2022-05-13 19:15:59,542 INFO [train.py:876] (5/8) Training started
2022-05-13 19:15:59,542 INFO [train.py:886] (5/8) Device: cuda:5
2022-05-13 19:15:59,545 INFO [train.py:895] (5/8) {'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 19:15:59,546 INFO [train.py:897] (5/8) About to create model
2022-05-13 19:16:00,240 INFO [train.py:901] (5/8) Number of model parameters: 116553580
2022-05-13 19:16:07,938 INFO [train.py:916] (5/8) Using DDP
2022-05-13 19:16:09,395 INFO [asr_datamodule.py:391] (5/8) About to get train-clean-100 cuts
2022-05-13 19:16:17,914 INFO [asr_datamodule.py:398] (5/8) About to get train-clean-360 cuts
2022-05-13 19:16:51,732 INFO [asr_datamodule.py:405] (5/8) About to get train-other-500 cuts
2022-05-13 19:17:47,003 INFO [asr_datamodule.py:209] (5/8) Enable MUSAN
2022-05-13 19:17:47,003 INFO [asr_datamodule.py:210] (5/8) About to get Musan cuts
2022-05-13 19:17:48,925 INFO [asr_datamodule.py:238] (5/8) Enable SpecAugment
2022-05-13 19:17:48,925 INFO [asr_datamodule.py:239] (5/8) Time warp factor: 80
2022-05-13 19:17:48,925 INFO [asr_datamodule.py:251] (5/8) Num frame mask: 10
2022-05-13 19:17:48,926 INFO [asr_datamodule.py:264] (5/8) About to create train dataset
2022-05-13 19:17:48,926 INFO [asr_datamodule.py:292] (5/8) Using BucketingSampler.
2022-05-13 19:17:54,440 INFO [asr_datamodule.py:308] (5/8) About to create train dataloader
2022-05-13 19:17:54,441 INFO [asr_datamodule.py:412] (5/8) About to get dev-clean cuts
2022-05-13 19:17:54,795 INFO [asr_datamodule.py:417] (5/8) About to get dev-other cuts
2022-05-13 19:17:55,076 INFO [asr_datamodule.py:339] (5/8) About to create dev dataset
2022-05-13 19:17:55,089 INFO [asr_datamodule.py:358] (5/8) About to create dev dataloader
2022-05-13 19:17:55,089 INFO [train.py:1078] (5/8) Sanity check -- see if any of the batches in epoch 1 would cause OOM.
2022-05-13 19:18:18,397 INFO [distributed.py:874] (5/8) Reducer buckets have been rebuilt in this iteration.
2022-05-13 19:18:41,992 INFO [train.py:812] (5/8) Epoch 1, batch 0, loss[loss=0.7638, simple_loss=1.528, pruned_loss=6.537, over 7273.00 frames.], tot_loss[loss=0.7638, simple_loss=1.528, pruned_loss=6.537, over 7273.00 frames.], batch size: 17, lr: 3.00e-03
2022-05-13 19:19:41,273 INFO [train.py:812] (5/8) Epoch 1, batch 50, loss[loss=0.4665, simple_loss=0.933, pruned_loss=7.027, over 7159.00 frames.], tot_loss[loss=0.5522, simple_loss=1.104, pruned_loss=7.105, over 323165.15 frames.], batch size: 19, lr: 3.00e-03
2022-05-13 19:20:39,812 INFO [train.py:812] (5/8) Epoch 1, batch 100, loss[loss=0.3923, simple_loss=0.7845, pruned_loss=6.59, over 7014.00 frames.], tot_loss[loss=0.4944, simple_loss=0.9888, pruned_loss=6.962, over 566707.79 frames.], batch size: 16, lr: 3.00e-03
2022-05-13 19:21:38,644 INFO [train.py:812] (5/8) Epoch 1, batch 150, loss[loss=0.3633, simple_loss=0.7265, pruned_loss=6.679, over 7014.00 frames.], tot_loss[loss=0.464, simple_loss=0.928, pruned_loss=6.875, over 758707.81 frames.], batch size: 16, lr: 3.00e-03
2022-05-13 19:22:36,950 INFO [train.py:812] (5/8) Epoch 1, batch 200, loss[loss=0.4013, simple_loss=0.8025, pruned_loss=6.692, over 7298.00 frames.], tot_loss[loss=0.4443, simple_loss=0.8886, pruned_loss=6.846, over 908711.24 frames.], batch size: 25, lr: 3.00e-03
2022-05-13 19:23:35,680 INFO [train.py:812] (5/8) Epoch 1, batch 250, loss[loss=0.4364, simple_loss=0.8728, pruned_loss=6.947, over 7332.00 frames.], tot_loss[loss=0.4297, simple_loss=0.8594, pruned_loss=6.834, over 1017284.57 frames.], batch size: 21, lr: 3.00e-03
2022-05-13 19:24:34,092 INFO [train.py:812] (5/8) Epoch 1, batch 300, loss[loss=0.3991, simple_loss=0.7981, pruned_loss=6.838, over 7307.00 frames.], tot_loss[loss=0.4191, simple_loss=0.8383, pruned_loss=6.829, over 1109663.75 frames.], batch size: 25, lr: 3.00e-03
2022-05-13 19:25:33,444 INFO [train.py:812] (5/8) Epoch 1, batch 350, loss[loss=0.3912, simple_loss=0.7825, pruned_loss=6.837, over 7260.00 frames.], tot_loss[loss=0.4111, simple_loss=0.8222, pruned_loss=6.822, over 1178939.80 frames.], batch size: 19, lr: 3.00e-03
2022-05-13 19:26:31,630 INFO [train.py:812] (5/8) Epoch 1, batch 400, loss[loss=0.3714, simple_loss=0.7429, pruned_loss=6.885, over 7410.00 frames.], tot_loss[loss=0.4031, simple_loss=0.8062, pruned_loss=6.806, over 1231027.84 frames.], batch size: 21, lr: 3.00e-03
2022-05-13 19:27:30,026 INFO [train.py:812] (5/8) Epoch 1, batch 450, loss[loss=0.3398, simple_loss=0.6797, pruned_loss=6.733, over 7421.00 frames.], tot_loss[loss=0.393, simple_loss=0.786, pruned_loss=6.791, over 1267472.88 frames.], batch size: 21, lr: 2.99e-03
2022-05-13 19:28:29,379 INFO [train.py:812] (5/8) Epoch 1, batch 500, loss[loss=0.3234, simple_loss=0.6468, pruned_loss=6.767, over 7195.00 frames.], tot_loss[loss=0.3777, simple_loss=0.7555, pruned_loss=6.776, over 1303749.18 frames.], batch size: 22, lr: 2.99e-03
2022-05-13 19:29:27,215 INFO [train.py:812] (5/8) Epoch 1, batch 550, loss[loss=0.2865, simple_loss=0.573, pruned_loss=6.729, over 7341.00 frames.], tot_loss[loss=0.3628, simple_loss=0.7256, pruned_loss=6.771, over 1330646.29 frames.], batch size: 22, lr: 2.99e-03
2022-05-13 19:30:26,694 INFO [train.py:812] (5/8) Epoch 1, batch 600, loss[loss=0.3154, simple_loss=0.6308, pruned_loss=6.784, over 7116.00 frames.], tot_loss[loss=0.3469, simple_loss=0.6938, pruned_loss=6.763, over 1351560.91 frames.], batch size: 21, lr: 2.99e-03
2022-05-13 19:31:24,423 INFO [train.py:812] (5/8) Epoch 1, batch 650, loss[loss=0.245, simple_loss=0.4901, pruned_loss=6.611, over 6998.00 frames.], tot_loss[loss=0.3322, simple_loss=0.6644, pruned_loss=6.757, over 1369585.21 frames.], batch size: 16, lr: 2.99e-03
2022-05-13 19:32:22,730 INFO [train.py:812] (5/8) Epoch 1, batch 700, loss[loss=0.2931, simple_loss=0.5862, pruned_loss=6.85, over 7222.00 frames.], tot_loss[loss=0.3161, simple_loss=0.6322, pruned_loss=6.747, over 1381417.14 frames.], batch size: 23, lr: 2.99e-03
2022-05-13 19:33:21,778 INFO [train.py:812] (5/8) Epoch 1, batch 750, loss[loss=0.2405, simple_loss=0.4811, pruned_loss=6.576, over 7263.00 frames.], tot_loss[loss=0.3013, simple_loss=0.6027, pruned_loss=6.737, over 1392615.01 frames.], batch size: 17, lr: 2.98e-03
2022-05-13 19:34:19,615 INFO [train.py:812] (5/8) Epoch 1, batch 800, loss[loss=0.2508, simple_loss=0.5016, pruned_loss=6.629, over 7117.00 frames.], tot_loss[loss=0.2901, simple_loss=0.5801, pruned_loss=6.737, over 1397517.41 frames.], batch size: 21, lr: 2.98e-03
2022-05-13 19:35:17,942 INFO [train.py:812] (5/8) Epoch 1, batch 850, loss[loss=0.252, simple_loss=0.5041, pruned_loss=6.845, over 7233.00 frames.], tot_loss[loss=0.2793, simple_loss=0.5586, pruned_loss=6.734, over 1402481.34 frames.], batch size: 21, lr: 2.98e-03
2022-05-13 19:36:17,406 INFO [train.py:812] (5/8) Epoch 1, batch 900, loss[loss=0.2633, simple_loss=0.5267, pruned_loss=6.85, over 7322.00 frames.], tot_loss[loss=0.2692, simple_loss=0.5384, pruned_loss=6.732, over 1407372.65 frames.], batch size: 21, lr: 2.98e-03
2022-05-13 19:37:15,467 INFO [train.py:812] (5/8) Epoch 1, batch 950, loss[loss=0.2078, simple_loss=0.4155, pruned_loss=6.567, over 7014.00 frames.], tot_loss[loss=0.2622, simple_loss=0.5244, pruned_loss=6.736, over 1403749.14 frames.], batch size: 16, lr: 2.97e-03
2022-05-13 19:38:15,214 INFO [train.py:812] (5/8) Epoch 1, batch 1000, loss[loss=0.1985, simple_loss=0.3971, pruned_loss=6.562, over 6997.00 frames.], tot_loss[loss=0.2562, simple_loss=0.5123, pruned_loss=6.739, over 1404078.82 frames.], batch size: 16, lr: 2.97e-03
2022-05-13 19:39:14,090 INFO [train.py:812] (5/8) Epoch 1, batch 1050, loss[loss=0.1838, simple_loss=0.3675, pruned_loss=6.488, over 6998.00 frames.], tot_loss[loss=0.2503, simple_loss=0.5007, pruned_loss=6.743, over 1406437.23 frames.], batch size: 16, lr: 2.97e-03
2022-05-13 19:40:12,425 INFO [train.py:812] (5/8) Epoch 1, batch 1100, loss[loss=0.2312, simple_loss=0.4624, pruned_loss=6.859, over 7217.00 frames.], tot_loss[loss=0.2451, simple_loss=0.4902, pruned_loss=6.749, over 1409802.92 frames.], batch size: 22, lr: 2.96e-03
2022-05-13 19:41:10,370 INFO [train.py:812] (5/8) Epoch 1, batch 1150, loss[loss=0.2333, simple_loss=0.4666, pruned_loss=6.812, over 6786.00 frames.], tot_loss[loss=0.2396, simple_loss=0.4792, pruned_loss=6.748, over 1411342.29 frames.], batch size: 31, lr: 2.96e-03
2022-05-13 19:42:08,517 INFO [train.py:812] (5/8) Epoch 1, batch 1200, loss[loss=0.2201, simple_loss=0.4402, pruned_loss=6.864, over 7208.00 frames.], tot_loss[loss=0.2349, simple_loss=0.4698, pruned_loss=6.751, over 1419478.84 frames.], batch size: 26, lr: 2.96e-03
2022-05-13 19:43:07,162 INFO [train.py:812] (5/8) Epoch 1, batch 1250, loss[loss=0.2309, simple_loss=0.4618, pruned_loss=6.763, over 7372.00 frames.], tot_loss[loss=0.2311, simple_loss=0.4622, pruned_loss=6.752, over 1413079.34 frames.], batch size: 23, lr: 2.95e-03
2022-05-13 19:44:06,117 INFO [train.py:812] (5/8) Epoch 1, batch 1300, loss[loss=0.2232, simple_loss=0.4464, pruned_loss=6.842, over 7299.00 frames.], tot_loss[loss=0.2265, simple_loss=0.453, pruned_loss=6.754, over 1420490.03 frames.], batch size: 24, lr: 2.95e-03
2022-05-13 19:45:04,272 INFO [train.py:812] (5/8) Epoch 1, batch 1350, loss[loss=0.2231, simple_loss=0.4462, pruned_loss=6.672, over 7149.00 frames.], tot_loss[loss=0.2231, simple_loss=0.4462, pruned_loss=6.752, over 1421684.21 frames.], batch size: 20, lr: 2.95e-03
2022-05-13 19:46:03,469 INFO [train.py:812] (5/8) Epoch 1, batch 1400, loss[loss=0.221, simple_loss=0.442, pruned_loss=6.871, over 7266.00 frames.], tot_loss[loss=0.2209, simple_loss=0.4418, pruned_loss=6.758, over 1418266.41 frames.], batch size: 24, lr: 2.94e-03
2022-05-13 19:47:02,115 INFO [train.py:812] (5/8) Epoch 1, batch 1450, loss[loss=0.1808, simple_loss=0.3616, pruned_loss=6.648, over 7132.00 frames.], tot_loss[loss=0.218, simple_loss=0.4359, pruned_loss=6.759, over 1418947.32 frames.], batch size: 17, lr: 2.94e-03
2022-05-13 19:48:00,920 INFO [train.py:812] (5/8) Epoch 1, batch 1500, loss[loss=0.1982, simple_loss=0.3963, pruned_loss=6.787, over 7286.00 frames.], tot_loss[loss=0.2157, simple_loss=0.4314, pruned_loss=6.759, over 1422010.64 frames.], batch size: 24, lr: 2.94e-03
2022-05-13 19:48:59,488 INFO [train.py:812] (5/8) Epoch 1, batch 1550, loss[loss=0.2235, simple_loss=0.447, pruned_loss=6.845, over 7109.00 frames.], tot_loss[loss=0.2127, simple_loss=0.4254, pruned_loss=6.758, over 1422038.66 frames.], batch size: 21, lr: 2.93e-03
2022-05-13 19:49:59,129 INFO [train.py:812] (5/8) Epoch 1, batch 1600, loss[loss=0.2117, simple_loss=0.4234, pruned_loss=6.745, over 7331.00 frames.], tot_loss[loss=0.2101, simple_loss=0.4202, pruned_loss=6.757, over 1419947.73 frames.], batch size: 20, lr: 2.93e-03
2022-05-13 19:50:58,998 INFO [train.py:812] (5/8) Epoch 1, batch 1650, loss[loss=0.19, simple_loss=0.3801, pruned_loss=6.539, over 7162.00 frames.], tot_loss[loss=0.2079, simple_loss=0.4158, pruned_loss=6.754, over 1422039.04 frames.], batch size: 18, lr: 2.92e-03
2022-05-13 19:51:59,060 INFO [train.py:812] (5/8) Epoch 1, batch 1700, loss[loss=0.2208, simple_loss=0.4416, pruned_loss=6.835, over 6309.00 frames.], tot_loss[loss=0.2066, simple_loss=0.4132, pruned_loss=6.758, over 1416658.07 frames.], batch size: 38, lr: 2.92e-03
2022-05-13 19:52:58,902 INFO [train.py:812] (5/8) Epoch 1, batch 1750, loss[loss=0.1998, simple_loss=0.3996, pruned_loss=6.778, over 6285.00 frames.], tot_loss[loss=0.2043, simple_loss=0.4085, pruned_loss=6.755, over 1416741.43 frames.], batch size: 37, lr: 2.91e-03
2022-05-13 19:54:00,189 INFO [train.py:812] (5/8) Epoch 1, batch 1800, loss[loss=0.2006, simple_loss=0.4012, pruned_loss=6.825, over 7099.00 frames.], tot_loss[loss=0.2033, simple_loss=0.4066, pruned_loss=6.757, over 1417824.57 frames.], batch size: 28, lr: 2.91e-03
2022-05-13 19:54:58,670 INFO [train.py:812] (5/8) Epoch 1, batch 1850, loss[loss=0.2336, simple_loss=0.4671, pruned_loss=6.849, over 4863.00 frames.], tot_loss[loss=0.2012, simple_loss=0.4025, pruned_loss=6.76, over 1420006.01 frames.], batch size: 52, lr: 2.91e-03
2022-05-13 19:55:57,059 INFO [train.py:812] (5/8) Epoch 1, batch 1900, loss[loss=0.2249, simple_loss=0.4497, pruned_loss=6.81, over 7258.00 frames.], tot_loss[loss=0.1995, simple_loss=0.3991, pruned_loss=6.76, over 1420108.23 frames.], batch size: 19, lr: 2.90e-03
2022-05-13 19:56:55,442 INFO [train.py:812] (5/8) Epoch 1, batch 1950, loss[loss=0.1768, simple_loss=0.3537, pruned_loss=6.652, over 7328.00 frames.], tot_loss[loss=0.1976, simple_loss=0.3953, pruned_loss=6.755, over 1422956.42 frames.], batch size: 21, lr: 2.90e-03
2022-05-13 19:57:54,322 INFO [train.py:812] (5/8) Epoch 1, batch 2000, loss[loss=0.1955, simple_loss=0.391, pruned_loss=6.728, over 7196.00 frames.], tot_loss[loss=0.1965, simple_loss=0.393, pruned_loss=6.754, over 1423589.12 frames.], batch size: 16, lr: 2.89e-03
2022-05-13 19:58:53,055 INFO [train.py:812] (5/8) Epoch 1, batch 2050, loss[loss=0.2064, simple_loss=0.4127, pruned_loss=6.76, over 7192.00 frames.], tot_loss[loss=0.1957, simple_loss=0.3913, pruned_loss=6.756, over 1421559.87 frames.], batch size: 26, lr: 2.89e-03
2022-05-13 19:59:51,416 INFO [train.py:812] (5/8) Epoch 1, batch 2100, loss[loss=0.1924, simple_loss=0.3848, pruned_loss=6.751, over 7174.00 frames.], tot_loss[loss=0.1946, simple_loss=0.3893, pruned_loss=6.757, over 1418813.93 frames.], batch size: 18, lr: 2.88e-03
2022-05-13 20:00:49,528 INFO [train.py:812] (5/8) Epoch 1, batch 2150, loss[loss=0.2212, simple_loss=0.4425, pruned_loss=6.792, over 7347.00 frames.], tot_loss[loss=0.1934, simple_loss=0.3869, pruned_loss=6.755, over 1422357.03 frames.], batch size: 22, lr: 2.88e-03
2022-05-13 20:01:48,623 INFO [train.py:812] (5/8) Epoch 1, batch 2200, loss[loss=0.2048, simple_loss=0.4096, pruned_loss=6.679, over 7294.00 frames.], tot_loss[loss=0.1928, simple_loss=0.3856, pruned_loss=6.757, over 1421459.40 frames.], batch size: 25, lr: 2.87e-03
2022-05-13 20:02:47,462 INFO [train.py:812] (5/8) Epoch 1, batch 2250, loss[loss=0.179, simple_loss=0.3579, pruned_loss=6.803, over 7230.00 frames.], tot_loss[loss=0.1912, simple_loss=0.3824, pruned_loss=6.752, over 1420032.64 frames.], batch size: 21, lr: 2.86e-03
2022-05-13 20:03:45,864 INFO [train.py:812] (5/8) Epoch 1, batch 2300, loss[loss=0.1838, simple_loss=0.3676, pruned_loss=6.78, over 7268.00 frames.], tot_loss[loss=0.1909, simple_loss=0.3817, pruned_loss=6.75, over 1414782.55 frames.], batch size: 19, lr: 2.86e-03
