2022-05-13 19:15:59,543 INFO [train.py:876] (6/8) Training started
2022-05-13 19:15:59,544 INFO [train.py:886] (6/8) Device: cuda:6
2022-05-13 19:15:59,547 INFO [train.py:895] (6/8) {'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, 'model_warm_step': 3000, 'env_info': {'k2-version': '1.15.1', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': 'f8d2dba06c000ffee36aab5b66f24e7c9809f116', 'k2-git-date': 'Thu Apr 21 12:20:34 2022', 'lhotse-version': '1.1.0.dev+missing.version.file', 'torch-version': '1.10.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'deeper-conformer-without-random-combiner', 'icefall-git-sha1': '7b786ce-dirty', 'icefall-git-date': 'Fri May 13 18:53:22 2022', 'icefall-path': '/ceph-fj/fangjun/open-source-2/icefall-deeper-conformer-2', 'k2-path': '/ceph-fj/fangjun/open-source-2/k2-multi-22/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-fj/fangjun/open-source-2/lhotse-multi-3/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-6-0415002726-7dc5bf9fdc-w24k9', 'IP address': '10.177.28.71'}, 'world_size': 8, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 40, 'start_epoch': 1, 'start_batch': 0, 'exp_dir': PosixPath('pruned_transducer_stateless5/exp-L'), 'bpe_model': 'data/lang_bpe_500/bpe.model', 'initial_lr': 0.003, 'lr_batches': 5000, 'lr_epochs': 6, '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': 4000, 'keep_last_k': 30, 'average_period': 100, 'use_fp16': False, 'num_encoder_layers': 18, 'dim_feedforward': 2048, 'nhead': 8, 'encoder_dim': 512, 'decoder_dim': 512, 'joiner_dim': 512, 'full_libri': True, 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 300, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'blank_id': 0, 'vocab_size': 500}
2022-05-13 19:15:59,547 INFO [train.py:897] (6/8) About to create model
2022-05-13 19:16:00,241 INFO [train.py:901] (6/8) Number of model parameters: 116553580
2022-05-13 19:16:07,842 INFO [train.py:916] (6/8) Using DDP
2022-05-13 19:16:09,395 INFO [asr_datamodule.py:391] (6/8) About to get train-clean-100 cuts
2022-05-13 19:16:17,935 INFO [asr_datamodule.py:398] (6/8) About to get train-clean-360 cuts
2022-05-13 19:16:51,763 INFO [asr_datamodule.py:405] (6/8) About to get train-other-500 cuts
2022-05-13 19:17:46,594 INFO [asr_datamodule.py:209] (6/8) Enable MUSAN
2022-05-13 19:17:46,594 INFO [asr_datamodule.py:210] (6/8) About to get Musan cuts
2022-05-13 19:17:48,541 INFO [asr_datamodule.py:238] (6/8) Enable SpecAugment
2022-05-13 19:17:48,542 INFO [asr_datamodule.py:239] (6/8) Time warp factor: 80
2022-05-13 19:17:48,542 INFO [asr_datamodule.py:251] (6/8) Num frame mask: 10
2022-05-13 19:17:48,542 INFO [asr_datamodule.py:264] (6/8) About to create train dataset
2022-05-13 19:17:48,542 INFO [asr_datamodule.py:292] (6/8) Using BucketingSampler.
2022-05-13 19:17:54,051 INFO [asr_datamodule.py:308] (6/8) About to create train dataloader
2022-05-13 19:17:54,052 INFO [asr_datamodule.py:412] (6/8) About to get dev-clean cuts
2022-05-13 19:17:54,468 INFO [asr_datamodule.py:417] (6/8) About to get dev-other cuts
2022-05-13 19:17:54,679 INFO [asr_datamodule.py:339] (6/8) About to create dev dataset
2022-05-13 19:17:54,692 INFO [asr_datamodule.py:358] (6/8) About to create dev dataloader
2022-05-13 19:17:54,692 INFO [train.py:1078] (6/8) Sanity check -- see if any of the batches in epoch 1 would cause OOM.
2022-05-13 19:18:18,398 INFO [distributed.py:874] (6/8) Reducer buckets have been rebuilt in this iteration.
2022-05-13 19:18:41,999 INFO [train.py:812] (6/8) Epoch 1, batch 0, loss[loss=0.8204, simple_loss=1.641, pruned_loss=6.706, over 7297.00 frames.], tot_loss[loss=0.8204, simple_loss=1.641, pruned_loss=6.706, over 7297.00 frames.], batch size: 17, lr: 3.00e-03
2022-05-13 19:19:41,278 INFO [train.py:812] (6/8) Epoch 1, batch 50, loss[loss=0.4672, simple_loss=0.9345, pruned_loss=7.036, over 7148.00 frames.], tot_loss[loss=0.5599, simple_loss=1.12, pruned_loss=7.126, over 324141.85 frames.], batch size: 19, lr: 3.00e-03
2022-05-13 19:20:39,900 INFO [train.py:812] (6/8) Epoch 1, batch 100, loss[loss=0.3837, simple_loss=0.7674, pruned_loss=6.606, over 7004.00 frames.], tot_loss[loss=0.4967, simple_loss=0.9934, pruned_loss=6.975, over 566163.62 frames.], batch size: 16, lr: 3.00e-03
2022-05-13 19:21:38,653 INFO [train.py:812] (6/8) Epoch 1, batch 150, loss[loss=0.3622, simple_loss=0.7244, pruned_loss=6.714, over 7018.00 frames.], tot_loss[loss=0.4652, simple_loss=0.9303, pruned_loss=6.882, over 758105.89 frames.], batch size: 16, lr: 3.00e-03
2022-05-13 19:22:36,958 INFO [train.py:812] (6/8) Epoch 1, batch 200, loss[loss=0.4449, simple_loss=0.8899, pruned_loss=6.834, over 7301.00 frames.], tot_loss[loss=0.4435, simple_loss=0.887, pruned_loss=6.845, over 908133.15 frames.], batch size: 25, lr: 3.00e-03
2022-05-13 19:23:35,690 INFO [train.py:812] (6/8) Epoch 1, batch 250, loss[loss=0.4228, simple_loss=0.8457, pruned_loss=6.965, over 7316.00 frames.], tot_loss[loss=0.4298, simple_loss=0.8596, pruned_loss=6.837, over 1018264.53 frames.], batch size: 21, lr: 3.00e-03
2022-05-13 19:24:34,039 INFO [train.py:812] (6/8) Epoch 1, batch 300, loss[loss=0.3924, simple_loss=0.7848, pruned_loss=6.803, over 7287.00 frames.], tot_loss[loss=0.4192, simple_loss=0.8385, pruned_loss=6.831, over 1109938.09 frames.], batch size: 25, lr: 3.00e-03
2022-05-13 19:25:33,451 INFO [train.py:812] (6/8) Epoch 1, batch 350, loss[loss=0.371, simple_loss=0.742, pruned_loss=6.835, over 7253.00 frames.], tot_loss[loss=0.4103, simple_loss=0.8205, pruned_loss=6.823, over 1178924.48 frames.], batch size: 19, lr: 3.00e-03
2022-05-13 19:26:31,634 INFO [train.py:812] (6/8) Epoch 1, batch 400, loss[loss=0.3864, simple_loss=0.7728, pruned_loss=6.904, over 7409.00 frames.], tot_loss[loss=0.4032, simple_loss=0.8065, pruned_loss=6.806, over 1231744.66 frames.], batch size: 21, lr: 3.00e-03
2022-05-13 19:27:30,037 INFO [train.py:812] (6/8) Epoch 1, batch 450, loss[loss=0.3586, simple_loss=0.7172, pruned_loss=6.809, over 7405.00 frames.], tot_loss[loss=0.3932, simple_loss=0.7864, pruned_loss=6.79, over 1268056.65 frames.], batch size: 21, lr: 2.99e-03
2022-05-13 19:28:29,389 INFO [train.py:812] (6/8) Epoch 1, batch 500, loss[loss=0.3409, simple_loss=0.6818, pruned_loss=6.755, over 7204.00 frames.], tot_loss[loss=0.3778, simple_loss=0.7556, pruned_loss=6.773, over 1303694.99 frames.], batch size: 22, lr: 2.99e-03
2022-05-13 19:29:27,296 INFO [train.py:812] (6/8) Epoch 1, batch 550, loss[loss=0.3379, simple_loss=0.6758, pruned_loss=6.792, over 7336.00 frames.], tot_loss[loss=0.3633, simple_loss=0.7265, pruned_loss=6.766, over 1329447.44 frames.], batch size: 22, lr: 2.99e-03
2022-05-13 19:30:26,701 INFO [train.py:812] (6/8) Epoch 1, batch 600, loss[loss=0.2959, simple_loss=0.5918, pruned_loss=6.797, over 7106.00 frames.], tot_loss[loss=0.347, simple_loss=0.6941, pruned_loss=6.759, over 1350747.85 frames.], batch size: 21, lr: 2.99e-03
2022-05-13 19:31:24,377 INFO [train.py:812] (6/8) Epoch 1, batch 650, loss[loss=0.2096, simple_loss=0.4191, pruned_loss=6.571, over 6983.00 frames.], tot_loss[loss=0.3306, simple_loss=0.6612, pruned_loss=6.751, over 1369199.11 frames.], batch size: 16, lr: 2.99e-03
2022-05-13 19:32:22,739 INFO [train.py:812] (6/8) Epoch 1, batch 700, loss[loss=0.2659, simple_loss=0.5319, pruned_loss=6.798, over 7196.00 frames.], tot_loss[loss=0.315, simple_loss=0.63, pruned_loss=6.738, over 1380324.83 frames.], batch size: 23, lr: 2.99e-03
2022-05-13 19:33:21,786 INFO [train.py:812] (6/8) Epoch 1, batch 750, loss[loss=0.2038, simple_loss=0.4076, pruned_loss=6.471, over 7281.00 frames.], tot_loss[loss=0.3007, simple_loss=0.6014, pruned_loss=6.732, over 1391924.05 frames.], batch size: 17, lr: 2.98e-03
2022-05-13 19:34:19,638 INFO [train.py:812] (6/8) Epoch 1, batch 800, loss[loss=0.2693, simple_loss=0.5386, pruned_loss=6.715, over 7111.00 frames.], tot_loss[loss=0.2903, simple_loss=0.5805, pruned_loss=6.732, over 1397300.71 frames.], batch size: 21, lr: 2.98e-03
2022-05-13 19:35:17,947 INFO [train.py:812] (6/8) Epoch 1, batch 850, loss[loss=0.2638, simple_loss=0.5276, pruned_loss=6.817, over 7218.00 frames.], tot_loss[loss=0.2799, simple_loss=0.5599, pruned_loss=6.732, over 1402963.18 frames.], batch size: 21, lr: 2.98e-03
2022-05-13 19:36:17,415 INFO [train.py:812] (6/8) Epoch 1, batch 900, loss[loss=0.251, simple_loss=0.502, pruned_loss=6.855, over 7321.00 frames.], tot_loss[loss=0.2702, simple_loss=0.5404, pruned_loss=6.732, over 1407585.82 frames.], batch size: 21, lr: 2.98e-03
2022-05-13 19:37:15,487 INFO [train.py:812] (6/8) Epoch 1, batch 950, loss[loss=0.1991, simple_loss=0.3983, pruned_loss=6.577, over 7420.00 frames.], tot_loss[loss=0.263, simple_loss=0.5259, pruned_loss=6.737, over 1404809.15 frames.], batch size: 17, lr: 2.97e-03
2022-05-13 19:38:15,222 INFO [train.py:812] (6/8) Epoch 1, batch 1000, loss[loss=0.2191, simple_loss=0.4382, pruned_loss=6.59, over 6998.00 frames.], tot_loss[loss=0.2569, simple_loss=0.5137, pruned_loss=6.738, over 1405139.57 frames.], batch size: 16, lr: 2.97e-03
2022-05-13 19:39:14,099 INFO [train.py:812] (6/8) Epoch 1, batch 1050, loss[loss=0.199, simple_loss=0.398, pruned_loss=6.609, over 7017.00 frames.], tot_loss[loss=0.2511, simple_loss=0.5022, pruned_loss=6.746, over 1407929.99 frames.], batch size: 16, lr: 2.97e-03
2022-05-13 19:40:12,432 INFO [train.py:812] (6/8) Epoch 1, batch 1100, loss[loss=0.2197, simple_loss=0.4395, pruned_loss=6.795, over 7203.00 frames.], tot_loss[loss=0.246, simple_loss=0.4919, pruned_loss=6.754, over 1412141.16 frames.], batch size: 22, lr: 2.96e-03
2022-05-13 19:41:10,383 INFO [train.py:812] (6/8) Epoch 1, batch 1150, loss[loss=0.2356, simple_loss=0.4712, pruned_loss=6.876, over 6940.00 frames.], tot_loss[loss=0.2399, simple_loss=0.4799, pruned_loss=6.752, over 1412444.18 frames.], batch size: 32, lr: 2.96e-03
2022-05-13 19:42:08,526 INFO [train.py:812] (6/8) Epoch 1, batch 1200, loss[loss=0.2203, simple_loss=0.4406, pruned_loss=6.807, over 7141.00 frames.], tot_loss[loss=0.2355, simple_loss=0.471, pruned_loss=6.752, over 1420104.27 frames.], batch size: 26, lr: 2.96e-03
2022-05-13 19:43:07,170 INFO [train.py:812] (6/8) Epoch 1, batch 1250, loss[loss=0.2316, simple_loss=0.4632, pruned_loss=6.843, over 7375.00 frames.], tot_loss[loss=0.232, simple_loss=0.4639, pruned_loss=6.753, over 1414147.38 frames.], batch size: 23, lr: 2.95e-03
2022-05-13 19:44:06,131 INFO [train.py:812] (6/8) Epoch 1, batch 1300, loss[loss=0.2195, simple_loss=0.439, pruned_loss=6.852, over 7299.00 frames.], tot_loss[loss=0.2271, simple_loss=0.4542, pruned_loss=6.757, over 1421760.39 frames.], batch size: 24, lr: 2.95e-03
2022-05-13 19:45:04,350 INFO [train.py:812] (6/8) Epoch 1, batch 1350, loss[loss=0.245, simple_loss=0.4901, pruned_loss=6.783, over 7141.00 frames.], tot_loss[loss=0.2235, simple_loss=0.447, pruned_loss=6.754, over 1422938.85 frames.], batch size: 20, lr: 2.95e-03
2022-05-13 19:46:03,487 INFO [train.py:812] (6/8) Epoch 1, batch 1400, loss[loss=0.2352, simple_loss=0.4704, pruned_loss=6.886, over 7294.00 frames.], tot_loss[loss=0.2218, simple_loss=0.4437, pruned_loss=6.763, over 1419768.78 frames.], batch size: 24, lr: 2.94e-03
2022-05-13 19:47:02,123 INFO [train.py:812] (6/8) Epoch 1, batch 1450, loss[loss=0.1805, simple_loss=0.361, pruned_loss=6.641, over 7128.00 frames.], tot_loss[loss=0.218, simple_loss=0.4361, pruned_loss=6.763, over 1420386.89 frames.], batch size: 17, lr: 2.94e-03
2022-05-13 19:48:00,937 INFO [train.py:812] (6/8) Epoch 1, batch 1500, loss[loss=0.2156, simple_loss=0.4313, pruned_loss=6.804, over 7319.00 frames.], tot_loss[loss=0.2156, simple_loss=0.4311, pruned_loss=6.763, over 1423301.52 frames.], batch size: 24, lr: 2.94e-03
2022-05-13 19:48:59,497 INFO [train.py:812] (6/8) Epoch 1, batch 1550, loss[loss=0.2059, simple_loss=0.4118, pruned_loss=6.701, over 7099.00 frames.], tot_loss[loss=0.2131, simple_loss=0.4261, pruned_loss=6.763, over 1423485.47 frames.], batch size: 21, lr: 2.93e-03
2022-05-13 19:49:59,137 INFO [train.py:812] (6/8) Epoch 1, batch 1600, loss[loss=0.1993, simple_loss=0.3986, pruned_loss=6.739, over 7326.00 frames.], tot_loss[loss=0.2104, simple_loss=0.4207, pruned_loss=6.757, over 1421284.40 frames.], batch size: 20, lr: 2.93e-03
2022-05-13 19:50:59,017 INFO [train.py:812] (6/8) Epoch 1, batch 1650, loss[loss=0.1842, simple_loss=0.3684, pruned_loss=6.749, over 7165.00 frames.], tot_loss[loss=0.2086, simple_loss=0.4173, pruned_loss=6.755, over 1422848.37 frames.], batch size: 18, lr: 2.92e-03
2022-05-13 19:51:59,138 INFO [train.py:812] (6/8) Epoch 1, batch 1700, loss[loss=0.2185, simple_loss=0.4371, pruned_loss=6.868, over 6446.00 frames.], tot_loss[loss=0.2068, simple_loss=0.4136, pruned_loss=6.762, over 1419108.85 frames.], batch size: 38, lr: 2.92e-03
2022-05-13 19:52:58,928 INFO [train.py:812] (6/8) Epoch 1, batch 1750, loss[loss=0.209, simple_loss=0.4179, pruned_loss=6.768, over 6380.00 frames.], tot_loss[loss=0.2039, simple_loss=0.4078, pruned_loss=6.757, over 1418472.39 frames.], batch size: 38, lr: 2.91e-03
2022-05-13 19:54:00,198 INFO [train.py:812] (6/8) Epoch 1, batch 1800, loss[loss=0.2037, simple_loss=0.4074, pruned_loss=6.813, over 7055.00 frames.], tot_loss[loss=0.2023, simple_loss=0.4045, pruned_loss=6.758, over 1418544.03 frames.], batch size: 28, lr: 2.91e-03
2022-05-13 19:54:58,672 INFO [train.py:812] (6/8) Epoch 1, batch 1850, loss[loss=0.2391, simple_loss=0.4782, pruned_loss=6.878, over 5058.00 frames.], tot_loss[loss=0.2007, simple_loss=0.4013, pruned_loss=6.76, over 1420512.91 frames.], batch size: 52, lr: 2.91e-03
2022-05-13 19:55:57,008 INFO [train.py:812] (6/8) Epoch 1, batch 1900, loss[loss=0.1986, simple_loss=0.3972, pruned_loss=6.725, over 7261.00 frames.], tot_loss[loss=0.1997, simple_loss=0.3995, pruned_loss=6.759, over 1420682.66 frames.], batch size: 19, lr: 2.90e-03
2022-05-13 19:56:55,446 INFO [train.py:812] (6/8) Epoch 1, batch 1950, loss[loss=0.2014, simple_loss=0.4029, pruned_loss=6.696, over 7320.00 frames.], tot_loss[loss=0.1981, simple_loss=0.3962, pruned_loss=6.756, over 1422708.38 frames.], batch size: 21, lr: 2.90e-03
2022-05-13 19:57:54,273 INFO [train.py:812] (6/8) Epoch 1, batch 2000, loss[loss=0.172, simple_loss=0.344, pruned_loss=6.652, over 6840.00 frames.], tot_loss[loss=0.1964, simple_loss=0.3928, pruned_loss=6.754, over 1423747.31 frames.], batch size: 15, lr: 2.89e-03
2022-05-13 19:58:53,070 INFO [train.py:812] (6/8) Epoch 1, batch 2050, loss[loss=0.2104, simple_loss=0.4208, pruned_loss=6.888, over 7150.00 frames.], tot_loss[loss=0.195, simple_loss=0.39, pruned_loss=6.757, over 1421489.54 frames.], batch size: 26, lr: 2.89e-03
2022-05-13 19:59:51,419 INFO [train.py:812] (6/8) Epoch 1, batch 2100, loss[loss=0.1791, simple_loss=0.3582, pruned_loss=6.661, over 7157.00 frames.], tot_loss[loss=0.1936, simple_loss=0.3872, pruned_loss=6.754, over 1418209.13 frames.], batch size: 18, lr: 2.88e-03
2022-05-13 20:00:49,602 INFO [train.py:812] (6/8) Epoch 1, batch 2150, loss[loss=0.1846, simple_loss=0.3692, pruned_loss=6.696, over 7335.00 frames.], tot_loss[loss=0.1919, simple_loss=0.3838, pruned_loss=6.748, over 1422283.12 frames.], batch size: 22, lr: 2.88e-03
2022-05-13 20:01:48,637 INFO [train.py:812] (6/8) Epoch 1, batch 2200, loss[loss=0.2134, simple_loss=0.4269, pruned_loss=6.809, over 7284.00 frames.], tot_loss[loss=0.192, simple_loss=0.3839, pruned_loss=6.75, over 1421139.27 frames.], batch size: 25, lr: 2.87e-03
2022-05-13 20:02:47,474 INFO [train.py:812] (6/8) Epoch 1, batch 2250, loss[loss=0.1826, simple_loss=0.3651, pruned_loss=6.78, over 7219.00 frames.], tot_loss[loss=0.1914, simple_loss=0.3828, pruned_loss=6.746, over 1420822.56 frames.], batch size: 21, lr: 2.86e-03
2022-05-13 20:03:45,873 INFO [train.py:812] (6/8) Epoch 1, batch 2300, loss[loss=0.1811, simple_loss=0.3622, pruned_loss=6.795, over 7256.00 frames.], tot_loss[loss=0.1911, simple_loss=0.3823, pruned_loss=6.746, over 1416328.35 frames.], batch size: 19, lr: 2.86e-03
2022-05-13 20:04:43,229 INFO [train.py:812] (6/8) Epoch 1, batch 2350, loss[loss=0.2199, simple_loss=0.4397, pruned_loss=6.819, over 5085.00 frames.], tot_loss[loss=0.1904, simple_loss=0.3807, pruned_loss=6.753, over 1415813.52 frames.], batch size: 52, lr: 2.85e-03
2022-05-13 20:05:42,794 INFO [train.py:812] (6/8) Epoch 1, batch 2400, loss[loss=0.1903, simple_loss=0.3805, pruned_loss=6.788, over 7437.00 frames.], tot_loss[loss=0.1899, simple_loss=0.3798, pruned_loss=6.754, over 1412455.20 frames.], batch size: 20, lr: 2.85e-03
2022-05-13 20:06:41,416 INFO [train.py:812] (6/8) Epoch 1, batch 2450, loss[loss=0.1963, simple_loss=0.3927, pruned_loss=6.731, over 4725.00 frames.], tot_loss[loss=0.1891, simple_loss=0.3783, pruned_loss=6.754, over 1412801.50 frames.], batch size: 52, lr: 2.84e-03
2022-05-13 20:07:40,735 INFO [train.py:812] (6/8) Epoch 1, batch 2500, loss[loss=0.1962, simple_loss=0.3924, pruned_loss=6.801, over 7320.00 frames.], tot_loss[loss=0.1881, simple_loss=0.3763, pruned_loss=6.748, over 1418123.33 frames.], batch size: 20, lr: 2.84e-03
2022-05-13 20:08:39,350 INFO [train.py:812] (6/8) Epoch 1, batch 2550, loss[loss=0.1546, simple_loss=0.3093, pruned_loss=6.638, over 7419.00 frames.], tot_loss[loss=0.1878, simple_loss=0.3755, pruned_loss=6.745, over 1418781.15 frames.], batch size: 18, lr: 2.83e-03
2022-05-13 20:09:37,914 INFO [train.py:812] (6/8) Epoch 1, batch 2600, loss[loss=0.2039, simple_loss=0.4078, pruned_loss=6.895, over 7236.00 frames.], tot_loss[loss=0.1867, simple_loss=0.3735, pruned_loss=6.74, over 1422480.88 frames.], batch size: 20, lr: 2.83e-03
2022-05-13 20:10:35,870 INFO [train.py:812] (6/8) Epoch 1, batch 2650, loss[loss=0.1668, simple_loss=0.3337, pruned_loss=6.764, over 7235.00 frames.], tot_loss[loss=0.1854, simple_loss=0.3709, pruned_loss=6.739, over 1424080.10 frames.], batch size: 20, lr: 2.82e-03
2022-05-13 20:11:35,635 INFO [train.py:812] (6/8) Epoch 1, batch 2700, loss[loss=0.1851, simple_loss=0.3703, pruned_loss=6.803, over 7144.00 frames.], tot_loss[loss=0.1853, simple_loss=0.3707, pruned_loss=6.742, over 1423125.16 frames.], batch size: 20, lr: 2.81e-03
2022-05-13 20:12:32,560 INFO [train.py:812] (6/8) Epoch 1, batch 2750, loss[loss=0.1655, simple_loss=0.3309, pruned_loss=6.824, over 7321.00 frames.], tot_loss[loss=0.1848, simple_loss=0.3697, pruned_loss=6.746, over 1423539.88 frames.], batch size: 20, lr: 2.81e-03
2022-05-13 20:13:32,049 INFO [train.py:812] (6/8) Epoch 1, batch 2800, loss[loss=0.1912, simple_loss=0.3825, pruned_loss=6.793, over 7149.00 frames.], tot_loss[loss=0.1848, simple_loss=0.3697, pruned_loss=6.743, over 1422023.17 frames.], batch size: 20, lr: 2.80e-03
2022-05-13 20:14:30,984 INFO [train.py:812] (6/8) Epoch 1, batch 2850, loss[loss=0.1655, simple_loss=0.3309, pruned_loss=6.678, over 7368.00 frames.], tot_loss[loss=0.1838, simple_loss=0.3676, pruned_loss=6.741, over 1425173.99 frames.], batch size: 19, lr: 2.80e-03
2022-05-13 20:15:28,501 INFO [train.py:812] (6/8) Epoch 1, batch 2900, loss[loss=0.1893, simple_loss=0.3786, pruned_loss=6.889, over 7313.00 frames.], tot_loss[loss=0.1843, simple_loss=0.3686, pruned_loss=6.746, over 1420421.67 frames.], batch size: 20, lr: 2.79e-03
2022-05-13 20:16:27,589 INFO [train.py:812] (6/8) Epoch 1, batch 2950, loss[loss=0.1698, simple_loss=0.3396, pruned_loss=6.75, over 7182.00 frames.], tot_loss[loss=0.1828, simple_loss=0.3655, pruned_loss=6.738, over 1416274.50 frames.], batch size: 26, lr: 2.78e-03
2022-05-13 20:17:26,756 INFO [train.py:812] (6/8) Epoch 1, batch 3000, loss[loss=0.3428, simple_loss=0.366, pruned_loss=1.598, over 7293.00 frames.], tot_loss[loss=0.2164, simple_loss=0.365, pruned_loss=6.716, over 1420042.12 frames.], batch size: 17, lr: 2.78e-03
2022-05-13 20:17:26,757 INFO [train.py:832] (6/8) Computing validation loss
2022-05-13 20:17:34,929 INFO [train.py:841] (6/8) Epoch 1, validation: loss=2.094, simple_loss=0.4148, pruned_loss=1.887, over 698248.00 frames.
2022-05-13 20:18:33,877 INFO [train.py:812] (6/8) Epoch 1, batch 3050, loss[loss=0.3011, simple_loss=0.4045, pruned_loss=0.9885, over 6560.00 frames.], tot_loss[loss=0.2415, simple_loss=0.3745, pruned_loss=5.508, over 1419410.56 frames.], batch size: 38, lr: 2.77e-03
2022-05-13 20:19:33,931 INFO [train.py:812] (6/8) Epoch 1, batch 3100, loss[loss=0.2534, simple_loss=0.3874, pruned_loss=0.5969, over 7402.00 frames.], tot_loss[loss=0.243, simple_loss=0.3695, pruned_loss=4.432, over 1425034.93 frames.], batch size: 21, lr: 2.77e-03
2022-05-13 20:20:32,563 INFO [train.py:812] (6/8) Epoch 1, batch 3150, loss[loss=0.2176, simple_loss=0.3619, pruned_loss=0.3668, over 7417.00 frames.], tot_loss[loss=0.2383, simple_loss=0.3668, pruned_loss=3.542, over 1426560.64 frames.], batch size: 21, lr: 2.76e-03
2022-05-13 20:21:30,566 INFO [train.py:812] (6/8) Epoch 1, batch 3200, loss[loss=0.1985, simple_loss=0.3483, pruned_loss=0.2433, over 7271.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3653, pruned_loss=2.831, over 1422748.80 frames.], batch size: 24, lr: 2.75e-03
2022-05-13 20:22:29,486 INFO [train.py:812] (6/8) Epoch 1, batch 3250, loss[loss=0.1953, simple_loss=0.3487, pruned_loss=0.209, over 7145.00 frames.], tot_loss[loss=0.226, simple_loss=0.3636, pruned_loss=2.26, over 1422504.67 frames.], batch size: 20, lr: 2.75e-03
2022-05-13 20:23:28,332 INFO [train.py:812] (6/8) Epoch 1, batch 3300, loss[loss=0.2098, simple_loss=0.3721, pruned_loss=0.2373, over 7369.00 frames.], tot_loss[loss=0.2216, simple_loss=0.3639, pruned_loss=1.816, over 1418552.47 frames.], batch size: 23, lr: 2.74e-03
2022-05-13 20:24:25,743 INFO [train.py:812] (6/8) Epoch 1, batch 3350, loss[loss=0.2001, simple_loss=0.3607, pruned_loss=0.1977, over 7266.00 frames.], tot_loss[loss=0.2165, simple_loss=0.3622, pruned_loss=1.455, over 1422946.61 frames.], batch size: 24, lr: 2.73e-03
2022-05-13 20:25:24,241 INFO [train.py:812] (6/8) Epoch 1, batch 3400, loss[loss=0.1636, simple_loss=0.3007, pruned_loss=0.1325, over 7251.00 frames.], tot_loss[loss=0.2127, simple_loss=0.3614, pruned_loss=1.177, over 1423416.60 frames.], batch size: 19, lr: 2.73e-03
2022-05-13 20:26:22,133 INFO [train.py:812] (6/8) Epoch 1, batch 3450, loss[loss=0.2091, simple_loss=0.3774, pruned_loss=0.2043, over 7285.00 frames.], tot_loss[loss=0.2098, simple_loss=0.3611, pruned_loss=0.9592, over 1423901.83 frames.], batch size: 25, lr: 2.72e-03
2022-05-13 20:27:20,159 INFO [train.py:812] (6/8) Epoch 1, batch 3500, loss[loss=0.2149, simple_loss=0.3855, pruned_loss=0.2217, over 7164.00 frames.], tot_loss[loss=0.2065, simple_loss=0.3595, pruned_loss=0.787, over 1421641.57 frames.], batch size: 26, lr: 2.72e-03
2022-05-13 20:28:19,228 INFO [train.py:812] (6/8) Epoch 1, batch 3550, loss[loss=0.2097, simple_loss=0.3826, pruned_loss=0.1843, over 7213.00 frames.], tot_loss[loss=0.2034, simple_loss=0.3575, pruned_loss=0.651, over 1422947.14 frames.], batch size: 21, lr: 2.71e-03
2022-05-13 20:29:18,105 INFO [train.py:812] (6/8) Epoch 1, batch 3600, loss[loss=0.1712, simple_loss=0.3096, pruned_loss=0.1644, over 7025.00 frames.], tot_loss[loss=0.2007, simple_loss=0.3554, pruned_loss=0.5448, over 1420816.67 frames.], batch size: 16, lr: 2.70e-03
2022-05-13 20:30:25,539 INFO [train.py:812] (6/8) Epoch 1, batch 3650, loss[loss=0.1954, simple_loss=0.3569, pruned_loss=0.1698, over 7211.00 frames.], tot_loss[loss=0.199, simple_loss=0.3546, pruned_loss=0.4616, over 1421092.68 frames.], batch size: 21, lr: 2.70e-03
2022-05-13 20:32:10,024 INFO [train.py:812] (6/8) Epoch 1, batch 3700, loss[loss=0.1906, simple_loss=0.3521, pruned_loss=0.1457, over 6698.00 frames.], tot_loss[loss=0.1963, simple_loss=0.3519, pruned_loss=0.3931, over 1425367.15 frames.], batch size: 31, lr: 2.69e-03
2022-05-13 20:33:27,109 INFO [train.py:812] (6/8) Epoch 1, batch 3750, loss[loss=0.1756, simple_loss=0.3221, pruned_loss=0.1456, over 7267.00 frames.], tot_loss[loss=0.1952, simple_loss=0.3515, pruned_loss=0.343, over 1417794.93 frames.], batch size: 18, lr: 2.68e-03
2022-05-13 20:34:26,661 INFO [train.py:812] (6/8) Epoch 1, batch 3800, loss[loss=0.1569, simple_loss=0.2922, pruned_loss=0.108, over 7123.00 frames.], tot_loss[loss=0.1934, simple_loss=0.3497, pruned_loss=0.3014, over 1418043.31 frames.], batch size: 17, lr: 2.68e-03
2022-05-13 20:35:25,753 INFO [train.py:812] (6/8) Epoch 1, batch 3850, loss[loss=0.1703, simple_loss=0.3158, pruned_loss=0.1245, over 7130.00 frames.], tot_loss[loss=0.1925, simple_loss=0.3492, pruned_loss=0.2686, over 1423300.22 frames.], batch size: 17, lr: 2.67e-03
2022-05-13 20:36:24,070 INFO [train.py:812] (6/8) Epoch 1, batch 3900, loss[loss=0.1645, simple_loss=0.3042, pruned_loss=0.1242, over 7258.00 frames.], tot_loss[loss=0.1917, simple_loss=0.3488, pruned_loss=0.2433, over 1420845.55 frames.], batch size: 16, lr: 2.66e-03
2022-05-13 20:37:21,128 INFO [train.py:812] (6/8) Epoch 1, batch 3950, loss[loss=0.1582, simple_loss=0.2938, pruned_loss=0.113, over 6792.00 frames.], tot_loss[loss=0.1908, simple_loss=0.348, pruned_loss=0.223, over 1418879.45 frames.], batch size: 15, lr: 2.66e-03
2022-05-13 20:38:27,961 INFO [train.py:812] (6/8) Epoch 1, batch 4000, loss[loss=0.1862, simple_loss=0.346, pruned_loss=0.1318, over 7314.00 frames.], tot_loss[loss=0.1907, simple_loss=0.3483, pruned_loss=0.208, over 1421258.75 frames.], batch size: 21, lr: 2.65e-03
2022-05-13 20:39:26,732 INFO [train.py:812] (6/8) Epoch 1, batch 4050, loss[loss=0.1941, simple_loss=0.356, pruned_loss=0.161, over 7025.00 frames.], tot_loss[loss=0.1903, simple_loss=0.3481, pruned_loss=0.1954, over 1421813.03 frames.], batch size: 28, lr: 2.64e-03
2022-05-13 20:40:25,272 INFO [train.py:812] (6/8) Epoch 1, batch 4100, loss[loss=0.178, simple_loss=0.3283, pruned_loss=0.1381, over 7260.00 frames.], tot_loss[loss=0.1892, simple_loss=0.3465, pruned_loss=0.1848, over 1421903.21 frames.], batch size: 19, lr: 2.64e-03
2022-05-13 20:41:23,933 INFO [train.py:812] (6/8) Epoch 1, batch 4150, loss[loss=0.1589, simple_loss=0.2989, pruned_loss=0.09398, over 7062.00 frames.], tot_loss[loss=0.1897, simple_loss=0.3479, pruned_loss=0.1773, over 1426161.71 frames.], batch size: 18, lr: 2.63e-03
2022-05-13 20:42:22,993 INFO [train.py:812] (6/8) Epoch 1, batch 4200, loss[loss=0.205, simple_loss=0.3765, pruned_loss=0.1677, over 7206.00 frames.], tot_loss[loss=0.1893, simple_loss=0.3477, pruned_loss=0.1701, over 1425590.18 frames.], batch size: 22, lr: 2.63e-03
2022-05-13 20:43:21,445 INFO [train.py:812] (6/8) Epoch 1, batch 4250, loss[loss=0.1955, simple_loss=0.357, pruned_loss=0.1702, over 7433.00 frames.], tot_loss[loss=0.1894, simple_loss=0.3482, pruned_loss=0.1655, over 1423612.75 frames.], batch size: 20, lr: 2.62e-03
2022-05-13 20:44:20,460 INFO [train.py:812] (6/8) Epoch 1, batch 4300, loss[loss=0.1793, simple_loss=0.3349, pruned_loss=0.1191, over 7069.00 frames.], tot_loss[loss=0.189, simple_loss=0.3477, pruned_loss=0.1607, over 1422863.93 frames.], batch size: 28, lr: 2.61e-03
2022-05-13 20:45:18,967 INFO [train.py:812] (6/8) Epoch 1, batch 4350, loss[loss=0.1666, simple_loss=0.3108, pruned_loss=0.1123, over 7426.00 frames.], tot_loss[loss=0.1884, simple_loss=0.347, pruned_loss=0.1561, over 1426502.45 frames.], batch size: 20, lr: 2.61e-03
2022-05-13 20:46:18,360 INFO [train.py:812] (6/8) Epoch 1, batch 4400, loss[loss=0.1958, simple_loss=0.3564, pruned_loss=0.176, over 7288.00 frames.], tot_loss[loss=0.1893, simple_loss=0.3487, pruned_loss=0.155, over 1424427.84 frames.], batch size: 18, lr: 2.60e-03
2022-05-13 20:47:17,294 INFO [train.py:812] (6/8) Epoch 1, batch 4450, loss[loss=0.188, simple_loss=0.3474, pruned_loss=0.1434, over 7430.00 frames.], tot_loss[loss=0.1894, simple_loss=0.3492, pruned_loss=0.1528, over 1423720.99 frames.], batch size: 20, lr: 2.59e-03
2022-05-13 20:48:16,741 INFO [train.py:812] (6/8) Epoch 1, batch 4500, loss[loss=0.2019, simple_loss=0.3705, pruned_loss=0.1667, over 6335.00 frames.], tot_loss[loss=0.189, simple_loss=0.3487, pruned_loss=0.1504, over 1413810.27 frames.], batch size: 37, lr: 2.59e-03
2022-05-13 20:49:13,813 INFO [train.py:812] (6/8) Epoch 1, batch 4550, loss[loss=0.1953, simple_loss=0.3589, pruned_loss=0.1591, over 5110.00 frames.], tot_loss[loss=0.1892, simple_loss=0.349, pruned_loss=0.1494, over 1394892.33 frames.], batch size: 52, lr: 2.58e-03
2022-05-13 20:50:25,950 INFO [train.py:812] (6/8) Epoch 2, batch 0, loss[loss=0.2075, simple_loss=0.3799, pruned_loss=0.175, over 7224.00 frames.], tot_loss[loss=0.2075, simple_loss=0.3799, pruned_loss=0.175, over 7224.00 frames.], batch size: 26, lr: 2.56e-03
2022-05-13 20:51:25,921 INFO [train.py:812] (6/8) Epoch 2, batch 50, loss[loss=0.1966, simple_loss=0.3649, pruned_loss=0.1416, over 7238.00 frames.], tot_loss[loss=0.184, simple_loss=0.34, pruned_loss=0.1399, over 312415.65 frames.], batch size: 20, lr: 2.55e-03
2022-05-13 20:52:24,862 INFO [train.py:812] (6/8) Epoch 2, batch 100, loss[loss=0.1799, simple_loss=0.3352, pruned_loss=0.123, over 7423.00 frames.], tot_loss[loss=0.1832, simple_loss=0.3391, pruned_loss=0.1362, over 560384.75 frames.], batch size: 20, lr: 2.54e-03
2022-05-13 20:53:23,910 INFO [train.py:812] (6/8) Epoch 2, batch 150, loss[loss=0.1578, simple_loss=0.2965, pruned_loss=0.09552, over 7330.00 frames.], tot_loss[loss=0.1824, simple_loss=0.3379, pruned_loss=0.1349, over 751245.11 frames.], batch size: 20, lr: 2.54e-03
2022-05-13 20:54:21,311 INFO [train.py:812] (6/8) Epoch 2, batch 200, loss[loss=0.1824, simple_loss=0.3404, pruned_loss=0.1214, over 7164.00 frames.], tot_loss[loss=0.1824, simple_loss=0.338, pruned_loss=0.1341, over 900541.92 frames.], batch size: 19, lr: 2.53e-03
2022-05-13 20:55:19,843 INFO [train.py:812] (6/8) Epoch 2, batch 250, loss[loss=0.21, simple_loss=0.386, pruned_loss=0.1697, over 7385.00 frames.], tot_loss[loss=0.184, simple_loss=0.3409, pruned_loss=0.1358, over 1015700.35 frames.], batch size: 23, lr: 2.53e-03
2022-05-13 20:56:18,134 INFO [train.py:812] (6/8) Epoch 2, batch 300, loss[loss=0.1664, simple_loss=0.31, pruned_loss=0.1142, over 7253.00 frames.], tot_loss[loss=0.1839, simple_loss=0.3408, pruned_loss=0.1347, over 1104941.54 frames.], batch size: 19, lr: 2.52e-03
2022-05-13 20:57:16,226 INFO [train.py:812] (6/8) Epoch 2, batch 350, loss[loss=0.1879, simple_loss=0.3483, pruned_loss=0.138, over 7215.00 frames.], tot_loss[loss=0.1834, simple_loss=0.3399, pruned_loss=0.1341, over 1174582.67 frames.], batch size: 21, lr: 2.51e-03
2022-05-13 20:58:14,752 INFO [train.py:812] (6/8) Epoch 2, batch 400, loss[loss=0.219, simple_loss=0.4018, pruned_loss=0.1812, over 7145.00 frames.], tot_loss[loss=0.1835, simple_loss=0.3402, pruned_loss=0.134, over 1231260.26 frames.], batch size: 20, lr: 2.51e-03
2022-05-13 20:59:13,916 INFO [train.py:812] (6/8) Epoch 2, batch 450, loss[loss=0.1875, simple_loss=0.3465, pruned_loss=0.1429, over 7160.00 frames.], tot_loss[loss=0.1832, simple_loss=0.3398, pruned_loss=0.1329, over 1276085.35 frames.], batch size: 19, lr: 2.50e-03
2022-05-13 21:00:12,354 INFO [train.py:812] (6/8) Epoch 2, batch 500, loss[loss=0.1677, simple_loss=0.313, pruned_loss=0.1124, over 7166.00 frames.], tot_loss[loss=0.1822, simple_loss=0.3383, pruned_loss=0.1309, over 1307549.85 frames.], batch size: 18, lr: 2.49e-03
2022-05-13 21:01:12,113 INFO [train.py:812] (6/8) Epoch 2, batch 550, loss[loss=0.1621, simple_loss=0.3049, pruned_loss=0.09682, over 7366.00 frames.], tot_loss[loss=0.182, simple_loss=0.338, pruned_loss=0.1306, over 1332106.57 frames.], batch size: 19, lr: 2.49e-03
2022-05-13 21:02:09,996 INFO [train.py:812] (6/8) Epoch 2, batch 600, loss[loss=0.1886, simple_loss=0.3526, pruned_loss=0.1228, over 7375.00 frames.], tot_loss[loss=0.1818, simple_loss=0.3377, pruned_loss=0.1296, over 1353831.97 frames.], batch size: 23, lr: 2.48e-03
2022-05-13 21:03:09,006 INFO [train.py:812] (6/8) Epoch 2, batch 650, loss[loss=0.1425, simple_loss=0.2691, pruned_loss=0.07971, over 7276.00 frames.], tot_loss[loss=0.1816, simple_loss=0.3371, pruned_loss=0.1302, over 1368108.59 frames.], batch size: 18, lr: 2.48e-03
2022-05-13 21:04:08,351 INFO [train.py:812] (6/8) Epoch 2, batch 700, loss[loss=0.1851, simple_loss=0.3426, pruned_loss=0.1379, over 4966.00 frames.], tot_loss[loss=0.1809, simple_loss=0.3359, pruned_loss=0.1295, over 1379561.64 frames.], batch size: 52, lr: 2.47e-03
2022-05-13 21:05:07,232 INFO [train.py:812] (6/8) Epoch 2, batch 750, loss[loss=0.1816, simple_loss=0.3381, pruned_loss=0.1259, over 7259.00 frames.], tot_loss[loss=0.1804, simple_loss=0.3352, pruned_loss=0.1281, over 1390554.25 frames.], batch size: 19, lr: 2.46e-03
2022-05-13 21:06:06,465 INFO [train.py:812] (6/8) Epoch 2, batch 800, loss[loss=0.1508, simple_loss=0.2859, pruned_loss=0.07891, over 7064.00 frames.], tot_loss[loss=0.1799, simple_loss=0.3343, pruned_loss=0.1274, over 1399965.39 frames.], batch size: 18, lr: 2.46e-03
2022-05-13 21:07:06,098 INFO [train.py:812] (6/8) Epoch 2, batch 850, loss[loss=0.1593, simple_loss=0.3011, pruned_loss=0.08812, over 7330.00 frames.], tot_loss[loss=0.179, simple_loss=0.3329, pruned_loss=0.1256, over 1407670.66 frames.], batch size: 20, lr: 2.45e-03
2022-05-13 21:08:05,131 INFO [train.py:812] (6/8) Epoch 2, batch 900, loss[loss=0.1601, simple_loss=0.3019, pruned_loss=0.0915, over 7426.00 frames.], tot_loss[loss=0.1792, simple_loss=0.3333, pruned_loss=0.1254, over 1412400.03 frames.], batch size: 20, lr: 2.45e-03
2022-05-13 21:09:04,134 INFO [train.py:812] (6/8) Epoch 2, batch 950, loss[loss=0.1737, simple_loss=0.325, pruned_loss=0.1122, over 7270.00 frames.], tot_loss[loss=0.1795, simple_loss=0.3338, pruned_loss=0.1256, over 1415164.11 frames.], batch size: 19, lr: 2.44e-03
2022-05-13 21:10:02,117 INFO [train.py:812] (6/8) Epoch 2, batch 1000, loss[loss=0.213, simple_loss=0.3928, pruned_loss=0.1663, over 6948.00 frames.], tot_loss[loss=0.1795, simple_loss=0.334, pruned_loss=0.1252, over 1416752.35 frames.], batch size: 32, lr: 2.43e-03
2022-05-13 21:11:00,264 INFO [train.py:812] (6/8) Epoch 2, batch 1050, loss[loss=0.1656, simple_loss=0.3097, pruned_loss=0.1076, over 7438.00 frames.], tot_loss[loss=0.1782, simple_loss=0.3318, pruned_loss=0.1234, over 1419065.86 frames.], batch size: 20, lr: 2.43e-03
2022-05-13 21:11:59,253 INFO [train.py:812] (6/8) Epoch 2, batch 1100, loss[loss=0.1615, simple_loss=0.3024, pruned_loss=0.1024, over 7164.00 frames.], tot_loss[loss=0.1784, simple_loss=0.3322, pruned_loss=0.1229, over 1419785.40 frames.], batch size: 18, lr: 2.42e-03
2022-05-13 21:12:57,579 INFO [train.py:812] (6/8) Epoch 2, batch 1150, loss[loss=0.1561, simple_loss=0.2924, pruned_loss=0.09894, over 7238.00 frames.], tot_loss[loss=0.1778, simple_loss=0.331, pruned_loss=0.1226, over 1423983.44 frames.], batch size: 20, lr: 2.41e-03
2022-05-13 21:13:56,184 INFO [train.py:812] (6/8) Epoch 2, batch 1200, loss[loss=0.1822, simple_loss=0.3386, pruned_loss=0.1291, over 7134.00 frames.], tot_loss[loss=0.1776, simple_loss=0.3308, pruned_loss=0.1222, over 1423942.20 frames.], batch size: 28, lr: 2.41e-03
2022-05-13 21:14:54,781 INFO [train.py:812] (6/8) Epoch 2, batch 1250, loss[loss=0.1544, simple_loss=0.2918, pruned_loss=0.08499, over 7283.00 frames.], tot_loss[loss=0.1782, simple_loss=0.3319, pruned_loss=0.1227, over 1422994.39 frames.], batch size: 18, lr: 2.40e-03
2022-05-13 21:15:53,354 INFO [train.py:812] (6/8) Epoch 2, batch 1300, loss[loss=0.1936, simple_loss=0.3596, pruned_loss=0.1376, over 7215.00 frames.], tot_loss[loss=0.1781, simple_loss=0.3317, pruned_loss=0.1228, over 1417535.69 frames.], batch size: 21, lr: 2.40e-03
2022-05-13 21:16:52,363 INFO [train.py:812] (6/8) Epoch 2, batch 1350, loss[loss=0.164, simple_loss=0.3059, pruned_loss=0.111, over 7275.00 frames.], tot_loss[loss=0.1773, simple_loss=0.3302, pruned_loss=0.1218, over 1421139.58 frames.], batch size: 17, lr: 2.39e-03
2022-05-13 21:17:49,952 INFO [train.py:812] (6/8) Epoch 2, batch 1400, loss[loss=0.1721, simple_loss=0.3226, pruned_loss=0.1075, over 7224.00 frames.], tot_loss[loss=0.178, simple_loss=0.3316, pruned_loss=0.1224, over 1419741.88 frames.], batch size: 21, lr: 2.39e-03
2022-05-13 21:18:49,269 INFO [train.py:812] (6/8) Epoch 2, batch 1450, loss[loss=0.3228, simple_loss=0.3543, pruned_loss=0.1457, over 7143.00 frames.], tot_loss[loss=0.2006, simple_loss=0.3325, pruned_loss=0.1243, over 1423148.89 frames.], batch size: 26, lr: 2.38e-03
2022-05-13 21:19:47,689 INFO [train.py:812] (6/8) Epoch 2, batch 1500, loss[loss=0.3206, simple_loss=0.3478, pruned_loss=0.1467, over 6479.00 frames.], tot_loss[loss=0.2226, simple_loss=0.3342, pruned_loss=0.1255, over 1423710.46 frames.], batch size: 38, lr: 2.37e-03
2022-05-13 21:20:45,901 INFO [train.py:812] (6/8) Epoch 2, batch 1550, loss[loss=0.266, simple_loss=0.3234, pruned_loss=0.1043, over 7437.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3356, pruned_loss=0.1251, over 1427473.65 frames.], batch size: 20, lr: 2.37e-03
2022-05-13 21:21:43,124 INFO [train.py:812] (6/8) Epoch 2, batch 1600, loss[loss=0.2818, simple_loss=0.329, pruned_loss=0.1174, over 7164.00 frames.], tot_loss[loss=0.2483, simple_loss=0.334, pruned_loss=0.1236, over 1425903.04 frames.], batch size: 18, lr: 2.36e-03
2022-05-13 21:22:41,926 INFO [train.py:812] (6/8) Epoch 2, batch 1650, loss[loss=0.255, simple_loss=0.3071, pruned_loss=0.1014, over 7422.00 frames.], tot_loss[loss=0.2572, simple_loss=0.334, pruned_loss=0.1232, over 1427129.10 frames.], batch size: 20, lr: 2.36e-03
2022-05-13 21:23:40,005 INFO [train.py:812] (6/8) Epoch 2, batch 1700, loss[loss=0.3101, simple_loss=0.3615, pruned_loss=0.1294, over 7413.00 frames.], tot_loss[loss=0.2625, simple_loss=0.3333, pruned_loss=0.1214, over 1424740.26 frames.], batch size: 21, lr: 2.35e-03
2022-05-13 21:24:39,037 INFO [train.py:812] (6/8) Epoch 2, batch 1750, loss[loss=0.281, simple_loss=0.3194, pruned_loss=0.1214, over 7291.00 frames.], tot_loss[loss=0.2692, simple_loss=0.3352, pruned_loss=0.1216, over 1424315.63 frames.], batch size: 18, lr: 2.34e-03
2022-05-13 21:25:38,314 INFO [train.py:812] (6/8) Epoch 2, batch 1800, loss[loss=0.2487, simple_loss=0.301, pruned_loss=0.0982, over 7357.00 frames.], tot_loss[loss=0.2733, simple_loss=0.3355, pruned_loss=0.1211, over 1425704.77 frames.], batch size: 19, lr: 2.34e-03
2022-05-13 21:26:37,488 INFO [train.py:812] (6/8) Epoch 2, batch 1850, loss[loss=0.2611, simple_loss=0.3277, pruned_loss=0.09727, over 7326.00 frames.], tot_loss[loss=0.2738, simple_loss=0.3337, pruned_loss=0.119, over 1425410.65 frames.], batch size: 20, lr: 2.33e-03
2022-05-13 21:27:35,759 INFO [train.py:812] (6/8) Epoch 2, batch 1900, loss[loss=0.2264, simple_loss=0.2763, pruned_loss=0.08828, over 7001.00 frames.], tot_loss[loss=0.2744, simple_loss=0.3331, pruned_loss=0.1173, over 1429252.94 frames.], batch size: 16, lr: 2.33e-03
2022-05-13 21:28:33,677 INFO [train.py:812] (6/8) Epoch 2, batch 1950, loss[loss=0.239, simple_loss=0.3034, pruned_loss=0.08727, over 7270.00 frames.], tot_loss[loss=0.2756, simple_loss=0.3329, pruned_loss=0.1164, over 1429809.69 frames.], batch size: 18, lr: 2.32e-03
2022-05-13 21:29:31,791 INFO [train.py:812] (6/8) Epoch 2, batch 2000, loss[loss=0.3227, simple_loss=0.3718, pruned_loss=0.1368, over 7108.00 frames.], tot_loss[loss=0.2775, simple_loss=0.3341, pruned_loss=0.1161, over 1423316.97 frames.], batch size: 21, lr: 2.32e-03
2022-05-13 21:30:31,558 INFO [train.py:812] (6/8) Epoch 2, batch 2050, loss[loss=0.3662, simple_loss=0.3963, pruned_loss=0.168, over 7024.00 frames.], tot_loss[loss=0.2773, simple_loss=0.3333, pruned_loss=0.1151, over 1424237.80 frames.], batch size: 28, lr: 2.31e-03
2022-05-13 21:31:31,048 INFO [train.py:812] (6/8) Epoch 2, batch 2100, loss[loss=0.2417, simple_loss=0.2986, pruned_loss=0.09241, over 7404.00 frames.], tot_loss[loss=0.2772, simple_loss=0.3329, pruned_loss=0.1142, over 1424783.07 frames.], batch size: 18, lr: 2.31e-03
2022-05-13 21:32:30,584 INFO [train.py:812] (6/8) Epoch 2, batch 2150, loss[loss=0.2974, simple_loss=0.358, pruned_loss=0.1184, over 7404.00 frames.], tot_loss[loss=0.2768, simple_loss=0.3322, pruned_loss=0.1134, over 1423192.93 frames.], batch size: 21, lr: 2.30e-03
2022-05-13 21:33:29,521 INFO [train.py:812] (6/8) Epoch 2, batch 2200, loss[loss=0.2734, simple_loss=0.3388, pruned_loss=0.104, over 7116.00 frames.], tot_loss[loss=0.2758, simple_loss=0.331, pruned_loss=0.1124, over 1422787.09 frames.], batch size: 21, lr: 2.29e-03
2022-05-13 21:34:29,373 INFO [train.py:812] (6/8) Epoch 2, batch 2250, loss[loss=0.2283, simple_loss=0.3118, pruned_loss=0.07243, over 7213.00 frames.], tot_loss[loss=0.2748, simple_loss=0.3304, pruned_loss=0.1113, over 1423864.38 frames.], batch size: 21, lr: 2.29e-03
2022-05-13 21:35:27,856 INFO [train.py:812] (6/8) Epoch 2, batch 2300, loss[loss=0.2794, simple_loss=0.333, pruned_loss=0.1129, over 7212.00 frames.], tot_loss[loss=0.2743, simple_loss=0.3303, pruned_loss=0.1104, over 1425350.55 frames.], batch size: 22, lr: 2.28e-03
2022-05-13 21:36:26,837 INFO [train.py:812] (6/8) Epoch 2, batch 2350, loss[loss=0.2929, simple_loss=0.3575, pruned_loss=0.1142, over 7241.00 frames.], tot_loss[loss=0.2764, simple_loss=0.3316, pruned_loss=0.1116, over 1423522.08 frames.], batch size: 20, lr: 2.28e-03
2022-05-13 21:37:24,988 INFO [train.py:812] (6/8) Epoch 2, batch 2400, loss[loss=0.3237, simple_loss=0.3711, pruned_loss=0.1382, over 7324.00 frames.], tot_loss[loss=0.2764, simple_loss=0.3317, pruned_loss=0.1113, over 1423948.09 frames.], batch size: 21, lr: 2.27e-03
2022-05-13 21:38:23,798 INFO [train.py:812] (6/8) Epoch 2, batch 2450, loss[loss=0.2693, simple_loss=0.3419, pruned_loss=0.09836, over 7321.00 frames.], tot_loss[loss=0.2772, simple_loss=0.3325, pruned_loss=0.1116, over 1427732.65 frames.], batch size: 21, lr: 2.27e-03
2022-05-13 21:39:23,292 INFO [train.py:812] (6/8) Epoch 2, batch 2500, loss[loss=0.321, simple_loss=0.3733, pruned_loss=0.1343, over 7190.00 frames.], tot_loss[loss=0.2773, simple_loss=0.3327, pruned_loss=0.1114, over 1427748.48 frames.], batch size: 26, lr: 2.26e-03
2022-05-13 21:40:22,003 INFO [train.py:812] (6/8) Epoch 2, batch 2550, loss[loss=0.248, simple_loss=0.3103, pruned_loss=0.09283, over 6995.00 frames.], tot_loss[loss=0.2764, simple_loss=0.3324, pruned_loss=0.1105, over 1427880.56 frames.], batch size: 16, lr: 2.26e-03
2022-05-13 21:41:21,139 INFO [train.py:812] (6/8) Epoch 2, batch 2600, loss[loss=0.3246, simple_loss=0.369, pruned_loss=0.1401, over 7148.00 frames.], tot_loss[loss=0.2736, simple_loss=0.3301, pruned_loss=0.1088, over 1429931.60 frames.], batch size: 26, lr: 2.25e-03
2022-05-13 21:42:20,633 INFO [train.py:812] (6/8) Epoch 2, batch 2650, loss[loss=0.3149, simple_loss=0.3649, pruned_loss=0.1324, over 6331.00 frames.], tot_loss[loss=0.2737, simple_loss=0.3302, pruned_loss=0.1089, over 1428530.21 frames.], batch size: 37, lr: 2.25e-03
2022-05-13 21:43:18,322 INFO [train.py:812] (6/8) Epoch 2, batch 2700, loss[loss=0.301, simple_loss=0.3475, pruned_loss=0.1273, over 6781.00 frames.], tot_loss[loss=0.2708, simple_loss=0.3275, pruned_loss=0.1072, over 1427759.94 frames.], batch size: 31, lr: 2.24e-03
2022-05-13 21:44:17,972 INFO [train.py:812] (6/8) Epoch 2, batch 2750, loss[loss=0.2857, simple_loss=0.3472, pruned_loss=0.1121, over 7274.00 frames.], tot_loss[loss=0.2715, simple_loss=0.3281, pruned_loss=0.1076, over 1423218.31 frames.], batch size: 24, lr: 2.24e-03
2022-05-13 21:45:15,693 INFO [train.py:812] (6/8) Epoch 2, batch 2800, loss[loss=0.2622, simple_loss=0.338, pruned_loss=0.09321, over 7208.00 frames.], tot_loss[loss=0.2697, simple_loss=0.3276, pruned_loss=0.106, over 1425947.48 frames.], batch size: 23, lr: 2.23e-03
2022-05-13 21:46:14,856 INFO [train.py:812] (6/8) Epoch 2, batch 2850, loss[loss=0.2505, simple_loss=0.3228, pruned_loss=0.08903, over 7299.00 frames.], tot_loss[loss=0.269, simple_loss=0.3272, pruned_loss=0.1055, over 1425804.86 frames.], batch size: 24, lr: 2.23e-03
2022-05-13 21:47:13,487 INFO [train.py:812] (6/8) Epoch 2, batch 2900, loss[loss=0.2741, simple_loss=0.3461, pruned_loss=0.101, over 7246.00 frames.], tot_loss[loss=0.2712, simple_loss=0.3291, pruned_loss=0.1067, over 1420964.47 frames.], batch size: 20, lr: 2.22e-03
2022-05-13 21:48:11,752 INFO [train.py:812] (6/8) Epoch 2, batch 2950, loss[loss=0.2635, simple_loss=0.3311, pruned_loss=0.09794, over 7241.00 frames.], tot_loss[loss=0.2707, simple_loss=0.3291, pruned_loss=0.1062, over 1422225.32 frames.], batch size: 20, lr: 2.22e-03
2022-05-13 21:49:10,907 INFO [train.py:812] (6/8) Epoch 2, batch 3000, loss[loss=0.2342, simple_loss=0.2877, pruned_loss=0.09033, over 7289.00 frames.], tot_loss[loss=0.2688, simple_loss=0.3278, pruned_loss=0.105, over 1425305.87 frames.], batch size: 17, lr: 2.21e-03
2022-05-13 21:49:10,908 INFO [train.py:832] (6/8) Computing validation loss
2022-05-13 21:49:18,580 INFO [train.py:841] (6/8) Epoch 2, validation: loss=0.2016, simple_loss=0.2977, pruned_loss=0.0527, over 698248.00 frames.
2022-05-13 21:50:17,425 INFO [train.py:812] (6/8) Epoch 2, batch 3050, loss[loss=0.2265, simple_loss=0.2901, pruned_loss=0.0815, over 7283.00 frames.], tot_loss[loss=0.2685, simple_loss=0.3274, pruned_loss=0.1048, over 1421561.40 frames.], batch size: 18, lr: 2.20e-03
2022-05-13 21:51:15,129 INFO [train.py:812] (6/8) Epoch 2, batch 3100, loss[loss=0.3326, simple_loss=0.3648, pruned_loss=0.1502, over 5167.00 frames.], tot_loss[loss=0.2686, simple_loss=0.3279, pruned_loss=0.1047, over 1421580.94 frames.], batch size: 52, lr: 2.20e-03
2022-05-13 21:52:14,008 INFO [train.py:812] (6/8) Epoch 2, batch 3150, loss[loss=0.2782, simple_loss=0.3165, pruned_loss=0.12, over 6785.00 frames.], tot_loss[loss=0.2691, simple_loss=0.3281, pruned_loss=0.105, over 1423600.41 frames.], batch size: 15, lr: 2.19e-03
2022-05-13 21:53:13,106 INFO [train.py:812] (6/8) Epoch 2, batch 3200, loss[loss=0.3294, simple_loss=0.365, pruned_loss=0.1469, over 5025.00 frames.], tot_loss[loss=0.2701, simple_loss=0.3291, pruned_loss=0.1056, over 1413087.32 frames.], batch size: 52, lr: 2.19e-03
2022-05-13 21:54:12,682 INFO [train.py:812] (6/8) Epoch 2, batch 3250, loss[loss=0.299, simple_loss=0.3551, pruned_loss=0.1214, over 7196.00 frames.], tot_loss[loss=0.2694, simple_loss=0.3289, pruned_loss=0.1049, over 1416020.91 frames.], batch size: 23, lr: 2.18e-03
2022-05-13 21:55:12,304 INFO [train.py:812] (6/8) Epoch 2, batch 3300, loss[loss=0.231, simple_loss=0.308, pruned_loss=0.07697, over 7198.00 frames.], tot_loss[loss=0.2677, simple_loss=0.3275, pruned_loss=0.104, over 1421231.48 frames.], batch size: 22, lr: 2.18e-03
2022-05-13 21:56:12,056 INFO [train.py:812] (6/8) Epoch 2, batch 3350, loss[loss=0.2759, simple_loss=0.3503, pruned_loss=0.1007, over 7211.00 frames.], tot_loss[loss=0.2673, simple_loss=0.328, pruned_loss=0.1033, over 1423926.24 frames.], batch size: 26, lr: 2.18e-03
2022-05-13 21:57:11,193 INFO [train.py:812] (6/8) Epoch 2, batch 3400, loss[loss=0.2303, simple_loss=0.302, pruned_loss=0.0793, over 7116.00 frames.], tot_loss[loss=0.2665, simple_loss=0.3272, pruned_loss=0.1029, over 1425704.00 frames.], batch size: 17, lr: 2.17e-03
2022-05-13 21:58:14,498 INFO [train.py:812] (6/8) Epoch 2, batch 3450, loss[loss=0.3307, simple_loss=0.3725, pruned_loss=0.1445, over 7298.00 frames.], tot_loss[loss=0.2671, simple_loss=0.3277, pruned_loss=0.1033, over 1427769.04 frames.], batch size: 24, lr: 2.17e-03
2022-05-13 21:59:13,392 INFO [train.py:812] (6/8) Epoch 2, batch 3500, loss[loss=0.2901, simple_loss=0.3345, pruned_loss=0.1229, over 6402.00 frames.], tot_loss[loss=0.2667, simple_loss=0.3273, pruned_loss=0.1031, over 1424803.14 frames.], batch size: 38, lr: 2.16e-03
2022-05-13 22:00:12,709 INFO [train.py:812] (6/8) Epoch 2, batch 3550, loss[loss=0.2611, simple_loss=0.3274, pruned_loss=0.09742, over 7315.00 frames.], tot_loss[loss=0.265, simple_loss=0.3258, pruned_loss=0.1021, over 1424593.48 frames.], batch size: 25, lr: 2.16e-03
2022-05-13 22:01:11,602 INFO [train.py:812] (6/8) Epoch 2, batch 3600, loss[loss=0.2527, simple_loss=0.3233, pruned_loss=0.09108, over 7234.00 frames.], tot_loss[loss=0.2659, simple_loss=0.3268, pruned_loss=0.1024, over 1425590.75 frames.], batch size: 20, lr: 2.15e-03
2022-05-13 22:02:11,452 INFO [train.py:812] (6/8) Epoch 2, batch 3650, loss[loss=0.263, simple_loss=0.3223, pruned_loss=0.1018, over 7230.00 frames.], tot_loss[loss=0.2656, simple_loss=0.3265, pruned_loss=0.1023, over 1427354.35 frames.], batch size: 16, lr: 2.15e-03
2022-05-13 22:03:10,453 INFO [train.py:812] (6/8) Epoch 2, batch 3700, loss[loss=0.2763, simple_loss=0.3378, pruned_loss=0.1073, over 7161.00 frames.], tot_loss[loss=0.2659, simple_loss=0.3272, pruned_loss=0.1023, over 1428975.40 frames.], batch size: 19, lr: 2.14e-03
2022-05-13 22:04:09,872 INFO [train.py:812] (6/8) Epoch 2, batch 3750, loss[loss=0.2825, simple_loss=0.3469, pruned_loss=0.1091, over 7272.00 frames.], tot_loss[loss=0.2658, simple_loss=0.3273, pruned_loss=0.1022, over 1430211.10 frames.], batch size: 24, lr: 2.14e-03
2022-05-13 22:05:09,272 INFO [train.py:812] (6/8) Epoch 2, batch 3800, loss[loss=0.2416, simple_loss=0.2979, pruned_loss=0.0927, over 6797.00 frames.], tot_loss[loss=0.2654, simple_loss=0.3265, pruned_loss=0.1022, over 1429995.49 frames.], batch size: 15, lr: 2.13e-03
2022-05-13 22:06:07,966 INFO [train.py:812] (6/8) Epoch 2, batch 3850, loss[loss=0.2816, simple_loss=0.3449, pruned_loss=0.1092, over 7118.00 frames.], tot_loss[loss=0.2662, simple_loss=0.3275, pruned_loss=0.1024, over 1430993.18 frames.], batch size: 26, lr: 2.13e-03
2022-05-13 22:07:06,195 INFO [train.py:812] (6/8) Epoch 2, batch 3900, loss[loss=0.2585, simple_loss=0.3358, pruned_loss=0.09064, over 7293.00 frames.], tot_loss[loss=0.264, simple_loss=0.3261, pruned_loss=0.1009, over 1429986.37 frames.], batch size: 24, lr: 2.12e-03
2022-05-13 22:08:05,678 INFO [train.py:812] (6/8) Epoch 2, batch 3950, loss[loss=0.2669, simple_loss=0.321, pruned_loss=0.1064, over 7108.00 frames.], tot_loss[loss=0.2625, simple_loss=0.3245, pruned_loss=0.1003, over 1428460.37 frames.], batch size: 21, lr: 2.12e-03
2022-05-13 22:09:04,775 INFO [train.py:812] (6/8) Epoch 2, batch 4000, loss[loss=0.244, simple_loss=0.3275, pruned_loss=0.08029, over 7210.00 frames.], tot_loss[loss=0.2614, simple_loss=0.3239, pruned_loss=0.09942, over 1428730.16 frames.], batch size: 22, lr: 2.11e-03
2022-05-13 22:10:02,688 INFO [train.py:812] (6/8) Epoch 2, batch 4050, loss[loss=0.28, simple_loss=0.3431, pruned_loss=0.1084, over 6678.00 frames.], tot_loss[loss=0.2627, simple_loss=0.3248, pruned_loss=0.1003, over 1426758.81 frames.], batch size: 31, lr: 2.11e-03
2022-05-13 22:11:01,218 INFO [train.py:812] (6/8) Epoch 2, batch 4100, loss[loss=0.2551, simple_loss=0.3256, pruned_loss=0.09231, over 7222.00 frames.], tot_loss[loss=0.2639, simple_loss=0.3257, pruned_loss=0.101, over 1420880.32 frames.], batch size: 21, lr: 2.10e-03
2022-05-13 22:11:59,879 INFO [train.py:812] (6/8) Epoch 2, batch 4150, loss[loss=0.2887, simple_loss=0.3448, pruned_loss=0.1163, over 6774.00 frames.], tot_loss[loss=0.2617, simple_loss=0.3242, pruned_loss=0.09955, over 1420368.40 frames.], batch size: 31, lr: 2.10e-03
2022-05-13 22:12:58,526 INFO [train.py:812] (6/8) Epoch 2, batch 4200, loss[loss=0.2271, simple_loss=0.2952, pruned_loss=0.07952, over 7288.00 frames.], tot_loss[loss=0.2617, simple_loss=0.3238, pruned_loss=0.09977, over 1419506.74 frames.], batch size: 18, lr: 2.10e-03
2022-05-13 22:13:58,095 INFO [train.py:812] (6/8) Epoch 2, batch 4250, loss[loss=0.2139, simple_loss=0.2745, pruned_loss=0.0767, over 7274.00 frames.], tot_loss[loss=0.2631, simple_loss=0.3249, pruned_loss=0.1007, over 1414607.66 frames.], batch size: 18, lr: 2.09e-03
2022-05-13 22:14:56,723 INFO [train.py:812] (6/8) Epoch 2, batch 4300, loss[loss=0.2564, simple_loss=0.3245, pruned_loss=0.09413, over 7323.00 frames.], tot_loss[loss=0.2632, simple_loss=0.3249, pruned_loss=0.1007, over 1413059.00 frames.], batch size: 25, lr: 2.09e-03
2022-05-13 22:15:55,438 INFO [train.py:812] (6/8) Epoch 2, batch 4350, loss[loss=0.2111, simple_loss=0.27, pruned_loss=0.07614, over 6994.00 frames.], tot_loss[loss=0.2629, simple_loss=0.3246, pruned_loss=0.1006, over 1413662.38 frames.], batch size: 16, lr: 2.08e-03
2022-05-13 22:16:54,217 INFO [train.py:812] (6/8) Epoch 2, batch 4400, loss[loss=0.2563, simple_loss=0.3233, pruned_loss=0.09469, over 7318.00 frames.], tot_loss[loss=0.2627, simple_loss=0.3246, pruned_loss=0.1004, over 1407996.78 frames.], batch size: 21, lr: 2.08e-03
2022-05-13 22:17:52,748 INFO [train.py:812] (6/8) Epoch 2, batch 4450, loss[loss=0.3197, simple_loss=0.3699, pruned_loss=0.1347, over 6372.00 frames.], tot_loss[loss=0.2644, simple_loss=0.3258, pruned_loss=0.1015, over 1400148.97 frames.], batch size: 38, lr: 2.07e-03
2022-05-13 22:18:50,620 INFO [train.py:812] (6/8) Epoch 2, batch 4500, loss[loss=0.2943, simple_loss=0.3479, pruned_loss=0.1204, over 6212.00 frames.], tot_loss[loss=0.2634, simple_loss=0.3247, pruned_loss=0.101, over 1385893.14 frames.], batch size: 37, lr: 2.07e-03
2022-05-13 22:19:49,239 INFO [train.py:812] (6/8) Epoch 2, batch 4550, loss[loss=0.3243, simple_loss=0.3682, pruned_loss=0.1402, over 5092.00 frames.], tot_loss[loss=0.2665, simple_loss=0.3272, pruned_loss=0.1029, over 1354056.21 frames.], batch size: 52, lr: 2.06e-03
2022-05-13 22:20:58,934 INFO [train.py:812] (6/8) Epoch 3, batch 0, loss[loss=0.2329, simple_loss=0.2847, pruned_loss=0.09055, over 7280.00 frames.], tot_loss[loss=0.2329, simple_loss=0.2847, pruned_loss=0.09055, over 7280.00 frames.], batch size: 17, lr: 2.02e-03
2022-05-13 22:21:58,069 INFO [train.py:812] (6/8) Epoch 3, batch 50, loss[loss=0.266, simple_loss=0.3363, pruned_loss=0.09787, over 7283.00 frames.], tot_loss[loss=0.2623, simple_loss=0.3252, pruned_loss=0.09976, over 321241.17 frames.], batch size: 25, lr: 2.02e-03
2022-05-13 22:22:56,169 INFO [train.py:812] (6/8) Epoch 3, batch 100, loss[loss=0.2252, simple_loss=0.2834, pruned_loss=0.08355, over 6998.00 frames.], tot_loss[loss=0.2579, simple_loss=0.3227, pruned_loss=0.09658, over 568512.86 frames.], batch size: 16, lr: 2.01e-03
2022-05-13 22:23:56,173 INFO [train.py:812] (6/8) Epoch 3, batch 150, loss[loss=0.2651, simple_loss=0.3491, pruned_loss=0.0905, over 6826.00 frames.], tot_loss[loss=0.2556, simple_loss=0.3198, pruned_loss=0.09569, over 761177.59 frames.], batch size: 31, lr: 2.01e-03
2022-05-13 22:24:53,596 INFO [train.py:812] (6/8) Epoch 3, batch 200, loss[loss=0.2064, simple_loss=0.2676, pruned_loss=0.07258, over 6849.00 frames.], tot_loss[loss=0.2538, simple_loss=0.318, pruned_loss=0.0948, over 900879.98 frames.], batch size: 15, lr: 2.00e-03
2022-05-13 22:25:53,024 INFO [train.py:812] (6/8) Epoch 3, batch 250, loss[loss=0.2599, simple_loss=0.3276, pruned_loss=0.09609, over 7359.00 frames.], tot_loss[loss=0.2541, simple_loss=0.3185, pruned_loss=0.09483, over 1011191.07 frames.], batch size: 19, lr: 2.00e-03
2022-05-13 22:26:52,124 INFO [train.py:812] (6/8) Epoch 3, batch 300, loss[loss=0.274, simple_loss=0.3483, pruned_loss=0.09981, over 6745.00 frames.], tot_loss[loss=0.2546, simple_loss=0.3196, pruned_loss=0.09475, over 1101417.44 frames.], batch size: 31, lr: 2.00e-03
2022-05-13 22:27:51,986 INFO [train.py:812] (6/8) Epoch 3, batch 350, loss[loss=0.2592, simple_loss=0.3211, pruned_loss=0.0986, over 7324.00 frames.], tot_loss[loss=0.2549, simple_loss=0.3202, pruned_loss=0.09483, over 1172001.70 frames.], batch size: 21, lr: 1.99e-03
2022-05-13 22:29:00,810 INFO [train.py:812] (6/8) Epoch 3, batch 400, loss[loss=0.2796, simple_loss=0.3343, pruned_loss=0.1124, over 7282.00 frames.], tot_loss[loss=0.2561, simple_loss=0.3208, pruned_loss=0.09571, over 1224064.91 frames.], batch size: 24, lr: 1.99e-03
2022-05-13 22:29:59,470 INFO [train.py:812] (6/8) Epoch 3, batch 450, loss[loss=0.2723, simple_loss=0.337, pruned_loss=0.1038, over 7205.00 frames.], tot_loss[loss=0.257, simple_loss=0.3223, pruned_loss=0.09589, over 1263835.46 frames.], batch size: 22, lr: 1.98e-03
2022-05-13 22:31:07,401 INFO [train.py:812] (6/8) Epoch 3, batch 500, loss[loss=0.2432, simple_loss=0.3131, pruned_loss=0.08663, over 7012.00 frames.], tot_loss[loss=0.2559, simple_loss=0.3216, pruned_loss=0.09515, over 1302332.95 frames.], batch size: 16, lr: 1.98e-03
2022-05-13 22:32:54,342 INFO [train.py:812] (6/8) Epoch 3, batch 550, loss[loss=0.2696, simple_loss=0.3463, pruned_loss=0.09647, over 7224.00 frames.], tot_loss[loss=0.2551, simple_loss=0.321, pruned_loss=0.09455, over 1331955.28 frames.], batch size: 21, lr: 1.98e-03
2022-05-13 22:34:03,108 INFO [train.py:812] (6/8) Epoch 3, batch 600, loss[loss=0.3237, simple_loss=0.3841, pruned_loss=0.1317, over 7291.00 frames.], tot_loss[loss=0.2559, simple_loss=0.3212, pruned_loss=0.09532, over 1353345.83 frames.], batch size: 25, lr: 1.97e-03
2022-05-13 22:35:02,667 INFO [train.py:812] (6/8) Epoch 3, batch 650, loss[loss=0.2429, simple_loss=0.3088, pruned_loss=0.08849, over 7353.00 frames.], tot_loss[loss=0.2555, simple_loss=0.3208, pruned_loss=0.09509, over 1367251.10 frames.], batch size: 19, lr: 1.97e-03
2022-05-13 22:36:02,061 INFO [train.py:812] (6/8) Epoch 3, batch 700, loss[loss=0.2287, simple_loss=0.3121, pruned_loss=0.07269, over 7211.00 frames.], tot_loss[loss=0.2543, simple_loss=0.3202, pruned_loss=0.0942, over 1377529.58 frames.], batch size: 21, lr: 1.96e-03
2022-05-13 22:37:01,828 INFO [train.py:812] (6/8) Epoch 3, batch 750, loss[loss=0.2688, simple_loss=0.3346, pruned_loss=0.1015, over 7208.00 frames.], tot_loss[loss=0.2546, simple_loss=0.3205, pruned_loss=0.09435, over 1391142.13 frames.], batch size: 23, lr: 1.96e-03
2022-05-13 22:38:00,549 INFO [train.py:812] (6/8) Epoch 3, batch 800, loss[loss=0.2787, simple_loss=0.3524, pruned_loss=0.1025, over 7211.00 frames.], tot_loss[loss=0.2537, simple_loss=0.3204, pruned_loss=0.09354, over 1401989.90 frames.], batch size: 23, lr: 1.96e-03
2022-05-13 22:38:59,720 INFO [train.py:812] (6/8) Epoch 3, batch 850, loss[loss=0.2836, simple_loss=0.3496, pruned_loss=0.1088, over 7304.00 frames.], tot_loss[loss=0.2521, simple_loss=0.319, pruned_loss=0.09259, over 1409668.86 frames.], batch size: 25, lr: 1.95e-03
2022-05-13 22:39:58,504 INFO [train.py:812] (6/8) Epoch 3, batch 900, loss[loss=0.2595, simple_loss=0.3294, pruned_loss=0.09478, over 7056.00 frames.], tot_loss[loss=0.2548, simple_loss=0.3209, pruned_loss=0.09431, over 1412331.11 frames.], batch size: 18, lr: 1.95e-03
2022-05-13 22:40:58,636 INFO [train.py:812] (6/8) Epoch 3, batch 950, loss[loss=0.2585, simple_loss=0.3296, pruned_loss=0.09367, over 7144.00 frames.], tot_loss[loss=0.2546, simple_loss=0.3209, pruned_loss=0.09415, over 1417210.20 frames.], batch size: 20, lr: 1.94e-03
2022-05-13 22:41:58,352 INFO [train.py:812] (6/8) Epoch 3, batch 1000, loss[loss=0.2552, simple_loss=0.3226, pruned_loss=0.09385, over 6731.00 frames.], tot_loss[loss=0.2557, simple_loss=0.3216, pruned_loss=0.09486, over 1416915.62 frames.], batch size: 31, lr: 1.94e-03
2022-05-13 22:42:57,498 INFO [train.py:812] (6/8) Epoch 3, batch 1050, loss[loss=0.1974, simple_loss=0.2678, pruned_loss=0.06353, over 7284.00 frames.], tot_loss[loss=0.2546, simple_loss=0.3209, pruned_loss=0.09413, over 1414255.61 frames.], batch size: 18, lr: 1.94e-03
2022-05-13 22:43:56,796 INFO [train.py:812] (6/8) Epoch 3, batch 1100, loss[loss=0.2438, simple_loss=0.318, pruned_loss=0.08478, over 7221.00 frames.], tot_loss[loss=0.2547, simple_loss=0.3215, pruned_loss=0.09391, over 1419263.44 frames.], batch size: 21, lr: 1.93e-03
2022-05-13 22:44:56,340 INFO [train.py:812] (6/8) Epoch 3, batch 1150, loss[loss=0.2118, simple_loss=0.2842, pruned_loss=0.0697, over 7227.00 frames.], tot_loss[loss=0.2523, simple_loss=0.3191, pruned_loss=0.0927, over 1421243.29 frames.], batch size: 20, lr: 1.93e-03
2022-05-13 22:45:54,833 INFO [train.py:812] (6/8) Epoch 3, batch 1200, loss[loss=0.2067, simple_loss=0.2922, pruned_loss=0.06057, over 7416.00 frames.], tot_loss[loss=0.2512, simple_loss=0.3183, pruned_loss=0.09199, over 1424224.86 frames.], batch size: 20, lr: 1.93e-03
2022-05-13 22:46:52,765 INFO [train.py:812] (6/8) Epoch 3, batch 1250, loss[loss=0.2488, simple_loss=0.3127, pruned_loss=0.0925, over 7411.00 frames.], tot_loss[loss=0.2509, simple_loss=0.3179, pruned_loss=0.09198, over 1424684.90 frames.], batch size: 21, lr: 1.92e-03
2022-05-13 22:47:52,037 INFO [train.py:812] (6/8) Epoch 3, batch 1300, loss[loss=0.2375, simple_loss=0.3081, pruned_loss=0.08343, over 7304.00 frames.], tot_loss[loss=0.2491, simple_loss=0.3166, pruned_loss=0.09085, over 1426500.69 frames.], batch size: 21, lr: 1.92e-03
2022-05-13 22:48:50,092 INFO [train.py:812] (6/8) Epoch 3, batch 1350, loss[loss=0.2094, simple_loss=0.2891, pruned_loss=0.06486, over 7431.00 frames.], tot_loss[loss=0.249, simple_loss=0.3169, pruned_loss=0.09054, over 1426428.70 frames.], batch size: 20, lr: 1.91e-03
2022-05-13 22:49:48,134 INFO [train.py:812] (6/8) Epoch 3, batch 1400, loss[loss=0.2071, simple_loss=0.2837, pruned_loss=0.0653, over 7158.00 frames.], tot_loss[loss=0.2489, simple_loss=0.317, pruned_loss=0.0904, over 1423768.86 frames.], batch size: 19, lr: 1.91e-03
2022-05-13 22:50:48,095 INFO [train.py:812] (6/8) Epoch 3, batch 1450, loss[loss=0.2152, simple_loss=0.2811, pruned_loss=0.07466, over 7130.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3165, pruned_loss=0.09031, over 1420767.87 frames.], batch size: 17, lr: 1.91e-03
2022-05-13 22:51:46,944 INFO [train.py:812] (6/8) Epoch 3, batch 1500, loss[loss=0.2552, simple_loss=0.3244, pruned_loss=0.09303, over 7308.00 frames.], tot_loss[loss=0.2498, simple_loss=0.3171, pruned_loss=0.09125, over 1419016.41 frames.], batch size: 21, lr: 1.90e-03
2022-05-13 22:52:47,341 INFO [train.py:812] (6/8) Epoch 3, batch 1550, loss[loss=0.2211, simple_loss=0.3054, pruned_loss=0.0684, over 7156.00 frames.], tot_loss[loss=0.2505, simple_loss=0.3178, pruned_loss=0.09157, over 1422990.63 frames.], batch size: 19, lr: 1.90e-03
2022-05-13 22:53:45,780 INFO [train.py:812] (6/8) Epoch 3, batch 1600, loss[loss=0.2232, simple_loss=0.2965, pruned_loss=0.07495, over 7171.00 frames.], tot_loss[loss=0.2493, simple_loss=0.3169, pruned_loss=0.09086, over 1424568.16 frames.], batch size: 19, lr: 1.90e-03
2022-05-13 22:54:44,642 INFO [train.py:812] (6/8) Epoch 3, batch 1650, loss[loss=0.2272, simple_loss=0.309, pruned_loss=0.07266, over 7431.00 frames.], tot_loss[loss=0.2484, simple_loss=0.3164, pruned_loss=0.09016, over 1426669.31 frames.], batch size: 20, lr: 1.89e-03
2022-05-13 22:55:42,311 INFO [train.py:812] (6/8) Epoch 3, batch 1700, loss[loss=0.2565, simple_loss=0.3225, pruned_loss=0.09518, over 7147.00 frames.], tot_loss[loss=0.2506, simple_loss=0.3179, pruned_loss=0.09168, over 1416635.30 frames.], batch size: 20, lr: 1.89e-03
2022-05-13 22:56:41,872 INFO [train.py:812] (6/8) Epoch 3, batch 1750, loss[loss=0.2296, simple_loss=0.3134, pruned_loss=0.07295, over 7237.00 frames.], tot_loss[loss=0.2491, simple_loss=0.3169, pruned_loss=0.09062, over 1423991.52 frames.], batch size: 20, lr: 1.88e-03
2022-05-13 22:57:40,297 INFO [train.py:812] (6/8) Epoch 3, batch 1800, loss[loss=0.2514, simple_loss=0.3215, pruned_loss=0.09063, over 7113.00 frames.], tot_loss[loss=0.2498, simple_loss=0.3173, pruned_loss=0.09119, over 1417072.68 frames.], batch size: 21, lr: 1.88e-03
2022-05-13 22:58:39,770 INFO [train.py:812] (6/8) Epoch 3, batch 1850, loss[loss=0.2597, simple_loss=0.3311, pruned_loss=0.09414, over 7411.00 frames.], tot_loss[loss=0.2484, simple_loss=0.3161, pruned_loss=0.09038, over 1419950.24 frames.], batch size: 21, lr: 1.88e-03
2022-05-13 22:59:38,886 INFO [train.py:812] (6/8) Epoch 3, batch 1900, loss[loss=0.2353, simple_loss=0.3099, pruned_loss=0.08033, over 7160.00 frames.], tot_loss[loss=0.248, simple_loss=0.3157, pruned_loss=0.09016, over 1418154.49 frames.], batch size: 18, lr: 1.87e-03
2022-05-13 23:00:38,448 INFO [train.py:812] (6/8) Epoch 3, batch 1950, loss[loss=0.2635, simple_loss=0.3399, pruned_loss=0.09351, over 6870.00 frames.], tot_loss[loss=0.2468, simple_loss=0.3147, pruned_loss=0.08948, over 1419585.78 frames.], batch size: 31, lr: 1.87e-03
2022-05-13 23:01:37,614 INFO [train.py:812] (6/8) Epoch 3, batch 2000, loss[loss=0.228, simple_loss=0.3089, pruned_loss=0.07357, over 7147.00 frames.], tot_loss[loss=0.2472, simple_loss=0.3148, pruned_loss=0.08984, over 1424006.92 frames.], batch size: 19, lr: 1.87e-03
2022-05-13 23:02:36,940 INFO [train.py:812] (6/8) Epoch 3, batch 2050, loss[loss=0.3223, simple_loss=0.3717, pruned_loss=0.1364, over 4836.00 frames.], tot_loss[loss=0.2487, simple_loss=0.3166, pruned_loss=0.0904, over 1423370.45 frames.], batch size: 52, lr: 1.86e-03
2022-05-13 23:03:35,458 INFO [train.py:812] (6/8) Epoch 3, batch 2100, loss[loss=0.251, simple_loss=0.3307, pruned_loss=0.08567, over 7317.00 frames.], tot_loss[loss=0.2483, simple_loss=0.3165, pruned_loss=0.09, over 1425916.31 frames.], batch size: 21, lr: 1.86e-03
2022-05-13 23:04:34,079 INFO [train.py:812] (6/8) Epoch 3, batch 2150, loss[loss=0.2287, simple_loss=0.3078, pruned_loss=0.0748, over 7226.00 frames.], tot_loss[loss=0.2474, simple_loss=0.3159, pruned_loss=0.08949, over 1427328.02 frames.], batch size: 20, lr: 1.86e-03
2022-05-13 23:05:32,841 INFO [train.py:812] (6/8) Epoch 3, batch 2200, loss[loss=0.2672, simple_loss=0.3314, pruned_loss=0.1015, over 7138.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3147, pruned_loss=0.0885, over 1425602.81 frames.], batch size: 20, lr: 1.85e-03
2022-05-13 23:06:32,274 INFO [train.py:812] (6/8) Epoch 3, batch 2250, loss[loss=0.2351, simple_loss=0.3149, pruned_loss=0.07763, over 7323.00 frames.], tot_loss[loss=0.2465, simple_loss=0.3152, pruned_loss=0.08889, over 1424972.37 frames.], batch size: 20, lr: 1.85e-03
2022-05-13 23:07:31,568 INFO [train.py:812] (6/8) Epoch 3, batch 2300, loss[loss=0.216, simple_loss=0.2841, pruned_loss=0.07394, over 7378.00 frames.], tot_loss[loss=0.2467, simple_loss=0.3149, pruned_loss=0.08923, over 1414088.82 frames.], batch size: 19, lr: 1.85e-03
2022-05-13 23:08:31,282 INFO [train.py:812] (6/8) Epoch 3, batch 2350, loss[loss=0.2534, simple_loss=0.3097, pruned_loss=0.09857, over 7252.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3139, pruned_loss=0.0888, over 1415637.77 frames.], batch size: 19, lr: 1.84e-03
2022-05-13 23:09:29,613 INFO [train.py:812] (6/8) Epoch 3, batch 2400, loss[loss=0.2081, simple_loss=0.2767, pruned_loss=0.06979, over 7258.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3147, pruned_loss=0.08974, over 1418730.22 frames.], batch size: 19, lr: 1.84e-03
2022-05-13 23:10:29,115 INFO [train.py:812] (6/8) Epoch 3, batch 2450, loss[loss=0.3246, simple_loss=0.3676, pruned_loss=0.1408, over 7237.00 frames.], tot_loss[loss=0.2483, simple_loss=0.316, pruned_loss=0.09034, over 1416773.42 frames.], batch size: 20, lr: 1.84e-03
2022-05-13 23:11:28,081 INFO [train.py:812] (6/8) Epoch 3, batch 2500, loss[loss=0.2373, simple_loss=0.3148, pruned_loss=0.07993, over 7152.00 frames.], tot_loss[loss=0.2474, simple_loss=0.3151, pruned_loss=0.08986, over 1414335.41 frames.], batch size: 19, lr: 1.83e-03
2022-05-13 23:12:27,754 INFO [train.py:812] (6/8) Epoch 3, batch 2550, loss[loss=0.2263, simple_loss=0.2997, pruned_loss=0.07642, over 7220.00 frames.], tot_loss[loss=0.2463, simple_loss=0.3138, pruned_loss=0.08938, over 1413970.50 frames.], batch size: 21, lr: 1.83e-03
2022-05-13 23:13:27,076 INFO [train.py:812] (6/8) Epoch 3, batch 2600, loss[loss=0.1942, simple_loss=0.2835, pruned_loss=0.05247, over 7275.00 frames.], tot_loss[loss=0.2441, simple_loss=0.3125, pruned_loss=0.08781, over 1419922.67 frames.], batch size: 18, lr: 1.83e-03
2022-05-13 23:14:26,428 INFO [train.py:812] (6/8) Epoch 3, batch 2650, loss[loss=0.2173, simple_loss=0.3047, pruned_loss=0.06501, over 7319.00 frames.], tot_loss[loss=0.2439, simple_loss=0.3123, pruned_loss=0.08768, over 1419642.00 frames.], batch size: 20, lr: 1.82e-03
2022-05-13 23:15:24,419 INFO [train.py:812] (6/8) Epoch 3, batch 2700, loss[loss=0.2942, simple_loss=0.3221, pruned_loss=0.1331, over 7062.00 frames.], tot_loss[loss=0.244, simple_loss=0.3128, pruned_loss=0.08759, over 1420525.20 frames.], batch size: 18, lr: 1.82e-03
2022-05-13 23:16:23,944 INFO [train.py:812] (6/8) Epoch 3, batch 2750, loss[loss=0.2561, simple_loss=0.3367, pruned_loss=0.08776, over 7187.00 frames.], tot_loss[loss=0.2436, simple_loss=0.3129, pruned_loss=0.08712, over 1419320.98 frames.], batch size: 26, lr: 1.82e-03
2022-05-13 23:17:22,915 INFO [train.py:812] (6/8) Epoch 3, batch 2800, loss[loss=0.3039, simple_loss=0.3504, pruned_loss=0.1287, over 5137.00 frames.], tot_loss[loss=0.2433, simple_loss=0.3128, pruned_loss=0.0869, over 1419037.63 frames.], batch size: 52, lr: 1.81e-03
2022-05-13 23:18:30,783 INFO [train.py:812] (6/8) Epoch 3, batch 2850, loss[loss=0.2257, simple_loss=0.3073, pruned_loss=0.07206, over 7210.00 frames.], tot_loss[loss=0.2415, simple_loss=0.3113, pruned_loss=0.08585, over 1421091.80 frames.], batch size: 21, lr: 1.81e-03
2022-05-13 23:19:29,910 INFO [train.py:812] (6/8) Epoch 3, batch 2900, loss[loss=0.2741, simple_loss=0.3284, pruned_loss=0.1099, over 6428.00 frames.], tot_loss[loss=0.2419, simple_loss=0.3114, pruned_loss=0.08621, over 1416954.72 frames.], batch size: 37, lr: 1.81e-03
2022-05-13 23:20:29,322 INFO [train.py:812] (6/8) Epoch 3, batch 2950, loss[loss=0.2481, simple_loss=0.3316, pruned_loss=0.08226, over 7161.00 frames.], tot_loss[loss=0.2432, simple_loss=0.3129, pruned_loss=0.08674, over 1416858.56 frames.], batch size: 26, lr: 1.80e-03
2022-05-13 23:21:28,548 INFO [train.py:812] (6/8) Epoch 3, batch 3000, loss[loss=0.2431, simple_loss=0.308, pruned_loss=0.08904, over 7329.00 frames.], tot_loss[loss=0.2446, simple_loss=0.3137, pruned_loss=0.08774, over 1419569.21 frames.], batch size: 22, lr: 1.80e-03
2022-05-13 23:21:28,549 INFO [train.py:832] (6/8) Computing validation loss
2022-05-13 23:21:36,069 INFO [train.py:841] (6/8) Epoch 3, validation: loss=0.1862, simple_loss=0.2867, pruned_loss=0.04278, over 698248.00 frames.
2022-05-13 23:22:33,850 INFO [train.py:812] (6/8) Epoch 3, batch 3050, loss[loss=0.2116, simple_loss=0.2921, pruned_loss=0.0656, over 7410.00 frames.], tot_loss[loss=0.2447, simple_loss=0.3138, pruned_loss=0.08783, over 1425140.77 frames.], batch size: 21, lr: 1.80e-03
2022-05-13 23:23:30,796 INFO [train.py:812] (6/8) Epoch 3, batch 3100, loss[loss=0.2459, simple_loss=0.2953, pruned_loss=0.09824, over 7271.00 frames.], tot_loss[loss=0.2439, simple_loss=0.313, pruned_loss=0.08745, over 1428572.45 frames.], batch size: 18, lr: 1.79e-03
2022-05-13 23:24:30,092 INFO [train.py:812] (6/8) Epoch 3, batch 3150, loss[loss=0.2388, simple_loss=0.3252, pruned_loss=0.07614, over 7212.00 frames.], tot_loss[loss=0.2429, simple_loss=0.3118, pruned_loss=0.08697, over 1422710.66 frames.], batch size: 21, lr: 1.79e-03
2022-05-13 23:25:29,465 INFO [train.py:812] (6/8) Epoch 3, batch 3200, loss[loss=0.253, simple_loss=0.328, pruned_loss=0.08899, over 7370.00 frames.], tot_loss[loss=0.2451, simple_loss=0.3142, pruned_loss=0.08802, over 1425789.19 frames.], batch size: 23, lr: 1.79e-03
2022-05-13 23:26:29,190 INFO [train.py:812] (6/8) Epoch 3, batch 3250, loss[loss=0.1958, simple_loss=0.2776, pruned_loss=0.05698, over 7170.00 frames.], tot_loss[loss=0.2445, simple_loss=0.314, pruned_loss=0.08747, over 1427470.30 frames.], batch size: 19, lr: 1.79e-03
2022-05-13 23:27:27,205 INFO [train.py:812] (6/8) Epoch 3, batch 3300, loss[loss=0.2862, simple_loss=0.3446, pruned_loss=0.1139, over 7136.00 frames.], tot_loss[loss=0.2433, simple_loss=0.3133, pruned_loss=0.08663, over 1429594.03 frames.], batch size: 26, lr: 1.78e-03
2022-05-13 23:28:26,196 INFO [train.py:812] (6/8) Epoch 3, batch 3350, loss[loss=0.2856, simple_loss=0.3426, pruned_loss=0.1143, over 7278.00 frames.], tot_loss[loss=0.2441, simple_loss=0.3143, pruned_loss=0.08694, over 1426953.28 frames.], batch size: 18, lr: 1.78e-03
2022-05-13 23:29:23,913 INFO [train.py:812] (6/8) Epoch 3, batch 3400, loss[loss=0.2269, simple_loss=0.2928, pruned_loss=0.0805, over 7394.00 frames.], tot_loss[loss=0.2435, simple_loss=0.3142, pruned_loss=0.08639, over 1424939.93 frames.], batch size: 18, lr: 1.78e-03
2022-05-13 23:30:22,233 INFO [train.py:812] (6/8) Epoch 3, batch 3450, loss[loss=0.2173, simple_loss=0.282, pruned_loss=0.07631, over 7256.00 frames.], tot_loss[loss=0.2423, simple_loss=0.3129, pruned_loss=0.08589, over 1420890.05 frames.], batch size: 19, lr: 1.77e-03
2022-05-13 23:31:20,928 INFO [train.py:812] (6/8) Epoch 3, batch 3500, loss[loss=0.283, simple_loss=0.3513, pruned_loss=0.1073, over 7286.00 frames.], tot_loss[loss=0.2416, simple_loss=0.312, pruned_loss=0.08561, over 1422136.85 frames.], batch size: 25, lr: 1.77e-03
2022-05-13 23:32:20,549 INFO [train.py:812] (6/8) Epoch 3, batch 3550, loss[loss=0.239, simple_loss=0.323, pruned_loss=0.07746, over 7222.00 frames.], tot_loss[loss=0.2409, simple_loss=0.3116, pruned_loss=0.08511, over 1421060.81 frames.], batch size: 21, lr: 1.77e-03
2022-05-13 23:33:19,839 INFO [train.py:812] (6/8) Epoch 3, batch 3600, loss[loss=0.2561, simple_loss=0.315, pruned_loss=0.09857, over 7267.00 frames.], tot_loss[loss=0.2402, simple_loss=0.3107, pruned_loss=0.08485, over 1422069.17 frames.], batch size: 24, lr: 1.76e-03
2022-05-13 23:34:19,482 INFO [train.py:812] (6/8) Epoch 3, batch 3650, loss[loss=0.2639, simple_loss=0.3323, pruned_loss=0.09773, over 7378.00 frames.], tot_loss[loss=0.2405, simple_loss=0.3106, pruned_loss=0.08517, over 1421945.10 frames.], batch size: 23, lr: 1.76e-03
2022-05-13 23:35:18,560 INFO [train.py:812] (6/8) Epoch 3, batch 3700, loss[loss=0.1952, simple_loss=0.2692, pruned_loss=0.06056, over 7421.00 frames.], tot_loss[loss=0.2392, simple_loss=0.3098, pruned_loss=0.08432, over 1418496.12 frames.], batch size: 18, lr: 1.76e-03
2022-05-13 23:36:18,213 INFO [train.py:812] (6/8) Epoch 3, batch 3750, loss[loss=0.2239, simple_loss=0.2906, pruned_loss=0.07859, over 7286.00 frames.], tot_loss[loss=0.238, simple_loss=0.3086, pruned_loss=0.08368, over 1424116.89 frames.], batch size: 18, lr: 1.76e-03
2022-05-13 23:37:16,807 INFO [train.py:812] (6/8) Epoch 3, batch 3800, loss[loss=0.2213, simple_loss=0.2966, pruned_loss=0.07304, over 7169.00 frames.], tot_loss[loss=0.2375, simple_loss=0.3082, pruned_loss=0.08346, over 1424393.34 frames.], batch size: 18, lr: 1.75e-03
2022-05-13 23:38:16,213 INFO [train.py:812] (6/8) Epoch 3, batch 3850, loss[loss=0.2418, simple_loss=0.3209, pruned_loss=0.0813, over 7340.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3088, pruned_loss=0.08417, over 1422974.53 frames.], batch size: 22, lr: 1.75e-03
2022-05-13 23:39:15,492 INFO [train.py:812] (6/8) Epoch 3, batch 3900, loss[loss=0.2346, simple_loss=0.3084, pruned_loss=0.08043, over 7326.00 frames.], tot_loss[loss=0.2391, simple_loss=0.3095, pruned_loss=0.08437, over 1424687.83 frames.], batch size: 20, lr: 1.75e-03
2022-05-13 23:40:14,828 INFO [train.py:812] (6/8) Epoch 3, batch 3950, loss[loss=0.2566, simple_loss=0.3201, pruned_loss=0.09649, over 7321.00 frames.], tot_loss[loss=0.2393, simple_loss=0.3096, pruned_loss=0.08443, over 1421605.29 frames.], batch size: 21, lr: 1.74e-03
2022-05-13 23:41:13,974 INFO [train.py:812] (6/8) Epoch 3, batch 4000, loss[loss=0.211, simple_loss=0.2957, pruned_loss=0.06313, over 7340.00 frames.], tot_loss[loss=0.2385, simple_loss=0.3094, pruned_loss=0.0838, over 1426086.29 frames.], batch size: 22, lr: 1.74e-03
2022-05-13 23:42:13,695 INFO [train.py:812] (6/8) Epoch 3, batch 4050, loss[loss=0.2617, simple_loss=0.3369, pruned_loss=0.09322, over 7450.00 frames.], tot_loss[loss=0.2378, simple_loss=0.3086, pruned_loss=0.0835, over 1426354.90 frames.], batch size: 20, lr: 1.74e-03
2022-05-13 23:43:12,793 INFO [train.py:812] (6/8) Epoch 3, batch 4100, loss[loss=0.224, simple_loss=0.292, pruned_loss=0.07802, over 7069.00 frames.], tot_loss[loss=0.2399, simple_loss=0.3104, pruned_loss=0.08476, over 1416568.31 frames.], batch size: 18, lr: 1.73e-03
2022-05-13 23:44:12,474 INFO [train.py:812] (6/8) Epoch 3, batch 4150, loss[loss=0.2183, simple_loss=0.3046, pruned_loss=0.06599, over 7103.00 frames.], tot_loss[loss=0.2393, simple_loss=0.3102, pruned_loss=0.0842, over 1420944.00 frames.], batch size: 21, lr: 1.73e-03
2022-05-13 23:45:10,721 INFO [train.py:812] (6/8) Epoch 3, batch 4200, loss[loss=0.306, simple_loss=0.3757, pruned_loss=0.1182, over 7036.00 frames.], tot_loss[loss=0.2392, simple_loss=0.3097, pruned_loss=0.08428, over 1421892.04 frames.], batch size: 28, lr: 1.73e-03
2022-05-13 23:46:09,940 INFO [train.py:812] (6/8) Epoch 3, batch 4250, loss[loss=0.2692, simple_loss=0.34, pruned_loss=0.09922, over 7194.00 frames.], tot_loss[loss=0.2379, simple_loss=0.309, pruned_loss=0.08344, over 1421926.21 frames.], batch size: 22, lr: 1.73e-03
2022-05-13 23:47:09,095 INFO [train.py:812] (6/8) Epoch 3, batch 4300, loss[loss=0.1992, simple_loss=0.2737, pruned_loss=0.0623, over 7452.00 frames.], tot_loss[loss=0.239, simple_loss=0.3102, pruned_loss=0.08395, over 1424636.35 frames.], batch size: 19, lr: 1.72e-03
2022-05-13 23:48:08,289 INFO [train.py:812] (6/8) Epoch 3, batch 4350, loss[loss=0.2146, simple_loss=0.2999, pruned_loss=0.06467, over 7141.00 frames.], tot_loss[loss=0.2381, simple_loss=0.3091, pruned_loss=0.08348, over 1425492.93 frames.], batch size: 20, lr: 1.72e-03
2022-05-13 23:49:06,733 INFO [train.py:812] (6/8) Epoch 3, batch 4400, loss[loss=0.2724, simple_loss=0.3446, pruned_loss=0.1001, over 7327.00 frames.], tot_loss[loss=0.2385, simple_loss=0.3093, pruned_loss=0.08384, over 1419742.06 frames.], batch size: 25, lr: 1.72e-03
2022-05-13 23:50:05,682 INFO [train.py:812] (6/8) Epoch 3, batch 4450, loss[loss=0.2362, simple_loss=0.3203, pruned_loss=0.07604, over 7338.00 frames.], tot_loss[loss=0.2403, simple_loss=0.3112, pruned_loss=0.08474, over 1412366.19 frames.], batch size: 22, lr: 1.71e-03
2022-05-13 23:51:04,267 INFO [train.py:812] (6/8) Epoch 3, batch 4500, loss[loss=0.2259, simple_loss=0.3071, pruned_loss=0.07236, over 7123.00 frames.], tot_loss[loss=0.2398, simple_loss=0.311, pruned_loss=0.08428, over 1406390.03 frames.], batch size: 21, lr: 1.71e-03
2022-05-13 23:52:01,836 INFO [train.py:812] (6/8) Epoch 3, batch 4550, loss[loss=0.2804, simple_loss=0.3413, pruned_loss=0.1097, over 6194.00 frames.], tot_loss[loss=0.2436, simple_loss=0.3141, pruned_loss=0.08655, over 1378210.46 frames.], batch size: 37, lr: 1.71e-03
2022-05-13 23:53:11,498 INFO [train.py:812] (6/8) Epoch 4, batch 0, loss[loss=0.26, simple_loss=0.3349, pruned_loss=0.0926, over 7191.00 frames.], tot_loss[loss=0.26, simple_loss=0.3349, pruned_loss=0.0926, over 7191.00 frames.], batch size: 23, lr: 1.66e-03
2022-05-13 23:54:10,711 INFO [train.py:812] (6/8) Epoch 4, batch 50, loss[loss=0.2104, simple_loss=0.2824, pruned_loss=0.0692, over 7276.00 frames.], tot_loss[loss=0.2319, simple_loss=0.3048, pruned_loss=0.07948, over 317596.52 frames.], batch size: 17, lr: 1.66e-03
2022-05-13 23:55:09,424 INFO [train.py:812] (6/8) Epoch 4, batch 100, loss[loss=0.2212, simple_loss=0.2827, pruned_loss=0.07987, over 7261.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3041, pruned_loss=0.08011, over 564523.47 frames.], batch size: 17, lr: 1.65e-03
2022-05-13 23:56:09,353 INFO [train.py:812] (6/8) Epoch 4, batch 150, loss[loss=0.2487, simple_loss=0.3222, pruned_loss=0.08765, over 7325.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3027, pruned_loss=0.07876, over 755216.76 frames.], batch size: 22, lr: 1.65e-03
2022-05-13 23:57:08,462 INFO [train.py:812] (6/8) Epoch 4, batch 200, loss[loss=0.3205, simple_loss=0.3573, pruned_loss=0.1418, over 7209.00 frames.], tot_loss[loss=0.2321, simple_loss=0.305, pruned_loss=0.07957, over 904108.32 frames.], batch size: 23, lr: 1.65e-03
2022-05-13 23:58:07,227 INFO [train.py:812] (6/8) Epoch 4, batch 250, loss[loss=0.2199, simple_loss=0.2951, pruned_loss=0.07237, over 7346.00 frames.], tot_loss[loss=0.233, simple_loss=0.3061, pruned_loss=0.07994, over 1016824.20 frames.], batch size: 22, lr: 1.64e-03
2022-05-13 23:59:06,619 INFO [train.py:812] (6/8) Epoch 4, batch 300, loss[loss=0.2865, simple_loss=0.3525, pruned_loss=0.1103, over 7355.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3059, pruned_loss=0.07973, over 1110847.99 frames.], batch size: 23, lr: 1.64e-03
2022-05-14 00:00:06,140 INFO [train.py:812] (6/8) Epoch 4, batch 350, loss[loss=0.2368, simple_loss=0.3162, pruned_loss=0.0787, over 7322.00 frames.], tot_loss[loss=0.231, simple_loss=0.3049, pruned_loss=0.07852, over 1181820.52 frames.], batch size: 21, lr: 1.64e-03
2022-05-14 00:01:05,136 INFO [train.py:812] (6/8) Epoch 4, batch 400, loss[loss=0.2229, simple_loss=0.3031, pruned_loss=0.07129, over 7233.00 frames.], tot_loss[loss=0.2306, simple_loss=0.3041, pruned_loss=0.07857, over 1232865.18 frames.], batch size: 20, lr: 1.64e-03
2022-05-14 00:02:04,533 INFO [train.py:812] (6/8) Epoch 4, batch 450, loss[loss=0.3135, simple_loss=0.3668, pruned_loss=0.1301, over 7148.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3049, pruned_loss=0.07961, over 1274482.01 frames.], batch size: 20, lr: 1.63e-03
2022-05-14 00:03:03,243 INFO [train.py:812] (6/8) Epoch 4, batch 500, loss[loss=0.2266, simple_loss=0.3036, pruned_loss=0.07475, over 7155.00 frames.], tot_loss[loss=0.2337, simple_loss=0.3068, pruned_loss=0.08026, over 1303506.58 frames.], batch size: 19, lr: 1.63e-03
2022-05-14 00:04:02,754 INFO [train.py:812] (6/8) Epoch 4, batch 550, loss[loss=0.234, simple_loss=0.3027, pruned_loss=0.08271, over 7148.00 frames.], tot_loss[loss=0.234, simple_loss=0.3068, pruned_loss=0.08061, over 1329080.07 frames.], batch size: 18, lr: 1.63e-03
2022-05-14 00:05:01,395 INFO [train.py:812] (6/8) Epoch 4, batch 600, loss[loss=0.3023, simple_loss=0.3573, pruned_loss=0.1237, over 6359.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3054, pruned_loss=0.07993, over 1346635.96 frames.], batch size: 38, lr: 1.63e-03
2022-05-14 00:06:00,850 INFO [train.py:812] (6/8) Epoch 4, batch 650, loss[loss=0.2569, simple_loss=0.3315, pruned_loss=0.09118, over 7431.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3053, pruned_loss=0.0795, over 1367163.69 frames.], batch size: 20, lr: 1.62e-03
2022-05-14 00:07:00,187 INFO [train.py:812] (6/8) Epoch 4, batch 700, loss[loss=0.23, simple_loss=0.3057, pruned_loss=0.07716, over 7300.00 frames.], tot_loss[loss=0.2316, simple_loss=0.3046, pruned_loss=0.07928, over 1383787.95 frames.], batch size: 24, lr: 1.62e-03
2022-05-14 00:07:59,283 INFO [train.py:812] (6/8) Epoch 4, batch 750, loss[loss=0.2887, simple_loss=0.3463, pruned_loss=0.1155, over 7292.00 frames.], tot_loss[loss=0.2311, simple_loss=0.3042, pruned_loss=0.079, over 1392381.89 frames.], batch size: 24, lr: 1.62e-03
2022-05-14 00:08:58,474 INFO [train.py:812] (6/8) Epoch 4, batch 800, loss[loss=0.2247, simple_loss=0.2955, pruned_loss=0.07699, over 7259.00 frames.], tot_loss[loss=0.2314, simple_loss=0.3042, pruned_loss=0.07926, over 1396435.07 frames.], batch size: 19, lr: 1.62e-03
2022-05-14 00:09:58,469 INFO [train.py:812] (6/8) Epoch 4, batch 850, loss[loss=0.246, simple_loss=0.3173, pruned_loss=0.08737, over 7063.00 frames.], tot_loss[loss=0.2311, simple_loss=0.3044, pruned_loss=0.07885, over 1406328.77 frames.], batch size: 18, lr: 1.61e-03
2022-05-14 00:10:57,739 INFO [train.py:812] (6/8) Epoch 4, batch 900, loss[loss=0.2362, simple_loss=0.3117, pruned_loss=0.08034, over 7104.00 frames.], tot_loss[loss=0.2292, simple_loss=0.3032, pruned_loss=0.07758, over 1413843.30 frames.], batch size: 21, lr: 1.61e-03
2022-05-14 00:11:56,773 INFO [train.py:812] (6/8) Epoch 4, batch 950, loss[loss=0.2316, simple_loss=0.3034, pruned_loss=0.07994, over 7165.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3042, pruned_loss=0.07855, over 1419317.55 frames.], batch size: 26, lr: 1.61e-03
2022-05-14 00:12:55,435 INFO [train.py:812] (6/8) Epoch 4, batch 1000, loss[loss=0.2133, simple_loss=0.2837, pruned_loss=0.0714, over 7266.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3038, pruned_loss=0.07887, over 1420376.91 frames.], batch size: 18, lr: 1.61e-03
2022-05-14 00:13:54,507 INFO [train.py:812] (6/8) Epoch 4, batch 1050, loss[loss=0.2917, simple_loss=0.3461, pruned_loss=0.1186, over 6856.00 frames.], tot_loss[loss=0.2314, simple_loss=0.3046, pruned_loss=0.07907, over 1419266.23 frames.], batch size: 31, lr: 1.60e-03
2022-05-14 00:14:53,505 INFO [train.py:812] (6/8) Epoch 4, batch 1100, loss[loss=0.2192, simple_loss=0.2967, pruned_loss=0.07082, over 7405.00 frames.], tot_loss[loss=0.2324, simple_loss=0.3055, pruned_loss=0.07966, over 1419851.51 frames.], batch size: 21, lr: 1.60e-03
2022-05-14 00:15:52,725 INFO [train.py:812] (6/8) Epoch 4, batch 1150, loss[loss=0.221, simple_loss=0.3056, pruned_loss=0.06818, over 7328.00 frames.], tot_loss[loss=0.2331, simple_loss=0.3065, pruned_loss=0.07989, over 1417855.20 frames.], batch size: 21, lr: 1.60e-03
2022-05-14 00:16:51,393 INFO [train.py:812] (6/8) Epoch 4, batch 1200, loss[loss=0.2442, simple_loss=0.3111, pruned_loss=0.08859, over 7319.00 frames.], tot_loss[loss=0.2341, simple_loss=0.3075, pruned_loss=0.08032, over 1415621.48 frames.], batch size: 21, lr: 1.60e-03
2022-05-14 00:17:50,417 INFO [train.py:812] (6/8) Epoch 4, batch 1250, loss[loss=0.2109, simple_loss=0.2769, pruned_loss=0.07244, over 6792.00 frames.], tot_loss[loss=0.2343, simple_loss=0.3074, pruned_loss=0.08061, over 1414372.11 frames.], batch size: 15, lr: 1.59e-03
2022-05-14 00:18:48,758 INFO [train.py:812] (6/8) Epoch 4, batch 1300, loss[loss=0.2137, simple_loss=0.3011, pruned_loss=0.06317, over 7222.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3068, pruned_loss=0.08049, over 1417711.22 frames.], batch size: 23, lr: 1.59e-03
2022-05-14 00:19:47,571 INFO [train.py:812] (6/8) Epoch 4, batch 1350, loss[loss=0.2506, simple_loss=0.3202, pruned_loss=0.09049, over 7236.00 frames.], tot_loss[loss=0.2332, simple_loss=0.3059, pruned_loss=0.08021, over 1416670.19 frames.], batch size: 20, lr: 1.59e-03
2022-05-14 00:20:44,928 INFO [train.py:812] (6/8) Epoch 4, batch 1400, loss[loss=0.2243, simple_loss=0.3141, pruned_loss=0.06718, over 7210.00 frames.], tot_loss[loss=0.2324, simple_loss=0.305, pruned_loss=0.07993, over 1419474.97 frames.], batch size: 22, lr: 1.59e-03
2022-05-14 00:21:44,729 INFO [train.py:812] (6/8) Epoch 4, batch 1450, loss[loss=0.2194, simple_loss=0.3046, pruned_loss=0.0671, over 7298.00 frames.], tot_loss[loss=0.2331, simple_loss=0.3061, pruned_loss=0.08001, over 1420890.52 frames.], batch size: 24, lr: 1.59e-03
2022-05-14 00:22:43,716 INFO [train.py:812] (6/8) Epoch 4, batch 1500, loss[loss=0.2548, simple_loss=0.3169, pruned_loss=0.09632, over 7293.00 frames.], tot_loss[loss=0.2336, simple_loss=0.3062, pruned_loss=0.08056, over 1417559.28 frames.], batch size: 24, lr: 1.58e-03
2022-05-14 00:23:43,458 INFO [train.py:812] (6/8) Epoch 4, batch 1550, loss[loss=0.3207, simple_loss=0.3531, pruned_loss=0.1441, over 4920.00 frames.], tot_loss[loss=0.2344, simple_loss=0.3068, pruned_loss=0.08106, over 1416098.68 frames.], batch size: 52, lr: 1.58e-03
2022-05-14 00:24:41,305 INFO [train.py:812] (6/8) Epoch 4, batch 1600, loss[loss=0.2696, simple_loss=0.3243, pruned_loss=0.1074, over 7296.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3069, pruned_loss=0.08048, over 1413402.78 frames.], batch size: 25, lr: 1.58e-03
2022-05-14 00:25:40,748 INFO [train.py:812] (6/8) Epoch 4, batch 1650, loss[loss=0.2125, simple_loss=0.2878, pruned_loss=0.0686, over 7319.00 frames.], tot_loss[loss=0.2328, simple_loss=0.3059, pruned_loss=0.07989, over 1415270.19 frames.], batch size: 20, lr: 1.58e-03
2022-05-14 00:26:39,539 INFO [train.py:812] (6/8) Epoch 4, batch 1700, loss[loss=0.2282, simple_loss=0.3202, pruned_loss=0.06808, over 7155.00 frames.], tot_loss[loss=0.2334, simple_loss=0.3069, pruned_loss=0.07991, over 1418963.02 frames.], batch size: 20, lr: 1.57e-03
2022-05-14 00:27:38,797 INFO [train.py:812] (6/8) Epoch 4, batch 1750, loss[loss=0.2064, simple_loss=0.2837, pruned_loss=0.06451, over 7203.00 frames.], tot_loss[loss=0.2325, simple_loss=0.3065, pruned_loss=0.07928, over 1418727.74 frames.], batch size: 22, lr: 1.57e-03
2022-05-14 00:28:45,539 INFO [train.py:812] (6/8) Epoch 4, batch 1800, loss[loss=0.2398, simple_loss=0.3122, pruned_loss=0.08366, over 7224.00 frames.], tot_loss[loss=0.2325, simple_loss=0.3068, pruned_loss=0.0791, over 1420824.24 frames.], batch size: 21, lr: 1.57e-03
2022-05-14 00:29:45,172 INFO [train.py:812] (6/8) Epoch 4, batch 1850, loss[loss=0.1997, simple_loss=0.2708, pruned_loss=0.06434, over 7143.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3068, pruned_loss=0.0788, over 1419895.03 frames.], batch size: 17, lr: 1.57e-03
2022-05-14 00:30:44,408 INFO [train.py:812] (6/8) Epoch 4, batch 1900, loss[loss=0.2094, simple_loss=0.2929, pruned_loss=0.06299, over 7159.00 frames.], tot_loss[loss=0.2325, simple_loss=0.3068, pruned_loss=0.07913, over 1422788.80 frames.], batch size: 19, lr: 1.56e-03
2022-05-14 00:31:43,805 INFO [train.py:812] (6/8) Epoch 4, batch 1950, loss[loss=0.2959, simple_loss=0.3407, pruned_loss=0.1255, over 6572.00 frames.], tot_loss[loss=0.2329, simple_loss=0.307, pruned_loss=0.07935, over 1427844.76 frames.], batch size: 38, lr: 1.56e-03
2022-05-14 00:32:40,432 INFO [train.py:812] (6/8) Epoch 4, batch 2000, loss[loss=0.2567, simple_loss=0.3239, pruned_loss=0.0947, over 7125.00 frames.], tot_loss[loss=0.2333, simple_loss=0.3071, pruned_loss=0.07974, over 1425033.59 frames.], batch size: 21, lr: 1.56e-03
2022-05-14 00:34:15,606 INFO [train.py:812] (6/8) Epoch 4, batch 2050, loss[loss=0.224, simple_loss=0.2985, pruned_loss=0.07476, over 6772.00 frames.], tot_loss[loss=0.2342, simple_loss=0.3075, pruned_loss=0.08048, over 1422024.49 frames.], batch size: 31, lr: 1.56e-03
2022-05-14 00:35:41,835 INFO [train.py:812] (6/8) Epoch 4, batch 2100, loss[loss=0.2096, simple_loss=0.2981, pruned_loss=0.06058, over 7329.00 frames.], tot_loss[loss=0.2326, simple_loss=0.306, pruned_loss=0.07958, over 1419890.43 frames.], batch size: 21, lr: 1.56e-03
2022-05-14 00:36:41,412 INFO [train.py:812] (6/8) Epoch 4, batch 2150, loss[loss=0.2338, simple_loss=0.3118, pruned_loss=0.07787, over 7333.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3043, pruned_loss=0.07804, over 1422742.99 frames.], batch size: 22, lr: 1.55e-03
2022-05-14 00:37:40,379 INFO [train.py:812] (6/8) Epoch 4, batch 2200, loss[loss=0.2424, simple_loss=0.3228, pruned_loss=0.08103, over 7223.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3042, pruned_loss=0.07809, over 1425118.90 frames.], batch size: 21, lr: 1.55e-03
2022-05-14 00:38:47,589 INFO [train.py:812] (6/8) Epoch 4, batch 2250, loss[loss=0.3396, simple_loss=0.3588, pruned_loss=0.1602, over 4747.00 frames.], tot_loss[loss=0.2298, simple_loss=0.3044, pruned_loss=0.07761, over 1426197.77 frames.], batch size: 52, lr: 1.55e-03
2022-05-14 00:39:45,621 INFO [train.py:812] (6/8) Epoch 4, batch 2300, loss[loss=0.2516, simple_loss=0.3158, pruned_loss=0.09374, over 7157.00 frames.], tot_loss[loss=0.2295, simple_loss=0.304, pruned_loss=0.07752, over 1429519.32 frames.], batch size: 19, lr: 1.55e-03
2022-05-14 00:40:45,382 INFO [train.py:812] (6/8) Epoch 4, batch 2350, loss[loss=0.2227, simple_loss=0.3034, pruned_loss=0.07101, over 7327.00 frames.], tot_loss[loss=0.2292, simple_loss=0.3037, pruned_loss=0.07735, over 1431042.01 frames.], batch size: 20, lr: 1.54e-03
2022-05-14 00:41:44,137 INFO [train.py:812] (6/8) Epoch 4, batch 2400, loss[loss=0.2191, simple_loss=0.2983, pruned_loss=0.06998, over 7282.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3055, pruned_loss=0.07807, over 1433141.49 frames.], batch size: 25, lr: 1.54e-03
2022-05-14 00:42:43,288 INFO [train.py:812] (6/8) Epoch 4, batch 2450, loss[loss=0.199, simple_loss=0.2778, pruned_loss=0.06009, over 7378.00 frames.], tot_loss[loss=0.2289, simple_loss=0.3041, pruned_loss=0.07684, over 1435950.59 frames.], batch size: 23, lr: 1.54e-03
2022-05-14 00:43:42,508 INFO [train.py:812] (6/8) Epoch 4, batch 2500, loss[loss=0.2325, simple_loss=0.2989, pruned_loss=0.0831, over 7148.00 frames.], tot_loss[loss=0.228, simple_loss=0.3033, pruned_loss=0.07636, over 1434625.75 frames.], batch size: 19, lr: 1.54e-03
2022-05-14 00:44:40,450 INFO [train.py:812] (6/8) Epoch 4, batch 2550, loss[loss=0.2342, simple_loss=0.2946, pruned_loss=0.08688, over 7412.00 frames.], tot_loss[loss=0.2286, simple_loss=0.3034, pruned_loss=0.0769, over 1425422.82 frames.], batch size: 18, lr: 1.54e-03
2022-05-14 00:45:38,435 INFO [train.py:812] (6/8) Epoch 4, batch 2600, loss[loss=0.2026, simple_loss=0.2802, pruned_loss=0.06249, over 7237.00 frames.], tot_loss[loss=0.2293, simple_loss=0.3041, pruned_loss=0.0773, over 1425473.81 frames.], batch size: 20, lr: 1.53e-03
2022-05-14 00:46:37,714 INFO [train.py:812] (6/8) Epoch 4, batch 2650, loss[loss=0.2015, simple_loss=0.2651, pruned_loss=0.06888, over 7005.00 frames.], tot_loss[loss=0.2292, simple_loss=0.304, pruned_loss=0.07715, over 1418640.25 frames.], batch size: 16, lr: 1.53e-03
2022-05-14 00:47:36,761 INFO [train.py:812] (6/8) Epoch 4, batch 2700, loss[loss=0.1795, simple_loss=0.2516, pruned_loss=0.05369, over 6786.00 frames.], tot_loss[loss=0.2286, simple_loss=0.3036, pruned_loss=0.07677, over 1417331.11 frames.], batch size: 15, lr: 1.53e-03
2022-05-14 00:48:35,484 INFO [train.py:812] (6/8) Epoch 4, batch 2750, loss[loss=0.2063, simple_loss=0.2821, pruned_loss=0.06521, over 7257.00 frames.], tot_loss[loss=0.228, simple_loss=0.3032, pruned_loss=0.07644, over 1420912.32 frames.], batch size: 19, lr: 1.53e-03
2022-05-14 00:49:34,182 INFO [train.py:812] (6/8) Epoch 4, batch 2800, loss[loss=0.2332, simple_loss=0.3001, pruned_loss=0.08318, over 7161.00 frames.], tot_loss[loss=0.2262, simple_loss=0.3014, pruned_loss=0.07551, over 1423671.47 frames.], batch size: 19, lr: 1.53e-03
2022-05-14 00:50:32,977 INFO [train.py:812] (6/8) Epoch 4, batch 2850, loss[loss=0.2903, simple_loss=0.3456, pruned_loss=0.1175, over 5202.00 frames.], tot_loss[loss=0.2258, simple_loss=0.3011, pruned_loss=0.07523, over 1423088.63 frames.], batch size: 53, lr: 1.52e-03
2022-05-14 00:51:31,222 INFO [train.py:812] (6/8) Epoch 4, batch 2900, loss[loss=0.2294, simple_loss=0.3036, pruned_loss=0.07755, over 6700.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3009, pruned_loss=0.07521, over 1423913.19 frames.], batch size: 31, lr: 1.52e-03
2022-05-14 00:52:31,107 INFO [train.py:812] (6/8) Epoch 4, batch 2950, loss[loss=0.211, simple_loss=0.2918, pruned_loss=0.06512, over 7027.00 frames.], tot_loss[loss=0.2259, simple_loss=0.3014, pruned_loss=0.07523, over 1427577.96 frames.], batch size: 28, lr: 1.52e-03
2022-05-14 00:53:30,070 INFO [train.py:812] (6/8) Epoch 4, batch 3000, loss[loss=0.2254, simple_loss=0.3149, pruned_loss=0.06798, over 7147.00 frames.], tot_loss[loss=0.2262, simple_loss=0.3021, pruned_loss=0.07519, over 1426113.51 frames.], batch size: 20, lr: 1.52e-03
2022-05-14 00:53:30,071 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 00:53:37,753 INFO [train.py:841] (6/8) Epoch 4, validation: loss=0.1771, simple_loss=0.279, pruned_loss=0.03761, over 698248.00 frames.
2022-05-14 00:54:36,388 INFO [train.py:812] (6/8) Epoch 4, batch 3050, loss[loss=0.2057, simple_loss=0.2895, pruned_loss=0.061, over 7124.00 frames.], tot_loss[loss=0.2257, simple_loss=0.3013, pruned_loss=0.07502, over 1421409.31 frames.], batch size: 21, lr: 1.51e-03
2022-05-14 00:55:35,353 INFO [train.py:812] (6/8) Epoch 4, batch 3100, loss[loss=0.2768, simple_loss=0.335, pruned_loss=0.1093, over 7301.00 frames.], tot_loss[loss=0.226, simple_loss=0.3011, pruned_loss=0.07546, over 1417229.22 frames.], batch size: 24, lr: 1.51e-03
2022-05-14 00:56:35,152 INFO [train.py:812] (6/8) Epoch 4, batch 3150, loss[loss=0.2182, simple_loss=0.306, pruned_loss=0.06519, over 7319.00 frames.], tot_loss[loss=0.2253, simple_loss=0.3002, pruned_loss=0.07519, over 1421969.43 frames.], batch size: 25, lr: 1.51e-03
2022-05-14 00:57:33,603 INFO [train.py:812] (6/8) Epoch 4, batch 3200, loss[loss=0.183, simple_loss=0.2581, pruned_loss=0.05395, over 7075.00 frames.], tot_loss[loss=0.2245, simple_loss=0.2995, pruned_loss=0.07477, over 1423221.57 frames.], batch size: 18, lr: 1.51e-03
2022-05-14 00:58:32,702 INFO [train.py:812] (6/8) Epoch 4, batch 3250, loss[loss=0.2326, simple_loss=0.3057, pruned_loss=0.07977, over 7261.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3006, pruned_loss=0.07524, over 1423907.45 frames.], batch size: 19, lr: 1.51e-03
2022-05-14 00:59:30,515 INFO [train.py:812] (6/8) Epoch 4, batch 3300, loss[loss=0.2086, simple_loss=0.3035, pruned_loss=0.05685, over 7211.00 frames.], tot_loss[loss=0.2261, simple_loss=0.3015, pruned_loss=0.0754, over 1422022.08 frames.], batch size: 23, lr: 1.50e-03
2022-05-14 01:00:29,666 INFO [train.py:812] (6/8) Epoch 4, batch 3350, loss[loss=0.2838, simple_loss=0.3487, pruned_loss=0.1095, over 6475.00 frames.], tot_loss[loss=0.2253, simple_loss=0.3004, pruned_loss=0.07507, over 1420442.01 frames.], batch size: 38, lr: 1.50e-03
2022-05-14 01:01:28,331 INFO [train.py:812] (6/8) Epoch 4, batch 3400, loss[loss=0.1704, simple_loss=0.2549, pruned_loss=0.043, over 6999.00 frames.], tot_loss[loss=0.2258, simple_loss=0.3009, pruned_loss=0.07533, over 1421113.23 frames.], batch size: 16, lr: 1.50e-03
2022-05-14 01:02:28,062 INFO [train.py:812] (6/8) Epoch 4, batch 3450, loss[loss=0.1934, simple_loss=0.2783, pruned_loss=0.05423, over 7167.00 frames.], tot_loss[loss=0.2233, simple_loss=0.2987, pruned_loss=0.07398, over 1426012.94 frames.], batch size: 18, lr: 1.50e-03
2022-05-14 01:03:26,393 INFO [train.py:812] (6/8) Epoch 4, batch 3500, loss[loss=0.2115, simple_loss=0.2987, pruned_loss=0.06216, over 7370.00 frames.], tot_loss[loss=0.2231, simple_loss=0.2983, pruned_loss=0.07395, over 1428098.23 frames.], batch size: 23, lr: 1.50e-03
2022-05-14 01:04:26,092 INFO [train.py:812] (6/8) Epoch 4, batch 3550, loss[loss=0.2364, simple_loss=0.3037, pruned_loss=0.08452, over 7279.00 frames.], tot_loss[loss=0.2228, simple_loss=0.2983, pruned_loss=0.07367, over 1429275.81 frames.], batch size: 24, lr: 1.49e-03
2022-05-14 01:05:25,252 INFO [train.py:812] (6/8) Epoch 4, batch 3600, loss[loss=0.1945, simple_loss=0.2555, pruned_loss=0.06673, over 7009.00 frames.], tot_loss[loss=0.2228, simple_loss=0.2983, pruned_loss=0.07364, over 1428273.27 frames.], batch size: 16, lr: 1.49e-03
2022-05-14 01:06:24,754 INFO [train.py:812] (6/8) Epoch 4, batch 3650, loss[loss=0.2271, simple_loss=0.2871, pruned_loss=0.08351, over 7144.00 frames.], tot_loss[loss=0.2235, simple_loss=0.2993, pruned_loss=0.07385, over 1428574.52 frames.], batch size: 17, lr: 1.49e-03
2022-05-14 01:07:24,241 INFO [train.py:812] (6/8) Epoch 4, batch 3700, loss[loss=0.257, simple_loss=0.311, pruned_loss=0.1014, over 7003.00 frames.], tot_loss[loss=0.2242, simple_loss=0.3, pruned_loss=0.07425, over 1427851.41 frames.], batch size: 16, lr: 1.49e-03
2022-05-14 01:08:24,382 INFO [train.py:812] (6/8) Epoch 4, batch 3750, loss[loss=0.208, simple_loss=0.3022, pruned_loss=0.05687, over 7427.00 frames.], tot_loss[loss=0.2234, simple_loss=0.2989, pruned_loss=0.07391, over 1426305.18 frames.], batch size: 20, lr: 1.49e-03
2022-05-14 01:09:22,780 INFO [train.py:812] (6/8) Epoch 4, batch 3800, loss[loss=0.2231, simple_loss=0.2927, pruned_loss=0.07676, over 7060.00 frames.], tot_loss[loss=0.2239, simple_loss=0.299, pruned_loss=0.07438, over 1422592.52 frames.], batch size: 18, lr: 1.48e-03
2022-05-14 01:10:22,624 INFO [train.py:812] (6/8) Epoch 4, batch 3850, loss[loss=0.1727, simple_loss=0.2424, pruned_loss=0.05148, over 7409.00 frames.], tot_loss[loss=0.2222, simple_loss=0.2979, pruned_loss=0.07327, over 1426330.71 frames.], batch size: 18, lr: 1.48e-03
2022-05-14 01:11:21,441 INFO [train.py:812] (6/8) Epoch 4, batch 3900, loss[loss=0.3009, simple_loss=0.3523, pruned_loss=0.1247, over 5207.00 frames.], tot_loss[loss=0.2235, simple_loss=0.2992, pruned_loss=0.0739, over 1427577.01 frames.], batch size: 52, lr: 1.48e-03
2022-05-14 01:12:20,488 INFO [train.py:812] (6/8) Epoch 4, batch 3950, loss[loss=0.2163, simple_loss=0.2793, pruned_loss=0.07664, over 6786.00 frames.], tot_loss[loss=0.2229, simple_loss=0.2984, pruned_loss=0.07365, over 1425774.89 frames.], batch size: 15, lr: 1.48e-03
2022-05-14 01:13:19,478 INFO [train.py:812] (6/8) Epoch 4, batch 4000, loss[loss=0.2449, simple_loss=0.319, pruned_loss=0.08537, over 7230.00 frames.], tot_loss[loss=0.2243, simple_loss=0.2996, pruned_loss=0.07453, over 1418781.04 frames.], batch size: 21, lr: 1.48e-03
2022-05-14 01:14:18,997 INFO [train.py:812] (6/8) Epoch 4, batch 4050, loss[loss=0.2552, simple_loss=0.3323, pruned_loss=0.08901, over 7414.00 frames.], tot_loss[loss=0.2246, simple_loss=0.2996, pruned_loss=0.07478, over 1420278.48 frames.], batch size: 21, lr: 1.47e-03
2022-05-14 01:15:18,248 INFO [train.py:812] (6/8) Epoch 4, batch 4100, loss[loss=0.2522, simple_loss=0.3216, pruned_loss=0.09138, over 6371.00 frames.], tot_loss[loss=0.2263, simple_loss=0.3008, pruned_loss=0.07588, over 1421808.68 frames.], batch size: 37, lr: 1.47e-03
2022-05-14 01:16:17,171 INFO [train.py:812] (6/8) Epoch 4, batch 4150, loss[loss=0.1971, simple_loss=0.2721, pruned_loss=0.06107, over 6992.00 frames.], tot_loss[loss=0.2245, simple_loss=0.2995, pruned_loss=0.07473, over 1423969.89 frames.], batch size: 16, lr: 1.47e-03
2022-05-14 01:17:15,992 INFO [train.py:812] (6/8) Epoch 4, batch 4200, loss[loss=0.207, simple_loss=0.2947, pruned_loss=0.05965, over 7160.00 frames.], tot_loss[loss=0.2246, simple_loss=0.2995, pruned_loss=0.07483, over 1421591.31 frames.], batch size: 19, lr: 1.47e-03
2022-05-14 01:18:15,846 INFO [train.py:812] (6/8) Epoch 4, batch 4250, loss[loss=0.2299, simple_loss=0.3048, pruned_loss=0.07751, over 7362.00 frames.], tot_loss[loss=0.2238, simple_loss=0.2985, pruned_loss=0.07452, over 1413985.80 frames.], batch size: 19, lr: 1.47e-03
2022-05-14 01:19:14,836 INFO [train.py:812] (6/8) Epoch 4, batch 4300, loss[loss=0.2525, simple_loss=0.3164, pruned_loss=0.09429, over 7350.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2966, pruned_loss=0.07375, over 1412903.89 frames.], batch size: 19, lr: 1.47e-03
2022-05-14 01:20:14,380 INFO [train.py:812] (6/8) Epoch 4, batch 4350, loss[loss=0.2789, simple_loss=0.3446, pruned_loss=0.1066, over 6553.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2953, pruned_loss=0.07364, over 1411430.78 frames.], batch size: 38, lr: 1.46e-03
2022-05-14 01:21:13,831 INFO [train.py:812] (6/8) Epoch 4, batch 4400, loss[loss=0.188, simple_loss=0.2784, pruned_loss=0.04886, over 7075.00 frames.], tot_loss[loss=0.2214, simple_loss=0.2953, pruned_loss=0.0738, over 1409471.51 frames.], batch size: 18, lr: 1.46e-03
2022-05-14 01:22:13,444 INFO [train.py:812] (6/8) Epoch 4, batch 4450, loss[loss=0.2078, simple_loss=0.3008, pruned_loss=0.05746, over 7384.00 frames.], tot_loss[loss=0.2212, simple_loss=0.2949, pruned_loss=0.07378, over 1402098.95 frames.], batch size: 23, lr: 1.46e-03
2022-05-14 01:23:11,953 INFO [train.py:812] (6/8) Epoch 4, batch 4500, loss[loss=0.2701, simple_loss=0.3463, pruned_loss=0.09698, over 6441.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2946, pruned_loss=0.07355, over 1396078.53 frames.], batch size: 38, lr: 1.46e-03
2022-05-14 01:24:10,696 INFO [train.py:812] (6/8) Epoch 4, batch 4550, loss[loss=0.2719, simple_loss=0.3419, pruned_loss=0.1009, over 5082.00 frames.], tot_loss[loss=0.2252, simple_loss=0.2986, pruned_loss=0.07595, over 1361919.76 frames.], batch size: 53, lr: 1.46e-03
2022-05-14 01:25:17,926 INFO [train.py:812] (6/8) Epoch 5, batch 0, loss[loss=0.2304, simple_loss=0.3164, pruned_loss=0.07218, over 7216.00 frames.], tot_loss[loss=0.2304, simple_loss=0.3164, pruned_loss=0.07218, over 7216.00 frames.], batch size: 23, lr: 1.40e-03
2022-05-14 01:26:16,024 INFO [train.py:812] (6/8) Epoch 5, batch 50, loss[loss=0.2352, simple_loss=0.3153, pruned_loss=0.07759, over 7337.00 frames.], tot_loss[loss=0.221, simple_loss=0.2972, pruned_loss=0.07237, over 320613.30 frames.], batch size: 22, lr: 1.40e-03
2022-05-14 01:27:13,779 INFO [train.py:812] (6/8) Epoch 5, batch 100, loss[loss=0.1981, simple_loss=0.2929, pruned_loss=0.05164, over 7331.00 frames.], tot_loss[loss=0.2254, simple_loss=0.3015, pruned_loss=0.07461, over 567065.49 frames.], batch size: 22, lr: 1.40e-03
2022-05-14 01:28:13,088 INFO [train.py:812] (6/8) Epoch 5, batch 150, loss[loss=0.2655, simple_loss=0.3338, pruned_loss=0.09862, over 4831.00 frames.], tot_loss[loss=0.2254, simple_loss=0.3014, pruned_loss=0.07468, over 755952.70 frames.], batch size: 52, lr: 1.40e-03
2022-05-14 01:29:12,462 INFO [train.py:812] (6/8) Epoch 5, batch 200, loss[loss=0.2377, simple_loss=0.3128, pruned_loss=0.08134, over 7155.00 frames.], tot_loss[loss=0.2248, simple_loss=0.3008, pruned_loss=0.0744, over 904580.81 frames.], batch size: 19, lr: 1.40e-03
2022-05-14 01:30:12,042 INFO [train.py:812] (6/8) Epoch 5, batch 250, loss[loss=0.218, simple_loss=0.3027, pruned_loss=0.06661, over 7352.00 frames.], tot_loss[loss=0.2247, simple_loss=0.3021, pruned_loss=0.07367, over 1021817.57 frames.], batch size: 22, lr: 1.39e-03
2022-05-14 01:31:10,363 INFO [train.py:812] (6/8) Epoch 5, batch 300, loss[loss=0.2065, simple_loss=0.2737, pruned_loss=0.06962, over 7277.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2989, pruned_loss=0.07232, over 1113736.68 frames.], batch size: 17, lr: 1.39e-03
2022-05-14 01:32:09,257 INFO [train.py:812] (6/8) Epoch 5, batch 350, loss[loss=0.182, simple_loss=0.257, pruned_loss=0.05347, over 7162.00 frames.], tot_loss[loss=0.2212, simple_loss=0.298, pruned_loss=0.07219, over 1181458.45 frames.], batch size: 19, lr: 1.39e-03
2022-05-14 01:33:06,935 INFO [train.py:812] (6/8) Epoch 5, batch 400, loss[loss=0.2417, simple_loss=0.3169, pruned_loss=0.08322, over 7101.00 frames.], tot_loss[loss=0.2214, simple_loss=0.298, pruned_loss=0.07241, over 1232575.53 frames.], batch size: 28, lr: 1.39e-03
2022-05-14 01:34:05,739 INFO [train.py:812] (6/8) Epoch 5, batch 450, loss[loss=0.2254, simple_loss=0.2987, pruned_loss=0.07608, over 7090.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2979, pruned_loss=0.07256, over 1274049.17 frames.], batch size: 28, lr: 1.39e-03
2022-05-14 01:35:05,180 INFO [train.py:812] (6/8) Epoch 5, batch 500, loss[loss=0.1978, simple_loss=0.29, pruned_loss=0.05285, over 7325.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2969, pruned_loss=0.07203, over 1309796.52 frames.], batch size: 21, lr: 1.39e-03
2022-05-14 01:36:04,765 INFO [train.py:812] (6/8) Epoch 5, batch 550, loss[loss=0.233, simple_loss=0.3047, pruned_loss=0.08062, over 6598.00 frames.], tot_loss[loss=0.2207, simple_loss=0.2969, pruned_loss=0.07227, over 1334170.36 frames.], batch size: 31, lr: 1.38e-03
2022-05-14 01:37:04,109 INFO [train.py:812] (6/8) Epoch 5, batch 600, loss[loss=0.1796, simple_loss=0.2595, pruned_loss=0.04985, over 6994.00 frames.], tot_loss[loss=0.2203, simple_loss=0.296, pruned_loss=0.07225, over 1355846.63 frames.], batch size: 16, lr: 1.38e-03
2022-05-14 01:38:03,185 INFO [train.py:812] (6/8) Epoch 5, batch 650, loss[loss=0.2512, simple_loss=0.3163, pruned_loss=0.09309, over 7321.00 frames.], tot_loss[loss=0.2206, simple_loss=0.2966, pruned_loss=0.07229, over 1370477.16 frames.], batch size: 20, lr: 1.38e-03
2022-05-14 01:39:02,175 INFO [train.py:812] (6/8) Epoch 5, batch 700, loss[loss=0.2494, simple_loss=0.3382, pruned_loss=0.0803, over 7303.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2976, pruned_loss=0.07278, over 1381084.57 frames.], batch size: 25, lr: 1.38e-03
2022-05-14 01:40:01,983 INFO [train.py:812] (6/8) Epoch 5, batch 750, loss[loss=0.2241, simple_loss=0.2988, pruned_loss=0.07471, over 7064.00 frames.], tot_loss[loss=0.2208, simple_loss=0.2969, pruned_loss=0.07237, over 1386514.89 frames.], batch size: 18, lr: 1.38e-03
2022-05-14 01:40:59,764 INFO [train.py:812] (6/8) Epoch 5, batch 800, loss[loss=0.2007, simple_loss=0.2692, pruned_loss=0.0661, over 7057.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2954, pruned_loss=0.07196, over 1397255.79 frames.], batch size: 18, lr: 1.38e-03
2022-05-14 01:41:57,420 INFO [train.py:812] (6/8) Epoch 5, batch 850, loss[loss=0.187, simple_loss=0.2706, pruned_loss=0.05173, over 7072.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2949, pruned_loss=0.07186, over 1395070.54 frames.], batch size: 18, lr: 1.37e-03
2022-05-14 01:42:55,839 INFO [train.py:812] (6/8) Epoch 5, batch 900, loss[loss=0.2309, simple_loss=0.3133, pruned_loss=0.07428, over 7318.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2949, pruned_loss=0.0714, over 1401841.41 frames.], batch size: 21, lr: 1.37e-03
2022-05-14 01:43:53,352 INFO [train.py:812] (6/8) Epoch 5, batch 950, loss[loss=0.2636, simple_loss=0.3291, pruned_loss=0.0991, over 6980.00 frames.], tot_loss[loss=0.22, simple_loss=0.2958, pruned_loss=0.07209, over 1405889.92 frames.], batch size: 28, lr: 1.37e-03
2022-05-14 01:44:52,029 INFO [train.py:812] (6/8) Epoch 5, batch 1000, loss[loss=0.2206, simple_loss=0.3023, pruned_loss=0.06942, over 7057.00 frames.], tot_loss[loss=0.2183, simple_loss=0.2945, pruned_loss=0.07104, over 1410771.09 frames.], batch size: 18, lr: 1.37e-03
2022-05-14 01:45:49,427 INFO [train.py:812] (6/8) Epoch 5, batch 1050, loss[loss=0.2235, simple_loss=0.3095, pruned_loss=0.06872, over 7275.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2953, pruned_loss=0.07111, over 1416301.41 frames.], batch size: 24, lr: 1.37e-03
2022-05-14 01:46:47,344 INFO [train.py:812] (6/8) Epoch 5, batch 1100, loss[loss=0.2299, simple_loss=0.3077, pruned_loss=0.07609, over 6329.00 frames.], tot_loss[loss=0.2202, simple_loss=0.2968, pruned_loss=0.07185, over 1411974.85 frames.], batch size: 37, lr: 1.37e-03
2022-05-14 01:47:47,106 INFO [train.py:812] (6/8) Epoch 5, batch 1150, loss[loss=0.2359, simple_loss=0.3064, pruned_loss=0.08273, over 7428.00 frames.], tot_loss[loss=0.2198, simple_loss=0.2971, pruned_loss=0.07131, over 1415186.48 frames.], batch size: 20, lr: 1.36e-03
2022-05-14 01:48:46,036 INFO [train.py:812] (6/8) Epoch 5, batch 1200, loss[loss=0.205, simple_loss=0.2872, pruned_loss=0.06139, over 6500.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2952, pruned_loss=0.07052, over 1417336.80 frames.], batch size: 38, lr: 1.36e-03
2022-05-14 01:49:45,452 INFO [train.py:812] (6/8) Epoch 5, batch 1250, loss[loss=0.2324, simple_loss=0.2998, pruned_loss=0.0825, over 7251.00 frames.], tot_loss[loss=0.2186, simple_loss=0.295, pruned_loss=0.07109, over 1413500.50 frames.], batch size: 19, lr: 1.36e-03
2022-05-14 01:50:43,668 INFO [train.py:812] (6/8) Epoch 5, batch 1300, loss[loss=0.2063, simple_loss=0.2889, pruned_loss=0.06181, over 7333.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2958, pruned_loss=0.07092, over 1416673.46 frames.], batch size: 20, lr: 1.36e-03
2022-05-14 01:51:42,409 INFO [train.py:812] (6/8) Epoch 5, batch 1350, loss[loss=0.2035, simple_loss=0.2772, pruned_loss=0.06493, over 7134.00 frames.], tot_loss[loss=0.2193, simple_loss=0.296, pruned_loss=0.07133, over 1423474.17 frames.], batch size: 17, lr: 1.36e-03
2022-05-14 01:52:39,826 INFO [train.py:812] (6/8) Epoch 5, batch 1400, loss[loss=0.2459, simple_loss=0.3171, pruned_loss=0.08738, over 7230.00 frames.], tot_loss[loss=0.2211, simple_loss=0.2973, pruned_loss=0.07243, over 1418987.91 frames.], batch size: 20, lr: 1.36e-03
2022-05-14 01:53:37,459 INFO [train.py:812] (6/8) Epoch 5, batch 1450, loss[loss=0.1883, simple_loss=0.2492, pruned_loss=0.06369, over 6997.00 frames.], tot_loss[loss=0.2206, simple_loss=0.2973, pruned_loss=0.07196, over 1419516.16 frames.], batch size: 16, lr: 1.35e-03
2022-05-14 01:54:35,162 INFO [train.py:812] (6/8) Epoch 5, batch 1500, loss[loss=0.2497, simple_loss=0.3148, pruned_loss=0.09227, over 7328.00 frames.], tot_loss[loss=0.2199, simple_loss=0.2964, pruned_loss=0.07171, over 1422975.06 frames.], batch size: 20, lr: 1.35e-03
2022-05-14 01:55:34,691 INFO [train.py:812] (6/8) Epoch 5, batch 1550, loss[loss=0.2326, simple_loss=0.3102, pruned_loss=0.07749, over 7375.00 frames.], tot_loss[loss=0.2181, simple_loss=0.295, pruned_loss=0.07059, over 1425390.19 frames.], batch size: 23, lr: 1.35e-03
2022-05-14 01:56:33,047 INFO [train.py:812] (6/8) Epoch 5, batch 1600, loss[loss=0.2229, simple_loss=0.3083, pruned_loss=0.06874, over 7290.00 frames.], tot_loss[loss=0.218, simple_loss=0.295, pruned_loss=0.07048, over 1424401.77 frames.], batch size: 25, lr: 1.35e-03
2022-05-14 01:57:37,119 INFO [train.py:812] (6/8) Epoch 5, batch 1650, loss[loss=0.2352, simple_loss=0.3117, pruned_loss=0.07932, over 7117.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2953, pruned_loss=0.07093, over 1422527.51 frames.], batch size: 21, lr: 1.35e-03
2022-05-14 01:58:36,687 INFO [train.py:812] (6/8) Epoch 5, batch 1700, loss[loss=0.2303, simple_loss=0.313, pruned_loss=0.07382, over 7343.00 frames.], tot_loss[loss=0.2169, simple_loss=0.294, pruned_loss=0.06986, over 1424538.48 frames.], batch size: 22, lr: 1.35e-03
2022-05-14 01:59:35,638 INFO [train.py:812] (6/8) Epoch 5, batch 1750, loss[loss=0.221, simple_loss=0.31, pruned_loss=0.06599, over 7296.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2932, pruned_loss=0.06997, over 1423971.77 frames.], batch size: 24, lr: 1.34e-03
2022-05-14 02:00:35,029 INFO [train.py:812] (6/8) Epoch 5, batch 1800, loss[loss=0.2507, simple_loss=0.3369, pruned_loss=0.08221, over 7318.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2937, pruned_loss=0.06997, over 1426460.84 frames.], batch size: 21, lr: 1.34e-03
2022-05-14 02:01:33,496 INFO [train.py:812] (6/8) Epoch 5, batch 1850, loss[loss=0.2548, simple_loss=0.329, pruned_loss=0.09028, over 6334.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2943, pruned_loss=0.07009, over 1426896.49 frames.], batch size: 38, lr: 1.34e-03
2022-05-14 02:02:31,910 INFO [train.py:812] (6/8) Epoch 5, batch 1900, loss[loss=0.2473, simple_loss=0.3256, pruned_loss=0.08446, over 7102.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2954, pruned_loss=0.07048, over 1427758.53 frames.], batch size: 21, lr: 1.34e-03
2022-05-14 02:03:30,601 INFO [train.py:812] (6/8) Epoch 5, batch 1950, loss[loss=0.1968, simple_loss=0.2765, pruned_loss=0.05849, over 7144.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2952, pruned_loss=0.07006, over 1428022.74 frames.], batch size: 18, lr: 1.34e-03
2022-05-14 02:04:28,256 INFO [train.py:812] (6/8) Epoch 5, batch 2000, loss[loss=0.2463, simple_loss=0.3099, pruned_loss=0.09141, over 7330.00 frames.], tot_loss[loss=0.218, simple_loss=0.2949, pruned_loss=0.07051, over 1424330.12 frames.], batch size: 25, lr: 1.34e-03
2022-05-14 02:05:26,872 INFO [train.py:812] (6/8) Epoch 5, batch 2050, loss[loss=0.2529, simple_loss=0.3335, pruned_loss=0.08611, over 7290.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2945, pruned_loss=0.07023, over 1429346.60 frames.], batch size: 24, lr: 1.34e-03
2022-05-14 02:06:25,385 INFO [train.py:812] (6/8) Epoch 5, batch 2100, loss[loss=0.2188, simple_loss=0.2884, pruned_loss=0.07463, over 7410.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2934, pruned_loss=0.06917, over 1432895.39 frames.], batch size: 18, lr: 1.33e-03
2022-05-14 02:07:23,979 INFO [train.py:812] (6/8) Epoch 5, batch 2150, loss[loss=0.1804, simple_loss=0.2573, pruned_loss=0.05177, over 7063.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2948, pruned_loss=0.06957, over 1431112.97 frames.], batch size: 18, lr: 1.33e-03
2022-05-14 02:08:21,805 INFO [train.py:812] (6/8) Epoch 5, batch 2200, loss[loss=0.2548, simple_loss=0.3368, pruned_loss=0.08637, over 7352.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2941, pruned_loss=0.06921, over 1432817.99 frames.], batch size: 22, lr: 1.33e-03
2022-05-14 02:09:20,791 INFO [train.py:812] (6/8) Epoch 5, batch 2250, loss[loss=0.2503, simple_loss=0.3299, pruned_loss=0.08537, over 7376.00 frames.], tot_loss[loss=0.2166, simple_loss=0.294, pruned_loss=0.06962, over 1430636.70 frames.], batch size: 23, lr: 1.33e-03
2022-05-14 02:10:20,195 INFO [train.py:812] (6/8) Epoch 5, batch 2300, loss[loss=0.1838, simple_loss=0.2599, pruned_loss=0.05389, over 7278.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2946, pruned_loss=0.0702, over 1429712.21 frames.], batch size: 17, lr: 1.33e-03
2022-05-14 02:11:18,998 INFO [train.py:812] (6/8) Epoch 5, batch 2350, loss[loss=0.1721, simple_loss=0.2449, pruned_loss=0.04965, over 7407.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2946, pruned_loss=0.06958, over 1433127.63 frames.], batch size: 18, lr: 1.33e-03
2022-05-14 02:12:18,596 INFO [train.py:812] (6/8) Epoch 5, batch 2400, loss[loss=0.2131, simple_loss=0.3039, pruned_loss=0.06113, over 7216.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2938, pruned_loss=0.06904, over 1434481.98 frames.], batch size: 21, lr: 1.32e-03
2022-05-14 02:13:16,804 INFO [train.py:812] (6/8) Epoch 5, batch 2450, loss[loss=0.2169, simple_loss=0.2837, pruned_loss=0.07504, over 7286.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2944, pruned_loss=0.06966, over 1434199.44 frames.], batch size: 18, lr: 1.32e-03
2022-05-14 02:14:14,140 INFO [train.py:812] (6/8) Epoch 5, batch 2500, loss[loss=0.2511, simple_loss=0.3284, pruned_loss=0.08694, over 7202.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2929, pruned_loss=0.06911, over 1431901.74 frames.], batch size: 22, lr: 1.32e-03
2022-05-14 02:15:13,122 INFO [train.py:812] (6/8) Epoch 5, batch 2550, loss[loss=0.1982, simple_loss=0.2836, pruned_loss=0.05641, over 7140.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2931, pruned_loss=0.06907, over 1432399.66 frames.], batch size: 20, lr: 1.32e-03
2022-05-14 02:16:11,211 INFO [train.py:812] (6/8) Epoch 5, batch 2600, loss[loss=0.1956, simple_loss=0.2828, pruned_loss=0.05421, over 7329.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2937, pruned_loss=0.06957, over 1430764.92 frames.], batch size: 21, lr: 1.32e-03
2022-05-14 02:17:10,917 INFO [train.py:812] (6/8) Epoch 5, batch 2650, loss[loss=0.1674, simple_loss=0.2484, pruned_loss=0.04318, over 7016.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2934, pruned_loss=0.06917, over 1429496.50 frames.], batch size: 16, lr: 1.32e-03
2022-05-14 02:18:10,530 INFO [train.py:812] (6/8) Epoch 5, batch 2700, loss[loss=0.2217, simple_loss=0.2946, pruned_loss=0.07444, over 7283.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2922, pruned_loss=0.06828, over 1432232.01 frames.], batch size: 18, lr: 1.32e-03
2022-05-14 02:19:10,300 INFO [train.py:812] (6/8) Epoch 5, batch 2750, loss[loss=0.2249, simple_loss=0.2948, pruned_loss=0.07751, over 7367.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2925, pruned_loss=0.06831, over 1432516.93 frames.], batch size: 19, lr: 1.31e-03
2022-05-14 02:20:09,514 INFO [train.py:812] (6/8) Epoch 5, batch 2800, loss[loss=0.1982, simple_loss=0.2626, pruned_loss=0.06687, over 7129.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2914, pruned_loss=0.06781, over 1432801.00 frames.], batch size: 17, lr: 1.31e-03
2022-05-14 02:21:07,416 INFO [train.py:812] (6/8) Epoch 5, batch 2850, loss[loss=0.2192, simple_loss=0.3039, pruned_loss=0.06722, over 6722.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2916, pruned_loss=0.06758, over 1430612.06 frames.], batch size: 31, lr: 1.31e-03
2022-05-14 02:22:06,268 INFO [train.py:812] (6/8) Epoch 5, batch 2900, loss[loss=0.2344, simple_loss=0.3227, pruned_loss=0.07302, over 7300.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2927, pruned_loss=0.068, over 1429912.40 frames.], batch size: 24, lr: 1.31e-03
2022-05-14 02:23:05,649 INFO [train.py:812] (6/8) Epoch 5, batch 2950, loss[loss=0.2119, simple_loss=0.2879, pruned_loss=0.06793, over 7333.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2919, pruned_loss=0.06757, over 1430156.56 frames.], batch size: 22, lr: 1.31e-03
2022-05-14 02:24:04,420 INFO [train.py:812] (6/8) Epoch 5, batch 3000, loss[loss=0.2272, simple_loss=0.3096, pruned_loss=0.07234, over 7154.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2916, pruned_loss=0.06773, over 1425867.14 frames.], batch size: 26, lr: 1.31e-03
2022-05-14 02:24:04,421 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 02:24:12,114 INFO [train.py:841] (6/8) Epoch 5, validation: loss=0.1705, simple_loss=0.2732, pruned_loss=0.03391, over 698248.00 frames.