2022-05-13 20:04:43,283 INFO [train.py:812] (5/8) Epoch 1, batch 2350, loss[loss=0.2123, simple_loss=0.4247, pruned_loss=6.72, over 5111.00 frames.], tot_loss[loss=0.1904, simple_loss=0.3807, pruned_loss=6.754, over 1414756.69 frames.], batch size: 52, lr: 2.85e-03
2022-05-13 20:05:42,786 INFO [train.py:812] (5/8) Epoch 1, batch 2400, loss[loss=0.1612, simple_loss=0.3224, pruned_loss=6.645, over 7431.00 frames.], tot_loss[loss=0.1895, simple_loss=0.379, pruned_loss=6.754, over 1411552.78 frames.], batch size: 20, lr: 2.85e-03
2022-05-13 20:06:41,409 INFO [train.py:812] (5/8) Epoch 1, batch 2450, loss[loss=0.2311, simple_loss=0.4621, pruned_loss=6.841, over 4810.00 frames.], tot_loss[loss=0.1892, simple_loss=0.3784, pruned_loss=6.757, over 1411276.09 frames.], batch size: 52, lr: 2.84e-03
2022-05-13 20:07:40,726 INFO [train.py:812] (5/8) Epoch 1, batch 2500, loss[loss=0.1955, simple_loss=0.3909, pruned_loss=6.772, over 7338.00 frames.], tot_loss[loss=0.1887, simple_loss=0.3774, pruned_loss=6.752, over 1417579.93 frames.], batch size: 20, lr: 2.84e-03
2022-05-13 20:08:39,340 INFO [train.py:812] (5/8) Epoch 1, batch 2550, loss[loss=0.1784, simple_loss=0.3569, pruned_loss=6.68, over 7383.00 frames.], tot_loss[loss=0.1879, simple_loss=0.3758, pruned_loss=6.746, over 1418376.06 frames.], batch size: 18, lr: 2.83e-03
2022-05-13 20:09:37,893 INFO [train.py:812] (5/8) Epoch 1, batch 2600, loss[loss=0.2052, simple_loss=0.4104, pruned_loss=6.733, over 7235.00 frames.], tot_loss[loss=0.1866, simple_loss=0.3733, pruned_loss=6.739, over 1421147.50 frames.], batch size: 20, lr: 2.83e-03
2022-05-13 20:10:35,860 INFO [train.py:812] (5/8) Epoch 1, batch 2650, loss[loss=0.1924, simple_loss=0.3848, pruned_loss=6.795, over 7230.00 frames.], tot_loss[loss=0.1854, simple_loss=0.3708, pruned_loss=6.74, over 1422588.03 frames.], batch size: 20, lr: 2.82e-03
2022-05-13 20:11:35,624 INFO [train.py:812] (5/8) Epoch 1, batch 2700, loss[loss=0.1915, simple_loss=0.383, pruned_loss=6.787, over 7147.00 frames.], tot_loss[loss=0.1849, simple_loss=0.3699, pruned_loss=6.74, over 1422262.71 frames.], batch size: 20, lr: 2.81e-03
2022-05-13 20:12:32,555 INFO [train.py:812] (5/8) Epoch 1, batch 2750, loss[loss=0.1946, simple_loss=0.3892, pruned_loss=6.865, over 7319.00 frames.], tot_loss[loss=0.1851, simple_loss=0.3701, pruned_loss=6.747, over 1423478.64 frames.], batch size: 20, lr: 2.81e-03
2022-05-13 20:13:32,037 INFO [train.py:812] (5/8) Epoch 1, batch 2800, loss[loss=0.1689, simple_loss=0.3378, pruned_loss=6.729, over 7152.00 frames.], tot_loss[loss=0.1842, simple_loss=0.3684, pruned_loss=6.745, over 1422544.55 frames.], batch size: 20, lr: 2.80e-03
2022-05-13 20:14:30,973 INFO [train.py:812] (5/8) Epoch 1, batch 2850, loss[loss=0.1704, simple_loss=0.3409, pruned_loss=6.626, over 7362.00 frames.], tot_loss[loss=0.1836, simple_loss=0.3671, pruned_loss=6.744, over 1425559.87 frames.], batch size: 19, lr: 2.80e-03
2022-05-13 20:15:28,487 INFO [train.py:812] (5/8) Epoch 1, batch 2900, loss[loss=0.1665, simple_loss=0.3331, pruned_loss=6.691, over 7325.00 frames.], tot_loss[loss=0.1845, simple_loss=0.3691, pruned_loss=6.747, over 1421492.94 frames.], batch size: 20, lr: 2.79e-03
2022-05-13 20:16:27,580 INFO [train.py:812] (5/8) Epoch 1, batch 2950, loss[loss=0.1717, simple_loss=0.3434, pruned_loss=6.667, over 7144.00 frames.], tot_loss[loss=0.1834, simple_loss=0.3667, pruned_loss=6.742, over 1417375.63 frames.], batch size: 26, lr: 2.78e-03
2022-05-13 20:17:26,812 INFO [train.py:812] (5/8) Epoch 1, batch 3000, loss[loss=0.3189, simple_loss=0.334, pruned_loss=1.519, over 7293.00 frames.], tot_loss[loss=0.2158, simple_loss=0.3641, pruned_loss=6.715, over 1420995.67 frames.], batch size: 17, lr: 2.78e-03
2022-05-13 20:17:26,813 INFO [train.py:832] (5/8) Computing validation loss
2022-05-13 20:17:34,929 INFO [train.py:841] (5/8) Epoch 1, validation: loss=2.094, simple_loss=0.4148, pruned_loss=1.887, over 698248.00 frames.
2022-05-13 20:18:33,865 INFO [train.py:812] (5/8) Epoch 1, batch 3050, loss[loss=0.2948, simple_loss=0.3989, pruned_loss=0.9536, over 6380.00 frames.], tot_loss[loss=0.2406, simple_loss=0.3732, pruned_loss=5.507, over 1419996.99 frames.], batch size: 38, lr: 2.77e-03
2022-05-13 20:19:33,921 INFO [train.py:812] (5/8) Epoch 1, batch 3100, loss[loss=0.2603, simple_loss=0.3948, pruned_loss=0.6296, over 7416.00 frames.], tot_loss[loss=0.2424, simple_loss=0.3688, pruned_loss=4.431, over 1425647.36 frames.], batch size: 21, lr: 2.77e-03
2022-05-13 20:20:32,545 INFO [train.py:812] (5/8) Epoch 1, batch 3150, loss[loss=0.2161, simple_loss=0.3621, pruned_loss=0.3506, over 7397.00 frames.], tot_loss[loss=0.2382, simple_loss=0.3668, pruned_loss=3.543, over 1426684.51 frames.], batch size: 21, lr: 2.76e-03
2022-05-13 20:21:30,566 INFO [train.py:812] (5/8) Epoch 1, batch 3200, loss[loss=0.2126, simple_loss=0.3719, pruned_loss=0.2667, over 7297.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3661, pruned_loss=2.832, over 1423777.62 frames.], batch size: 24, lr: 2.75e-03
2022-05-13 20:22:29,537 INFO [train.py:812] (5/8) Epoch 1, batch 3250, loss[loss=0.1966, simple_loss=0.3467, pruned_loss=0.2321, over 7145.00 frames.], tot_loss[loss=0.2261, simple_loss=0.3637, pruned_loss=2.26, over 1423657.45 frames.], batch size: 20, lr: 2.75e-03
2022-05-13 20:23:28,327 INFO [train.py:812] (5/8) Epoch 1, batch 3300, loss[loss=0.219, simple_loss=0.3861, pruned_loss=0.2597, over 7385.00 frames.], tot_loss[loss=0.2215, simple_loss=0.3637, pruned_loss=1.817, over 1418525.74 frames.], batch size: 23, lr: 2.74e-03
2022-05-13 20:24:25,730 INFO [train.py:812] (5/8) Epoch 1, batch 3350, loss[loss=0.2048, simple_loss=0.369, pruned_loss=0.2029, over 7286.00 frames.], tot_loss[loss=0.2171, simple_loss=0.3631, pruned_loss=1.457, over 1423304.58 frames.], batch size: 24, lr: 2.73e-03
2022-05-13 20:25:24,229 INFO [train.py:812] (5/8) Epoch 1, batch 3400, loss[loss=0.2042, simple_loss=0.3691, pruned_loss=0.197, over 7259.00 frames.], tot_loss[loss=0.2138, simple_loss=0.3632, pruned_loss=1.18, over 1423706.77 frames.], batch size: 19, lr: 2.73e-03
2022-05-13 20:26:22,126 INFO [train.py:812] (5/8) Epoch 1, batch 3450, loss[loss=0.2095, simple_loss=0.3796, pruned_loss=0.1966, over 7291.00 frames.], tot_loss[loss=0.2106, simple_loss=0.3624, pruned_loss=0.9617, over 1423518.25 frames.], batch size: 25, lr: 2.72e-03
2022-05-13 20:27:20,151 INFO [train.py:812] (5/8) Epoch 1, batch 3500, loss[loss=0.2032, simple_loss=0.3694, pruned_loss=0.1852, over 7157.00 frames.], tot_loss[loss=0.2075, simple_loss=0.3612, pruned_loss=0.7894, over 1420971.26 frames.], batch size: 26, lr: 2.72e-03
2022-05-13 20:28:19,220 INFO [train.py:812] (5/8) Epoch 1, batch 3550, loss[loss=0.2172, simple_loss=0.3937, pruned_loss=0.2041, over 7221.00 frames.], tot_loss[loss=0.2035, simple_loss=0.3576, pruned_loss=0.6514, over 1422831.02 frames.], batch size: 21, lr: 2.71e-03
2022-05-13 20:29:18,099 INFO [train.py:812] (5/8) Epoch 1, batch 3600, loss[loss=0.189, simple_loss=0.3415, pruned_loss=0.182, over 6985.00 frames.], tot_loss[loss=0.2011, simple_loss=0.3561, pruned_loss=0.5459, over 1421084.27 frames.], batch size: 16, lr: 2.70e-03
2022-05-13 20:30:25,532 INFO [train.py:812] (5/8) Epoch 1, batch 3650, loss[loss=0.2175, simple_loss=0.3967, pruned_loss=0.1911, over 7217.00 frames.], tot_loss[loss=0.199, simple_loss=0.3547, pruned_loss=0.4618, over 1421168.42 frames.], batch size: 21, lr: 2.70e-03
2022-05-13 20:32:10,077 INFO [train.py:812] (5/8) Epoch 1, batch 3700, loss[loss=0.2039, simple_loss=0.3718, pruned_loss=0.1797, over 6778.00 frames.], tot_loss[loss=0.1968, simple_loss=0.3528, pruned_loss=0.3944, over 1426350.53 frames.], batch size: 31, lr: 2.69e-03
2022-05-13 20:33:27,165 INFO [train.py:812] (5/8) Epoch 1, batch 3750, loss[loss=0.1879, simple_loss=0.3442, pruned_loss=0.1575, over 7283.00 frames.], tot_loss[loss=0.1951, simple_loss=0.3513, pruned_loss=0.3432, over 1418565.44 frames.], batch size: 18, lr: 2.68e-03
2022-05-13 20:34:26,653 INFO [train.py:812] (5/8) Epoch 1, batch 3800, loss[loss=0.1709, simple_loss=0.3156, pruned_loss=0.1306, over 7138.00 frames.], tot_loss[loss=0.1936, simple_loss=0.35, pruned_loss=0.3023, over 1417995.08 frames.], batch size: 17, lr: 2.68e-03
2022-05-13 20:35:25,744 INFO [train.py:812] (5/8) Epoch 1, batch 3850, loss[loss=0.1753, simple_loss=0.3217, pruned_loss=0.1443, over 7127.00 frames.], tot_loss[loss=0.1925, simple_loss=0.3491, pruned_loss=0.2687, over 1423280.69 frames.], batch size: 17, lr: 2.67e-03
2022-05-13 20:36:24,057 INFO [train.py:812] (5/8) Epoch 1, batch 3900, loss[loss=0.1957, simple_loss=0.3554, pruned_loss=0.1804, over 6783.00 frames.], tot_loss[loss=0.1924, simple_loss=0.35, pruned_loss=0.2445, over 1419532.25 frames.], batch size: 15, lr: 2.66e-03
2022-05-13 20:37:21,118 INFO [train.py:812] (5/8) Epoch 1, batch 3950, loss[loss=0.1768, simple_loss=0.3232, pruned_loss=0.1517, over 6784.00 frames.], tot_loss[loss=0.1906, simple_loss=0.3475, pruned_loss=0.2228, over 1417371.66 frames.], batch size: 15, lr: 2.66e-03
2022-05-13 20:38:27,952 INFO [train.py:812] (5/8) Epoch 1, batch 4000, loss[loss=0.1658, simple_loss=0.308, pruned_loss=0.1177, over 7311.00 frames.], tot_loss[loss=0.1904, simple_loss=0.348, pruned_loss=0.2064, over 1420110.39 frames.], batch size: 21, lr: 2.65e-03
2022-05-13 20:39:26,722 INFO [train.py:812] (5/8) Epoch 1, batch 4050, loss[loss=0.2105, simple_loss=0.3851, pruned_loss=0.1799, over 7077.00 frames.], tot_loss[loss=0.1899, simple_loss=0.3477, pruned_loss=0.1938, over 1420525.25 frames.], batch size: 28, lr: 2.64e-03
2022-05-13 20:40:25,260 INFO [train.py:812] (5/8) Epoch 1, batch 4100, loss[loss=0.1933, simple_loss=0.3535, pruned_loss=0.1653, over 7256.00 frames.], tot_loss[loss=0.1887, simple_loss=0.3459, pruned_loss=0.183, over 1420733.32 frames.], batch size: 19, lr: 2.64e-03
2022-05-13 20:41:23,930 INFO [train.py:812] (5/8) Epoch 1, batch 4150, loss[loss=0.1727, simple_loss=0.3222, pruned_loss=0.1161, over 7071.00 frames.], tot_loss[loss=0.1881, simple_loss=0.3455, pruned_loss=0.1741, over 1425343.30 frames.], batch size: 18, lr: 2.63e-03
2022-05-13 20:42:22,986 INFO [train.py:812] (5/8) Epoch 1, batch 4200, loss[loss=0.1898, simple_loss=0.3532, pruned_loss=0.1323, over 7205.00 frames.], tot_loss[loss=0.1876, simple_loss=0.3449, pruned_loss=0.1668, over 1424762.06 frames.], batch size: 22, lr: 2.63e-03
2022-05-13 20:43:21,441 INFO [train.py:812] (5/8) Epoch 1, batch 4250, loss[loss=0.1825, simple_loss=0.3383, pruned_loss=0.1333, over 7422.00 frames.], tot_loss[loss=0.1878, simple_loss=0.3456, pruned_loss=0.1626, over 1423074.46 frames.], batch size: 20, lr: 2.62e-03
2022-05-13 20:44:20,457 INFO [train.py:812] (5/8) Epoch 1, batch 4300, loss[loss=0.1868, simple_loss=0.3439, pruned_loss=0.1485, over 7107.00 frames.], tot_loss[loss=0.1879, simple_loss=0.3458, pruned_loss=0.159, over 1422591.06 frames.], batch size: 28, lr: 2.61e-03
2022-05-13 20:45:18,959 INFO [train.py:812] (5/8) Epoch 1, batch 4350, loss[loss=0.1847, simple_loss=0.3414, pruned_loss=0.1402, over 7440.00 frames.], tot_loss[loss=0.1878, simple_loss=0.3461, pruned_loss=0.1552, over 1426524.82 frames.], batch size: 20, lr: 2.61e-03
2022-05-13 20:46:18,354 INFO [train.py:812] (5/8) Epoch 1, batch 4400, loss[loss=0.1784, simple_loss=0.329, pruned_loss=0.1396, over 7283.00 frames.], tot_loss[loss=0.1877, simple_loss=0.3461, pruned_loss=0.1526, over 1423973.58 frames.], batch size: 18, lr: 2.60e-03
2022-05-13 20:47:17,287 INFO [train.py:812] (5/8) Epoch 1, batch 4450, loss[loss=0.1575, simple_loss=0.2994, pruned_loss=0.07789, over 7430.00 frames.], tot_loss[loss=0.1883, simple_loss=0.3472, pruned_loss=0.151, over 1423480.68 frames.], batch size: 20, lr: 2.59e-03
2022-05-13 20:48:16,733 INFO [train.py:812] (5/8) Epoch 1, batch 4500, loss[loss=0.1965, simple_loss=0.3603, pruned_loss=0.1641, over 6339.00 frames.], tot_loss[loss=0.1883, simple_loss=0.3475, pruned_loss=0.1492, over 1413963.02 frames.], batch size: 37, lr: 2.59e-03
2022-05-13 20:49:13,802 INFO [train.py:812] (5/8) Epoch 1, batch 4550, loss[loss=0.1945, simple_loss=0.3553, pruned_loss=0.169, over 4767.00 frames.], tot_loss[loss=0.189, simple_loss=0.3488, pruned_loss=0.1489, over 1394447.31 frames.], batch size: 52, lr: 2.58e-03
2022-05-13 20:50:25,938 INFO [train.py:812] (5/8) Epoch 2, batch 0, loss[loss=0.1909, simple_loss=0.3507, pruned_loss=0.155, over 7148.00 frames.], tot_loss[loss=0.1909, simple_loss=0.3507, pruned_loss=0.155, over 7148.00 frames.], batch size: 26, lr: 2.56e-03
2022-05-13 20:51:25,846 INFO [train.py:812] (5/8) Epoch 2, batch 50, loss[loss=0.1736, simple_loss=0.3262, pruned_loss=0.1046, over 7234.00 frames.], tot_loss[loss=0.184, simple_loss=0.3405, pruned_loss=0.1376, over 311774.24 frames.], batch size: 20, lr: 2.55e-03
2022-05-13 20:52:24,854 INFO [train.py:812] (5/8) Epoch 2, batch 100, loss[loss=0.1779, simple_loss=0.328, pruned_loss=0.139, over 7424.00 frames.], tot_loss[loss=0.1828, simple_loss=0.3385, pruned_loss=0.1359, over 560155.24 frames.], batch size: 20, lr: 2.54e-03
2022-05-13 20:53:23,905 INFO [train.py:812] (5/8) Epoch 2, batch 150, loss[loss=0.1731, simple_loss=0.322, pruned_loss=0.1204, over 7330.00 frames.], tot_loss[loss=0.1834, simple_loss=0.3397, pruned_loss=0.1358, over 751178.15 frames.], batch size: 20, lr: 2.54e-03
2022-05-13 20:54:21,374 INFO [train.py:812] (5/8) Epoch 2, batch 200, loss[loss=0.166, simple_loss=0.3081, pruned_loss=0.1198, over 7160.00 frames.], tot_loss[loss=0.1822, simple_loss=0.3377, pruned_loss=0.1335, over 901251.69 frames.], batch size: 19, lr: 2.53e-03
2022-05-13 20:55:19,831 INFO [train.py:812] (5/8) Epoch 2, batch 250, loss[loss=0.1814, simple_loss=0.3375, pruned_loss=0.1265, over 7375.00 frames.], tot_loss[loss=0.1816, simple_loss=0.3367, pruned_loss=0.1322, over 1015711.86 frames.], batch size: 23, lr: 2.53e-03
2022-05-13 20:56:18,132 INFO [train.py:812] (5/8) Epoch 2, batch 300, loss[loss=0.1666, simple_loss=0.3119, pruned_loss=0.1062, over 7268.00 frames.], tot_loss[loss=0.1824, simple_loss=0.3383, pruned_loss=0.1327, over 1104806.40 frames.], batch size: 19, lr: 2.52e-03
2022-05-13 20:57:16,218 INFO [train.py:812] (5/8) Epoch 2, batch 350, loss[loss=0.183, simple_loss=0.3414, pruned_loss=0.1227, over 7226.00 frames.], tot_loss[loss=0.1821, simple_loss=0.3379, pruned_loss=0.1319, over 1173896.30 frames.], batch size: 21, lr: 2.51e-03
2022-05-13 20:58:14,749 INFO [train.py:812] (5/8) Epoch 2, batch 400, loss[loss=0.2179, simple_loss=0.4018, pruned_loss=0.1699, over 7145.00 frames.], tot_loss[loss=0.1821, simple_loss=0.3378, pruned_loss=0.1315, over 1230795.64 frames.], batch size: 20, lr: 2.51e-03
2022-05-13 20:59:13,910 INFO [train.py:812] (5/8) Epoch 2, batch 450, loss[loss=0.178, simple_loss=0.3305, pruned_loss=0.1276, over 7144.00 frames.], tot_loss[loss=0.1821, simple_loss=0.338, pruned_loss=0.1312, over 1275552.33 frames.], batch size: 19, lr: 2.50e-03
2022-05-13 21:00:12,345 INFO [train.py:812] (5/8) Epoch 2, batch 500, loss[loss=0.1806, simple_loss=0.334, pruned_loss=0.1355, over 7168.00 frames.], tot_loss[loss=0.1817, simple_loss=0.3374, pruned_loss=0.1306, over 1307750.66 frames.], batch size: 18, lr: 2.49e-03
2022-05-13 21:01:12,103 INFO [train.py:812] (5/8) Epoch 2, batch 550, loss[loss=0.185, simple_loss=0.3429, pruned_loss=0.135, over 7360.00 frames.], tot_loss[loss=0.1813, simple_loss=0.3365, pruned_loss=0.13, over 1333000.77 frames.], batch size: 19, lr: 2.49e-03
2022-05-13 21:02:09,995 INFO [train.py:812] (5/8) Epoch 2, batch 600, loss[loss=0.1942, simple_loss=0.3595, pruned_loss=0.1439, over 7387.00 frames.], tot_loss[loss=0.1818, simple_loss=0.3376, pruned_loss=0.1303, over 1354088.84 frames.], batch size: 23, lr: 2.48e-03
2022-05-13 21:03:09,063 INFO [train.py:812] (5/8) Epoch 2, batch 650, loss[loss=0.1549, simple_loss=0.29, pruned_loss=0.0984, over 7281.00 frames.], tot_loss[loss=0.1817, simple_loss=0.3373, pruned_loss=0.1307, over 1367837.83 frames.], batch size: 18, lr: 2.48e-03
2022-05-13 21:04:08,339 INFO [train.py:812] (5/8) Epoch 2, batch 700, loss[loss=0.2069, simple_loss=0.3779, pruned_loss=0.1796, over 4716.00 frames.], tot_loss[loss=0.1814, simple_loss=0.3368, pruned_loss=0.1304, over 1379699.69 frames.], batch size: 52, lr: 2.47e-03
2022-05-13 21:05:07,288 INFO [train.py:812] (5/8) Epoch 2, batch 750, loss[loss=0.1802, simple_loss=0.3338, pruned_loss=0.133, over 7257.00 frames.], tot_loss[loss=0.1807, simple_loss=0.3357, pruned_loss=0.1288, over 1391532.21 frames.], batch size: 19, lr: 2.46e-03
2022-05-13 21:06:06,459 INFO [train.py:812] (5/8) Epoch 2, batch 800, loss[loss=0.1558, simple_loss=0.2924, pruned_loss=0.09609, over 7074.00 frames.], tot_loss[loss=0.1797, simple_loss=0.3339, pruned_loss=0.1272, over 1400742.66 frames.], batch size: 18, lr: 2.46e-03
2022-05-13 21:07:06,091 INFO [train.py:812] (5/8) Epoch 2, batch 850, loss[loss=0.1677, simple_loss=0.315, pruned_loss=0.1027, over 7329.00 frames.], tot_loss[loss=0.1788, simple_loss=0.3325, pruned_loss=0.1256, over 1408578.70 frames.], batch size: 20, lr: 2.45e-03
2022-05-13 21:08:05,127 INFO [train.py:812] (5/8) Epoch 2, batch 900, loss[loss=0.1684, simple_loss=0.3165, pruned_loss=0.1013, over 7426.00 frames.], tot_loss[loss=0.1789, simple_loss=0.3327, pruned_loss=0.1255, over 1412806.71 frames.], batch size: 20, lr: 2.45e-03
2022-05-13 21:09:04,126 INFO [train.py:812] (5/8) Epoch 2, batch 950, loss[loss=0.1513, simple_loss=0.2836, pruned_loss=0.09482, over 7261.00 frames.], tot_loss[loss=0.1788, simple_loss=0.3325, pruned_loss=0.1253, over 1415459.05 frames.], batch size: 19, lr: 2.44e-03
2022-05-13 21:10:02,103 INFO [train.py:812] (5/8) Epoch 2, batch 1000, loss[loss=0.1868, simple_loss=0.3486, pruned_loss=0.1253, over 6657.00 frames.], tot_loss[loss=0.178, simple_loss=0.3313, pruned_loss=0.1239, over 1417254.07 frames.], batch size: 31, lr: 2.43e-03
2022-05-13 21:11:00,322 INFO [train.py:812] (5/8) Epoch 2, batch 1050, loss[loss=0.1515, simple_loss=0.2869, pruned_loss=0.08012, over 7420.00 frames.], tot_loss[loss=0.1778, simple_loss=0.3309, pruned_loss=0.1232, over 1418574.01 frames.], batch size: 20, lr: 2.43e-03
2022-05-13 21:11:59,253 INFO [train.py:812] (5/8) Epoch 2, batch 1100, loss[loss=0.1546, simple_loss=0.2902, pruned_loss=0.09458, over 7154.00 frames.], tot_loss[loss=0.1772, simple_loss=0.3302, pruned_loss=0.1214, over 1419554.26 frames.], batch size: 18, lr: 2.42e-03
2022-05-13 21:12:57,569 INFO [train.py:812] (5/8) Epoch 2, batch 1150, loss[loss=0.1826, simple_loss=0.3389, pruned_loss=0.1315, over 7245.00 frames.], tot_loss[loss=0.1771, simple_loss=0.3299, pruned_loss=0.1213, over 1423261.01 frames.], batch size: 20, lr: 2.41e-03
2022-05-13 21:13:56,178 INFO [train.py:812] (5/8) Epoch 2, batch 1200, loss[loss=0.1679, simple_loss=0.3152, pruned_loss=0.1029, over 6980.00 frames.], tot_loss[loss=0.1772, simple_loss=0.3301, pruned_loss=0.1219, over 1422761.11 frames.], batch size: 28, lr: 2.41e-03
2022-05-13 21:14:54,775 INFO [train.py:812] (5/8) Epoch 2, batch 1250, loss[loss=0.1743, simple_loss=0.3226, pruned_loss=0.1305, over 7283.00 frames.], tot_loss[loss=0.1775, simple_loss=0.3307, pruned_loss=0.1217, over 1422930.91 frames.], batch size: 18, lr: 2.40e-03
2022-05-13 21:15:53,347 INFO [train.py:812] (5/8) Epoch 2, batch 1300, loss[loss=0.1937, simple_loss=0.361, pruned_loss=0.1323, over 7220.00 frames.], tot_loss[loss=0.1786, simple_loss=0.3326, pruned_loss=0.1234, over 1417377.23 frames.], batch size: 21, lr: 2.40e-03
2022-05-13 21:16:52,423 INFO [train.py:812] (5/8) Epoch 2, batch 1350, loss[loss=0.1658, simple_loss=0.3085, pruned_loss=0.1153, over 7291.00 frames.], tot_loss[loss=0.1777, simple_loss=0.3309, pruned_loss=0.1223, over 1420716.34 frames.], batch size: 17, lr: 2.39e-03
2022-05-13 21:17:49,941 INFO [train.py:812] (5/8) Epoch 2, batch 1400, loss[loss=0.2094, simple_loss=0.3832, pruned_loss=0.1777, over 7236.00 frames.], tot_loss[loss=0.1774, simple_loss=0.3303, pruned_loss=0.1221, over 1419266.27 frames.], batch size: 21, lr: 2.39e-03
2022-05-13 21:18:49,259 INFO [train.py:812] (5/8) Epoch 2, batch 1450, loss[loss=0.3456, simple_loss=0.3813, pruned_loss=0.155, over 7196.00 frames.], tot_loss[loss=0.2, simple_loss=0.3313, pruned_loss=0.1239, over 1422633.72 frames.], batch size: 26, lr: 2.38e-03
2022-05-13 21:19:47,677 INFO [train.py:812] (5/8) Epoch 2, batch 1500, loss[loss=0.3187, simple_loss=0.3496, pruned_loss=0.1439, over 6607.00 frames.], tot_loss[loss=0.2211, simple_loss=0.333, pruned_loss=0.1242, over 1423247.05 frames.], batch size: 38, lr: 2.37e-03
2022-05-13 21:20:45,892 INFO [train.py:812] (5/8) Epoch 2, batch 1550, loss[loss=0.254, simple_loss=0.3113, pruned_loss=0.09834, over 7432.00 frames.], tot_loss[loss=0.2366, simple_loss=0.3342, pruned_loss=0.1236, over 1426495.67 frames.], batch size: 20, lr: 2.37e-03
2022-05-13 21:21:43,116 INFO [train.py:812] (5/8) Epoch 2, batch 1600, loss[loss=0.275, simple_loss=0.3252, pruned_loss=0.1124, over 7167.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3335, pruned_loss=0.1224, over 1425473.04 frames.], batch size: 18, lr: 2.36e-03
2022-05-13 21:22:41,922 INFO [train.py:812] (5/8) Epoch 2, batch 1650, loss[loss=0.2702, simple_loss=0.3302, pruned_loss=0.1051, over 7420.00 frames.], tot_loss[loss=0.2545, simple_loss=0.3326, pruned_loss=0.121, over 1425295.00 frames.], batch size: 20, lr: 2.36e-03
2022-05-13 21:23:40,002 INFO [train.py:812] (5/8) Epoch 2, batch 1700, loss[loss=0.3513, simple_loss=0.3778, pruned_loss=0.1624, over 7416.00 frames.], tot_loss[loss=0.2617, simple_loss=0.3332, pruned_loss=0.1206, over 1424122.91 frames.], batch size: 21, lr: 2.35e-03
2022-05-13 21:24:38,975 INFO [train.py:812] (5/8) Epoch 2, batch 1750, loss[loss=0.2424, simple_loss=0.2923, pruned_loss=0.09625, over 7286.00 frames.], tot_loss[loss=0.2671, simple_loss=0.3342, pruned_loss=0.1199, over 1424349.27 frames.], batch size: 18, lr: 2.34e-03
2022-05-13 21:25:38,367 INFO [train.py:812] (5/8) Epoch 2, batch 1800, loss[loss=0.2181, simple_loss=0.2851, pruned_loss=0.07557, over 7361.00 frames.], tot_loss[loss=0.2723, simple_loss=0.3351, pruned_loss=0.1202, over 1424973.01 frames.], batch size: 19, lr: 2.34e-03
2022-05-13 21:26:37,486 INFO [train.py:812] (5/8) Epoch 2, batch 1850, loss[loss=0.2743, simple_loss=0.3299, pruned_loss=0.1093, over 7321.00 frames.], tot_loss[loss=0.2742, simple_loss=0.334, pruned_loss=0.1193, over 1426199.60 frames.], batch size: 20, lr: 2.33e-03
2022-05-13 21:27:35,687 INFO [train.py:812] (5/8) Epoch 2, batch 1900, loss[loss=0.2413, simple_loss=0.2928, pruned_loss=0.09487, over 7008.00 frames.], tot_loss[loss=0.2753, simple_loss=0.3337, pruned_loss=0.1178, over 1429286.43 frames.], batch size: 16, lr: 2.33e-03
2022-05-13 21:28:33,670 INFO [train.py:812] (5/8) Epoch 2, batch 1950, loss[loss=0.2432, simple_loss=0.3032, pruned_loss=0.09163, over 7276.00 frames.], tot_loss[loss=0.2775, simple_loss=0.3342, pruned_loss=0.1177, over 1429429.93 frames.], batch size: 18, lr: 2.32e-03
2022-05-13 21:29:31,784 INFO [train.py:812] (5/8) Epoch 2, batch 2000, loss[loss=0.2602, simple_loss=0.3196, pruned_loss=0.1004, over 7123.00 frames.], tot_loss[loss=0.2786, simple_loss=0.3345, pruned_loss=0.1171, over 1423593.32 frames.], batch size: 21, lr: 2.32e-03
2022-05-13 21:30:31,549 INFO [train.py:812] (5/8) Epoch 2, batch 2050, loss[loss=0.3021, simple_loss=0.3554, pruned_loss=0.1244, over 7060.00 frames.], tot_loss[loss=0.2796, simple_loss=0.3343, pruned_loss=0.1169, over 1424699.21 frames.], batch size: 28, lr: 2.31e-03
2022-05-13 21:31:31,044 INFO [train.py:812] (5/8) Epoch 2, batch 2100, loss[loss=0.2685, simple_loss=0.309, pruned_loss=0.114, over 7406.00 frames.], tot_loss[loss=0.2786, simple_loss=0.333, pruned_loss=0.1156, over 1424696.86 frames.], batch size: 18, lr: 2.31e-03
2022-05-13 21:32:30,576 INFO [train.py:812] (5/8) Epoch 2, batch 2150, loss[loss=0.2286, simple_loss=0.2946, pruned_loss=0.08126, over 7409.00 frames.], tot_loss[loss=0.2755, simple_loss=0.3305, pruned_loss=0.1129, over 1423926.51 frames.], batch size: 21, lr: 2.30e-03
2022-05-13 21:33:29,450 INFO [train.py:812] (5/8) Epoch 2, batch 2200, loss[loss=0.3033, simple_loss=0.3503, pruned_loss=0.1282, over 7134.00 frames.], tot_loss[loss=0.2759, simple_loss=0.3305, pruned_loss=0.1127, over 1423096.21 frames.], batch size: 21, lr: 2.29e-03
2022-05-13 21:34:29,304 INFO [train.py:812] (5/8) Epoch 2, batch 2250, loss[loss=0.2525, simple_loss=0.3205, pruned_loss=0.09221, over 7223.00 frames.], tot_loss[loss=0.2743, simple_loss=0.3292, pruned_loss=0.1113, over 1424521.84 frames.], batch size: 21, lr: 2.29e-03
2022-05-13 21:35:27,783 INFO [train.py:812] (5/8) Epoch 2, batch 2300, loss[loss=0.258, simple_loss=0.3263, pruned_loss=0.09485, over 7219.00 frames.], tot_loss[loss=0.274, simple_loss=0.3291, pruned_loss=0.1107, over 1425331.17 frames.], batch size: 22, lr: 2.28e-03
2022-05-13 21:36:26,825 INFO [train.py:812] (5/8) Epoch 2, batch 2350, loss[loss=0.2748, simple_loss=0.3436, pruned_loss=0.103, over 7237.00 frames.], tot_loss[loss=0.277, simple_loss=0.3312, pruned_loss=0.1124, over 1424084.81 frames.], batch size: 20, lr: 2.28e-03
2022-05-13 21:37:24,978 INFO [train.py:812] (5/8) Epoch 2, batch 2400, loss[loss=0.2509, simple_loss=0.3224, pruned_loss=0.08973, over 7313.00 frames.], tot_loss[loss=0.2767, simple_loss=0.3315, pruned_loss=0.1117, over 1423164.51 frames.], batch size: 21, lr: 2.27e-03
2022-05-13 21:38:23,788 INFO [train.py:812] (5/8) Epoch 2, batch 2450, loss[loss=0.3191, simple_loss=0.3655, pruned_loss=0.1363, over 7333.00 frames.], tot_loss[loss=0.2765, simple_loss=0.3315, pruned_loss=0.1114, over 1426579.13 frames.], batch size: 21, lr: 2.27e-03
2022-05-13 21:39:23,283 INFO [train.py:812] (5/8) Epoch 2, batch 2500, loss[loss=0.2288, simple_loss=0.31, pruned_loss=0.07379, over 7172.00 frames.], tot_loss[loss=0.2756, simple_loss=0.3316, pruned_loss=0.1103, over 1426985.51 frames.], batch size: 26, lr: 2.26e-03
2022-05-13 21:40:21,937 INFO [train.py:812] (5/8) Epoch 2, batch 2550, loss[loss=0.2447, simple_loss=0.2944, pruned_loss=0.09743, over 6990.00 frames.], tot_loss[loss=0.2755, simple_loss=0.3312, pruned_loss=0.1102, over 1427184.23 frames.], batch size: 16, lr: 2.26e-03
2022-05-13 21:41:21,068 INFO [train.py:812] (5/8) Epoch 2, batch 2600, loss[loss=0.2638, simple_loss=0.342, pruned_loss=0.09278, over 7190.00 frames.], tot_loss[loss=0.2752, simple_loss=0.3312, pruned_loss=0.1099, over 1429088.41 frames.], batch size: 26, lr: 2.25e-03
2022-05-13 21:42:20,633 INFO [train.py:812] (5/8) Epoch 2, batch 2650, loss[loss=0.289, simple_loss=0.3499, pruned_loss=0.114, over 6490.00 frames.], tot_loss[loss=0.275, simple_loss=0.331, pruned_loss=0.1097, over 1428164.98 frames.], batch size: 37, lr: 2.25e-03
2022-05-13 21:43:18,319 INFO [train.py:812] (5/8) Epoch 2, batch 2700, loss[loss=0.3689, simple_loss=0.4096, pruned_loss=0.1642, over 6828.00 frames.], tot_loss[loss=0.2737, simple_loss=0.3303, pruned_loss=0.1087, over 1428014.58 frames.], batch size: 31, lr: 2.24e-03
2022-05-13 21:44:17,964 INFO [train.py:812] (5/8) Epoch 2, batch 2750, loss[loss=0.2756, simple_loss=0.3374, pruned_loss=0.1069, over 7292.00 frames.], tot_loss[loss=0.2733, simple_loss=0.3298, pruned_loss=0.1086, over 1424059.22 frames.], batch size: 24, lr: 2.24e-03
2022-05-13 21:45:15,754 INFO [train.py:812] (5/8) Epoch 2, batch 2800, loss[loss=0.3221, simple_loss=0.3711, pruned_loss=0.1366, over 7187.00 frames.], tot_loss[loss=0.2717, simple_loss=0.3292, pruned_loss=0.1072, over 1427157.17 frames.], batch size: 23, lr: 2.23e-03
2022-05-13 21:46:14,844 INFO [train.py:812] (5/8) Epoch 2, batch 2850, loss[loss=0.2573, simple_loss=0.3189, pruned_loss=0.09781, over 7302.00 frames.], tot_loss[loss=0.2698, simple_loss=0.328, pruned_loss=0.1059, over 1426939.53 frames.], batch size: 24, lr: 2.23e-03
2022-05-13 21:47:13,475 INFO [train.py:812] (5/8) Epoch 2, batch 2900, loss[loss=0.2391, simple_loss=0.3116, pruned_loss=0.08331, over 7228.00 frames.], tot_loss[loss=0.2723, simple_loss=0.3299, pruned_loss=0.1074, over 1421064.84 frames.], batch size: 20, lr: 2.22e-03
2022-05-13 21:48:11,747 INFO [train.py:812] (5/8) Epoch 2, batch 2950, loss[loss=0.217, simple_loss=0.2909, pruned_loss=0.07152, over 7234.00 frames.], tot_loss[loss=0.2701, simple_loss=0.3286, pruned_loss=0.1059, over 1421738.02 frames.], batch size: 20, lr: 2.22e-03
2022-05-13 21:49:10,836 INFO [train.py:812] (5/8) Epoch 2, batch 3000, loss[loss=0.2201, simple_loss=0.2848, pruned_loss=0.07768, over 7292.00 frames.], tot_loss[loss=0.2688, simple_loss=0.3276, pruned_loss=0.1051, over 1425123.16 frames.], batch size: 17, lr: 2.21e-03
2022-05-13 21:49:10,837 INFO [train.py:832] (5/8) Computing validation loss
2022-05-13 21:49:18,580 INFO [train.py:841] (5/8) Epoch 2, validation: loss=0.2016, simple_loss=0.2977, pruned_loss=0.0527, over 698248.00 frames.