2022-05-14 02:25:11,807 INFO [train.py:812] (6/8) Epoch 5, batch 3050, loss[loss=0.2122, simple_loss=0.2883, pruned_loss=0.068, over 7209.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2925, pruned_loss=0.06796, over 1429742.65 frames.], batch size: 22, lr: 1.31e-03
2022-05-14 02:26:09,633 INFO [train.py:812] (6/8) Epoch 5, batch 3100, loss[loss=0.2142, simple_loss=0.2902, pruned_loss=0.0691, over 7223.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2928, pruned_loss=0.0684, over 1428544.59 frames.], batch size: 20, lr: 1.30e-03
2022-05-14 02:27:19,147 INFO [train.py:812] (6/8) Epoch 5, batch 3150, loss[loss=0.2285, simple_loss=0.312, pruned_loss=0.07253, over 7288.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2932, pruned_loss=0.06874, over 1428882.31 frames.], batch size: 25, lr: 1.30e-03
2022-05-14 02:28:18,327 INFO [train.py:812] (6/8) Epoch 5, batch 3200, loss[loss=0.2059, simple_loss=0.2849, pruned_loss=0.06347, over 7345.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2936, pruned_loss=0.06958, over 1429985.77 frames.], batch size: 19, lr: 1.30e-03
2022-05-14 02:29:17,247 INFO [train.py:812] (6/8) Epoch 5, batch 3250, loss[loss=0.1957, simple_loss=0.2688, pruned_loss=0.06128, over 7171.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2936, pruned_loss=0.06972, over 1428376.64 frames.], batch size: 18, lr: 1.30e-03
2022-05-14 02:30:15,407 INFO [train.py:812] (6/8) Epoch 5, batch 3300, loss[loss=0.2107, simple_loss=0.2964, pruned_loss=0.06254, over 7141.00 frames.], tot_loss[loss=0.2171, simple_loss=0.294, pruned_loss=0.07011, over 1423394.07 frames.], batch size: 26, lr: 1.30e-03
2022-05-14 02:31:14,132 INFO [train.py:812] (6/8) Epoch 5, batch 3350, loss[loss=0.2538, simple_loss=0.3193, pruned_loss=0.09415, over 7118.00 frames.], tot_loss[loss=0.216, simple_loss=0.2935, pruned_loss=0.06928, over 1426566.56 frames.], batch size: 21, lr: 1.30e-03
2022-05-14 02:32:12,544 INFO [train.py:812] (6/8) Epoch 5, batch 3400, loss[loss=0.2023, simple_loss=0.2849, pruned_loss=0.05987, over 7227.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2936, pruned_loss=0.06937, over 1428594.01 frames.], batch size: 20, lr: 1.30e-03
2022-05-14 02:33:11,753 INFO [train.py:812] (6/8) Epoch 5, batch 3450, loss[loss=0.2374, simple_loss=0.3023, pruned_loss=0.08624, over 7201.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2927, pruned_loss=0.06925, over 1428183.31 frames.], batch size: 23, lr: 1.29e-03
2022-05-14 02:34:10,785 INFO [train.py:812] (6/8) Epoch 5, batch 3500, loss[loss=0.2231, simple_loss=0.3025, pruned_loss=0.07179, over 7327.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2936, pruned_loss=0.06926, over 1430233.96 frames.], batch size: 20, lr: 1.29e-03
2022-05-14 02:35:38,318 INFO [train.py:812] (6/8) Epoch 5, batch 3550, loss[loss=0.2476, simple_loss=0.323, pruned_loss=0.08609, over 7416.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2926, pruned_loss=0.06859, over 1424933.60 frames.], batch size: 21, lr: 1.29e-03
2022-05-14 02:36:46,068 INFO [train.py:812] (6/8) Epoch 5, batch 3600, loss[loss=0.1976, simple_loss=0.276, pruned_loss=0.0596, over 7268.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2928, pruned_loss=0.06886, over 1421619.62 frames.], batch size: 19, lr: 1.29e-03
2022-05-14 02:38:13,289 INFO [train.py:812] (6/8) Epoch 5, batch 3650, loss[loss=0.2312, simple_loss=0.3089, pruned_loss=0.0768, over 6783.00 frames.], tot_loss[loss=0.216, simple_loss=0.2938, pruned_loss=0.06912, over 1415690.61 frames.], batch size: 31, lr: 1.29e-03
2022-05-14 02:39:13,001 INFO [train.py:812] (6/8) Epoch 5, batch 3700, loss[loss=0.1864, simple_loss=0.2733, pruned_loss=0.04977, over 7149.00 frames.], tot_loss[loss=0.2131, simple_loss=0.291, pruned_loss=0.06755, over 1419344.90 frames.], batch size: 18, lr: 1.29e-03
2022-05-14 02:40:11,655 INFO [train.py:812] (6/8) Epoch 5, batch 3750, loss[loss=0.1885, simple_loss=0.2682, pruned_loss=0.0544, over 6837.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2921, pruned_loss=0.06821, over 1419633.64 frames.], batch size: 15, lr: 1.29e-03
2022-05-14 02:41:09,965 INFO [train.py:812] (6/8) Epoch 5, batch 3800, loss[loss=0.2167, simple_loss=0.2786, pruned_loss=0.07742, over 7278.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2921, pruned_loss=0.06808, over 1421010.13 frames.], batch size: 18, lr: 1.28e-03
2022-05-14 02:42:07,621 INFO [train.py:812] (6/8) Epoch 5, batch 3850, loss[loss=0.2168, simple_loss=0.3001, pruned_loss=0.06669, over 7407.00 frames.], tot_loss[loss=0.213, simple_loss=0.2912, pruned_loss=0.06745, over 1421199.80 frames.], batch size: 21, lr: 1.28e-03
2022-05-14 02:43:06,312 INFO [train.py:812] (6/8) Epoch 5, batch 3900, loss[loss=0.1809, simple_loss=0.2595, pruned_loss=0.0512, over 7157.00 frames.], tot_loss[loss=0.2134, simple_loss=0.291, pruned_loss=0.06786, over 1417845.32 frames.], batch size: 18, lr: 1.28e-03
2022-05-14 02:44:04,250 INFO [train.py:812] (6/8) Epoch 5, batch 3950, loss[loss=0.1996, simple_loss=0.2948, pruned_loss=0.05219, over 7418.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2913, pruned_loss=0.06799, over 1414818.21 frames.], batch size: 21, lr: 1.28e-03
2022-05-14 02:45:02,233 INFO [train.py:812] (6/8) Epoch 5, batch 4000, loss[loss=0.2106, simple_loss=0.2984, pruned_loss=0.06141, over 7434.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2915, pruned_loss=0.06749, over 1417246.87 frames.], batch size: 20, lr: 1.28e-03
2022-05-14 02:46:01,629 INFO [train.py:812] (6/8) Epoch 5, batch 4050, loss[loss=0.2244, simple_loss=0.3071, pruned_loss=0.07087, over 7217.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2914, pruned_loss=0.06739, over 1419473.29 frames.], batch size: 21, lr: 1.28e-03
2022-05-14 02:46:59,624 INFO [train.py:812] (6/8) Epoch 5, batch 4100, loss[loss=0.2145, simple_loss=0.2838, pruned_loss=0.07259, over 7267.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2936, pruned_loss=0.06844, over 1416478.35 frames.], batch size: 18, lr: 1.28e-03
2022-05-14 02:47:58,920 INFO [train.py:812] (6/8) Epoch 5, batch 4150, loss[loss=0.2211, simple_loss=0.294, pruned_loss=0.07411, over 7197.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2937, pruned_loss=0.06831, over 1415312.49 frames.], batch size: 22, lr: 1.27e-03
2022-05-14 02:48:57,897 INFO [train.py:812] (6/8) Epoch 5, batch 4200, loss[loss=0.2117, simple_loss=0.284, pruned_loss=0.06973, over 7143.00 frames.], tot_loss[loss=0.2154, simple_loss=0.294, pruned_loss=0.0684, over 1413410.88 frames.], batch size: 17, lr: 1.27e-03
2022-05-14 02:49:57,173 INFO [train.py:812] (6/8) Epoch 5, batch 4250, loss[loss=0.1978, simple_loss=0.2774, pruned_loss=0.05906, over 7450.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2946, pruned_loss=0.06899, over 1414838.50 frames.], batch size: 19, lr: 1.27e-03
2022-05-14 02:50:54,456 INFO [train.py:812] (6/8) Epoch 5, batch 4300, loss[loss=0.2254, simple_loss=0.3103, pruned_loss=0.07026, over 7144.00 frames.], tot_loss[loss=0.2169, simple_loss=0.295, pruned_loss=0.0694, over 1414978.50 frames.], batch size: 20, lr: 1.27e-03
2022-05-14 02:51:52,728 INFO [train.py:812] (6/8) Epoch 5, batch 4350, loss[loss=0.2454, simple_loss=0.3259, pruned_loss=0.08239, over 7410.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2949, pruned_loss=0.06906, over 1413946.33 frames.], batch size: 21, lr: 1.27e-03
2022-05-14 02:52:52,066 INFO [train.py:812] (6/8) Epoch 5, batch 4400, loss[loss=0.2126, simple_loss=0.2996, pruned_loss=0.06285, over 7264.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2944, pruned_loss=0.06893, over 1408904.85 frames.], batch size: 19, lr: 1.27e-03
2022-05-14 02:53:51,766 INFO [train.py:812] (6/8) Epoch 5, batch 4450, loss[loss=0.1998, simple_loss=0.2824, pruned_loss=0.05863, over 6777.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2948, pruned_loss=0.06923, over 1402480.96 frames.], batch size: 31, lr: 1.27e-03
2022-05-14 02:54:49,535 INFO [train.py:812] (6/8) Epoch 5, batch 4500, loss[loss=0.2576, simple_loss=0.3216, pruned_loss=0.09682, over 4996.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2971, pruned_loss=0.0703, over 1392132.78 frames.], batch size: 54, lr: 1.27e-03
2022-05-14 02:55:48,819 INFO [train.py:812] (6/8) Epoch 5, batch 4550, loss[loss=0.2649, simple_loss=0.3157, pruned_loss=0.1071, over 5343.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3001, pruned_loss=0.0735, over 1337434.40 frames.], batch size: 53, lr: 1.26e-03
2022-05-14 02:56:57,109 INFO [train.py:812] (6/8) Epoch 6, batch 0, loss[loss=0.1912, simple_loss=0.2725, pruned_loss=0.05495, over 7155.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2725, pruned_loss=0.05495, over 7155.00 frames.], batch size: 19, lr: 1.21e-03
2022-05-14 02:57:56,769 INFO [train.py:812] (6/8) Epoch 6, batch 50, loss[loss=0.2314, simple_loss=0.296, pruned_loss=0.08338, over 4898.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2884, pruned_loss=0.0639, over 318399.27 frames.], batch size: 52, lr: 1.21e-03
2022-05-14 02:58:56,413 INFO [train.py:812] (6/8) Epoch 6, batch 100, loss[loss=0.2019, simple_loss=0.2799, pruned_loss=0.06199, over 7140.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2933, pruned_loss=0.06686, over 560698.94 frames.], batch size: 20, lr: 1.21e-03
2022-05-14 02:59:55,400 INFO [train.py:812] (6/8) Epoch 6, batch 150, loss[loss=0.2559, simple_loss=0.3252, pruned_loss=0.09328, over 6678.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2912, pruned_loss=0.06582, over 749399.36 frames.], batch size: 31, lr: 1.21e-03
2022-05-14 03:00:54,931 INFO [train.py:812] (6/8) Epoch 6, batch 200, loss[loss=0.2361, simple_loss=0.2961, pruned_loss=0.08801, over 7419.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2909, pruned_loss=0.06668, over 898930.88 frames.], batch size: 18, lr: 1.21e-03
2022-05-14 03:01:54,491 INFO [train.py:812] (6/8) Epoch 6, batch 250, loss[loss=0.2302, simple_loss=0.3088, pruned_loss=0.07575, over 7323.00 frames.], tot_loss[loss=0.211, simple_loss=0.2904, pruned_loss=0.06577, over 1019279.20 frames.], batch size: 22, lr: 1.21e-03
2022-05-14 03:02:54,580 INFO [train.py:812] (6/8) Epoch 6, batch 300, loss[loss=0.2131, simple_loss=0.296, pruned_loss=0.06513, over 7228.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2906, pruned_loss=0.06635, over 1111890.04 frames.], batch size: 20, lr: 1.21e-03
2022-05-14 03:03:51,881 INFO [train.py:812] (6/8) Epoch 6, batch 350, loss[loss=0.1988, simple_loss=0.2832, pruned_loss=0.05716, over 7322.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2905, pruned_loss=0.06641, over 1185718.03 frames.], batch size: 20, lr: 1.20e-03
2022-05-14 03:04:49,997 INFO [train.py:812] (6/8) Epoch 6, batch 400, loss[loss=0.2158, simple_loss=0.291, pruned_loss=0.07028, over 7379.00 frames.], tot_loss[loss=0.2107, simple_loss=0.29, pruned_loss=0.06569, over 1236993.37 frames.], batch size: 23, lr: 1.20e-03
2022-05-14 03:05:47,812 INFO [train.py:812] (6/8) Epoch 6, batch 450, loss[loss=0.1835, simple_loss=0.2588, pruned_loss=0.05407, over 7232.00 frames.], tot_loss[loss=0.2094, simple_loss=0.289, pruned_loss=0.06486, over 1280029.90 frames.], batch size: 16, lr: 1.20e-03
2022-05-14 03:06:47,299 INFO [train.py:812] (6/8) Epoch 6, batch 500, loss[loss=0.2456, simple_loss=0.3169, pruned_loss=0.08717, over 5189.00 frames.], tot_loss[loss=0.21, simple_loss=0.2897, pruned_loss=0.06519, over 1310620.87 frames.], batch size: 53, lr: 1.20e-03
2022-05-14 03:07:45,169 INFO [train.py:812] (6/8) Epoch 6, batch 550, loss[loss=0.2791, simple_loss=0.352, pruned_loss=0.1031, over 6626.00 frames.], tot_loss[loss=0.2103, simple_loss=0.29, pruned_loss=0.06535, over 1334175.12 frames.], batch size: 38, lr: 1.20e-03
2022-05-14 03:08:44,006 INFO [train.py:812] (6/8) Epoch 6, batch 600, loss[loss=0.2291, simple_loss=0.3102, pruned_loss=0.07403, over 7145.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2887, pruned_loss=0.06509, over 1352923.22 frames.], batch size: 20, lr: 1.20e-03
2022-05-14 03:09:42,706 INFO [train.py:812] (6/8) Epoch 6, batch 650, loss[loss=0.1761, simple_loss=0.2693, pruned_loss=0.04151, over 7416.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2883, pruned_loss=0.06503, over 1367283.90 frames.], batch size: 21, lr: 1.20e-03
2022-05-14 03:10:42,224 INFO [train.py:812] (6/8) Epoch 6, batch 700, loss[loss=0.1761, simple_loss=0.2644, pruned_loss=0.04388, over 7242.00 frames.], tot_loss[loss=0.2084, simple_loss=0.288, pruned_loss=0.06443, over 1379933.18 frames.], batch size: 16, lr: 1.20e-03
2022-05-14 03:11:41,184 INFO [train.py:812] (6/8) Epoch 6, batch 750, loss[loss=0.2151, simple_loss=0.3037, pruned_loss=0.0632, over 7229.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2895, pruned_loss=0.06517, over 1389333.91 frames.], batch size: 21, lr: 1.19e-03
2022-05-14 03:12:41,106 INFO [train.py:812] (6/8) Epoch 6, batch 800, loss[loss=0.2422, simple_loss=0.3189, pruned_loss=0.08277, over 7220.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2889, pruned_loss=0.06489, over 1399648.53 frames.], batch size: 21, lr: 1.19e-03
2022-05-14 03:13:40,556 INFO [train.py:812] (6/8) Epoch 6, batch 850, loss[loss=0.2312, simple_loss=0.3109, pruned_loss=0.07576, over 7194.00 frames.], tot_loss[loss=0.2104, simple_loss=0.29, pruned_loss=0.06539, over 1404327.08 frames.], batch size: 23, lr: 1.19e-03
2022-05-14 03:14:39,882 INFO [train.py:812] (6/8) Epoch 6, batch 900, loss[loss=0.2113, simple_loss=0.2917, pruned_loss=0.06546, over 7414.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2896, pruned_loss=0.06493, over 1405794.61 frames.], batch size: 21, lr: 1.19e-03
2022-05-14 03:15:38,565 INFO [train.py:812] (6/8) Epoch 6, batch 950, loss[loss=0.1891, simple_loss=0.2631, pruned_loss=0.05752, over 7125.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2897, pruned_loss=0.06494, over 1406612.44 frames.], batch size: 17, lr: 1.19e-03
2022-05-14 03:16:37,964 INFO [train.py:812] (6/8) Epoch 6, batch 1000, loss[loss=0.2155, simple_loss=0.298, pruned_loss=0.06648, over 7402.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2899, pruned_loss=0.06527, over 1408985.18 frames.], batch size: 21, lr: 1.19e-03
2022-05-14 03:17:36,246 INFO [train.py:812] (6/8) Epoch 6, batch 1050, loss[loss=0.1995, simple_loss=0.2793, pruned_loss=0.05991, over 7327.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2898, pruned_loss=0.06563, over 1414019.55 frames.], batch size: 20, lr: 1.19e-03
2022-05-14 03:18:39,072 INFO [train.py:812] (6/8) Epoch 6, batch 1100, loss[loss=0.2028, simple_loss=0.2922, pruned_loss=0.05667, over 7325.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2897, pruned_loss=0.06573, over 1409094.51 frames.], batch size: 21, lr: 1.19e-03
2022-05-14 03:19:37,387 INFO [train.py:812] (6/8) Epoch 6, batch 1150, loss[loss=0.1865, simple_loss=0.268, pruned_loss=0.05248, over 7160.00 frames.], tot_loss[loss=0.2105, simple_loss=0.29, pruned_loss=0.06553, over 1414410.09 frames.], batch size: 20, lr: 1.19e-03
2022-05-14 03:20:36,651 INFO [train.py:812] (6/8) Epoch 6, batch 1200, loss[loss=0.2055, simple_loss=0.2763, pruned_loss=0.06729, over 7179.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2892, pruned_loss=0.06521, over 1414654.60 frames.], batch size: 26, lr: 1.18e-03
2022-05-14 03:21:34,828 INFO [train.py:812] (6/8) Epoch 6, batch 1250, loss[loss=0.1945, simple_loss=0.2899, pruned_loss=0.04951, over 7139.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2892, pruned_loss=0.06493, over 1413530.12 frames.], batch size: 20, lr: 1.18e-03
2022-05-14 03:22:34,583 INFO [train.py:812] (6/8) Epoch 6, batch 1300, loss[loss=0.2138, simple_loss=0.2865, pruned_loss=0.0706, over 7359.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2879, pruned_loss=0.06465, over 1411682.71 frames.], batch size: 19, lr: 1.18e-03
2022-05-14 03:23:33,481 INFO [train.py:812] (6/8) Epoch 6, batch 1350, loss[loss=0.261, simple_loss=0.3408, pruned_loss=0.09058, over 7060.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2872, pruned_loss=0.06381, over 1415219.21 frames.], batch size: 28, lr: 1.18e-03
2022-05-14 03:24:32,561 INFO [train.py:812] (6/8) Epoch 6, batch 1400, loss[loss=0.2012, simple_loss=0.2843, pruned_loss=0.05904, over 7331.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2873, pruned_loss=0.06419, over 1418431.51 frames.], batch size: 20, lr: 1.18e-03
2022-05-14 03:25:31,756 INFO [train.py:812] (6/8) Epoch 6, batch 1450, loss[loss=0.1857, simple_loss=0.2789, pruned_loss=0.0463, over 7435.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2871, pruned_loss=0.06391, over 1419671.50 frames.], batch size: 20, lr: 1.18e-03
2022-05-14 03:26:31,160 INFO [train.py:812] (6/8) Epoch 6, batch 1500, loss[loss=0.211, simple_loss=0.2925, pruned_loss=0.06476, over 7149.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2877, pruned_loss=0.06426, over 1419870.92 frames.], batch size: 20, lr: 1.18e-03
2022-05-14 03:27:30,169 INFO [train.py:812] (6/8) Epoch 6, batch 1550, loss[loss=0.1649, simple_loss=0.2399, pruned_loss=0.0449, over 7279.00 frames.], tot_loss[loss=0.208, simple_loss=0.2881, pruned_loss=0.06394, over 1421567.65 frames.], batch size: 17, lr: 1.18e-03
2022-05-14 03:28:29,824 INFO [train.py:812] (6/8) Epoch 6, batch 1600, loss[loss=0.2021, simple_loss=0.2766, pruned_loss=0.0638, over 7428.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2875, pruned_loss=0.06392, over 1414868.95 frames.], batch size: 20, lr: 1.17e-03
2022-05-14 03:29:29,250 INFO [train.py:812] (6/8) Epoch 6, batch 1650, loss[loss=0.2474, simple_loss=0.3255, pruned_loss=0.08464, over 7299.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2873, pruned_loss=0.06379, over 1415333.61 frames.], batch size: 25, lr: 1.17e-03
2022-05-14 03:30:27,838 INFO [train.py:812] (6/8) Epoch 6, batch 1700, loss[loss=0.2097, simple_loss=0.2954, pruned_loss=0.06196, over 7213.00 frames.], tot_loss[loss=0.2089, simple_loss=0.2882, pruned_loss=0.06474, over 1414139.37 frames.], batch size: 22, lr: 1.17e-03
2022-05-14 03:31:26,915 INFO [train.py:812] (6/8) Epoch 6, batch 1750, loss[loss=0.1837, simple_loss=0.2532, pruned_loss=0.05712, over 7280.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2887, pruned_loss=0.06503, over 1411083.24 frames.], batch size: 18, lr: 1.17e-03
2022-05-14 03:32:26,473 INFO [train.py:812] (6/8) Epoch 6, batch 1800, loss[loss=0.2585, simple_loss=0.3159, pruned_loss=0.1006, over 5296.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2885, pruned_loss=0.0646, over 1412389.29 frames.], batch size: 52, lr: 1.17e-03
2022-05-14 03:33:25,541 INFO [train.py:812] (6/8) Epoch 6, batch 1850, loss[loss=0.1958, simple_loss=0.2836, pruned_loss=0.054, over 7162.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2874, pruned_loss=0.0638, over 1416685.51 frames.], batch size: 18, lr: 1.17e-03
2022-05-14 03:34:24,888 INFO [train.py:812] (6/8) Epoch 6, batch 1900, loss[loss=0.1868, simple_loss=0.2586, pruned_loss=0.05749, over 7146.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2883, pruned_loss=0.06402, over 1416101.74 frames.], batch size: 17, lr: 1.17e-03
2022-05-14 03:35:23,976 INFO [train.py:812] (6/8) Epoch 6, batch 1950, loss[loss=0.2291, simple_loss=0.3152, pruned_loss=0.07148, over 7111.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2886, pruned_loss=0.06395, over 1420203.43 frames.], batch size: 21, lr: 1.17e-03
2022-05-14 03:36:21,531 INFO [train.py:812] (6/8) Epoch 6, batch 2000, loss[loss=0.1972, simple_loss=0.27, pruned_loss=0.06219, over 7257.00 frames.], tot_loss[loss=0.208, simple_loss=0.2882, pruned_loss=0.06387, over 1423398.85 frames.], batch size: 18, lr: 1.17e-03
2022-05-14 03:37:19,522 INFO [train.py:812] (6/8) Epoch 6, batch 2050, loss[loss=0.2098, simple_loss=0.292, pruned_loss=0.06374, over 7021.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2883, pruned_loss=0.06398, over 1423138.75 frames.], batch size: 28, lr: 1.16e-03
2022-05-14 03:38:19,357 INFO [train.py:812] (6/8) Epoch 6, batch 2100, loss[loss=0.2036, simple_loss=0.2919, pruned_loss=0.05762, over 6159.00 frames.], tot_loss[loss=0.2075, simple_loss=0.288, pruned_loss=0.06351, over 1424685.29 frames.], batch size: 37, lr: 1.16e-03
2022-05-14 03:39:18,995 INFO [train.py:812] (6/8) Epoch 6, batch 2150, loss[loss=0.2033, simple_loss=0.271, pruned_loss=0.06776, over 7143.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2873, pruned_loss=0.06293, over 1429954.09 frames.], batch size: 20, lr: 1.16e-03
2022-05-14 03:40:18,687 INFO [train.py:812] (6/8) Epoch 6, batch 2200, loss[loss=0.2062, simple_loss=0.2932, pruned_loss=0.05959, over 7130.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2871, pruned_loss=0.06295, over 1426002.92 frames.], batch size: 20, lr: 1.16e-03
2022-05-14 03:41:17,714 INFO [train.py:812] (6/8) Epoch 6, batch 2250, loss[loss=0.1677, simple_loss=0.2564, pruned_loss=0.03945, over 7368.00 frames.], tot_loss[loss=0.207, simple_loss=0.2876, pruned_loss=0.06318, over 1424929.59 frames.], batch size: 19, lr: 1.16e-03
2022-05-14 03:42:16,660 INFO [train.py:812] (6/8) Epoch 6, batch 2300, loss[loss=0.2161, simple_loss=0.2931, pruned_loss=0.06953, over 7288.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2869, pruned_loss=0.06301, over 1421994.05 frames.], batch size: 24, lr: 1.16e-03
2022-05-14 03:43:15,834 INFO [train.py:812] (6/8) Epoch 6, batch 2350, loss[loss=0.2132, simple_loss=0.2988, pruned_loss=0.06379, over 7219.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2867, pruned_loss=0.06313, over 1421347.45 frames.], batch size: 21, lr: 1.16e-03
2022-05-14 03:44:16,020 INFO [train.py:812] (6/8) Epoch 6, batch 2400, loss[loss=0.1935, simple_loss=0.2718, pruned_loss=0.05757, over 7335.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2861, pruned_loss=0.06306, over 1421880.24 frames.], batch size: 20, lr: 1.16e-03
2022-05-14 03:45:14,571 INFO [train.py:812] (6/8) Epoch 6, batch 2450, loss[loss=0.1862, simple_loss=0.2691, pruned_loss=0.05164, over 7191.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2854, pruned_loss=0.06261, over 1421474.52 frames.], batch size: 16, lr: 1.16e-03
2022-05-14 03:46:13,728 INFO [train.py:812] (6/8) Epoch 6, batch 2500, loss[loss=0.1855, simple_loss=0.2712, pruned_loss=0.04993, over 7329.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2857, pruned_loss=0.06274, over 1421121.70 frames.], batch size: 22, lr: 1.15e-03
2022-05-14 03:47:11,227 INFO [train.py:812] (6/8) Epoch 6, batch 2550, loss[loss=0.2218, simple_loss=0.2813, pruned_loss=0.0811, over 6816.00 frames.], tot_loss[loss=0.206, simple_loss=0.2858, pruned_loss=0.06311, over 1422904.42 frames.], batch size: 15, lr: 1.15e-03
2022-05-14 03:48:09,677 INFO [train.py:812] (6/8) Epoch 6, batch 2600, loss[loss=0.2139, simple_loss=0.3088, pruned_loss=0.05948, over 7336.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2859, pruned_loss=0.06293, over 1425881.34 frames.], batch size: 21, lr: 1.15e-03
2022-05-14 03:49:08,332 INFO [train.py:812] (6/8) Epoch 6, batch 2650, loss[loss=0.1929, simple_loss=0.2904, pruned_loss=0.04773, over 7298.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2871, pruned_loss=0.06333, over 1424060.04 frames.], batch size: 25, lr: 1.15e-03
2022-05-14 03:50:08,430 INFO [train.py:812] (6/8) Epoch 6, batch 2700, loss[loss=0.1813, simple_loss=0.251, pruned_loss=0.05583, over 7254.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2861, pruned_loss=0.06242, over 1426614.12 frames.], batch size: 16, lr: 1.15e-03
2022-05-14 03:51:06,545 INFO [train.py:812] (6/8) Epoch 6, batch 2750, loss[loss=0.2222, simple_loss=0.3063, pruned_loss=0.06903, over 7233.00 frames.], tot_loss[loss=0.2064, simple_loss=0.287, pruned_loss=0.06286, over 1424048.16 frames.], batch size: 20, lr: 1.15e-03
2022-05-14 03:52:05,474 INFO [train.py:812] (6/8) Epoch 6, batch 2800, loss[loss=0.2108, simple_loss=0.2982, pruned_loss=0.06164, over 7272.00 frames.], tot_loss[loss=0.2067, simple_loss=0.287, pruned_loss=0.06317, over 1421898.24 frames.], batch size: 18, lr: 1.15e-03
2022-05-14 03:53:03,396 INFO [train.py:812] (6/8) Epoch 6, batch 2850, loss[loss=0.163, simple_loss=0.2397, pruned_loss=0.04321, over 7275.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2875, pruned_loss=0.06307, over 1419515.19 frames.], batch size: 17, lr: 1.15e-03
2022-05-14 03:54:00,923 INFO [train.py:812] (6/8) Epoch 6, batch 2900, loss[loss=0.2151, simple_loss=0.2994, pruned_loss=0.06539, over 6758.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2878, pruned_loss=0.0634, over 1420897.98 frames.], batch size: 31, lr: 1.15e-03
2022-05-14 03:54:58,724 INFO [train.py:812] (6/8) Epoch 6, batch 2950, loss[loss=0.2253, simple_loss=0.2839, pruned_loss=0.08332, over 7142.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2867, pruned_loss=0.06287, over 1420596.13 frames.], batch size: 20, lr: 1.14e-03
2022-05-14 03:55:55,722 INFO [train.py:812] (6/8) Epoch 6, batch 3000, loss[loss=0.1735, simple_loss=0.2636, pruned_loss=0.04168, over 7237.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2871, pruned_loss=0.06286, over 1420615.46 frames.], batch size: 20, lr: 1.14e-03
2022-05-14 03:55:55,724 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 03:56:03,338 INFO [train.py:841] (6/8) Epoch 6, validation: loss=0.1668, simple_loss=0.2696, pruned_loss=0.03205, over 698248.00 frames.
2022-05-14 03:57:02,216 INFO [train.py:812] (6/8) Epoch 6, batch 3050, loss[loss=0.2292, simple_loss=0.3074, pruned_loss=0.07548, over 7193.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2858, pruned_loss=0.06198, over 1426300.00 frames.], batch size: 23, lr: 1.14e-03
2022-05-14 03:58:01,687 INFO [train.py:812] (6/8) Epoch 6, batch 3100, loss[loss=0.2246, simple_loss=0.3125, pruned_loss=0.06834, over 7332.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2845, pruned_loss=0.06202, over 1423807.10 frames.], batch size: 22, lr: 1.14e-03
2022-05-14 03:58:58,850 INFO [train.py:812] (6/8) Epoch 6, batch 3150, loss[loss=0.2107, simple_loss=0.2909, pruned_loss=0.06528, over 7207.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2862, pruned_loss=0.06232, over 1424452.59 frames.], batch size: 23, lr: 1.14e-03
2022-05-14 03:59:57,541 INFO [train.py:812] (6/8) Epoch 6, batch 3200, loss[loss=0.2003, simple_loss=0.2785, pruned_loss=0.06109, over 7227.00 frames.], tot_loss[loss=0.206, simple_loss=0.2865, pruned_loss=0.06272, over 1425451.39 frames.], batch size: 21, lr: 1.14e-03
2022-05-14 04:00:56,308 INFO [train.py:812] (6/8) Epoch 6, batch 3250, loss[loss=0.215, simple_loss=0.2908, pruned_loss=0.06961, over 7361.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2876, pruned_loss=0.06326, over 1425557.84 frames.], batch size: 19, lr: 1.14e-03
2022-05-14 04:01:55,537 INFO [train.py:812] (6/8) Epoch 6, batch 3300, loss[loss=0.2003, simple_loss=0.2804, pruned_loss=0.0601, over 7204.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2885, pruned_loss=0.06386, over 1421633.17 frames.], batch size: 23, lr: 1.14e-03
2022-05-14 04:02:54,524 INFO [train.py:812] (6/8) Epoch 6, batch 3350, loss[loss=0.1831, simple_loss=0.2567, pruned_loss=0.05471, over 7257.00 frames.], tot_loss[loss=0.207, simple_loss=0.2874, pruned_loss=0.06328, over 1425979.45 frames.], batch size: 19, lr: 1.14e-03
2022-05-14 04:03:53,911 INFO [train.py:812] (6/8) Epoch 6, batch 3400, loss[loss=0.2235, simple_loss=0.3065, pruned_loss=0.07027, over 7286.00 frames.], tot_loss[loss=0.2067, simple_loss=0.287, pruned_loss=0.06321, over 1424828.32 frames.], batch size: 24, lr: 1.14e-03
2022-05-14 04:04:52,394 INFO [train.py:812] (6/8) Epoch 6, batch 3450, loss[loss=0.2192, simple_loss=0.3067, pruned_loss=0.06584, over 7417.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2879, pruned_loss=0.06351, over 1427404.42 frames.], batch size: 21, lr: 1.13e-03
2022-05-14 04:05:50,790 INFO [train.py:812] (6/8) Epoch 6, batch 3500, loss[loss=0.243, simple_loss=0.3239, pruned_loss=0.08107, over 7204.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2871, pruned_loss=0.06325, over 1424021.31 frames.], batch size: 22, lr: 1.13e-03
2022-05-14 04:06:49,095 INFO [train.py:812] (6/8) Epoch 6, batch 3550, loss[loss=0.2353, simple_loss=0.3132, pruned_loss=0.07869, over 7322.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2864, pruned_loss=0.06296, over 1427077.57 frames.], batch size: 21, lr: 1.13e-03
2022-05-14 04:07:47,627 INFO [train.py:812] (6/8) Epoch 6, batch 3600, loss[loss=0.1989, simple_loss=0.2709, pruned_loss=0.06345, over 7170.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2857, pruned_loss=0.0628, over 1428898.67 frames.], batch size: 18, lr: 1.13e-03
2022-05-14 04:08:46,813 INFO [train.py:812] (6/8) Epoch 6, batch 3650, loss[loss=0.2072, simple_loss=0.2855, pruned_loss=0.06449, over 7409.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2858, pruned_loss=0.06266, over 1428143.08 frames.], batch size: 21, lr: 1.13e-03
2022-05-14 04:09:44,232 INFO [train.py:812] (6/8) Epoch 6, batch 3700, loss[loss=0.1934, simple_loss=0.2775, pruned_loss=0.05462, over 7227.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2854, pruned_loss=0.06222, over 1426219.01 frames.], batch size: 20, lr: 1.13e-03
2022-05-14 04:10:41,366 INFO [train.py:812] (6/8) Epoch 6, batch 3750, loss[loss=0.2371, simple_loss=0.3146, pruned_loss=0.07981, over 7369.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2857, pruned_loss=0.06236, over 1423541.71 frames.], batch size: 23, lr: 1.13e-03
2022-05-14 04:11:40,667 INFO [train.py:812] (6/8) Epoch 6, batch 3800, loss[loss=0.2194, simple_loss=0.3075, pruned_loss=0.06558, over 7236.00 frames.], tot_loss[loss=0.206, simple_loss=0.2858, pruned_loss=0.06308, over 1418760.42 frames.], batch size: 20, lr: 1.13e-03
2022-05-14 04:12:39,831 INFO [train.py:812] (6/8) Epoch 6, batch 3850, loss[loss=0.1909, simple_loss=0.2759, pruned_loss=0.05299, over 7436.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2867, pruned_loss=0.06245, over 1419434.04 frames.], batch size: 20, lr: 1.13e-03
2022-05-14 04:13:39,023 INFO [train.py:812] (6/8) Epoch 6, batch 3900, loss[loss=0.2109, simple_loss=0.2883, pruned_loss=0.06674, over 7409.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2877, pruned_loss=0.06308, over 1423971.89 frames.], batch size: 18, lr: 1.13e-03
2022-05-14 04:14:38,347 INFO [train.py:812] (6/8) Epoch 6, batch 3950, loss[loss=0.2153, simple_loss=0.2993, pruned_loss=0.06569, over 7295.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2859, pruned_loss=0.06231, over 1423186.86 frames.], batch size: 24, lr: 1.12e-03
2022-05-14 04:15:37,080 INFO [train.py:812] (6/8) Epoch 6, batch 4000, loss[loss=0.2355, simple_loss=0.3081, pruned_loss=0.08142, over 7200.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2867, pruned_loss=0.063, over 1426211.00 frames.], batch size: 23, lr: 1.12e-03
2022-05-14 04:16:34,899 INFO [train.py:812] (6/8) Epoch 6, batch 4050, loss[loss=0.2679, simple_loss=0.3412, pruned_loss=0.09726, over 7267.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2873, pruned_loss=0.06329, over 1427525.88 frames.], batch size: 24, lr: 1.12e-03
2022-05-14 04:17:34,630 INFO [train.py:812] (6/8) Epoch 6, batch 4100, loss[loss=0.1929, simple_loss=0.2708, pruned_loss=0.05747, over 7413.00 frames.], tot_loss[loss=0.206, simple_loss=0.2861, pruned_loss=0.06297, over 1428107.57 frames.], batch size: 18, lr: 1.12e-03
2022-05-14 04:18:33,848 INFO [train.py:812] (6/8) Epoch 6, batch 4150, loss[loss=0.2202, simple_loss=0.3111, pruned_loss=0.06465, over 6790.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2841, pruned_loss=0.06221, over 1427583.06 frames.], batch size: 31, lr: 1.12e-03
2022-05-14 04:19:32,914 INFO [train.py:812] (6/8) Epoch 6, batch 4200, loss[loss=0.252, simple_loss=0.3233, pruned_loss=0.09039, over 7109.00 frames.], tot_loss[loss=0.2045, simple_loss=0.284, pruned_loss=0.06252, over 1428757.27 frames.], batch size: 21, lr: 1.12e-03
2022-05-14 04:20:33,102 INFO [train.py:812] (6/8) Epoch 6, batch 4250, loss[loss=0.2102, simple_loss=0.3028, pruned_loss=0.05882, over 7381.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2837, pruned_loss=0.06226, over 1429451.15 frames.], batch size: 23, lr: 1.12e-03
2022-05-14 04:21:32,393 INFO [train.py:812] (6/8) Epoch 6, batch 4300, loss[loss=0.2022, simple_loss=0.2805, pruned_loss=0.06195, over 7065.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2847, pruned_loss=0.06326, over 1425203.43 frames.], batch size: 18, lr: 1.12e-03
2022-05-14 04:22:31,658 INFO [train.py:812] (6/8) Epoch 6, batch 4350, loss[loss=0.1918, simple_loss=0.2844, pruned_loss=0.04962, over 7229.00 frames.], tot_loss[loss=0.2048, simple_loss=0.284, pruned_loss=0.06274, over 1424922.84 frames.], batch size: 21, lr: 1.12e-03
2022-05-14 04:23:31,436 INFO [train.py:812] (6/8) Epoch 6, batch 4400, loss[loss=0.2182, simple_loss=0.2981, pruned_loss=0.06917, over 7434.00 frames.], tot_loss[loss=0.2038, simple_loss=0.283, pruned_loss=0.06232, over 1422634.60 frames.], batch size: 20, lr: 1.12e-03
2022-05-14 04:24:30,578 INFO [train.py:812] (6/8) Epoch 6, batch 4450, loss[loss=0.1872, simple_loss=0.2693, pruned_loss=0.05257, over 7289.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2836, pruned_loss=0.06229, over 1409817.38 frames.], batch size: 17, lr: 1.11e-03
2022-05-14 04:25:38,571 INFO [train.py:812] (6/8) Epoch 6, batch 4500, loss[loss=0.1907, simple_loss=0.2763, pruned_loss=0.05257, over 7232.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2809, pruned_loss=0.06112, over 1408970.51 frames.], batch size: 20, lr: 1.11e-03
2022-05-14 04:26:36,436 INFO [train.py:812] (6/8) Epoch 6, batch 4550, loss[loss=0.3325, simple_loss=0.3813, pruned_loss=0.1419, over 5387.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2844, pruned_loss=0.06452, over 1360550.07 frames.], batch size: 52, lr: 1.11e-03
2022-05-14 04:27:44,585 INFO [train.py:812] (6/8) Epoch 7, batch 0, loss[loss=0.1875, simple_loss=0.2645, pruned_loss=0.05523, over 7407.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2645, pruned_loss=0.05523, over 7407.00 frames.], batch size: 18, lr: 1.07e-03
2022-05-14 04:28:43,315 INFO [train.py:812] (6/8) Epoch 7, batch 50, loss[loss=0.1844, simple_loss=0.2708, pruned_loss=0.04906, over 7397.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2808, pruned_loss=0.06108, over 322499.02 frames.], batch size: 18, lr: 1.07e-03
2022-05-14 04:29:42,468 INFO [train.py:812] (6/8) Epoch 7, batch 100, loss[loss=0.1939, simple_loss=0.2722, pruned_loss=0.05781, over 7151.00 frames.], tot_loss[loss=0.2021, simple_loss=0.282, pruned_loss=0.06105, over 566510.92 frames.], batch size: 19, lr: 1.06e-03
2022-05-14 04:30:41,787 INFO [train.py:812] (6/8) Epoch 7, batch 150, loss[loss=0.2315, simple_loss=0.3002, pruned_loss=0.08143, over 7168.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2834, pruned_loss=0.06084, over 756604.46 frames.], batch size: 19, lr: 1.06e-03
2022-05-14 04:31:41,619 INFO [train.py:812] (6/8) Epoch 7, batch 200, loss[loss=0.2161, simple_loss=0.3044, pruned_loss=0.06396, over 7375.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2836, pruned_loss=0.06113, over 906701.88 frames.], batch size: 23, lr: 1.06e-03
2022-05-14 04:32:39,942 INFO [train.py:812] (6/8) Epoch 7, batch 250, loss[loss=0.2292, simple_loss=0.3123, pruned_loss=0.07303, over 7136.00 frames.], tot_loss[loss=0.2022, simple_loss=0.284, pruned_loss=0.06026, over 1020935.40 frames.], batch size: 20, lr: 1.06e-03
2022-05-14 04:33:39,371 INFO [train.py:812] (6/8) Epoch 7, batch 300, loss[loss=0.1785, simple_loss=0.2564, pruned_loss=0.0503, over 6816.00 frames.], tot_loss[loss=0.2038, simple_loss=0.285, pruned_loss=0.06128, over 1107141.74 frames.], batch size: 15, lr: 1.06e-03
2022-05-14 04:34:57,033 INFO [train.py:812] (6/8) Epoch 7, batch 350, loss[loss=0.1894, simple_loss=0.276, pruned_loss=0.05145, over 7108.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2841, pruned_loss=0.06009, over 1177620.29 frames.], batch size: 21, lr: 1.06e-03
2022-05-14 04:35:53,860 INFO [train.py:812] (6/8) Epoch 7, batch 400, loss[loss=0.1566, simple_loss=0.2379, pruned_loss=0.03769, over 7166.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2837, pruned_loss=0.05961, over 1230230.12 frames.], batch size: 18, lr: 1.06e-03
2022-05-14 04:37:20,608 INFO [train.py:812] (6/8) Epoch 7, batch 450, loss[loss=0.2112, simple_loss=0.2831, pruned_loss=0.06963, over 7364.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2838, pruned_loss=0.0599, over 1276326.85 frames.], batch size: 19, lr: 1.06e-03
2022-05-14 04:38:43,169 INFO [train.py:812] (6/8) Epoch 7, batch 500, loss[loss=0.1866, simple_loss=0.2726, pruned_loss=0.05027, over 6491.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2838, pruned_loss=0.05974, over 1305546.07 frames.], batch size: 38, lr: 1.06e-03
2022-05-14 04:39:42,054 INFO [train.py:812] (6/8) Epoch 7, batch 550, loss[loss=0.2103, simple_loss=0.2938, pruned_loss=0.06337, over 7118.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2833, pruned_loss=0.05958, over 1330416.55 frames.], batch size: 21, lr: 1.06e-03
2022-05-14 04:40:39,519 INFO [train.py:812] (6/8) Epoch 7, batch 600, loss[loss=0.2232, simple_loss=0.3073, pruned_loss=0.06958, over 7099.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2846, pruned_loss=0.06062, over 1349080.04 frames.], batch size: 28, lr: 1.06e-03
2022-05-14 04:41:38,894 INFO [train.py:812] (6/8) Epoch 7, batch 650, loss[loss=0.2538, simple_loss=0.3106, pruned_loss=0.09856, over 5115.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2834, pruned_loss=0.06013, over 1365266.91 frames.], batch size: 54, lr: 1.05e-03
2022-05-14 04:42:37,560 INFO [train.py:812] (6/8) Epoch 7, batch 700, loss[loss=0.1872, simple_loss=0.2626, pruned_loss=0.05592, over 7157.00 frames.], tot_loss[loss=0.202, simple_loss=0.2835, pruned_loss=0.06021, over 1379362.74 frames.], batch size: 18, lr: 1.05e-03
2022-05-14 04:43:36,180 INFO [train.py:812] (6/8) Epoch 7, batch 750, loss[loss=0.1739, simple_loss=0.2718, pruned_loss=0.03793, over 6755.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2823, pruned_loss=0.05931, over 1392732.28 frames.], batch size: 31, lr: 1.05e-03
2022-05-14 04:44:33,733 INFO [train.py:812] (6/8) Epoch 7, batch 800, loss[loss=0.197, simple_loss=0.2823, pruned_loss=0.05586, over 7322.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2807, pruned_loss=0.05853, over 1392632.73 frames.], batch size: 20, lr: 1.05e-03
2022-05-14 04:45:32,941 INFO [train.py:812] (6/8) Epoch 7, batch 850, loss[loss=0.2476, simple_loss=0.3264, pruned_loss=0.08436, over 7287.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2813, pruned_loss=0.05894, over 1399387.23 frames.], batch size: 24, lr: 1.05e-03
2022-05-14 04:46:32,286 INFO [train.py:812] (6/8) Epoch 7, batch 900, loss[loss=0.3361, simple_loss=0.3892, pruned_loss=0.1415, over 7366.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2821, pruned_loss=0.05969, over 1404324.03 frames.], batch size: 23, lr: 1.05e-03
2022-05-14 04:47:31,120 INFO [train.py:812] (6/8) Epoch 7, batch 950, loss[loss=0.1985, simple_loss=0.289, pruned_loss=0.05402, over 7377.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2837, pruned_loss=0.06023, over 1408359.11 frames.], batch size: 23, lr: 1.05e-03
2022-05-14 04:48:29,756 INFO [train.py:812] (6/8) Epoch 7, batch 1000, loss[loss=0.2258, simple_loss=0.3045, pruned_loss=0.07351, over 7394.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2828, pruned_loss=0.06004, over 1408794.23 frames.], batch size: 23, lr: 1.05e-03
2022-05-14 04:49:29,143 INFO [train.py:812] (6/8) Epoch 7, batch 1050, loss[loss=0.1729, simple_loss=0.2557, pruned_loss=0.04508, over 7165.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2817, pruned_loss=0.05925, over 1415623.08 frames.], batch size: 19, lr: 1.05e-03
2022-05-14 04:50:29,075 INFO [train.py:812] (6/8) Epoch 7, batch 1100, loss[loss=0.2489, simple_loss=0.3315, pruned_loss=0.08317, over 7307.00 frames.], tot_loss[loss=0.2003, simple_loss=0.282, pruned_loss=0.05933, over 1419886.84 frames.], batch size: 25, lr: 1.05e-03
2022-05-14 04:51:28,391 INFO [train.py:812] (6/8) Epoch 7, batch 1150, loss[loss=0.1979, simple_loss=0.2689, pruned_loss=0.06347, over 7152.00 frames.], tot_loss[loss=0.201, simple_loss=0.2824, pruned_loss=0.05975, over 1418080.60 frames.], batch size: 17, lr: 1.05e-03
2022-05-14 04:52:28,301 INFO [train.py:812] (6/8) Epoch 7, batch 1200, loss[loss=0.1784, simple_loss=0.2519, pruned_loss=0.05247, over 7215.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2828, pruned_loss=0.06028, over 1413092.66 frames.], batch size: 16, lr: 1.04e-03
2022-05-14 04:53:27,884 INFO [train.py:812] (6/8) Epoch 7, batch 1250, loss[loss=0.193, simple_loss=0.2875, pruned_loss=0.04928, over 7221.00 frames.], tot_loss[loss=0.2012, simple_loss=0.282, pruned_loss=0.06015, over 1414027.81 frames.], batch size: 20, lr: 1.04e-03
2022-05-14 04:54:25,631 INFO [train.py:812] (6/8) Epoch 7, batch 1300, loss[loss=0.1458, simple_loss=0.2283, pruned_loss=0.03161, over 7283.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2816, pruned_loss=0.05972, over 1414885.84 frames.], batch size: 17, lr: 1.04e-03
2022-05-14 04:55:24,145 INFO [train.py:812] (6/8) Epoch 7, batch 1350, loss[loss=0.24, simple_loss=0.3197, pruned_loss=0.08011, over 7424.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2817, pruned_loss=0.05926, over 1420456.64 frames.], batch size: 21, lr: 1.04e-03
2022-05-14 04:56:22,899 INFO [train.py:812] (6/8) Epoch 7, batch 1400, loss[loss=0.1983, simple_loss=0.2765, pruned_loss=0.06006, over 7173.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2825, pruned_loss=0.05985, over 1418685.30 frames.], batch size: 19, lr: 1.04e-03
2022-05-14 04:57:22,038 INFO [train.py:812] (6/8) Epoch 7, batch 1450, loss[loss=0.2024, simple_loss=0.2935, pruned_loss=0.05567, over 6713.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2825, pruned_loss=0.05992, over 1418423.86 frames.], batch size: 31, lr: 1.04e-03
2022-05-14 04:58:20,161 INFO [train.py:812] (6/8) Epoch 7, batch 1500, loss[loss=0.1835, simple_loss=0.2708, pruned_loss=0.04817, over 7420.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2816, pruned_loss=0.05909, over 1422138.28 frames.], batch size: 21, lr: 1.04e-03
2022-05-14 04:59:18,881 INFO [train.py:812] (6/8) Epoch 7, batch 1550, loss[loss=0.2254, simple_loss=0.3037, pruned_loss=0.07354, over 7203.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2821, pruned_loss=0.05952, over 1415934.94 frames.], batch size: 26, lr: 1.04e-03
2022-05-14 05:00:18,919 INFO [train.py:812] (6/8) Epoch 7, batch 1600, loss[loss=0.223, simple_loss=0.3149, pruned_loss=0.06556, over 7107.00 frames.], tot_loss[loss=0.1999, simple_loss=0.282, pruned_loss=0.05886, over 1422486.16 frames.], batch size: 21, lr: 1.04e-03
2022-05-14 05:01:18,263 INFO [train.py:812] (6/8) Epoch 7, batch 1650, loss[loss=0.2003, simple_loss=0.2728, pruned_loss=0.06396, over 7454.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2812, pruned_loss=0.05899, over 1417739.51 frames.], batch size: 19, lr: 1.04e-03
2022-05-14 05:02:16,824 INFO [train.py:812] (6/8) Epoch 7, batch 1700, loss[loss=0.2358, simple_loss=0.3028, pruned_loss=0.08435, over 7226.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2805, pruned_loss=0.05893, over 1416283.14 frames.], batch size: 22, lr: 1.04e-03
2022-05-14 05:03:15,991 INFO [train.py:812] (6/8) Epoch 7, batch 1750, loss[loss=0.1771, simple_loss=0.2739, pruned_loss=0.04011, over 7336.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2814, pruned_loss=0.05963, over 1412197.84 frames.], batch size: 22, lr: 1.04e-03
2022-05-14 05:04:14,623 INFO [train.py:812] (6/8) Epoch 7, batch 1800, loss[loss=0.2272, simple_loss=0.3023, pruned_loss=0.07606, over 7265.00 frames.], tot_loss[loss=0.2019, simple_loss=0.283, pruned_loss=0.06035, over 1414716.03 frames.], batch size: 25, lr: 1.03e-03
2022-05-14 05:05:13,141 INFO [train.py:812] (6/8) Epoch 7, batch 1850, loss[loss=0.1798, simple_loss=0.259, pruned_loss=0.05035, over 7002.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2829, pruned_loss=0.06004, over 1417398.54 frames.], batch size: 16, lr: 1.03e-03
2022-05-14 05:06:10,522 INFO [train.py:812] (6/8) Epoch 7, batch 1900, loss[loss=0.1742, simple_loss=0.2482, pruned_loss=0.05012, over 7065.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2817, pruned_loss=0.05967, over 1414284.22 frames.], batch size: 18, lr: 1.03e-03
2022-05-14 05:07:08,634 INFO [train.py:812] (6/8) Epoch 7, batch 1950, loss[loss=0.1958, simple_loss=0.2715, pruned_loss=0.06007, over 7275.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2809, pruned_loss=0.05919, over 1418232.50 frames.], batch size: 18, lr: 1.03e-03
2022-05-14 05:08:07,329 INFO [train.py:812] (6/8) Epoch 7, batch 2000, loss[loss=0.2242, simple_loss=0.302, pruned_loss=0.07318, over 7268.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2815, pruned_loss=0.05982, over 1418140.78 frames.], batch size: 25, lr: 1.03e-03
2022-05-14 05:09:04,300 INFO [train.py:812] (6/8) Epoch 7, batch 2050, loss[loss=0.1958, simple_loss=0.2788, pruned_loss=0.05643, over 7295.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2822, pruned_loss=0.06018, over 1414645.26 frames.], batch size: 24, lr: 1.03e-03
2022-05-14 05:10:01,756 INFO [train.py:812] (6/8) Epoch 7, batch 2100, loss[loss=0.1887, simple_loss=0.2602, pruned_loss=0.05858, over 7005.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2823, pruned_loss=0.06001, over 1417704.61 frames.], batch size: 16, lr: 1.03e-03
2022-05-14 05:11:00,092 INFO [train.py:812] (6/8) Epoch 7, batch 2150, loss[loss=0.1876, simple_loss=0.2718, pruned_loss=0.05169, over 7410.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2824, pruned_loss=0.05924, over 1422952.83 frames.], batch size: 21, lr: 1.03e-03
2022-05-14 05:11:57,841 INFO [train.py:812] (6/8) Epoch 7, batch 2200, loss[loss=0.1794, simple_loss=0.2481, pruned_loss=0.05535, over 7136.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2814, pruned_loss=0.05894, over 1422033.79 frames.], batch size: 17, lr: 1.03e-03
2022-05-14 05:12:56,736 INFO [train.py:812] (6/8) Epoch 7, batch 2250, loss[loss=0.1786, simple_loss=0.2599, pruned_loss=0.04863, over 7293.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2827, pruned_loss=0.06003, over 1417553.31 frames.], batch size: 17, lr: 1.03e-03
2022-05-14 05:13:54,324 INFO [train.py:812] (6/8) Epoch 7, batch 2300, loss[loss=0.2001, simple_loss=0.283, pruned_loss=0.05863, over 7210.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2819, pruned_loss=0.05942, over 1420328.29 frames.], batch size: 23, lr: 1.03e-03
2022-05-14 05:14:53,679 INFO [train.py:812] (6/8) Epoch 7, batch 2350, loss[loss=0.2129, simple_loss=0.2996, pruned_loss=0.06309, over 7406.00 frames.], tot_loss[loss=0.2, simple_loss=0.2815, pruned_loss=0.05926, over 1418497.45 frames.], batch size: 21, lr: 1.02e-03
2022-05-14 05:15:53,759 INFO [train.py:812] (6/8) Epoch 7, batch 2400, loss[loss=0.1669, simple_loss=0.2437, pruned_loss=0.04506, over 7264.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2811, pruned_loss=0.05931, over 1421905.23 frames.], batch size: 18, lr: 1.02e-03
2022-05-14 05:16:51,116 INFO [train.py:812] (6/8) Epoch 7, batch 2450, loss[loss=0.1913, simple_loss=0.2849, pruned_loss=0.04887, over 7416.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2813, pruned_loss=0.05915, over 1418298.66 frames.], batch size: 21, lr: 1.02e-03
2022-05-14 05:17:49,492 INFO [train.py:812] (6/8) Epoch 7, batch 2500, loss[loss=0.2362, simple_loss=0.3188, pruned_loss=0.07685, over 7321.00 frames.], tot_loss[loss=0.2005, simple_loss=0.282, pruned_loss=0.05951, over 1418510.07 frames.], batch size: 21, lr: 1.02e-03
2022-05-14 05:18:48,423 INFO [train.py:812] (6/8) Epoch 7, batch 2550, loss[loss=0.2068, simple_loss=0.2901, pruned_loss=0.06174, over 7429.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2822, pruned_loss=0.05932, over 1424483.04 frames.], batch size: 20, lr: 1.02e-03
2022-05-14 05:19:47,260 INFO [train.py:812] (6/8) Epoch 7, batch 2600, loss[loss=0.1636, simple_loss=0.2494, pruned_loss=0.03889, over 7170.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2824, pruned_loss=0.05956, over 1418337.40 frames.], batch size: 18, lr: 1.02e-03
2022-05-14 05:20:45,577 INFO [train.py:812] (6/8) Epoch 7, batch 2650, loss[loss=0.1689, simple_loss=0.255, pruned_loss=0.04143, over 7163.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2817, pruned_loss=0.05922, over 1417056.27 frames.], batch size: 18, lr: 1.02e-03
2022-05-14 05:21:44,788 INFO [train.py:812] (6/8) Epoch 7, batch 2700, loss[loss=0.1943, simple_loss=0.2651, pruned_loss=0.06175, over 6862.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2817, pruned_loss=0.059, over 1419034.42 frames.], batch size: 15, lr: 1.02e-03
2022-05-14 05:22:44,414 INFO [train.py:812] (6/8) Epoch 7, batch 2750, loss[loss=0.1687, simple_loss=0.2498, pruned_loss=0.04383, over 7414.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2821, pruned_loss=0.05929, over 1419554.38 frames.], batch size: 18, lr: 1.02e-03
2022-05-14 05:23:44,425 INFO [train.py:812] (6/8) Epoch 7, batch 2800, loss[loss=0.1575, simple_loss=0.2372, pruned_loss=0.03892, over 7001.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2803, pruned_loss=0.05891, over 1418219.48 frames.], batch size: 16, lr: 1.02e-03
2022-05-14 05:24:43,864 INFO [train.py:812] (6/8) Epoch 7, batch 2850, loss[loss=0.1843, simple_loss=0.2672, pruned_loss=0.05068, over 7321.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2794, pruned_loss=0.05871, over 1422788.86 frames.], batch size: 21, lr: 1.02e-03
2022-05-14 05:25:43,752 INFO [train.py:812] (6/8) Epoch 7, batch 2900, loss[loss=0.2044, simple_loss=0.2868, pruned_loss=0.06097, over 5259.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2793, pruned_loss=0.05846, over 1424674.71 frames.], batch size: 53, lr: 1.02e-03
2022-05-14 05:26:42,762 INFO [train.py:812] (6/8) Epoch 7, batch 2950, loss[loss=0.2214, simple_loss=0.3065, pruned_loss=0.06816, over 7308.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2807, pruned_loss=0.05908, over 1424273.71 frames.], batch size: 25, lr: 1.01e-03
2022-05-14 05:27:42,383 INFO [train.py:812] (6/8) Epoch 7, batch 3000, loss[loss=0.1978, simple_loss=0.2851, pruned_loss=0.05526, over 7256.00 frames.], tot_loss[loss=0.1993, simple_loss=0.281, pruned_loss=0.05878, over 1426549.83 frames.], batch size: 26, lr: 1.01e-03
2022-05-14 05:27:42,384 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 05:27:49,661 INFO [train.py:841] (6/8) Epoch 7, validation: loss=0.1637, simple_loss=0.2662, pruned_loss=0.03066, over 698248.00 frames.
2022-05-14 05:28:49,047 INFO [train.py:812] (6/8) Epoch 7, batch 3050, loss[loss=0.2065, simple_loss=0.2982, pruned_loss=0.0574, over 7112.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2814, pruned_loss=0.05908, over 1427648.77 frames.], batch size: 26, lr: 1.01e-03
2022-05-14 05:29:48,823 INFO [train.py:812] (6/8) Epoch 7, batch 3100, loss[loss=0.2348, simple_loss=0.3083, pruned_loss=0.08068, over 7164.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2823, pruned_loss=0.05923, over 1425028.31 frames.], batch size: 26, lr: 1.01e-03
2022-05-14 05:30:48,420 INFO [train.py:812] (6/8) Epoch 7, batch 3150, loss[loss=0.2261, simple_loss=0.3032, pruned_loss=0.07451, over 7081.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2819, pruned_loss=0.05884, over 1428130.61 frames.], batch size: 28, lr: 1.01e-03
2022-05-14 05:31:47,462 INFO [train.py:812] (6/8) Epoch 7, batch 3200, loss[loss=0.1912, simple_loss=0.2908, pruned_loss=0.0458, over 7343.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2831, pruned_loss=0.05938, over 1423784.21 frames.], batch size: 22, lr: 1.01e-03
2022-05-14 05:32:46,893 INFO [train.py:812] (6/8) Epoch 7, batch 3250, loss[loss=0.2069, simple_loss=0.2902, pruned_loss=0.06185, over 7029.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2818, pruned_loss=0.05867, over 1423854.54 frames.], batch size: 28, lr: 1.01e-03
2022-05-14 05:33:46,265 INFO [train.py:812] (6/8) Epoch 7, batch 3300, loss[loss=0.188, simple_loss=0.2849, pruned_loss=0.0455, over 7146.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2825, pruned_loss=0.059, over 1418911.24 frames.], batch size: 20, lr: 1.01e-03
2022-05-14 05:34:45,950 INFO [train.py:812] (6/8) Epoch 7, batch 3350, loss[loss=0.1796, simple_loss=0.2631, pruned_loss=0.04802, over 7149.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2822, pruned_loss=0.05874, over 1420466.44 frames.], batch size: 19, lr: 1.01e-03
2022-05-14 05:35:44,960 INFO [train.py:812] (6/8) Epoch 7, batch 3400, loss[loss=0.2019, simple_loss=0.291, pruned_loss=0.05643, over 7121.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2827, pruned_loss=0.05895, over 1423097.02 frames.], batch size: 21, lr: 1.01e-03
2022-05-14 05:36:43,532 INFO [train.py:812] (6/8) Epoch 7, batch 3450, loss[loss=0.1988, simple_loss=0.2937, pruned_loss=0.05197, over 7283.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2828, pruned_loss=0.05897, over 1420513.34 frames.], batch size: 24, lr: 1.01e-03
2022-05-14 05:37:43,019 INFO [train.py:812] (6/8) Epoch 7, batch 3500, loss[loss=0.2569, simple_loss=0.3436, pruned_loss=0.08506, over 7214.00 frames.], tot_loss[loss=0.2006, simple_loss=0.283, pruned_loss=0.0591, over 1422579.53 frames.], batch size: 21, lr: 1.01e-03
2022-05-14 05:38:41,464 INFO [train.py:812] (6/8) Epoch 7, batch 3550, loss[loss=0.2253, simple_loss=0.3115, pruned_loss=0.06956, over 7386.00 frames.], tot_loss[loss=0.1997, simple_loss=0.282, pruned_loss=0.05871, over 1424139.48 frames.], batch size: 23, lr: 1.01e-03
2022-05-14 05:39:40,572 INFO [train.py:812] (6/8) Epoch 7, batch 3600, loss[loss=0.2021, simple_loss=0.3028, pruned_loss=0.0507, over 7222.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2822, pruned_loss=0.05865, over 1425549.54 frames.], batch size: 21, lr: 1.00e-03
2022-05-14 05:40:39,026 INFO [train.py:812] (6/8) Epoch 7, batch 3650, loss[loss=0.2021, simple_loss=0.2802, pruned_loss=0.06195, over 7089.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2818, pruned_loss=0.05853, over 1421487.07 frames.], batch size: 28, lr: 1.00e-03
2022-05-14 05:41:38,749 INFO [train.py:812] (6/8) Epoch 7, batch 3700, loss[loss=0.1992, simple_loss=0.28, pruned_loss=0.0592, over 7438.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2811, pruned_loss=0.0586, over 1423204.04 frames.], batch size: 20, lr: 1.00e-03
2022-05-14 05:42:37,964 INFO [train.py:812] (6/8) Epoch 7, batch 3750, loss[loss=0.2435, simple_loss=0.32, pruned_loss=0.08348, over 5003.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2817, pruned_loss=0.05895, over 1423549.14 frames.], batch size: 52, lr: 1.00e-03
2022-05-14 05:43:37,520 INFO [train.py:812] (6/8) Epoch 7, batch 3800, loss[loss=0.1641, simple_loss=0.2493, pruned_loss=0.03946, over 7355.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2809, pruned_loss=0.05839, over 1420898.30 frames.], batch size: 19, lr: 1.00e-03
2022-05-14 05:44:35,604 INFO [train.py:812] (6/8) Epoch 7, batch 3850, loss[loss=0.1566, simple_loss=0.2383, pruned_loss=0.03746, over 7150.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2793, pruned_loss=0.05806, over 1424279.26 frames.], batch size: 17, lr: 1.00e-03
2022-05-14 05:45:34,815 INFO [train.py:812] (6/8) Epoch 7, batch 3900, loss[loss=0.1722, simple_loss=0.2518, pruned_loss=0.04625, over 7159.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2796, pruned_loss=0.05857, over 1425008.91 frames.], batch size: 18, lr: 1.00e-03
2022-05-14 05:46:31,687 INFO [train.py:812] (6/8) Epoch 7, batch 3950, loss[loss=0.1888, simple_loss=0.2918, pruned_loss=0.04289, over 7323.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2797, pruned_loss=0.05848, over 1426796.05 frames.], batch size: 22, lr: 9.99e-04
2022-05-14 05:47:30,578 INFO [train.py:812] (6/8) Epoch 7, batch 4000, loss[loss=0.2227, simple_loss=0.2956, pruned_loss=0.07489, over 6717.00 frames.], tot_loss[loss=0.198, simple_loss=0.2794, pruned_loss=0.0583, over 1431139.75 frames.], batch size: 31, lr: 9.98e-04
2022-05-14 05:48:29,688 INFO [train.py:812] (6/8) Epoch 7, batch 4050, loss[loss=0.1886, simple_loss=0.2774, pruned_loss=0.04989, over 7148.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2803, pruned_loss=0.05899, over 1430001.71 frames.], batch size: 18, lr: 9.98e-04
2022-05-14 05:49:28,788 INFO [train.py:812] (6/8) Epoch 7, batch 4100, loss[loss=0.1939, simple_loss=0.2817, pruned_loss=0.05308, over 7104.00 frames.], tot_loss[loss=0.2002, simple_loss=0.281, pruned_loss=0.05972, over 1425325.42 frames.], batch size: 21, lr: 9.97e-04
2022-05-14 05:50:26,081 INFO [train.py:812] (6/8) Epoch 7, batch 4150, loss[loss=0.2032, simple_loss=0.2905, pruned_loss=0.05792, over 7207.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2816, pruned_loss=0.0599, over 1425241.70 frames.], batch size: 23, lr: 9.96e-04
2022-05-14 05:51:25,286 INFO [train.py:812] (6/8) Epoch 7, batch 4200, loss[loss=0.1735, simple_loss=0.251, pruned_loss=0.04802, over 7275.00 frames.], tot_loss[loss=0.199, simple_loss=0.2805, pruned_loss=0.05878, over 1427329.60 frames.], batch size: 17, lr: 9.95e-04
2022-05-14 05:52:24,638 INFO [train.py:812] (6/8) Epoch 7, batch 4250, loss[loss=0.1943, simple_loss=0.2776, pruned_loss=0.05548, over 7437.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2803, pruned_loss=0.05871, over 1422468.14 frames.], batch size: 20, lr: 9.95e-04
2022-05-14 05:53:23,925 INFO [train.py:812] (6/8) Epoch 7, batch 4300, loss[loss=0.1905, simple_loss=0.2856, pruned_loss=0.04773, over 7227.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2819, pruned_loss=0.0592, over 1417014.21 frames.], batch size: 20, lr: 9.94e-04
2022-05-14 05:54:23,300 INFO [train.py:812] (6/8) Epoch 7, batch 4350, loss[loss=0.2175, simple_loss=0.2958, pruned_loss=0.06961, over 6502.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2806, pruned_loss=0.05818, over 1410280.97 frames.], batch size: 38, lr: 9.93e-04
2022-05-14 05:55:22,299 INFO [train.py:812] (6/8) Epoch 7, batch 4400, loss[loss=0.2207, simple_loss=0.3112, pruned_loss=0.06504, over 6758.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2809, pruned_loss=0.0588, over 1411844.24 frames.], batch size: 31, lr: 9.92e-04
2022-05-14 05:56:20,603 INFO [train.py:812] (6/8) Epoch 7, batch 4450, loss[loss=0.2086, simple_loss=0.2985, pruned_loss=0.05936, over 7203.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2814, pruned_loss=0.0589, over 1407336.35 frames.], batch size: 22, lr: 9.92e-04
2022-05-14 05:57:24,432 INFO [train.py:812] (6/8) Epoch 7, batch 4500, loss[loss=0.2356, simple_loss=0.3108, pruned_loss=0.08024, over 7214.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2817, pruned_loss=0.0589, over 1404380.55 frames.], batch size: 22, lr: 9.91e-04
2022-05-14 05:58:22,221 INFO [train.py:812] (6/8) Epoch 7, batch 4550, loss[loss=0.2974, simple_loss=0.3524, pruned_loss=0.1212, over 5049.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2835, pruned_loss=0.05972, over 1389758.97 frames.], batch size: 53, lr: 9.90e-04
2022-05-14 05:59:32,593 INFO [train.py:812] (6/8) Epoch 8, batch 0, loss[loss=0.1952, simple_loss=0.2837, pruned_loss=0.05336, over 7348.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2837, pruned_loss=0.05336, over 7348.00 frames.], batch size: 22, lr: 9.49e-04
2022-05-14 06:00:31,166 INFO [train.py:812] (6/8) Epoch 8, batch 50, loss[loss=0.1774, simple_loss=0.249, pruned_loss=0.05286, over 7129.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2817, pruned_loss=0.0573, over 320487.31 frames.], batch size: 17, lr: 9.48e-04
2022-05-14 06:01:30,404 INFO [train.py:812] (6/8) Epoch 8, batch 100, loss[loss=0.1851, simple_loss=0.2684, pruned_loss=0.05085, over 7308.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2814, pruned_loss=0.0564, over 568678.30 frames.], batch size: 25, lr: 9.48e-04
2022-05-14 06:02:29,686 INFO [train.py:812] (6/8) Epoch 8, batch 150, loss[loss=0.1981, simple_loss=0.2919, pruned_loss=0.05218, over 7123.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2779, pruned_loss=0.05561, over 757943.58 frames.], batch size: 21, lr: 9.47e-04
2022-05-14 06:03:26,765 INFO [train.py:812] (6/8) Epoch 8, batch 200, loss[loss=0.1718, simple_loss=0.2632, pruned_loss=0.04018, over 7217.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2794, pruned_loss=0.05663, over 906231.25 frames.], batch size: 22, lr: 9.46e-04
2022-05-14 06:04:24,370 INFO [train.py:812] (6/8) Epoch 8, batch 250, loss[loss=0.197, simple_loss=0.2804, pruned_loss=0.05681, over 7122.00 frames.], tot_loss[loss=0.1968, simple_loss=0.28, pruned_loss=0.05679, over 1019486.88 frames.], batch size: 21, lr: 9.46e-04
2022-05-14 06:05:21,326 INFO [train.py:812] (6/8) Epoch 8, batch 300, loss[loss=0.1898, simple_loss=0.2711, pruned_loss=0.05426, over 7065.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2805, pruned_loss=0.05745, over 1105544.78 frames.], batch size: 18, lr: 9.45e-04
2022-05-14 06:06:19,896 INFO [train.py:812] (6/8) Epoch 8, batch 350, loss[loss=0.2568, simple_loss=0.3381, pruned_loss=0.08776, over 7110.00 frames.], tot_loss[loss=0.198, simple_loss=0.2804, pruned_loss=0.05786, over 1177728.78 frames.], batch size: 21, lr: 9.44e-04
2022-05-14 06:07:19,505 INFO [train.py:812] (6/8) Epoch 8, batch 400, loss[loss=0.253, simple_loss=0.3136, pruned_loss=0.09616, over 5005.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2816, pruned_loss=0.05833, over 1230576.25 frames.], batch size: 52, lr: 9.43e-04
2022-05-14 06:08:18,811 INFO [train.py:812] (6/8) Epoch 8, batch 450, loss[loss=0.1652, simple_loss=0.2394, pruned_loss=0.04545, over 7190.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2805, pruned_loss=0.05814, over 1271997.03 frames.], batch size: 16, lr: 9.43e-04
2022-05-14 06:09:18,371 INFO [train.py:812] (6/8) Epoch 8, batch 500, loss[loss=0.2014, simple_loss=0.2808, pruned_loss=0.06101, over 7208.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2785, pruned_loss=0.05646, over 1304772.97 frames.], batch size: 23, lr: 9.42e-04
2022-05-14 06:10:16,963 INFO [train.py:812] (6/8) Epoch 8, batch 550, loss[loss=0.192, simple_loss=0.2766, pruned_loss=0.05372, over 7213.00 frames.], tot_loss[loss=0.196, simple_loss=0.2787, pruned_loss=0.05666, over 1332195.24 frames.], batch size: 23, lr: 9.41e-04
2022-05-14 06:11:16,914 INFO [train.py:812] (6/8) Epoch 8, batch 600, loss[loss=0.2302, simple_loss=0.3104, pruned_loss=0.075, over 7221.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2807, pruned_loss=0.05757, over 1352587.08 frames.], batch size: 21, lr: 9.41e-04
2022-05-14 06:12:15,329 INFO [train.py:812] (6/8) Epoch 8, batch 650, loss[loss=0.2001, simple_loss=0.2821, pruned_loss=0.05909, over 7267.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2795, pruned_loss=0.05668, over 1368165.76 frames.], batch size: 19, lr: 9.40e-04
2022-05-14 06:13:14,194 INFO [train.py:812] (6/8) Epoch 8, batch 700, loss[loss=0.2545, simple_loss=0.3114, pruned_loss=0.09884, over 4827.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2804, pruned_loss=0.05723, over 1376157.46 frames.], batch size: 53, lr: 9.39e-04
2022-05-14 06:14:13,413 INFO [train.py:812] (6/8) Epoch 8, batch 750, loss[loss=0.1787, simple_loss=0.2665, pruned_loss=0.04549, over 7348.00 frames.], tot_loss[loss=0.197, simple_loss=0.2799, pruned_loss=0.05706, over 1385250.71 frames.], batch size: 19, lr: 9.39e-04
2022-05-14 06:15:12,823 INFO [train.py:812] (6/8) Epoch 8, batch 800, loss[loss=0.2244, simple_loss=0.3051, pruned_loss=0.07186, over 6440.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2813, pruned_loss=0.05773, over 1390506.14 frames.], batch size: 37, lr: 9.38e-04
2022-05-14 06:16:12,246 INFO [train.py:812] (6/8) Epoch 8, batch 850, loss[loss=0.1525, simple_loss=0.2361, pruned_loss=0.03446, over 7416.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2786, pruned_loss=0.05655, over 1399351.85 frames.], batch size: 18, lr: 9.37e-04
2022-05-14 06:17:11,311 INFO [train.py:812] (6/8) Epoch 8, batch 900, loss[loss=0.2362, simple_loss=0.3193, pruned_loss=0.07657, over 6898.00 frames.], tot_loss[loss=0.1956, simple_loss=0.278, pruned_loss=0.0566, over 1398539.68 frames.], batch size: 31, lr: 9.36e-04
2022-05-14 06:18:09,043 INFO [train.py:812] (6/8) Epoch 8, batch 950, loss[loss=0.2023, simple_loss=0.2827, pruned_loss=0.06092, over 7248.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2785, pruned_loss=0.05662, over 1404095.88 frames.], batch size: 20, lr: 9.36e-04
2022-05-14 06:19:08,081 INFO [train.py:812] (6/8) Epoch 8, batch 1000, loss[loss=0.2215, simple_loss=0.31, pruned_loss=0.06651, over 7224.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2789, pruned_loss=0.05646, over 1408461.79 frames.], batch size: 21, lr: 9.35e-04
2022-05-14 06:20:06,238 INFO [train.py:812] (6/8) Epoch 8, batch 1050, loss[loss=0.1757, simple_loss=0.2727, pruned_loss=0.03937, over 7145.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2793, pruned_loss=0.05618, over 1406248.00 frames.], batch size: 17, lr: 9.34e-04
2022-05-14 06:21:04,773 INFO [train.py:812] (6/8) Epoch 8, batch 1100, loss[loss=0.226, simple_loss=0.3016, pruned_loss=0.07515, over 7203.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2785, pruned_loss=0.05641, over 1410488.14 frames.], batch size: 22, lr: 9.34e-04
2022-05-14 06:22:02,869 INFO [train.py:812] (6/8) Epoch 8, batch 1150, loss[loss=0.2481, simple_loss=0.3157, pruned_loss=0.09028, over 5068.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2795, pruned_loss=0.05635, over 1415974.62 frames.], batch size: 52, lr: 9.33e-04
2022-05-14 06:23:10,869 INFO [train.py:812] (6/8) Epoch 8, batch 1200, loss[loss=0.1876, simple_loss=0.2726, pruned_loss=0.0513, over 7143.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2787, pruned_loss=0.05628, over 1419598.22 frames.], batch size: 20, lr: 9.32e-04
2022-05-14 06:24:10,087 INFO [train.py:812] (6/8) Epoch 8, batch 1250, loss[loss=0.1382, simple_loss=0.2225, pruned_loss=0.02697, over 7285.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2771, pruned_loss=0.0559, over 1418792.69 frames.], batch size: 18, lr: 9.32e-04
2022-05-14 06:25:09,386 INFO [train.py:812] (6/8) Epoch 8, batch 1300, loss[loss=0.1959, simple_loss=0.2844, pruned_loss=0.05373, over 7159.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2786, pruned_loss=0.05647, over 1415723.98 frames.], batch size: 20, lr: 9.31e-04
2022-05-14 06:26:08,276 INFO [train.py:812] (6/8) Epoch 8, batch 1350, loss[loss=0.2021, simple_loss=0.2802, pruned_loss=0.06199, over 7160.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2787, pruned_loss=0.05654, over 1415097.25 frames.], batch size: 19, lr: 9.30e-04
2022-05-14 06:27:08,020 INFO [train.py:812] (6/8) Epoch 8, batch 1400, loss[loss=0.1798, simple_loss=0.2671, pruned_loss=0.04623, over 7295.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2789, pruned_loss=0.05618, over 1416363.76 frames.], batch size: 18, lr: 9.30e-04
2022-05-14 06:28:06,831 INFO [train.py:812] (6/8) Epoch 8, batch 1450, loss[loss=0.1911, simple_loss=0.2699, pruned_loss=0.05618, over 7172.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2786, pruned_loss=0.05595, over 1415954.91 frames.], batch size: 18, lr: 9.29e-04
2022-05-14 06:29:06,647 INFO [train.py:812] (6/8) Epoch 8, batch 1500, loss[loss=0.178, simple_loss=0.247, pruned_loss=0.05455, over 7416.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2774, pruned_loss=0.05602, over 1416270.00 frames.], batch size: 18, lr: 9.28e-04
2022-05-14 06:30:05,552 INFO [train.py:812] (6/8) Epoch 8, batch 1550, loss[loss=0.2058, simple_loss=0.2896, pruned_loss=0.06105, over 7210.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2783, pruned_loss=0.05655, over 1421046.12 frames.], batch size: 22, lr: 9.28e-04
2022-05-14 06:31:05,149 INFO [train.py:812] (6/8) Epoch 8, batch 1600, loss[loss=0.236, simple_loss=0.3173, pruned_loss=0.07736, over 6305.00 frames.], tot_loss[loss=0.197, simple_loss=0.2795, pruned_loss=0.05724, over 1421279.67 frames.], batch size: 38, lr: 9.27e-04
2022-05-14 06:32:04,316 INFO [train.py:812] (6/8) Epoch 8, batch 1650, loss[loss=0.2014, simple_loss=0.2867, pruned_loss=0.05808, over 7285.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2797, pruned_loss=0.05726, over 1419477.42 frames.], batch size: 24, lr: 9.26e-04
2022-05-14 06:33:04,122 INFO [train.py:812] (6/8) Epoch 8, batch 1700, loss[loss=0.2018, simple_loss=0.2852, pruned_loss=0.05919, over 7313.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2788, pruned_loss=0.05665, over 1420269.50 frames.], batch size: 21, lr: 9.26e-04
2022-05-14 06:34:03,608 INFO [train.py:812] (6/8) Epoch 8, batch 1750, loss[loss=0.2028, simple_loss=0.2895, pruned_loss=0.05806, over 7329.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2783, pruned_loss=0.05634, over 1422076.25 frames.], batch size: 22, lr: 9.25e-04
2022-05-14 06:35:12,528 INFO [train.py:812] (6/8) Epoch 8, batch 1800, loss[loss=0.2101, simple_loss=0.2986, pruned_loss=0.0608, over 7337.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2765, pruned_loss=0.05547, over 1422908.48 frames.], batch size: 22, lr: 9.24e-04
2022-05-14 06:36:21,382 INFO [train.py:812] (6/8) Epoch 8, batch 1850, loss[loss=0.1864, simple_loss=0.286, pruned_loss=0.04337, over 7237.00 frames.], tot_loss[loss=0.1953, simple_loss=0.278, pruned_loss=0.05626, over 1424913.45 frames.], batch size: 20, lr: 9.24e-04
2022-05-14 06:37:30,790 INFO [train.py:812] (6/8) Epoch 8, batch 1900, loss[loss=0.1908, simple_loss=0.2729, pruned_loss=0.05436, over 7284.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2765, pruned_loss=0.05549, over 1422842.06 frames.], batch size: 25, lr: 9.23e-04
2022-05-14 06:38:48,532 INFO [train.py:812] (6/8) Epoch 8, batch 1950, loss[loss=0.2066, simple_loss=0.2674, pruned_loss=0.07297, over 7007.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2765, pruned_loss=0.05524, over 1427006.92 frames.], batch size: 16, lr: 9.22e-04
2022-05-14 06:40:07,025 INFO [train.py:812] (6/8) Epoch 8, batch 2000, loss[loss=0.2366, simple_loss=0.3135, pruned_loss=0.07981, over 7116.00 frames.], tot_loss[loss=0.193, simple_loss=0.2762, pruned_loss=0.05493, over 1427466.10 frames.], batch size: 21, lr: 9.22e-04
2022-05-14 06:41:06,028 INFO [train.py:812] (6/8) Epoch 8, batch 2050, loss[loss=0.1967, simple_loss=0.2739, pruned_loss=0.05979, over 5280.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2768, pruned_loss=0.05572, over 1422408.39 frames.], batch size: 52, lr: 9.21e-04
2022-05-14 06:42:04,907 INFO [train.py:812] (6/8) Epoch 8, batch 2100, loss[loss=0.1715, simple_loss=0.2611, pruned_loss=0.04096, over 7231.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2773, pruned_loss=0.056, over 1419284.56 frames.], batch size: 20, lr: 9.20e-04
2022-05-14 06:43:04,000 INFO [train.py:812] (6/8) Epoch 8, batch 2150, loss[loss=0.1972, simple_loss=0.282, pruned_loss=0.05619, over 7196.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2776, pruned_loss=0.05625, over 1420089.67 frames.], batch size: 22, lr: 9.20e-04
2022-05-14 06:44:02,982 INFO [train.py:812] (6/8) Epoch 8, batch 2200, loss[loss=0.187, simple_loss=0.2707, pruned_loss=0.05171, over 7294.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2762, pruned_loss=0.0557, over 1417713.42 frames.], batch size: 24, lr: 9.19e-04
2022-05-14 06:45:01,878 INFO [train.py:812] (6/8) Epoch 8, batch 2250, loss[loss=0.2133, simple_loss=0.2986, pruned_loss=0.06398, over 7218.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2757, pruned_loss=0.05557, over 1412940.37 frames.], batch size: 23, lr: 9.18e-04
2022-05-14 06:46:00,859 INFO [train.py:812] (6/8) Epoch 8, batch 2300, loss[loss=0.1908, simple_loss=0.2703, pruned_loss=0.05561, over 7403.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2762, pruned_loss=0.05568, over 1413404.12 frames.], batch size: 18, lr: 9.18e-04
2022-05-14 06:46:59,526 INFO [train.py:812] (6/8) Epoch 8, batch 2350, loss[loss=0.1966, simple_loss=0.2785, pruned_loss=0.05734, over 7061.00 frames.], tot_loss[loss=0.194, simple_loss=0.2767, pruned_loss=0.05564, over 1413427.29 frames.], batch size: 18, lr: 9.17e-04
2022-05-14 06:47:58,475 INFO [train.py:812] (6/8) Epoch 8, batch 2400, loss[loss=0.189, simple_loss=0.2636, pruned_loss=0.05717, over 7259.00 frames.], tot_loss[loss=0.1932, simple_loss=0.276, pruned_loss=0.05519, over 1417415.80 frames.], batch size: 19, lr: 9.16e-04
2022-05-14 06:48:57,539 INFO [train.py:812] (6/8) Epoch 8, batch 2450, loss[loss=0.2138, simple_loss=0.2954, pruned_loss=0.06609, over 7287.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2759, pruned_loss=0.05537, over 1423502.00 frames.], batch size: 24, lr: 9.16e-04
2022-05-14 06:49:57,004 INFO [train.py:812] (6/8) Epoch 8, batch 2500, loss[loss=0.2292, simple_loss=0.3123, pruned_loss=0.07302, over 7321.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2765, pruned_loss=0.05536, over 1422278.27 frames.], batch size: 21, lr: 9.15e-04
2022-05-14 06:50:55,705 INFO [train.py:812] (6/8) Epoch 8, batch 2550, loss[loss=0.1682, simple_loss=0.2498, pruned_loss=0.04331, over 7362.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2749, pruned_loss=0.05464, over 1426093.30 frames.], batch size: 19, lr: 9.14e-04
2022-05-14 06:51:54,452 INFO [train.py:812] (6/8) Epoch 8, batch 2600, loss[loss=0.2182, simple_loss=0.2909, pruned_loss=0.07276, over 6821.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2755, pruned_loss=0.05498, over 1426149.33 frames.], batch size: 15, lr: 9.14e-04
2022-05-14 06:52:51,853 INFO [train.py:812] (6/8) Epoch 8, batch 2650, loss[loss=0.1838, simple_loss=0.2807, pruned_loss=0.04344, over 7121.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2763, pruned_loss=0.05521, over 1426671.43 frames.], batch size: 21, lr: 9.13e-04
2022-05-14 06:53:49,756 INFO [train.py:812] (6/8) Epoch 8, batch 2700, loss[loss=0.1601, simple_loss=0.2397, pruned_loss=0.04023, over 6811.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2749, pruned_loss=0.05441, over 1429088.34 frames.], batch size: 15, lr: 9.12e-04
2022-05-14 06:54:48,264 INFO [train.py:812] (6/8) Epoch 8, batch 2750, loss[loss=0.1542, simple_loss=0.2349, pruned_loss=0.03673, over 6999.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2745, pruned_loss=0.05443, over 1428096.53 frames.], batch size: 16, lr: 9.12e-04
2022-05-14 06:55:46,859 INFO [train.py:812] (6/8) Epoch 8, batch 2800, loss[loss=0.1987, simple_loss=0.2796, pruned_loss=0.05889, over 7144.00 frames.], tot_loss[loss=0.1921, simple_loss=0.275, pruned_loss=0.05465, over 1429044.89 frames.], batch size: 20, lr: 9.11e-04
2022-05-14 06:56:44,441 INFO [train.py:812] (6/8) Epoch 8, batch 2850, loss[loss=0.1946, simple_loss=0.2783, pruned_loss=0.0554, over 7207.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2753, pruned_loss=0.05469, over 1426760.24 frames.], batch size: 22, lr: 9.11e-04
2022-05-14 06:57:43,816 INFO [train.py:812] (6/8) Epoch 8, batch 2900, loss[loss=0.1595, simple_loss=0.2359, pruned_loss=0.04155, over 7130.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2757, pruned_loss=0.05465, over 1426573.05 frames.], batch size: 17, lr: 9.10e-04
2022-05-14 06:58:42,767 INFO [train.py:812] (6/8) Epoch 8, batch 2950, loss[loss=0.1854, simple_loss=0.2601, pruned_loss=0.05534, over 7065.00 frames.], tot_loss[loss=0.1931, simple_loss=0.276, pruned_loss=0.05513, over 1425374.75 frames.], batch size: 18, lr: 9.09e-04
2022-05-14 06:59:42,245 INFO [train.py:812] (6/8) Epoch 8, batch 3000, loss[loss=0.2633, simple_loss=0.3271, pruned_loss=0.09969, over 5183.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2767, pruned_loss=0.05554, over 1422345.17 frames.], batch size: 52, lr: 9.09e-04
2022-05-14 06:59:42,246 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 06:59:50,552 INFO [train.py:841] (6/8) Epoch 8, validation: loss=0.1612, simple_loss=0.2635, pruned_loss=0.0294, over 698248.00 frames.
2022-05-14 07:00:48,461 INFO [train.py:812] (6/8) Epoch 8, batch 3050, loss[loss=0.2202, simple_loss=0.3079, pruned_loss=0.06619, over 6579.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2769, pruned_loss=0.05603, over 1415218.05 frames.], batch size: 38, lr: 9.08e-04
2022-05-14 07:01:48,164 INFO [train.py:812] (6/8) Epoch 8, batch 3100, loss[loss=0.1934, simple_loss=0.2745, pruned_loss=0.05617, over 7260.00 frames.], tot_loss[loss=0.1945, simple_loss=0.277, pruned_loss=0.056, over 1419553.07 frames.], batch size: 19, lr: 9.07e-04
2022-05-14 07:02:45,311 INFO [train.py:812] (6/8) Epoch 8, batch 3150, loss[loss=0.1894, simple_loss=0.2695, pruned_loss=0.05462, over 7418.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2758, pruned_loss=0.05522, over 1420690.00 frames.], batch size: 20, lr: 9.07e-04
2022-05-14 07:03:44,360 INFO [train.py:812] (6/8) Epoch 8, batch 3200, loss[loss=0.1864, simple_loss=0.2719, pruned_loss=0.05044, over 7428.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2753, pruned_loss=0.05453, over 1424212.20 frames.], batch size: 20, lr: 9.06e-04
2022-05-14 07:04:43,383 INFO [train.py:812] (6/8) Epoch 8, batch 3250, loss[loss=0.2174, simple_loss=0.3002, pruned_loss=0.06729, over 7063.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2764, pruned_loss=0.05502, over 1422928.78 frames.], batch size: 28, lr: 9.05e-04
2022-05-14 07:05:41,278 INFO [train.py:812] (6/8) Epoch 8, batch 3300, loss[loss=0.2281, simple_loss=0.3146, pruned_loss=0.07078, over 6685.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2754, pruned_loss=0.05441, over 1421834.09 frames.], batch size: 31, lr: 9.05e-04
2022-05-14 07:06:40,388 INFO [train.py:812] (6/8) Epoch 8, batch 3350, loss[loss=0.1801, simple_loss=0.2639, pruned_loss=0.04814, over 7428.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2758, pruned_loss=0.05467, over 1419957.89 frames.], batch size: 20, lr: 9.04e-04
2022-05-14 07:07:39,825 INFO [train.py:812] (6/8) Epoch 8, batch 3400, loss[loss=0.2064, simple_loss=0.2944, pruned_loss=0.05924, over 6804.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2769, pruned_loss=0.05522, over 1417384.65 frames.], batch size: 31, lr: 9.04e-04
2022-05-14 07:08:38,484 INFO [train.py:812] (6/8) Epoch 8, batch 3450, loss[loss=0.1822, simple_loss=0.2635, pruned_loss=0.0505, over 7412.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2776, pruned_loss=0.05509, over 1420418.72 frames.], batch size: 18, lr: 9.03e-04
2022-05-14 07:09:37,930 INFO [train.py:812] (6/8) Epoch 8, batch 3500, loss[loss=0.198, simple_loss=0.2884, pruned_loss=0.05383, over 7381.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2779, pruned_loss=0.0549, over 1419802.97 frames.], batch size: 23, lr: 9.02e-04
2022-05-14 07:10:37,048 INFO [train.py:812] (6/8) Epoch 8, batch 3550, loss[loss=0.1747, simple_loss=0.2669, pruned_loss=0.04125, over 7260.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2778, pruned_loss=0.05462, over 1421759.14 frames.], batch size: 19, lr: 9.02e-04
2022-05-14 07:11:36,665 INFO [train.py:812] (6/8) Epoch 8, batch 3600, loss[loss=0.1733, simple_loss=0.246, pruned_loss=0.05029, over 7286.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2775, pruned_loss=0.05513, over 1420162.20 frames.], batch size: 17, lr: 9.01e-04
2022-05-14 07:12:33,633 INFO [train.py:812] (6/8) Epoch 8, batch 3650, loss[loss=0.1627, simple_loss=0.2541, pruned_loss=0.03565, over 7421.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2782, pruned_loss=0.05555, over 1414823.17 frames.], batch size: 21, lr: 9.01e-04
2022-05-14 07:13:32,617 INFO [train.py:812] (6/8) Epoch 8, batch 3700, loss[loss=0.1832, simple_loss=0.2724, pruned_loss=0.04695, over 7222.00 frames.], tot_loss[loss=0.1934, simple_loss=0.277, pruned_loss=0.05488, over 1419167.00 frames.], batch size: 21, lr: 9.00e-04
2022-05-14 07:14:31,411 INFO [train.py:812] (6/8) Epoch 8, batch 3750, loss[loss=0.1573, simple_loss=0.2476, pruned_loss=0.03356, over 7161.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2763, pruned_loss=0.0547, over 1416854.11 frames.], batch size: 19, lr: 8.99e-04
2022-05-14 07:15:30,617 INFO [train.py:812] (6/8) Epoch 8, batch 3800, loss[loss=0.2073, simple_loss=0.2837, pruned_loss=0.06542, over 7300.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2777, pruned_loss=0.05548, over 1419939.31 frames.], batch size: 24, lr: 8.99e-04
2022-05-14 07:16:28,752 INFO [train.py:812] (6/8) Epoch 8, batch 3850, loss[loss=0.2201, simple_loss=0.3186, pruned_loss=0.06079, over 7221.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2779, pruned_loss=0.05545, over 1418062.63 frames.], batch size: 21, lr: 8.98e-04
2022-05-14 07:17:33,263 INFO [train.py:812] (6/8) Epoch 8, batch 3900, loss[loss=0.1901, simple_loss=0.2783, pruned_loss=0.05098, over 7435.00 frames.], tot_loss[loss=0.1928, simple_loss=0.276, pruned_loss=0.05484, over 1422054.50 frames.], batch size: 20, lr: 8.97e-04
2022-05-14 07:18:32,373 INFO [train.py:812] (6/8) Epoch 8, batch 3950, loss[loss=0.1679, simple_loss=0.2558, pruned_loss=0.03997, over 7436.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2752, pruned_loss=0.05456, over 1425806.74 frames.], batch size: 17, lr: 8.97e-04
2022-05-14 07:19:31,331 INFO [train.py:812] (6/8) Epoch 8, batch 4000, loss[loss=0.2269, simple_loss=0.3075, pruned_loss=0.07314, over 7154.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2762, pruned_loss=0.05471, over 1423962.77 frames.], batch size: 20, lr: 8.96e-04
2022-05-14 07:20:29,701 INFO [train.py:812] (6/8) Epoch 8, batch 4050, loss[loss=0.1801, simple_loss=0.2682, pruned_loss=0.04598, over 7423.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2756, pruned_loss=0.05455, over 1426683.87 frames.], batch size: 21, lr: 8.96e-04
2022-05-14 07:21:29,477 INFO [train.py:812] (6/8) Epoch 8, batch 4100, loss[loss=0.1456, simple_loss=0.2252, pruned_loss=0.03296, over 7268.00 frames.], tot_loss[loss=0.193, simple_loss=0.2762, pruned_loss=0.05491, over 1419977.13 frames.], batch size: 17, lr: 8.95e-04
2022-05-14 07:22:28,436 INFO [train.py:812] (6/8) Epoch 8, batch 4150, loss[loss=0.2262, simple_loss=0.3098, pruned_loss=0.07133, over 7335.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2767, pruned_loss=0.05516, over 1412483.11 frames.], batch size: 22, lr: 8.94e-04
2022-05-14 07:23:28,106 INFO [train.py:812] (6/8) Epoch 8, batch 4200, loss[loss=0.1854, simple_loss=0.2677, pruned_loss=0.05152, over 7151.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2776, pruned_loss=0.05546, over 1415146.36 frames.], batch size: 20, lr: 8.94e-04
2022-05-14 07:24:27,294 INFO [train.py:812] (6/8) Epoch 8, batch 4250, loss[loss=0.21, simple_loss=0.2984, pruned_loss=0.06082, over 7202.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2771, pruned_loss=0.05525, over 1419532.23 frames.], batch size: 22, lr: 8.93e-04
2022-05-14 07:25:26,248 INFO [train.py:812] (6/8) Epoch 8, batch 4300, loss[loss=0.182, simple_loss=0.2675, pruned_loss=0.04828, over 7321.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2768, pruned_loss=0.05575, over 1417158.50 frames.], batch size: 21, lr: 8.93e-04
2022-05-14 07:26:25,355 INFO [train.py:812] (6/8) Epoch 8, batch 4350, loss[loss=0.2118, simple_loss=0.3006, pruned_loss=0.06148, over 7112.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2758, pruned_loss=0.05537, over 1413417.07 frames.], batch size: 21, lr: 8.92e-04
2022-05-14 07:27:24,400 INFO [train.py:812] (6/8) Epoch 8, batch 4400, loss[loss=0.186, simple_loss=0.2805, pruned_loss=0.04575, over 7106.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2747, pruned_loss=0.05481, over 1416101.37 frames.], batch size: 28, lr: 8.91e-04
2022-05-14 07:28:23,676 INFO [train.py:812] (6/8) Epoch 8, batch 4450, loss[loss=0.1797, simple_loss=0.264, pruned_loss=0.04769, over 7332.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2742, pruned_loss=0.05472, over 1416055.04 frames.], batch size: 20, lr: 8.91e-04
2022-05-14 07:29:23,603 INFO [train.py:812] (6/8) Epoch 8, batch 4500, loss[loss=0.2137, simple_loss=0.2882, pruned_loss=0.06963, over 7161.00 frames.], tot_loss[loss=0.1918, simple_loss=0.274, pruned_loss=0.05477, over 1413752.75 frames.], batch size: 18, lr: 8.90e-04
2022-05-14 07:30:22,914 INFO [train.py:812] (6/8) Epoch 8, batch 4550, loss[loss=0.1443, simple_loss=0.2293, pruned_loss=0.02967, over 7266.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2742, pruned_loss=0.05584, over 1396257.79 frames.], batch size: 17, lr: 8.90e-04
2022-05-14 07:31:33,247 INFO [train.py:812] (6/8) Epoch 9, batch 0, loss[loss=0.2157, simple_loss=0.2936, pruned_loss=0.06891, over 7180.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2936, pruned_loss=0.06891, over 7180.00 frames.], batch size: 23, lr: 8.54e-04
2022-05-14 07:32:31,247 INFO [train.py:812] (6/8) Epoch 9, batch 50, loss[loss=0.1918, simple_loss=0.277, pruned_loss=0.05335, over 7111.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2787, pruned_loss=0.05503, over 319353.86 frames.], batch size: 28, lr: 8.53e-04
2022-05-14 07:33:31,154 INFO [train.py:812] (6/8) Epoch 9, batch 100, loss[loss=0.1753, simple_loss=0.2651, pruned_loss=0.04271, over 7240.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2765, pruned_loss=0.05433, over 566330.34 frames.], batch size: 20, lr: 8.53e-04
2022-05-14 07:34:29,335 INFO [train.py:812] (6/8) Epoch 9, batch 150, loss[loss=0.2374, simple_loss=0.3093, pruned_loss=0.08277, over 5218.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2751, pruned_loss=0.05301, over 753870.48 frames.], batch size: 52, lr: 8.52e-04
2022-05-14 07:35:29,210 INFO [train.py:812] (6/8) Epoch 9, batch 200, loss[loss=0.217, simple_loss=0.2948, pruned_loss=0.06966, over 7203.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2741, pruned_loss=0.05237, over 903110.88 frames.], batch size: 22, lr: 8.51e-04
2022-05-14 07:36:28,019 INFO [train.py:812] (6/8) Epoch 9, batch 250, loss[loss=0.1871, simple_loss=0.2749, pruned_loss=0.04961, over 7435.00 frames.], tot_loss[loss=0.1894, simple_loss=0.274, pruned_loss=0.05242, over 1019610.47 frames.], batch size: 20, lr: 8.51e-04
2022-05-14 07:37:25,199 INFO [train.py:812] (6/8) Epoch 9, batch 300, loss[loss=0.2026, simple_loss=0.2838, pruned_loss=0.06075, over 7335.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2753, pruned_loss=0.05311, over 1105120.90 frames.], batch size: 22, lr: 8.50e-04
2022-05-14 07:38:24,956 INFO [train.py:812] (6/8) Epoch 9, batch 350, loss[loss=0.1661, simple_loss=0.2579, pruned_loss=0.0371, over 7163.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2735, pruned_loss=0.0525, over 1178670.52 frames.], batch size: 19, lr: 8.50e-04
2022-05-14 07:39:24,194 INFO [train.py:812] (6/8) Epoch 9, batch 400, loss[loss=0.1795, simple_loss=0.2523, pruned_loss=0.05329, over 7131.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2743, pruned_loss=0.05305, over 1237650.59 frames.], batch size: 17, lr: 8.49e-04
2022-05-14 07:40:21,419 INFO [train.py:812] (6/8) Epoch 9, batch 450, loss[loss=0.1744, simple_loss=0.2638, pruned_loss=0.04249, over 7264.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2736, pruned_loss=0.05283, over 1278266.35 frames.], batch size: 19, lr: 8.49e-04
2022-05-14 07:41:19,796 INFO [train.py:812] (6/8) Epoch 9, batch 500, loss[loss=0.1556, simple_loss=0.2438, pruned_loss=0.0337, over 7406.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2747, pruned_loss=0.05332, over 1310933.45 frames.], batch size: 18, lr: 8.48e-04
2022-05-14 07:42:19,044 INFO [train.py:812] (6/8) Epoch 9, batch 550, loss[loss=0.1687, simple_loss=0.2483, pruned_loss=0.04461, over 7055.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2734, pruned_loss=0.05245, over 1338644.71 frames.], batch size: 18, lr: 8.48e-04
2022-05-14 07:43:17,534 INFO [train.py:812] (6/8) Epoch 9, batch 600, loss[loss=0.1825, simple_loss=0.2599, pruned_loss=0.05259, over 7080.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2722, pruned_loss=0.05154, over 1360115.01 frames.], batch size: 18, lr: 8.47e-04
2022-05-14 07:44:16,650 INFO [train.py:812] (6/8) Epoch 9, batch 650, loss[loss=0.1668, simple_loss=0.2485, pruned_loss=0.04257, over 7357.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2721, pruned_loss=0.05148, over 1373047.92 frames.], batch size: 19, lr: 8.46e-04
2022-05-14 07:45:15,380 INFO [train.py:812] (6/8) Epoch 9, batch 700, loss[loss=0.1596, simple_loss=0.2563, pruned_loss=0.03143, over 7427.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2719, pruned_loss=0.05135, over 1385602.68 frames.], batch size: 20, lr: 8.46e-04
2022-05-14 07:46:13,725 INFO [train.py:812] (6/8) Epoch 9, batch 750, loss[loss=0.1461, simple_loss=0.2242, pruned_loss=0.03402, over 7166.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2726, pruned_loss=0.05203, over 1389674.07 frames.], batch size: 18, lr: 8.45e-04
2022-05-14 07:47:13,062 INFO [train.py:812] (6/8) Epoch 9, batch 800, loss[loss=0.1985, simple_loss=0.2846, pruned_loss=0.05621, over 7392.00 frames.], tot_loss[loss=0.1889, simple_loss=0.273, pruned_loss=0.05243, over 1396449.49 frames.], batch size: 23, lr: 8.45e-04
2022-05-14 07:48:11,397 INFO [train.py:812] (6/8) Epoch 9, batch 850, loss[loss=0.1957, simple_loss=0.2905, pruned_loss=0.05046, over 7323.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2735, pruned_loss=0.05282, over 1401674.89 frames.], batch size: 21, lr: 8.44e-04
2022-05-14 07:49:11,223 INFO [train.py:812] (6/8) Epoch 9, batch 900, loss[loss=0.1833, simple_loss=0.2752, pruned_loss=0.04567, over 7225.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2728, pruned_loss=0.05218, over 1411085.45 frames.], batch size: 21, lr: 8.44e-04
2022-05-14 07:50:10,506 INFO [train.py:812] (6/8) Epoch 9, batch 950, loss[loss=0.1998, simple_loss=0.2832, pruned_loss=0.05819, over 7335.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2735, pruned_loss=0.05273, over 1408654.20 frames.], batch size: 20, lr: 8.43e-04
2022-05-14 07:51:10,501 INFO [train.py:812] (6/8) Epoch 9, batch 1000, loss[loss=0.1742, simple_loss=0.2705, pruned_loss=0.03892, over 7430.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2727, pruned_loss=0.05223, over 1413398.31 frames.], batch size: 20, lr: 8.43e-04
2022-05-14 07:52:08,974 INFO [train.py:812] (6/8) Epoch 9, batch 1050, loss[loss=0.1653, simple_loss=0.2575, pruned_loss=0.03658, over 7259.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2732, pruned_loss=0.05218, over 1417136.08 frames.], batch size: 19, lr: 8.42e-04
2022-05-14 07:53:07,750 INFO [train.py:812] (6/8) Epoch 9, batch 1100, loss[loss=0.1664, simple_loss=0.2472, pruned_loss=0.04281, over 7283.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2743, pruned_loss=0.05264, over 1420255.62 frames.], batch size: 17, lr: 8.41e-04
2022-05-14 07:54:04,877 INFO [train.py:812] (6/8) Epoch 9, batch 1150, loss[loss=0.1838, simple_loss=0.28, pruned_loss=0.04379, over 7283.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2737, pruned_loss=0.0523, over 1420992.33 frames.], batch size: 25, lr: 8.41e-04
2022-05-14 07:55:04,934 INFO [train.py:812] (6/8) Epoch 9, batch 1200, loss[loss=0.1821, simple_loss=0.2674, pruned_loss=0.04846, over 7424.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2734, pruned_loss=0.0521, over 1421436.68 frames.], batch size: 20, lr: 8.40e-04
2022-05-14 07:56:02,853 INFO [train.py:812] (6/8) Epoch 9, batch 1250, loss[loss=0.1904, simple_loss=0.2663, pruned_loss=0.05725, over 6773.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2728, pruned_loss=0.05247, over 1416957.63 frames.], batch size: 15, lr: 8.40e-04
2022-05-14 07:57:02,092 INFO [train.py:812] (6/8) Epoch 9, batch 1300, loss[loss=0.239, simple_loss=0.3255, pruned_loss=0.07626, over 7157.00 frames.], tot_loss[loss=0.1904, simple_loss=0.274, pruned_loss=0.05341, over 1413454.01 frames.], batch size: 19, lr: 8.39e-04
2022-05-14 07:58:01,415 INFO [train.py:812] (6/8) Epoch 9, batch 1350, loss[loss=0.1805, simple_loss=0.27, pruned_loss=0.04548, over 7436.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2744, pruned_loss=0.05359, over 1418301.16 frames.], batch size: 20, lr: 8.39e-04
2022-05-14 07:59:00,876 INFO [train.py:812] (6/8) Epoch 9, batch 1400, loss[loss=0.1742, simple_loss=0.2657, pruned_loss=0.04131, over 7232.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2739, pruned_loss=0.05345, over 1415081.44 frames.], batch size: 21, lr: 8.38e-04
2022-05-14 07:59:57,901 INFO [train.py:812] (6/8) Epoch 9, batch 1450, loss[loss=0.1862, simple_loss=0.2804, pruned_loss=0.04596, over 7319.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2733, pruned_loss=0.05304, over 1420041.30 frames.], batch size: 21, lr: 8.38e-04
2022-05-14 08:00:55,538 INFO [train.py:812] (6/8) Epoch 9, batch 1500, loss[loss=0.1811, simple_loss=0.2678, pruned_loss=0.04722, over 7230.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2736, pruned_loss=0.05272, over 1422786.03 frames.], batch size: 20, lr: 8.37e-04
2022-05-14 08:01:53,812 INFO [train.py:812] (6/8) Epoch 9, batch 1550, loss[loss=0.1758, simple_loss=0.261, pruned_loss=0.04531, over 7200.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2734, pruned_loss=0.05297, over 1421825.98 frames.], batch size: 22, lr: 8.37e-04
2022-05-14 08:02:52,007 INFO [train.py:812] (6/8) Epoch 9, batch 1600, loss[loss=0.1511, simple_loss=0.2403, pruned_loss=0.03099, over 7059.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2733, pruned_loss=0.0527, over 1420485.68 frames.], batch size: 18, lr: 8.36e-04
2022-05-14 08:03:49,510 INFO [train.py:812] (6/8) Epoch 9, batch 1650, loss[loss=0.2022, simple_loss=0.2886, pruned_loss=0.05797, over 7126.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2737, pruned_loss=0.05287, over 1422207.14 frames.], batch size: 21, lr: 8.35e-04
2022-05-14 08:04:47,913 INFO [train.py:812] (6/8) Epoch 9, batch 1700, loss[loss=0.1812, simple_loss=0.2752, pruned_loss=0.04366, over 7145.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2749, pruned_loss=0.05314, over 1421004.38 frames.], batch size: 20, lr: 8.35e-04
2022-05-14 08:05:46,551 INFO [train.py:812] (6/8) Epoch 9, batch 1750, loss[loss=0.1684, simple_loss=0.2489, pruned_loss=0.04395, over 7317.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2741, pruned_loss=0.05282, over 1422286.42 frames.], batch size: 21, lr: 8.34e-04
2022-05-14 08:06:45,663 INFO [train.py:812] (6/8) Epoch 9, batch 1800, loss[loss=0.1934, simple_loss=0.2851, pruned_loss=0.05081, over 7241.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2737, pruned_loss=0.05257, over 1419321.14 frames.], batch size: 20, lr: 8.34e-04
2022-05-14 08:07:44,996 INFO [train.py:812] (6/8) Epoch 9, batch 1850, loss[loss=0.1851, simple_loss=0.2719, pruned_loss=0.04916, over 7236.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2755, pruned_loss=0.05306, over 1422039.07 frames.], batch size: 20, lr: 8.33e-04
2022-05-14 08:08:44,865 INFO [train.py:812] (6/8) Epoch 9, batch 1900, loss[loss=0.196, simple_loss=0.2704, pruned_loss=0.06073, over 7154.00 frames.], tot_loss[loss=0.1899, simple_loss=0.275, pruned_loss=0.05244, over 1420133.99 frames.], batch size: 19, lr: 8.33e-04
2022-05-14 08:09:44,237 INFO [train.py:812] (6/8) Epoch 9, batch 1950, loss[loss=0.2064, simple_loss=0.2902, pruned_loss=0.06126, over 7113.00 frames.], tot_loss[loss=0.189, simple_loss=0.274, pruned_loss=0.05199, over 1421267.50 frames.], batch size: 21, lr: 8.32e-04
2022-05-14 08:10:44,132 INFO [train.py:812] (6/8) Epoch 9, batch 2000, loss[loss=0.2141, simple_loss=0.2874, pruned_loss=0.07046, over 7306.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2737, pruned_loss=0.05209, over 1422035.15 frames.], batch size: 24, lr: 8.32e-04
2022-05-14 08:11:43,580 INFO [train.py:812] (6/8) Epoch 9, batch 2050, loss[loss=0.1667, simple_loss=0.2336, pruned_loss=0.04987, over 7283.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2736, pruned_loss=0.05254, over 1421789.53 frames.], batch size: 17, lr: 8.31e-04
2022-05-14 08:12:43,246 INFO [train.py:812] (6/8) Epoch 9, batch 2100, loss[loss=0.1764, simple_loss=0.258, pruned_loss=0.04742, over 7259.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2732, pruned_loss=0.05232, over 1423428.70 frames.], batch size: 19, lr: 8.31e-04
2022-05-14 08:13:42,072 INFO [train.py:812] (6/8) Epoch 9, batch 2150, loss[loss=0.1686, simple_loss=0.2518, pruned_loss=0.04272, over 7066.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2731, pruned_loss=0.0518, over 1425818.00 frames.], batch size: 18, lr: 8.30e-04
2022-05-14 08:14:40,844 INFO [train.py:812] (6/8) Epoch 9, batch 2200, loss[loss=0.1714, simple_loss=0.2496, pruned_loss=0.04665, over 7266.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2726, pruned_loss=0.0518, over 1423888.36 frames.], batch size: 17, lr: 8.30e-04
2022-05-14 08:15:40,325 INFO [train.py:812] (6/8) Epoch 9, batch 2250, loss[loss=0.1699, simple_loss=0.2487, pruned_loss=0.04552, over 7156.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2727, pruned_loss=0.05172, over 1424658.16 frames.], batch size: 18, lr: 8.29e-04
2022-05-14 08:16:40,264 INFO [train.py:812] (6/8) Epoch 9, batch 2300, loss[loss=0.1712, simple_loss=0.2609, pruned_loss=0.04081, over 7144.00 frames.], tot_loss[loss=0.1886, simple_loss=0.273, pruned_loss=0.05213, over 1425402.38 frames.], batch size: 20, lr: 8.29e-04
2022-05-14 08:17:37,473 INFO [train.py:812] (6/8) Epoch 9, batch 2350, loss[loss=0.1928, simple_loss=0.2875, pruned_loss=0.04908, over 6890.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2733, pruned_loss=0.05212, over 1424318.71 frames.], batch size: 31, lr: 8.28e-04
2022-05-14 08:18:37,035 INFO [train.py:812] (6/8) Epoch 9, batch 2400, loss[loss=0.1832, simple_loss=0.2644, pruned_loss=0.05099, over 7279.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2731, pruned_loss=0.05214, over 1425111.40 frames.], batch size: 18, lr: 8.28e-04
2022-05-14 08:19:36,168 INFO [train.py:812] (6/8) Epoch 9, batch 2450, loss[loss=0.1579, simple_loss=0.2402, pruned_loss=0.03776, over 7410.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2729, pruned_loss=0.05204, over 1426233.09 frames.], batch size: 18, lr: 8.27e-04
2022-05-14 08:20:34,805 INFO [train.py:812] (6/8) Epoch 9, batch 2500, loss[loss=0.2016, simple_loss=0.2949, pruned_loss=0.05415, over 7199.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2734, pruned_loss=0.05244, over 1424362.87 frames.], batch size: 22, lr: 8.27e-04
2022-05-14 08:21:43,999 INFO [train.py:812] (6/8) Epoch 9, batch 2550, loss[loss=0.1496, simple_loss=0.2373, pruned_loss=0.03094, over 7149.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2723, pruned_loss=0.05196, over 1421650.45 frames.], batch size: 17, lr: 8.26e-04
2022-05-14 08:22:42,427 INFO [train.py:812] (6/8) Epoch 9, batch 2600, loss[loss=0.2119, simple_loss=0.3007, pruned_loss=0.06149, over 7378.00 frames.], tot_loss[loss=0.1882, simple_loss=0.273, pruned_loss=0.05166, over 1418162.00 frames.], batch size: 23, lr: 8.25e-04
2022-05-14 08:23:41,191 INFO [train.py:812] (6/8) Epoch 9, batch 2650, loss[loss=0.2488, simple_loss=0.3157, pruned_loss=0.09091, over 4654.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2723, pruned_loss=0.05142, over 1416667.98 frames.], batch size: 53, lr: 8.25e-04
2022-05-14 08:24:39,389 INFO [train.py:812] (6/8) Epoch 9, batch 2700, loss[loss=0.18, simple_loss=0.2758, pruned_loss=0.04217, over 7338.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2734, pruned_loss=0.05157, over 1418522.85 frames.], batch size: 22, lr: 8.24e-04
2022-05-14 08:25:38,217 INFO [train.py:812] (6/8) Epoch 9, batch 2750, loss[loss=0.1987, simple_loss=0.2849, pruned_loss=0.0563, over 7326.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2736, pruned_loss=0.05161, over 1423387.92 frames.], batch size: 20, lr: 8.24e-04
2022-05-14 08:26:37,742 INFO [train.py:812] (6/8) Epoch 9, batch 2800, loss[loss=0.2095, simple_loss=0.2895, pruned_loss=0.0648, over 7207.00 frames.], tot_loss[loss=0.1887, simple_loss=0.274, pruned_loss=0.05177, over 1426262.43 frames.], batch size: 22, lr: 8.23e-04
2022-05-14 08:27:35,915 INFO [train.py:812] (6/8) Epoch 9, batch 2850, loss[loss=0.1785, simple_loss=0.2606, pruned_loss=0.04822, over 7150.00 frames.], tot_loss[loss=0.188, simple_loss=0.2729, pruned_loss=0.05154, over 1429116.48 frames.], batch size: 19, lr: 8.23e-04
2022-05-14 08:28:33,958 INFO [train.py:812] (6/8) Epoch 9, batch 2900, loss[loss=0.2029, simple_loss=0.2796, pruned_loss=0.06311, over 7312.00 frames.], tot_loss[loss=0.188, simple_loss=0.273, pruned_loss=0.05152, over 1427780.40 frames.], batch size: 21, lr: 8.22e-04
2022-05-14 08:29:31,243 INFO [train.py:812] (6/8) Epoch 9, batch 2950, loss[loss=0.171, simple_loss=0.2563, pruned_loss=0.04288, over 7278.00 frames.], tot_loss[loss=0.1882, simple_loss=0.273, pruned_loss=0.05171, over 1424469.55 frames.], batch size: 18, lr: 8.22e-04
2022-05-14 08:30:30,205 INFO [train.py:812] (6/8) Epoch 9, batch 3000, loss[loss=0.1838, simple_loss=0.2758, pruned_loss=0.04587, over 7277.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2724, pruned_loss=0.05158, over 1422664.01 frames.], batch size: 24, lr: 8.21e-04
2022-05-14 08:30:30,206 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 08:30:38,337 INFO [train.py:841] (6/8) Epoch 9, validation: loss=0.1602, simple_loss=0.262, pruned_loss=0.0292, over 698248.00 frames.
2022-05-14 08:31:37,232 INFO [train.py:812] (6/8) Epoch 9, batch 3050, loss[loss=0.1728, simple_loss=0.2642, pruned_loss=0.04075, over 7332.00 frames.], tot_loss[loss=0.188, simple_loss=0.2721, pruned_loss=0.05192, over 1419287.00 frames.], batch size: 20, lr: 8.21e-04
2022-05-14 08:32:34,702 INFO [train.py:812] (6/8) Epoch 9, batch 3100, loss[loss=0.2182, simple_loss=0.2923, pruned_loss=0.07206, over 6764.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2747, pruned_loss=0.05313, over 1413891.20 frames.], batch size: 31, lr: 8.20e-04
2022-05-14 08:33:32,686 INFO [train.py:812] (6/8) Epoch 9, batch 3150, loss[loss=0.1593, simple_loss=0.2501, pruned_loss=0.0343, over 7158.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2729, pruned_loss=0.05245, over 1417359.10 frames.], batch size: 19, lr: 8.20e-04
2022-05-14 08:34:32,442 INFO [train.py:812] (6/8) Epoch 9, batch 3200, loss[loss=0.1789, simple_loss=0.2752, pruned_loss=0.04129, over 7161.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2734, pruned_loss=0.05274, over 1421571.28 frames.], batch size: 20, lr: 8.19e-04
2022-05-14 08:35:31,374 INFO [train.py:812] (6/8) Epoch 9, batch 3250, loss[loss=0.2725, simple_loss=0.339, pruned_loss=0.103, over 5241.00 frames.], tot_loss[loss=0.19, simple_loss=0.2741, pruned_loss=0.05292, over 1419755.48 frames.], batch size: 53, lr: 8.19e-04
2022-05-14 08:36:46,159 INFO [train.py:812] (6/8) Epoch 9, batch 3300, loss[loss=0.199, simple_loss=0.2911, pruned_loss=0.05343, over 7206.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2735, pruned_loss=0.05232, over 1420282.17 frames.], batch size: 22, lr: 8.18e-04
2022-05-14 08:37:52,679 INFO [train.py:812] (6/8) Epoch 9, batch 3350, loss[loss=0.178, simple_loss=0.2671, pruned_loss=0.04446, over 7261.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2728, pruned_loss=0.05177, over 1423734.04 frames.], batch size: 19, lr: 8.18e-04
2022-05-14 08:38:51,551 INFO [train.py:812] (6/8) Epoch 9, batch 3400, loss[loss=0.2062, simple_loss=0.2843, pruned_loss=0.06409, over 6711.00 frames.], tot_loss[loss=0.1878, simple_loss=0.273, pruned_loss=0.05128, over 1421661.70 frames.], batch size: 31, lr: 8.17e-04
2022-05-14 08:39:59,442 INFO [train.py:812] (6/8) Epoch 9, batch 3450, loss[loss=0.1729, simple_loss=0.2555, pruned_loss=0.04518, over 7394.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2726, pruned_loss=0.05113, over 1424283.65 frames.], batch size: 18, lr: 8.17e-04
2022-05-14 08:41:27,465 INFO [train.py:812] (6/8) Epoch 9, batch 3500, loss[loss=0.185, simple_loss=0.272, pruned_loss=0.04902, over 7156.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2722, pruned_loss=0.05104, over 1425482.47 frames.], batch size: 19, lr: 8.16e-04
2022-05-14 08:42:35,755 INFO [train.py:812] (6/8) Epoch 9, batch 3550, loss[loss=0.1622, simple_loss=0.2375, pruned_loss=0.04341, over 7163.00 frames.], tot_loss[loss=0.187, simple_loss=0.2716, pruned_loss=0.05119, over 1427256.37 frames.], batch size: 18, lr: 8.16e-04
2022-05-14 08:43:34,799 INFO [train.py:812] (6/8) Epoch 9, batch 3600, loss[loss=0.1463, simple_loss=0.2302, pruned_loss=0.03122, over 7302.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2725, pruned_loss=0.05161, over 1425217.37 frames.], batch size: 18, lr: 8.15e-04
2022-05-14 08:44:32,184 INFO [train.py:812] (6/8) Epoch 9, batch 3650, loss[loss=0.1807, simple_loss=0.2555, pruned_loss=0.05294, over 7125.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2717, pruned_loss=0.05127, over 1426637.97 frames.], batch size: 17, lr: 8.15e-04
2022-05-14 08:45:31,316 INFO [train.py:812] (6/8) Epoch 9, batch 3700, loss[loss=0.1955, simple_loss=0.2787, pruned_loss=0.05618, over 7269.00 frames.], tot_loss[loss=0.1887, simple_loss=0.273, pruned_loss=0.05223, over 1427094.24 frames.], batch size: 25, lr: 8.14e-04
2022-05-14 08:46:29,966 INFO [train.py:812] (6/8) Epoch 9, batch 3750, loss[loss=0.1715, simple_loss=0.2469, pruned_loss=0.04812, over 7433.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2739, pruned_loss=0.05267, over 1426139.28 frames.], batch size: 20, lr: 8.14e-04
2022-05-14 08:47:28,944 INFO [train.py:812] (6/8) Epoch 9, batch 3800, loss[loss=0.1774, simple_loss=0.2561, pruned_loss=0.04936, over 7398.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2736, pruned_loss=0.05242, over 1429181.84 frames.], batch size: 18, lr: 8.13e-04
2022-05-14 08:48:27,808 INFO [train.py:812] (6/8) Epoch 9, batch 3850, loss[loss=0.1483, simple_loss=0.235, pruned_loss=0.03077, over 7302.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2735, pruned_loss=0.0521, over 1430539.67 frames.], batch size: 17, lr: 8.13e-04
2022-05-14 08:49:26,816 INFO [train.py:812] (6/8) Epoch 9, batch 3900, loss[loss=0.2257, simple_loss=0.299, pruned_loss=0.07619, over 4997.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2744, pruned_loss=0.05235, over 1427488.15 frames.], batch size: 52, lr: 8.12e-04
2022-05-14 08:50:26,284 INFO [train.py:812] (6/8) Epoch 9, batch 3950, loss[loss=0.1939, simple_loss=0.2849, pruned_loss=0.05151, over 6721.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2736, pruned_loss=0.05211, over 1427878.81 frames.], batch size: 31, lr: 8.12e-04
2022-05-14 08:51:25,806 INFO [train.py:812] (6/8) Epoch 9, batch 4000, loss[loss=0.1739, simple_loss=0.2709, pruned_loss=0.03844, over 7222.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2743, pruned_loss=0.05271, over 1427191.78 frames.], batch size: 21, lr: 8.11e-04
2022-05-14 08:52:25,230 INFO [train.py:812] (6/8) Epoch 9, batch 4050, loss[loss=0.1554, simple_loss=0.2418, pruned_loss=0.03451, over 7426.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2725, pruned_loss=0.0521, over 1426278.55 frames.], batch size: 18, lr: 8.11e-04
2022-05-14 08:53:25,064 INFO [train.py:812] (6/8) Epoch 9, batch 4100, loss[loss=0.1962, simple_loss=0.2638, pruned_loss=0.06428, over 7137.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2725, pruned_loss=0.05223, over 1426930.80 frames.], batch size: 17, lr: 8.10e-04
2022-05-14 08:54:24,745 INFO [train.py:812] (6/8) Epoch 9, batch 4150, loss[loss=0.1982, simple_loss=0.288, pruned_loss=0.05423, over 7066.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2726, pruned_loss=0.05239, over 1422704.21 frames.], batch size: 28, lr: 8.10e-04
2022-05-14 08:55:24,392 INFO [train.py:812] (6/8) Epoch 9, batch 4200, loss[loss=0.186, simple_loss=0.2726, pruned_loss=0.04966, over 7333.00 frames.], tot_loss[loss=0.1882, simple_loss=0.272, pruned_loss=0.05218, over 1423774.62 frames.], batch size: 20, lr: 8.09e-04
2022-05-14 08:56:23,078 INFO [train.py:812] (6/8) Epoch 9, batch 4250, loss[loss=0.1722, simple_loss=0.253, pruned_loss=0.04575, over 7143.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2715, pruned_loss=0.05219, over 1419436.20 frames.], batch size: 17, lr: 8.09e-04
2022-05-14 08:57:23,058 INFO [train.py:812] (6/8) Epoch 9, batch 4300, loss[loss=0.2002, simple_loss=0.2888, pruned_loss=0.05574, over 7413.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2707, pruned_loss=0.05195, over 1414297.77 frames.], batch size: 21, lr: 8.08e-04
2022-05-14 08:58:21,487 INFO [train.py:812] (6/8) Epoch 9, batch 4350, loss[loss=0.1531, simple_loss=0.2375, pruned_loss=0.03438, over 7290.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2703, pruned_loss=0.05156, over 1420279.56 frames.], batch size: 17, lr: 8.08e-04
2022-05-14 08:59:21,333 INFO [train.py:812] (6/8) Epoch 9, batch 4400, loss[loss=0.2378, simple_loss=0.3127, pruned_loss=0.08142, over 7103.00 frames.], tot_loss[loss=0.1867, simple_loss=0.27, pruned_loss=0.05173, over 1417578.32 frames.], batch size: 28, lr: 8.07e-04
2022-05-14 09:00:19,279 INFO [train.py:812] (6/8) Epoch 9, batch 4450, loss[loss=0.1767, simple_loss=0.2664, pruned_loss=0.04354, over 7039.00 frames.], tot_loss[loss=0.186, simple_loss=0.2688, pruned_loss=0.05162, over 1413389.60 frames.], batch size: 28, lr: 8.07e-04
2022-05-14 09:01:19,100 INFO [train.py:812] (6/8) Epoch 9, batch 4500, loss[loss=0.1945, simple_loss=0.28, pruned_loss=0.05446, over 6999.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2694, pruned_loss=0.05215, over 1394789.17 frames.], batch size: 28, lr: 8.07e-04
2022-05-14 09:02:17,095 INFO [train.py:812] (6/8) Epoch 9, batch 4550, loss[loss=0.1787, simple_loss=0.2782, pruned_loss=0.03966, over 6272.00 frames.], tot_loss[loss=0.1912, simple_loss=0.274, pruned_loss=0.05419, over 1355375.76 frames.], batch size: 37, lr: 8.06e-04
2022-05-14 09:03:24,870 INFO [train.py:812] (6/8) Epoch 10, batch 0, loss[loss=0.1771, simple_loss=0.2612, pruned_loss=0.04648, over 7417.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2612, pruned_loss=0.04648, over 7417.00 frames.], batch size: 21, lr: 7.75e-04
2022-05-14 09:04:24,015 INFO [train.py:812] (6/8) Epoch 10, batch 50, loss[loss=0.1892, simple_loss=0.2845, pruned_loss=0.0469, over 7222.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2684, pruned_loss=0.04904, over 321792.01 frames.], batch size: 23, lr: 7.74e-04
2022-05-14 09:05:23,107 INFO [train.py:812] (6/8) Epoch 10, batch 100, loss[loss=0.2025, simple_loss=0.2898, pruned_loss=0.0576, over 5115.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2689, pruned_loss=0.04979, over 557788.97 frames.], batch size: 53, lr: 7.74e-04
2022-05-14 09:06:22,306 INFO [train.py:812] (6/8) Epoch 10, batch 150, loss[loss=0.1594, simple_loss=0.2474, pruned_loss=0.03567, over 7439.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2693, pruned_loss=0.04961, over 751721.17 frames.], batch size: 20, lr: 7.73e-04
2022-05-14 09:07:20,634 INFO [train.py:812] (6/8) Epoch 10, batch 200, loss[loss=0.1709, simple_loss=0.2569, pruned_loss=0.04249, over 7427.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2691, pruned_loss=0.04975, over 899244.82 frames.], batch size: 20, lr: 7.73e-04
2022-05-14 09:08:19,903 INFO [train.py:812] (6/8) Epoch 10, batch 250, loss[loss=0.1654, simple_loss=0.2517, pruned_loss=0.03953, over 7168.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2714, pruned_loss=0.05111, over 1011766.75 frames.], batch size: 18, lr: 7.72e-04
2022-05-14 09:09:19,095 INFO [train.py:812] (6/8) Epoch 10, batch 300, loss[loss=0.1975, simple_loss=0.2843, pruned_loss=0.05534, over 7322.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2721, pruned_loss=0.05157, over 1104927.41 frames.], batch size: 20, lr: 7.72e-04
2022-05-14 09:10:16,352 INFO [train.py:812] (6/8) Epoch 10, batch 350, loss[loss=0.1811, simple_loss=0.2709, pruned_loss=0.04569, over 7194.00 frames.], tot_loss[loss=0.1861, simple_loss=0.271, pruned_loss=0.05059, over 1173394.70 frames.], batch size: 23, lr: 7.71e-04
2022-05-14 09:11:15,064 INFO [train.py:812] (6/8) Epoch 10, batch 400, loss[loss=0.1863, simple_loss=0.2762, pruned_loss=0.04823, over 7182.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2725, pruned_loss=0.05129, over 1222513.11 frames.], batch size: 26, lr: 7.71e-04
2022-05-14 09:12:14,074 INFO [train.py:812] (6/8) Epoch 10, batch 450, loss[loss=0.2111, simple_loss=0.2935, pruned_loss=0.06441, over 6287.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2733, pruned_loss=0.05148, over 1260617.53 frames.], batch size: 37, lr: 7.71e-04
2022-05-14 09:13:13,644 INFO [train.py:812] (6/8) Epoch 10, batch 500, loss[loss=0.1602, simple_loss=0.2439, pruned_loss=0.0383, over 7171.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2739, pruned_loss=0.05165, over 1296676.76 frames.], batch size: 19, lr: 7.70e-04
2022-05-14 09:14:12,347 INFO [train.py:812] (6/8) Epoch 10, batch 550, loss[loss=0.182, simple_loss=0.256, pruned_loss=0.05403, over 7136.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2732, pruned_loss=0.05132, over 1324636.24 frames.], batch size: 17, lr: 7.70e-04
2022-05-14 09:15:10,145 INFO [train.py:812] (6/8) Epoch 10, batch 600, loss[loss=0.1594, simple_loss=0.2394, pruned_loss=0.0397, over 7284.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2732, pruned_loss=0.05171, over 1346170.17 frames.], batch size: 18, lr: 7.69e-04
2022-05-14 09:16:08,342 INFO [train.py:812] (6/8) Epoch 10, batch 650, loss[loss=0.2198, simple_loss=0.3002, pruned_loss=0.06964, over 7181.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2735, pruned_loss=0.05182, over 1362724.21 frames.], batch size: 26, lr: 7.69e-04
2022-05-14 09:17:07,954 INFO [train.py:812] (6/8) Epoch 10, batch 700, loss[loss=0.1795, simple_loss=0.2747, pruned_loss=0.04213, over 7334.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2727, pruned_loss=0.05122, over 1377202.71 frames.], batch size: 25, lr: 7.68e-04
2022-05-14 09:18:07,546 INFO [train.py:812] (6/8) Epoch 10, batch 750, loss[loss=0.1553, simple_loss=0.2439, pruned_loss=0.03341, over 7436.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2719, pruned_loss=0.05081, over 1387069.75 frames.], batch size: 20, lr: 7.68e-04
2022-05-14 09:19:06,542 INFO [train.py:812] (6/8) Epoch 10, batch 800, loss[loss=0.1854, simple_loss=0.2747, pruned_loss=0.04808, over 7290.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2712, pruned_loss=0.0507, over 1394121.36 frames.], batch size: 24, lr: 7.67e-04
2022-05-14 09:20:06,010 INFO [train.py:812] (6/8) Epoch 10, batch 850, loss[loss=0.2206, simple_loss=0.2967, pruned_loss=0.07227, over 6396.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2714, pruned_loss=0.05068, over 1396817.24 frames.], batch size: 37, lr: 7.67e-04
2022-05-14 09:21:05,091 INFO [train.py:812] (6/8) Epoch 10, batch 900, loss[loss=0.1928, simple_loss=0.2841, pruned_loss=0.05073, over 7306.00 frames.], tot_loss[loss=0.1859, simple_loss=0.271, pruned_loss=0.05039, over 1406706.03 frames.], batch size: 21, lr: 7.66e-04
2022-05-14 09:22:03,792 INFO [train.py:812] (6/8) Epoch 10, batch 950, loss[loss=0.1956, simple_loss=0.2918, pruned_loss=0.04971, over 7201.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2714, pruned_loss=0.05086, over 1406267.43 frames.], batch size: 26, lr: 7.66e-04
2022-05-14 09:23:02,574 INFO [train.py:812] (6/8) Epoch 10, batch 1000, loss[loss=0.1542, simple_loss=0.248, pruned_loss=0.03019, over 7323.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2706, pruned_loss=0.05005, over 1413548.71 frames.], batch size: 20, lr: 7.66e-04
2022-05-14 09:24:00,837 INFO [train.py:812] (6/8) Epoch 10, batch 1050, loss[loss=0.2201, simple_loss=0.3155, pruned_loss=0.0624, over 7035.00 frames.], tot_loss[loss=0.185, simple_loss=0.2703, pruned_loss=0.04987, over 1415611.59 frames.], batch size: 28, lr: 7.65e-04
2022-05-14 09:24:59,375 INFO [train.py:812] (6/8) Epoch 10, batch 1100, loss[loss=0.1995, simple_loss=0.2905, pruned_loss=0.0543, over 7051.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2718, pruned_loss=0.05044, over 1416063.83 frames.], batch size: 28, lr: 7.65e-04
2022-05-14 09:25:57,296 INFO [train.py:812] (6/8) Epoch 10, batch 1150, loss[loss=0.1877, simple_loss=0.2809, pruned_loss=0.04726, over 7329.00 frames.], tot_loss[loss=0.186, simple_loss=0.2715, pruned_loss=0.05031, over 1420776.14 frames.], batch size: 20, lr: 7.64e-04
2022-05-14 09:26:55,711 INFO [train.py:812] (6/8) Epoch 10, batch 1200, loss[loss=0.1977, simple_loss=0.2948, pruned_loss=0.05029, over 7201.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2727, pruned_loss=0.05095, over 1420001.39 frames.], batch size: 23, lr: 7.64e-04
2022-05-14 09:27:55,418 INFO [train.py:812] (6/8) Epoch 10, batch 1250, loss[loss=0.1677, simple_loss=0.2502, pruned_loss=0.04261, over 7270.00 frames.], tot_loss[loss=0.1869, simple_loss=0.272, pruned_loss=0.05088, over 1418694.21 frames.], batch size: 17, lr: 7.63e-04
2022-05-14 09:28:54,706 INFO [train.py:812] (6/8) Epoch 10, batch 1300, loss[loss=0.1492, simple_loss=0.2267, pruned_loss=0.03585, over 7004.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2705, pruned_loss=0.05027, over 1417215.80 frames.], batch size: 16, lr: 7.63e-04
2022-05-14 09:29:54,200 INFO [train.py:812] (6/8) Epoch 10, batch 1350, loss[loss=0.1955, simple_loss=0.2847, pruned_loss=0.05311, over 7309.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2705, pruned_loss=0.05023, over 1415551.01 frames.], batch size: 21, lr: 7.62e-04
2022-05-14 09:30:53,024 INFO [train.py:812] (6/8) Epoch 10, batch 1400, loss[loss=0.1813, simple_loss=0.2612, pruned_loss=0.05069, over 7119.00 frames.], tot_loss[loss=0.185, simple_loss=0.2704, pruned_loss=0.04976, over 1418817.32 frames.], batch size: 21, lr: 7.62e-04
2022-05-14 09:31:52,548 INFO [train.py:812] (6/8) Epoch 10, batch 1450, loss[loss=0.1852, simple_loss=0.2667, pruned_loss=0.05183, over 7285.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2702, pruned_loss=0.04978, over 1420185.99 frames.], batch size: 25, lr: 7.62e-04
2022-05-14 09:32:51,612 INFO [train.py:812] (6/8) Epoch 10, batch 1500, loss[loss=0.2126, simple_loss=0.2896, pruned_loss=0.06781, over 5000.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2703, pruned_loss=0.04965, over 1415796.12 frames.], batch size: 52, lr: 7.61e-04
2022-05-14 09:33:51,570 INFO [train.py:812] (6/8) Epoch 10, batch 1550, loss[loss=0.1711, simple_loss=0.256, pruned_loss=0.0431, over 7370.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2706, pruned_loss=0.04927, over 1419068.30 frames.], batch size: 19, lr: 7.61e-04
2022-05-14 09:34:49,182 INFO [train.py:812] (6/8) Epoch 10, batch 1600, loss[loss=0.1721, simple_loss=0.2687, pruned_loss=0.03776, over 7258.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2715, pruned_loss=0.04978, over 1417573.66 frames.], batch size: 19, lr: 7.60e-04
2022-05-14 09:35:46,394 INFO [train.py:812] (6/8) Epoch 10, batch 1650, loss[loss=0.1846, simple_loss=0.2693, pruned_loss=0.04997, over 7403.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2713, pruned_loss=0.05002, over 1415328.18 frames.], batch size: 21, lr: 7.60e-04
2022-05-14 09:36:44,429 INFO [train.py:812] (6/8) Epoch 10, batch 1700, loss[loss=0.2343, simple_loss=0.3268, pruned_loss=0.07086, over 7296.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2708, pruned_loss=0.04993, over 1413846.37 frames.], batch size: 24, lr: 7.59e-04
2022-05-14 09:37:43,577 INFO [train.py:812] (6/8) Epoch 10, batch 1750, loss[loss=0.1778, simple_loss=0.2478, pruned_loss=0.0539, over 7238.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2714, pruned_loss=0.05063, over 1406059.10 frames.], batch size: 16, lr: 7.59e-04
2022-05-14 09:38:41,652 INFO [train.py:812] (6/8) Epoch 10, batch 1800, loss[loss=0.1918, simple_loss=0.2741, pruned_loss=0.05471, over 7363.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2711, pruned_loss=0.0504, over 1410736.41 frames.], batch size: 19, lr: 7.59e-04
2022-05-14 09:39:39,855 INFO [train.py:812] (6/8) Epoch 10, batch 1850, loss[loss=0.1736, simple_loss=0.2512, pruned_loss=0.04796, over 7357.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2708, pruned_loss=0.05031, over 1411603.15 frames.], batch size: 19, lr: 7.58e-04
2022-05-14 09:40:38,492 INFO [train.py:812] (6/8) Epoch 10, batch 1900, loss[loss=0.1929, simple_loss=0.268, pruned_loss=0.05885, over 7275.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2699, pruned_loss=0.0494, over 1416151.37 frames.], batch size: 18, lr: 7.58e-04
2022-05-14 09:41:37,160 INFO [train.py:812] (6/8) Epoch 10, batch 1950, loss[loss=0.2277, simple_loss=0.3197, pruned_loss=0.06791, over 7196.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2699, pruned_loss=0.04972, over 1414953.79 frames.], batch size: 23, lr: 7.57e-04
2022-05-14 09:42:35,064 INFO [train.py:812] (6/8) Epoch 10, batch 2000, loss[loss=0.1776, simple_loss=0.264, pruned_loss=0.04559, over 7231.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2689, pruned_loss=0.04939, over 1417925.42 frames.], batch size: 20, lr: 7.57e-04
2022-05-14 09:43:34,872 INFO [train.py:812] (6/8) Epoch 10, batch 2050, loss[loss=0.1807, simple_loss=0.2689, pruned_loss=0.04628, over 7194.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2688, pruned_loss=0.04926, over 1419579.64 frames.], batch size: 23, lr: 7.56e-04
2022-05-14 09:44:34,088 INFO [train.py:812] (6/8) Epoch 10, batch 2100, loss[loss=0.1745, simple_loss=0.2612, pruned_loss=0.04393, over 7149.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2681, pruned_loss=0.04877, over 1424462.39 frames.], batch size: 20, lr: 7.56e-04
2022-05-14 09:45:31,443 INFO [train.py:812] (6/8) Epoch 10, batch 2150, loss[loss=0.1586, simple_loss=0.2378, pruned_loss=0.03965, over 7399.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2671, pruned_loss=0.04815, over 1426683.70 frames.], batch size: 18, lr: 7.56e-04
2022-05-14 09:46:28,659 INFO [train.py:812] (6/8) Epoch 10, batch 2200, loss[loss=0.1801, simple_loss=0.2628, pruned_loss=0.04872, over 6628.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2683, pruned_loss=0.04864, over 1427203.76 frames.], batch size: 38, lr: 7.55e-04
2022-05-14 09:47:27,370 INFO [train.py:812] (6/8) Epoch 10, batch 2250, loss[loss=0.1595, simple_loss=0.2504, pruned_loss=0.03433, over 7322.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2685, pruned_loss=0.04893, over 1429217.46 frames.], batch size: 21, lr: 7.55e-04
2022-05-14 09:48:25,585 INFO [train.py:812] (6/8) Epoch 10, batch 2300, loss[loss=0.1868, simple_loss=0.2773, pruned_loss=0.04818, over 7140.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2689, pruned_loss=0.04897, over 1426505.68 frames.], batch size: 20, lr: 7.54e-04
2022-05-14 09:49:24,926 INFO [train.py:812] (6/8) Epoch 10, batch 2350, loss[loss=0.1805, simple_loss=0.2709, pruned_loss=0.04507, over 7217.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2679, pruned_loss=0.04868, over 1423908.15 frames.], batch size: 22, lr: 7.54e-04
2022-05-14 09:50:22,147 INFO [train.py:812] (6/8) Epoch 10, batch 2400, loss[loss=0.1767, simple_loss=0.2537, pruned_loss=0.04978, over 7278.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2683, pruned_loss=0.0486, over 1426153.85 frames.], batch size: 18, lr: 7.53e-04
2022-05-14 09:51:20,808 INFO [train.py:812] (6/8) Epoch 10, batch 2450, loss[loss=0.1688, simple_loss=0.2508, pruned_loss=0.04339, over 7061.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2691, pruned_loss=0.04887, over 1429219.95 frames.], batch size: 18, lr: 7.53e-04
2022-05-14 09:52:18,492 INFO [train.py:812] (6/8) Epoch 10, batch 2500, loss[loss=0.1647, simple_loss=0.2624, pruned_loss=0.03348, over 7321.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2681, pruned_loss=0.04809, over 1427773.83 frames.], batch size: 21, lr: 7.53e-04
2022-05-14 09:53:18,342 INFO [train.py:812] (6/8) Epoch 10, batch 2550, loss[loss=0.1978, simple_loss=0.282, pruned_loss=0.05674, over 7227.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2684, pruned_loss=0.04876, over 1425822.76 frames.], batch size: 21, lr: 7.52e-04
2022-05-14 09:54:18,084 INFO [train.py:812] (6/8) Epoch 10, batch 2600, loss[loss=0.204, simple_loss=0.286, pruned_loss=0.06096, over 7138.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2687, pruned_loss=0.04886, over 1429630.08 frames.], batch size: 26, lr: 7.52e-04
2022-05-14 09:55:17,735 INFO [train.py:812] (6/8) Epoch 10, batch 2650, loss[loss=0.2062, simple_loss=0.2919, pruned_loss=0.06027, over 7326.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2702, pruned_loss=0.04945, over 1424926.58 frames.], batch size: 22, lr: 7.51e-04
2022-05-14 09:56:16,835 INFO [train.py:812] (6/8) Epoch 10, batch 2700, loss[loss=0.199, simple_loss=0.291, pruned_loss=0.05354, over 6907.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2692, pruned_loss=0.04918, over 1426574.12 frames.], batch size: 31, lr: 7.51e-04
2022-05-14 09:57:23,649 INFO [train.py:812] (6/8) Epoch 10, batch 2750, loss[loss=0.1601, simple_loss=0.2544, pruned_loss=0.03288, over 6876.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2685, pruned_loss=0.04896, over 1424055.06 frames.], batch size: 32, lr: 7.50e-04
2022-05-14 09:58:22,154 INFO [train.py:812] (6/8) Epoch 10, batch 2800, loss[loss=0.2142, simple_loss=0.2933, pruned_loss=0.06752, over 7382.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2681, pruned_loss=0.04867, over 1428717.95 frames.], batch size: 23, lr: 7.50e-04
2022-05-14 09:59:21,344 INFO [train.py:812] (6/8) Epoch 10, batch 2850, loss[loss=0.1962, simple_loss=0.288, pruned_loss=0.05218, over 7344.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2691, pruned_loss=0.04908, over 1426662.33 frames.], batch size: 22, lr: 7.50e-04
2022-05-14 10:00:20,942 INFO [train.py:812] (6/8) Epoch 10, batch 2900, loss[loss=0.1804, simple_loss=0.2767, pruned_loss=0.04205, over 7109.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2694, pruned_loss=0.04958, over 1426308.23 frames.], batch size: 21, lr: 7.49e-04
2022-05-14 10:01:19,230 INFO [train.py:812] (6/8) Epoch 10, batch 2950, loss[loss=0.1662, simple_loss=0.2535, pruned_loss=0.03946, over 7285.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2685, pruned_loss=0.04917, over 1426168.50 frames.], batch size: 18, lr: 7.49e-04
2022-05-14 10:02:18,294 INFO [train.py:812] (6/8) Epoch 10, batch 3000, loss[loss=0.1508, simple_loss=0.2309, pruned_loss=0.03536, over 7292.00 frames.], tot_loss[loss=0.1829, simple_loss=0.268, pruned_loss=0.04896, over 1425885.49 frames.], batch size: 17, lr: 7.48e-04
2022-05-14 10:02:18,295 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 10:02:25,810 INFO [train.py:841] (6/8) Epoch 10, validation: loss=0.1584, simple_loss=0.26, pruned_loss=0.0284, over 698248.00 frames.