2022-05-13 21:50:17,416 INFO [train.py:812] (5/8) Epoch 2, batch 3050, loss[loss=0.2338, simple_loss=0.3108, pruned_loss=0.07843, over 7284.00 frames.], tot_loss[loss=0.2688, simple_loss=0.3275, pruned_loss=0.105, over 1420794.91 frames.], batch size: 18, lr: 2.20e-03
2022-05-13 21:51:15,123 INFO [train.py:812] (5/8) Epoch 2, batch 3100, loss[loss=0.3405, simple_loss=0.3595, pruned_loss=0.1608, over 4893.00 frames.], tot_loss[loss=0.2691, simple_loss=0.328, pruned_loss=0.1052, over 1420481.61 frames.], batch size: 52, lr: 2.20e-03
2022-05-13 21:52:13,941 INFO [train.py:812] (5/8) Epoch 2, batch 3150, loss[loss=0.2679, simple_loss=0.3149, pruned_loss=0.1105, over 6759.00 frames.], tot_loss[loss=0.2694, simple_loss=0.3283, pruned_loss=0.1053, over 1422692.60 frames.], batch size: 15, lr: 2.19e-03
2022-05-13 21:53:13,039 INFO [train.py:812] (5/8) Epoch 2, batch 3200, loss[loss=0.2838, simple_loss=0.3289, pruned_loss=0.1194, over 4951.00 frames.], tot_loss[loss=0.2722, simple_loss=0.3305, pruned_loss=0.107, over 1413066.31 frames.], batch size: 53, lr: 2.19e-03
2022-05-13 21:54:12,612 INFO [train.py:812] (5/8) Epoch 2, batch 3250, loss[loss=0.2994, simple_loss=0.3521, pruned_loss=0.1233, over 7214.00 frames.], tot_loss[loss=0.2716, simple_loss=0.3303, pruned_loss=0.1064, over 1415696.94 frames.], batch size: 23, lr: 2.18e-03
2022-05-13 21:55:12,233 INFO [train.py:812] (5/8) Epoch 2, batch 3300, loss[loss=0.2551, simple_loss=0.3216, pruned_loss=0.09429, over 7195.00 frames.], tot_loss[loss=0.2693, simple_loss=0.3289, pruned_loss=0.1049, over 1420005.41 frames.], batch size: 22, lr: 2.18e-03
2022-05-13 21:56:11,985 INFO [train.py:812] (5/8) Epoch 2, batch 3350, loss[loss=0.2718, simple_loss=0.346, pruned_loss=0.09878, over 7184.00 frames.], tot_loss[loss=0.2682, simple_loss=0.3286, pruned_loss=0.1039, over 1423022.70 frames.], batch size: 26, lr: 2.18e-03
2022-05-13 21:57:11,183 INFO [train.py:812] (5/8) Epoch 2, batch 3400, loss[loss=0.2495, simple_loss=0.3068, pruned_loss=0.09607, over 7116.00 frames.], tot_loss[loss=0.2665, simple_loss=0.3272, pruned_loss=0.1029, over 1424989.96 frames.], batch size: 17, lr: 2.17e-03
2022-05-13 21:58:14,486 INFO [train.py:812] (5/8) Epoch 2, batch 3450, loss[loss=0.3648, simple_loss=0.3983, pruned_loss=0.1657, over 7321.00 frames.], tot_loss[loss=0.267, simple_loss=0.3279, pruned_loss=0.1031, over 1427437.39 frames.], batch size: 24, lr: 2.17e-03
2022-05-13 21:59:13,378 INFO [train.py:812] (5/8) Epoch 2, batch 3500, loss[loss=0.2859, simple_loss=0.3354, pruned_loss=0.1181, over 6372.00 frames.], tot_loss[loss=0.2678, simple_loss=0.3283, pruned_loss=0.1036, over 1423612.69 frames.], batch size: 37, lr: 2.16e-03
2022-05-13 22:00:12,696 INFO [train.py:812] (5/8) Epoch 2, batch 3550, loss[loss=0.2804, simple_loss=0.3473, pruned_loss=0.1068, over 7307.00 frames.], tot_loss[loss=0.2668, simple_loss=0.3276, pruned_loss=0.1029, over 1423777.99 frames.], batch size: 25, lr: 2.16e-03
2022-05-13 22:01:11,592 INFO [train.py:812] (5/8) Epoch 2, batch 3600, loss[loss=0.2689, simple_loss=0.3314, pruned_loss=0.1032, over 7244.00 frames.], tot_loss[loss=0.2666, simple_loss=0.3279, pruned_loss=0.1027, over 1425173.49 frames.], batch size: 20, lr: 2.15e-03
2022-05-13 22:02:11,441 INFO [train.py:812] (5/8) Epoch 2, batch 3650, loss[loss=0.229, simple_loss=0.2895, pruned_loss=0.08426, over 6802.00 frames.], tot_loss[loss=0.2665, simple_loss=0.3273, pruned_loss=0.1029, over 1426592.07 frames.], batch size: 15, lr: 2.15e-03
2022-05-13 22:03:10,511 INFO [train.py:812] (5/8) Epoch 2, batch 3700, loss[loss=0.2192, simple_loss=0.291, pruned_loss=0.07371, over 7163.00 frames.], tot_loss[loss=0.2662, simple_loss=0.3274, pruned_loss=0.1025, over 1428630.84 frames.], batch size: 19, lr: 2.14e-03
2022-05-13 22:04:09,799 INFO [train.py:812] (5/8) Epoch 2, batch 3750, loss[loss=0.3072, simple_loss=0.3697, pruned_loss=0.1223, over 7286.00 frames.], tot_loss[loss=0.2662, simple_loss=0.3278, pruned_loss=0.1022, over 1429581.37 frames.], batch size: 24, lr: 2.14e-03
2022-05-13 22:05:09,265 INFO [train.py:812] (5/8) Epoch 2, batch 3800, loss[loss=0.2054, simple_loss=0.2757, pruned_loss=0.06752, over 6737.00 frames.], tot_loss[loss=0.2651, simple_loss=0.3273, pruned_loss=0.1015, over 1428980.26 frames.], batch size: 15, lr: 2.13e-03
2022-05-13 22:06:07,959 INFO [train.py:812] (5/8) Epoch 2, batch 3850, loss[loss=0.309, simple_loss=0.3634, pruned_loss=0.1273, over 7099.00 frames.], tot_loss[loss=0.2653, simple_loss=0.3281, pruned_loss=0.1012, over 1430631.26 frames.], batch size: 26, lr: 2.13e-03
2022-05-13 22:07:06,188 INFO [train.py:812] (5/8) Epoch 2, batch 3900, loss[loss=0.2683, simple_loss=0.3228, pruned_loss=0.1069, over 7319.00 frames.], tot_loss[loss=0.2643, simple_loss=0.3274, pruned_loss=0.1006, over 1429662.76 frames.], batch size: 24, lr: 2.12e-03
2022-05-13 22:08:05,732 INFO [train.py:812] (5/8) Epoch 2, batch 3950, loss[loss=0.2769, simple_loss=0.3389, pruned_loss=0.1074, over 7104.00 frames.], tot_loss[loss=0.2636, simple_loss=0.3266, pruned_loss=0.1003, over 1427311.84 frames.], batch size: 21, lr: 2.12e-03
2022-05-13 22:09:04,767 INFO [train.py:812] (5/8) Epoch 2, batch 4000, loss[loss=0.259, simple_loss=0.3284, pruned_loss=0.09486, over 7200.00 frames.], tot_loss[loss=0.2632, simple_loss=0.3262, pruned_loss=0.1001, over 1428001.64 frames.], batch size: 22, lr: 2.11e-03
2022-05-13 22:10:02,677 INFO [train.py:812] (5/8) Epoch 2, batch 4050, loss[loss=0.3182, simple_loss=0.3677, pruned_loss=0.1343, over 6819.00 frames.], tot_loss[loss=0.2642, simple_loss=0.3268, pruned_loss=0.1008, over 1426597.60 frames.], batch size: 31, lr: 2.11e-03
2022-05-13 22:11:01,211 INFO [train.py:812] (5/8) Epoch 2, batch 4100, loss[loss=0.2772, simple_loss=0.3437, pruned_loss=0.1053, over 7217.00 frames.], tot_loss[loss=0.2637, simple_loss=0.3263, pruned_loss=0.1005, over 1420661.03 frames.], batch size: 21, lr: 2.10e-03
2022-05-13 22:11:59,872 INFO [train.py:812] (5/8) Epoch 2, batch 4150, loss[loss=0.2916, simple_loss=0.3659, pruned_loss=0.1087, over 6774.00 frames.], tot_loss[loss=0.263, simple_loss=0.3257, pruned_loss=0.1002, over 1419856.02 frames.], batch size: 31, lr: 2.10e-03
2022-05-13 22:12:58,519 INFO [train.py:812] (5/8) Epoch 2, batch 4200, loss[loss=0.1945, simple_loss=0.2733, pruned_loss=0.05786, over 7283.00 frames.], tot_loss[loss=0.2622, simple_loss=0.3248, pruned_loss=0.09978, over 1417283.87 frames.], batch size: 18, lr: 2.10e-03
2022-05-13 22:13:58,088 INFO [train.py:812] (5/8) Epoch 2, batch 4250, loss[loss=0.2473, simple_loss=0.3019, pruned_loss=0.09642, over 7291.00 frames.], tot_loss[loss=0.263, simple_loss=0.3249, pruned_loss=0.1005, over 1413199.71 frames.], batch size: 18, lr: 2.09e-03
2022-05-13 22:14:56,766 INFO [train.py:812] (5/8) Epoch 2, batch 4300, loss[loss=0.2895, simple_loss=0.3429, pruned_loss=0.1181, over 7277.00 frames.], tot_loss[loss=0.2632, simple_loss=0.3251, pruned_loss=0.1006, over 1413434.86 frames.], batch size: 25, lr: 2.09e-03
2022-05-13 22:15:55,431 INFO [train.py:812] (5/8) Epoch 2, batch 4350, loss[loss=0.1927, simple_loss=0.2637, pruned_loss=0.06083, over 6993.00 frames.], tot_loss[loss=0.2617, simple_loss=0.3242, pruned_loss=0.0996, over 1414208.50 frames.], batch size: 16, lr: 2.08e-03
2022-05-13 22:16:54,207 INFO [train.py:812] (5/8) Epoch 2, batch 4400, loss[loss=0.2583, simple_loss=0.3334, pruned_loss=0.09157, over 7312.00 frames.], tot_loss[loss=0.2607, simple_loss=0.3234, pruned_loss=0.09902, over 1409167.01 frames.], batch size: 21, lr: 2.08e-03
2022-05-13 22:17:52,790 INFO [train.py:812] (5/8) Epoch 2, batch 4450, loss[loss=0.2753, simple_loss=0.3288, pruned_loss=0.1109, over 6412.00 frames.], tot_loss[loss=0.2612, simple_loss=0.3239, pruned_loss=0.09923, over 1401692.57 frames.], batch size: 37, lr: 2.07e-03
2022-05-13 22:18:50,562 INFO [train.py:812] (5/8) Epoch 2, batch 4500, loss[loss=0.2647, simple_loss=0.3338, pruned_loss=0.09777, over 6211.00 frames.], tot_loss[loss=0.2615, simple_loss=0.3238, pruned_loss=0.09961, over 1386690.91 frames.], batch size: 37, lr: 2.07e-03
2022-05-13 22:19:49,227 INFO [train.py:812] (5/8) Epoch 2, batch 4550, loss[loss=0.3066, simple_loss=0.3495, pruned_loss=0.1318, over 5041.00 frames.], tot_loss[loss=0.2642, simple_loss=0.3258, pruned_loss=0.1013, over 1356199.21 frames.], batch size: 52, lr: 2.06e-03
2022-05-13 22:20:58,925 INFO [train.py:812] (5/8) Epoch 3, batch 0, loss[loss=0.2071, simple_loss=0.2842, pruned_loss=0.06494, over 7301.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2842, pruned_loss=0.06494, over 7301.00 frames.], batch size: 17, lr: 2.02e-03
2022-05-13 22:21:58,060 INFO [train.py:812] (5/8) Epoch 3, batch 50, loss[loss=0.3012, simple_loss=0.3635, pruned_loss=0.1195, over 7285.00 frames.], tot_loss[loss=0.259, simple_loss=0.322, pruned_loss=0.09806, over 321707.03 frames.], batch size: 25, lr: 2.02e-03
2022-05-13 22:22:56,158 INFO [train.py:812] (5/8) Epoch 3, batch 100, loss[loss=0.2681, simple_loss=0.3079, pruned_loss=0.1141, over 6988.00 frames.], tot_loss[loss=0.2567, simple_loss=0.3219, pruned_loss=0.09571, over 568912.89 frames.], batch size: 16, lr: 2.01e-03
2022-05-13 22:23:56,092 INFO [train.py:812] (5/8) Epoch 3, batch 150, loss[loss=0.2963, simple_loss=0.3491, pruned_loss=0.1218, over 6715.00 frames.], tot_loss[loss=0.2528, simple_loss=0.318, pruned_loss=0.09385, over 760970.38 frames.], batch size: 31, lr: 2.01e-03
2022-05-13 22:24:53,584 INFO [train.py:812] (5/8) Epoch 3, batch 200, loss[loss=0.2017, simple_loss=0.2758, pruned_loss=0.06382, over 6762.00 frames.], tot_loss[loss=0.2511, simple_loss=0.3167, pruned_loss=0.09279, over 900128.61 frames.], batch size: 15, lr: 2.00e-03
2022-05-13 22:25:53,083 INFO [train.py:812] (5/8) Epoch 3, batch 250, loss[loss=0.235, simple_loss=0.3079, pruned_loss=0.08102, over 7366.00 frames.], tot_loss[loss=0.253, simple_loss=0.3185, pruned_loss=0.09374, over 1011166.49 frames.], batch size: 19, lr: 2.00e-03
2022-05-13 22:26:52,116 INFO [train.py:812] (5/8) Epoch 3, batch 300, loss[loss=0.2997, simple_loss=0.3548, pruned_loss=0.1223, over 6804.00 frames.], tot_loss[loss=0.2557, simple_loss=0.3206, pruned_loss=0.09537, over 1101059.51 frames.], batch size: 31, lr: 2.00e-03
2022-05-13 22:27:51,974 INFO [train.py:812] (5/8) Epoch 3, batch 350, loss[loss=0.2918, simple_loss=0.3536, pruned_loss=0.115, over 7316.00 frames.], tot_loss[loss=0.2568, simple_loss=0.3216, pruned_loss=0.09603, over 1171755.43 frames.], batch size: 21, lr: 1.99e-03
2022-05-13 22:29:00,803 INFO [train.py:812] (5/8) Epoch 3, batch 400, loss[loss=0.2322, simple_loss=0.3048, pruned_loss=0.07977, over 7296.00 frames.], tot_loss[loss=0.2574, simple_loss=0.3221, pruned_loss=0.09636, over 1223138.63 frames.], batch size: 24, lr: 1.99e-03
2022-05-13 22:29:59,462 INFO [train.py:812] (5/8) Epoch 3, batch 450, loss[loss=0.2747, simple_loss=0.3332, pruned_loss=0.1081, over 7199.00 frames.], tot_loss[loss=0.2568, simple_loss=0.3219, pruned_loss=0.09581, over 1263362.17 frames.], batch size: 22, lr: 1.98e-03
2022-05-13 22:31:07,389 INFO [train.py:812] (5/8) Epoch 3, batch 500, loss[loss=0.2118, simple_loss=0.2827, pruned_loss=0.07049, over 6994.00 frames.], tot_loss[loss=0.2555, simple_loss=0.321, pruned_loss=0.09493, over 1302115.88 frames.], batch size: 16, lr: 1.98e-03
2022-05-13 22:32:54,395 INFO [train.py:812] (5/8) Epoch 3, batch 550, loss[loss=0.2506, simple_loss=0.3216, pruned_loss=0.08984, over 7222.00 frames.], tot_loss[loss=0.2553, simple_loss=0.3212, pruned_loss=0.09473, over 1331971.79 frames.], batch size: 21, lr: 1.98e-03
2022-05-13 22:34:03,168 INFO [train.py:812] (5/8) Epoch 3, batch 600, loss[loss=0.2952, simple_loss=0.365, pruned_loss=0.1127, over 7297.00 frames.], tot_loss[loss=0.255, simple_loss=0.3204, pruned_loss=0.09479, over 1352497.30 frames.], batch size: 25, lr: 1.97e-03
2022-05-13 22:35:02,656 INFO [train.py:812] (5/8) Epoch 3, batch 650, loss[loss=0.2293, simple_loss=0.2945, pruned_loss=0.08208, over 7351.00 frames.], tot_loss[loss=0.2531, simple_loss=0.3189, pruned_loss=0.09366, over 1367415.98 frames.], batch size: 19, lr: 1.97e-03
2022-05-13 22:36:02,056 INFO [train.py:812] (5/8) Epoch 3, batch 700, loss[loss=0.2388, simple_loss=0.3222, pruned_loss=0.07765, over 7220.00 frames.], tot_loss[loss=0.2532, simple_loss=0.3193, pruned_loss=0.09357, over 1378148.72 frames.], batch size: 21, lr: 1.96e-03
2022-05-13 22:37:01,818 INFO [train.py:812] (5/8) Epoch 3, batch 750, loss[loss=0.2861, simple_loss=0.353, pruned_loss=0.1095, over 7197.00 frames.], tot_loss[loss=0.2539, simple_loss=0.3199, pruned_loss=0.09396, over 1391329.14 frames.], batch size: 23, lr: 1.96e-03
2022-05-13 22:38:00,545 INFO [train.py:812] (5/8) Epoch 3, batch 800, loss[loss=0.2464, simple_loss=0.318, pruned_loss=0.08743, over 7205.00 frames.], tot_loss[loss=0.2545, simple_loss=0.3207, pruned_loss=0.09411, over 1402492.25 frames.], batch size: 23, lr: 1.96e-03
2022-05-13 22:38:59,709 INFO [train.py:812] (5/8) Epoch 3, batch 850, loss[loss=0.3184, simple_loss=0.3653, pruned_loss=0.1358, over 7300.00 frames.], tot_loss[loss=0.2541, simple_loss=0.3201, pruned_loss=0.09409, over 1409804.18 frames.], batch size: 25, lr: 1.95e-03
2022-05-13 22:39:58,492 INFO [train.py:812] (5/8) Epoch 3, batch 900, loss[loss=0.2508, simple_loss=0.3125, pruned_loss=0.09459, over 7077.00 frames.], tot_loss[loss=0.2542, simple_loss=0.3203, pruned_loss=0.09405, over 1411818.73 frames.], batch size: 18, lr: 1.95e-03
2022-05-13 22:40:58,630 INFO [train.py:812] (5/8) Epoch 3, batch 950, loss[loss=0.2502, simple_loss=0.3215, pruned_loss=0.08949, over 7142.00 frames.], tot_loss[loss=0.2531, simple_loss=0.3197, pruned_loss=0.09321, over 1417028.19 frames.], batch size: 20, lr: 1.94e-03
2022-05-13 22:41:58,335 INFO [train.py:812] (5/8) Epoch 3, batch 1000, loss[loss=0.2682, simple_loss=0.3433, pruned_loss=0.09655, over 6830.00 frames.], tot_loss[loss=0.2536, simple_loss=0.3203, pruned_loss=0.09346, over 1417160.71 frames.], batch size: 31, lr: 1.94e-03
2022-05-13 22:42:57,492 INFO [train.py:812] (5/8) Epoch 3, batch 1050, loss[loss=0.2408, simple_loss=0.2909, pruned_loss=0.09535, over 7295.00 frames.], tot_loss[loss=0.2525, simple_loss=0.3192, pruned_loss=0.09289, over 1414898.47 frames.], batch size: 18, lr: 1.94e-03
2022-05-13 22:43:56,853 INFO [train.py:812] (5/8) Epoch 3, batch 1100, loss[loss=0.2689, simple_loss=0.325, pruned_loss=0.1064, over 7219.00 frames.], tot_loss[loss=0.2533, simple_loss=0.3203, pruned_loss=0.09319, over 1419539.80 frames.], batch size: 21, lr: 1.93e-03
2022-05-13 22:44:56,404 INFO [train.py:812] (5/8) Epoch 3, batch 1150, loss[loss=0.2731, simple_loss=0.3385, pruned_loss=0.1038, over 7226.00 frames.], tot_loss[loss=0.2518, simple_loss=0.3186, pruned_loss=0.09247, over 1421008.77 frames.], batch size: 20, lr: 1.93e-03
2022-05-13 22:45:54,822 INFO [train.py:812] (5/8) Epoch 3, batch 1200, loss[loss=0.2307, simple_loss=0.3105, pruned_loss=0.07547, over 7419.00 frames.], tot_loss[loss=0.253, simple_loss=0.3194, pruned_loss=0.09324, over 1424496.33 frames.], batch size: 20, lr: 1.93e-03
2022-05-13 22:46:52,755 INFO [train.py:812] (5/8) Epoch 3, batch 1250, loss[loss=0.282, simple_loss=0.3496, pruned_loss=0.1073, over 7408.00 frames.], tot_loss[loss=0.2525, simple_loss=0.3193, pruned_loss=0.09282, over 1425283.30 frames.], batch size: 21, lr: 1.92e-03
2022-05-13 22:47:52,028 INFO [train.py:812] (5/8) Epoch 3, batch 1300, loss[loss=0.2162, simple_loss=0.3037, pruned_loss=0.06435, over 7334.00 frames.], tot_loss[loss=0.2513, simple_loss=0.3183, pruned_loss=0.09213, over 1426990.70 frames.], batch size: 21, lr: 1.92e-03
2022-05-13 22:48:50,083 INFO [train.py:812] (5/8) Epoch 3, batch 1350, loss[loss=0.2458, simple_loss=0.3153, pruned_loss=0.0882, over 7430.00 frames.], tot_loss[loss=0.2526, simple_loss=0.3193, pruned_loss=0.09288, over 1426659.07 frames.], batch size: 20, lr: 1.91e-03
2022-05-13 22:49:48,129 INFO [train.py:812] (5/8) Epoch 3, batch 1400, loss[loss=0.2233, simple_loss=0.3091, pruned_loss=0.06874, over 7159.00 frames.], tot_loss[loss=0.2518, simple_loss=0.3191, pruned_loss=0.09224, over 1423438.33 frames.], batch size: 19, lr: 1.91e-03
2022-05-13 22:50:48,081 INFO [train.py:812] (5/8) Epoch 3, batch 1450, loss[loss=0.1952, simple_loss=0.2567, pruned_loss=0.06686, over 7114.00 frames.], tot_loss[loss=0.2508, simple_loss=0.318, pruned_loss=0.09184, over 1420103.32 frames.], batch size: 17, lr: 1.91e-03
2022-05-13 22:51:46,937 INFO [train.py:812] (5/8) Epoch 3, batch 1500, loss[loss=0.272, simple_loss=0.3432, pruned_loss=0.1003, over 7313.00 frames.], tot_loss[loss=0.2517, simple_loss=0.3186, pruned_loss=0.09238, over 1419309.19 frames.], batch size: 21, lr: 1.90e-03
2022-05-13 22:52:47,272 INFO [train.py:812] (5/8) Epoch 3, batch 1550, loss[loss=0.2606, simple_loss=0.321, pruned_loss=0.1001, over 7165.00 frames.], tot_loss[loss=0.251, simple_loss=0.3181, pruned_loss=0.09189, over 1423273.34 frames.], batch size: 19, lr: 1.90e-03
2022-05-13 22:53:45,772 INFO [train.py:812] (5/8) Epoch 3, batch 1600, loss[loss=0.2915, simple_loss=0.3423, pruned_loss=0.1203, over 7162.00 frames.], tot_loss[loss=0.2506, simple_loss=0.3178, pruned_loss=0.09175, over 1424197.00 frames.], batch size: 19, lr: 1.90e-03
2022-05-13 22:54:44,634 INFO [train.py:812] (5/8) Epoch 3, batch 1650, loss[loss=0.2277, simple_loss=0.2914, pruned_loss=0.08195, over 7433.00 frames.], tot_loss[loss=0.2496, simple_loss=0.3168, pruned_loss=0.09115, over 1426322.33 frames.], batch size: 20, lr: 1.89e-03
2022-05-13 22:55:42,301 INFO [train.py:812] (5/8) Epoch 3, batch 1700, loss[loss=0.2345, simple_loss=0.3052, pruned_loss=0.08193, over 7141.00 frames.], tot_loss[loss=0.2501, simple_loss=0.3173, pruned_loss=0.09144, over 1416350.49 frames.], batch size: 20, lr: 1.89e-03
2022-05-13 22:56:41,930 INFO [train.py:812] (5/8) Epoch 3, batch 1750, loss[loss=0.2513, simple_loss=0.3255, pruned_loss=0.08853, over 7226.00 frames.], tot_loss[loss=0.2497, simple_loss=0.3172, pruned_loss=0.09108, over 1423661.09 frames.], batch size: 20, lr: 1.88e-03
2022-05-13 22:57:40,289 INFO [train.py:812] (5/8) Epoch 3, batch 1800, loss[loss=0.259, simple_loss=0.3223, pruned_loss=0.09787, over 7110.00 frames.], tot_loss[loss=0.2505, simple_loss=0.3172, pruned_loss=0.09186, over 1417201.89 frames.], batch size: 21, lr: 1.88e-03
2022-05-13 22:58:39,826 INFO [train.py:812] (5/8) Epoch 3, batch 1850, loss[loss=0.2624, simple_loss=0.3243, pruned_loss=0.1002, over 7415.00 frames.], tot_loss[loss=0.2497, simple_loss=0.3167, pruned_loss=0.09132, over 1418882.58 frames.], batch size: 21, lr: 1.88e-03
2022-05-13 22:59:38,944 INFO [train.py:812] (5/8) Epoch 3, batch 1900, loss[loss=0.2085, simple_loss=0.2931, pruned_loss=0.06196, over 7156.00 frames.], tot_loss[loss=0.2496, simple_loss=0.3169, pruned_loss=0.09114, over 1416358.78 frames.], batch size: 18, lr: 1.87e-03
2022-05-13 23:00:38,505 INFO [train.py:812] (5/8) Epoch 3, batch 1950, loss[loss=0.2853, simple_loss=0.3525, pruned_loss=0.109, over 6778.00 frames.], tot_loss[loss=0.2488, simple_loss=0.3158, pruned_loss=0.09085, over 1418212.53 frames.], batch size: 31, lr: 1.87e-03
2022-05-13 23:01:37,672 INFO [train.py:812] (5/8) Epoch 3, batch 2000, loss[loss=0.2275, simple_loss=0.3029, pruned_loss=0.07604, over 7164.00 frames.], tot_loss[loss=0.2461, simple_loss=0.3141, pruned_loss=0.08904, over 1422109.59 frames.], batch size: 19, lr: 1.87e-03
2022-05-13 23:02:36,994 INFO [train.py:812] (5/8) Epoch 3, batch 2050, loss[loss=0.2885, simple_loss=0.3363, pruned_loss=0.1203, over 5066.00 frames.], tot_loss[loss=0.2477, simple_loss=0.3157, pruned_loss=0.08988, over 1421591.67 frames.], batch size: 52, lr: 1.86e-03
2022-05-13 23:03:35,448 INFO [train.py:812] (5/8) Epoch 3, batch 2100, loss[loss=0.2318, simple_loss=0.2995, pruned_loss=0.08201, over 7323.00 frames.], tot_loss[loss=0.2469, simple_loss=0.3151, pruned_loss=0.08938, over 1424838.71 frames.], batch size: 21, lr: 1.86e-03
2022-05-13 23:04:34,133 INFO [train.py:812] (5/8) Epoch 3, batch 2150, loss[loss=0.2737, simple_loss=0.3318, pruned_loss=0.1078, over 7227.00 frames.], tot_loss[loss=0.2481, simple_loss=0.3159, pruned_loss=0.09019, over 1425845.77 frames.], batch size: 20, lr: 1.86e-03
2022-05-13 23:05:32,769 INFO [train.py:812] (5/8) Epoch 3, batch 2200, loss[loss=0.2299, simple_loss=0.3074, pruned_loss=0.07619, over 7133.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3151, pruned_loss=0.08956, over 1424927.63 frames.], batch size: 20, lr: 1.85e-03
2022-05-13 23:06:32,198 INFO [train.py:812] (5/8) Epoch 3, batch 2250, loss[loss=0.241, simple_loss=0.3243, pruned_loss=0.07887, over 7328.00 frames.], tot_loss[loss=0.2482, simple_loss=0.3166, pruned_loss=0.08995, over 1424912.89 frames.], batch size: 20, lr: 1.85e-03
2022-05-13 23:07:31,559 INFO [train.py:812] (5/8) Epoch 3, batch 2300, loss[loss=0.2665, simple_loss=0.3257, pruned_loss=0.1037, over 7355.00 frames.], tot_loss[loss=0.2466, simple_loss=0.3148, pruned_loss=0.08923, over 1413505.44 frames.], batch size: 19, lr: 1.85e-03
2022-05-13 23:08:31,341 INFO [train.py:812] (5/8) Epoch 3, batch 2350, loss[loss=0.2357, simple_loss=0.2988, pruned_loss=0.08627, over 7257.00 frames.], tot_loss[loss=0.2457, simple_loss=0.3139, pruned_loss=0.08872, over 1415317.98 frames.], batch size: 19, lr: 1.84e-03
2022-05-13 23:09:29,603 INFO [train.py:812] (5/8) Epoch 3, batch 2400, loss[loss=0.1957, simple_loss=0.2669, pruned_loss=0.0623, over 7249.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3152, pruned_loss=0.08953, over 1418741.56 frames.], batch size: 19, lr: 1.84e-03
2022-05-13 23:10:29,109 INFO [train.py:812] (5/8) Epoch 3, batch 2450, loss[loss=0.2578, simple_loss=0.3218, pruned_loss=0.09686, over 7223.00 frames.], tot_loss[loss=0.2483, simple_loss=0.316, pruned_loss=0.09027, over 1415968.05 frames.], batch size: 20, lr: 1.84e-03
2022-05-13 23:11:28,134 INFO [train.py:812] (5/8) Epoch 3, batch 2500, loss[loss=0.2457, simple_loss=0.316, pruned_loss=0.08764, over 7152.00 frames.], tot_loss[loss=0.2479, simple_loss=0.3156, pruned_loss=0.09008, over 1414628.59 frames.], batch size: 19, lr: 1.83e-03
2022-05-13 23:12:27,741 INFO [train.py:812] (5/8) Epoch 3, batch 2550, loss[loss=0.2441, simple_loss=0.3302, pruned_loss=0.07903, over 7210.00 frames.], tot_loss[loss=0.2472, simple_loss=0.3148, pruned_loss=0.08977, over 1413921.09 frames.], batch size: 21, lr: 1.83e-03
2022-05-13 23:13:27,068 INFO [train.py:812] (5/8) Epoch 3, batch 2600, loss[loss=0.2152, simple_loss=0.2836, pruned_loss=0.07342, over 7274.00 frames.], tot_loss[loss=0.2462, simple_loss=0.3142, pruned_loss=0.08911, over 1419599.00 frames.], batch size: 18, lr: 1.83e-03
2022-05-13 23:14:26,423 INFO [train.py:812] (5/8) Epoch 3, batch 2650, loss[loss=0.2343, simple_loss=0.306, pruned_loss=0.08132, over 7325.00 frames.], tot_loss[loss=0.2449, simple_loss=0.3133, pruned_loss=0.08829, over 1419374.59 frames.], batch size: 20, lr: 1.82e-03
2022-05-13 23:15:24,400 INFO [train.py:812] (5/8) Epoch 3, batch 2700, loss[loss=0.1923, simple_loss=0.2738, pruned_loss=0.05535, over 7068.00 frames.], tot_loss[loss=0.2435, simple_loss=0.3127, pruned_loss=0.08714, over 1420084.99 frames.], batch size: 18, lr: 1.82e-03
2022-05-13 23:16:23,934 INFO [train.py:812] (5/8) Epoch 3, batch 2750, loss[loss=0.2789, simple_loss=0.3594, pruned_loss=0.09918, over 7227.00 frames.], tot_loss[loss=0.2438, simple_loss=0.313, pruned_loss=0.08729, over 1419491.09 frames.], batch size: 26, lr: 1.82e-03
2022-05-13 23:17:22,904 INFO [train.py:812] (5/8) Epoch 3, batch 2800, loss[loss=0.3684, simple_loss=0.3967, pruned_loss=0.1701, over 5120.00 frames.], tot_loss[loss=0.2439, simple_loss=0.3131, pruned_loss=0.08735, over 1418565.77 frames.], batch size: 53, lr: 1.81e-03
2022-05-13 23:18:30,780 INFO [train.py:812] (5/8) Epoch 3, batch 2850, loss[loss=0.2974, simple_loss=0.3607, pruned_loss=0.117, over 7214.00 frames.], tot_loss[loss=0.2438, simple_loss=0.3129, pruned_loss=0.08737, over 1422013.51 frames.], batch size: 21, lr: 1.81e-03
2022-05-13 23:19:29,900 INFO [train.py:812] (5/8) Epoch 3, batch 2900, loss[loss=0.2642, simple_loss=0.3337, pruned_loss=0.09736, over 6521.00 frames.], tot_loss[loss=0.2436, simple_loss=0.3126, pruned_loss=0.08731, over 1418947.88 frames.], batch size: 37, lr: 1.81e-03
2022-05-13 23:20:29,314 INFO [train.py:812] (5/8) Epoch 3, batch 2950, loss[loss=0.2533, simple_loss=0.3151, pruned_loss=0.09575, over 7174.00 frames.], tot_loss[loss=0.2434, simple_loss=0.3127, pruned_loss=0.08702, over 1417264.45 frames.], batch size: 26, lr: 1.80e-03
2022-05-13 23:21:28,543 INFO [train.py:812] (5/8) Epoch 3, batch 3000, loss[loss=0.1986, simple_loss=0.2832, pruned_loss=0.05694, over 7331.00 frames.], tot_loss[loss=0.243, simple_loss=0.3122, pruned_loss=0.08686, over 1420805.31 frames.], batch size: 22, lr: 1.80e-03
2022-05-13 23:21:28,544 INFO [train.py:832] (5/8) Computing validation loss
2022-05-13 23:21:36,069 INFO [train.py:841] (5/8) Epoch 3, validation: loss=0.1862, simple_loss=0.2867, pruned_loss=0.04278, over 698248.00 frames.