2022-05-14 10:03:25,424 INFO [train.py:812] (6/8) Epoch 10, batch 3050, loss[loss=0.1774, simple_loss=0.2703, pruned_loss=0.04226, over 7152.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2687, pruned_loss=0.04935, over 1425669.18 frames.], batch size: 19, lr: 7.48e-04
2022-05-14 10:04:24,567 INFO [train.py:812] (6/8) Epoch 10, batch 3100, loss[loss=0.1837, simple_loss=0.2771, pruned_loss=0.04513, over 7111.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2686, pruned_loss=0.049, over 1428855.91 frames.], batch size: 21, lr: 7.47e-04
2022-05-14 10:05:24,326 INFO [train.py:812] (6/8) Epoch 10, batch 3150, loss[loss=0.1575, simple_loss=0.2504, pruned_loss=0.03228, over 7319.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2686, pruned_loss=0.04901, over 1424945.02 frames.], batch size: 21, lr: 7.47e-04
2022-05-14 10:06:23,654 INFO [train.py:812] (6/8) Epoch 10, batch 3200, loss[loss=0.1875, simple_loss=0.2724, pruned_loss=0.05132, over 7238.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2684, pruned_loss=0.04889, over 1425475.26 frames.], batch size: 20, lr: 7.47e-04
2022-05-14 10:07:23,042 INFO [train.py:812] (6/8) Epoch 10, batch 3250, loss[loss=0.2002, simple_loss=0.2962, pruned_loss=0.05209, over 7411.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2689, pruned_loss=0.0488, over 1426425.32 frames.], batch size: 21, lr: 7.46e-04
2022-05-14 10:08:22,161 INFO [train.py:812] (6/8) Epoch 10, batch 3300, loss[loss=0.194, simple_loss=0.2896, pruned_loss=0.04923, over 7198.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2692, pruned_loss=0.04862, over 1428335.84 frames.], batch size: 22, lr: 7.46e-04
2022-05-14 10:09:21,731 INFO [train.py:812] (6/8) Epoch 10, batch 3350, loss[loss=0.1932, simple_loss=0.2874, pruned_loss=0.04944, over 7206.00 frames.], tot_loss[loss=0.183, simple_loss=0.2692, pruned_loss=0.04835, over 1428933.05 frames.], batch size: 23, lr: 7.45e-04
2022-05-14 10:10:20,638 INFO [train.py:812] (6/8) Epoch 10, batch 3400, loss[loss=0.1568, simple_loss=0.234, pruned_loss=0.03987, over 7292.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2695, pruned_loss=0.04866, over 1425010.75 frames.], batch size: 17, lr: 7.45e-04
2022-05-14 10:11:20,103 INFO [train.py:812] (6/8) Epoch 10, batch 3450, loss[loss=0.2136, simple_loss=0.2839, pruned_loss=0.07163, over 7318.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2699, pruned_loss=0.04922, over 1423856.96 frames.], batch size: 24, lr: 7.45e-04
2022-05-14 10:12:19,092 INFO [train.py:812] (6/8) Epoch 10, batch 3500, loss[loss=0.2005, simple_loss=0.301, pruned_loss=0.05002, over 7418.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2705, pruned_loss=0.04905, over 1424447.62 frames.], batch size: 21, lr: 7.44e-04
2022-05-14 10:13:18,726 INFO [train.py:812] (6/8) Epoch 10, batch 3550, loss[loss=0.1949, simple_loss=0.278, pruned_loss=0.05593, over 7080.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2688, pruned_loss=0.0481, over 1427097.76 frames.], batch size: 28, lr: 7.44e-04
2022-05-14 10:14:16,921 INFO [train.py:812] (6/8) Epoch 10, batch 3600, loss[loss=0.2188, simple_loss=0.2985, pruned_loss=0.06952, over 7052.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2679, pruned_loss=0.04778, over 1427264.62 frames.], batch size: 28, lr: 7.43e-04
2022-05-14 10:15:16,472 INFO [train.py:812] (6/8) Epoch 10, batch 3650, loss[loss=0.1569, simple_loss=0.2404, pruned_loss=0.03667, over 7061.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2673, pruned_loss=0.0477, over 1423036.72 frames.], batch size: 18, lr: 7.43e-04
2022-05-14 10:16:15,510 INFO [train.py:812] (6/8) Epoch 10, batch 3700, loss[loss=0.1525, simple_loss=0.2294, pruned_loss=0.03778, over 7284.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2672, pruned_loss=0.0473, over 1425657.35 frames.], batch size: 17, lr: 7.43e-04
2022-05-14 10:17:15,211 INFO [train.py:812] (6/8) Epoch 10, batch 3750, loss[loss=0.2094, simple_loss=0.289, pruned_loss=0.0649, over 7165.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2683, pruned_loss=0.04771, over 1427679.31 frames.], batch size: 19, lr: 7.42e-04
2022-05-14 10:18:14,401 INFO [train.py:812] (6/8) Epoch 10, batch 3800, loss[loss=0.2078, simple_loss=0.2846, pruned_loss=0.06548, over 7428.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2684, pruned_loss=0.04772, over 1426656.87 frames.], batch size: 20, lr: 7.42e-04
2022-05-14 10:19:12,965 INFO [train.py:812] (6/8) Epoch 10, batch 3850, loss[loss=0.1519, simple_loss=0.2441, pruned_loss=0.02989, over 7062.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2692, pruned_loss=0.04797, over 1425768.44 frames.], batch size: 18, lr: 7.41e-04
2022-05-14 10:20:21,779 INFO [train.py:812] (6/8) Epoch 10, batch 3900, loss[loss=0.1668, simple_loss=0.252, pruned_loss=0.04079, over 7156.00 frames.], tot_loss[loss=0.1824, simple_loss=0.269, pruned_loss=0.04792, over 1426861.06 frames.], batch size: 19, lr: 7.41e-04
2022-05-14 10:21:21,332 INFO [train.py:812] (6/8) Epoch 10, batch 3950, loss[loss=0.2212, simple_loss=0.2877, pruned_loss=0.07731, over 5130.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2688, pruned_loss=0.04837, over 1420467.40 frames.], batch size: 54, lr: 7.41e-04
2022-05-14 10:22:19,910 INFO [train.py:812] (6/8) Epoch 10, batch 4000, loss[loss=0.1708, simple_loss=0.2565, pruned_loss=0.04259, over 7262.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2693, pruned_loss=0.04886, over 1421216.23 frames.], batch size: 19, lr: 7.40e-04
2022-05-14 10:23:18,831 INFO [train.py:812] (6/8) Epoch 10, batch 4050, loss[loss=0.1775, simple_loss=0.2583, pruned_loss=0.04839, over 7138.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2691, pruned_loss=0.04929, over 1422279.93 frames.], batch size: 17, lr: 7.40e-04
2022-05-14 10:24:16,998 INFO [train.py:812] (6/8) Epoch 10, batch 4100, loss[loss=0.1815, simple_loss=0.2775, pruned_loss=0.04274, over 7317.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2684, pruned_loss=0.04834, over 1424421.51 frames.], batch size: 21, lr: 7.39e-04
2022-05-14 10:25:16,592 INFO [train.py:812] (6/8) Epoch 10, batch 4150, loss[loss=0.1589, simple_loss=0.2425, pruned_loss=0.03765, over 7402.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2683, pruned_loss=0.04855, over 1425123.22 frames.], batch size: 18, lr: 7.39e-04
2022-05-14 10:26:14,798 INFO [train.py:812] (6/8) Epoch 10, batch 4200, loss[loss=0.175, simple_loss=0.2625, pruned_loss=0.04369, over 7270.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2687, pruned_loss=0.04907, over 1427021.41 frames.], batch size: 24, lr: 7.39e-04
2022-05-14 10:27:13,959 INFO [train.py:812] (6/8) Epoch 10, batch 4250, loss[loss=0.1834, simple_loss=0.2566, pruned_loss=0.05516, over 7284.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2697, pruned_loss=0.04965, over 1422942.17 frames.], batch size: 17, lr: 7.38e-04
2022-05-14 10:28:13,117 INFO [train.py:812] (6/8) Epoch 10, batch 4300, loss[loss=0.1973, simple_loss=0.2926, pruned_loss=0.05099, over 7269.00 frames.], tot_loss[loss=0.1848, simple_loss=0.27, pruned_loss=0.04975, over 1415806.50 frames.], batch size: 24, lr: 7.38e-04
2022-05-14 10:29:10,980 INFO [train.py:812] (6/8) Epoch 10, batch 4350, loss[loss=0.2729, simple_loss=0.3297, pruned_loss=0.1081, over 4867.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2713, pruned_loss=0.05045, over 1405931.22 frames.], batch size: 52, lr: 7.37e-04
2022-05-14 10:30:10,325 INFO [train.py:812] (6/8) Epoch 10, batch 4400, loss[loss=0.2223, simple_loss=0.3078, pruned_loss=0.06833, over 7211.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2713, pruned_loss=0.0502, over 1409277.38 frames.], batch size: 22, lr: 7.37e-04
2022-05-14 10:31:10,101 INFO [train.py:812] (6/8) Epoch 10, batch 4450, loss[loss=0.2594, simple_loss=0.3244, pruned_loss=0.0972, over 4928.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2723, pruned_loss=0.05112, over 1394055.24 frames.], batch size: 52, lr: 7.37e-04
2022-05-14 10:32:09,143 INFO [train.py:812] (6/8) Epoch 10, batch 4500, loss[loss=0.1995, simple_loss=0.2826, pruned_loss=0.05819, over 7136.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2717, pruned_loss=0.0516, over 1391661.36 frames.], batch size: 20, lr: 7.36e-04
2022-05-14 10:33:08,623 INFO [train.py:812] (6/8) Epoch 10, batch 4550, loss[loss=0.2057, simple_loss=0.2946, pruned_loss=0.05842, over 7144.00 frames.], tot_loss[loss=0.1883, simple_loss=0.272, pruned_loss=0.05225, over 1372226.29 frames.], batch size: 26, lr: 7.36e-04
2022-05-14 10:34:22,340 INFO [train.py:812] (6/8) Epoch 11, batch 0, loss[loss=0.2258, simple_loss=0.2981, pruned_loss=0.0767, over 7417.00 frames.], tot_loss[loss=0.2258, simple_loss=0.2981, pruned_loss=0.0767, over 7417.00 frames.], batch size: 20, lr: 7.08e-04
2022-05-14 10:35:21,216 INFO [train.py:812] (6/8) Epoch 11, batch 50, loss[loss=0.1818, simple_loss=0.263, pruned_loss=0.05033, over 7431.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2709, pruned_loss=0.0488, over 322431.29 frames.], batch size: 20, lr: 7.08e-04
2022-05-14 10:36:19,859 INFO [train.py:812] (6/8) Epoch 11, batch 100, loss[loss=0.1515, simple_loss=0.2263, pruned_loss=0.03831, over 7275.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2712, pruned_loss=0.04933, over 566683.95 frames.], batch size: 18, lr: 7.08e-04
2022-05-14 10:37:28,471 INFO [train.py:812] (6/8) Epoch 11, batch 150, loss[loss=0.1955, simple_loss=0.2755, pruned_loss=0.05769, over 6766.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2735, pruned_loss=0.05064, over 760061.70 frames.], batch size: 15, lr: 7.07e-04
2022-05-14 10:38:36,334 INFO [train.py:812] (6/8) Epoch 11, batch 200, loss[loss=0.1904, simple_loss=0.2595, pruned_loss=0.0607, over 7424.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2714, pruned_loss=0.04938, over 907594.77 frames.], batch size: 18, lr: 7.07e-04
2022-05-14 10:39:34,530 INFO [train.py:812] (6/8) Epoch 11, batch 250, loss[loss=0.1758, simple_loss=0.2631, pruned_loss=0.0442, over 6310.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2703, pruned_loss=0.04907, over 1023107.68 frames.], batch size: 37, lr: 7.06e-04
2022-05-14 10:40:50,474 INFO [train.py:812] (6/8) Epoch 11, batch 300, loss[loss=0.2241, simple_loss=0.2976, pruned_loss=0.07525, over 5335.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2687, pruned_loss=0.04796, over 1114227.25 frames.], batch size: 52, lr: 7.06e-04
2022-05-14 10:41:47,791 INFO [train.py:812] (6/8) Epoch 11, batch 350, loss[loss=0.1822, simple_loss=0.2619, pruned_loss=0.05129, over 6760.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2681, pruned_loss=0.04769, over 1186883.46 frames.], batch size: 31, lr: 7.06e-04
2022-05-14 10:43:03,923 INFO [train.py:812] (6/8) Epoch 11, batch 400, loss[loss=0.1771, simple_loss=0.2704, pruned_loss=0.0419, over 7416.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2679, pruned_loss=0.04787, over 1240673.80 frames.], batch size: 20, lr: 7.05e-04
2022-05-14 10:44:13,175 INFO [train.py:812] (6/8) Epoch 11, batch 450, loss[loss=0.1842, simple_loss=0.2678, pruned_loss=0.05027, over 7246.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2668, pruned_loss=0.04789, over 1280457.04 frames.], batch size: 20, lr: 7.05e-04
2022-05-14 10:45:12,591 INFO [train.py:812] (6/8) Epoch 11, batch 500, loss[loss=0.1908, simple_loss=0.2762, pruned_loss=0.0527, over 7322.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2659, pruned_loss=0.04746, over 1314920.71 frames.], batch size: 20, lr: 7.04e-04
2022-05-14 10:46:12,013 INFO [train.py:812] (6/8) Epoch 11, batch 550, loss[loss=0.1519, simple_loss=0.2416, pruned_loss=0.03105, over 7076.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2662, pruned_loss=0.04727, over 1340113.96 frames.], batch size: 18, lr: 7.04e-04
2022-05-14 10:47:11,383 INFO [train.py:812] (6/8) Epoch 11, batch 600, loss[loss=0.1448, simple_loss=0.2291, pruned_loss=0.03022, over 7001.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2671, pruned_loss=0.04754, over 1359649.30 frames.], batch size: 16, lr: 7.04e-04
2022-05-14 10:48:09,830 INFO [train.py:812] (6/8) Epoch 11, batch 650, loss[loss=0.1484, simple_loss=0.2253, pruned_loss=0.0357, over 7139.00 frames.], tot_loss[loss=0.181, simple_loss=0.267, pruned_loss=0.04754, over 1364063.94 frames.], batch size: 17, lr: 7.03e-04
2022-05-14 10:49:08,411 INFO [train.py:812] (6/8) Epoch 11, batch 700, loss[loss=0.1866, simple_loss=0.2644, pruned_loss=0.05443, over 7216.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2676, pruned_loss=0.04804, over 1374732.73 frames.], batch size: 16, lr: 7.03e-04
2022-05-14 10:50:07,700 INFO [train.py:812] (6/8) Epoch 11, batch 750, loss[loss=0.1949, simple_loss=0.2839, pruned_loss=0.05291, over 7139.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2676, pruned_loss=0.04802, over 1381996.22 frames.], batch size: 20, lr: 7.03e-04
2022-05-14 10:51:05,922 INFO [train.py:812] (6/8) Epoch 11, batch 800, loss[loss=0.1817, simple_loss=0.2752, pruned_loss=0.04413, over 7162.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2676, pruned_loss=0.04802, over 1393884.02 frames.], batch size: 26, lr: 7.02e-04
2022-05-14 10:52:03,631 INFO [train.py:812] (6/8) Epoch 11, batch 850, loss[loss=0.1772, simple_loss=0.2644, pruned_loss=0.045, over 7330.00 frames.], tot_loss[loss=0.182, simple_loss=0.2678, pruned_loss=0.04806, over 1398280.45 frames.], batch size: 20, lr: 7.02e-04
2022-05-14 10:53:01,763 INFO [train.py:812] (6/8) Epoch 11, batch 900, loss[loss=0.1732, simple_loss=0.2538, pruned_loss=0.04636, over 7430.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2673, pruned_loss=0.04817, over 1406855.77 frames.], batch size: 20, lr: 7.02e-04
2022-05-14 10:54:00,395 INFO [train.py:812] (6/8) Epoch 11, batch 950, loss[loss=0.1927, simple_loss=0.2705, pruned_loss=0.05747, over 6995.00 frames.], tot_loss[loss=0.1816, simple_loss=0.267, pruned_loss=0.04808, over 1408629.32 frames.], batch size: 16, lr: 7.01e-04
2022-05-14 10:54:58,957 INFO [train.py:812] (6/8) Epoch 11, batch 1000, loss[loss=0.2005, simple_loss=0.2832, pruned_loss=0.05888, over 7289.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2672, pruned_loss=0.04814, over 1413353.02 frames.], batch size: 25, lr: 7.01e-04
2022-05-14 10:55:58,069 INFO [train.py:812] (6/8) Epoch 11, batch 1050, loss[loss=0.17, simple_loss=0.2533, pruned_loss=0.04332, over 7261.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2682, pruned_loss=0.04871, over 1408354.42 frames.], batch size: 19, lr: 7.00e-04
2022-05-14 10:56:57,299 INFO [train.py:812] (6/8) Epoch 11, batch 1100, loss[loss=0.1548, simple_loss=0.2422, pruned_loss=0.03368, over 7153.00 frames.], tot_loss[loss=0.182, simple_loss=0.2677, pruned_loss=0.04812, over 1412676.99 frames.], batch size: 18, lr: 7.00e-04
2022-05-14 10:57:56,867 INFO [train.py:812] (6/8) Epoch 11, batch 1150, loss[loss=0.1759, simple_loss=0.2598, pruned_loss=0.04604, over 7065.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2674, pruned_loss=0.04788, over 1416144.26 frames.], batch size: 18, lr: 7.00e-04
2022-05-14 10:58:55,497 INFO [train.py:812] (6/8) Epoch 11, batch 1200, loss[loss=0.1759, simple_loss=0.2488, pruned_loss=0.05149, over 7190.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2659, pruned_loss=0.04745, over 1419456.90 frames.], batch size: 16, lr: 6.99e-04
2022-05-14 10:59:53,790 INFO [train.py:812] (6/8) Epoch 11, batch 1250, loss[loss=0.1599, simple_loss=0.2382, pruned_loss=0.04075, over 7147.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2652, pruned_loss=0.04698, over 1423184.38 frames.], batch size: 17, lr: 6.99e-04
2022-05-14 11:00:50,432 INFO [train.py:812] (6/8) Epoch 11, batch 1300, loss[loss=0.1835, simple_loss=0.2758, pruned_loss=0.04556, over 7310.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2654, pruned_loss=0.04707, over 1420106.81 frames.], batch size: 21, lr: 6.99e-04
2022-05-14 11:01:49,337 INFO [train.py:812] (6/8) Epoch 11, batch 1350, loss[loss=0.1777, simple_loss=0.2674, pruned_loss=0.04403, over 7314.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2668, pruned_loss=0.04774, over 1422996.53 frames.], batch size: 21, lr: 6.98e-04
2022-05-14 11:02:46,395 INFO [train.py:812] (6/8) Epoch 11, batch 1400, loss[loss=0.1498, simple_loss=0.2389, pruned_loss=0.03036, over 7147.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2667, pruned_loss=0.04772, over 1426435.49 frames.], batch size: 19, lr: 6.98e-04
2022-05-14 11:03:44,654 INFO [train.py:812] (6/8) Epoch 11, batch 1450, loss[loss=0.1637, simple_loss=0.2479, pruned_loss=0.03977, over 7283.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2668, pruned_loss=0.04737, over 1427382.75 frames.], batch size: 17, lr: 6.97e-04
2022-05-14 11:04:41,559 INFO [train.py:812] (6/8) Epoch 11, batch 1500, loss[loss=0.1556, simple_loss=0.2548, pruned_loss=0.0282, over 7051.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2669, pruned_loss=0.04724, over 1424723.10 frames.], batch size: 28, lr: 6.97e-04
2022-05-14 11:05:41,365 INFO [train.py:812] (6/8) Epoch 11, batch 1550, loss[loss=0.1543, simple_loss=0.2443, pruned_loss=0.03219, over 7427.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2676, pruned_loss=0.04771, over 1423420.60 frames.], batch size: 20, lr: 6.97e-04
2022-05-14 11:06:38,942 INFO [train.py:812] (6/8) Epoch 11, batch 1600, loss[loss=0.1868, simple_loss=0.2659, pruned_loss=0.05382, over 6716.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2674, pruned_loss=0.04797, over 1418541.76 frames.], batch size: 31, lr: 6.96e-04
2022-05-14 11:07:38,270 INFO [train.py:812] (6/8) Epoch 11, batch 1650, loss[loss=0.1745, simple_loss=0.247, pruned_loss=0.05101, over 6784.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2662, pruned_loss=0.04752, over 1418459.90 frames.], batch size: 15, lr: 6.96e-04
2022-05-14 11:08:37,024 INFO [train.py:812] (6/8) Epoch 11, batch 1700, loss[loss=0.1508, simple_loss=0.2291, pruned_loss=0.03622, over 7264.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2669, pruned_loss=0.04768, over 1417760.74 frames.], batch size: 16, lr: 6.96e-04
2022-05-14 11:09:36,823 INFO [train.py:812] (6/8) Epoch 11, batch 1750, loss[loss=0.1699, simple_loss=0.2647, pruned_loss=0.03751, over 7119.00 frames.], tot_loss[loss=0.181, simple_loss=0.2661, pruned_loss=0.04791, over 1414783.76 frames.], batch size: 21, lr: 6.95e-04
2022-05-14 11:10:35,684 INFO [train.py:812] (6/8) Epoch 11, batch 1800, loss[loss=0.2107, simple_loss=0.2865, pruned_loss=0.06749, over 5496.00 frames.], tot_loss[loss=0.181, simple_loss=0.2661, pruned_loss=0.0479, over 1414207.64 frames.], batch size: 52, lr: 6.95e-04
2022-05-14 11:11:35,363 INFO [train.py:812] (6/8) Epoch 11, batch 1850, loss[loss=0.1786, simple_loss=0.2593, pruned_loss=0.04893, over 6588.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2659, pruned_loss=0.04758, over 1418524.98 frames.], batch size: 38, lr: 6.95e-04
2022-05-14 11:12:33,316 INFO [train.py:812] (6/8) Epoch 11, batch 1900, loss[loss=0.1802, simple_loss=0.2783, pruned_loss=0.041, over 7315.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2658, pruned_loss=0.0474, over 1422701.96 frames.], batch size: 21, lr: 6.94e-04
2022-05-14 11:13:32,953 INFO [train.py:812] (6/8) Epoch 11, batch 1950, loss[loss=0.217, simple_loss=0.3012, pruned_loss=0.06642, over 7353.00 frames.], tot_loss[loss=0.18, simple_loss=0.2656, pruned_loss=0.04713, over 1421728.78 frames.], batch size: 19, lr: 6.94e-04
2022-05-14 11:14:32,029 INFO [train.py:812] (6/8) Epoch 11, batch 2000, loss[loss=0.1824, simple_loss=0.2743, pruned_loss=0.04524, over 7161.00 frames.], tot_loss[loss=0.1802, simple_loss=0.266, pruned_loss=0.04719, over 1423161.86 frames.], batch size: 18, lr: 6.93e-04
2022-05-14 11:15:30,897 INFO [train.py:812] (6/8) Epoch 11, batch 2050, loss[loss=0.1442, simple_loss=0.2248, pruned_loss=0.03183, over 7281.00 frames.], tot_loss[loss=0.1791, simple_loss=0.265, pruned_loss=0.04667, over 1424831.84 frames.], batch size: 17, lr: 6.93e-04
2022-05-14 11:16:30,470 INFO [train.py:812] (6/8) Epoch 11, batch 2100, loss[loss=0.1764, simple_loss=0.2662, pruned_loss=0.0433, over 7378.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2656, pruned_loss=0.04701, over 1424216.65 frames.], batch size: 23, lr: 6.93e-04
2022-05-14 11:17:37,602 INFO [train.py:812] (6/8) Epoch 11, batch 2150, loss[loss=0.1937, simple_loss=0.2728, pruned_loss=0.05732, over 7163.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2662, pruned_loss=0.04709, over 1424524.17 frames.], batch size: 18, lr: 6.92e-04
2022-05-14 11:18:36,042 INFO [train.py:812] (6/8) Epoch 11, batch 2200, loss[loss=0.1834, simple_loss=0.2746, pruned_loss=0.04611, over 7239.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2667, pruned_loss=0.04735, over 1421858.16 frames.], batch size: 20, lr: 6.92e-04
2022-05-14 11:19:35,029 INFO [train.py:812] (6/8) Epoch 11, batch 2250, loss[loss=0.1822, simple_loss=0.2751, pruned_loss=0.04461, over 7335.00 frames.], tot_loss[loss=0.1818, simple_loss=0.268, pruned_loss=0.04778, over 1425024.23 frames.], batch size: 22, lr: 6.92e-04
2022-05-14 11:20:34,393 INFO [train.py:812] (6/8) Epoch 11, batch 2300, loss[loss=0.1841, simple_loss=0.2769, pruned_loss=0.04566, over 7130.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2666, pruned_loss=0.04748, over 1425675.99 frames.], batch size: 26, lr: 6.91e-04
2022-05-14 11:21:33,311 INFO [train.py:812] (6/8) Epoch 11, batch 2350, loss[loss=0.1955, simple_loss=0.2707, pruned_loss=0.06009, over 6852.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2657, pruned_loss=0.04679, over 1428334.30 frames.], batch size: 31, lr: 6.91e-04
2022-05-14 11:22:32,016 INFO [train.py:812] (6/8) Epoch 11, batch 2400, loss[loss=0.1675, simple_loss=0.2626, pruned_loss=0.03622, over 7315.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2667, pruned_loss=0.04723, over 1422710.67 frames.], batch size: 21, lr: 6.91e-04
2022-05-14 11:23:31,130 INFO [train.py:812] (6/8) Epoch 11, batch 2450, loss[loss=0.1848, simple_loss=0.2654, pruned_loss=0.05206, over 7019.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2655, pruned_loss=0.04703, over 1423381.00 frames.], batch size: 16, lr: 6.90e-04
2022-05-14 11:24:30,218 INFO [train.py:812] (6/8) Epoch 11, batch 2500, loss[loss=0.1669, simple_loss=0.2461, pruned_loss=0.04381, over 7152.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2663, pruned_loss=0.04718, over 1422803.70 frames.], batch size: 19, lr: 6.90e-04
2022-05-14 11:25:29,329 INFO [train.py:812] (6/8) Epoch 11, batch 2550, loss[loss=0.172, simple_loss=0.2512, pruned_loss=0.04635, over 6803.00 frames.], tot_loss[loss=0.179, simple_loss=0.2652, pruned_loss=0.04642, over 1426762.98 frames.], batch size: 15, lr: 6.90e-04
2022-05-14 11:26:27,801 INFO [train.py:812] (6/8) Epoch 11, batch 2600, loss[loss=0.1805, simple_loss=0.2691, pruned_loss=0.04593, over 7366.00 frames.], tot_loss[loss=0.1798, simple_loss=0.266, pruned_loss=0.04679, over 1428231.74 frames.], batch size: 23, lr: 6.89e-04
2022-05-14 11:27:26,098 INFO [train.py:812] (6/8) Epoch 11, batch 2650, loss[loss=0.1366, simple_loss=0.2218, pruned_loss=0.0257, over 6997.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2669, pruned_loss=0.04712, over 1423385.57 frames.], batch size: 16, lr: 6.89e-04
2022-05-14 11:28:23,561 INFO [train.py:812] (6/8) Epoch 11, batch 2700, loss[loss=0.1696, simple_loss=0.2638, pruned_loss=0.03773, over 7417.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2671, pruned_loss=0.04654, over 1426189.39 frames.], batch size: 21, lr: 6.89e-04
2022-05-14 11:29:21,002 INFO [train.py:812] (6/8) Epoch 11, batch 2750, loss[loss=0.1671, simple_loss=0.2551, pruned_loss=0.03958, over 7270.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2648, pruned_loss=0.04576, over 1425056.86 frames.], batch size: 18, lr: 6.88e-04
2022-05-14 11:30:17,978 INFO [train.py:812] (6/8) Epoch 11, batch 2800, loss[loss=0.2026, simple_loss=0.2886, pruned_loss=0.0583, over 7177.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2651, pruned_loss=0.04603, over 1423861.20 frames.], batch size: 19, lr: 6.88e-04
2022-05-14 11:31:17,639 INFO [train.py:812] (6/8) Epoch 11, batch 2850, loss[loss=0.148, simple_loss=0.2344, pruned_loss=0.03083, over 7318.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2644, pruned_loss=0.04593, over 1424203.29 frames.], batch size: 21, lr: 6.87e-04
2022-05-14 11:32:14,498 INFO [train.py:812] (6/8) Epoch 11, batch 2900, loss[loss=0.224, simple_loss=0.3077, pruned_loss=0.07018, over 7203.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2655, pruned_loss=0.04636, over 1426559.26 frames.], batch size: 23, lr: 6.87e-04
2022-05-14 11:33:13,354 INFO [train.py:812] (6/8) Epoch 11, batch 2950, loss[loss=0.174, simple_loss=0.2625, pruned_loss=0.0427, over 7212.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2672, pruned_loss=0.04687, over 1423843.90 frames.], batch size: 22, lr: 6.87e-04
2022-05-14 11:34:12,330 INFO [train.py:812] (6/8) Epoch 11, batch 3000, loss[loss=0.1392, simple_loss=0.2203, pruned_loss=0.02907, over 7159.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2671, pruned_loss=0.04677, over 1422368.82 frames.], batch size: 18, lr: 6.86e-04
2022-05-14 11:34:12,331 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 11:34:19,823 INFO [train.py:841] (6/8) Epoch 11, validation: loss=0.1564, simple_loss=0.2581, pruned_loss=0.02737, over 698248.00 frames.
2022-05-14 11:35:18,273 INFO [train.py:812] (6/8) Epoch 11, batch 3050, loss[loss=0.1817, simple_loss=0.2834, pruned_loss=0.04003, over 7160.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2664, pruned_loss=0.04665, over 1427093.89 frames.], batch size: 26, lr: 6.86e-04
2022-05-14 11:36:16,730 INFO [train.py:812] (6/8) Epoch 11, batch 3100, loss[loss=0.165, simple_loss=0.2514, pruned_loss=0.03931, over 7398.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2674, pruned_loss=0.0474, over 1424782.12 frames.], batch size: 18, lr: 6.86e-04
2022-05-14 11:37:16,248 INFO [train.py:812] (6/8) Epoch 11, batch 3150, loss[loss=0.1742, simple_loss=0.2592, pruned_loss=0.04459, over 7286.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2668, pruned_loss=0.04743, over 1427234.85 frames.], batch size: 18, lr: 6.85e-04
2022-05-14 11:38:15,173 INFO [train.py:812] (6/8) Epoch 11, batch 3200, loss[loss=0.1899, simple_loss=0.2734, pruned_loss=0.05319, over 7167.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2657, pruned_loss=0.04692, over 1429304.73 frames.], batch size: 18, lr: 6.85e-04
2022-05-14 11:39:14,893 INFO [train.py:812] (6/8) Epoch 11, batch 3250, loss[loss=0.1434, simple_loss=0.2327, pruned_loss=0.02706, over 7066.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2658, pruned_loss=0.04702, over 1430886.35 frames.], batch size: 18, lr: 6.85e-04
2022-05-14 11:40:14,284 INFO [train.py:812] (6/8) Epoch 11, batch 3300, loss[loss=0.1897, simple_loss=0.2895, pruned_loss=0.04493, over 6433.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2669, pruned_loss=0.04714, over 1429307.34 frames.], batch size: 38, lr: 6.84e-04
2022-05-14 11:41:13,853 INFO [train.py:812] (6/8) Epoch 11, batch 3350, loss[loss=0.1796, simple_loss=0.2576, pruned_loss=0.05077, over 7115.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2668, pruned_loss=0.04721, over 1424384.29 frames.], batch size: 21, lr: 6.84e-04
2022-05-14 11:42:12,414 INFO [train.py:812] (6/8) Epoch 11, batch 3400, loss[loss=0.1723, simple_loss=0.2447, pruned_loss=0.04991, over 6995.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2673, pruned_loss=0.04766, over 1421699.32 frames.], batch size: 16, lr: 6.84e-04
2022-05-14 11:43:11,492 INFO [train.py:812] (6/8) Epoch 11, batch 3450, loss[loss=0.1821, simple_loss=0.2602, pruned_loss=0.052, over 7134.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2675, pruned_loss=0.04774, over 1424272.08 frames.], batch size: 21, lr: 6.83e-04
2022-05-14 11:44:10,171 INFO [train.py:812] (6/8) Epoch 11, batch 3500, loss[loss=0.1706, simple_loss=0.246, pruned_loss=0.04761, over 7414.00 frames.], tot_loss[loss=0.1812, simple_loss=0.267, pruned_loss=0.04771, over 1425144.85 frames.], batch size: 18, lr: 6.83e-04
2022-05-14 11:45:10,026 INFO [train.py:812] (6/8) Epoch 11, batch 3550, loss[loss=0.2056, simple_loss=0.2893, pruned_loss=0.06096, over 6383.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2669, pruned_loss=0.0477, over 1423838.36 frames.], batch size: 37, lr: 6.83e-04
2022-05-14 11:46:08,754 INFO [train.py:812] (6/8) Epoch 11, batch 3600, loss[loss=0.1901, simple_loss=0.2745, pruned_loss=0.05288, over 6089.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2673, pruned_loss=0.04816, over 1418815.39 frames.], batch size: 37, lr: 6.82e-04
2022-05-14 11:47:07,769 INFO [train.py:812] (6/8) Epoch 11, batch 3650, loss[loss=0.1873, simple_loss=0.2785, pruned_loss=0.04802, over 7123.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2662, pruned_loss=0.04724, over 1422434.04 frames.], batch size: 21, lr: 6.82e-04
2022-05-14 11:48:06,848 INFO [train.py:812] (6/8) Epoch 11, batch 3700, loss[loss=0.2051, simple_loss=0.2935, pruned_loss=0.05839, over 7132.00 frames.], tot_loss[loss=0.181, simple_loss=0.267, pruned_loss=0.04748, over 1418969.76 frames.], batch size: 21, lr: 6.82e-04
2022-05-14 11:49:06,480 INFO [train.py:812] (6/8) Epoch 11, batch 3750, loss[loss=0.1665, simple_loss=0.2553, pruned_loss=0.03882, over 7424.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2677, pruned_loss=0.04705, over 1424714.66 frames.], batch size: 20, lr: 6.81e-04
2022-05-14 11:50:05,394 INFO [train.py:812] (6/8) Epoch 11, batch 3800, loss[loss=0.1916, simple_loss=0.2765, pruned_loss=0.05336, over 7314.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2675, pruned_loss=0.04749, over 1422580.74 frames.], batch size: 24, lr: 6.81e-04
2022-05-14 11:51:04,562 INFO [train.py:812] (6/8) Epoch 11, batch 3850, loss[loss=0.2132, simple_loss=0.3005, pruned_loss=0.06299, over 7210.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2676, pruned_loss=0.04765, over 1426553.44 frames.], batch size: 22, lr: 6.81e-04
2022-05-14 11:52:01,427 INFO [train.py:812] (6/8) Epoch 11, batch 3900, loss[loss=0.1822, simple_loss=0.2711, pruned_loss=0.04669, over 7379.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2674, pruned_loss=0.04778, over 1427657.89 frames.], batch size: 23, lr: 6.80e-04
2022-05-14 11:53:00,862 INFO [train.py:812] (6/8) Epoch 11, batch 3950, loss[loss=0.1885, simple_loss=0.2765, pruned_loss=0.05019, over 7433.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2661, pruned_loss=0.04723, over 1427422.75 frames.], batch size: 20, lr: 6.80e-04
2022-05-14 11:53:59,474 INFO [train.py:812] (6/8) Epoch 11, batch 4000, loss[loss=0.1468, simple_loss=0.2469, pruned_loss=0.02334, over 7235.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2667, pruned_loss=0.0475, over 1418186.42 frames.], batch size: 21, lr: 6.80e-04
2022-05-14 11:54:58,928 INFO [train.py:812] (6/8) Epoch 11, batch 4050, loss[loss=0.1754, simple_loss=0.264, pruned_loss=0.04337, over 7216.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2677, pruned_loss=0.04804, over 1417823.68 frames.], batch size: 22, lr: 6.79e-04
2022-05-14 11:55:57,987 INFO [train.py:812] (6/8) Epoch 11, batch 4100, loss[loss=0.1672, simple_loss=0.2552, pruned_loss=0.03956, over 7196.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2674, pruned_loss=0.04741, over 1417766.95 frames.], batch size: 22, lr: 6.79e-04
2022-05-14 11:56:56,032 INFO [train.py:812] (6/8) Epoch 11, batch 4150, loss[loss=0.1899, simple_loss=0.2784, pruned_loss=0.05066, over 6698.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2683, pruned_loss=0.04807, over 1415207.71 frames.], batch size: 31, lr: 6.79e-04
2022-05-14 11:57:54,864 INFO [train.py:812] (6/8) Epoch 11, batch 4200, loss[loss=0.1767, simple_loss=0.2679, pruned_loss=0.04275, over 7042.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2685, pruned_loss=0.04809, over 1415834.49 frames.], batch size: 28, lr: 6.78e-04
2022-05-14 11:58:54,368 INFO [train.py:812] (6/8) Epoch 11, batch 4250, loss[loss=0.2468, simple_loss=0.3124, pruned_loss=0.09062, over 4739.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2671, pruned_loss=0.04765, over 1414803.80 frames.], batch size: 52, lr: 6.78e-04
2022-05-14 11:59:53,062 INFO [train.py:812] (6/8) Epoch 11, batch 4300, loss[loss=0.2198, simple_loss=0.3019, pruned_loss=0.0688, over 5014.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2675, pruned_loss=0.04777, over 1411233.17 frames.], batch size: 52, lr: 6.78e-04
2022-05-14 12:00:52,222 INFO [train.py:812] (6/8) Epoch 11, batch 4350, loss[loss=0.1655, simple_loss=0.2599, pruned_loss=0.03556, over 7230.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2672, pruned_loss=0.04756, over 1410717.92 frames.], batch size: 20, lr: 6.77e-04
2022-05-14 12:01:50,118 INFO [train.py:812] (6/8) Epoch 11, batch 4400, loss[loss=0.1887, simple_loss=0.2781, pruned_loss=0.04964, over 7213.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2684, pruned_loss=0.04829, over 1415632.72 frames.], batch size: 22, lr: 6.77e-04
2022-05-14 12:02:49,059 INFO [train.py:812] (6/8) Epoch 11, batch 4450, loss[loss=0.1353, simple_loss=0.2274, pruned_loss=0.02165, over 7242.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2699, pruned_loss=0.04875, over 1418859.13 frames.], batch size: 20, lr: 6.77e-04
2022-05-14 12:03:48,140 INFO [train.py:812] (6/8) Epoch 11, batch 4500, loss[loss=0.2503, simple_loss=0.3175, pruned_loss=0.09156, over 5197.00 frames.], tot_loss[loss=0.1846, simple_loss=0.271, pruned_loss=0.04915, over 1410941.36 frames.], batch size: 52, lr: 6.76e-04
2022-05-14 12:04:46,803 INFO [train.py:812] (6/8) Epoch 11, batch 4550, loss[loss=0.2028, simple_loss=0.2809, pruned_loss=0.06237, over 5009.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2733, pruned_loss=0.05148, over 1347627.22 frames.], batch size: 53, lr: 6.76e-04
2022-05-14 12:05:54,964 INFO [train.py:812] (6/8) Epoch 12, batch 0, loss[loss=0.1818, simple_loss=0.2683, pruned_loss=0.04758, over 7413.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2683, pruned_loss=0.04758, over 7413.00 frames.], batch size: 21, lr: 6.52e-04
2022-05-14 12:06:54,752 INFO [train.py:812] (6/8) Epoch 12, batch 50, loss[loss=0.2403, simple_loss=0.3036, pruned_loss=0.08854, over 4815.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2663, pruned_loss=0.04666, over 319014.97 frames.], batch size: 54, lr: 6.52e-04
2022-05-14 12:07:53,911 INFO [train.py:812] (6/8) Epoch 12, batch 100, loss[loss=0.1819, simple_loss=0.273, pruned_loss=0.04542, over 6435.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2652, pruned_loss=0.04599, over 558631.87 frames.], batch size: 38, lr: 6.51e-04
2022-05-14 12:08:53,465 INFO [train.py:812] (6/8) Epoch 12, batch 150, loss[loss=0.1621, simple_loss=0.2466, pruned_loss=0.03881, over 7297.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2669, pruned_loss=0.04628, over 748720.15 frames.], batch size: 17, lr: 6.51e-04
2022-05-14 12:09:52,501 INFO [train.py:812] (6/8) Epoch 12, batch 200, loss[loss=0.2103, simple_loss=0.2984, pruned_loss=0.06114, over 7201.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2671, pruned_loss=0.04666, over 895571.23 frames.], batch size: 22, lr: 6.51e-04
2022-05-14 12:10:51,926 INFO [train.py:812] (6/8) Epoch 12, batch 250, loss[loss=0.1878, simple_loss=0.2771, pruned_loss=0.04923, over 6698.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2664, pruned_loss=0.04588, over 1013737.17 frames.], batch size: 31, lr: 6.50e-04
2022-05-14 12:11:51,104 INFO [train.py:812] (6/8) Epoch 12, batch 300, loss[loss=0.1795, simple_loss=0.2687, pruned_loss=0.04517, over 7193.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2665, pruned_loss=0.0455, over 1098190.36 frames.], batch size: 22, lr: 6.50e-04
2022-05-14 12:12:50,850 INFO [train.py:812] (6/8) Epoch 12, batch 350, loss[loss=0.1782, simple_loss=0.2643, pruned_loss=0.04606, over 7336.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2658, pruned_loss=0.04532, over 1164278.62 frames.], batch size: 22, lr: 6.50e-04
2022-05-14 12:13:50,325 INFO [train.py:812] (6/8) Epoch 12, batch 400, loss[loss=0.1816, simple_loss=0.2689, pruned_loss=0.04714, over 7338.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2657, pruned_loss=0.04558, over 1219533.37 frames.], batch size: 22, lr: 6.49e-04
2022-05-14 12:14:48,365 INFO [train.py:812] (6/8) Epoch 12, batch 450, loss[loss=0.1679, simple_loss=0.2674, pruned_loss=0.03421, over 7155.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2654, pruned_loss=0.04542, over 1268018.41 frames.], batch size: 19, lr: 6.49e-04
2022-05-14 12:15:47,361 INFO [train.py:812] (6/8) Epoch 12, batch 500, loss[loss=0.2498, simple_loss=0.3354, pruned_loss=0.08208, over 7376.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2656, pruned_loss=0.04558, over 1302151.36 frames.], batch size: 23, lr: 6.49e-04
2022-05-14 12:16:45,624 INFO [train.py:812] (6/8) Epoch 12, batch 550, loss[loss=0.176, simple_loss=0.2705, pruned_loss=0.04073, over 7403.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2648, pruned_loss=0.04537, over 1328250.99 frames.], batch size: 21, lr: 6.48e-04
2022-05-14 12:17:43,515 INFO [train.py:812] (6/8) Epoch 12, batch 600, loss[loss=0.1729, simple_loss=0.2641, pruned_loss=0.04078, over 7340.00 frames.], tot_loss[loss=0.1782, simple_loss=0.265, pruned_loss=0.04567, over 1347374.76 frames.], batch size: 22, lr: 6.48e-04
2022-05-14 12:18:41,737 INFO [train.py:812] (6/8) Epoch 12, batch 650, loss[loss=0.2, simple_loss=0.2848, pruned_loss=0.05759, over 7381.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2644, pruned_loss=0.04552, over 1368478.16 frames.], batch size: 23, lr: 6.48e-04
2022-05-14 12:19:49,861 INFO [train.py:812] (6/8) Epoch 12, batch 700, loss[loss=0.1772, simple_loss=0.2621, pruned_loss=0.04612, over 7270.00 frames.], tot_loss[loss=0.177, simple_loss=0.264, pruned_loss=0.04494, over 1379822.28 frames.], batch size: 24, lr: 6.47e-04
2022-05-14 12:20:48,664 INFO [train.py:812] (6/8) Epoch 12, batch 750, loss[loss=0.1592, simple_loss=0.2535, pruned_loss=0.03244, over 7330.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2648, pruned_loss=0.04548, over 1385915.66 frames.], batch size: 20, lr: 6.47e-04
2022-05-14 12:21:47,962 INFO [train.py:812] (6/8) Epoch 12, batch 800, loss[loss=0.149, simple_loss=0.2253, pruned_loss=0.03633, over 7402.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2643, pruned_loss=0.04557, over 1398537.69 frames.], batch size: 18, lr: 6.47e-04
2022-05-14 12:22:46,132 INFO [train.py:812] (6/8) Epoch 12, batch 850, loss[loss=0.194, simple_loss=0.2776, pruned_loss=0.05516, over 6828.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2661, pruned_loss=0.04638, over 1402829.23 frames.], batch size: 31, lr: 6.46e-04
2022-05-14 12:23:43,982 INFO [train.py:812] (6/8) Epoch 12, batch 900, loss[loss=0.1693, simple_loss=0.2641, pruned_loss=0.03726, over 7337.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2654, pruned_loss=0.04591, over 1406905.61 frames.], batch size: 22, lr: 6.46e-04
2022-05-14 12:24:43,697 INFO [train.py:812] (6/8) Epoch 12, batch 950, loss[loss=0.1503, simple_loss=0.2417, pruned_loss=0.02943, over 7426.00 frames.], tot_loss[loss=0.1794, simple_loss=0.266, pruned_loss=0.04636, over 1412504.28 frames.], batch size: 20, lr: 6.46e-04
2022-05-14 12:25:42,178 INFO [train.py:812] (6/8) Epoch 12, batch 1000, loss[loss=0.1677, simple_loss=0.2589, pruned_loss=0.03826, over 7156.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2663, pruned_loss=0.04612, over 1415863.75 frames.], batch size: 19, lr: 6.46e-04
2022-05-14 12:26:41,697 INFO [train.py:812] (6/8) Epoch 12, batch 1050, loss[loss=0.1528, simple_loss=0.2367, pruned_loss=0.03446, over 7421.00 frames.], tot_loss[loss=0.1799, simple_loss=0.267, pruned_loss=0.04644, over 1416039.47 frames.], batch size: 17, lr: 6.45e-04
2022-05-14 12:27:40,725 INFO [train.py:812] (6/8) Epoch 12, batch 1100, loss[loss=0.1666, simple_loss=0.2565, pruned_loss=0.0383, over 7156.00 frames.], tot_loss[loss=0.18, simple_loss=0.2673, pruned_loss=0.04631, over 1418962.54 frames.], batch size: 19, lr: 6.45e-04
2022-05-14 12:28:40,262 INFO [train.py:812] (6/8) Epoch 12, batch 1150, loss[loss=0.1843, simple_loss=0.2661, pruned_loss=0.05126, over 4803.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2663, pruned_loss=0.04593, over 1421581.08 frames.], batch size: 52, lr: 6.45e-04
2022-05-14 12:29:38,133 INFO [train.py:812] (6/8) Epoch 12, batch 1200, loss[loss=0.1951, simple_loss=0.281, pruned_loss=0.05465, over 7114.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2662, pruned_loss=0.04584, over 1424000.92 frames.], batch size: 21, lr: 6.44e-04
2022-05-14 12:30:37,019 INFO [train.py:812] (6/8) Epoch 12, batch 1250, loss[loss=0.1512, simple_loss=0.2317, pruned_loss=0.03534, over 6990.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2653, pruned_loss=0.04556, over 1425452.53 frames.], batch size: 16, lr: 6.44e-04
2022-05-14 12:31:36,646 INFO [train.py:812] (6/8) Epoch 12, batch 1300, loss[loss=0.1816, simple_loss=0.2716, pruned_loss=0.04575, over 7327.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2648, pruned_loss=0.04518, over 1427596.31 frames.], batch size: 20, lr: 6.44e-04
2022-05-14 12:32:34,818 INFO [train.py:812] (6/8) Epoch 12, batch 1350, loss[loss=0.203, simple_loss=0.2923, pruned_loss=0.05687, over 7313.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2657, pruned_loss=0.046, over 1423997.56 frames.], batch size: 21, lr: 6.43e-04
2022-05-14 12:33:34,088 INFO [train.py:812] (6/8) Epoch 12, batch 1400, loss[loss=0.1954, simple_loss=0.2814, pruned_loss=0.05469, over 7315.00 frames.], tot_loss[loss=0.1782, simple_loss=0.265, pruned_loss=0.0457, over 1420602.50 frames.], batch size: 21, lr: 6.43e-04
2022-05-14 12:34:33,356 INFO [train.py:812] (6/8) Epoch 12, batch 1450, loss[loss=0.1447, simple_loss=0.2416, pruned_loss=0.02391, over 7067.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2659, pruned_loss=0.04622, over 1421990.05 frames.], batch size: 18, lr: 6.43e-04
2022-05-14 12:35:32,033 INFO [train.py:812] (6/8) Epoch 12, batch 1500, loss[loss=0.2176, simple_loss=0.2947, pruned_loss=0.07021, over 7192.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2654, pruned_loss=0.0461, over 1425586.90 frames.], batch size: 23, lr: 6.42e-04
2022-05-14 12:36:36,805 INFO [train.py:812] (6/8) Epoch 12, batch 1550, loss[loss=0.1593, simple_loss=0.256, pruned_loss=0.03124, over 7244.00 frames.], tot_loss[loss=0.178, simple_loss=0.2645, pruned_loss=0.04575, over 1424653.42 frames.], batch size: 20, lr: 6.42e-04
2022-05-14 12:37:35,865 INFO [train.py:812] (6/8) Epoch 12, batch 1600, loss[loss=0.1722, simple_loss=0.253, pruned_loss=0.04566, over 7356.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2651, pruned_loss=0.04571, over 1425020.15 frames.], batch size: 19, lr: 6.42e-04
2022-05-14 12:38:44,932 INFO [train.py:812] (6/8) Epoch 12, batch 1650, loss[loss=0.1836, simple_loss=0.2726, pruned_loss=0.04735, over 7396.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2654, pruned_loss=0.04582, over 1425979.15 frames.], batch size: 23, lr: 6.42e-04
2022-05-14 12:39:52,056 INFO [train.py:812] (6/8) Epoch 12, batch 1700, loss[loss=0.2182, simple_loss=0.3049, pruned_loss=0.06578, over 7222.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2657, pruned_loss=0.04536, over 1427076.52 frames.], batch size: 21, lr: 6.41e-04
2022-05-14 12:40:51,352 INFO [train.py:812] (6/8) Epoch 12, batch 1750, loss[loss=0.1769, simple_loss=0.2668, pruned_loss=0.04352, over 7174.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2657, pruned_loss=0.04525, over 1427487.83 frames.], batch size: 26, lr: 6.41e-04
2022-05-14 12:41:58,746 INFO [train.py:812] (6/8) Epoch 12, batch 1800, loss[loss=0.1606, simple_loss=0.2415, pruned_loss=0.03991, over 7000.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2645, pruned_loss=0.04469, over 1427275.25 frames.], batch size: 16, lr: 6.41e-04
2022-05-14 12:43:08,056 INFO [train.py:812] (6/8) Epoch 12, batch 1850, loss[loss=0.1924, simple_loss=0.2661, pruned_loss=0.05935, over 7141.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2645, pruned_loss=0.04508, over 1426626.08 frames.], batch size: 26, lr: 6.40e-04
2022-05-14 12:44:16,866 INFO [train.py:812] (6/8) Epoch 12, batch 1900, loss[loss=0.181, simple_loss=0.2641, pruned_loss=0.04896, over 7425.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2639, pruned_loss=0.04493, over 1428909.54 frames.], batch size: 20, lr: 6.40e-04
2022-05-14 12:45:34,972 INFO [train.py:812] (6/8) Epoch 12, batch 1950, loss[loss=0.2228, simple_loss=0.2839, pruned_loss=0.08087, over 6972.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2647, pruned_loss=0.046, over 1427249.59 frames.], batch size: 16, lr: 6.40e-04
2022-05-14 12:46:34,729 INFO [train.py:812] (6/8) Epoch 12, batch 2000, loss[loss=0.1939, simple_loss=0.2743, pruned_loss=0.05678, over 6338.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2648, pruned_loss=0.04624, over 1425227.15 frames.], batch size: 37, lr: 6.39e-04
2022-05-14 12:47:34,773 INFO [train.py:812] (6/8) Epoch 12, batch 2050, loss[loss=0.1825, simple_loss=0.279, pruned_loss=0.04306, over 7396.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2643, pruned_loss=0.04596, over 1422734.76 frames.], batch size: 23, lr: 6.39e-04
2022-05-14 12:48:34,253 INFO [train.py:812] (6/8) Epoch 12, batch 2100, loss[loss=0.1717, simple_loss=0.2586, pruned_loss=0.04241, over 6823.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2647, pruned_loss=0.04601, over 1427153.58 frames.], batch size: 31, lr: 6.39e-04
2022-05-14 12:49:34,273 INFO [train.py:812] (6/8) Epoch 12, batch 2150, loss[loss=0.1785, simple_loss=0.2644, pruned_loss=0.04629, over 6807.00 frames.], tot_loss[loss=0.178, simple_loss=0.2644, pruned_loss=0.04587, over 1422359.85 frames.], batch size: 15, lr: 6.38e-04
2022-05-14 12:50:33,523 INFO [train.py:812] (6/8) Epoch 12, batch 2200, loss[loss=0.1599, simple_loss=0.263, pruned_loss=0.02842, over 7424.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2635, pruned_loss=0.0453, over 1426822.44 frames.], batch size: 20, lr: 6.38e-04
2022-05-14 12:51:31,633 INFO [train.py:812] (6/8) Epoch 12, batch 2250, loss[loss=0.1712, simple_loss=0.2599, pruned_loss=0.0413, over 7137.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2635, pruned_loss=0.04539, over 1426324.83 frames.], batch size: 17, lr: 6.38e-04
2022-05-14 12:52:29,476 INFO [train.py:812] (6/8) Epoch 12, batch 2300, loss[loss=0.1679, simple_loss=0.2461, pruned_loss=0.04484, over 7350.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2645, pruned_loss=0.04597, over 1424719.64 frames.], batch size: 19, lr: 6.38e-04
2022-05-14 12:53:28,566 INFO [train.py:812] (6/8) Epoch 12, batch 2350, loss[loss=0.1922, simple_loss=0.2802, pruned_loss=0.05215, over 7277.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2638, pruned_loss=0.04553, over 1426201.44 frames.], batch size: 24, lr: 6.37e-04
2022-05-14 12:54:27,662 INFO [train.py:812] (6/8) Epoch 12, batch 2400, loss[loss=0.1799, simple_loss=0.2737, pruned_loss=0.04307, over 7117.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2644, pruned_loss=0.04519, over 1428060.57 frames.], batch size: 21, lr: 6.37e-04
2022-05-14 12:55:26,379 INFO [train.py:812] (6/8) Epoch 12, batch 2450, loss[loss=0.1784, simple_loss=0.2679, pruned_loss=0.04439, over 7235.00 frames.], tot_loss[loss=0.177, simple_loss=0.2647, pruned_loss=0.04466, over 1426521.81 frames.], batch size: 20, lr: 6.37e-04
2022-05-14 12:56:25,372 INFO [train.py:812] (6/8) Epoch 12, batch 2500, loss[loss=0.2147, simple_loss=0.2765, pruned_loss=0.07641, over 7071.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2645, pruned_loss=0.04496, over 1424958.36 frames.], batch size: 18, lr: 6.36e-04
2022-05-14 12:57:24,989 INFO [train.py:812] (6/8) Epoch 12, batch 2550, loss[loss=0.1554, simple_loss=0.24, pruned_loss=0.0354, over 7273.00 frames.], tot_loss[loss=0.1777, simple_loss=0.265, pruned_loss=0.04521, over 1428003.71 frames.], batch size: 17, lr: 6.36e-04
2022-05-14 12:58:23,581 INFO [train.py:812] (6/8) Epoch 12, batch 2600, loss[loss=0.208, simple_loss=0.294, pruned_loss=0.06101, over 7262.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2654, pruned_loss=0.04561, over 1422303.40 frames.], batch size: 24, lr: 6.36e-04
2022-05-14 12:59:22,477 INFO [train.py:812] (6/8) Epoch 12, batch 2650, loss[loss=0.1538, simple_loss=0.2383, pruned_loss=0.03468, over 7252.00 frames.], tot_loss[loss=0.1787, simple_loss=0.266, pruned_loss=0.04571, over 1419141.14 frames.], batch size: 19, lr: 6.36e-04
2022-05-14 13:00:21,656 INFO [train.py:812] (6/8) Epoch 12, batch 2700, loss[loss=0.181, simple_loss=0.2648, pruned_loss=0.04855, over 7283.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2651, pruned_loss=0.04524, over 1422977.24 frames.], batch size: 25, lr: 6.35e-04
2022-05-14 13:01:21,328 INFO [train.py:812] (6/8) Epoch 12, batch 2750, loss[loss=0.1713, simple_loss=0.2705, pruned_loss=0.03609, over 7436.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2649, pruned_loss=0.04484, over 1426155.13 frames.], batch size: 20, lr: 6.35e-04
2022-05-14 13:02:20,438 INFO [train.py:812] (6/8) Epoch 12, batch 2800, loss[loss=0.2137, simple_loss=0.3046, pruned_loss=0.06143, over 7111.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2656, pruned_loss=0.04554, over 1426914.58 frames.], batch size: 21, lr: 6.35e-04
2022-05-14 13:03:19,820 INFO [train.py:812] (6/8) Epoch 12, batch 2850, loss[loss=0.157, simple_loss=0.2504, pruned_loss=0.03177, over 7318.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2642, pruned_loss=0.04503, over 1429002.34 frames.], batch size: 21, lr: 6.34e-04
2022-05-14 13:04:19,020 INFO [train.py:812] (6/8) Epoch 12, batch 2900, loss[loss=0.192, simple_loss=0.2835, pruned_loss=0.05026, over 7282.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2658, pruned_loss=0.04586, over 1424905.19 frames.], batch size: 24, lr: 6.34e-04
2022-05-14 13:05:18,680 INFO [train.py:812] (6/8) Epoch 12, batch 2950, loss[loss=0.1552, simple_loss=0.2468, pruned_loss=0.03184, over 7218.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2649, pruned_loss=0.04589, over 1420303.27 frames.], batch size: 21, lr: 6.34e-04
2022-05-14 13:06:17,610 INFO [train.py:812] (6/8) Epoch 12, batch 3000, loss[loss=0.2018, simple_loss=0.2837, pruned_loss=0.06, over 7316.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2645, pruned_loss=0.04563, over 1421615.63 frames.], batch size: 25, lr: 6.33e-04
2022-05-14 13:06:17,611 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 13:06:26,032 INFO [train.py:841] (6/8) Epoch 12, validation: loss=0.1553, simple_loss=0.2571, pruned_loss=0.02678, over 698248.00 frames.
2022-05-14 13:07:25,178 INFO [train.py:812] (6/8) Epoch 12, batch 3050, loss[loss=0.1963, simple_loss=0.2796, pruned_loss=0.05649, over 7377.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2654, pruned_loss=0.04577, over 1419783.51 frames.], batch size: 23, lr: 6.33e-04
2022-05-14 13:08:24,612 INFO [train.py:812] (6/8) Epoch 12, batch 3100, loss[loss=0.165, simple_loss=0.2555, pruned_loss=0.0373, over 7325.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2649, pruned_loss=0.04532, over 1421741.43 frames.], batch size: 20, lr: 6.33e-04
2022-05-14 13:09:23,907 INFO [train.py:812] (6/8) Epoch 12, batch 3150, loss[loss=0.1914, simple_loss=0.2792, pruned_loss=0.05184, over 7398.00 frames.], tot_loss[loss=0.1778, simple_loss=0.265, pruned_loss=0.0453, over 1423913.43 frames.], batch size: 23, lr: 6.33e-04
2022-05-14 13:10:22,802 INFO [train.py:812] (6/8) Epoch 12, batch 3200, loss[loss=0.1778, simple_loss=0.2746, pruned_loss=0.04049, over 7119.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2653, pruned_loss=0.04547, over 1423556.62 frames.], batch size: 21, lr: 6.32e-04
2022-05-14 13:11:22,019 INFO [train.py:812] (6/8) Epoch 12, batch 3250, loss[loss=0.1783, simple_loss=0.2779, pruned_loss=0.03933, over 7424.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2652, pruned_loss=0.04509, over 1425022.25 frames.], batch size: 21, lr: 6.32e-04
2022-05-14 13:12:21,130 INFO [train.py:812] (6/8) Epoch 12, batch 3300, loss[loss=0.1502, simple_loss=0.2274, pruned_loss=0.03651, over 6993.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2656, pruned_loss=0.04531, over 1425283.79 frames.], batch size: 16, lr: 6.32e-04
2022-05-14 13:13:18,559 INFO [train.py:812] (6/8) Epoch 12, batch 3350, loss[loss=0.1658, simple_loss=0.2523, pruned_loss=0.03965, over 7277.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2662, pruned_loss=0.04566, over 1425680.26 frames.], batch size: 18, lr: 6.31e-04
2022-05-14 13:14:17,042 INFO [train.py:812] (6/8) Epoch 12, batch 3400, loss[loss=0.2224, simple_loss=0.2928, pruned_loss=0.07597, over 6507.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2658, pruned_loss=0.0458, over 1421010.96 frames.], batch size: 38, lr: 6.31e-04
2022-05-14 13:15:16,602 INFO [train.py:812] (6/8) Epoch 12, batch 3450, loss[loss=0.1916, simple_loss=0.2735, pruned_loss=0.05484, over 7117.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2652, pruned_loss=0.04576, over 1418022.79 frames.], batch size: 21, lr: 6.31e-04
2022-05-14 13:16:15,033 INFO [train.py:812] (6/8) Epoch 12, batch 3500, loss[loss=0.1981, simple_loss=0.2872, pruned_loss=0.05449, over 7315.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2647, pruned_loss=0.04517, over 1424080.83 frames.], batch size: 21, lr: 6.31e-04
2022-05-14 13:17:13,779 INFO [train.py:812] (6/8) Epoch 12, batch 3550, loss[loss=0.1477, simple_loss=0.2333, pruned_loss=0.03106, over 7001.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2648, pruned_loss=0.04525, over 1422797.41 frames.], batch size: 16, lr: 6.30e-04
2022-05-14 13:18:12,633 INFO [train.py:812] (6/8) Epoch 12, batch 3600, loss[loss=0.1777, simple_loss=0.2698, pruned_loss=0.04276, over 7227.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2654, pruned_loss=0.04543, over 1424574.98 frames.], batch size: 20, lr: 6.30e-04
2022-05-14 13:19:11,484 INFO [train.py:812] (6/8) Epoch 12, batch 3650, loss[loss=0.2032, simple_loss=0.2865, pruned_loss=0.05994, over 7424.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2663, pruned_loss=0.04568, over 1424177.95 frames.], batch size: 20, lr: 6.30e-04
2022-05-14 13:20:08,361 INFO [train.py:812] (6/8) Epoch 12, batch 3700, loss[loss=0.1966, simple_loss=0.2826, pruned_loss=0.05534, over 6782.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2655, pruned_loss=0.04549, over 1421328.42 frames.], batch size: 31, lr: 6.29e-04
2022-05-14 13:21:06,294 INFO [train.py:812] (6/8) Epoch 12, batch 3750, loss[loss=0.1757, simple_loss=0.2667, pruned_loss=0.04239, over 7365.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2645, pruned_loss=0.04504, over 1425356.61 frames.], batch size: 23, lr: 6.29e-04
2022-05-14 13:22:05,722 INFO [train.py:812] (6/8) Epoch 12, batch 3800, loss[loss=0.1841, simple_loss=0.273, pruned_loss=0.04758, over 7184.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2638, pruned_loss=0.04435, over 1428011.98 frames.], batch size: 26, lr: 6.29e-04
2022-05-14 13:23:04,549 INFO [train.py:812] (6/8) Epoch 12, batch 3850, loss[loss=0.173, simple_loss=0.2696, pruned_loss=0.0382, over 7115.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2644, pruned_loss=0.04471, over 1428430.55 frames.], batch size: 21, lr: 6.29e-04
2022-05-14 13:24:03,550 INFO [train.py:812] (6/8) Epoch 12, batch 3900, loss[loss=0.1535, simple_loss=0.2446, pruned_loss=0.0312, over 7432.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2648, pruned_loss=0.04512, over 1429106.69 frames.], batch size: 20, lr: 6.28e-04
2022-05-14 13:25:02,812 INFO [train.py:812] (6/8) Epoch 12, batch 3950, loss[loss=0.183, simple_loss=0.2811, pruned_loss=0.04251, over 7234.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2644, pruned_loss=0.04527, over 1431142.52 frames.], batch size: 20, lr: 6.28e-04
2022-05-14 13:26:01,760 INFO [train.py:812] (6/8) Epoch 12, batch 4000, loss[loss=0.1973, simple_loss=0.2848, pruned_loss=0.05492, over 7421.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2648, pruned_loss=0.04526, over 1425888.31 frames.], batch size: 21, lr: 6.28e-04
2022-05-14 13:27:01,243 INFO [train.py:812] (6/8) Epoch 12, batch 4050, loss[loss=0.1778, simple_loss=0.2673, pruned_loss=0.04411, over 7417.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2641, pruned_loss=0.04486, over 1424515.45 frames.], batch size: 20, lr: 6.27e-04
2022-05-14 13:28:00,404 INFO [train.py:812] (6/8) Epoch 12, batch 4100, loss[loss=0.169, simple_loss=0.2623, pruned_loss=0.03785, over 7328.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2641, pruned_loss=0.04501, over 1421897.88 frames.], batch size: 20, lr: 6.27e-04
2022-05-14 13:28:59,936 INFO [train.py:812] (6/8) Epoch 12, batch 4150, loss[loss=0.1564, simple_loss=0.2504, pruned_loss=0.03118, over 7229.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2648, pruned_loss=0.04583, over 1422530.60 frames.], batch size: 20, lr: 6.27e-04
2022-05-14 13:29:59,298 INFO [train.py:812] (6/8) Epoch 12, batch 4200, loss[loss=0.1529, simple_loss=0.2431, pruned_loss=0.03135, over 7333.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2649, pruned_loss=0.04598, over 1421717.80 frames.], batch size: 22, lr: 6.27e-04
2022-05-14 13:30:59,190 INFO [train.py:812] (6/8) Epoch 12, batch 4250, loss[loss=0.1726, simple_loss=0.2516, pruned_loss=0.04684, over 7428.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2641, pruned_loss=0.04587, over 1424618.24 frames.], batch size: 18, lr: 6.26e-04
2022-05-14 13:31:58,499 INFO [train.py:812] (6/8) Epoch 12, batch 4300, loss[loss=0.1722, simple_loss=0.2681, pruned_loss=0.03814, over 7234.00 frames.], tot_loss[loss=0.1767, simple_loss=0.263, pruned_loss=0.04522, over 1418064.52 frames.], batch size: 20, lr: 6.26e-04
2022-05-14 13:32:57,472 INFO [train.py:812] (6/8) Epoch 12, batch 4350, loss[loss=0.2055, simple_loss=0.2885, pruned_loss=0.06122, over 7209.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2618, pruned_loss=0.0452, over 1419605.25 frames.], batch size: 22, lr: 6.26e-04
2022-05-14 13:33:56,607 INFO [train.py:812] (6/8) Epoch 12, batch 4400, loss[loss=0.1719, simple_loss=0.2553, pruned_loss=0.04431, over 7316.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2615, pruned_loss=0.04501, over 1417767.32 frames.], batch size: 21, lr: 6.25e-04
2022-05-14 13:34:56,723 INFO [train.py:812] (6/8) Epoch 12, batch 4450, loss[loss=0.1982, simple_loss=0.2776, pruned_loss=0.0594, over 6285.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2612, pruned_loss=0.04552, over 1405682.76 frames.], batch size: 37, lr: 6.25e-04
2022-05-14 13:35:55,762 INFO [train.py:812] (6/8) Epoch 12, batch 4500, loss[loss=0.1704, simple_loss=0.2614, pruned_loss=0.03969, over 6577.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2613, pruned_loss=0.04599, over 1389418.55 frames.], batch size: 37, lr: 6.25e-04
2022-05-14 13:36:54,586 INFO [train.py:812] (6/8) Epoch 12, batch 4550, loss[loss=0.2055, simple_loss=0.278, pruned_loss=0.06651, over 4751.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2634, pruned_loss=0.04759, over 1348775.19 frames.], batch size: 52, lr: 6.25e-04
2022-05-14 13:38:08,557 INFO [train.py:812] (6/8) Epoch 13, batch 0, loss[loss=0.1692, simple_loss=0.2631, pruned_loss=0.03769, over 7157.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2631, pruned_loss=0.03769, over 7157.00 frames.], batch size: 20, lr: 6.03e-04
2022-05-14 13:39:08,086 INFO [train.py:812] (6/8) Epoch 13, batch 50, loss[loss=0.168, simple_loss=0.2601, pruned_loss=0.03799, over 7236.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2649, pruned_loss=0.04502, over 318213.32 frames.], batch size: 20, lr: 6.03e-04
2022-05-14 13:40:06,204 INFO [train.py:812] (6/8) Epoch 13, batch 100, loss[loss=0.1923, simple_loss=0.2749, pruned_loss=0.05485, over 7172.00 frames.], tot_loss[loss=0.176, simple_loss=0.2638, pruned_loss=0.04411, over 564657.48 frames.], batch size: 23, lr: 6.03e-04
2022-05-14 13:41:05,028 INFO [train.py:812] (6/8) Epoch 13, batch 150, loss[loss=0.1656, simple_loss=0.2504, pruned_loss=0.04044, over 7143.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2644, pruned_loss=0.04392, over 753840.63 frames.], batch size: 20, lr: 6.03e-04
2022-05-14 13:42:04,240 INFO [train.py:812] (6/8) Epoch 13, batch 200, loss[loss=0.1823, simple_loss=0.2786, pruned_loss=0.04298, over 7150.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2647, pruned_loss=0.04403, over 900671.17 frames.], batch size: 20, lr: 6.02e-04
2022-05-14 13:43:03,744 INFO [train.py:812] (6/8) Epoch 13, batch 250, loss[loss=0.1457, simple_loss=0.2327, pruned_loss=0.02934, over 6850.00 frames.], tot_loss[loss=0.1771, simple_loss=0.265, pruned_loss=0.04461, over 1014309.23 frames.], batch size: 15, lr: 6.02e-04
2022-05-14 13:44:02,533 INFO [train.py:812] (6/8) Epoch 13, batch 300, loss[loss=0.185, simple_loss=0.283, pruned_loss=0.04351, over 7137.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2652, pruned_loss=0.04459, over 1104320.55 frames.], batch size: 20, lr: 6.02e-04
2022-05-14 13:45:01,963 INFO [train.py:812] (6/8) Epoch 13, batch 350, loss[loss=0.1929, simple_loss=0.2769, pruned_loss=0.05448, over 7023.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2658, pruned_loss=0.04451, over 1176549.56 frames.], batch size: 28, lr: 6.01e-04
2022-05-14 13:46:00,668 INFO [train.py:812] (6/8) Epoch 13, batch 400, loss[loss=0.1538, simple_loss=0.2411, pruned_loss=0.03324, over 7359.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2652, pruned_loss=0.04449, over 1233489.45 frames.], batch size: 19, lr: 6.01e-04
2022-05-14 13:46:57,914 INFO [train.py:812] (6/8) Epoch 13, batch 450, loss[loss=0.1557, simple_loss=0.2536, pruned_loss=0.02895, over 7325.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2643, pruned_loss=0.04429, over 1276999.74 frames.], batch size: 21, lr: 6.01e-04
2022-05-14 13:47:55,561 INFO [train.py:812] (6/8) Epoch 13, batch 500, loss[loss=0.1988, simple_loss=0.2818, pruned_loss=0.05789, over 6438.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2624, pruned_loss=0.04398, over 1311239.57 frames.], batch size: 38, lr: 6.01e-04
2022-05-14 13:48:55,169 INFO [train.py:812] (6/8) Epoch 13, batch 550, loss[loss=0.2155, simple_loss=0.3012, pruned_loss=0.06489, over 7391.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2629, pruned_loss=0.04432, over 1333808.61 frames.], batch size: 23, lr: 6.00e-04
2022-05-14 13:49:53,977 INFO [train.py:812] (6/8) Epoch 13, batch 600, loss[loss=0.1486, simple_loss=0.2308, pruned_loss=0.03317, over 6782.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2617, pruned_loss=0.04429, over 1347689.04 frames.], batch size: 15, lr: 6.00e-04
2022-05-14 13:50:53,017 INFO [train.py:812] (6/8) Epoch 13, batch 650, loss[loss=0.1778, simple_loss=0.2631, pruned_loss=0.04621, over 7267.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2622, pruned_loss=0.04419, over 1366603.65 frames.], batch size: 18, lr: 6.00e-04
2022-05-14 13:51:52,327 INFO [train.py:812] (6/8) Epoch 13, batch 700, loss[loss=0.1756, simple_loss=0.2552, pruned_loss=0.04798, over 6825.00 frames.], tot_loss[loss=0.176, simple_loss=0.2632, pruned_loss=0.04439, over 1383348.95 frames.], batch size: 15, lr: 6.00e-04
2022-05-14 13:52:51,788 INFO [train.py:812] (6/8) Epoch 13, batch 750, loss[loss=0.2189, simple_loss=0.2957, pruned_loss=0.07105, over 7192.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2639, pruned_loss=0.04423, over 1395678.59 frames.], batch size: 23, lr: 5.99e-04
2022-05-14 13:53:50,418 INFO [train.py:812] (6/8) Epoch 13, batch 800, loss[loss=0.1817, simple_loss=0.2719, pruned_loss=0.04573, over 7202.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2636, pruned_loss=0.04397, over 1405350.58 frames.], batch size: 22, lr: 5.99e-04
2022-05-14 13:54:49,220 INFO [train.py:812] (6/8) Epoch 13, batch 850, loss[loss=0.154, simple_loss=0.2227, pruned_loss=0.0427, over 7134.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2643, pruned_loss=0.04434, over 1412168.20 frames.], batch size: 17, lr: 5.99e-04
2022-05-14 13:55:48,212 INFO [train.py:812] (6/8) Epoch 13, batch 900, loss[loss=0.184, simple_loss=0.2778, pruned_loss=0.04505, over 7336.00 frames.], tot_loss[loss=0.175, simple_loss=0.2626, pruned_loss=0.04372, over 1414922.33 frames.], batch size: 20, lr: 5.99e-04
2022-05-14 13:56:53,069 INFO [train.py:812] (6/8) Epoch 13, batch 950, loss[loss=0.1835, simple_loss=0.2701, pruned_loss=0.04842, over 7219.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2631, pruned_loss=0.04392, over 1414968.09 frames.], batch size: 26, lr: 5.98e-04
2022-05-14 13:57:52,301 INFO [train.py:812] (6/8) Epoch 13, batch 1000, loss[loss=0.178, simple_loss=0.265, pruned_loss=0.04552, over 6507.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2641, pruned_loss=0.04448, over 1415864.70 frames.], batch size: 38, lr: 5.98e-04
2022-05-14 13:58:51,884 INFO [train.py:812] (6/8) Epoch 13, batch 1050, loss[loss=0.1897, simple_loss=0.2691, pruned_loss=0.05514, over 7252.00 frames.], tot_loss[loss=0.1756, simple_loss=0.263, pruned_loss=0.04409, over 1416641.14 frames.], batch size: 19, lr: 5.98e-04
2022-05-14 13:59:49,639 INFO [train.py:812] (6/8) Epoch 13, batch 1100, loss[loss=0.1736, simple_loss=0.273, pruned_loss=0.03709, over 7390.00 frames.], tot_loss[loss=0.176, simple_loss=0.2634, pruned_loss=0.04433, over 1422567.37 frames.], batch size: 23, lr: 5.97e-04
2022-05-14 14:00:49,269 INFO [train.py:812] (6/8) Epoch 13, batch 1150, loss[loss=0.1789, simple_loss=0.2729, pruned_loss=0.0425, over 7324.00 frames.], tot_loss[loss=0.176, simple_loss=0.2636, pruned_loss=0.04424, over 1426085.90 frames.], batch size: 20, lr: 5.97e-04
2022-05-14 14:01:48,651 INFO [train.py:812] (6/8) Epoch 13, batch 1200, loss[loss=0.2222, simple_loss=0.2987, pruned_loss=0.07284, over 4629.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2639, pruned_loss=0.04441, over 1422280.38 frames.], batch size: 53, lr: 5.97e-04
2022-05-14 14:02:48,271 INFO [train.py:812] (6/8) Epoch 13, batch 1250, loss[loss=0.1693, simple_loss=0.266, pruned_loss=0.0363, over 7153.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2637, pruned_loss=0.04438, over 1419221.51 frames.], batch size: 19, lr: 5.97e-04
2022-05-14 14:03:47,351 INFO [train.py:812] (6/8) Epoch 13, batch 1300, loss[loss=0.138, simple_loss=0.2243, pruned_loss=0.02587, over 7071.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2628, pruned_loss=0.0441, over 1419181.14 frames.], batch size: 18, lr: 5.96e-04
2022-05-14 14:04:46,591 INFO [train.py:812] (6/8) Epoch 13, batch 1350, loss[loss=0.2029, simple_loss=0.2771, pruned_loss=0.06439, over 4840.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2635, pruned_loss=0.04457, over 1416190.63 frames.], batch size: 54, lr: 5.96e-04
2022-05-14 14:05:45,569 INFO [train.py:812] (6/8) Epoch 13, batch 1400, loss[loss=0.198, simple_loss=0.2781, pruned_loss=0.05899, over 7303.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2637, pruned_loss=0.04476, over 1415224.49 frames.], batch size: 25, lr: 5.96e-04
2022-05-14 14:06:43,989 INFO [train.py:812] (6/8) Epoch 13, batch 1450, loss[loss=0.157, simple_loss=0.2517, pruned_loss=0.03113, over 7319.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2632, pruned_loss=0.04428, over 1413779.26 frames.], batch size: 21, lr: 5.96e-04
2022-05-14 14:07:42,624 INFO [train.py:812] (6/8) Epoch 13, batch 1500, loss[loss=0.1977, simple_loss=0.2899, pruned_loss=0.0527, over 7209.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2635, pruned_loss=0.04447, over 1417443.13 frames.], batch size: 23, lr: 5.95e-04
2022-05-14 14:08:42,697 INFO [train.py:812] (6/8) Epoch 13, batch 1550, loss[loss=0.1939, simple_loss=0.293, pruned_loss=0.04734, over 7050.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2636, pruned_loss=0.04481, over 1419343.94 frames.], batch size: 28, lr: 5.95e-04
2022-05-14 14:09:41,293 INFO [train.py:812] (6/8) Epoch 13, batch 1600, loss[loss=0.1917, simple_loss=0.2907, pruned_loss=0.04635, over 7306.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2635, pruned_loss=0.04468, over 1418446.19 frames.], batch size: 25, lr: 5.95e-04
2022-05-14 14:10:39,366 INFO [train.py:812] (6/8) Epoch 13, batch 1650, loss[loss=0.2018, simple_loss=0.2839, pruned_loss=0.05988, over 7301.00 frames.], tot_loss[loss=0.1761, simple_loss=0.263, pruned_loss=0.0446, over 1421561.41 frames.], batch size: 24, lr: 5.95e-04
2022-05-14 14:11:36,477 INFO [train.py:812] (6/8) Epoch 13, batch 1700, loss[loss=0.131, simple_loss=0.2164, pruned_loss=0.02283, over 7124.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2626, pruned_loss=0.04402, over 1417671.08 frames.], batch size: 17, lr: 5.94e-04
2022-05-14 14:12:34,790 INFO [train.py:812] (6/8) Epoch 13, batch 1750, loss[loss=0.2193, simple_loss=0.2923, pruned_loss=0.07315, over 7146.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2612, pruned_loss=0.04327, over 1420982.31 frames.], batch size: 26, lr: 5.94e-04
2022-05-14 14:13:34,202 INFO [train.py:812] (6/8) Epoch 13, batch 1800, loss[loss=0.1532, simple_loss=0.2338, pruned_loss=0.03627, over 7012.00 frames.], tot_loss[loss=0.1737, simple_loss=0.261, pruned_loss=0.04323, over 1426723.34 frames.], batch size: 16, lr: 5.94e-04
2022-05-14 14:14:33,884 INFO [train.py:812] (6/8) Epoch 13, batch 1850, loss[loss=0.1883, simple_loss=0.2844, pruned_loss=0.04617, over 7324.00 frames.], tot_loss[loss=0.1736, simple_loss=0.261, pruned_loss=0.04309, over 1427250.31 frames.], batch size: 22, lr: 5.94e-04
2022-05-14 14:15:33,210 INFO [train.py:812] (6/8) Epoch 13, batch 1900, loss[loss=0.1542, simple_loss=0.2497, pruned_loss=0.02937, over 7229.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2619, pruned_loss=0.04326, over 1428441.10 frames.], batch size: 20, lr: 5.93e-04
2022-05-14 14:16:32,247 INFO [train.py:812] (6/8) Epoch 13, batch 1950, loss[loss=0.1606, simple_loss=0.238, pruned_loss=0.04162, over 7282.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2613, pruned_loss=0.04302, over 1428663.94 frames.], batch size: 17, lr: 5.93e-04
2022-05-14 14:17:31,545 INFO [train.py:812] (6/8) Epoch 13, batch 2000, loss[loss=0.1666, simple_loss=0.2452, pruned_loss=0.04394, over 7013.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2605, pruned_loss=0.04288, over 1427718.90 frames.], batch size: 16, lr: 5.93e-04
2022-05-14 14:18:40,147 INFO [train.py:812] (6/8) Epoch 13, batch 2050, loss[loss=0.1399, simple_loss=0.2292, pruned_loss=0.02527, over 7154.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2602, pruned_loss=0.04314, over 1421125.27 frames.], batch size: 19, lr: 5.93e-04
2022-05-14 14:19:39,738 INFO [train.py:812] (6/8) Epoch 13, batch 2100, loss[loss=0.1427, simple_loss=0.2376, pruned_loss=0.02387, over 7158.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2607, pruned_loss=0.0433, over 1421124.98 frames.], batch size: 19, lr: 5.92e-04
2022-05-14 14:20:39,445 INFO [train.py:812] (6/8) Epoch 13, batch 2150, loss[loss=0.165, simple_loss=0.2416, pruned_loss=0.04423, over 7285.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2614, pruned_loss=0.04356, over 1421407.11 frames.], batch size: 18, lr: 5.92e-04
2022-05-14 14:21:36,916 INFO [train.py:812] (6/8) Epoch 13, batch 2200, loss[loss=0.1655, simple_loss=0.2513, pruned_loss=0.03978, over 7333.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2615, pruned_loss=0.04359, over 1422181.61 frames.], batch size: 20, lr: 5.92e-04
2022-05-14 14:22:35,600 INFO [train.py:812] (6/8) Epoch 13, batch 2250, loss[loss=0.1668, simple_loss=0.2607, pruned_loss=0.03647, over 7087.00 frames.], tot_loss[loss=0.1737, simple_loss=0.261, pruned_loss=0.04317, over 1420773.87 frames.], batch size: 28, lr: 5.91e-04
2022-05-14 14:23:34,272 INFO [train.py:812] (6/8) Epoch 13, batch 2300, loss[loss=0.1633, simple_loss=0.2625, pruned_loss=0.03208, over 7118.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2616, pruned_loss=0.04348, over 1424280.65 frames.], batch size: 21, lr: 5.91e-04
2022-05-14 14:24:34,077 INFO [train.py:812] (6/8) Epoch 13, batch 2350, loss[loss=0.1695, simple_loss=0.2552, pruned_loss=0.04185, over 7157.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2624, pruned_loss=0.04375, over 1425396.56 frames.], batch size: 19, lr: 5.91e-04
2022-05-14 14:25:33,618 INFO [train.py:812] (6/8) Epoch 13, batch 2400, loss[loss=0.1638, simple_loss=0.2408, pruned_loss=0.04344, over 7133.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2622, pruned_loss=0.04344, over 1425486.98 frames.], batch size: 17, lr: 5.91e-04
2022-05-14 14:26:31,982 INFO [train.py:812] (6/8) Epoch 13, batch 2450, loss[loss=0.1727, simple_loss=0.2636, pruned_loss=0.04095, over 7215.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2622, pruned_loss=0.04344, over 1424617.56 frames.], batch size: 21, lr: 5.90e-04
2022-05-14 14:27:30,767 INFO [train.py:812] (6/8) Epoch 13, batch 2500, loss[loss=0.1598, simple_loss=0.2445, pruned_loss=0.0376, over 7290.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2632, pruned_loss=0.0441, over 1425724.81 frames.], batch size: 18, lr: 5.90e-04
2022-05-14 14:28:30,447 INFO [train.py:812] (6/8) Epoch 13, batch 2550, loss[loss=0.1529, simple_loss=0.2352, pruned_loss=0.03534, over 7318.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2638, pruned_loss=0.04443, over 1428155.38 frames.], batch size: 16, lr: 5.90e-04
2022-05-14 14:29:29,647 INFO [train.py:812] (6/8) Epoch 13, batch 2600, loss[loss=0.1763, simple_loss=0.2543, pruned_loss=0.04911, over 7187.00 frames.], tot_loss[loss=0.176, simple_loss=0.2633, pruned_loss=0.04434, over 1425001.56 frames.], batch size: 16, lr: 5.90e-04
2022-05-14 14:30:29,030 INFO [train.py:812] (6/8) Epoch 13, batch 2650, loss[loss=0.1594, simple_loss=0.2328, pruned_loss=0.04307, over 6994.00 frames.], tot_loss[loss=0.1764, simple_loss=0.264, pruned_loss=0.04436, over 1422453.47 frames.], batch size: 16, lr: 5.89e-04
2022-05-14 14:31:27,716 INFO [train.py:812] (6/8) Epoch 13, batch 2700, loss[loss=0.1375, simple_loss=0.2204, pruned_loss=0.02731, over 7001.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2636, pruned_loss=0.04453, over 1423869.75 frames.], batch size: 16, lr: 5.89e-04
2022-05-14 14:32:27,076 INFO [train.py:812] (6/8) Epoch 13, batch 2750, loss[loss=0.1937, simple_loss=0.2746, pruned_loss=0.05642, over 7114.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2633, pruned_loss=0.04422, over 1421303.72 frames.], batch size: 21, lr: 5.89e-04
2022-05-14 14:33:24,888 INFO [train.py:812] (6/8) Epoch 13, batch 2800, loss[loss=0.145, simple_loss=0.2358, pruned_loss=0.02707, over 7140.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2643, pruned_loss=0.04422, over 1420867.46 frames.], batch size: 17, lr: 5.89e-04
2022-05-14 14:34:24,976 INFO [train.py:812] (6/8) Epoch 13, batch 2850, loss[loss=0.1818, simple_loss=0.2661, pruned_loss=0.0488, over 7394.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2638, pruned_loss=0.04372, over 1427053.54 frames.], batch size: 23, lr: 5.88e-04
2022-05-14 14:35:22,595 INFO [train.py:812] (6/8) Epoch 13, batch 2900, loss[loss=0.1527, simple_loss=0.2373, pruned_loss=0.0341, over 7355.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2648, pruned_loss=0.04417, over 1425009.30 frames.], batch size: 19, lr: 5.88e-04
2022-05-14 14:36:22,036 INFO [train.py:812] (6/8) Epoch 13, batch 2950, loss[loss=0.1694, simple_loss=0.2644, pruned_loss=0.03714, over 7118.00 frames.], tot_loss[loss=0.1759, simple_loss=0.264, pruned_loss=0.04389, over 1426315.61 frames.], batch size: 21, lr: 5.88e-04
2022-05-14 14:37:20,738 INFO [train.py:812] (6/8) Epoch 13, batch 3000, loss[loss=0.1304, simple_loss=0.2125, pruned_loss=0.02412, over 7266.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2629, pruned_loss=0.0437, over 1427168.19 frames.], batch size: 17, lr: 5.88e-04
2022-05-14 14:37:20,739 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 14:37:28,227 INFO [train.py:841] (6/8) Epoch 13, validation: loss=0.1549, simple_loss=0.2559, pruned_loss=0.02694, over 698248.00 frames.
2022-05-14 14:38:28,333 INFO [train.py:812] (6/8) Epoch 13, batch 3050, loss[loss=0.1415, simple_loss=0.2228, pruned_loss=0.03017, over 7156.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2615, pruned_loss=0.04317, over 1427641.06 frames.], batch size: 17, lr: 5.87e-04
2022-05-14 14:39:27,860 INFO [train.py:812] (6/8) Epoch 13, batch 3100, loss[loss=0.1666, simple_loss=0.2599, pruned_loss=0.03665, over 7102.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2605, pruned_loss=0.043, over 1427015.41 frames.], batch size: 21, lr: 5.87e-04
2022-05-14 14:40:36,460 INFO [train.py:812] (6/8) Epoch 13, batch 3150, loss[loss=0.236, simple_loss=0.3185, pruned_loss=0.07676, over 7269.00 frames.], tot_loss[loss=0.1746, simple_loss=0.262, pruned_loss=0.0436, over 1424804.56 frames.], batch size: 25, lr: 5.87e-04
2022-05-14 14:41:35,469 INFO [train.py:812] (6/8) Epoch 13, batch 3200, loss[loss=0.2564, simple_loss=0.3169, pruned_loss=0.09799, over 5025.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2634, pruned_loss=0.04404, over 1426014.39 frames.], batch size: 52, lr: 5.87e-04
2022-05-14 14:42:44,512 INFO [train.py:812] (6/8) Epoch 13, batch 3250, loss[loss=0.1516, simple_loss=0.233, pruned_loss=0.0351, over 7288.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2621, pruned_loss=0.04337, over 1428540.57 frames.], batch size: 17, lr: 5.86e-04
2022-05-14 14:43:53,105 INFO [train.py:812] (6/8) Epoch 13, batch 3300, loss[loss=0.1567, simple_loss=0.247, pruned_loss=0.03322, over 7329.00 frames.], tot_loss[loss=0.175, simple_loss=0.2625, pruned_loss=0.04376, over 1428272.50 frames.], batch size: 20, lr: 5.86e-04
2022-05-14 14:44:51,614 INFO [train.py:812] (6/8) Epoch 13, batch 3350, loss[loss=0.1533, simple_loss=0.232, pruned_loss=0.03727, over 7007.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2626, pruned_loss=0.04388, over 1421695.27 frames.], batch size: 16, lr: 5.86e-04
2022-05-14 14:46:18,933 INFO [train.py:812] (6/8) Epoch 13, batch 3400, loss[loss=0.1729, simple_loss=0.264, pruned_loss=0.04087, over 7374.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2637, pruned_loss=0.04468, over 1425394.16 frames.], batch size: 23, lr: 5.86e-04
2022-05-14 14:47:27,799 INFO [train.py:812] (6/8) Epoch 13, batch 3450, loss[loss=0.1435, simple_loss=0.2224, pruned_loss=0.03228, over 7427.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2635, pruned_loss=0.04478, over 1414458.14 frames.], batch size: 18, lr: 5.85e-04
2022-05-14 14:48:26,514 INFO [train.py:812] (6/8) Epoch 13, batch 3500, loss[loss=0.2032, simple_loss=0.2901, pruned_loss=0.05814, over 6674.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2642, pruned_loss=0.04484, over 1416275.02 frames.], batch size: 31, lr: 5.85e-04
2022-05-14 14:49:26,048 INFO [train.py:812] (6/8) Epoch 13, batch 3550, loss[loss=0.1647, simple_loss=0.24, pruned_loss=0.04466, over 6991.00 frames.], tot_loss[loss=0.1758, simple_loss=0.263, pruned_loss=0.04432, over 1421344.28 frames.], batch size: 16, lr: 5.85e-04
2022-05-14 14:50:24,025 INFO [train.py:812] (6/8) Epoch 13, batch 3600, loss[loss=0.151, simple_loss=0.2381, pruned_loss=0.03194, over 7300.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2628, pruned_loss=0.04401, over 1421586.64 frames.], batch size: 18, lr: 5.85e-04
2022-05-14 14:51:22,137 INFO [train.py:812] (6/8) Epoch 13, batch 3650, loss[loss=0.1706, simple_loss=0.2632, pruned_loss=0.03902, over 7409.00 frames.], tot_loss[loss=0.1754, simple_loss=0.263, pruned_loss=0.04387, over 1424297.54 frames.], batch size: 21, lr: 5.84e-04
2022-05-14 14:52:20,930 INFO [train.py:812] (6/8) Epoch 13, batch 3700, loss[loss=0.1712, simple_loss=0.2615, pruned_loss=0.04046, over 7268.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2621, pruned_loss=0.04402, over 1424905.34 frames.], batch size: 19, lr: 5.84e-04
2022-05-14 14:53:20,354 INFO [train.py:812] (6/8) Epoch 13, batch 3750, loss[loss=0.1772, simple_loss=0.2836, pruned_loss=0.03545, over 7414.00 frames.], tot_loss[loss=0.174, simple_loss=0.2617, pruned_loss=0.04318, over 1424848.09 frames.], batch size: 21, lr: 5.84e-04
2022-05-14 14:54:19,197 INFO [train.py:812] (6/8) Epoch 13, batch 3800, loss[loss=0.1783, simple_loss=0.2714, pruned_loss=0.04261, over 7006.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2623, pruned_loss=0.04351, over 1428468.84 frames.], batch size: 28, lr: 5.84e-04
2022-05-14 14:55:18,397 INFO [train.py:812] (6/8) Epoch 13, batch 3850, loss[loss=0.2127, simple_loss=0.2982, pruned_loss=0.06359, over 7196.00 frames.], tot_loss[loss=0.176, simple_loss=0.264, pruned_loss=0.04401, over 1425984.33 frames.], batch size: 22, lr: 5.83e-04
2022-05-14 14:56:17,005 INFO [train.py:812] (6/8) Epoch 13, batch 3900, loss[loss=0.1836, simple_loss=0.2731, pruned_loss=0.04702, over 7289.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2624, pruned_loss=0.04327, over 1424436.43 frames.], batch size: 24, lr: 5.83e-04
2022-05-14 14:57:16,835 INFO [train.py:812] (6/8) Epoch 13, batch 3950, loss[loss=0.2246, simple_loss=0.3036, pruned_loss=0.0728, over 7204.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2631, pruned_loss=0.04382, over 1422955.30 frames.], batch size: 23, lr: 5.83e-04
2022-05-14 14:58:15,084 INFO [train.py:812] (6/8) Epoch 13, batch 4000, loss[loss=0.139, simple_loss=0.2179, pruned_loss=0.03009, over 7134.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2632, pruned_loss=0.044, over 1422570.25 frames.], batch size: 17, lr: 5.83e-04
2022-05-14 14:59:14,640 INFO [train.py:812] (6/8) Epoch 13, batch 4050, loss[loss=0.1634, simple_loss=0.2468, pruned_loss=0.03999, over 7237.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2631, pruned_loss=0.04405, over 1423805.85 frames.], batch size: 20, lr: 5.82e-04
2022-05-14 15:00:14,087 INFO [train.py:812] (6/8) Epoch 13, batch 4100, loss[loss=0.1871, simple_loss=0.2768, pruned_loss=0.04868, over 7147.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2622, pruned_loss=0.04385, over 1423845.06 frames.], batch size: 20, lr: 5.82e-04
2022-05-14 15:01:13,275 INFO [train.py:812] (6/8) Epoch 13, batch 4150, loss[loss=0.1492, simple_loss=0.2373, pruned_loss=0.03058, over 7437.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2627, pruned_loss=0.04412, over 1418612.89 frames.], batch size: 20, lr: 5.82e-04
2022-05-14 15:02:11,348 INFO [train.py:812] (6/8) Epoch 13, batch 4200, loss[loss=0.1831, simple_loss=0.268, pruned_loss=0.04914, over 7153.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2613, pruned_loss=0.04387, over 1420896.12 frames.], batch size: 20, lr: 5.82e-04
2022-05-14 15:03:10,135 INFO [train.py:812] (6/8) Epoch 13, batch 4250, loss[loss=0.1697, simple_loss=0.2583, pruned_loss=0.04053, over 7140.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2612, pruned_loss=0.04388, over 1417954.73 frames.], batch size: 26, lr: 5.81e-04
2022-05-14 15:04:08,204 INFO [train.py:812] (6/8) Epoch 13, batch 4300, loss[loss=0.1766, simple_loss=0.2567, pruned_loss=0.04826, over 7415.00 frames.], tot_loss[loss=0.1753, simple_loss=0.262, pruned_loss=0.04432, over 1415569.23 frames.], batch size: 20, lr: 5.81e-04
2022-05-14 15:05:06,781 INFO [train.py:812] (6/8) Epoch 13, batch 4350, loss[loss=0.146, simple_loss=0.2324, pruned_loss=0.02986, over 7017.00 frames.], tot_loss[loss=0.1755, simple_loss=0.262, pruned_loss=0.04448, over 1410506.30 frames.], batch size: 16, lr: 5.81e-04
2022-05-14 15:06:06,058 INFO [train.py:812] (6/8) Epoch 13, batch 4400, loss[loss=0.2529, simple_loss=0.3228, pruned_loss=0.09146, over 5012.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2612, pruned_loss=0.04414, over 1409881.85 frames.], batch size: 52, lr: 5.81e-04
2022-05-14 15:07:04,953 INFO [train.py:812] (6/8) Epoch 13, batch 4450, loss[loss=0.2078, simple_loss=0.3005, pruned_loss=0.05756, over 7291.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2608, pruned_loss=0.04382, over 1407831.76 frames.], batch size: 24, lr: 5.81e-04
2022-05-14 15:08:03,281 INFO [train.py:812] (6/8) Epoch 13, batch 4500, loss[loss=0.1447, simple_loss=0.2463, pruned_loss=0.02157, over 7414.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2613, pruned_loss=0.04416, over 1388676.77 frames.], batch size: 21, lr: 5.80e-04
2022-05-14 15:09:01,467 INFO [train.py:812] (6/8) Epoch 13, batch 4550, loss[loss=0.2486, simple_loss=0.3204, pruned_loss=0.08836, over 5444.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2644, pruned_loss=0.04613, over 1353215.75 frames.], batch size: 52, lr: 5.80e-04
2022-05-14 15:10:14,235 INFO [train.py:812] (6/8) Epoch 14, batch 0, loss[loss=0.1961, simple_loss=0.2834, pruned_loss=0.05442, over 7369.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2834, pruned_loss=0.05442, over 7369.00 frames.], batch size: 23, lr: 5.61e-04
2022-05-14 15:11:14,110 INFO [train.py:812] (6/8) Epoch 14, batch 50, loss[loss=0.2101, simple_loss=0.2921, pruned_loss=0.06407, over 7126.00 frames.], tot_loss[loss=0.174, simple_loss=0.2595, pruned_loss=0.04426, over 322252.25 frames.], batch size: 21, lr: 5.61e-04
2022-05-14 15:12:13,755 INFO [train.py:812] (6/8) Epoch 14, batch 100, loss[loss=0.2153, simple_loss=0.3004, pruned_loss=0.06511, over 7147.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2614, pruned_loss=0.04385, over 572067.67 frames.], batch size: 20, lr: 5.61e-04
2022-05-14 15:13:13,207 INFO [train.py:812] (6/8) Epoch 14, batch 150, loss[loss=0.1497, simple_loss=0.2302, pruned_loss=0.03463, over 6985.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2602, pruned_loss=0.04357, over 762385.20 frames.], batch size: 16, lr: 5.61e-04
2022-05-14 15:14:11,622 INFO [train.py:812] (6/8) Epoch 14, batch 200, loss[loss=0.1886, simple_loss=0.2781, pruned_loss=0.04955, over 7189.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2596, pruned_loss=0.04292, over 909581.35 frames.], batch size: 22, lr: 5.60e-04
2022-05-14 15:15:09,359 INFO [train.py:812] (6/8) Epoch 14, batch 250, loss[loss=0.1779, simple_loss=0.2813, pruned_loss=0.03724, over 7195.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2602, pruned_loss=0.04263, over 1025330.09 frames.], batch size: 22, lr: 5.60e-04
2022-05-14 15:16:07,608 INFO [train.py:812] (6/8) Epoch 14, batch 300, loss[loss=0.1658, simple_loss=0.2532, pruned_loss=0.03915, over 7409.00 frames.], tot_loss[loss=0.174, simple_loss=0.2619, pruned_loss=0.04302, over 1111797.37 frames.], batch size: 21, lr: 5.60e-04
2022-05-14 15:17:06,830 INFO [train.py:812] (6/8) Epoch 14, batch 350, loss[loss=0.1857, simple_loss=0.2761, pruned_loss=0.04762, over 7431.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2613, pruned_loss=0.04375, over 1179788.00 frames.], batch size: 20, lr: 5.60e-04
2022-05-14 15:18:11,722 INFO [train.py:812] (6/8) Epoch 14, batch 400, loss[loss=0.163, simple_loss=0.2534, pruned_loss=0.03624, over 6998.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2611, pruned_loss=0.04375, over 1230089.36 frames.], batch size: 28, lr: 5.59e-04
2022-05-14 15:19:10,173 INFO [train.py:812] (6/8) Epoch 14, batch 450, loss[loss=0.2035, simple_loss=0.2991, pruned_loss=0.05399, over 6393.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2624, pruned_loss=0.044, over 1272085.23 frames.], batch size: 37, lr: 5.59e-04
2022-05-14 15:20:09,620 INFO [train.py:812] (6/8) Epoch 14, batch 500, loss[loss=0.2059, simple_loss=0.2868, pruned_loss=0.06254, over 7066.00 frames.], tot_loss[loss=0.1749, simple_loss=0.262, pruned_loss=0.04391, over 1299152.66 frames.], batch size: 28, lr: 5.59e-04
2022-05-14 15:21:08,843 INFO [train.py:812] (6/8) Epoch 14, batch 550, loss[loss=0.1793, simple_loss=0.2693, pruned_loss=0.04469, over 6366.00 frames.], tot_loss[loss=0.174, simple_loss=0.2614, pruned_loss=0.04331, over 1325425.74 frames.], batch size: 38, lr: 5.59e-04
2022-05-14 15:22:08,391 INFO [train.py:812] (6/8) Epoch 14, batch 600, loss[loss=0.1932, simple_loss=0.2858, pruned_loss=0.05027, over 7324.00 frames.], tot_loss[loss=0.174, simple_loss=0.2614, pruned_loss=0.04335, over 1347736.23 frames.], batch size: 21, lr: 5.59e-04
2022-05-14 15:23:07,044 INFO [train.py:812] (6/8) Epoch 14, batch 650, loss[loss=0.1617, simple_loss=0.2457, pruned_loss=0.03884, over 7065.00 frames.], tot_loss[loss=0.174, simple_loss=0.2616, pruned_loss=0.04318, over 1360424.07 frames.], batch size: 18, lr: 5.58e-04
2022-05-14 15:24:06,569 INFO [train.py:812] (6/8) Epoch 14, batch 700, loss[loss=0.157, simple_loss=0.2372, pruned_loss=0.03839, over 7275.00 frames.], tot_loss[loss=0.1731, simple_loss=0.261, pruned_loss=0.04261, over 1375888.13 frames.], batch size: 18, lr: 5.58e-04
2022-05-14 15:25:05,448 INFO [train.py:812] (6/8) Epoch 14, batch 750, loss[loss=0.1832, simple_loss=0.2717, pruned_loss=0.04732, over 7202.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2606, pruned_loss=0.04222, over 1382549.35 frames.], batch size: 23, lr: 5.58e-04
2022-05-14 15:26:04,476 INFO [train.py:812] (6/8) Epoch 14, batch 800, loss[loss=0.1623, simple_loss=0.2586, pruned_loss=0.03299, over 7335.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2611, pruned_loss=0.04238, over 1391835.01 frames.], batch size: 25, lr: 5.58e-04
2022-05-14 15:27:03,673 INFO [train.py:812] (6/8) Epoch 14, batch 850, loss[loss=0.1613, simple_loss=0.2484, pruned_loss=0.03705, over 7225.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2607, pruned_loss=0.04219, over 1400689.30 frames.], batch size: 21, lr: 5.57e-04
2022-05-14 15:28:02,928 INFO [train.py:812] (6/8) Epoch 14, batch 900, loss[loss=0.1579, simple_loss=0.2431, pruned_loss=0.03635, over 7162.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2617, pruned_loss=0.04273, over 1404228.38 frames.], batch size: 18, lr: 5.57e-04
2022-05-14 15:29:01,742 INFO [train.py:812] (6/8) Epoch 14, batch 950, loss[loss=0.1708, simple_loss=0.2659, pruned_loss=0.03782, over 7222.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2619, pruned_loss=0.04297, over 1404836.86 frames.], batch size: 21, lr: 5.57e-04
2022-05-14 15:30:01,413 INFO [train.py:812] (6/8) Epoch 14, batch 1000, loss[loss=0.1571, simple_loss=0.2511, pruned_loss=0.03158, over 7213.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2614, pruned_loss=0.04272, over 1411658.56 frames.], batch size: 22, lr: 5.57e-04
2022-05-14 15:31:00,189 INFO [train.py:812] (6/8) Epoch 14, batch 1050, loss[loss=0.1882, simple_loss=0.2786, pruned_loss=0.0489, over 7411.00 frames.], tot_loss[loss=0.1734, simple_loss=0.261, pruned_loss=0.0429, over 1411582.19 frames.], batch size: 21, lr: 5.56e-04
2022-05-14 15:31:57,367 INFO [train.py:812] (6/8) Epoch 14, batch 1100, loss[loss=0.1872, simple_loss=0.2723, pruned_loss=0.0511, over 6623.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2604, pruned_loss=0.04258, over 1410519.65 frames.], batch size: 31, lr: 5.56e-04
2022-05-14 15:32:55,047 INFO [train.py:812] (6/8) Epoch 14, batch 1150, loss[loss=0.184, simple_loss=0.2754, pruned_loss=0.0463, over 7333.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2615, pruned_loss=0.0428, over 1410679.95 frames.], batch size: 22, lr: 5.56e-04
2022-05-14 15:33:54,478 INFO [train.py:812] (6/8) Epoch 14, batch 1200, loss[loss=0.2108, simple_loss=0.2888, pruned_loss=0.0664, over 5278.00 frames.], tot_loss[loss=0.174, simple_loss=0.2615, pruned_loss=0.0432, over 1410677.37 frames.], batch size: 53, lr: 5.56e-04
2022-05-14 15:34:52,757 INFO [train.py:812] (6/8) Epoch 14, batch 1250, loss[loss=0.1711, simple_loss=0.2558, pruned_loss=0.04319, over 7434.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2621, pruned_loss=0.04316, over 1414739.57 frames.], batch size: 20, lr: 5.56e-04
2022-05-14 15:35:51,082 INFO [train.py:812] (6/8) Epoch 14, batch 1300, loss[loss=0.1654, simple_loss=0.256, pruned_loss=0.03745, over 7262.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2619, pruned_loss=0.04276, over 1418479.52 frames.], batch size: 19, lr: 5.55e-04
2022-05-14 15:36:49,470 INFO [train.py:812] (6/8) Epoch 14, batch 1350, loss[loss=0.1428, simple_loss=0.2279, pruned_loss=0.02886, over 7278.00 frames.], tot_loss[loss=0.1729, simple_loss=0.261, pruned_loss=0.04244, over 1422270.70 frames.], batch size: 18, lr: 5.55e-04
2022-05-14 15:37:48,230 INFO [train.py:812] (6/8) Epoch 14, batch 1400, loss[loss=0.1504, simple_loss=0.2363, pruned_loss=0.03226, over 7162.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2609, pruned_loss=0.04237, over 1418200.59 frames.], batch size: 18, lr: 5.55e-04
2022-05-14 15:38:45,047 INFO [train.py:812] (6/8) Epoch 14, batch 1450, loss[loss=0.1459, simple_loss=0.2267, pruned_loss=0.03253, over 7284.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2611, pruned_loss=0.04252, over 1421482.57 frames.], batch size: 17, lr: 5.55e-04
2022-05-14 15:39:43,876 INFO [train.py:812] (6/8) Epoch 14, batch 1500, loss[loss=0.1724, simple_loss=0.2569, pruned_loss=0.04398, over 7296.00 frames.], tot_loss[loss=0.1721, simple_loss=0.26, pruned_loss=0.04205, over 1423012.28 frames.], batch size: 17, lr: 5.54e-04
2022-05-14 15:40:41,997 INFO [train.py:812] (6/8) Epoch 14, batch 1550, loss[loss=0.1852, simple_loss=0.2797, pruned_loss=0.04538, over 6285.00 frames.], tot_loss[loss=0.173, simple_loss=0.261, pruned_loss=0.04244, over 1417596.98 frames.], batch size: 37, lr: 5.54e-04
2022-05-14 15:41:40,147 INFO [train.py:812] (6/8) Epoch 14, batch 1600, loss[loss=0.1803, simple_loss=0.2697, pruned_loss=0.04542, over 7414.00 frames.], tot_loss[loss=0.173, simple_loss=0.2612, pruned_loss=0.04238, over 1416731.53 frames.], batch size: 21, lr: 5.54e-04
2022-05-14 15:42:38,935 INFO [train.py:812] (6/8) Epoch 14, batch 1650, loss[loss=0.1828, simple_loss=0.2666, pruned_loss=0.04945, over 7229.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2614, pruned_loss=0.04217, over 1418933.87 frames.], batch size: 20, lr: 5.54e-04
2022-05-14 15:43:38,146 INFO [train.py:812] (6/8) Epoch 14, batch 1700, loss[loss=0.1716, simple_loss=0.2619, pruned_loss=0.0407, over 6423.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2608, pruned_loss=0.04169, over 1418441.41 frames.], batch size: 38, lr: 5.54e-04
2022-05-14 15:44:37,155 INFO [train.py:812] (6/8) Epoch 14, batch 1750, loss[loss=0.1535, simple_loss=0.2414, pruned_loss=0.03284, over 7277.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2596, pruned_loss=0.04126, over 1421731.20 frames.], batch size: 17, lr: 5.53e-04
2022-05-14 15:45:37,339 INFO [train.py:812] (6/8) Epoch 14, batch 1800, loss[loss=0.1516, simple_loss=0.2458, pruned_loss=0.02865, over 7141.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2595, pruned_loss=0.04158, over 1426083.32 frames.], batch size: 20, lr: 5.53e-04
2022-05-14 15:46:35,095 INFO [train.py:812] (6/8) Epoch 14, batch 1850, loss[loss=0.1978, simple_loss=0.2769, pruned_loss=0.05938, over 7288.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2603, pruned_loss=0.0422, over 1425909.90 frames.], batch size: 25, lr: 5.53e-04
2022-05-14 15:47:33,732 INFO [train.py:812] (6/8) Epoch 14, batch 1900, loss[loss=0.166, simple_loss=0.2497, pruned_loss=0.04115, over 6265.00 frames.], tot_loss[loss=0.1733, simple_loss=0.261, pruned_loss=0.04278, over 1421051.33 frames.], batch size: 37, lr: 5.53e-04
2022-05-14 15:48:32,641 INFO [train.py:812] (6/8) Epoch 14, batch 1950, loss[loss=0.1604, simple_loss=0.2571, pruned_loss=0.03181, over 7261.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2619, pruned_loss=0.04278, over 1421977.65 frames.], batch size: 19, lr: 5.52e-04
2022-05-14 15:49:32,358 INFO [train.py:812] (6/8) Epoch 14, batch 2000, loss[loss=0.1698, simple_loss=0.2687, pruned_loss=0.03548, over 7338.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2617, pruned_loss=0.04261, over 1423218.03 frames.], batch size: 22, lr: 5.52e-04
2022-05-14 15:50:31,428 INFO [train.py:812] (6/8) Epoch 14, batch 2050, loss[loss=0.1742, simple_loss=0.2747, pruned_loss=0.0368, over 7385.00 frames.], tot_loss[loss=0.1727, simple_loss=0.261, pruned_loss=0.04226, over 1424496.17 frames.], batch size: 23, lr: 5.52e-04
2022-05-14 15:51:31,159 INFO [train.py:812] (6/8) Epoch 14, batch 2100, loss[loss=0.1778, simple_loss=0.2668, pruned_loss=0.04445, over 7237.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2615, pruned_loss=0.04235, over 1424322.61 frames.], batch size: 20, lr: 5.52e-04
2022-05-14 15:52:30,503 INFO [train.py:812] (6/8) Epoch 14, batch 2150, loss[loss=0.1653, simple_loss=0.2622, pruned_loss=0.03416, over 7171.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2606, pruned_loss=0.04162, over 1426942.69 frames.], batch size: 26, lr: 5.52e-04
2022-05-14 15:53:29,903 INFO [train.py:812] (6/8) Epoch 14, batch 2200, loss[loss=0.1456, simple_loss=0.235, pruned_loss=0.02813, over 7432.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2613, pruned_loss=0.04227, over 1425797.84 frames.], batch size: 20, lr: 5.51e-04
2022-05-14 15:54:28,295 INFO [train.py:812] (6/8) Epoch 14, batch 2250, loss[loss=0.1387, simple_loss=0.2319, pruned_loss=0.02275, over 7236.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2604, pruned_loss=0.04206, over 1427362.28 frames.], batch size: 20, lr: 5.51e-04
2022-05-14 15:55:26,905 INFO [train.py:812] (6/8) Epoch 14, batch 2300, loss[loss=0.1764, simple_loss=0.2667, pruned_loss=0.04304, over 7047.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2596, pruned_loss=0.04174, over 1427651.44 frames.], batch size: 28, lr: 5.51e-04
2022-05-14 15:56:25,016 INFO [train.py:812] (6/8) Epoch 14, batch 2350, loss[loss=0.2477, simple_loss=0.3067, pruned_loss=0.09439, over 5018.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2602, pruned_loss=0.04198, over 1427501.64 frames.], batch size: 53, lr: 5.51e-04
2022-05-14 15:57:24,252 INFO [train.py:812] (6/8) Epoch 14, batch 2400, loss[loss=0.1555, simple_loss=0.2374, pruned_loss=0.0368, over 7269.00 frames.], tot_loss[loss=0.171, simple_loss=0.2594, pruned_loss=0.04135, over 1429160.88 frames.], batch size: 17, lr: 5.50e-04
2022-05-14 15:58:23,295 INFO [train.py:812] (6/8) Epoch 14, batch 2450, loss[loss=0.2091, simple_loss=0.288, pruned_loss=0.06509, over 6838.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2605, pruned_loss=0.04164, over 1431673.03 frames.], batch size: 31, lr: 5.50e-04
2022-05-14 15:59:21,607 INFO [train.py:812] (6/8) Epoch 14, batch 2500, loss[loss=0.1467, simple_loss=0.2308, pruned_loss=0.03134, over 7286.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2615, pruned_loss=0.04249, over 1427827.94 frames.], batch size: 17, lr: 5.50e-04
2022-05-14 16:00:20,034 INFO [train.py:812] (6/8) Epoch 14, batch 2550, loss[loss=0.1768, simple_loss=0.268, pruned_loss=0.04282, over 7287.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2619, pruned_loss=0.04282, over 1423052.57 frames.], batch size: 25, lr: 5.50e-04
2022-05-14 16:01:19,229 INFO [train.py:812] (6/8) Epoch 14, batch 2600, loss[loss=0.179, simple_loss=0.279, pruned_loss=0.03948, over 7414.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2616, pruned_loss=0.04255, over 1418991.96 frames.], batch size: 21, lr: 5.50e-04
2022-05-14 16:02:16,359 INFO [train.py:812] (6/8) Epoch 14, batch 2650, loss[loss=0.1721, simple_loss=0.2693, pruned_loss=0.03741, over 7118.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2613, pruned_loss=0.04263, over 1418405.70 frames.], batch size: 21, lr: 5.49e-04
2022-05-14 16:03:15,390 INFO [train.py:812] (6/8) Epoch 14, batch 2700, loss[loss=0.1644, simple_loss=0.2423, pruned_loss=0.0433, over 6998.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2607, pruned_loss=0.04231, over 1423429.92 frames.], batch size: 16, lr: 5.49e-04
2022-05-14 16:04:13,425 INFO [train.py:812] (6/8) Epoch 14, batch 2750, loss[loss=0.1663, simple_loss=0.2494, pruned_loss=0.04166, over 7292.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2603, pruned_loss=0.04201, over 1428579.93 frames.], batch size: 24, lr: 5.49e-04
2022-05-14 16:05:11,593 INFO [train.py:812] (6/8) Epoch 14, batch 2800, loss[loss=0.1657, simple_loss=0.2526, pruned_loss=0.03944, over 7120.00 frames.], tot_loss[loss=0.172, simple_loss=0.2601, pruned_loss=0.04193, over 1427024.88 frames.], batch size: 17, lr: 5.49e-04
2022-05-14 16:06:10,722 INFO [train.py:812] (6/8) Epoch 14, batch 2850, loss[loss=0.1559, simple_loss=0.2506, pruned_loss=0.03061, over 7409.00 frames.], tot_loss[loss=0.172, simple_loss=0.2598, pruned_loss=0.04211, over 1428272.84 frames.], batch size: 21, lr: 5.48e-04
2022-05-14 16:07:10,260 INFO [train.py:812] (6/8) Epoch 14, batch 2900, loss[loss=0.1795, simple_loss=0.2824, pruned_loss=0.03831, over 7115.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2597, pruned_loss=0.04174, over 1429139.30 frames.], batch size: 21, lr: 5.48e-04
2022-05-14 16:08:08,952 INFO [train.py:812] (6/8) Epoch 14, batch 2950, loss[loss=0.2193, simple_loss=0.3031, pruned_loss=0.06779, over 7189.00 frames.], tot_loss[loss=0.1716, simple_loss=0.26, pruned_loss=0.04156, over 1430480.65 frames.], batch size: 23, lr: 5.48e-04
2022-05-14 16:09:07,591 INFO [train.py:812] (6/8) Epoch 14, batch 3000, loss[loss=0.1917, simple_loss=0.2854, pruned_loss=0.04899, over 7320.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2589, pruned_loss=0.04106, over 1431272.19 frames.], batch size: 24, lr: 5.48e-04
2022-05-14 16:09:07,592 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 16:09:15,055 INFO [train.py:841] (6/8) Epoch 14, validation: loss=0.1549, simple_loss=0.2556, pruned_loss=0.02713, over 698248.00 frames.
2022-05-14 16:10:14,207 INFO [train.py:812] (6/8) Epoch 14, batch 3050, loss[loss=0.1608, simple_loss=0.2395, pruned_loss=0.04109, over 7258.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2602, pruned_loss=0.04176, over 1431296.36 frames.], batch size: 17, lr: 5.48e-04
2022-05-14 16:11:13,772 INFO [train.py:812] (6/8) Epoch 14, batch 3100, loss[loss=0.186, simple_loss=0.2782, pruned_loss=0.04692, over 7185.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2604, pruned_loss=0.04201, over 1431355.82 frames.], batch size: 23, lr: 5.47e-04
2022-05-14 16:12:13,434 INFO [train.py:812] (6/8) Epoch 14, batch 3150, loss[loss=0.1983, simple_loss=0.2777, pruned_loss=0.05946, over 5129.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2595, pruned_loss=0.04172, over 1430073.88 frames.], batch size: 52, lr: 5.47e-04
2022-05-14 16:13:13,725 INFO [train.py:812] (6/8) Epoch 14, batch 3200, loss[loss=0.1677, simple_loss=0.262, pruned_loss=0.03672, over 7337.00 frames.], tot_loss[loss=0.172, simple_loss=0.26, pruned_loss=0.04205, over 1429282.83 frames.], batch size: 22, lr: 5.47e-04
2022-05-14 16:14:11,601 INFO [train.py:812] (6/8) Epoch 14, batch 3250, loss[loss=0.2199, simple_loss=0.3026, pruned_loss=0.06854, over 7208.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2604, pruned_loss=0.0424, over 1426336.44 frames.], batch size: 26, lr: 5.47e-04
2022-05-14 16:15:10,532 INFO [train.py:812] (6/8) Epoch 14, batch 3300, loss[loss=0.1701, simple_loss=0.2526, pruned_loss=0.04382, over 7161.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2604, pruned_loss=0.04249, over 1424024.96 frames.], batch size: 18, lr: 5.46e-04
2022-05-14 16:16:09,535 INFO [train.py:812] (6/8) Epoch 14, batch 3350, loss[loss=0.161, simple_loss=0.2384, pruned_loss=0.04181, over 7416.00 frames.], tot_loss[loss=0.173, simple_loss=0.2605, pruned_loss=0.04276, over 1425674.13 frames.], batch size: 18, lr: 5.46e-04
2022-05-14 16:17:08,400 INFO [train.py:812] (6/8) Epoch 14, batch 3400, loss[loss=0.2202, simple_loss=0.3128, pruned_loss=0.06383, over 7158.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2607, pruned_loss=0.04244, over 1425994.19 frames.], batch size: 18, lr: 5.46e-04
2022-05-14 16:18:17,674 INFO [train.py:812] (6/8) Epoch 14, batch 3450, loss[loss=0.1685, simple_loss=0.2611, pruned_loss=0.0379, over 7117.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2613, pruned_loss=0.04274, over 1424687.98 frames.], batch size: 21, lr: 5.46e-04
2022-05-14 16:19:16,726 INFO [train.py:812] (6/8) Epoch 14, batch 3500, loss[loss=0.196, simple_loss=0.2861, pruned_loss=0.05299, over 7336.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2601, pruned_loss=0.0423, over 1426425.70 frames.], batch size: 22, lr: 5.46e-04
2022-05-14 16:20:15,521 INFO [train.py:812] (6/8) Epoch 14, batch 3550, loss[loss=0.1703, simple_loss=0.2568, pruned_loss=0.04191, over 7321.00 frames.], tot_loss[loss=0.1721, simple_loss=0.26, pruned_loss=0.04205, over 1426439.97 frames.], batch size: 21, lr: 5.45e-04
2022-05-14 16:21:14,199 INFO [train.py:812] (6/8) Epoch 14, batch 3600, loss[loss=0.1466, simple_loss=0.2323, pruned_loss=0.03039, over 7350.00 frames.], tot_loss[loss=0.1702, simple_loss=0.258, pruned_loss=0.04118, over 1429999.30 frames.], batch size: 19, lr: 5.45e-04
2022-05-14 16:22:13,056 INFO [train.py:812] (6/8) Epoch 14, batch 3650, loss[loss=0.1693, simple_loss=0.2438, pruned_loss=0.04741, over 7230.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2585, pruned_loss=0.04156, over 1429608.33 frames.], batch size: 20, lr: 5.45e-04
2022-05-14 16:23:12,488 INFO [train.py:812] (6/8) Epoch 14, batch 3700, loss[loss=0.2235, simple_loss=0.3073, pruned_loss=0.06983, over 7312.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2599, pruned_loss=0.04228, over 1421708.76 frames.], batch size: 24, lr: 5.45e-04
2022-05-14 16:24:11,525 INFO [train.py:812] (6/8) Epoch 14, batch 3750, loss[loss=0.2288, simple_loss=0.2845, pruned_loss=0.08654, over 4807.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2607, pruned_loss=0.04278, over 1419819.80 frames.], batch size: 52, lr: 5.45e-04
2022-05-14 16:25:11,065 INFO [train.py:812] (6/8) Epoch 14, batch 3800, loss[loss=0.1418, simple_loss=0.2199, pruned_loss=0.03188, over 7018.00 frames.], tot_loss[loss=0.173, simple_loss=0.2606, pruned_loss=0.04274, over 1419104.09 frames.], batch size: 16, lr: 5.44e-04
2022-05-14 16:26:09,830 INFO [train.py:812] (6/8) Epoch 14, batch 3850, loss[loss=0.1713, simple_loss=0.2548, pruned_loss=0.04397, over 7189.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2602, pruned_loss=0.04218, over 1420297.08 frames.], batch size: 22, lr: 5.44e-04
2022-05-14 16:27:08,440 INFO [train.py:812] (6/8) Epoch 14, batch 3900, loss[loss=0.1777, simple_loss=0.2725, pruned_loss=0.04149, over 7318.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2606, pruned_loss=0.04218, over 1421864.53 frames.], batch size: 21, lr: 5.44e-04
2022-05-14 16:28:07,620 INFO [train.py:812] (6/8) Epoch 14, batch 3950, loss[loss=0.2092, simple_loss=0.2917, pruned_loss=0.06329, over 5191.00 frames.], tot_loss[loss=0.1722, simple_loss=0.26, pruned_loss=0.04219, over 1420290.76 frames.], batch size: 53, lr: 5.44e-04
2022-05-14 16:29:06,383 INFO [train.py:812] (6/8) Epoch 14, batch 4000, loss[loss=0.1731, simple_loss=0.2659, pruned_loss=0.04013, over 7334.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2611, pruned_loss=0.04204, over 1422133.37 frames.], batch size: 22, lr: 5.43e-04
2022-05-14 16:30:03,970 INFO [train.py:812] (6/8) Epoch 14, batch 4050, loss[loss=0.1555, simple_loss=0.2321, pruned_loss=0.03945, over 7167.00 frames.], tot_loss[loss=0.172, simple_loss=0.2601, pruned_loss=0.04192, over 1423901.03 frames.], batch size: 16, lr: 5.43e-04
2022-05-14 16:31:03,501 INFO [train.py:812] (6/8) Epoch 14, batch 4100, loss[loss=0.1846, simple_loss=0.2691, pruned_loss=0.05002, over 6787.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2598, pruned_loss=0.04245, over 1421162.33 frames.], batch size: 31, lr: 5.43e-04
2022-05-14 16:32:02,260 INFO [train.py:812] (6/8) Epoch 14, batch 4150, loss[loss=0.1613, simple_loss=0.2521, pruned_loss=0.03519, over 7218.00 frames.], tot_loss[loss=0.172, simple_loss=0.2594, pruned_loss=0.0423, over 1420022.38 frames.], batch size: 21, lr: 5.43e-04
2022-05-14 16:33:01,726 INFO [train.py:812] (6/8) Epoch 14, batch 4200, loss[loss=0.152, simple_loss=0.2292, pruned_loss=0.03739, over 7297.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2588, pruned_loss=0.04213, over 1421839.29 frames.], batch size: 17, lr: 5.43e-04
2022-05-14 16:34:00,232 INFO [train.py:812] (6/8) Epoch 14, batch 4250, loss[loss=0.1921, simple_loss=0.2805, pruned_loss=0.05181, over 6440.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2592, pruned_loss=0.04219, over 1415597.02 frames.], batch size: 38, lr: 5.42e-04
2022-05-14 16:34:59,092 INFO [train.py:812] (6/8) Epoch 14, batch 4300, loss[loss=0.1767, simple_loss=0.272, pruned_loss=0.04068, over 7213.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2597, pruned_loss=0.04234, over 1411411.89 frames.], batch size: 21, lr: 5.42e-04
2022-05-14 16:35:56,907 INFO [train.py:812] (6/8) Epoch 14, batch 4350, loss[loss=0.1305, simple_loss=0.2128, pruned_loss=0.02412, over 7212.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2602, pruned_loss=0.04271, over 1408276.66 frames.], batch size: 16, lr: 5.42e-04
2022-05-14 16:37:01,666 INFO [train.py:812] (6/8) Epoch 14, batch 4400, loss[loss=0.1401, simple_loss=0.2334, pruned_loss=0.02338, over 7147.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2599, pruned_loss=0.04251, over 1401442.28 frames.], batch size: 20, lr: 5.42e-04
2022-05-14 16:38:00,484 INFO [train.py:812] (6/8) Epoch 14, batch 4450, loss[loss=0.2087, simple_loss=0.2852, pruned_loss=0.06611, over 4868.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2609, pruned_loss=0.04321, over 1391890.40 frames.], batch size: 52, lr: 5.42e-04
2022-05-14 16:38:59,697 INFO [train.py:812] (6/8) Epoch 14, batch 4500, loss[loss=0.2054, simple_loss=0.2839, pruned_loss=0.06344, over 4963.00 frames.], tot_loss[loss=0.1748, simple_loss=0.262, pruned_loss=0.04386, over 1375916.46 frames.], batch size: 54, lr: 5.41e-04
2022-05-14 16:40:07,832 INFO [train.py:812] (6/8) Epoch 14, batch 4550, loss[loss=0.1707, simple_loss=0.259, pruned_loss=0.04117, over 6739.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2615, pruned_loss=0.04347, over 1365716.98 frames.], batch size: 31, lr: 5.41e-04
2022-05-14 16:41:16,669 INFO [train.py:812] (6/8) Epoch 15, batch 0, loss[loss=0.1702, simple_loss=0.2665, pruned_loss=0.03698, over 7091.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2665, pruned_loss=0.03698, over 7091.00 frames.], batch size: 28, lr: 5.25e-04
2022-05-14 16:42:15,487 INFO [train.py:812] (6/8) Epoch 15, batch 50, loss[loss=0.1799, simple_loss=0.2667, pruned_loss=0.04653, over 4879.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2586, pruned_loss=0.04035, over 321499.61 frames.], batch size: 52, lr: 5.24e-04
2022-05-14 16:43:15,423 INFO [train.py:812] (6/8) Epoch 15, batch 100, loss[loss=0.1806, simple_loss=0.2763, pruned_loss=0.0424, over 7163.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2605, pruned_loss=0.04107, over 568443.55 frames.], batch size: 18, lr: 5.24e-04
2022-05-14 16:44:31,109 INFO [train.py:812] (6/8) Epoch 15, batch 150, loss[loss=0.1542, simple_loss=0.2567, pruned_loss=0.02585, over 7114.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2619, pruned_loss=0.04135, over 758969.82 frames.], batch size: 21, lr: 5.24e-04
2022-05-14 16:45:30,990 INFO [train.py:812] (6/8) Epoch 15, batch 200, loss[loss=0.1618, simple_loss=0.2671, pruned_loss=0.02829, over 7324.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2628, pruned_loss=0.042, over 903055.43 frames.], batch size: 20, lr: 5.24e-04
2022-05-14 16:46:49,173 INFO [train.py:812] (6/8) Epoch 15, batch 250, loss[loss=0.1998, simple_loss=0.2943, pruned_loss=0.0527, over 6114.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2613, pruned_loss=0.04131, over 1019494.18 frames.], batch size: 37, lr: 5.24e-04
2022-05-14 16:48:07,507 INFO [train.py:812] (6/8) Epoch 15, batch 300, loss[loss=0.1422, simple_loss=0.2244, pruned_loss=0.02996, over 7116.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2589, pruned_loss=0.04039, over 1109282.41 frames.], batch size: 17, lr: 5.23e-04
2022-05-14 16:49:06,743 INFO [train.py:812] (6/8) Epoch 15, batch 350, loss[loss=0.1426, simple_loss=0.2165, pruned_loss=0.0344, over 6788.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2584, pruned_loss=0.0404, over 1171847.60 frames.], batch size: 15, lr: 5.23e-04
2022-05-14 16:50:06,792 INFO [train.py:812] (6/8) Epoch 15, batch 400, loss[loss=0.1837, simple_loss=0.2807, pruned_loss=0.04337, over 7140.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2581, pruned_loss=0.04057, over 1227481.68 frames.], batch size: 20, lr: 5.23e-04
2022-05-14 16:51:05,898 INFO [train.py:812] (6/8) Epoch 15, batch 450, loss[loss=0.1643, simple_loss=0.2483, pruned_loss=0.0401, over 7153.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2581, pruned_loss=0.04061, over 1271932.91 frames.], batch size: 19, lr: 5.23e-04
2022-05-14 16:52:05,400 INFO [train.py:812] (6/8) Epoch 15, batch 500, loss[loss=0.1714, simple_loss=0.2634, pruned_loss=0.03966, over 7424.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2588, pruned_loss=0.04122, over 1304732.16 frames.], batch size: 20, lr: 5.23e-04
2022-05-14 16:53:04,828 INFO [train.py:812] (6/8) Epoch 15, batch 550, loss[loss=0.1545, simple_loss=0.246, pruned_loss=0.03145, over 7290.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2574, pruned_loss=0.04037, over 1333669.64 frames.], batch size: 18, lr: 5.22e-04
2022-05-14 16:54:04,586 INFO [train.py:812] (6/8) Epoch 15, batch 600, loss[loss=0.1795, simple_loss=0.2711, pruned_loss=0.0439, over 7236.00 frames.], tot_loss[loss=0.1687, simple_loss=0.257, pruned_loss=0.04021, over 1356079.02 frames.], batch size: 20, lr: 5.22e-04
2022-05-14 16:55:03,733 INFO [train.py:812] (6/8) Epoch 15, batch 650, loss[loss=0.1687, simple_loss=0.2631, pruned_loss=0.0372, over 7330.00 frames.], tot_loss[loss=0.1696, simple_loss=0.258, pruned_loss=0.04064, over 1370779.35 frames.], batch size: 22, lr: 5.22e-04
2022-05-14 16:56:03,049 INFO [train.py:812] (6/8) Epoch 15, batch 700, loss[loss=0.1387, simple_loss=0.2318, pruned_loss=0.02282, over 7323.00 frames.], tot_loss[loss=0.1706, simple_loss=0.259, pruned_loss=0.04108, over 1383215.64 frames.], batch size: 20, lr: 5.22e-04
2022-05-14 16:57:02,262 INFO [train.py:812] (6/8) Epoch 15, batch 750, loss[loss=0.2132, simple_loss=0.2997, pruned_loss=0.06331, over 7347.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2587, pruned_loss=0.04081, over 1391493.88 frames.], batch size: 22, lr: 5.22e-04
2022-05-14 16:58:01,676 INFO [train.py:812] (6/8) Epoch 15, batch 800, loss[loss=0.1772, simple_loss=0.2679, pruned_loss=0.04323, over 7338.00 frames.], tot_loss[loss=0.1703, simple_loss=0.259, pruned_loss=0.0408, over 1398895.32 frames.], batch size: 22, lr: 5.21e-04
2022-05-14 16:59:01,003 INFO [train.py:812] (6/8) Epoch 15, batch 850, loss[loss=0.1558, simple_loss=0.2427, pruned_loss=0.03446, over 7143.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2593, pruned_loss=0.04123, over 1401835.40 frames.], batch size: 17, lr: 5.21e-04
2022-05-14 17:00:00,547 INFO [train.py:812] (6/8) Epoch 15, batch 900, loss[loss=0.1758, simple_loss=0.2651, pruned_loss=0.04326, over 7261.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2587, pruned_loss=0.04098, over 1397080.96 frames.], batch size: 19, lr: 5.21e-04
2022-05-14 17:00:59,895 INFO [train.py:812] (6/8) Epoch 15, batch 950, loss[loss=0.1495, simple_loss=0.2463, pruned_loss=0.02632, over 7335.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2591, pruned_loss=0.04072, over 1406002.73 frames.], batch size: 22, lr: 5.21e-04
2022-05-14 17:01:59,715 INFO [train.py:812] (6/8) Epoch 15, batch 1000, loss[loss=0.1848, simple_loss=0.2727, pruned_loss=0.0485, over 7008.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2596, pruned_loss=0.04095, over 1407585.67 frames.], batch size: 28, lr: 5.21e-04
2022-05-14 17:02:57,925 INFO [train.py:812] (6/8) Epoch 15, batch 1050, loss[loss=0.141, simple_loss=0.2298, pruned_loss=0.02612, over 7276.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2586, pruned_loss=0.04059, over 1413550.09 frames.], batch size: 18, lr: 5.20e-04
2022-05-14 17:03:56,834 INFO [train.py:812] (6/8) Epoch 15, batch 1100, loss[loss=0.1426, simple_loss=0.232, pruned_loss=0.02666, over 7288.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2593, pruned_loss=0.04098, over 1418389.73 frames.], batch size: 17, lr: 5.20e-04
2022-05-14 17:04:54,411 INFO [train.py:812] (6/8) Epoch 15, batch 1150, loss[loss=0.1721, simple_loss=0.2622, pruned_loss=0.04096, over 7420.00 frames.], tot_loss[loss=0.1698, simple_loss=0.258, pruned_loss=0.04075, over 1422820.00 frames.], batch size: 21, lr: 5.20e-04
2022-05-14 17:05:54,086 INFO [train.py:812] (6/8) Epoch 15, batch 1200, loss[loss=0.1631, simple_loss=0.2522, pruned_loss=0.03701, over 7427.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2578, pruned_loss=0.04056, over 1424068.77 frames.], batch size: 20, lr: 5.20e-04
2022-05-14 17:06:52,041 INFO [train.py:812] (6/8) Epoch 15, batch 1250, loss[loss=0.1761, simple_loss=0.2573, pruned_loss=0.04744, over 7356.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2584, pruned_loss=0.04058, over 1427479.67 frames.], batch size: 19, lr: 5.20e-04
2022-05-14 17:07:51,293 INFO [train.py:812] (6/8) Epoch 15, batch 1300, loss[loss=0.1952, simple_loss=0.2825, pruned_loss=0.05398, over 6431.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2588, pruned_loss=0.04104, over 1420385.00 frames.], batch size: 38, lr: 5.19e-04
2022-05-14 17:08:51,299 INFO [train.py:812] (6/8) Epoch 15, batch 1350, loss[loss=0.1772, simple_loss=0.2583, pruned_loss=0.04804, over 6999.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2588, pruned_loss=0.04108, over 1421611.87 frames.], batch size: 16, lr: 5.19e-04
2022-05-14 17:09:50,520 INFO [train.py:812] (6/8) Epoch 15, batch 1400, loss[loss=0.1677, simple_loss=0.2578, pruned_loss=0.0388, over 7293.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2575, pruned_loss=0.04088, over 1421106.92 frames.], batch size: 24, lr: 5.19e-04
2022-05-14 17:10:49,143 INFO [train.py:812] (6/8) Epoch 15, batch 1450, loss[loss=0.1808, simple_loss=0.2762, pruned_loss=0.04264, over 7392.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2577, pruned_loss=0.0409, over 1418829.70 frames.], batch size: 23, lr: 5.19e-04
2022-05-14 17:11:46,397 INFO [train.py:812] (6/8) Epoch 15, batch 1500, loss[loss=0.1808, simple_loss=0.2666, pruned_loss=0.04753, over 7144.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2589, pruned_loss=0.04166, over 1412852.87 frames.], batch size: 20, lr: 5.19e-04
2022-05-14 17:12:45,417 INFO [train.py:812] (6/8) Epoch 15, batch 1550, loss[loss=0.1736, simple_loss=0.2635, pruned_loss=0.04182, over 7109.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2579, pruned_loss=0.04074, over 1417129.15 frames.], batch size: 21, lr: 5.18e-04
2022-05-14 17:13:44,528 INFO [train.py:812] (6/8) Epoch 15, batch 1600, loss[loss=0.1806, simple_loss=0.2785, pruned_loss=0.0414, over 7415.00 frames.], tot_loss[loss=0.1699, simple_loss=0.258, pruned_loss=0.04086, over 1419295.04 frames.], batch size: 21, lr: 5.18e-04
2022-05-14 17:14:43,368 INFO [train.py:812] (6/8) Epoch 15, batch 1650, loss[loss=0.1944, simple_loss=0.2815, pruned_loss=0.05359, over 7225.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2578, pruned_loss=0.04062, over 1424376.22 frames.], batch size: 23, lr: 5.18e-04
2022-05-14 17:15:42,320 INFO [train.py:812] (6/8) Epoch 15, batch 1700, loss[loss=0.1884, simple_loss=0.2733, pruned_loss=0.05177, over 7294.00 frames.], tot_loss[loss=0.1687, simple_loss=0.257, pruned_loss=0.04023, over 1428006.32 frames.], batch size: 25, lr: 5.18e-04
2022-05-14 17:16:41,928 INFO [train.py:812] (6/8) Epoch 15, batch 1750, loss[loss=0.1732, simple_loss=0.2774, pruned_loss=0.03444, over 7094.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2578, pruned_loss=0.04041, over 1431316.99 frames.], batch size: 28, lr: 5.18e-04
2022-05-14 17:17:41,426 INFO [train.py:812] (6/8) Epoch 15, batch 1800, loss[loss=0.1417, simple_loss=0.2297, pruned_loss=0.0268, over 7289.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2578, pruned_loss=0.04047, over 1428174.62 frames.], batch size: 17, lr: 5.17e-04
2022-05-14 17:18:41,090 INFO [train.py:812] (6/8) Epoch 15, batch 1850, loss[loss=0.1428, simple_loss=0.2284, pruned_loss=0.02863, over 7159.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2574, pruned_loss=0.03997, over 1432324.10 frames.], batch size: 18, lr: 5.17e-04
2022-05-14 17:19:41,054 INFO [train.py:812] (6/8) Epoch 15, batch 1900, loss[loss=0.1984, simple_loss=0.2816, pruned_loss=0.05763, over 7113.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2578, pruned_loss=0.04025, over 1431949.39 frames.], batch size: 21, lr: 5.17e-04
2022-05-14 17:20:40,336 INFO [train.py:812] (6/8) Epoch 15, batch 1950, loss[loss=0.1835, simple_loss=0.2896, pruned_loss=0.03872, over 7263.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2575, pruned_loss=0.04004, over 1432105.76 frames.], batch size: 18, lr: 5.17e-04
2022-05-14 17:21:39,016 INFO [train.py:812] (6/8) Epoch 15, batch 2000, loss[loss=0.161, simple_loss=0.2655, pruned_loss=0.02824, over 6438.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2573, pruned_loss=0.04022, over 1428176.57 frames.], batch size: 38, lr: 5.17e-04
2022-05-14 17:22:38,362 INFO [train.py:812] (6/8) Epoch 15, batch 2050, loss[loss=0.182, simple_loss=0.2774, pruned_loss=0.04326, over 7323.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2576, pruned_loss=0.04036, over 1429502.15 frames.], batch size: 25, lr: 5.16e-04
2022-05-14 17:23:37,401 INFO [train.py:812] (6/8) Epoch 15, batch 2100, loss[loss=0.1485, simple_loss=0.2283, pruned_loss=0.03431, over 7413.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2573, pruned_loss=0.0407, over 1424219.93 frames.], batch size: 18, lr: 5.16e-04
2022-05-14 17:24:36,110 INFO [train.py:812] (6/8) Epoch 15, batch 2150, loss[loss=0.2187, simple_loss=0.2992, pruned_loss=0.06907, over 7181.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2565, pruned_loss=0.04, over 1421480.04 frames.], batch size: 22, lr: 5.16e-04
2022-05-14 17:25:35,473 INFO [train.py:812] (6/8) Epoch 15, batch 2200, loss[loss=0.1677, simple_loss=0.2614, pruned_loss=0.03702, over 7420.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2577, pruned_loss=0.04038, over 1420932.55 frames.], batch size: 20, lr: 5.16e-04
2022-05-14 17:26:33,950 INFO [train.py:812] (6/8) Epoch 15, batch 2250, loss[loss=0.2131, simple_loss=0.3051, pruned_loss=0.06059, over 7056.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2576, pruned_loss=0.04016, over 1421201.40 frames.], batch size: 28, lr: 5.16e-04
2022-05-14 17:27:32,336 INFO [train.py:812] (6/8) Epoch 15, batch 2300, loss[loss=0.1526, simple_loss=0.2323, pruned_loss=0.03642, over 6831.00 frames.], tot_loss[loss=0.17, simple_loss=0.2585, pruned_loss=0.0408, over 1420980.80 frames.], batch size: 15, lr: 5.15e-04
2022-05-14 17:28:30,797 INFO [train.py:812] (6/8) Epoch 15, batch 2350, loss[loss=0.1428, simple_loss=0.2281, pruned_loss=0.0288, over 7410.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2578, pruned_loss=0.04038, over 1423980.83 frames.], batch size: 18, lr: 5.15e-04
2022-05-14 17:29:30,889 INFO [train.py:812] (6/8) Epoch 15, batch 2400, loss[loss=0.157, simple_loss=0.2438, pruned_loss=0.03514, over 7398.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2593, pruned_loss=0.04112, over 1422084.91 frames.], batch size: 18, lr: 5.15e-04
2022-05-14 17:30:30,113 INFO [train.py:812] (6/8) Epoch 15, batch 2450, loss[loss=0.1675, simple_loss=0.2602, pruned_loss=0.03736, over 7409.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2597, pruned_loss=0.04147, over 1423682.51 frames.], batch size: 21, lr: 5.15e-04
2022-05-14 17:31:29,573 INFO [train.py:812] (6/8) Epoch 15, batch 2500, loss[loss=0.1715, simple_loss=0.2766, pruned_loss=0.03321, over 7324.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2603, pruned_loss=0.04156, over 1425413.57 frames.], batch size: 21, lr: 5.15e-04
2022-05-14 17:32:27,955 INFO [train.py:812] (6/8) Epoch 15, batch 2550, loss[loss=0.1542, simple_loss=0.2477, pruned_loss=0.03034, over 7165.00 frames.], tot_loss[loss=0.171, simple_loss=0.2596, pruned_loss=0.04121, over 1427557.20 frames.], batch size: 18, lr: 5.14e-04
2022-05-14 17:33:27,561 INFO [train.py:812] (6/8) Epoch 15, batch 2600, loss[loss=0.1779, simple_loss=0.2735, pruned_loss=0.04117, over 7199.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2606, pruned_loss=0.0416, over 1421011.35 frames.], batch size: 23, lr: 5.14e-04
2022-05-14 17:34:25,793 INFO [train.py:812] (6/8) Epoch 15, batch 2650, loss[loss=0.1669, simple_loss=0.2573, pruned_loss=0.03821, over 7293.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2598, pruned_loss=0.04135, over 1421923.59 frames.], batch size: 25, lr: 5.14e-04
2022-05-14 17:35:25,141 INFO [train.py:812] (6/8) Epoch 15, batch 2700, loss[loss=0.1718, simple_loss=0.2708, pruned_loss=0.03643, over 7315.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2607, pruned_loss=0.04159, over 1424005.59 frames.], batch size: 21, lr: 5.14e-04
2022-05-14 17:36:24,199 INFO [train.py:812] (6/8) Epoch 15, batch 2750, loss[loss=0.1827, simple_loss=0.2639, pruned_loss=0.05069, over 7287.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2605, pruned_loss=0.04143, over 1424822.52 frames.], batch size: 24, lr: 5.14e-04
2022-05-14 17:37:23,476 INFO [train.py:812] (6/8) Epoch 15, batch 2800, loss[loss=0.1672, simple_loss=0.2587, pruned_loss=0.03785, over 7148.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2598, pruned_loss=0.04094, over 1427959.29 frames.], batch size: 20, lr: 5.14e-04
2022-05-14 17:38:20,806 INFO [train.py:812] (6/8) Epoch 15, batch 2850, loss[loss=0.1691, simple_loss=0.2574, pruned_loss=0.04043, over 6786.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2598, pruned_loss=0.0408, over 1428246.28 frames.], batch size: 15, lr: 5.13e-04
2022-05-14 17:39:21,016 INFO [train.py:812] (6/8) Epoch 15, batch 2900, loss[loss=0.1798, simple_loss=0.2673, pruned_loss=0.04614, over 7387.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2598, pruned_loss=0.04144, over 1423292.59 frames.], batch size: 23, lr: 5.13e-04
2022-05-14 17:40:20,007 INFO [train.py:812] (6/8) Epoch 15, batch 2950, loss[loss=0.1526, simple_loss=0.2359, pruned_loss=0.03464, over 7432.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2598, pruned_loss=0.0415, over 1424239.27 frames.], batch size: 20, lr: 5.13e-04
2022-05-14 17:41:19,164 INFO [train.py:812] (6/8) Epoch 15, batch 3000, loss[loss=0.2033, simple_loss=0.296, pruned_loss=0.05527, over 7157.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2594, pruned_loss=0.04146, over 1422596.13 frames.], batch size: 19, lr: 5.13e-04
2022-05-14 17:41:19,166 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 17:41:26,767 INFO [train.py:841] (6/8) Epoch 15, validation: loss=0.1543, simple_loss=0.2544, pruned_loss=0.02713, over 698248.00 frames.