2022-05-13 23:22:33,845 INFO [train.py:812] (5/8) Epoch 3, batch 3050, loss[loss=0.2237, simple_loss=0.3049, pruned_loss=0.07127, over 7418.00 frames.], tot_loss[loss=0.2426, simple_loss=0.3122, pruned_loss=0.08655, over 1425700.70 frames.], batch size: 21, lr: 1.80e-03
2022-05-13 23:23:30,791 INFO [train.py:812] (5/8) Epoch 3, batch 3100, loss[loss=0.218, simple_loss=0.2914, pruned_loss=0.07233, over 7291.00 frames.], tot_loss[loss=0.2426, simple_loss=0.3123, pruned_loss=0.08643, over 1428968.95 frames.], batch size: 18, lr: 1.79e-03
2022-05-13 23:24:30,032 INFO [train.py:812] (5/8) Epoch 3, batch 3150, loss[loss=0.243, simple_loss=0.3225, pruned_loss=0.08175, over 7219.00 frames.], tot_loss[loss=0.2433, simple_loss=0.3126, pruned_loss=0.08701, over 1423871.94 frames.], batch size: 21, lr: 1.79e-03
2022-05-13 23:25:29,519 INFO [train.py:812] (5/8) Epoch 3, batch 3200, loss[loss=0.2568, simple_loss=0.3326, pruned_loss=0.09046, over 7390.00 frames.], tot_loss[loss=0.2436, simple_loss=0.313, pruned_loss=0.08707, over 1426391.56 frames.], batch size: 23, lr: 1.79e-03
2022-05-13 23:26:29,122 INFO [train.py:812] (5/8) Epoch 3, batch 3250, loss[loss=0.2629, simple_loss=0.3178, pruned_loss=0.104, over 7158.00 frames.], tot_loss[loss=0.2429, simple_loss=0.3128, pruned_loss=0.08653, over 1426637.33 frames.], batch size: 19, lr: 1.79e-03
2022-05-13 23:27:27,200 INFO [train.py:812] (5/8) Epoch 3, batch 3300, loss[loss=0.2341, simple_loss=0.3104, pruned_loss=0.0789, over 7209.00 frames.], tot_loss[loss=0.2416, simple_loss=0.3116, pruned_loss=0.08577, over 1428717.65 frames.], batch size: 26, lr: 1.78e-03
2022-05-13 23:28:26,176 INFO [train.py:812] (5/8) Epoch 3, batch 3350, loss[loss=0.1863, simple_loss=0.2699, pruned_loss=0.05133, over 7278.00 frames.], tot_loss[loss=0.2423, simple_loss=0.3125, pruned_loss=0.0861, over 1425547.93 frames.], batch size: 18, lr: 1.78e-03
2022-05-13 23:29:23,905 INFO [train.py:812] (5/8) Epoch 3, batch 3400, loss[loss=0.201, simple_loss=0.2669, pruned_loss=0.06757, over 7422.00 frames.], tot_loss[loss=0.243, simple_loss=0.3131, pruned_loss=0.0865, over 1423323.38 frames.], batch size: 18, lr: 1.78e-03
2022-05-13 23:30:22,225 INFO [train.py:812] (5/8) Epoch 3, batch 3450, loss[loss=0.245, simple_loss=0.323, pruned_loss=0.08348, over 7265.00 frames.], tot_loss[loss=0.2424, simple_loss=0.3125, pruned_loss=0.08612, over 1419631.83 frames.], batch size: 19, lr: 1.77e-03
2022-05-13 23:31:20,918 INFO [train.py:812] (5/8) Epoch 3, batch 3500, loss[loss=0.2554, simple_loss=0.3298, pruned_loss=0.09053, over 7317.00 frames.], tot_loss[loss=0.2413, simple_loss=0.3112, pruned_loss=0.08571, over 1420959.03 frames.], batch size: 25, lr: 1.77e-03
2022-05-13 23:32:20,545 INFO [train.py:812] (5/8) Epoch 3, batch 3550, loss[loss=0.2269, simple_loss=0.2978, pruned_loss=0.07804, over 7208.00 frames.], tot_loss[loss=0.2412, simple_loss=0.3115, pruned_loss=0.08545, over 1419775.99 frames.], batch size: 21, lr: 1.77e-03
2022-05-13 23:33:19,830 INFO [train.py:812] (5/8) Epoch 3, batch 3600, loss[loss=0.22, simple_loss=0.2959, pruned_loss=0.07207, over 7284.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3091, pruned_loss=0.08409, over 1421002.07 frames.], batch size: 24, lr: 1.76e-03
2022-05-13 23:34:19,468 INFO [train.py:812] (5/8) Epoch 3, batch 3650, loss[loss=0.2501, simple_loss=0.3263, pruned_loss=0.08692, over 7383.00 frames.], tot_loss[loss=0.2391, simple_loss=0.3095, pruned_loss=0.08432, over 1421412.17 frames.], batch size: 23, lr: 1.76e-03
2022-05-13 23:35:18,552 INFO [train.py:812] (5/8) Epoch 3, batch 3700, loss[loss=0.2575, simple_loss=0.3074, pruned_loss=0.1038, over 7418.00 frames.], tot_loss[loss=0.239, simple_loss=0.3094, pruned_loss=0.08427, over 1416586.27 frames.], batch size: 18, lr: 1.76e-03
2022-05-13 23:36:18,273 INFO [train.py:812] (5/8) Epoch 3, batch 3750, loss[loss=0.1971, simple_loss=0.2626, pruned_loss=0.06577, over 7279.00 frames.], tot_loss[loss=0.2374, simple_loss=0.3081, pruned_loss=0.08332, over 1422251.38 frames.], batch size: 18, lr: 1.76e-03
2022-05-13 23:37:16,798 INFO [train.py:812] (5/8) Epoch 3, batch 3800, loss[loss=0.2026, simple_loss=0.2731, pruned_loss=0.06606, over 7168.00 frames.], tot_loss[loss=0.2374, simple_loss=0.3079, pruned_loss=0.08346, over 1423268.09 frames.], batch size: 18, lr: 1.75e-03
2022-05-13 23:38:16,274 INFO [train.py:812] (5/8) Epoch 3, batch 3850, loss[loss=0.2752, simple_loss=0.352, pruned_loss=0.09918, over 7336.00 frames.], tot_loss[loss=0.2375, simple_loss=0.308, pruned_loss=0.08354, over 1422166.25 frames.], batch size: 22, lr: 1.75e-03
2022-05-13 23:39:15,545 INFO [train.py:812] (5/8) Epoch 3, batch 3900, loss[loss=0.2379, simple_loss=0.3038, pruned_loss=0.08601, over 7329.00 frames.], tot_loss[loss=0.2362, simple_loss=0.3068, pruned_loss=0.08273, over 1423926.48 frames.], batch size: 20, lr: 1.75e-03
2022-05-13 23:40:14,883 INFO [train.py:812] (5/8) Epoch 3, batch 3950, loss[loss=0.2376, simple_loss=0.3173, pruned_loss=0.07896, over 7319.00 frames.], tot_loss[loss=0.2371, simple_loss=0.3076, pruned_loss=0.08336, over 1421214.16 frames.], batch size: 21, lr: 1.74e-03
2022-05-13 23:41:13,968 INFO [train.py:812] (5/8) Epoch 3, batch 4000, loss[loss=0.2399, simple_loss=0.3125, pruned_loss=0.08364, over 7342.00 frames.], tot_loss[loss=0.2373, simple_loss=0.3081, pruned_loss=0.08321, over 1425845.56 frames.], batch size: 22, lr: 1.74e-03
2022-05-13 23:42:13,686 INFO [train.py:812] (5/8) Epoch 3, batch 4050, loss[loss=0.2815, simple_loss=0.3586, pruned_loss=0.1022, over 7428.00 frames.], tot_loss[loss=0.2372, simple_loss=0.308, pruned_loss=0.08321, over 1425677.52 frames.], batch size: 20, lr: 1.74e-03
2022-05-13 23:43:12,785 INFO [train.py:812] (5/8) Epoch 3, batch 4100, loss[loss=0.2146, simple_loss=0.2856, pruned_loss=0.07181, over 7054.00 frames.], tot_loss[loss=0.238, simple_loss=0.3088, pruned_loss=0.08363, over 1416209.24 frames.], batch size: 18, lr: 1.73e-03
2022-05-13 23:44:12,463 INFO [train.py:812] (5/8) Epoch 3, batch 4150, loss[loss=0.2187, simple_loss=0.2995, pruned_loss=0.06895, over 7112.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3093, pruned_loss=0.0839, over 1421482.15 frames.], batch size: 21, lr: 1.73e-03
2022-05-13 23:45:10,714 INFO [train.py:812] (5/8) Epoch 3, batch 4200, loss[loss=0.233, simple_loss=0.3188, pruned_loss=0.0736, over 6975.00 frames.], tot_loss[loss=0.2395, simple_loss=0.3099, pruned_loss=0.08452, over 1420801.99 frames.], batch size: 28, lr: 1.73e-03
2022-05-13 23:46:09,930 INFO [train.py:812] (5/8) Epoch 3, batch 4250, loss[loss=0.2327, simple_loss=0.3146, pruned_loss=0.07543, over 7197.00 frames.], tot_loss[loss=0.2382, simple_loss=0.309, pruned_loss=0.08371, over 1420857.91 frames.], batch size: 22, lr: 1.73e-03
2022-05-13 23:47:09,083 INFO [train.py:812] (5/8) Epoch 3, batch 4300, loss[loss=0.2279, simple_loss=0.2927, pruned_loss=0.08157, over 7437.00 frames.], tot_loss[loss=0.239, simple_loss=0.3097, pruned_loss=0.08415, over 1423557.80 frames.], batch size: 19, lr: 1.72e-03
2022-05-13 23:48:08,223 INFO [train.py:812] (5/8) Epoch 3, batch 4350, loss[loss=0.2306, simple_loss=0.3163, pruned_loss=0.07244, over 7141.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3087, pruned_loss=0.08288, over 1425112.80 frames.], batch size: 20, lr: 1.72e-03
2022-05-13 23:49:06,727 INFO [train.py:812] (5/8) Epoch 3, batch 4400, loss[loss=0.2662, simple_loss=0.3355, pruned_loss=0.0984, over 7278.00 frames.], tot_loss[loss=0.2371, simple_loss=0.3082, pruned_loss=0.08305, over 1419927.09 frames.], batch size: 25, lr: 1.72e-03
2022-05-13 23:50:05,671 INFO [train.py:812] (5/8) Epoch 3, batch 4450, loss[loss=0.2552, simple_loss=0.3317, pruned_loss=0.08933, over 7335.00 frames.], tot_loss[loss=0.2383, simple_loss=0.3094, pruned_loss=0.08357, over 1411939.76 frames.], batch size: 22, lr: 1.71e-03
2022-05-13 23:51:04,266 INFO [train.py:812] (5/8) Epoch 3, batch 4500, loss[loss=0.2203, simple_loss=0.2936, pruned_loss=0.07348, over 7132.00 frames.], tot_loss[loss=0.2394, simple_loss=0.3108, pruned_loss=0.08407, over 1405192.60 frames.], batch size: 21, lr: 1.71e-03
2022-05-13 23:52:01,894 INFO [train.py:812] (5/8) Epoch 3, batch 4550, loss[loss=0.2352, simple_loss=0.3074, pruned_loss=0.08149, over 6499.00 frames.], tot_loss[loss=0.2442, simple_loss=0.3141, pruned_loss=0.08714, over 1378616.61 frames.], batch size: 38, lr: 1.71e-03
2022-05-13 23:53:11,540 INFO [train.py:812] (5/8) Epoch 4, batch 0, loss[loss=0.2651, simple_loss=0.3456, pruned_loss=0.09234, over 7188.00 frames.], tot_loss[loss=0.2651, simple_loss=0.3456, pruned_loss=0.09234, over 7188.00 frames.], batch size: 23, lr: 1.66e-03
2022-05-13 23:54:10,703 INFO [train.py:812] (5/8) Epoch 4, batch 50, loss[loss=0.2133, simple_loss=0.278, pruned_loss=0.07434, over 7282.00 frames.], tot_loss[loss=0.2323, simple_loss=0.3046, pruned_loss=0.07998, over 317629.10 frames.], batch size: 17, lr: 1.66e-03
2022-05-13 23:55:09,411 INFO [train.py:812] (5/8) Epoch 4, batch 100, loss[loss=0.2021, simple_loss=0.268, pruned_loss=0.06812, over 7288.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3058, pruned_loss=0.08264, over 564299.49 frames.], batch size: 17, lr: 1.65e-03
2022-05-13 23:56:09,344 INFO [train.py:812] (5/8) Epoch 4, batch 150, loss[loss=0.2459, simple_loss=0.3229, pruned_loss=0.0845, over 7343.00 frames.], tot_loss[loss=0.2341, simple_loss=0.3061, pruned_loss=0.08104, over 755309.63 frames.], batch size: 22, lr: 1.65e-03
2022-05-13 23:57:08,517 INFO [train.py:812] (5/8) Epoch 4, batch 200, loss[loss=0.2572, simple_loss=0.3153, pruned_loss=0.09955, over 7206.00 frames.], tot_loss[loss=0.2351, simple_loss=0.3071, pruned_loss=0.08153, over 904354.38 frames.], batch size: 23, lr: 1.65e-03
2022-05-13 23:58:07,153 INFO [train.py:812] (5/8) Epoch 4, batch 250, loss[loss=0.2387, simple_loss=0.3227, pruned_loss=0.0774, over 7323.00 frames.], tot_loss[loss=0.2368, simple_loss=0.3088, pruned_loss=0.08238, over 1016178.86 frames.], batch size: 22, lr: 1.64e-03
2022-05-13 23:59:06,672 INFO [train.py:812] (5/8) Epoch 4, batch 300, loss[loss=0.2778, simple_loss=0.3433, pruned_loss=0.1061, over 7382.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3078, pruned_loss=0.08164, over 1110523.92 frames.], batch size: 23, lr: 1.64e-03
2022-05-14 00:00:06,193 INFO [train.py:812] (5/8) Epoch 4, batch 350, loss[loss=0.2272, simple_loss=0.3165, pruned_loss=0.069, over 7320.00 frames.], tot_loss[loss=0.235, simple_loss=0.3082, pruned_loss=0.08086, over 1182166.58 frames.], batch size: 21, lr: 1.64e-03
2022-05-14 00:01:05,185 INFO [train.py:812] (5/8) Epoch 4, batch 400, loss[loss=0.2506, simple_loss=0.3248, pruned_loss=0.0882, over 7237.00 frames.], tot_loss[loss=0.235, simple_loss=0.3076, pruned_loss=0.08123, over 1232260.52 frames.], batch size: 20, lr: 1.64e-03
2022-05-14 00:02:04,585 INFO [train.py:812] (5/8) Epoch 4, batch 450, loss[loss=0.2561, simple_loss=0.3252, pruned_loss=0.09356, over 7148.00 frames.], tot_loss[loss=0.2348, simple_loss=0.307, pruned_loss=0.08137, over 1273822.63 frames.], batch size: 20, lr: 1.63e-03
2022-05-14 00:03:03,294 INFO [train.py:812] (5/8) Epoch 4, batch 500, loss[loss=0.2092, simple_loss=0.2878, pruned_loss=0.06533, over 7170.00 frames.], tot_loss[loss=0.2362, simple_loss=0.3086, pruned_loss=0.08188, over 1304250.13 frames.], batch size: 19, lr: 1.63e-03
2022-05-14 00:04:02,808 INFO [train.py:812] (5/8) Epoch 4, batch 550, loss[loss=0.1903, simple_loss=0.2714, pruned_loss=0.0546, over 7157.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3081, pruned_loss=0.08146, over 1330040.93 frames.], batch size: 18, lr: 1.63e-03
2022-05-14 00:05:01,379 INFO [train.py:812] (5/8) Epoch 4, batch 600, loss[loss=0.2701, simple_loss=0.3319, pruned_loss=0.1041, over 6435.00 frames.], tot_loss[loss=0.235, simple_loss=0.3073, pruned_loss=0.08134, over 1347668.01 frames.], batch size: 38, lr: 1.63e-03
2022-05-14 00:06:00,844 INFO [train.py:812] (5/8) Epoch 4, batch 650, loss[loss=0.2129, simple_loss=0.2936, pruned_loss=0.06615, over 7421.00 frames.], tot_loss[loss=0.2329, simple_loss=0.3059, pruned_loss=0.0799, over 1368630.43 frames.], batch size: 20, lr: 1.62e-03
2022-05-14 00:07:00,175 INFO [train.py:812] (5/8) Epoch 4, batch 700, loss[loss=0.2252, simple_loss=0.3204, pruned_loss=0.06507, over 7305.00 frames.], tot_loss[loss=0.2317, simple_loss=0.3048, pruned_loss=0.07932, over 1385533.94 frames.], batch size: 24, lr: 1.62e-03
2022-05-14 00:07:59,216 INFO [train.py:812] (5/8) Epoch 4, batch 750, loss[loss=0.3279, simple_loss=0.3744, pruned_loss=0.1407, over 7301.00 frames.], tot_loss[loss=0.2326, simple_loss=0.305, pruned_loss=0.08007, over 1393623.13 frames.], batch size: 24, lr: 1.62e-03
2022-05-14 00:08:58,470 INFO [train.py:812] (5/8) Epoch 4, batch 800, loss[loss=0.2039, simple_loss=0.2803, pruned_loss=0.0638, over 7258.00 frames.], tot_loss[loss=0.2326, simple_loss=0.3052, pruned_loss=0.07997, over 1397495.87 frames.], batch size: 19, lr: 1.62e-03
2022-05-14 00:09:58,458 INFO [train.py:812] (5/8) Epoch 4, batch 850, loss[loss=0.2516, simple_loss=0.3225, pruned_loss=0.09034, over 7050.00 frames.], tot_loss[loss=0.2328, simple_loss=0.3061, pruned_loss=0.07971, over 1407491.52 frames.], batch size: 18, lr: 1.61e-03
2022-05-14 00:10:57,735 INFO [train.py:812] (5/8) Epoch 4, batch 900, loss[loss=0.221, simple_loss=0.3022, pruned_loss=0.0699, over 7109.00 frames.], tot_loss[loss=0.2319, simple_loss=0.3055, pruned_loss=0.07915, over 1415166.60 frames.], batch size: 21, lr: 1.61e-03
2022-05-14 00:11:56,766 INFO [train.py:812] (5/8) Epoch 4, batch 950, loss[loss=0.2627, simple_loss=0.3389, pruned_loss=0.09322, over 7177.00 frames.], tot_loss[loss=0.233, simple_loss=0.3062, pruned_loss=0.07993, over 1420234.62 frames.], batch size: 26, lr: 1.61e-03
2022-05-14 00:12:55,417 INFO [train.py:812] (5/8) Epoch 4, batch 1000, loss[loss=0.2037, simple_loss=0.2715, pruned_loss=0.06793, over 7276.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3049, pruned_loss=0.0793, over 1420513.66 frames.], batch size: 18, lr: 1.61e-03
2022-05-14 00:13:54,498 INFO [train.py:812] (5/8) Epoch 4, batch 1050, loss[loss=0.2791, simple_loss=0.3446, pruned_loss=0.1068, over 6669.00 frames.], tot_loss[loss=0.2324, simple_loss=0.3059, pruned_loss=0.07946, over 1418597.04 frames.], batch size: 31, lr: 1.60e-03
2022-05-14 00:14:53,491 INFO [train.py:812] (5/8) Epoch 4, batch 1100, loss[loss=0.2427, simple_loss=0.3215, pruned_loss=0.08198, over 7414.00 frames.], tot_loss[loss=0.232, simple_loss=0.3051, pruned_loss=0.07941, over 1419837.77 frames.], batch size: 21, lr: 1.60e-03
2022-05-14 00:15:52,781 INFO [train.py:812] (5/8) Epoch 4, batch 1150, loss[loss=0.2876, simple_loss=0.3543, pruned_loss=0.1104, over 7321.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3057, pruned_loss=0.07933, over 1417558.12 frames.], batch size: 21, lr: 1.60e-03
2022-05-14 00:16:51,388 INFO [train.py:812] (5/8) Epoch 4, batch 1200, loss[loss=0.2517, simple_loss=0.3186, pruned_loss=0.09239, over 7313.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3071, pruned_loss=0.08031, over 1415972.26 frames.], batch size: 21, lr: 1.60e-03
2022-05-14 00:17:50,408 INFO [train.py:812] (5/8) Epoch 4, batch 1250, loss[loss=0.1838, simple_loss=0.2622, pruned_loss=0.05276, over 6782.00 frames.], tot_loss[loss=0.2325, simple_loss=0.3061, pruned_loss=0.07948, over 1414236.93 frames.], batch size: 15, lr: 1.59e-03
2022-05-14 00:18:48,754 INFO [train.py:812] (5/8) Epoch 4, batch 1300, loss[loss=0.2708, simple_loss=0.3346, pruned_loss=0.1035, over 7222.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3054, pruned_loss=0.07914, over 1417013.77 frames.], batch size: 23, lr: 1.59e-03
2022-05-14 00:19:47,561 INFO [train.py:812] (5/8) Epoch 4, batch 1350, loss[loss=0.2305, simple_loss=0.3072, pruned_loss=0.07691, over 7240.00 frames.], tot_loss[loss=0.2317, simple_loss=0.3052, pruned_loss=0.07907, over 1416191.13 frames.], batch size: 20, lr: 1.59e-03
2022-05-14 00:20:44,850 INFO [train.py:812] (5/8) Epoch 4, batch 1400, loss[loss=0.2569, simple_loss=0.3413, pruned_loss=0.08629, over 7212.00 frames.], tot_loss[loss=0.2315, simple_loss=0.3047, pruned_loss=0.07916, over 1419066.02 frames.], batch size: 22, lr: 1.59e-03
2022-05-14 00:21:44,658 INFO [train.py:812] (5/8) Epoch 4, batch 1450, loss[loss=0.319, simple_loss=0.3837, pruned_loss=0.1271, over 7282.00 frames.], tot_loss[loss=0.2331, simple_loss=0.3064, pruned_loss=0.07985, over 1421356.33 frames.], batch size: 24, lr: 1.59e-03
2022-05-14 00:22:43,707 INFO [train.py:812] (5/8) Epoch 4, batch 1500, loss[loss=0.276, simple_loss=0.347, pruned_loss=0.1025, over 7279.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3062, pruned_loss=0.0796, over 1418566.64 frames.], batch size: 24, lr: 1.58e-03
2022-05-14 00:23:43,447 INFO [train.py:812] (5/8) Epoch 4, batch 1550, loss[loss=0.3533, simple_loss=0.3802, pruned_loss=0.1631, over 5179.00 frames.], tot_loss[loss=0.2337, simple_loss=0.3069, pruned_loss=0.0803, over 1418310.91 frames.], batch size: 53, lr: 1.58e-03
2022-05-14 00:24:41,299 INFO [train.py:812] (5/8) Epoch 4, batch 1600, loss[loss=0.2528, simple_loss=0.3278, pruned_loss=0.08895, over 7292.00 frames.], tot_loss[loss=0.2345, simple_loss=0.3079, pruned_loss=0.08054, over 1415225.14 frames.], batch size: 25, lr: 1.58e-03
2022-05-14 00:25:40,739 INFO [train.py:812] (5/8) Epoch 4, batch 1650, loss[loss=0.2342, simple_loss=0.313, pruned_loss=0.07767, over 7323.00 frames.], tot_loss[loss=0.2334, simple_loss=0.3067, pruned_loss=0.08007, over 1416912.29 frames.], batch size: 20, lr: 1.58e-03
2022-05-14 00:26:39,596 INFO [train.py:812] (5/8) Epoch 4, batch 1700, loss[loss=0.2576, simple_loss=0.32, pruned_loss=0.09758, over 7138.00 frames.], tot_loss[loss=0.2324, simple_loss=0.3061, pruned_loss=0.07933, over 1420158.45 frames.], batch size: 20, lr: 1.57e-03
2022-05-14 00:27:38,851 INFO [train.py:812] (5/8) Epoch 4, batch 1750, loss[loss=0.2537, simple_loss=0.3161, pruned_loss=0.09567, over 7209.00 frames.], tot_loss[loss=0.2324, simple_loss=0.306, pruned_loss=0.07934, over 1419481.92 frames.], batch size: 22, lr: 1.57e-03
2022-05-14 00:28:45,525 INFO [train.py:812] (5/8) Epoch 4, batch 1800, loss[loss=0.3008, simple_loss=0.3648, pruned_loss=0.1184, over 7223.00 frames.], tot_loss[loss=0.2329, simple_loss=0.3067, pruned_loss=0.07951, over 1421691.36 frames.], batch size: 21, lr: 1.57e-03
2022-05-14 00:29:45,156 INFO [train.py:812] (5/8) Epoch 4, batch 1850, loss[loss=0.192, simple_loss=0.2637, pruned_loss=0.06017, over 7135.00 frames.], tot_loss[loss=0.2317, simple_loss=0.306, pruned_loss=0.0787, over 1420615.88 frames.], batch size: 17, lr: 1.57e-03
2022-05-14 00:30:44,458 INFO [train.py:812] (5/8) Epoch 4, batch 1900, loss[loss=0.2135, simple_loss=0.287, pruned_loss=0.06997, over 7150.00 frames.], tot_loss[loss=0.2329, simple_loss=0.3069, pruned_loss=0.07941, over 1423568.45 frames.], batch size: 19, lr: 1.56e-03
2022-05-14 00:31:43,796 INFO [train.py:812] (5/8) Epoch 4, batch 1950, loss[loss=0.2771, simple_loss=0.3382, pruned_loss=0.108, over 6553.00 frames.], tot_loss[loss=0.2319, simple_loss=0.3061, pruned_loss=0.07883, over 1428613.21 frames.], batch size: 38, lr: 1.56e-03
2022-05-14 00:32:40,424 INFO [train.py:812] (5/8) Epoch 4, batch 2000, loss[loss=0.2309, simple_loss=0.3075, pruned_loss=0.07721, over 7111.00 frames.], tot_loss[loss=0.2329, simple_loss=0.307, pruned_loss=0.07942, over 1425933.43 frames.], batch size: 21, lr: 1.56e-03
2022-05-14 00:34:15,593 INFO [train.py:812] (5/8) Epoch 4, batch 2050, loss[loss=0.287, simple_loss=0.346, pruned_loss=0.114, over 6805.00 frames.], tot_loss[loss=0.2332, simple_loss=0.307, pruned_loss=0.07972, over 1422980.62 frames.], batch size: 31, lr: 1.56e-03
2022-05-14 00:35:41,816 INFO [train.py:812] (5/8) Epoch 4, batch 2100, loss[loss=0.2149, simple_loss=0.2975, pruned_loss=0.06619, over 7317.00 frames.], tot_loss[loss=0.2317, simple_loss=0.3054, pruned_loss=0.07902, over 1421240.84 frames.], batch size: 21, lr: 1.56e-03
2022-05-14 00:36:41,403 INFO [train.py:812] (5/8) Epoch 4, batch 2150, loss[loss=0.2301, simple_loss=0.3127, pruned_loss=0.07377, over 7332.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3043, pruned_loss=0.07801, over 1423888.78 frames.], batch size: 22, lr: 1.55e-03
2022-05-14 00:37:40,368 INFO [train.py:812] (5/8) Epoch 4, batch 2200, loss[loss=0.2034, simple_loss=0.2899, pruned_loss=0.05842, over 7221.00 frames.], tot_loss[loss=0.2291, simple_loss=0.3033, pruned_loss=0.0775, over 1426162.84 frames.], batch size: 21, lr: 1.55e-03
2022-05-14 00:38:47,584 INFO [train.py:812] (5/8) Epoch 4, batch 2250, loss[loss=0.2836, simple_loss=0.3374, pruned_loss=0.1149, over 5229.00 frames.], tot_loss[loss=0.2289, simple_loss=0.3036, pruned_loss=0.07707, over 1427881.53 frames.], batch size: 52, lr: 1.55e-03
2022-05-14 00:39:45,544 INFO [train.py:812] (5/8) Epoch 4, batch 2300, loss[loss=0.2676, simple_loss=0.331, pruned_loss=0.1021, over 7144.00 frames.], tot_loss[loss=0.2295, simple_loss=0.3043, pruned_loss=0.07735, over 1430364.80 frames.], batch size: 19, lr: 1.55e-03
2022-05-14 00:40:45,374 INFO [train.py:812] (5/8) Epoch 4, batch 2350, loss[loss=0.2222, simple_loss=0.3042, pruned_loss=0.07014, over 7328.00 frames.], tot_loss[loss=0.2286, simple_loss=0.3035, pruned_loss=0.07685, over 1431082.12 frames.], batch size: 20, lr: 1.54e-03
2022-05-14 00:41:44,160 INFO [train.py:812] (5/8) Epoch 4, batch 2400, loss[loss=0.2312, simple_loss=0.312, pruned_loss=0.07521, over 7287.00 frames.], tot_loss[loss=0.2289, simple_loss=0.3041, pruned_loss=0.07685, over 1433379.41 frames.], batch size: 25, lr: 1.54e-03
2022-05-14 00:42:43,343 INFO [train.py:812] (5/8) Epoch 4, batch 2450, loss[loss=0.2996, simple_loss=0.3488, pruned_loss=0.1252, over 7360.00 frames.], tot_loss[loss=0.2292, simple_loss=0.3041, pruned_loss=0.07714, over 1436533.08 frames.], batch size: 23, lr: 1.54e-03
2022-05-14 00:43:42,437 INFO [train.py:812] (5/8) Epoch 4, batch 2500, loss[loss=0.1942, simple_loss=0.2787, pruned_loss=0.05488, over 7152.00 frames.], tot_loss[loss=0.229, simple_loss=0.3038, pruned_loss=0.0771, over 1434352.46 frames.], batch size: 19, lr: 1.54e-03
2022-05-14 00:44:40,437 INFO [train.py:812] (5/8) Epoch 4, batch 2550, loss[loss=0.2091, simple_loss=0.2697, pruned_loss=0.07424, over 7405.00 frames.], tot_loss[loss=0.2295, simple_loss=0.3039, pruned_loss=0.07752, over 1425864.23 frames.], batch size: 18, lr: 1.54e-03
2022-05-14 00:45:38,431 INFO [train.py:812] (5/8) Epoch 4, batch 2600, loss[loss=0.2245, simple_loss=0.3186, pruned_loss=0.06518, over 7226.00 frames.], tot_loss[loss=0.2305, simple_loss=0.3051, pruned_loss=0.078, over 1425462.49 frames.], batch size: 20, lr: 1.53e-03
2022-05-14 00:46:37,707 INFO [train.py:812] (5/8) Epoch 4, batch 2650, loss[loss=0.1915, simple_loss=0.2557, pruned_loss=0.06369, over 6988.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3056, pruned_loss=0.078, over 1418954.97 frames.], batch size: 16, lr: 1.53e-03
2022-05-14 00:47:36,756 INFO [train.py:812] (5/8) Epoch 4, batch 2700, loss[loss=0.196, simple_loss=0.2597, pruned_loss=0.06616, over 6775.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3048, pruned_loss=0.07765, over 1417637.32 frames.], batch size: 15, lr: 1.53e-03
2022-05-14 00:48:35,479 INFO [train.py:812] (5/8) Epoch 4, batch 2750, loss[loss=0.2396, simple_loss=0.3169, pruned_loss=0.08109, over 7260.00 frames.], tot_loss[loss=0.2312, simple_loss=0.3059, pruned_loss=0.07823, over 1421323.14 frames.], batch size: 19, lr: 1.53e-03
2022-05-14 00:49:34,105 INFO [train.py:812] (5/8) Epoch 4, batch 2800, loss[loss=0.2562, simple_loss=0.3207, pruned_loss=0.0958, over 7165.00 frames.], tot_loss[loss=0.2295, simple_loss=0.3046, pruned_loss=0.07717, over 1423613.46 frames.], batch size: 19, lr: 1.53e-03
2022-05-14 00:50:32,970 INFO [train.py:812] (5/8) Epoch 4, batch 2850, loss[loss=0.2857, simple_loss=0.3479, pruned_loss=0.1117, over 5174.00 frames.], tot_loss[loss=0.2291, simple_loss=0.304, pruned_loss=0.07712, over 1422781.38 frames.], batch size: 52, lr: 1.52e-03
2022-05-14 00:51:31,216 INFO [train.py:812] (5/8) Epoch 4, batch 2900, loss[loss=0.2412, simple_loss=0.315, pruned_loss=0.0837, over 6708.00 frames.], tot_loss[loss=0.2285, simple_loss=0.3034, pruned_loss=0.07679, over 1423271.08 frames.], batch size: 31, lr: 1.52e-03
2022-05-14 00:52:31,097 INFO [train.py:812] (5/8) Epoch 4, batch 2950, loss[loss=0.2402, simple_loss=0.3195, pruned_loss=0.08045, over 7050.00 frames.], tot_loss[loss=0.2276, simple_loss=0.3023, pruned_loss=0.07642, over 1427345.10 frames.], batch size: 28, lr: 1.52e-03
2022-05-14 00:53:30,063 INFO [train.py:812] (5/8) Epoch 4, batch 3000, loss[loss=0.2237, simple_loss=0.3022, pruned_loss=0.07258, over 7155.00 frames.], tot_loss[loss=0.2279, simple_loss=0.3027, pruned_loss=0.07648, over 1425369.11 frames.], batch size: 20, lr: 1.52e-03
2022-05-14 00:53:30,064 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 00:53:37,752 INFO [train.py:841] (5/8) Epoch 4, validation: loss=0.1771, simple_loss=0.279, pruned_loss=0.03761, over 698248.00 frames.
2022-05-14 00:54:36,379 INFO [train.py:812] (5/8) Epoch 4, batch 3050, loss[loss=0.2454, simple_loss=0.3189, pruned_loss=0.08589, over 7113.00 frames.], tot_loss[loss=0.2284, simple_loss=0.303, pruned_loss=0.07693, over 1419857.42 frames.], batch size: 21, lr: 1.51e-03
2022-05-14 00:55:35,280 INFO [train.py:812] (5/8) Epoch 4, batch 3100, loss[loss=0.2432, simple_loss=0.3187, pruned_loss=0.08382, over 7272.00 frames.], tot_loss[loss=0.2275, simple_loss=0.3019, pruned_loss=0.07651, over 1416259.11 frames.], batch size: 24, lr: 1.51e-03
2022-05-14 00:56:35,141 INFO [train.py:812] (5/8) Epoch 4, batch 3150, loss[loss=0.2348, simple_loss=0.3154, pruned_loss=0.07715, over 7313.00 frames.], tot_loss[loss=0.2261, simple_loss=0.3006, pruned_loss=0.07579, over 1421017.07 frames.], batch size: 25, lr: 1.51e-03
2022-05-14 00:57:33,655 INFO [train.py:812] (5/8) Epoch 4, batch 3200, loss[loss=0.2345, simple_loss=0.3112, pruned_loss=0.07888, over 7075.00 frames.], tot_loss[loss=0.225, simple_loss=0.2995, pruned_loss=0.07521, over 1422223.17 frames.], batch size: 18, lr: 1.51e-03
2022-05-14 00:58:32,689 INFO [train.py:812] (5/8) Epoch 4, batch 3250, loss[loss=0.2042, simple_loss=0.2849, pruned_loss=0.06177, over 7251.00 frames.], tot_loss[loss=0.226, simple_loss=0.3005, pruned_loss=0.0758, over 1423886.22 frames.], batch size: 19, lr: 1.51e-03
2022-05-14 00:59:30,506 INFO [train.py:812] (5/8) Epoch 4, batch 3300, loss[loss=0.266, simple_loss=0.3256, pruned_loss=0.1032, over 7209.00 frames.], tot_loss[loss=0.2268, simple_loss=0.3016, pruned_loss=0.07598, over 1422573.73 frames.], batch size: 23, lr: 1.50e-03
2022-05-14 01:00:29,712 INFO [train.py:812] (5/8) Epoch 4, batch 3350, loss[loss=0.2775, simple_loss=0.3389, pruned_loss=0.108, over 6201.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3004, pruned_loss=0.0753, over 1419863.92 frames.], batch size: 37, lr: 1.50e-03
2022-05-14 01:01:28,325 INFO [train.py:812] (5/8) Epoch 4, batch 3400, loss[loss=0.1999, simple_loss=0.2642, pruned_loss=0.06781, over 7005.00 frames.], tot_loss[loss=0.2252, simple_loss=0.3002, pruned_loss=0.07512, over 1420893.33 frames.], batch size: 16, lr: 1.50e-03
2022-05-14 01:02:28,059 INFO [train.py:812] (5/8) Epoch 4, batch 3450, loss[loss=0.1807, simple_loss=0.2496, pruned_loss=0.05591, over 7167.00 frames.], tot_loss[loss=0.2231, simple_loss=0.2983, pruned_loss=0.07391, over 1426111.60 frames.], batch size: 18, lr: 1.50e-03
2022-05-14 01:03:26,448 INFO [train.py:812] (5/8) Epoch 4, batch 3500, loss[loss=0.2283, simple_loss=0.3102, pruned_loss=0.07316, over 7392.00 frames.], tot_loss[loss=0.2236, simple_loss=0.2985, pruned_loss=0.07433, over 1428117.08 frames.], batch size: 23, lr: 1.50e-03
2022-05-14 01:04:26,015 INFO [train.py:812] (5/8) Epoch 4, batch 3550, loss[loss=0.2575, simple_loss=0.323, pruned_loss=0.096, over 7280.00 frames.], tot_loss[loss=0.2224, simple_loss=0.2976, pruned_loss=0.07366, over 1429099.44 frames.], batch size: 24, lr: 1.49e-03
2022-05-14 01:05:25,244 INFO [train.py:812] (5/8) Epoch 4, batch 3600, loss[loss=0.2012, simple_loss=0.2782, pruned_loss=0.06204, over 7002.00 frames.], tot_loss[loss=0.223, simple_loss=0.2978, pruned_loss=0.07404, over 1427442.09 frames.], batch size: 16, lr: 1.49e-03
2022-05-14 01:06:24,744 INFO [train.py:812] (5/8) Epoch 4, batch 3650, loss[loss=0.1819, simple_loss=0.2626, pruned_loss=0.05056, over 7132.00 frames.], tot_loss[loss=0.2237, simple_loss=0.2986, pruned_loss=0.07445, over 1428411.34 frames.], batch size: 17, lr: 1.49e-03
2022-05-14 01:07:24,233 INFO [train.py:812] (5/8) Epoch 4, batch 3700, loss[loss=0.1871, simple_loss=0.2484, pruned_loss=0.06292, over 6987.00 frames.], tot_loss[loss=0.2233, simple_loss=0.2983, pruned_loss=0.07418, over 1427053.87 frames.], batch size: 16, lr: 1.49e-03
2022-05-14 01:08:24,436 INFO [train.py:812] (5/8) Epoch 4, batch 3750, loss[loss=0.214, simple_loss=0.299, pruned_loss=0.06444, over 7425.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2972, pruned_loss=0.07332, over 1424522.41 frames.], batch size: 20, lr: 1.49e-03
2022-05-14 01:09:22,771 INFO [train.py:812] (5/8) Epoch 4, batch 3800, loss[loss=0.2089, simple_loss=0.2867, pruned_loss=0.06553, over 7059.00 frames.], tot_loss[loss=0.2226, simple_loss=0.298, pruned_loss=0.07356, over 1421068.26 frames.], batch size: 18, lr: 1.48e-03
2022-05-14 01:10:22,616 INFO [train.py:812] (5/8) Epoch 4, batch 3850, loss[loss=0.2287, simple_loss=0.2964, pruned_loss=0.08054, over 7424.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2973, pruned_loss=0.07297, over 1425006.08 frames.], batch size: 18, lr: 1.48e-03
2022-05-14 01:11:21,435 INFO [train.py:812] (5/8) Epoch 4, batch 3900, loss[loss=0.2798, simple_loss=0.3414, pruned_loss=0.1091, over 5201.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2978, pruned_loss=0.07339, over 1426986.88 frames.], batch size: 52, lr: 1.48e-03