2022-05-14 17:42:25,638 INFO [train.py:812] (6/8) Epoch 15, batch 3050, loss[loss=0.1614, simple_loss=0.2454, pruned_loss=0.0387, over 6756.00 frames.], tot_loss[loss=0.171, simple_loss=0.2597, pruned_loss=0.04111, over 1425823.26 frames.], batch size: 15, lr: 5.13e-04
2022-05-14 17:43:23,126 INFO [train.py:812] (6/8) Epoch 15, batch 3100, loss[loss=0.1499, simple_loss=0.2487, pruned_loss=0.02555, over 7325.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2603, pruned_loss=0.0412, over 1421349.83 frames.], batch size: 20, lr: 5.12e-04
2022-05-14 17:44:21,952 INFO [train.py:812] (6/8) Epoch 15, batch 3150, loss[loss=0.1775, simple_loss=0.2496, pruned_loss=0.05266, over 7275.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2604, pruned_loss=0.04167, over 1426671.78 frames.], batch size: 17, lr: 5.12e-04
2022-05-14 17:45:20,576 INFO [train.py:812] (6/8) Epoch 15, batch 3200, loss[loss=0.1818, simple_loss=0.2818, pruned_loss=0.04095, over 7074.00 frames.], tot_loss[loss=0.1714, simple_loss=0.26, pruned_loss=0.04146, over 1426956.26 frames.], batch size: 28, lr: 5.12e-04
2022-05-14 17:46:20,200 INFO [train.py:812] (6/8) Epoch 15, batch 3250, loss[loss=0.148, simple_loss=0.2506, pruned_loss=0.02273, over 7075.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2595, pruned_loss=0.04162, over 1427455.82 frames.], batch size: 18, lr: 5.12e-04
2022-05-14 17:47:18,753 INFO [train.py:812] (6/8) Epoch 15, batch 3300, loss[loss=0.1368, simple_loss=0.2155, pruned_loss=0.02902, over 7281.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2585, pruned_loss=0.04104, over 1426804.05 frames.], batch size: 17, lr: 5.12e-04
2022-05-14 17:48:17,430 INFO [train.py:812] (6/8) Epoch 15, batch 3350, loss[loss=0.1947, simple_loss=0.2892, pruned_loss=0.05015, over 7208.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2592, pruned_loss=0.04104, over 1426774.78 frames.], batch size: 23, lr: 5.11e-04
2022-05-14 17:49:14,767 INFO [train.py:812] (6/8) Epoch 15, batch 3400, loss[loss=0.1623, simple_loss=0.2518, pruned_loss=0.0364, over 7219.00 frames.], tot_loss[loss=0.171, simple_loss=0.26, pruned_loss=0.04103, over 1423823.19 frames.], batch size: 21, lr: 5.11e-04
2022-05-14 17:50:13,367 INFO [train.py:812] (6/8) Epoch 15, batch 3450, loss[loss=0.1746, simple_loss=0.2613, pruned_loss=0.044, over 7055.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2604, pruned_loss=0.04143, over 1420874.57 frames.], batch size: 28, lr: 5.11e-04
2022-05-14 17:51:13,253 INFO [train.py:812] (6/8) Epoch 15, batch 3500, loss[loss=0.2015, simple_loss=0.2897, pruned_loss=0.05665, over 7132.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2596, pruned_loss=0.04107, over 1426112.88 frames.], batch size: 26, lr: 5.11e-04
2022-05-14 17:52:12,828 INFO [train.py:812] (6/8) Epoch 15, batch 3550, loss[loss=0.1515, simple_loss=0.2507, pruned_loss=0.02613, over 7231.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2599, pruned_loss=0.04083, over 1427427.43 frames.], batch size: 20, lr: 5.11e-04
2022-05-14 17:53:11,381 INFO [train.py:812] (6/8) Epoch 15, batch 3600, loss[loss=0.1792, simple_loss=0.272, pruned_loss=0.04314, over 7333.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2592, pruned_loss=0.04095, over 1423457.47 frames.], batch size: 21, lr: 5.11e-04
2022-05-14 17:54:10,561 INFO [train.py:812] (6/8) Epoch 15, batch 3650, loss[loss=0.192, simple_loss=0.2866, pruned_loss=0.04863, over 7256.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2598, pruned_loss=0.04126, over 1423948.02 frames.], batch size: 19, lr: 5.10e-04
2022-05-14 17:55:10,198 INFO [train.py:812] (6/8) Epoch 15, batch 3700, loss[loss=0.1624, simple_loss=0.2455, pruned_loss=0.03971, over 7438.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2602, pruned_loss=0.04131, over 1421014.17 frames.], batch size: 20, lr: 5.10e-04
2022-05-14 17:56:09,478 INFO [train.py:812] (6/8) Epoch 15, batch 3750, loss[loss=0.2049, simple_loss=0.2915, pruned_loss=0.05915, over 5046.00 frames.], tot_loss[loss=0.1717, simple_loss=0.26, pruned_loss=0.04173, over 1422863.97 frames.], batch size: 52, lr: 5.10e-04
2022-05-14 17:57:14,373 INFO [train.py:812] (6/8) Epoch 15, batch 3800, loss[loss=0.1503, simple_loss=0.2308, pruned_loss=0.03488, over 7061.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2601, pruned_loss=0.04171, over 1425471.62 frames.], batch size: 18, lr: 5.10e-04
2022-05-14 17:58:12,113 INFO [train.py:812] (6/8) Epoch 15, batch 3850, loss[loss=0.1667, simple_loss=0.2506, pruned_loss=0.04146, over 7241.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2597, pruned_loss=0.04102, over 1427954.70 frames.], batch size: 20, lr: 5.10e-04
2022-05-14 17:59:11,815 INFO [train.py:812] (6/8) Epoch 15, batch 3900, loss[loss=0.1555, simple_loss=0.2423, pruned_loss=0.03438, over 7249.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2593, pruned_loss=0.04106, over 1426578.19 frames.], batch size: 19, lr: 5.09e-04
2022-05-14 18:00:10,986 INFO [train.py:812] (6/8) Epoch 15, batch 3950, loss[loss=0.1841, simple_loss=0.272, pruned_loss=0.04811, over 7348.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2584, pruned_loss=0.04055, over 1422231.73 frames.], batch size: 19, lr: 5.09e-04
2022-05-14 18:01:10,527 INFO [train.py:812] (6/8) Epoch 15, batch 4000, loss[loss=0.1815, simple_loss=0.2733, pruned_loss=0.04481, over 7216.00 frames.], tot_loss[loss=0.1691, simple_loss=0.258, pruned_loss=0.04009, over 1423041.57 frames.], batch size: 21, lr: 5.09e-04
2022-05-14 18:02:09,527 INFO [train.py:812] (6/8) Epoch 15, batch 4050, loss[loss=0.1724, simple_loss=0.2573, pruned_loss=0.04376, over 7226.00 frames.], tot_loss[loss=0.169, simple_loss=0.2583, pruned_loss=0.03991, over 1427232.62 frames.], batch size: 21, lr: 5.09e-04
2022-05-14 18:03:08,722 INFO [train.py:812] (6/8) Epoch 15, batch 4100, loss[loss=0.1867, simple_loss=0.2752, pruned_loss=0.0491, over 7208.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2581, pruned_loss=0.04005, over 1419584.03 frames.], batch size: 23, lr: 5.09e-04
2022-05-14 18:04:07,548 INFO [train.py:812] (6/8) Epoch 15, batch 4150, loss[loss=0.212, simple_loss=0.2876, pruned_loss=0.06818, over 5132.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2584, pruned_loss=0.04032, over 1413189.81 frames.], batch size: 52, lr: 5.08e-04
2022-05-14 18:05:07,024 INFO [train.py:812] (6/8) Epoch 15, batch 4200, loss[loss=0.1707, simple_loss=0.2702, pruned_loss=0.03559, over 7229.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2578, pruned_loss=0.04024, over 1411665.14 frames.], batch size: 20, lr: 5.08e-04
2022-05-14 18:06:05,968 INFO [train.py:812] (6/8) Epoch 15, batch 4250, loss[loss=0.162, simple_loss=0.2434, pruned_loss=0.04027, over 7069.00 frames.], tot_loss[loss=0.1697, simple_loss=0.258, pruned_loss=0.04074, over 1409634.59 frames.], batch size: 18, lr: 5.08e-04
2022-05-14 18:07:05,147 INFO [train.py:812] (6/8) Epoch 15, batch 4300, loss[loss=0.1413, simple_loss=0.2274, pruned_loss=0.02763, over 6795.00 frames.], tot_loss[loss=0.1694, simple_loss=0.258, pruned_loss=0.04039, over 1404841.98 frames.], batch size: 15, lr: 5.08e-04
2022-05-14 18:08:04,074 INFO [train.py:812] (6/8) Epoch 15, batch 4350, loss[loss=0.1631, simple_loss=0.2542, pruned_loss=0.03603, over 7317.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2578, pruned_loss=0.03978, over 1407994.23 frames.], batch size: 21, lr: 5.08e-04
2022-05-14 18:09:03,515 INFO [train.py:812] (6/8) Epoch 15, batch 4400, loss[loss=0.1697, simple_loss=0.2613, pruned_loss=0.03908, over 7140.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2573, pruned_loss=0.03958, over 1410708.52 frames.], batch size: 19, lr: 5.08e-04
2022-05-14 18:10:02,441 INFO [train.py:812] (6/8) Epoch 15, batch 4450, loss[loss=0.1417, simple_loss=0.2274, pruned_loss=0.02797, over 7170.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2559, pruned_loss=0.03966, over 1403396.05 frames.], batch size: 18, lr: 5.07e-04
2022-05-14 18:11:01,302 INFO [train.py:812] (6/8) Epoch 15, batch 4500, loss[loss=0.1652, simple_loss=0.2493, pruned_loss=0.0405, over 7070.00 frames.], tot_loss[loss=0.169, simple_loss=0.2571, pruned_loss=0.04049, over 1394572.51 frames.], batch size: 18, lr: 5.07e-04
2022-05-14 18:11:59,586 INFO [train.py:812] (6/8) Epoch 15, batch 4550, loss[loss=0.2319, simple_loss=0.3128, pruned_loss=0.07553, over 5317.00 frames.], tot_loss[loss=0.17, simple_loss=0.2579, pruned_loss=0.04106, over 1366856.68 frames.], batch size: 52, lr: 5.07e-04
2022-05-14 18:13:08,760 INFO [train.py:812] (6/8) Epoch 16, batch 0, loss[loss=0.1741, simple_loss=0.2704, pruned_loss=0.0389, over 7291.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2704, pruned_loss=0.0389, over 7291.00 frames.], batch size: 24, lr: 4.92e-04
2022-05-14 18:14:07,995 INFO [train.py:812] (6/8) Epoch 16, batch 50, loss[loss=0.1419, simple_loss=0.2335, pruned_loss=0.02519, over 7408.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2549, pruned_loss=0.03681, over 320856.68 frames.], batch size: 18, lr: 4.92e-04
2022-05-14 18:15:07,193 INFO [train.py:812] (6/8) Epoch 16, batch 100, loss[loss=0.1771, simple_loss=0.2641, pruned_loss=0.04507, over 7337.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2543, pruned_loss=0.03738, over 564200.18 frames.], batch size: 20, lr: 4.92e-04
2022-05-14 18:16:06,285 INFO [train.py:812] (6/8) Epoch 16, batch 150, loss[loss=0.1809, simple_loss=0.2706, pruned_loss=0.0456, over 7145.00 frames.], tot_loss[loss=0.1667, simple_loss=0.256, pruned_loss=0.03868, over 754263.57 frames.], batch size: 20, lr: 4.92e-04
2022-05-14 18:17:15,054 INFO [train.py:812] (6/8) Epoch 16, batch 200, loss[loss=0.2, simple_loss=0.2847, pruned_loss=0.05761, over 7116.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2561, pruned_loss=0.03875, over 897302.42 frames.], batch size: 21, lr: 4.91e-04
2022-05-14 18:18:13,079 INFO [train.py:812] (6/8) Epoch 16, batch 250, loss[loss=0.1738, simple_loss=0.2645, pruned_loss=0.0415, over 7158.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2569, pruned_loss=0.03911, over 1014092.58 frames.], batch size: 19, lr: 4.91e-04
2022-05-14 18:19:12,336 INFO [train.py:812] (6/8) Epoch 16, batch 300, loss[loss=0.165, simple_loss=0.2623, pruned_loss=0.03387, over 7167.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2566, pruned_loss=0.03921, over 1108818.77 frames.], batch size: 19, lr: 4.91e-04
2022-05-14 18:20:11,394 INFO [train.py:812] (6/8) Epoch 16, batch 350, loss[loss=0.1419, simple_loss=0.2212, pruned_loss=0.03127, over 7287.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2573, pruned_loss=0.03997, over 1180107.90 frames.], batch size: 18, lr: 4.91e-04
2022-05-14 18:21:11,302 INFO [train.py:812] (6/8) Epoch 16, batch 400, loss[loss=0.1495, simple_loss=0.2405, pruned_loss=0.0293, over 7268.00 frames.], tot_loss[loss=0.169, simple_loss=0.2578, pruned_loss=0.04012, over 1234930.70 frames.], batch size: 19, lr: 4.91e-04
2022-05-14 18:22:10,139 INFO [train.py:812] (6/8) Epoch 16, batch 450, loss[loss=0.1586, simple_loss=0.2487, pruned_loss=0.03421, over 7427.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2593, pruned_loss=0.04067, over 1281657.37 frames.], batch size: 20, lr: 4.91e-04
2022-05-14 18:23:09,264 INFO [train.py:812] (6/8) Epoch 16, batch 500, loss[loss=0.1611, simple_loss=0.2577, pruned_loss=0.0322, over 7208.00 frames.], tot_loss[loss=0.1708, simple_loss=0.26, pruned_loss=0.0408, over 1318329.41 frames.], batch size: 23, lr: 4.90e-04
2022-05-14 18:24:07,729 INFO [train.py:812] (6/8) Epoch 16, batch 550, loss[loss=0.1563, simple_loss=0.2424, pruned_loss=0.03514, over 7271.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2581, pruned_loss=0.04019, over 1345518.27 frames.], batch size: 18, lr: 4.90e-04
2022-05-14 18:25:07,656 INFO [train.py:812] (6/8) Epoch 16, batch 600, loss[loss=0.1577, simple_loss=0.2426, pruned_loss=0.03644, over 7158.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2566, pruned_loss=0.03927, over 1361494.52 frames.], batch size: 19, lr: 4.90e-04
2022-05-14 18:26:06,748 INFO [train.py:812] (6/8) Epoch 16, batch 650, loss[loss=0.1686, simple_loss=0.2666, pruned_loss=0.03526, over 6316.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2567, pruned_loss=0.0391, over 1373480.50 frames.], batch size: 37, lr: 4.90e-04
2022-05-14 18:27:05,476 INFO [train.py:812] (6/8) Epoch 16, batch 700, loss[loss=0.1771, simple_loss=0.2699, pruned_loss=0.04213, over 7090.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2567, pruned_loss=0.03944, over 1386140.06 frames.], batch size: 28, lr: 4.90e-04
2022-05-14 18:28:04,365 INFO [train.py:812] (6/8) Epoch 16, batch 750, loss[loss=0.1517, simple_loss=0.2369, pruned_loss=0.0333, over 7162.00 frames.], tot_loss[loss=0.167, simple_loss=0.256, pruned_loss=0.03902, over 1396060.58 frames.], batch size: 19, lr: 4.89e-04
2022-05-14 18:29:03,822 INFO [train.py:812] (6/8) Epoch 16, batch 800, loss[loss=0.1926, simple_loss=0.2659, pruned_loss=0.05966, over 7267.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2561, pruned_loss=0.03911, over 1403464.91 frames.], batch size: 19, lr: 4.89e-04
2022-05-14 18:30:02,575 INFO [train.py:812] (6/8) Epoch 16, batch 850, loss[loss=0.1523, simple_loss=0.2517, pruned_loss=0.02643, over 7145.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2569, pruned_loss=0.03948, over 1405631.77 frames.], batch size: 20, lr: 4.89e-04
2022-05-14 18:31:02,437 INFO [train.py:812] (6/8) Epoch 16, batch 900, loss[loss=0.1389, simple_loss=0.2249, pruned_loss=0.02641, over 7352.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2565, pruned_loss=0.03954, over 1404283.82 frames.], batch size: 19, lr: 4.89e-04
2022-05-14 18:32:01,980 INFO [train.py:812] (6/8) Epoch 16, batch 950, loss[loss=0.1593, simple_loss=0.2438, pruned_loss=0.03737, over 7434.00 frames.], tot_loss[loss=0.168, simple_loss=0.2561, pruned_loss=0.03996, over 1407689.46 frames.], batch size: 20, lr: 4.89e-04
2022-05-14 18:33:00,795 INFO [train.py:812] (6/8) Epoch 16, batch 1000, loss[loss=0.1879, simple_loss=0.2797, pruned_loss=0.04811, over 7306.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2554, pruned_loss=0.03919, over 1412945.95 frames.], batch size: 25, lr: 4.89e-04
2022-05-14 18:33:59,682 INFO [train.py:812] (6/8) Epoch 16, batch 1050, loss[loss=0.1766, simple_loss=0.2617, pruned_loss=0.04582, over 7334.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2565, pruned_loss=0.03931, over 1417895.72 frames.], batch size: 20, lr: 4.88e-04
2022-05-14 18:34:59,630 INFO [train.py:812] (6/8) Epoch 16, batch 1100, loss[loss=0.1675, simple_loss=0.2517, pruned_loss=0.0417, over 7355.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2562, pruned_loss=0.03924, over 1420968.43 frames.], batch size: 19, lr: 4.88e-04
2022-05-14 18:35:59,379 INFO [train.py:812] (6/8) Epoch 16, batch 1150, loss[loss=0.1871, simple_loss=0.2816, pruned_loss=0.04627, over 5129.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2552, pruned_loss=0.03885, over 1422203.14 frames.], batch size: 52, lr: 4.88e-04
2022-05-14 18:36:59,299 INFO [train.py:812] (6/8) Epoch 16, batch 1200, loss[loss=0.1537, simple_loss=0.2484, pruned_loss=0.02953, over 7103.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2558, pruned_loss=0.03929, over 1419029.98 frames.], batch size: 21, lr: 4.88e-04
2022-05-14 18:37:58,917 INFO [train.py:812] (6/8) Epoch 16, batch 1250, loss[loss=0.1405, simple_loss=0.2192, pruned_loss=0.03095, over 6783.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2555, pruned_loss=0.03941, over 1420145.46 frames.], batch size: 15, lr: 4.88e-04
2022-05-14 18:38:58,852 INFO [train.py:812] (6/8) Epoch 16, batch 1300, loss[loss=0.189, simple_loss=0.279, pruned_loss=0.04952, over 7214.00 frames.], tot_loss[loss=0.168, simple_loss=0.2568, pruned_loss=0.03957, over 1425963.85 frames.], batch size: 22, lr: 4.88e-04
2022-05-14 18:39:58,374 INFO [train.py:812] (6/8) Epoch 16, batch 1350, loss[loss=0.1579, simple_loss=0.2425, pruned_loss=0.03669, over 7154.00 frames.], tot_loss[loss=0.168, simple_loss=0.2568, pruned_loss=0.03957, over 1418299.77 frames.], batch size: 19, lr: 4.87e-04
2022-05-14 18:40:58,078 INFO [train.py:812] (6/8) Epoch 16, batch 1400, loss[loss=0.1503, simple_loss=0.2509, pruned_loss=0.02489, over 7333.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2571, pruned_loss=0.0397, over 1417094.64 frames.], batch size: 22, lr: 4.87e-04
2022-05-14 18:41:57,587 INFO [train.py:812] (6/8) Epoch 16, batch 1450, loss[loss=0.1998, simple_loss=0.2896, pruned_loss=0.05504, over 7413.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2572, pruned_loss=0.03997, over 1422762.51 frames.], batch size: 21, lr: 4.87e-04
2022-05-14 18:43:06,648 INFO [train.py:812] (6/8) Epoch 16, batch 1500, loss[loss=0.1801, simple_loss=0.268, pruned_loss=0.0461, over 7169.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2575, pruned_loss=0.03984, over 1422535.74 frames.], batch size: 23, lr: 4.87e-04
2022-05-14 18:44:06,131 INFO [train.py:812] (6/8) Epoch 16, batch 1550, loss[loss=0.1551, simple_loss=0.2356, pruned_loss=0.03729, over 7214.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2577, pruned_loss=0.03989, over 1420856.11 frames.], batch size: 16, lr: 4.87e-04
2022-05-14 18:45:06,028 INFO [train.py:812] (6/8) Epoch 16, batch 1600, loss[loss=0.1421, simple_loss=0.2321, pruned_loss=0.02602, over 6859.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2581, pruned_loss=0.04016, over 1422992.09 frames.], batch size: 15, lr: 4.87e-04
2022-05-14 18:46:05,530 INFO [train.py:812] (6/8) Epoch 16, batch 1650, loss[loss=0.1714, simple_loss=0.2591, pruned_loss=0.04189, over 7151.00 frames.], tot_loss[loss=0.169, simple_loss=0.2578, pruned_loss=0.04009, over 1424358.54 frames.], batch size: 20, lr: 4.86e-04
2022-05-14 18:47:14,971 INFO [train.py:812] (6/8) Epoch 16, batch 1700, loss[loss=0.1661, simple_loss=0.2442, pruned_loss=0.04395, over 7407.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2566, pruned_loss=0.03953, over 1424787.82 frames.], batch size: 18, lr: 4.86e-04
2022-05-14 18:48:31,561 INFO [train.py:812] (6/8) Epoch 16, batch 1750, loss[loss=0.1633, simple_loss=0.261, pruned_loss=0.0328, over 7380.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2569, pruned_loss=0.03952, over 1424316.98 frames.], batch size: 23, lr: 4.86e-04
2022-05-14 18:49:49,356 INFO [train.py:812] (6/8) Epoch 16, batch 1800, loss[loss=0.1514, simple_loss=0.2304, pruned_loss=0.03615, over 7352.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2568, pruned_loss=0.03944, over 1423020.80 frames.], batch size: 19, lr: 4.86e-04
2022-05-14 18:50:57,680 INFO [train.py:812] (6/8) Epoch 16, batch 1850, loss[loss=0.18, simple_loss=0.2815, pruned_loss=0.03922, over 7150.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2559, pruned_loss=0.03926, over 1425706.83 frames.], batch size: 20, lr: 4.86e-04
2022-05-14 18:51:57,587 INFO [train.py:812] (6/8) Epoch 16, batch 1900, loss[loss=0.2025, simple_loss=0.3033, pruned_loss=0.05088, over 7295.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2562, pruned_loss=0.0394, over 1429385.36 frames.], batch size: 25, lr: 4.86e-04
2022-05-14 18:52:55,113 INFO [train.py:812] (6/8) Epoch 16, batch 1950, loss[loss=0.2086, simple_loss=0.2977, pruned_loss=0.05976, over 7198.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2575, pruned_loss=0.03994, over 1429981.03 frames.], batch size: 23, lr: 4.85e-04
2022-05-14 18:53:54,425 INFO [train.py:812] (6/8) Epoch 16, batch 2000, loss[loss=0.2439, simple_loss=0.305, pruned_loss=0.09139, over 4870.00 frames.], tot_loss[loss=0.1692, simple_loss=0.258, pruned_loss=0.04016, over 1423211.83 frames.], batch size: 52, lr: 4.85e-04
2022-05-14 18:54:53,363 INFO [train.py:812] (6/8) Epoch 16, batch 2050, loss[loss=0.1706, simple_loss=0.2614, pruned_loss=0.03992, over 6311.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2589, pruned_loss=0.04046, over 1421760.85 frames.], batch size: 37, lr: 4.85e-04
2022-05-14 18:55:52,725 INFO [train.py:812] (6/8) Epoch 16, batch 2100, loss[loss=0.1752, simple_loss=0.2614, pruned_loss=0.04456, over 7117.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2592, pruned_loss=0.04078, over 1422421.84 frames.], batch size: 21, lr: 4.85e-04
2022-05-14 18:56:51,672 INFO [train.py:812] (6/8) Epoch 16, batch 2150, loss[loss=0.1644, simple_loss=0.255, pruned_loss=0.03685, over 7258.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2587, pruned_loss=0.0404, over 1417414.67 frames.], batch size: 19, lr: 4.85e-04
2022-05-14 18:57:50,999 INFO [train.py:812] (6/8) Epoch 16, batch 2200, loss[loss=0.1848, simple_loss=0.2771, pruned_loss=0.04623, over 7208.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2585, pruned_loss=0.04035, over 1414155.35 frames.], batch size: 22, lr: 4.84e-04
2022-05-14 18:58:50,191 INFO [train.py:812] (6/8) Epoch 16, batch 2250, loss[loss=0.1718, simple_loss=0.27, pruned_loss=0.03686, over 7415.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2574, pruned_loss=0.03992, over 1416039.77 frames.], batch size: 21, lr: 4.84e-04
2022-05-14 18:59:49,569 INFO [train.py:812] (6/8) Epoch 16, batch 2300, loss[loss=0.1768, simple_loss=0.2683, pruned_loss=0.04266, over 7216.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2573, pruned_loss=0.03986, over 1417967.83 frames.], batch size: 23, lr: 4.84e-04
2022-05-14 19:00:48,698 INFO [train.py:812] (6/8) Epoch 16, batch 2350, loss[loss=0.2051, simple_loss=0.2881, pruned_loss=0.0611, over 7316.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2574, pruned_loss=0.04005, over 1421124.12 frames.], batch size: 25, lr: 4.84e-04
2022-05-14 19:01:48,358 INFO [train.py:812] (6/8) Epoch 16, batch 2400, loss[loss=0.2105, simple_loss=0.2969, pruned_loss=0.06202, over 7299.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2576, pruned_loss=0.03997, over 1425148.93 frames.], batch size: 25, lr: 4.84e-04
2022-05-14 19:02:47,262 INFO [train.py:812] (6/8) Epoch 16, batch 2450, loss[loss=0.1661, simple_loss=0.2603, pruned_loss=0.03597, over 6798.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2583, pruned_loss=0.03998, over 1424007.52 frames.], batch size: 31, lr: 4.84e-04
2022-05-14 19:03:46,840 INFO [train.py:812] (6/8) Epoch 16, batch 2500, loss[loss=0.1593, simple_loss=0.2531, pruned_loss=0.03277, over 7211.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2579, pruned_loss=0.03984, over 1426682.73 frames.], batch size: 21, lr: 4.83e-04
2022-05-14 19:04:46,112 INFO [train.py:812] (6/8) Epoch 16, batch 2550, loss[loss=0.1578, simple_loss=0.2555, pruned_loss=0.03008, over 7145.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2571, pruned_loss=0.03985, over 1423028.00 frames.], batch size: 20, lr: 4.83e-04
2022-05-14 19:05:45,588 INFO [train.py:812] (6/8) Epoch 16, batch 2600, loss[loss=0.1788, simple_loss=0.2692, pruned_loss=0.04419, over 7355.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2574, pruned_loss=0.04044, over 1421813.58 frames.], batch size: 19, lr: 4.83e-04
2022-05-14 19:06:45,278 INFO [train.py:812] (6/8) Epoch 16, batch 2650, loss[loss=0.1818, simple_loss=0.2664, pruned_loss=0.04859, over 7387.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2573, pruned_loss=0.04068, over 1422765.90 frames.], batch size: 23, lr: 4.83e-04
2022-05-14 19:07:45,175 INFO [train.py:812] (6/8) Epoch 16, batch 2700, loss[loss=0.1859, simple_loss=0.2748, pruned_loss=0.04848, over 7182.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2576, pruned_loss=0.04076, over 1418816.03 frames.], batch size: 26, lr: 4.83e-04
2022-05-14 19:08:44,249 INFO [train.py:812] (6/8) Epoch 16, batch 2750, loss[loss=0.1448, simple_loss=0.2413, pruned_loss=0.02416, over 7279.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2579, pruned_loss=0.04065, over 1423065.51 frames.], batch size: 18, lr: 4.83e-04
2022-05-14 19:09:44,124 INFO [train.py:812] (6/8) Epoch 16, batch 2800, loss[loss=0.1732, simple_loss=0.2764, pruned_loss=0.03493, over 7214.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2581, pruned_loss=0.04022, over 1425209.11 frames.], batch size: 21, lr: 4.82e-04
2022-05-14 19:10:43,386 INFO [train.py:812] (6/8) Epoch 16, batch 2850, loss[loss=0.1662, simple_loss=0.2497, pruned_loss=0.04133, over 7158.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2581, pruned_loss=0.04014, over 1425038.19 frames.], batch size: 18, lr: 4.82e-04
2022-05-14 19:11:42,836 INFO [train.py:812] (6/8) Epoch 16, batch 2900, loss[loss=0.1538, simple_loss=0.2408, pruned_loss=0.03341, over 7174.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2579, pruned_loss=0.04032, over 1427606.85 frames.], batch size: 18, lr: 4.82e-04
2022-05-14 19:12:41,629 INFO [train.py:812] (6/8) Epoch 16, batch 2950, loss[loss=0.1439, simple_loss=0.2465, pruned_loss=0.02067, over 7323.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2582, pruned_loss=0.04046, over 1423739.09 frames.], batch size: 22, lr: 4.82e-04
2022-05-14 19:13:40,906 INFO [train.py:812] (6/8) Epoch 16, batch 3000, loss[loss=0.1665, simple_loss=0.249, pruned_loss=0.04202, over 7403.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2584, pruned_loss=0.04035, over 1427760.36 frames.], batch size: 21, lr: 4.82e-04
2022-05-14 19:13:40,908 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 19:13:48,992 INFO [train.py:841] (6/8) Epoch 16, validation: loss=0.1537, simple_loss=0.2535, pruned_loss=0.02695, over 698248.00 frames.
2022-05-14 19:14:47,151 INFO [train.py:812] (6/8) Epoch 16, batch 3050, loss[loss=0.1771, simple_loss=0.2466, pruned_loss=0.05381, over 7423.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2579, pruned_loss=0.04022, over 1426135.48 frames.], batch size: 18, lr: 4.82e-04
2022-05-14 19:15:46,685 INFO [train.py:812] (6/8) Epoch 16, batch 3100, loss[loss=0.1768, simple_loss=0.2694, pruned_loss=0.04209, over 7215.00 frames.], tot_loss[loss=0.1691, simple_loss=0.258, pruned_loss=0.04012, over 1426448.00 frames.], batch size: 23, lr: 4.81e-04
2022-05-14 19:16:44,982 INFO [train.py:812] (6/8) Epoch 16, batch 3150, loss[loss=0.155, simple_loss=0.2314, pruned_loss=0.03932, over 7146.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2581, pruned_loss=0.04011, over 1424116.40 frames.], batch size: 18, lr: 4.81e-04
2022-05-14 19:17:47,871 INFO [train.py:812] (6/8) Epoch 16, batch 3200, loss[loss=0.1629, simple_loss=0.2497, pruned_loss=0.03804, over 7295.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2584, pruned_loss=0.04019, over 1424219.88 frames.], batch size: 24, lr: 4.81e-04
2022-05-14 19:18:47,177 INFO [train.py:812] (6/8) Epoch 16, batch 3250, loss[loss=0.1557, simple_loss=0.2488, pruned_loss=0.03133, over 7318.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2573, pruned_loss=0.04004, over 1425380.43 frames.], batch size: 21, lr: 4.81e-04
2022-05-14 19:19:45,425 INFO [train.py:812] (6/8) Epoch 16, batch 3300, loss[loss=0.184, simple_loss=0.2845, pruned_loss=0.04171, over 7308.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2576, pruned_loss=0.03965, over 1429112.00 frames.], batch size: 25, lr: 4.81e-04
2022-05-14 19:20:42,565 INFO [train.py:812] (6/8) Epoch 16, batch 3350, loss[loss=0.172, simple_loss=0.2682, pruned_loss=0.03787, over 7236.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2577, pruned_loss=0.03982, over 1431455.29 frames.], batch size: 20, lr: 4.81e-04
2022-05-14 19:21:41,204 INFO [train.py:812] (6/8) Epoch 16, batch 3400, loss[loss=0.2047, simple_loss=0.2866, pruned_loss=0.06147, over 7056.00 frames.], tot_loss[loss=0.169, simple_loss=0.2579, pruned_loss=0.04007, over 1428683.15 frames.], batch size: 28, lr: 4.80e-04
2022-05-14 19:22:40,401 INFO [train.py:812] (6/8) Epoch 16, batch 3450, loss[loss=0.1626, simple_loss=0.246, pruned_loss=0.03956, over 7352.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2574, pruned_loss=0.03997, over 1430377.06 frames.], batch size: 19, lr: 4.80e-04
2022-05-14 19:23:40,286 INFO [train.py:812] (6/8) Epoch 16, batch 3500, loss[loss=0.1622, simple_loss=0.2618, pruned_loss=0.03132, over 7314.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2573, pruned_loss=0.03981, over 1429037.25 frames.], batch size: 21, lr: 4.80e-04
2022-05-14 19:24:39,292 INFO [train.py:812] (6/8) Epoch 16, batch 3550, loss[loss=0.2001, simple_loss=0.2941, pruned_loss=0.05307, over 7195.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2584, pruned_loss=0.04029, over 1425015.88 frames.], batch size: 26, lr: 4.80e-04
2022-05-14 19:25:38,826 INFO [train.py:812] (6/8) Epoch 16, batch 3600, loss[loss=0.2022, simple_loss=0.3042, pruned_loss=0.05006, over 7317.00 frames.], tot_loss[loss=0.169, simple_loss=0.2583, pruned_loss=0.03991, over 1426734.83 frames.], batch size: 21, lr: 4.80e-04
2022-05-14 19:26:37,932 INFO [train.py:812] (6/8) Epoch 16, batch 3650, loss[loss=0.1711, simple_loss=0.2446, pruned_loss=0.04875, over 7271.00 frames.], tot_loss[loss=0.169, simple_loss=0.2584, pruned_loss=0.03985, over 1426745.46 frames.], batch size: 18, lr: 4.80e-04
2022-05-14 19:27:36,138 INFO [train.py:812] (6/8) Epoch 16, batch 3700, loss[loss=0.1619, simple_loss=0.2369, pruned_loss=0.04342, over 6770.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2581, pruned_loss=0.0398, over 1423516.89 frames.], batch size: 15, lr: 4.79e-04
2022-05-14 19:28:35,324 INFO [train.py:812] (6/8) Epoch 16, batch 3750, loss[loss=0.2066, simple_loss=0.2953, pruned_loss=0.05895, over 7325.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2574, pruned_loss=0.03967, over 1421655.51 frames.], batch size: 25, lr: 4.79e-04
2022-05-14 19:29:33,349 INFO [train.py:812] (6/8) Epoch 16, batch 3800, loss[loss=0.1626, simple_loss=0.2472, pruned_loss=0.039, over 7134.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2573, pruned_loss=0.03977, over 1425458.67 frames.], batch size: 17, lr: 4.79e-04
2022-05-14 19:30:31,496 INFO [train.py:812] (6/8) Epoch 16, batch 3850, loss[loss=0.1678, simple_loss=0.2464, pruned_loss=0.04467, over 7278.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2571, pruned_loss=0.03972, over 1421729.58 frames.], batch size: 18, lr: 4.79e-04
2022-05-14 19:31:29,714 INFO [train.py:812] (6/8) Epoch 16, batch 3900, loss[loss=0.1488, simple_loss=0.2497, pruned_loss=0.02391, over 7217.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2567, pruned_loss=0.03912, over 1423261.88 frames.], batch size: 21, lr: 4.79e-04
2022-05-14 19:32:28,911 INFO [train.py:812] (6/8) Epoch 16, batch 3950, loss[loss=0.156, simple_loss=0.2511, pruned_loss=0.03043, over 7232.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2574, pruned_loss=0.0395, over 1422523.08 frames.], batch size: 20, lr: 4.79e-04
2022-05-14 19:33:27,702 INFO [train.py:812] (6/8) Epoch 16, batch 4000, loss[loss=0.1683, simple_loss=0.2695, pruned_loss=0.03359, over 7325.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2584, pruned_loss=0.03993, over 1420140.13 frames.], batch size: 21, lr: 4.79e-04
2022-05-14 19:34:27,233 INFO [train.py:812] (6/8) Epoch 16, batch 4050, loss[loss=0.1618, simple_loss=0.247, pruned_loss=0.03827, over 7162.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2576, pruned_loss=0.03952, over 1418433.53 frames.], batch size: 18, lr: 4.78e-04
2022-05-14 19:35:27,412 INFO [train.py:812] (6/8) Epoch 16, batch 4100, loss[loss=0.1642, simple_loss=0.2503, pruned_loss=0.039, over 7177.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2567, pruned_loss=0.03929, over 1423458.83 frames.], batch size: 18, lr: 4.78e-04
2022-05-14 19:36:26,222 INFO [train.py:812] (6/8) Epoch 16, batch 4150, loss[loss=0.1569, simple_loss=0.2402, pruned_loss=0.03679, over 7090.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2569, pruned_loss=0.03932, over 1418657.30 frames.], batch size: 28, lr: 4.78e-04
2022-05-14 19:37:25,132 INFO [train.py:812] (6/8) Epoch 16, batch 4200, loss[loss=0.1433, simple_loss=0.2264, pruned_loss=0.03011, over 7007.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2559, pruned_loss=0.03892, over 1417644.95 frames.], batch size: 16, lr: 4.78e-04
2022-05-14 19:38:24,453 INFO [train.py:812] (6/8) Epoch 16, batch 4250, loss[loss=0.1678, simple_loss=0.2426, pruned_loss=0.04645, over 7168.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2549, pruned_loss=0.03902, over 1416628.41 frames.], batch size: 18, lr: 4.78e-04
2022-05-14 19:39:23,848 INFO [train.py:812] (6/8) Epoch 16, batch 4300, loss[loss=0.193, simple_loss=0.2829, pruned_loss=0.05153, over 6666.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2549, pruned_loss=0.03915, over 1411955.21 frames.], batch size: 31, lr: 4.78e-04
2022-05-14 19:40:22,739 INFO [train.py:812] (6/8) Epoch 16, batch 4350, loss[loss=0.159, simple_loss=0.2449, pruned_loss=0.0366, over 7162.00 frames.], tot_loss[loss=0.1664, simple_loss=0.255, pruned_loss=0.03894, over 1415544.77 frames.], batch size: 18, lr: 4.77e-04
2022-05-14 19:41:21,979 INFO [train.py:812] (6/8) Epoch 16, batch 4400, loss[loss=0.1564, simple_loss=0.2472, pruned_loss=0.03286, over 7116.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2555, pruned_loss=0.03918, over 1415453.28 frames.], batch size: 21, lr: 4.77e-04
2022-05-14 19:42:18,623 INFO [train.py:812] (6/8) Epoch 16, batch 4450, loss[loss=0.1877, simple_loss=0.2717, pruned_loss=0.05185, over 7201.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2559, pruned_loss=0.03935, over 1410767.46 frames.], batch size: 22, lr: 4.77e-04
2022-05-14 19:43:16,038 INFO [train.py:812] (6/8) Epoch 16, batch 4500, loss[loss=0.1335, simple_loss=0.2083, pruned_loss=0.02935, over 7146.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2558, pruned_loss=0.03918, over 1401652.94 frames.], batch size: 17, lr: 4.77e-04
2022-05-14 19:44:12,910 INFO [train.py:812] (6/8) Epoch 16, batch 4550, loss[loss=0.1892, simple_loss=0.2793, pruned_loss=0.04955, over 4856.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2592, pruned_loss=0.0416, over 1349149.16 frames.], batch size: 52, lr: 4.77e-04
2022-05-14 19:45:27,046 INFO [train.py:812] (6/8) Epoch 17, batch 0, loss[loss=0.1598, simple_loss=0.2467, pruned_loss=0.03643, over 7445.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2467, pruned_loss=0.03643, over 7445.00 frames.], batch size: 22, lr: 4.63e-04
2022-05-14 19:46:26,110 INFO [train.py:812] (6/8) Epoch 17, batch 50, loss[loss=0.1702, simple_loss=0.265, pruned_loss=0.03768, over 7324.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2605, pruned_loss=0.04065, over 317602.03 frames.], batch size: 21, lr: 4.63e-04
2022-05-14 19:47:25,021 INFO [train.py:812] (6/8) Epoch 17, batch 100, loss[loss=0.1846, simple_loss=0.2777, pruned_loss=0.04571, over 7162.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2574, pruned_loss=0.03878, over 559779.17 frames.], batch size: 20, lr: 4.63e-04
2022-05-14 19:48:23,540 INFO [train.py:812] (6/8) Epoch 17, batch 150, loss[loss=0.1518, simple_loss=0.228, pruned_loss=0.03774, over 7016.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2556, pruned_loss=0.03829, over 747364.48 frames.], batch size: 16, lr: 4.63e-04
2022-05-14 19:49:23,011 INFO [train.py:812] (6/8) Epoch 17, batch 200, loss[loss=0.1562, simple_loss=0.2409, pruned_loss=0.03576, over 7128.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2581, pruned_loss=0.03909, over 896513.07 frames.], batch size: 17, lr: 4.63e-04
2022-05-14 19:50:21,386 INFO [train.py:812] (6/8) Epoch 17, batch 250, loss[loss=0.1618, simple_loss=0.2542, pruned_loss=0.03468, over 7262.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2585, pruned_loss=0.03948, over 1015773.28 frames.], batch size: 19, lr: 4.63e-04
2022-05-14 19:51:20,300 INFO [train.py:812] (6/8) Epoch 17, batch 300, loss[loss=0.1604, simple_loss=0.2469, pruned_loss=0.03698, over 7059.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2594, pruned_loss=0.04014, over 1100847.11 frames.], batch size: 18, lr: 4.62e-04
2022-05-14 19:52:19,503 INFO [train.py:812] (6/8) Epoch 17, batch 350, loss[loss=0.142, simple_loss=0.223, pruned_loss=0.0305, over 6785.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2589, pruned_loss=0.04006, over 1171320.78 frames.], batch size: 15, lr: 4.62e-04
2022-05-14 19:53:18,633 INFO [train.py:812] (6/8) Epoch 17, batch 400, loss[loss=0.1968, simple_loss=0.2743, pruned_loss=0.05967, over 5429.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2583, pruned_loss=0.03923, over 1227972.21 frames.], batch size: 52, lr: 4.62e-04
2022-05-14 19:54:16,207 INFO [train.py:812] (6/8) Epoch 17, batch 450, loss[loss=0.1672, simple_loss=0.2563, pruned_loss=0.03906, over 7373.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2581, pruned_loss=0.03904, over 1268855.83 frames.], batch size: 19, lr: 4.62e-04
2022-05-14 19:55:14,841 INFO [train.py:812] (6/8) Epoch 17, batch 500, loss[loss=0.1475, simple_loss=0.2349, pruned_loss=0.03006, over 7159.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2571, pruned_loss=0.03859, over 1301522.85 frames.], batch size: 18, lr: 4.62e-04
2022-05-14 19:56:13,710 INFO [train.py:812] (6/8) Epoch 17, batch 550, loss[loss=0.1453, simple_loss=0.2273, pruned_loss=0.03167, over 7134.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2568, pruned_loss=0.03893, over 1326613.05 frames.], batch size: 17, lr: 4.62e-04
2022-05-14 19:57:12,595 INFO [train.py:812] (6/8) Epoch 17, batch 600, loss[loss=0.1631, simple_loss=0.2511, pruned_loss=0.03752, over 7155.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2572, pruned_loss=0.03946, over 1341427.59 frames.], batch size: 28, lr: 4.62e-04
2022-05-14 19:58:11,570 INFO [train.py:812] (6/8) Epoch 17, batch 650, loss[loss=0.1652, simple_loss=0.2633, pruned_loss=0.03352, over 7328.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2577, pruned_loss=0.03944, over 1359740.09 frames.], batch size: 20, lr: 4.61e-04
2022-05-14 19:59:10,295 INFO [train.py:812] (6/8) Epoch 17, batch 700, loss[loss=0.1683, simple_loss=0.2506, pruned_loss=0.04303, over 7258.00 frames.], tot_loss[loss=0.1685, simple_loss=0.258, pruned_loss=0.03949, over 1366889.56 frames.], batch size: 19, lr: 4.61e-04
2022-05-14 20:00:09,357 INFO [train.py:812] (6/8) Epoch 17, batch 750, loss[loss=0.166, simple_loss=0.2613, pruned_loss=0.03536, over 7146.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2584, pruned_loss=0.03971, over 1375185.09 frames.], batch size: 20, lr: 4.61e-04
2022-05-14 20:01:08,217 INFO [train.py:812] (6/8) Epoch 17, batch 800, loss[loss=0.1354, simple_loss=0.2316, pruned_loss=0.01959, over 7163.00 frames.], tot_loss[loss=0.168, simple_loss=0.2573, pruned_loss=0.03935, over 1386496.13 frames.], batch size: 19, lr: 4.61e-04
2022-05-14 20:02:07,182 INFO [train.py:812] (6/8) Epoch 17, batch 850, loss[loss=0.1861, simple_loss=0.2728, pruned_loss=0.04976, over 6377.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2562, pruned_loss=0.03904, over 1394404.17 frames.], batch size: 38, lr: 4.61e-04
2022-05-14 20:03:05,150 INFO [train.py:812] (6/8) Epoch 17, batch 900, loss[loss=0.1876, simple_loss=0.2678, pruned_loss=0.05368, over 7325.00 frames.], tot_loss[loss=0.1672, simple_loss=0.256, pruned_loss=0.03922, over 1406167.91 frames.], batch size: 20, lr: 4.61e-04
2022-05-14 20:04:03,155 INFO [train.py:812] (6/8) Epoch 17, batch 950, loss[loss=0.14, simple_loss=0.2105, pruned_loss=0.03472, over 7146.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2562, pruned_loss=0.03903, over 1411270.46 frames.], batch size: 17, lr: 4.60e-04
2022-05-14 20:05:01,754 INFO [train.py:812] (6/8) Epoch 17, batch 1000, loss[loss=0.1573, simple_loss=0.2561, pruned_loss=0.02921, over 7118.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2564, pruned_loss=0.03901, over 1415825.59 frames.], batch size: 21, lr: 4.60e-04
2022-05-14 20:06:00,366 INFO [train.py:812] (6/8) Epoch 17, batch 1050, loss[loss=0.1908, simple_loss=0.2938, pruned_loss=0.04387, over 7345.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2557, pruned_loss=0.03888, over 1420120.96 frames.], batch size: 22, lr: 4.60e-04
2022-05-14 20:06:59,586 INFO [train.py:812] (6/8) Epoch 17, batch 1100, loss[loss=0.1673, simple_loss=0.2613, pruned_loss=0.03667, over 7288.00 frames.], tot_loss[loss=0.167, simple_loss=0.2562, pruned_loss=0.03885, over 1420603.01 frames.], batch size: 24, lr: 4.60e-04
2022-05-14 20:07:58,277 INFO [train.py:812] (6/8) Epoch 17, batch 1150, loss[loss=0.1964, simple_loss=0.2857, pruned_loss=0.05354, over 7306.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2563, pruned_loss=0.03879, over 1422345.50 frames.], batch size: 24, lr: 4.60e-04
2022-05-14 20:08:57,635 INFO [train.py:812] (6/8) Epoch 17, batch 1200, loss[loss=0.2373, simple_loss=0.328, pruned_loss=0.07325, over 7295.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2567, pruned_loss=0.03905, over 1419958.09 frames.], batch size: 25, lr: 4.60e-04
2022-05-14 20:09:55,629 INFO [train.py:812] (6/8) Epoch 17, batch 1250, loss[loss=0.1412, simple_loss=0.2281, pruned_loss=0.02712, over 7271.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2567, pruned_loss=0.03926, over 1415113.07 frames.], batch size: 18, lr: 4.60e-04
2022-05-14 20:10:53,536 INFO [train.py:812] (6/8) Epoch 17, batch 1300, loss[loss=0.1996, simple_loss=0.2891, pruned_loss=0.055, over 7349.00 frames.], tot_loss[loss=0.1682, simple_loss=0.257, pruned_loss=0.0397, over 1413679.47 frames.], batch size: 22, lr: 4.59e-04
2022-05-14 20:11:51,666 INFO [train.py:812] (6/8) Epoch 17, batch 1350, loss[loss=0.1682, simple_loss=0.2444, pruned_loss=0.04603, over 7003.00 frames.], tot_loss[loss=0.1678, simple_loss=0.257, pruned_loss=0.03937, over 1418932.65 frames.], batch size: 16, lr: 4.59e-04
2022-05-14 20:12:51,173 INFO [train.py:812] (6/8) Epoch 17, batch 1400, loss[loss=0.19, simple_loss=0.2757, pruned_loss=0.05213, over 7143.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2558, pruned_loss=0.03906, over 1420430.24 frames.], batch size: 20, lr: 4.59e-04
2022-05-14 20:13:49,622 INFO [train.py:812] (6/8) Epoch 17, batch 1450, loss[loss=0.1986, simple_loss=0.2832, pruned_loss=0.05695, over 7344.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2562, pruned_loss=0.03921, over 1419125.97 frames.], batch size: 22, lr: 4.59e-04
2022-05-14 20:14:48,960 INFO [train.py:812] (6/8) Epoch 17, batch 1500, loss[loss=0.134, simple_loss=0.2181, pruned_loss=0.02493, over 7253.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2546, pruned_loss=0.03879, over 1424722.98 frames.], batch size: 19, lr: 4.59e-04
2022-05-14 20:15:57,368 INFO [train.py:812] (6/8) Epoch 17, batch 1550, loss[loss=0.1807, simple_loss=0.269, pruned_loss=0.0462, over 7215.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2547, pruned_loss=0.03891, over 1422381.92 frames.], batch size: 21, lr: 4.59e-04
2022-05-14 20:16:56,760 INFO [train.py:812] (6/8) Epoch 17, batch 1600, loss[loss=0.156, simple_loss=0.2492, pruned_loss=0.03137, over 7429.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2553, pruned_loss=0.03874, over 1426607.59 frames.], batch size: 20, lr: 4.58e-04
2022-05-14 20:17:55,355 INFO [train.py:812] (6/8) Epoch 17, batch 1650, loss[loss=0.1561, simple_loss=0.2521, pruned_loss=0.03004, over 7408.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2557, pruned_loss=0.03871, over 1429378.52 frames.], batch size: 21, lr: 4.58e-04
2022-05-14 20:18:53,701 INFO [train.py:812] (6/8) Epoch 17, batch 1700, loss[loss=0.2338, simple_loss=0.3002, pruned_loss=0.0837, over 5428.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2559, pruned_loss=0.03846, over 1423300.00 frames.], batch size: 52, lr: 4.58e-04
2022-05-14 20:19:52,414 INFO [train.py:812] (6/8) Epoch 17, batch 1750, loss[loss=0.1851, simple_loss=0.2775, pruned_loss=0.04632, over 7391.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2571, pruned_loss=0.03925, over 1415392.04 frames.], batch size: 23, lr: 4.58e-04
2022-05-14 20:20:51,561 INFO [train.py:812] (6/8) Epoch 17, batch 1800, loss[loss=0.1556, simple_loss=0.2522, pruned_loss=0.02955, over 7187.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2569, pruned_loss=0.03899, over 1416090.55 frames.], batch size: 23, lr: 4.58e-04
2022-05-14 20:21:48,767 INFO [train.py:812] (6/8) Epoch 17, batch 1850, loss[loss=0.1595, simple_loss=0.2564, pruned_loss=0.03134, over 6395.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2569, pruned_loss=0.03872, over 1417081.24 frames.], batch size: 38, lr: 4.58e-04
2022-05-14 20:22:47,377 INFO [train.py:812] (6/8) Epoch 17, batch 1900, loss[loss=0.1619, simple_loss=0.2481, pruned_loss=0.03785, over 7437.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2565, pruned_loss=0.03848, over 1420977.54 frames.], batch size: 20, lr: 4.58e-04
2022-05-14 20:23:46,076 INFO [train.py:812] (6/8) Epoch 17, batch 1950, loss[loss=0.1587, simple_loss=0.2493, pruned_loss=0.03404, over 7318.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2563, pruned_loss=0.03873, over 1422909.92 frames.], batch size: 21, lr: 4.57e-04
2022-05-14 20:24:44,624 INFO [train.py:812] (6/8) Epoch 17, batch 2000, loss[loss=0.1732, simple_loss=0.2554, pruned_loss=0.04545, over 7251.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2574, pruned_loss=0.03904, over 1424643.87 frames.], batch size: 19, lr: 4.57e-04
2022-05-14 20:25:43,676 INFO [train.py:812] (6/8) Epoch 17, batch 2050, loss[loss=0.1308, simple_loss=0.2198, pruned_loss=0.02091, over 7420.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2561, pruned_loss=0.03872, over 1428822.47 frames.], batch size: 18, lr: 4.57e-04
2022-05-14 20:26:43,371 INFO [train.py:812] (6/8) Epoch 17, batch 2100, loss[loss=0.1675, simple_loss=0.2616, pruned_loss=0.03669, over 7414.00 frames.], tot_loss[loss=0.167, simple_loss=0.2567, pruned_loss=0.03866, over 1428870.72 frames.], batch size: 21, lr: 4.57e-04
2022-05-14 20:27:42,673 INFO [train.py:812] (6/8) Epoch 17, batch 2150, loss[loss=0.1609, simple_loss=0.2645, pruned_loss=0.02863, over 7362.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2566, pruned_loss=0.03843, over 1425729.73 frames.], batch size: 19, lr: 4.57e-04
2022-05-14 20:28:40,072 INFO [train.py:812] (6/8) Epoch 17, batch 2200, loss[loss=0.1484, simple_loss=0.241, pruned_loss=0.0279, over 7325.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2563, pruned_loss=0.03826, over 1422634.11 frames.], batch size: 22, lr: 4.57e-04
2022-05-14 20:29:39,226 INFO [train.py:812] (6/8) Epoch 17, batch 2250, loss[loss=0.152, simple_loss=0.2541, pruned_loss=0.02492, over 7413.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2564, pruned_loss=0.0383, over 1425608.91 frames.], batch size: 21, lr: 4.56e-04
2022-05-14 20:30:37,981 INFO [train.py:812] (6/8) Epoch 17, batch 2300, loss[loss=0.1807, simple_loss=0.2734, pruned_loss=0.04404, over 7285.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2565, pruned_loss=0.03862, over 1424546.72 frames.], batch size: 24, lr: 4.56e-04
2022-05-14 20:31:36,717 INFO [train.py:812] (6/8) Epoch 17, batch 2350, loss[loss=0.1745, simple_loss=0.2716, pruned_loss=0.03868, over 7390.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2559, pruned_loss=0.03844, over 1427455.81 frames.], batch size: 23, lr: 4.56e-04
2022-05-14 20:32:36,109 INFO [train.py:812] (6/8) Epoch 17, batch 2400, loss[loss=0.1462, simple_loss=0.2346, pruned_loss=0.02892, over 6984.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2548, pruned_loss=0.03813, over 1424794.06 frames.], batch size: 16, lr: 4.56e-04
2022-05-14 20:33:34,537 INFO [train.py:812] (6/8) Epoch 17, batch 2450, loss[loss=0.1646, simple_loss=0.2619, pruned_loss=0.03366, over 7345.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2544, pruned_loss=0.038, over 1424471.97 frames.], batch size: 22, lr: 4.56e-04
2022-05-14 20:34:34,272 INFO [train.py:812] (6/8) Epoch 17, batch 2500, loss[loss=0.186, simple_loss=0.2801, pruned_loss=0.04597, over 7228.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2534, pruned_loss=0.03746, over 1424441.92 frames.], batch size: 21, lr: 4.56e-04
2022-05-14 20:35:31,573 INFO [train.py:812] (6/8) Epoch 17, batch 2550, loss[loss=0.1505, simple_loss=0.25, pruned_loss=0.02547, over 7221.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2532, pruned_loss=0.03753, over 1419917.45 frames.], batch size: 21, lr: 4.56e-04
2022-05-14 20:36:37,570 INFO [train.py:812] (6/8) Epoch 17, batch 2600, loss[loss=0.1701, simple_loss=0.2623, pruned_loss=0.03892, over 7084.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2549, pruned_loss=0.03814, over 1423383.77 frames.], batch size: 28, lr: 4.55e-04
2022-05-14 20:37:36,701 INFO [train.py:812] (6/8) Epoch 17, batch 2650, loss[loss=0.1646, simple_loss=0.255, pruned_loss=0.03715, over 7362.00 frames.], tot_loss[loss=0.1668, simple_loss=0.256, pruned_loss=0.03877, over 1421737.47 frames.], batch size: 19, lr: 4.55e-04
2022-05-14 20:38:34,791 INFO [train.py:812] (6/8) Epoch 17, batch 2700, loss[loss=0.1745, simple_loss=0.2676, pruned_loss=0.04074, over 7334.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2551, pruned_loss=0.03821, over 1424047.05 frames.], batch size: 22, lr: 4.55e-04
2022-05-14 20:39:32,818 INFO [train.py:812] (6/8) Epoch 17, batch 2750, loss[loss=0.1618, simple_loss=0.2519, pruned_loss=0.0359, over 7166.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2544, pruned_loss=0.03811, over 1423162.92 frames.], batch size: 19, lr: 4.55e-04
2022-05-14 20:40:31,889 INFO [train.py:812] (6/8) Epoch 17, batch 2800, loss[loss=0.2144, simple_loss=0.3024, pruned_loss=0.06318, over 5163.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2542, pruned_loss=0.03813, over 1422301.87 frames.], batch size: 55, lr: 4.55e-04
2022-05-14 20:41:30,554 INFO [train.py:812] (6/8) Epoch 17, batch 2850, loss[loss=0.1757, simple_loss=0.2749, pruned_loss=0.03823, over 7315.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2554, pruned_loss=0.03855, over 1421547.69 frames.], batch size: 21, lr: 4.55e-04
2022-05-14 20:42:28,906 INFO [train.py:812] (6/8) Epoch 17, batch 2900, loss[loss=0.1428, simple_loss=0.2335, pruned_loss=0.02604, over 7228.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2556, pruned_loss=0.03875, over 1417919.26 frames.], batch size: 20, lr: 4.55e-04
2022-05-14 20:43:27,767 INFO [train.py:812] (6/8) Epoch 17, batch 2950, loss[loss=0.1558, simple_loss=0.2457, pruned_loss=0.03299, over 7276.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2559, pruned_loss=0.03871, over 1418204.32 frames.], batch size: 18, lr: 4.54e-04
2022-05-14 20:44:36,166 INFO [train.py:812] (6/8) Epoch 17, batch 3000, loss[loss=0.1628, simple_loss=0.258, pruned_loss=0.03382, over 7148.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2559, pruned_loss=0.03858, over 1423022.48 frames.], batch size: 20, lr: 4.54e-04
2022-05-14 20:44:36,167 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 20:44:43,902 INFO [train.py:841] (6/8) Epoch 17, validation: loss=0.1538, simple_loss=0.2534, pruned_loss=0.02708, over 698248.00 frames.
2022-05-14 20:45:42,781 INFO [train.py:812] (6/8) Epoch 17, batch 3050, loss[loss=0.1752, simple_loss=0.2634, pruned_loss=0.04349, over 6476.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2551, pruned_loss=0.03812, over 1422999.89 frames.], batch size: 38, lr: 4.54e-04
2022-05-14 20:46:41,081 INFO [train.py:812] (6/8) Epoch 17, batch 3100, loss[loss=0.1738, simple_loss=0.2653, pruned_loss=0.04115, over 7321.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2552, pruned_loss=0.03834, over 1419802.21 frames.], batch size: 25, lr: 4.54e-04
2022-05-14 20:47:58,636 INFO [train.py:812] (6/8) Epoch 17, batch 3150, loss[loss=0.1458, simple_loss=0.2405, pruned_loss=0.02558, over 7325.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2553, pruned_loss=0.03851, over 1418548.10 frames.], batch size: 20, lr: 4.54e-04
2022-05-14 20:49:07,278 INFO [train.py:812] (6/8) Epoch 17, batch 3200, loss[loss=0.1482, simple_loss=0.2351, pruned_loss=0.03065, over 7356.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2559, pruned_loss=0.03893, over 1418238.69 frames.], batch size: 19, lr: 4.54e-04
2022-05-14 20:50:25,539 INFO [train.py:812] (6/8) Epoch 17, batch 3250, loss[loss=0.1562, simple_loss=0.2427, pruned_loss=0.0348, over 7078.00 frames.], tot_loss[loss=0.167, simple_loss=0.2564, pruned_loss=0.03886, over 1423554.42 frames.], batch size: 18, lr: 4.54e-04
2022-05-14 20:51:34,403 INFO [train.py:812] (6/8) Epoch 17, batch 3300, loss[loss=0.1753, simple_loss=0.2708, pruned_loss=0.03995, over 7148.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2572, pruned_loss=0.03916, over 1424976.86 frames.], batch size: 19, lr: 4.53e-04
2022-05-14 20:52:33,328 INFO [train.py:812] (6/8) Epoch 17, batch 3350, loss[loss=0.1704, simple_loss=0.2704, pruned_loss=0.03525, over 7337.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2582, pruned_loss=0.0392, over 1425983.38 frames.], batch size: 22, lr: 4.53e-04
2022-05-14 20:53:32,430 INFO [train.py:812] (6/8) Epoch 17, batch 3400, loss[loss=0.1947, simple_loss=0.2779, pruned_loss=0.05572, over 7144.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2576, pruned_loss=0.03896, over 1422556.63 frames.], batch size: 20, lr: 4.53e-04
2022-05-14 20:54:31,699 INFO [train.py:812] (6/8) Epoch 17, batch 3450, loss[loss=0.1562, simple_loss=0.2433, pruned_loss=0.03452, over 7331.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2553, pruned_loss=0.03848, over 1423688.94 frames.], batch size: 20, lr: 4.53e-04
2022-05-14 20:55:30,349 INFO [train.py:812] (6/8) Epoch 17, batch 3500, loss[loss=0.1626, simple_loss=0.2551, pruned_loss=0.035, over 7203.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2543, pruned_loss=0.03794, over 1423150.44 frames.], batch size: 22, lr: 4.53e-04
2022-05-14 20:56:29,318 INFO [train.py:812] (6/8) Epoch 17, batch 3550, loss[loss=0.1688, simple_loss=0.258, pruned_loss=0.0398, over 7439.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2551, pruned_loss=0.03816, over 1426306.39 frames.], batch size: 22, lr: 4.53e-04
2022-05-14 20:57:28,823 INFO [train.py:812] (6/8) Epoch 17, batch 3600, loss[loss=0.1514, simple_loss=0.2332, pruned_loss=0.03484, over 7285.00 frames.], tot_loss[loss=0.166, simple_loss=0.2556, pruned_loss=0.03824, over 1426802.39 frames.], batch size: 18, lr: 4.52e-04
2022-05-14 20:58:27,848 INFO [train.py:812] (6/8) Epoch 17, batch 3650, loss[loss=0.1778, simple_loss=0.2722, pruned_loss=0.04172, over 7305.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2551, pruned_loss=0.03857, over 1430949.26 frames.], batch size: 21, lr: 4.52e-04
2022-05-14 20:59:27,710 INFO [train.py:812] (6/8) Epoch 17, batch 3700, loss[loss=0.1639, simple_loss=0.2574, pruned_loss=0.03518, over 7149.00 frames.], tot_loss[loss=0.1659, simple_loss=0.255, pruned_loss=0.03844, over 1431146.43 frames.], batch size: 20, lr: 4.52e-04
2022-05-14 21:00:26,368 INFO [train.py:812] (6/8) Epoch 17, batch 3750, loss[loss=0.1871, simple_loss=0.2924, pruned_loss=0.04087, over 6314.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2555, pruned_loss=0.03846, over 1428457.27 frames.], batch size: 38, lr: 4.52e-04
2022-05-14 21:01:24,386 INFO [train.py:812] (6/8) Epoch 17, batch 3800, loss[loss=0.1731, simple_loss=0.2545, pruned_loss=0.04586, over 6407.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2561, pruned_loss=0.03834, over 1427088.81 frames.], batch size: 38, lr: 4.52e-04
2022-05-14 21:02:23,100 INFO [train.py:812] (6/8) Epoch 17, batch 3850, loss[loss=0.1458, simple_loss=0.2276, pruned_loss=0.03204, over 6992.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2561, pruned_loss=0.03821, over 1426291.40 frames.], batch size: 16, lr: 4.52e-04
2022-05-14 21:03:22,493 INFO [train.py:812] (6/8) Epoch 17, batch 3900, loss[loss=0.1774, simple_loss=0.2605, pruned_loss=0.04713, over 7204.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2545, pruned_loss=0.03798, over 1428479.27 frames.], batch size: 22, lr: 4.52e-04
2022-05-14 21:04:21,501 INFO [train.py:812] (6/8) Epoch 17, batch 3950, loss[loss=0.1722, simple_loss=0.2651, pruned_loss=0.0397, over 7187.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2555, pruned_loss=0.03844, over 1428025.98 frames.], batch size: 23, lr: 4.51e-04
2022-05-14 21:05:20,853 INFO [train.py:812] (6/8) Epoch 17, batch 4000, loss[loss=0.1434, simple_loss=0.2322, pruned_loss=0.02729, over 7274.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2555, pruned_loss=0.03872, over 1428006.78 frames.], batch size: 18, lr: 4.51e-04
2022-05-14 21:06:19,945 INFO [train.py:812] (6/8) Epoch 17, batch 4050, loss[loss=0.1568, simple_loss=0.2597, pruned_loss=0.02701, over 6822.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2555, pruned_loss=0.03851, over 1423896.00 frames.], batch size: 31, lr: 4.51e-04
2022-05-14 21:07:19,013 INFO [train.py:812] (6/8) Epoch 17, batch 4100, loss[loss=0.1776, simple_loss=0.2729, pruned_loss=0.04116, over 6639.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2569, pruned_loss=0.0393, over 1423627.46 frames.], batch size: 38, lr: 4.51e-04
2022-05-14 21:08:18,283 INFO [train.py:812] (6/8) Epoch 17, batch 4150, loss[loss=0.1545, simple_loss=0.238, pruned_loss=0.03545, over 7129.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2562, pruned_loss=0.03933, over 1422779.33 frames.], batch size: 17, lr: 4.51e-04
2022-05-14 21:09:17,062 INFO [train.py:812] (6/8) Epoch 17, batch 4200, loss[loss=0.1747, simple_loss=0.2741, pruned_loss=0.03767, over 7154.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2564, pruned_loss=0.03938, over 1421805.70 frames.], batch size: 26, lr: 4.51e-04
2022-05-14 21:10:16,233 INFO [train.py:812] (6/8) Epoch 17, batch 4250, loss[loss=0.1712, simple_loss=0.2598, pruned_loss=0.04133, over 7282.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2566, pruned_loss=0.0392, over 1423480.61 frames.], batch size: 18, lr: 4.51e-04
2022-05-14 21:11:15,277 INFO [train.py:812] (6/8) Epoch 17, batch 4300, loss[loss=0.1566, simple_loss=0.242, pruned_loss=0.03566, over 7067.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2556, pruned_loss=0.03909, over 1422206.46 frames.], batch size: 18, lr: 4.50e-04
2022-05-14 21:12:14,030 INFO [train.py:812] (6/8) Epoch 17, batch 4350, loss[loss=0.1803, simple_loss=0.2598, pruned_loss=0.05039, over 7165.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2558, pruned_loss=0.03902, over 1421682.73 frames.], batch size: 18, lr: 4.50e-04
2022-05-14 21:13:12,865 INFO [train.py:812] (6/8) Epoch 17, batch 4400, loss[loss=0.1742, simple_loss=0.2674, pruned_loss=0.0405, over 7210.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2557, pruned_loss=0.03894, over 1419523.28 frames.], batch size: 21, lr: 4.50e-04
2022-05-14 21:14:12,284 INFO [train.py:812] (6/8) Epoch 17, batch 4450, loss[loss=0.1482, simple_loss=0.2279, pruned_loss=0.03426, over 7139.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2563, pruned_loss=0.03929, over 1415786.73 frames.], batch size: 17, lr: 4.50e-04
2022-05-14 21:15:12,256 INFO [train.py:812] (6/8) Epoch 17, batch 4500, loss[loss=0.1603, simple_loss=0.2549, pruned_loss=0.03282, over 7232.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2553, pruned_loss=0.03913, over 1416191.07 frames.], batch size: 20, lr: 4.50e-04
2022-05-14 21:16:11,546 INFO [train.py:812] (6/8) Epoch 17, batch 4550, loss[loss=0.1612, simple_loss=0.2562, pruned_loss=0.03306, over 4993.00 frames.], tot_loss[loss=0.1673, simple_loss=0.255, pruned_loss=0.03981, over 1381755.46 frames.], batch size: 52, lr: 4.50e-04
2022-05-14 21:17:18,370 INFO [train.py:812] (6/8) Epoch 18, batch 0, loss[loss=0.1841, simple_loss=0.2735, pruned_loss=0.0473, over 7232.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2735, pruned_loss=0.0473, over 7232.00 frames.], batch size: 20, lr: 4.38e-04
2022-05-14 21:18:18,299 INFO [train.py:812] (6/8) Epoch 18, batch 50, loss[loss=0.1642, simple_loss=0.2387, pruned_loss=0.04486, over 6983.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2549, pruned_loss=0.03835, over 323573.32 frames.], batch size: 16, lr: 4.38e-04
2022-05-14 21:19:17,376 INFO [train.py:812] (6/8) Epoch 18, batch 100, loss[loss=0.1564, simple_loss=0.2441, pruned_loss=0.03435, over 7151.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2568, pruned_loss=0.03919, over 565932.19 frames.], batch size: 18, lr: 4.37e-04
2022-05-14 21:20:15,730 INFO [train.py:812] (6/8) Epoch 18, batch 150, loss[loss=0.1621, simple_loss=0.2499, pruned_loss=0.03713, over 7150.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2581, pruned_loss=0.03953, over 753211.91 frames.], batch size: 20, lr: 4.37e-04
2022-05-14 21:21:13,528 INFO [train.py:812] (6/8) Epoch 18, batch 200, loss[loss=0.1475, simple_loss=0.2418, pruned_loss=0.02663, over 7159.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2568, pruned_loss=0.03835, over 904775.91 frames.], batch size: 18, lr: 4.37e-04
2022-05-14 21:22:12,919 INFO [train.py:812] (6/8) Epoch 18, batch 250, loss[loss=0.166, simple_loss=0.2556, pruned_loss=0.0382, over 6829.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2565, pruned_loss=0.03828, over 1022475.21 frames.], batch size: 31, lr: 4.37e-04
2022-05-14 21:23:11,936 INFO [train.py:812] (6/8) Epoch 18, batch 300, loss[loss=0.1634, simple_loss=0.2624, pruned_loss=0.03217, over 7094.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2571, pruned_loss=0.0382, over 1106263.77 frames.], batch size: 28, lr: 4.37e-04
2022-05-14 21:24:11,153 INFO [train.py:812] (6/8) Epoch 18, batch 350, loss[loss=0.1817, simple_loss=0.2778, pruned_loss=0.04277, over 7330.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2549, pruned_loss=0.03793, over 1174658.14 frames.], batch size: 22, lr: 4.37e-04
2022-05-14 21:25:08,912 INFO [train.py:812] (6/8) Epoch 18, batch 400, loss[loss=0.1497, simple_loss=0.2352, pruned_loss=0.03204, over 6816.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2556, pruned_loss=0.03762, over 1233966.59 frames.], batch size: 15, lr: 4.37e-04
2022-05-14 21:26:06,618 INFO [train.py:812] (6/8) Epoch 18, batch 450, loss[loss=0.1524, simple_loss=0.2447, pruned_loss=0.03006, over 7215.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2558, pruned_loss=0.03772, over 1276898.24 frames.], batch size: 22, lr: 4.36e-04
2022-05-14 21:27:06,227 INFO [train.py:812] (6/8) Epoch 18, batch 500, loss[loss=0.1525, simple_loss=0.2534, pruned_loss=0.02574, over 7343.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2555, pruned_loss=0.03751, over 1313593.55 frames.], batch size: 22, lr: 4.36e-04
2022-05-14 21:28:04,635 INFO [train.py:812] (6/8) Epoch 18, batch 550, loss[loss=0.1615, simple_loss=0.2511, pruned_loss=0.03597, over 7129.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2556, pruned_loss=0.03763, over 1339647.27 frames.], batch size: 17, lr: 4.36e-04
2022-05-14 21:29:02,271 INFO [train.py:812] (6/8) Epoch 18, batch 600, loss[loss=0.177, simple_loss=0.2674, pruned_loss=0.04331, over 6162.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2571, pruned_loss=0.03824, over 1356857.58 frames.], batch size: 37, lr: 4.36e-04
2022-05-14 21:30:01,254 INFO [train.py:812] (6/8) Epoch 18, batch 650, loss[loss=0.1888, simple_loss=0.2667, pruned_loss=0.05546, over 5130.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2564, pruned_loss=0.03759, over 1369485.43 frames.], batch size: 54, lr: 4.36e-04
2022-05-14 21:30:59,634 INFO [train.py:812] (6/8) Epoch 18, batch 700, loss[loss=0.1576, simple_loss=0.2558, pruned_loss=0.02973, over 7317.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2558, pruned_loss=0.03769, over 1380844.96 frames.], batch size: 21, lr: 4.36e-04
2022-05-14 21:31:59,640 INFO [train.py:812] (6/8) Epoch 18, batch 750, loss[loss=0.1365, simple_loss=0.2177, pruned_loss=0.02766, over 7423.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2548, pruned_loss=0.03768, over 1391325.04 frames.], batch size: 18, lr: 4.36e-04
2022-05-14 21:32:57,582 INFO [train.py:812] (6/8) Epoch 18, batch 800, loss[loss=0.1715, simple_loss=0.2697, pruned_loss=0.03661, over 7323.00 frames.], tot_loss[loss=0.164, simple_loss=0.2542, pruned_loss=0.03688, over 1403743.39 frames.], batch size: 21, lr: 4.36e-04
2022-05-14 21:33:57,272 INFO [train.py:812] (6/8) Epoch 18, batch 850, loss[loss=0.1467, simple_loss=0.2434, pruned_loss=0.02499, over 7417.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2536, pruned_loss=0.03689, over 1407921.61 frames.], batch size: 21, lr: 4.35e-04
2022-05-14 21:34:56,179 INFO [train.py:812] (6/8) Epoch 18, batch 900, loss[loss=0.164, simple_loss=0.2571, pruned_loss=0.03546, over 7207.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2549, pruned_loss=0.03747, over 1408535.63 frames.], batch size: 22, lr: 4.35e-04
2022-05-14 21:35:54,678 INFO [train.py:812] (6/8) Epoch 18, batch 950, loss[loss=0.1703, simple_loss=0.2635, pruned_loss=0.0386, over 7257.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2552, pruned_loss=0.03748, over 1411239.58 frames.], batch size: 19, lr: 4.35e-04
2022-05-14 21:36:52,273 INFO [train.py:812] (6/8) Epoch 18, batch 1000, loss[loss=0.1739, simple_loss=0.2708, pruned_loss=0.03846, over 7316.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2539, pruned_loss=0.0368, over 1415503.92 frames.], batch size: 24, lr: 4.35e-04
2022-05-14 21:37:51,929 INFO [train.py:812] (6/8) Epoch 18, batch 1050, loss[loss=0.1541, simple_loss=0.2289, pruned_loss=0.03965, over 7285.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2538, pruned_loss=0.03724, over 1417792.05 frames.], batch size: 17, lr: 4.35e-04
2022-05-14 21:38:50,555 INFO [train.py:812] (6/8) Epoch 18, batch 1100, loss[loss=0.1858, simple_loss=0.2764, pruned_loss=0.04756, over 7299.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2541, pruned_loss=0.03736, over 1420734.59 frames.], batch size: 25, lr: 4.35e-04
2022-05-14 21:39:48,094 INFO [train.py:812] (6/8) Epoch 18, batch 1150, loss[loss=0.1912, simple_loss=0.2783, pruned_loss=0.05209, over 7377.00 frames.], tot_loss[loss=0.1657, simple_loss=0.255, pruned_loss=0.03817, over 1419703.67 frames.], batch size: 23, lr: 4.35e-04
2022-05-14 21:40:45,350 INFO [train.py:812] (6/8) Epoch 18, batch 1200, loss[loss=0.1689, simple_loss=0.2489, pruned_loss=0.04444, over 7281.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2553, pruned_loss=0.03848, over 1417379.30 frames.], batch size: 18, lr: 4.34e-04
2022-05-14 21:41:44,620 INFO [train.py:812] (6/8) Epoch 18, batch 1250, loss[loss=0.164, simple_loss=0.2499, pruned_loss=0.03902, over 7412.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2554, pruned_loss=0.03853, over 1419343.94 frames.], batch size: 21, lr: 4.34e-04
2022-05-14 21:42:42,173 INFO [train.py:812] (6/8) Epoch 18, batch 1300, loss[loss=0.177, simple_loss=0.2732, pruned_loss=0.0404, over 7149.00 frames.], tot_loss[loss=0.166, simple_loss=0.255, pruned_loss=0.03852, over 1419793.87 frames.], batch size: 26, lr: 4.34e-04
2022-05-14 21:43:41,349 INFO [train.py:812] (6/8) Epoch 18, batch 1350, loss[loss=0.1723, simple_loss=0.2373, pruned_loss=0.05363, over 7006.00 frames.], tot_loss[loss=0.166, simple_loss=0.2551, pruned_loss=0.03851, over 1422630.75 frames.], batch size: 16, lr: 4.34e-04
2022-05-14 21:44:39,601 INFO [train.py:812] (6/8) Epoch 18, batch 1400, loss[loss=0.1824, simple_loss=0.2821, pruned_loss=0.04134, over 7108.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2556, pruned_loss=0.03834, over 1424504.04 frames.], batch size: 21, lr: 4.34e-04
2022-05-14 21:45:38,207 INFO [train.py:812] (6/8) Epoch 18, batch 1450, loss[loss=0.1694, simple_loss=0.2643, pruned_loss=0.03722, over 7149.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2557, pruned_loss=0.03832, over 1421993.59 frames.], batch size: 20, lr: 4.34e-04
2022-05-14 21:46:36,916 INFO [train.py:812] (6/8) Epoch 18, batch 1500, loss[loss=0.1753, simple_loss=0.2631, pruned_loss=0.04371, over 7277.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2554, pruned_loss=0.03836, over 1413714.48 frames.], batch size: 25, lr: 4.34e-04
2022-05-14 21:47:35,833 INFO [train.py:812] (6/8) Epoch 18, batch 1550, loss[loss=0.1835, simple_loss=0.2599, pruned_loss=0.05351, over 7149.00 frames.], tot_loss[loss=0.165, simple_loss=0.2543, pruned_loss=0.03781, over 1421219.01 frames.], batch size: 19, lr: 4.33e-04
2022-05-14 21:48:33,682 INFO [train.py:812] (6/8) Epoch 18, batch 1600, loss[loss=0.1496, simple_loss=0.2426, pruned_loss=0.02829, over 7432.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2542, pruned_loss=0.03768, over 1421887.79 frames.], batch size: 20, lr: 4.33e-04
2022-05-14 21:49:33,288 INFO [train.py:812] (6/8) Epoch 18, batch 1650, loss[loss=0.1624, simple_loss=0.2484, pruned_loss=0.03822, over 7274.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2559, pruned_loss=0.03822, over 1421506.78 frames.], batch size: 17, lr: 4.33e-04
2022-05-14 21:50:30,811 INFO [train.py:812] (6/8) Epoch 18, batch 1700, loss[loss=0.1414, simple_loss=0.2242, pruned_loss=0.02929, over 7348.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2553, pruned_loss=0.03765, over 1424227.49 frames.], batch size: 19, lr: 4.33e-04
2022-05-14 21:51:29,638 INFO [train.py:812] (6/8) Epoch 18, batch 1750, loss[loss=0.1681, simple_loss=0.2602, pruned_loss=0.03794, over 7317.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2554, pruned_loss=0.03776, over 1424753.47 frames.], batch size: 21, lr: 4.33e-04
2022-05-14 21:52:27,488 INFO [train.py:812] (6/8) Epoch 18, batch 1800, loss[loss=0.195, simple_loss=0.2817, pruned_loss=0.0542, over 7237.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2545, pruned_loss=0.03717, over 1429472.51 frames.], batch size: 20, lr: 4.33e-04
2022-05-14 21:53:27,320 INFO [train.py:812] (6/8) Epoch 18, batch 1850, loss[loss=0.1728, simple_loss=0.2576, pruned_loss=0.04396, over 5003.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2532, pruned_loss=0.03707, over 1428017.91 frames.], batch size: 52, lr: 4.33e-04
2022-05-14 21:54:25,911 INFO [train.py:812] (6/8) Epoch 18, batch 1900, loss[loss=0.1736, simple_loss=0.2696, pruned_loss=0.03875, over 7322.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2546, pruned_loss=0.03743, over 1428376.44 frames.], batch size: 21, lr: 4.33e-04
2022-05-14 21:55:25,280 INFO [train.py:812] (6/8) Epoch 18, batch 1950, loss[loss=0.1653, simple_loss=0.2742, pruned_loss=0.02821, over 7322.00 frames.], tot_loss[loss=0.1657, simple_loss=0.256, pruned_loss=0.03776, over 1425195.84 frames.], batch size: 21, lr: 4.32e-04
2022-05-14 21:56:23,572 INFO [train.py:812] (6/8) Epoch 18, batch 2000, loss[loss=0.1584, simple_loss=0.2436, pruned_loss=0.0366, over 4981.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2551, pruned_loss=0.03764, over 1425617.83 frames.], batch size: 52, lr: 4.32e-04
2022-05-14 21:57:27,195 INFO [train.py:812] (6/8) Epoch 18, batch 2050, loss[loss=0.1663, simple_loss=0.2619, pruned_loss=0.03532, over 7122.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2549, pruned_loss=0.0377, over 1421641.82 frames.], batch size: 21, lr: 4.32e-04
2022-05-14 21:58:25,566 INFO [train.py:812] (6/8) Epoch 18, batch 2100, loss[loss=0.2052, simple_loss=0.3021, pruned_loss=0.05411, over 6749.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2558, pruned_loss=0.03817, over 1417084.55 frames.], batch size: 31, lr: 4.32e-04
2022-05-14 21:59:24,614 INFO [train.py:812] (6/8) Epoch 18, batch 2150, loss[loss=0.1745, simple_loss=0.2676, pruned_loss=0.0407, over 7221.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2554, pruned_loss=0.038, over 1419262.05 frames.], batch size: 21, lr: 4.32e-04
2022-05-14 22:00:22,642 INFO [train.py:812] (6/8) Epoch 18, batch 2200, loss[loss=0.165, simple_loss=0.2334, pruned_loss=0.04827, over 7251.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2554, pruned_loss=0.03805, over 1421538.93 frames.], batch size: 16, lr: 4.32e-04
2022-05-14 22:01:22,006 INFO [train.py:812] (6/8) Epoch 18, batch 2250, loss[loss=0.1234, simple_loss=0.2139, pruned_loss=0.01646, over 7012.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2547, pruned_loss=0.03795, over 1424866.43 frames.], batch size: 16, lr: 4.32e-04
2022-05-14 22:02:21,450 INFO [train.py:812] (6/8) Epoch 18, batch 2300, loss[loss=0.1717, simple_loss=0.2564, pruned_loss=0.04353, over 7140.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2557, pruned_loss=0.0386, over 1427441.81 frames.], batch size: 20, lr: 4.31e-04
2022-05-14 22:03:21,288 INFO [train.py:812] (6/8) Epoch 18, batch 2350, loss[loss=0.2014, simple_loss=0.2965, pruned_loss=0.05315, over 7150.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2552, pruned_loss=0.03828, over 1427069.21 frames.], batch size: 26, lr: 4.31e-04
2022-05-14 22:04:20,463 INFO [train.py:812] (6/8) Epoch 18, batch 2400, loss[loss=0.226, simple_loss=0.3079, pruned_loss=0.07203, over 6550.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2558, pruned_loss=0.03848, over 1425769.95 frames.], batch size: 38, lr: 4.31e-04
2022-05-14 22:05:18,793 INFO [train.py:812] (6/8) Epoch 18, batch 2450, loss[loss=0.1555, simple_loss=0.2397, pruned_loss=0.03562, over 7157.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2551, pruned_loss=0.03833, over 1426876.03 frames.], batch size: 19, lr: 4.31e-04
2022-05-14 22:06:16,648 INFO [train.py:812] (6/8) Epoch 18, batch 2500, loss[loss=0.1611, simple_loss=0.2511, pruned_loss=0.03553, over 7116.00 frames.], tot_loss[loss=0.167, simple_loss=0.2565, pruned_loss=0.03878, over 1419960.21 frames.], batch size: 21, lr: 4.31e-04
2022-05-14 22:07:15,279 INFO [train.py:812] (6/8) Epoch 18, batch 2550, loss[loss=0.1868, simple_loss=0.2671, pruned_loss=0.05331, over 7324.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2557, pruned_loss=0.03845, over 1420265.88 frames.], batch size: 21, lr: 4.31e-04
2022-05-14 22:08:14,577 INFO [train.py:812] (6/8) Epoch 18, batch 2600, loss[loss=0.1242, simple_loss=0.2106, pruned_loss=0.01891, over 6808.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2555, pruned_loss=0.03817, over 1419830.56 frames.], batch size: 15, lr: 4.31e-04
2022-05-14 22:09:14,564 INFO [train.py:812] (6/8) Epoch 18, batch 2650, loss[loss=0.1679, simple_loss=0.25, pruned_loss=0.0429, over 7361.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2553, pruned_loss=0.03789, over 1420733.20 frames.], batch size: 19, lr: 4.31e-04
2022-05-14 22:10:13,356 INFO [train.py:812] (6/8) Epoch 18, batch 2700, loss[loss=0.1381, simple_loss=0.2171, pruned_loss=0.02957, over 7290.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2543, pruned_loss=0.03747, over 1420562.67 frames.], batch size: 18, lr: 4.30e-04
2022-05-14 22:11:12,908 INFO [train.py:812] (6/8) Epoch 18, batch 2750, loss[loss=0.1601, simple_loss=0.258, pruned_loss=0.03113, over 7129.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2541, pruned_loss=0.03758, over 1419077.96 frames.], batch size: 20, lr: 4.30e-04
2022-05-14 22:12:10,435 INFO [train.py:812] (6/8) Epoch 18, batch 2800, loss[loss=0.1608, simple_loss=0.2625, pruned_loss=0.02951, over 7305.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2537, pruned_loss=0.03762, over 1418064.67 frames.], batch size: 21, lr: 4.30e-04
2022-05-14 22:13:09,215 INFO [train.py:812] (6/8) Epoch 18, batch 2850, loss[loss=0.1931, simple_loss=0.2815, pruned_loss=0.0523, over 7299.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2535, pruned_loss=0.03738, over 1421109.18 frames.], batch size: 25, lr: 4.30e-04
2022-05-14 22:14:17,949 INFO [train.py:812] (6/8) Epoch 18, batch 2900, loss[loss=0.1887, simple_loss=0.2708, pruned_loss=0.05327, over 7203.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2534, pruned_loss=0.03736, over 1423415.36 frames.], batch size: 22, lr: 4.30e-04
2022-05-14 22:15:17,303 INFO [train.py:812] (6/8) Epoch 18, batch 2950, loss[loss=0.1734, simple_loss=0.2683, pruned_loss=0.03922, over 6352.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2546, pruned_loss=0.03804, over 1420387.56 frames.], batch size: 37, lr: 4.30e-04
2022-05-14 22:16:16,216 INFO [train.py:812] (6/8) Epoch 18, batch 3000, loss[loss=0.1787, simple_loss=0.275, pruned_loss=0.04124, over 7307.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2547, pruned_loss=0.03761, over 1420093.71 frames.], batch size: 25, lr: 4.30e-04
2022-05-14 22:16:16,217 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 22:16:23,835 INFO [train.py:841] (6/8) Epoch 18, validation: loss=0.153, simple_loss=0.2523, pruned_loss=0.02686, over 698248.00 frames.
2022-05-14 22:17:22,920 INFO [train.py:812] (6/8) Epoch 18, batch 3050, loss[loss=0.1602, simple_loss=0.2497, pruned_loss=0.03536, over 7107.00 frames.], tot_loss[loss=0.1653, simple_loss=0.255, pruned_loss=0.03778, over 1418370.22 frames.], batch size: 21, lr: 4.29e-04
2022-05-14 22:18:21,086 INFO [train.py:812] (6/8) Epoch 18, batch 3100, loss[loss=0.1489, simple_loss=0.2359, pruned_loss=0.03091, over 7230.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2555, pruned_loss=0.03807, over 1419761.73 frames.], batch size: 20, lr: 4.29e-04
2022-05-14 22:19:19,579 INFO [train.py:812] (6/8) Epoch 18, batch 3150, loss[loss=0.1551, simple_loss=0.2466, pruned_loss=0.03174, over 7250.00 frames.], tot_loss[loss=0.166, simple_loss=0.2555, pruned_loss=0.03828, over 1422023.24 frames.], batch size: 19, lr: 4.29e-04
2022-05-14 22:20:18,667 INFO [train.py:812] (6/8) Epoch 18, batch 3200, loss[loss=0.1578, simple_loss=0.2564, pruned_loss=0.02961, over 6731.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2553, pruned_loss=0.0383, over 1420331.75 frames.], batch size: 31, lr: 4.29e-04
2022-05-14 22:21:17,372 INFO [train.py:812] (6/8) Epoch 18, batch 3250, loss[loss=0.1817, simple_loss=0.2702, pruned_loss=0.04654, over 7380.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2545, pruned_loss=0.03786, over 1423004.18 frames.], batch size: 23, lr: 4.29e-04
2022-05-14 22:22:16,116 INFO [train.py:812] (6/8) Epoch 18, batch 3300, loss[loss=0.1458, simple_loss=0.2315, pruned_loss=0.03004, over 7159.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2539, pruned_loss=0.03745, over 1427264.34 frames.], batch size: 18, lr: 4.29e-04
2022-05-14 22:23:15,283 INFO [train.py:812] (6/8) Epoch 18, batch 3350, loss[loss=0.1328, simple_loss=0.216, pruned_loss=0.0248, over 7416.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2535, pruned_loss=0.03709, over 1426539.98 frames.], batch size: 18, lr: 4.29e-04
2022-05-14 22:24:13,580 INFO [train.py:812] (6/8) Epoch 18, batch 3400, loss[loss=0.1868, simple_loss=0.2877, pruned_loss=0.04301, over 7388.00 frames.], tot_loss[loss=0.1641, simple_loss=0.254, pruned_loss=0.03711, over 1430435.01 frames.], batch size: 23, lr: 4.29e-04
2022-05-14 22:25:13,427 INFO [train.py:812] (6/8) Epoch 18, batch 3450, loss[loss=0.1477, simple_loss=0.2278, pruned_loss=0.03382, over 7412.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2542, pruned_loss=0.03716, over 1430628.38 frames.], batch size: 18, lr: 4.28e-04
2022-05-14 22:26:12,113 INFO [train.py:812] (6/8) Epoch 18, batch 3500, loss[loss=0.1672, simple_loss=0.2691, pruned_loss=0.03265, over 6330.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2537, pruned_loss=0.037, over 1432901.89 frames.], batch size: 38, lr: 4.28e-04
2022-05-14 22:27:09,555 INFO [train.py:812] (6/8) Epoch 18, batch 3550, loss[loss=0.1771, simple_loss=0.2692, pruned_loss=0.0425, over 7209.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2547, pruned_loss=0.03716, over 1430852.91 frames.], batch size: 23, lr: 4.28e-04
2022-05-14 22:28:09,184 INFO [train.py:812] (6/8) Epoch 18, batch 3600, loss[loss=0.1606, simple_loss=0.2522, pruned_loss=0.03449, over 7206.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2547, pruned_loss=0.0371, over 1432138.59 frames.], batch size: 21, lr: 4.28e-04
2022-05-14 22:29:08,012 INFO [train.py:812] (6/8) Epoch 18, batch 3650, loss[loss=0.1692, simple_loss=0.2707, pruned_loss=0.03383, over 7340.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2541, pruned_loss=0.03683, over 1422336.16 frames.], batch size: 22, lr: 4.28e-04
2022-05-14 22:30:06,386 INFO [train.py:812] (6/8) Epoch 18, batch 3700, loss[loss=0.1383, simple_loss=0.223, pruned_loss=0.02678, over 6984.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2548, pruned_loss=0.03711, over 1423621.29 frames.], batch size: 16, lr: 4.28e-04
2022-05-14 22:31:03,716 INFO [train.py:812] (6/8) Epoch 18, batch 3750, loss[loss=0.1632, simple_loss=0.2617, pruned_loss=0.03237, over 7269.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2553, pruned_loss=0.03713, over 1426177.49 frames.], batch size: 25, lr: 4.28e-04
2022-05-14 22:32:02,182 INFO [train.py:812] (6/8) Epoch 18, batch 3800, loss[loss=0.15, simple_loss=0.2446, pruned_loss=0.02773, over 7357.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2545, pruned_loss=0.03686, over 1425909.46 frames.], batch size: 19, lr: 4.28e-04
2022-05-14 22:33:01,947 INFO [train.py:812] (6/8) Epoch 18, batch 3850, loss[loss=0.1842, simple_loss=0.2593, pruned_loss=0.05458, over 7421.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2537, pruned_loss=0.03674, over 1424527.03 frames.], batch size: 18, lr: 4.27e-04
2022-05-14 22:34:00,996 INFO [train.py:812] (6/8) Epoch 18, batch 3900, loss[loss=0.1434, simple_loss=0.2458, pruned_loss=0.02052, over 7121.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2544, pruned_loss=0.03694, over 1420605.22 frames.], batch size: 21, lr: 4.27e-04
2022-05-14 22:35:00,691 INFO [train.py:812] (6/8) Epoch 18, batch 3950, loss[loss=0.1957, simple_loss=0.2867, pruned_loss=0.05229, over 7079.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2541, pruned_loss=0.03711, over 1422172.95 frames.], batch size: 28, lr: 4.27e-04
2022-05-14 22:35:58,142 INFO [train.py:812] (6/8) Epoch 18, batch 4000, loss[loss=0.147, simple_loss=0.2281, pruned_loss=0.03301, over 6850.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2539, pruned_loss=0.03712, over 1423101.04 frames.], batch size: 15, lr: 4.27e-04
2022-05-14 22:36:56,558 INFO [train.py:812] (6/8) Epoch 18, batch 4050, loss[loss=0.1596, simple_loss=0.2492, pruned_loss=0.03504, over 7053.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2542, pruned_loss=0.03756, over 1427221.39 frames.], batch size: 28, lr: 4.27e-04
2022-05-14 22:37:55,341 INFO [train.py:812] (6/8) Epoch 18, batch 4100, loss[loss=0.1974, simple_loss=0.2833, pruned_loss=0.05573, over 7148.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2544, pruned_loss=0.03787, over 1423729.34 frames.], batch size: 20, lr: 4.27e-04
2022-05-14 22:38:54,635 INFO [train.py:812] (6/8) Epoch 18, batch 4150, loss[loss=0.1864, simple_loss=0.2789, pruned_loss=0.04699, over 7325.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2547, pruned_loss=0.03805, over 1422455.86 frames.], batch size: 20, lr: 4.27e-04
2022-05-14 22:39:53,808 INFO [train.py:812] (6/8) Epoch 18, batch 4200, loss[loss=0.1479, simple_loss=0.2273, pruned_loss=0.03431, over 7431.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2537, pruned_loss=0.03783, over 1422180.20 frames.], batch size: 17, lr: 4.26e-04
2022-05-14 22:40:53,078 INFO [train.py:812] (6/8) Epoch 18, batch 4250, loss[loss=0.1632, simple_loss=0.2523, pruned_loss=0.03704, over 6865.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2538, pruned_loss=0.03793, over 1417956.05 frames.], batch size: 31, lr: 4.26e-04
2022-05-14 22:41:52,058 INFO [train.py:812] (6/8) Epoch 18, batch 4300, loss[loss=0.1572, simple_loss=0.2386, pruned_loss=0.03786, over 6989.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2527, pruned_loss=0.03724, over 1418733.91 frames.], batch size: 16, lr: 4.26e-04
2022-05-14 22:42:51,542 INFO [train.py:812] (6/8) Epoch 18, batch 4350, loss[loss=0.1786, simple_loss=0.2711, pruned_loss=0.04305, over 7218.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2529, pruned_loss=0.03782, over 1407126.04 frames.], batch size: 21, lr: 4.26e-04
2022-05-14 22:43:50,339 INFO [train.py:812] (6/8) Epoch 18, batch 4400, loss[loss=0.1489, simple_loss=0.23, pruned_loss=0.03387, over 7064.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2532, pruned_loss=0.03782, over 1400785.07 frames.], batch size: 18, lr: 4.26e-04
2022-05-14 22:44:47,960 INFO [train.py:812] (6/8) Epoch 18, batch 4450, loss[loss=0.176, simple_loss=0.2732, pruned_loss=0.03941, over 6265.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2556, pruned_loss=0.03836, over 1393155.32 frames.], batch size: 37, lr: 4.26e-04
2022-05-14 22:45:55,882 INFO [train.py:812] (6/8) Epoch 18, batch 4500, loss[loss=0.1543, simple_loss=0.2315, pruned_loss=0.03858, over 6986.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2563, pruned_loss=0.03863, over 1380371.70 frames.], batch size: 16, lr: 4.26e-04
2022-05-14 22:46:55,069 INFO [train.py:812] (6/8) Epoch 18, batch 4550, loss[loss=0.1754, simple_loss=0.2716, pruned_loss=0.03958, over 7162.00 frames.], tot_loss[loss=0.1668, simple_loss=0.256, pruned_loss=0.0388, over 1371748.03 frames.], batch size: 19, lr: 4.26e-04
2022-05-14 22:48:10,091 INFO [train.py:812] (6/8) Epoch 19, batch 0, loss[loss=0.1605, simple_loss=0.2547, pruned_loss=0.03314, over 7289.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2547, pruned_loss=0.03314, over 7289.00 frames.], batch size: 25, lr: 4.15e-04
2022-05-14 22:49:27,410 INFO [train.py:812] (6/8) Epoch 19, batch 50, loss[loss=0.1604, simple_loss=0.2546, pruned_loss=0.03312, over 7345.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2528, pruned_loss=0.03734, over 324847.89 frames.], batch size: 22, lr: 4.15e-04
2022-05-14 22:50:35,558 INFO [train.py:812] (6/8) Epoch 19, batch 100, loss[loss=0.1748, simple_loss=0.2658, pruned_loss=0.04192, over 7328.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2549, pruned_loss=0.03682, over 574103.59 frames.], batch size: 22, lr: 4.14e-04
2022-05-14 22:51:34,814 INFO [train.py:812] (6/8) Epoch 19, batch 150, loss[loss=0.16, simple_loss=0.26, pruned_loss=0.03004, over 7220.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2539, pruned_loss=0.03682, over 763722.42 frames.], batch size: 21, lr: 4.14e-04
2022-05-14 22:53:02,403 INFO [train.py:812] (6/8) Epoch 19, batch 200, loss[loss=0.1445, simple_loss=0.2313, pruned_loss=0.0288, over 7268.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2536, pruned_loss=0.03673, over 908836.18 frames.], batch size: 17, lr: 4.14e-04
2022-05-14 22:54:01,944 INFO [train.py:812] (6/8) Epoch 19, batch 250, loss[loss=0.1705, simple_loss=0.26, pruned_loss=0.04054, over 6708.00 frames.], tot_loss[loss=0.164, simple_loss=0.254, pruned_loss=0.03705, over 1024194.53 frames.], batch size: 31, lr: 4.14e-04
2022-05-14 22:55:01,159 INFO [train.py:812] (6/8) Epoch 19, batch 300, loss[loss=0.1472, simple_loss=0.2347, pruned_loss=0.02984, over 7237.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2548, pruned_loss=0.03764, over 1114554.30 frames.], batch size: 20, lr: 4.14e-04
2022-05-14 22:56:00,991 INFO [train.py:812] (6/8) Epoch 19, batch 350, loss[loss=0.1776, simple_loss=0.2749, pruned_loss=0.0401, over 6749.00 frames.], tot_loss[loss=0.164, simple_loss=0.2535, pruned_loss=0.0373, over 1182168.46 frames.], batch size: 31, lr: 4.14e-04
2022-05-14 22:56:59,188 INFO [train.py:812] (6/8) Epoch 19, batch 400, loss[loss=0.1625, simple_loss=0.2489, pruned_loss=0.03808, over 7064.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2549, pruned_loss=0.03781, over 1233938.05 frames.], batch size: 18, lr: 4.14e-04
2022-05-14 22:57:58,724 INFO [train.py:812] (6/8) Epoch 19, batch 450, loss[loss=0.1568, simple_loss=0.2579, pruned_loss=0.02782, over 7341.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2545, pruned_loss=0.03692, over 1275598.07 frames.], batch size: 22, lr: 4.14e-04
2022-05-14 22:58:57,686 INFO [train.py:812] (6/8) Epoch 19, batch 500, loss[loss=0.1588, simple_loss=0.2392, pruned_loss=0.03923, over 7127.00 frames.], tot_loss[loss=0.1647, simple_loss=0.255, pruned_loss=0.03717, over 1306387.85 frames.], batch size: 17, lr: 4.13e-04
2022-05-14 22:59:57,494 INFO [train.py:812] (6/8) Epoch 19, batch 550, loss[loss=0.1449, simple_loss=0.2263, pruned_loss=0.03171, over 7272.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2543, pruned_loss=0.03638, over 1335716.73 frames.], batch size: 17, lr: 4.13e-04
2022-05-14 23:00:56,155 INFO [train.py:812] (6/8) Epoch 19, batch 600, loss[loss=0.1658, simple_loss=0.2502, pruned_loss=0.04075, over 7291.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2548, pruned_loss=0.03705, over 1356853.19 frames.], batch size: 18, lr: 4.13e-04
2022-05-14 23:01:55,600 INFO [train.py:812] (6/8) Epoch 19, batch 650, loss[loss=0.1533, simple_loss=0.255, pruned_loss=0.02577, over 7120.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2529, pruned_loss=0.03637, over 1375603.35 frames.], batch size: 21, lr: 4.13e-04
2022-05-14 23:02:54,281 INFO [train.py:812] (6/8) Epoch 19, batch 700, loss[loss=0.2023, simple_loss=0.2835, pruned_loss=0.06052, over 4872.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2537, pruned_loss=0.03674, over 1385808.99 frames.], batch size: 53, lr: 4.13e-04
2022-05-14 23:03:53,350 INFO [train.py:812] (6/8) Epoch 19, batch 750, loss[loss=0.1501, simple_loss=0.2369, pruned_loss=0.03166, over 7149.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2533, pruned_loss=0.0368, over 1395096.92 frames.], batch size: 19, lr: 4.13e-04
2022-05-14 23:04:52,310 INFO [train.py:812] (6/8) Epoch 19, batch 800, loss[loss=0.1803, simple_loss=0.2783, pruned_loss=0.04121, over 6749.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2536, pruned_loss=0.03693, over 1397355.43 frames.], batch size: 31, lr: 4.13e-04
2022-05-14 23:05:50,874 INFO [train.py:812] (6/8) Epoch 19, batch 850, loss[loss=0.1389, simple_loss=0.2199, pruned_loss=0.02891, over 7073.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2538, pruned_loss=0.03679, over 1405107.21 frames.], batch size: 18, lr: 4.13e-04
2022-05-14 23:06:49,953 INFO [train.py:812] (6/8) Epoch 19, batch 900, loss[loss=0.1967, simple_loss=0.268, pruned_loss=0.06273, over 6786.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2546, pruned_loss=0.03688, over 1410118.89 frames.], batch size: 15, lr: 4.12e-04
2022-05-14 23:07:49,379 INFO [train.py:812] (6/8) Epoch 19, batch 950, loss[loss=0.1727, simple_loss=0.2671, pruned_loss=0.03915, over 7379.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2537, pruned_loss=0.03661, over 1413237.73 frames.], batch size: 23, lr: 4.12e-04
2022-05-14 23:08:48,643 INFO [train.py:812] (6/8) Epoch 19, batch 1000, loss[loss=0.1498, simple_loss=0.248, pruned_loss=0.02583, over 7137.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2544, pruned_loss=0.03663, over 1419880.48 frames.], batch size: 20, lr: 4.12e-04
2022-05-14 23:09:47,747 INFO [train.py:812] (6/8) Epoch 19, batch 1050, loss[loss=0.1894, simple_loss=0.2776, pruned_loss=0.05056, over 7309.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2538, pruned_loss=0.03683, over 1417650.70 frames.], batch size: 25, lr: 4.12e-04
2022-05-14 23:10:45,909 INFO [train.py:812] (6/8) Epoch 19, batch 1100, loss[loss=0.1656, simple_loss=0.2522, pruned_loss=0.03957, over 7339.00 frames.], tot_loss[loss=0.163, simple_loss=0.2526, pruned_loss=0.0367, over 1418894.61 frames.], batch size: 20, lr: 4.12e-04
2022-05-14 23:11:43,632 INFO [train.py:812] (6/8) Epoch 19, batch 1150, loss[loss=0.1686, simple_loss=0.2644, pruned_loss=0.03641, over 7303.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2529, pruned_loss=0.03688, over 1419502.93 frames.], batch size: 24, lr: 4.12e-04
2022-05-14 23:12:42,332 INFO [train.py:812] (6/8) Epoch 19, batch 1200, loss[loss=0.1931, simple_loss=0.2606, pruned_loss=0.06278, over 5379.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2524, pruned_loss=0.0369, over 1415089.64 frames.], batch size: 53, lr: 4.12e-04
2022-05-14 23:13:40,445 INFO [train.py:812] (6/8) Epoch 19, batch 1250, loss[loss=0.1568, simple_loss=0.2515, pruned_loss=0.03107, over 7118.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2526, pruned_loss=0.03709, over 1415179.37 frames.], batch size: 21, lr: 4.12e-04
2022-05-14 23:14:39,638 INFO [train.py:812] (6/8) Epoch 19, batch 1300, loss[loss=0.1808, simple_loss=0.2633, pruned_loss=0.04909, over 7157.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2539, pruned_loss=0.03717, over 1414466.10 frames.], batch size: 19, lr: 4.12e-04
2022-05-14 23:15:38,806 INFO [train.py:812] (6/8) Epoch 19, batch 1350, loss[loss=0.1758, simple_loss=0.2704, pruned_loss=0.04062, over 7104.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2544, pruned_loss=0.03718, over 1413267.04 frames.], batch size: 28, lr: 4.11e-04
2022-05-14 23:16:38,133 INFO [train.py:812] (6/8) Epoch 19, batch 1400, loss[loss=0.1688, simple_loss=0.2576, pruned_loss=0.04001, over 7063.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2543, pruned_loss=0.03738, over 1411641.42 frames.], batch size: 18, lr: 4.11e-04
2022-05-14 23:17:42,366 INFO [train.py:812] (6/8) Epoch 19, batch 1450, loss[loss=0.1689, simple_loss=0.264, pruned_loss=0.03695, over 7319.00 frames.], tot_loss[loss=0.164, simple_loss=0.2541, pruned_loss=0.03695, over 1418500.68 frames.], batch size: 21, lr: 4.11e-04
2022-05-14 23:18:41,303 INFO [train.py:812] (6/8) Epoch 19, batch 1500, loss[loss=0.1486, simple_loss=0.2379, pruned_loss=0.02968, over 7252.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2543, pruned_loss=0.03713, over 1421833.36 frames.], batch size: 19, lr: 4.11e-04
2022-05-14 23:19:40,449 INFO [train.py:812] (6/8) Epoch 19, batch 1550, loss[loss=0.1498, simple_loss=0.2489, pruned_loss=0.02539, over 7412.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2538, pruned_loss=0.0369, over 1424595.99 frames.], batch size: 21, lr: 4.11e-04
2022-05-14 23:20:39,984 INFO [train.py:812] (6/8) Epoch 19, batch 1600, loss[loss=0.1566, simple_loss=0.243, pruned_loss=0.03515, over 7212.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2541, pruned_loss=0.03721, over 1423579.65 frames.], batch size: 22, lr: 4.11e-04
2022-05-14 23:21:39,525 INFO [train.py:812] (6/8) Epoch 19, batch 1650, loss[loss=0.1467, simple_loss=0.2391, pruned_loss=0.02709, over 7160.00 frames.], tot_loss[loss=0.1644, simple_loss=0.254, pruned_loss=0.03736, over 1422186.08 frames.], batch size: 18, lr: 4.11e-04
2022-05-14 23:22:38,874 INFO [train.py:812] (6/8) Epoch 19, batch 1700, loss[loss=0.1561, simple_loss=0.2401, pruned_loss=0.03599, over 7163.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2539, pruned_loss=0.03701, over 1422947.25 frames.], batch size: 18, lr: 4.11e-04
2022-05-14 23:23:37,801 INFO [train.py:812] (6/8) Epoch 19, batch 1750, loss[loss=0.1709, simple_loss=0.2656, pruned_loss=0.03811, over 7148.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2553, pruned_loss=0.03767, over 1416377.87 frames.], batch size: 20, lr: 4.10e-04
2022-05-14 23:24:36,358 INFO [train.py:812] (6/8) Epoch 19, batch 1800, loss[loss=0.1538, simple_loss=0.2377, pruned_loss=0.03498, over 7257.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2562, pruned_loss=0.03758, over 1416538.02 frames.], batch size: 19, lr: 4.10e-04
2022-05-14 23:25:35,730 INFO [train.py:812] (6/8) Epoch 19, batch 1850, loss[loss=0.2248, simple_loss=0.295, pruned_loss=0.07725, over 7304.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2561, pruned_loss=0.03754, over 1422363.35 frames.], batch size: 24, lr: 4.10e-04
2022-05-14 23:26:34,643 INFO [train.py:812] (6/8) Epoch 19, batch 1900, loss[loss=0.1615, simple_loss=0.2628, pruned_loss=0.03007, over 7072.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2557, pruned_loss=0.03749, over 1420543.05 frames.], batch size: 28, lr: 4.10e-04
2022-05-14 23:27:34,103 INFO [train.py:812] (6/8) Epoch 19, batch 1950, loss[loss=0.1231, simple_loss=0.2054, pruned_loss=0.02035, over 7001.00 frames.], tot_loss[loss=0.166, simple_loss=0.2563, pruned_loss=0.03784, over 1421112.56 frames.], batch size: 16, lr: 4.10e-04
2022-05-14 23:28:32,913 INFO [train.py:812] (6/8) Epoch 19, batch 2000, loss[loss=0.1778, simple_loss=0.2633, pruned_loss=0.04614, over 7148.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2554, pruned_loss=0.03737, over 1424182.65 frames.], batch size: 20, lr: 4.10e-04
2022-05-14 23:29:32,690 INFO [train.py:812] (6/8) Epoch 19, batch 2050, loss[loss=0.1731, simple_loss=0.2611, pruned_loss=0.0425, over 7305.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2551, pruned_loss=0.03724, over 1424343.52 frames.], batch size: 25, lr: 4.10e-04
2022-05-14 23:30:30,728 INFO [train.py:812] (6/8) Epoch 19, batch 2100, loss[loss=0.1397, simple_loss=0.2256, pruned_loss=0.02687, over 7155.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2544, pruned_loss=0.03667, over 1424816.69 frames.], batch size: 19, lr: 4.10e-04
2022-05-14 23:31:30,585 INFO [train.py:812] (6/8) Epoch 19, batch 2150, loss[loss=0.1926, simple_loss=0.2853, pruned_loss=0.05001, over 7217.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2544, pruned_loss=0.03703, over 1421624.69 frames.], batch size: 21, lr: 4.09e-04
2022-05-14 23:32:29,978 INFO [train.py:812] (6/8) Epoch 19, batch 2200, loss[loss=0.2074, simple_loss=0.3053, pruned_loss=0.05478, over 7445.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2533, pruned_loss=0.03658, over 1425868.94 frames.], batch size: 22, lr: 4.09e-04
2022-05-14 23:33:29,286 INFO [train.py:812] (6/8) Epoch 19, batch 2250, loss[loss=0.1561, simple_loss=0.2438, pruned_loss=0.03415, over 6480.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2546, pruned_loss=0.03714, over 1424773.56 frames.], batch size: 37, lr: 4.09e-04
2022-05-14 23:34:27,808 INFO [train.py:812] (6/8) Epoch 19, batch 2300, loss[loss=0.1758, simple_loss=0.2679, pruned_loss=0.04185, over 7372.00 frames.], tot_loss[loss=0.1639, simple_loss=0.254, pruned_loss=0.03688, over 1426501.00 frames.], batch size: 23, lr: 4.09e-04
2022-05-14 23:35:25,974 INFO [train.py:812] (6/8) Epoch 19, batch 2350, loss[loss=0.14, simple_loss=0.2221, pruned_loss=0.02898, over 7274.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2536, pruned_loss=0.03687, over 1423793.55 frames.], batch size: 17, lr: 4.09e-04
2022-05-14 23:36:25,416 INFO [train.py:812] (6/8) Epoch 19, batch 2400, loss[loss=0.1594, simple_loss=0.2543, pruned_loss=0.03226, over 7150.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2543, pruned_loss=0.03701, over 1419871.17 frames.], batch size: 20, lr: 4.09e-04
2022-05-14 23:37:24,215 INFO [train.py:812] (6/8) Epoch 19, batch 2450, loss[loss=0.1661, simple_loss=0.26, pruned_loss=0.03605, over 7147.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2537, pruned_loss=0.03679, over 1422074.45 frames.], batch size: 20, lr: 4.09e-04
2022-05-14 23:38:23,519 INFO [train.py:812] (6/8) Epoch 19, batch 2500, loss[loss=0.1663, simple_loss=0.2604, pruned_loss=0.03607, over 7154.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2541, pruned_loss=0.03741, over 1421445.67 frames.], batch size: 26, lr: 4.09e-04
2022-05-14 23:39:22,990 INFO [train.py:812] (6/8) Epoch 19, batch 2550, loss[loss=0.1736, simple_loss=0.2676, pruned_loss=0.03977, over 7266.00 frames.], tot_loss[loss=0.164, simple_loss=0.2536, pruned_loss=0.03722, over 1421211.52 frames.], batch size: 24, lr: 4.08e-04
2022-05-14 23:40:21,749 INFO [train.py:812] (6/8) Epoch 19, batch 2600, loss[loss=0.1692, simple_loss=0.2424, pruned_loss=0.04797, over 6992.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2546, pruned_loss=0.03751, over 1424605.93 frames.], batch size: 16, lr: 4.08e-04
2022-05-14 23:41:20,971 INFO [train.py:812] (6/8) Epoch 19, batch 2650, loss[loss=0.1887, simple_loss=0.269, pruned_loss=0.05425, over 7274.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2543, pruned_loss=0.03751, over 1426853.30 frames.], batch size: 24, lr: 4.08e-04
2022-05-14 23:42:20,911 INFO [train.py:812] (6/8) Epoch 19, batch 2700, loss[loss=0.1776, simple_loss=0.2729, pruned_loss=0.04115, over 7312.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2531, pruned_loss=0.03663, over 1430302.57 frames.], batch size: 25, lr: 4.08e-04
2022-05-14 23:43:20,355 INFO [train.py:812] (6/8) Epoch 19, batch 2750, loss[loss=0.1455, simple_loss=0.2425, pruned_loss=0.02422, over 7413.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2539, pruned_loss=0.03696, over 1430282.30 frames.], batch size: 21, lr: 4.08e-04
2022-05-14 23:44:19,880 INFO [train.py:812] (6/8) Epoch 19, batch 2800, loss[loss=0.1743, simple_loss=0.2714, pruned_loss=0.03863, over 7064.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2541, pruned_loss=0.03701, over 1431091.90 frames.], batch size: 18, lr: 4.08e-04
2022-05-14 23:45:18,639 INFO [train.py:812] (6/8) Epoch 19, batch 2850, loss[loss=0.1592, simple_loss=0.2558, pruned_loss=0.0313, over 7159.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2542, pruned_loss=0.03706, over 1427901.72 frames.], batch size: 19, lr: 4.08e-04
2022-05-14 23:46:17,165 INFO [train.py:812] (6/8) Epoch 19, batch 2900, loss[loss=0.1672, simple_loss=0.2559, pruned_loss=0.03922, over 7182.00 frames.], tot_loss[loss=0.164, simple_loss=0.2539, pruned_loss=0.03708, over 1425085.67 frames.], batch size: 26, lr: 4.08e-04
2022-05-14 23:47:15,893 INFO [train.py:812] (6/8) Epoch 19, batch 2950, loss[loss=0.1609, simple_loss=0.2298, pruned_loss=0.04594, over 7271.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2542, pruned_loss=0.03728, over 1430640.78 frames.], batch size: 17, lr: 4.08e-04
2022-05-14 23:48:15,120 INFO [train.py:812] (6/8) Epoch 19, batch 3000, loss[loss=0.1849, simple_loss=0.2681, pruned_loss=0.05086, over 5316.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2546, pruned_loss=0.03731, over 1431330.93 frames.], batch size: 52, lr: 4.07e-04
2022-05-14 23:48:15,121 INFO [train.py:832] (6/8) Computing validation loss
2022-05-14 23:48:22,685 INFO [train.py:841] (6/8) Epoch 19, validation: loss=0.1531, simple_loss=0.2523, pruned_loss=0.02694, over 698248.00 frames.
2022-05-14 23:49:22,405 INFO [train.py:812] (6/8) Epoch 19, batch 3050, loss[loss=0.1767, simple_loss=0.2614, pruned_loss=0.046, over 7194.00 frames.], tot_loss[loss=0.1639, simple_loss=0.254, pruned_loss=0.03687, over 1432800.16 frames.], batch size: 23, lr: 4.07e-04
2022-05-14 23:50:21,368 INFO [train.py:812] (6/8) Epoch 19, batch 3100, loss[loss=0.1937, simple_loss=0.2756, pruned_loss=0.05594, over 6512.00 frames.], tot_loss[loss=0.1641, simple_loss=0.254, pruned_loss=0.03704, over 1433786.82 frames.], batch size: 38, lr: 4.07e-04
2022-05-14 23:51:20,047 INFO [train.py:812] (6/8) Epoch 19, batch 3150, loss[loss=0.1397, simple_loss=0.2272, pruned_loss=0.02606, over 7269.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2544, pruned_loss=0.03709, over 1431099.14 frames.], batch size: 18, lr: 4.07e-04
2022-05-14 23:52:18,568 INFO [train.py:812] (6/8) Epoch 19, batch 3200, loss[loss=0.1423, simple_loss=0.2345, pruned_loss=0.02508, over 7163.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2542, pruned_loss=0.03711, over 1428874.20 frames.], batch size: 19, lr: 4.07e-04
2022-05-14 23:53:18,023 INFO [train.py:812] (6/8) Epoch 19, batch 3250, loss[loss=0.1495, simple_loss=0.233, pruned_loss=0.03297, over 7363.00 frames.], tot_loss[loss=0.165, simple_loss=0.2553, pruned_loss=0.03737, over 1425773.48 frames.], batch size: 19, lr: 4.07e-04
2022-05-14 23:54:16,320 INFO [train.py:812] (6/8) Epoch 19, batch 3300, loss[loss=0.1672, simple_loss=0.2593, pruned_loss=0.03754, over 6332.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2557, pruned_loss=0.03727, over 1425556.16 frames.], batch size: 37, lr: 4.07e-04
2022-05-14 23:55:15,324 INFO [train.py:812] (6/8) Epoch 19, batch 3350, loss[loss=0.1541, simple_loss=0.2507, pruned_loss=0.02877, over 7113.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2541, pruned_loss=0.03637, over 1424534.64 frames.], batch size: 21, lr: 4.07e-04
2022-05-14 23:56:14,426 INFO [train.py:812] (6/8) Epoch 19, batch 3400, loss[loss=0.1609, simple_loss=0.2392, pruned_loss=0.04133, over 7276.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2542, pruned_loss=0.03634, over 1424685.16 frames.], batch size: 18, lr: 4.06e-04
2022-05-14 23:57:14,029 INFO [train.py:812] (6/8) Epoch 19, batch 3450, loss[loss=0.1593, simple_loss=0.2419, pruned_loss=0.03838, over 7354.00 frames.], tot_loss[loss=0.1627, simple_loss=0.253, pruned_loss=0.03626, over 1421624.11 frames.], batch size: 19, lr: 4.06e-04
2022-05-14 23:58:13,028 INFO [train.py:812] (6/8) Epoch 19, batch 3500, loss[loss=0.1544, simple_loss=0.2464, pruned_loss=0.03117, over 7287.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2529, pruned_loss=0.0362, over 1423990.74 frames.], batch size: 18, lr: 4.06e-04
2022-05-14 23:59:12,615 INFO [train.py:812] (6/8) Epoch 19, batch 3550, loss[loss=0.1463, simple_loss=0.2331, pruned_loss=0.0298, over 7142.00 frames.], tot_loss[loss=0.162, simple_loss=0.2524, pruned_loss=0.03583, over 1423822.15 frames.], batch size: 17, lr: 4.06e-04
2022-05-15 00:00:11,614 INFO [train.py:812] (6/8) Epoch 19, batch 3600, loss[loss=0.2213, simple_loss=0.2908, pruned_loss=0.07587, over 7186.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2534, pruned_loss=0.03619, over 1421121.09 frames.], batch size: 23, lr: 4.06e-04
2022-05-15 00:01:11,002 INFO [train.py:812] (6/8) Epoch 19, batch 3650, loss[loss=0.152, simple_loss=0.2496, pruned_loss=0.0272, over 7320.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2539, pruned_loss=0.03641, over 1415122.38 frames.], batch size: 20, lr: 4.06e-04
2022-05-15 00:02:10,024 INFO [train.py:812] (6/8) Epoch 19, batch 3700, loss[loss=0.1649, simple_loss=0.2548, pruned_loss=0.0375, over 7421.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2542, pruned_loss=0.03659, over 1416875.52 frames.], batch size: 21, lr: 4.06e-04
2022-05-15 00:03:09,355 INFO [train.py:812] (6/8) Epoch 19, batch 3750, loss[loss=0.1673, simple_loss=0.2658, pruned_loss=0.03437, over 7390.00 frames.], tot_loss[loss=0.1642, simple_loss=0.254, pruned_loss=0.03723, over 1412918.92 frames.], batch size: 23, lr: 4.06e-04
2022-05-15 00:04:08,163 INFO [train.py:812] (6/8) Epoch 19, batch 3800, loss[loss=0.1665, simple_loss=0.244, pruned_loss=0.04457, over 7369.00 frames.], tot_loss[loss=0.164, simple_loss=0.254, pruned_loss=0.03696, over 1418688.92 frames.], batch size: 19, lr: 4.06e-04
2022-05-15 00:05:06,757 INFO [train.py:812] (6/8) Epoch 19, batch 3850, loss[loss=0.149, simple_loss=0.2284, pruned_loss=0.03485, over 7165.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2534, pruned_loss=0.03681, over 1417368.69 frames.], batch size: 18, lr: 4.05e-04
2022-05-15 00:06:04,449 INFO [train.py:812] (6/8) Epoch 19, batch 3900, loss[loss=0.1834, simple_loss=0.2738, pruned_loss=0.04656, over 7108.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2538, pruned_loss=0.03692, over 1414429.10 frames.], batch size: 21, lr: 4.05e-04
2022-05-15 00:07:04,213 INFO [train.py:812] (6/8) Epoch 19, batch 3950, loss[loss=0.1798, simple_loss=0.2701, pruned_loss=0.04474, over 7153.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2542, pruned_loss=0.03737, over 1416794.51 frames.], batch size: 18, lr: 4.05e-04
2022-05-15 00:08:03,288 INFO [train.py:812] (6/8) Epoch 19, batch 4000, loss[loss=0.1792, simple_loss=0.2724, pruned_loss=0.04305, over 5723.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2545, pruned_loss=0.03734, over 1418146.93 frames.], batch size: 54, lr: 4.05e-04
2022-05-15 00:09:00,807 INFO [train.py:812] (6/8) Epoch 19, batch 4050, loss[loss=0.1546, simple_loss=0.2365, pruned_loss=0.03636, over 6807.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2542, pruned_loss=0.03697, over 1416251.81 frames.], batch size: 15, lr: 4.05e-04
2022-05-15 00:09:59,482 INFO [train.py:812] (6/8) Epoch 19, batch 4100, loss[loss=0.1765, simple_loss=0.2569, pruned_loss=0.04806, over 5395.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2546, pruned_loss=0.03734, over 1416497.12 frames.], batch size: 52, lr: 4.05e-04
2022-05-15 00:10:57,151 INFO [train.py:812] (6/8) Epoch 19, batch 4150, loss[loss=0.147, simple_loss=0.2364, pruned_loss=0.02883, over 7390.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2531, pruned_loss=0.03677, over 1421864.03 frames.], batch size: 23, lr: 4.05e-04
2022-05-15 00:11:56,840 INFO [train.py:812] (6/8) Epoch 19, batch 4200, loss[loss=0.1651, simple_loss=0.2631, pruned_loss=0.03351, over 7201.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2531, pruned_loss=0.03672, over 1420360.47 frames.], batch size: 23, lr: 4.05e-04
2022-05-15 00:12:56,152 INFO [train.py:812] (6/8) Epoch 19, batch 4250, loss[loss=0.1662, simple_loss=0.2385, pruned_loss=0.04692, over 6773.00 frames.], tot_loss[loss=0.1629, simple_loss=0.253, pruned_loss=0.03639, over 1419902.36 frames.], batch size: 15, lr: 4.04e-04
2022-05-15 00:14:05,180 INFO [train.py:812] (6/8) Epoch 19, batch 4300, loss[loss=0.1736, simple_loss=0.2715, pruned_loss=0.03787, over 7124.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2533, pruned_loss=0.03624, over 1419778.32 frames.], batch size: 26, lr: 4.04e-04
2022-05-15 00:15:04,952 INFO [train.py:812] (6/8) Epoch 19, batch 4350, loss[loss=0.1836, simple_loss=0.2646, pruned_loss=0.05136, over 7171.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2526, pruned_loss=0.03612, over 1417212.96 frames.], batch size: 18, lr: 4.04e-04
2022-05-15 00:16:03,314 INFO [train.py:812] (6/8) Epoch 19, batch 4400, loss[loss=0.1734, simple_loss=0.2701, pruned_loss=0.03835, over 6447.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2524, pruned_loss=0.03639, over 1413750.61 frames.], batch size: 38, lr: 4.04e-04
2022-05-15 00:17:02,502 INFO [train.py:812] (6/8) Epoch 19, batch 4450, loss[loss=0.1406, simple_loss=0.2246, pruned_loss=0.02825, over 7253.00 frames.], tot_loss[loss=0.162, simple_loss=0.2514, pruned_loss=0.03633, over 1408024.47 frames.], batch size: 16, lr: 4.04e-04
2022-05-15 00:18:02,041 INFO [train.py:812] (6/8) Epoch 19, batch 4500, loss[loss=0.1602, simple_loss=0.2617, pruned_loss=0.02939, over 7143.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2531, pruned_loss=0.03735, over 1393637.92 frames.], batch size: 20, lr: 4.04e-04
2022-05-15 00:19:01,083 INFO [train.py:812] (6/8) Epoch 19, batch 4550, loss[loss=0.1743, simple_loss=0.2612, pruned_loss=0.04365, over 6342.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2526, pruned_loss=0.03785, over 1367192.63 frames.], batch size: 37, lr: 4.04e-04
2022-05-15 00:20:09,406 INFO [train.py:812] (6/8) Epoch 20, batch 0, loss[loss=0.167, simple_loss=0.2552, pruned_loss=0.03935, over 7360.00 frames.], tot_loss[loss=0.167, simple_loss=0.2552, pruned_loss=0.03935, over 7360.00 frames.], batch size: 19, lr: 3.94e-04
2022-05-15 00:21:09,548 INFO [train.py:812] (6/8) Epoch 20, batch 50, loss[loss=0.1412, simple_loss=0.2292, pruned_loss=0.02655, over 7279.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2545, pruned_loss=0.036, over 321416.92 frames.], batch size: 18, lr: 3.94e-04
2022-05-15 00:22:08,834 INFO [train.py:812] (6/8) Epoch 20, batch 100, loss[loss=0.1953, simple_loss=0.2778, pruned_loss=0.05637, over 5475.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2532, pruned_loss=0.036, over 566553.84 frames.], batch size: 53, lr: 3.94e-04
2022-05-15 00:23:08,494 INFO [train.py:812] (6/8) Epoch 20, batch 150, loss[loss=0.157, simple_loss=0.2621, pruned_loss=0.02589, over 7318.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2555, pruned_loss=0.03634, over 756434.00 frames.], batch size: 21, lr: 3.94e-04
2022-05-15 00:24:07,759 INFO [train.py:812] (6/8) Epoch 20, batch 200, loss[loss=0.1451, simple_loss=0.2427, pruned_loss=0.02373, over 7335.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2545, pruned_loss=0.03622, over 903397.45 frames.], batch size: 22, lr: 3.93e-04
2022-05-15 00:25:08,008 INFO [train.py:812] (6/8) Epoch 20, batch 250, loss[loss=0.1817, simple_loss=0.2792, pruned_loss=0.04212, over 7347.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2531, pruned_loss=0.03599, over 1022652.46 frames.], batch size: 22, lr: 3.93e-04
2022-05-15 00:26:07,277 INFO [train.py:812] (6/8) Epoch 20, batch 300, loss[loss=0.1715, simple_loss=0.2599, pruned_loss=0.04155, over 7201.00 frames.], tot_loss[loss=0.163, simple_loss=0.254, pruned_loss=0.03605, over 1112132.26 frames.], batch size: 23, lr: 3.93e-04
2022-05-15 00:27:07,181 INFO [train.py:812] (6/8) Epoch 20, batch 350, loss[loss=0.144, simple_loss=0.2399, pruned_loss=0.02401, over 7140.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2545, pruned_loss=0.03647, over 1184519.87 frames.], batch size: 20, lr: 3.93e-04
2022-05-15 00:28:05,129 INFO [train.py:812] (6/8) Epoch 20, batch 400, loss[loss=0.1758, simple_loss=0.2581, pruned_loss=0.0468, over 7146.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2554, pruned_loss=0.03676, over 1237615.82 frames.], batch size: 20, lr: 3.93e-04
2022-05-15 00:29:03,601 INFO [train.py:812] (6/8) Epoch 20, batch 450, loss[loss=0.2047, simple_loss=0.2883, pruned_loss=0.06056, over 7369.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2559, pruned_loss=0.03717, over 1274387.35 frames.], batch size: 23, lr: 3.93e-04
2022-05-15 00:30:01,860 INFO [train.py:812] (6/8) Epoch 20, batch 500, loss[loss=0.1577, simple_loss=0.2602, pruned_loss=0.02758, over 7230.00 frames.], tot_loss[loss=0.1642, simple_loss=0.255, pruned_loss=0.03666, over 1306410.92 frames.], batch size: 21, lr: 3.93e-04
2022-05-15 00:31:00,469 INFO [train.py:812] (6/8) Epoch 20, batch 550, loss[loss=0.1661, simple_loss=0.2675, pruned_loss=0.03232, over 6796.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2545, pruned_loss=0.03643, over 1332169.12 frames.], batch size: 31, lr: 3.93e-04
2022-05-15 00:32:00,105 INFO [train.py:812] (6/8) Epoch 20, batch 600, loss[loss=0.13, simple_loss=0.2233, pruned_loss=0.01836, over 7161.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2527, pruned_loss=0.03614, over 1355337.79 frames.], batch size: 18, lr: 3.93e-04
2022-05-15 00:32:59,183 INFO [train.py:812] (6/8) Epoch 20, batch 650, loss[loss=0.1602, simple_loss=0.2439, pruned_loss=0.03829, over 7160.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2525, pruned_loss=0.03617, over 1369301.39 frames.], batch size: 18, lr: 3.92e-04
2022-05-15 00:33:55,668 INFO [train.py:812] (6/8) Epoch 20, batch 700, loss[loss=0.1316, simple_loss=0.2215, pruned_loss=0.02083, over 7226.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2529, pruned_loss=0.03582, over 1382793.70 frames.], batch size: 20, lr: 3.92e-04
2022-05-15 00:34:54,561 INFO [train.py:812] (6/8) Epoch 20, batch 750, loss[loss=0.1602, simple_loss=0.261, pruned_loss=0.02967, over 7308.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2525, pruned_loss=0.036, over 1393616.28 frames.], batch size: 25, lr: 3.92e-04
2022-05-15 00:35:51,674 INFO [train.py:812] (6/8) Epoch 20, batch 800, loss[loss=0.1355, simple_loss=0.2188, pruned_loss=0.02608, over 7411.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2525, pruned_loss=0.03608, over 1403075.64 frames.], batch size: 18, lr: 3.92e-04
2022-05-15 00:36:56,569 INFO [train.py:812] (6/8) Epoch 20, batch 850, loss[loss=0.1622, simple_loss=0.2605, pruned_loss=0.03197, over 7007.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2529, pruned_loss=0.03635, over 1410965.92 frames.], batch size: 28, lr: 3.92e-04
2022-05-15 00:37:55,375 INFO [train.py:812] (6/8) Epoch 20, batch 900, loss[loss=0.1589, simple_loss=0.2462, pruned_loss=0.03581, over 7358.00 frames.], tot_loss[loss=0.1617, simple_loss=0.252, pruned_loss=0.03576, over 1416086.54 frames.], batch size: 19, lr: 3.92e-04
2022-05-15 00:38:53,717 INFO [train.py:812] (6/8) Epoch 20, batch 950, loss[loss=0.1816, simple_loss=0.2805, pruned_loss=0.0414, over 7239.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2527, pruned_loss=0.03571, over 1420341.39 frames.], batch size: 20, lr: 3.92e-04
2022-05-15 00:39:52,447 INFO [train.py:812] (6/8) Epoch 20, batch 1000, loss[loss=0.1793, simple_loss=0.2789, pruned_loss=0.03981, over 7305.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2525, pruned_loss=0.03552, over 1420551.41 frames.], batch size: 24, lr: 3.92e-04
2022-05-15 00:40:51,842 INFO [train.py:812] (6/8) Epoch 20, batch 1050, loss[loss=0.1511, simple_loss=0.2397, pruned_loss=0.03123, over 7206.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2531, pruned_loss=0.03626, over 1420179.09 frames.], batch size: 22, lr: 3.92e-04
2022-05-15 00:41:50,558 INFO [train.py:812] (6/8) Epoch 20, batch 1100, loss[loss=0.2163, simple_loss=0.3042, pruned_loss=0.06419, over 7200.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2534, pruned_loss=0.03666, over 1416504.84 frames.], batch size: 22, lr: 3.91e-04
2022-05-15 00:42:49,024 INFO [train.py:812] (6/8) Epoch 20, batch 1150, loss[loss=0.1839, simple_loss=0.2818, pruned_loss=0.04301, over 7291.00 frames.], tot_loss[loss=0.1639, simple_loss=0.254, pruned_loss=0.03687, over 1419980.64 frames.], batch size: 24, lr: 3.91e-04
2022-05-15 00:43:48,225 INFO [train.py:812] (6/8) Epoch 20, batch 1200, loss[loss=0.1682, simple_loss=0.26, pruned_loss=0.03816, over 7328.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2526, pruned_loss=0.03647, over 1424469.91 frames.], batch size: 22, lr: 3.91e-04
2022-05-15 00:44:47,697 INFO [train.py:812] (6/8) Epoch 20, batch 1250, loss[loss=0.1465, simple_loss=0.2343, pruned_loss=0.02935, over 7127.00 frames.], tot_loss[loss=0.1633, simple_loss=0.253, pruned_loss=0.0368, over 1424955.00 frames.], batch size: 17, lr: 3.91e-04
2022-05-15 00:45:46,815 INFO [train.py:812] (6/8) Epoch 20, batch 1300, loss[loss=0.1665, simple_loss=0.2563, pruned_loss=0.03838, over 7109.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2522, pruned_loss=0.03624, over 1426909.16 frames.], batch size: 21, lr: 3.91e-04
2022-05-15 00:46:46,863 INFO [train.py:812] (6/8) Epoch 20, batch 1350, loss[loss=0.1928, simple_loss=0.2846, pruned_loss=0.0505, over 7199.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2521, pruned_loss=0.03563, over 1428927.92 frames.], batch size: 22, lr: 3.91e-04
2022-05-15 00:47:55,970 INFO [train.py:812] (6/8) Epoch 20, batch 1400, loss[loss=0.1752, simple_loss=0.2661, pruned_loss=0.04222, over 7152.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2525, pruned_loss=0.03585, over 1430806.15 frames.], batch size: 26, lr: 3.91e-04
2022-05-15 00:48:55,566 INFO [train.py:812] (6/8) Epoch 20, batch 1450, loss[loss=0.1622, simple_loss=0.2566, pruned_loss=0.03385, over 7181.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2537, pruned_loss=0.03641, over 1429738.38 frames.], batch size: 26, lr: 3.91e-04
2022-05-15 00:49:54,808 INFO [train.py:812] (6/8) Epoch 20, batch 1500, loss[loss=0.1946, simple_loss=0.28, pruned_loss=0.05455, over 7384.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2545, pruned_loss=0.03684, over 1427758.31 frames.], batch size: 23, lr: 3.91e-04
2022-05-15 00:51:04,088 INFO [train.py:812] (6/8) Epoch 20, batch 1550, loss[loss=0.1665, simple_loss=0.253, pruned_loss=0.03996, over 7432.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2537, pruned_loss=0.03651, over 1429711.04 frames.], batch size: 20, lr: 3.91e-04
2022-05-15 00:52:22,077 INFO [train.py:812] (6/8) Epoch 20, batch 1600, loss[loss=0.1659, simple_loss=0.2657, pruned_loss=0.03306, over 7347.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2534, pruned_loss=0.03605, over 1424340.07 frames.], batch size: 22, lr: 3.90e-04
2022-05-15 00:53:19,534 INFO [train.py:812] (6/8) Epoch 20, batch 1650, loss[loss=0.1897, simple_loss=0.2651, pruned_loss=0.05721, over 7180.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2543, pruned_loss=0.03666, over 1421423.46 frames.], batch size: 23, lr: 3.90e-04
2022-05-15 00:54:36,075 INFO [train.py:812] (6/8) Epoch 20, batch 1700, loss[loss=0.143, simple_loss=0.2372, pruned_loss=0.02441, over 7156.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2544, pruned_loss=0.03662, over 1420769.60 frames.], batch size: 19, lr: 3.90e-04
2022-05-15 00:55:43,693 INFO [train.py:812] (6/8) Epoch 20, batch 1750, loss[loss=0.1588, simple_loss=0.2568, pruned_loss=0.03041, over 7337.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2539, pruned_loss=0.03642, over 1425931.92 frames.], batch size: 22, lr: 3.90e-04
2022-05-15 00:56:42,601 INFO [train.py:812] (6/8) Epoch 20, batch 1800, loss[loss=0.1813, simple_loss=0.2715, pruned_loss=0.04556, over 7297.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2536, pruned_loss=0.03627, over 1425593.56 frames.], batch size: 25, lr: 3.90e-04
2022-05-15 00:57:42,403 INFO [train.py:812] (6/8) Epoch 20, batch 1850, loss[loss=0.149, simple_loss=0.2315, pruned_loss=0.03328, over 7070.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2534, pruned_loss=0.0361, over 1428242.42 frames.], batch size: 18, lr: 3.90e-04
2022-05-15 00:58:41,752 INFO [train.py:812] (6/8) Epoch 20, batch 1900, loss[loss=0.1834, simple_loss=0.2727, pruned_loss=0.04704, over 7234.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2543, pruned_loss=0.03629, over 1428747.82 frames.], batch size: 20, lr: 3.90e-04
2022-05-15 00:59:40,067 INFO [train.py:812] (6/8) Epoch 20, batch 1950, loss[loss=0.1866, simple_loss=0.2729, pruned_loss=0.05014, over 6454.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2535, pruned_loss=0.03615, over 1428861.38 frames.], batch size: 38, lr: 3.90e-04
2022-05-15 01:00:37,512 INFO [train.py:812] (6/8) Epoch 20, batch 2000, loss[loss=0.1608, simple_loss=0.2604, pruned_loss=0.0306, over 7228.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2523, pruned_loss=0.03575, over 1429730.76 frames.], batch size: 20, lr: 3.90e-04
2022-05-15 01:01:35,468 INFO [train.py:812] (6/8) Epoch 20, batch 2050, loss[loss=0.1725, simple_loss=0.2597, pruned_loss=0.04266, over 7222.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2524, pruned_loss=0.03607, over 1429557.45 frames.], batch size: 21, lr: 3.89e-04
2022-05-15 01:02:33,053 INFO [train.py:812] (6/8) Epoch 20, batch 2100, loss[loss=0.1549, simple_loss=0.242, pruned_loss=0.03392, over 7445.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2522, pruned_loss=0.0361, over 1431528.34 frames.], batch size: 20, lr: 3.89e-04
2022-05-15 01:03:30,911 INFO [train.py:812] (6/8) Epoch 20, batch 2150, loss[loss=0.1962, simple_loss=0.2822, pruned_loss=0.05507, over 7214.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2531, pruned_loss=0.03657, over 1425677.63 frames.], batch size: 22, lr: 3.89e-04
2022-05-15 01:04:30,280 INFO [train.py:812] (6/8) Epoch 20, batch 2200, loss[loss=0.1675, simple_loss=0.2488, pruned_loss=0.04311, over 6801.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2533, pruned_loss=0.03684, over 1420821.70 frames.], batch size: 15, lr: 3.89e-04
2022-05-15 01:05:28,872 INFO [train.py:812] (6/8) Epoch 20, batch 2250, loss[loss=0.1655, simple_loss=0.2509, pruned_loss=0.04004, over 7158.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2524, pruned_loss=0.0364, over 1423323.19 frames.], batch size: 20, lr: 3.89e-04
2022-05-15 01:06:27,825 INFO [train.py:812] (6/8) Epoch 20, batch 2300, loss[loss=0.1796, simple_loss=0.2636, pruned_loss=0.04782, over 7371.00 frames.], tot_loss[loss=0.1624, simple_loss=0.252, pruned_loss=0.03638, over 1423078.68 frames.], batch size: 23, lr: 3.89e-04
2022-05-15 01:07:25,477 INFO [train.py:812] (6/8) Epoch 20, batch 2350, loss[loss=0.1653, simple_loss=0.2491, pruned_loss=0.04075, over 7318.00 frames.], tot_loss[loss=0.163, simple_loss=0.2528, pruned_loss=0.03665, over 1421977.33 frames.], batch size: 21, lr: 3.89e-04
2022-05-15 01:08:24,277 INFO [train.py:812] (6/8) Epoch 20, batch 2400, loss[loss=0.1548, simple_loss=0.2453, pruned_loss=0.03213, over 7418.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2517, pruned_loss=0.03585, over 1424025.57 frames.], batch size: 20, lr: 3.89e-04
2022-05-15 01:09:23,910 INFO [train.py:812] (6/8) Epoch 20, batch 2450, loss[loss=0.1719, simple_loss=0.2649, pruned_loss=0.03947, over 7159.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2513, pruned_loss=0.03593, over 1426818.81 frames.], batch size: 28, lr: 3.89e-04
2022-05-15 01:10:23,082 INFO [train.py:812] (6/8) Epoch 20, batch 2500, loss[loss=0.1603, simple_loss=0.2593, pruned_loss=0.0306, over 7167.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2504, pruned_loss=0.0355, over 1425675.37 frames.], batch size: 26, lr: 3.88e-04
2022-05-15 01:11:22,885 INFO [train.py:812] (6/8) Epoch 20, batch 2550, loss[loss=0.1627, simple_loss=0.2532, pruned_loss=0.03611, over 7329.00 frames.], tot_loss[loss=0.1613, simple_loss=0.251, pruned_loss=0.03578, over 1423719.30 frames.], batch size: 20, lr: 3.88e-04
2022-05-15 01:12:22,127 INFO [train.py:812] (6/8) Epoch 20, batch 2600, loss[loss=0.1767, simple_loss=0.2718, pruned_loss=0.04081, over 6702.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2523, pruned_loss=0.03632, over 1425008.16 frames.], batch size: 31, lr: 3.88e-04
2022-05-15 01:13:22,246 INFO [train.py:812] (6/8) Epoch 20, batch 2650, loss[loss=0.1411, simple_loss=0.2205, pruned_loss=0.03085, over 7006.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2519, pruned_loss=0.0363, over 1426438.69 frames.], batch size: 16, lr: 3.88e-04
2022-05-15 01:14:21,720 INFO [train.py:812] (6/8) Epoch 20, batch 2700, loss[loss=0.169, simple_loss=0.2641, pruned_loss=0.03693, over 7388.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2507, pruned_loss=0.03588, over 1427014.77 frames.], batch size: 23, lr: 3.88e-04
2022-05-15 01:15:21,567 INFO [train.py:812] (6/8) Epoch 20, batch 2750, loss[loss=0.1696, simple_loss=0.2578, pruned_loss=0.04071, over 7198.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2511, pruned_loss=0.03565, over 1426173.41 frames.], batch size: 23, lr: 3.88e-04
2022-05-15 01:16:21,051 INFO [train.py:812] (6/8) Epoch 20, batch 2800, loss[loss=0.1459, simple_loss=0.2407, pruned_loss=0.02552, over 7159.00 frames.], tot_loss[loss=0.1615, simple_loss=0.252, pruned_loss=0.03552, over 1430180.78 frames.], batch size: 18, lr: 3.88e-04
2022-05-15 01:17:20,897 INFO [train.py:812] (6/8) Epoch 20, batch 2850, loss[loss=0.1605, simple_loss=0.257, pruned_loss=0.03197, over 7411.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2518, pruned_loss=0.03526, over 1432025.92 frames.], batch size: 21, lr: 3.88e-04
2022-05-15 01:18:20,017 INFO [train.py:812] (6/8) Epoch 20, batch 2900, loss[loss=0.1919, simple_loss=0.2741, pruned_loss=0.05482, over 7159.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2509, pruned_loss=0.0352, over 1427703.92 frames.], batch size: 26, lr: 3.88e-04
2022-05-15 01:19:19,553 INFO [train.py:812] (6/8) Epoch 20, batch 2950, loss[loss=0.159, simple_loss=0.2596, pruned_loss=0.02918, over 7243.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2516, pruned_loss=0.03533, over 1431538.81 frames.], batch size: 20, lr: 3.87e-04
2022-05-15 01:20:18,539 INFO [train.py:812] (6/8) Epoch 20, batch 3000, loss[loss=0.1837, simple_loss=0.2842, pruned_loss=0.04158, over 7379.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2529, pruned_loss=0.03583, over 1430793.77 frames.], batch size: 23, lr: 3.87e-04
2022-05-15 01:20:18,540 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 01:20:27,134 INFO [train.py:841] (6/8) Epoch 20, validation: loss=0.1532, simple_loss=0.2519, pruned_loss=0.02723, over 698248.00 frames.
2022-05-15 01:21:26,369 INFO [train.py:812] (6/8) Epoch 20, batch 3050, loss[loss=0.1543, simple_loss=0.2412, pruned_loss=0.03372, over 7157.00 frames.], tot_loss[loss=0.162, simple_loss=0.2523, pruned_loss=0.03582, over 1432282.49 frames.], batch size: 19, lr: 3.87e-04
2022-05-15 01:22:25,325 INFO [train.py:812] (6/8) Epoch 20, batch 3100, loss[loss=0.1782, simple_loss=0.2693, pruned_loss=0.04357, over 7106.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2529, pruned_loss=0.03584, over 1432447.14 frames.], batch size: 21, lr: 3.87e-04
2022-05-15 01:23:24,554 INFO [train.py:812] (6/8) Epoch 20, batch 3150, loss[loss=0.1492, simple_loss=0.2321, pruned_loss=0.03311, over 7286.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2532, pruned_loss=0.03594, over 1432887.66 frames.], batch size: 18, lr: 3.87e-04
2022-05-15 01:24:21,339 INFO [train.py:812] (6/8) Epoch 20, batch 3200, loss[loss=0.1814, simple_loss=0.2783, pruned_loss=0.04226, over 6711.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2532, pruned_loss=0.03606, over 1432052.07 frames.], batch size: 31, lr: 3.87e-04
2022-05-15 01:25:18,754 INFO [train.py:812] (6/8) Epoch 20, batch 3250, loss[loss=0.1461, simple_loss=0.2294, pruned_loss=0.03136, over 7071.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2532, pruned_loss=0.03628, over 1428561.55 frames.], batch size: 18, lr: 3.87e-04
2022-05-15 01:26:16,476 INFO [train.py:812] (6/8) Epoch 20, batch 3300, loss[loss=0.1473, simple_loss=0.2439, pruned_loss=0.02538, over 7139.00 frames.], tot_loss[loss=0.1628, simple_loss=0.253, pruned_loss=0.03634, over 1426935.56 frames.], batch size: 17, lr: 3.87e-04
2022-05-15 01:27:14,071 INFO [train.py:812] (6/8) Epoch 20, batch 3350, loss[loss=0.1653, simple_loss=0.2657, pruned_loss=0.03244, over 7147.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2527, pruned_loss=0.03634, over 1427120.05 frames.], batch size: 20, lr: 3.87e-04
2022-05-15 01:28:13,194 INFO [train.py:812] (6/8) Epoch 20, batch 3400, loss[loss=0.1419, simple_loss=0.2283, pruned_loss=0.02769, over 7281.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2523, pruned_loss=0.03598, over 1426455.61 frames.], batch size: 17, lr: 3.87e-04
2022-05-15 01:29:12,310 INFO [train.py:812] (6/8) Epoch 20, batch 3450, loss[loss=0.1539, simple_loss=0.2471, pruned_loss=0.03032, over 7239.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2529, pruned_loss=0.03596, over 1425322.32 frames.], batch size: 20, lr: 3.86e-04
2022-05-15 01:30:11,855 INFO [train.py:812] (6/8) Epoch 20, batch 3500, loss[loss=0.1366, simple_loss=0.2221, pruned_loss=0.02552, over 7265.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2526, pruned_loss=0.03564, over 1424118.81 frames.], batch size: 19, lr: 3.86e-04
2022-05-15 01:31:11,506 INFO [train.py:812] (6/8) Epoch 20, batch 3550, loss[loss=0.1601, simple_loss=0.2581, pruned_loss=0.03105, over 7115.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2531, pruned_loss=0.03591, over 1427381.42 frames.], batch size: 21, lr: 3.86e-04
2022-05-15 01:32:11,010 INFO [train.py:812] (6/8) Epoch 20, batch 3600, loss[loss=0.1838, simple_loss=0.2765, pruned_loss=0.04557, over 7192.00 frames.], tot_loss[loss=0.162, simple_loss=0.2522, pruned_loss=0.03596, over 1429335.00 frames.], batch size: 23, lr: 3.86e-04
2022-05-15 01:33:10,985 INFO [train.py:812] (6/8) Epoch 20, batch 3650, loss[loss=0.1738, simple_loss=0.2744, pruned_loss=0.03659, over 7314.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2521, pruned_loss=0.03577, over 1430314.74 frames.], batch size: 21, lr: 3.86e-04
2022-05-15 01:34:09,105 INFO [train.py:812] (6/8) Epoch 20, batch 3700, loss[loss=0.132, simple_loss=0.2258, pruned_loss=0.01908, over 7149.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2521, pruned_loss=0.03572, over 1432530.31 frames.], batch size: 18, lr: 3.86e-04
2022-05-15 01:35:08,076 INFO [train.py:812] (6/8) Epoch 20, batch 3750, loss[loss=0.1721, simple_loss=0.2638, pruned_loss=0.04021, over 7098.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2517, pruned_loss=0.03563, over 1426017.76 frames.], batch size: 28, lr: 3.86e-04
2022-05-15 01:36:06,438 INFO [train.py:812] (6/8) Epoch 20, batch 3800, loss[loss=0.1372, simple_loss=0.2309, pruned_loss=0.02177, over 7322.00 frames.], tot_loss[loss=0.1619, simple_loss=0.252, pruned_loss=0.03585, over 1421643.10 frames.], batch size: 20, lr: 3.86e-04
2022-05-15 01:37:04,410 INFO [train.py:812] (6/8) Epoch 20, batch 3850, loss[loss=0.1428, simple_loss=0.2237, pruned_loss=0.03094, over 7275.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2515, pruned_loss=0.0357, over 1419513.63 frames.], batch size: 17, lr: 3.86e-04
2022-05-15 01:38:02,237 INFO [train.py:812] (6/8) Epoch 20, batch 3900, loss[loss=0.1485, simple_loss=0.2431, pruned_loss=0.02699, over 7109.00 frames.], tot_loss[loss=0.1617, simple_loss=0.252, pruned_loss=0.0357, over 1417070.10 frames.], batch size: 21, lr: 3.85e-04
2022-05-15 01:39:01,359 INFO [train.py:812] (6/8) Epoch 20, batch 3950, loss[loss=0.1658, simple_loss=0.2547, pruned_loss=0.03844, over 7335.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2515, pruned_loss=0.03573, over 1411370.30 frames.], batch size: 20, lr: 3.85e-04
2022-05-15 01:39:59,114 INFO [train.py:812] (6/8) Epoch 20, batch 4000, loss[loss=0.1576, simple_loss=0.2387, pruned_loss=0.03826, over 7160.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2515, pruned_loss=0.03574, over 1408900.24 frames.], batch size: 18, lr: 3.85e-04
2022-05-15 01:40:58,264 INFO [train.py:812] (6/8) Epoch 20, batch 4050, loss[loss=0.1453, simple_loss=0.2336, pruned_loss=0.02849, over 7327.00 frames.], tot_loss[loss=0.162, simple_loss=0.2519, pruned_loss=0.03604, over 1406637.80 frames.], batch size: 20, lr: 3.85e-04
2022-05-15 01:41:57,281 INFO [train.py:812] (6/8) Epoch 20, batch 4100, loss[loss=0.1646, simple_loss=0.2466, pruned_loss=0.04133, over 7280.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2515, pruned_loss=0.03598, over 1407748.18 frames.], batch size: 18, lr: 3.85e-04
2022-05-15 01:42:56,569 INFO [train.py:812] (6/8) Epoch 20, batch 4150, loss[loss=0.1624, simple_loss=0.2492, pruned_loss=0.03775, over 7057.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2511, pruned_loss=0.03584, over 1411056.89 frames.], batch size: 18, lr: 3.85e-04
2022-05-15 01:43:53,663 INFO [train.py:812] (6/8) Epoch 20, batch 4200, loss[loss=0.1486, simple_loss=0.2216, pruned_loss=0.03782, over 6774.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2513, pruned_loss=0.03582, over 1406319.76 frames.], batch size: 15, lr: 3.85e-04
2022-05-15 01:44:52,599 INFO [train.py:812] (6/8) Epoch 20, batch 4250, loss[loss=0.1726, simple_loss=0.2649, pruned_loss=0.04014, over 7191.00 frames.], tot_loss[loss=0.161, simple_loss=0.2505, pruned_loss=0.03576, over 1403181.70 frames.], batch size: 23, lr: 3.85e-04
2022-05-15 01:45:49,909 INFO [train.py:812] (6/8) Epoch 20, batch 4300, loss[loss=0.1719, simple_loss=0.2656, pruned_loss=0.03905, over 7225.00 frames.], tot_loss[loss=0.1615, simple_loss=0.251, pruned_loss=0.03604, over 1400939.57 frames.], batch size: 21, lr: 3.85e-04
2022-05-15 01:46:48,984 INFO [train.py:812] (6/8) Epoch 20, batch 4350, loss[loss=0.1572, simple_loss=0.241, pruned_loss=0.03668, over 5289.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2497, pruned_loss=0.03548, over 1404901.32 frames.], batch size: 53, lr: 3.84e-04
2022-05-15 01:47:48,040 INFO [train.py:812] (6/8) Epoch 20, batch 4400, loss[loss=0.1634, simple_loss=0.2522, pruned_loss=0.03731, over 7159.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2501, pruned_loss=0.03584, over 1399504.49 frames.], batch size: 19, lr: 3.84e-04
2022-05-15 01:48:47,120 INFO [train.py:812] (6/8) Epoch 20, batch 4450, loss[loss=0.1433, simple_loss=0.2223, pruned_loss=0.03213, over 7238.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2497, pruned_loss=0.03598, over 1390214.02 frames.], batch size: 16, lr: 3.84e-04
2022-05-15 01:49:45,854 INFO [train.py:812] (6/8) Epoch 20, batch 4500, loss[loss=0.1867, simple_loss=0.2722, pruned_loss=0.05054, over 7201.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2515, pruned_loss=0.03642, over 1383095.72 frames.], batch size: 23, lr: 3.84e-04
2022-05-15 01:50:44,410 INFO [train.py:812] (6/8) Epoch 20, batch 4550, loss[loss=0.1442, simple_loss=0.2375, pruned_loss=0.02543, over 6649.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2539, pruned_loss=0.03786, over 1338191.68 frames.], batch size: 38, lr: 3.84e-04
2022-05-15 01:51:55,161 INFO [train.py:812] (6/8) Epoch 21, batch 0, loss[loss=0.1443, simple_loss=0.2267, pruned_loss=0.03096, over 7002.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2267, pruned_loss=0.03096, over 7002.00 frames.], batch size: 16, lr: 3.75e-04
2022-05-15 01:52:54,971 INFO [train.py:812] (6/8) Epoch 21, batch 50, loss[loss=0.1398, simple_loss=0.2392, pruned_loss=0.02026, over 6446.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2521, pruned_loss=0.03416, over 322586.32 frames.], batch size: 38, lr: 3.75e-04
2022-05-15 01:53:53,844 INFO [train.py:812] (6/8) Epoch 21, batch 100, loss[loss=0.1671, simple_loss=0.248, pruned_loss=0.04307, over 6814.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2518, pruned_loss=0.03454, over 566517.45 frames.], batch size: 15, lr: 3.75e-04
2022-05-15 01:54:52,719 INFO [train.py:812] (6/8) Epoch 21, batch 150, loss[loss=0.1328, simple_loss=0.2168, pruned_loss=0.02435, over 7152.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2507, pruned_loss=0.0345, over 755886.48 frames.], batch size: 18, lr: 3.75e-04
2022-05-15 01:55:51,319 INFO [train.py:812] (6/8) Epoch 21, batch 200, loss[loss=0.1775, simple_loss=0.2762, pruned_loss=0.03944, over 6826.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2527, pruned_loss=0.03523, over 901310.17 frames.], batch size: 31, lr: 3.75e-04
2022-05-15 01:56:53,963 INFO [train.py:812] (6/8) Epoch 21, batch 250, loss[loss=0.1393, simple_loss=0.2338, pruned_loss=0.02239, over 7162.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2527, pruned_loss=0.0355, over 1013650.19 frames.], batch size: 19, lr: 3.75e-04
2022-05-15 01:57:52,823 INFO [train.py:812] (6/8) Epoch 21, batch 300, loss[loss=0.1394, simple_loss=0.2281, pruned_loss=0.02536, over 7283.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2528, pruned_loss=0.03569, over 1102469.08 frames.], batch size: 18, lr: 3.75e-04
2022-05-15 01:58:49,831 INFO [train.py:812] (6/8) Epoch 21, batch 350, loss[loss=0.1366, simple_loss=0.2211, pruned_loss=0.02604, over 7255.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2529, pruned_loss=0.03586, over 1170392.92 frames.], batch size: 19, lr: 3.74e-04
2022-05-15 01:59:47,334 INFO [train.py:812] (6/8) Epoch 21, batch 400, loss[loss=0.148, simple_loss=0.2438, pruned_loss=0.02606, over 7063.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2514, pruned_loss=0.03549, over 1229312.99 frames.], batch size: 18, lr: 3.74e-04
2022-05-15 02:00:46,719 INFO [train.py:812] (6/8) Epoch 21, batch 450, loss[loss=0.1426, simple_loss=0.2301, pruned_loss=0.02759, over 7062.00 frames.], tot_loss[loss=0.161, simple_loss=0.2511, pruned_loss=0.03545, over 1272342.93 frames.], batch size: 18, lr: 3.74e-04
2022-05-15 02:01:45,891 INFO [train.py:812] (6/8) Epoch 21, batch 500, loss[loss=0.1586, simple_loss=0.2551, pruned_loss=0.03101, over 6954.00 frames.], tot_loss[loss=0.1609, simple_loss=0.251, pruned_loss=0.03537, over 1310655.15 frames.], batch size: 28, lr: 3.74e-04
2022-05-15 02:02:44,652 INFO [train.py:812] (6/8) Epoch 21, batch 550, loss[loss=0.1218, simple_loss=0.2024, pruned_loss=0.02056, over 7201.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2495, pruned_loss=0.03466, over 1337146.07 frames.], batch size: 16, lr: 3.74e-04
2022-05-15 02:03:42,721 INFO [train.py:812] (6/8) Epoch 21, batch 600, loss[loss=0.1919, simple_loss=0.2853, pruned_loss=0.04922, over 7217.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2504, pruned_loss=0.03498, over 1356202.95 frames.], batch size: 22, lr: 3.74e-04
2022-05-15 02:04:42,164 INFO [train.py:812] (6/8) Epoch 21, batch 650, loss[loss=0.1598, simple_loss=0.2475, pruned_loss=0.03607, over 7136.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2495, pruned_loss=0.035, over 1370131.41 frames.], batch size: 17, lr: 3.74e-04
2022-05-15 02:05:41,128 INFO [train.py:812] (6/8) Epoch 21, batch 700, loss[loss=0.1931, simple_loss=0.2864, pruned_loss=0.04985, over 7227.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2509, pruned_loss=0.03522, over 1379774.94 frames.], batch size: 20, lr: 3.74e-04
2022-05-15 02:06:40,206 INFO [train.py:812] (6/8) Epoch 21, batch 750, loss[loss=0.1325, simple_loss=0.2235, pruned_loss=0.02072, over 7397.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2518, pruned_loss=0.03586, over 1385707.05 frames.], batch size: 18, lr: 3.74e-04
2022-05-15 02:07:37,523 INFO [train.py:812] (6/8) Epoch 21, batch 800, loss[loss=0.1402, simple_loss=0.2292, pruned_loss=0.02559, over 7239.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2507, pruned_loss=0.03571, over 1384564.54 frames.], batch size: 20, lr: 3.73e-04
2022-05-15 02:08:37,264 INFO [train.py:812] (6/8) Epoch 21, batch 850, loss[loss=0.1816, simple_loss=0.2701, pruned_loss=0.04651, over 7295.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2503, pruned_loss=0.03558, over 1391807.17 frames.], batch size: 25, lr: 3.73e-04
2022-05-15 02:09:36,871 INFO [train.py:812] (6/8) Epoch 21, batch 900, loss[loss=0.1596, simple_loss=0.2535, pruned_loss=0.03283, over 7231.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2497, pruned_loss=0.03548, over 1400348.36 frames.], batch size: 20, lr: 3.73e-04
2022-05-15 02:10:36,715 INFO [train.py:812] (6/8) Epoch 21, batch 950, loss[loss=0.1757, simple_loss=0.2599, pruned_loss=0.0457, over 7338.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2501, pruned_loss=0.03573, over 1406091.88 frames.], batch size: 22, lr: 3.73e-04
2022-05-15 02:11:34,902 INFO [train.py:812] (6/8) Epoch 21, batch 1000, loss[loss=0.1682, simple_loss=0.2645, pruned_loss=0.03594, over 7195.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2505, pruned_loss=0.03545, over 1405876.81 frames.], batch size: 23, lr: 3.73e-04
2022-05-15 02:12:42,514 INFO [train.py:812] (6/8) Epoch 21, batch 1050, loss[loss=0.1651, simple_loss=0.2647, pruned_loss=0.03281, over 7423.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2518, pruned_loss=0.03589, over 1406489.35 frames.], batch size: 21, lr: 3.73e-04
2022-05-15 02:13:41,837 INFO [train.py:812] (6/8) Epoch 21, batch 1100, loss[loss=0.1682, simple_loss=0.2459, pruned_loss=0.04525, over 6798.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2515, pruned_loss=0.03565, over 1407467.23 frames.], batch size: 15, lr: 3.73e-04
2022-05-15 02:14:40,547 INFO [train.py:812] (6/8) Epoch 21, batch 1150, loss[loss=0.1734, simple_loss=0.2659, pruned_loss=0.04049, over 7284.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2519, pruned_loss=0.03582, over 1412955.93 frames.], batch size: 24, lr: 3.73e-04
2022-05-15 02:15:37,797 INFO [train.py:812] (6/8) Epoch 21, batch 1200, loss[loss=0.1748, simple_loss=0.266, pruned_loss=0.04178, over 7266.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2531, pruned_loss=0.0362, over 1415774.12 frames.], batch size: 18, lr: 3.73e-04
2022-05-15 02:16:37,268 INFO [train.py:812] (6/8) Epoch 21, batch 1250, loss[loss=0.1934, simple_loss=0.289, pruned_loss=0.04892, over 7277.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2521, pruned_loss=0.03576, over 1418195.88 frames.], batch size: 24, lr: 3.73e-04
2022-05-15 02:17:36,471 INFO [train.py:812] (6/8) Epoch 21, batch 1300, loss[loss=0.1434, simple_loss=0.2319, pruned_loss=0.02744, over 7075.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2517, pruned_loss=0.0357, over 1417442.04 frames.], batch size: 18, lr: 3.72e-04
2022-05-15 02:18:34,039 INFO [train.py:812] (6/8) Epoch 21, batch 1350, loss[loss=0.1577, simple_loss=0.2487, pruned_loss=0.03331, over 7342.00 frames.], tot_loss[loss=0.1607, simple_loss=0.251, pruned_loss=0.03522, over 1424208.70 frames.], batch size: 22, lr: 3.72e-04
2022-05-15 02:19:32,915 INFO [train.py:812] (6/8) Epoch 21, batch 1400, loss[loss=0.1622, simple_loss=0.2577, pruned_loss=0.03341, over 7376.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2518, pruned_loss=0.03537, over 1426250.50 frames.], batch size: 23, lr: 3.72e-04
2022-05-15 02:20:31,811 INFO [train.py:812] (6/8) Epoch 21, batch 1450, loss[loss=0.1607, simple_loss=0.2446, pruned_loss=0.03841, over 5080.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2512, pruned_loss=0.03555, over 1421079.73 frames.], batch size: 52, lr: 3.72e-04
2022-05-15 02:21:30,174 INFO [train.py:812] (6/8) Epoch 21, batch 1500, loss[loss=0.1597, simple_loss=0.2463, pruned_loss=0.03657, over 7337.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2521, pruned_loss=0.03601, over 1418619.17 frames.], batch size: 22, lr: 3.72e-04
2022-05-15 02:22:29,910 INFO [train.py:812] (6/8) Epoch 21, batch 1550, loss[loss=0.1464, simple_loss=0.2377, pruned_loss=0.02749, over 6718.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2526, pruned_loss=0.03626, over 1420370.09 frames.], batch size: 31, lr: 3.72e-04
2022-05-15 02:23:26,749 INFO [train.py:812] (6/8) Epoch 21, batch 1600, loss[loss=0.1666, simple_loss=0.2543, pruned_loss=0.03949, over 7346.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2537, pruned_loss=0.03651, over 1420850.59 frames.], batch size: 22, lr: 3.72e-04
2022-05-15 02:24:25,709 INFO [train.py:812] (6/8) Epoch 21, batch 1650, loss[loss=0.1552, simple_loss=0.2429, pruned_loss=0.03373, over 7331.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2534, pruned_loss=0.03678, over 1422007.19 frames.], batch size: 20, lr: 3.72e-04
2022-05-15 02:25:24,261 INFO [train.py:812] (6/8) Epoch 21, batch 1700, loss[loss=0.185, simple_loss=0.2781, pruned_loss=0.04596, over 7338.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2523, pruned_loss=0.03623, over 1422105.25 frames.], batch size: 22, lr: 3.72e-04
2022-05-15 02:26:22,323 INFO [train.py:812] (6/8) Epoch 21, batch 1750, loss[loss=0.1746, simple_loss=0.2439, pruned_loss=0.05265, over 7413.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2522, pruned_loss=0.03611, over 1423074.67 frames.], batch size: 18, lr: 3.72e-04
2022-05-15 02:27:21,274 INFO [train.py:812] (6/8) Epoch 21, batch 1800, loss[loss=0.197, simple_loss=0.2872, pruned_loss=0.05341, over 7201.00 frames.], tot_loss[loss=0.161, simple_loss=0.2509, pruned_loss=0.03553, over 1424567.53 frames.], batch size: 23, lr: 3.71e-04
2022-05-15 02:28:20,364 INFO [train.py:812] (6/8) Epoch 21, batch 1850, loss[loss=0.1568, simple_loss=0.2293, pruned_loss=0.0422, over 7408.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2511, pruned_loss=0.03572, over 1423478.23 frames.], batch size: 18, lr: 3.71e-04
2022-05-15 02:29:19,110 INFO [train.py:812] (6/8) Epoch 21, batch 1900, loss[loss=0.1499, simple_loss=0.2379, pruned_loss=0.03099, over 7163.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2516, pruned_loss=0.03605, over 1424057.67 frames.], batch size: 19, lr: 3.71e-04
2022-05-15 02:30:18,957 INFO [train.py:812] (6/8) Epoch 21, batch 1950, loss[loss=0.1373, simple_loss=0.2295, pruned_loss=0.02261, over 7260.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2514, pruned_loss=0.03603, over 1427767.25 frames.], batch size: 19, lr: 3.71e-04
2022-05-15 02:31:18,453 INFO [train.py:812] (6/8) Epoch 21, batch 2000, loss[loss=0.1648, simple_loss=0.2575, pruned_loss=0.03603, over 6711.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2511, pruned_loss=0.03585, over 1424219.01 frames.], batch size: 31, lr: 3.71e-04
2022-05-15 02:32:18,161 INFO [train.py:812] (6/8) Epoch 21, batch 2050, loss[loss=0.1785, simple_loss=0.2657, pruned_loss=0.04562, over 7226.00 frames.], tot_loss[loss=0.162, simple_loss=0.252, pruned_loss=0.03602, over 1423593.69 frames.], batch size: 21, lr: 3.71e-04
2022-05-15 02:33:17,374 INFO [train.py:812] (6/8) Epoch 21, batch 2100, loss[loss=0.1468, simple_loss=0.2237, pruned_loss=0.03499, over 7071.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2519, pruned_loss=0.03583, over 1422814.83 frames.], batch size: 18, lr: 3.71e-04
2022-05-15 02:34:16,891 INFO [train.py:812] (6/8) Epoch 21, batch 2150, loss[loss=0.1412, simple_loss=0.2217, pruned_loss=0.03033, over 6825.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2527, pruned_loss=0.03614, over 1420862.79 frames.], batch size: 15, lr: 3.71e-04
2022-05-15 02:35:14,487 INFO [train.py:812] (6/8) Epoch 21, batch 2200, loss[loss=0.1908, simple_loss=0.2807, pruned_loss=0.05048, over 7211.00 frames.], tot_loss[loss=0.162, simple_loss=0.2521, pruned_loss=0.03599, over 1423305.96 frames.], batch size: 22, lr: 3.71e-04
2022-05-15 02:36:12,375 INFO [train.py:812] (6/8) Epoch 21, batch 2250, loss[loss=0.1904, simple_loss=0.276, pruned_loss=0.05241, over 7206.00 frames.], tot_loss[loss=0.1619, simple_loss=0.252, pruned_loss=0.03592, over 1424018.70 frames.], batch size: 22, lr: 3.71e-04
2022-05-15 02:37:12,525 INFO [train.py:812] (6/8) Epoch 21, batch 2300, loss[loss=0.1864, simple_loss=0.2582, pruned_loss=0.05725, over 4908.00 frames.], tot_loss[loss=0.1615, simple_loss=0.251, pruned_loss=0.03598, over 1421842.21 frames.], batch size: 52, lr: 3.71e-04
2022-05-15 02:38:11,399 INFO [train.py:812] (6/8) Epoch 21, batch 2350, loss[loss=0.1605, simple_loss=0.2454, pruned_loss=0.03779, over 7308.00 frames.], tot_loss[loss=0.1623, simple_loss=0.252, pruned_loss=0.03629, over 1416377.03 frames.], batch size: 24, lr: 3.70e-04
2022-05-15 02:39:10,744 INFO [train.py:812] (6/8) Epoch 21, batch 2400, loss[loss=0.1797, simple_loss=0.2744, pruned_loss=0.04245, over 7204.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2516, pruned_loss=0.03575, over 1420351.73 frames.], batch size: 23, lr: 3.70e-04
2022-05-15 02:40:10,459 INFO [train.py:812] (6/8) Epoch 21, batch 2450, loss[loss=0.1626, simple_loss=0.2479, pruned_loss=0.03863, over 7161.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2509, pruned_loss=0.03539, over 1421159.42 frames.], batch size: 19, lr: 3.70e-04
2022-05-15 02:41:09,432 INFO [train.py:812] (6/8) Epoch 21, batch 2500, loss[loss=0.1741, simple_loss=0.2648, pruned_loss=0.04169, over 7412.00 frames.], tot_loss[loss=0.1611, simple_loss=0.251, pruned_loss=0.0356, over 1422012.99 frames.], batch size: 21, lr: 3.70e-04
2022-05-15 02:42:07,855 INFO [train.py:812] (6/8) Epoch 21, batch 2550, loss[loss=0.2012, simple_loss=0.269, pruned_loss=0.06675, over 5116.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2517, pruned_loss=0.03562, over 1420053.88 frames.], batch size: 54, lr: 3.70e-04
2022-05-15 02:43:06,169 INFO [train.py:812] (6/8) Epoch 21, batch 2600, loss[loss=0.1426, simple_loss=0.2354, pruned_loss=0.02491, over 7065.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2521, pruned_loss=0.03565, over 1420921.68 frames.], batch size: 18, lr: 3.70e-04
2022-05-15 02:44:05,932 INFO [train.py:812] (6/8) Epoch 21, batch 2650, loss[loss=0.1675, simple_loss=0.2675, pruned_loss=0.03375, over 7335.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2526, pruned_loss=0.03606, over 1417654.89 frames.], batch size: 20, lr: 3.70e-04
2022-05-15 02:45:04,661 INFO [train.py:812] (6/8) Epoch 21, batch 2700, loss[loss=0.1397, simple_loss=0.2255, pruned_loss=0.02698, over 7411.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2516, pruned_loss=0.03571, over 1420823.28 frames.], batch size: 18, lr: 3.70e-04
2022-05-15 02:46:03,784 INFO [train.py:812] (6/8) Epoch 21, batch 2750, loss[loss=0.154, simple_loss=0.2416, pruned_loss=0.03319, over 7152.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2514, pruned_loss=0.03542, over 1421966.89 frames.], batch size: 18, lr: 3.70e-04
2022-05-15 02:47:03,050 INFO [train.py:812] (6/8) Epoch 21, batch 2800, loss[loss=0.1632, simple_loss=0.2467, pruned_loss=0.03988, over 7363.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2505, pruned_loss=0.03526, over 1425010.50 frames.], batch size: 23, lr: 3.70e-04
2022-05-15 02:48:12,166 INFO [train.py:812] (6/8) Epoch 21, batch 2850, loss[loss=0.196, simple_loss=0.29, pruned_loss=0.05095, over 7199.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2504, pruned_loss=0.03526, over 1419320.90 frames.], batch size: 23, lr: 3.69e-04
2022-05-15 02:49:11,146 INFO [train.py:812] (6/8) Epoch 21, batch 2900, loss[loss=0.1711, simple_loss=0.2653, pruned_loss=0.03846, over 7068.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2504, pruned_loss=0.03535, over 1414758.31 frames.], batch size: 28, lr: 3.69e-04
2022-05-15 02:50:09,832 INFO [train.py:812] (6/8) Epoch 21, batch 2950, loss[loss=0.148, simple_loss=0.2413, pruned_loss=0.02731, over 7355.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2509, pruned_loss=0.03535, over 1412460.95 frames.], batch size: 19, lr: 3.69e-04
2022-05-15 02:51:09,049 INFO [train.py:812] (6/8) Epoch 21, batch 3000, loss[loss=0.1619, simple_loss=0.2567, pruned_loss=0.03357, over 6878.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2502, pruned_loss=0.03514, over 1412321.56 frames.], batch size: 31, lr: 3.69e-04
2022-05-15 02:51:09,050 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 02:51:16,349 INFO [train.py:841] (6/8) Epoch 21, validation: loss=0.153, simple_loss=0.2519, pruned_loss=0.02704, over 698248.00 frames.
2022-05-15 02:52:35,386 INFO [train.py:812] (6/8) Epoch 21, batch 3050, loss[loss=0.1646, simple_loss=0.2473, pruned_loss=0.04095, over 7276.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2499, pruned_loss=0.03521, over 1413873.62 frames.], batch size: 18, lr: 3.69e-04
2022-05-15 02:53:32,977 INFO [train.py:812] (6/8) Epoch 21, batch 3100, loss[loss=0.1853, simple_loss=0.2619, pruned_loss=0.05437, over 7382.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2511, pruned_loss=0.03572, over 1413100.08 frames.], batch size: 23, lr: 3.69e-04
2022-05-15 02:55:01,545 INFO [train.py:812] (6/8) Epoch 21, batch 3150, loss[loss=0.1887, simple_loss=0.2829, pruned_loss=0.04722, over 7284.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2515, pruned_loss=0.03648, over 1417517.63 frames.], batch size: 24, lr: 3.69e-04
2022-05-15 02:56:00,666 INFO [train.py:812] (6/8) Epoch 21, batch 3200, loss[loss=0.1424, simple_loss=0.23, pruned_loss=0.02744, over 7322.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2532, pruned_loss=0.03694, over 1422106.50 frames.], batch size: 21, lr: 3.69e-04
2022-05-15 02:57:00,421 INFO [train.py:812] (6/8) Epoch 21, batch 3250, loss[loss=0.1559, simple_loss=0.2486, pruned_loss=0.03159, over 7065.00 frames.], tot_loss[loss=0.1631, simple_loss=0.253, pruned_loss=0.03666, over 1421112.17 frames.], batch size: 18, lr: 3.69e-04
2022-05-15 02:58:08,770 INFO [train.py:812] (6/8) Epoch 21, batch 3300, loss[loss=0.139, simple_loss=0.2264, pruned_loss=0.02581, over 7129.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2517, pruned_loss=0.036, over 1422681.55 frames.], batch size: 17, lr: 3.69e-04
2022-05-15 02:59:08,388 INFO [train.py:812] (6/8) Epoch 21, batch 3350, loss[loss=0.166, simple_loss=0.2646, pruned_loss=0.0337, over 7233.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2518, pruned_loss=0.0359, over 1418217.81 frames.], batch size: 20, lr: 3.68e-04
2022-05-15 03:00:06,804 INFO [train.py:812] (6/8) Epoch 21, batch 3400, loss[loss=0.1872, simple_loss=0.2683, pruned_loss=0.0531, over 6381.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2518, pruned_loss=0.0353, over 1416083.46 frames.], batch size: 37, lr: 3.68e-04
2022-05-15 03:01:06,189 INFO [train.py:812] (6/8) Epoch 21, batch 3450, loss[loss=0.1459, simple_loss=0.2432, pruned_loss=0.02435, over 7316.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2522, pruned_loss=0.03572, over 1414388.37 frames.], batch size: 21, lr: 3.68e-04
2022-05-15 03:02:05,078 INFO [train.py:812] (6/8) Epoch 21, batch 3500, loss[loss=0.1874, simple_loss=0.2703, pruned_loss=0.05228, over 7107.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2525, pruned_loss=0.03546, over 1410123.71 frames.], batch size: 28, lr: 3.68e-04
2022-05-15 03:03:04,145 INFO [train.py:812] (6/8) Epoch 21, batch 3550, loss[loss=0.1358, simple_loss=0.2145, pruned_loss=0.02851, over 7280.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2514, pruned_loss=0.03517, over 1413982.43 frames.], batch size: 17, lr: 3.68e-04
2022-05-15 03:04:02,982 INFO [train.py:812] (6/8) Epoch 21, batch 3600, loss[loss=0.1776, simple_loss=0.2681, pruned_loss=0.04352, over 7367.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2517, pruned_loss=0.03507, over 1411954.02 frames.], batch size: 23, lr: 3.68e-04
2022-05-15 03:05:02,891 INFO [train.py:812] (6/8) Epoch 21, batch 3650, loss[loss=0.1585, simple_loss=0.2578, pruned_loss=0.02962, over 7155.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2514, pruned_loss=0.03492, over 1413320.81 frames.], batch size: 26, lr: 3.68e-04
2022-05-15 03:06:01,402 INFO [train.py:812] (6/8) Epoch 21, batch 3700, loss[loss=0.1512, simple_loss=0.2485, pruned_loss=0.027, over 7305.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2514, pruned_loss=0.03471, over 1414752.49 frames.], batch size: 21, lr: 3.68e-04
2022-05-15 03:07:01,127 INFO [train.py:812] (6/8) Epoch 21, batch 3750, loss[loss=0.168, simple_loss=0.2622, pruned_loss=0.0369, over 7308.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2514, pruned_loss=0.0349, over 1417496.80 frames.], batch size: 25, lr: 3.68e-04
2022-05-15 03:07:59,616 INFO [train.py:812] (6/8) Epoch 21, batch 3800, loss[loss=0.1886, simple_loss=0.2775, pruned_loss=0.04985, over 7149.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2508, pruned_loss=0.03479, over 1418355.05 frames.], batch size: 26, lr: 3.68e-04
2022-05-15 03:08:58,696 INFO [train.py:812] (6/8) Epoch 21, batch 3850, loss[loss=0.1676, simple_loss=0.2657, pruned_loss=0.03476, over 7324.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2504, pruned_loss=0.03445, over 1419182.76 frames.], batch size: 20, lr: 3.68e-04
2022-05-15 03:09:55,537 INFO [train.py:812] (6/8) Epoch 21, batch 3900, loss[loss=0.1629, simple_loss=0.256, pruned_loss=0.03489, over 7254.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2509, pruned_loss=0.03468, over 1423087.39 frames.], batch size: 19, lr: 3.67e-04
2022-05-15 03:10:53,481 INFO [train.py:812] (6/8) Epoch 21, batch 3950, loss[loss=0.1427, simple_loss=0.2227, pruned_loss=0.03129, over 7396.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2521, pruned_loss=0.03527, over 1418611.34 frames.], batch size: 18, lr: 3.67e-04
2022-05-15 03:11:51,935 INFO [train.py:812] (6/8) Epoch 21, batch 4000, loss[loss=0.1597, simple_loss=0.2515, pruned_loss=0.03395, over 7356.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2522, pruned_loss=0.03513, over 1422497.62 frames.], batch size: 19, lr: 3.67e-04
2022-05-15 03:12:50,968 INFO [train.py:812] (6/8) Epoch 21, batch 4050, loss[loss=0.1978, simple_loss=0.2778, pruned_loss=0.05889, over 4985.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2511, pruned_loss=0.03527, over 1419955.83 frames.], batch size: 53, lr: 3.67e-04
2022-05-15 03:13:49,292 INFO [train.py:812] (6/8) Epoch 21, batch 4100, loss[loss=0.1598, simple_loss=0.2541, pruned_loss=0.03279, over 7221.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2517, pruned_loss=0.03556, over 1411332.21 frames.], batch size: 21, lr: 3.67e-04
2022-05-15 03:14:46,160 INFO [train.py:812] (6/8) Epoch 21, batch 4150, loss[loss=0.1501, simple_loss=0.2362, pruned_loss=0.03203, over 7069.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2536, pruned_loss=0.03603, over 1412100.94 frames.], batch size: 18, lr: 3.67e-04
2022-05-15 03:15:43,942 INFO [train.py:812] (6/8) Epoch 21, batch 4200, loss[loss=0.1536, simple_loss=0.2502, pruned_loss=0.0285, over 6902.00 frames.], tot_loss[loss=0.1622, simple_loss=0.253, pruned_loss=0.03576, over 1411766.11 frames.], batch size: 32, lr: 3.67e-04
2022-05-15 03:16:47,879 INFO [train.py:812] (6/8) Epoch 21, batch 4250, loss[loss=0.1389, simple_loss=0.2447, pruned_loss=0.01652, over 7230.00 frames.], tot_loss[loss=0.1613, simple_loss=0.252, pruned_loss=0.03532, over 1416238.32 frames.], batch size: 21, lr: 3.67e-04
2022-05-15 03:17:46,899 INFO [train.py:812] (6/8) Epoch 21, batch 4300, loss[loss=0.1725, simple_loss=0.2624, pruned_loss=0.0413, over 7288.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2512, pruned_loss=0.03484, over 1417156.27 frames.], batch size: 24, lr: 3.67e-04
2022-05-15 03:18:45,862 INFO [train.py:812] (6/8) Epoch 21, batch 4350, loss[loss=0.1549, simple_loss=0.2537, pruned_loss=0.02806, over 7211.00 frames.], tot_loss[loss=0.16, simple_loss=0.2508, pruned_loss=0.03464, over 1417195.63 frames.], batch size: 21, lr: 3.67e-04
2022-05-15 03:19:43,042 INFO [train.py:812] (6/8) Epoch 21, batch 4400, loss[loss=0.1348, simple_loss=0.2207, pruned_loss=0.02451, over 7156.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2508, pruned_loss=0.03455, over 1416306.97 frames.], batch size: 18, lr: 3.66e-04
2022-05-15 03:20:42,018 INFO [train.py:812] (6/8) Epoch 21, batch 4450, loss[loss=0.148, simple_loss=0.2326, pruned_loss=0.03169, over 7016.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2515, pruned_loss=0.03504, over 1408167.41 frames.], batch size: 16, lr: 3.66e-04
2022-05-15 03:21:40,353 INFO [train.py:812] (6/8) Epoch 21, batch 4500, loss[loss=0.1262, simple_loss=0.2162, pruned_loss=0.0181, over 6994.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2508, pruned_loss=0.0345, over 1409800.42 frames.], batch size: 16, lr: 3.66e-04
2022-05-15 03:22:39,949 INFO [train.py:812] (6/8) Epoch 21, batch 4550, loss[loss=0.164, simple_loss=0.2567, pruned_loss=0.03567, over 5285.00 frames.], tot_loss[loss=0.16, simple_loss=0.2502, pruned_loss=0.03493, over 1394714.57 frames.], batch size: 52, lr: 3.66e-04
2022-05-15 03:23:52,261 INFO [train.py:812] (6/8) Epoch 22, batch 0, loss[loss=0.1638, simple_loss=0.2592, pruned_loss=0.03419, over 7292.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2592, pruned_loss=0.03419, over 7292.00 frames.], batch size: 25, lr: 3.58e-04
2022-05-15 03:24:50,153 INFO [train.py:812] (6/8) Epoch 22, batch 50, loss[loss=0.1406, simple_loss=0.2246, pruned_loss=0.02829, over 7163.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2533, pruned_loss=0.03613, over 317523.59 frames.], batch size: 18, lr: 3.58e-04
2022-05-15 03:25:49,157 INFO [train.py:812] (6/8) Epoch 22, batch 100, loss[loss=0.1553, simple_loss=0.2629, pruned_loss=0.0239, over 7116.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2507, pruned_loss=0.03446, over 564319.64 frames.], batch size: 21, lr: 3.58e-04
2022-05-15 03:26:47,192 INFO [train.py:812] (6/8) Epoch 22, batch 150, loss[loss=0.1807, simple_loss=0.2703, pruned_loss=0.04561, over 7313.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2498, pruned_loss=0.0343, over 753963.42 frames.], batch size: 21, lr: 3.58e-04
2022-05-15 03:27:46,015 INFO [train.py:812] (6/8) Epoch 22, batch 200, loss[loss=0.1477, simple_loss=0.2427, pruned_loss=0.0263, over 7345.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2512, pruned_loss=0.03474, over 901553.85 frames.], batch size: 22, lr: 3.58e-04
2022-05-15 03:28:43,588 INFO [train.py:812] (6/8) Epoch 22, batch 250, loss[loss=0.156, simple_loss=0.2475, pruned_loss=0.03222, over 7249.00 frames.], tot_loss[loss=0.1604, simple_loss=0.251, pruned_loss=0.03491, over 1014515.78 frames.], batch size: 19, lr: 3.57e-04
2022-05-15 03:29:41,578 INFO [train.py:812] (6/8) Epoch 22, batch 300, loss[loss=0.1469, simple_loss=0.2346, pruned_loss=0.02962, over 7241.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2514, pruned_loss=0.03533, over 1106952.30 frames.], batch size: 20, lr: 3.57e-04
2022-05-15 03:30:39,475 INFO [train.py:812] (6/8) Epoch 22, batch 350, loss[loss=0.1571, simple_loss=0.2529, pruned_loss=0.03064, over 7165.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2505, pruned_loss=0.03518, over 1176904.29 frames.], batch size: 19, lr: 3.57e-04
2022-05-15 03:31:38,302 INFO [train.py:812] (6/8) Epoch 22, batch 400, loss[loss=0.1917, simple_loss=0.2951, pruned_loss=0.04412, over 7222.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2507, pruned_loss=0.03502, over 1230407.61 frames.], batch size: 21, lr: 3.57e-04
2022-05-15 03:32:37,211 INFO [train.py:812] (6/8) Epoch 22, batch 450, loss[loss=0.1939, simple_loss=0.278, pruned_loss=0.0549, over 5022.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2499, pruned_loss=0.03489, over 1273955.39 frames.], batch size: 52, lr: 3.57e-04
2022-05-15 03:33:36,438 INFO [train.py:812] (6/8) Epoch 22, batch 500, loss[loss=0.1768, simple_loss=0.2773, pruned_loss=0.03814, over 7295.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2506, pruned_loss=0.03452, over 1309488.06 frames.], batch size: 25, lr: 3.57e-04
2022-05-15 03:34:33,239 INFO [train.py:812] (6/8) Epoch 22, batch 550, loss[loss=0.1502, simple_loss=0.2413, pruned_loss=0.02951, over 7435.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2512, pruned_loss=0.03447, over 1331974.56 frames.], batch size: 20, lr: 3.57e-04
2022-05-15 03:35:32,164 INFO [train.py:812] (6/8) Epoch 22, batch 600, loss[loss=0.175, simple_loss=0.2742, pruned_loss=0.03785, over 7327.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2504, pruned_loss=0.03395, over 1353663.41 frames.], batch size: 22, lr: 3.57e-04
2022-05-15 03:36:31,015 INFO [train.py:812] (6/8) Epoch 22, batch 650, loss[loss=0.1594, simple_loss=0.2551, pruned_loss=0.03187, over 7346.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2519, pruned_loss=0.03456, over 1370111.14 frames.], batch size: 22, lr: 3.57e-04
2022-05-15 03:37:30,485 INFO [train.py:812] (6/8) Epoch 22, batch 700, loss[loss=0.1818, simple_loss=0.2681, pruned_loss=0.04773, over 7291.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2515, pruned_loss=0.03487, over 1378818.66 frames.], batch size: 25, lr: 3.57e-04
2022-05-15 03:38:28,402 INFO [train.py:812] (6/8) Epoch 22, batch 750, loss[loss=0.1643, simple_loss=0.2477, pruned_loss=0.04038, over 7164.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2502, pruned_loss=0.03454, over 1387601.65 frames.], batch size: 18, lr: 3.57e-04
2022-05-15 03:39:28,270 INFO [train.py:812] (6/8) Epoch 22, batch 800, loss[loss=0.1576, simple_loss=0.2522, pruned_loss=0.03148, over 7274.00 frames.], tot_loss[loss=0.1593, simple_loss=0.25, pruned_loss=0.03432, over 1399791.27 frames.], batch size: 25, lr: 3.56e-04
2022-05-15 03:40:27,696 INFO [train.py:812] (6/8) Epoch 22, batch 850, loss[loss=0.1698, simple_loss=0.2541, pruned_loss=0.04275, over 7407.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2498, pruned_loss=0.0344, over 1405320.25 frames.], batch size: 18, lr: 3.56e-04
2022-05-15 03:41:26,083 INFO [train.py:812] (6/8) Epoch 22, batch 900, loss[loss=0.1986, simple_loss=0.2939, pruned_loss=0.05166, over 6392.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2495, pruned_loss=0.03458, over 1409724.53 frames.], batch size: 37, lr: 3.56e-04
2022-05-15 03:42:25,452 INFO [train.py:812] (6/8) Epoch 22, batch 950, loss[loss=0.1315, simple_loss=0.2171, pruned_loss=0.02294, over 7277.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2489, pruned_loss=0.03437, over 1411759.53 frames.], batch size: 18, lr: 3.56e-04
2022-05-15 03:43:24,207 INFO [train.py:812] (6/8) Epoch 22, batch 1000, loss[loss=0.1767, simple_loss=0.2657, pruned_loss=0.04388, over 7140.00 frames.], tot_loss[loss=0.1596, simple_loss=0.25, pruned_loss=0.03464, over 1411892.85 frames.], batch size: 19, lr: 3.56e-04
2022-05-15 03:44:23,461 INFO [train.py:812] (6/8) Epoch 22, batch 1050, loss[loss=0.167, simple_loss=0.2575, pruned_loss=0.03824, over 7326.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2491, pruned_loss=0.03474, over 1414985.45 frames.], batch size: 22, lr: 3.56e-04
2022-05-15 03:45:23,006 INFO [train.py:812] (6/8) Epoch 22, batch 1100, loss[loss=0.2051, simple_loss=0.2878, pruned_loss=0.06116, over 6430.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2496, pruned_loss=0.03494, over 1418750.67 frames.], batch size: 38, lr: 3.56e-04
2022-05-15 03:46:20,343 INFO [train.py:812] (6/8) Epoch 22, batch 1150, loss[loss=0.1447, simple_loss=0.236, pruned_loss=0.0267, over 7255.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2494, pruned_loss=0.03465, over 1419808.19 frames.], batch size: 19, lr: 3.56e-04
2022-05-15 03:47:19,505 INFO [train.py:812] (6/8) Epoch 22, batch 1200, loss[loss=0.1862, simple_loss=0.2685, pruned_loss=0.0519, over 7294.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2494, pruned_loss=0.03452, over 1420576.09 frames.], batch size: 25, lr: 3.56e-04
2022-05-15 03:48:19,016 INFO [train.py:812] (6/8) Epoch 22, batch 1250, loss[loss=0.1292, simple_loss=0.2106, pruned_loss=0.02388, over 6998.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2496, pruned_loss=0.03472, over 1420254.96 frames.], batch size: 16, lr: 3.56e-04
2022-05-15 03:49:19,111 INFO [train.py:812] (6/8) Epoch 22, batch 1300, loss[loss=0.1498, simple_loss=0.2375, pruned_loss=0.03108, over 7163.00 frames.], tot_loss[loss=0.1591, simple_loss=0.249, pruned_loss=0.03463, over 1418397.31 frames.], batch size: 19, lr: 3.56e-04
2022-05-15 03:50:16,182 INFO [train.py:812] (6/8) Epoch 22, batch 1350, loss[loss=0.1654, simple_loss=0.2596, pruned_loss=0.0356, over 7414.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2487, pruned_loss=0.03457, over 1422447.62 frames.], batch size: 21, lr: 3.55e-04
2022-05-15 03:51:15,340 INFO [train.py:812] (6/8) Epoch 22, batch 1400, loss[loss=0.2034, simple_loss=0.2954, pruned_loss=0.05572, over 7203.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2489, pruned_loss=0.03471, over 1419351.50 frames.], batch size: 22, lr: 3.55e-04
2022-05-15 03:52:14,145 INFO [train.py:812] (6/8) Epoch 22, batch 1450, loss[loss=0.1557, simple_loss=0.2494, pruned_loss=0.03102, over 7433.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2487, pruned_loss=0.03424, over 1424510.70 frames.], batch size: 20, lr: 3.55e-04
2022-05-15 03:53:13,835 INFO [train.py:812] (6/8) Epoch 22, batch 1500, loss[loss=0.1596, simple_loss=0.2531, pruned_loss=0.03306, over 7228.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2493, pruned_loss=0.03454, over 1426351.60 frames.], batch size: 20, lr: 3.55e-04
2022-05-15 03:54:13,333 INFO [train.py:812] (6/8) Epoch 22, batch 1550, loss[loss=0.1745, simple_loss=0.2653, pruned_loss=0.04184, over 7242.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2486, pruned_loss=0.03417, over 1429069.15 frames.], batch size: 20, lr: 3.55e-04
2022-05-15 03:55:12,247 INFO [train.py:812] (6/8) Epoch 22, batch 1600, loss[loss=0.1531, simple_loss=0.2345, pruned_loss=0.03587, over 6751.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2485, pruned_loss=0.03403, over 1429890.54 frames.], batch size: 15, lr: 3.55e-04
2022-05-15 03:56:08,996 INFO [train.py:812] (6/8) Epoch 22, batch 1650, loss[loss=0.1588, simple_loss=0.2538, pruned_loss=0.03188, over 6763.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2495, pruned_loss=0.03452, over 1432185.15 frames.], batch size: 31, lr: 3.55e-04
2022-05-15 03:57:06,980 INFO [train.py:812] (6/8) Epoch 22, batch 1700, loss[loss=0.1685, simple_loss=0.2681, pruned_loss=0.03448, over 7335.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2479, pruned_loss=0.03353, over 1435330.76 frames.], batch size: 22, lr: 3.55e-04
2022-05-15 03:58:03,883 INFO [train.py:812] (6/8) Epoch 22, batch 1750, loss[loss=0.1507, simple_loss=0.2494, pruned_loss=0.02596, over 7225.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2484, pruned_loss=0.03359, over 1433864.95 frames.], batch size: 20, lr: 3.55e-04
2022-05-15 03:59:03,640 INFO [train.py:812] (6/8) Epoch 22, batch 1800, loss[loss=0.1371, simple_loss=0.2211, pruned_loss=0.02661, over 7288.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2482, pruned_loss=0.0336, over 1429976.77 frames.], batch size: 17, lr: 3.55e-04
2022-05-15 04:00:02,111 INFO [train.py:812] (6/8) Epoch 22, batch 1850, loss[loss=0.1697, simple_loss=0.2585, pruned_loss=0.04041, over 6394.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2485, pruned_loss=0.03395, over 1425785.83 frames.], batch size: 38, lr: 3.55e-04
2022-05-15 04:01:00,871 INFO [train.py:812] (6/8) Epoch 22, batch 1900, loss[loss=0.173, simple_loss=0.2565, pruned_loss=0.0448, over 5045.00 frames.], tot_loss[loss=0.159, simple_loss=0.2495, pruned_loss=0.03426, over 1423377.52 frames.], batch size: 52, lr: 3.54e-04
2022-05-15 04:02:00,147 INFO [train.py:812] (6/8) Epoch 22, batch 1950, loss[loss=0.1663, simple_loss=0.2478, pruned_loss=0.0424, over 7289.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2501, pruned_loss=0.0347, over 1424344.09 frames.], batch size: 17, lr: 3.54e-04
2022-05-15 04:02:59,578 INFO [train.py:812] (6/8) Epoch 22, batch 2000, loss[loss=0.1761, simple_loss=0.2692, pruned_loss=0.04149, over 7337.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2502, pruned_loss=0.03484, over 1427057.89 frames.], batch size: 20, lr: 3.54e-04
2022-05-15 04:03:58,515 INFO [train.py:812] (6/8) Epoch 22, batch 2050, loss[loss=0.1362, simple_loss=0.2153, pruned_loss=0.02857, over 7280.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2506, pruned_loss=0.03528, over 1428151.35 frames.], batch size: 17, lr: 3.54e-04
2022-05-15 04:04:58,116 INFO [train.py:812] (6/8) Epoch 22, batch 2100, loss[loss=0.157, simple_loss=0.2309, pruned_loss=0.04157, over 7418.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2508, pruned_loss=0.03528, over 1427213.31 frames.], batch size: 18, lr: 3.54e-04
2022-05-15 04:05:56,585 INFO [train.py:812] (6/8) Epoch 22, batch 2150, loss[loss=0.1486, simple_loss=0.2438, pruned_loss=0.02669, over 7159.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2502, pruned_loss=0.03515, over 1422886.17 frames.], batch size: 18, lr: 3.54e-04
2022-05-15 04:06:54,951 INFO [train.py:812] (6/8) Epoch 22, batch 2200, loss[loss=0.1495, simple_loss=0.2398, pruned_loss=0.02957, over 7121.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2495, pruned_loss=0.03464, over 1425933.47 frames.], batch size: 21, lr: 3.54e-04
2022-05-15 04:07:52,624 INFO [train.py:812] (6/8) Epoch 22, batch 2250, loss[loss=0.1475, simple_loss=0.2282, pruned_loss=0.03342, over 7274.00 frames.], tot_loss[loss=0.1599, simple_loss=0.25, pruned_loss=0.03493, over 1423819.66 frames.], batch size: 16, lr: 3.54e-04
2022-05-15 04:08:49,589 INFO [train.py:812] (6/8) Epoch 22, batch 2300, loss[loss=0.2053, simple_loss=0.2857, pruned_loss=0.06251, over 4984.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2503, pruned_loss=0.03491, over 1424926.70 frames.], batch size: 54, lr: 3.54e-04
2022-05-15 04:09:47,988 INFO [train.py:812] (6/8) Epoch 22, batch 2350, loss[loss=0.1681, simple_loss=0.2641, pruned_loss=0.03599, over 6558.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2497, pruned_loss=0.03462, over 1427334.78 frames.], batch size: 38, lr: 3.54e-04
2022-05-15 04:10:57,214 INFO [train.py:812] (6/8) Epoch 22, batch 2400, loss[loss=0.1315, simple_loss=0.2104, pruned_loss=0.02629, over 7133.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2488, pruned_loss=0.03448, over 1425987.61 frames.], batch size: 17, lr: 3.54e-04
2022-05-15 04:11:56,431 INFO [train.py:812] (6/8) Epoch 22, batch 2450, loss[loss=0.1436, simple_loss=0.229, pruned_loss=0.02903, over 7280.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2491, pruned_loss=0.03451, over 1424436.10 frames.], batch size: 17, lr: 3.54e-04
2022-05-15 04:12:56,113 INFO [train.py:812] (6/8) Epoch 22, batch 2500, loss[loss=0.1556, simple_loss=0.253, pruned_loss=0.0291, over 7409.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2486, pruned_loss=0.03426, over 1421989.95 frames.], batch size: 21, lr: 3.53e-04
2022-05-15 04:13:55,283 INFO [train.py:812] (6/8) Epoch 22, batch 2550, loss[loss=0.1877, simple_loss=0.2745, pruned_loss=0.05038, over 7064.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2492, pruned_loss=0.03462, over 1420706.15 frames.], batch size: 18, lr: 3.53e-04
2022-05-15 04:14:54,434 INFO [train.py:812] (6/8) Epoch 22, batch 2600, loss[loss=0.1647, simple_loss=0.2427, pruned_loss=0.04334, over 7157.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2507, pruned_loss=0.03497, over 1416925.01 frames.], batch size: 19, lr: 3.53e-04
2022-05-15 04:15:53,327 INFO [train.py:812] (6/8) Epoch 22, batch 2650, loss[loss=0.1544, simple_loss=0.2493, pruned_loss=0.02976, over 7271.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2499, pruned_loss=0.03464, over 1420225.92 frames.], batch size: 19, lr: 3.53e-04
2022-05-15 04:16:52,258 INFO [train.py:812] (6/8) Epoch 22, batch 2700, loss[loss=0.1458, simple_loss=0.2323, pruned_loss=0.02968, over 7164.00 frames.], tot_loss[loss=0.1598, simple_loss=0.25, pruned_loss=0.03481, over 1419314.20 frames.], batch size: 18, lr: 3.53e-04
2022-05-15 04:17:51,016 INFO [train.py:812] (6/8) Epoch 22, batch 2750, loss[loss=0.1444, simple_loss=0.2326, pruned_loss=0.02813, over 7060.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2502, pruned_loss=0.03473, over 1419537.21 frames.], batch size: 18, lr: 3.53e-04
2022-05-15 04:18:49,830 INFO [train.py:812] (6/8) Epoch 22, batch 2800, loss[loss=0.1389, simple_loss=0.2316, pruned_loss=0.02311, over 7272.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2502, pruned_loss=0.03472, over 1419953.40 frames.], batch size: 18, lr: 3.53e-04
2022-05-15 04:19:48,491 INFO [train.py:812] (6/8) Epoch 22, batch 2850, loss[loss=0.1528, simple_loss=0.2373, pruned_loss=0.03412, over 7154.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2498, pruned_loss=0.03442, over 1418898.15 frames.], batch size: 19, lr: 3.53e-04
2022-05-15 04:20:47,858 INFO [train.py:812] (6/8) Epoch 22, batch 2900, loss[loss=0.1337, simple_loss=0.2283, pruned_loss=0.01955, over 7164.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2497, pruned_loss=0.03443, over 1421351.21 frames.], batch size: 19, lr: 3.53e-04
2022-05-15 04:21:47,235 INFO [train.py:812] (6/8) Epoch 22, batch 2950, loss[loss=0.149, simple_loss=0.2493, pruned_loss=0.02429, over 7410.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2494, pruned_loss=0.03446, over 1421829.14 frames.], batch size: 21, lr: 3.53e-04
2022-05-15 04:22:47,053 INFO [train.py:812] (6/8) Epoch 22, batch 3000, loss[loss=0.1493, simple_loss=0.2367, pruned_loss=0.03091, over 7150.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2499, pruned_loss=0.03434, over 1425881.70 frames.], batch size: 18, lr: 3.53e-04
2022-05-15 04:22:47,054 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 04:22:54,482 INFO [train.py:841] (6/8) Epoch 22, validation: loss=0.1529, simple_loss=0.2512, pruned_loss=0.02731, over 698248.00 frames.
2022-05-15 04:23:53,746 INFO [train.py:812] (6/8) Epoch 22, batch 3050, loss[loss=0.2097, simple_loss=0.3074, pruned_loss=0.05599, over 6967.00 frames.], tot_loss[loss=0.16, simple_loss=0.2506, pruned_loss=0.03472, over 1427756.31 frames.], batch size: 28, lr: 3.52e-04
2022-05-15 04:24:53,796 INFO [train.py:812] (6/8) Epoch 22, batch 3100, loss[loss=0.2, simple_loss=0.2769, pruned_loss=0.06157, over 5078.00 frames.], tot_loss[loss=0.16, simple_loss=0.2504, pruned_loss=0.03482, over 1428459.63 frames.], batch size: 52, lr: 3.52e-04
2022-05-15 04:25:52,326 INFO [train.py:812] (6/8) Epoch 22, batch 3150, loss[loss=0.1751, simple_loss=0.2693, pruned_loss=0.04049, over 7417.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2503, pruned_loss=0.03495, over 1425774.92 frames.], batch size: 21, lr: 3.52e-04
2022-05-15 04:26:51,020 INFO [train.py:812] (6/8) Epoch 22, batch 3200, loss[loss=0.1661, simple_loss=0.2584, pruned_loss=0.03691, over 7076.00 frames.], tot_loss[loss=0.1596, simple_loss=0.25, pruned_loss=0.03466, over 1426695.83 frames.], batch size: 18, lr: 3.52e-04
2022-05-15 04:27:50,215 INFO [train.py:812] (6/8) Epoch 22, batch 3250, loss[loss=0.1488, simple_loss=0.2383, pruned_loss=0.02964, over 7003.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2512, pruned_loss=0.03507, over 1427643.33 frames.], batch size: 16, lr: 3.52e-04
2022-05-15 04:28:47,791 INFO [train.py:812] (6/8) Epoch 22, batch 3300, loss[loss=0.1608, simple_loss=0.2486, pruned_loss=0.03656, over 7433.00 frames.], tot_loss[loss=0.161, simple_loss=0.2515, pruned_loss=0.0352, over 1430254.25 frames.], batch size: 20, lr: 3.52e-04
2022-05-15 04:29:46,990 INFO [train.py:812] (6/8) Epoch 22, batch 3350, loss[loss=0.1545, simple_loss=0.2424, pruned_loss=0.03334, over 7366.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2518, pruned_loss=0.03516, over 1428740.83 frames.], batch size: 19, lr: 3.52e-04
2022-05-15 04:30:46,413 INFO [train.py:812] (6/8) Epoch 22, batch 3400, loss[loss=0.1652, simple_loss=0.2544, pruned_loss=0.03799, over 7123.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2509, pruned_loss=0.03499, over 1425056.17 frames.], batch size: 17, lr: 3.52e-04
2022-05-15 04:31:45,557 INFO [train.py:812] (6/8) Epoch 22, batch 3450, loss[loss=0.1566, simple_loss=0.2525, pruned_loss=0.03032, over 7344.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2515, pruned_loss=0.03496, over 1427112.17 frames.], batch size: 22, lr: 3.52e-04
2022-05-15 04:32:45,142 INFO [train.py:812] (6/8) Epoch 22, batch 3500, loss[loss=0.168, simple_loss=0.2602, pruned_loss=0.03787, over 7328.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2504, pruned_loss=0.03447, over 1429592.40 frames.], batch size: 22, lr: 3.52e-04
2022-05-15 04:33:44,173 INFO [train.py:812] (6/8) Epoch 22, batch 3550, loss[loss=0.1497, simple_loss=0.244, pruned_loss=0.0277, over 6748.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2515, pruned_loss=0.03476, over 1428420.32 frames.], batch size: 31, lr: 3.52e-04
2022-05-15 04:34:43,579 INFO [train.py:812] (6/8) Epoch 22, batch 3600, loss[loss=0.138, simple_loss=0.2264, pruned_loss=0.02476, over 7286.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2512, pruned_loss=0.0348, over 1423386.91 frames.], batch size: 17, lr: 3.51e-04
2022-05-15 04:35:42,265 INFO [train.py:812] (6/8) Epoch 22, batch 3650, loss[loss=0.173, simple_loss=0.258, pruned_loss=0.04406, over 7375.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2517, pruned_loss=0.03478, over 1424538.39 frames.], batch size: 23, lr: 3.51e-04
2022-05-15 04:36:47,193 INFO [train.py:812] (6/8) Epoch 22, batch 3700, loss[loss=0.1473, simple_loss=0.2418, pruned_loss=0.02639, over 7221.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2511, pruned_loss=0.03458, over 1426829.99 frames.], batch size: 21, lr: 3.51e-04
2022-05-15 04:37:46,506 INFO [train.py:812] (6/8) Epoch 22, batch 3750, loss[loss=0.1361, simple_loss=0.2222, pruned_loss=0.02507, over 6983.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2503, pruned_loss=0.03425, over 1430382.52 frames.], batch size: 16, lr: 3.51e-04
2022-05-15 04:38:46,129 INFO [train.py:812] (6/8) Epoch 22, batch 3800, loss[loss=0.225, simple_loss=0.2932, pruned_loss=0.07845, over 4910.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2501, pruned_loss=0.0346, over 1424288.92 frames.], batch size: 52, lr: 3.51e-04
2022-05-15 04:39:43,956 INFO [train.py:812] (6/8) Epoch 22, batch 3850, loss[loss=0.181, simple_loss=0.2754, pruned_loss=0.04332, over 7233.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2511, pruned_loss=0.03484, over 1427145.83 frames.], batch size: 20, lr: 3.51e-04
2022-05-15 04:40:43,472 INFO [train.py:812] (6/8) Epoch 22, batch 3900, loss[loss=0.1523, simple_loss=0.2506, pruned_loss=0.02694, over 6574.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2502, pruned_loss=0.03433, over 1426931.63 frames.], batch size: 38, lr: 3.51e-04
2022-05-15 04:41:41,337 INFO [train.py:812] (6/8) Epoch 22, batch 3950, loss[loss=0.1524, simple_loss=0.2349, pruned_loss=0.03492, over 7274.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2499, pruned_loss=0.03452, over 1425507.29 frames.], batch size: 17, lr: 3.51e-04
2022-05-15 04:42:39,870 INFO [train.py:812] (6/8) Epoch 22, batch 4000, loss[loss=0.158, simple_loss=0.2625, pruned_loss=0.02677, over 7310.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2507, pruned_loss=0.03484, over 1425678.95 frames.], batch size: 21, lr: 3.51e-04
2022-05-15 04:43:37,317 INFO [train.py:812] (6/8) Epoch 22, batch 4050, loss[loss=0.1478, simple_loss=0.236, pruned_loss=0.02978, over 7357.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2499, pruned_loss=0.03447, over 1424364.26 frames.], batch size: 19, lr: 3.51e-04
2022-05-15 04:44:35,628 INFO [train.py:812] (6/8) Epoch 22, batch 4100, loss[loss=0.1512, simple_loss=0.2426, pruned_loss=0.0299, over 7325.00 frames.], tot_loss[loss=0.1593, simple_loss=0.25, pruned_loss=0.03428, over 1425357.64 frames.], batch size: 20, lr: 3.51e-04
2022-05-15 04:45:34,805 INFO [train.py:812] (6/8) Epoch 22, batch 4150, loss[loss=0.156, simple_loss=0.2451, pruned_loss=0.03348, over 7060.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2492, pruned_loss=0.03395, over 1421035.61 frames.], batch size: 18, lr: 3.51e-04
2022-05-15 04:46:33,516 INFO [train.py:812] (6/8) Epoch 22, batch 4200, loss[loss=0.1584, simple_loss=0.2512, pruned_loss=0.03286, over 7139.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2499, pruned_loss=0.03438, over 1416671.63 frames.], batch size: 20, lr: 3.50e-04
2022-05-15 04:47:30,300 INFO [train.py:812] (6/8) Epoch 22, batch 4250, loss[loss=0.1791, simple_loss=0.2735, pruned_loss=0.04236, over 6886.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2515, pruned_loss=0.03538, over 1410533.76 frames.], batch size: 31, lr: 3.50e-04
2022-05-15 04:48:27,308 INFO [train.py:812] (6/8) Epoch 22, batch 4300, loss[loss=0.164, simple_loss=0.2642, pruned_loss=0.03189, over 7278.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2521, pruned_loss=0.03519, over 1411946.16 frames.], batch size: 24, lr: 3.50e-04
2022-05-15 04:49:26,479 INFO [train.py:812] (6/8) Epoch 22, batch 4350, loss[loss=0.1789, simple_loss=0.2897, pruned_loss=0.03408, over 7336.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2528, pruned_loss=0.0352, over 1409056.08 frames.], batch size: 22, lr: 3.50e-04
2022-05-15 04:50:35,341 INFO [train.py:812] (6/8) Epoch 22, batch 4400, loss[loss=0.1448, simple_loss=0.2436, pruned_loss=0.02302, over 7094.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2525, pruned_loss=0.03535, over 1402930.01 frames.], batch size: 21, lr: 3.50e-04
2022-05-15 04:51:33,844 INFO [train.py:812] (6/8) Epoch 22, batch 4450, loss[loss=0.1672, simple_loss=0.2636, pruned_loss=0.03543, over 7339.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2534, pruned_loss=0.03562, over 1399716.89 frames.], batch size: 22, lr: 3.50e-04
2022-05-15 04:52:33,365 INFO [train.py:812] (6/8) Epoch 22, batch 4500, loss[loss=0.1579, simple_loss=0.2554, pruned_loss=0.03014, over 7051.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2545, pruned_loss=0.03611, over 1389921.34 frames.], batch size: 28, lr: 3.50e-04
2022-05-15 04:53:50,578 INFO [train.py:812] (6/8) Epoch 22, batch 4550, loss[loss=0.1696, simple_loss=0.2522, pruned_loss=0.04348, over 5090.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2553, pruned_loss=0.03679, over 1347580.00 frames.], batch size: 52, lr: 3.50e-04
2022-05-15 04:55:29,971 INFO [train.py:812] (6/8) Epoch 23, batch 0, loss[loss=0.1525, simple_loss=0.2392, pruned_loss=0.0329, over 6807.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2392, pruned_loss=0.0329, over 6807.00 frames.], batch size: 15, lr: 3.42e-04
2022-05-15 04:56:28,546 INFO [train.py:812] (6/8) Epoch 23, batch 50, loss[loss=0.1272, simple_loss=0.213, pruned_loss=0.02074, over 7154.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2473, pruned_loss=0.03297, over 319970.24 frames.], batch size: 19, lr: 3.42e-04
2022-05-15 04:57:26,793 INFO [train.py:812] (6/8) Epoch 23, batch 100, loss[loss=0.1364, simple_loss=0.2225, pruned_loss=0.02518, over 7277.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2504, pruned_loss=0.03462, over 566119.79 frames.], batch size: 18, lr: 3.42e-04
2022-05-15 04:58:25,230 INFO [train.py:812] (6/8) Epoch 23, batch 150, loss[loss=0.154, simple_loss=0.2438, pruned_loss=0.03207, over 7281.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2518, pruned_loss=0.03489, over 753576.45 frames.], batch size: 24, lr: 3.42e-04
2022-05-15 04:59:34,132 INFO [train.py:812] (6/8) Epoch 23, batch 200, loss[loss=0.1558, simple_loss=0.2485, pruned_loss=0.03155, over 6517.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2508, pruned_loss=0.03449, over 902668.03 frames.], batch size: 37, lr: 3.42e-04
2022-05-15 05:00:33,217 INFO [train.py:812] (6/8) Epoch 23, batch 250, loss[loss=0.1697, simple_loss=0.2604, pruned_loss=0.03949, over 7206.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2521, pruned_loss=0.03532, over 1017723.01 frames.], batch size: 23, lr: 3.42e-04
2022-05-15 05:01:30,576 INFO [train.py:812] (6/8) Epoch 23, batch 300, loss[loss=0.1558, simple_loss=0.2475, pruned_loss=0.03208, over 7158.00 frames.], tot_loss[loss=0.1606, simple_loss=0.251, pruned_loss=0.03509, over 1104337.96 frames.], batch size: 19, lr: 3.42e-04
2022-05-15 05:02:29,206 INFO [train.py:812] (6/8) Epoch 23, batch 350, loss[loss=0.1555, simple_loss=0.2445, pruned_loss=0.03322, over 7340.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2509, pruned_loss=0.03494, over 1178853.10 frames.], batch size: 22, lr: 3.42e-04
2022-05-15 05:03:27,255 INFO [train.py:812] (6/8) Epoch 23, batch 400, loss[loss=0.1749, simple_loss=0.2584, pruned_loss=0.04563, over 7211.00 frames.], tot_loss[loss=0.1606, simple_loss=0.251, pruned_loss=0.03514, over 1231266.97 frames.], batch size: 23, lr: 3.42e-04
2022-05-15 05:04:26,599 INFO [train.py:812] (6/8) Epoch 23, batch 450, loss[loss=0.1919, simple_loss=0.2781, pruned_loss=0.05285, over 7289.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2511, pruned_loss=0.035, over 1271889.18 frames.], batch size: 24, lr: 3.42e-04
2022-05-15 05:05:24,850 INFO [train.py:812] (6/8) Epoch 23, batch 500, loss[loss=0.1387, simple_loss=0.2255, pruned_loss=0.02596, over 7227.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2511, pruned_loss=0.03461, over 1307864.89 frames.], batch size: 16, lr: 3.41e-04
2022-05-15 05:06:21,998 INFO [train.py:812] (6/8) Epoch 23, batch 550, loss[loss=0.1614, simple_loss=0.2566, pruned_loss=0.03313, over 7293.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2503, pruned_loss=0.03432, over 1337472.11 frames.], batch size: 24, lr: 3.41e-04
2022-05-15 05:07:20,814 INFO [train.py:812] (6/8) Epoch 23, batch 600, loss[loss=0.187, simple_loss=0.2727, pruned_loss=0.05063, over 7104.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2506, pruned_loss=0.03448, over 1359464.95 frames.], batch size: 21, lr: 3.41e-04
2022-05-15 05:08:19,861 INFO [train.py:812] (6/8) Epoch 23, batch 650, loss[loss=0.1565, simple_loss=0.2429, pruned_loss=0.03503, over 6728.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2503, pruned_loss=0.03431, over 1373808.35 frames.], batch size: 31, lr: 3.41e-04
2022-05-15 05:09:19,439 INFO [train.py:812] (6/8) Epoch 23, batch 700, loss[loss=0.1978, simple_loss=0.2869, pruned_loss=0.05436, over 5356.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2505, pruned_loss=0.03439, over 1381008.42 frames.], batch size: 54, lr: 3.41e-04
2022-05-15 05:10:18,467 INFO [train.py:812] (6/8) Epoch 23, batch 750, loss[loss=0.138, simple_loss=0.2366, pruned_loss=0.01972, over 7199.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2508, pruned_loss=0.03423, over 1391800.87 frames.], batch size: 23, lr: 3.41e-04
2022-05-15 05:11:17,833 INFO [train.py:812] (6/8) Epoch 23, batch 800, loss[loss=0.1418, simple_loss=0.2286, pruned_loss=0.02755, over 7349.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2508, pruned_loss=0.03403, over 1396560.65 frames.], batch size: 19, lr: 3.41e-04
2022-05-15 05:12:15,582 INFO [train.py:812] (6/8) Epoch 23, batch 850, loss[loss=0.1666, simple_loss=0.2525, pruned_loss=0.04038, over 7421.00 frames.], tot_loss[loss=0.159, simple_loss=0.2502, pruned_loss=0.03392, over 1405305.40 frames.], batch size: 20, lr: 3.41e-04
2022-05-15 05:13:14,543 INFO [train.py:812] (6/8) Epoch 23, batch 900, loss[loss=0.1545, simple_loss=0.2533, pruned_loss=0.02782, over 7155.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2507, pruned_loss=0.03423, over 1409563.27 frames.], batch size: 19, lr: 3.41e-04
2022-05-15 05:14:13,177 INFO [train.py:812] (6/8) Epoch 23, batch 950, loss[loss=0.203, simple_loss=0.2951, pruned_loss=0.05545, over 7086.00 frames.], tot_loss[loss=0.1595, simple_loss=0.251, pruned_loss=0.03404, over 1411439.88 frames.], batch size: 28, lr: 3.41e-04
2022-05-15 05:15:13,127 INFO [train.py:812] (6/8) Epoch 23, batch 1000, loss[loss=0.1502, simple_loss=0.239, pruned_loss=0.03071, over 7359.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2511, pruned_loss=0.03409, over 1418534.15 frames.], batch size: 19, lr: 3.41e-04
2022-05-15 05:16:12,073 INFO [train.py:812] (6/8) Epoch 23, batch 1050, loss[loss=0.1753, simple_loss=0.2615, pruned_loss=0.04452, over 4856.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2506, pruned_loss=0.03399, over 1418880.47 frames.], batch size: 52, lr: 3.41e-04
2022-05-15 05:17:10,938 INFO [train.py:812] (6/8) Epoch 23, batch 1100, loss[loss=0.1337, simple_loss=0.2263, pruned_loss=0.02053, over 7258.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2505, pruned_loss=0.0336, over 1418463.37 frames.], batch size: 17, lr: 3.40e-04
2022-05-15 05:18:09,891 INFO [train.py:812] (6/8) Epoch 23, batch 1150, loss[loss=0.159, simple_loss=0.2453, pruned_loss=0.03638, over 7428.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2509, pruned_loss=0.03387, over 1422405.60 frames.], batch size: 20, lr: 3.40e-04
2022-05-15 05:19:09,553 INFO [train.py:812] (6/8) Epoch 23, batch 1200, loss[loss=0.135, simple_loss=0.2196, pruned_loss=0.02514, over 7290.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2508, pruned_loss=0.03386, over 1421197.00 frames.], batch size: 18, lr: 3.40e-04
2022-05-15 05:20:07,300 INFO [train.py:812] (6/8) Epoch 23, batch 1250, loss[loss=0.1327, simple_loss=0.2168, pruned_loss=0.0243, over 7233.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2496, pruned_loss=0.03376, over 1424817.75 frames.], batch size: 16, lr: 3.40e-04
2022-05-15 05:21:05,625 INFO [train.py:812] (6/8) Epoch 23, batch 1300, loss[loss=0.1596, simple_loss=0.2524, pruned_loss=0.03337, over 7203.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2494, pruned_loss=0.03374, over 1427443.55 frames.], batch size: 23, lr: 3.40e-04
2022-05-15 05:22:03,031 INFO [train.py:812] (6/8) Epoch 23, batch 1350, loss[loss=0.1426, simple_loss=0.229, pruned_loss=0.0281, over 7282.00 frames.], tot_loss[loss=0.158, simple_loss=0.2486, pruned_loss=0.03368, over 1428033.70 frames.], batch size: 18, lr: 3.40e-04
2022-05-15 05:23:02,518 INFO [train.py:812] (6/8) Epoch 23, batch 1400, loss[loss=0.1657, simple_loss=0.2591, pruned_loss=0.03618, over 7114.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2489, pruned_loss=0.03379, over 1428544.41 frames.], batch size: 21, lr: 3.40e-04
2022-05-15 05:24:01,141 INFO [train.py:812] (6/8) Epoch 23, batch 1450, loss[loss=0.1294, simple_loss=0.2128, pruned_loss=0.02302, over 7405.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2489, pruned_loss=0.03375, over 1422738.09 frames.], batch size: 18, lr: 3.40e-04
2022-05-15 05:24:59,750 INFO [train.py:812] (6/8) Epoch 23, batch 1500, loss[loss=0.1743, simple_loss=0.2629, pruned_loss=0.04289, over 7129.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2474, pruned_loss=0.03356, over 1423423.13 frames.], batch size: 28, lr: 3.40e-04
2022-05-15 05:25:58,360 INFO [train.py:812] (6/8) Epoch 23, batch 1550, loss[loss=0.1442, simple_loss=0.2422, pruned_loss=0.02309, over 7355.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2483, pruned_loss=0.03372, over 1414288.41 frames.], batch size: 19, lr: 3.40e-04
2022-05-15 05:26:57,176 INFO [train.py:812] (6/8) Epoch 23, batch 1600, loss[loss=0.1746, simple_loss=0.2695, pruned_loss=0.03984, over 7216.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2484, pruned_loss=0.03394, over 1412107.93 frames.], batch size: 21, lr: 3.40e-04
2022-05-15 05:27:55,187 INFO [train.py:812] (6/8) Epoch 23, batch 1650, loss[loss=0.1719, simple_loss=0.2656, pruned_loss=0.03915, over 7350.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2486, pruned_loss=0.03394, over 1414935.31 frames.], batch size: 23, lr: 3.40e-04
2022-05-15 05:28:54,106 INFO [train.py:812] (6/8) Epoch 23, batch 1700, loss[loss=0.1507, simple_loss=0.232, pruned_loss=0.03472, over 7410.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2486, pruned_loss=0.03363, over 1416549.38 frames.], batch size: 18, lr: 3.39e-04
2022-05-15 05:29:50,568 INFO [train.py:812] (6/8) Epoch 23, batch 1750, loss[loss=0.2103, simple_loss=0.2933, pruned_loss=0.06368, over 7168.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2499, pruned_loss=0.03399, over 1415021.79 frames.], batch size: 26, lr: 3.39e-04
2022-05-15 05:30:48,772 INFO [train.py:812] (6/8) Epoch 23, batch 1800, loss[loss=0.1934, simple_loss=0.283, pruned_loss=0.05187, over 4991.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2504, pruned_loss=0.03423, over 1411920.73 frames.], batch size: 52, lr: 3.39e-04
2022-05-15 05:31:46,096 INFO [train.py:812] (6/8) Epoch 23, batch 1850, loss[loss=0.1495, simple_loss=0.236, pruned_loss=0.03154, over 7428.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2499, pruned_loss=0.03386, over 1417061.29 frames.], batch size: 20, lr: 3.39e-04
2022-05-15 05:32:44,002 INFO [train.py:812] (6/8) Epoch 23, batch 1900, loss[loss=0.1688, simple_loss=0.2609, pruned_loss=0.03834, over 7154.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2492, pruned_loss=0.03375, over 1420402.16 frames.], batch size: 20, lr: 3.39e-04
2022-05-15 05:33:42,355 INFO [train.py:812] (6/8) Epoch 23, batch 1950, loss[loss=0.1562, simple_loss=0.2532, pruned_loss=0.02956, over 7146.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2496, pruned_loss=0.03387, over 1417474.76 frames.], batch size: 20, lr: 3.39e-04
2022-05-15 05:34:41,208 INFO [train.py:812] (6/8) Epoch 23, batch 2000, loss[loss=0.1507, simple_loss=0.2396, pruned_loss=0.03085, over 7262.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2506, pruned_loss=0.03411, over 1421097.30 frames.], batch size: 19, lr: 3.39e-04
2022-05-15 05:35:40,300 INFO [train.py:812] (6/8) Epoch 23, batch 2050, loss[loss=0.17, simple_loss=0.2624, pruned_loss=0.03883, over 7231.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2505, pruned_loss=0.0338, over 1425143.33 frames.], batch size: 20, lr: 3.39e-04
2022-05-15 05:36:39,479 INFO [train.py:812] (6/8) Epoch 23, batch 2100, loss[loss=0.1667, simple_loss=0.2641, pruned_loss=0.03462, over 7199.00 frames.], tot_loss[loss=0.1588, simple_loss=0.25, pruned_loss=0.03374, over 1420440.89 frames.], batch size: 23, lr: 3.39e-04
2022-05-15 05:37:37,961 INFO [train.py:812] (6/8) Epoch 23, batch 2150, loss[loss=0.1388, simple_loss=0.2289, pruned_loss=0.02433, over 7154.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2491, pruned_loss=0.03325, over 1421975.65 frames.], batch size: 19, lr: 3.39e-04
2022-05-15 05:38:37,712 INFO [train.py:812] (6/8) Epoch 23, batch 2200, loss[loss=0.1623, simple_loss=0.2548, pruned_loss=0.03488, over 7144.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2491, pruned_loss=0.03352, over 1417263.41 frames.], batch size: 20, lr: 3.39e-04
2022-05-15 05:39:36,706 INFO [train.py:812] (6/8) Epoch 23, batch 2250, loss[loss=0.1597, simple_loss=0.2577, pruned_loss=0.03084, over 7169.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2492, pruned_loss=0.03389, over 1412975.78 frames.], batch size: 19, lr: 3.39e-04
2022-05-15 05:40:35,586 INFO [train.py:812] (6/8) Epoch 23, batch 2300, loss[loss=0.1697, simple_loss=0.2653, pruned_loss=0.03706, over 7307.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2491, pruned_loss=0.03417, over 1414357.45 frames.], batch size: 21, lr: 3.38e-04
2022-05-15 05:41:34,393 INFO [train.py:812] (6/8) Epoch 23, batch 2350, loss[loss=0.1552, simple_loss=0.2602, pruned_loss=0.02515, over 7323.00 frames.], tot_loss[loss=0.159, simple_loss=0.2496, pruned_loss=0.03423, over 1416067.03 frames.], batch size: 22, lr: 3.38e-04
2022-05-15 05:42:33,226 INFO [train.py:812] (6/8) Epoch 23, batch 2400, loss[loss=0.1723, simple_loss=0.2618, pruned_loss=0.04142, over 7311.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2504, pruned_loss=0.03441, over 1417937.02 frames.], batch size: 24, lr: 3.38e-04
2022-05-15 05:43:31,227 INFO [train.py:812] (6/8) Epoch 23, batch 2450, loss[loss=0.1721, simple_loss=0.2625, pruned_loss=0.04086, over 7202.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2512, pruned_loss=0.03457, over 1421987.92 frames.], batch size: 22, lr: 3.38e-04
2022-05-15 05:44:30,350 INFO [train.py:812] (6/8) Epoch 23, batch 2500, loss[loss=0.1689, simple_loss=0.2674, pruned_loss=0.03514, over 6401.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2497, pruned_loss=0.03446, over 1420384.52 frames.], batch size: 38, lr: 3.38e-04
2022-05-15 05:45:29,334 INFO [train.py:812] (6/8) Epoch 23, batch 2550, loss[loss=0.1366, simple_loss=0.2292, pruned_loss=0.02197, over 7365.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2494, pruned_loss=0.03408, over 1421351.64 frames.], batch size: 23, lr: 3.38e-04
2022-05-15 05:46:26,782 INFO [train.py:812] (6/8) Epoch 23, batch 2600, loss[loss=0.1526, simple_loss=0.2423, pruned_loss=0.03144, over 7336.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2493, pruned_loss=0.03397, over 1426031.03 frames.], batch size: 22, lr: 3.38e-04
2022-05-15 05:47:25,323 INFO [train.py:812] (6/8) Epoch 23, batch 2650, loss[loss=0.1878, simple_loss=0.2807, pruned_loss=0.04744, over 7318.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2486, pruned_loss=0.03423, over 1423317.90 frames.], batch size: 25, lr: 3.38e-04
2022-05-15 05:48:25,325 INFO [train.py:812] (6/8) Epoch 23, batch 2700, loss[loss=0.1499, simple_loss=0.2416, pruned_loss=0.02916, over 7149.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2491, pruned_loss=0.03455, over 1423433.45 frames.], batch size: 19, lr: 3.38e-04
2022-05-15 05:49:24,356 INFO [train.py:812] (6/8) Epoch 23, batch 2750, loss[loss=0.1498, simple_loss=0.239, pruned_loss=0.03032, over 7154.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2493, pruned_loss=0.03489, over 1421187.83 frames.], batch size: 18, lr: 3.38e-04
2022-05-15 05:50:23,660 INFO [train.py:812] (6/8) Epoch 23, batch 2800, loss[loss=0.1288, simple_loss=0.2199, pruned_loss=0.01887, over 7155.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2495, pruned_loss=0.03494, over 1421024.07 frames.], batch size: 18, lr: 3.38e-04
2022-05-15 05:51:22,645 INFO [train.py:812] (6/8) Epoch 23, batch 2850, loss[loss=0.1873, simple_loss=0.286, pruned_loss=0.04428, over 7108.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2492, pruned_loss=0.03491, over 1422257.18 frames.], batch size: 28, lr: 3.38e-04
2022-05-15 05:52:22,322 INFO [train.py:812] (6/8) Epoch 23, batch 2900, loss[loss=0.1731, simple_loss=0.2623, pruned_loss=0.04197, over 7277.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2497, pruned_loss=0.03501, over 1423817.93 frames.], batch size: 25, lr: 3.37e-04
2022-05-15 05:53:20,369 INFO [train.py:812] (6/8) Epoch 23, batch 2950, loss[loss=0.1592, simple_loss=0.2506, pruned_loss=0.03396, over 7203.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2498, pruned_loss=0.03467, over 1424363.30 frames.], batch size: 22, lr: 3.37e-04
2022-05-15 05:54:18,731 INFO [train.py:812] (6/8) Epoch 23, batch 3000, loss[loss=0.1391, simple_loss=0.2148, pruned_loss=0.03171, over 6986.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2491, pruned_loss=0.03424, over 1424213.58 frames.], batch size: 16, lr: 3.37e-04
2022-05-15 05:54:18,732 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 05:54:28,114 INFO [train.py:841] (6/8) Epoch 23, validation: loss=0.153, simple_loss=0.251, pruned_loss=0.02752, over 698248.00 frames.
2022-05-15 05:55:26,707 INFO [train.py:812] (6/8) Epoch 23, batch 3050, loss[loss=0.1629, simple_loss=0.245, pruned_loss=0.04039, over 7170.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2489, pruned_loss=0.03421, over 1426352.64 frames.], batch size: 19, lr: 3.37e-04
2022-05-15 05:56:31,605 INFO [train.py:812] (6/8) Epoch 23, batch 3100, loss[loss=0.1468, simple_loss=0.2431, pruned_loss=0.0253, over 7235.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2481, pruned_loss=0.03401, over 1424775.42 frames.], batch size: 20, lr: 3.37e-04
2022-05-15 05:57:30,942 INFO [train.py:812] (6/8) Epoch 23, batch 3150, loss[loss=0.1777, simple_loss=0.263, pruned_loss=0.04625, over 7318.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2488, pruned_loss=0.03433, over 1426363.28 frames.], batch size: 20, lr: 3.37e-04
2022-05-15 05:58:30,539 INFO [train.py:812] (6/8) Epoch 23, batch 3200, loss[loss=0.1668, simple_loss=0.2652, pruned_loss=0.03422, over 7123.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2483, pruned_loss=0.03396, over 1427210.66 frames.], batch size: 21, lr: 3.37e-04
2022-05-15 05:59:29,511 INFO [train.py:812] (6/8) Epoch 23, batch 3250, loss[loss=0.1669, simple_loss=0.2675, pruned_loss=0.03315, over 6306.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2498, pruned_loss=0.03442, over 1422198.81 frames.], batch size: 37, lr: 3.37e-04
2022-05-15 06:00:29,771 INFO [train.py:812] (6/8) Epoch 23, batch 3300, loss[loss=0.1674, simple_loss=0.2569, pruned_loss=0.03894, over 7286.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2496, pruned_loss=0.03406, over 1422660.36 frames.], batch size: 24, lr: 3.37e-04
2022-05-15 06:01:29,050 INFO [train.py:812] (6/8) Epoch 23, batch 3350, loss[loss=0.1607, simple_loss=0.2572, pruned_loss=0.03207, over 7140.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2481, pruned_loss=0.03373, over 1427166.64 frames.], batch size: 26, lr: 3.37e-04
2022-05-15 06:02:28,586 INFO [train.py:812] (6/8) Epoch 23, batch 3400, loss[loss=0.1422, simple_loss=0.2375, pruned_loss=0.02347, over 7156.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2477, pruned_loss=0.03352, over 1428560.59 frames.], batch size: 19, lr: 3.37e-04
2022-05-15 06:03:27,802 INFO [train.py:812] (6/8) Epoch 23, batch 3450, loss[loss=0.1506, simple_loss=0.2282, pruned_loss=0.03646, over 6769.00 frames.], tot_loss[loss=0.157, simple_loss=0.247, pruned_loss=0.03349, over 1429754.92 frames.], batch size: 15, lr: 3.37e-04
2022-05-15 06:04:27,371 INFO [train.py:812] (6/8) Epoch 23, batch 3500, loss[loss=0.1473, simple_loss=0.2313, pruned_loss=0.03165, over 6849.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2471, pruned_loss=0.03335, over 1430966.40 frames.], batch size: 15, lr: 3.37e-04
2022-05-15 06:05:25,907 INFO [train.py:812] (6/8) Epoch 23, batch 3550, loss[loss=0.122, simple_loss=0.2084, pruned_loss=0.01777, over 7413.00 frames.], tot_loss[loss=0.157, simple_loss=0.2475, pruned_loss=0.03326, over 1431073.78 frames.], batch size: 18, lr: 3.36e-04
2022-05-15 06:06:25,047 INFO [train.py:812] (6/8) Epoch 23, batch 3600, loss[loss=0.1375, simple_loss=0.227, pruned_loss=0.02402, over 7275.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2485, pruned_loss=0.03341, over 1431921.39 frames.], batch size: 17, lr: 3.36e-04
2022-05-15 06:07:24,145 INFO [train.py:812] (6/8) Epoch 23, batch 3650, loss[loss=0.1546, simple_loss=0.25, pruned_loss=0.02965, over 6392.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2486, pruned_loss=0.03322, over 1431285.84 frames.], batch size: 38, lr: 3.36e-04
2022-05-15 06:08:33,463 INFO [train.py:812] (6/8) Epoch 23, batch 3700, loss[loss=0.1793, simple_loss=0.2681, pruned_loss=0.04522, over 7159.00 frames.], tot_loss[loss=0.158, simple_loss=0.2489, pruned_loss=0.03352, over 1430073.11 frames.], batch size: 19, lr: 3.36e-04
2022-05-15 06:09:32,136 INFO [train.py:812] (6/8) Epoch 23, batch 3750, loss[loss=0.1394, simple_loss=0.2293, pruned_loss=0.02481, over 7276.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2487, pruned_loss=0.03317, over 1428290.78 frames.], batch size: 17, lr: 3.36e-04
2022-05-15 06:10:31,415 INFO [train.py:812] (6/8) Epoch 23, batch 3800, loss[loss=0.1671, simple_loss=0.2476, pruned_loss=0.04329, over 7361.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2492, pruned_loss=0.03358, over 1429524.84 frames.], batch size: 23, lr: 3.36e-04
2022-05-15 06:11:30,191 INFO [train.py:812] (6/8) Epoch 23, batch 3850, loss[loss=0.1621, simple_loss=0.2579, pruned_loss=0.03314, over 7120.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2485, pruned_loss=0.0332, over 1430963.02 frames.], batch size: 28, lr: 3.36e-04
2022-05-15 06:12:28,315 INFO [train.py:812] (6/8) Epoch 23, batch 3900, loss[loss=0.1608, simple_loss=0.2548, pruned_loss=0.03337, over 7448.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2493, pruned_loss=0.03398, over 1431570.31 frames.], batch size: 22, lr: 3.36e-04
2022-05-15 06:13:25,769 INFO [train.py:812] (6/8) Epoch 23, batch 3950, loss[loss=0.1954, simple_loss=0.2665, pruned_loss=0.06213, over 7167.00 frames.], tot_loss[loss=0.1592, simple_loss=0.25, pruned_loss=0.03423, over 1430831.67 frames.], batch size: 19, lr: 3.36e-04
2022-05-15 06:14:23,004 INFO [train.py:812] (6/8) Epoch 23, batch 4000, loss[loss=0.1434, simple_loss=0.2234, pruned_loss=0.03168, over 7279.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2494, pruned_loss=0.0341, over 1427287.29 frames.], batch size: 17, lr: 3.36e-04
2022-05-15 06:15:21,478 INFO [train.py:812] (6/8) Epoch 23, batch 4050, loss[loss=0.1302, simple_loss=0.2142, pruned_loss=0.02313, over 6815.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2498, pruned_loss=0.03397, over 1421734.05 frames.], batch size: 15, lr: 3.36e-04
2022-05-15 06:16:21,800 INFO [train.py:812] (6/8) Epoch 23, batch 4100, loss[loss=0.1441, simple_loss=0.2301, pruned_loss=0.0291, over 6827.00 frames.], tot_loss[loss=0.159, simple_loss=0.2496, pruned_loss=0.03416, over 1418643.55 frames.], batch size: 15, lr: 3.36e-04
2022-05-15 06:17:19,472 INFO [train.py:812] (6/8) Epoch 23, batch 4150, loss[loss=0.1395, simple_loss=0.2266, pruned_loss=0.02615, over 7312.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2503, pruned_loss=0.03427, over 1418367.68 frames.], batch size: 21, lr: 3.35e-04
2022-05-15 06:18:18,906 INFO [train.py:812] (6/8) Epoch 23, batch 4200, loss[loss=0.1327, simple_loss=0.214, pruned_loss=0.02569, over 7412.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2506, pruned_loss=0.03423, over 1423003.59 frames.], batch size: 17, lr: 3.35e-04
2022-05-15 06:19:17,852 INFO [train.py:812] (6/8) Epoch 23, batch 4250, loss[loss=0.1531, simple_loss=0.2412, pruned_loss=0.03247, over 7233.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2503, pruned_loss=0.03398, over 1424035.68 frames.], batch size: 20, lr: 3.35e-04
2022-05-15 06:20:16,346 INFO [train.py:812] (6/8) Epoch 23, batch 4300, loss[loss=0.1436, simple_loss=0.233, pruned_loss=0.02716, over 7158.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2487, pruned_loss=0.03333, over 1420973.66 frames.], batch size: 18, lr: 3.35e-04
2022-05-15 06:21:15,787 INFO [train.py:812] (6/8) Epoch 23, batch 4350, loss[loss=0.1416, simple_loss=0.2221, pruned_loss=0.03056, over 6840.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2481, pruned_loss=0.0334, over 1422363.88 frames.], batch size: 15, lr: 3.35e-04
2022-05-15 06:22:15,643 INFO [train.py:812] (6/8) Epoch 23, batch 4400, loss[loss=0.1482, simple_loss=0.2314, pruned_loss=0.03247, over 7072.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2482, pruned_loss=0.03344, over 1419237.50 frames.], batch size: 18, lr: 3.35e-04
2022-05-15 06:23:14,869 INFO [train.py:812] (6/8) Epoch 23, batch 4450, loss[loss=0.2167, simple_loss=0.2955, pruned_loss=0.06891, over 4816.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2491, pruned_loss=0.0341, over 1413019.81 frames.], batch size: 53, lr: 3.35e-04
2022-05-15 06:24:12,967 INFO [train.py:812] (6/8) Epoch 23, batch 4500, loss[loss=0.1355, simple_loss=0.229, pruned_loss=0.02102, over 7066.00 frames.], tot_loss[loss=0.158, simple_loss=0.2489, pruned_loss=0.0336, over 1412465.12 frames.], batch size: 18, lr: 3.35e-04
2022-05-15 06:25:11,008 INFO [train.py:812] (6/8) Epoch 23, batch 4550, loss[loss=0.1921, simple_loss=0.2664, pruned_loss=0.05894, over 5117.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2515, pruned_loss=0.03529, over 1354385.29 frames.], batch size: 52, lr: 3.35e-04
2022-05-15 06:26:16,428 INFO [train.py:812] (6/8) Epoch 24, batch 0, loss[loss=0.1228, simple_loss=0.2092, pruned_loss=0.0182, over 6802.00 frames.], tot_loss[loss=0.1228, simple_loss=0.2092, pruned_loss=0.0182, over 6802.00 frames.], batch size: 15, lr: 3.28e-04
2022-05-15 06:27:14,056 INFO [train.py:812] (6/8) Epoch 24, batch 50, loss[loss=0.1332, simple_loss=0.2265, pruned_loss=0.01998, over 7279.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2466, pruned_loss=0.03333, over 316994.30 frames.], batch size: 17, lr: 3.28e-04
2022-05-15 06:28:13,409 INFO [train.py:812] (6/8) Epoch 24, batch 100, loss[loss=0.1833, simple_loss=0.275, pruned_loss=0.04582, over 7324.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2477, pruned_loss=0.03285, over 567927.96 frames.], batch size: 20, lr: 3.28e-04
2022-05-15 06:29:11,054 INFO [train.py:812] (6/8) Epoch 24, batch 150, loss[loss=0.1916, simple_loss=0.2784, pruned_loss=0.05238, over 7393.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2478, pruned_loss=0.03294, over 753860.87 frames.], batch size: 23, lr: 3.28e-04
2022-05-15 06:30:10,089 INFO [train.py:812] (6/8) Epoch 24, batch 200, loss[loss=0.1747, simple_loss=0.2737, pruned_loss=0.03787, over 7200.00 frames.], tot_loss[loss=0.1583, simple_loss=0.249, pruned_loss=0.03376, over 904301.40 frames.], batch size: 22, lr: 3.28e-04
2022-05-15 06:31:07,649 INFO [train.py:812] (6/8) Epoch 24, batch 250, loss[loss=0.1715, simple_loss=0.2659, pruned_loss=0.0385, over 7415.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2495, pruned_loss=0.03404, over 1015993.92 frames.], batch size: 21, lr: 3.28e-04
2022-05-15 06:32:07,196 INFO [train.py:812] (6/8) Epoch 24, batch 300, loss[loss=0.1554, simple_loss=0.2553, pruned_loss=0.02772, over 7146.00 frames.], tot_loss[loss=0.159, simple_loss=0.2502, pruned_loss=0.03392, over 1107249.64 frames.], batch size: 20, lr: 3.27e-04
2022-05-15 06:33:04,002 INFO [train.py:812] (6/8) Epoch 24, batch 350, loss[loss=0.1922, simple_loss=0.2778, pruned_loss=0.05324, over 7318.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2498, pruned_loss=0.03353, over 1179289.05 frames.], batch size: 25, lr: 3.27e-04
2022-05-15 06:34:01,091 INFO [train.py:812] (6/8) Epoch 24, batch 400, loss[loss=0.1773, simple_loss=0.2708, pruned_loss=0.04191, over 7296.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2491, pruned_loss=0.03363, over 1230249.15 frames.], batch size: 24, lr: 3.27e-04
2022-05-15 06:34:58,903 INFO [train.py:812] (6/8) Epoch 24, batch 450, loss[loss=0.154, simple_loss=0.2366, pruned_loss=0.03572, over 7145.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2495, pruned_loss=0.03332, over 1275651.52 frames.], batch size: 20, lr: 3.27e-04
2022-05-15 06:35:57,378 INFO [train.py:812] (6/8) Epoch 24, batch 500, loss[loss=0.1551, simple_loss=0.2417, pruned_loss=0.03427, over 7354.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2493, pruned_loss=0.03313, over 1307552.60 frames.], batch size: 19, lr: 3.27e-04
2022-05-15 06:36:55,898 INFO [train.py:812] (6/8) Epoch 24, batch 550, loss[loss=0.1581, simple_loss=0.2515, pruned_loss=0.03231, over 7194.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2492, pruned_loss=0.03322, over 1336237.85 frames.], batch size: 22, lr: 3.27e-04
2022-05-15 06:37:55,369 INFO [train.py:812] (6/8) Epoch 24, batch 600, loss[loss=0.1699, simple_loss=0.2562, pruned_loss=0.04176, over 7356.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2484, pruned_loss=0.03324, over 1353445.54 frames.], batch size: 19, lr: 3.27e-04
2022-05-15 06:38:54,604 INFO [train.py:812] (6/8) Epoch 24, batch 650, loss[loss=0.1593, simple_loss=0.2365, pruned_loss=0.04103, over 7356.00 frames.], tot_loss[loss=0.1573, simple_loss=0.248, pruned_loss=0.03327, over 1364343.90 frames.], batch size: 19, lr: 3.27e-04
2022-05-15 06:39:54,728 INFO [train.py:812] (6/8) Epoch 24, batch 700, loss[loss=0.1605, simple_loss=0.2575, pruned_loss=0.0317, over 7192.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2462, pruned_loss=0.03279, over 1381375.27 frames.], batch size: 26, lr: 3.27e-04
2022-05-15 06:40:53,843 INFO [train.py:812] (6/8) Epoch 24, batch 750, loss[loss=0.1556, simple_loss=0.2381, pruned_loss=0.03653, over 7011.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2471, pruned_loss=0.03307, over 1393223.89 frames.], batch size: 16, lr: 3.27e-04
2022-05-15 06:41:53,044 INFO [train.py:812] (6/8) Epoch 24, batch 800, loss[loss=0.1405, simple_loss=0.2348, pruned_loss=0.02309, over 7256.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2468, pruned_loss=0.03288, over 1399965.42 frames.], batch size: 19, lr: 3.27e-04
2022-05-15 06:42:52,221 INFO [train.py:812] (6/8) Epoch 24, batch 850, loss[loss=0.1595, simple_loss=0.2594, pruned_loss=0.0298, over 6595.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2462, pruned_loss=0.03252, over 1405629.37 frames.], batch size: 31, lr: 3.27e-04
2022-05-15 06:43:51,476 INFO [train.py:812] (6/8) Epoch 24, batch 900, loss[loss=0.1415, simple_loss=0.226, pruned_loss=0.02846, over 7419.00 frames.], tot_loss[loss=0.1555, simple_loss=0.246, pruned_loss=0.03252, over 1410815.97 frames.], batch size: 20, lr: 3.27e-04
2022-05-15 06:44:50,519 INFO [train.py:812] (6/8) Epoch 24, batch 950, loss[loss=0.1986, simple_loss=0.2917, pruned_loss=0.05279, over 6462.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2463, pruned_loss=0.03306, over 1415266.51 frames.], batch size: 37, lr: 3.26e-04
2022-05-15 06:45:49,549 INFO [train.py:812] (6/8) Epoch 24, batch 1000, loss[loss=0.1724, simple_loss=0.2686, pruned_loss=0.03807, over 7316.00 frames.], tot_loss[loss=0.157, simple_loss=0.2469, pruned_loss=0.03352, over 1417573.36 frames.], batch size: 21, lr: 3.26e-04
2022-05-15 06:46:47,317 INFO [train.py:812] (6/8) Epoch 24, batch 1050, loss[loss=0.1822, simple_loss=0.2765, pruned_loss=0.04395, over 7238.00 frames.], tot_loss[loss=0.1579, simple_loss=0.248, pruned_loss=0.03392, over 1412741.91 frames.], batch size: 20, lr: 3.26e-04
2022-05-15 06:47:46,430 INFO [train.py:812] (6/8) Epoch 24, batch 1100, loss[loss=0.1599, simple_loss=0.2548, pruned_loss=0.03248, over 7147.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2478, pruned_loss=0.03365, over 1412323.32 frames.], batch size: 20, lr: 3.26e-04
2022-05-15 06:48:44,908 INFO [train.py:812] (6/8) Epoch 24, batch 1150, loss[loss=0.1548, simple_loss=0.252, pruned_loss=0.02882, over 6382.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2477, pruned_loss=0.03347, over 1415929.65 frames.], batch size: 38, lr: 3.26e-04
2022-05-15 06:49:42,953 INFO [train.py:812] (6/8) Epoch 24, batch 1200, loss[loss=0.15, simple_loss=0.2379, pruned_loss=0.031, over 7162.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2482, pruned_loss=0.03334, over 1418699.17 frames.], batch size: 18, lr: 3.26e-04
2022-05-15 06:50:50,717 INFO [train.py:812] (6/8) Epoch 24, batch 1250, loss[loss=0.1606, simple_loss=0.2552, pruned_loss=0.03299, over 7328.00 frames.], tot_loss[loss=0.1577, simple_loss=0.248, pruned_loss=0.03372, over 1420043.15 frames.], batch size: 20, lr: 3.26e-04
2022-05-15 06:51:49,906 INFO [train.py:812] (6/8) Epoch 24, batch 1300, loss[loss=0.1613, simple_loss=0.2556, pruned_loss=0.03355, over 6716.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2481, pruned_loss=0.03358, over 1421161.18 frames.], batch size: 31, lr: 3.26e-04
2022-05-15 06:52:48,837 INFO [train.py:812] (6/8) Epoch 24, batch 1350, loss[loss=0.1335, simple_loss=0.2185, pruned_loss=0.02429, over 7417.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2484, pruned_loss=0.03333, over 1426296.49 frames.], batch size: 18, lr: 3.26e-04
2022-05-15 06:53:46,302 INFO [train.py:812] (6/8) Epoch 24, batch 1400, loss[loss=0.1827, simple_loss=0.2867, pruned_loss=0.03934, over 7185.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2482, pruned_loss=0.03336, over 1424237.10 frames.], batch size: 26, lr: 3.26e-04
2022-05-15 06:55:13,456 INFO [train.py:812] (6/8) Epoch 24, batch 1450, loss[loss=0.145, simple_loss=0.242, pruned_loss=0.02402, over 7146.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2482, pruned_loss=0.03319, over 1422681.96 frames.], batch size: 20, lr: 3.26e-04
2022-05-15 06:56:21,952 INFO [train.py:812] (6/8) Epoch 24, batch 1500, loss[loss=0.1753, simple_loss=0.2749, pruned_loss=0.03788, over 7147.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2479, pruned_loss=0.03339, over 1421606.23 frames.], batch size: 20, lr: 3.26e-04
2022-05-15 06:57:21,247 INFO [train.py:812] (6/8) Epoch 24, batch 1550, loss[loss=0.2156, simple_loss=0.309, pruned_loss=0.06108, over 6761.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2485, pruned_loss=0.03355, over 1421284.10 frames.], batch size: 31, lr: 3.26e-04
2022-05-15 06:58:39,400 INFO [train.py:812] (6/8) Epoch 24, batch 1600, loss[loss=0.1418, simple_loss=0.2388, pruned_loss=0.02241, over 7324.00 frames.], tot_loss[loss=0.1579, simple_loss=0.249, pruned_loss=0.0334, over 1422327.82 frames.], batch size: 20, lr: 3.25e-04
2022-05-15 06:59:37,743 INFO [train.py:812] (6/8) Epoch 24, batch 1650, loss[loss=0.1403, simple_loss=0.2282, pruned_loss=0.02614, over 6862.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2495, pruned_loss=0.03376, over 1414338.56 frames.], batch size: 15, lr: 3.25e-04
2022-05-15 07:00:36,795 INFO [train.py:812] (6/8) Epoch 24, batch 1700, loss[loss=0.1882, simple_loss=0.2884, pruned_loss=0.04395, over 7319.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2483, pruned_loss=0.03322, over 1417759.86 frames.], batch size: 21, lr: 3.25e-04
2022-05-15 07:01:34,461 INFO [train.py:812] (6/8) Epoch 24, batch 1750, loss[loss=0.1523, simple_loss=0.2368, pruned_loss=0.03394, over 7071.00 frames.], tot_loss[loss=0.1577, simple_loss=0.249, pruned_loss=0.03325, over 1419649.54 frames.], batch size: 18, lr: 3.25e-04
2022-05-15 07:02:33,283 INFO [train.py:812] (6/8) Epoch 24, batch 1800, loss[loss=0.1474, simple_loss=0.2404, pruned_loss=0.02726, over 7332.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2491, pruned_loss=0.03337, over 1420505.25 frames.], batch size: 22, lr: 3.25e-04
2022-05-15 07:03:31,335 INFO [train.py:812] (6/8) Epoch 24, batch 1850, loss[loss=0.1555, simple_loss=0.2462, pruned_loss=0.03242, over 7306.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2497, pruned_loss=0.0337, over 1424359.22 frames.], batch size: 24, lr: 3.25e-04
2022-05-15 07:04:30,232 INFO [train.py:812] (6/8) Epoch 24, batch 1900, loss[loss=0.1869, simple_loss=0.2795, pruned_loss=0.04722, over 7105.00 frames.], tot_loss[loss=0.1592, simple_loss=0.25, pruned_loss=0.03419, over 1422387.07 frames.], batch size: 28, lr: 3.25e-04
2022-05-15 07:05:29,106 INFO [train.py:812] (6/8) Epoch 24, batch 1950, loss[loss=0.1604, simple_loss=0.2595, pruned_loss=0.03067, over 7124.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2503, pruned_loss=0.03421, over 1423511.91 frames.], batch size: 21, lr: 3.25e-04
2022-05-15 07:06:27,371 INFO [train.py:812] (6/8) Epoch 24, batch 2000, loss[loss=0.1836, simple_loss=0.2706, pruned_loss=0.04833, over 5092.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2508, pruned_loss=0.03443, over 1421213.73 frames.], batch size: 52, lr: 3.25e-04
2022-05-15 07:07:25,822 INFO [train.py:812] (6/8) Epoch 24, batch 2050, loss[loss=0.1467, simple_loss=0.2388, pruned_loss=0.02735, over 7440.00 frames.], tot_loss[loss=0.16, simple_loss=0.251, pruned_loss=0.0345, over 1421291.29 frames.], batch size: 20, lr: 3.25e-04
2022-05-15 07:08:23,664 INFO [train.py:812] (6/8) Epoch 24, batch 2100, loss[loss=0.1535, simple_loss=0.2425, pruned_loss=0.03223, over 7016.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2506, pruned_loss=0.03423, over 1422956.64 frames.], batch size: 16, lr: 3.25e-04
2022-05-15 07:09:22,555 INFO [train.py:812] (6/8) Epoch 24, batch 2150, loss[loss=0.1793, simple_loss=0.2743, pruned_loss=0.04221, over 5140.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2494, pruned_loss=0.03382, over 1420788.56 frames.], batch size: 52, lr: 3.25e-04
2022-05-15 07:10:21,921 INFO [train.py:812] (6/8) Epoch 24, batch 2200, loss[loss=0.1407, simple_loss=0.2277, pruned_loss=0.02684, over 7138.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2496, pruned_loss=0.03403, over 1420186.03 frames.], batch size: 17, lr: 3.25e-04
2022-05-15 07:11:20,865 INFO [train.py:812] (6/8) Epoch 24, batch 2250, loss[loss=0.171, simple_loss=0.2655, pruned_loss=0.03825, over 7268.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2503, pruned_loss=0.03446, over 1410250.64 frames.], batch size: 25, lr: 3.24e-04
2022-05-15 07:12:19,966 INFO [train.py:812] (6/8) Epoch 24, batch 2300, loss[loss=0.1313, simple_loss=0.2187, pruned_loss=0.022, over 7299.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2494, pruned_loss=0.03401, over 1416942.08 frames.], batch size: 17, lr: 3.24e-04
2022-05-15 07:13:18,783 INFO [train.py:812] (6/8) Epoch 24, batch 2350, loss[loss=0.1532, simple_loss=0.2549, pruned_loss=0.02573, over 7343.00 frames.], tot_loss[loss=0.1591, simple_loss=0.25, pruned_loss=0.03412, over 1418083.96 frames.], batch size: 22, lr: 3.24e-04
2022-05-15 07:14:18,409 INFO [train.py:812] (6/8) Epoch 24, batch 2400, loss[loss=0.1444, simple_loss=0.2283, pruned_loss=0.03021, over 6750.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2505, pruned_loss=0.03401, over 1420969.54 frames.], batch size: 15, lr: 3.24e-04
2022-05-15 07:15:15,769 INFO [train.py:812] (6/8) Epoch 24, batch 2450, loss[loss=0.1704, simple_loss=0.2642, pruned_loss=0.03836, over 7235.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2502, pruned_loss=0.03406, over 1417308.69 frames.], batch size: 20, lr: 3.24e-04
2022-05-15 07:16:21,383 INFO [train.py:812] (6/8) Epoch 24, batch 2500, loss[loss=0.1623, simple_loss=0.2473, pruned_loss=0.03859, over 7323.00 frames.], tot_loss[loss=0.1592, simple_loss=0.25, pruned_loss=0.03414, over 1417668.95 frames.], batch size: 21, lr: 3.24e-04
2022-05-15 07:17:19,936 INFO [train.py:812] (6/8) Epoch 24, batch 2550, loss[loss=0.1794, simple_loss=0.2637, pruned_loss=0.0475, over 4933.00 frames.], tot_loss[loss=0.159, simple_loss=0.2495, pruned_loss=0.03424, over 1412874.38 frames.], batch size: 52, lr: 3.24e-04
2022-05-15 07:18:18,707 INFO [train.py:812] (6/8) Epoch 24, batch 2600, loss[loss=0.1446, simple_loss=0.2365, pruned_loss=0.0263, over 7266.00 frames.], tot_loss[loss=0.1599, simple_loss=0.251, pruned_loss=0.03443, over 1416523.62 frames.], batch size: 18, lr: 3.24e-04
2022-05-15 07:19:17,329 INFO [train.py:812] (6/8) Epoch 24, batch 2650, loss[loss=0.1537, simple_loss=0.2548, pruned_loss=0.02633, over 7309.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2503, pruned_loss=0.03445, over 1416028.94 frames.], batch size: 21, lr: 3.24e-04
2022-05-15 07:20:16,537 INFO [train.py:812] (6/8) Epoch 24, batch 2700, loss[loss=0.178, simple_loss=0.2776, pruned_loss=0.0392, over 7336.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2504, pruned_loss=0.03439, over 1421347.36 frames.], batch size: 22, lr: 3.24e-04
2022-05-15 07:21:15,989 INFO [train.py:812] (6/8) Epoch 24, batch 2750, loss[loss=0.1678, simple_loss=0.2553, pruned_loss=0.04017, over 7406.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2496, pruned_loss=0.03368, over 1424609.82 frames.], batch size: 21, lr: 3.24e-04
2022-05-15 07:22:15,057 INFO [train.py:812] (6/8) Epoch 24, batch 2800, loss[loss=0.1483, simple_loss=0.2356, pruned_loss=0.03051, over 7228.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2503, pruned_loss=0.0336, over 1420625.14 frames.], batch size: 20, lr: 3.24e-04
2022-05-15 07:23:13,167 INFO [train.py:812] (6/8) Epoch 24, batch 2850, loss[loss=0.1772, simple_loss=0.2683, pruned_loss=0.04305, over 7361.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2513, pruned_loss=0.03421, over 1420842.59 frames.], batch size: 19, lr: 3.24e-04
2022-05-15 07:24:12,166 INFO [train.py:812] (6/8) Epoch 24, batch 2900, loss[loss=0.1462, simple_loss=0.2433, pruned_loss=0.02452, over 7304.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2514, pruned_loss=0.03441, over 1421230.09 frames.], batch size: 25, lr: 3.24e-04
2022-05-15 07:25:09,877 INFO [train.py:812] (6/8) Epoch 24, batch 2950, loss[loss=0.1537, simple_loss=0.2377, pruned_loss=0.03483, over 7310.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2516, pruned_loss=0.03441, over 1425211.90 frames.], batch size: 17, lr: 3.23e-04
2022-05-15 07:26:08,044 INFO [train.py:812] (6/8) Epoch 24, batch 3000, loss[loss=0.1702, simple_loss=0.2633, pruned_loss=0.03861, over 7125.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2518, pruned_loss=0.03476, over 1420969.33 frames.], batch size: 21, lr: 3.23e-04
2022-05-15 07:26:08,045 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 07:26:15,600 INFO [train.py:841] (6/8) Epoch 24, validation: loss=0.1537, simple_loss=0.2513, pruned_loss=0.02802, over 698248.00 frames.
2022-05-15 07:27:15,069 INFO [train.py:812] (6/8) Epoch 24, batch 3050, loss[loss=0.1559, simple_loss=0.237, pruned_loss=0.03736, over 7277.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2508, pruned_loss=0.03428, over 1416251.32 frames.], batch size: 18, lr: 3.23e-04
2022-05-15 07:28:13,639 INFO [train.py:812] (6/8) Epoch 24, batch 3100, loss[loss=0.1667, simple_loss=0.2534, pruned_loss=0.03994, over 6730.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2497, pruned_loss=0.03408, over 1419606.93 frames.], batch size: 31, lr: 3.23e-04
2022-05-15 07:29:12,205 INFO [train.py:812] (6/8) Epoch 24, batch 3150, loss[loss=0.1467, simple_loss=0.2231, pruned_loss=0.03511, over 6992.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2501, pruned_loss=0.03408, over 1420571.00 frames.], batch size: 16, lr: 3.23e-04
2022-05-15 07:30:11,697 INFO [train.py:812] (6/8) Epoch 24, batch 3200, loss[loss=0.1565, simple_loss=0.254, pruned_loss=0.02952, over 7322.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2495, pruned_loss=0.03379, over 1425452.41 frames.], batch size: 21, lr: 3.23e-04
2022-05-15 07:31:10,176 INFO [train.py:812] (6/8) Epoch 24, batch 3250, loss[loss=0.1564, simple_loss=0.2343, pruned_loss=0.03929, over 7152.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2492, pruned_loss=0.03353, over 1427731.46 frames.], batch size: 18, lr: 3.23e-04
2022-05-15 07:32:09,050 INFO [train.py:812] (6/8) Epoch 24, batch 3300, loss[loss=0.185, simple_loss=0.2777, pruned_loss=0.04617, over 7309.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2489, pruned_loss=0.03369, over 1427879.56 frames.], batch size: 24, lr: 3.23e-04
2022-05-15 07:33:06,620 INFO [train.py:812] (6/8) Epoch 24, batch 3350, loss[loss=0.1518, simple_loss=0.2454, pruned_loss=0.02914, over 7313.00 frames.], tot_loss[loss=0.1583, simple_loss=0.249, pruned_loss=0.03382, over 1422921.72 frames.], batch size: 24, lr: 3.23e-04
2022-05-15 07:34:04,984 INFO [train.py:812] (6/8) Epoch 24, batch 3400, loss[loss=0.1538, simple_loss=0.2526, pruned_loss=0.0275, over 7352.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2495, pruned_loss=0.03385, over 1426891.98 frames.], batch size: 19, lr: 3.23e-04
2022-05-15 07:35:03,138 INFO [train.py:812] (6/8) Epoch 24, batch 3450, loss[loss=0.1713, simple_loss=0.2597, pruned_loss=0.04149, over 7349.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2498, pruned_loss=0.03393, over 1422958.92 frames.], batch size: 22, lr: 3.23e-04
2022-05-15 07:36:01,810 INFO [train.py:812] (6/8) Epoch 24, batch 3500, loss[loss=0.1419, simple_loss=0.2331, pruned_loss=0.02536, over 7188.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2484, pruned_loss=0.03338, over 1421425.43 frames.], batch size: 16, lr: 3.23e-04
2022-05-15 07:37:00,369 INFO [train.py:812] (6/8) Epoch 24, batch 3550, loss[loss=0.1546, simple_loss=0.2448, pruned_loss=0.03214, over 7111.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2482, pruned_loss=0.03356, over 1423048.73 frames.], batch size: 21, lr: 3.23e-04
2022-05-15 07:38:00,182 INFO [train.py:812] (6/8) Epoch 24, batch 3600, loss[loss=0.1721, simple_loss=0.2646, pruned_loss=0.03975, over 7074.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2496, pruned_loss=0.03406, over 1421698.27 frames.], batch size: 18, lr: 3.22e-04
2022-05-15 07:38:57,467 INFO [train.py:812] (6/8) Epoch 24, batch 3650, loss[loss=0.1357, simple_loss=0.2285, pruned_loss=0.02139, over 7361.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2503, pruned_loss=0.03415, over 1422312.43 frames.], batch size: 19, lr: 3.22e-04
2022-05-15 07:39:55,863 INFO [train.py:812] (6/8) Epoch 24, batch 3700, loss[loss=0.1644, simple_loss=0.2598, pruned_loss=0.03453, over 6514.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2505, pruned_loss=0.03446, over 1419929.86 frames.], batch size: 38, lr: 3.22e-04
2022-05-15 07:40:52,819 INFO [train.py:812] (6/8) Epoch 24, batch 3750, loss[loss=0.1518, simple_loss=0.2432, pruned_loss=0.03017, over 7254.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2502, pruned_loss=0.03427, over 1421847.36 frames.], batch size: 18, lr: 3.22e-04
2022-05-15 07:41:51,843 INFO [train.py:812] (6/8) Epoch 24, batch 3800, loss[loss=0.1404, simple_loss=0.2334, pruned_loss=0.02367, over 7429.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2492, pruned_loss=0.03365, over 1423244.96 frames.], batch size: 20, lr: 3.22e-04
2022-05-15 07:42:51,154 INFO [train.py:812] (6/8) Epoch 24, batch 3850, loss[loss=0.1718, simple_loss=0.2603, pruned_loss=0.04166, over 5250.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2491, pruned_loss=0.03366, over 1418772.70 frames.], batch size: 52, lr: 3.22e-04
2022-05-15 07:43:50,686 INFO [train.py:812] (6/8) Epoch 24, batch 3900, loss[loss=0.1532, simple_loss=0.2442, pruned_loss=0.0311, over 6687.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2485, pruned_loss=0.03351, over 1415083.64 frames.], batch size: 31, lr: 3.22e-04
2022-05-15 07:44:49,679 INFO [train.py:812] (6/8) Epoch 24, batch 3950, loss[loss=0.1394, simple_loss=0.2267, pruned_loss=0.0261, over 7148.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2493, pruned_loss=0.03377, over 1415633.84 frames.], batch size: 17, lr: 3.22e-04
2022-05-15 07:45:48,724 INFO [train.py:812] (6/8) Epoch 24, batch 4000, loss[loss=0.1619, simple_loss=0.2588, pruned_loss=0.03245, over 7206.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2503, pruned_loss=0.03429, over 1413781.09 frames.], batch size: 22, lr: 3.22e-04
2022-05-15 07:46:47,131 INFO [train.py:812] (6/8) Epoch 24, batch 4050, loss[loss=0.193, simple_loss=0.2709, pruned_loss=0.05755, over 4675.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2497, pruned_loss=0.0341, over 1414527.50 frames.], batch size: 52, lr: 3.22e-04
2022-05-15 07:47:46,793 INFO [train.py:812] (6/8) Epoch 24, batch 4100, loss[loss=0.1274, simple_loss=0.215, pruned_loss=0.0199, over 7277.00 frames.], tot_loss[loss=0.159, simple_loss=0.2496, pruned_loss=0.03413, over 1414648.23 frames.], batch size: 18, lr: 3.22e-04
2022-05-15 07:48:45,779 INFO [train.py:812] (6/8) Epoch 24, batch 4150, loss[loss=0.1273, simple_loss=0.2164, pruned_loss=0.01912, over 6986.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2491, pruned_loss=0.03408, over 1416179.79 frames.], batch size: 16, lr: 3.22e-04
2022-05-15 07:49:44,874 INFO [train.py:812] (6/8) Epoch 24, batch 4200, loss[loss=0.1335, simple_loss=0.216, pruned_loss=0.02546, over 7271.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2496, pruned_loss=0.03382, over 1417594.73 frames.], batch size: 18, lr: 3.22e-04
2022-05-15 07:50:44,115 INFO [train.py:812] (6/8) Epoch 24, batch 4250, loss[loss=0.1887, simple_loss=0.2834, pruned_loss=0.047, over 7383.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2498, pruned_loss=0.034, over 1415871.83 frames.], batch size: 23, lr: 3.22e-04
2022-05-15 07:51:43,433 INFO [train.py:812] (6/8) Epoch 24, batch 4300, loss[loss=0.1519, simple_loss=0.2399, pruned_loss=0.032, over 7224.00 frames.], tot_loss[loss=0.158, simple_loss=0.2488, pruned_loss=0.03354, over 1415605.20 frames.], batch size: 16, lr: 3.21e-04
2022-05-15 07:52:41,817 INFO [train.py:812] (6/8) Epoch 24, batch 4350, loss[loss=0.1621, simple_loss=0.2551, pruned_loss=0.03455, over 6775.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2488, pruned_loss=0.03337, over 1412944.00 frames.], batch size: 31, lr: 3.21e-04
2022-05-15 07:53:40,672 INFO [train.py:812] (6/8) Epoch 24, batch 4400, loss[loss=0.1608, simple_loss=0.2551, pruned_loss=0.0332, over 6323.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2495, pruned_loss=0.03386, over 1406488.94 frames.], batch size: 37, lr: 3.21e-04
2022-05-15 07:54:38,579 INFO [train.py:812] (6/8) Epoch 24, batch 4450, loss[loss=0.1998, simple_loss=0.2893, pruned_loss=0.0552, over 6509.00 frames.], tot_loss[loss=0.1582, simple_loss=0.249, pruned_loss=0.0337, over 1409775.29 frames.], batch size: 38, lr: 3.21e-04
2022-05-15 07:55:37,573 INFO [train.py:812] (6/8) Epoch 24, batch 4500, loss[loss=0.145, simple_loss=0.2459, pruned_loss=0.02201, over 6617.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2491, pruned_loss=0.03366, over 1398691.35 frames.], batch size: 38, lr: 3.21e-04
2022-05-15 07:56:36,610 INFO [train.py:812] (6/8) Epoch 24, batch 4550, loss[loss=0.1785, simple_loss=0.2685, pruned_loss=0.04423, over 7301.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2494, pruned_loss=0.03398, over 1388873.20 frames.], batch size: 24, lr: 3.21e-04
2022-05-15 07:57:47,822 INFO [train.py:812] (6/8) Epoch 25, batch 0, loss[loss=0.1571, simple_loss=0.2522, pruned_loss=0.03107, over 7076.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2522, pruned_loss=0.03107, over 7076.00 frames.], batch size: 18, lr: 3.15e-04
2022-05-15 07:58:47,074 INFO [train.py:812] (6/8) Epoch 25, batch 50, loss[loss=0.1562, simple_loss=0.2494, pruned_loss=0.03153, over 7250.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2508, pruned_loss=0.03487, over 322061.20 frames.], batch size: 19, lr: 3.15e-04
2022-05-15 07:59:46,733 INFO [train.py:812] (6/8) Epoch 25, batch 100, loss[loss=0.1618, simple_loss=0.2597, pruned_loss=0.032, over 7323.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2492, pruned_loss=0.03355, over 569932.71 frames.], batch size: 20, lr: 3.15e-04
2022-05-15 08:00:45,705 INFO [train.py:812] (6/8) Epoch 25, batch 150, loss[loss=0.1544, simple_loss=0.2565, pruned_loss=0.02617, over 7322.00 frames.], tot_loss[loss=0.157, simple_loss=0.248, pruned_loss=0.03306, over 761032.92 frames.], batch size: 21, lr: 3.14e-04
2022-05-15 08:01:45,478 INFO [train.py:812] (6/8) Epoch 25, batch 200, loss[loss=0.128, simple_loss=0.2114, pruned_loss=0.02228, over 6775.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2481, pruned_loss=0.03289, over 906145.89 frames.], batch size: 15, lr: 3.14e-04
2022-05-15 08:02:44,407 INFO [train.py:812] (6/8) Epoch 25, batch 250, loss[loss=0.162, simple_loss=0.261, pruned_loss=0.03148, over 7236.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2476, pruned_loss=0.03273, over 1017646.74 frames.], batch size: 20, lr: 3.14e-04
2022-05-15 08:03:43,905 INFO [train.py:812] (6/8) Epoch 25, batch 300, loss[loss=0.1506, simple_loss=0.2465, pruned_loss=0.02738, over 7163.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2487, pruned_loss=0.03301, over 1111933.10 frames.], batch size: 19, lr: 3.14e-04
2022-05-15 08:04:42,724 INFO [train.py:812] (6/8) Epoch 25, batch 350, loss[loss=0.1661, simple_loss=0.267, pruned_loss=0.03259, over 7208.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2489, pruned_loss=0.03323, over 1181699.56 frames.], batch size: 23, lr: 3.14e-04
2022-05-15 08:05:50,929 INFO [train.py:812] (6/8) Epoch 25, batch 400, loss[loss=0.1376, simple_loss=0.2218, pruned_loss=0.02668, over 7225.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2484, pruned_loss=0.03286, over 1236286.58 frames.], batch size: 20, lr: 3.14e-04
2022-05-15 08:06:49,152 INFO [train.py:812] (6/8) Epoch 25, batch 450, loss[loss=0.1771, simple_loss=0.2753, pruned_loss=0.03942, over 7083.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2478, pruned_loss=0.03284, over 1277511.43 frames.], batch size: 28, lr: 3.14e-04
2022-05-15 08:07:48,550 INFO [train.py:812] (6/8) Epoch 25, batch 500, loss[loss=0.1542, simple_loss=0.2418, pruned_loss=0.0333, over 7167.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2472, pruned_loss=0.03276, over 1312463.51 frames.], batch size: 18, lr: 3.14e-04
2022-05-15 08:08:47,666 INFO [train.py:812] (6/8) Epoch 25, batch 550, loss[loss=0.1516, simple_loss=0.2396, pruned_loss=0.03178, over 7164.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2472, pruned_loss=0.03246, over 1339517.19 frames.], batch size: 18, lr: 3.14e-04
2022-05-15 08:09:45,630 INFO [train.py:812] (6/8) Epoch 25, batch 600, loss[loss=0.1639, simple_loss=0.2597, pruned_loss=0.0341, over 7211.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2474, pruned_loss=0.03237, over 1358914.08 frames.], batch size: 23, lr: 3.14e-04
2022-05-15 08:10:45,017 INFO [train.py:812] (6/8) Epoch 25, batch 650, loss[loss=0.1465, simple_loss=0.2274, pruned_loss=0.03284, over 7289.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2466, pruned_loss=0.03245, over 1371420.39 frames.], batch size: 17, lr: 3.14e-04
2022-05-15 08:11:43,802 INFO [train.py:812] (6/8) Epoch 25, batch 700, loss[loss=0.1314, simple_loss=0.2187, pruned_loss=0.02202, over 6772.00 frames.], tot_loss[loss=0.156, simple_loss=0.2471, pruned_loss=0.03249, over 1387509.02 frames.], batch size: 15, lr: 3.14e-04
2022-05-15 08:12:42,961 INFO [train.py:812] (6/8) Epoch 25, batch 750, loss[loss=0.1499, simple_loss=0.244, pruned_loss=0.02786, over 7239.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2482, pruned_loss=0.03286, over 1399022.33 frames.], batch size: 20, lr: 3.14e-04
2022-05-15 08:13:42,753 INFO [train.py:812] (6/8) Epoch 25, batch 800, loss[loss=0.1777, simple_loss=0.2624, pruned_loss=0.04654, over 7408.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2483, pruned_loss=0.03302, over 1406043.73 frames.], batch size: 21, lr: 3.14e-04
2022-05-15 08:14:42,183 INFO [train.py:812] (6/8) Epoch 25, batch 850, loss[loss=0.1652, simple_loss=0.2579, pruned_loss=0.03623, over 7322.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2486, pruned_loss=0.03353, over 1407774.16 frames.], batch size: 21, lr: 3.13e-04
2022-05-15 08:15:39,814 INFO [train.py:812] (6/8) Epoch 25, batch 900, loss[loss=0.1839, simple_loss=0.2815, pruned_loss=0.04321, over 7292.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2497, pruned_loss=0.03363, over 1409494.56 frames.], batch size: 25, lr: 3.13e-04
2022-05-15 08:16:38,411 INFO [train.py:812] (6/8) Epoch 25, batch 950, loss[loss=0.1615, simple_loss=0.2524, pruned_loss=0.03529, over 4967.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2499, pruned_loss=0.03391, over 1403845.83 frames.], batch size: 52, lr: 3.13e-04
2022-05-15 08:17:38,416 INFO [train.py:812] (6/8) Epoch 25, batch 1000, loss[loss=0.1685, simple_loss=0.2637, pruned_loss=0.03664, over 7409.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2495, pruned_loss=0.03366, over 1411428.25 frames.], batch size: 21, lr: 3.13e-04
2022-05-15 08:18:37,813 INFO [train.py:812] (6/8) Epoch 25, batch 1050, loss[loss=0.145, simple_loss=0.2365, pruned_loss=0.02675, over 7319.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2497, pruned_loss=0.03369, over 1418199.58 frames.], batch size: 20, lr: 3.13e-04
2022-05-15 08:19:35,314 INFO [train.py:812] (6/8) Epoch 25, batch 1100, loss[loss=0.1482, simple_loss=0.2494, pruned_loss=0.0235, over 7345.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2485, pruned_loss=0.03336, over 1420761.00 frames.], batch size: 22, lr: 3.13e-04
2022-05-15 08:20:32,129 INFO [train.py:812] (6/8) Epoch 25, batch 1150, loss[loss=0.1758, simple_loss=0.2771, pruned_loss=0.03725, over 7197.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2487, pruned_loss=0.03337, over 1423341.29 frames.], batch size: 23, lr: 3.13e-04
2022-05-15 08:21:31,801 INFO [train.py:812] (6/8) Epoch 25, batch 1200, loss[loss=0.1845, simple_loss=0.2718, pruned_loss=0.04856, over 7373.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2495, pruned_loss=0.03382, over 1422913.05 frames.], batch size: 23, lr: 3.13e-04
2022-05-15 08:22:29,886 INFO [train.py:812] (6/8) Epoch 25, batch 1250, loss[loss=0.1435, simple_loss=0.2406, pruned_loss=0.02315, over 7148.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2492, pruned_loss=0.03378, over 1421245.46 frames.], batch size: 20, lr: 3.13e-04
2022-05-15 08:23:28,198 INFO [train.py:812] (6/8) Epoch 25, batch 1300, loss[loss=0.1565, simple_loss=0.2394, pruned_loss=0.03674, over 7208.00 frames.], tot_loss[loss=0.1573, simple_loss=0.248, pruned_loss=0.03333, over 1421164.26 frames.], batch size: 16, lr: 3.13e-04
2022-05-15 08:24:27,541 INFO [train.py:812] (6/8) Epoch 25, batch 1350, loss[loss=0.1591, simple_loss=0.2494, pruned_loss=0.03437, over 6463.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2482, pruned_loss=0.03337, over 1420531.45 frames.], batch size: 38, lr: 3.13e-04
2022-05-15 08:25:27,003 INFO [train.py:812] (6/8) Epoch 25, batch 1400, loss[loss=0.1459, simple_loss=0.2228, pruned_loss=0.03456, over 7287.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2494, pruned_loss=0.03365, over 1425769.71 frames.], batch size: 17, lr: 3.13e-04
2022-05-15 08:26:26,009 INFO [train.py:812] (6/8) Epoch 25, batch 1450, loss[loss=0.1686, simple_loss=0.2676, pruned_loss=0.03483, over 7143.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2488, pruned_loss=0.03342, over 1422209.45 frames.], batch size: 20, lr: 3.13e-04
2022-05-15 08:27:24,399 INFO [train.py:812] (6/8) Epoch 25, batch 1500, loss[loss=0.1751, simple_loss=0.2717, pruned_loss=0.03922, over 6711.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2491, pruned_loss=0.03353, over 1421072.75 frames.], batch size: 31, lr: 3.13e-04
2022-05-15 08:28:23,108 INFO [train.py:812] (6/8) Epoch 25, batch 1550, loss[loss=0.1406, simple_loss=0.2211, pruned_loss=0.03003, over 7281.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2496, pruned_loss=0.03367, over 1422399.72 frames.], batch size: 18, lr: 3.12e-04
2022-05-15 08:29:22,791 INFO [train.py:812] (6/8) Epoch 25, batch 1600, loss[loss=0.1777, simple_loss=0.2609, pruned_loss=0.04723, over 6842.00 frames.], tot_loss[loss=0.158, simple_loss=0.2489, pruned_loss=0.0336, over 1421091.43 frames.], batch size: 15, lr: 3.12e-04
2022-05-15 08:30:21,922 INFO [train.py:812] (6/8) Epoch 25, batch 1650, loss[loss=0.15, simple_loss=0.2423, pruned_loss=0.02882, over 7236.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2486, pruned_loss=0.03357, over 1422175.59 frames.], batch size: 21, lr: 3.12e-04
2022-05-15 08:31:21,081 INFO [train.py:812] (6/8) Epoch 25, batch 1700, loss[loss=0.1492, simple_loss=0.2381, pruned_loss=0.03018, over 7389.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2487, pruned_loss=0.03385, over 1420803.03 frames.], batch size: 23, lr: 3.12e-04
2022-05-15 08:32:19,167 INFO [train.py:812] (6/8) Epoch 25, batch 1750, loss[loss=0.1407, simple_loss=0.2373, pruned_loss=0.02203, over 7127.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2485, pruned_loss=0.03328, over 1422704.00 frames.], batch size: 17, lr: 3.12e-04
2022-05-15 08:33:18,571 INFO [train.py:812] (6/8) Epoch 25, batch 1800, loss[loss=0.1267, simple_loss=0.2107, pruned_loss=0.02131, over 7020.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2481, pruned_loss=0.03288, over 1423164.91 frames.], batch size: 16, lr: 3.12e-04
2022-05-15 08:34:17,252 INFO [train.py:812] (6/8) Epoch 25, batch 1850, loss[loss=0.1287, simple_loss=0.2189, pruned_loss=0.01928, over 6872.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2478, pruned_loss=0.03279, over 1420148.27 frames.], batch size: 15, lr: 3.12e-04
2022-05-15 08:35:20,962 INFO [train.py:812] (6/8) Epoch 25, batch 1900, loss[loss=0.1577, simple_loss=0.2488, pruned_loss=0.03332, over 7314.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2484, pruned_loss=0.0331, over 1422257.71 frames.], batch size: 25, lr: 3.12e-04
2022-05-15 08:36:19,547 INFO [train.py:812] (6/8) Epoch 25, batch 1950, loss[loss=0.1505, simple_loss=0.2391, pruned_loss=0.03099, over 7251.00 frames.], tot_loss[loss=0.157, simple_loss=0.2478, pruned_loss=0.03309, over 1423551.33 frames.], batch size: 19, lr: 3.12e-04
2022-05-15 08:37:18,334 INFO [train.py:812] (6/8) Epoch 25, batch 2000, loss[loss=0.1728, simple_loss=0.2666, pruned_loss=0.03954, over 7149.00 frames.], tot_loss[loss=0.156, simple_loss=0.2468, pruned_loss=0.03266, over 1423730.25 frames.], batch size: 18, lr: 3.12e-04
2022-05-15 08:38:16,616 INFO [train.py:812] (6/8) Epoch 25, batch 2050, loss[loss=0.1761, simple_loss=0.2766, pruned_loss=0.03784, over 7323.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2464, pruned_loss=0.03236, over 1426407.28 frames.], batch size: 21, lr: 3.12e-04
2022-05-15 08:39:15,911 INFO [train.py:812] (6/8) Epoch 25, batch 2100, loss[loss=0.1629, simple_loss=0.2593, pruned_loss=0.03326, over 7257.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2469, pruned_loss=0.03232, over 1423171.48 frames.], batch size: 19, lr: 3.12e-04
2022-05-15 08:40:13,582 INFO [train.py:812] (6/8) Epoch 25, batch 2150, loss[loss=0.1588, simple_loss=0.251, pruned_loss=0.03328, over 7433.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2476, pruned_loss=0.03257, over 1421715.78 frames.], batch size: 20, lr: 3.12e-04
2022-05-15 08:41:13,387 INFO [train.py:812] (6/8) Epoch 25, batch 2200, loss[loss=0.155, simple_loss=0.2319, pruned_loss=0.03899, over 6775.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2468, pruned_loss=0.0324, over 1420242.99 frames.], batch size: 15, lr: 3.12e-04
2022-05-15 08:42:11,785 INFO [train.py:812] (6/8) Epoch 25, batch 2250, loss[loss=0.1476, simple_loss=0.2357, pruned_loss=0.02975, over 7070.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2473, pruned_loss=0.03279, over 1416187.37 frames.], batch size: 18, lr: 3.12e-04
2022-05-15 08:43:09,227 INFO [train.py:812] (6/8) Epoch 25, batch 2300, loss[loss=0.1429, simple_loss=0.2222, pruned_loss=0.03176, over 7254.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2468, pruned_loss=0.0324, over 1417596.36 frames.], batch size: 16, lr: 3.11e-04
2022-05-15 08:44:06,021 INFO [train.py:812] (6/8) Epoch 25, batch 2350, loss[loss=0.1646, simple_loss=0.2643, pruned_loss=0.03245, over 7319.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2468, pruned_loss=0.03268, over 1418076.06 frames.], batch size: 21, lr: 3.11e-04
2022-05-15 08:45:05,381 INFO [train.py:812] (6/8) Epoch 25, batch 2400, loss[loss=0.1656, simple_loss=0.2511, pruned_loss=0.04003, over 7362.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2482, pruned_loss=0.03309, over 1423545.02 frames.], batch size: 19, lr: 3.11e-04
2022-05-15 08:46:04,726 INFO [train.py:812] (6/8) Epoch 25, batch 2450, loss[loss=0.1327, simple_loss=0.2187, pruned_loss=0.02335, over 7139.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2487, pruned_loss=0.0332, over 1422559.00 frames.], batch size: 17, lr: 3.11e-04
2022-05-15 08:47:04,388 INFO [train.py:812] (6/8) Epoch 25, batch 2500, loss[loss=0.1628, simple_loss=0.2599, pruned_loss=0.03283, over 7425.00 frames.], tot_loss[loss=0.157, simple_loss=0.2482, pruned_loss=0.03294, over 1423002.29 frames.], batch size: 21, lr: 3.11e-04
2022-05-15 08:48:03,402 INFO [train.py:812] (6/8) Epoch 25, batch 2550, loss[loss=0.1534, simple_loss=0.2555, pruned_loss=0.02563, over 7437.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2493, pruned_loss=0.03318, over 1423775.43 frames.], batch size: 20, lr: 3.11e-04
2022-05-15 08:49:03,060 INFO [train.py:812] (6/8) Epoch 25, batch 2600, loss[loss=0.1517, simple_loss=0.2418, pruned_loss=0.03078, over 7118.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2494, pruned_loss=0.03348, over 1420635.41 frames.], batch size: 17, lr: 3.11e-04
2022-05-15 08:50:01,844 INFO [train.py:812] (6/8) Epoch 25, batch 2650, loss[loss=0.1362, simple_loss=0.2251, pruned_loss=0.02365, over 7209.00 frames.], tot_loss[loss=0.1586, simple_loss=0.25, pruned_loss=0.03355, over 1422625.94 frames.], batch size: 22, lr: 3.11e-04
2022-05-15 08:51:09,496 INFO [train.py:812] (6/8) Epoch 25, batch 2700, loss[loss=0.1681, simple_loss=0.2642, pruned_loss=0.03597, over 7059.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2491, pruned_loss=0.03327, over 1425493.87 frames.], batch size: 18, lr: 3.11e-04
2022-05-15 08:52:06,920 INFO [train.py:812] (6/8) Epoch 25, batch 2750, loss[loss=0.1658, simple_loss=0.2534, pruned_loss=0.03913, over 7149.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2478, pruned_loss=0.03302, over 1421124.97 frames.], batch size: 20, lr: 3.11e-04
2022-05-15 08:53:06,511 INFO [train.py:812] (6/8) Epoch 25, batch 2800, loss[loss=0.171, simple_loss=0.2587, pruned_loss=0.04158, over 7261.00 frames.], tot_loss[loss=0.156, simple_loss=0.2468, pruned_loss=0.03256, over 1421601.97 frames.], batch size: 19, lr: 3.11e-04
2022-05-15 08:54:05,463 INFO [train.py:812] (6/8) Epoch 25, batch 2850, loss[loss=0.1744, simple_loss=0.2623, pruned_loss=0.04326, over 7431.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2469, pruned_loss=0.03231, over 1419352.13 frames.], batch size: 20, lr: 3.11e-04
2022-05-15 08:55:04,583 INFO [train.py:812] (6/8) Epoch 25, batch 2900, loss[loss=0.166, simple_loss=0.2582, pruned_loss=0.03688, over 7213.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2479, pruned_loss=0.03271, over 1420440.42 frames.], batch size: 23, lr: 3.11e-04
2022-05-15 08:56:02,087 INFO [train.py:812] (6/8) Epoch 25, batch 2950, loss[loss=0.1603, simple_loss=0.2493, pruned_loss=0.03568, over 7128.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2472, pruned_loss=0.03255, over 1426268.28 frames.], batch size: 21, lr: 3.11e-04
2022-05-15 08:57:29,024 INFO [train.py:812] (6/8) Epoch 25, batch 3000, loss[loss=0.156, simple_loss=0.2544, pruned_loss=0.02882, over 6812.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2465, pruned_loss=0.03232, over 1428883.14 frames.], batch size: 31, lr: 3.10e-04
2022-05-15 08:57:29,025 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 08:57:46,643 INFO [train.py:841] (6/8) Epoch 25, validation: loss=0.1532, simple_loss=0.2507, pruned_loss=0.02787, over 698248.00 frames.
2022-05-15 08:58:45,936 INFO [train.py:812] (6/8) Epoch 25, batch 3050, loss[loss=0.1269, simple_loss=0.2258, pruned_loss=0.01396, over 7112.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2464, pruned_loss=0.03264, over 1429659.06 frames.], batch size: 21, lr: 3.10e-04
2022-05-15 08:59:53,878 INFO [train.py:812] (6/8) Epoch 25, batch 3100, loss[loss=0.12, simple_loss=0.2017, pruned_loss=0.01912, over 7202.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2455, pruned_loss=0.03247, over 1430948.20 frames.], batch size: 16, lr: 3.10e-04
2022-05-15 09:01:01,461 INFO [train.py:812] (6/8) Epoch 25, batch 3150, loss[loss=0.1385, simple_loss=0.2297, pruned_loss=0.02361, over 7253.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2462, pruned_loss=0.03237, over 1432221.39 frames.], batch size: 19, lr: 3.10e-04
2022-05-15 09:02:01,451 INFO [train.py:812] (6/8) Epoch 25, batch 3200, loss[loss=0.1898, simple_loss=0.2692, pruned_loss=0.05517, over 5202.00 frames.], tot_loss[loss=0.155, simple_loss=0.2459, pruned_loss=0.03203, over 1430738.85 frames.], batch size: 52, lr: 3.10e-04
2022-05-15 09:03:00,417 INFO [train.py:812] (6/8) Epoch 25, batch 3250, loss[loss=0.1731, simple_loss=0.2628, pruned_loss=0.04176, over 7245.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2471, pruned_loss=0.03265, over 1429401.29 frames.], batch size: 20, lr: 3.10e-04
2022-05-15 09:03:59,281 INFO [train.py:812] (6/8) Epoch 25, batch 3300, loss[loss=0.1435, simple_loss=0.2347, pruned_loss=0.02617, over 7161.00 frames.], tot_loss[loss=0.156, simple_loss=0.2472, pruned_loss=0.03239, over 1428174.46 frames.], batch size: 19, lr: 3.10e-04
2022-05-15 09:04:58,428 INFO [train.py:812] (6/8) Epoch 25, batch 3350, loss[loss=0.1448, simple_loss=0.2324, pruned_loss=0.02863, over 7260.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2475, pruned_loss=0.0326, over 1424042.36 frames.], batch size: 19, lr: 3.10e-04
2022-05-15 09:05:57,559 INFO [train.py:812] (6/8) Epoch 25, batch 3400, loss[loss=0.1253, simple_loss=0.2098, pruned_loss=0.02033, over 7283.00 frames.], tot_loss[loss=0.1559, simple_loss=0.247, pruned_loss=0.03234, over 1425700.56 frames.], batch size: 17, lr: 3.10e-04
2022-05-15 09:06:55,968 INFO [train.py:812] (6/8) Epoch 25, batch 3450, loss[loss=0.1492, simple_loss=0.2443, pruned_loss=0.02701, over 7196.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2467, pruned_loss=0.03228, over 1421909.30 frames.], batch size: 21, lr: 3.10e-04
2022-05-15 09:07:54,102 INFO [train.py:812] (6/8) Epoch 25, batch 3500, loss[loss=0.1532, simple_loss=0.2272, pruned_loss=0.03966, over 7144.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2468, pruned_loss=0.03237, over 1423074.56 frames.], batch size: 17, lr: 3.10e-04
2022-05-15 09:08:53,545 INFO [train.py:812] (6/8) Epoch 25, batch 3550, loss[loss=0.1546, simple_loss=0.2422, pruned_loss=0.0335, over 7329.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2476, pruned_loss=0.0328, over 1424983.49 frames.], batch size: 20, lr: 3.10e-04
2022-05-15 09:09:52,747 INFO [train.py:812] (6/8) Epoch 25, batch 3600, loss[loss=0.162, simple_loss=0.2508, pruned_loss=0.03663, over 7215.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2477, pruned_loss=0.03294, over 1423268.44 frames.], batch size: 23, lr: 3.10e-04
2022-05-15 09:10:51,702 INFO [train.py:812] (6/8) Epoch 25, batch 3650, loss[loss=0.1667, simple_loss=0.2544, pruned_loss=0.03956, over 6676.00 frames.], tot_loss[loss=0.157, simple_loss=0.2477, pruned_loss=0.03314, over 1419394.18 frames.], batch size: 38, lr: 3.10e-04
2022-05-15 09:11:51,266 INFO [train.py:812] (6/8) Epoch 25, batch 3700, loss[loss=0.1325, simple_loss=0.2192, pruned_loss=0.02294, over 7443.00 frames.], tot_loss[loss=0.1564, simple_loss=0.247, pruned_loss=0.03283, over 1422249.31 frames.], batch size: 20, lr: 3.10e-04
2022-05-15 09:12:50,507 INFO [train.py:812] (6/8) Epoch 25, batch 3750, loss[loss=0.171, simple_loss=0.2574, pruned_loss=0.04231, over 7366.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2471, pruned_loss=0.03293, over 1424391.42 frames.], batch size: 23, lr: 3.09e-04
2022-05-15 09:13:50,128 INFO [train.py:812] (6/8) Epoch 25, batch 3800, loss[loss=0.2179, simple_loss=0.299, pruned_loss=0.06845, over 5063.00 frames.], tot_loss[loss=0.1576, simple_loss=0.248, pruned_loss=0.03362, over 1422696.92 frames.], batch size: 52, lr: 3.09e-04
2022-05-15 09:14:48,011 INFO [train.py:812] (6/8) Epoch 25, batch 3850, loss[loss=0.1319, simple_loss=0.2145, pruned_loss=0.02466, over 7284.00 frames.], tot_loss[loss=0.1574, simple_loss=0.248, pruned_loss=0.03342, over 1422759.28 frames.], batch size: 18, lr: 3.09e-04
2022-05-15 09:15:47,055 INFO [train.py:812] (6/8) Epoch 25, batch 3900, loss[loss=0.1496, simple_loss=0.2399, pruned_loss=0.0297, over 7261.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2485, pruned_loss=0.03329, over 1422057.08 frames.], batch size: 19, lr: 3.09e-04
2022-05-15 09:16:44,720 INFO [train.py:812] (6/8) Epoch 25, batch 3950, loss[loss=0.1225, simple_loss=0.2073, pruned_loss=0.01882, over 7416.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2479, pruned_loss=0.03267, over 1424254.15 frames.], batch size: 18, lr: 3.09e-04
2022-05-15 09:17:43,604 INFO [train.py:812] (6/8) Epoch 25, batch 4000, loss[loss=0.1619, simple_loss=0.2609, pruned_loss=0.03148, over 7316.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2481, pruned_loss=0.03279, over 1424185.10 frames.], batch size: 21, lr: 3.09e-04
2022-05-15 09:18:42,649 INFO [train.py:812] (6/8) Epoch 25, batch 4050, loss[loss=0.1517, simple_loss=0.2381, pruned_loss=0.03267, over 7429.00 frames.], tot_loss[loss=0.1557, simple_loss=0.247, pruned_loss=0.03226, over 1422455.75 frames.], batch size: 20, lr: 3.09e-04
2022-05-15 09:19:41,951 INFO [train.py:812] (6/8) Epoch 25, batch 4100, loss[loss=0.1629, simple_loss=0.2454, pruned_loss=0.04016, over 6360.00 frames.], tot_loss[loss=0.1558, simple_loss=0.247, pruned_loss=0.03227, over 1422491.77 frames.], batch size: 37, lr: 3.09e-04
2022-05-15 09:20:41,119 INFO [train.py:812] (6/8) Epoch 25, batch 4150, loss[loss=0.1473, simple_loss=0.2381, pruned_loss=0.02828, over 7226.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2471, pruned_loss=0.03236, over 1419167.59 frames.], batch size: 21, lr: 3.09e-04
2022-05-15 09:21:39,851 INFO [train.py:812] (6/8) Epoch 25, batch 4200, loss[loss=0.1606, simple_loss=0.2627, pruned_loss=0.02927, over 7190.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2494, pruned_loss=0.03311, over 1420003.75 frames.], batch size: 23, lr: 3.09e-04
2022-05-15 09:22:38,432 INFO [train.py:812] (6/8) Epoch 25, batch 4250, loss[loss=0.1697, simple_loss=0.2642, pruned_loss=0.03763, over 6347.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2493, pruned_loss=0.03327, over 1414382.69 frames.], batch size: 37, lr: 3.09e-04
2022-05-15 09:23:37,042 INFO [train.py:812] (6/8) Epoch 25, batch 4300, loss[loss=0.1392, simple_loss=0.2326, pruned_loss=0.02289, over 7161.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2489, pruned_loss=0.03312, over 1413859.15 frames.], batch size: 19, lr: 3.09e-04
2022-05-15 09:24:36,171 INFO [train.py:812] (6/8) Epoch 25, batch 4350, loss[loss=0.1622, simple_loss=0.2662, pruned_loss=0.02911, over 7281.00 frames.], tot_loss[loss=0.1571, simple_loss=0.248, pruned_loss=0.03309, over 1414081.33 frames.], batch size: 25, lr: 3.09e-04
2022-05-15 09:25:35,376 INFO [train.py:812] (6/8) Epoch 25, batch 4400, loss[loss=0.1589, simple_loss=0.2514, pruned_loss=0.03315, over 7284.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2489, pruned_loss=0.0334, over 1412625.40 frames.], batch size: 24, lr: 3.09e-04
2022-05-15 09:26:34,036 INFO [train.py:812] (6/8) Epoch 25, batch 4450, loss[loss=0.1675, simple_loss=0.2659, pruned_loss=0.0346, over 7290.00 frames.], tot_loss[loss=0.158, simple_loss=0.2494, pruned_loss=0.03332, over 1403049.47 frames.], batch size: 25, lr: 3.09e-04
2022-05-15 09:27:33,023 INFO [train.py:812] (6/8) Epoch 25, batch 4500, loss[loss=0.1809, simple_loss=0.2646, pruned_loss=0.04861, over 4813.00 frames.], tot_loss[loss=0.1595, simple_loss=0.251, pruned_loss=0.03401, over 1387954.95 frames.], batch size: 52, lr: 3.08e-04
2022-05-15 09:28:30,329 INFO [train.py:812] (6/8) Epoch 25, batch 4550, loss[loss=0.2042, simple_loss=0.2778, pruned_loss=0.06526, over 5075.00 frames.], tot_loss[loss=0.1605, simple_loss=0.252, pruned_loss=0.03449, over 1350683.58 frames.], batch size: 52, lr: 3.08e-04
2022-05-15 09:29:36,553 INFO [train.py:812] (6/8) Epoch 26, batch 0, loss[loss=0.1668, simple_loss=0.2579, pruned_loss=0.0378, over 7219.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2579, pruned_loss=0.0378, over 7219.00 frames.], batch size: 21, lr: 3.02e-04
2022-05-15 09:30:35,859 INFO [train.py:812] (6/8) Epoch 26, batch 50, loss[loss=0.1841, simple_loss=0.2756, pruned_loss=0.04629, over 7316.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2472, pruned_loss=0.03173, over 323458.61 frames.], batch size: 21, lr: 3.02e-04
2022-05-15 09:31:35,519 INFO [train.py:812] (6/8) Epoch 26, batch 100, loss[loss=0.1627, simple_loss=0.2505, pruned_loss=0.03741, over 4949.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2491, pruned_loss=0.03309, over 567162.17 frames.], batch size: 54, lr: 3.02e-04
2022-05-15 09:32:35,339 INFO [train.py:812] (6/8) Epoch 26, batch 150, loss[loss=0.1431, simple_loss=0.2276, pruned_loss=0.02931, over 7268.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2486, pruned_loss=0.03296, over 760869.03 frames.], batch size: 17, lr: 3.02e-04
2022-05-15 09:33:34,916 INFO [train.py:812] (6/8) Epoch 26, batch 200, loss[loss=0.1713, simple_loss=0.2609, pruned_loss=0.04081, over 7379.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2481, pruned_loss=0.03276, over 908708.55 frames.], batch size: 23, lr: 3.02e-04
2022-05-15 09:34:32,556 INFO [train.py:812] (6/8) Epoch 26, batch 250, loss[loss=0.1631, simple_loss=0.2507, pruned_loss=0.03778, over 7185.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2484, pruned_loss=0.03272, over 1021211.22 frames.], batch size: 22, lr: 3.02e-04
2022-05-15 09:35:31,870 INFO [train.py:812] (6/8) Epoch 26, batch 300, loss[loss=0.1441, simple_loss=0.2411, pruned_loss=0.02355, over 7333.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2489, pruned_loss=0.03308, over 1107584.98 frames.], batch size: 20, lr: 3.02e-04
2022-05-15 09:36:29,851 INFO [train.py:812] (6/8) Epoch 26, batch 350, loss[loss=0.1302, simple_loss=0.2154, pruned_loss=0.02244, over 7170.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2474, pruned_loss=0.03283, over 1176881.80 frames.], batch size: 18, lr: 3.02e-04
2022-05-15 09:37:29,645 INFO [train.py:812] (6/8) Epoch 26, batch 400, loss[loss=0.1422, simple_loss=0.218, pruned_loss=0.03322, over 7408.00 frames.], tot_loss[loss=0.156, simple_loss=0.2468, pruned_loss=0.03255, over 1233886.88 frames.], batch size: 18, lr: 3.02e-04
2022-05-15 09:38:28,210 INFO [train.py:812] (6/8) Epoch 26, batch 450, loss[loss=0.1651, simple_loss=0.2599, pruned_loss=0.03509, over 7423.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2463, pruned_loss=0.03191, over 1275113.99 frames.], batch size: 21, lr: 3.02e-04
2022-05-15 09:39:25,645 INFO [train.py:812] (6/8) Epoch 26, batch 500, loss[loss=0.1649, simple_loss=0.2543, pruned_loss=0.03771, over 7372.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2468, pruned_loss=0.03222, over 1302986.58 frames.], batch size: 23, lr: 3.02e-04
2022-05-15 09:40:22,337 INFO [train.py:812] (6/8) Epoch 26, batch 550, loss[loss=0.159, simple_loss=0.2595, pruned_loss=0.02927, over 7240.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2452, pruned_loss=0.03165, over 1329914.56 frames.], batch size: 20, lr: 3.02e-04
2022-05-15 09:41:20,617 INFO [train.py:812] (6/8) Epoch 26, batch 600, loss[loss=0.1438, simple_loss=0.2469, pruned_loss=0.02029, over 7080.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2459, pruned_loss=0.03159, over 1347993.12 frames.], batch size: 28, lr: 3.02e-04
2022-05-15 09:42:19,404 INFO [train.py:812] (6/8) Epoch 26, batch 650, loss[loss=0.1546, simple_loss=0.2482, pruned_loss=0.03056, over 7331.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2447, pruned_loss=0.03142, over 1362296.27 frames.], batch size: 20, lr: 3.02e-04
2022-05-15 09:43:17,909 INFO [train.py:812] (6/8) Epoch 26, batch 700, loss[loss=0.1664, simple_loss=0.265, pruned_loss=0.03394, over 7141.00 frames.], tot_loss[loss=0.1542, simple_loss=0.245, pruned_loss=0.03165, over 1374934.56 frames.], batch size: 20, lr: 3.02e-04
2022-05-15 09:44:17,501 INFO [train.py:812] (6/8) Epoch 26, batch 750, loss[loss=0.1569, simple_loss=0.2448, pruned_loss=0.03443, over 7426.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2455, pruned_loss=0.03157, over 1389860.21 frames.], batch size: 20, lr: 3.01e-04
2022-05-15 09:45:17,296 INFO [train.py:812] (6/8) Epoch 26, batch 800, loss[loss=0.1729, simple_loss=0.2783, pruned_loss=0.03372, over 6694.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2465, pruned_loss=0.032, over 1395049.14 frames.], batch size: 31, lr: 3.01e-04
2022-05-15 09:46:14,835 INFO [train.py:812] (6/8) Epoch 26, batch 850, loss[loss=0.1446, simple_loss=0.2393, pruned_loss=0.02497, over 7122.00 frames.], tot_loss[loss=0.155, simple_loss=0.2468, pruned_loss=0.03166, over 1405905.65 frames.], batch size: 21, lr: 3.01e-04
2022-05-15 09:47:13,172 INFO [train.py:812] (6/8) Epoch 26, batch 900, loss[loss=0.1287, simple_loss=0.2113, pruned_loss=0.02308, over 7188.00 frames.], tot_loss[loss=0.155, simple_loss=0.2467, pruned_loss=0.03167, over 1406953.78 frames.], batch size: 16, lr: 3.01e-04
2022-05-15 09:48:12,077 INFO [train.py:812] (6/8) Epoch 26, batch 950, loss[loss=0.1569, simple_loss=0.238, pruned_loss=0.03789, over 7272.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2466, pruned_loss=0.03156, over 1413145.06 frames.], batch size: 17, lr: 3.01e-04
2022-05-15 09:49:11,026 INFO [train.py:812] (6/8) Epoch 26, batch 1000, loss[loss=0.1595, simple_loss=0.2569, pruned_loss=0.03104, over 7118.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2471, pruned_loss=0.03212, over 1412152.30 frames.], batch size: 21, lr: 3.01e-04
2022-05-15 09:50:10,509 INFO [train.py:812] (6/8) Epoch 26, batch 1050, loss[loss=0.1624, simple_loss=0.2465, pruned_loss=0.03918, over 4955.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2476, pruned_loss=0.03211, over 1412215.13 frames.], batch size: 52, lr: 3.01e-04
2022-05-15 09:51:08,653 INFO [train.py:812] (6/8) Epoch 26, batch 1100, loss[loss=0.1549, simple_loss=0.2449, pruned_loss=0.0325, over 7115.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2473, pruned_loss=0.03217, over 1413654.16 frames.], batch size: 21, lr: 3.01e-04
2022-05-15 09:52:08,148 INFO [train.py:812] (6/8) Epoch 26, batch 1150, loss[loss=0.1431, simple_loss=0.2327, pruned_loss=0.02675, over 7379.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2471, pruned_loss=0.0323, over 1416768.23 frames.], batch size: 23, lr: 3.01e-04
2022-05-15 09:53:08,274 INFO [train.py:812] (6/8) Epoch 26, batch 1200, loss[loss=0.1471, simple_loss=0.2304, pruned_loss=0.03196, over 7148.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2465, pruned_loss=0.03219, over 1420472.28 frames.], batch size: 17, lr: 3.01e-04
2022-05-15 09:54:07,370 INFO [train.py:812] (6/8) Epoch 26, batch 1250, loss[loss=0.1624, simple_loss=0.2569, pruned_loss=0.03397, over 7320.00 frames.], tot_loss[loss=0.156, simple_loss=0.247, pruned_loss=0.03245, over 1422724.98 frames.], batch size: 21, lr: 3.01e-04
2022-05-15 09:55:11,144 INFO [train.py:812] (6/8) Epoch 26, batch 1300, loss[loss=0.15, simple_loss=0.2348, pruned_loss=0.03263, over 7434.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2467, pruned_loss=0.03237, over 1426093.06 frames.], batch size: 20, lr: 3.01e-04
2022-05-15 09:56:09,549 INFO [train.py:812] (6/8) Epoch 26, batch 1350, loss[loss=0.1736, simple_loss=0.2645, pruned_loss=0.04139, over 7316.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2474, pruned_loss=0.03266, over 1426121.02 frames.], batch size: 21, lr: 3.01e-04
2022-05-15 09:57:07,835 INFO [train.py:812] (6/8) Epoch 26, batch 1400, loss[loss=0.1622, simple_loss=0.2557, pruned_loss=0.03439, over 7329.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2482, pruned_loss=0.03302, over 1426044.48 frames.], batch size: 22, lr: 3.01e-04
2022-05-15 09:58:05,649 INFO [train.py:812] (6/8) Epoch 26, batch 1450, loss[loss=0.1488, simple_loss=0.2318, pruned_loss=0.03293, over 6986.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2479, pruned_loss=0.0328, over 1428133.34 frames.], batch size: 16, lr: 3.01e-04
2022-05-15 09:59:03,798 INFO [train.py:812] (6/8) Epoch 26, batch 1500, loss[loss=0.1525, simple_loss=0.2504, pruned_loss=0.0273, over 7230.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2473, pruned_loss=0.03252, over 1428003.14 frames.], batch size: 21, lr: 3.00e-04
2022-05-15 10:00:02,491 INFO [train.py:812] (6/8) Epoch 26, batch 1550, loss[loss=0.1434, simple_loss=0.2213, pruned_loss=0.03271, over 7145.00 frames.], tot_loss[loss=0.156, simple_loss=0.247, pruned_loss=0.03247, over 1427583.07 frames.], batch size: 17, lr: 3.00e-04
2022-05-15 10:01:01,526 INFO [train.py:812] (6/8) Epoch 26, batch 1600, loss[loss=0.1551, simple_loss=0.2504, pruned_loss=0.02988, over 7151.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2486, pruned_loss=0.0328, over 1424805.69 frames.], batch size: 20, lr: 3.00e-04
2022-05-15 10:02:00,522 INFO [train.py:812] (6/8) Epoch 26, batch 1650, loss[loss=0.1609, simple_loss=0.2521, pruned_loss=0.03481, over 7173.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2471, pruned_loss=0.03221, over 1426237.93 frames.], batch size: 28, lr: 3.00e-04
2022-05-15 10:02:59,723 INFO [train.py:812] (6/8) Epoch 26, batch 1700, loss[loss=0.1652, simple_loss=0.261, pruned_loss=0.03468, over 7323.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2475, pruned_loss=0.03238, over 1426374.99 frames.], batch size: 21, lr: 3.00e-04
2022-05-15 10:04:07,544 INFO [train.py:812] (6/8) Epoch 26, batch 1750, loss[loss=0.126, simple_loss=0.2082, pruned_loss=0.02194, over 7133.00 frames.], tot_loss[loss=0.1566, simple_loss=0.248, pruned_loss=0.03255, over 1425425.87 frames.], batch size: 17, lr: 3.00e-04
2022-05-15 10:05:06,533 INFO [train.py:812] (6/8) Epoch 26, batch 1800, loss[loss=0.1708, simple_loss=0.2602, pruned_loss=0.04073, over 7143.00 frames.], tot_loss[loss=0.157, simple_loss=0.2484, pruned_loss=0.03277, over 1421641.61 frames.], batch size: 20, lr: 3.00e-04
2022-05-15 10:06:05,267 INFO [train.py:812] (6/8) Epoch 26, batch 1850, loss[loss=0.1615, simple_loss=0.2629, pruned_loss=0.03012, over 7430.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2484, pruned_loss=0.03271, over 1422352.56 frames.], batch size: 20, lr: 3.00e-04
2022-05-15 10:07:04,828 INFO [train.py:812] (6/8) Epoch 26, batch 1900, loss[loss=0.1581, simple_loss=0.2308, pruned_loss=0.04272, over 7126.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2488, pruned_loss=0.03283, over 1422640.89 frames.], batch size: 17, lr: 3.00e-04
2022-05-15 10:08:02,595 INFO [train.py:812] (6/8) Epoch 26, batch 1950, loss[loss=0.1853, simple_loss=0.272, pruned_loss=0.04928, over 5416.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2484, pruned_loss=0.03286, over 1421392.37 frames.], batch size: 53, lr: 3.00e-04
2022-05-15 10:09:00,918 INFO [train.py:812] (6/8) Epoch 26, batch 2000, loss[loss=0.1498, simple_loss=0.2377, pruned_loss=0.03092, over 7159.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2479, pruned_loss=0.03285, over 1417248.96 frames.], batch size: 19, lr: 3.00e-04
2022-05-15 10:10:00,127 INFO [train.py:812] (6/8) Epoch 26, batch 2050, loss[loss=0.1493, simple_loss=0.2466, pruned_loss=0.02596, over 7320.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2474, pruned_loss=0.03284, over 1418536.06 frames.], batch size: 20, lr: 3.00e-04
2022-05-15 10:10:59,287 INFO [train.py:812] (6/8) Epoch 26, batch 2100, loss[loss=0.1786, simple_loss=0.2628, pruned_loss=0.04716, over 7196.00 frames.], tot_loss[loss=0.157, simple_loss=0.2482, pruned_loss=0.03291, over 1418166.69 frames.], batch size: 22, lr: 3.00e-04
2022-05-15 10:11:58,151 INFO [train.py:812] (6/8) Epoch 26, batch 2150, loss[loss=0.1337, simple_loss=0.2272, pruned_loss=0.02008, over 7161.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2491, pruned_loss=0.03289, over 1420682.62 frames.], batch size: 18, lr: 3.00e-04
2022-05-15 10:12:57,690 INFO [train.py:812] (6/8) Epoch 26, batch 2200, loss[loss=0.1543, simple_loss=0.2461, pruned_loss=0.0313, over 7061.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2488, pruned_loss=0.03267, over 1422448.48 frames.], batch size: 28, lr: 3.00e-04
2022-05-15 10:13:56,441 INFO [train.py:812] (6/8) Epoch 26, batch 2250, loss[loss=0.162, simple_loss=0.2589, pruned_loss=0.03257, over 7395.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2483, pruned_loss=0.03246, over 1425306.89 frames.], batch size: 23, lr: 3.00e-04
2022-05-15 10:14:54,812 INFO [train.py:812] (6/8) Epoch 26, batch 2300, loss[loss=0.1445, simple_loss=0.2339, pruned_loss=0.02753, over 7064.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2483, pruned_loss=0.03217, over 1425475.65 frames.], batch size: 18, lr: 2.99e-04
2022-05-15 10:15:54,100 INFO [train.py:812] (6/8) Epoch 26, batch 2350, loss[loss=0.1402, simple_loss=0.2195, pruned_loss=0.03044, over 7257.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2476, pruned_loss=0.03245, over 1425567.19 frames.], batch size: 19, lr: 2.99e-04
2022-05-15 10:16:53,720 INFO [train.py:812] (6/8) Epoch 26, batch 2400, loss[loss=0.1796, simple_loss=0.2712, pruned_loss=0.04404, over 7381.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2475, pruned_loss=0.03257, over 1423349.35 frames.], batch size: 23, lr: 2.99e-04
2022-05-15 10:17:52,718 INFO [train.py:812] (6/8) Epoch 26, batch 2450, loss[loss=0.1362, simple_loss=0.2356, pruned_loss=0.0184, over 6746.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2483, pruned_loss=0.03292, over 1422095.37 frames.], batch size: 31, lr: 2.99e-04
2022-05-15 10:18:50,835 INFO [train.py:812] (6/8) Epoch 26, batch 2500, loss[loss=0.1592, simple_loss=0.2553, pruned_loss=0.03156, over 7356.00 frames.], tot_loss[loss=0.156, simple_loss=0.2471, pruned_loss=0.03247, over 1424203.15 frames.], batch size: 19, lr: 2.99e-04
2022-05-15 10:19:48,023 INFO [train.py:812] (6/8) Epoch 26, batch 2550, loss[loss=0.1344, simple_loss=0.2158, pruned_loss=0.02648, over 7415.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2466, pruned_loss=0.03244, over 1426671.30 frames.], batch size: 18, lr: 2.99e-04
2022-05-15 10:20:46,863 INFO [train.py:812] (6/8) Epoch 26, batch 2600, loss[loss=0.1387, simple_loss=0.2338, pruned_loss=0.02183, over 7154.00 frames.], tot_loss[loss=0.1562, simple_loss=0.247, pruned_loss=0.03271, over 1424148.29 frames.], batch size: 19, lr: 2.99e-04
2022-05-15 10:21:44,655 INFO [train.py:812] (6/8) Epoch 26, batch 2650, loss[loss=0.1626, simple_loss=0.2552, pruned_loss=0.03498, over 7047.00 frames.], tot_loss[loss=0.156, simple_loss=0.2472, pruned_loss=0.03239, over 1419618.26 frames.], batch size: 28, lr: 2.99e-04
2022-05-15 10:22:43,740 INFO [train.py:812] (6/8) Epoch 26, batch 2700, loss[loss=0.1469, simple_loss=0.231, pruned_loss=0.03139, over 7257.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2471, pruned_loss=0.03256, over 1419925.44 frames.], batch size: 19, lr: 2.99e-04
2022-05-15 10:23:42,396 INFO [train.py:812] (6/8) Epoch 26, batch 2750, loss[loss=0.1627, simple_loss=0.2638, pruned_loss=0.03079, over 7291.00 frames.], tot_loss[loss=0.157, simple_loss=0.2479, pruned_loss=0.03308, over 1413367.56 frames.], batch size: 25, lr: 2.99e-04
2022-05-15 10:24:40,500 INFO [train.py:812] (6/8) Epoch 26, batch 2800, loss[loss=0.1599, simple_loss=0.2514, pruned_loss=0.03421, over 7281.00 frames.], tot_loss[loss=0.157, simple_loss=0.2476, pruned_loss=0.03313, over 1416700.89 frames.], batch size: 18, lr: 2.99e-04
2022-05-15 10:25:38,147 INFO [train.py:812] (6/8) Epoch 26, batch 2850, loss[loss=0.1489, simple_loss=0.2492, pruned_loss=0.0243, over 7421.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2471, pruned_loss=0.03293, over 1412069.36 frames.], batch size: 21, lr: 2.99e-04
2022-05-15 10:26:37,774 INFO [train.py:812] (6/8) Epoch 26, batch 2900, loss[loss=0.1276, simple_loss=0.2203, pruned_loss=0.01744, over 7157.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2462, pruned_loss=0.03246, over 1418110.66 frames.], batch size: 20, lr: 2.99e-04
2022-05-15 10:27:35,293 INFO [train.py:812] (6/8) Epoch 26, batch 2950, loss[loss=0.1392, simple_loss=0.2347, pruned_loss=0.02191, over 7331.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2466, pruned_loss=0.03251, over 1418343.56 frames.], batch size: 20, lr: 2.99e-04
2022-05-15 10:28:33,144 INFO [train.py:812] (6/8) Epoch 26, batch 3000, loss[loss=0.1436, simple_loss=0.2437, pruned_loss=0.02177, over 6395.00 frames.], tot_loss[loss=0.1556, simple_loss=0.247, pruned_loss=0.03217, over 1422511.75 frames.], batch size: 37, lr: 2.99e-04
2022-05-15 10:28:33,145 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 10:28:40,783 INFO [train.py:841] (6/8) Epoch 26, validation: loss=0.1534, simple_loss=0.2507, pruned_loss=0.02805, over 698248.00 frames.
2022-05-15 10:29:38,775 INFO [train.py:812] (6/8) Epoch 26, batch 3050, loss[loss=0.1356, simple_loss=0.2308, pruned_loss=0.02019, over 7335.00 frames.], tot_loss[loss=0.156, simple_loss=0.2478, pruned_loss=0.03212, over 1421393.09 frames.], batch size: 22, lr: 2.99e-04
2022-05-15 10:30:38,723 INFO [train.py:812] (6/8) Epoch 26, batch 3100, loss[loss=0.1562, simple_loss=0.2456, pruned_loss=0.03342, over 7257.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2482, pruned_loss=0.03203, over 1419662.10 frames.], batch size: 19, lr: 2.98e-04
2022-05-15 10:31:36,307 INFO [train.py:812] (6/8) Epoch 26, batch 3150, loss[loss=0.1308, simple_loss=0.2114, pruned_loss=0.0251, over 7152.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2484, pruned_loss=0.03234, over 1417854.34 frames.], batch size: 17, lr: 2.98e-04
2022-05-15 10:32:35,718 INFO [train.py:812] (6/8) Epoch 26, batch 3200, loss[loss=0.138, simple_loss=0.2286, pruned_loss=0.02374, over 7153.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2481, pruned_loss=0.03229, over 1420975.33 frames.], batch size: 19, lr: 2.98e-04
2022-05-15 10:33:35,089 INFO [train.py:812] (6/8) Epoch 26, batch 3250, loss[loss=0.1572, simple_loss=0.2459, pruned_loss=0.03428, over 7278.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2469, pruned_loss=0.03198, over 1423726.99 frames.], batch size: 18, lr: 2.98e-04
2022-05-15 10:34:33,022 INFO [train.py:812] (6/8) Epoch 26, batch 3300, loss[loss=0.1662, simple_loss=0.2652, pruned_loss=0.03358, over 7152.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2472, pruned_loss=0.03212, over 1417616.19 frames.], batch size: 26, lr: 2.98e-04
2022-05-15 10:35:31,819 INFO [train.py:812] (6/8) Epoch 26, batch 3350, loss[loss=0.1752, simple_loss=0.2756, pruned_loss=0.03741, over 7326.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2467, pruned_loss=0.03185, over 1414236.37 frames.], batch size: 21, lr: 2.98e-04
2022-05-15 10:36:31,849 INFO [train.py:812] (6/8) Epoch 26, batch 3400, loss[loss=0.1534, simple_loss=0.2573, pruned_loss=0.02475, over 6619.00 frames.], tot_loss[loss=0.155, simple_loss=0.246, pruned_loss=0.03198, over 1419924.83 frames.], batch size: 38, lr: 2.98e-04
2022-05-15 10:37:30,439 INFO [train.py:812] (6/8) Epoch 26, batch 3450, loss[loss=0.1559, simple_loss=0.24, pruned_loss=0.03592, over 7155.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2458, pruned_loss=0.03167, over 1420054.34 frames.], batch size: 18, lr: 2.98e-04
2022-05-15 10:38:29,767 INFO [train.py:812] (6/8) Epoch 26, batch 3500, loss[loss=0.1765, simple_loss=0.2611, pruned_loss=0.0459, over 7381.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2463, pruned_loss=0.03177, over 1418830.39 frames.], batch size: 23, lr: 2.98e-04
2022-05-15 10:39:28,322 INFO [train.py:812] (6/8) Epoch 26, batch 3550, loss[loss=0.1341, simple_loss=0.2363, pruned_loss=0.01594, over 7416.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2463, pruned_loss=0.03178, over 1421371.73 frames.], batch size: 21, lr: 2.98e-04
2022-05-15 10:40:26,273 INFO [train.py:812] (6/8) Epoch 26, batch 3600, loss[loss=0.1689, simple_loss=0.2679, pruned_loss=0.03498, over 7182.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2464, pruned_loss=0.03187, over 1425528.85 frames.], batch size: 23, lr: 2.98e-04
2022-05-15 10:41:25,878 INFO [train.py:812] (6/8) Epoch 26, batch 3650, loss[loss=0.15, simple_loss=0.2419, pruned_loss=0.02901, over 7267.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2462, pruned_loss=0.03146, over 1426756.75 frames.], batch size: 19, lr: 2.98e-04
2022-05-15 10:42:23,893 INFO [train.py:812] (6/8) Epoch 26, batch 3700, loss[loss=0.143, simple_loss=0.2373, pruned_loss=0.02435, over 7067.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2464, pruned_loss=0.03202, over 1424558.12 frames.], batch size: 18, lr: 2.98e-04
2022-05-15 10:43:22,978 INFO [train.py:812] (6/8) Epoch 26, batch 3750, loss[loss=0.1666, simple_loss=0.2618, pruned_loss=0.0357, over 7156.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2471, pruned_loss=0.03212, over 1423223.45 frames.], batch size: 19, lr: 2.98e-04
2022-05-15 10:44:21,261 INFO [train.py:812] (6/8) Epoch 26, batch 3800, loss[loss=0.1644, simple_loss=0.2576, pruned_loss=0.03557, over 6426.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2469, pruned_loss=0.03175, over 1421159.95 frames.], batch size: 38, lr: 2.98e-04
2022-05-15 10:45:20,420 INFO [train.py:812] (6/8) Epoch 26, batch 3850, loss[loss=0.1704, simple_loss=0.2674, pruned_loss=0.0367, over 7147.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2468, pruned_loss=0.0318, over 1418413.12 frames.], batch size: 20, lr: 2.97e-04
2022-05-15 10:46:19,975 INFO [train.py:812] (6/8) Epoch 26, batch 3900, loss[loss=0.1387, simple_loss=0.2217, pruned_loss=0.02783, over 7401.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2482, pruned_loss=0.03233, over 1420939.44 frames.], batch size: 18, lr: 2.97e-04
2022-05-15 10:47:17,439 INFO [train.py:812] (6/8) Epoch 26, batch 3950, loss[loss=0.1605, simple_loss=0.2525, pruned_loss=0.03421, over 7227.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2479, pruned_loss=0.03258, over 1425667.35 frames.], batch size: 20, lr: 2.97e-04
2022-05-15 10:48:16,894 INFO [train.py:812] (6/8) Epoch 26, batch 4000, loss[loss=0.1474, simple_loss=0.241, pruned_loss=0.0269, over 7424.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2471, pruned_loss=0.03235, over 1418328.03 frames.], batch size: 20, lr: 2.97e-04
2022-05-15 10:49:15,505 INFO [train.py:812] (6/8) Epoch 26, batch 4050, loss[loss=0.1491, simple_loss=0.251, pruned_loss=0.02361, over 7407.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2472, pruned_loss=0.03213, over 1419393.44 frames.], batch size: 21, lr: 2.97e-04
2022-05-15 10:50:14,959 INFO [train.py:812] (6/8) Epoch 26, batch 4100, loss[loss=0.148, simple_loss=0.242, pruned_loss=0.02701, over 7407.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2481, pruned_loss=0.03274, over 1417502.41 frames.], batch size: 21, lr: 2.97e-04
2022-05-15 10:51:14,803 INFO [train.py:812] (6/8) Epoch 26, batch 4150, loss[loss=0.135, simple_loss=0.2172, pruned_loss=0.02638, over 7272.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2476, pruned_loss=0.03229, over 1422364.43 frames.], batch size: 19, lr: 2.97e-04
2022-05-15 10:52:13,199 INFO [train.py:812] (6/8) Epoch 26, batch 4200, loss[loss=0.1617, simple_loss=0.2502, pruned_loss=0.03655, over 7063.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2483, pruned_loss=0.03282, over 1419110.77 frames.], batch size: 28, lr: 2.97e-04
2022-05-15 10:53:19,418 INFO [train.py:812] (6/8) Epoch 26, batch 4250, loss[loss=0.1544, simple_loss=0.2385, pruned_loss=0.03512, over 7165.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2479, pruned_loss=0.03289, over 1418416.37 frames.], batch size: 18, lr: 2.97e-04
2022-05-15 10:54:17,974 INFO [train.py:812] (6/8) Epoch 26, batch 4300, loss[loss=0.1489, simple_loss=0.2453, pruned_loss=0.02623, over 7211.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2484, pruned_loss=0.03287, over 1422362.63 frames.], batch size: 26, lr: 2.97e-04
2022-05-15 10:55:15,857 INFO [train.py:812] (6/8) Epoch 26, batch 4350, loss[loss=0.1493, simple_loss=0.2463, pruned_loss=0.0261, over 7228.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2479, pruned_loss=0.03249, over 1414571.71 frames.], batch size: 20, lr: 2.97e-04
2022-05-15 10:56:15,075 INFO [train.py:812] (6/8) Epoch 26, batch 4400, loss[loss=0.153, simple_loss=0.2371, pruned_loss=0.0344, over 7061.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2482, pruned_loss=0.03225, over 1414384.83 frames.], batch size: 18, lr: 2.97e-04
2022-05-15 10:57:23,164 INFO [train.py:812] (6/8) Epoch 26, batch 4450, loss[loss=0.1596, simple_loss=0.256, pruned_loss=0.03159, over 7279.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2485, pruned_loss=0.03235, over 1414200.12 frames.], batch size: 24, lr: 2.97e-04
2022-05-15 10:58:40,631 INFO [train.py:812] (6/8) Epoch 26, batch 4500, loss[loss=0.169, simple_loss=0.254, pruned_loss=0.04199, over 7329.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2488, pruned_loss=0.03292, over 1399136.03 frames.], batch size: 20, lr: 2.97e-04
2022-05-15 10:59:48,365 INFO [train.py:812] (6/8) Epoch 26, batch 4550, loss[loss=0.1901, simple_loss=0.2673, pruned_loss=0.05642, over 4948.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2493, pruned_loss=0.0336, over 1389378.31 frames.], batch size: 52, lr: 2.97e-04
2022-05-15 11:01:05,805 INFO [train.py:812] (6/8) Epoch 27, batch 0, loss[loss=0.1369, simple_loss=0.2176, pruned_loss=0.02812, over 7157.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2176, pruned_loss=0.02812, over 7157.00 frames.], batch size: 18, lr: 2.91e-04
2022-05-15 11:02:14,202 INFO [train.py:812] (6/8) Epoch 27, batch 50, loss[loss=0.1424, simple_loss=0.222, pruned_loss=0.03143, over 7279.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2436, pruned_loss=0.03167, over 318418.99 frames.], batch size: 17, lr: 2.91e-04
2022-05-15 11:03:12,404 INFO [train.py:812] (6/8) Epoch 27, batch 100, loss[loss=0.143, simple_loss=0.2236, pruned_loss=0.03119, over 7280.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2455, pruned_loss=0.03166, over 562093.38 frames.], batch size: 17, lr: 2.91e-04
2022-05-15 11:04:11,563 INFO [train.py:812] (6/8) Epoch 27, batch 150, loss[loss=0.1496, simple_loss=0.2379, pruned_loss=0.03063, over 6497.00 frames.], tot_loss[loss=0.155, simple_loss=0.2463, pruned_loss=0.03182, over 751572.84 frames.], batch size: 38, lr: 2.91e-04
2022-05-15 11:05:08,314 INFO [train.py:812] (6/8) Epoch 27, batch 200, loss[loss=0.1567, simple_loss=0.2536, pruned_loss=0.02987, over 7161.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2469, pruned_loss=0.03222, over 894413.16 frames.], batch size: 26, lr: 2.91e-04
2022-05-15 11:06:06,643 INFO [train.py:812] (6/8) Epoch 27, batch 250, loss[loss=0.1481, simple_loss=0.2475, pruned_loss=0.0244, over 6368.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2466, pruned_loss=0.03178, over 1006320.89 frames.], batch size: 37, lr: 2.91e-04
2022-05-15 11:07:05,736 INFO [train.py:812] (6/8) Epoch 27, batch 300, loss[loss=0.1771, simple_loss=0.2752, pruned_loss=0.03951, over 6143.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2467, pruned_loss=0.03173, over 1100140.14 frames.], batch size: 37, lr: 2.91e-04
2022-05-15 11:08:04,243 INFO [train.py:812] (6/8) Epoch 27, batch 350, loss[loss=0.1805, simple_loss=0.2669, pruned_loss=0.04709, over 6737.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2462, pruned_loss=0.03176, over 1167480.33 frames.], batch size: 31, lr: 2.91e-04
2022-05-15 11:09:03,276 INFO [train.py:812] (6/8) Epoch 27, batch 400, loss[loss=0.1417, simple_loss=0.2294, pruned_loss=0.02704, over 7144.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2458, pruned_loss=0.03182, over 1227469.98 frames.], batch size: 20, lr: 2.91e-04
2022-05-15 11:10:01,857 INFO [train.py:812] (6/8) Epoch 27, batch 450, loss[loss=0.168, simple_loss=0.2531, pruned_loss=0.04143, over 7231.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2461, pruned_loss=0.03184, over 1275469.55 frames.], batch size: 20, lr: 2.91e-04
2022-05-15 11:10:59,678 INFO [train.py:812] (6/8) Epoch 27, batch 500, loss[loss=0.1705, simple_loss=0.2583, pruned_loss=0.04134, over 5113.00 frames.], tot_loss[loss=0.154, simple_loss=0.2453, pruned_loss=0.03132, over 1307438.60 frames.], batch size: 52, lr: 2.91e-04
2022-05-15 11:11:59,510 INFO [train.py:812] (6/8) Epoch 27, batch 550, loss[loss=0.1678, simple_loss=0.2573, pruned_loss=0.03912, over 7202.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2456, pruned_loss=0.03129, over 1331364.93 frames.], batch size: 22, lr: 2.90e-04
2022-05-15 11:12:58,979 INFO [train.py:812] (6/8) Epoch 27, batch 600, loss[loss=0.1469, simple_loss=0.2392, pruned_loss=0.02728, over 7262.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2455, pruned_loss=0.03138, over 1354147.45 frames.], batch size: 19, lr: 2.90e-04
2022-05-15 11:13:58,676 INFO [train.py:812] (6/8) Epoch 27, batch 650, loss[loss=0.143, simple_loss=0.2256, pruned_loss=0.03017, over 7270.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2449, pruned_loss=0.03117, over 1371538.67 frames.], batch size: 18, lr: 2.90e-04
2022-05-15 11:14:57,641 INFO [train.py:812] (6/8) Epoch 27, batch 700, loss[loss=0.154, simple_loss=0.2426, pruned_loss=0.03263, over 7119.00 frames.], tot_loss[loss=0.154, simple_loss=0.2457, pruned_loss=0.03119, over 1380455.76 frames.], batch size: 21, lr: 2.90e-04
2022-05-15 11:16:01,101 INFO [train.py:812] (6/8) Epoch 27, batch 750, loss[loss=0.1438, simple_loss=0.2334, pruned_loss=0.02704, over 7139.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2456, pruned_loss=0.03099, over 1388159.08 frames.], batch size: 20, lr: 2.90e-04
2022-05-15 11:17:00,057 INFO [train.py:812] (6/8) Epoch 27, batch 800, loss[loss=0.1609, simple_loss=0.2538, pruned_loss=0.03399, over 7232.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2459, pruned_loss=0.03129, over 1394702.88 frames.], batch size: 20, lr: 2.90e-04
2022-05-15 11:17:59,359 INFO [train.py:812] (6/8) Epoch 27, batch 850, loss[loss=0.1716, simple_loss=0.2576, pruned_loss=0.04281, over 5040.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2473, pruned_loss=0.03179, over 1397729.81 frames.], batch size: 52, lr: 2.90e-04
2022-05-15 11:18:57,713 INFO [train.py:812] (6/8) Epoch 27, batch 900, loss[loss=0.1267, simple_loss=0.2169, pruned_loss=0.01829, over 7388.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2464, pruned_loss=0.03151, over 1407055.47 frames.], batch size: 18, lr: 2.90e-04
2022-05-15 11:19:56,354 INFO [train.py:812] (6/8) Epoch 27, batch 950, loss[loss=0.1587, simple_loss=0.236, pruned_loss=0.04067, over 6823.00 frames.], tot_loss[loss=0.1559, simple_loss=0.248, pruned_loss=0.03193, over 1408086.28 frames.], batch size: 15, lr: 2.90e-04
2022-05-15 11:20:55,302 INFO [train.py:812] (6/8) Epoch 27, batch 1000, loss[loss=0.1876, simple_loss=0.2785, pruned_loss=0.04838, over 7275.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2479, pruned_loss=0.03178, over 1411034.63 frames.], batch size: 24, lr: 2.90e-04
2022-05-15 11:21:53,191 INFO [train.py:812] (6/8) Epoch 27, batch 1050, loss[loss=0.1566, simple_loss=0.2545, pruned_loss=0.02938, over 7212.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2478, pruned_loss=0.03163, over 1416713.98 frames.], batch size: 23, lr: 2.90e-04
2022-05-15 11:22:52,385 INFO [train.py:812] (6/8) Epoch 27, batch 1100, loss[loss=0.1631, simple_loss=0.2613, pruned_loss=0.03247, over 7205.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2468, pruned_loss=0.03136, over 1421431.61 frames.], batch size: 22, lr: 2.90e-04
2022-05-15 11:23:52,076 INFO [train.py:812] (6/8) Epoch 27, batch 1150, loss[loss=0.1528, simple_loss=0.2396, pruned_loss=0.033, over 7156.00 frames.], tot_loss[loss=0.155, simple_loss=0.247, pruned_loss=0.0315, over 1422516.72 frames.], batch size: 19, lr: 2.90e-04
2022-05-15 11:24:50,283 INFO [train.py:812] (6/8) Epoch 27, batch 1200, loss[loss=0.1534, simple_loss=0.2444, pruned_loss=0.03124, over 7308.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2476, pruned_loss=0.03187, over 1426273.18 frames.], batch size: 24, lr: 2.90e-04
2022-05-15 11:25:49,804 INFO [train.py:812] (6/8) Epoch 27, batch 1250, loss[loss=0.1703, simple_loss=0.27, pruned_loss=0.03527, over 6333.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2464, pruned_loss=0.03136, over 1425685.81 frames.], batch size: 37, lr: 2.90e-04
2022-05-15 11:26:48,363 INFO [train.py:812] (6/8) Epoch 27, batch 1300, loss[loss=0.1234, simple_loss=0.208, pruned_loss=0.0194, over 7276.00 frames.], tot_loss[loss=0.1546, simple_loss=0.246, pruned_loss=0.03164, over 1421489.02 frames.], batch size: 18, lr: 2.90e-04
2022-05-15 11:27:46,506 INFO [train.py:812] (6/8) Epoch 27, batch 1350, loss[loss=0.1273, simple_loss=0.2138, pruned_loss=0.02039, over 7412.00 frames.], tot_loss[loss=0.1532, simple_loss=0.244, pruned_loss=0.03119, over 1425575.28 frames.], batch size: 18, lr: 2.89e-04
2022-05-15 11:28:44,277 INFO [train.py:812] (6/8) Epoch 27, batch 1400, loss[loss=0.1818, simple_loss=0.2668, pruned_loss=0.04836, over 7199.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2444, pruned_loss=0.03133, over 1418857.50 frames.], batch size: 23, lr: 2.89e-04
2022-05-15 11:29:43,180 INFO [train.py:812] (6/8) Epoch 27, batch 1450, loss[loss=0.1508, simple_loss=0.2459, pruned_loss=0.02789, over 7277.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2454, pruned_loss=0.03167, over 1420926.54 frames.], batch size: 18, lr: 2.89e-04
2022-05-15 11:30:41,592 INFO [train.py:812] (6/8) Epoch 27, batch 1500, loss[loss=0.1755, simple_loss=0.264, pruned_loss=0.04348, over 4978.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2454, pruned_loss=0.03142, over 1417257.22 frames.], batch size: 52, lr: 2.89e-04
2022-05-15 11:31:41,142 INFO [train.py:812] (6/8) Epoch 27, batch 1550, loss[loss=0.1373, simple_loss=0.2359, pruned_loss=0.01934, over 7117.00 frames.], tot_loss[loss=0.1547, simple_loss=0.246, pruned_loss=0.0317, over 1420887.57 frames.], batch size: 21, lr: 2.89e-04
2022-05-15 11:32:40,457 INFO [train.py:812] (6/8) Epoch 27, batch 1600, loss[loss=0.1454, simple_loss=0.238, pruned_loss=0.02638, over 7253.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2453, pruned_loss=0.03117, over 1424568.24 frames.], batch size: 19, lr: 2.89e-04
2022-05-15 11:33:39,629 INFO [train.py:812] (6/8) Epoch 27, batch 1650, loss[loss=0.1734, simple_loss=0.272, pruned_loss=0.03745, over 7169.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2458, pruned_loss=0.03144, over 1428745.78 frames.], batch size: 26, lr: 2.89e-04
2022-05-15 11:34:37,994 INFO [train.py:812] (6/8) Epoch 27, batch 1700, loss[loss=0.157, simple_loss=0.2624, pruned_loss=0.02576, over 7324.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2459, pruned_loss=0.03137, over 1430480.57 frames.], batch size: 22, lr: 2.89e-04
2022-05-15 11:35:35,794 INFO [train.py:812] (6/8) Epoch 27, batch 1750, loss[loss=0.1791, simple_loss=0.2772, pruned_loss=0.04052, over 7160.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2469, pruned_loss=0.03188, over 1430923.55 frames.], batch size: 26, lr: 2.89e-04
2022-05-15 11:36:34,361 INFO [train.py:812] (6/8) Epoch 27, batch 1800, loss[loss=0.141, simple_loss=0.2379, pruned_loss=0.02206, over 7127.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2473, pruned_loss=0.0323, over 1428963.04 frames.], batch size: 21, lr: 2.89e-04
2022-05-15 11:37:32,443 INFO [train.py:812] (6/8) Epoch 27, batch 1850, loss[loss=0.1837, simple_loss=0.2742, pruned_loss=0.04656, over 4904.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2477, pruned_loss=0.03223, over 1429089.58 frames.], batch size: 54, lr: 2.89e-04
2022-05-15 11:38:30,744 INFO [train.py:812] (6/8) Epoch 27, batch 1900, loss[loss=0.1407, simple_loss=0.2254, pruned_loss=0.02801, over 7366.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2459, pruned_loss=0.0318, over 1427791.73 frames.], batch size: 19, lr: 2.89e-04
2022-05-15 11:39:30,054 INFO [train.py:812] (6/8) Epoch 27, batch 1950, loss[loss=0.1509, simple_loss=0.2468, pruned_loss=0.02748, over 6398.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2462, pruned_loss=0.03198, over 1423785.01 frames.], batch size: 37, lr: 2.89e-04
2022-05-15 11:40:29,366 INFO [train.py:812] (6/8) Epoch 27, batch 2000, loss[loss=0.1765, simple_loss=0.2612, pruned_loss=0.04588, over 6837.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2462, pruned_loss=0.03224, over 1421817.13 frames.], batch size: 31, lr: 2.89e-04
2022-05-15 11:41:28,630 INFO [train.py:812] (6/8) Epoch 27, batch 2050, loss[loss=0.1767, simple_loss=0.2651, pruned_loss=0.04419, over 7174.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2474, pruned_loss=0.03252, over 1425193.89 frames.], batch size: 26, lr: 2.89e-04
2022-05-15 11:42:27,682 INFO [train.py:812] (6/8) Epoch 27, batch 2100, loss[loss=0.1592, simple_loss=0.2541, pruned_loss=0.0322, over 7214.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2468, pruned_loss=0.03236, over 1423393.17 frames.], batch size: 22, lr: 2.89e-04
2022-05-15 11:43:25,353 INFO [train.py:812] (6/8) Epoch 27, batch 2150, loss[loss=0.1679, simple_loss=0.265, pruned_loss=0.03544, over 7285.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2482, pruned_loss=0.03262, over 1427287.53 frames.], batch size: 25, lr: 2.89e-04
2022-05-15 11:44:23,717 INFO [train.py:812] (6/8) Epoch 27, batch 2200, loss[loss=0.1567, simple_loss=0.2517, pruned_loss=0.03082, over 7243.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2478, pruned_loss=0.03233, over 1425677.91 frames.], batch size: 20, lr: 2.88e-04
2022-05-15 11:45:23,017 INFO [train.py:812] (6/8) Epoch 27, batch 2250, loss[loss=0.1222, simple_loss=0.2118, pruned_loss=0.01626, over 7004.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2476, pruned_loss=0.03241, over 1430849.53 frames.], batch size: 16, lr: 2.88e-04
2022-05-15 11:46:21,536 INFO [train.py:812] (6/8) Epoch 27, batch 2300, loss[loss=0.1644, simple_loss=0.2437, pruned_loss=0.04256, over 7142.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2474, pruned_loss=0.03209, over 1432218.10 frames.], batch size: 17, lr: 2.88e-04
2022-05-15 11:47:19,558 INFO [train.py:812] (6/8) Epoch 27, batch 2350, loss[loss=0.171, simple_loss=0.2628, pruned_loss=0.03962, over 7143.00 frames.], tot_loss[loss=0.157, simple_loss=0.2485, pruned_loss=0.03278, over 1431238.28 frames.], batch size: 20, lr: 2.88e-04
2022-05-15 11:48:16,606 INFO [train.py:812] (6/8) Epoch 27, batch 2400, loss[loss=0.1704, simple_loss=0.2634, pruned_loss=0.03868, over 7290.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2479, pruned_loss=0.03239, over 1432864.86 frames.], batch size: 24, lr: 2.88e-04
2022-05-15 11:49:16,171 INFO [train.py:812] (6/8) Epoch 27, batch 2450, loss[loss=0.166, simple_loss=0.2613, pruned_loss=0.03539, over 7236.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2474, pruned_loss=0.03198, over 1435876.28 frames.], batch size: 20, lr: 2.88e-04
2022-05-15 11:50:15,244 INFO [train.py:812] (6/8) Epoch 27, batch 2500, loss[loss=0.1845, simple_loss=0.2795, pruned_loss=0.04474, over 7210.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2469, pruned_loss=0.0321, over 1437405.08 frames.], batch size: 21, lr: 2.88e-04
2022-05-15 11:51:13,687 INFO [train.py:812] (6/8) Epoch 27, batch 2550, loss[loss=0.1601, simple_loss=0.2566, pruned_loss=0.03184, over 6751.00 frames.], tot_loss[loss=0.155, simple_loss=0.2463, pruned_loss=0.03182, over 1435137.63 frames.], batch size: 31, lr: 2.88e-04
2022-05-15 11:52:12,752 INFO [train.py:812] (6/8) Epoch 27, batch 2600, loss[loss=0.1382, simple_loss=0.2299, pruned_loss=0.02324, over 7229.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2473, pruned_loss=0.03212, over 1435757.65 frames.], batch size: 16, lr: 2.88e-04
2022-05-15 11:53:12,242 INFO [train.py:812] (6/8) Epoch 27, batch 2650, loss[loss=0.1662, simple_loss=0.2602, pruned_loss=0.03613, over 7255.00 frames.], tot_loss[loss=0.1557, simple_loss=0.247, pruned_loss=0.03215, over 1431958.14 frames.], batch size: 24, lr: 2.88e-04
2022-05-15 11:54:11,607 INFO [train.py:812] (6/8) Epoch 27, batch 2700, loss[loss=0.1665, simple_loss=0.2588, pruned_loss=0.03708, over 7332.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2465, pruned_loss=0.03187, over 1430490.54 frames.], batch size: 22, lr: 2.88e-04
2022-05-15 11:55:10,422 INFO [train.py:812] (6/8) Epoch 27, batch 2750, loss[loss=0.2124, simple_loss=0.2687, pruned_loss=0.07806, over 7158.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2465, pruned_loss=0.0317, over 1430401.30 frames.], batch size: 19, lr: 2.88e-04
2022-05-15 11:56:08,590 INFO [train.py:812] (6/8) Epoch 27, batch 2800, loss[loss=0.1473, simple_loss=0.2428, pruned_loss=0.02587, over 7282.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2466, pruned_loss=0.03194, over 1428964.09 frames.], batch size: 25, lr: 2.88e-04
2022-05-15 11:57:08,023 INFO [train.py:812] (6/8) Epoch 27, batch 2850, loss[loss=0.1647, simple_loss=0.2534, pruned_loss=0.03804, over 7248.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2461, pruned_loss=0.03163, over 1428197.89 frames.], batch size: 19, lr: 2.88e-04
2022-05-15 11:58:06,992 INFO [train.py:812] (6/8) Epoch 27, batch 2900, loss[loss=0.1393, simple_loss=0.2296, pruned_loss=0.02452, over 7170.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2461, pruned_loss=0.03147, over 1426972.23 frames.], batch size: 19, lr: 2.88e-04
2022-05-15 11:59:06,489 INFO [train.py:812] (6/8) Epoch 27, batch 2950, loss[loss=0.1464, simple_loss=0.2386, pruned_loss=0.02708, over 7121.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2459, pruned_loss=0.0313, over 1420617.91 frames.], batch size: 21, lr: 2.88e-04
2022-05-15 12:00:05,434 INFO [train.py:812] (6/8) Epoch 27, batch 3000, loss[loss=0.1744, simple_loss=0.2737, pruned_loss=0.03753, over 7417.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2456, pruned_loss=0.03136, over 1418838.13 frames.], batch size: 21, lr: 2.88e-04
2022-05-15 12:00:05,435 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 12:00:12,945 INFO [train.py:841] (6/8) Epoch 27, validation: loss=0.1528, simple_loss=0.25, pruned_loss=0.02785, over 698248.00 frames.
2022-05-15 12:01:11,839 INFO [train.py:812] (6/8) Epoch 27, batch 3050, loss[loss=0.1559, simple_loss=0.2554, pruned_loss=0.02826, over 7105.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2453, pruned_loss=0.03159, over 1410850.67 frames.], batch size: 21, lr: 2.87e-04
2022-05-15 12:02:10,778 INFO [train.py:812] (6/8) Epoch 27, batch 3100, loss[loss=0.1749, simple_loss=0.2743, pruned_loss=0.03781, over 7321.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2468, pruned_loss=0.03205, over 1416521.05 frames.], batch size: 21, lr: 2.87e-04
2022-05-15 12:03:20,254 INFO [train.py:812] (6/8) Epoch 27, batch 3150, loss[loss=0.1674, simple_loss=0.2593, pruned_loss=0.03769, over 7196.00 frames.], tot_loss[loss=0.1556, simple_loss=0.247, pruned_loss=0.03208, over 1417896.05 frames.], batch size: 22, lr: 2.87e-04
2022-05-15 12:04:19,268 INFO [train.py:812] (6/8) Epoch 27, batch 3200, loss[loss=0.1775, simple_loss=0.2651, pruned_loss=0.045, over 7199.00 frames.], tot_loss[loss=0.156, simple_loss=0.2473, pruned_loss=0.03236, over 1419843.50 frames.], batch size: 23, lr: 2.87e-04
2022-05-15 12:05:18,868 INFO [train.py:812] (6/8) Epoch 27, batch 3250, loss[loss=0.1531, simple_loss=0.245, pruned_loss=0.03054, over 6356.00 frames.], tot_loss[loss=0.1558, simple_loss=0.247, pruned_loss=0.03233, over 1420170.16 frames.], batch size: 38, lr: 2.87e-04
2022-05-15 12:06:17,725 INFO [train.py:812] (6/8) Epoch 27, batch 3300, loss[loss=0.1494, simple_loss=0.248, pruned_loss=0.0254, over 6717.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2469, pruned_loss=0.03233, over 1420190.63 frames.], batch size: 31, lr: 2.87e-04
2022-05-15 12:07:17,065 INFO [train.py:812] (6/8) Epoch 27, batch 3350, loss[loss=0.1789, simple_loss=0.2749, pruned_loss=0.04151, over 7344.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2479, pruned_loss=0.03219, over 1421394.89 frames.], batch size: 22, lr: 2.87e-04
2022-05-15 12:08:16,177 INFO [train.py:812] (6/8) Epoch 27, batch 3400, loss[loss=0.1605, simple_loss=0.2563, pruned_loss=0.03237, over 7153.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2483, pruned_loss=0.03228, over 1418473.80 frames.], batch size: 20, lr: 2.87e-04
2022-05-15 12:09:15,051 INFO [train.py:812] (6/8) Epoch 27, batch 3450, loss[loss=0.1552, simple_loss=0.2358, pruned_loss=0.03733, over 7346.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2478, pruned_loss=0.03219, over 1421610.40 frames.], batch size: 22, lr: 2.87e-04
2022-05-15 12:10:13,339 INFO [train.py:812] (6/8) Epoch 27, batch 3500, loss[loss=0.1223, simple_loss=0.2028, pruned_loss=0.0209, over 6820.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2475, pruned_loss=0.03213, over 1424024.63 frames.], batch size: 15, lr: 2.87e-04
2022-05-15 12:11:13,150 INFO [train.py:812] (6/8) Epoch 27, batch 3550, loss[loss=0.1593, simple_loss=0.2448, pruned_loss=0.03691, over 5106.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2471, pruned_loss=0.03206, over 1417389.37 frames.], batch size: 53, lr: 2.87e-04
2022-05-15 12:12:10,933 INFO [train.py:812] (6/8) Epoch 27, batch 3600, loss[loss=0.1579, simple_loss=0.261, pruned_loss=0.02741, over 7158.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2467, pruned_loss=0.03193, over 1414645.26 frames.], batch size: 19, lr: 2.87e-04
2022-05-15 12:13:10,321 INFO [train.py:812] (6/8) Epoch 27, batch 3650, loss[loss=0.171, simple_loss=0.2667, pruned_loss=0.03762, over 7060.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2459, pruned_loss=0.03149, over 1413339.48 frames.], batch size: 18, lr: 2.87e-04
2022-05-15 12:14:09,333 INFO [train.py:812] (6/8) Epoch 27, batch 3700, loss[loss=0.1336, simple_loss=0.2206, pruned_loss=0.02327, over 7268.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2459, pruned_loss=0.03191, over 1412484.21 frames.], batch size: 18, lr: 2.87e-04
2022-05-15 12:15:08,334 INFO [train.py:812] (6/8) Epoch 27, batch 3750, loss[loss=0.1575, simple_loss=0.2554, pruned_loss=0.02981, over 7211.00 frames.], tot_loss[loss=0.1544, simple_loss=0.245, pruned_loss=0.03188, over 1416332.71 frames.], batch size: 21, lr: 2.87e-04
2022-05-15 12:16:08,065 INFO [train.py:812] (6/8) Epoch 27, batch 3800, loss[loss=0.1547, simple_loss=0.2551, pruned_loss=0.02712, over 7333.00 frames.], tot_loss[loss=0.154, simple_loss=0.2446, pruned_loss=0.03177, over 1420513.64 frames.], batch size: 20, lr: 2.87e-04
2022-05-15 12:17:07,850 INFO [train.py:812] (6/8) Epoch 27, batch 3850, loss[loss=0.1267, simple_loss=0.2024, pruned_loss=0.02546, over 7426.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2464, pruned_loss=0.03206, over 1414814.01 frames.], batch size: 18, lr: 2.87e-04
2022-05-15 12:18:06,253 INFO [train.py:812] (6/8) Epoch 27, batch 3900, loss[loss=0.1758, simple_loss=0.2741, pruned_loss=0.03875, over 7054.00 frames.], tot_loss[loss=0.1556, simple_loss=0.247, pruned_loss=0.03209, over 1415470.03 frames.], batch size: 28, lr: 2.86e-04
2022-05-15 12:19:04,975 INFO [train.py:812] (6/8) Epoch 27, batch 3950, loss[loss=0.1599, simple_loss=0.2459, pruned_loss=0.03696, over 7354.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2469, pruned_loss=0.03203, over 1420016.40 frames.], batch size: 19, lr: 2.86e-04
2022-05-15 12:20:04,293 INFO [train.py:812] (6/8) Epoch 27, batch 4000, loss[loss=0.1627, simple_loss=0.2579, pruned_loss=0.03378, over 7051.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2468, pruned_loss=0.03179, over 1424733.90 frames.], batch size: 28, lr: 2.86e-04
2022-05-15 12:21:04,181 INFO [train.py:812] (6/8) Epoch 27, batch 4050, loss[loss=0.1564, simple_loss=0.2407, pruned_loss=0.03608, over 7322.00 frames.], tot_loss[loss=0.1555, simple_loss=0.247, pruned_loss=0.03204, over 1425526.65 frames.], batch size: 20, lr: 2.86e-04
2022-05-15 12:22:03,615 INFO [train.py:812] (6/8) Epoch 27, batch 4100, loss[loss=0.1449, simple_loss=0.2451, pruned_loss=0.02239, over 7334.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2464, pruned_loss=0.0316, over 1425015.88 frames.], batch size: 20, lr: 2.86e-04
2022-05-15 12:23:02,360 INFO [train.py:812] (6/8) Epoch 27, batch 4150, loss[loss=0.145, simple_loss=0.2369, pruned_loss=0.02654, over 7123.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2465, pruned_loss=0.03169, over 1421555.17 frames.], batch size: 21, lr: 2.86e-04
2022-05-15 12:23:59,506 INFO [train.py:812] (6/8) Epoch 27, batch 4200, loss[loss=0.1375, simple_loss=0.2365, pruned_loss=0.01926, over 7335.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2454, pruned_loss=0.03113, over 1422412.02 frames.], batch size: 22, lr: 2.86e-04
2022-05-15 12:24:57,517 INFO [train.py:812] (6/8) Epoch 27, batch 4250, loss[loss=0.166, simple_loss=0.2597, pruned_loss=0.03616, over 7412.00 frames.], tot_loss[loss=0.155, simple_loss=0.2468, pruned_loss=0.03158, over 1415253.83 frames.], batch size: 21, lr: 2.86e-04
2022-05-15 12:25:55,561 INFO [train.py:812] (6/8) Epoch 27, batch 4300, loss[loss=0.1732, simple_loss=0.265, pruned_loss=0.04077, over 6788.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2478, pruned_loss=0.03203, over 1412982.27 frames.], batch size: 31, lr: 2.86e-04
2022-05-15 12:26:54,780 INFO [train.py:812] (6/8) Epoch 27, batch 4350, loss[loss=0.1348, simple_loss=0.2193, pruned_loss=0.02516, over 7000.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2471, pruned_loss=0.03178, over 1412601.47 frames.], batch size: 16, lr: 2.86e-04
2022-05-15 12:27:53,347 INFO [train.py:812] (6/8) Epoch 27, batch 4400, loss[loss=0.1612, simple_loss=0.245, pruned_loss=0.03871, over 6361.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2478, pruned_loss=0.03254, over 1399561.49 frames.], batch size: 37, lr: 2.86e-04
2022-05-15 12:28:51,276 INFO [train.py:812] (6/8) Epoch 27, batch 4450, loss[loss=0.1625, simple_loss=0.2549, pruned_loss=0.03511, over 7347.00 frames.], tot_loss[loss=0.156, simple_loss=0.2471, pruned_loss=0.03244, over 1395031.97 frames.], batch size: 22, lr: 2.86e-04
2022-05-15 12:29:50,479 INFO [train.py:812] (6/8) Epoch 27, batch 4500, loss[loss=0.1717, simple_loss=0.2794, pruned_loss=0.03202, over 7164.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2474, pruned_loss=0.03255, over 1386216.59 frames.], batch size: 18, lr: 2.86e-04
2022-05-15 12:30:49,300 INFO [train.py:812] (6/8) Epoch 27, batch 4550, loss[loss=0.1838, simple_loss=0.2659, pruned_loss=0.05086, over 5106.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2458, pruned_loss=0.03242, over 1369938.90 frames.], batch size: 52, lr: 2.86e-04
2022-05-15 12:32:00,107 INFO [train.py:812] (6/8) Epoch 28, batch 0, loss[loss=0.1571, simple_loss=0.2495, pruned_loss=0.03234, over 7273.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2495, pruned_loss=0.03234, over 7273.00 frames.], batch size: 19, lr: 2.81e-04
2022-05-15 12:32:59,369 INFO [train.py:812] (6/8) Epoch 28, batch 50, loss[loss=0.1484, simple_loss=0.2301, pruned_loss=0.03332, over 7255.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2434, pruned_loss=0.02979, over 322301.12 frames.], batch size: 19, lr: 2.81e-04
2022-05-15 12:33:58,545 INFO [train.py:812] (6/8) Epoch 28, batch 100, loss[loss=0.1553, simple_loss=0.2495, pruned_loss=0.03058, over 7139.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2462, pruned_loss=0.03138, over 565756.80 frames.], batch size: 20, lr: 2.80e-04
2022-05-15 12:35:03,235 INFO [train.py:812] (6/8) Epoch 28, batch 150, loss[loss=0.1605, simple_loss=0.2515, pruned_loss=0.0348, over 6346.00 frames.], tot_loss[loss=0.1536, simple_loss=0.246, pruned_loss=0.03062, over 753387.23 frames.], batch size: 38, lr: 2.80e-04
2022-05-15 12:36:01,539 INFO [train.py:812] (6/8) Epoch 28, batch 200, loss[loss=0.1813, simple_loss=0.2698, pruned_loss=0.04646, over 7208.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2467, pruned_loss=0.03096, over 899503.03 frames.], batch size: 23, lr: 2.80e-04
2022-05-15 12:36:59,632 INFO [train.py:812] (6/8) Epoch 28, batch 250, loss[loss=0.1654, simple_loss=0.2572, pruned_loss=0.0368, over 7298.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2468, pruned_loss=0.03138, over 1015517.80 frames.], batch size: 24, lr: 2.80e-04
2022-05-15 12:37:58,316 INFO [train.py:812] (6/8) Epoch 28, batch 300, loss[loss=0.17, simple_loss=0.2661, pruned_loss=0.03699, over 6739.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2471, pruned_loss=0.03137, over 1105587.19 frames.], batch size: 31, lr: 2.80e-04
2022-05-15 12:38:57,256 INFO [train.py:812] (6/8) Epoch 28, batch 350, loss[loss=0.1507, simple_loss=0.2511, pruned_loss=0.02517, over 7162.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2456, pruned_loss=0.03097, over 1178290.70 frames.], batch size: 19, lr: 2.80e-04
2022-05-15 12:39:55,237 INFO [train.py:812] (6/8) Epoch 28, batch 400, loss[loss=0.1447, simple_loss=0.2421, pruned_loss=0.02363, over 7148.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2461, pruned_loss=0.03139, over 1233911.69 frames.], batch size: 17, lr: 2.80e-04
2022-05-15 12:40:54,511 INFO [train.py:812] (6/8) Epoch 28, batch 450, loss[loss=0.1591, simple_loss=0.256, pruned_loss=0.0311, over 7323.00 frames.], tot_loss[loss=0.155, simple_loss=0.2466, pruned_loss=0.03175, over 1270840.04 frames.], batch size: 25, lr: 2.80e-04
2022-05-15 12:41:53,069 INFO [train.py:812] (6/8) Epoch 28, batch 500, loss[loss=0.1484, simple_loss=0.2432, pruned_loss=0.0268, over 7315.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2466, pruned_loss=0.03163, over 1308593.63 frames.], batch size: 21, lr: 2.80e-04
2022-05-15 12:42:52,294 INFO [train.py:812] (6/8) Epoch 28, batch 550, loss[loss=0.1567, simple_loss=0.2474, pruned_loss=0.03304, over 7060.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2457, pruned_loss=0.03169, over 1330344.95 frames.], batch size: 18, lr: 2.80e-04
2022-05-15 12:43:51,392 INFO [train.py:812] (6/8) Epoch 28, batch 600, loss[loss=0.1496, simple_loss=0.2503, pruned_loss=0.0244, over 7328.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2458, pruned_loss=0.03177, over 1348473.73 frames.], batch size: 20, lr: 2.80e-04
2022-05-15 12:44:49,191 INFO [train.py:812] (6/8) Epoch 28, batch 650, loss[loss=0.1857, simple_loss=0.2852, pruned_loss=0.04312, over 7151.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2461, pruned_loss=0.0318, over 1365831.57 frames.], batch size: 28, lr: 2.80e-04
2022-05-15 12:45:48,010 INFO [train.py:812] (6/8) Epoch 28, batch 700, loss[loss=0.1402, simple_loss=0.2276, pruned_loss=0.02634, over 7052.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2456, pruned_loss=0.03144, over 1380171.92 frames.], batch size: 18, lr: 2.80e-04
2022-05-15 12:46:48,153 INFO [train.py:812] (6/8) Epoch 28, batch 750, loss[loss=0.1472, simple_loss=0.2365, pruned_loss=0.02892, over 7220.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2443, pruned_loss=0.03097, over 1391357.27 frames.], batch size: 21, lr: 2.80e-04
2022-05-15 12:47:47,189 INFO [train.py:812] (6/8) Epoch 28, batch 800, loss[loss=0.1673, simple_loss=0.2624, pruned_loss=0.03603, over 7014.00 frames.], tot_loss[loss=0.154, simple_loss=0.2451, pruned_loss=0.03139, over 1398863.67 frames.], batch size: 28, lr: 2.80e-04
2022-05-15 12:48:46,808 INFO [train.py:812] (6/8) Epoch 28, batch 850, loss[loss=0.1659, simple_loss=0.2595, pruned_loss=0.03614, over 7255.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2455, pruned_loss=0.03169, over 1406392.67 frames.], batch size: 25, lr: 2.80e-04
2022-05-15 12:49:45,710 INFO [train.py:812] (6/8) Epoch 28, batch 900, loss[loss=0.1269, simple_loss=0.2146, pruned_loss=0.01965, over 6995.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2463, pruned_loss=0.0319, over 1408196.07 frames.], batch size: 16, lr: 2.80e-04
2022-05-15 12:50:45,009 INFO [train.py:812] (6/8) Epoch 28, batch 950, loss[loss=0.1537, simple_loss=0.2354, pruned_loss=0.036, over 7173.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2464, pruned_loss=0.03196, over 1411089.06 frames.], batch size: 18, lr: 2.80e-04
2022-05-15 12:51:43,951 INFO [train.py:812] (6/8) Epoch 28, batch 1000, loss[loss=0.1505, simple_loss=0.2474, pruned_loss=0.02686, over 7423.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2462, pruned_loss=0.03238, over 1416946.55 frames.], batch size: 20, lr: 2.79e-04
2022-05-15 12:52:42,481 INFO [train.py:812] (6/8) Epoch 28, batch 1050, loss[loss=0.1637, simple_loss=0.2537, pruned_loss=0.03686, over 7420.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2465, pruned_loss=0.03246, over 1416482.68 frames.], batch size: 21, lr: 2.79e-04
2022-05-15 12:53:50,442 INFO [train.py:812] (6/8) Epoch 28, batch 1100, loss[loss=0.1334, simple_loss=0.2125, pruned_loss=0.02713, over 7450.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2469, pruned_loss=0.03224, over 1415896.14 frames.], batch size: 19, lr: 2.79e-04
2022-05-15 12:54:49,782 INFO [train.py:812] (6/8) Epoch 28, batch 1150, loss[loss=0.1561, simple_loss=0.256, pruned_loss=0.0281, over 7187.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2464, pruned_loss=0.03199, over 1420502.15 frames.], batch size: 23, lr: 2.79e-04
2022-05-15 12:55:48,183 INFO [train.py:812] (6/8) Epoch 28, batch 1200, loss[loss=0.1413, simple_loss=0.2345, pruned_loss=0.02405, over 7135.00 frames.], tot_loss[loss=0.1547, simple_loss=0.246, pruned_loss=0.03168, over 1425317.03 frames.], batch size: 17, lr: 2.79e-04
2022-05-15 12:56:47,578 INFO [train.py:812] (6/8) Epoch 28, batch 1250, loss[loss=0.1367, simple_loss=0.2244, pruned_loss=0.02452, over 7130.00 frames.], tot_loss[loss=0.1545, simple_loss=0.246, pruned_loss=0.03151, over 1423415.53 frames.], batch size: 17, lr: 2.79e-04
2022-05-15 12:57:56,223 INFO [train.py:812] (6/8) Epoch 28, batch 1300, loss[loss=0.1362, simple_loss=0.2209, pruned_loss=0.02574, over 7275.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2458, pruned_loss=0.03133, over 1419478.21 frames.], batch size: 18, lr: 2.79e-04
2022-05-15 12:58:55,629 INFO [train.py:812] (6/8) Epoch 28, batch 1350, loss[loss=0.1566, simple_loss=0.2431, pruned_loss=0.03512, over 7348.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2461, pruned_loss=0.03142, over 1420065.23 frames.], batch size: 19, lr: 2.79e-04
2022-05-15 13:00:02,714 INFO [train.py:812] (6/8) Epoch 28, batch 1400, loss[loss=0.1485, simple_loss=0.2342, pruned_loss=0.03146, over 7064.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2462, pruned_loss=0.0315, over 1420405.29 frames.], batch size: 18, lr: 2.79e-04
2022-05-15 13:01:30,488 INFO [train.py:812] (6/8) Epoch 28, batch 1450, loss[loss=0.1506, simple_loss=0.2388, pruned_loss=0.03124, over 7329.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2459, pruned_loss=0.03157, over 1422442.28 frames.], batch size: 20, lr: 2.79e-04
2022-05-15 13:02:27,766 INFO [train.py:812] (6/8) Epoch 28, batch 1500, loss[loss=0.1888, simple_loss=0.2838, pruned_loss=0.04687, over 7115.00 frames.], tot_loss[loss=0.156, simple_loss=0.2477, pruned_loss=0.03213, over 1424743.86 frames.], batch size: 21, lr: 2.79e-04
2022-05-15 13:03:25,164 INFO [train.py:812] (6/8) Epoch 28, batch 1550, loss[loss=0.1299, simple_loss=0.2198, pruned_loss=0.02002, over 7229.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2468, pruned_loss=0.03196, over 1421897.03 frames.], batch size: 16, lr: 2.79e-04
2022-05-15 13:04:33,688 INFO [train.py:812] (6/8) Epoch 28, batch 1600, loss[loss=0.1545, simple_loss=0.2535, pruned_loss=0.02776, over 7427.00 frames.], tot_loss[loss=0.1546, simple_loss=0.246, pruned_loss=0.03155, over 1425871.22 frames.], batch size: 21, lr: 2.79e-04
2022-05-15 13:05:32,123 INFO [train.py:812] (6/8) Epoch 28, batch 1650, loss[loss=0.1578, simple_loss=0.2432, pruned_loss=0.0362, over 7071.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2457, pruned_loss=0.03142, over 1426769.80 frames.], batch size: 18, lr: 2.79e-04
2022-05-15 13:06:30,582 INFO [train.py:812] (6/8) Epoch 28, batch 1700, loss[loss=0.1485, simple_loss=0.2363, pruned_loss=0.03037, over 7351.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2465, pruned_loss=0.03147, over 1427673.58 frames.], batch size: 19, lr: 2.79e-04
2022-05-15 13:07:29,485 INFO [train.py:812] (6/8) Epoch 28, batch 1750, loss[loss=0.1524, simple_loss=0.2478, pruned_loss=0.02847, over 6748.00 frames.], tot_loss[loss=0.1543, simple_loss=0.246, pruned_loss=0.03125, over 1429313.95 frames.], batch size: 31, lr: 2.79e-04
2022-05-15 13:08:28,871 INFO [train.py:812] (6/8) Epoch 28, batch 1800, loss[loss=0.1501, simple_loss=0.2441, pruned_loss=0.02808, over 7229.00 frames.], tot_loss[loss=0.1544, simple_loss=0.246, pruned_loss=0.03142, over 1428819.46 frames.], batch size: 20, lr: 2.79e-04
2022-05-15 13:09:27,172 INFO [train.py:812] (6/8) Epoch 28, batch 1850, loss[loss=0.1232, simple_loss=0.2192, pruned_loss=0.01357, over 7163.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2463, pruned_loss=0.03152, over 1431162.94 frames.], batch size: 19, lr: 2.79e-04
2022-05-15 13:10:26,319 INFO [train.py:812] (6/8) Epoch 28, batch 1900, loss[loss=0.1488, simple_loss=0.2313, pruned_loss=0.03312, over 7257.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2467, pruned_loss=0.0319, over 1431147.63 frames.], batch size: 17, lr: 2.78e-04
2022-05-15 13:11:24,514 INFO [train.py:812] (6/8) Epoch 28, batch 1950, loss[loss=0.1527, simple_loss=0.253, pruned_loss=0.02621, over 6460.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2471, pruned_loss=0.03231, over 1426439.66 frames.], batch size: 38, lr: 2.78e-04
2022-05-15 13:12:23,346 INFO [train.py:812] (6/8) Epoch 28, batch 2000, loss[loss=0.1535, simple_loss=0.2516, pruned_loss=0.02773, over 7215.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2468, pruned_loss=0.03219, over 1425223.27 frames.], batch size: 21, lr: 2.78e-04
2022-05-15 13:13:21,540 INFO [train.py:812] (6/8) Epoch 28, batch 2050, loss[loss=0.1826, simple_loss=0.2658, pruned_loss=0.04965, over 7191.00 frames.], tot_loss[loss=0.157, simple_loss=0.248, pruned_loss=0.03294, over 1423993.05 frames.], batch size: 23, lr: 2.78e-04
2022-05-15 13:14:21,008 INFO [train.py:812] (6/8) Epoch 28, batch 2100, loss[loss=0.1807, simple_loss=0.2765, pruned_loss=0.04242, over 7319.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2475, pruned_loss=0.03262, over 1423622.41 frames.], batch size: 25, lr: 2.78e-04
2022-05-15 13:15:20,659 INFO [train.py:812] (6/8) Epoch 28, batch 2150, loss[loss=0.145, simple_loss=0.2251, pruned_loss=0.03244, over 7149.00 frames.], tot_loss[loss=0.1557, simple_loss=0.247, pruned_loss=0.03217, over 1422012.86 frames.], batch size: 17, lr: 2.78e-04
2022-05-15 13:16:19,078 INFO [train.py:812] (6/8) Epoch 28, batch 2200, loss[loss=0.17, simple_loss=0.2632, pruned_loss=0.03844, over 7263.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2468, pruned_loss=0.03221, over 1420436.12 frames.], batch size: 24, lr: 2.78e-04
2022-05-15 13:17:18,183 INFO [train.py:812] (6/8) Epoch 28, batch 2250, loss[loss=0.1659, simple_loss=0.2616, pruned_loss=0.03511, over 7337.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2465, pruned_loss=0.03225, over 1423870.48 frames.], batch size: 22, lr: 2.78e-04
2022-05-15 13:18:16,764 INFO [train.py:812] (6/8) Epoch 28, batch 2300, loss[loss=0.1621, simple_loss=0.2497, pruned_loss=0.03728, over 7153.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2468, pruned_loss=0.03243, over 1421843.55 frames.], batch size: 20, lr: 2.78e-04
2022-05-15 13:19:16,302 INFO [train.py:812] (6/8) Epoch 28, batch 2350, loss[loss=0.1307, simple_loss=0.2255, pruned_loss=0.01792, over 7159.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2463, pruned_loss=0.03222, over 1419298.78 frames.], batch size: 19, lr: 2.78e-04
2022-05-15 13:20:14,248 INFO [train.py:812] (6/8) Epoch 28, batch 2400, loss[loss=0.1768, simple_loss=0.2607, pruned_loss=0.04644, over 7187.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2472, pruned_loss=0.03259, over 1422473.76 frames.], batch size: 23, lr: 2.78e-04
2022-05-15 13:21:14,095 INFO [train.py:812] (6/8) Epoch 28, batch 2450, loss[loss=0.1473, simple_loss=0.2422, pruned_loss=0.0262, over 6586.00 frames.], tot_loss[loss=0.155, simple_loss=0.2461, pruned_loss=0.03198, over 1423849.33 frames.], batch size: 38, lr: 2.78e-04
2022-05-15 13:22:13,026 INFO [train.py:812] (6/8) Epoch 28, batch 2500, loss[loss=0.1316, simple_loss=0.2122, pruned_loss=0.02551, over 6786.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2454, pruned_loss=0.03186, over 1419698.82 frames.], batch size: 15, lr: 2.78e-04
2022-05-15 13:23:12,476 INFO [train.py:812] (6/8) Epoch 28, batch 2550, loss[loss=0.1427, simple_loss=0.2354, pruned_loss=0.025, over 7255.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2458, pruned_loss=0.03191, over 1420428.06 frames.], batch size: 19, lr: 2.78e-04
2022-05-15 13:24:10,669 INFO [train.py:812] (6/8) Epoch 28, batch 2600, loss[loss=0.164, simple_loss=0.255, pruned_loss=0.03654, over 7228.00 frames.], tot_loss[loss=0.1542, simple_loss=0.245, pruned_loss=0.03175, over 1420540.05 frames.], batch size: 20, lr: 2.78e-04
2022-05-15 13:25:09,895 INFO [train.py:812] (6/8) Epoch 28, batch 2650, loss[loss=0.1584, simple_loss=0.2443, pruned_loss=0.03622, over 7004.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2452, pruned_loss=0.03184, over 1419684.77 frames.], batch size: 16, lr: 2.78e-04
2022-05-15 13:26:08,949 INFO [train.py:812] (6/8) Epoch 28, batch 2700, loss[loss=0.1546, simple_loss=0.2454, pruned_loss=0.03185, over 7322.00 frames.], tot_loss[loss=0.154, simple_loss=0.245, pruned_loss=0.03153, over 1421461.47 frames.], batch size: 21, lr: 2.78e-04
2022-05-15 13:27:07,545 INFO [train.py:812] (6/8) Epoch 28, batch 2750, loss[loss=0.1468, simple_loss=0.2459, pruned_loss=0.02383, over 7261.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2447, pruned_loss=0.03147, over 1420916.96 frames.], batch size: 19, lr: 2.78e-04
2022-05-15 13:28:05,912 INFO [train.py:812] (6/8) Epoch 28, batch 2800, loss[loss=0.1463, simple_loss=0.2454, pruned_loss=0.0236, over 7242.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2451, pruned_loss=0.03171, over 1417002.76 frames.], batch size: 20, lr: 2.77e-04
2022-05-15 13:29:05,162 INFO [train.py:812] (6/8) Epoch 28, batch 2850, loss[loss=0.1298, simple_loss=0.2194, pruned_loss=0.02011, over 7123.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2441, pruned_loss=0.03122, over 1421395.12 frames.], batch size: 17, lr: 2.77e-04
2022-05-15 13:30:03,013 INFO [train.py:812] (6/8) Epoch 28, batch 2900, loss[loss=0.1515, simple_loss=0.2443, pruned_loss=0.02932, over 7274.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2455, pruned_loss=0.03163, over 1420607.64 frames.], batch size: 25, lr: 2.77e-04
2022-05-15 13:31:01,413 INFO [train.py:812] (6/8) Epoch 28, batch 2950, loss[loss=0.156, simple_loss=0.2542, pruned_loss=0.02889, over 7211.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2456, pruned_loss=0.03141, over 1423409.83 frames.], batch size: 23, lr: 2.77e-04
2022-05-15 13:32:00,686 INFO [train.py:812] (6/8) Epoch 28, batch 3000, loss[loss=0.1461, simple_loss=0.2398, pruned_loss=0.02624, over 7029.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2465, pruned_loss=0.03162, over 1425083.87 frames.], batch size: 28, lr: 2.77e-04
2022-05-15 13:32:00,687 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 13:32:08,091 INFO [train.py:841] (6/8) Epoch 28, validation: loss=0.1523, simple_loss=0.2496, pruned_loss=0.02748, over 698248.00 frames.
2022-05-15 13:33:05,914 INFO [train.py:812] (6/8) Epoch 28, batch 3050, loss[loss=0.1223, simple_loss=0.1954, pruned_loss=0.02456, over 7129.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2463, pruned_loss=0.0317, over 1426899.33 frames.], batch size: 17, lr: 2.77e-04
2022-05-15 13:34:04,044 INFO [train.py:812] (6/8) Epoch 28, batch 3100, loss[loss=0.1605, simple_loss=0.253, pruned_loss=0.03402, over 7369.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2456, pruned_loss=0.03178, over 1425408.31 frames.], batch size: 23, lr: 2.77e-04
2022-05-15 13:35:03,616 INFO [train.py:812] (6/8) Epoch 28, batch 3150, loss[loss=0.1347, simple_loss=0.2264, pruned_loss=0.02153, over 7415.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2452, pruned_loss=0.03154, over 1424240.22 frames.], batch size: 18, lr: 2.77e-04
2022-05-15 13:36:02,697 INFO [train.py:812] (6/8) Epoch 28, batch 3200, loss[loss=0.1667, simple_loss=0.2611, pruned_loss=0.03618, over 7319.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2459, pruned_loss=0.03164, over 1425056.58 frames.], batch size: 21, lr: 2.77e-04
2022-05-15 13:37:02,651 INFO [train.py:812] (6/8) Epoch 28, batch 3250, loss[loss=0.1456, simple_loss=0.2303, pruned_loss=0.03046, over 7157.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2453, pruned_loss=0.03146, over 1424618.15 frames.], batch size: 18, lr: 2.77e-04
2022-05-15 13:37:59,654 INFO [train.py:812] (6/8) Epoch 28, batch 3300, loss[loss=0.1359, simple_loss=0.2235, pruned_loss=0.02419, over 6987.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2456, pruned_loss=0.03139, over 1423314.65 frames.], batch size: 16, lr: 2.77e-04
2022-05-15 13:38:57,919 INFO [train.py:812] (6/8) Epoch 28, batch 3350, loss[loss=0.184, simple_loss=0.2801, pruned_loss=0.04397, over 7383.00 frames.], tot_loss[loss=0.155, simple_loss=0.2464, pruned_loss=0.03177, over 1420914.45 frames.], batch size: 23, lr: 2.77e-04
2022-05-15 13:39:56,940 INFO [train.py:812] (6/8) Epoch 28, batch 3400, loss[loss=0.1507, simple_loss=0.2494, pruned_loss=0.02599, over 7324.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2464, pruned_loss=0.03196, over 1422873.86 frames.], batch size: 20, lr: 2.77e-04
2022-05-15 13:40:56,438 INFO [train.py:812] (6/8) Epoch 28, batch 3450, loss[loss=0.1506, simple_loss=0.2482, pruned_loss=0.02654, over 7212.00 frames.], tot_loss[loss=0.1546, simple_loss=0.246, pruned_loss=0.0316, over 1424124.49 frames.], batch size: 22, lr: 2.77e-04
2022-05-15 13:41:55,475 INFO [train.py:812] (6/8) Epoch 28, batch 3500, loss[loss=0.1622, simple_loss=0.2513, pruned_loss=0.03652, over 7069.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2467, pruned_loss=0.03183, over 1423175.90 frames.], batch size: 18, lr: 2.77e-04
2022-05-15 13:42:54,610 INFO [train.py:812] (6/8) Epoch 28, batch 3550, loss[loss=0.1344, simple_loss=0.2291, pruned_loss=0.01984, over 7343.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2469, pruned_loss=0.03163, over 1423856.10 frames.], batch size: 22, lr: 2.77e-04
2022-05-15 13:43:53,665 INFO [train.py:812] (6/8) Epoch 28, batch 3600, loss[loss=0.1386, simple_loss=0.2319, pruned_loss=0.02265, over 7069.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2468, pruned_loss=0.03137, over 1423115.59 frames.], batch size: 18, lr: 2.77e-04
2022-05-15 13:44:53,080 INFO [train.py:812] (6/8) Epoch 28, batch 3650, loss[loss=0.209, simple_loss=0.2835, pruned_loss=0.06725, over 7409.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2466, pruned_loss=0.03143, over 1423208.89 frames.], batch size: 21, lr: 2.77e-04
2022-05-15 13:45:51,492 INFO [train.py:812] (6/8) Epoch 28, batch 3700, loss[loss=0.1414, simple_loss=0.2359, pruned_loss=0.02346, over 7435.00 frames.], tot_loss[loss=0.1541, simple_loss=0.246, pruned_loss=0.03109, over 1423582.17 frames.], batch size: 20, lr: 2.77e-04
2022-05-15 13:46:50,231 INFO [train.py:812] (6/8) Epoch 28, batch 3750, loss[loss=0.1791, simple_loss=0.2708, pruned_loss=0.04366, over 5261.00 frames.], tot_loss[loss=0.154, simple_loss=0.2459, pruned_loss=0.03106, over 1419218.27 frames.], batch size: 52, lr: 2.76e-04
2022-05-15 13:47:49,325 INFO [train.py:812] (6/8) Epoch 28, batch 3800, loss[loss=0.1423, simple_loss=0.2214, pruned_loss=0.03158, over 7274.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2462, pruned_loss=0.03085, over 1421042.87 frames.], batch size: 17, lr: 2.76e-04
2022-05-15 13:48:48,432 INFO [train.py:812] (6/8) Epoch 28, batch 3850, loss[loss=0.1787, simple_loss=0.2783, pruned_loss=0.03952, over 7152.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2467, pruned_loss=0.03106, over 1424754.53 frames.], batch size: 19, lr: 2.76e-04
2022-05-15 13:49:47,467 INFO [train.py:812] (6/8) Epoch 28, batch 3900, loss[loss=0.1562, simple_loss=0.2502, pruned_loss=0.03114, over 7208.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2465, pruned_loss=0.03114, over 1423713.59 frames.], batch size: 22, lr: 2.76e-04
2022-05-15 13:50:47,238 INFO [train.py:812] (6/8) Epoch 28, batch 3950, loss[loss=0.1651, simple_loss=0.2543, pruned_loss=0.03798, over 7204.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2458, pruned_loss=0.03116, over 1424951.98 frames.], batch size: 22, lr: 2.76e-04
2022-05-15 13:51:46,173 INFO [train.py:812] (6/8) Epoch 28, batch 4000, loss[loss=0.1607, simple_loss=0.2498, pruned_loss=0.03575, over 6751.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2449, pruned_loss=0.03116, over 1422397.21 frames.], batch size: 31, lr: 2.76e-04
2022-05-15 13:52:45,732 INFO [train.py:812] (6/8) Epoch 28, batch 4050, loss[loss=0.1569, simple_loss=0.2493, pruned_loss=0.03231, over 4954.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2451, pruned_loss=0.03159, over 1416120.55 frames.], batch size: 52, lr: 2.76e-04
2022-05-15 13:53:44,809 INFO [train.py:812] (6/8) Epoch 28, batch 4100, loss[loss=0.1418, simple_loss=0.2287, pruned_loss=0.02744, over 7150.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2456, pruned_loss=0.03191, over 1417788.19 frames.], batch size: 17, lr: 2.76e-04
2022-05-15 13:54:49,283 INFO [train.py:812] (6/8) Epoch 28, batch 4150, loss[loss=0.1279, simple_loss=0.219, pruned_loss=0.01842, over 7151.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2463, pruned_loss=0.03197, over 1423365.07 frames.], batch size: 19, lr: 2.76e-04
2022-05-15 13:55:47,974 INFO [train.py:812] (6/8) Epoch 28, batch 4200, loss[loss=0.1836, simple_loss=0.2823, pruned_loss=0.04249, over 4950.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2477, pruned_loss=0.03262, over 1417154.36 frames.], batch size: 53, lr: 2.76e-04
2022-05-15 13:56:46,306 INFO [train.py:812] (6/8) Epoch 28, batch 4250, loss[loss=0.1244, simple_loss=0.217, pruned_loss=0.01596, over 7069.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2472, pruned_loss=0.03201, over 1414343.58 frames.], batch size: 18, lr: 2.76e-04
2022-05-15 13:57:45,191 INFO [train.py:812] (6/8) Epoch 28, batch 4300, loss[loss=0.1346, simple_loss=0.2267, pruned_loss=0.02123, over 7143.00 frames.], tot_loss[loss=0.155, simple_loss=0.2465, pruned_loss=0.03179, over 1415801.74 frames.], batch size: 17, lr: 2.76e-04
2022-05-15 13:58:44,145 INFO [train.py:812] (6/8) Epoch 28, batch 4350, loss[loss=0.1593, simple_loss=0.2533, pruned_loss=0.03266, over 7218.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2473, pruned_loss=0.03214, over 1416932.37 frames.], batch size: 21, lr: 2.76e-04
2022-05-15 13:59:42,364 INFO [train.py:812] (6/8) Epoch 28, batch 4400, loss[loss=0.1546, simple_loss=0.251, pruned_loss=0.02911, over 6445.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2475, pruned_loss=0.03218, over 1408953.15 frames.], batch size: 38, lr: 2.76e-04
2022-05-15 14:00:51,461 INFO [train.py:812] (6/8) Epoch 28, batch 4450, loss[loss=0.1534, simple_loss=0.2352, pruned_loss=0.03582, over 6815.00 frames.], tot_loss[loss=0.1564, simple_loss=0.248, pruned_loss=0.03246, over 1403266.44 frames.], batch size: 15, lr: 2.76e-04
2022-05-15 14:01:50,473 INFO [train.py:812] (6/8) Epoch 28, batch 4500, loss[loss=0.1477, simple_loss=0.2496, pruned_loss=0.02286, over 7209.00 frames.], tot_loss[loss=0.1566, simple_loss=0.248, pruned_loss=0.03254, over 1391483.89 frames.], batch size: 21, lr: 2.76e-04
2022-05-15 14:02:49,659 INFO [train.py:812] (6/8) Epoch 28, batch 4550, loss[loss=0.1525, simple_loss=0.2517, pruned_loss=0.02665, over 6310.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2486, pruned_loss=0.03312, over 1360533.83 frames.], batch size: 37, lr: 2.76e-04
2022-05-15 14:04:01,553 INFO [train.py:812] (6/8) Epoch 29, batch 0, loss[loss=0.1398, simple_loss=0.228, pruned_loss=0.02578, over 7135.00 frames.], tot_loss[loss=0.1398, simple_loss=0.228, pruned_loss=0.02578, over 7135.00 frames.], batch size: 28, lr: 2.71e-04
2022-05-15 14:05:00,866 INFO [train.py:812] (6/8) Epoch 29, batch 50, loss[loss=0.1615, simple_loss=0.2483, pruned_loss=0.03738, over 7302.00 frames.], tot_loss[loss=0.1559, simple_loss=0.247, pruned_loss=0.03237, over 323734.98 frames.], batch size: 24, lr: 2.71e-04
2022-05-15 14:05:59,928 INFO [train.py:812] (6/8) Epoch 29, batch 100, loss[loss=0.1947, simple_loss=0.281, pruned_loss=0.05424, over 7316.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2472, pruned_loss=0.03264, over 570251.87 frames.], batch size: 21, lr: 2.71e-04
2022-05-15 14:06:58,570 INFO [train.py:812] (6/8) Epoch 29, batch 150, loss[loss=0.1648, simple_loss=0.2662, pruned_loss=0.03171, over 7236.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2465, pruned_loss=0.03185, over 759997.51 frames.], batch size: 20, lr: 2.71e-04
2022-05-15 14:07:56,843 INFO [train.py:812] (6/8) Epoch 29, batch 200, loss[loss=0.1448, simple_loss=0.2332, pruned_loss=0.02824, over 7056.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2457, pruned_loss=0.03106, over 908919.46 frames.], batch size: 18, lr: 2.71e-04
2022-05-15 14:08:56,097 INFO [train.py:812] (6/8) Epoch 29, batch 250, loss[loss=0.1621, simple_loss=0.2581, pruned_loss=0.03303, over 5052.00 frames.], tot_loss[loss=0.154, simple_loss=0.2453, pruned_loss=0.03128, over 1019721.94 frames.], batch size: 53, lr: 2.71e-04
2022-05-15 14:09:54,914 INFO [train.py:812] (6/8) Epoch 29, batch 300, loss[loss=0.1526, simple_loss=0.2329, pruned_loss=0.03608, over 7170.00 frames.], tot_loss[loss=0.1535, simple_loss=0.245, pruned_loss=0.03101, over 1109774.69 frames.], batch size: 18, lr: 2.70e-04
2022-05-15 14:10:53,116 INFO [train.py:812] (6/8) Epoch 29, batch 350, loss[loss=0.1317, simple_loss=0.2223, pruned_loss=0.0205, over 7070.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2456, pruned_loss=0.03081, over 1181743.62 frames.], batch size: 18, lr: 2.70e-04
2022-05-15 14:11:51,417 INFO [train.py:812] (6/8) Epoch 29, batch 400, loss[loss=0.157, simple_loss=0.2545, pruned_loss=0.02976, over 7150.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2467, pruned_loss=0.03095, over 1237086.18 frames.], batch size: 20, lr: 2.70e-04
2022-05-15 14:12:49,839 INFO [train.py:812] (6/8) Epoch 29, batch 450, loss[loss=0.1429, simple_loss=0.2371, pruned_loss=0.02437, over 7118.00 frames.], tot_loss[loss=0.1539, simple_loss=0.246, pruned_loss=0.03087, over 1282988.82 frames.], batch size: 21, lr: 2.70e-04
2022-05-15 14:13:47,252 INFO [train.py:812] (6/8) Epoch 29, batch 500, loss[loss=0.1741, simple_loss=0.266, pruned_loss=0.0411, over 5073.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2459, pruned_loss=0.03131, over 1310603.78 frames.], batch size: 54, lr: 2.70e-04
2022-05-15 14:14:46,088 INFO [train.py:812] (6/8) Epoch 29, batch 550, loss[loss=0.1418, simple_loss=0.242, pruned_loss=0.02079, over 7220.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2458, pruned_loss=0.03138, over 1332742.50 frames.], batch size: 21, lr: 2.70e-04
2022-05-15 14:15:44,283 INFO [train.py:812] (6/8) Epoch 29, batch 600, loss[loss=0.1609, simple_loss=0.255, pruned_loss=0.0334, over 7250.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2447, pruned_loss=0.03099, over 1349503.49 frames.], batch size: 19, lr: 2.70e-04
2022-05-15 14:16:43,608 INFO [train.py:812] (6/8) Epoch 29, batch 650, loss[loss=0.1606, simple_loss=0.2457, pruned_loss=0.03779, over 7059.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2447, pruned_loss=0.03127, over 1368090.06 frames.], batch size: 18, lr: 2.70e-04
2022-05-15 14:17:43,348 INFO [train.py:812] (6/8) Epoch 29, batch 700, loss[loss=0.1971, simple_loss=0.2646, pruned_loss=0.06482, over 4996.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2457, pruned_loss=0.03173, over 1376182.79 frames.], batch size: 52, lr: 2.70e-04
2022-05-15 14:18:41,560 INFO [train.py:812] (6/8) Epoch 29, batch 750, loss[loss=0.1437, simple_loss=0.2372, pruned_loss=0.0251, over 7427.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2451, pruned_loss=0.03157, over 1383638.17 frames.], batch size: 20, lr: 2.70e-04
2022-05-15 14:19:40,281 INFO [train.py:812] (6/8) Epoch 29, batch 800, loss[loss=0.1635, simple_loss=0.2571, pruned_loss=0.03493, over 7115.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2455, pruned_loss=0.03144, over 1389003.93 frames.], batch size: 21, lr: 2.70e-04
2022-05-15 14:20:39,298 INFO [train.py:812] (6/8) Epoch 29, batch 850, loss[loss=0.1593, simple_loss=0.2567, pruned_loss=0.03098, over 6302.00 frames.], tot_loss[loss=0.155, simple_loss=0.2464, pruned_loss=0.03176, over 1393004.42 frames.], batch size: 37, lr: 2.70e-04
2022-05-15 14:21:38,045 INFO [train.py:812] (6/8) Epoch 29, batch 900, loss[loss=0.175, simple_loss=0.2634, pruned_loss=0.04334, over 6800.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2455, pruned_loss=0.03156, over 1399912.88 frames.], batch size: 31, lr: 2.70e-04
2022-05-15 14:22:37,049 INFO [train.py:812] (6/8) Epoch 29, batch 950, loss[loss=0.1695, simple_loss=0.2669, pruned_loss=0.03604, over 7217.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2457, pruned_loss=0.0318, over 1409348.07 frames.], batch size: 22, lr: 2.70e-04
2022-05-15 14:23:36,651 INFO [train.py:812] (6/8) Epoch 29, batch 1000, loss[loss=0.1383, simple_loss=0.2255, pruned_loss=0.0255, over 6801.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2447, pruned_loss=0.03118, over 1415197.63 frames.], batch size: 15, lr: 2.70e-04
2022-05-15 14:24:36,131 INFO [train.py:812] (6/8) Epoch 29, batch 1050, loss[loss=0.1465, simple_loss=0.247, pruned_loss=0.02298, over 7408.00 frames.], tot_loss[loss=0.153, simple_loss=0.2445, pruned_loss=0.03075, over 1420089.69 frames.], batch size: 21, lr: 2.70e-04
2022-05-15 14:25:35,358 INFO [train.py:812] (6/8) Epoch 29, batch 1100, loss[loss=0.1537, simple_loss=0.2394, pruned_loss=0.03397, over 7279.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2448, pruned_loss=0.03103, over 1422622.83 frames.], batch size: 17, lr: 2.70e-04
2022-05-15 14:26:34,885 INFO [train.py:812] (6/8) Epoch 29, batch 1150, loss[loss=0.1965, simple_loss=0.2815, pruned_loss=0.05574, over 7053.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2444, pruned_loss=0.0309, over 1420972.84 frames.], batch size: 28, lr: 2.70e-04
2022-05-15 14:27:33,682 INFO [train.py:812] (6/8) Epoch 29, batch 1200, loss[loss=0.1541, simple_loss=0.2478, pruned_loss=0.03016, over 7095.00 frames.], tot_loss[loss=0.1541, simple_loss=0.246, pruned_loss=0.03112, over 1423549.85 frames.], batch size: 28, lr: 2.70e-04
2022-05-15 14:28:32,483 INFO [train.py:812] (6/8) Epoch 29, batch 1250, loss[loss=0.1792, simple_loss=0.2712, pruned_loss=0.04361, over 7223.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2459, pruned_loss=0.03128, over 1416769.96 frames.], batch size: 22, lr: 2.70e-04
2022-05-15 14:29:29,511 INFO [train.py:812] (6/8) Epoch 29, batch 1300, loss[loss=0.1492, simple_loss=0.246, pruned_loss=0.0262, over 7141.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2461, pruned_loss=0.03149, over 1419580.91 frames.], batch size: 20, lr: 2.69e-04
2022-05-15 14:30:28,430 INFO [train.py:812] (6/8) Epoch 29, batch 1350, loss[loss=0.1482, simple_loss=0.2457, pruned_loss=0.02538, over 7102.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2457, pruned_loss=0.031, over 1425335.97 frames.], batch size: 21, lr: 2.69e-04
2022-05-15 14:31:27,375 INFO [train.py:812] (6/8) Epoch 29, batch 1400, loss[loss=0.1581, simple_loss=0.233, pruned_loss=0.04157, over 7275.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2461, pruned_loss=0.0313, over 1426735.23 frames.], batch size: 17, lr: 2.69e-04
2022-05-15 14:32:26,409 INFO [train.py:812] (6/8) Epoch 29, batch 1450, loss[loss=0.1735, simple_loss=0.2674, pruned_loss=0.03979, over 7290.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2456, pruned_loss=0.03128, over 1430643.53 frames.], batch size: 24, lr: 2.69e-04
2022-05-15 14:33:24,407 INFO [train.py:812] (6/8) Epoch 29, batch 1500, loss[loss=0.1594, simple_loss=0.2505, pruned_loss=0.03413, over 7331.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2458, pruned_loss=0.03133, over 1428201.27 frames.], batch size: 20, lr: 2.69e-04
2022-05-15 14:34:23,851 INFO [train.py:812] (6/8) Epoch 29, batch 1550, loss[loss=0.154, simple_loss=0.2501, pruned_loss=0.02892, over 7231.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2458, pruned_loss=0.0313, over 1430651.64 frames.], batch size: 21, lr: 2.69e-04
2022-05-15 14:35:22,711 INFO [train.py:812] (6/8) Epoch 29, batch 1600, loss[loss=0.1397, simple_loss=0.2302, pruned_loss=0.0246, over 6811.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2459, pruned_loss=0.03126, over 1427386.96 frames.], batch size: 15, lr: 2.69e-04
2022-05-15 14:36:22,780 INFO [train.py:812] (6/8) Epoch 29, batch 1650, loss[loss=0.1316, simple_loss=0.2187, pruned_loss=0.02227, over 6784.00 frames.], tot_loss[loss=0.1533, simple_loss=0.245, pruned_loss=0.03082, over 1428457.10 frames.], batch size: 15, lr: 2.69e-04
2022-05-15 14:37:22,119 INFO [train.py:812] (6/8) Epoch 29, batch 1700, loss[loss=0.1452, simple_loss=0.2357, pruned_loss=0.02733, over 7261.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2449, pruned_loss=0.03078, over 1431057.59 frames.], batch size: 19, lr: 2.69e-04
2022-05-15 14:38:21,736 INFO [train.py:812] (6/8) Epoch 29, batch 1750, loss[loss=0.1407, simple_loss=0.2415, pruned_loss=0.01996, over 7123.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2449, pruned_loss=0.03066, over 1433035.34 frames.], batch size: 21, lr: 2.69e-04
2022-05-15 14:39:20,848 INFO [train.py:812] (6/8) Epoch 29, batch 1800, loss[loss=0.1407, simple_loss=0.2176, pruned_loss=0.03187, over 7005.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2447, pruned_loss=0.03102, over 1423871.89 frames.], batch size: 16, lr: 2.69e-04
2022-05-15 14:40:20,282 INFO [train.py:812] (6/8) Epoch 29, batch 1850, loss[loss=0.1436, simple_loss=0.2292, pruned_loss=0.02907, over 7406.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2462, pruned_loss=0.03153, over 1426407.11 frames.], batch size: 18, lr: 2.69e-04
2022-05-15 14:41:18,744 INFO [train.py:812] (6/8) Epoch 29, batch 1900, loss[loss=0.1556, simple_loss=0.252, pruned_loss=0.02961, over 7114.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2458, pruned_loss=0.03135, over 1426647.92 frames.], batch size: 26, lr: 2.69e-04
2022-05-15 14:42:17,749 INFO [train.py:812] (6/8) Epoch 29, batch 1950, loss[loss=0.1827, simple_loss=0.2752, pruned_loss=0.04511, over 7317.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2467, pruned_loss=0.03176, over 1428728.49 frames.], batch size: 25, lr: 2.69e-04
2022-05-15 14:43:16,660 INFO [train.py:812] (6/8) Epoch 29, batch 2000, loss[loss=0.1677, simple_loss=0.2636, pruned_loss=0.03589, over 7198.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2462, pruned_loss=0.03168, over 1431535.86 frames.], batch size: 23, lr: 2.69e-04
2022-05-15 14:44:14,146 INFO [train.py:812] (6/8) Epoch 29, batch 2050, loss[loss=0.1447, simple_loss=0.245, pruned_loss=0.02219, over 7327.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2462, pruned_loss=0.03163, over 1424694.50 frames.], batch size: 21, lr: 2.69e-04
2022-05-15 14:45:11,938 INFO [train.py:812] (6/8) Epoch 29, batch 2100, loss[loss=0.1663, simple_loss=0.2569, pruned_loss=0.0379, over 7316.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2448, pruned_loss=0.03104, over 1427471.24 frames.], batch size: 25, lr: 2.69e-04
2022-05-15 14:46:11,713 INFO [train.py:812] (6/8) Epoch 29, batch 2150, loss[loss=0.1312, simple_loss=0.2303, pruned_loss=0.0161, over 7220.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2451, pruned_loss=0.03076, over 1428245.66 frames.], batch size: 21, lr: 2.69e-04
2022-05-15 14:47:09,927 INFO [train.py:812] (6/8) Epoch 29, batch 2200, loss[loss=0.1618, simple_loss=0.2537, pruned_loss=0.03498, over 7285.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2451, pruned_loss=0.03076, over 1422673.44 frames.], batch size: 25, lr: 2.69e-04
2022-05-15 14:48:08,327 INFO [train.py:812] (6/8) Epoch 29, batch 2250, loss[loss=0.1398, simple_loss=0.2296, pruned_loss=0.025, over 7113.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2457, pruned_loss=0.03122, over 1426445.84 frames.], batch size: 21, lr: 2.68e-04
2022-05-15 14:49:05,774 INFO [train.py:812] (6/8) Epoch 29, batch 2300, loss[loss=0.1762, simple_loss=0.2684, pruned_loss=0.04195, over 7282.00 frames.], tot_loss[loss=0.153, simple_loss=0.2446, pruned_loss=0.03071, over 1427747.62 frames.], batch size: 24, lr: 2.68e-04
2022-05-15 14:50:03,896 INFO [train.py:812] (6/8) Epoch 29, batch 2350, loss[loss=0.1351, simple_loss=0.2314, pruned_loss=0.01944, over 7073.00 frames.], tot_loss[loss=0.153, simple_loss=0.2448, pruned_loss=0.03057, over 1425081.87 frames.], batch size: 18, lr: 2.68e-04
2022-05-15 14:51:02,218 INFO [train.py:812] (6/8) Epoch 29, batch 2400, loss[loss=0.1596, simple_loss=0.2589, pruned_loss=0.03014, over 7362.00 frames.], tot_loss[loss=0.1524, simple_loss=0.244, pruned_loss=0.03038, over 1426402.79 frames.], batch size: 19, lr: 2.68e-04
2022-05-15 14:51:59,589 INFO [train.py:812] (6/8) Epoch 29, batch 2450, loss[loss=0.1424, simple_loss=0.2414, pruned_loss=0.02169, over 7104.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2451, pruned_loss=0.03089, over 1417222.57 frames.], batch size: 21, lr: 2.68e-04
2022-05-15 14:52:57,684 INFO [train.py:812] (6/8) Epoch 29, batch 2500, loss[loss=0.1307, simple_loss=0.2228, pruned_loss=0.01926, over 7410.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2442, pruned_loss=0.03049, over 1420141.81 frames.], batch size: 18, lr: 2.68e-04
2022-05-15 14:53:56,711 INFO [train.py:812] (6/8) Epoch 29, batch 2550, loss[loss=0.143, simple_loss=0.2245, pruned_loss=0.03079, over 7160.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2439, pruned_loss=0.03066, over 1416869.28 frames.], batch size: 18, lr: 2.68e-04
2022-05-15 14:54:55,324 INFO [train.py:812] (6/8) Epoch 29, batch 2600, loss[loss=0.1719, simple_loss=0.2638, pruned_loss=0.03993, over 7218.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2451, pruned_loss=0.03158, over 1415441.47 frames.], batch size: 23, lr: 2.68e-04
2022-05-15 14:56:04,297 INFO [train.py:812] (6/8) Epoch 29, batch 2650, loss[loss=0.1171, simple_loss=0.2032, pruned_loss=0.01548, over 7415.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2448, pruned_loss=0.03151, over 1418010.43 frames.], batch size: 18, lr: 2.68e-04
2022-05-15 14:57:02,553 INFO [train.py:812] (6/8) Epoch 29, batch 2700, loss[loss=0.1476, simple_loss=0.25, pruned_loss=0.02264, over 5405.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2435, pruned_loss=0.03085, over 1418472.16 frames.], batch size: 52, lr: 2.68e-04
2022-05-15 14:58:00,041 INFO [train.py:812] (6/8) Epoch 29, batch 2750, loss[loss=0.1556, simple_loss=0.2564, pruned_loss=0.02743, over 7311.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2445, pruned_loss=0.03129, over 1414789.96 frames.], batch size: 21, lr: 2.68e-04
2022-05-15 14:59:07,979 INFO [train.py:812] (6/8) Epoch 29, batch 2800, loss[loss=0.1503, simple_loss=0.2478, pruned_loss=0.02641, over 7334.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2447, pruned_loss=0.03117, over 1418607.09 frames.], batch size: 22, lr: 2.68e-04
2022-05-15 15:00:06,420 INFO [train.py:812] (6/8) Epoch 29, batch 2850, loss[loss=0.1379, simple_loss=0.2288, pruned_loss=0.02357, over 7255.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2438, pruned_loss=0.03075, over 1419645.18 frames.], batch size: 19, lr: 2.68e-04
2022-05-15 15:01:14,259 INFO [train.py:812] (6/8) Epoch 29, batch 2900, loss[loss=0.1364, simple_loss=0.2197, pruned_loss=0.02649, over 7260.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2444, pruned_loss=0.03061, over 1418759.64 frames.], batch size: 17, lr: 2.68e-04
2022-05-15 15:02:42,670 INFO [train.py:812] (6/8) Epoch 29, batch 2950, loss[loss=0.1467, simple_loss=0.2384, pruned_loss=0.0275, over 7138.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2438, pruned_loss=0.03065, over 1418602.55 frames.], batch size: 17, lr: 2.68e-04
2022-05-15 15:03:40,343 INFO [train.py:812] (6/8) Epoch 29, batch 3000, loss[loss=0.1492, simple_loss=0.2409, pruned_loss=0.02874, over 7223.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2445, pruned_loss=0.03119, over 1418730.21 frames.], batch size: 20, lr: 2.68e-04
2022-05-15 15:03:40,343 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 15:03:47,851 INFO [train.py:841] (6/8) Epoch 29, validation: loss=0.153, simple_loss=0.2498, pruned_loss=0.02809, over 698248.00 frames.
2022-05-15 15:04:46,873 INFO [train.py:812] (6/8) Epoch 29, batch 3050, loss[loss=0.1266, simple_loss=0.2146, pruned_loss=0.01931, over 7155.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2441, pruned_loss=0.03104, over 1421268.26 frames.], batch size: 18, lr: 2.68e-04
2022-05-15 15:05:54,537 INFO [train.py:812] (6/8) Epoch 29, batch 3100, loss[loss=0.1559, simple_loss=0.2421, pruned_loss=0.03491, over 7285.00 frames.], tot_loss[loss=0.1532, simple_loss=0.244, pruned_loss=0.03119, over 1417827.69 frames.], batch size: 18, lr: 2.68e-04
2022-05-15 15:06:53,596 INFO [train.py:812] (6/8) Epoch 29, batch 3150, loss[loss=0.1938, simple_loss=0.294, pruned_loss=0.04683, over 7207.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2451, pruned_loss=0.03122, over 1421358.61 frames.], batch size: 21, lr: 2.68e-04
2022-05-15 15:07:52,389 INFO [train.py:812] (6/8) Epoch 29, batch 3200, loss[loss=0.1608, simple_loss=0.2629, pruned_loss=0.02928, over 7125.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2462, pruned_loss=0.03112, over 1421650.70 frames.], batch size: 21, lr: 2.68e-04
2022-05-15 15:08:52,069 INFO [train.py:812] (6/8) Epoch 29, batch 3250, loss[loss=0.1457, simple_loss=0.2246, pruned_loss=0.03347, over 7253.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2454, pruned_loss=0.03096, over 1421534.94 frames.], batch size: 16, lr: 2.67e-04
2022-05-15 15:09:50,372 INFO [train.py:812] (6/8) Epoch 29, batch 3300, loss[loss=0.1495, simple_loss=0.2521, pruned_loss=0.02348, over 7212.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2473, pruned_loss=0.03178, over 1420914.85 frames.], batch size: 21, lr: 2.67e-04
2022-05-15 15:10:48,359 INFO [train.py:812] (6/8) Epoch 29, batch 3350, loss[loss=0.1697, simple_loss=0.2549, pruned_loss=0.04225, over 7021.00 frames.], tot_loss[loss=0.1553, simple_loss=0.247, pruned_loss=0.0318, over 1418188.57 frames.], batch size: 28, lr: 2.67e-04
2022-05-15 15:11:47,211 INFO [train.py:812] (6/8) Epoch 29, batch 3400, loss[loss=0.1527, simple_loss=0.2451, pruned_loss=0.03016, over 7060.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2474, pruned_loss=0.03197, over 1417339.21 frames.], batch size: 18, lr: 2.67e-04
2022-05-15 15:12:46,920 INFO [train.py:812] (6/8) Epoch 29, batch 3450, loss[loss=0.1325, simple_loss=0.2154, pruned_loss=0.02483, over 7286.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2463, pruned_loss=0.03164, over 1419689.41 frames.], batch size: 17, lr: 2.67e-04
2022-05-15 15:13:45,923 INFO [train.py:812] (6/8) Epoch 29, batch 3500, loss[loss=0.1647, simple_loss=0.2549, pruned_loss=0.03719, over 6932.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2458, pruned_loss=0.03136, over 1419473.91 frames.], batch size: 32, lr: 2.67e-04
2022-05-15 15:14:51,721 INFO [train.py:812] (6/8) Epoch 29, batch 3550, loss[loss=0.1246, simple_loss=0.2135, pruned_loss=0.01783, over 7288.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2446, pruned_loss=0.03098, over 1422401.78 frames.], batch size: 18, lr: 2.67e-04
2022-05-15 15:15:51,054 INFO [train.py:812] (6/8) Epoch 29, batch 3600, loss[loss=0.136, simple_loss=0.2188, pruned_loss=0.0266, over 7227.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2456, pruned_loss=0.03178, over 1422930.75 frames.], batch size: 16, lr: 2.67e-04
2022-05-15 15:16:50,746 INFO [train.py:812] (6/8) Epoch 29, batch 3650, loss[loss=0.1481, simple_loss=0.2389, pruned_loss=0.02869, over 7337.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2452, pruned_loss=0.03168, over 1425973.14 frames.], batch size: 22, lr: 2.67e-04
2022-05-15 15:17:49,911 INFO [train.py:812] (6/8) Epoch 29, batch 3700, loss[loss=0.1761, simple_loss=0.263, pruned_loss=0.04462, over 7231.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2444, pruned_loss=0.03152, over 1425224.70 frames.], batch size: 23, lr: 2.67e-04
2022-05-15 15:18:49,048 INFO [train.py:812] (6/8) Epoch 29, batch 3750, loss[loss=0.1873, simple_loss=0.2896, pruned_loss=0.04247, over 4995.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2451, pruned_loss=0.03134, over 1425600.18 frames.], batch size: 53, lr: 2.67e-04
2022-05-15 15:19:48,078 INFO [train.py:812] (6/8) Epoch 29, batch 3800, loss[loss=0.144, simple_loss=0.2377, pruned_loss=0.0252, over 7427.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2459, pruned_loss=0.03139, over 1426031.52 frames.], batch size: 20, lr: 2.67e-04
2022-05-15 15:20:46,944 INFO [train.py:812] (6/8) Epoch 29, batch 3850, loss[loss=0.1469, simple_loss=0.233, pruned_loss=0.03043, over 7358.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2453, pruned_loss=0.03122, over 1426511.23 frames.], batch size: 23, lr: 2.67e-04
2022-05-15 15:21:44,971 INFO [train.py:812] (6/8) Epoch 29, batch 3900, loss[loss=0.1632, simple_loss=0.2581, pruned_loss=0.03412, over 7303.00 frames.], tot_loss[loss=0.154, simple_loss=0.2454, pruned_loss=0.03126, over 1429099.41 frames.], batch size: 24, lr: 2.67e-04
2022-05-15 15:22:44,177 INFO [train.py:812] (6/8) Epoch 29, batch 3950, loss[loss=0.1287, simple_loss=0.2178, pruned_loss=0.01981, over 7423.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2463, pruned_loss=0.03112, over 1430065.75 frames.], batch size: 18, lr: 2.67e-04
2022-05-15 15:23:43,023 INFO [train.py:812] (6/8) Epoch 29, batch 4000, loss[loss=0.1533, simple_loss=0.2583, pruned_loss=0.02414, over 7336.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2465, pruned_loss=0.03113, over 1430164.40 frames.], batch size: 22, lr: 2.67e-04
2022-05-15 15:24:42,296 INFO [train.py:812] (6/8) Epoch 29, batch 4050, loss[loss=0.1204, simple_loss=0.2084, pruned_loss=0.0162, over 7282.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2466, pruned_loss=0.03153, over 1429044.10 frames.], batch size: 17, lr: 2.67e-04
2022-05-15 15:25:40,988 INFO [train.py:812] (6/8) Epoch 29, batch 4100, loss[loss=0.16, simple_loss=0.2596, pruned_loss=0.03021, over 7340.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2464, pruned_loss=0.03124, over 1429713.70 frames.], batch size: 22, lr: 2.67e-04
2022-05-15 15:26:40,402 INFO [train.py:812] (6/8) Epoch 29, batch 4150, loss[loss=0.1391, simple_loss=0.2332, pruned_loss=0.02255, over 7335.00 frames.], tot_loss[loss=0.155, simple_loss=0.2467, pruned_loss=0.03169, over 1423779.38 frames.], batch size: 21, lr: 2.67e-04
2022-05-15 15:27:39,257 INFO [train.py:812] (6/8) Epoch 29, batch 4200, loss[loss=0.1431, simple_loss=0.2287, pruned_loss=0.02877, over 7262.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2471, pruned_loss=0.03158, over 1421063.17 frames.], batch size: 19, lr: 2.66e-04
2022-05-15 15:28:38,677 INFO [train.py:812] (6/8) Epoch 29, batch 4250, loss[loss=0.1561, simple_loss=0.246, pruned_loss=0.03308, over 6706.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2466, pruned_loss=0.03117, over 1421326.77 frames.], batch size: 31, lr: 2.66e-04
2022-05-15 15:29:36,731 INFO [train.py:812] (6/8) Epoch 29, batch 4300, loss[loss=0.1541, simple_loss=0.2311, pruned_loss=0.03851, over 7169.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2464, pruned_loss=0.03093, over 1417451.88 frames.], batch size: 18, lr: 2.66e-04
2022-05-15 15:30:35,692 INFO [train.py:812] (6/8) Epoch 29, batch 4350, loss[loss=0.1295, simple_loss=0.2257, pruned_loss=0.01665, over 7316.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2452, pruned_loss=0.03085, over 1419097.81 frames.], batch size: 21, lr: 2.66e-04
2022-05-15 15:31:34,539 INFO [train.py:812] (6/8) Epoch 29, batch 4400, loss[loss=0.1633, simple_loss=0.2625, pruned_loss=0.03205, over 7273.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2453, pruned_loss=0.03124, over 1409555.77 frames.], batch size: 24, lr: 2.66e-04
2022-05-15 15:32:33,474 INFO [train.py:812] (6/8) Epoch 29, batch 4450, loss[loss=0.1441, simple_loss=0.2364, pruned_loss=0.02593, over 6328.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2456, pruned_loss=0.03142, over 1400673.93 frames.], batch size: 37, lr: 2.66e-04
2022-05-15 15:33:31,925 INFO [train.py:812] (6/8) Epoch 29, batch 4500, loss[loss=0.1651, simple_loss=0.2647, pruned_loss=0.03278, over 7207.00 frames.], tot_loss[loss=0.155, simple_loss=0.2463, pruned_loss=0.03181, over 1378228.90 frames.], batch size: 22, lr: 2.66e-04
2022-05-15 15:34:29,710 INFO [train.py:812] (6/8) Epoch 29, batch 4550, loss[loss=0.2153, simple_loss=0.2967, pruned_loss=0.06695, over 5108.00 frames.], tot_loss[loss=0.1564, simple_loss=0.248, pruned_loss=0.03237, over 1359954.95 frames.], batch size: 52, lr: 2.66e-04
2022-05-15 15:35:40,771 INFO [train.py:812] (6/8) Epoch 30, batch 0, loss[loss=0.1493, simple_loss=0.2456, pruned_loss=0.02653, over 7313.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2456, pruned_loss=0.02653, over 7313.00 frames.], batch size: 20, lr: 2.62e-04
2022-05-15 15:36:39,963 INFO [train.py:812] (6/8) Epoch 30, batch 50, loss[loss=0.1263, simple_loss=0.2205, pruned_loss=0.01603, over 7283.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2443, pruned_loss=0.02901, over 323739.92 frames.], batch size: 18, lr: 2.62e-04
2022-05-15 15:37:39,037 INFO [train.py:812] (6/8) Epoch 30, batch 100, loss[loss=0.1202, simple_loss=0.2055, pruned_loss=0.01743, over 7266.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2417, pruned_loss=0.02939, over 571804.44 frames.], batch size: 17, lr: 2.62e-04
2022-05-15 15:38:38,764 INFO [train.py:812] (6/8) Epoch 30, batch 150, loss[loss=0.1839, simple_loss=0.2738, pruned_loss=0.04698, over 7321.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2446, pruned_loss=0.03076, over 748449.44 frames.], batch size: 24, lr: 2.62e-04
2022-05-15 15:39:36,206 INFO [train.py:812] (6/8) Epoch 30, batch 200, loss[loss=0.1382, simple_loss=0.2229, pruned_loss=0.02671, over 7356.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2443, pruned_loss=0.03103, over 898600.51 frames.], batch size: 19, lr: 2.61e-04
2022-05-15 15:40:35,803 INFO [train.py:812] (6/8) Epoch 30, batch 250, loss[loss=0.1291, simple_loss=0.2178, pruned_loss=0.02019, over 7260.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2452, pruned_loss=0.03119, over 1015075.77 frames.], batch size: 16, lr: 2.61e-04
2022-05-15 15:41:34,908 INFO [train.py:812] (6/8) Epoch 30, batch 300, loss[loss=0.1614, simple_loss=0.2414, pruned_loss=0.0407, over 7268.00 frames.], tot_loss[loss=0.1543, simple_loss=0.246, pruned_loss=0.0313, over 1107819.75 frames.], batch size: 18, lr: 2.61e-04
2022-05-15 15:42:33,935 INFO [train.py:812] (6/8) Epoch 30, batch 350, loss[loss=0.1471, simple_loss=0.2421, pruned_loss=0.02604, over 7330.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2455, pruned_loss=0.03145, over 1181061.21 frames.], batch size: 20, lr: 2.61e-04
2022-05-15 15:43:32,166 INFO [train.py:812] (6/8) Epoch 30, batch 400, loss[loss=0.1606, simple_loss=0.2607, pruned_loss=0.03031, over 7293.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2461, pruned_loss=0.03158, over 1237296.34 frames.], batch size: 24, lr: 2.61e-04
2022-05-15 15:44:30,906 INFO [train.py:812] (6/8) Epoch 30, batch 450, loss[loss=0.1427, simple_loss=0.2393, pruned_loss=0.02303, over 7413.00 frames.], tot_loss[loss=0.154, simple_loss=0.2454, pruned_loss=0.03132, over 1279527.77 frames.], batch size: 21, lr: 2.61e-04
2022-05-15 15:45:28,642 INFO [train.py:812] (6/8) Epoch 30, batch 500, loss[loss=0.1536, simple_loss=0.2452, pruned_loss=0.031, over 7330.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2458, pruned_loss=0.0318, over 1308781.72 frames.], batch size: 20, lr: 2.61e-04
2022-05-15 15:46:27,340 INFO [train.py:812] (6/8) Epoch 30, batch 550, loss[loss=0.1566, simple_loss=0.2566, pruned_loss=0.02827, over 7253.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2463, pruned_loss=0.03162, over 1336640.11 frames.], batch size: 24, lr: 2.61e-04
2022-05-15 15:47:24,865 INFO [train.py:812] (6/8) Epoch 30, batch 600, loss[loss=0.1696, simple_loss=0.2548, pruned_loss=0.04217, over 7207.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2463, pruned_loss=0.03142, over 1351870.32 frames.], batch size: 22, lr: 2.61e-04
2022-05-15 15:48:22,465 INFO [train.py:812] (6/8) Epoch 30, batch 650, loss[loss=0.1579, simple_loss=0.246, pruned_loss=0.03491, over 7062.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2462, pruned_loss=0.03148, over 1366497.39 frames.], batch size: 18, lr: 2.61e-04
2022-05-15 15:49:20,307 INFO [train.py:812] (6/8) Epoch 30, batch 700, loss[loss=0.1309, simple_loss=0.2196, pruned_loss=0.02104, over 7333.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2461, pruned_loss=0.03122, over 1374707.72 frames.], batch size: 20, lr: 2.61e-04
2022-05-15 15:50:18,878 INFO [train.py:812] (6/8) Epoch 30, batch 750, loss[loss=0.1403, simple_loss=0.2318, pruned_loss=0.02442, over 7229.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2459, pruned_loss=0.03121, over 1381913.12 frames.], batch size: 20, lr: 2.61e-04
2022-05-15 15:51:17,433 INFO [train.py:812] (6/8) Epoch 30, batch 800, loss[loss=0.1419, simple_loss=0.2383, pruned_loss=0.02277, over 7325.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2449, pruned_loss=0.03101, over 1389056.11 frames.], batch size: 22, lr: 2.61e-04
2022-05-15 15:52:16,541 INFO [train.py:812] (6/8) Epoch 30, batch 850, loss[loss=0.1445, simple_loss=0.2372, pruned_loss=0.0259, over 7061.00 frames.], tot_loss[loss=0.153, simple_loss=0.2443, pruned_loss=0.03082, over 1397863.25 frames.], batch size: 18, lr: 2.61e-04
2022-05-15 15:53:14,191 INFO [train.py:812] (6/8) Epoch 30, batch 900, loss[loss=0.1742, simple_loss=0.2712, pruned_loss=0.03857, over 7228.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2442, pruned_loss=0.0308, over 1401924.25 frames.], batch size: 21, lr: 2.61e-04
2022-05-15 15:54:13,183 INFO [train.py:812] (6/8) Epoch 30, batch 950, loss[loss=0.151, simple_loss=0.2514, pruned_loss=0.02532, over 7117.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2454, pruned_loss=0.0311, over 1407566.02 frames.], batch size: 21, lr: 2.61e-04
2022-05-15 15:55:11,588 INFO [train.py:812] (6/8) Epoch 30, batch 1000, loss[loss=0.1615, simple_loss=0.2622, pruned_loss=0.03042, over 7143.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2461, pruned_loss=0.03113, over 1411032.59 frames.], batch size: 20, lr: 2.61e-04
2022-05-15 15:56:10,074 INFO [train.py:812] (6/8) Epoch 30, batch 1050, loss[loss=0.1394, simple_loss=0.2174, pruned_loss=0.03066, over 7269.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2468, pruned_loss=0.0317, over 1407679.89 frames.], batch size: 18, lr: 2.61e-04
2022-05-15 15:57:08,336 INFO [train.py:812] (6/8) Epoch 30, batch 1100, loss[loss=0.1574, simple_loss=0.2569, pruned_loss=0.02894, over 7318.00 frames.], tot_loss[loss=0.156, simple_loss=0.248, pruned_loss=0.032, over 1417101.52 frames.], batch size: 21, lr: 2.61e-04
2022-05-15 15:58:07,698 INFO [train.py:812] (6/8) Epoch 30, batch 1150, loss[loss=0.1408, simple_loss=0.2278, pruned_loss=0.02688, over 7020.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2474, pruned_loss=0.03158, over 1417672.87 frames.], batch size: 16, lr: 2.61e-04
2022-05-15 15:59:06,109 INFO [train.py:812] (6/8) Epoch 30, batch 1200, loss[loss=0.1287, simple_loss=0.2179, pruned_loss=0.01972, over 7156.00 frames.], tot_loss[loss=0.1547, simple_loss=0.247, pruned_loss=0.0312, over 1421713.04 frames.], batch size: 19, lr: 2.61e-04
2022-05-15 16:00:14,959 INFO [train.py:812] (6/8) Epoch 30, batch 1250, loss[loss=0.2169, simple_loss=0.2894, pruned_loss=0.07218, over 4949.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2456, pruned_loss=0.03082, over 1416684.68 frames.], batch size: 53, lr: 2.60e-04
2022-05-15 16:01:13,802 INFO [train.py:812] (6/8) Epoch 30, batch 1300, loss[loss=0.1481, simple_loss=0.2503, pruned_loss=0.02295, over 7332.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2456, pruned_loss=0.0309, over 1418613.64 frames.], batch size: 22, lr: 2.60e-04
2022-05-15 16:02:13,360 INFO [train.py:812] (6/8) Epoch 30, batch 1350, loss[loss=0.1481, simple_loss=0.2453, pruned_loss=0.02543, over 6202.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2461, pruned_loss=0.03138, over 1419304.90 frames.], batch size: 37, lr: 2.60e-04
2022-05-15 16:03:12,430 INFO [train.py:812] (6/8) Epoch 30, batch 1400, loss[loss=0.1626, simple_loss=0.2449, pruned_loss=0.04014, over 6839.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2452, pruned_loss=0.03121, over 1419220.42 frames.], batch size: 15, lr: 2.60e-04
2022-05-15 16:04:10,803 INFO [train.py:812] (6/8) Epoch 30, batch 1450, loss[loss=0.148, simple_loss=0.2495, pruned_loss=0.02324, over 7121.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2454, pruned_loss=0.03097, over 1418427.06 frames.], batch size: 21, lr: 2.60e-04
2022-05-15 16:05:09,056 INFO [train.py:812] (6/8) Epoch 30, batch 1500, loss[loss=0.1495, simple_loss=0.2372, pruned_loss=0.0309, over 7262.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2458, pruned_loss=0.03117, over 1417606.41 frames.], batch size: 19, lr: 2.60e-04
2022-05-15 16:06:06,405 INFO [train.py:812] (6/8) Epoch 30, batch 1550, loss[loss=0.1542, simple_loss=0.2526, pruned_loss=0.02785, over 7225.00 frames.], tot_loss[loss=0.1543, simple_loss=0.246, pruned_loss=0.03128, over 1418407.16 frames.], batch size: 23, lr: 2.60e-04
2022-05-15 16:07:03,146 INFO [train.py:812] (6/8) Epoch 30, batch 1600, loss[loss=0.1443, simple_loss=0.2451, pruned_loss=0.02177, over 7319.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2464, pruned_loss=0.03156, over 1419571.08 frames.], batch size: 21, lr: 2.60e-04
2022-05-15 16:08:02,739 INFO [train.py:812] (6/8) Epoch 30, batch 1650, loss[loss=0.1781, simple_loss=0.2711, pruned_loss=0.0426, over 7158.00 frames.], tot_loss[loss=0.1542, simple_loss=0.246, pruned_loss=0.03125, over 1423519.25 frames.], batch size: 26, lr: 2.60e-04
2022-05-15 16:09:00,143 INFO [train.py:812] (6/8) Epoch 30, batch 1700, loss[loss=0.1543, simple_loss=0.2476, pruned_loss=0.03054, over 7124.00 frames.], tot_loss[loss=0.155, simple_loss=0.2468, pruned_loss=0.03161, over 1426671.46 frames.], batch size: 17, lr: 2.60e-04
2022-05-15 16:09:58,747 INFO [train.py:812] (6/8) Epoch 30, batch 1750, loss[loss=0.1546, simple_loss=0.252, pruned_loss=0.02862, over 7148.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2461, pruned_loss=0.03113, over 1422833.31 frames.], batch size: 20, lr: 2.60e-04
2022-05-15 16:10:56,862 INFO [train.py:812] (6/8) Epoch 30, batch 1800, loss[loss=0.1687, simple_loss=0.2648, pruned_loss=0.03629, over 5193.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2456, pruned_loss=0.03101, over 1420293.70 frames.], batch size: 53, lr: 2.60e-04
2022-05-15 16:11:55,134 INFO [train.py:812] (6/8) Epoch 30, batch 1850, loss[loss=0.1516, simple_loss=0.2491, pruned_loss=0.02709, over 7116.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2446, pruned_loss=0.03075, over 1424557.85 frames.], batch size: 21, lr: 2.60e-04
2022-05-15 16:12:53,272 INFO [train.py:812] (6/8) Epoch 30, batch 1900, loss[loss=0.1135, simple_loss=0.1969, pruned_loss=0.01506, over 7193.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2444, pruned_loss=0.03071, over 1427253.48 frames.], batch size: 16, lr: 2.60e-04
2022-05-15 16:13:52,787 INFO [train.py:812] (6/8) Epoch 30, batch 1950, loss[loss=0.1223, simple_loss=0.2122, pruned_loss=0.01617, over 7266.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2447, pruned_loss=0.03072, over 1428552.61 frames.], batch size: 17, lr: 2.60e-04
2022-05-15 16:14:51,469 INFO [train.py:812] (6/8) Epoch 30, batch 2000, loss[loss=0.1401, simple_loss=0.245, pruned_loss=0.01763, over 7327.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2451, pruned_loss=0.03089, over 1430612.86 frames.], batch size: 22, lr: 2.60e-04
2022-05-15 16:15:50,908 INFO [train.py:812] (6/8) Epoch 30, batch 2050, loss[loss=0.2091, simple_loss=0.2947, pruned_loss=0.06178, over 7213.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2455, pruned_loss=0.03107, over 1430929.33 frames.], batch size: 23, lr: 2.60e-04
2022-05-15 16:16:49,868 INFO [train.py:812] (6/8) Epoch 30, batch 2100, loss[loss=0.1802, simple_loss=0.266, pruned_loss=0.04715, over 7144.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2451, pruned_loss=0.03108, over 1429789.77 frames.], batch size: 20, lr: 2.60e-04
2022-05-15 16:17:48,133 INFO [train.py:812] (6/8) Epoch 30, batch 2150, loss[loss=0.1537, simple_loss=0.2391, pruned_loss=0.03412, over 7135.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2452, pruned_loss=0.03127, over 1428411.16 frames.], batch size: 17, lr: 2.60e-04
2022-05-15 16:18:47,075 INFO [train.py:812] (6/8) Epoch 30, batch 2200, loss[loss=0.1625, simple_loss=0.2517, pruned_loss=0.03668, over 7282.00 frames.], tot_loss[loss=0.154, simple_loss=0.2451, pruned_loss=0.03149, over 1423175.68 frames.], batch size: 24, lr: 2.60e-04
2022-05-15 16:19:45,894 INFO [train.py:812] (6/8) Epoch 30, batch 2250, loss[loss=0.1534, simple_loss=0.2523, pruned_loss=0.02726, over 7149.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2453, pruned_loss=0.03128, over 1421491.21 frames.], batch size: 26, lr: 2.59e-04
2022-05-15 16:20:43,585 INFO [train.py:812] (6/8) Epoch 30, batch 2300, loss[loss=0.1531, simple_loss=0.2348, pruned_loss=0.03566, over 7327.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2458, pruned_loss=0.03142, over 1417795.86 frames.], batch size: 20, lr: 2.59e-04
2022-05-15 16:21:42,633 INFO [train.py:812] (6/8) Epoch 30, batch 2350, loss[loss=0.1446, simple_loss=0.2377, pruned_loss=0.02574, over 7339.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2452, pruned_loss=0.0311, over 1420231.89 frames.], batch size: 22, lr: 2.59e-04
2022-05-15 16:22:41,701 INFO [train.py:812] (6/8) Epoch 30, batch 2400, loss[loss=0.158, simple_loss=0.2622, pruned_loss=0.02687, over 7289.00 frames.], tot_loss[loss=0.154, simple_loss=0.2456, pruned_loss=0.03125, over 1421651.40 frames.], batch size: 25, lr: 2.59e-04
2022-05-15 16:23:41,333 INFO [train.py:812] (6/8) Epoch 30, batch 2450, loss[loss=0.1481, simple_loss=0.2478, pruned_loss=0.02423, over 7147.00 frames.], tot_loss[loss=0.153, simple_loss=0.2445, pruned_loss=0.03076, over 1426013.99 frames.], batch size: 20, lr: 2.59e-04
2022-05-15 16:24:39,664 INFO [train.py:812] (6/8) Epoch 30, batch 2500, loss[loss=0.1345, simple_loss=0.2171, pruned_loss=0.026, over 6815.00 frames.], tot_loss[loss=0.1533, simple_loss=0.245, pruned_loss=0.03076, over 1430540.14 frames.], batch size: 15, lr: 2.59e-04
2022-05-15 16:25:38,966 INFO [train.py:812] (6/8) Epoch 30, batch 2550, loss[loss=0.1381, simple_loss=0.2242, pruned_loss=0.02594, over 7401.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2444, pruned_loss=0.03072, over 1427541.76 frames.], batch size: 18, lr: 2.59e-04
2022-05-15 16:26:37,727 INFO [train.py:812] (6/8) Epoch 30, batch 2600, loss[loss=0.1389, simple_loss=0.235, pruned_loss=0.02143, over 7118.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2442, pruned_loss=0.03077, over 1426837.97 frames.], batch size: 21, lr: 2.59e-04
2022-05-15 16:27:37,207 INFO [train.py:812] (6/8) Epoch 30, batch 2650, loss[loss=0.1326, simple_loss=0.2152, pruned_loss=0.02496, over 7126.00 frames.], tot_loss[loss=0.152, simple_loss=0.2433, pruned_loss=0.03033, over 1428758.83 frames.], batch size: 17, lr: 2.59e-04
2022-05-15 16:28:36,174 INFO [train.py:812] (6/8) Epoch 30, batch 2700, loss[loss=0.1553, simple_loss=0.2468, pruned_loss=0.03186, over 7119.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2444, pruned_loss=0.03102, over 1429407.17 frames.], batch size: 21, lr: 2.59e-04
2022-05-15 16:29:34,413 INFO [train.py:812] (6/8) Epoch 30, batch 2750, loss[loss=0.1491, simple_loss=0.2455, pruned_loss=0.02634, over 7236.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2449, pruned_loss=0.03078, over 1425573.16 frames.], batch size: 20, lr: 2.59e-04
2022-05-15 16:30:32,057 INFO [train.py:812] (6/8) Epoch 30, batch 2800, loss[loss=0.1369, simple_loss=0.2328, pruned_loss=0.02054, over 7327.00 frames.], tot_loss[loss=0.154, simple_loss=0.2457, pruned_loss=0.03112, over 1424493.34 frames.], batch size: 22, lr: 2.59e-04
2022-05-15 16:31:31,633 INFO [train.py:812] (6/8) Epoch 30, batch 2850, loss[loss=0.155, simple_loss=0.2405, pruned_loss=0.03475, over 7240.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2449, pruned_loss=0.03084, over 1418743.71 frames.], batch size: 20, lr: 2.59e-04
2022-05-15 16:32:29,839 INFO [train.py:812] (6/8) Epoch 30, batch 2900, loss[loss=0.1354, simple_loss=0.2206, pruned_loss=0.02505, over 6990.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2446, pruned_loss=0.03106, over 1421576.29 frames.], batch size: 16, lr: 2.59e-04
2022-05-15 16:33:36,402 INFO [train.py:812] (6/8) Epoch 30, batch 2950, loss[loss=0.1487, simple_loss=0.2532, pruned_loss=0.02216, over 6527.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2441, pruned_loss=0.03042, over 1422281.52 frames.], batch size: 38, lr: 2.59e-04
2022-05-15 16:34:35,502 INFO [train.py:812] (6/8) Epoch 30, batch 3000, loss[loss=0.1643, simple_loss=0.2511, pruned_loss=0.03879, over 7119.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2439, pruned_loss=0.03042, over 1425382.42 frames.], batch size: 21, lr: 2.59e-04
2022-05-15 16:34:35,503 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 16:34:43,057 INFO [train.py:841] (6/8) Epoch 30, validation: loss=0.1528, simple_loss=0.2494, pruned_loss=0.02809, over 698248.00 frames.
2022-05-15 16:35:41,804 INFO [train.py:812] (6/8) Epoch 30, batch 3050, loss[loss=0.1291, simple_loss=0.2189, pruned_loss=0.01965, over 7116.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2443, pruned_loss=0.03019, over 1426779.85 frames.], batch size: 21, lr: 2.59e-04
2022-05-15 16:36:40,857 INFO [train.py:812] (6/8) Epoch 30, batch 3100, loss[loss=0.1396, simple_loss=0.2365, pruned_loss=0.0214, over 7418.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2442, pruned_loss=0.03005, over 1426885.99 frames.], batch size: 21, lr: 2.59e-04
2022-05-15 16:37:40,574 INFO [train.py:812] (6/8) Epoch 30, batch 3150, loss[loss=0.1449, simple_loss=0.2286, pruned_loss=0.03056, over 7158.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2435, pruned_loss=0.02987, over 1422441.26 frames.], batch size: 18, lr: 2.59e-04
2022-05-15 16:38:39,690 INFO [train.py:812] (6/8) Epoch 30, batch 3200, loss[loss=0.162, simple_loss=0.2419, pruned_loss=0.04102, over 7262.00 frames.], tot_loss[loss=0.151, simple_loss=0.2425, pruned_loss=0.02975, over 1425763.96 frames.], batch size: 19, lr: 2.59e-04
2022-05-15 16:39:38,887 INFO [train.py:812] (6/8) Epoch 30, batch 3250, loss[loss=0.1557, simple_loss=0.2468, pruned_loss=0.03231, over 7092.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2433, pruned_loss=0.03017, over 1421427.99 frames.], batch size: 28, lr: 2.59e-04
2022-05-15 16:40:36,551 INFO [train.py:812] (6/8) Epoch 30, batch 3300, loss[loss=0.1248, simple_loss=0.2154, pruned_loss=0.01712, over 7334.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2443, pruned_loss=0.03056, over 1423904.20 frames.], batch size: 20, lr: 2.58e-04
2022-05-15 16:41:35,379 INFO [train.py:812] (6/8) Epoch 30, batch 3350, loss[loss=0.1444, simple_loss=0.2298, pruned_loss=0.02946, over 7283.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2436, pruned_loss=0.03045, over 1427937.62 frames.], batch size: 17, lr: 2.58e-04
2022-05-15 16:42:33,357 INFO [train.py:812] (6/8) Epoch 30, batch 3400, loss[loss=0.174, simple_loss=0.2645, pruned_loss=0.04177, over 5169.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2438, pruned_loss=0.03037, over 1424552.20 frames.], batch size: 52, lr: 2.58e-04
2022-05-15 16:43:31,895 INFO [train.py:812] (6/8) Epoch 30, batch 3450, loss[loss=0.162, simple_loss=0.253, pruned_loss=0.03552, over 7287.00 frames.], tot_loss[loss=0.1523, simple_loss=0.244, pruned_loss=0.03033, over 1421337.07 frames.], batch size: 24, lr: 2.58e-04
2022-05-15 16:44:30,302 INFO [train.py:812] (6/8) Epoch 30, batch 3500, loss[loss=0.174, simple_loss=0.2792, pruned_loss=0.03441, over 7138.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2445, pruned_loss=0.03064, over 1422900.06 frames.], batch size: 26, lr: 2.58e-04
2022-05-15 16:45:29,437 INFO [train.py:812] (6/8) Epoch 30, batch 3550, loss[loss=0.1262, simple_loss=0.2111, pruned_loss=0.02064, over 7178.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2446, pruned_loss=0.03057, over 1422319.19 frames.], batch size: 18, lr: 2.58e-04
2022-05-15 16:46:28,178 INFO [train.py:812] (6/8) Epoch 30, batch 3600, loss[loss=0.1637, simple_loss=0.255, pruned_loss=0.0362, over 7265.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2442, pruned_loss=0.03046, over 1427101.09 frames.], batch size: 19, lr: 2.58e-04
2022-05-15 16:47:27,385 INFO [train.py:812] (6/8) Epoch 30, batch 3650, loss[loss=0.1535, simple_loss=0.2507, pruned_loss=0.02818, over 6822.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2451, pruned_loss=0.03086, over 1428834.53 frames.], batch size: 31, lr: 2.58e-04
2022-05-15 16:48:25,018 INFO [train.py:812] (6/8) Epoch 30, batch 3700, loss[loss=0.1375, simple_loss=0.2147, pruned_loss=0.03016, over 7282.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2443, pruned_loss=0.03064, over 1429788.94 frames.], batch size: 17, lr: 2.58e-04
2022-05-15 16:49:23,814 INFO [train.py:812] (6/8) Epoch 30, batch 3750, loss[loss=0.1564, simple_loss=0.2452, pruned_loss=0.03374, over 6940.00 frames.], tot_loss[loss=0.1526, simple_loss=0.244, pruned_loss=0.03057, over 1432629.57 frames.], batch size: 28, lr: 2.58e-04
2022-05-15 16:50:21,212 INFO [train.py:812] (6/8) Epoch 30, batch 3800, loss[loss=0.1714, simple_loss=0.2741, pruned_loss=0.03435, over 7204.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2448, pruned_loss=0.03054, over 1425285.65 frames.], batch size: 22, lr: 2.58e-04
2022-05-15 16:51:18,903 INFO [train.py:812] (6/8) Epoch 30, batch 3850, loss[loss=0.1354, simple_loss=0.2207, pruned_loss=0.02505, over 7210.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2444, pruned_loss=0.03029, over 1425997.28 frames.], batch size: 16, lr: 2.58e-04
2022-05-15 16:52:16,802 INFO [train.py:812] (6/8) Epoch 30, batch 3900, loss[loss=0.162, simple_loss=0.2366, pruned_loss=0.0437, over 7149.00 frames.], tot_loss[loss=0.1533, simple_loss=0.245, pruned_loss=0.03085, over 1426589.15 frames.], batch size: 17, lr: 2.58e-04
2022-05-15 16:53:15,088 INFO [train.py:812] (6/8) Epoch 30, batch 3950, loss[loss=0.1819, simple_loss=0.2775, pruned_loss=0.0432, over 7366.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2464, pruned_loss=0.03134, over 1420911.45 frames.], batch size: 23, lr: 2.58e-04
2022-05-15 16:54:13,805 INFO [train.py:812] (6/8) Epoch 30, batch 4000, loss[loss=0.1641, simple_loss=0.2593, pruned_loss=0.03444, over 7296.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2469, pruned_loss=0.03136, over 1418637.88 frames.], batch size: 25, lr: 2.58e-04
2022-05-15 16:55:12,887 INFO [train.py:812] (6/8) Epoch 30, batch 4050, loss[loss=0.1566, simple_loss=0.2511, pruned_loss=0.03104, over 7090.00 frames.], tot_loss[loss=0.1543, simple_loss=0.246, pruned_loss=0.03126, over 1418293.73 frames.], batch size: 28, lr: 2.58e-04
2022-05-15 16:56:10,901 INFO [train.py:812] (6/8) Epoch 30, batch 4100, loss[loss=0.1456, simple_loss=0.2413, pruned_loss=0.02495, over 7325.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2449, pruned_loss=0.03095, over 1420290.44 frames.], batch size: 21, lr: 2.58e-04
2022-05-15 16:57:19,280 INFO [train.py:812] (6/8) Epoch 30, batch 4150, loss[loss=0.1512, simple_loss=0.2511, pruned_loss=0.02569, over 7212.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2442, pruned_loss=0.03048, over 1420699.35 frames.], batch size: 21, lr: 2.58e-04
2022-05-15 16:58:17,933 INFO [train.py:812] (6/8) Epoch 30, batch 4200, loss[loss=0.1349, simple_loss=0.2304, pruned_loss=0.01971, over 7422.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2444, pruned_loss=0.03066, over 1421778.18 frames.], batch size: 20, lr: 2.58e-04
2022-05-15 16:59:24,886 INFO [train.py:812] (6/8) Epoch 30, batch 4250, loss[loss=0.1672, simple_loss=0.2644, pruned_loss=0.03497, over 7381.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2462, pruned_loss=0.03117, over 1416797.38 frames.], batch size: 23, lr: 2.58e-04
2022-05-15 17:00:23,131 INFO [train.py:812] (6/8) Epoch 30, batch 4300, loss[loss=0.1369, simple_loss=0.2211, pruned_loss=0.02633, over 7288.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2456, pruned_loss=0.03113, over 1420135.38 frames.], batch size: 17, lr: 2.58e-04
2022-05-15 17:01:31,697 INFO [train.py:812] (6/8) Epoch 30, batch 4350, loss[loss=0.1322, simple_loss=0.2274, pruned_loss=0.01846, over 7232.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2453, pruned_loss=0.03097, over 1421662.13 frames.], batch size: 20, lr: 2.58e-04
2022-05-15 17:02:30,816 INFO [train.py:812] (6/8) Epoch 30, batch 4400, loss[loss=0.1715, simple_loss=0.2706, pruned_loss=0.03617, over 7239.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2453, pruned_loss=0.03123, over 1418016.77 frames.], batch size: 20, lr: 2.57e-04
2022-05-15 17:03:47,900 INFO [train.py:812] (6/8) Epoch 30, batch 4450, loss[loss=0.1618, simple_loss=0.2621, pruned_loss=0.03075, over 6547.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2445, pruned_loss=0.03063, over 1412940.39 frames.], batch size: 38, lr: 2.57e-04
2022-05-15 17:04:54,631 INFO [train.py:812] (6/8) Epoch 30, batch 4500, loss[loss=0.1937, simple_loss=0.2794, pruned_loss=0.05402, over 4873.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2455, pruned_loss=0.03075, over 1398538.24 frames.], batch size: 52, lr: 2.57e-04
2022-05-15 17:05:52,208 INFO [train.py:812] (6/8) Epoch 30, batch 4550, loss[loss=0.1853, simple_loss=0.2699, pruned_loss=0.05031, over 4817.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2477, pruned_loss=0.03195, over 1357412.74 frames.], batch size: 52, lr: 2.57e-04
2022-05-15 17:07:08,084 INFO [train.py:812] (6/8) Epoch 31, batch 0, loss[loss=0.1431, simple_loss=0.2339, pruned_loss=0.02608, over 7319.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2339, pruned_loss=0.02608, over 7319.00 frames.], batch size: 20, lr: 2.53e-04
2022-05-15 17:08:07,417 INFO [train.py:812] (6/8) Epoch 31, batch 50, loss[loss=0.1519, simple_loss=0.2511, pruned_loss=0.02631, over 7253.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2462, pruned_loss=0.031, over 316842.56 frames.], batch size: 19, lr: 2.53e-04
2022-05-15 17:09:06,201 INFO [train.py:812] (6/8) Epoch 31, batch 100, loss[loss=0.1486, simple_loss=0.2407, pruned_loss=0.02826, over 7385.00 frames.], tot_loss[loss=0.154, simple_loss=0.2462, pruned_loss=0.03092, over 561966.19 frames.], batch size: 23, lr: 2.53e-04
2022-05-15 17:10:05,073 INFO [train.py:812] (6/8) Epoch 31, batch 150, loss[loss=0.1773, simple_loss=0.271, pruned_loss=0.04183, over 7211.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2436, pruned_loss=0.0303, over 756126.08 frames.], batch size: 22, lr: 2.53e-04
2022-05-15 17:11:03,891 INFO [train.py:812] (6/8) Epoch 31, batch 200, loss[loss=0.1664, simple_loss=0.2558, pruned_loss=0.03851, over 4954.00 frames.], tot_loss[loss=0.152, simple_loss=0.2432, pruned_loss=0.03043, over 901296.27 frames.], batch size: 52, lr: 2.53e-04
2022-05-15 17:12:02,434 INFO [train.py:812] (6/8) Epoch 31, batch 250, loss[loss=0.1627, simple_loss=0.2611, pruned_loss=0.0321, over 7257.00 frames.], tot_loss[loss=0.1524, simple_loss=0.244, pruned_loss=0.03038, over 1015757.26 frames.], batch size: 25, lr: 2.53e-04
2022-05-15 17:13:01,766 INFO [train.py:812] (6/8) Epoch 31, batch 300, loss[loss=0.1286, simple_loss=0.231, pruned_loss=0.01309, over 7325.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2444, pruned_loss=0.03037, over 1107227.72 frames.], batch size: 21, lr: 2.53e-04
2022-05-15 17:13:59,737 INFO [train.py:812] (6/8) Epoch 31, batch 350, loss[loss=0.1524, simple_loss=0.2384, pruned_loss=0.03322, over 7168.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2436, pruned_loss=0.03005, over 1174267.48 frames.], batch size: 18, lr: 2.53e-04
2022-05-15 17:14:57,255 INFO [train.py:812] (6/8) Epoch 31, batch 400, loss[loss=0.166, simple_loss=0.262, pruned_loss=0.03503, over 7217.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2438, pruned_loss=0.02993, over 1225727.09 frames.], batch size: 21, lr: 2.53e-04
2022-05-15 17:15:56,100 INFO [train.py:812] (6/8) Epoch 31, batch 450, loss[loss=0.1542, simple_loss=0.2484, pruned_loss=0.03004, over 7125.00 frames.], tot_loss[loss=0.153, simple_loss=0.2451, pruned_loss=0.03042, over 1267445.85 frames.], batch size: 26, lr: 2.53e-04
2022-05-15 17:16:55,576 INFO [train.py:812] (6/8) Epoch 31, batch 500, loss[loss=0.1353, simple_loss=0.2145, pruned_loss=0.02805, over 7278.00 frames.], tot_loss[loss=0.153, simple_loss=0.2446, pruned_loss=0.03065, over 1303159.28 frames.], batch size: 17, lr: 2.53e-04
2022-05-15 17:17:54,448 INFO [train.py:812] (6/8) Epoch 31, batch 550, loss[loss=0.158, simple_loss=0.2608, pruned_loss=0.02759, over 7416.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2451, pruned_loss=0.0308, over 1329664.68 frames.], batch size: 21, lr: 2.53e-04
2022-05-15 17:18:53,063 INFO [train.py:812] (6/8) Epoch 31, batch 600, loss[loss=0.147, simple_loss=0.2341, pruned_loss=0.02994, over 7065.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2467, pruned_loss=0.03133, over 1348888.59 frames.], batch size: 18, lr: 2.53e-04
2022-05-15 17:19:50,589 INFO [train.py:812] (6/8) Epoch 31, batch 650, loss[loss=0.1736, simple_loss=0.2681, pruned_loss=0.03955, over 7142.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2459, pruned_loss=0.03078, over 1369569.28 frames.], batch size: 20, lr: 2.53e-04
2022-05-15 17:20:49,372 INFO [train.py:812] (6/8) Epoch 31, batch 700, loss[loss=0.1242, simple_loss=0.2134, pruned_loss=0.0175, over 7241.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2455, pruned_loss=0.03063, over 1378972.01 frames.], batch size: 16, lr: 2.52e-04
2022-05-15 17:21:47,379 INFO [train.py:812] (6/8) Epoch 31, batch 750, loss[loss=0.1629, simple_loss=0.2603, pruned_loss=0.03278, over 7228.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2458, pruned_loss=0.03082, over 1386772.30 frames.], batch size: 20, lr: 2.52e-04
2022-05-15 17:22:46,097 INFO [train.py:812] (6/8) Epoch 31, batch 800, loss[loss=0.1476, simple_loss=0.2339, pruned_loss=0.03067, over 7331.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2453, pruned_loss=0.03061, over 1395109.48 frames.], batch size: 20, lr: 2.52e-04
2022-05-15 17:23:44,742 INFO [train.py:812] (6/8) Epoch 31, batch 850, loss[loss=0.1586, simple_loss=0.2488, pruned_loss=0.03413, over 7437.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2443, pruned_loss=0.03062, over 1398816.59 frames.], batch size: 20, lr: 2.52e-04
2022-05-15 17:24:43,301 INFO [train.py:812] (6/8) Epoch 31, batch 900, loss[loss=0.149, simple_loss=0.2281, pruned_loss=0.03498, over 7197.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2446, pruned_loss=0.03097, over 1403726.18 frames.], batch size: 16, lr: 2.52e-04
2022-05-15 17:25:42,280 INFO [train.py:812] (6/8) Epoch 31, batch 950, loss[loss=0.1493, simple_loss=0.2473, pruned_loss=0.02569, over 7106.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2444, pruned_loss=0.03092, over 1405849.95 frames.], batch size: 28, lr: 2.52e-04
2022-05-15 17:26:41,338 INFO [train.py:812] (6/8) Epoch 31, batch 1000, loss[loss=0.1591, simple_loss=0.2623, pruned_loss=0.02795, over 7342.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2445, pruned_loss=0.03106, over 1407825.77 frames.], batch size: 22, lr: 2.52e-04
2022-05-15 17:27:40,602 INFO [train.py:812] (6/8) Epoch 31, batch 1050, loss[loss=0.1559, simple_loss=0.2471, pruned_loss=0.03235, over 7060.00 frames.], tot_loss[loss=0.153, simple_loss=0.2445, pruned_loss=0.03071, over 1410744.58 frames.], batch size: 28, lr: 2.52e-04
2022-05-15 17:28:39,407 INFO [train.py:812] (6/8) Epoch 31, batch 1100, loss[loss=0.1555, simple_loss=0.24, pruned_loss=0.03546, over 7063.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2444, pruned_loss=0.03071, over 1414951.61 frames.], batch size: 18, lr: 2.52e-04
2022-05-15 17:29:38,139 INFO [train.py:812] (6/8) Epoch 31, batch 1150, loss[loss=0.1485, simple_loss=0.2377, pruned_loss=0.02963, over 7446.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2441, pruned_loss=0.03081, over 1417204.00 frames.], batch size: 19, lr: 2.52e-04
2022-05-15 17:30:36,930 INFO [train.py:812] (6/8) Epoch 31, batch 1200, loss[loss=0.1783, simple_loss=0.2621, pruned_loss=0.04729, over 7208.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2433, pruned_loss=0.03076, over 1419243.35 frames.], batch size: 22, lr: 2.52e-04
2022-05-15 17:31:36,142 INFO [train.py:812] (6/8) Epoch 31, batch 1250, loss[loss=0.134, simple_loss=0.2215, pruned_loss=0.02328, over 7406.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2435, pruned_loss=0.03053, over 1418521.37 frames.], batch size: 18, lr: 2.52e-04
2022-05-15 17:32:35,757 INFO [train.py:812] (6/8) Epoch 31, batch 1300, loss[loss=0.2007, simple_loss=0.2915, pruned_loss=0.05494, over 7153.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2441, pruned_loss=0.03048, over 1417149.47 frames.], batch size: 26, lr: 2.52e-04
2022-05-15 17:33:34,090 INFO [train.py:812] (6/8) Epoch 31, batch 1350, loss[loss=0.1376, simple_loss=0.2239, pruned_loss=0.02562, over 7139.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2461, pruned_loss=0.03102, over 1414689.01 frames.], batch size: 17, lr: 2.52e-04
2022-05-15 17:34:32,634 INFO [train.py:812] (6/8) Epoch 31, batch 1400, loss[loss=0.1802, simple_loss=0.2815, pruned_loss=0.03948, over 7346.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2465, pruned_loss=0.03114, over 1418766.22 frames.], batch size: 22, lr: 2.52e-04
2022-05-15 17:35:31,411 INFO [train.py:812] (6/8) Epoch 31, batch 1450, loss[loss=0.1471, simple_loss=0.2396, pruned_loss=0.02729, over 7140.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2454, pruned_loss=0.03056, over 1420073.32 frames.], batch size: 20, lr: 2.52e-04
2022-05-15 17:36:30,423 INFO [train.py:812] (6/8) Epoch 31, batch 1500, loss[loss=0.1648, simple_loss=0.2615, pruned_loss=0.03409, over 7308.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2465, pruned_loss=0.03096, over 1425839.26 frames.], batch size: 25, lr: 2.52e-04
2022-05-15 17:37:27,934 INFO [train.py:812] (6/8) Epoch 31, batch 1550, loss[loss=0.1718, simple_loss=0.2618, pruned_loss=0.04091, over 7297.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2457, pruned_loss=0.03091, over 1427941.56 frames.], batch size: 25, lr: 2.52e-04
2022-05-15 17:38:27,313 INFO [train.py:812] (6/8) Epoch 31, batch 1600, loss[loss=0.1419, simple_loss=0.2375, pruned_loss=0.02319, over 7256.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2451, pruned_loss=0.03061, over 1429122.95 frames.], batch size: 19, lr: 2.52e-04
2022-05-15 17:39:26,052 INFO [train.py:812] (6/8) Epoch 31, batch 1650, loss[loss=0.1494, simple_loss=0.2456, pruned_loss=0.0266, over 7121.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2457, pruned_loss=0.03062, over 1429260.66 frames.], batch size: 21, lr: 2.52e-04
2022-05-15 17:40:24,544 INFO [train.py:812] (6/8) Epoch 31, batch 1700, loss[loss=0.1666, simple_loss=0.2616, pruned_loss=0.03578, over 7312.00 frames.], tot_loss[loss=0.153, simple_loss=0.2447, pruned_loss=0.03064, over 1425921.50 frames.], batch size: 24, lr: 2.52e-04
2022-05-15 17:41:22,585 INFO [train.py:812] (6/8) Epoch 31, batch 1750, loss[loss=0.2071, simple_loss=0.2833, pruned_loss=0.06539, over 7360.00 frames.], tot_loss[loss=0.153, simple_loss=0.245, pruned_loss=0.03054, over 1427978.99 frames.], batch size: 23, lr: 2.52e-04
2022-05-15 17:42:21,644 INFO [train.py:812] (6/8) Epoch 31, batch 1800, loss[loss=0.1515, simple_loss=0.2417, pruned_loss=0.03065, over 7428.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2444, pruned_loss=0.03056, over 1423595.81 frames.], batch size: 20, lr: 2.51e-04
2022-05-15 17:43:20,024 INFO [train.py:812] (6/8) Epoch 31, batch 1850, loss[loss=0.1426, simple_loss=0.2276, pruned_loss=0.02879, over 7134.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2438, pruned_loss=0.03005, over 1421789.34 frames.], batch size: 17, lr: 2.51e-04
2022-05-15 17:44:19,020 INFO [train.py:812] (6/8) Epoch 31, batch 1900, loss[loss=0.1777, simple_loss=0.2645, pruned_loss=0.04548, over 7322.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2446, pruned_loss=0.03045, over 1425295.99 frames.], batch size: 20, lr: 2.51e-04
2022-05-15 17:45:17,766 INFO [train.py:812] (6/8) Epoch 31, batch 1950, loss[loss=0.1896, simple_loss=0.2735, pruned_loss=0.05289, over 7365.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2446, pruned_loss=0.03024, over 1425201.60 frames.], batch size: 23, lr: 2.51e-04
2022-05-15 17:46:16,479 INFO [train.py:812] (6/8) Epoch 31, batch 2000, loss[loss=0.169, simple_loss=0.2587, pruned_loss=0.03962, over 7168.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2437, pruned_loss=0.03007, over 1426878.01 frames.], batch size: 18, lr: 2.51e-04
2022-05-15 17:47:15,235 INFO [train.py:812] (6/8) Epoch 31, batch 2050, loss[loss=0.1561, simple_loss=0.2424, pruned_loss=0.03488, over 7199.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2427, pruned_loss=0.02978, over 1424290.28 frames.], batch size: 22, lr: 2.51e-04
2022-05-15 17:48:13,828 INFO [train.py:812] (6/8) Epoch 31, batch 2100, loss[loss=0.1717, simple_loss=0.2577, pruned_loss=0.04283, over 7159.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2435, pruned_loss=0.03006, over 1423496.43 frames.], batch size: 19, lr: 2.51e-04
2022-05-15 17:49:12,926 INFO [train.py:812] (6/8) Epoch 31, batch 2150, loss[loss=0.1282, simple_loss=0.2114, pruned_loss=0.02254, over 7171.00 frames.], tot_loss[loss=0.1503, simple_loss=0.242, pruned_loss=0.02934, over 1427452.85 frames.], batch size: 18, lr: 2.51e-04
2022-05-15 17:50:11,063 INFO [train.py:812] (6/8) Epoch 31, batch 2200, loss[loss=0.1382, simple_loss=0.2211, pruned_loss=0.0277, over 7079.00 frames.], tot_loss[loss=0.1512, simple_loss=0.243, pruned_loss=0.02968, over 1428551.40 frames.], batch size: 18, lr: 2.51e-04
2022-05-15 17:51:08,609 INFO [train.py:812] (6/8) Epoch 31, batch 2250, loss[loss=0.1832, simple_loss=0.2802, pruned_loss=0.04312, over 7192.00 frames.], tot_loss[loss=0.152, simple_loss=0.2441, pruned_loss=0.02994, over 1427496.77 frames.], batch size: 23, lr: 2.51e-04
2022-05-15 17:52:08,130 INFO [train.py:812] (6/8) Epoch 31, batch 2300, loss[loss=0.1351, simple_loss=0.2325, pruned_loss=0.0188, over 7254.00 frames.], tot_loss[loss=0.1518, simple_loss=0.244, pruned_loss=0.02984, over 1429575.30 frames.], batch size: 19, lr: 2.51e-04
2022-05-15 17:53:06,342 INFO [train.py:812] (6/8) Epoch 31, batch 2350, loss[loss=0.1707, simple_loss=0.264, pruned_loss=0.03866, over 7060.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2436, pruned_loss=0.02985, over 1430519.46 frames.], batch size: 18, lr: 2.51e-04
2022-05-15 17:54:10,963 INFO [train.py:812] (6/8) Epoch 31, batch 2400, loss[loss=0.1701, simple_loss=0.267, pruned_loss=0.03656, over 7227.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2446, pruned_loss=0.03042, over 1429427.70 frames.], batch size: 21, lr: 2.51e-04
2022-05-15 17:55:08,410 INFO [train.py:812] (6/8) Epoch 31, batch 2450, loss[loss=0.1381, simple_loss=0.241, pruned_loss=0.01765, over 7225.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2451, pruned_loss=0.03027, over 1425587.71 frames.], batch size: 21, lr: 2.51e-04
2022-05-15 17:56:07,150 INFO [train.py:812] (6/8) Epoch 31, batch 2500, loss[loss=0.1716, simple_loss=0.2737, pruned_loss=0.03477, over 7330.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2448, pruned_loss=0.03006, over 1427719.14 frames.], batch size: 22, lr: 2.51e-04
2022-05-15 17:57:05,831 INFO [train.py:812] (6/8) Epoch 31, batch 2550, loss[loss=0.1757, simple_loss=0.2583, pruned_loss=0.04658, over 7184.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2448, pruned_loss=0.03012, over 1428836.63 frames.], batch size: 23, lr: 2.51e-04
2022-05-15 17:58:14,115 INFO [train.py:812] (6/8) Epoch 31, batch 2600, loss[loss=0.141, simple_loss=0.2256, pruned_loss=0.02815, over 7423.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2445, pruned_loss=0.03021, over 1428141.25 frames.], batch size: 18, lr: 2.51e-04
2022-05-15 17:59:11,569 INFO [train.py:812] (6/8) Epoch 31, batch 2650, loss[loss=0.1765, simple_loss=0.2789, pruned_loss=0.03706, over 7410.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2445, pruned_loss=0.03025, over 1425662.65 frames.], batch size: 21, lr: 2.51e-04
2022-05-15 18:00:10,535 INFO [train.py:812] (6/8) Epoch 31, batch 2700, loss[loss=0.1642, simple_loss=0.2546, pruned_loss=0.03692, over 7281.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2451, pruned_loss=0.03055, over 1419288.87 frames.], batch size: 25, lr: 2.51e-04
2022-05-15 18:01:09,687 INFO [train.py:812] (6/8) Epoch 31, batch 2750, loss[loss=0.1459, simple_loss=0.2465, pruned_loss=0.02266, over 7145.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2441, pruned_loss=0.03006, over 1419938.48 frames.], batch size: 20, lr: 2.51e-04
2022-05-15 18:02:08,953 INFO [train.py:812] (6/8) Epoch 31, batch 2800, loss[loss=0.1451, simple_loss=0.2361, pruned_loss=0.027, over 7173.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2445, pruned_loss=0.03016, over 1422429.09 frames.], batch size: 18, lr: 2.51e-04
2022-05-15 18:03:06,858 INFO [train.py:812] (6/8) Epoch 31, batch 2850, loss[loss=0.1576, simple_loss=0.2565, pruned_loss=0.02937, over 7200.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2449, pruned_loss=0.03023, over 1419851.37 frames.], batch size: 22, lr: 2.51e-04
2022-05-15 18:04:06,626 INFO [train.py:812] (6/8) Epoch 31, batch 2900, loss[loss=0.1472, simple_loss=0.25, pruned_loss=0.0222, over 7110.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2442, pruned_loss=0.02962, over 1423240.63 frames.], batch size: 21, lr: 2.51e-04
2022-05-15 18:05:04,901 INFO [train.py:812] (6/8) Epoch 31, batch 2950, loss[loss=0.1425, simple_loss=0.2255, pruned_loss=0.02969, over 7258.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2443, pruned_loss=0.02965, over 1422500.14 frames.], batch size: 19, lr: 2.50e-04
2022-05-15 18:06:03,516 INFO [train.py:812] (6/8) Epoch 31, batch 3000, loss[loss=0.1507, simple_loss=0.2412, pruned_loss=0.03013, over 7327.00 frames.], tot_loss[loss=0.1519, simple_loss=0.244, pruned_loss=0.02986, over 1422862.37 frames.], batch size: 20, lr: 2.50e-04
2022-05-15 18:06:03,517 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 18:06:10,970 INFO [train.py:841] (6/8) Epoch 31, validation: loss=0.1541, simple_loss=0.25, pruned_loss=0.02913, over 698248.00 frames.
2022-05-15 18:07:09,532 INFO [train.py:812] (6/8) Epoch 31, batch 3050, loss[loss=0.128, simple_loss=0.2165, pruned_loss=0.01971, over 6997.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2445, pruned_loss=0.03032, over 1422385.39 frames.], batch size: 16, lr: 2.50e-04
2022-05-15 18:08:09,160 INFO [train.py:812] (6/8) Epoch 31, batch 3100, loss[loss=0.1756, simple_loss=0.2736, pruned_loss=0.03876, over 7241.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2451, pruned_loss=0.031, over 1425934.99 frames.], batch size: 25, lr: 2.50e-04
2022-05-15 18:09:08,145 INFO [train.py:812] (6/8) Epoch 31, batch 3150, loss[loss=0.1168, simple_loss=0.2003, pruned_loss=0.01664, over 7010.00 frames.], tot_loss[loss=0.1546, simple_loss=0.246, pruned_loss=0.03164, over 1425759.42 frames.], batch size: 16, lr: 2.50e-04
2022-05-15 18:10:05,076 INFO [train.py:812] (6/8) Epoch 31, batch 3200, loss[loss=0.1505, simple_loss=0.2488, pruned_loss=0.02614, over 7200.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2459, pruned_loss=0.03156, over 1417491.81 frames.], batch size: 23, lr: 2.50e-04
2022-05-15 18:11:03,072 INFO [train.py:812] (6/8) Epoch 31, batch 3250, loss[loss=0.162, simple_loss=0.277, pruned_loss=0.02349, over 7150.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2468, pruned_loss=0.03153, over 1417159.28 frames.], batch size: 20, lr: 2.50e-04
2022-05-15 18:12:02,666 INFO [train.py:812] (6/8) Epoch 31, batch 3300, loss[loss=0.1572, simple_loss=0.24, pruned_loss=0.03714, over 7272.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2456, pruned_loss=0.03137, over 1423247.67 frames.], batch size: 17, lr: 2.50e-04
2022-05-15 18:13:01,658 INFO [train.py:812] (6/8) Epoch 31, batch 3350, loss[loss=0.1599, simple_loss=0.2571, pruned_loss=0.03132, over 7217.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2448, pruned_loss=0.03085, over 1422491.52 frames.], batch size: 21, lr: 2.50e-04
2022-05-15 18:14:00,862 INFO [train.py:812] (6/8) Epoch 31, batch 3400, loss[loss=0.1525, simple_loss=0.2511, pruned_loss=0.027, over 7300.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2439, pruned_loss=0.03062, over 1421959.55 frames.], batch size: 25, lr: 2.50e-04
2022-05-15 18:14:57,864 INFO [train.py:812] (6/8) Epoch 31, batch 3450, loss[loss=0.1483, simple_loss=0.253, pruned_loss=0.02183, over 6235.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2443, pruned_loss=0.0304, over 1426116.96 frames.], batch size: 37, lr: 2.50e-04
2022-05-15 18:15:56,026 INFO [train.py:812] (6/8) Epoch 31, batch 3500, loss[loss=0.2147, simple_loss=0.2998, pruned_loss=0.06481, over 7388.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2442, pruned_loss=0.0303, over 1427068.73 frames.], batch size: 23, lr: 2.50e-04
2022-05-15 18:16:54,987 INFO [train.py:812] (6/8) Epoch 31, batch 3550, loss[loss=0.1567, simple_loss=0.2521, pruned_loss=0.03064, over 7437.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2447, pruned_loss=0.03031, over 1428252.80 frames.], batch size: 20, lr: 2.50e-04
2022-05-15 18:17:52,447 INFO [train.py:812] (6/8) Epoch 31, batch 3600, loss[loss=0.1495, simple_loss=0.2392, pruned_loss=0.02992, over 7289.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2456, pruned_loss=0.03074, over 1422960.28 frames.], batch size: 24, lr: 2.50e-04
2022-05-15 18:18:51,257 INFO [train.py:812] (6/8) Epoch 31, batch 3650, loss[loss=0.1391, simple_loss=0.23, pruned_loss=0.02409, over 7133.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2452, pruned_loss=0.03057, over 1422512.41 frames.], batch size: 17, lr: 2.50e-04
2022-05-15 18:19:50,493 INFO [train.py:812] (6/8) Epoch 31, batch 3700, loss[loss=0.1274, simple_loss=0.2114, pruned_loss=0.02174, over 7286.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2439, pruned_loss=0.0299, over 1425493.57 frames.], batch size: 17, lr: 2.50e-04
2022-05-15 18:20:49,384 INFO [train.py:812] (6/8) Epoch 31, batch 3750, loss[loss=0.138, simple_loss=0.2266, pruned_loss=0.02471, over 7257.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2439, pruned_loss=0.03014, over 1423992.50 frames.], batch size: 19, lr: 2.50e-04
2022-05-15 18:21:49,300 INFO [train.py:812] (6/8) Epoch 31, batch 3800, loss[loss=0.1225, simple_loss=0.2063, pruned_loss=0.01934, over 7269.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2431, pruned_loss=0.02971, over 1426437.58 frames.], batch size: 18, lr: 2.50e-04
2022-05-15 18:22:47,397 INFO [train.py:812] (6/8) Epoch 31, batch 3850, loss[loss=0.1483, simple_loss=0.2472, pruned_loss=0.02468, over 7056.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2442, pruned_loss=0.03021, over 1425513.65 frames.], batch size: 18, lr: 2.50e-04
2022-05-15 18:23:45,724 INFO [train.py:812] (6/8) Epoch 31, batch 3900, loss[loss=0.1783, simple_loss=0.2719, pruned_loss=0.04235, over 7259.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2436, pruned_loss=0.02992, over 1429039.07 frames.], batch size: 24, lr: 2.50e-04
2022-05-15 18:24:43,625 INFO [train.py:812] (6/8) Epoch 31, batch 3950, loss[loss=0.1624, simple_loss=0.2422, pruned_loss=0.04131, over 7354.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2432, pruned_loss=0.03011, over 1429296.34 frames.], batch size: 19, lr: 2.50e-04
2022-05-15 18:25:41,731 INFO [train.py:812] (6/8) Epoch 31, batch 4000, loss[loss=0.1543, simple_loss=0.2434, pruned_loss=0.03255, over 7175.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2437, pruned_loss=0.03034, over 1426506.47 frames.], batch size: 18, lr: 2.50e-04
2022-05-15 18:26:41,011 INFO [train.py:812] (6/8) Epoch 31, batch 4050, loss[loss=0.1553, simple_loss=0.251, pruned_loss=0.02982, over 7305.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2444, pruned_loss=0.03055, over 1425702.40 frames.], batch size: 24, lr: 2.49e-04
2022-05-15 18:27:40,606 INFO [train.py:812] (6/8) Epoch 31, batch 4100, loss[loss=0.1311, simple_loss=0.2238, pruned_loss=0.01918, over 7163.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2446, pruned_loss=0.0305, over 1427600.81 frames.], batch size: 19, lr: 2.49e-04
2022-05-15 18:28:39,540 INFO [train.py:812] (6/8) Epoch 31, batch 4150, loss[loss=0.1581, simple_loss=0.2646, pruned_loss=0.02582, over 7109.00 frames.], tot_loss[loss=0.152, simple_loss=0.2439, pruned_loss=0.03003, over 1429466.69 frames.], batch size: 21, lr: 2.49e-04
2022-05-15 18:29:38,565 INFO [train.py:812] (6/8) Epoch 31, batch 4200, loss[loss=0.1172, simple_loss=0.1997, pruned_loss=0.0174, over 6839.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2429, pruned_loss=0.02984, over 1431113.96 frames.], batch size: 15, lr: 2.49e-04
2022-05-15 18:30:36,496 INFO [train.py:812] (6/8) Epoch 31, batch 4250, loss[loss=0.1731, simple_loss=0.2674, pruned_loss=0.0394, over 7179.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2432, pruned_loss=0.03, over 1427688.45 frames.], batch size: 26, lr: 2.49e-04
2022-05-15 18:31:35,837 INFO [train.py:812] (6/8) Epoch 31, batch 4300, loss[loss=0.199, simple_loss=0.2842, pruned_loss=0.05692, over 7288.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2432, pruned_loss=0.02994, over 1431430.91 frames.], batch size: 24, lr: 2.49e-04
2022-05-15 18:32:33,419 INFO [train.py:812] (6/8) Epoch 31, batch 4350, loss[loss=0.1291, simple_loss=0.2197, pruned_loss=0.01927, over 7124.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2434, pruned_loss=0.03014, over 1422678.37 frames.], batch size: 21, lr: 2.49e-04
2022-05-15 18:33:32,250 INFO [train.py:812] (6/8) Epoch 31, batch 4400, loss[loss=0.1463, simple_loss=0.2441, pruned_loss=0.02422, over 7122.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2438, pruned_loss=0.03026, over 1412199.78 frames.], batch size: 21, lr: 2.49e-04
2022-05-15 18:34:30,889 INFO [train.py:812] (6/8) Epoch 31, batch 4450, loss[loss=0.1671, simple_loss=0.2675, pruned_loss=0.03339, over 6348.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2439, pruned_loss=0.03027, over 1410494.89 frames.], batch size: 38, lr: 2.49e-04
2022-05-15 18:35:30,072 INFO [train.py:812] (6/8) Epoch 31, batch 4500, loss[loss=0.1503, simple_loss=0.2469, pruned_loss=0.02691, over 6468.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2453, pruned_loss=0.03072, over 1386205.53 frames.], batch size: 38, lr: 2.49e-04
2022-05-15 18:36:28,949 INFO [train.py:812] (6/8) Epoch 31, batch 4550, loss[loss=0.1596, simple_loss=0.2439, pruned_loss=0.03766, over 5261.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2464, pruned_loss=0.03126, over 1356252.32 frames.], batch size: 52, lr: 2.49e-04
2022-05-15 18:37:36,654 INFO [train.py:812] (6/8) Epoch 32, batch 0, loss[loss=0.1579, simple_loss=0.2552, pruned_loss=0.03026, over 5345.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2552, pruned_loss=0.03026, over 5345.00 frames.], batch size: 52, lr: 2.45e-04
2022-05-15 18:38:34,889 INFO [train.py:812] (6/8) Epoch 32, batch 50, loss[loss=0.1681, simple_loss=0.2624, pruned_loss=0.03692, over 6335.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2516, pruned_loss=0.03179, over 319930.44 frames.], batch size: 37, lr: 2.45e-04
2022-05-15 18:39:33,416 INFO [train.py:812] (6/8) Epoch 32, batch 100, loss[loss=0.1601, simple_loss=0.2479, pruned_loss=0.03621, over 7281.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2465, pruned_loss=0.03096, over 567283.82 frames.], batch size: 25, lr: 2.45e-04
2022-05-15 18:40:32,546 INFO [train.py:812] (6/8) Epoch 32, batch 150, loss[loss=0.1407, simple_loss=0.244, pruned_loss=0.0187, over 7209.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2453, pruned_loss=0.03083, over 758655.01 frames.], batch size: 26, lr: 2.45e-04
2022-05-15 18:41:31,138 INFO [train.py:812] (6/8) Epoch 32, batch 200, loss[loss=0.1408, simple_loss=0.2249, pruned_loss=0.02838, over 6988.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2446, pruned_loss=0.0299, over 903525.60 frames.], batch size: 16, lr: 2.45e-04
2022-05-15 18:42:29,428 INFO [train.py:812] (6/8) Epoch 32, batch 250, loss[loss=0.1528, simple_loss=0.2435, pruned_loss=0.03109, over 7287.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2458, pruned_loss=0.03053, over 1023161.75 frames.], batch size: 24, lr: 2.45e-04
2022-05-15 18:43:28,940 INFO [train.py:812] (6/8) Epoch 32, batch 300, loss[loss=0.2259, simple_loss=0.3091, pruned_loss=0.07138, over 7285.00 frames.], tot_loss[loss=0.1536, simple_loss=0.246, pruned_loss=0.03058, over 1113882.90 frames.], batch size: 24, lr: 2.45e-04
2022-05-15 18:44:28,374 INFO [train.py:812] (6/8) Epoch 32, batch 350, loss[loss=0.1484, simple_loss=0.2434, pruned_loss=0.02674, over 7035.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2447, pruned_loss=0.03006, over 1181643.05 frames.], batch size: 28, lr: 2.45e-04
2022-05-15 18:45:27,078 INFO [train.py:812] (6/8) Epoch 32, batch 400, loss[loss=0.1668, simple_loss=0.2584, pruned_loss=0.0376, over 7181.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2445, pruned_loss=0.03018, over 1236876.86 frames.], batch size: 26, lr: 2.45e-04
2022-05-15 18:46:25,926 INFO [train.py:812] (6/8) Epoch 32, batch 450, loss[loss=0.154, simple_loss=0.2574, pruned_loss=0.02529, over 7321.00 frames.], tot_loss[loss=0.152, simple_loss=0.2441, pruned_loss=0.02994, over 1277001.79 frames.], batch size: 21, lr: 2.45e-04
2022-05-15 18:47:25,091 INFO [train.py:812] (6/8) Epoch 32, batch 500, loss[loss=0.175, simple_loss=0.2684, pruned_loss=0.04085, over 7318.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2441, pruned_loss=0.0298, over 1313050.21 frames.], batch size: 22, lr: 2.45e-04
2022-05-15 18:48:23,095 INFO [train.py:812] (6/8) Epoch 32, batch 550, loss[loss=0.1661, simple_loss=0.2646, pruned_loss=0.03378, over 7334.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2442, pruned_loss=0.03002, over 1341712.51 frames.], batch size: 22, lr: 2.45e-04
2022-05-15 18:49:22,858 INFO [train.py:812] (6/8) Epoch 32, batch 600, loss[loss=0.1374, simple_loss=0.2148, pruned_loss=0.03004, over 7134.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2435, pruned_loss=0.02988, over 1364173.46 frames.], batch size: 17, lr: 2.45e-04
2022-05-15 18:50:21,232 INFO [train.py:812] (6/8) Epoch 32, batch 650, loss[loss=0.1309, simple_loss=0.215, pruned_loss=0.02335, over 6983.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2429, pruned_loss=0.02983, over 1379601.12 frames.], batch size: 16, lr: 2.45e-04
2022-05-15 18:51:18,834 INFO [train.py:812] (6/8) Epoch 32, batch 700, loss[loss=0.1569, simple_loss=0.2499, pruned_loss=0.03189, over 7198.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2439, pruned_loss=0.03031, over 1388245.74 frames.], batch size: 23, lr: 2.45e-04
2022-05-15 18:52:17,855 INFO [train.py:812] (6/8) Epoch 32, batch 750, loss[loss=0.16, simple_loss=0.2651, pruned_loss=0.02749, over 7122.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2444, pruned_loss=0.0304, over 1396277.54 frames.], batch size: 21, lr: 2.44e-04
2022-05-15 18:53:17,323 INFO [train.py:812] (6/8) Epoch 32, batch 800, loss[loss=0.139, simple_loss=0.2308, pruned_loss=0.02356, over 7289.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2445, pruned_loss=0.0302, over 1400862.64 frames.], batch size: 18, lr: 2.44e-04
2022-05-15 18:54:15,847 INFO [train.py:812] (6/8) Epoch 32, batch 850, loss[loss=0.1536, simple_loss=0.2418, pruned_loss=0.03274, over 7267.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2449, pruned_loss=0.03014, over 1408473.17 frames.], batch size: 25, lr: 2.44e-04
2022-05-15 18:55:14,228 INFO [train.py:812] (6/8) Epoch 32, batch 900, loss[loss=0.1435, simple_loss=0.2469, pruned_loss=0.02006, over 7337.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2456, pruned_loss=0.0301, over 1411417.67 frames.], batch size: 22, lr: 2.44e-04
2022-05-15 18:56:22,086 INFO [train.py:812] (6/8) Epoch 32, batch 950, loss[loss=0.1362, simple_loss=0.2275, pruned_loss=0.02243, over 7257.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2449, pruned_loss=0.03024, over 1413323.02 frames.], batch size: 16, lr: 2.44e-04
2022-05-15 18:57:31,057 INFO [train.py:812] (6/8) Epoch 32, batch 1000, loss[loss=0.1525, simple_loss=0.2575, pruned_loss=0.02374, over 7436.00 frames.], tot_loss[loss=0.1518, simple_loss=0.244, pruned_loss=0.02973, over 1417444.26 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 18:58:30,376 INFO [train.py:812] (6/8) Epoch 32, batch 1050, loss[loss=0.1639, simple_loss=0.2617, pruned_loss=0.03302, over 7239.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2437, pruned_loss=0.02968, over 1421111.08 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 18:59:29,294 INFO [train.py:812] (6/8) Epoch 32, batch 1100, loss[loss=0.1654, simple_loss=0.2582, pruned_loss=0.03627, over 7190.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2433, pruned_loss=0.02955, over 1418999.75 frames.], batch size: 22, lr: 2.44e-04
2022-05-15 19:00:36,750 INFO [train.py:812] (6/8) Epoch 32, batch 1150, loss[loss=0.1403, simple_loss=0.2192, pruned_loss=0.03073, over 7130.00 frames.], tot_loss[loss=0.1521, simple_loss=0.244, pruned_loss=0.03015, over 1422997.94 frames.], batch size: 17, lr: 2.44e-04
2022-05-15 19:01:36,499 INFO [train.py:812] (6/8) Epoch 32, batch 1200, loss[loss=0.1559, simple_loss=0.2488, pruned_loss=0.03147, over 7425.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2432, pruned_loss=0.02988, over 1425472.24 frames.], batch size: 21, lr: 2.44e-04
2022-05-15 19:02:45,193 INFO [train.py:812] (6/8) Epoch 32, batch 1250, loss[loss=0.1609, simple_loss=0.2536, pruned_loss=0.03414, over 7201.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2432, pruned_loss=0.02993, over 1418941.50 frames.], batch size: 23, lr: 2.44e-04
2022-05-15 19:03:53,741 INFO [train.py:812] (6/8) Epoch 32, batch 1300, loss[loss=0.176, simple_loss=0.2743, pruned_loss=0.03885, over 7151.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2432, pruned_loss=0.02987, over 1423735.94 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 19:05:00,953 INFO [train.py:812] (6/8) Epoch 32, batch 1350, loss[loss=0.1316, simple_loss=0.2214, pruned_loss=0.02094, over 7337.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2435, pruned_loss=0.02994, over 1421838.66 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 19:05:59,746 INFO [train.py:812] (6/8) Epoch 32, batch 1400, loss[loss=0.1446, simple_loss=0.2401, pruned_loss=0.02455, over 7239.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2435, pruned_loss=0.02979, over 1422229.62 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 19:06:57,270 INFO [train.py:812] (6/8) Epoch 32, batch 1450, loss[loss=0.1407, simple_loss=0.2378, pruned_loss=0.02185, over 7331.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2443, pruned_loss=0.02993, over 1423825.40 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 19:08:05,691 INFO [train.py:812] (6/8) Epoch 32, batch 1500, loss[loss=0.169, simple_loss=0.2569, pruned_loss=0.04058, over 5045.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2434, pruned_loss=0.02957, over 1422271.54 frames.], batch size: 52, lr: 2.44e-04
2022-05-15 19:09:04,133 INFO [train.py:812] (6/8) Epoch 32, batch 1550, loss[loss=0.1427, simple_loss=0.228, pruned_loss=0.02873, over 7397.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2429, pruned_loss=0.02943, over 1420895.62 frames.], batch size: 18, lr: 2.44e-04
2022-05-15 19:10:03,437 INFO [train.py:812] (6/8) Epoch 32, batch 1600, loss[loss=0.1669, simple_loss=0.2572, pruned_loss=0.03832, over 7195.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2431, pruned_loss=0.02981, over 1416962.76 frames.], batch size: 23, lr: 2.44e-04
2022-05-15 19:11:01,508 INFO [train.py:812] (6/8) Epoch 32, batch 1650, loss[loss=0.1434, simple_loss=0.2351, pruned_loss=0.02583, over 7412.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2442, pruned_loss=0.03016, over 1416673.87 frames.], batch size: 21, lr: 2.44e-04
2022-05-15 19:12:00,711 INFO [train.py:812] (6/8) Epoch 32, batch 1700, loss[loss=0.1424, simple_loss=0.2409, pruned_loss=0.022, over 7109.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2436, pruned_loss=0.02996, over 1412304.36 frames.], batch size: 21, lr: 2.44e-04
2022-05-15 19:12:59,712 INFO [train.py:812] (6/8) Epoch 32, batch 1750, loss[loss=0.1835, simple_loss=0.2588, pruned_loss=0.05403, over 5004.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2433, pruned_loss=0.02968, over 1410269.23 frames.], batch size: 53, lr: 2.44e-04
2022-05-15 19:14:04,618 INFO [train.py:812] (6/8) Epoch 32, batch 1800, loss[loss=0.1567, simple_loss=0.2476, pruned_loss=0.0329, over 7233.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2445, pruned_loss=0.0302, over 1411396.95 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 19:15:03,163 INFO [train.py:812] (6/8) Epoch 32, batch 1850, loss[loss=0.1316, simple_loss=0.2181, pruned_loss=0.02256, over 6992.00 frames.], tot_loss[loss=0.1523, simple_loss=0.244, pruned_loss=0.03032, over 1405183.63 frames.], batch size: 16, lr: 2.44e-04
2022-05-15 19:16:02,097 INFO [train.py:812] (6/8) Epoch 32, batch 1900, loss[loss=0.1372, simple_loss=0.2226, pruned_loss=0.02588, over 7352.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2421, pruned_loss=0.02974, over 1411898.75 frames.], batch size: 19, lr: 2.44e-04
2022-05-15 19:17:00,604 INFO [train.py:812] (6/8) Epoch 32, batch 1950, loss[loss=0.1352, simple_loss=0.2218, pruned_loss=0.02436, over 7362.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2418, pruned_loss=0.02962, over 1418106.26 frames.], batch size: 19, lr: 2.43e-04
2022-05-15 19:18:00,500 INFO [train.py:812] (6/8) Epoch 32, batch 2000, loss[loss=0.1364, simple_loss=0.2246, pruned_loss=0.02414, over 7283.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2427, pruned_loss=0.02977, over 1419338.02 frames.], batch size: 18, lr: 2.43e-04
2022-05-15 19:18:57,520 INFO [train.py:812] (6/8) Epoch 32, batch 2050, loss[loss=0.1596, simple_loss=0.2532, pruned_loss=0.03297, over 7137.00 frames.], tot_loss[loss=0.1516, simple_loss=0.243, pruned_loss=0.03008, over 1415758.59 frames.], batch size: 20, lr: 2.43e-04
2022-05-15 19:19:56,219 INFO [train.py:812] (6/8) Epoch 32, batch 2100, loss[loss=0.1268, simple_loss=0.2158, pruned_loss=0.01893, over 7239.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2448, pruned_loss=0.03042, over 1415942.67 frames.], batch size: 16, lr: 2.43e-04
2022-05-15 19:20:54,969 INFO [train.py:812] (6/8) Epoch 32, batch 2150, loss[loss=0.1474, simple_loss=0.2493, pruned_loss=0.02273, over 7212.00 frames.], tot_loss[loss=0.153, simple_loss=0.2452, pruned_loss=0.03038, over 1420134.22 frames.], batch size: 21, lr: 2.43e-04
2022-05-15 19:21:53,685 INFO [train.py:812] (6/8) Epoch 32, batch 2200, loss[loss=0.1707, simple_loss=0.263, pruned_loss=0.03915, over 7216.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2448, pruned_loss=0.03045, over 1423002.17 frames.], batch size: 26, lr: 2.43e-04
2022-05-15 19:22:52,779 INFO [train.py:812] (6/8) Epoch 32, batch 2250, loss[loss=0.1395, simple_loss=0.2364, pruned_loss=0.02129, over 7457.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2449, pruned_loss=0.03045, over 1425343.34 frames.], batch size: 19, lr: 2.43e-04
2022-05-15 19:23:52,316 INFO [train.py:812] (6/8) Epoch 32, batch 2300, loss[loss=0.1579, simple_loss=0.2596, pruned_loss=0.0281, over 7343.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2452, pruned_loss=0.03084, over 1422769.62 frames.], batch size: 22, lr: 2.43e-04
2022-05-15 19:24:49,789 INFO [train.py:812] (6/8) Epoch 32, batch 2350, loss[loss=0.1464, simple_loss=0.2284, pruned_loss=0.03215, over 7280.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2463, pruned_loss=0.03113, over 1426274.95 frames.], batch size: 17, lr: 2.43e-04
2022-05-15 19:25:48,464 INFO [train.py:812] (6/8) Epoch 32, batch 2400, loss[loss=0.1417, simple_loss=0.2286, pruned_loss=0.02737, over 7328.00 frames.], tot_loss[loss=0.1542, simple_loss=0.246, pruned_loss=0.03113, over 1422161.02 frames.], batch size: 20, lr: 2.43e-04
2022-05-15 19:26:47,729 INFO [train.py:812] (6/8) Epoch 32, batch 2450, loss[loss=0.1751, simple_loss=0.282, pruned_loss=0.03412, over 7105.00 frames.], tot_loss[loss=0.154, simple_loss=0.2457, pruned_loss=0.03113, over 1423491.99 frames.], batch size: 26, lr: 2.43e-04
2022-05-15 19:27:46,283 INFO [train.py:812] (6/8) Epoch 32, batch 2500, loss[loss=0.127, simple_loss=0.2164, pruned_loss=0.01882, over 7281.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2454, pruned_loss=0.0308, over 1425567.42 frames.], batch size: 17, lr: 2.43e-04
2022-05-15 19:28:44,163 INFO [train.py:812] (6/8) Epoch 32, batch 2550, loss[loss=0.1553, simple_loss=0.2453, pruned_loss=0.03268, over 7334.00 frames.], tot_loss[loss=0.1531, simple_loss=0.245, pruned_loss=0.03064, over 1423164.90 frames.], batch size: 20, lr: 2.43e-04
2022-05-15 19:29:41,350 INFO [train.py:812] (6/8) Epoch 32, batch 2600, loss[loss=0.1493, simple_loss=0.2387, pruned_loss=0.02996, over 7126.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2445, pruned_loss=0.03051, over 1421838.21 frames.], batch size: 17, lr: 2.43e-04
2022-05-15 19:30:39,828 INFO [train.py:812] (6/8) Epoch 32, batch 2650, loss[loss=0.1727, simple_loss=0.2691, pruned_loss=0.03818, over 7173.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2445, pruned_loss=0.03026, over 1424313.23 frames.], batch size: 26, lr: 2.43e-04
2022-05-15 19:31:39,501 INFO [train.py:812] (6/8) Epoch 32, batch 2700, loss[loss=0.1432, simple_loss=0.251, pruned_loss=0.01772, over 7331.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2441, pruned_loss=0.03029, over 1423385.05 frames.], batch size: 20, lr: 2.43e-04
2022-05-15 19:32:37,309 INFO [train.py:812] (6/8) Epoch 32, batch 2750, loss[loss=0.1385, simple_loss=0.2346, pruned_loss=0.02117, over 7029.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2435, pruned_loss=0.02988, over 1424917.77 frames.], batch size: 28, lr: 2.43e-04
2022-05-15 19:33:35,486 INFO [train.py:812] (6/8) Epoch 32, batch 2800, loss[loss=0.142, simple_loss=0.2219, pruned_loss=0.0311, over 7412.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2432, pruned_loss=0.0299, over 1423800.35 frames.], batch size: 18, lr: 2.43e-04
2022-05-15 19:34:34,372 INFO [train.py:812] (6/8) Epoch 32, batch 2850, loss[loss=0.1642, simple_loss=0.2727, pruned_loss=0.02781, over 6435.00 frames.], tot_loss[loss=0.1513, simple_loss=0.243, pruned_loss=0.02977, over 1420633.16 frames.], batch size: 38, lr: 2.43e-04
2022-05-15 19:35:32,687 INFO [train.py:812] (6/8) Epoch 32, batch 2900, loss[loss=0.1472, simple_loss=0.2416, pruned_loss=0.02635, over 7233.00 frames.], tot_loss[loss=0.152, simple_loss=0.2443, pruned_loss=0.02985, over 1424798.21 frames.], batch size: 20, lr: 2.43e-04
2022-05-15 19:36:30,947 INFO [train.py:812] (6/8) Epoch 32, batch 2950, loss[loss=0.1377, simple_loss=0.2318, pruned_loss=0.02178, over 7205.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2454, pruned_loss=0.03043, over 1418427.11 frames.], batch size: 23, lr: 2.43e-04
2022-05-15 19:37:29,695 INFO [train.py:812] (6/8) Epoch 32, batch 3000, loss[loss=0.1542, simple_loss=0.2431, pruned_loss=0.03264, over 7420.00 frames.], tot_loss[loss=0.153, simple_loss=0.2453, pruned_loss=0.03037, over 1418998.04 frames.], batch size: 20, lr: 2.43e-04
2022-05-15 19:37:29,696 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 19:37:37,095 INFO [train.py:841] (6/8) Epoch 32, validation: loss=0.1532, simple_loss=0.2494, pruned_loss=0.02852, over 698248.00 frames.
2022-05-15 19:38:35,490 INFO [train.py:812] (6/8) Epoch 32, batch 3050, loss[loss=0.1734, simple_loss=0.2723, pruned_loss=0.03726, over 7326.00 frames.], tot_loss[loss=0.1529, simple_loss=0.245, pruned_loss=0.03042, over 1422922.26 frames.], batch size: 25, lr: 2.43e-04
2022-05-15 19:39:34,754 INFO [train.py:812] (6/8) Epoch 32, batch 3100, loss[loss=0.1505, simple_loss=0.2492, pruned_loss=0.02586, over 7123.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2452, pruned_loss=0.03052, over 1425874.39 frames.], batch size: 28, lr: 2.42e-04
2022-05-15 19:40:34,198 INFO [train.py:812] (6/8) Epoch 32, batch 3150, loss[loss=0.1288, simple_loss=0.213, pruned_loss=0.02233, over 7260.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2447, pruned_loss=0.03034, over 1423199.72 frames.], batch size: 17, lr: 2.42e-04
2022-05-15 19:41:32,556 INFO [train.py:812] (6/8) Epoch 32, batch 3200, loss[loss=0.1414, simple_loss=0.2442, pruned_loss=0.01925, over 7119.00 frames.], tot_loss[loss=0.1525, simple_loss=0.245, pruned_loss=0.02999, over 1426187.25 frames.], batch size: 21, lr: 2.42e-04
2022-05-15 19:42:31,646 INFO [train.py:812] (6/8) Epoch 32, batch 3250, loss[loss=0.1431, simple_loss=0.235, pruned_loss=0.02563, over 7339.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2446, pruned_loss=0.02982, over 1427024.94 frames.], batch size: 22, lr: 2.42e-04
2022-05-15 19:43:31,275 INFO [train.py:812] (6/8) Epoch 32, batch 3300, loss[loss=0.1627, simple_loss=0.2494, pruned_loss=0.038, over 7443.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2441, pruned_loss=0.02982, over 1423176.83 frames.], batch size: 20, lr: 2.42e-04
2022-05-15 19:44:30,454 INFO [train.py:812] (6/8) Epoch 32, batch 3350, loss[loss=0.1572, simple_loss=0.2585, pruned_loss=0.02795, over 7328.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2426, pruned_loss=0.02949, over 1425224.26 frames.], batch size: 21, lr: 2.42e-04
2022-05-15 19:45:29,633 INFO [train.py:812] (6/8) Epoch 32, batch 3400, loss[loss=0.1623, simple_loss=0.2653, pruned_loss=0.02968, over 7328.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2438, pruned_loss=0.03016, over 1422569.09 frames.], batch size: 20, lr: 2.42e-04
2022-05-15 19:46:27,581 INFO [train.py:812] (6/8) Epoch 32, batch 3450, loss[loss=0.1588, simple_loss=0.2494, pruned_loss=0.03412, over 7204.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2449, pruned_loss=0.03044, over 1425620.02 frames.], batch size: 22, lr: 2.42e-04
2022-05-15 19:47:26,352 INFO [train.py:812] (6/8) Epoch 32, batch 3500, loss[loss=0.1386, simple_loss=0.2321, pruned_loss=0.02256, over 7288.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2453, pruned_loss=0.0305, over 1428503.71 frames.], batch size: 24, lr: 2.42e-04
2022-05-15 19:48:25,223 INFO [train.py:812] (6/8) Epoch 32, batch 3550, loss[loss=0.1597, simple_loss=0.267, pruned_loss=0.0262, over 7380.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2449, pruned_loss=0.03043, over 1431714.81 frames.], batch size: 23, lr: 2.42e-04
2022-05-15 19:49:24,754 INFO [train.py:812] (6/8) Epoch 32, batch 3600, loss[loss=0.1414, simple_loss=0.2386, pruned_loss=0.02213, over 6350.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2445, pruned_loss=0.03033, over 1428600.35 frames.], batch size: 37, lr: 2.42e-04
2022-05-15 19:50:24,101 INFO [train.py:812] (6/8) Epoch 32, batch 3650, loss[loss=0.1539, simple_loss=0.2555, pruned_loss=0.02617, over 7226.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2448, pruned_loss=0.03003, over 1428466.39 frames.], batch size: 20, lr: 2.42e-04
2022-05-15 19:51:24,146 INFO [train.py:812] (6/8) Epoch 32, batch 3700, loss[loss=0.1377, simple_loss=0.2204, pruned_loss=0.02745, over 7139.00 frames.], tot_loss[loss=0.152, simple_loss=0.2439, pruned_loss=0.02999, over 1430270.74 frames.], batch size: 17, lr: 2.42e-04
2022-05-15 19:52:22,805 INFO [train.py:812] (6/8) Epoch 32, batch 3750, loss[loss=0.1692, simple_loss=0.2605, pruned_loss=0.03898, over 7197.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2437, pruned_loss=0.03008, over 1425302.12 frames.], batch size: 23, lr: 2.42e-04
2022-05-15 19:53:21,638 INFO [train.py:812] (6/8) Epoch 32, batch 3800, loss[loss=0.1522, simple_loss=0.2424, pruned_loss=0.03103, over 7365.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2444, pruned_loss=0.02992, over 1426434.95 frames.], batch size: 23, lr: 2.42e-04
2022-05-15 19:54:19,353 INFO [train.py:812] (6/8) Epoch 32, batch 3850, loss[loss=0.1545, simple_loss=0.2524, pruned_loss=0.02829, over 7442.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2444, pruned_loss=0.03008, over 1428207.78 frames.], batch size: 20, lr: 2.42e-04
2022-05-15 19:55:27,962 INFO [train.py:812] (6/8) Epoch 32, batch 3900, loss[loss=0.1425, simple_loss=0.2354, pruned_loss=0.02485, over 7169.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2448, pruned_loss=0.02999, over 1429516.21 frames.], batch size: 18, lr: 2.42e-04
2022-05-15 19:56:25,328 INFO [train.py:812] (6/8) Epoch 32, batch 3950, loss[loss=0.1553, simple_loss=0.2546, pruned_loss=0.028, over 7209.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2458, pruned_loss=0.0305, over 1424215.40 frames.], batch size: 21, lr: 2.42e-04
2022-05-15 19:57:24,507 INFO [train.py:812] (6/8) Epoch 32, batch 4000, loss[loss=0.1367, simple_loss=0.2229, pruned_loss=0.02526, over 7417.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2446, pruned_loss=0.03016, over 1422458.30 frames.], batch size: 18, lr: 2.42e-04
2022-05-15 19:58:22,819 INFO [train.py:812] (6/8) Epoch 32, batch 4050, loss[loss=0.1839, simple_loss=0.2847, pruned_loss=0.04158, over 7397.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2444, pruned_loss=0.03047, over 1419837.83 frames.], batch size: 23, lr: 2.42e-04
2022-05-15 19:59:20,926 INFO [train.py:812] (6/8) Epoch 32, batch 4100, loss[loss=0.1601, simple_loss=0.2545, pruned_loss=0.0328, over 7202.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2451, pruned_loss=0.03079, over 1418148.42 frames.], batch size: 22, lr: 2.42e-04
2022-05-15 20:00:19,886 INFO [train.py:812] (6/8) Epoch 32, batch 4150, loss[loss=0.1585, simple_loss=0.2616, pruned_loss=0.02767, over 7210.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2442, pruned_loss=0.03011, over 1421576.05 frames.], batch size: 21, lr: 2.42e-04
2022-05-15 20:01:19,549 INFO [train.py:812] (6/8) Epoch 32, batch 4200, loss[loss=0.1554, simple_loss=0.2495, pruned_loss=0.03061, over 7330.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2432, pruned_loss=0.03008, over 1421549.55 frames.], batch size: 20, lr: 2.42e-04
2022-05-15 20:02:17,838 INFO [train.py:812] (6/8) Epoch 32, batch 4250, loss[loss=0.1343, simple_loss=0.2278, pruned_loss=0.02043, over 7245.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2431, pruned_loss=0.03013, over 1421279.60 frames.], batch size: 19, lr: 2.42e-04
2022-05-15 20:03:17,430 INFO [train.py:812] (6/8) Epoch 32, batch 4300, loss[loss=0.1286, simple_loss=0.2045, pruned_loss=0.02639, over 7409.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2424, pruned_loss=0.02977, over 1421273.75 frames.], batch size: 18, lr: 2.42e-04
2022-05-15 20:04:16,092 INFO [train.py:812] (6/8) Epoch 32, batch 4350, loss[loss=0.1481, simple_loss=0.244, pruned_loss=0.02609, over 7174.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2434, pruned_loss=0.0301, over 1421205.75 frames.], batch size: 18, lr: 2.41e-04
2022-05-15 20:05:14,967 INFO [train.py:812] (6/8) Epoch 32, batch 4400, loss[loss=0.1574, simple_loss=0.2585, pruned_loss=0.02815, over 7289.00 frames.], tot_loss[loss=0.152, simple_loss=0.2437, pruned_loss=0.03016, over 1408136.41 frames.], batch size: 25, lr: 2.41e-04
2022-05-15 20:06:12,568 INFO [train.py:812] (6/8) Epoch 32, batch 4450, loss[loss=0.1466, simple_loss=0.2305, pruned_loss=0.03134, over 6755.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2441, pruned_loss=0.03018, over 1404977.83 frames.], batch size: 15, lr: 2.41e-04
2022-05-15 20:07:11,390 INFO [train.py:812] (6/8) Epoch 32, batch 4500, loss[loss=0.151, simple_loss=0.2505, pruned_loss=0.02571, over 6812.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2446, pruned_loss=0.03025, over 1396924.05 frames.], batch size: 31, lr: 2.41e-04
2022-05-15 20:08:09,898 INFO [train.py:812] (6/8) Epoch 32, batch 4550, loss[loss=0.1592, simple_loss=0.2525, pruned_loss=0.03296, over 4976.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2446, pruned_loss=0.03097, over 1358903.04 frames.], batch size: 53, lr: 2.41e-04
2022-05-15 20:09:17,687 INFO [train.py:812] (6/8) Epoch 33, batch 0, loss[loss=0.1574, simple_loss=0.2485, pruned_loss=0.0332, over 6831.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2485, pruned_loss=0.0332, over 6831.00 frames.], batch size: 31, lr: 2.38e-04
2022-05-15 20:10:15,662 INFO [train.py:812] (6/8) Epoch 33, batch 50, loss[loss=0.1688, simple_loss=0.2691, pruned_loss=0.03429, over 5101.00 frames.], tot_loss[loss=0.1535, simple_loss=0.246, pruned_loss=0.03048, over 314714.02 frames.], batch size: 52, lr: 2.38e-04
2022-05-15 20:11:14,531 INFO [train.py:812] (6/8) Epoch 33, batch 100, loss[loss=0.1362, simple_loss=0.2352, pruned_loss=0.01855, over 6458.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2455, pruned_loss=0.0302, over 560159.46 frames.], batch size: 38, lr: 2.38e-04
2022-05-15 20:12:13,181 INFO [train.py:812] (6/8) Epoch 33, batch 150, loss[loss=0.1645, simple_loss=0.2574, pruned_loss=0.0358, over 7199.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2464, pruned_loss=0.03048, over 752579.24 frames.], batch size: 23, lr: 2.37e-04
2022-05-15 20:13:12,830 INFO [train.py:812] (6/8) Epoch 33, batch 200, loss[loss=0.1394, simple_loss=0.2208, pruned_loss=0.02903, over 7025.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2458, pruned_loss=0.03021, over 896162.79 frames.], batch size: 16, lr: 2.37e-04
2022-05-15 20:14:10,276 INFO [train.py:812] (6/8) Epoch 33, batch 250, loss[loss=0.1301, simple_loss=0.221, pruned_loss=0.01963, over 7237.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2457, pruned_loss=0.03032, over 1011011.70 frames.], batch size: 20, lr: 2.37e-04
2022-05-15 20:15:08,985 INFO [train.py:812] (6/8) Epoch 33, batch 300, loss[loss=0.1616, simple_loss=0.2579, pruned_loss=0.0326, over 6818.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2453, pruned_loss=0.03026, over 1094866.42 frames.], batch size: 31, lr: 2.37e-04
2022-05-15 20:16:07,609 INFO [train.py:812] (6/8) Epoch 33, batch 350, loss[loss=0.1166, simple_loss=0.2005, pruned_loss=0.01637, over 7399.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2445, pruned_loss=0.03015, over 1164998.62 frames.], batch size: 18, lr: 2.37e-04
2022-05-15 20:17:07,056 INFO [train.py:812] (6/8) Epoch 33, batch 400, loss[loss=0.1573, simple_loss=0.2431, pruned_loss=0.03569, over 7429.00 frames.], tot_loss[loss=0.151, simple_loss=0.2432, pruned_loss=0.02939, over 1222638.56 frames.], batch size: 20, lr: 2.37e-04
2022-05-15 20:18:06,473 INFO [train.py:812] (6/8) Epoch 33, batch 450, loss[loss=0.1538, simple_loss=0.2556, pruned_loss=0.02602, over 6722.00 frames.], tot_loss[loss=0.1511, simple_loss=0.243, pruned_loss=0.0296, over 1264063.51 frames.], batch size: 31, lr: 2.37e-04
2022-05-15 20:19:06,088 INFO [train.py:812] (6/8) Epoch 33, batch 500, loss[loss=0.1582, simple_loss=0.2525, pruned_loss=0.03193, over 7205.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2438, pruned_loss=0.02998, over 1302147.11 frames.], batch size: 23, lr: 2.37e-04
2022-05-15 20:20:04,297 INFO [train.py:812] (6/8) Epoch 33, batch 550, loss[loss=0.1605, simple_loss=0.2566, pruned_loss=0.03219, over 7321.00 frames.], tot_loss[loss=0.1529, simple_loss=0.245, pruned_loss=0.03033, over 1330383.35 frames.], batch size: 21, lr: 2.37e-04
2022-05-15 20:21:03,120 INFO [train.py:812] (6/8) Epoch 33, batch 600, loss[loss=0.1679, simple_loss=0.2584, pruned_loss=0.03869, over 7276.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2449, pruned_loss=0.0302, over 1348513.84 frames.], batch size: 24, lr: 2.37e-04
2022-05-15 20:22:00,741 INFO [train.py:812] (6/8) Epoch 33, batch 650, loss[loss=0.1567, simple_loss=0.2512, pruned_loss=0.03112, over 7208.00 frames.], tot_loss[loss=0.153, simple_loss=0.2455, pruned_loss=0.03019, over 1365465.76 frames.], batch size: 26, lr: 2.37e-04
2022-05-15 20:23:00,336 INFO [train.py:812] (6/8) Epoch 33, batch 700, loss[loss=0.1158, simple_loss=0.1982, pruned_loss=0.01666, over 7135.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2457, pruned_loss=0.03022, over 1375724.80 frames.], batch size: 17, lr: 2.37e-04
2022-05-15 20:23:58,666 INFO [train.py:812] (6/8) Epoch 33, batch 750, loss[loss=0.1607, simple_loss=0.2524, pruned_loss=0.03454, over 7213.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2462, pruned_loss=0.03025, over 1381387.17 frames.], batch size: 21, lr: 2.37e-04
2022-05-15 20:24:57,937 INFO [train.py:812] (6/8) Epoch 33, batch 800, loss[loss=0.1599, simple_loss=0.2524, pruned_loss=0.03364, over 7427.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2452, pruned_loss=0.03046, over 1393876.66 frames.], batch size: 20, lr: 2.37e-04
2022-05-15 20:25:55,909 INFO [train.py:812] (6/8) Epoch 33, batch 850, loss[loss=0.1674, simple_loss=0.2635, pruned_loss=0.03562, over 7382.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2453, pruned_loss=0.03062, over 1401501.58 frames.], batch size: 23, lr: 2.37e-04
2022-05-15 20:26:54,557 INFO [train.py:812] (6/8) Epoch 33, batch 900, loss[loss=0.1478, simple_loss=0.2459, pruned_loss=0.02482, over 7210.00 frames.], tot_loss[loss=0.152, simple_loss=0.244, pruned_loss=0.03, over 1411407.40 frames.], batch size: 23, lr: 2.37e-04
2022-05-15 20:27:51,777 INFO [train.py:812] (6/8) Epoch 33, batch 950, loss[loss=0.1499, simple_loss=0.2485, pruned_loss=0.0257, over 7431.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2443, pruned_loss=0.03021, over 1415758.89 frames.], batch size: 20, lr: 2.37e-04
2022-05-15 20:28:51,362 INFO [train.py:812] (6/8) Epoch 33, batch 1000, loss[loss=0.1556, simple_loss=0.2481, pruned_loss=0.03151, over 7210.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2441, pruned_loss=0.03017, over 1414930.30 frames.], batch size: 23, lr: 2.37e-04
2022-05-15 20:29:49,409 INFO [train.py:812] (6/8) Epoch 33, batch 1050, loss[loss=0.1855, simple_loss=0.285, pruned_loss=0.04305, over 7033.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2442, pruned_loss=0.0301, over 1413894.91 frames.], batch size: 28, lr: 2.37e-04
2022-05-15 20:30:48,563 INFO [train.py:812] (6/8) Epoch 33, batch 1100, loss[loss=0.152, simple_loss=0.2529, pruned_loss=0.02555, over 7271.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2447, pruned_loss=0.03036, over 1419147.99 frames.], batch size: 24, lr: 2.37e-04
2022-05-15 20:31:47,040 INFO [train.py:812] (6/8) Epoch 33, batch 1150, loss[loss=0.1566, simple_loss=0.2559, pruned_loss=0.02869, over 7199.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2445, pruned_loss=0.03026, over 1419857.42 frames.], batch size: 23, lr: 2.37e-04
2022-05-15 20:32:51,457 INFO [train.py:812] (6/8) Epoch 33, batch 1200, loss[loss=0.1856, simple_loss=0.2783, pruned_loss=0.04646, over 7186.00 frames.], tot_loss[loss=0.153, simple_loss=0.2451, pruned_loss=0.03048, over 1422501.35 frames.], batch size: 26, lr: 2.37e-04
2022-05-15 20:33:50,448 INFO [train.py:812] (6/8) Epoch 33, batch 1250, loss[loss=0.1585, simple_loss=0.2501, pruned_loss=0.03346, over 6277.00 frames.], tot_loss[loss=0.1529, simple_loss=0.245, pruned_loss=0.03037, over 1421245.04 frames.], batch size: 37, lr: 2.37e-04
2022-05-15 20:34:50,218 INFO [train.py:812] (6/8) Epoch 33, batch 1300, loss[loss=0.1451, simple_loss=0.2389, pruned_loss=0.02568, over 7220.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2448, pruned_loss=0.03044, over 1421635.05 frames.], batch size: 21, lr: 2.37e-04
2022-05-15 20:35:49,535 INFO [train.py:812] (6/8) Epoch 33, batch 1350, loss[loss=0.1351, simple_loss=0.2162, pruned_loss=0.02698, over 7274.00 frames.], tot_loss[loss=0.1521, simple_loss=0.244, pruned_loss=0.03007, over 1420428.93 frames.], batch size: 17, lr: 2.37e-04
2022-05-15 20:36:48,926 INFO [train.py:812] (6/8) Epoch 33, batch 1400, loss[loss=0.1613, simple_loss=0.252, pruned_loss=0.03526, over 7144.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2439, pruned_loss=0.03011, over 1422753.44 frames.], batch size: 20, lr: 2.36e-04
2022-05-15 20:37:47,565 INFO [train.py:812] (6/8) Epoch 33, batch 1450, loss[loss=0.1536, simple_loss=0.2499, pruned_loss=0.02868, over 6870.00 frames.], tot_loss[loss=0.152, simple_loss=0.2441, pruned_loss=0.03, over 1425351.78 frames.], batch size: 31, lr: 2.36e-04
2022-05-15 20:38:46,322 INFO [train.py:812] (6/8) Epoch 33, batch 1500, loss[loss=0.1617, simple_loss=0.2479, pruned_loss=0.03778, over 4857.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2444, pruned_loss=0.03011, over 1422432.94 frames.], batch size: 52, lr: 2.36e-04
2022-05-15 20:39:44,944 INFO [train.py:812] (6/8) Epoch 33, batch 1550, loss[loss=0.1438, simple_loss=0.2399, pruned_loss=0.02387, over 7211.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2445, pruned_loss=0.03023, over 1418542.51 frames.], batch size: 21, lr: 2.36e-04
2022-05-15 20:40:43,908 INFO [train.py:812] (6/8) Epoch 33, batch 1600, loss[loss=0.1678, simple_loss=0.2586, pruned_loss=0.0385, over 7418.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2449, pruned_loss=0.03036, over 1419808.71 frames.], batch size: 21, lr: 2.36e-04
2022-05-15 20:41:42,722 INFO [train.py:812] (6/8) Epoch 33, batch 1650, loss[loss=0.1451, simple_loss=0.2417, pruned_loss=0.02422, over 7223.00 frames.], tot_loss[loss=0.153, simple_loss=0.2447, pruned_loss=0.03061, over 1420463.48 frames.], batch size: 21, lr: 2.36e-04
2022-05-15 20:42:41,760 INFO [train.py:812] (6/8) Epoch 33, batch 1700, loss[loss=0.1938, simple_loss=0.2841, pruned_loss=0.05178, over 7281.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2447, pruned_loss=0.03052, over 1422956.50 frames.], batch size: 24, lr: 2.36e-04
2022-05-15 20:43:40,831 INFO [train.py:812] (6/8) Epoch 33, batch 1750, loss[loss=0.1465, simple_loss=0.2445, pruned_loss=0.02422, over 7066.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2455, pruned_loss=0.03085, over 1416002.95 frames.], batch size: 28, lr: 2.36e-04
2022-05-15 20:44:40,005 INFO [train.py:812] (6/8) Epoch 33, batch 1800, loss[loss=0.1297, simple_loss=0.2182, pruned_loss=0.02059, over 7259.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2455, pruned_loss=0.03067, over 1419259.30 frames.], batch size: 19, lr: 2.36e-04
2022-05-15 20:45:38,888 INFO [train.py:812] (6/8) Epoch 33, batch 1850, loss[loss=0.1522, simple_loss=0.2442, pruned_loss=0.03005, over 7322.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2458, pruned_loss=0.03042, over 1422334.48 frames.], batch size: 21, lr: 2.36e-04
2022-05-15 20:46:37,357 INFO [train.py:812] (6/8) Epoch 33, batch 1900, loss[loss=0.1629, simple_loss=0.2533, pruned_loss=0.03627, over 7387.00 frames.], tot_loss[loss=0.1528, simple_loss=0.245, pruned_loss=0.03031, over 1424945.82 frames.], batch size: 23, lr: 2.36e-04
2022-05-15 20:47:35,906 INFO [train.py:812] (6/8) Epoch 33, batch 1950, loss[loss=0.17, simple_loss=0.2585, pruned_loss=0.04076, over 7314.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2446, pruned_loss=0.03023, over 1423841.83 frames.], batch size: 24, lr: 2.36e-04
2022-05-15 20:48:34,899 INFO [train.py:812] (6/8) Epoch 33, batch 2000, loss[loss=0.1479, simple_loss=0.2437, pruned_loss=0.02604, over 6341.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2444, pruned_loss=0.03035, over 1424939.19 frames.], batch size: 37, lr: 2.36e-04
2022-05-15 20:49:32,718 INFO [train.py:812] (6/8) Epoch 33, batch 2050, loss[loss=0.1512, simple_loss=0.2446, pruned_loss=0.02884, over 7159.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2444, pruned_loss=0.03027, over 1425361.11 frames.], batch size: 18, lr: 2.36e-04
2022-05-15 20:50:32,332 INFO [train.py:812] (6/8) Epoch 33, batch 2100, loss[loss=0.1353, simple_loss=0.2382, pruned_loss=0.01625, over 7179.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2439, pruned_loss=0.03035, over 1426809.86 frames.], batch size: 19, lr: 2.36e-04
2022-05-15 20:51:30,246 INFO [train.py:812] (6/8) Epoch 33, batch 2150, loss[loss=0.1278, simple_loss=0.2106, pruned_loss=0.02246, over 7408.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2436, pruned_loss=0.03013, over 1427137.97 frames.], batch size: 18, lr: 2.36e-04
2022-05-15 20:52:28,387 INFO [train.py:812] (6/8) Epoch 33, batch 2200, loss[loss=0.2018, simple_loss=0.2936, pruned_loss=0.05501, over 5140.00 frames.], tot_loss[loss=0.152, simple_loss=0.2436, pruned_loss=0.0302, over 1422356.15 frames.], batch size: 52, lr: 2.36e-04
2022-05-15 20:53:26,618 INFO [train.py:812] (6/8) Epoch 33, batch 2250, loss[loss=0.158, simple_loss=0.2462, pruned_loss=0.03489, over 7210.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2431, pruned_loss=0.02986, over 1420354.82 frames.], batch size: 26, lr: 2.36e-04
2022-05-15 20:54:25,523 INFO [train.py:812] (6/8) Epoch 33, batch 2300, loss[loss=0.1484, simple_loss=0.2417, pruned_loss=0.02756, over 7216.00 frames.], tot_loss[loss=0.1517, simple_loss=0.243, pruned_loss=0.0302, over 1419471.48 frames.], batch size: 22, lr: 2.36e-04
2022-05-15 20:55:24,391 INFO [train.py:812] (6/8) Epoch 33, batch 2350, loss[loss=0.1379, simple_loss=0.2252, pruned_loss=0.02525, over 7273.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2423, pruned_loss=0.02977, over 1422629.35 frames.], batch size: 16, lr: 2.36e-04
2022-05-15 20:56:22,967 INFO [train.py:812] (6/8) Epoch 33, batch 2400, loss[loss=0.1618, simple_loss=0.2606, pruned_loss=0.03152, over 7428.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2423, pruned_loss=0.02962, over 1425354.67 frames.], batch size: 20, lr: 2.36e-04
2022-05-15 20:57:40,448 INFO [train.py:812] (6/8) Epoch 33, batch 2450, loss[loss=0.1504, simple_loss=0.2446, pruned_loss=0.02812, over 7263.00 frames.], tot_loss[loss=0.1504, simple_loss=0.242, pruned_loss=0.02938, over 1427148.57 frames.], batch size: 19, lr: 2.36e-04
2022-05-15 20:58:40,023 INFO [train.py:812] (6/8) Epoch 33, batch 2500, loss[loss=0.1616, simple_loss=0.2582, pruned_loss=0.03249, over 7322.00 frames.], tot_loss[loss=0.1503, simple_loss=0.242, pruned_loss=0.02928, over 1428837.80 frames.], batch size: 21, lr: 2.36e-04
2022-05-15 20:59:48,292 INFO [train.py:812] (6/8) Epoch 33, batch 2550, loss[loss=0.1459, simple_loss=0.2429, pruned_loss=0.02449, over 7378.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2412, pruned_loss=0.02912, over 1427467.23 frames.], batch size: 23, lr: 2.36e-04
2022-05-15 21:00:46,746 INFO [train.py:812] (6/8) Epoch 33, batch 2600, loss[loss=0.1796, simple_loss=0.2746, pruned_loss=0.04227, over 7204.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2414, pruned_loss=0.02906, over 1427160.77 frames.], batch size: 23, lr: 2.36e-04
2022-05-15 21:01:44,981 INFO [train.py:812] (6/8) Epoch 33, batch 2650, loss[loss=0.134, simple_loss=0.2157, pruned_loss=0.02615, over 7214.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2417, pruned_loss=0.02927, over 1422653.54 frames.], batch size: 16, lr: 2.35e-04
2022-05-15 21:02:52,798 INFO [train.py:812] (6/8) Epoch 33, batch 2700, loss[loss=0.1339, simple_loss=0.2194, pruned_loss=0.02417, over 7435.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2424, pruned_loss=0.02955, over 1424714.01 frames.], batch size: 20, lr: 2.35e-04
2022-05-15 21:04:10,673 INFO [train.py:812] (6/8) Epoch 33, batch 2750, loss[loss=0.1577, simple_loss=0.2449, pruned_loss=0.03521, over 7269.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2431, pruned_loss=0.02986, over 1425437.83 frames.], batch size: 18, lr: 2.35e-04
2022-05-15 21:05:09,534 INFO [train.py:812] (6/8) Epoch 33, batch 2800, loss[loss=0.1602, simple_loss=0.2632, pruned_loss=0.02859, over 7184.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2424, pruned_loss=0.02989, over 1424670.37 frames.], batch size: 23, lr: 2.35e-04
2022-05-15 21:06:07,224 INFO [train.py:812] (6/8) Epoch 33, batch 2850, loss[loss=0.1625, simple_loss=0.2528, pruned_loss=0.03605, over 7311.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2421, pruned_loss=0.02975, over 1426213.58 frames.], batch size: 21, lr: 2.35e-04
2022-05-15 21:07:06,365 INFO [train.py:812] (6/8) Epoch 33, batch 2900, loss[loss=0.1433, simple_loss=0.2434, pruned_loss=0.02157, over 7276.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2429, pruned_loss=0.03006, over 1425351.98 frames.], batch size: 25, lr: 2.35e-04
2022-05-15 21:08:04,498 INFO [train.py:812] (6/8) Epoch 33, batch 2950, loss[loss=0.151, simple_loss=0.2444, pruned_loss=0.02879, over 7423.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2437, pruned_loss=0.03007, over 1427408.85 frames.], batch size: 20, lr: 2.35e-04
2022-05-15 21:09:12,197 INFO [train.py:812] (6/8) Epoch 33, batch 3000, loss[loss=0.1329, simple_loss=0.2232, pruned_loss=0.02127, over 7058.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2436, pruned_loss=0.02987, over 1426159.29 frames.], batch size: 18, lr: 2.35e-04
2022-05-15 21:09:12,198 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 21:09:19,691 INFO [train.py:841] (6/8) Epoch 33, validation: loss=0.1535, simple_loss=0.2493, pruned_loss=0.02886, over 698248.00 frames.
2022-05-15 21:10:18,076 INFO [train.py:812] (6/8) Epoch 33, batch 3050, loss[loss=0.1695, simple_loss=0.2652, pruned_loss=0.03694, over 6412.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2428, pruned_loss=0.02964, over 1422932.31 frames.], batch size: 38, lr: 2.35e-04
2022-05-15 21:11:15,935 INFO [train.py:812] (6/8) Epoch 33, batch 3100, loss[loss=0.1628, simple_loss=0.255, pruned_loss=0.03526, over 7372.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2433, pruned_loss=0.02971, over 1424189.30 frames.], batch size: 23, lr: 2.35e-04
2022-05-15 21:12:14,887 INFO [train.py:812] (6/8) Epoch 33, batch 3150, loss[loss=0.1226, simple_loss=0.2115, pruned_loss=0.01686, over 7065.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2426, pruned_loss=0.02954, over 1422412.38 frames.], batch size: 18, lr: 2.35e-04
2022-05-15 21:13:13,037 INFO [train.py:812] (6/8) Epoch 33, batch 3200, loss[loss=0.1581, simple_loss=0.2396, pruned_loss=0.03829, over 7180.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2432, pruned_loss=0.02951, over 1422959.67 frames.], batch size: 16, lr: 2.35e-04
2022-05-15 21:14:11,711 INFO [train.py:812] (6/8) Epoch 33, batch 3250, loss[loss=0.1433, simple_loss=0.2351, pruned_loss=0.02574, over 7267.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2435, pruned_loss=0.02982, over 1420775.47 frames.], batch size: 18, lr: 2.35e-04
2022-05-15 21:15:11,686 INFO [train.py:812] (6/8) Epoch 33, batch 3300, loss[loss=0.1613, simple_loss=0.2597, pruned_loss=0.03146, over 7231.00 frames.], tot_loss[loss=0.1504, simple_loss=0.242, pruned_loss=0.02934, over 1425537.31 frames.], batch size: 20, lr: 2.35e-04
2022-05-15 21:16:10,456 INFO [train.py:812] (6/8) Epoch 33, batch 3350, loss[loss=0.1526, simple_loss=0.2432, pruned_loss=0.03096, over 7317.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2419, pruned_loss=0.02893, over 1428690.81 frames.], batch size: 21, lr: 2.35e-04
2022-05-15 21:17:09,955 INFO [train.py:812] (6/8) Epoch 33, batch 3400, loss[loss=0.1187, simple_loss=0.2026, pruned_loss=0.01743, over 7277.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2423, pruned_loss=0.02941, over 1428321.93 frames.], batch size: 18, lr: 2.35e-04
2022-05-15 21:18:09,774 INFO [train.py:812] (6/8) Epoch 33, batch 3450, loss[loss=0.1433, simple_loss=0.2402, pruned_loss=0.02317, over 7328.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2426, pruned_loss=0.02945, over 1431872.95 frames.], batch size: 20, lr: 2.35e-04
2022-05-15 21:19:07,589 INFO [train.py:812] (6/8) Epoch 33, batch 3500, loss[loss=0.1424, simple_loss=0.2299, pruned_loss=0.02741, over 7376.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2435, pruned_loss=0.02972, over 1428012.86 frames.], batch size: 23, lr: 2.35e-04
2022-05-15 21:20:05,741 INFO [train.py:812] (6/8) Epoch 33, batch 3550, loss[loss=0.1273, simple_loss=0.2108, pruned_loss=0.0219, over 7415.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2426, pruned_loss=0.02958, over 1426970.75 frames.], batch size: 18, lr: 2.35e-04
2022-05-15 21:21:04,482 INFO [train.py:812] (6/8) Epoch 33, batch 3600, loss[loss=0.1473, simple_loss=0.2408, pruned_loss=0.02686, over 7329.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2422, pruned_loss=0.02926, over 1424244.47 frames.], batch size: 20, lr: 2.35e-04
2022-05-15 21:22:03,606 INFO [train.py:812] (6/8) Epoch 33, batch 3650, loss[loss=0.1363, simple_loss=0.231, pruned_loss=0.02082, over 7327.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2418, pruned_loss=0.02921, over 1423735.72 frames.], batch size: 20, lr: 2.35e-04
2022-05-15 21:23:02,545 INFO [train.py:812] (6/8) Epoch 33, batch 3700, loss[loss=0.1182, simple_loss=0.2004, pruned_loss=0.01801, over 7273.00 frames.], tot_loss[loss=0.1512, simple_loss=0.243, pruned_loss=0.02972, over 1427291.49 frames.], batch size: 17, lr: 2.35e-04
2022-05-15 21:24:01,178 INFO [train.py:812] (6/8) Epoch 33, batch 3750, loss[loss=0.1299, simple_loss=0.2288, pruned_loss=0.01549, over 7216.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2432, pruned_loss=0.02991, over 1426897.17 frames.], batch size: 21, lr: 2.35e-04
2022-05-15 21:25:00,733 INFO [train.py:812] (6/8) Epoch 33, batch 3800, loss[loss=0.1552, simple_loss=0.256, pruned_loss=0.02714, over 7207.00 frames.], tot_loss[loss=0.1514, simple_loss=0.243, pruned_loss=0.02986, over 1427930.77 frames.], batch size: 23, lr: 2.35e-04
2022-05-15 21:25:58,511 INFO [train.py:812] (6/8) Epoch 33, batch 3850, loss[loss=0.1636, simple_loss=0.2605, pruned_loss=0.03334, over 7324.00 frames.], tot_loss[loss=0.1512, simple_loss=0.243, pruned_loss=0.02975, over 1428214.61 frames.], batch size: 21, lr: 2.35e-04
2022-05-15 21:26:57,082 INFO [train.py:812] (6/8) Epoch 33, batch 3900, loss[loss=0.1272, simple_loss=0.2103, pruned_loss=0.02204, over 6809.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2444, pruned_loss=0.02992, over 1428341.60 frames.], batch size: 15, lr: 2.35e-04
2022-05-15 21:27:55,774 INFO [train.py:812] (6/8) Epoch 33, batch 3950, loss[loss=0.1388, simple_loss=0.2248, pruned_loss=0.02643, over 7406.00 frames.], tot_loss[loss=0.1524, simple_loss=0.245, pruned_loss=0.02993, over 1430808.84 frames.], batch size: 18, lr: 2.34e-04
2022-05-15 21:28:55,542 INFO [train.py:812] (6/8) Epoch 33, batch 4000, loss[loss=0.1695, simple_loss=0.2673, pruned_loss=0.03582, over 6276.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2433, pruned_loss=0.02945, over 1430866.76 frames.], batch size: 37, lr: 2.34e-04
2022-05-15 21:29:54,331 INFO [train.py:812] (6/8) Epoch 33, batch 4050, loss[loss=0.1466, simple_loss=0.2402, pruned_loss=0.02648, over 7272.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2437, pruned_loss=0.02975, over 1427513.90 frames.], batch size: 18, lr: 2.34e-04
2022-05-15 21:30:52,676 INFO [train.py:812] (6/8) Epoch 33, batch 4100, loss[loss=0.1518, simple_loss=0.2545, pruned_loss=0.02459, over 7198.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2433, pruned_loss=0.02987, over 1422257.22 frames.], batch size: 26, lr: 2.34e-04
2022-05-15 21:31:50,564 INFO [train.py:812] (6/8) Epoch 33, batch 4150, loss[loss=0.1182, simple_loss=0.1993, pruned_loss=0.01852, over 7271.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2435, pruned_loss=0.02997, over 1422410.56 frames.], batch size: 16, lr: 2.34e-04
2022-05-15 21:32:49,108 INFO [train.py:812] (6/8) Epoch 33, batch 4200, loss[loss=0.1449, simple_loss=0.2423, pruned_loss=0.02376, over 7257.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2431, pruned_loss=0.0297, over 1421548.85 frames.], batch size: 19, lr: 2.34e-04
2022-05-15 21:33:48,274 INFO [train.py:812] (6/8) Epoch 33, batch 4250, loss[loss=0.1353, simple_loss=0.2287, pruned_loss=0.02098, over 7419.00 frames.], tot_loss[loss=0.1509, simple_loss=0.243, pruned_loss=0.02935, over 1421153.14 frames.], batch size: 20, lr: 2.34e-04
2022-05-15 21:34:46,563 INFO [train.py:812] (6/8) Epoch 33, batch 4300, loss[loss=0.1677, simple_loss=0.2623, pruned_loss=0.03658, over 6740.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2431, pruned_loss=0.02934, over 1419606.40 frames.], batch size: 31, lr: 2.34e-04
2022-05-15 21:35:44,812 INFO [train.py:812] (6/8) Epoch 33, batch 4350, loss[loss=0.1368, simple_loss=0.2345, pruned_loss=0.01954, over 7227.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2417, pruned_loss=0.02888, over 1416427.68 frames.], batch size: 21, lr: 2.34e-04
2022-05-15 21:36:43,643 INFO [train.py:812] (6/8) Epoch 33, batch 4400, loss[loss=0.1634, simple_loss=0.2605, pruned_loss=0.03317, over 7141.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2414, pruned_loss=0.02866, over 1414669.16 frames.], batch size: 20, lr: 2.34e-04
2022-05-15 21:37:42,039 INFO [train.py:812] (6/8) Epoch 33, batch 4450, loss[loss=0.148, simple_loss=0.2466, pruned_loss=0.02472, over 7321.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2422, pruned_loss=0.02908, over 1406773.68 frames.], batch size: 22, lr: 2.34e-04
2022-05-15 21:38:41,162 INFO [train.py:812] (6/8) Epoch 33, batch 4500, loss[loss=0.143, simple_loss=0.2466, pruned_loss=0.01973, over 7149.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2429, pruned_loss=0.02933, over 1395870.77 frames.], batch size: 20, lr: 2.34e-04
2022-05-15 21:39:39,855 INFO [train.py:812] (6/8) Epoch 33, batch 4550, loss[loss=0.1715, simple_loss=0.2608, pruned_loss=0.04107, over 5229.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2439, pruned_loss=0.02998, over 1374694.55 frames.], batch size: 52, lr: 2.34e-04
2022-05-15 21:40:52,131 INFO [train.py:812] (6/8) Epoch 34, batch 0, loss[loss=0.1667, simple_loss=0.2535, pruned_loss=0.03992, over 7418.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2535, pruned_loss=0.03992, over 7418.00 frames.], batch size: 20, lr: 2.31e-04
2022-05-15 21:41:51,346 INFO [train.py:812] (6/8) Epoch 34, batch 50, loss[loss=0.1508, simple_loss=0.2453, pruned_loss=0.02811, over 7121.00 frames.], tot_loss[loss=0.1483, simple_loss=0.239, pruned_loss=0.02881, over 324405.69 frames.], batch size: 28, lr: 2.30e-04
2022-05-15 21:42:51,074 INFO [train.py:812] (6/8) Epoch 34, batch 100, loss[loss=0.1444, simple_loss=0.2452, pruned_loss=0.02177, over 7122.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2427, pruned_loss=0.03022, over 565417.97 frames.], batch size: 21, lr: 2.30e-04
2022-05-15 21:43:50,308 INFO [train.py:812] (6/8) Epoch 34, batch 150, loss[loss=0.1435, simple_loss=0.2346, pruned_loss=0.02624, over 7061.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2418, pruned_loss=0.02988, over 755275.28 frames.], batch size: 18, lr: 2.30e-04
2022-05-15 21:44:49,641 INFO [train.py:812] (6/8) Epoch 34, batch 200, loss[loss=0.1354, simple_loss=0.2254, pruned_loss=0.02273, over 7270.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2413, pruned_loss=0.02928, over 905192.66 frames.], batch size: 17, lr: 2.30e-04
2022-05-15 21:45:48,786 INFO [train.py:812] (6/8) Epoch 34, batch 250, loss[loss=0.1786, simple_loss=0.2625, pruned_loss=0.0473, over 4792.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2412, pruned_loss=0.02916, over 1011036.74 frames.], batch size: 52, lr: 2.30e-04
2022-05-15 21:46:48,768 INFO [train.py:812] (6/8) Epoch 34, batch 300, loss[loss=0.1495, simple_loss=0.2503, pruned_loss=0.02434, over 7384.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2423, pruned_loss=0.02961, over 1101909.20 frames.], batch size: 23, lr: 2.30e-04
2022-05-15 21:47:46,245 INFO [train.py:812] (6/8) Epoch 34, batch 350, loss[loss=0.1299, simple_loss=0.2153, pruned_loss=0.02227, over 7137.00 frames.], tot_loss[loss=0.1522, simple_loss=0.244, pruned_loss=0.03019, over 1167266.61 frames.], batch size: 17, lr: 2.30e-04
2022-05-15 21:48:46,234 INFO [train.py:812] (6/8) Epoch 34, batch 400, loss[loss=0.1815, simple_loss=0.2712, pruned_loss=0.04586, over 7422.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2432, pruned_loss=0.02986, over 1227954.01 frames.], batch size: 21, lr: 2.30e-04
2022-05-15 21:49:44,747 INFO [train.py:812] (6/8) Epoch 34, batch 450, loss[loss=0.1428, simple_loss=0.2293, pruned_loss=0.02814, over 7400.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2433, pruned_loss=0.02985, over 1272782.47 frames.], batch size: 18, lr: 2.30e-04
2022-05-15 21:50:44,155 INFO [train.py:812] (6/8) Epoch 34, batch 500, loss[loss=0.1326, simple_loss=0.2383, pruned_loss=0.01347, over 7303.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2443, pruned_loss=0.03016, over 1305505.87 frames.], batch size: 24, lr: 2.30e-04
2022-05-15 21:51:42,572 INFO [train.py:812] (6/8) Epoch 34, batch 550, loss[loss=0.15, simple_loss=0.2496, pruned_loss=0.02524, over 6325.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2441, pruned_loss=0.03009, over 1329055.91 frames.], batch size: 37, lr: 2.30e-04
2022-05-15 21:52:57,384 INFO [train.py:812] (6/8) Epoch 34, batch 600, loss[loss=0.1662, simple_loss=0.2625, pruned_loss=0.03499, over 7316.00 frames.], tot_loss[loss=0.152, simple_loss=0.2443, pruned_loss=0.0299, over 1351594.53 frames.], batch size: 25, lr: 2.30e-04
2022-05-15 21:53:55,934 INFO [train.py:812] (6/8) Epoch 34, batch 650, loss[loss=0.1449, simple_loss=0.2254, pruned_loss=0.03218, over 7158.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2443, pruned_loss=0.03011, over 1369781.96 frames.], batch size: 18, lr: 2.30e-04
2022-05-15 21:54:54,875 INFO [train.py:812] (6/8) Epoch 34, batch 700, loss[loss=0.1427, simple_loss=0.2207, pruned_loss=0.03236, over 7140.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2431, pruned_loss=0.02978, over 1377555.31 frames.], batch size: 17, lr: 2.30e-04
2022-05-15 21:55:51,398 INFO [train.py:812] (6/8) Epoch 34, batch 750, loss[loss=0.1592, simple_loss=0.2521, pruned_loss=0.03315, over 7201.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2433, pruned_loss=0.02952, over 1388778.20 frames.], batch size: 23, lr: 2.30e-04
2022-05-15 21:56:50,534 INFO [train.py:812] (6/8) Epoch 34, batch 800, loss[loss=0.1296, simple_loss=0.2082, pruned_loss=0.02553, over 7260.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2436, pruned_loss=0.02976, over 1394794.14 frames.], batch size: 18, lr: 2.30e-04
2022-05-15 21:57:49,853 INFO [train.py:812] (6/8) Epoch 34, batch 850, loss[loss=0.154, simple_loss=0.2572, pruned_loss=0.02545, over 6535.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2438, pruned_loss=0.02992, over 1404980.25 frames.], batch size: 38, lr: 2.30e-04
2022-05-15 21:58:48,172 INFO [train.py:812] (6/8) Epoch 34, batch 900, loss[loss=0.1736, simple_loss=0.2606, pruned_loss=0.04337, over 5128.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2433, pruned_loss=0.02983, over 1409791.34 frames.], batch size: 52, lr: 2.30e-04
2022-05-15 21:59:45,399 INFO [train.py:812] (6/8) Epoch 34, batch 950, loss[loss=0.147, simple_loss=0.2393, pruned_loss=0.02734, over 7279.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2434, pruned_loss=0.03019, over 1408779.41 frames.], batch size: 18, lr: 2.30e-04
2022-05-15 22:00:43,723 INFO [train.py:812] (6/8) Epoch 34, batch 1000, loss[loss=0.1462, simple_loss=0.2343, pruned_loss=0.02902, over 7428.00 frames.], tot_loss[loss=0.1519, simple_loss=0.243, pruned_loss=0.03037, over 1409959.96 frames.], batch size: 20, lr: 2.30e-04
2022-05-15 22:01:41,773 INFO [train.py:812] (6/8) Epoch 34, batch 1050, loss[loss=0.1401, simple_loss=0.238, pruned_loss=0.02112, over 7174.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2425, pruned_loss=0.02995, over 1415501.72 frames.], batch size: 19, lr: 2.30e-04
2022-05-15 22:02:40,801 INFO [train.py:812] (6/8) Epoch 34, batch 1100, loss[loss=0.1608, simple_loss=0.2626, pruned_loss=0.02952, over 6277.00 frames.], tot_loss[loss=0.151, simple_loss=0.2429, pruned_loss=0.02957, over 1413821.94 frames.], batch size: 38, lr: 2.30e-04
2022-05-15 22:03:39,424 INFO [train.py:812] (6/8) Epoch 34, batch 1150, loss[loss=0.1548, simple_loss=0.2417, pruned_loss=0.03394, over 7438.00 frames.], tot_loss[loss=0.1505, simple_loss=0.242, pruned_loss=0.02948, over 1416033.50 frames.], batch size: 20, lr: 2.30e-04
2022-05-15 22:04:38,155 INFO [train.py:812] (6/8) Epoch 34, batch 1200, loss[loss=0.1867, simple_loss=0.2729, pruned_loss=0.05027, over 7218.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2423, pruned_loss=0.02972, over 1420290.47 frames.], batch size: 23, lr: 2.30e-04
2022-05-15 22:05:35,696 INFO [train.py:812] (6/8) Epoch 34, batch 1250, loss[loss=0.1446, simple_loss=0.24, pruned_loss=0.02454, over 7322.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2425, pruned_loss=0.02988, over 1418105.78 frames.], batch size: 22, lr: 2.30e-04
2022-05-15 22:06:34,735 INFO [train.py:812] (6/8) Epoch 34, batch 1300, loss[loss=0.1513, simple_loss=0.2458, pruned_loss=0.02837, over 7234.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2424, pruned_loss=0.03002, over 1417954.69 frames.], batch size: 26, lr: 2.30e-04
2022-05-15 22:07:33,176 INFO [train.py:812] (6/8) Epoch 34, batch 1350, loss[loss=0.1417, simple_loss=0.236, pruned_loss=0.02369, over 7215.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2417, pruned_loss=0.02958, over 1418629.98 frames.], batch size: 21, lr: 2.29e-04
2022-05-15 22:08:32,148 INFO [train.py:812] (6/8) Epoch 34, batch 1400, loss[loss=0.1376, simple_loss=0.2272, pruned_loss=0.02405, over 7255.00 frames.], tot_loss[loss=0.15, simple_loss=0.2412, pruned_loss=0.02937, over 1423054.63 frames.], batch size: 19, lr: 2.29e-04
2022-05-15 22:09:31,156 INFO [train.py:812] (6/8) Epoch 34, batch 1450, loss[loss=0.1718, simple_loss=0.2504, pruned_loss=0.04665, over 7423.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2418, pruned_loss=0.02925, over 1426081.82 frames.], batch size: 21, lr: 2.29e-04
2022-05-15 22:10:29,319 INFO [train.py:812] (6/8) Epoch 34, batch 1500, loss[loss=0.1511, simple_loss=0.2425, pruned_loss=0.02985, over 7370.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2426, pruned_loss=0.02958, over 1424898.88 frames.], batch size: 23, lr: 2.29e-04
2022-05-15 22:11:28,527 INFO [train.py:812] (6/8) Epoch 34, batch 1550, loss[loss=0.1485, simple_loss=0.2414, pruned_loss=0.0278, over 7296.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2431, pruned_loss=0.02966, over 1422187.34 frames.], batch size: 24, lr: 2.29e-04
2022-05-15 22:12:27,933 INFO [train.py:812] (6/8) Epoch 34, batch 1600, loss[loss=0.1291, simple_loss=0.2218, pruned_loss=0.01822, over 7330.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2429, pruned_loss=0.02942, over 1422828.26 frames.], batch size: 20, lr: 2.29e-04
2022-05-15 22:13:26,017 INFO [train.py:812] (6/8) Epoch 34, batch 1650, loss[loss=0.1815, simple_loss=0.2689, pruned_loss=0.047, over 7197.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2443, pruned_loss=0.03009, over 1422483.38 frames.], batch size: 22, lr: 2.29e-04
2022-05-15 22:14:25,175 INFO [train.py:812] (6/8) Epoch 34, batch 1700, loss[loss=0.1685, simple_loss=0.2539, pruned_loss=0.04151, over 7376.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2445, pruned_loss=0.03007, over 1426769.34 frames.], batch size: 23, lr: 2.29e-04
2022-05-15 22:15:24,027 INFO [train.py:812] (6/8) Epoch 34, batch 1750, loss[loss=0.1535, simple_loss=0.2513, pruned_loss=0.02785, over 7018.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2446, pruned_loss=0.03025, over 1421472.99 frames.], batch size: 28, lr: 2.29e-04
2022-05-15 22:16:22,639 INFO [train.py:812] (6/8) Epoch 34, batch 1800, loss[loss=0.135, simple_loss=0.2173, pruned_loss=0.02637, over 7288.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2448, pruned_loss=0.03035, over 1422670.08 frames.], batch size: 17, lr: 2.29e-04
2022-05-15 22:17:21,605 INFO [train.py:812] (6/8) Epoch 34, batch 1850, loss[loss=0.1491, simple_loss=0.2471, pruned_loss=0.02555, over 7312.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2445, pruned_loss=0.03027, over 1415896.02 frames.], batch size: 21, lr: 2.29e-04
2022-05-15 22:18:20,747 INFO [train.py:812] (6/8) Epoch 34, batch 1900, loss[loss=0.1509, simple_loss=0.2433, pruned_loss=0.02928, over 6772.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2443, pruned_loss=0.02995, over 1412257.41 frames.], batch size: 31, lr: 2.29e-04
2022-05-15 22:19:17,935 INFO [train.py:812] (6/8) Epoch 34, batch 1950, loss[loss=0.1326, simple_loss=0.2101, pruned_loss=0.02754, over 6995.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2442, pruned_loss=0.02995, over 1418354.37 frames.], batch size: 16, lr: 2.29e-04
2022-05-15 22:20:16,785 INFO [train.py:812] (6/8) Epoch 34, batch 2000, loss[loss=0.1202, simple_loss=0.2077, pruned_loss=0.01638, over 7403.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2444, pruned_loss=0.03007, over 1422879.51 frames.], batch size: 18, lr: 2.29e-04
2022-05-15 22:21:15,733 INFO [train.py:812] (6/8) Epoch 34, batch 2050, loss[loss=0.1622, simple_loss=0.2637, pruned_loss=0.03031, over 7214.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2433, pruned_loss=0.02964, over 1422188.56 frames.], batch size: 26, lr: 2.29e-04
2022-05-15 22:22:14,742 INFO [train.py:812] (6/8) Epoch 34, batch 2100, loss[loss=0.1745, simple_loss=0.2655, pruned_loss=0.04171, over 7196.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2441, pruned_loss=0.02983, over 1424330.40 frames.], batch size: 23, lr: 2.29e-04
2022-05-15 22:23:12,305 INFO [train.py:812] (6/8) Epoch 34, batch 2150, loss[loss=0.1734, simple_loss=0.2747, pruned_loss=0.03609, over 7281.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2435, pruned_loss=0.02957, over 1422634.16 frames.], batch size: 24, lr: 2.29e-04
2022-05-15 22:24:11,549 INFO [train.py:812] (6/8) Epoch 34, batch 2200, loss[loss=0.1611, simple_loss=0.261, pruned_loss=0.03058, over 7320.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2442, pruned_loss=0.02954, over 1425696.85 frames.], batch size: 21, lr: 2.29e-04
2022-05-15 22:25:10,886 INFO [train.py:812] (6/8) Epoch 34, batch 2250, loss[loss=0.1376, simple_loss=0.2282, pruned_loss=0.02352, over 7290.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2441, pruned_loss=0.02979, over 1423186.45 frames.], batch size: 18, lr: 2.29e-04
2022-05-15 22:26:09,583 INFO [train.py:812] (6/8) Epoch 34, batch 2300, loss[loss=0.1743, simple_loss=0.2649, pruned_loss=0.04189, over 7144.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2446, pruned_loss=0.03001, over 1424644.15 frames.], batch size: 19, lr: 2.29e-04
2022-05-15 22:27:08,008 INFO [train.py:812] (6/8) Epoch 34, batch 2350, loss[loss=0.1352, simple_loss=0.2262, pruned_loss=0.02211, over 7165.00 frames.], tot_loss[loss=0.152, simple_loss=0.244, pruned_loss=0.03, over 1425941.71 frames.], batch size: 19, lr: 2.29e-04
2022-05-15 22:28:06,479 INFO [train.py:812] (6/8) Epoch 34, batch 2400, loss[loss=0.1633, simple_loss=0.2678, pruned_loss=0.02941, over 7374.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2431, pruned_loss=0.02987, over 1426893.83 frames.], batch size: 23, lr: 2.29e-04
2022-05-15 22:29:04,661 INFO [train.py:812] (6/8) Epoch 34, batch 2450, loss[loss=0.1397, simple_loss=0.2465, pruned_loss=0.01649, over 7227.00 frames.], tot_loss[loss=0.151, simple_loss=0.243, pruned_loss=0.02952, over 1420533.61 frames.], batch size: 21, lr: 2.29e-04
2022-05-15 22:30:04,450 INFO [train.py:812] (6/8) Epoch 34, batch 2500, loss[loss=0.129, simple_loss=0.2135, pruned_loss=0.02221, over 6989.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2434, pruned_loss=0.02953, over 1418949.32 frames.], batch size: 16, lr: 2.29e-04
2022-05-15 22:31:02,281 INFO [train.py:812] (6/8) Epoch 34, batch 2550, loss[loss=0.1538, simple_loss=0.2573, pruned_loss=0.02519, over 7342.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2436, pruned_loss=0.02964, over 1420504.58 frames.], batch size: 22, lr: 2.29e-04
2022-05-15 22:32:00,060 INFO [train.py:812] (6/8) Epoch 34, batch 2600, loss[loss=0.131, simple_loss=0.2198, pruned_loss=0.02107, over 7063.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2438, pruned_loss=0.02986, over 1419636.64 frames.], batch size: 18, lr: 2.29e-04
2022-05-15 22:32:58,093 INFO [train.py:812] (6/8) Epoch 34, batch 2650, loss[loss=0.1387, simple_loss=0.2311, pruned_loss=0.02318, over 7340.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2434, pruned_loss=0.02963, over 1420629.50 frames.], batch size: 22, lr: 2.29e-04
2022-05-15 22:33:56,987 INFO [train.py:812] (6/8) Epoch 34, batch 2700, loss[loss=0.1485, simple_loss=0.2338, pruned_loss=0.03164, over 7278.00 frames.], tot_loss[loss=0.1509, simple_loss=0.243, pruned_loss=0.02937, over 1425553.48 frames.], batch size: 18, lr: 2.28e-04
2022-05-15 22:34:55,308 INFO [train.py:812] (6/8) Epoch 34, batch 2750, loss[loss=0.1668, simple_loss=0.2657, pruned_loss=0.03392, over 7308.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2436, pruned_loss=0.02974, over 1424443.93 frames.], batch size: 21, lr: 2.28e-04
2022-05-15 22:35:54,070 INFO [train.py:812] (6/8) Epoch 34, batch 2800, loss[loss=0.134, simple_loss=0.2262, pruned_loss=0.02092, over 7408.00 frames.], tot_loss[loss=0.151, simple_loss=0.2435, pruned_loss=0.02932, over 1429482.97 frames.], batch size: 18, lr: 2.28e-04
2022-05-15 22:36:52,783 INFO [train.py:812] (6/8) Epoch 34, batch 2850, loss[loss=0.1574, simple_loss=0.2549, pruned_loss=0.02995, over 7206.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2435, pruned_loss=0.02948, over 1430498.10 frames.], batch size: 23, lr: 2.28e-04
2022-05-15 22:37:50,509 INFO [train.py:812] (6/8) Epoch 34, batch 2900, loss[loss=0.1344, simple_loss=0.2351, pruned_loss=0.01685, over 7145.00 frames.], tot_loss[loss=0.151, simple_loss=0.2434, pruned_loss=0.02931, over 1427569.74 frames.], batch size: 20, lr: 2.28e-04
2022-05-15 22:38:49,637 INFO [train.py:812] (6/8) Epoch 34, batch 2950, loss[loss=0.1511, simple_loss=0.2502, pruned_loss=0.02605, over 7144.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2429, pruned_loss=0.02901, over 1428396.36 frames.], batch size: 20, lr: 2.28e-04
2022-05-15 22:39:49,331 INFO [train.py:812] (6/8) Epoch 34, batch 3000, loss[loss=0.1536, simple_loss=0.2447, pruned_loss=0.03129, over 7357.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2426, pruned_loss=0.02893, over 1428146.63 frames.], batch size: 19, lr: 2.28e-04
2022-05-15 22:39:49,332 INFO [train.py:832] (6/8) Computing validation loss
2022-05-15 22:39:56,835 INFO [train.py:841] (6/8) Epoch 34, validation: loss=0.1534, simple_loss=0.2492, pruned_loss=0.02878, over 698248.00 frames.
2022-05-15 22:40:55,241 INFO [train.py:812] (6/8) Epoch 34, batch 3050, loss[loss=0.1623, simple_loss=0.2601, pruned_loss=0.0322, over 7359.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2439, pruned_loss=0.02934, over 1428416.96 frames.], batch size: 19, lr: 2.28e-04
2022-05-15 22:41:53,731 INFO [train.py:812] (6/8) Epoch 34, batch 3100, loss[loss=0.1419, simple_loss=0.2298, pruned_loss=0.02698, over 6813.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2441, pruned_loss=0.02968, over 1429888.72 frames.], batch size: 15, lr: 2.28e-04
2022-05-15 22:42:52,709 INFO [train.py:812] (6/8) Epoch 34, batch 3150, loss[loss=0.1178, simple_loss=0.2069, pruned_loss=0.01429, over 7282.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2438, pruned_loss=0.0294, over 1430022.22 frames.], batch size: 17, lr: 2.28e-04
2022-05-15 22:43:51,469 INFO [train.py:812] (6/8) Epoch 34, batch 3200, loss[loss=0.1781, simple_loss=0.2648, pruned_loss=0.04575, over 4943.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2439, pruned_loss=0.02976, over 1425553.66 frames.], batch size: 52, lr: 2.28e-04
2022-05-15 22:44:49,463 INFO [train.py:812] (6/8) Epoch 34, batch 3250, loss[loss=0.1456, simple_loss=0.2347, pruned_loss=0.02821, over 7131.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2431, pruned_loss=0.02959, over 1422969.01 frames.], batch size: 17, lr: 2.28e-04
2022-05-15 22:45:48,024 INFO [train.py:812] (6/8) Epoch 34, batch 3300, loss[loss=0.1763, simple_loss=0.2735, pruned_loss=0.03961, over 7052.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2434, pruned_loss=0.02991, over 1419785.16 frames.], batch size: 28, lr: 2.28e-04
2022-05-15 22:46:47,416 INFO [train.py:812] (6/8) Epoch 34, batch 3350, loss[loss=0.131, simple_loss=0.2296, pruned_loss=0.01617, over 7154.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2419, pruned_loss=0.02918, over 1422099.37 frames.], batch size: 20, lr: 2.28e-04
2022-05-15 22:47:45,307 INFO [train.py:812] (6/8) Epoch 34, batch 3400, loss[loss=0.1558, simple_loss=0.2544, pruned_loss=0.02854, over 7207.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2427, pruned_loss=0.02943, over 1422388.52 frames.], batch size: 23, lr: 2.28e-04
2022-05-15 22:48:43,918 INFO [train.py:812] (6/8) Epoch 34, batch 3450, loss[loss=0.1451, simple_loss=0.2326, pruned_loss=0.02881, over 7021.00 frames.], tot_loss[loss=0.1503, simple_loss=0.242, pruned_loss=0.02931, over 1427793.27 frames.], batch size: 16, lr: 2.28e-04
2022-05-15 22:49:41,442 INFO [train.py:812] (6/8) Epoch 34, batch 3500, loss[loss=0.1703, simple_loss=0.264, pruned_loss=0.03826, over 7194.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2431, pruned_loss=0.02926, over 1429485.14 frames.], batch size: 23, lr: 2.28e-04
2022-05-15 22:50:38,745 INFO [train.py:812] (6/8) Epoch 34, batch 3550, loss[loss=0.1345, simple_loss=0.2172, pruned_loss=0.0259, over 7286.00 frames.], tot_loss[loss=0.1496, simple_loss=0.242, pruned_loss=0.02867, over 1431801.02 frames.], batch size: 17, lr: 2.28e-04
2022-05-15 22:51:37,815 INFO [train.py:812] (6/8) Epoch 34, batch 3600, loss[loss=0.152, simple_loss=0.2536, pruned_loss=0.02514, over 7326.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2424, pruned_loss=0.0289, over 1433482.62 frames.], batch size: 21, lr: 2.28e-04
2022-05-15 22:52:35,109 INFO [train.py:812] (6/8) Epoch 34, batch 3650, loss[loss=0.1462, simple_loss=0.2426, pruned_loss=0.02492, over 6311.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2426, pruned_loss=0.02913, over 1428985.98 frames.], batch size: 38, lr: 2.28e-04
2022-05-15 22:53:34,815 INFO [train.py:812] (6/8) Epoch 34, batch 3700, loss[loss=0.1503, simple_loss=0.2445, pruned_loss=0.02798, over 7238.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2411, pruned_loss=0.02902, over 1424595.18 frames.], batch size: 20, lr: 2.28e-04
2022-05-15 22:54:33,350 INFO [train.py:812] (6/8) Epoch 34, batch 3750, loss[loss=0.1406, simple_loss=0.2325, pruned_loss=0.02436, over 7300.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2407, pruned_loss=0.0291, over 1423033.04 frames.], batch size: 24, lr: 2.28e-04
2022-05-15 22:55:32,426 INFO [train.py:812] (6/8) Epoch 34, batch 3800, loss[loss=0.1514, simple_loss=0.2504, pruned_loss=0.02621, over 7148.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2418, pruned_loss=0.0294, over 1426944.61 frames.], batch size: 20, lr: 2.28e-04
2022-05-15 22:56:31,647 INFO [train.py:812] (6/8) Epoch 34, batch 3850, loss[loss=0.1874, simple_loss=0.2775, pruned_loss=0.04862, over 7211.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2415, pruned_loss=0.02959, over 1428569.46 frames.], batch size: 23, lr: 2.28e-04
2022-05-15 22:57:28,731 INFO [train.py:812] (6/8) Epoch 34, batch 3900, loss[loss=0.1759, simple_loss=0.2727, pruned_loss=0.03957, over 7198.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2415, pruned_loss=0.02955, over 1427173.26 frames.], batch size: 23, lr: 2.28e-04
2022-05-15 22:58:46,460 INFO [train.py:812] (6/8) Epoch 34, batch 3950, loss[loss=0.1493, simple_loss=0.243, pruned_loss=0.02784, over 7325.00 frames.], tot_loss[loss=0.1504, simple_loss=0.242, pruned_loss=0.0294, over 1424011.30 frames.], batch size: 20, lr: 2.28e-04
2022-05-15 22:59:45,593 INFO [train.py:812] (6/8) Epoch 34, batch 4000, loss[loss=0.1411, simple_loss=0.2323, pruned_loss=0.02496, over 7062.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2429, pruned_loss=0.02951, over 1424346.51 frames.], batch size: 18, lr: 2.28e-04
2022-05-15 23:00:53,104 INFO [train.py:812] (6/8) Epoch 34, batch 4050, loss[loss=0.156, simple_loss=0.2513, pruned_loss=0.03032, over 7178.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2436, pruned_loss=0.02949, over 1419640.93 frames.], batch size: 26, lr: 2.27e-04
2022-05-15 23:01:51,550 INFO [train.py:812] (6/8) Epoch 34, batch 4100, loss[loss=0.1716, simple_loss=0.2617, pruned_loss=0.04075, over 6237.00 frames.], tot_loss[loss=0.151, simple_loss=0.2432, pruned_loss=0.02939, over 1419489.28 frames.], batch size: 37, lr: 2.27e-04
2022-05-15 23:02:49,339 INFO [train.py:812] (6/8) Epoch 34, batch 4150, loss[loss=0.1587, simple_loss=0.2432, pruned_loss=0.03713, over 7408.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2438, pruned_loss=0.0298, over 1417941.55 frames.], batch size: 18, lr: 2.27e-04
2022-05-15 23:03:57,831 INFO [train.py:812] (6/8) Epoch 34, batch 4200, loss[loss=0.1439, simple_loss=0.2311, pruned_loss=0.02839, over 7244.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2437, pruned_loss=0.02986, over 1420972.73 frames.], batch size: 20, lr: 2.27e-04
2022-05-15 23:05:06,376 INFO [train.py:812] (6/8) Epoch 34, batch 4250, loss[loss=0.132, simple_loss=0.2096, pruned_loss=0.02721, over 7150.00 frames.], tot_loss[loss=0.152, simple_loss=0.2439, pruned_loss=0.03005, over 1420414.92 frames.], batch size: 17, lr: 2.27e-04
2022-05-15 23:06:05,069 INFO [train.py:812] (6/8) Epoch 34, batch 4300, loss[loss=0.138, simple_loss=0.216, pruned_loss=0.03005, over 7435.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2444, pruned_loss=0.03026, over 1421010.78 frames.], batch size: 17, lr: 2.27e-04
2022-05-15 23:07:13,196 INFO [train.py:812] (6/8) Epoch 34, batch 4350, loss[loss=0.1376, simple_loss=0.2223, pruned_loss=0.02648, over 6795.00 frames.], tot_loss[loss=0.1529, simple_loss=0.245, pruned_loss=0.03045, over 1416190.24 frames.], batch size: 15, lr: 2.27e-04
2022-05-15 23:08:12,769 INFO [train.py:812] (6/8) Epoch 34, batch 4400, loss[loss=0.1684, simple_loss=0.2568, pruned_loss=0.03997, over 7155.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2443, pruned_loss=0.03034, over 1416770.09 frames.], batch size: 18, lr: 2.27e-04
2022-05-15 23:09:11,168 INFO [train.py:812] (6/8) Epoch 34, batch 4450, loss[loss=0.1619, simple_loss=0.2523, pruned_loss=0.03574, over 7182.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2443, pruned_loss=0.03012, over 1401223.70 frames.], batch size: 23, lr: 2.27e-04
2022-05-15 23:10:19,465 INFO [train.py:812] (6/8) Epoch 34, batch 4500, loss[loss=0.185, simple_loss=0.2751, pruned_loss=0.04749, over 5520.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2445, pruned_loss=0.03026, over 1392988.81 frames.], batch size: 54, lr: 2.27e-04
2022-05-15 23:11:16,030 INFO [train.py:812] (6/8) Epoch 34, batch 4550, loss[loss=0.1651, simple_loss=0.2488, pruned_loss=0.04063, over 5159.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2465, pruned_loss=0.03108, over 1352179.35 frames.], batch size: 52, lr: 2.27e-04
2022-05-15 23:12:20,547 INFO [train.py:812] (6/8) Epoch 35, batch 0, loss[loss=0.1709, simple_loss=0.2665, pruned_loss=0.03762, over 7224.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2665, pruned_loss=0.03762, over 7224.00 frames.], batch size: 20, lr: 2.24e-04
2022-05-15 23:13:24,543 INFO [train.py:812] (6/8) Epoch 35, batch 50, loss[loss=0.1619, simple_loss=0.2571, pruned_loss=0.03337, over 7289.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2471, pruned_loss=0.03153, over 317857.30 frames.], batch size: 24, lr: 2.24e-04
2022-05-15 23:14:23,045 INFO [train.py:812] (6/8) Epoch 35, batch 100, loss[loss=0.165, simple_loss=0.262, pruned_loss=0.03398, over 7117.00 frames.], tot_loss[loss=0.151, simple_loss=0.2431, pruned_loss=0.02942, over 567561.32 frames.], batch size: 26, lr: 2.24e-04
2022-05-15 23:15:22,500 INFO [train.py:812] (6/8) Epoch 35, batch 150, loss[loss=0.1457, simple_loss=0.2402, pruned_loss=0.02565, over 7382.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2439, pruned_loss=0.02967, over 760431.13 frames.], batch size: 23, lr: 2.24e-04
2022-05-15 23:16:21,273 INFO [train.py:812] (6/8) Epoch 35, batch 200, loss[loss=0.1383, simple_loss=0.2348, pruned_loss=0.02085, over 7077.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2436, pruned_loss=0.02947, over 909183.83 frames.], batch size: 18, lr: 2.24e-04
2022-05-15 23:17:21,212 INFO [train.py:812] (6/8) Epoch 35, batch 250, loss[loss=0.1626, simple_loss=0.2544, pruned_loss=0.0354, over 7231.00 frames.], tot_loss[loss=0.1509, simple_loss=0.243, pruned_loss=0.02937, over 1026617.93 frames.], batch size: 20, lr: 2.24e-04
2022-05-15 23:18:18,842 INFO [train.py:812] (6/8) Epoch 35, batch 300, loss[loss=0.1386, simple_loss=0.2319, pruned_loss=0.02266, over 7161.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2427, pruned_loss=0.02934, over 1113998.18 frames.], batch size: 19, lr: 2.24e-04
2022-05-15 23:19:18,453 INFO [train.py:812] (6/8) Epoch 35, batch 350, loss[loss=0.1748, simple_loss=0.2621, pruned_loss=0.04377, over 7202.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2429, pruned_loss=0.02947, over 1185388.96 frames.], batch size: 23, lr: 2.24e-04
2022-05-15 23:20:16,887 INFO [train.py:812] (6/8) Epoch 35, batch 400, loss[loss=0.1401, simple_loss=0.2384, pruned_loss=0.02087, over 7327.00 frames.], tot_loss[loss=0.151, simple_loss=0.2428, pruned_loss=0.02956, over 1239728.90 frames.], batch size: 20, lr: 2.24e-04
2022-05-15 23:21:15,067 INFO [train.py:812] (6/8) Epoch 35, batch 450, loss[loss=0.161, simple_loss=0.2663, pruned_loss=0.02785, over 6732.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2426, pruned_loss=0.02942, over 1283890.73 frames.], batch size: 31, lr: 2.24e-04
2022-05-15 23:22:13,117 INFO [train.py:812] (6/8) Epoch 35, batch 500, loss[loss=0.1389, simple_loss=0.2315, pruned_loss=0.02317, over 7336.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2424, pruned_loss=0.02943, over 1312737.43 frames.], batch size: 20, lr: 2.23e-04
2022-05-15 23:23:12,702 INFO [train.py:812] (6/8) Epoch 35, batch 550, loss[loss=0.149, simple_loss=0.2408, pruned_loss=0.02863, over 7064.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2416, pruned_loss=0.02877, over 1334427.34 frames.], batch size: 18, lr: 2.23e-04
2022-05-15 23:24:10,898 INFO [train.py:812] (6/8) Epoch 35, batch 600, loss[loss=0.1663, simple_loss=0.2602, pruned_loss=0.03625, over 7336.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2428, pruned_loss=0.02944, over 1353169.42 frames.], batch size: 22, lr: 2.23e-04
2022-05-15 23:25:10,160 INFO [train.py:812] (6/8) Epoch 35, batch 650, loss[loss=0.1466, simple_loss=0.2251, pruned_loss=0.03404, over 7160.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2428, pruned_loss=0.029, over 1372069.17 frames.], batch size: 18, lr: 2.23e-04
2022-05-15 23:26:08,925 INFO [train.py:812] (6/8) Epoch 35, batch 700, loss[loss=0.1498, simple_loss=0.2328, pruned_loss=0.03334, over 7266.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2429, pruned_loss=0.02917, over 1386295.31 frames.], batch size: 17, lr: 2.23e-04
2022-05-15 23:27:08,862 INFO [train.py:812] (6/8) Epoch 35, batch 750, loss[loss=0.1394, simple_loss=0.2254, pruned_loss=0.02671, over 7255.00 frames.], tot_loss[loss=0.15, simple_loss=0.242, pruned_loss=0.02903, over 1393971.77 frames.], batch size: 19, lr: 2.23e-04
2022-05-15 23:28:07,093 INFO [train.py:812] (6/8) Epoch 35, batch 800, loss[loss=0.1696, simple_loss=0.2601, pruned_loss=0.03955, over 7217.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2427, pruned_loss=0.02921, over 1402192.70 frames.], batch size: 21, lr: 2.23e-04
2022-05-15 23:29:06,744 INFO [train.py:812] (6/8) Epoch 35, batch 850, loss[loss=0.1745, simple_loss=0.2689, pruned_loss=0.04007, over 7280.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2434, pruned_loss=0.02901, over 1402909.47 frames.], batch size: 24, lr: 2.23e-04
2022-05-15 23:30:05,629 INFO [train.py:812] (6/8) Epoch 35, batch 900, loss[loss=0.1879, simple_loss=0.2688, pruned_loss=0.05355, over 4750.00 frames.], tot_loss[loss=0.151, simple_loss=0.2437, pruned_loss=0.02914, over 1405704.22 frames.], batch size: 52, lr: 2.23e-04
2022-05-15 23:31:04,516 INFO [train.py:812] (6/8) Epoch 35, batch 950, loss[loss=0.142, simple_loss=0.2362, pruned_loss=0.0239, over 7268.00 frames.], tot_loss[loss=0.1507, simple_loss=0.243, pruned_loss=0.02916, over 1409260.33 frames.], batch size: 19, lr: 2.23e-04
2022-05-15 23:32:02,590 INFO [train.py:812] (6/8) Epoch 35, batch 1000, loss[loss=0.1408, simple_loss=0.2374, pruned_loss=0.02208, over 6757.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2436, pruned_loss=0.02937, over 1410603.62 frames.], batch size: 31, lr: 2.23e-04
2022-05-15 23:33:01,154 INFO [train.py:812] (6/8) Epoch 35, batch 1050, loss[loss=0.1451, simple_loss=0.2411, pruned_loss=0.02461, over 7418.00 frames.], tot_loss[loss=0.15, simple_loss=0.2422, pruned_loss=0.02889, over 1415741.16 frames.], batch size: 21, lr: 2.23e-04
2022-05-15 23:33:59,698 INFO [train.py:812] (6/8) Epoch 35, batch 1100, loss[loss=0.1419, simple_loss=0.2299, pruned_loss=0.02698, over 7358.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2408, pruned_loss=0.02844, over 1420063.54 frames.], batch size: 19, lr: 2.23e-04
2022-05-15 23:34:58,677 INFO [train.py:812] (6/8) Epoch 35, batch 1150, loss[loss=0.1845, simple_loss=0.2824, pruned_loss=0.04332, over 7193.00 frames.], tot_loss[loss=0.149, simple_loss=0.2413, pruned_loss=0.0284, over 1421476.69 frames.], batch size: 23, lr: 2.23e-04
2022-05-15 23:35:56,584 INFO [train.py:812] (6/8) Epoch 35, batch 1200, loss[loss=0.1542, simple_loss=0.2468, pruned_loss=0.03082, over 7266.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2415, pruned_loss=0.02865, over 1424979.89 frames.], batch size: 18, lr: 2.23e-04
2022-05-15 23:36:55,003 INFO [train.py:812] (6/8) Epoch 35, batch 1250, loss[loss=0.172, simple_loss=0.268, pruned_loss=0.03798, over 7321.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2425, pruned_loss=0.02923, over 1424806.19 frames.], batch size: 22, lr: 2.23e-04
2022-05-15 23:37:53,441 INFO [train.py:812] (6/8) Epoch 35, batch 1300, loss[loss=0.1813, simple_loss=0.2646, pruned_loss=0.04901, over 7108.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2436, pruned_loss=0.02995, over 1421358.77 frames.], batch size: 28, lr: 2.23e-04
2022-05-15 23:38:52,787 INFO [train.py:812] (6/8) Epoch 35, batch 1350, loss[loss=0.1692, simple_loss=0.2622, pruned_loss=0.03815, over 7061.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2442, pruned_loss=0.03009, over 1423854.56 frames.], batch size: 28, lr: 2.23e-04
2022-05-15 23:39:51,269 INFO [train.py:812] (6/8) Epoch 35, batch 1400, loss[loss=0.1344, simple_loss=0.2238, pruned_loss=0.02254, over 7328.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2447, pruned_loss=0.03013, over 1421438.48 frames.], batch size: 20, lr: 2.23e-04
2022-05-15 23:40:50,644 INFO [train.py:812] (6/8) Epoch 35, batch 1450, loss[loss=0.1434, simple_loss=0.2365, pruned_loss=0.02512, over 7268.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2439, pruned_loss=0.03031, over 1419356.38 frames.], batch size: 19, lr: 2.23e-04
2022-05-15 23:41:50,040 INFO [train.py:812] (6/8) Epoch 35, batch 1500, loss[loss=0.147, simple_loss=0.2292, pruned_loss=0.03242, over 7133.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2437, pruned_loss=0.03042, over 1420417.29 frames.], batch size: 17, lr: 2.23e-04
2022-05-15 23:42:48,892 INFO [train.py:812] (6/8) Epoch 35, batch 1550, loss[loss=0.2108, simple_loss=0.3173, pruned_loss=0.05218, over 7216.00 frames.], tot_loss[loss=0.153, simple_loss=0.2446, pruned_loss=0.03067, over 1420706.44 frames.], batch size: 21, lr: 2.23e-04
2022-05-15 23:43:47,286 INFO [train.py:812] (6/8) Epoch 35, batch 1600, loss[loss=0.1493, simple_loss=0.2394, pruned_loss=0.0296, over 7045.00 frames.], tot_loss[loss=0.152, simple_loss=0.2437, pruned_loss=0.03016, over 1422360.12 frames.], batch size: 28, lr: 2.23e-04
2022-05-15 23:44:46,448 INFO [train.py:812] (6/8) Epoch 35, batch 1650, loss[loss=0.1304, simple_loss=0.2147, pruned_loss=0.02308, over 7416.00 frames.], tot_loss[loss=0.152, simple_loss=0.2439, pruned_loss=0.03004, over 1426953.68 frames.], batch size: 18, lr: 2.23e-04
2022-05-15 23:45:45,300 INFO [train.py:812] (6/8) Epoch 35, batch 1700, loss[loss=0.1671, simple_loss=0.2515, pruned_loss=0.04138, over 5230.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2435, pruned_loss=0.02982, over 1426600.38 frames.], batch size: 52, lr: 2.23e-04
2022-05-15 23:46:45,284 INFO [train.py:812] (6/8) Epoch 35, batch 1750, loss[loss=0.1517, simple_loss=0.2351, pruned_loss=0.03413, over 7175.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2425, pruned_loss=0.02935, over 1426465.29 frames.], batch size: 18, lr: 2.23e-04
2022-05-15 23:47:44,625 INFO [train.py:812] (6/8) Epoch 35, batch 1800, loss[loss=0.1694, simple_loss=0.262, pruned_loss=0.03843, over 7296.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2417, pruned_loss=0.02908, over 1430245.41 frames.], batch size: 25, lr: 2.23e-04
2022-05-15 23:48:43,678 INFO [train.py:812] (6/8) Epoch 35, batch 1850, loss[loss=0.1244, simple_loss=0.2121, pruned_loss=0.01837, over 7071.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2416, pruned_loss=0.02929, over 1426420.63 frames.], batch size: 18, lr: 2.23e-04
2022-05-15 23:49:42,139 INFO [train.py:812] (6/8) Epoch 35, batch 1900, loss[loss=0.1518, simple_loss=0.2381, pruned_loss=0.03278, over 7385.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2418, pruned_loss=0.02954, over 1426331.08 frames.], batch size: 23, lr: 2.22e-04
2022-05-15 23:50:50,959 INFO [train.py:812] (6/8) Epoch 35, batch 1950, loss[loss=0.1279, simple_loss=0.2092, pruned_loss=0.02327, over 7160.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2422, pruned_loss=0.02958, over 1424478.25 frames.], batch size: 18, lr: 2.22e-04
2022-05-15 23:51:48,114 INFO [train.py:812] (6/8) Epoch 35, batch 2000, loss[loss=0.1362, simple_loss=0.2313, pruned_loss=0.02062, over 6371.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2429, pruned_loss=0.02979, over 1420385.37 frames.], batch size: 37, lr: 2.22e-04
2022-05-15 23:52:46,846 INFO [train.py:812] (6/8) Epoch 35, batch 2050, loss[loss=0.1458, simple_loss=0.2396, pruned_loss=0.026, over 7120.00 frames.], tot_loss[loss=0.1513, simple_loss=0.243, pruned_loss=0.02979, over 1421256.45 frames.], batch size: 21, lr: 2.22e-04
2022-05-15 23:53:45,622 INFO [train.py:812] (6/8) Epoch 35, batch 2100, loss[loss=0.1645, simple_loss=0.2633, pruned_loss=0.03288, over 7411.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2437, pruned_loss=0.02982, over 1424001.94 frames.], batch size: 21, lr: 2.22e-04
2022-05-15 23:54:43,314 INFO [train.py:812] (6/8) Epoch 35, batch 2150, loss[loss=0.147, simple_loss=0.2441, pruned_loss=0.02493, over 6307.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2431, pruned_loss=0.02936, over 1427290.43 frames.], batch size: 37, lr: 2.22e-04
2022-05-15 23:55:40,410 INFO [train.py:812] (6/8) Epoch 35, batch 2200, loss[loss=0.1322, simple_loss=0.2254, pruned_loss=0.01951, over 7432.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2428, pruned_loss=0.02951, over 1424452.41 frames.], batch size: 20, lr: 2.22e-04
2022-05-15 23:56:39,594 INFO [train.py:812] (6/8) Epoch 35, batch 2250, loss[loss=0.1482, simple_loss=0.2354, pruned_loss=0.03048, over 7272.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2423, pruned_loss=0.02918, over 1422242.18 frames.], batch size: 18, lr: 2.22e-04
2022-05-15 23:57:38,206 INFO [train.py:812] (6/8) Epoch 35, batch 2300, loss[loss=0.157, simple_loss=0.2531, pruned_loss=0.03046, over 7145.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2424, pruned_loss=0.02902, over 1419278.35 frames.], batch size: 26, lr: 2.22e-04
2022-05-15 23:58:36,530 INFO [train.py:812] (6/8) Epoch 35, batch 2350, loss[loss=0.1419, simple_loss=0.2395, pruned_loss=0.02218, over 7010.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2424, pruned_loss=0.02866, over 1417561.11 frames.], batch size: 28, lr: 2.22e-04
2022-05-15 23:59:34,369 INFO [train.py:812] (6/8) Epoch 35, batch 2400, loss[loss=0.1171, simple_loss=0.2004, pruned_loss=0.01685, over 7014.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2426, pruned_loss=0.02883, over 1422100.58 frames.], batch size: 16, lr: 2.22e-04
2022-05-16 00:00:32,069 INFO [train.py:812] (6/8) Epoch 35, batch 2450, loss[loss=0.1478, simple_loss=0.2415, pruned_loss=0.02706, over 7446.00 frames.], tot_loss[loss=0.149, simple_loss=0.2416, pruned_loss=0.02825, over 1423308.72 frames.], batch size: 20, lr: 2.22e-04
2022-05-16 00:01:31,445 INFO [train.py:812] (6/8) Epoch 35, batch 2500, loss[loss=0.1751, simple_loss=0.281, pruned_loss=0.03458, over 6570.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2408, pruned_loss=0.02819, over 1425242.77 frames.], batch size: 38, lr: 2.22e-04
2022-05-16 00:02:30,531 INFO [train.py:812] (6/8) Epoch 35, batch 2550, loss[loss=0.1419, simple_loss=0.2469, pruned_loss=0.01841, over 7108.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2407, pruned_loss=0.02807, over 1425036.57 frames.], batch size: 21, lr: 2.22e-04
2022-05-16 00:03:28,745 INFO [train.py:812] (6/8) Epoch 35, batch 2600, loss[loss=0.1826, simple_loss=0.2633, pruned_loss=0.05091, over 7200.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2408, pruned_loss=0.02848, over 1424084.61 frames.], batch size: 22, lr: 2.22e-04
2022-05-16 00:04:26,540 INFO [train.py:812] (6/8) Epoch 35, batch 2650, loss[loss=0.1511, simple_loss=0.2469, pruned_loss=0.02763, over 7233.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2407, pruned_loss=0.02849, over 1422680.95 frames.], batch size: 23, lr: 2.22e-04
2022-05-16 00:05:25,233 INFO [train.py:812] (6/8) Epoch 35, batch 2700, loss[loss=0.1272, simple_loss=0.223, pruned_loss=0.01569, over 7121.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2406, pruned_loss=0.02828, over 1424292.00 frames.], batch size: 21, lr: 2.22e-04
2022-05-16 00:06:24,239 INFO [train.py:812] (6/8) Epoch 35, batch 2750, loss[loss=0.1566, simple_loss=0.2498, pruned_loss=0.03167, over 7323.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2413, pruned_loss=0.02845, over 1423515.99 frames.], batch size: 21, lr: 2.22e-04
2022-05-16 00:07:23,055 INFO [train.py:812] (6/8) Epoch 35, batch 2800, loss[loss=0.1317, simple_loss=0.2262, pruned_loss=0.01855, over 7332.00 frames.], tot_loss[loss=0.1497, simple_loss=0.242, pruned_loss=0.02868, over 1424962.51 frames.], batch size: 20, lr: 2.22e-04
2022-05-16 00:08:20,731 INFO [train.py:812] (6/8) Epoch 35, batch 2850, loss[loss=0.1797, simple_loss=0.2696, pruned_loss=0.04495, over 7154.00 frames.], tot_loss[loss=0.15, simple_loss=0.2424, pruned_loss=0.02877, over 1423956.56 frames.], batch size: 19, lr: 2.22e-04
2022-05-16 00:09:20,182 INFO [train.py:812] (6/8) Epoch 35, batch 2900, loss[loss=0.1433, simple_loss=0.2388, pruned_loss=0.02386, over 6488.00 frames.], tot_loss[loss=0.15, simple_loss=0.2424, pruned_loss=0.02884, over 1422684.23 frames.], batch size: 38, lr: 2.22e-04
2022-05-16 00:10:18,333 INFO [train.py:812] (6/8) Epoch 35, batch 2950, loss[loss=0.1186, simple_loss=0.2058, pruned_loss=0.01568, over 6811.00 frames.], tot_loss[loss=0.151, simple_loss=0.2434, pruned_loss=0.02931, over 1416269.59 frames.], batch size: 15, lr: 2.22e-04
2022-05-16 00:11:17,555 INFO [train.py:812] (6/8) Epoch 35, batch 3000, loss[loss=0.171, simple_loss=0.2617, pruned_loss=0.04011, over 7387.00 frames.], tot_loss[loss=0.1507, simple_loss=0.243, pruned_loss=0.02922, over 1419819.76 frames.], batch size: 23, lr: 2.22e-04
2022-05-16 00:11:17,556 INFO [train.py:832] (6/8) Computing validation loss
2022-05-16 00:11:25,087 INFO [train.py:841] (6/8) Epoch 35, validation: loss=0.1528, simple_loss=0.2485, pruned_loss=0.02851, over 698248.00 frames.
2022-05-16 00:12:24,401 INFO [train.py:812] (6/8) Epoch 35, batch 3050, loss[loss=0.1335, simple_loss=0.2321, pruned_loss=0.01742, over 7234.00 frames.], tot_loss[loss=0.1509, simple_loss=0.243, pruned_loss=0.0294, over 1422705.37 frames.], batch size: 20, lr: 2.22e-04
2022-05-16 00:13:22,723 INFO [train.py:812] (6/8) Epoch 35, batch 3100, loss[loss=0.1483, simple_loss=0.2477, pruned_loss=0.02446, over 7397.00 frames.], tot_loss[loss=0.1513, simple_loss=0.243, pruned_loss=0.02978, over 1419622.03 frames.], batch size: 23, lr: 2.22e-04
2022-05-16 00:14:22,598 INFO [train.py:812] (6/8) Epoch 35, batch 3150, loss[loss=0.1576, simple_loss=0.2529, pruned_loss=0.03112, over 7204.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2417, pruned_loss=0.02933, over 1421417.47 frames.], batch size: 22, lr: 2.22e-04
2022-05-16 00:15:21,755 INFO [train.py:812] (6/8) Epoch 35, batch 3200, loss[loss=0.1618, simple_loss=0.2547, pruned_loss=0.03442, over 7198.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2431, pruned_loss=0.02963, over 1426319.44 frames.], batch size: 22, lr: 2.22e-04
2022-05-16 00:16:21,598 INFO [train.py:812] (6/8) Epoch 35, batch 3250, loss[loss=0.1405, simple_loss=0.2368, pruned_loss=0.02214, over 7424.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2434, pruned_loss=0.02965, over 1424678.12 frames.], batch size: 20, lr: 2.22e-04
2022-05-16 00:17:21,160 INFO [train.py:812] (6/8) Epoch 35, batch 3300, loss[loss=0.149, simple_loss=0.2431, pruned_loss=0.02743, over 7431.00 frames.], tot_loss[loss=0.1519, simple_loss=0.244, pruned_loss=0.02994, over 1425947.27 frames.], batch size: 20, lr: 2.22e-04
2022-05-16 00:18:19,933 INFO [train.py:812] (6/8) Epoch 35, batch 3350, loss[loss=0.1517, simple_loss=0.2442, pruned_loss=0.02964, over 7419.00 frames.], tot_loss[loss=0.152, simple_loss=0.2442, pruned_loss=0.02989, over 1429352.69 frames.], batch size: 20, lr: 2.21e-04
2022-05-16 00:19:17,064 INFO [train.py:812] (6/8) Epoch 35, batch 3400, loss[loss=0.1507, simple_loss=0.2392, pruned_loss=0.03109, over 7254.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2435, pruned_loss=0.02989, over 1426763.62 frames.], batch size: 18, lr: 2.21e-04
2022-05-16 00:20:15,924 INFO [train.py:812] (6/8) Epoch 35, batch 3450, loss[loss=0.1283, simple_loss=0.2198, pruned_loss=0.01838, over 7013.00 frames.], tot_loss[loss=0.152, simple_loss=0.2436, pruned_loss=0.03017, over 1429408.34 frames.], batch size: 16, lr: 2.21e-04
2022-05-16 00:21:14,734 INFO [train.py:812] (6/8) Epoch 35, batch 3500, loss[loss=0.1581, simple_loss=0.2612, pruned_loss=0.02749, over 7358.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2435, pruned_loss=0.02999, over 1428058.21 frames.], batch size: 22, lr: 2.21e-04
2022-05-16 00:22:12,835 INFO [train.py:812] (6/8) Epoch 35, batch 3550, loss[loss=0.1667, simple_loss=0.2583, pruned_loss=0.03754, over 6801.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2436, pruned_loss=0.02994, over 1421469.38 frames.], batch size: 31, lr: 2.21e-04
2022-05-16 00:23:10,737 INFO [train.py:812] (6/8) Epoch 35, batch 3600, loss[loss=0.1613, simple_loss=0.2534, pruned_loss=0.03458, over 7215.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2438, pruned_loss=0.02999, over 1419949.60 frames.], batch size: 22, lr: 2.21e-04
2022-05-16 00:24:08,626 INFO [train.py:812] (6/8) Epoch 35, batch 3650, loss[loss=0.165, simple_loss=0.2513, pruned_loss=0.03938, over 7291.00 frames.], tot_loss[loss=0.1527, simple_loss=0.245, pruned_loss=0.03023, over 1421355.61 frames.], batch size: 25, lr: 2.21e-04
2022-05-16 00:25:06,935 INFO [train.py:812] (6/8) Epoch 35, batch 3700, loss[loss=0.1439, simple_loss=0.2303, pruned_loss=0.02873, over 6534.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2443, pruned_loss=0.02992, over 1420993.68 frames.], batch size: 38, lr: 2.21e-04
2022-05-16 00:26:05,706 INFO [train.py:812] (6/8) Epoch 35, batch 3750, loss[loss=0.16, simple_loss=0.2442, pruned_loss=0.03791, over 5190.00 frames.], tot_loss[loss=0.1517, simple_loss=0.244, pruned_loss=0.02973, over 1418838.36 frames.], batch size: 52, lr: 2.21e-04
2022-05-16 00:27:04,275 INFO [train.py:812] (6/8) Epoch 35, batch 3800, loss[loss=0.1597, simple_loss=0.2547, pruned_loss=0.03234, over 6766.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2442, pruned_loss=0.02955, over 1419038.63 frames.], batch size: 31, lr: 2.21e-04
2022-05-16 00:28:02,115 INFO [train.py:812] (6/8) Epoch 35, batch 3850, loss[loss=0.1647, simple_loss=0.2568, pruned_loss=0.03633, over 7287.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2433, pruned_loss=0.02921, over 1422200.74 frames.], batch size: 24, lr: 2.21e-04
2022-05-16 00:29:00,967 INFO [train.py:812] (6/8) Epoch 35, batch 3900, loss[loss=0.1541, simple_loss=0.2393, pruned_loss=0.03447, over 6805.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2434, pruned_loss=0.02922, over 1417986.64 frames.], batch size: 15, lr: 2.21e-04
2022-05-16 00:30:00,073 INFO [train.py:812] (6/8) Epoch 35, batch 3950, loss[loss=0.1241, simple_loss=0.2075, pruned_loss=0.02034, over 7132.00 frames.], tot_loss[loss=0.151, simple_loss=0.2431, pruned_loss=0.02947, over 1418056.86 frames.], batch size: 17, lr: 2.21e-04
2022-05-16 00:30:58,312 INFO [train.py:812] (6/8) Epoch 35, batch 4000, loss[loss=0.1307, simple_loss=0.2055, pruned_loss=0.02789, over 7000.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2422, pruned_loss=0.02914, over 1418064.57 frames.], batch size: 16, lr: 2.21e-04
2022-05-16 00:32:02,099 INFO [train.py:812] (6/8) Epoch 35, batch 4050, loss[loss=0.1501, simple_loss=0.2441, pruned_loss=0.02807, over 6324.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2426, pruned_loss=0.02897, over 1420533.47 frames.], batch size: 37, lr: 2.21e-04
2022-05-16 00:33:00,887 INFO [train.py:812] (6/8) Epoch 35, batch 4100, loss[loss=0.1966, simple_loss=0.2835, pruned_loss=0.05491, over 7216.00 frames.], tot_loss[loss=0.151, simple_loss=0.2432, pruned_loss=0.02944, over 1425411.41 frames.], batch size: 21, lr: 2.21e-04
2022-05-16 00:33:59,523 INFO [train.py:812] (6/8) Epoch 35, batch 4150, loss[loss=0.1311, simple_loss=0.2238, pruned_loss=0.01922, over 7325.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2424, pruned_loss=0.02945, over 1424033.55 frames.], batch size: 21, lr: 2.21e-04
2022-05-16 00:34:58,352 INFO [train.py:812] (6/8) Epoch 35, batch 4200, loss[loss=0.1406, simple_loss=0.2351, pruned_loss=0.02303, over 7318.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2429, pruned_loss=0.02929, over 1421960.16 frames.], batch size: 21, lr: 2.21e-04
2022-05-16 00:35:57,146 INFO [train.py:812] (6/8) Epoch 35, batch 4250, loss[loss=0.1197, simple_loss=0.2052, pruned_loss=0.01708, over 7279.00 frames.], tot_loss[loss=0.15, simple_loss=0.2423, pruned_loss=0.02885, over 1426660.56 frames.], batch size: 17, lr: 2.21e-04
2022-05-16 00:36:55,268 INFO [train.py:812] (6/8) Epoch 35, batch 4300, loss[loss=0.1623, simple_loss=0.2612, pruned_loss=0.03173, over 7202.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2413, pruned_loss=0.02851, over 1417865.30 frames.], batch size: 26, lr: 2.21e-04
2022-05-16 00:37:53,252 INFO [train.py:812] (6/8) Epoch 35, batch 4350, loss[loss=0.1653, simple_loss=0.2538, pruned_loss=0.0384, over 7302.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2428, pruned_loss=0.02942, over 1413489.23 frames.], batch size: 24, lr: 2.21e-04
2022-05-16 00:38:52,049 INFO [train.py:812] (6/8) Epoch 35, batch 4400, loss[loss=0.1392, simple_loss=0.2231, pruned_loss=0.02763, over 7163.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2434, pruned_loss=0.02959, over 1409145.11 frames.], batch size: 19, lr: 2.21e-04
2022-05-16 00:39:50,125 INFO [train.py:812] (6/8) Epoch 35, batch 4450, loss[loss=0.152, simple_loss=0.2486, pruned_loss=0.0277, over 6686.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2438, pruned_loss=0.0297, over 1392674.75 frames.], batch size: 31, lr: 2.21e-04
2022-05-16 00:40:48,503 INFO [train.py:812] (6/8) Epoch 35, batch 4500, loss[loss=0.1591, simple_loss=0.2476, pruned_loss=0.03529, over 7134.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2442, pruned_loss=0.03016, over 1378812.94 frames.], batch size: 26, lr: 2.21e-04
2022-05-16 00:41:45,659 INFO [train.py:812] (6/8) Epoch 35, batch 4550, loss[loss=0.1727, simple_loss=0.2634, pruned_loss=0.04102, over 4925.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2466, pruned_loss=0.03099, over 1353297.33 frames.], batch size: 52, lr: 2.21e-04
2022-05-16 00:42:50,925 INFO [train.py:812] (6/8) Epoch 36, batch 0, loss[loss=0.1384, simple_loss=0.2374, pruned_loss=0.01976, over 7328.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2374, pruned_loss=0.01976, over 7328.00 frames.], batch size: 20, lr: 2.18e-04
2022-05-16 00:43:50,528 INFO [train.py:812] (6/8) Epoch 36, batch 50, loss[loss=0.1415, simple_loss=0.2355, pruned_loss=0.02378, over 7411.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2423, pruned_loss=0.02857, over 316269.88 frames.], batch size: 20, lr: 2.18e-04
2022-05-16 00:44:48,789 INFO [train.py:812] (6/8) Epoch 36, batch 100, loss[loss=0.1701, simple_loss=0.2513, pruned_loss=0.04441, over 4878.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2408, pruned_loss=0.02845, over 561694.14 frames.], batch size: 52, lr: 2.17e-04
2022-05-16 00:45:47,238 INFO [train.py:812] (6/8) Epoch 36, batch 150, loss[loss=0.156, simple_loss=0.2601, pruned_loss=0.02595, over 7233.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2408, pruned_loss=0.02881, over 751336.75 frames.], batch size: 20, lr: 2.17e-04
2022-05-16 00:46:46,280 INFO [train.py:812] (6/8) Epoch 36, batch 200, loss[loss=0.1434, simple_loss=0.2448, pruned_loss=0.02104, over 7319.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2429, pruned_loss=0.02897, over 901145.95 frames.], batch size: 21, lr: 2.17e-04
2022-05-16 00:47:45,333 INFO [train.py:812] (6/8) Epoch 36, batch 250, loss[loss=0.1519, simple_loss=0.2467, pruned_loss=0.02854, over 7158.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2412, pruned_loss=0.02863, over 1020490.61 frames.], batch size: 19, lr: 2.17e-04
2022-05-16 00:48:43,655 INFO [train.py:812] (6/8) Epoch 36, batch 300, loss[loss=0.1692, simple_loss=0.2691, pruned_loss=0.03465, over 7218.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2417, pruned_loss=0.0291, over 1104837.16 frames.], batch size: 26, lr: 2.17e-04
2022-05-16 00:49:42,225 INFO [train.py:812] (6/8) Epoch 36, batch 350, loss[loss=0.1534, simple_loss=0.2516, pruned_loss=0.02759, over 6833.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2421, pruned_loss=0.02924, over 1174185.72 frames.], batch size: 31, lr: 2.17e-04
2022-05-16 00:50:40,187 INFO [train.py:812] (6/8) Epoch 36, batch 400, loss[loss=0.1504, simple_loss=0.2442, pruned_loss=0.02833, over 7208.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2429, pruned_loss=0.02943, over 1230081.20 frames.], batch size: 22, lr: 2.17e-04
2022-05-16 00:51:39,738 INFO [train.py:812] (6/8) Epoch 36, batch 450, loss[loss=0.155, simple_loss=0.2457, pruned_loss=0.03214, over 7132.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2424, pruned_loss=0.02912, over 1277728.99 frames.], batch size: 26, lr: 2.17e-04
2022-05-16 00:52:38,616 INFO [train.py:812] (6/8) Epoch 36, batch 500, loss[loss=0.1524, simple_loss=0.2444, pruned_loss=0.03016, over 7199.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2429, pruned_loss=0.02926, over 1309473.04 frames.], batch size: 23, lr: 2.17e-04
2022-05-16 00:53:37,441 INFO [train.py:812] (6/8) Epoch 36, batch 550, loss[loss=0.1528, simple_loss=0.2456, pruned_loss=0.03002, over 7434.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2432, pruned_loss=0.02915, over 1335859.05 frames.], batch size: 20, lr: 2.17e-04
2022-05-16 00:54:35,759 INFO [train.py:812] (6/8) Epoch 36, batch 600, loss[loss=0.147, simple_loss=0.2378, pruned_loss=0.02804, over 7211.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2426, pruned_loss=0.02905, over 1358126.39 frames.], batch size: 23, lr: 2.17e-04
2022-05-16 00:55:34,860 INFO [train.py:812] (6/8) Epoch 36, batch 650, loss[loss=0.158, simple_loss=0.2521, pruned_loss=0.03197, over 7152.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2415, pruned_loss=0.02874, over 1372506.22 frames.], batch size: 19, lr: 2.17e-04
2022-05-16 00:56:33,874 INFO [train.py:812] (6/8) Epoch 36, batch 700, loss[loss=0.1438, simple_loss=0.232, pruned_loss=0.0278, over 7232.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2414, pruned_loss=0.02876, over 1384229.61 frames.], batch size: 19, lr: 2.17e-04
2022-05-16 00:57:42,580 INFO [train.py:812] (6/8) Epoch 36, batch 750, loss[loss=0.1418, simple_loss=0.2299, pruned_loss=0.02687, over 7329.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2418, pruned_loss=0.02899, over 1384283.57 frames.], batch size: 20, lr: 2.17e-04
2022-05-16 00:58:59,885 INFO [train.py:812] (6/8) Epoch 36, batch 800, loss[loss=0.1482, simple_loss=0.2537, pruned_loss=0.02135, over 7421.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2422, pruned_loss=0.02905, over 1393112.25 frames.], batch size: 21, lr: 2.17e-04
2022-05-16 00:59:58,246 INFO [train.py:812] (6/8) Epoch 36, batch 850, loss[loss=0.1402, simple_loss=0.2433, pruned_loss=0.01858, over 7220.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2426, pruned_loss=0.02894, over 1395103.50 frames.], batch size: 21, lr: 2.17e-04
2022-05-16 01:00:57,356 INFO [train.py:812] (6/8) Epoch 36, batch 900, loss[loss=0.1374, simple_loss=0.2347, pruned_loss=0.02003, over 6800.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2423, pruned_loss=0.02882, over 1402524.11 frames.], batch size: 31, lr: 2.17e-04
2022-05-16 01:01:55,213 INFO [train.py:812] (6/8) Epoch 36, batch 950, loss[loss=0.1249, simple_loss=0.2093, pruned_loss=0.02024, over 7008.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2429, pruned_loss=0.02917, over 1405509.55 frames.], batch size: 16, lr: 2.17e-04
2022-05-16 01:03:03,142 INFO [train.py:812] (6/8) Epoch 36, batch 1000, loss[loss=0.1505, simple_loss=0.2373, pruned_loss=0.03183, over 7266.00 frames.], tot_loss[loss=0.1506, simple_loss=0.243, pruned_loss=0.0291, over 1406600.72 frames.], batch size: 17, lr: 2.17e-04
2022-05-16 01:04:02,093 INFO [train.py:812] (6/8) Epoch 36, batch 1050, loss[loss=0.1392, simple_loss=0.2303, pruned_loss=0.02402, over 7356.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2429, pruned_loss=0.02936, over 1407125.18 frames.], batch size: 19, lr: 2.17e-04
2022-05-16 01:05:09,988 INFO [train.py:812] (6/8) Epoch 36, batch 1100, loss[loss=0.1836, simple_loss=0.2852, pruned_loss=0.04097, over 7206.00 frames.], tot_loss[loss=0.151, simple_loss=0.2432, pruned_loss=0.02937, over 1407411.25 frames.], batch size: 22, lr: 2.17e-04
2022-05-16 01:06:19,092 INFO [train.py:812] (6/8) Epoch 36, batch 1150, loss[loss=0.1423, simple_loss=0.2433, pruned_loss=0.02068, over 7265.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2425, pruned_loss=0.02886, over 1412360.84 frames.], batch size: 24, lr: 2.17e-04
2022-05-16 01:07:18,013 INFO [train.py:812] (6/8) Epoch 36, batch 1200, loss[loss=0.1338, simple_loss=0.2178, pruned_loss=0.0249, over 7281.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2431, pruned_loss=0.02931, over 1407780.77 frames.], batch size: 17, lr: 2.17e-04
2022-05-16 01:08:17,062 INFO [train.py:812] (6/8) Epoch 36, batch 1250, loss[loss=0.1399, simple_loss=0.2283, pruned_loss=0.0258, over 7002.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2426, pruned_loss=0.02885, over 1409208.47 frames.], batch size: 16, lr: 2.17e-04
2022-05-16 01:09:23,823 INFO [train.py:812] (6/8) Epoch 36, batch 1300, loss[loss=0.1215, simple_loss=0.2076, pruned_loss=0.01772, over 7156.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2425, pruned_loss=0.02889, over 1412822.32 frames.], batch size: 17, lr: 2.17e-04
2022-05-16 01:10:23,389 INFO [train.py:812] (6/8) Epoch 36, batch 1350, loss[loss=0.1411, simple_loss=0.2342, pruned_loss=0.02403, over 7254.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2425, pruned_loss=0.02903, over 1418324.81 frames.], batch size: 19, lr: 2.17e-04
2022-05-16 01:11:21,651 INFO [train.py:812] (6/8) Epoch 36, batch 1400, loss[loss=0.1205, simple_loss=0.1999, pruned_loss=0.02056, over 6994.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2432, pruned_loss=0.02951, over 1417093.20 frames.], batch size: 16, lr: 2.17e-04
2022-05-16 01:12:20,394 INFO [train.py:812] (6/8) Epoch 36, batch 1450, loss[loss=0.1357, simple_loss=0.2222, pruned_loss=0.02454, over 7194.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2425, pruned_loss=0.02926, over 1414661.01 frames.], batch size: 16, lr: 2.17e-04
2022-05-16 01:13:19,138 INFO [train.py:812] (6/8) Epoch 36, batch 1500, loss[loss=0.1495, simple_loss=0.2508, pruned_loss=0.02408, over 7327.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2427, pruned_loss=0.02915, over 1418499.70 frames.], batch size: 21, lr: 2.17e-04
2022-05-16 01:14:17,141 INFO [train.py:812] (6/8) Epoch 36, batch 1550, loss[loss=0.1371, simple_loss=0.2429, pruned_loss=0.01568, over 7233.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2427, pruned_loss=0.02877, over 1419567.52 frames.], batch size: 20, lr: 2.17e-04
2022-05-16 01:15:14,903 INFO [train.py:812] (6/8) Epoch 36, batch 1600, loss[loss=0.1728, simple_loss=0.2623, pruned_loss=0.04162, over 7374.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2416, pruned_loss=0.02868, over 1419575.04 frames.], batch size: 23, lr: 2.16e-04
2022-05-16 01:16:13,265 INFO [train.py:812] (6/8) Epoch 36, batch 1650, loss[loss=0.1453, simple_loss=0.2407, pruned_loss=0.02498, over 7154.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2418, pruned_loss=0.02888, over 1420474.78 frames.], batch size: 19, lr: 2.16e-04
2022-05-16 01:17:10,683 INFO [train.py:812] (6/8) Epoch 36, batch 1700, loss[loss=0.1664, simple_loss=0.2574, pruned_loss=0.03764, over 7271.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2425, pruned_loss=0.02902, over 1423524.37 frames.], batch size: 25, lr: 2.16e-04
2022-05-16 01:18:09,649 INFO [train.py:812] (6/8) Epoch 36, batch 1750, loss[loss=0.1422, simple_loss=0.2251, pruned_loss=0.02962, over 7260.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2424, pruned_loss=0.02873, over 1420566.90 frames.], batch size: 18, lr: 2.16e-04
2022-05-16 01:19:07,123 INFO [train.py:812] (6/8) Epoch 36, batch 1800, loss[loss=0.1861, simple_loss=0.2751, pruned_loss=0.04857, over 7207.00 frames.], tot_loss[loss=0.1503, simple_loss=0.243, pruned_loss=0.02882, over 1422179.35 frames.], batch size: 23, lr: 2.16e-04
2022-05-16 01:20:05,575 INFO [train.py:812] (6/8) Epoch 36, batch 1850, loss[loss=0.1596, simple_loss=0.2663, pruned_loss=0.02641, over 7120.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2427, pruned_loss=0.02886, over 1425274.53 frames.], batch size: 21, lr: 2.16e-04
2022-05-16 01:21:04,174 INFO [train.py:812] (6/8) Epoch 36, batch 1900, loss[loss=0.1824, simple_loss=0.2729, pruned_loss=0.04592, over 6756.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2427, pruned_loss=0.02897, over 1426875.48 frames.], batch size: 31, lr: 2.16e-04
2022-05-16 01:22:03,023 INFO [train.py:812] (6/8) Epoch 36, batch 1950, loss[loss=0.1568, simple_loss=0.2507, pruned_loss=0.03143, over 7232.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2428, pruned_loss=0.02929, over 1424079.77 frames.], batch size: 20, lr: 2.16e-04
2022-05-16 01:23:01,532 INFO [train.py:812] (6/8) Epoch 36, batch 2000, loss[loss=0.1368, simple_loss=0.2206, pruned_loss=0.02652, over 7012.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2442, pruned_loss=0.02977, over 1421513.11 frames.], batch size: 16, lr: 2.16e-04
2022-05-16 01:24:00,267 INFO [train.py:812] (6/8) Epoch 36, batch 2050, loss[loss=0.1745, simple_loss=0.2665, pruned_loss=0.04127, over 7313.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2437, pruned_loss=0.02952, over 1425763.56 frames.], batch size: 21, lr: 2.16e-04
2022-05-16 01:24:59,298 INFO [train.py:812] (6/8) Epoch 36, batch 2100, loss[loss=0.1643, simple_loss=0.2534, pruned_loss=0.03754, over 7412.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2432, pruned_loss=0.02954, over 1424035.47 frames.], batch size: 21, lr: 2.16e-04
2022-05-16 01:25:59,150 INFO [train.py:812] (6/8) Epoch 36, batch 2150, loss[loss=0.1459, simple_loss=0.2371, pruned_loss=0.02734, over 7256.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2427, pruned_loss=0.02898, over 1426313.75 frames.], batch size: 19, lr: 2.16e-04
2022-05-16 01:26:58,773 INFO [train.py:812] (6/8) Epoch 36, batch 2200, loss[loss=0.1387, simple_loss=0.2189, pruned_loss=0.02919, over 7414.00 frames.], tot_loss[loss=0.1506, simple_loss=0.243, pruned_loss=0.02911, over 1425270.01 frames.], batch size: 18, lr: 2.16e-04
2022-05-16 01:27:57,355 INFO [train.py:812] (6/8) Epoch 36, batch 2250, loss[loss=0.1623, simple_loss=0.2625, pruned_loss=0.03105, over 7328.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2432, pruned_loss=0.02915, over 1421493.88 frames.], batch size: 22, lr: 2.16e-04
2022-05-16 01:28:55,645 INFO [train.py:812] (6/8) Epoch 36, batch 2300, loss[loss=0.133, simple_loss=0.226, pruned_loss=0.02003, over 7125.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2422, pruned_loss=0.02904, over 1425009.79 frames.], batch size: 17, lr: 2.16e-04
2022-05-16 01:29:55,115 INFO [train.py:812] (6/8) Epoch 36, batch 2350, loss[loss=0.1593, simple_loss=0.2486, pruned_loss=0.03501, over 5022.00 frames.], tot_loss[loss=0.1504, simple_loss=0.243, pruned_loss=0.02883, over 1423804.79 frames.], batch size: 52, lr: 2.16e-04
2022-05-16 01:30:54,433 INFO [train.py:812] (6/8) Epoch 36, batch 2400, loss[loss=0.1512, simple_loss=0.2402, pruned_loss=0.03114, over 7417.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2427, pruned_loss=0.02852, over 1426583.99 frames.], batch size: 18, lr: 2.16e-04
2022-05-16 01:31:54,111 INFO [train.py:812] (6/8) Epoch 36, batch 2450, loss[loss=0.1245, simple_loss=0.2152, pruned_loss=0.01692, over 7174.00 frames.], tot_loss[loss=0.1495, simple_loss=0.242, pruned_loss=0.02851, over 1421865.27 frames.], batch size: 18, lr: 2.16e-04
2022-05-16 01:32:52,240 INFO [train.py:812] (6/8) Epoch 36, batch 2500, loss[loss=0.1405, simple_loss=0.2289, pruned_loss=0.02608, over 7144.00 frames.], tot_loss[loss=0.1496, simple_loss=0.242, pruned_loss=0.02857, over 1426713.54 frames.], batch size: 20, lr: 2.16e-04
2022-05-16 01:33:51,378 INFO [train.py:812] (6/8) Epoch 36, batch 2550, loss[loss=0.1386, simple_loss=0.222, pruned_loss=0.02765, over 7361.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2425, pruned_loss=0.02888, over 1424325.73 frames.], batch size: 19, lr: 2.16e-04
2022-05-16 01:34:50,041 INFO [train.py:812] (6/8) Epoch 36, batch 2600, loss[loss=0.1602, simple_loss=0.2496, pruned_loss=0.03543, over 7152.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2426, pruned_loss=0.02908, over 1425512.62 frames.], batch size: 19, lr: 2.16e-04
2022-05-16 01:35:48,606 INFO [train.py:812] (6/8) Epoch 36, batch 2650, loss[loss=0.1972, simple_loss=0.2896, pruned_loss=0.05239, over 4977.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2423, pruned_loss=0.02935, over 1424060.49 frames.], batch size: 53, lr: 2.16e-04
2022-05-16 01:36:47,062 INFO [train.py:812] (6/8) Epoch 36, batch 2700, loss[loss=0.1537, simple_loss=0.251, pruned_loss=0.02818, over 7308.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2415, pruned_loss=0.029, over 1425140.62 frames.], batch size: 21, lr: 2.16e-04
2022-05-16 01:37:45,797 INFO [train.py:812] (6/8) Epoch 36, batch 2750, loss[loss=0.1329, simple_loss=0.2336, pruned_loss=0.01613, over 7120.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2413, pruned_loss=0.02852, over 1426536.56 frames.], batch size: 21, lr: 2.16e-04
2022-05-16 01:38:44,975 INFO [train.py:812] (6/8) Epoch 36, batch 2800, loss[loss=0.1819, simple_loss=0.2832, pruned_loss=0.0403, over 7202.00 frames.], tot_loss[loss=0.1492, simple_loss=0.241, pruned_loss=0.02866, over 1428220.93 frames.], batch size: 22, lr: 2.16e-04
2022-05-16 01:39:44,910 INFO [train.py:812] (6/8) Epoch 36, batch 2850, loss[loss=0.1229, simple_loss=0.2136, pruned_loss=0.01611, over 7297.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2402, pruned_loss=0.02836, over 1428994.57 frames.], batch size: 17, lr: 2.16e-04
2022-05-16 01:40:43,914 INFO [train.py:812] (6/8) Epoch 36, batch 2900, loss[loss=0.1371, simple_loss=0.2278, pruned_loss=0.02325, over 7258.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2395, pruned_loss=0.02835, over 1428348.11 frames.], batch size: 19, lr: 2.16e-04
2022-05-16 01:41:42,725 INFO [train.py:812] (6/8) Epoch 36, batch 2950, loss[loss=0.1261, simple_loss=0.2177, pruned_loss=0.01731, over 7172.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2407, pruned_loss=0.02832, over 1425988.44 frames.], batch size: 18, lr: 2.16e-04
2022-05-16 01:42:41,197 INFO [train.py:812] (6/8) Epoch 36, batch 3000, loss[loss=0.1375, simple_loss=0.2224, pruned_loss=0.02633, over 7165.00 frames.], tot_loss[loss=0.15, simple_loss=0.2422, pruned_loss=0.0289, over 1423138.41 frames.], batch size: 19, lr: 2.16e-04
2022-05-16 01:42:41,198 INFO [train.py:832] (6/8) Computing validation loss
2022-05-16 01:42:48,526 INFO [train.py:841] (6/8) Epoch 36, validation: loss=0.1533, simple_loss=0.2487, pruned_loss=0.02893, over 698248.00 frames.
2022-05-16 01:43:48,429 INFO [train.py:812] (6/8) Epoch 36, batch 3050, loss[loss=0.1421, simple_loss=0.2413, pruned_loss=0.0215, over 7276.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2424, pruned_loss=0.02923, over 1424935.77 frames.], batch size: 24, lr: 2.16e-04
2022-05-16 01:44:47,702 INFO [train.py:812] (6/8) Epoch 36, batch 3100, loss[loss=0.1624, simple_loss=0.2572, pruned_loss=0.0338, over 7304.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2429, pruned_loss=0.02938, over 1428994.59 frames.], batch size: 25, lr: 2.15e-04
2022-05-16 01:45:47,524 INFO [train.py:812] (6/8) Epoch 36, batch 3150, loss[loss=0.1759, simple_loss=0.2658, pruned_loss=0.04299, over 7370.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2427, pruned_loss=0.02939, over 1427079.84 frames.], batch size: 23, lr: 2.15e-04
2022-05-16 01:46:46,136 INFO [train.py:812] (6/8) Epoch 36, batch 3200, loss[loss=0.134, simple_loss=0.2161, pruned_loss=0.02599, over 7150.00 frames.], tot_loss[loss=0.151, simple_loss=0.2428, pruned_loss=0.02955, over 1419977.77 frames.], batch size: 17, lr: 2.15e-04
2022-05-16 01:47:45,908 INFO [train.py:812] (6/8) Epoch 36, batch 3250, loss[loss=0.1629, simple_loss=0.2426, pruned_loss=0.04158, over 5106.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2427, pruned_loss=0.02987, over 1417714.38 frames.], batch size: 52, lr: 2.15e-04
2022-05-16 01:48:53,228 INFO [train.py:812] (6/8) Epoch 36, batch 3300, loss[loss=0.1637, simple_loss=0.2507, pruned_loss=0.03836, over 7193.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2431, pruned_loss=0.02978, over 1421357.57 frames.], batch size: 23, lr: 2.15e-04
2022-05-16 01:49:52,249 INFO [train.py:812] (6/8) Epoch 36, batch 3350, loss[loss=0.1498, simple_loss=0.2349, pruned_loss=0.03228, over 7202.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2431, pruned_loss=0.02975, over 1425083.62 frames.], batch size: 23, lr: 2.15e-04
2022-05-16 01:50:50,325 INFO [train.py:812] (6/8) Epoch 36, batch 3400, loss[loss=0.1409, simple_loss=0.2329, pruned_loss=0.02446, over 7261.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2425, pruned_loss=0.02958, over 1424457.89 frames.], batch size: 19, lr: 2.15e-04
2022-05-16 01:51:53,854 INFO [train.py:812] (6/8) Epoch 36, batch 3450, loss[loss=0.1277, simple_loss=0.2136, pruned_loss=0.02087, over 7278.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2432, pruned_loss=0.03007, over 1421555.86 frames.], batch size: 17, lr: 2.15e-04
2022-05-16 01:52:52,279 INFO [train.py:812] (6/8) Epoch 36, batch 3500, loss[loss=0.1459, simple_loss=0.2464, pruned_loss=0.02275, over 7421.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2421, pruned_loss=0.02944, over 1418729.05 frames.], batch size: 21, lr: 2.15e-04
2022-05-16 01:53:50,972 INFO [train.py:812] (6/8) Epoch 36, batch 3550, loss[loss=0.1393, simple_loss=0.2362, pruned_loss=0.02122, over 7073.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2412, pruned_loss=0.02893, over 1422696.24 frames.], batch size: 28, lr: 2.15e-04
2022-05-16 01:54:49,019 INFO [train.py:812] (6/8) Epoch 36, batch 3600, loss[loss=0.1635, simple_loss=0.2594, pruned_loss=0.03386, over 7259.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2413, pruned_loss=0.02887, over 1421609.68 frames.], batch size: 25, lr: 2.15e-04
2022-05-16 01:55:48,158 INFO [train.py:812] (6/8) Epoch 36, batch 3650, loss[loss=0.1598, simple_loss=0.2522, pruned_loss=0.03372, over 7287.00 frames.], tot_loss[loss=0.15, simple_loss=0.2417, pruned_loss=0.02913, over 1422812.53 frames.], batch size: 24, lr: 2.15e-04
2022-05-16 01:56:46,096 INFO [train.py:812] (6/8) Epoch 36, batch 3700, loss[loss=0.1594, simple_loss=0.2526, pruned_loss=0.0331, over 7109.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2415, pruned_loss=0.02897, over 1425739.86 frames.], batch size: 21, lr: 2.15e-04
2022-05-16 01:57:44,758 INFO [train.py:812] (6/8) Epoch 36, batch 3750, loss[loss=0.1518, simple_loss=0.2448, pruned_loss=0.02938, over 7327.00 frames.], tot_loss[loss=0.1501, simple_loss=0.242, pruned_loss=0.0291, over 1424945.12 frames.], batch size: 22, lr: 2.15e-04
2022-05-16 01:58:43,602 INFO [train.py:812] (6/8) Epoch 36, batch 3800, loss[loss=0.1318, simple_loss=0.218, pruned_loss=0.02285, over 7362.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2429, pruned_loss=0.02898, over 1427098.26 frames.], batch size: 19, lr: 2.15e-04
2022-05-16 01:59:42,804 INFO [train.py:812] (6/8) Epoch 36, batch 3850, loss[loss=0.1254, simple_loss=0.2102, pruned_loss=0.02035, over 7437.00 frames.], tot_loss[loss=0.1503, simple_loss=0.243, pruned_loss=0.02882, over 1423801.10 frames.], batch size: 17, lr: 2.15e-04
2022-05-16 02:00:41,857 INFO [train.py:812] (6/8) Epoch 36, batch 3900, loss[loss=0.1735, simple_loss=0.2683, pruned_loss=0.03932, over 7186.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2426, pruned_loss=0.02896, over 1426072.72 frames.], batch size: 23, lr: 2.15e-04
2022-05-16 02:01:40,068 INFO [train.py:812] (6/8) Epoch 36, batch 3950, loss[loss=0.1413, simple_loss=0.2388, pruned_loss=0.0219, over 6821.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2435, pruned_loss=0.02898, over 1424496.54 frames.], batch size: 31, lr: 2.15e-04
2022-05-16 02:02:38,475 INFO [train.py:812] (6/8) Epoch 36, batch 4000, loss[loss=0.1556, simple_loss=0.2582, pruned_loss=0.02652, over 7048.00 frames.], tot_loss[loss=0.1512, simple_loss=0.244, pruned_loss=0.02926, over 1424447.90 frames.], batch size: 28, lr: 2.15e-04
2022-05-16 02:03:36,278 INFO [train.py:812] (6/8) Epoch 36, batch 4050, loss[loss=0.1484, simple_loss=0.2371, pruned_loss=0.02984, over 7222.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2434, pruned_loss=0.02897, over 1427163.88 frames.], batch size: 21, lr: 2.15e-04
2022-05-16 02:04:34,969 INFO [train.py:812] (6/8) Epoch 36, batch 4100, loss[loss=0.1394, simple_loss=0.2264, pruned_loss=0.02622, over 7136.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2432, pruned_loss=0.02911, over 1426995.59 frames.], batch size: 17, lr: 2.15e-04
2022-05-16 02:05:34,477 INFO [train.py:812] (6/8) Epoch 36, batch 4150, loss[loss=0.1587, simple_loss=0.2537, pruned_loss=0.03186, over 7205.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2422, pruned_loss=0.02896, over 1420587.37 frames.], batch size: 23, lr: 2.15e-04
2022-05-16 02:06:32,913 INFO [train.py:812] (6/8) Epoch 36, batch 4200, loss[loss=0.1462, simple_loss=0.2445, pruned_loss=0.02397, over 7236.00 frames.], tot_loss[loss=0.1499, simple_loss=0.242, pruned_loss=0.02888, over 1417724.82 frames.], batch size: 20, lr: 2.15e-04
2022-05-16 02:07:31,856 INFO [train.py:812] (6/8) Epoch 36, batch 4250, loss[loss=0.1615, simple_loss=0.2465, pruned_loss=0.0383, over 7204.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2421, pruned_loss=0.02906, over 1416688.94 frames.], batch size: 22, lr: 2.15e-04
2022-05-16 02:08:31,009 INFO [train.py:812] (6/8) Epoch 36, batch 4300, loss[loss=0.1465, simple_loss=0.2378, pruned_loss=0.02756, over 7204.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2417, pruned_loss=0.02905, over 1413110.29 frames.], batch size: 22, lr: 2.15e-04
2022-05-16 02:09:30,587 INFO [train.py:812] (6/8) Epoch 36, batch 4350, loss[loss=0.1385, simple_loss=0.2369, pruned_loss=0.02005, over 7433.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2409, pruned_loss=0.02877, over 1411977.60 frames.], batch size: 20, lr: 2.15e-04
2022-05-16 02:10:29,644 INFO [train.py:812] (6/8) Epoch 36, batch 4400, loss[loss=0.1489, simple_loss=0.228, pruned_loss=0.03488, over 7353.00 frames.], tot_loss[loss=0.149, simple_loss=0.2406, pruned_loss=0.02875, over 1416692.13 frames.], batch size: 19, lr: 2.15e-04
2022-05-16 02:11:29,754 INFO [train.py:812] (6/8) Epoch 36, batch 4450, loss[loss=0.1519, simple_loss=0.2523, pruned_loss=0.02577, over 7223.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2401, pruned_loss=0.0284, over 1406150.94 frames.], batch size: 21, lr: 2.15e-04
2022-05-16 02:12:28,156 INFO [train.py:812] (6/8) Epoch 36, batch 4500, loss[loss=0.1389, simple_loss=0.2353, pruned_loss=0.02121, over 7231.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2406, pruned_loss=0.02848, over 1393929.80 frames.], batch size: 21, lr: 2.15e-04
2022-05-16 02:13:26,415 INFO [train.py:812] (6/8) Epoch 36, batch 4550, loss[loss=0.1681, simple_loss=0.2513, pruned_loss=0.04244, over 7258.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2421, pruned_loss=0.02959, over 1354892.50 frames.], batch size: 19, lr: 2.15e-04
2022-05-16 02:14:35,977 INFO [train.py:812] (6/8) Epoch 37, batch 0, loss[loss=0.1455, simple_loss=0.2453, pruned_loss=0.02285, over 7336.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2453, pruned_loss=0.02285, over 7336.00 frames.], batch size: 22, lr: 2.12e-04
2022-05-16 02:15:35,004 INFO [train.py:812] (6/8) Epoch 37, batch 50, loss[loss=0.1382, simple_loss=0.2275, pruned_loss=0.02451, over 7068.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2441, pruned_loss=0.02927, over 320659.74 frames.], batch size: 18, lr: 2.12e-04
2022-05-16 02:16:33,784 INFO [train.py:812] (6/8) Epoch 37, batch 100, loss[loss=0.1635, simple_loss=0.2486, pruned_loss=0.03924, over 7326.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2448, pruned_loss=0.02989, over 566617.66 frames.], batch size: 20, lr: 2.12e-04
2022-05-16 02:17:32,760 INFO [train.py:812] (6/8) Epoch 37, batch 150, loss[loss=0.1523, simple_loss=0.251, pruned_loss=0.02674, over 7078.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2438, pruned_loss=0.02979, over 753834.49 frames.], batch size: 28, lr: 2.11e-04
2022-05-16 02:18:31,120 INFO [train.py:812] (6/8) Epoch 37, batch 200, loss[loss=0.1544, simple_loss=0.2556, pruned_loss=0.02666, over 7315.00 frames.], tot_loss[loss=0.152, simple_loss=0.2452, pruned_loss=0.02944, over 905147.67 frames.], batch size: 21, lr: 2.11e-04
2022-05-16 02:19:29,655 INFO [train.py:812] (6/8) Epoch 37, batch 250, loss[loss=0.154, simple_loss=0.245, pruned_loss=0.03145, over 7263.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2442, pruned_loss=0.0294, over 1016792.30 frames.], batch size: 19, lr: 2.11e-04
2022-05-16 02:20:28,591 INFO [train.py:812] (6/8) Epoch 37, batch 300, loss[loss=0.1529, simple_loss=0.2537, pruned_loss=0.02608, over 7328.00 frames.], tot_loss[loss=0.151, simple_loss=0.2431, pruned_loss=0.02941, over 1102764.64 frames.], batch size: 22, lr: 2.11e-04
2022-05-16 02:21:27,118 INFO [train.py:812] (6/8) Epoch 37, batch 350, loss[loss=0.148, simple_loss=0.2388, pruned_loss=0.02864, over 7170.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2437, pruned_loss=0.02985, over 1171539.51 frames.], batch size: 18, lr: 2.11e-04
2022-05-16 02:22:25,747 INFO [train.py:812] (6/8) Epoch 37, batch 400, loss[loss=0.1515, simple_loss=0.2396, pruned_loss=0.03172, over 7235.00 frames.], tot_loss[loss=0.1508, simple_loss=0.243, pruned_loss=0.02932, over 1230800.37 frames.], batch size: 20, lr: 2.11e-04
2022-05-16 02:23:24,552 INFO [train.py:812] (6/8) Epoch 37, batch 450, loss[loss=0.151, simple_loss=0.2425, pruned_loss=0.02976, over 7153.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2425, pruned_loss=0.02885, over 1275604.80 frames.], batch size: 20, lr: 2.11e-04
2022-05-16 02:24:21,859 INFO [train.py:812] (6/8) Epoch 37, batch 500, loss[loss=0.1459, simple_loss=0.2404, pruned_loss=0.02571, over 7233.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2421, pruned_loss=0.0288, over 1306143.63 frames.], batch size: 20, lr: 2.11e-04
2022-05-16 02:25:21,121 INFO [train.py:812] (6/8) Epoch 37, batch 550, loss[loss=0.1354, simple_loss=0.2185, pruned_loss=0.02616, over 7073.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2424, pruned_loss=0.02909, over 1322941.58 frames.], batch size: 18, lr: 2.11e-04
2022-05-16 02:26:19,463 INFO [train.py:812] (6/8) Epoch 37, batch 600, loss[loss=0.1551, simple_loss=0.2442, pruned_loss=0.03304, over 7431.00 frames.], tot_loss[loss=0.1502, simple_loss=0.242, pruned_loss=0.02924, over 1348150.32 frames.], batch size: 20, lr: 2.11e-04
2022-05-16 02:27:18,175 INFO [train.py:812] (6/8) Epoch 37, batch 650, loss[loss=0.1287, simple_loss=0.2133, pruned_loss=0.02208, over 7128.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2405, pruned_loss=0.02881, over 1366965.64 frames.], batch size: 17, lr: 2.11e-04
2022-05-16 02:28:16,749 INFO [train.py:812] (6/8) Epoch 37, batch 700, loss[loss=0.1675, simple_loss=0.2654, pruned_loss=0.03479, over 7228.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2408, pruned_loss=0.02851, over 1379915.94 frames.], batch size: 20, lr: 2.11e-04
2022-05-16 02:29:16,768 INFO [train.py:812] (6/8) Epoch 37, batch 750, loss[loss=0.1683, simple_loss=0.2594, pruned_loss=0.0386, over 7160.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2401, pruned_loss=0.0282, over 1388475.03 frames.], batch size: 19, lr: 2.11e-04
2022-05-16 02:30:15,269 INFO [train.py:812] (6/8) Epoch 37, batch 800, loss[loss=0.1401, simple_loss=0.2264, pruned_loss=0.02696, over 7399.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2402, pruned_loss=0.02825, over 1398602.02 frames.], batch size: 18, lr: 2.11e-04
2022-05-16 02:31:14,047 INFO [train.py:812] (6/8) Epoch 37, batch 850, loss[loss=0.117, simple_loss=0.2065, pruned_loss=0.01369, over 7255.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2416, pruned_loss=0.02854, over 1398002.59 frames.], batch size: 19, lr: 2.11e-04
2022-05-16 02:32:12,869 INFO [train.py:812] (6/8) Epoch 37, batch 900, loss[loss=0.132, simple_loss=0.2187, pruned_loss=0.02264, over 7066.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2405, pruned_loss=0.02794, over 1407030.90 frames.], batch size: 18, lr: 2.11e-04
2022-05-16 02:33:11,837 INFO [train.py:812] (6/8) Epoch 37, batch 950, loss[loss=0.1426, simple_loss=0.2224, pruned_loss=0.03143, over 7296.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2408, pruned_loss=0.0281, over 1410962.55 frames.], batch size: 17, lr: 2.11e-04
2022-05-16 02:34:09,795 INFO [train.py:812] (6/8) Epoch 37, batch 1000, loss[loss=0.1698, simple_loss=0.275, pruned_loss=0.03233, over 6874.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2413, pruned_loss=0.02814, over 1413557.76 frames.], batch size: 31, lr: 2.11e-04
2022-05-16 02:35:08,660 INFO [train.py:812] (6/8) Epoch 37, batch 1050, loss[loss=0.1579, simple_loss=0.2511, pruned_loss=0.03235, over 7367.00 frames.], tot_loss[loss=0.149, simple_loss=0.2413, pruned_loss=0.02833, over 1417735.84 frames.], batch size: 23, lr: 2.11e-04
2022-05-16 02:36:07,866 INFO [train.py:812] (6/8) Epoch 37, batch 1100, loss[loss=0.1472, simple_loss=0.2469, pruned_loss=0.02373, over 7226.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2413, pruned_loss=0.02846, over 1418901.81 frames.], batch size: 21, lr: 2.11e-04
2022-05-16 02:37:06,619 INFO [train.py:812] (6/8) Epoch 37, batch 1150, loss[loss=0.1537, simple_loss=0.2408, pruned_loss=0.03333, over 4934.00 frames.], tot_loss[loss=0.1489, simple_loss=0.241, pruned_loss=0.0284, over 1418026.82 frames.], batch size: 52, lr: 2.11e-04
2022-05-16 02:38:04,307 INFO [train.py:812] (6/8) Epoch 37, batch 1200, loss[loss=0.1795, simple_loss=0.2673, pruned_loss=0.04589, over 7147.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2424, pruned_loss=0.02886, over 1419793.01 frames.], batch size: 20, lr: 2.11e-04
2022-05-16 02:39:03,479 INFO [train.py:812] (6/8) Epoch 37, batch 1250, loss[loss=0.1433, simple_loss=0.2414, pruned_loss=0.02262, over 7211.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2418, pruned_loss=0.02837, over 1420087.22 frames.], batch size: 22, lr: 2.11e-04
2022-05-16 02:40:01,895 INFO [train.py:812] (6/8) Epoch 37, batch 1300, loss[loss=0.1311, simple_loss=0.2146, pruned_loss=0.02383, over 7159.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2425, pruned_loss=0.02865, over 1422428.69 frames.], batch size: 17, lr: 2.11e-04
2022-05-16 02:41:00,875 INFO [train.py:812] (6/8) Epoch 37, batch 1350, loss[loss=0.1468, simple_loss=0.2239, pruned_loss=0.03485, over 7064.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2417, pruned_loss=0.02838, over 1418120.15 frames.], batch size: 18, lr: 2.11e-04
2022-05-16 02:42:00,031 INFO [train.py:812] (6/8) Epoch 37, batch 1400, loss[loss=0.1105, simple_loss=0.1943, pruned_loss=0.01336, over 6991.00 frames.], tot_loss[loss=0.15, simple_loss=0.2422, pruned_loss=0.02885, over 1417996.26 frames.], batch size: 16, lr: 2.11e-04
2022-05-16 02:42:58,502 INFO [train.py:812] (6/8) Epoch 37, batch 1450, loss[loss=0.1494, simple_loss=0.2473, pruned_loss=0.0258, over 7268.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2431, pruned_loss=0.02875, over 1419421.38 frames.], batch size: 24, lr: 2.11e-04
2022-05-16 02:43:56,640 INFO [train.py:812] (6/8) Epoch 37, batch 1500, loss[loss=0.2013, simple_loss=0.2955, pruned_loss=0.05353, over 7292.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2438, pruned_loss=0.02956, over 1416329.95 frames.], batch size: 24, lr: 2.11e-04
2022-05-16 02:44:55,823 INFO [train.py:812] (6/8) Epoch 37, batch 1550, loss[loss=0.1579, simple_loss=0.2563, pruned_loss=0.02976, over 6922.00 frames.], tot_loss[loss=0.151, simple_loss=0.2433, pruned_loss=0.02937, over 1412483.93 frames.], batch size: 32, lr: 2.11e-04
2022-05-16 02:45:54,010 INFO [train.py:812] (6/8) Epoch 37, batch 1600, loss[loss=0.1645, simple_loss=0.2616, pruned_loss=0.03372, over 7375.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2427, pruned_loss=0.02916, over 1412579.47 frames.], batch size: 23, lr: 2.11e-04
2022-05-16 02:46:52,099 INFO [train.py:812] (6/8) Epoch 37, batch 1650, loss[loss=0.1734, simple_loss=0.2736, pruned_loss=0.03663, over 7218.00 frames.], tot_loss[loss=0.15, simple_loss=0.2424, pruned_loss=0.02883, over 1415697.25 frames.], batch size: 22, lr: 2.11e-04
2022-05-16 02:47:50,662 INFO [train.py:812] (6/8) Epoch 37, batch 1700, loss[loss=0.1506, simple_loss=0.248, pruned_loss=0.0266, over 7160.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2429, pruned_loss=0.02879, over 1414413.29 frames.], batch size: 19, lr: 2.11e-04
2022-05-16 02:48:48,775 INFO [train.py:812] (6/8) Epoch 37, batch 1750, loss[loss=0.1378, simple_loss=0.2312, pruned_loss=0.02218, over 7359.00 frames.], tot_loss[loss=0.1505, simple_loss=0.243, pruned_loss=0.02902, over 1408872.02 frames.], batch size: 19, lr: 2.10e-04
2022-05-16 02:49:47,222 INFO [train.py:812] (6/8) Epoch 37, batch 1800, loss[loss=0.171, simple_loss=0.2628, pruned_loss=0.0396, over 7302.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2433, pruned_loss=0.02899, over 1411200.10 frames.], batch size: 24, lr: 2.10e-04
2022-05-16 02:50:46,355 INFO [train.py:812] (6/8) Epoch 37, batch 1850, loss[loss=0.1451, simple_loss=0.232, pruned_loss=0.02911, over 7271.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2428, pruned_loss=0.02891, over 1411424.22 frames.], batch size: 19, lr: 2.10e-04
2022-05-16 02:51:45,059 INFO [train.py:812] (6/8) Epoch 37, batch 1900, loss[loss=0.1446, simple_loss=0.2371, pruned_loss=0.02608, over 6729.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2434, pruned_loss=0.02901, over 1417038.31 frames.], batch size: 31, lr: 2.10e-04
2022-05-16 02:52:44,054 INFO [train.py:812] (6/8) Epoch 37, batch 1950, loss[loss=0.1556, simple_loss=0.246, pruned_loss=0.03263, over 7213.00 frames.], tot_loss[loss=0.1496, simple_loss=0.242, pruned_loss=0.02859, over 1419989.15 frames.], batch size: 21, lr: 2.10e-04
2022-05-16 02:53:42,347 INFO [train.py:812] (6/8) Epoch 37, batch 2000, loss[loss=0.1799, simple_loss=0.2742, pruned_loss=0.04285, over 7400.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2426, pruned_loss=0.0288, over 1416698.98 frames.], batch size: 21, lr: 2.10e-04
2022-05-16 02:54:41,779 INFO [train.py:812] (6/8) Epoch 37, batch 2050, loss[loss=0.1466, simple_loss=0.2458, pruned_loss=0.02375, over 7238.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2424, pruned_loss=0.02873, over 1420319.66 frames.], batch size: 20, lr: 2.10e-04
2022-05-16 02:55:38,572 INFO [train.py:812] (6/8) Epoch 37, batch 2100, loss[loss=0.1571, simple_loss=0.2525, pruned_loss=0.03083, over 7148.00 frames.], tot_loss[loss=0.1496, simple_loss=0.242, pruned_loss=0.02855, over 1420062.92 frames.], batch size: 20, lr: 2.10e-04
2022-05-16 02:56:46,823 INFO [train.py:812] (6/8) Epoch 37, batch 2150, loss[loss=0.1392, simple_loss=0.2387, pruned_loss=0.01984, over 7414.00 frames.], tot_loss[loss=0.15, simple_loss=0.2425, pruned_loss=0.0287, over 1417421.44 frames.], batch size: 21, lr: 2.10e-04
2022-05-16 02:57:45,107 INFO [train.py:812] (6/8) Epoch 37, batch 2200, loss[loss=0.1174, simple_loss=0.2153, pruned_loss=0.009706, over 7251.00 frames.], tot_loss[loss=0.1495, simple_loss=0.242, pruned_loss=0.02849, over 1419036.44 frames.], batch size: 19, lr: 2.10e-04
2022-05-16 02:58:53,444 INFO [train.py:812] (6/8) Epoch 37, batch 2250, loss[loss=0.1574, simple_loss=0.2561, pruned_loss=0.02931, over 7155.00 frames.], tot_loss[loss=0.15, simple_loss=0.2426, pruned_loss=0.02874, over 1419123.24 frames.], batch size: 20, lr: 2.10e-04
2022-05-16 03:00:01,348 INFO [train.py:812] (6/8) Epoch 37, batch 2300, loss[loss=0.1494, simple_loss=0.245, pruned_loss=0.02693, over 7192.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2429, pruned_loss=0.02873, over 1418644.37 frames.], batch size: 23, lr: 2.10e-04
2022-05-16 03:01:01,009 INFO [train.py:812] (6/8) Epoch 37, batch 2350, loss[loss=0.1276, simple_loss=0.2099, pruned_loss=0.02262, over 7277.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2431, pruned_loss=0.02889, over 1413266.78 frames.], batch size: 17, lr: 2.10e-04
2022-05-16 03:01:59,220 INFO [train.py:812] (6/8) Epoch 37, batch 2400, loss[loss=0.1766, simple_loss=0.2691, pruned_loss=0.04207, over 7287.00 frames.], tot_loss[loss=0.1504, simple_loss=0.243, pruned_loss=0.02888, over 1419717.84 frames.], batch size: 25, lr: 2.10e-04
2022-05-16 03:02:57,114 INFO [train.py:812] (6/8) Epoch 37, batch 2450, loss[loss=0.1602, simple_loss=0.2569, pruned_loss=0.03177, over 7186.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2433, pruned_loss=0.02888, over 1424923.54 frames.], batch size: 26, lr: 2.10e-04
2022-05-16 03:04:04,668 INFO [train.py:812] (6/8) Epoch 37, batch 2500, loss[loss=0.1481, simple_loss=0.2393, pruned_loss=0.02841, over 7159.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2421, pruned_loss=0.02862, over 1428336.38 frames.], batch size: 19, lr: 2.10e-04
2022-05-16 03:05:04,386 INFO [train.py:812] (6/8) Epoch 37, batch 2550, loss[loss=0.1495, simple_loss=0.2452, pruned_loss=0.02684, over 7284.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2418, pruned_loss=0.02829, over 1428226.03 frames.], batch size: 24, lr: 2.10e-04
2022-05-16 03:06:02,651 INFO [train.py:812] (6/8) Epoch 37, batch 2600, loss[loss=0.1375, simple_loss=0.2187, pruned_loss=0.0281, over 6804.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2417, pruned_loss=0.02822, over 1424597.80 frames.], batch size: 15, lr: 2.10e-04
2022-05-16 03:07:21,592 INFO [train.py:812] (6/8) Epoch 37, batch 2650, loss[loss=0.1478, simple_loss=0.2425, pruned_loss=0.02656, over 7197.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2424, pruned_loss=0.02868, over 1427922.23 frames.], batch size: 22, lr: 2.10e-04
2022-05-16 03:08:19,636 INFO [train.py:812] (6/8) Epoch 37, batch 2700, loss[loss=0.1563, simple_loss=0.2546, pruned_loss=0.02896, over 6532.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2433, pruned_loss=0.02915, over 1424146.39 frames.], batch size: 38, lr: 2.10e-04
2022-05-16 03:09:18,929 INFO [train.py:812] (6/8) Epoch 37, batch 2750, loss[loss=0.1972, simple_loss=0.2809, pruned_loss=0.05673, over 5334.00 frames.], tot_loss[loss=0.151, simple_loss=0.2441, pruned_loss=0.029, over 1425189.46 frames.], batch size: 52, lr: 2.10e-04
2022-05-16 03:10:17,003 INFO [train.py:812] (6/8) Epoch 37, batch 2800, loss[loss=0.143, simple_loss=0.2304, pruned_loss=0.02783, over 7284.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2431, pruned_loss=0.02899, over 1429399.99 frames.], batch size: 18, lr: 2.10e-04
2022-05-16 03:11:34,307 INFO [train.py:812] (6/8) Epoch 37, batch 2850, loss[loss=0.1414, simple_loss=0.2453, pruned_loss=0.01878, over 6347.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2425, pruned_loss=0.02868, over 1427927.82 frames.], batch size: 38, lr: 2.10e-04
2022-05-16 03:12:32,636 INFO [train.py:812] (6/8) Epoch 37, batch 2900, loss[loss=0.1366, simple_loss=0.2169, pruned_loss=0.02819, over 6986.00 frames.], tot_loss[loss=0.1494, simple_loss=0.242, pruned_loss=0.02842, over 1428628.86 frames.], batch size: 16, lr: 2.10e-04
2022-05-16 03:13:31,846 INFO [train.py:812] (6/8) Epoch 37, batch 2950, loss[loss=0.1535, simple_loss=0.2544, pruned_loss=0.0263, over 7436.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2423, pruned_loss=0.02863, over 1425232.70 frames.], batch size: 20, lr: 2.10e-04
2022-05-16 03:14:30,574 INFO [train.py:812] (6/8) Epoch 37, batch 3000, loss[loss=0.1487, simple_loss=0.2446, pruned_loss=0.02636, over 7208.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2421, pruned_loss=0.02848, over 1420370.25 frames.], batch size: 21, lr: 2.10e-04
2022-05-16 03:14:30,575 INFO [train.py:832] (6/8) Computing validation loss
2022-05-16 03:14:38,087 INFO [train.py:841] (6/8) Epoch 37, validation: loss=0.1539, simple_loss=0.2491, pruned_loss=0.02931, over 698248.00 frames.
2022-05-16 03:15:37,673 INFO [train.py:812] (6/8) Epoch 37, batch 3050, loss[loss=0.156, simple_loss=0.2455, pruned_loss=0.0333, over 6775.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2426, pruned_loss=0.02905, over 1418972.77 frames.], batch size: 15, lr: 2.10e-04
2022-05-16 03:16:36,467 INFO [train.py:812] (6/8) Epoch 37, batch 3100, loss[loss=0.1526, simple_loss=0.2335, pruned_loss=0.03584, over 7073.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2421, pruned_loss=0.02915, over 1417815.71 frames.], batch size: 18, lr: 2.10e-04
2022-05-16 03:17:34,879 INFO [train.py:812] (6/8) Epoch 37, batch 3150, loss[loss=0.1333, simple_loss=0.2163, pruned_loss=0.02519, over 6991.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2418, pruned_loss=0.02936, over 1417007.36 frames.], batch size: 16, lr: 2.10e-04
2022-05-16 03:18:33,965 INFO [train.py:812] (6/8) Epoch 37, batch 3200, loss[loss=0.1627, simple_loss=0.25, pruned_loss=0.03772, over 4992.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2413, pruned_loss=0.02894, over 1417417.88 frames.], batch size: 52, lr: 2.10e-04
2022-05-16 03:19:33,515 INFO [train.py:812] (6/8) Epoch 37, batch 3250, loss[loss=0.1455, simple_loss=0.246, pruned_loss=0.02248, over 7224.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2417, pruned_loss=0.02896, over 1417067.14 frames.], batch size: 22, lr: 2.10e-04
2022-05-16 03:20:31,497 INFO [train.py:812] (6/8) Epoch 37, batch 3300, loss[loss=0.1552, simple_loss=0.2498, pruned_loss=0.03033, over 7409.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2423, pruned_loss=0.02944, over 1414149.46 frames.], batch size: 21, lr: 2.10e-04
2022-05-16 03:21:29,317 INFO [train.py:812] (6/8) Epoch 37, batch 3350, loss[loss=0.1662, simple_loss=0.259, pruned_loss=0.03669, over 7378.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2431, pruned_loss=0.0296, over 1410931.78 frames.], batch size: 23, lr: 2.09e-04
2022-05-16 03:22:27,830 INFO [train.py:812] (6/8) Epoch 37, batch 3400, loss[loss=0.1319, simple_loss=0.2238, pruned_loss=0.01995, over 7152.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2423, pruned_loss=0.02949, over 1415789.72 frames.], batch size: 17, lr: 2.09e-04
2022-05-16 03:23:27,202 INFO [train.py:812] (6/8) Epoch 37, batch 3450, loss[loss=0.1339, simple_loss=0.2177, pruned_loss=0.02504, over 7282.00 frames.], tot_loss[loss=0.15, simple_loss=0.2416, pruned_loss=0.02914, over 1419013.40 frames.], batch size: 17, lr: 2.09e-04
2022-05-16 03:24:25,221 INFO [train.py:812] (6/8) Epoch 37, batch 3500, loss[loss=0.1384, simple_loss=0.2259, pruned_loss=0.02541, over 7369.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2421, pruned_loss=0.02938, over 1416708.68 frames.], batch size: 19, lr: 2.09e-04
2022-05-16 03:25:24,390 INFO [train.py:812] (6/8) Epoch 37, batch 3550, loss[loss=0.1327, simple_loss=0.214, pruned_loss=0.02573, over 6826.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2422, pruned_loss=0.02921, over 1413994.08 frames.], batch size: 15, lr: 2.09e-04
2022-05-16 03:26:23,196 INFO [train.py:812] (6/8) Epoch 37, batch 3600, loss[loss=0.1391, simple_loss=0.2218, pruned_loss=0.02816, over 6997.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2409, pruned_loss=0.02865, over 1420126.71 frames.], batch size: 16, lr: 2.09e-04
2022-05-16 03:27:22,043 INFO [train.py:812] (6/8) Epoch 37, batch 3650, loss[loss=0.1406, simple_loss=0.2267, pruned_loss=0.02726, over 7147.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2405, pruned_loss=0.02837, over 1422699.08 frames.], batch size: 19, lr: 2.09e-04
2022-05-16 03:28:20,577 INFO [train.py:812] (6/8) Epoch 37, batch 3700, loss[loss=0.1645, simple_loss=0.258, pruned_loss=0.0355, over 7235.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2413, pruned_loss=0.02885, over 1425632.81 frames.], batch size: 20, lr: 2.09e-04
2022-05-16 03:29:19,683 INFO [train.py:812] (6/8) Epoch 37, batch 3750, loss[loss=0.1747, simple_loss=0.2581, pruned_loss=0.04562, over 7307.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2419, pruned_loss=0.02879, over 1422090.57 frames.], batch size: 24, lr: 2.09e-04
2022-05-16 03:30:17,098 INFO [train.py:812] (6/8) Epoch 37, batch 3800, loss[loss=0.1393, simple_loss=0.2174, pruned_loss=0.03059, over 7292.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2412, pruned_loss=0.02833, over 1424040.00 frames.], batch size: 17, lr: 2.09e-04
2022-05-16 03:31:15,857 INFO [train.py:812] (6/8) Epoch 37, batch 3850, loss[loss=0.1481, simple_loss=0.2356, pruned_loss=0.03027, over 5239.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2411, pruned_loss=0.02867, over 1422977.16 frames.], batch size: 52, lr: 2.09e-04
2022-05-16 03:32:12,588 INFO [train.py:812] (6/8) Epoch 37, batch 3900, loss[loss=0.1332, simple_loss=0.2325, pruned_loss=0.01697, over 7326.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2402, pruned_loss=0.02824, over 1425178.68 frames.], batch size: 20, lr: 2.09e-04
2022-05-16 03:33:11,486 INFO [train.py:812] (6/8) Epoch 37, batch 3950, loss[loss=0.1633, simple_loss=0.2592, pruned_loss=0.03371, over 7274.00 frames.], tot_loss[loss=0.1489, simple_loss=0.241, pruned_loss=0.02841, over 1426582.77 frames.], batch size: 18, lr: 2.09e-04
2022-05-16 03:34:09,783 INFO [train.py:812] (6/8) Epoch 37, batch 4000, loss[loss=0.1644, simple_loss=0.2493, pruned_loss=0.03971, over 7154.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2414, pruned_loss=0.02874, over 1427496.87 frames.], batch size: 20, lr: 2.09e-04
2022-05-16 03:35:09,216 INFO [train.py:812] (6/8) Epoch 37, batch 4050, loss[loss=0.1481, simple_loss=0.2396, pruned_loss=0.02833, over 7138.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2412, pruned_loss=0.02873, over 1426803.59 frames.], batch size: 20, lr: 2.09e-04
2022-05-16 03:36:06,975 INFO [train.py:812] (6/8) Epoch 37, batch 4100, loss[loss=0.1845, simple_loss=0.27, pruned_loss=0.04952, over 7350.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2417, pruned_loss=0.02859, over 1424522.21 frames.], batch size: 25, lr: 2.09e-04
2022-05-16 03:37:05,679 INFO [train.py:812] (6/8) Epoch 37, batch 4150, loss[loss=0.1335, simple_loss=0.2288, pruned_loss=0.01912, over 7213.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2417, pruned_loss=0.02822, over 1426153.67 frames.], batch size: 21, lr: 2.09e-04
2022-05-16 03:38:02,989 INFO [train.py:812] (6/8) Epoch 37, batch 4200, loss[loss=0.1443, simple_loss=0.2365, pruned_loss=0.02604, over 7341.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2412, pruned_loss=0.02819, over 1428665.37 frames.], batch size: 22, lr: 2.09e-04
2022-05-16 03:39:02,457 INFO [train.py:812] (6/8) Epoch 37, batch 4250, loss[loss=0.1415, simple_loss=0.2425, pruned_loss=0.02025, over 7202.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2404, pruned_loss=0.02788, over 1431633.05 frames.], batch size: 22, lr: 2.09e-04
2022-05-16 03:40:00,864 INFO [train.py:812] (6/8) Epoch 37, batch 4300, loss[loss=0.1455, simple_loss=0.2471, pruned_loss=0.02196, over 7329.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2411, pruned_loss=0.0283, over 1426302.90 frames.], batch size: 20, lr: 2.09e-04
2022-05-16 03:41:00,630 INFO [train.py:812] (6/8) Epoch 37, batch 4350, loss[loss=0.1438, simple_loss=0.2457, pruned_loss=0.02099, over 7335.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2407, pruned_loss=0.02786, over 1430492.00 frames.], batch size: 22, lr: 2.09e-04
2022-05-16 03:41:59,230 INFO [train.py:812] (6/8) Epoch 37, batch 4400, loss[loss=0.148, simple_loss=0.2465, pruned_loss=0.02479, over 7344.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2407, pruned_loss=0.02781, over 1423138.91 frames.], batch size: 22, lr: 2.09e-04
2022-05-16 03:42:59,069 INFO [train.py:812] (6/8) Epoch 37, batch 4450, loss[loss=0.1409, simple_loss=0.2221, pruned_loss=0.02985, over 7409.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2414, pruned_loss=0.028, over 1422135.59 frames.], batch size: 18, lr: 2.09e-04
2022-05-16 03:43:58,024 INFO [train.py:812] (6/8) Epoch 37, batch 4500, loss[loss=0.1295, simple_loss=0.2216, pruned_loss=0.01866, over 7269.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2412, pruned_loss=0.02771, over 1416436.33 frames.], batch size: 18, lr: 2.09e-04
2022-05-16 03:44:56,300 INFO [train.py:812] (6/8) Epoch 37, batch 4550, loss[loss=0.1455, simple_loss=0.2479, pruned_loss=0.02153, over 6444.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2426, pruned_loss=0.02857, over 1391714.08 frames.], batch size: 38, lr: 2.09e-04
2022-05-16 03:46:01,499 INFO [train.py:812] (6/8) Epoch 38, batch 0, loss[loss=0.1336, simple_loss=0.2174, pruned_loss=0.02495, over 7367.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2174, pruned_loss=0.02495, over 7367.00 frames.], batch size: 19, lr: 2.06e-04
2022-05-16 03:47:10,867 INFO [train.py:812] (6/8) Epoch 38, batch 50, loss[loss=0.1346, simple_loss=0.2286, pruned_loss=0.02025, over 6295.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2384, pruned_loss=0.02807, over 322392.87 frames.], batch size: 37, lr: 2.06e-04
2022-05-16 03:48:09,442 INFO [train.py:812] (6/8) Epoch 38, batch 100, loss[loss=0.1436, simple_loss=0.2357, pruned_loss=0.02573, over 7256.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2418, pruned_loss=0.02899, over 560394.99 frames.], batch size: 19, lr: 2.06e-04
2022-05-16 03:49:08,233 INFO [train.py:812] (6/8) Epoch 38, batch 150, loss[loss=0.1678, simple_loss=0.2627, pruned_loss=0.03645, over 7378.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2441, pruned_loss=0.02936, over 748093.42 frames.], batch size: 23, lr: 2.06e-04
2022-05-16 03:50:07,485 INFO [train.py:812] (6/8) Epoch 38, batch 200, loss[loss=0.1317, simple_loss=0.2263, pruned_loss=0.01856, over 7400.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2413, pruned_loss=0.02843, over 897252.25 frames.], batch size: 21, lr: 2.06e-04
2022-05-16 03:51:06,655 INFO [train.py:812] (6/8) Epoch 38, batch 250, loss[loss=0.1523, simple_loss=0.2372, pruned_loss=0.03366, over 7358.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2411, pruned_loss=0.02821, over 1016589.15 frames.], batch size: 19, lr: 2.06e-04
2022-05-16 03:52:05,099 INFO [train.py:812] (6/8) Epoch 38, batch 300, loss[loss=0.1597, simple_loss=0.2581, pruned_loss=0.03064, over 7230.00 frames.], tot_loss[loss=0.1495, simple_loss=0.242, pruned_loss=0.02848, over 1106698.66 frames.], batch size: 20, lr: 2.06e-04
2022-05-16 03:53:04,650 INFO [train.py:812] (6/8) Epoch 38, batch 350, loss[loss=0.1346, simple_loss=0.228, pruned_loss=0.02061, over 7257.00 frames.], tot_loss[loss=0.1488, simple_loss=0.241, pruned_loss=0.02825, over 1174300.09 frames.], batch size: 19, lr: 2.06e-04
2022-05-16 03:54:02,519 INFO [train.py:812] (6/8) Epoch 38, batch 400, loss[loss=0.1349, simple_loss=0.2134, pruned_loss=0.02814, over 7292.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2402, pruned_loss=0.02814, over 1233827.99 frames.], batch size: 17, lr: 2.06e-04
2022-05-16 03:55:02,017 INFO [train.py:812] (6/8) Epoch 38, batch 450, loss[loss=0.137, simple_loss=0.2323, pruned_loss=0.02084, over 7114.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2402, pruned_loss=0.02814, over 1277239.98 frames.], batch size: 21, lr: 2.06e-04
2022-05-16 03:56:00,729 INFO [train.py:812] (6/8) Epoch 38, batch 500, loss[loss=0.1424, simple_loss=0.2299, pruned_loss=0.02743, over 7291.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2396, pruned_loss=0.02826, over 1313089.53 frames.], batch size: 18, lr: 2.06e-04
2022-05-16 03:56:58,590 INFO [train.py:812] (6/8) Epoch 38, batch 550, loss[loss=0.1393, simple_loss=0.2357, pruned_loss=0.02145, over 7324.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2404, pruned_loss=0.02812, over 1337027.78 frames.], batch size: 20, lr: 2.06e-04
2022-05-16 03:57:56,243 INFO [train.py:812] (6/8) Epoch 38, batch 600, loss[loss=0.1498, simple_loss=0.2487, pruned_loss=0.02548, over 7364.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2417, pruned_loss=0.02847, over 1358210.06 frames.], batch size: 23, lr: 2.06e-04
2022-05-16 03:58:54,210 INFO [train.py:812] (6/8) Epoch 38, batch 650, loss[loss=0.1398, simple_loss=0.2412, pruned_loss=0.0192, over 7345.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2419, pruned_loss=0.02839, over 1373836.04 frames.], batch size: 22, lr: 2.06e-04
2022-05-16 03:59:53,359 INFO [train.py:812] (6/8) Epoch 38, batch 700, loss[loss=0.1513, simple_loss=0.2458, pruned_loss=0.02841, over 7171.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2412, pruned_loss=0.02814, over 1385785.26 frames.], batch size: 18, lr: 2.06e-04
2022-05-16 04:00:52,143 INFO [train.py:812] (6/8) Epoch 38, batch 750, loss[loss=0.1523, simple_loss=0.25, pruned_loss=0.0273, over 7361.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2421, pruned_loss=0.02822, over 1400588.75 frames.], batch size: 23, lr: 2.05e-04
2022-05-16 04:01:50,306 INFO [train.py:812] (6/8) Epoch 38, batch 800, loss[loss=0.1363, simple_loss=0.2248, pruned_loss=0.02392, over 7406.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2423, pruned_loss=0.02818, over 1408219.76 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:02:49,131 INFO [train.py:812] (6/8) Epoch 38, batch 850, loss[loss=0.1445, simple_loss=0.2316, pruned_loss=0.02863, over 7355.00 frames.], tot_loss[loss=0.149, simple_loss=0.242, pruned_loss=0.02803, over 1411476.37 frames.], batch size: 19, lr: 2.05e-04
2022-05-16 04:03:47,725 INFO [train.py:812] (6/8) Epoch 38, batch 900, loss[loss=0.1528, simple_loss=0.2559, pruned_loss=0.02489, over 7266.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2425, pruned_loss=0.02852, over 1413445.63 frames.], batch size: 24, lr: 2.05e-04
2022-05-16 04:04:46,179 INFO [train.py:812] (6/8) Epoch 38, batch 950, loss[loss=0.1399, simple_loss=0.2347, pruned_loss=0.02258, over 7251.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2426, pruned_loss=0.02858, over 1418841.69 frames.], batch size: 19, lr: 2.05e-04
2022-05-16 04:05:44,617 INFO [train.py:812] (6/8) Epoch 38, batch 1000, loss[loss=0.1715, simple_loss=0.2558, pruned_loss=0.04361, over 7208.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2427, pruned_loss=0.02892, over 1421991.47 frames.], batch size: 22, lr: 2.05e-04
2022-05-16 04:06:43,936 INFO [train.py:812] (6/8) Epoch 38, batch 1050, loss[loss=0.141, simple_loss=0.2331, pruned_loss=0.02441, over 7322.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2428, pruned_loss=0.02896, over 1422494.51 frames.], batch size: 20, lr: 2.05e-04
2022-05-16 04:07:41,816 INFO [train.py:812] (6/8) Epoch 38, batch 1100, loss[loss=0.1419, simple_loss=0.223, pruned_loss=0.03043, over 6820.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2432, pruned_loss=0.02879, over 1425521.74 frames.], batch size: 15, lr: 2.05e-04
2022-05-16 04:08:41,060 INFO [train.py:812] (6/8) Epoch 38, batch 1150, loss[loss=0.1219, simple_loss=0.2074, pruned_loss=0.01815, over 7296.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2426, pruned_loss=0.02834, over 1422824.47 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:09:40,645 INFO [train.py:812] (6/8) Epoch 38, batch 1200, loss[loss=0.1503, simple_loss=0.2494, pruned_loss=0.02565, over 7136.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2428, pruned_loss=0.02848, over 1424042.81 frames.], batch size: 26, lr: 2.05e-04
2022-05-16 04:10:39,682 INFO [train.py:812] (6/8) Epoch 38, batch 1250, loss[loss=0.1473, simple_loss=0.2388, pruned_loss=0.02788, over 6396.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2432, pruned_loss=0.02855, over 1427608.27 frames.], batch size: 38, lr: 2.05e-04
2022-05-16 04:11:38,496 INFO [train.py:812] (6/8) Epoch 38, batch 1300, loss[loss=0.134, simple_loss=0.2247, pruned_loss=0.02162, over 7296.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2437, pruned_loss=0.02896, over 1427019.99 frames.], batch size: 17, lr: 2.05e-04
2022-05-16 04:12:36,180 INFO [train.py:812] (6/8) Epoch 38, batch 1350, loss[loss=0.1418, simple_loss=0.2422, pruned_loss=0.0207, over 7128.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2423, pruned_loss=0.02861, over 1420283.23 frames.], batch size: 21, lr: 2.05e-04
2022-05-16 04:13:33,884 INFO [train.py:812] (6/8) Epoch 38, batch 1400, loss[loss=0.1562, simple_loss=0.2595, pruned_loss=0.02642, over 7276.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2413, pruned_loss=0.02826, over 1421016.65 frames.], batch size: 24, lr: 2.05e-04
2022-05-16 04:14:32,958 INFO [train.py:812] (6/8) Epoch 38, batch 1450, loss[loss=0.1538, simple_loss=0.2479, pruned_loss=0.02979, over 7210.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2431, pruned_loss=0.02907, over 1425037.06 frames.], batch size: 22, lr: 2.05e-04
2022-05-16 04:15:31,420 INFO [train.py:812] (6/8) Epoch 38, batch 1500, loss[loss=0.1555, simple_loss=0.2451, pruned_loss=0.03291, over 7305.00 frames.], tot_loss[loss=0.1507, simple_loss=0.243, pruned_loss=0.0292, over 1425438.76 frames.], batch size: 25, lr: 2.05e-04
2022-05-16 04:16:30,126 INFO [train.py:812] (6/8) Epoch 38, batch 1550, loss[loss=0.1319, simple_loss=0.2235, pruned_loss=0.02012, over 7244.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2434, pruned_loss=0.02915, over 1422847.19 frames.], batch size: 20, lr: 2.05e-04
2022-05-16 04:17:27,396 INFO [train.py:812] (6/8) Epoch 38, batch 1600, loss[loss=0.1551, simple_loss=0.2471, pruned_loss=0.03157, over 7266.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2433, pruned_loss=0.02899, over 1426038.76 frames.], batch size: 19, lr: 2.05e-04
2022-05-16 04:18:25,553 INFO [train.py:812] (6/8) Epoch 38, batch 1650, loss[loss=0.1577, simple_loss=0.2614, pruned_loss=0.02702, over 7105.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2428, pruned_loss=0.02867, over 1425213.53 frames.], batch size: 28, lr: 2.05e-04
2022-05-16 04:19:24,093 INFO [train.py:812] (6/8) Epoch 38, batch 1700, loss[loss=0.1437, simple_loss=0.2363, pruned_loss=0.02551, over 7174.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2418, pruned_loss=0.02845, over 1423751.32 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:20:24,545 INFO [train.py:812] (6/8) Epoch 38, batch 1750, loss[loss=0.1752, simple_loss=0.2662, pruned_loss=0.04215, over 5275.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2411, pruned_loss=0.02813, over 1421891.64 frames.], batch size: 53, lr: 2.05e-04
2022-05-16 04:21:23,207 INFO [train.py:812] (6/8) Epoch 38, batch 1800, loss[loss=0.1555, simple_loss=0.2504, pruned_loss=0.03031, over 7330.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2407, pruned_loss=0.02829, over 1420165.30 frames.], batch size: 20, lr: 2.05e-04
2022-05-16 04:22:21,151 INFO [train.py:812] (6/8) Epoch 38, batch 1850, loss[loss=0.1564, simple_loss=0.234, pruned_loss=0.03935, over 7262.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2409, pruned_loss=0.02835, over 1422016.20 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:23:20,143 INFO [train.py:812] (6/8) Epoch 38, batch 1900, loss[loss=0.1267, simple_loss=0.2149, pruned_loss=0.0193, over 6821.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2416, pruned_loss=0.02843, over 1424726.08 frames.], batch size: 15, lr: 2.05e-04
2022-05-16 04:24:18,768 INFO [train.py:812] (6/8) Epoch 38, batch 1950, loss[loss=0.1503, simple_loss=0.2504, pruned_loss=0.02515, over 7252.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2424, pruned_loss=0.02832, over 1426827.92 frames.], batch size: 19, lr: 2.05e-04
2022-05-16 04:25:17,688 INFO [train.py:812] (6/8) Epoch 38, batch 2000, loss[loss=0.1326, simple_loss=0.2261, pruned_loss=0.01956, over 7407.00 frames.], tot_loss[loss=0.149, simple_loss=0.2416, pruned_loss=0.02822, over 1425457.05 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:26:16,385 INFO [train.py:812] (6/8) Epoch 38, batch 2050, loss[loss=0.1433, simple_loss=0.2484, pruned_loss=0.01916, over 7259.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2419, pruned_loss=0.02841, over 1423187.62 frames.], batch size: 19, lr: 2.05e-04
2022-05-16 04:27:14,058 INFO [train.py:812] (6/8) Epoch 38, batch 2100, loss[loss=0.1807, simple_loss=0.2857, pruned_loss=0.03782, over 7169.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2425, pruned_loss=0.02865, over 1417673.61 frames.], batch size: 26, lr: 2.05e-04
2022-05-16 04:28:12,421 INFO [train.py:812] (6/8) Epoch 38, batch 2150, loss[loss=0.1396, simple_loss=0.2234, pruned_loss=0.02791, over 7071.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2428, pruned_loss=0.02885, over 1418254.31 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:29:11,068 INFO [train.py:812] (6/8) Epoch 38, batch 2200, loss[loss=0.1187, simple_loss=0.2078, pruned_loss=0.01477, over 7065.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2435, pruned_loss=0.02914, over 1419199.44 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:30:15,091 INFO [train.py:812] (6/8) Epoch 38, batch 2250, loss[loss=0.1426, simple_loss=0.2374, pruned_loss=0.0239, over 6312.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2432, pruned_loss=0.02887, over 1418086.98 frames.], batch size: 38, lr: 2.05e-04
2022-05-16 04:31:14,140 INFO [train.py:812] (6/8) Epoch 38, batch 2300, loss[loss=0.1327, simple_loss=0.2179, pruned_loss=0.02376, over 7069.00 frames.], tot_loss[loss=0.15, simple_loss=0.243, pruned_loss=0.02849, over 1422495.07 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:32:13,297 INFO [train.py:812] (6/8) Epoch 38, batch 2350, loss[loss=0.1428, simple_loss=0.2387, pruned_loss=0.0234, over 7325.00 frames.], tot_loss[loss=0.15, simple_loss=0.2427, pruned_loss=0.02862, over 1419939.14 frames.], batch size: 20, lr: 2.05e-04
2022-05-16 04:33:12,149 INFO [train.py:812] (6/8) Epoch 38, batch 2400, loss[loss=0.1398, simple_loss=0.2207, pruned_loss=0.02946, over 7403.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2424, pruned_loss=0.02859, over 1425322.55 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:34:10,724 INFO [train.py:812] (6/8) Epoch 38, batch 2450, loss[loss=0.1522, simple_loss=0.2436, pruned_loss=0.03044, over 7334.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2424, pruned_loss=0.02853, over 1426806.36 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:35:08,796 INFO [train.py:812] (6/8) Epoch 38, batch 2500, loss[loss=0.136, simple_loss=0.2196, pruned_loss=0.02625, over 7167.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2419, pruned_loss=0.02836, over 1426731.08 frames.], batch size: 18, lr: 2.04e-04
2022-05-16 04:36:06,686 INFO [train.py:812] (6/8) Epoch 38, batch 2550, loss[loss=0.1238, simple_loss=0.2134, pruned_loss=0.01707, over 7167.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2418, pruned_loss=0.0283, over 1424315.28 frames.], batch size: 18, lr: 2.04e-04
2022-05-16 04:37:05,284 INFO [train.py:812] (6/8) Epoch 38, batch 2600, loss[loss=0.1573, simple_loss=0.2593, pruned_loss=0.02768, over 7432.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2411, pruned_loss=0.02814, over 1424300.33 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:38:03,417 INFO [train.py:812] (6/8) Epoch 38, batch 2650, loss[loss=0.1496, simple_loss=0.2454, pruned_loss=0.0269, over 7214.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2421, pruned_loss=0.02854, over 1425101.34 frames.], batch size: 23, lr: 2.04e-04
2022-05-16 04:39:01,028 INFO [train.py:812] (6/8) Epoch 38, batch 2700, loss[loss=0.1335, simple_loss=0.2309, pruned_loss=0.01807, over 7237.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2416, pruned_loss=0.02825, over 1423211.43 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:39:59,852 INFO [train.py:812] (6/8) Epoch 38, batch 2750, loss[loss=0.1546, simple_loss=0.2435, pruned_loss=0.03284, over 7344.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2422, pruned_loss=0.02856, over 1424836.54 frames.], batch size: 19, lr: 2.04e-04
2022-05-16 04:40:57,554 INFO [train.py:812] (6/8) Epoch 38, batch 2800, loss[loss=0.1659, simple_loss=0.2539, pruned_loss=0.03898, over 7281.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2422, pruned_loss=0.02843, over 1423287.43 frames.], batch size: 24, lr: 2.04e-04
2022-05-16 04:41:55,566 INFO [train.py:812] (6/8) Epoch 38, batch 2850, loss[loss=0.1389, simple_loss=0.2398, pruned_loss=0.01899, over 7420.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2422, pruned_loss=0.02839, over 1423528.77 frames.], batch size: 21, lr: 2.04e-04
2022-05-16 04:42:54,115 INFO [train.py:812] (6/8) Epoch 38, batch 2900, loss[loss=0.1337, simple_loss=0.2221, pruned_loss=0.02267, over 7142.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2409, pruned_loss=0.02798, over 1424604.46 frames.], batch size: 17, lr: 2.04e-04
2022-05-16 04:43:53,032 INFO [train.py:812] (6/8) Epoch 38, batch 2950, loss[loss=0.133, simple_loss=0.2171, pruned_loss=0.02445, over 7400.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2416, pruned_loss=0.02826, over 1428982.91 frames.], batch size: 18, lr: 2.04e-04
2022-05-16 04:44:52,018 INFO [train.py:812] (6/8) Epoch 38, batch 3000, loss[loss=0.1711, simple_loss=0.2528, pruned_loss=0.04468, over 7196.00 frames.], tot_loss[loss=0.149, simple_loss=0.2416, pruned_loss=0.02821, over 1428012.70 frames.], batch size: 23, lr: 2.04e-04
2022-05-16 04:44:52,019 INFO [train.py:832] (6/8) Computing validation loss
2022-05-16 04:44:59,416 INFO [train.py:841] (6/8) Epoch 38, validation: loss=0.1532, simple_loss=0.2484, pruned_loss=0.02898, over 698248.00 frames.
2022-05-16 04:45:58,537 INFO [train.py:812] (6/8) Epoch 38, batch 3050, loss[loss=0.1707, simple_loss=0.2588, pruned_loss=0.0413, over 7166.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2422, pruned_loss=0.02842, over 1428367.34 frames.], batch size: 18, lr: 2.04e-04
2022-05-16 04:46:56,204 INFO [train.py:812] (6/8) Epoch 38, batch 3100, loss[loss=0.147, simple_loss=0.2397, pruned_loss=0.02718, over 7219.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2425, pruned_loss=0.02892, over 1421895.47 frames.], batch size: 22, lr: 2.04e-04
2022-05-16 04:47:54,529 INFO [train.py:812] (6/8) Epoch 38, batch 3150, loss[loss=0.1467, simple_loss=0.2379, pruned_loss=0.02779, over 7377.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2421, pruned_loss=0.0291, over 1420165.89 frames.], batch size: 23, lr: 2.04e-04
2022-05-16 04:48:52,452 INFO [train.py:812] (6/8) Epoch 38, batch 3200, loss[loss=0.1455, simple_loss=0.239, pruned_loss=0.02597, over 7118.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2424, pruned_loss=0.02924, over 1425005.68 frames.], batch size: 21, lr: 2.04e-04
2022-05-16 04:49:51,333 INFO [train.py:812] (6/8) Epoch 38, batch 3250, loss[loss=0.1259, simple_loss=0.2089, pruned_loss=0.02143, over 7291.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2416, pruned_loss=0.02897, over 1426152.99 frames.], batch size: 18, lr: 2.04e-04
2022-05-16 04:50:49,199 INFO [train.py:812] (6/8) Epoch 38, batch 3300, loss[loss=0.1579, simple_loss=0.2514, pruned_loss=0.03218, over 7227.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2414, pruned_loss=0.02893, over 1426028.36 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:51:47,401 INFO [train.py:812] (6/8) Epoch 38, batch 3350, loss[loss=0.1398, simple_loss=0.235, pruned_loss=0.02227, over 7203.00 frames.], tot_loss[loss=0.1497, simple_loss=0.242, pruned_loss=0.02869, over 1426907.84 frames.], batch size: 22, lr: 2.04e-04
2022-05-16 04:52:45,602 INFO [train.py:812] (6/8) Epoch 38, batch 3400, loss[loss=0.1672, simple_loss=0.2757, pruned_loss=0.02929, over 6763.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2417, pruned_loss=0.0282, over 1430610.57 frames.], batch size: 31, lr: 2.04e-04
2022-05-16 04:53:45,207 INFO [train.py:812] (6/8) Epoch 38, batch 3450, loss[loss=0.145, simple_loss=0.2326, pruned_loss=0.02872, over 7434.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2421, pruned_loss=0.02858, over 1431421.03 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:54:43,562 INFO [train.py:812] (6/8) Epoch 38, batch 3500, loss[loss=0.1557, simple_loss=0.259, pruned_loss=0.02624, over 7234.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2422, pruned_loss=0.0286, over 1430852.93 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:55:41,800 INFO [train.py:812] (6/8) Epoch 38, batch 3550, loss[loss=0.1419, simple_loss=0.2404, pruned_loss=0.02169, over 7156.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2433, pruned_loss=0.02868, over 1431784.47 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:56:49,634 INFO [train.py:812] (6/8) Epoch 38, batch 3600, loss[loss=0.1538, simple_loss=0.2533, pruned_loss=0.02711, over 6762.00 frames.], tot_loss[loss=0.1499, simple_loss=0.243, pruned_loss=0.02833, over 1429763.06 frames.], batch size: 31, lr: 2.04e-04
2022-05-16 04:57:48,351 INFO [train.py:812] (6/8) Epoch 38, batch 3650, loss[loss=0.1712, simple_loss=0.2699, pruned_loss=0.03631, over 7068.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2432, pruned_loss=0.02865, over 1431782.25 frames.], batch size: 28, lr: 2.04e-04
2022-05-16 04:58:46,164 INFO [train.py:812] (6/8) Epoch 38, batch 3700, loss[loss=0.1593, simple_loss=0.2597, pruned_loss=0.02943, over 7300.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2425, pruned_loss=0.02865, over 1423138.10 frames.], batch size: 24, lr: 2.04e-04
2022-05-16 05:00:03,400 INFO [train.py:812] (6/8) Epoch 38, batch 3750, loss[loss=0.138, simple_loss=0.2248, pruned_loss=0.02562, over 7167.00 frames.], tot_loss[loss=0.1494, simple_loss=0.242, pruned_loss=0.02846, over 1418497.52 frames.], batch size: 19, lr: 2.04e-04
2022-05-16 05:01:01,786 INFO [train.py:812] (6/8) Epoch 38, batch 3800, loss[loss=0.1519, simple_loss=0.2416, pruned_loss=0.03116, over 7370.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2415, pruned_loss=0.02842, over 1418389.98 frames.], batch size: 23, lr: 2.04e-04
2022-05-16 05:02:01,307 INFO [train.py:812] (6/8) Epoch 38, batch 3850, loss[loss=0.1599, simple_loss=0.2593, pruned_loss=0.03028, over 7096.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2412, pruned_loss=0.02823, over 1420957.90 frames.], batch size: 21, lr: 2.04e-04
2022-05-16 05:03:01,108 INFO [train.py:812] (6/8) Epoch 38, batch 3900, loss[loss=0.1472, simple_loss=0.2426, pruned_loss=0.02591, over 7322.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2412, pruned_loss=0.02856, over 1422621.23 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 05:03:59,300 INFO [train.py:812] (6/8) Epoch 38, batch 3950, loss[loss=0.1709, simple_loss=0.2668, pruned_loss=0.03746, over 7203.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2405, pruned_loss=0.02835, over 1418386.03 frames.], batch size: 22, lr: 2.04e-04
2022-05-16 05:04:56,902 INFO [train.py:812] (6/8) Epoch 38, batch 4000, loss[loss=0.1343, simple_loss=0.2296, pruned_loss=0.01949, over 7159.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2406, pruned_loss=0.02837, over 1418869.92 frames.], batch size: 19, lr: 2.04e-04
2022-05-16 05:06:06,118 INFO [train.py:812] (6/8) Epoch 38, batch 4050, loss[loss=0.1447, simple_loss=0.2309, pruned_loss=0.02923, over 7270.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2409, pruned_loss=0.02865, over 1411910.38 frames.], batch size: 17, lr: 2.04e-04
2022-05-16 05:07:14,552 INFO [train.py:812] (6/8) Epoch 38, batch 4100, loss[loss=0.1474, simple_loss=0.2488, pruned_loss=0.02299, over 7220.00 frames.], tot_loss[loss=0.1497, simple_loss=0.242, pruned_loss=0.02868, over 1414334.90 frames.], batch size: 21, lr: 2.04e-04
2022-05-16 05:08:13,935 INFO [train.py:812] (6/8) Epoch 38, batch 4150, loss[loss=0.1488, simple_loss=0.242, pruned_loss=0.02775, over 7269.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2405, pruned_loss=0.02823, over 1413653.79 frames.], batch size: 19, lr: 2.03e-04
2022-05-16 05:09:21,219 INFO [train.py:812] (6/8) Epoch 38, batch 4200, loss[loss=0.148, simple_loss=0.2492, pruned_loss=0.02341, over 7271.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2411, pruned_loss=0.02805, over 1414449.34 frames.], batch size: 24, lr: 2.03e-04
2022-05-16 05:10:29,490 INFO [train.py:812] (6/8) Epoch 38, batch 4250, loss[loss=0.1636, simple_loss=0.2515, pruned_loss=0.03781, over 7238.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2414, pruned_loss=0.02799, over 1414608.67 frames.], batch size: 20, lr: 2.03e-04
2022-05-16 05:11:27,931 INFO [train.py:812] (6/8) Epoch 38, batch 4300, loss[loss=0.1531, simple_loss=0.2394, pruned_loss=0.03342, over 4597.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2394, pruned_loss=0.02743, over 1411729.04 frames.], batch size: 52, lr: 2.03e-04
2022-05-16 05:12:26,590 INFO [train.py:812] (6/8) Epoch 38, batch 4350, loss[loss=0.1379, simple_loss=0.225, pruned_loss=0.02542, over 7006.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2386, pruned_loss=0.02747, over 1414223.47 frames.], batch size: 16, lr: 2.03e-04
2022-05-16 05:13:26,098 INFO [train.py:812] (6/8) Epoch 38, batch 4400, loss[loss=0.1267, simple_loss=0.2128, pruned_loss=0.02028, over 6828.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2379, pruned_loss=0.02727, over 1413743.50 frames.], batch size: 15, lr: 2.03e-04
2022-05-16 05:14:25,947 INFO [train.py:812] (6/8) Epoch 38, batch 4450, loss[loss=0.1504, simple_loss=0.2298, pruned_loss=0.03553, over 7268.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2372, pruned_loss=0.02747, over 1405657.44 frames.], batch size: 16, lr: 2.03e-04
2022-05-16 05:15:24,207 INFO [train.py:812] (6/8) Epoch 38, batch 4500, loss[loss=0.1416, simple_loss=0.2359, pruned_loss=0.02364, over 6259.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2373, pruned_loss=0.02784, over 1382188.86 frames.], batch size: 38, lr: 2.03e-04
2022-05-16 05:16:23,043 INFO [train.py:812] (6/8) Epoch 38, batch 4550, loss[loss=0.1631, simple_loss=0.2395, pruned_loss=0.0434, over 5107.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2369, pruned_loss=0.02793, over 1356211.82 frames.], batch size: 52, lr: 2.03e-04
2022-05-16 05:17:28,550 INFO [train.py:812] (6/8) Epoch 39, batch 0, loss[loss=0.1569, simple_loss=0.2573, pruned_loss=0.02824, over 7270.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2573, pruned_loss=0.02824, over 7270.00 frames.], batch size: 19, lr: 2.01e-04
2022-05-16 05:18:26,914 INFO [train.py:812] (6/8) Epoch 39, batch 50, loss[loss=0.1523, simple_loss=0.2489, pruned_loss=0.02791, over 7145.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2449, pruned_loss=0.02845, over 320110.89 frames.], batch size: 20, lr: 2.01e-04
2022-05-16 05:19:25,803 INFO [train.py:812] (6/8) Epoch 39, batch 100, loss[loss=0.1268, simple_loss=0.2246, pruned_loss=0.01446, over 6857.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2425, pruned_loss=0.02747, over 565190.12 frames.], batch size: 31, lr: 2.01e-04
2022-05-16 05:20:24,091 INFO [train.py:812] (6/8) Epoch 39, batch 150, loss[loss=0.1277, simple_loss=0.2164, pruned_loss=0.01952, over 7160.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2409, pruned_loss=0.02793, over 754302.05 frames.], batch size: 18, lr: 2.01e-04
2022-05-16 05:21:22,517 INFO [train.py:812] (6/8) Epoch 39, batch 200, loss[loss=0.1204, simple_loss=0.2141, pruned_loss=0.01334, over 7419.00 frames.], tot_loss[loss=0.1497, simple_loss=0.242, pruned_loss=0.0287, over 901260.12 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:22:20,454 INFO [train.py:812] (6/8) Epoch 39, batch 250, loss[loss=0.139, simple_loss=0.231, pruned_loss=0.02353, over 6517.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2423, pruned_loss=0.02897, over 1017656.49 frames.], batch size: 38, lr: 2.00e-04
2022-05-16 05:23:19,083 INFO [train.py:812] (6/8) Epoch 39, batch 300, loss[loss=0.1524, simple_loss=0.2383, pruned_loss=0.03324, over 7425.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2423, pruned_loss=0.0286, over 1112845.65 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:24:17,718 INFO [train.py:812] (6/8) Epoch 39, batch 350, loss[loss=0.1532, simple_loss=0.251, pruned_loss=0.02768, over 7312.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2418, pruned_loss=0.02864, over 1178958.12 frames.], batch size: 24, lr: 2.00e-04
2022-05-16 05:25:17,175 INFO [train.py:812] (6/8) Epoch 39, batch 400, loss[loss=0.177, simple_loss=0.2779, pruned_loss=0.03808, over 7219.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2416, pruned_loss=0.0285, over 1228253.95 frames.], batch size: 21, lr: 2.00e-04
2022-05-16 05:26:16,276 INFO [train.py:812] (6/8) Epoch 39, batch 450, loss[loss=0.1562, simple_loss=0.2498, pruned_loss=0.03137, over 7188.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2412, pruned_loss=0.02832, over 1273061.12 frames.], batch size: 23, lr: 2.00e-04
2022-05-16 05:27:15,060 INFO [train.py:812] (6/8) Epoch 39, batch 500, loss[loss=0.1647, simple_loss=0.277, pruned_loss=0.02623, over 7154.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2412, pruned_loss=0.02851, over 1300203.07 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:28:14,653 INFO [train.py:812] (6/8) Epoch 39, batch 550, loss[loss=0.1482, simple_loss=0.2424, pruned_loss=0.02701, over 7417.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2412, pruned_loss=0.02864, over 1325557.59 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:29:14,831 INFO [train.py:812] (6/8) Epoch 39, batch 600, loss[loss=0.1422, simple_loss=0.2233, pruned_loss=0.03053, over 7168.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2412, pruned_loss=0.02845, over 1344366.01 frames.], batch size: 18, lr: 2.00e-04
2022-05-16 05:30:14,588 INFO [train.py:812] (6/8) Epoch 39, batch 650, loss[loss=0.1429, simple_loss=0.2231, pruned_loss=0.03134, over 7266.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2414, pruned_loss=0.0286, over 1364533.56 frames.], batch size: 17, lr: 2.00e-04
2022-05-16 05:31:13,710 INFO [train.py:812] (6/8) Epoch 39, batch 700, loss[loss=0.1461, simple_loss=0.2331, pruned_loss=0.02956, over 7216.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2402, pruned_loss=0.02827, over 1377835.50 frames.], batch size: 16, lr: 2.00e-04
2022-05-16 05:32:12,662 INFO [train.py:812] (6/8) Epoch 39, batch 750, loss[loss=0.1544, simple_loss=0.2565, pruned_loss=0.02614, over 6517.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2401, pruned_loss=0.0281, over 1386944.37 frames.], batch size: 38, lr: 2.00e-04
2022-05-16 05:33:12,267 INFO [train.py:812] (6/8) Epoch 39, batch 800, loss[loss=0.1467, simple_loss=0.2341, pruned_loss=0.02968, over 7232.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2405, pruned_loss=0.02831, over 1399310.61 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:34:10,581 INFO [train.py:812] (6/8) Epoch 39, batch 850, loss[loss=0.1724, simple_loss=0.2594, pruned_loss=0.04275, over 7081.00 frames.], tot_loss[loss=0.148, simple_loss=0.2399, pruned_loss=0.0281, over 1404832.07 frames.], batch size: 28, lr: 2.00e-04
2022-05-16 05:35:08,804 INFO [train.py:812] (6/8) Epoch 39, batch 900, loss[loss=0.1694, simple_loss=0.2766, pruned_loss=0.03104, over 7409.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2407, pruned_loss=0.02839, over 1403789.35 frames.], batch size: 21, lr: 2.00e-04
2022-05-16 05:36:07,917 INFO [train.py:812] (6/8) Epoch 39, batch 950, loss[loss=0.1505, simple_loss=0.2445, pruned_loss=0.02826, over 7142.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2419, pruned_loss=0.02877, over 1404872.69 frames.], batch size: 17, lr: 2.00e-04
2022-05-16 05:37:07,608 INFO [train.py:812] (6/8) Epoch 39, batch 1000, loss[loss=0.1366, simple_loss=0.2231, pruned_loss=0.02503, over 7360.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2416, pruned_loss=0.02884, over 1408381.22 frames.], batch size: 19, lr: 2.00e-04
2022-05-16 05:38:06,534 INFO [train.py:812] (6/8) Epoch 39, batch 1050, loss[loss=0.1587, simple_loss=0.2564, pruned_loss=0.03049, over 6727.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2412, pruned_loss=0.02872, over 1410884.29 frames.], batch size: 31, lr: 2.00e-04
2022-05-16 05:39:05,058 INFO [train.py:812] (6/8) Epoch 39, batch 1100, loss[loss=0.1426, simple_loss=0.2431, pruned_loss=0.021, over 7371.00 frames.], tot_loss[loss=0.1491, simple_loss=0.241, pruned_loss=0.02862, over 1416428.59 frames.], batch size: 23, lr: 2.00e-04
2022-05-16 05:40:03,850 INFO [train.py:812] (6/8) Epoch 39, batch 1150, loss[loss=0.1477, simple_loss=0.2378, pruned_loss=0.02878, over 7287.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2404, pruned_loss=0.02827, over 1419610.72 frames.], batch size: 18, lr: 2.00e-04
2022-05-16 05:41:02,332 INFO [train.py:812] (6/8) Epoch 39, batch 1200, loss[loss=0.167, simple_loss=0.2538, pruned_loss=0.04011, over 6650.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2406, pruned_loss=0.0286, over 1420725.24 frames.], batch size: 31, lr: 2.00e-04
2022-05-16 05:42:00,512 INFO [train.py:812] (6/8) Epoch 39, batch 1250, loss[loss=0.1326, simple_loss=0.2207, pruned_loss=0.02222, over 7419.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2408, pruned_loss=0.0285, over 1421522.00 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:42:59,389 INFO [train.py:812] (6/8) Epoch 39, batch 1300, loss[loss=0.1372, simple_loss=0.2229, pruned_loss=0.02578, over 7264.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2405, pruned_loss=0.02807, over 1425601.96 frames.], batch size: 17, lr: 2.00e-04
2022-05-16 05:43:56,604 INFO [train.py:812] (6/8) Epoch 39, batch 1350, loss[loss=0.1267, simple_loss=0.2113, pruned_loss=0.02108, over 7341.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2406, pruned_loss=0.02797, over 1425613.40 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:45:05,774 INFO [train.py:812] (6/8) Epoch 39, batch 1400, loss[loss=0.1261, simple_loss=0.224, pruned_loss=0.01414, over 7162.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2407, pruned_loss=0.02816, over 1425194.29 frames.], batch size: 19, lr: 2.00e-04
2022-05-16 05:46:03,935 INFO [train.py:812] (6/8) Epoch 39, batch 1450, loss[loss=0.1688, simple_loss=0.2626, pruned_loss=0.0375, over 7310.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2419, pruned_loss=0.02867, over 1425600.92 frames.], batch size: 25, lr: 2.00e-04
2022-05-16 05:47:01,547 INFO [train.py:812] (6/8) Epoch 39, batch 1500, loss[loss=0.1714, simple_loss=0.2764, pruned_loss=0.03314, over 7121.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2425, pruned_loss=0.02886, over 1424315.03 frames.], batch size: 21, lr: 2.00e-04
2022-05-16 05:48:00,121 INFO [train.py:812] (6/8) Epoch 39, batch 1550, loss[loss=0.1618, simple_loss=0.2523, pruned_loss=0.03561, over 7214.00 frames.], tot_loss[loss=0.1498, simple_loss=0.242, pruned_loss=0.0288, over 1424540.86 frames.], batch size: 22, lr: 2.00e-04
2022-05-16 05:48:59,913 INFO [train.py:812] (6/8) Epoch 39, batch 1600, loss[loss=0.1469, simple_loss=0.2393, pruned_loss=0.02723, over 6742.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2413, pruned_loss=0.02882, over 1426036.84 frames.], batch size: 31, lr: 2.00e-04
2022-05-16 05:49:57,809 INFO [train.py:812] (6/8) Epoch 39, batch 1650, loss[loss=0.1577, simple_loss=0.249, pruned_loss=0.03319, over 7217.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2412, pruned_loss=0.02892, over 1424790.79 frames.], batch size: 21, lr: 2.00e-04
2022-05-16 05:51:01,161 INFO [train.py:812] (6/8) Epoch 39, batch 1700, loss[loss=0.155, simple_loss=0.2507, pruned_loss=0.02968, over 7102.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2421, pruned_loss=0.02875, over 1426788.82 frames.], batch size: 28, lr: 2.00e-04
2022-05-16 05:51:59,357 INFO [train.py:812] (6/8) Epoch 39, batch 1750, loss[loss=0.1537, simple_loss=0.2435, pruned_loss=0.03201, over 7435.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2417, pruned_loss=0.02823, over 1426182.46 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:52:58,526 INFO [train.py:812] (6/8) Epoch 39, batch 1800, loss[loss=0.1431, simple_loss=0.2365, pruned_loss=0.02481, over 7191.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2421, pruned_loss=0.02834, over 1423547.68 frames.], batch size: 23, lr: 2.00e-04
2022-05-16 05:53:57,514 INFO [train.py:812] (6/8) Epoch 39, batch 1850, loss[loss=0.1395, simple_loss=0.2265, pruned_loss=0.0262, over 7151.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2415, pruned_loss=0.02838, over 1421040.02 frames.], batch size: 19, lr: 2.00e-04
2022-05-16 05:54:55,922 INFO [train.py:812] (6/8) Epoch 39, batch 1900, loss[loss=0.1451, simple_loss=0.2359, pruned_loss=0.02714, over 7279.00 frames.], tot_loss[loss=0.149, simple_loss=0.2411, pruned_loss=0.02842, over 1423969.12 frames.], batch size: 18, lr: 2.00e-04
2022-05-16 05:55:54,023 INFO [train.py:812] (6/8) Epoch 39, batch 1950, loss[loss=0.1545, simple_loss=0.2512, pruned_loss=0.0289, over 7328.00 frames.], tot_loss[loss=0.149, simple_loss=0.2415, pruned_loss=0.0283, over 1423687.40 frames.], batch size: 21, lr: 1.99e-04
2022-05-16 05:56:52,365 INFO [train.py:812] (6/8) Epoch 39, batch 2000, loss[loss=0.1422, simple_loss=0.2322, pruned_loss=0.02607, over 7265.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2417, pruned_loss=0.0283, over 1422942.73 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 05:57:50,327 INFO [train.py:812] (6/8) Epoch 39, batch 2050, loss[loss=0.1442, simple_loss=0.2442, pruned_loss=0.02213, over 7324.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2423, pruned_loss=0.02813, over 1420940.46 frames.], batch size: 20, lr: 1.99e-04
2022-05-16 05:58:49,546 INFO [train.py:812] (6/8) Epoch 39, batch 2100, loss[loss=0.1417, simple_loss=0.2271, pruned_loss=0.0282, over 7183.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2421, pruned_loss=0.02818, over 1422929.14 frames.], batch size: 16, lr: 1.99e-04
2022-05-16 05:59:47,706 INFO [train.py:812] (6/8) Epoch 39, batch 2150, loss[loss=0.1347, simple_loss=0.2289, pruned_loss=0.02028, over 7257.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2422, pruned_loss=0.02827, over 1420007.70 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 06:00:46,910 INFO [train.py:812] (6/8) Epoch 39, batch 2200, loss[loss=0.1641, simple_loss=0.252, pruned_loss=0.03812, over 7196.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2425, pruned_loss=0.02821, over 1421095.79 frames.], batch size: 22, lr: 1.99e-04
2022-05-16 06:01:45,969 INFO [train.py:812] (6/8) Epoch 39, batch 2250, loss[loss=0.1475, simple_loss=0.2417, pruned_loss=0.02665, over 7152.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2414, pruned_loss=0.02794, over 1423784.48 frames.], batch size: 20, lr: 1.99e-04
2022-05-16 06:02:45,408 INFO [train.py:812] (6/8) Epoch 39, batch 2300, loss[loss=0.157, simple_loss=0.2439, pruned_loss=0.03505, over 7167.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2413, pruned_loss=0.02788, over 1423350.93 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 06:03:45,429 INFO [train.py:812] (6/8) Epoch 39, batch 2350, loss[loss=0.1312, simple_loss=0.2315, pruned_loss=0.01548, over 7228.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2407, pruned_loss=0.02779, over 1424895.77 frames.], batch size: 20, lr: 1.99e-04
2022-05-16 06:04:43,841 INFO [train.py:812] (6/8) Epoch 39, batch 2400, loss[loss=0.1511, simple_loss=0.2445, pruned_loss=0.02886, over 7148.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2412, pruned_loss=0.02823, over 1427610.19 frames.], batch size: 20, lr: 1.99e-04
2022-05-16 06:05:41,797 INFO [train.py:812] (6/8) Epoch 39, batch 2450, loss[loss=0.1276, simple_loss=0.2178, pruned_loss=0.01867, over 7413.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2405, pruned_loss=0.02789, over 1428657.36 frames.], batch size: 18, lr: 1.99e-04
2022-05-16 06:06:40,892 INFO [train.py:812] (6/8) Epoch 39, batch 2500, loss[loss=0.1259, simple_loss=0.2161, pruned_loss=0.01787, over 7401.00 frames.], tot_loss[loss=0.148, simple_loss=0.2405, pruned_loss=0.02772, over 1427062.18 frames.], batch size: 18, lr: 1.99e-04
2022-05-16 06:07:38,144 INFO [train.py:812] (6/8) Epoch 39, batch 2550, loss[loss=0.1499, simple_loss=0.2393, pruned_loss=0.03026, over 7438.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2401, pruned_loss=0.0277, over 1431826.08 frames.], batch size: 20, lr: 1.99e-04
2022-05-16 06:08:37,356 INFO [train.py:812] (6/8) Epoch 39, batch 2600, loss[loss=0.1624, simple_loss=0.2598, pruned_loss=0.03252, over 7171.00 frames.], tot_loss[loss=0.148, simple_loss=0.2407, pruned_loss=0.02769, over 1429710.65 frames.], batch size: 26, lr: 1.99e-04
2022-05-16 06:09:36,156 INFO [train.py:812] (6/8) Epoch 39, batch 2650, loss[loss=0.1585, simple_loss=0.2537, pruned_loss=0.03163, over 7055.00 frames.], tot_loss[loss=0.1483, simple_loss=0.241, pruned_loss=0.02787, over 1430171.78 frames.], batch size: 28, lr: 1.99e-04
2022-05-16 06:10:34,098 INFO [train.py:812] (6/8) Epoch 39, batch 2700, loss[loss=0.1615, simple_loss=0.2549, pruned_loss=0.03399, over 7298.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2408, pruned_loss=0.02776, over 1428389.83 frames.], batch size: 25, lr: 1.99e-04
2022-05-16 06:11:32,694 INFO [train.py:812] (6/8) Epoch 39, batch 2750, loss[loss=0.1355, simple_loss=0.2298, pruned_loss=0.02063, over 7163.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2411, pruned_loss=0.02816, over 1428554.19 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 06:12:31,343 INFO [train.py:812] (6/8) Epoch 39, batch 2800, loss[loss=0.1614, simple_loss=0.2548, pruned_loss=0.03402, over 7318.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2418, pruned_loss=0.02832, over 1426246.58 frames.], batch size: 22, lr: 1.99e-04
2022-05-16 06:13:29,189 INFO [train.py:812] (6/8) Epoch 39, batch 2850, loss[loss=0.1465, simple_loss=0.2507, pruned_loss=0.0212, over 6208.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2421, pruned_loss=0.02849, over 1425685.66 frames.], batch size: 38, lr: 1.99e-04
2022-05-16 06:14:28,575 INFO [train.py:812] (6/8) Epoch 39, batch 2900, loss[loss=0.1433, simple_loss=0.2414, pruned_loss=0.02259, over 7313.00 frames.], tot_loss[loss=0.1493, simple_loss=0.242, pruned_loss=0.02831, over 1424887.70 frames.], batch size: 21, lr: 1.99e-04
2022-05-16 06:15:27,567 INFO [train.py:812] (6/8) Epoch 39, batch 2950, loss[loss=0.1543, simple_loss=0.2452, pruned_loss=0.03169, over 7341.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2409, pruned_loss=0.02813, over 1428675.81 frames.], batch size: 22, lr: 1.99e-04
2022-05-16 06:16:26,927 INFO [train.py:812] (6/8) Epoch 39, batch 3000, loss[loss=0.1512, simple_loss=0.2542, pruned_loss=0.0241, over 7231.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2416, pruned_loss=0.02841, over 1429084.54 frames.], batch size: 20, lr: 1.99e-04
2022-05-16 06:16:26,929 INFO [train.py:832] (6/8) Computing validation loss
2022-05-16 06:16:34,437 INFO [train.py:841] (6/8) Epoch 39, validation: loss=0.153, simple_loss=0.2484, pruned_loss=0.02885, over 698248.00 frames.
2022-05-16 06:17:33,445 INFO [train.py:812] (6/8) Epoch 39, batch 3050, loss[loss=0.1266, simple_loss=0.2186, pruned_loss=0.0173, over 7135.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2417, pruned_loss=0.02857, over 1426245.35 frames.], batch size: 17, lr: 1.99e-04
2022-05-16 06:18:32,182 INFO [train.py:812] (6/8) Epoch 39, batch 3100, loss[loss=0.1547, simple_loss=0.2496, pruned_loss=0.02993, over 6363.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2422, pruned_loss=0.02868, over 1418349.64 frames.], batch size: 38, lr: 1.99e-04
2022-05-16 06:19:30,265 INFO [train.py:812] (6/8) Epoch 39, batch 3150, loss[loss=0.152, simple_loss=0.2421, pruned_loss=0.03097, over 7411.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2421, pruned_loss=0.02852, over 1423851.46 frames.], batch size: 21, lr: 1.99e-04
2022-05-16 06:20:28,885 INFO [train.py:812] (6/8) Epoch 39, batch 3200, loss[loss=0.1476, simple_loss=0.2507, pruned_loss=0.02226, over 6337.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2422, pruned_loss=0.02853, over 1424769.86 frames.], batch size: 38, lr: 1.99e-04
2022-05-16 06:21:26,283 INFO [train.py:812] (6/8) Epoch 39, batch 3250, loss[loss=0.1495, simple_loss=0.2457, pruned_loss=0.0267, over 6439.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2417, pruned_loss=0.0283, over 1424442.04 frames.], batch size: 37, lr: 1.99e-04
2022-05-16 06:22:25,454 INFO [train.py:812] (6/8) Epoch 39, batch 3300, loss[loss=0.1201, simple_loss=0.2142, pruned_loss=0.01299, over 7159.00 frames.], tot_loss[loss=0.1485, simple_loss=0.241, pruned_loss=0.028, over 1423896.39 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 06:23:24,306 INFO [train.py:812] (6/8) Epoch 39, batch 3350, loss[loss=0.1492, simple_loss=0.2334, pruned_loss=0.03251, over 7148.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2401, pruned_loss=0.02753, over 1425435.21 frames.], batch size: 17, lr: 1.99e-04
2022-05-16 06:24:23,021 INFO [train.py:812] (6/8) Epoch 39, batch 3400, loss[loss=0.1684, simple_loss=0.258, pruned_loss=0.03938, over 7351.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2396, pruned_loss=0.02755, over 1426809.61 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 06:25:22,173 INFO [train.py:812] (6/8) Epoch 39, batch 3450, loss[loss=0.165, simple_loss=0.2622, pruned_loss=0.0339, over 7196.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2402, pruned_loss=0.02836, over 1419452.70 frames.], batch size: 23, lr: 1.99e-04
2022-05-16 06:26:21,439 INFO [train.py:812] (6/8) Epoch 39, batch 3500, loss[loss=0.1288, simple_loss=0.2085, pruned_loss=0.02451, over 7153.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2411, pruned_loss=0.02882, over 1420587.00 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 06:27:20,259 INFO [train.py:812] (6/8) Epoch 39, batch 3550, loss[loss=0.1607, simple_loss=0.2568, pruned_loss=0.03234, over 7348.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2409, pruned_loss=0.02906, over 1423757.56 frames.], batch size: 22, lr: 1.99e-04
2022-05-16 06:28:19,545 INFO [train.py:812] (6/8) Epoch 39, batch 3600, loss[loss=0.1294, simple_loss=0.2219, pruned_loss=0.01845, over 7271.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2412, pruned_loss=0.02865, over 1424625.71 frames.], batch size: 18, lr: 1.99e-04
2022-05-16 06:29:17,997 INFO [train.py:812] (6/8) Epoch 39, batch 3650, loss[loss=0.1481, simple_loss=0.234, pruned_loss=0.03111, over 7024.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2417, pruned_loss=0.02889, over 1425961.22 frames.], batch size: 28, lr: 1.99e-04
2022-05-16 06:30:16,888 INFO [train.py:812] (6/8) Epoch 39, batch 3700, loss[loss=0.1525, simple_loss=0.2451, pruned_loss=0.02991, over 6341.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2416, pruned_loss=0.02868, over 1422486.74 frames.], batch size: 37, lr: 1.99e-04
2022-05-16 06:31:16,267 INFO [train.py:812] (6/8) Epoch 39, batch 3750, loss[loss=0.1663, simple_loss=0.2616, pruned_loss=0.03549, over 7217.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2409, pruned_loss=0.02884, over 1416448.07 frames.], batch size: 23, lr: 1.98e-04
2022-05-16 06:32:15,553 INFO [train.py:812] (6/8) Epoch 39, batch 3800, loss[loss=0.1337, simple_loss=0.2186, pruned_loss=0.02443, over 7362.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2403, pruned_loss=0.02841, over 1422882.06 frames.], batch size: 19, lr: 1.98e-04
2022-05-16 06:33:12,761 INFO [train.py:812] (6/8) Epoch 39, batch 3850, loss[loss=0.1845, simple_loss=0.2629, pruned_loss=0.05307, over 4985.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2398, pruned_loss=0.02832, over 1419645.91 frames.], batch size: 52, lr: 1.98e-04
2022-05-16 06:34:10,695 INFO [train.py:812] (6/8) Epoch 39, batch 3900, loss[loss=0.1495, simple_loss=0.2441, pruned_loss=0.02747, over 7101.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2401, pruned_loss=0.02807, over 1420928.30 frames.], batch size: 28, lr: 1.98e-04
2022-05-16 06:35:09,073 INFO [train.py:812] (6/8) Epoch 39, batch 3950, loss[loss=0.1668, simple_loss=0.2651, pruned_loss=0.03422, over 7310.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2405, pruned_loss=0.02785, over 1422915.44 frames.], batch size: 25, lr: 1.98e-04
2022-05-16 06:36:07,267 INFO [train.py:812] (6/8) Epoch 39, batch 4000, loss[loss=0.1538, simple_loss=0.2547, pruned_loss=0.02648, over 6740.00 frames.], tot_loss[loss=0.1485, simple_loss=0.241, pruned_loss=0.02797, over 1425794.06 frames.], batch size: 31, lr: 1.98e-04
2022-05-16 06:37:03,585 INFO [train.py:812] (6/8) Epoch 39, batch 4050, loss[loss=0.1625, simple_loss=0.2517, pruned_loss=0.03662, over 6925.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2421, pruned_loss=0.02839, over 1424116.27 frames.], batch size: 31, lr: 1.98e-04
2022-05-16 06:38:02,741 INFO [train.py:812] (6/8) Epoch 39, batch 4100, loss[loss=0.1495, simple_loss=0.2491, pruned_loss=0.02495, over 7211.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2416, pruned_loss=0.02851, over 1423053.91 frames.], batch size: 21, lr: 1.98e-04
2022-05-16 06:39:01,694 INFO [train.py:812] (6/8) Epoch 39, batch 4150, loss[loss=0.159, simple_loss=0.2499, pruned_loss=0.03398, over 7211.00 frames.], tot_loss[loss=0.1486, simple_loss=0.241, pruned_loss=0.02809, over 1420565.07 frames.], batch size: 21, lr: 1.98e-04
2022-05-16 06:40:00,326 INFO [train.py:812] (6/8) Epoch 39, batch 4200, loss[loss=0.1641, simple_loss=0.2494, pruned_loss=0.03938, over 6774.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2412, pruned_loss=0.02797, over 1419667.18 frames.], batch size: 31, lr: 1.98e-04
2022-05-16 06:40:58,797 INFO [train.py:812] (6/8) Epoch 39, batch 4250, loss[loss=0.1472, simple_loss=0.2218, pruned_loss=0.03625, over 7138.00 frames.], tot_loss[loss=0.149, simple_loss=0.2415, pruned_loss=0.02821, over 1416629.98 frames.], batch size: 17, lr: 1.98e-04
2022-05-16 06:41:58,210 INFO [train.py:812] (6/8) Epoch 39, batch 4300, loss[loss=0.1534, simple_loss=0.2483, pruned_loss=0.0292, over 7298.00 frames.], tot_loss[loss=0.15, simple_loss=0.243, pruned_loss=0.02848, over 1417075.24 frames.], batch size: 25, lr: 1.98e-04
2022-05-16 06:42:57,006 INFO [train.py:812] (6/8) Epoch 39, batch 4350, loss[loss=0.1486, simple_loss=0.2399, pruned_loss=0.0287, over 7431.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2428, pruned_loss=0.02867, over 1413953.48 frames.], batch size: 20, lr: 1.98e-04
2022-05-16 06:43:56,263 INFO [train.py:812] (6/8) Epoch 39, batch 4400, loss[loss=0.1344, simple_loss=0.231, pruned_loss=0.01892, over 7334.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2438, pruned_loss=0.02887, over 1410767.00 frames.], batch size: 22, lr: 1.98e-04
2022-05-16 06:44:54,117 INFO [train.py:812] (6/8) Epoch 39, batch 4450, loss[loss=0.128, simple_loss=0.2157, pruned_loss=0.02012, over 6989.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2443, pruned_loss=0.02916, over 1397666.74 frames.], batch size: 16, lr: 1.98e-04
2022-05-16 06:45:52,388 INFO [train.py:812] (6/8) Epoch 39, batch 4500, loss[loss=0.1417, simple_loss=0.2285, pruned_loss=0.0274, over 7178.00 frames.], tot_loss[loss=0.152, simple_loss=0.2448, pruned_loss=0.02958, over 1385770.35 frames.], batch size: 18, lr: 1.98e-04
2022-05-16 06:46:49,716 INFO [train.py:812] (6/8) Epoch 39, batch 4550, loss[loss=0.1826, simple_loss=0.2739, pruned_loss=0.04569, over 4848.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2467, pruned_loss=0.03059, over 1347312.14 frames.], batch size: 52, lr: 1.98e-04
2022-05-16 06:47:54,899 INFO [train.py:812] (6/8) Epoch 40, batch 0, loss[loss=0.196, simple_loss=0.2893, pruned_loss=0.05133, over 7268.00 frames.], tot_loss[loss=0.196, simple_loss=0.2893, pruned_loss=0.05133, over 7268.00 frames.], batch size: 24, lr: 1.96e-04
2022-05-16 06:48:53,198 INFO [train.py:812] (6/8) Epoch 40, batch 50, loss[loss=0.1239, simple_loss=0.2086, pruned_loss=0.01957, over 7280.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2426, pruned_loss=0.0289, over 317289.99 frames.], batch size: 17, lr: 1.95e-04
2022-05-16 06:49:52,159 INFO [train.py:812] (6/8) Epoch 40, batch 100, loss[loss=0.1465, simple_loss=0.2483, pruned_loss=0.02239, over 7371.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2403, pruned_loss=0.02765, over 562396.80 frames.], batch size: 19, lr: 1.95e-04
2022-05-16 06:50:51,453 INFO [train.py:812] (6/8) Epoch 40, batch 150, loss[loss=0.1448, simple_loss=0.2368, pruned_loss=0.02637, over 7233.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2382, pruned_loss=0.02746, over 755323.79 frames.], batch size: 20, lr: 1.95e-04
2022-05-16 06:51:50,282 INFO [train.py:812] (6/8) Epoch 40, batch 200, loss[loss=0.1503, simple_loss=0.2358, pruned_loss=0.03239, over 7428.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2409, pruned_loss=0.02809, over 904211.49 frames.], batch size: 18, lr: 1.95e-04
2022-05-16 06:52:48,887 INFO [train.py:812] (6/8) Epoch 40, batch 250, loss[loss=0.1305, simple_loss=0.2304, pruned_loss=0.01526, over 7100.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2404, pruned_loss=0.02759, over 1017629.47 frames.], batch size: 21, lr: 1.95e-04
2022-05-16 06:53:47,841 INFO [train.py:812] (6/8) Epoch 40, batch 300, loss[loss=0.1542, simple_loss=0.2464, pruned_loss=0.03097, over 7301.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2403, pruned_loss=0.0273, over 1108240.51 frames.], batch size: 24, lr: 1.95e-04
2022-05-16 06:54:46,921 INFO [train.py:812] (6/8) Epoch 40, batch 350, loss[loss=0.1367, simple_loss=0.2335, pruned_loss=0.01996, over 7144.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2397, pruned_loss=0.0274, over 1172545.79 frames.], batch size: 20, lr: 1.95e-04
2022-05-16 06:55:45,296 INFO [train.py:812] (6/8) Epoch 40, batch 400, loss[loss=0.1497, simple_loss=0.2489, pruned_loss=0.02527, over 7157.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2407, pruned_loss=0.02755, over 1229569.14 frames.], batch size: 26, lr: 1.95e-04
2022-05-16 06:56:53,576 INFO [train.py:812] (6/8) Epoch 40, batch 450, loss[loss=0.1652, simple_loss=0.2583, pruned_loss=0.03603, over 7273.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2401, pruned_loss=0.02768, over 1273038.52 frames.], batch size: 25, lr: 1.95e-04
2022-05-16 06:57:52,478 INFO [train.py:812] (6/8) Epoch 40, batch 500, loss[loss=0.1433, simple_loss=0.2416, pruned_loss=0.02249, over 7321.00 frames.], tot_loss[loss=0.1477, simple_loss=0.24, pruned_loss=0.02764, over 1305064.21 frames.], batch size: 21, lr: 1.95e-04
2022-05-16 06:58:59,585 INFO [train.py:812] (6/8) Epoch 40, batch 550, loss[loss=0.1572, simple_loss=0.2533, pruned_loss=0.03054, over 7228.00 frames.], tot_loss[loss=0.149, simple_loss=0.2414, pruned_loss=0.02831, over 1326530.67 frames.], batch size: 20, lr: 1.95e-04
2022-05-16 06:59:58,462 INFO [train.py:812] (6/8) Epoch 40, batch 600, loss[loss=0.1538, simple_loss=0.2401, pruned_loss=0.03377, over 7261.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2405, pruned_loss=0.02802, over 1348726.08 frames.], batch size: 19, lr: 1.95e-04
2022-05-16 07:01:07,498 INFO [train.py:812] (6/8) Epoch 40, batch 650, loss[loss=0.1594, simple_loss=0.2457, pruned_loss=0.03657, over 7227.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2404, pruned_loss=0.02802, over 1367387.09 frames.], batch size: 20, lr: 1.95e-04
2022-05-16 07:02:07,016 INFO [train.py:812] (6/8) Epoch 40, batch 700, loss[loss=0.1349, simple_loss=0.224, pruned_loss=0.02292, over 7278.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2406, pruned_loss=0.02794, over 1380770.78 frames.], batch size: 18, lr: 1.95e-04
2022-05-16 07:03:06,185 INFO [train.py:812] (6/8) Epoch 40, batch 750, loss[loss=0.1387, simple_loss=0.2208, pruned_loss=0.02835, over 7363.00 frames.], tot_loss[loss=0.1478, simple_loss=0.24, pruned_loss=0.02778, over 1386658.16 frames.], batch size: 19, lr: 1.95e-04
2022-05-16 07:04:05,443 INFO [train.py:812] (6/8) Epoch 40, batch 800, loss[loss=0.1462, simple_loss=0.2399, pruned_loss=0.02621, over 7111.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2395, pruned_loss=0.02736, over 1396243.07 frames.], batch size: 21, lr: 1.95e-04
2022-05-16 07:05:03,695 INFO [train.py:812] (6/8) Epoch 40, batch 850, loss[loss=0.1218, simple_loss=0.2075, pruned_loss=0.01811, over 7136.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2402, pruned_loss=0.02765, over 1402885.33 frames.], batch size: 17, lr: 1.95e-04
2022-05-16 07:06:12,344 INFO [train.py:812] (6/8) Epoch 40, batch 900, loss[loss=0.1597, simple_loss=0.2565, pruned_loss=0.03149, over 7207.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2407, pruned_loss=0.0276, over 1408767.45 frames.], batch size: 23, lr: 1.95e-04
2022-05-16 07:07:10,693 INFO [train.py:812] (6/8) Epoch 40, batch 950, loss[loss=0.1699, simple_loss=0.2645, pruned_loss=0.03761, over 4948.00 frames.], tot_loss[loss=0.149, simple_loss=0.242, pruned_loss=0.02795, over 1411471.54 frames.], batch size: 52, lr: 1.95e-04
2022-05-16 07:08:20,269 INFO [train.py:812] (6/8) Epoch 40, batch 1000, loss[loss=0.1444, simple_loss=0.2411, pruned_loss=0.02384, over 7117.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2416, pruned_loss=0.02804, over 1409580.67 frames.], batch size: 21, lr: 1.95e-04
2022-05-16 07:09:19,157 INFO [train.py:812] (6/8) Epoch 40, batch 1050, loss[loss=0.1328, simple_loss=0.2344, pruned_loss=0.01554, over 7228.00 frames.], tot_loss[loss=0.149, simple_loss=0.2418, pruned_loss=0.02809, over 1409271.97 frames.], batch size: 21, lr: 1.95e-04
2022-05-16 07:10:42,485 INFO [train.py:812] (6/8) Epoch 40, batch 1100, loss[loss=0.1299, simple_loss=0.2209, pruned_loss=0.01947, over 7162.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2413, pruned_loss=0.02792, over 1407956.41 frames.], batch size: 18, lr: 1.95e-04
2022-05-16 07:11:40,924 INFO [train.py:812] (6/8) Epoch 40, batch 1150, loss[loss=0.1483, simple_loss=0.2445, pruned_loss=0.02609, over 6783.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2406, pruned_loss=0.02758, over 1415211.76 frames.], batch size: 31, lr: 1.95e-04
2022-05-16 07:12:38,590 INFO [train.py:812] (6/8) Epoch 40, batch 1200, loss[loss=0.1607, simple_loss=0.2551, pruned_loss=0.03317, over 6244.00 frames.], tot_loss[loss=0.1482, simple_loss=0.241, pruned_loss=0.02773, over 1417610.62 frames.], batch size: 38, lr: 1.95e-04
2022-05-16 07:13:37,107 INFO [train.py:812] (6/8) Epoch 40, batch 1250, loss[loss=0.1826, simple_loss=0.2767, pruned_loss=0.04419, over 7279.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2413, pruned_loss=0.02826, over 1422060.26 frames.], batch size: 25, lr: 1.95e-04
2022-05-16 07:14:35,228 INFO [train.py:812] (6/8) Epoch 40, batch 1300, loss[loss=0.1632, simple_loss=0.2639, pruned_loss=0.03127, over 7414.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2418, pruned_loss=0.0285, over 1422497.30 frames.], batch size: 20, lr: 1.95e-04
2022-05-16 07:15:33,954 INFO [train.py:812] (6/8) Epoch 40, batch 1350, loss[loss=0.1395, simple_loss=0.2432, pruned_loss=0.01786, over 6470.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2415, pruned_loss=0.02849, over 1422030.94 frames.], batch size: 37, lr: 1.95e-04
2022-05-16 07:16:32,351 INFO [train.py:812] (6/8) Epoch 40, batch 1400, loss[loss=0.1514, simple_loss=0.2471, pruned_loss=0.02781, over 6183.00 frames.], tot_loss[loss=0.149, simple_loss=0.2414, pruned_loss=0.02834, over 1423321.94 frames.], batch size: 37, lr: 1.95e-04
2022-05-16 07:17:30,660 INFO [train.py:812] (6/8) Epoch 40, batch 1450, loss[loss=0.1617, simple_loss=0.2591, pruned_loss=0.03212, over 7196.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2413, pruned_loss=0.02822, over 1424581.87 frames.], batch size: 23, lr: 1.95e-04
2022-05-16 07:18:29,829 INFO [train.py:812] (6/8) Epoch 40, batch 1500, loss[loss=0.1312, simple_loss=0.2236, pruned_loss=0.0194, over 7145.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2417, pruned_loss=0.02829, over 1425075.39 frames.], batch size: 17, lr: 1.95e-04
2022-05-16 07:19:28,059 INFO [train.py:812] (6/8) Epoch 40, batch 1550, loss[loss=0.1788, simple_loss=0.2758, pruned_loss=0.04084, over 7201.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2406, pruned_loss=0.02792, over 1423436.32 frames.], batch size: 23, lr: 1.95e-04
2022-05-16 07:20:27,063 INFO [train.py:812] (6/8) Epoch 40, batch 1600, loss[loss=0.1689, simple_loss=0.2673, pruned_loss=0.03521, over 7096.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2417, pruned_loss=0.02848, over 1426034.23 frames.], batch size: 28, lr: 1.95e-04
2022-05-16 07:21:25,483 INFO [train.py:812] (6/8) Epoch 40, batch 1650, loss[loss=0.1707, simple_loss=0.2592, pruned_loss=0.04111, over 5111.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2413, pruned_loss=0.02823, over 1419721.66 frames.], batch size: 52, lr: 1.95e-04
2022-05-16 07:22:23,887 INFO [train.py:812] (6/8) Epoch 40, batch 1700, loss[loss=0.1234, simple_loss=0.2066, pruned_loss=0.02004, over 6984.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2415, pruned_loss=0.02832, over 1412535.18 frames.], batch size: 16, lr: 1.95e-04
2022-05-16 07:23:23,268 INFO [train.py:812] (6/8) Epoch 40, batch 1750, loss[loss=0.1537, simple_loss=0.2539, pruned_loss=0.02681, over 7309.00 frames.], tot_loss[loss=0.1488, simple_loss=0.241, pruned_loss=0.02831, over 1414622.23 frames.], batch size: 21, lr: 1.95e-04
2022-05-16 07:24:22,414 INFO [train.py:812] (6/8) Epoch 40, batch 1800, loss[loss=0.1464, simple_loss=0.2527, pruned_loss=0.02002, over 7347.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2419, pruned_loss=0.0285, over 1417097.10 frames.], batch size: 22, lr: 1.95e-04
2022-05-16 07:25:21,054 INFO [train.py:812] (6/8) Epoch 40, batch 1850, loss[loss=0.1427, simple_loss=0.2216, pruned_loss=0.03188, over 7073.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2415, pruned_loss=0.02832, over 1420372.21 frames.], batch size: 18, lr: 1.95e-04
2022-05-16 07:26:20,236 INFO [train.py:812] (6/8) Epoch 40, batch 1900, loss[loss=0.1455, simple_loss=0.234, pruned_loss=0.02855, over 7174.00 frames.], tot_loss[loss=0.1492, simple_loss=0.242, pruned_loss=0.02819, over 1423749.92 frames.], batch size: 19, lr: 1.94e-04
2022-05-16 07:27:17,901 INFO [train.py:812] (6/8) Epoch 40, batch 1950, loss[loss=0.1893, simple_loss=0.2752, pruned_loss=0.05169, over 5137.00 frames.], tot_loss[loss=0.15, simple_loss=0.2425, pruned_loss=0.02869, over 1417964.54 frames.], batch size: 52, lr: 1.94e-04
2022-05-16 07:28:16,416 INFO [train.py:812] (6/8) Epoch 40, batch 2000, loss[loss=0.1395, simple_loss=0.2212, pruned_loss=0.02896, over 7079.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2422, pruned_loss=0.02851, over 1421737.32 frames.], batch size: 18, lr: 1.94e-04
2022-05-16 07:29:15,100 INFO [train.py:812] (6/8) Epoch 40, batch 2050, loss[loss=0.1424, simple_loss=0.2276, pruned_loss=0.02856, over 7434.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2421, pruned_loss=0.02866, over 1425927.22 frames.], batch size: 20, lr: 1.94e-04
2022-05-16 07:30:14,392 INFO [train.py:812] (6/8) Epoch 40, batch 2100, loss[loss=0.1174, simple_loss=0.1988, pruned_loss=0.01805, over 7406.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2415, pruned_loss=0.02841, over 1424766.87 frames.], batch size: 18, lr: 1.94e-04
2022-05-16 07:31:12,648 INFO [train.py:812] (6/8) Epoch 40, batch 2150, loss[loss=0.1908, simple_loss=0.2922, pruned_loss=0.04467, over 7144.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2412, pruned_loss=0.02804, over 1428974.78 frames.], batch size: 20, lr: 1.94e-04
2022-05-16 07:32:11,380 INFO [train.py:812] (6/8) Epoch 40, batch 2200, loss[loss=0.1641, simple_loss=0.2521, pruned_loss=0.0381, over 7236.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2412, pruned_loss=0.0281, over 1431664.25 frames.], batch size: 20, lr: 1.94e-04
2022-05-16 07:33:10,323 INFO [train.py:812] (6/8) Epoch 40, batch 2250, loss[loss=0.1728, simple_loss=0.2656, pruned_loss=0.04001, over 7197.00 frames.], tot_loss[loss=0.1483, simple_loss=0.241, pruned_loss=0.02777, over 1430135.03 frames.], batch size: 22, lr: 1.94e-04
2022-05-16 07:34:08,379 INFO [train.py:812] (6/8) Epoch 40, batch 2300, loss[loss=0.1527, simple_loss=0.2444, pruned_loss=0.03047, over 7434.00 frames.], tot_loss[loss=0.1477, simple_loss=0.24, pruned_loss=0.02764, over 1426304.40 frames.], batch size: 20, lr: 1.94e-04
2022-05-16 07:35:07,172 INFO [train.py:812] (6/8) Epoch 40, batch 2350, loss[loss=0.1615, simple_loss=0.2617, pruned_loss=0.03072, over 7347.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2389, pruned_loss=0.0274, over 1425061.33 frames.], batch size: 22, lr: 1.94e-04
2022-05-16 07:36:06,640 INFO [train.py:812] (6/8) Epoch 40, batch 2400, loss[loss=0.1678, simple_loss=0.2623, pruned_loss=0.03662, over 7198.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2394, pruned_loss=0.02768, over 1425182.76 frames.], batch size: 22, lr: 1.94e-04
2022-05-16 07:37:04,779 INFO [train.py:812] (6/8) Epoch 40, batch 2450, loss[loss=0.1772, simple_loss=0.2645, pruned_loss=0.04502, over 7133.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2402, pruned_loss=0.02765, over 1420489.54 frames.], batch size: 28, lr: 1.94e-04
2022-05-16 07:38:03,603 INFO [train.py:812] (6/8) Epoch 40, batch 2500, loss[loss=0.153, simple_loss=0.2501, pruned_loss=0.028, over 7422.00 frames.], tot_loss[loss=0.1475, simple_loss=0.24, pruned_loss=0.02744, over 1418122.07 frames.], batch size: 21, lr: 1.94e-04
2022-05-16 07:39:02,645 INFO [train.py:812] (6/8) Epoch 40, batch 2550, loss[loss=0.1474, simple_loss=0.2507, pruned_loss=0.02203, over 7048.00 frames.], tot_loss[loss=0.1479, simple_loss=0.241, pruned_loss=0.0274, over 1418103.94 frames.], batch size: 28, lr: 1.94e-04
2022-05-16 07:40:02,283 INFO [train.py:812] (6/8) Epoch 40, batch 2600, loss[loss=0.1505, simple_loss=0.2564, pruned_loss=0.02233, over 7337.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2403, pruned_loss=0.02724, over 1417305.07 frames.], batch size: 22, lr: 1.94e-04
2022-05-16 07:40:59,598 INFO [train.py:812] (6/8) Epoch 40, batch 2650, loss[loss=0.1479, simple_loss=0.2413, pruned_loss=0.02724, over 7158.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2407, pruned_loss=0.02747, over 1420043.01 frames.], batch size: 18, lr: 1.94e-04
2022-05-16 07:42:08,105 INFO [train.py:812] (6/8) Epoch 40, batch 2700, loss[loss=0.1661, simple_loss=0.2527, pruned_loss=0.03981, over 7174.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2409, pruned_loss=0.02743, over 1422096.51 frames.], batch size: 26, lr: 1.94e-04
2022-05-16 07:43:06,189 INFO [train.py:812] (6/8) Epoch 40, batch 2750, loss[loss=0.1565, simple_loss=0.2532, pruned_loss=0.02994, over 7287.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2407, pruned_loss=0.0273, over 1426070.58 frames.], batch size: 24, lr: 1.94e-04
2022-05-16 07:44:05,708 INFO [train.py:812] (6/8) Epoch 40, batch 2800, loss[loss=0.1614, simple_loss=0.2514, pruned_loss=0.03574, over 7069.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2415, pruned_loss=0.02772, over 1422784.89 frames.], batch size: 18, lr: 1.94e-04
2022-05-16 07:45:02,896 INFO [train.py:812] (6/8) Epoch 40, batch 2850, loss[loss=0.1639, simple_loss=0.273, pruned_loss=0.02738, over 6461.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2416, pruned_loss=0.02777, over 1418443.65 frames.], batch size: 38, lr: 1.94e-04
2022-05-16 07:46:01,101 INFO [train.py:812] (6/8) Epoch 40, batch 2900, loss[loss=0.1377, simple_loss=0.228, pruned_loss=0.02365, over 7072.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2408, pruned_loss=0.02719, over 1418612.84 frames.], batch size: 18, lr: 1.94e-04
2022-05-16 07:46:58,672 INFO [train.py:812] (6/8) Epoch 40, batch 2950, loss[loss=0.1507, simple_loss=0.2434, pruned_loss=0.02902, over 7279.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2417, pruned_loss=0.02755, over 1417541.88 frames.], batch size: 24, lr: 1.94e-04
2022-05-16 07:47:56,498 INFO [train.py:812] (6/8) Epoch 40, batch 3000, loss[loss=0.1515, simple_loss=0.2479, pruned_loss=0.02759, over 7343.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2426, pruned_loss=0.02799, over 1412448.94 frames.], batch size: 22, lr: 1.94e-04
2022-05-16 07:47:56,498 INFO [train.py:832] (6/8) Computing validation loss
2022-05-16 07:48:04,107 INFO [train.py:841] (6/8) Epoch 40, validation: loss=0.1534, simple_loss=0.2485, pruned_loss=0.02916, over 698248.00 frames.
2022-05-16 07:49:02,580 INFO [train.py:812] (6/8) Epoch 40, batch 3050, loss[loss=0.1408, simple_loss=0.2342, pruned_loss=0.02376, over 7363.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2426, pruned_loss=0.02839, over 1415204.39 frames.], batch size: 19, lr: 1.94e-04
2022-05-16 07:50:01,841 INFO [train.py:812] (6/8) Epoch 40, batch 3100, loss[loss=0.1432, simple_loss=0.2439, pruned_loss=0.02121, over 7179.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2423, pruned_loss=0.02827, over 1417763.14 frames.], batch size: 26, lr: 1.94e-04
2022-05-16 07:51:00,391 INFO [train.py:812] (6/8) Epoch 40, batch 3150, loss[loss=0.1312, simple_loss=0.2261, pruned_loss=0.01812, over 7142.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2423, pruned_loss=0.02803, over 1421483.89 frames.], batch size: 20, lr: 1.94e-04
2022-05-16 07:51:59,403 INFO [train.py:812] (6/8) Epoch 40, batch 3200, loss[loss=0.1683, simple_loss=0.2542, pruned_loss=0.0412, over 5162.00 frames.], tot_loss[loss=0.15, simple_loss=0.243, pruned_loss=0.0285, over 1421788.90 frames.], batch size: 53, lr: 1.94e-04
2022-05-16 07:52:57,293 INFO [train.py:812] (6/8) Epoch 40, batch 3250, loss[loss=0.1622, simple_loss=0.2465, pruned_loss=0.03893, over 7361.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2434, pruned_loss=0.02872, over 1420750.55 frames.], batch size: 23, lr: 1.94e-04
2022-05-16 07:53:57,125 INFO [train.py:812] (6/8) Epoch 40, batch 3300, loss[loss=0.1523, simple_loss=0.2465, pruned_loss=0.02907, over 7114.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2421, pruned_loss=0.02826, over 1419159.25 frames.], batch size: 21, lr: 1.94e-04
2022-05-16 07:54:55,921 INFO [train.py:812] (6/8) Epoch 40, batch 3350, loss[loss=0.1478, simple_loss=0.2473, pruned_loss=0.02419, over 7116.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2421, pruned_loss=0.02813, over 1417096.61 frames.], batch size: 21, lr: 1.94e-04
2022-05-16 07:55:55,676 INFO [train.py:812] (6/8) Epoch 40, batch 3400, loss[loss=0.1336, simple_loss=0.2342, pruned_loss=0.01653, over 7160.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2408, pruned_loss=0.02748, over 1418745.32 frames.], batch size: 19, lr: 1.94e-04
2022-05-16 07:56:54,703 INFO [train.py:812] (6/8) Epoch 40, batch 3450, loss[loss=0.1384, simple_loss=0.2183, pruned_loss=0.02923, over 7282.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2414, pruned_loss=0.0279, over 1417996.90 frames.], batch size: 17, lr: 1.94e-04
2022-05-16 07:57:54,437 INFO [train.py:812] (6/8) Epoch 40, batch 3500, loss[loss=0.1443, simple_loss=0.2431, pruned_loss=0.02274, over 7317.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2419, pruned_loss=0.02788, over 1418539.48 frames.], batch size: 21, lr: 1.94e-04
2022-05-16 07:58:53,149 INFO [train.py:812] (6/8) Epoch 40, batch 3550, loss[loss=0.1247, simple_loss=0.2154, pruned_loss=0.01703, over 7067.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2404, pruned_loss=0.02745, over 1420164.60 frames.], batch size: 18, lr: 1.94e-04
2022-05-16 07:59:51,365 INFO [train.py:812] (6/8) Epoch 40, batch 3600, loss[loss=0.1416, simple_loss=0.2335, pruned_loss=0.0249, over 4521.00 frames.], tot_loss[loss=0.1482, simple_loss=0.241, pruned_loss=0.02766, over 1417249.93 frames.], batch size: 53, lr: 1.94e-04
2022-05-16 08:00:51,223 INFO [train.py:812] (6/8) Epoch 40, batch 3650, loss[loss=0.1395, simple_loss=0.2311, pruned_loss=0.02394, over 6326.00 frames.], tot_loss[loss=0.148, simple_loss=0.2405, pruned_loss=0.02773, over 1418972.80 frames.], batch size: 37, lr: 1.94e-04
2022-05-16 08:01:49,916 INFO [train.py:812] (6/8) Epoch 40, batch 3700, loss[loss=0.1328, simple_loss=0.2211, pruned_loss=0.02221, over 7141.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2405, pruned_loss=0.02752, over 1423488.59 frames.], batch size: 17, lr: 1.94e-04
2022-05-16 08:02:47,002 INFO [train.py:812] (6/8) Epoch 40, batch 3750, loss[loss=0.1797, simple_loss=0.2601, pruned_loss=0.04961, over 7366.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2409, pruned_loss=0.02777, over 1419979.25 frames.], batch size: 19, lr: 1.93e-04
2022-05-16 08:03:45,487 INFO [train.py:812] (6/8) Epoch 40, batch 3800, loss[loss=0.1126, simple_loss=0.1949, pruned_loss=0.01511, over 6984.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2407, pruned_loss=0.02785, over 1424562.31 frames.], batch size: 16, lr: 1.93e-04
2022-05-16 08:04:42,353 INFO [train.py:812] (6/8) Epoch 40, batch 3850, loss[loss=0.1481, simple_loss=0.2487, pruned_loss=0.02375, over 7415.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2406, pruned_loss=0.028, over 1419568.23 frames.], batch size: 21, lr: 1.93e-04
2022-05-16 08:05:41,377 INFO [train.py:812] (6/8) Epoch 40, batch 3900, loss[loss=0.1802, simple_loss=0.2874, pruned_loss=0.03652, over 7208.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2405, pruned_loss=0.02788, over 1419492.45 frames.], batch size: 23, lr: 1.93e-04
2022-05-16 08:06:40,235 INFO [train.py:812] (6/8) Epoch 40, batch 3950, loss[loss=0.1336, simple_loss=0.2212, pruned_loss=0.02299, over 7046.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2392, pruned_loss=0.02775, over 1415653.30 frames.], batch size: 18, lr: 1.93e-04
2022-05-16 08:07:38,733 INFO [train.py:812] (6/8) Epoch 40, batch 4000, loss[loss=0.1554, simple_loss=0.2325, pruned_loss=0.03916, over 7132.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2395, pruned_loss=0.02787, over 1414836.14 frames.], batch size: 17, lr: 1.93e-04
2022-05-16 08:08:36,090 INFO [train.py:812] (6/8) Epoch 40, batch 4050, loss[loss=0.1657, simple_loss=0.2647, pruned_loss=0.03332, over 7211.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2397, pruned_loss=0.02778, over 1419111.26 frames.], batch size: 22, lr: 1.93e-04
2022-05-16 08:09:35,659 INFO [train.py:812] (6/8) Epoch 40, batch 4100, loss[loss=0.1329, simple_loss=0.2337, pruned_loss=0.01608, over 7241.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2395, pruned_loss=0.02764, over 1419175.24 frames.], batch size: 20, lr: 1.93e-04
2022-05-16 08:10:34,190 INFO [train.py:812] (6/8) Epoch 40, batch 4150, loss[loss=0.1643, simple_loss=0.243, pruned_loss=0.0428, over 7273.00 frames.], tot_loss[loss=0.1479, simple_loss=0.24, pruned_loss=0.02791, over 1421642.21 frames.], batch size: 18, lr: 1.93e-04
2022-05-16 08:11:32,972 INFO [train.py:812] (6/8) Epoch 40, batch 4200, loss[loss=0.1284, simple_loss=0.2179, pruned_loss=0.01952, over 7155.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2405, pruned_loss=0.02793, over 1423275.81 frames.], batch size: 18, lr: 1.93e-04
2022-05-16 08:12:32,014 INFO [train.py:812] (6/8) Epoch 40, batch 4250, loss[loss=0.1769, simple_loss=0.2761, pruned_loss=0.03889, over 7317.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2403, pruned_loss=0.02829, over 1419640.51 frames.], batch size: 21, lr: 1.93e-04
2022-05-16 08:13:30,186 INFO [train.py:812] (6/8) Epoch 40, batch 4300, loss[loss=0.1405, simple_loss=0.2263, pruned_loss=0.02732, over 7174.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2408, pruned_loss=0.02871, over 1420786.42 frames.], batch size: 18, lr: 1.93e-04
2022-05-16 08:14:29,479 INFO [train.py:812] (6/8) Epoch 40, batch 4350, loss[loss=0.1457, simple_loss=0.2382, pruned_loss=0.02659, over 7330.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2415, pruned_loss=0.02867, over 1422393.68 frames.], batch size: 20, lr: 1.93e-04
2022-05-16 08:15:29,018 INFO [train.py:812] (6/8) Epoch 40, batch 4400, loss[loss=0.1573, simple_loss=0.256, pruned_loss=0.02936, over 6785.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2419, pruned_loss=0.0286, over 1422208.21 frames.], batch size: 31, lr: 1.93e-04
2022-05-16 08:16:26,694 INFO [train.py:812] (6/8) Epoch 40, batch 4450, loss[loss=0.144, simple_loss=0.2262, pruned_loss=0.03093, over 7154.00 frames.], tot_loss[loss=0.1496, simple_loss=0.242, pruned_loss=0.02864, over 1408780.41 frames.], batch size: 18, lr: 1.93e-04
2022-05-16 08:17:25,878 INFO [train.py:812] (6/8) Epoch 40, batch 4500, loss[loss=0.1485, simple_loss=0.245, pruned_loss=0.02599, over 7216.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2423, pruned_loss=0.0287, over 1401562.16 frames.], batch size: 21, lr: 1.93e-04
2022-05-16 08:18:25,900 INFO [train.py:812] (6/8) Epoch 40, batch 4550, loss[loss=0.121, simple_loss=0.1991, pruned_loss=0.0214, over 7214.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2392, pruned_loss=0.02831, over 1394028.04 frames.], batch size: 16, lr: 1.93e-04
2022-05-16 08:19:10,720 INFO [train.py:1030] (6/8) Done!