2022-05-14 01:12:20,496 INFO [train.py:812] (5/8) Epoch 4, batch 3950, loss[loss=0.2016, simple_loss=0.2618, pruned_loss=0.07066, over 7216.00 frames.], tot_loss[loss=0.2228, simple_loss=0.298, pruned_loss=0.07375, over 1425351.74 frames.], batch size: 16, lr: 1.48e-03
2022-05-14 01:13:19,408 INFO [train.py:812] (5/8) Epoch 4, batch 4000, loss[loss=0.2788, simple_loss=0.3463, pruned_loss=0.1056, over 7210.00 frames.], tot_loss[loss=0.2231, simple_loss=0.2981, pruned_loss=0.07408, over 1417835.57 frames.], batch size: 21, lr: 1.48e-03
2022-05-14 01:14:19,055 INFO [train.py:812] (5/8) Epoch 4, batch 4050, loss[loss=0.2342, simple_loss=0.3083, pruned_loss=0.08011, over 7407.00 frames.], tot_loss[loss=0.224, simple_loss=0.2989, pruned_loss=0.07459, over 1419637.71 frames.], batch size: 21, lr: 1.47e-03
2022-05-14 01:15:18,240 INFO [train.py:812] (5/8) Epoch 4, batch 4100, loss[loss=0.2385, simple_loss=0.3136, pruned_loss=0.08166, over 6458.00 frames.], tot_loss[loss=0.2253, simple_loss=0.2997, pruned_loss=0.07552, over 1422029.28 frames.], batch size: 38, lr: 1.47e-03
2022-05-14 01:16:17,166 INFO [train.py:812] (5/8) Epoch 4, batch 4150, loss[loss=0.1989, simple_loss=0.2724, pruned_loss=0.06269, over 7004.00 frames.], tot_loss[loss=0.2245, simple_loss=0.2989, pruned_loss=0.075, over 1424454.55 frames.], batch size: 16, lr: 1.47e-03
2022-05-14 01:17:15,919 INFO [train.py:812] (5/8) Epoch 4, batch 4200, loss[loss=0.2368, simple_loss=0.3143, pruned_loss=0.07968, over 7150.00 frames.], tot_loss[loss=0.2243, simple_loss=0.2987, pruned_loss=0.07492, over 1422560.69 frames.], batch size: 19, lr: 1.47e-03
2022-05-14 01:18:15,838 INFO [train.py:812] (5/8) Epoch 4, batch 4250, loss[loss=0.1739, simple_loss=0.2544, pruned_loss=0.04675, over 7363.00 frames.], tot_loss[loss=0.2239, simple_loss=0.298, pruned_loss=0.07491, over 1413808.85 frames.], batch size: 19, lr: 1.47e-03
2022-05-14 01:19:14,763 INFO [train.py:812] (5/8) Epoch 4, batch 4300, loss[loss=0.2421, simple_loss=0.3057, pruned_loss=0.08921, over 7359.00 frames.], tot_loss[loss=0.2232, simple_loss=0.2968, pruned_loss=0.07479, over 1412239.91 frames.], batch size: 19, lr: 1.47e-03
2022-05-14 01:20:14,298 INFO [train.py:812] (5/8) Epoch 4, batch 4350, loss[loss=0.23, simple_loss=0.3106, pruned_loss=0.07465, over 6603.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2949, pruned_loss=0.07402, over 1410399.06 frames.], batch size: 38, lr: 1.46e-03
2022-05-14 01:21:13,821 INFO [train.py:812] (5/8) Epoch 4, batch 4400, loss[loss=0.2046, simple_loss=0.2775, pruned_loss=0.06588, over 7058.00 frames.], tot_loss[loss=0.2214, simple_loss=0.2946, pruned_loss=0.07411, over 1409983.55 frames.], batch size: 18, lr: 1.46e-03
2022-05-14 01:22:13,439 INFO [train.py:812] (5/8) Epoch 4, batch 4450, loss[loss=0.2061, simple_loss=0.299, pruned_loss=0.05663, over 7376.00 frames.], tot_loss[loss=0.223, simple_loss=0.2958, pruned_loss=0.07508, over 1400731.83 frames.], batch size: 23, lr: 1.46e-03
2022-05-14 01:23:11,878 INFO [train.py:812] (5/8) Epoch 4, batch 4500, loss[loss=0.2351, simple_loss=0.3164, pruned_loss=0.0769, over 6492.00 frames.], tot_loss[loss=0.2236, simple_loss=0.2965, pruned_loss=0.07537, over 1396426.83 frames.], batch size: 38, lr: 1.46e-03
2022-05-14 01:24:10,625 INFO [train.py:812] (5/8) Epoch 4, batch 4550, loss[loss=0.2614, simple_loss=0.3262, pruned_loss=0.09829, over 5338.00 frames.], tot_loss[loss=0.2277, simple_loss=0.3, pruned_loss=0.07767, over 1363078.50 frames.], batch size: 53, lr: 1.46e-03
2022-05-14 01:25:17,917 INFO [train.py:812] (5/8) Epoch 5, batch 0, loss[loss=0.2578, simple_loss=0.3389, pruned_loss=0.08834, over 7212.00 frames.], tot_loss[loss=0.2578, simple_loss=0.3389, pruned_loss=0.08834, over 7212.00 frames.], batch size: 23, lr: 1.40e-03
2022-05-14 01:26:16,016 INFO [train.py:812] (5/8) Epoch 5, batch 50, loss[loss=0.2237, simple_loss=0.3103, pruned_loss=0.06853, over 7341.00 frames.], tot_loss[loss=0.219, simple_loss=0.2958, pruned_loss=0.07109, over 320885.32 frames.], batch size: 22, lr: 1.40e-03
2022-05-14 01:27:13,775 INFO [train.py:812] (5/8) Epoch 5, batch 100, loss[loss=0.2209, simple_loss=0.3027, pruned_loss=0.06956, over 7334.00 frames.], tot_loss[loss=0.2222, simple_loss=0.2984, pruned_loss=0.07302, over 566740.25 frames.], batch size: 22, lr: 1.40e-03
2022-05-14 01:28:13,016 INFO [train.py:812] (5/8) Epoch 5, batch 150, loss[loss=0.247, simple_loss=0.3201, pruned_loss=0.08695, over 5186.00 frames.], tot_loss[loss=0.2233, simple_loss=0.2999, pruned_loss=0.07336, over 756319.69 frames.], batch size: 52, lr: 1.40e-03
2022-05-14 01:29:12,392 INFO [train.py:812] (5/8) Epoch 5, batch 200, loss[loss=0.2163, simple_loss=0.2909, pruned_loss=0.07091, over 7167.00 frames.], tot_loss[loss=0.2252, simple_loss=0.3016, pruned_loss=0.07436, over 904209.76 frames.], batch size: 19, lr: 1.40e-03
2022-05-14 01:30:11,972 INFO [train.py:812] (5/8) Epoch 5, batch 250, loss[loss=0.2455, simple_loss=0.3395, pruned_loss=0.07581, over 7352.00 frames.], tot_loss[loss=0.2263, simple_loss=0.3032, pruned_loss=0.07467, over 1021580.19 frames.], batch size: 22, lr: 1.39e-03
2022-05-14 01:31:10,334 INFO [train.py:812] (5/8) Epoch 5, batch 300, loss[loss=0.2223, simple_loss=0.2822, pruned_loss=0.0812, over 7285.00 frames.], tot_loss[loss=0.2241, simple_loss=0.301, pruned_loss=0.07357, over 1114136.69 frames.], batch size: 17, lr: 1.39e-03
2022-05-14 01:32:09,249 INFO [train.py:812] (5/8) Epoch 5, batch 350, loss[loss=0.1956, simple_loss=0.2792, pruned_loss=0.05602, over 7160.00 frames.], tot_loss[loss=0.223, simple_loss=0.2995, pruned_loss=0.07324, over 1182262.25 frames.], batch size: 19, lr: 1.39e-03
2022-05-14 01:33:06,987 INFO [train.py:812] (5/8) Epoch 5, batch 400, loss[loss=0.2084, simple_loss=0.284, pruned_loss=0.06637, over 7100.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2982, pruned_loss=0.07284, over 1232794.85 frames.], batch size: 28, lr: 1.39e-03
2022-05-14 01:34:05,733 INFO [train.py:812] (5/8) Epoch 5, batch 450, loss[loss=0.2273, simple_loss=0.3, pruned_loss=0.07725, over 7094.00 frames.], tot_loss[loss=0.2205, simple_loss=0.297, pruned_loss=0.07204, over 1274330.45 frames.], batch size: 28, lr: 1.39e-03
2022-05-14 01:35:05,167 INFO [train.py:812] (5/8) Epoch 5, batch 500, loss[loss=0.1916, simple_loss=0.2839, pruned_loss=0.04969, over 7307.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2956, pruned_loss=0.07099, over 1308469.67 frames.], batch size: 21, lr: 1.39e-03
2022-05-14 01:36:04,757 INFO [train.py:812] (5/8) Epoch 5, batch 550, loss[loss=0.2048, simple_loss=0.2911, pruned_loss=0.05927, over 6777.00 frames.], tot_loss[loss=0.218, simple_loss=0.2952, pruned_loss=0.07043, over 1333235.97 frames.], batch size: 31, lr: 1.38e-03
2022-05-14 01:37:04,108 INFO [train.py:812] (5/8) Epoch 5, batch 600, loss[loss=0.2214, simple_loss=0.2925, pruned_loss=0.07513, over 7009.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2951, pruned_loss=0.07034, over 1355441.91 frames.], batch size: 16, lr: 1.38e-03
2022-05-14 01:38:03,246 INFO [train.py:812] (5/8) Epoch 5, batch 650, loss[loss=0.2425, simple_loss=0.3123, pruned_loss=0.0863, over 7340.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2949, pruned_loss=0.06993, over 1370872.20 frames.], batch size: 20, lr: 1.38e-03
2022-05-14 01:39:02,103 INFO [train.py:812] (5/8) Epoch 5, batch 700, loss[loss=0.2617, simple_loss=0.3366, pruned_loss=0.09337, over 7314.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2959, pruned_loss=0.07075, over 1380294.00 frames.], batch size: 25, lr: 1.38e-03
2022-05-14 01:40:01,974 INFO [train.py:812] (5/8) Epoch 5, batch 750, loss[loss=0.1857, simple_loss=0.2648, pruned_loss=0.05334, over 7070.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2954, pruned_loss=0.07103, over 1384643.88 frames.], batch size: 18, lr: 1.38e-03
2022-05-14 01:40:59,763 INFO [train.py:812] (5/8) Epoch 5, batch 800, loss[loss=0.2033, simple_loss=0.2791, pruned_loss=0.06375, over 7444.00 frames.], tot_loss[loss=0.2173, simple_loss=0.2938, pruned_loss=0.07036, over 1396780.92 frames.], batch size: 19, lr: 1.38e-03
2022-05-14 01:41:57,350 INFO [train.py:812] (5/8) Epoch 5, batch 850, loss[loss=0.2021, simple_loss=0.2785, pruned_loss=0.06288, over 7063.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2936, pruned_loss=0.07058, over 1395693.90 frames.], batch size: 18, lr: 1.37e-03
2022-05-14 01:42:55,832 INFO [train.py:812] (5/8) Epoch 5, batch 900, loss[loss=0.2397, simple_loss=0.3129, pruned_loss=0.08322, over 7316.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2928, pruned_loss=0.06981, over 1403415.50 frames.], batch size: 21, lr: 1.37e-03
2022-05-14 01:43:53,342 INFO [train.py:812] (5/8) Epoch 5, batch 950, loss[loss=0.2303, simple_loss=0.3003, pruned_loss=0.08019, over 6975.00 frames.], tot_loss[loss=0.217, simple_loss=0.2934, pruned_loss=0.07035, over 1407888.98 frames.], batch size: 28, lr: 1.37e-03
2022-05-14 01:44:52,023 INFO [train.py:812] (5/8) Epoch 5, batch 1000, loss[loss=0.2014, simple_loss=0.2835, pruned_loss=0.05969, over 7082.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2926, pruned_loss=0.07007, over 1412039.75 frames.], batch size: 18, lr: 1.37e-03
2022-05-14 01:45:49,481 INFO [train.py:812] (5/8) Epoch 5, batch 1050, loss[loss=0.2561, simple_loss=0.3325, pruned_loss=0.0899, over 7285.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2943, pruned_loss=0.07094, over 1417217.78 frames.], batch size: 24, lr: 1.37e-03
2022-05-14 01:46:47,331 INFO [train.py:812] (5/8) Epoch 5, batch 1100, loss[loss=0.2462, simple_loss=0.3123, pruned_loss=0.0901, over 6285.00 frames.], tot_loss[loss=0.219, simple_loss=0.2957, pruned_loss=0.0712, over 1413190.70 frames.], batch size: 37, lr: 1.37e-03
2022-05-14 01:47:47,035 INFO [train.py:812] (5/8) Epoch 5, batch 1150, loss[loss=0.2743, simple_loss=0.3434, pruned_loss=0.1026, over 7427.00 frames.], tot_loss[loss=0.2193, simple_loss=0.296, pruned_loss=0.07126, over 1415700.45 frames.], batch size: 20, lr: 1.36e-03
2022-05-14 01:48:45,951 INFO [train.py:812] (5/8) Epoch 5, batch 1200, loss[loss=0.2173, simple_loss=0.3029, pruned_loss=0.06582, over 6224.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2953, pruned_loss=0.07107, over 1417297.10 frames.], batch size: 37, lr: 1.36e-03
2022-05-14 01:49:45,442 INFO [train.py:812] (5/8) Epoch 5, batch 1250, loss[loss=0.1902, simple_loss=0.2775, pruned_loss=0.05143, over 7262.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2954, pruned_loss=0.07141, over 1412190.36 frames.], batch size: 19, lr: 1.36e-03
2022-05-14 01:50:43,661 INFO [train.py:812] (5/8) Epoch 5, batch 1300, loss[loss=0.2118, simple_loss=0.2934, pruned_loss=0.06507, over 7335.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2957, pruned_loss=0.07099, over 1415452.87 frames.], batch size: 20, lr: 1.36e-03
2022-05-14 01:51:42,394 INFO [train.py:812] (5/8) Epoch 5, batch 1350, loss[loss=0.1947, simple_loss=0.2712, pruned_loss=0.05911, over 7141.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2958, pruned_loss=0.07067, over 1422289.00 frames.], batch size: 17, lr: 1.36e-03
2022-05-14 01:52:39,818 INFO [train.py:812] (5/8) Epoch 5, batch 1400, loss[loss=0.2087, simple_loss=0.2811, pruned_loss=0.06818, over 7241.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2977, pruned_loss=0.07166, over 1419123.79 frames.], batch size: 20, lr: 1.36e-03
2022-05-14 01:53:37,526 INFO [train.py:812] (5/8) Epoch 5, batch 1450, loss[loss=0.2103, simple_loss=0.2792, pruned_loss=0.07069, over 7001.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2979, pruned_loss=0.07154, over 1419703.47 frames.], batch size: 16, lr: 1.35e-03
2022-05-14 01:54:35,091 INFO [train.py:812] (5/8) Epoch 5, batch 1500, loss[loss=0.2173, simple_loss=0.2908, pruned_loss=0.07196, over 7315.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2968, pruned_loss=0.07114, over 1422476.91 frames.], batch size: 20, lr: 1.35e-03
2022-05-14 01:55:34,681 INFO [train.py:812] (5/8) Epoch 5, batch 1550, loss[loss=0.2689, simple_loss=0.3354, pruned_loss=0.1012, over 7371.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2957, pruned_loss=0.07108, over 1424580.74 frames.], batch size: 23, lr: 1.35e-03
2022-05-14 01:56:33,038 INFO [train.py:812] (5/8) Epoch 5, batch 1600, loss[loss=0.2205, simple_loss=0.2986, pruned_loss=0.0712, over 7293.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2956, pruned_loss=0.07084, over 1424006.58 frames.], batch size: 25, lr: 1.35e-03
2022-05-14 01:57:37,109 INFO [train.py:812] (5/8) Epoch 5, batch 1650, loss[loss=0.2471, simple_loss=0.3284, pruned_loss=0.08293, over 7111.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2956, pruned_loss=0.07078, over 1422185.81 frames.], batch size: 21, lr: 1.35e-03
2022-05-14 01:58:36,673 INFO [train.py:812] (5/8) Epoch 5, batch 1700, loss[loss=0.1936, simple_loss=0.2775, pruned_loss=0.05483, over 7336.00 frames.], tot_loss[loss=0.2183, simple_loss=0.2951, pruned_loss=0.07081, over 1423956.10 frames.], batch size: 22, lr: 1.35e-03
2022-05-14 01:59:35,632 INFO [train.py:812] (5/8) Epoch 5, batch 1750, loss[loss=0.2007, simple_loss=0.2866, pruned_loss=0.05734, over 7287.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2948, pruned_loss=0.07079, over 1423494.54 frames.], batch size: 24, lr: 1.34e-03
2022-05-14 02:00:34,956 INFO [train.py:812] (5/8) Epoch 5, batch 1800, loss[loss=0.2298, simple_loss=0.3279, pruned_loss=0.06588, over 7326.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2946, pruned_loss=0.07025, over 1426186.79 frames.], batch size: 21, lr: 1.34e-03
2022-05-14 02:01:33,549 INFO [train.py:812] (5/8) Epoch 5, batch 1850, loss[loss=0.2565, simple_loss=0.3361, pruned_loss=0.08846, over 6209.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2949, pruned_loss=0.06994, over 1426352.93 frames.], batch size: 37, lr: 1.34e-03
2022-05-14 02:02:31,897 INFO [train.py:812] (5/8) Epoch 5, batch 1900, loss[loss=0.2424, simple_loss=0.3254, pruned_loss=0.07973, over 7105.00 frames.], tot_loss[loss=0.2177, simple_loss=0.2953, pruned_loss=0.07007, over 1427202.51 frames.], batch size: 21, lr: 1.34e-03
2022-05-14 02:03:30,586 INFO [train.py:812] (5/8) Epoch 5, batch 1950, loss[loss=0.1889, simple_loss=0.2607, pruned_loss=0.0585, over 7160.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2943, pruned_loss=0.06922, over 1427257.19 frames.], batch size: 18, lr: 1.34e-03
2022-05-14 02:04:28,312 INFO [train.py:812] (5/8) Epoch 5, batch 2000, loss[loss=0.2193, simple_loss=0.303, pruned_loss=0.06778, over 7295.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2944, pruned_loss=0.06951, over 1424084.16 frames.], batch size: 25, lr: 1.34e-03
2022-05-14 02:05:26,866 INFO [train.py:812] (5/8) Epoch 5, batch 2050, loss[loss=0.2257, simple_loss=0.3005, pruned_loss=0.07546, over 7295.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2938, pruned_loss=0.06935, over 1429134.23 frames.], batch size: 24, lr: 1.34e-03
2022-05-14 02:06:25,379 INFO [train.py:812] (5/8) Epoch 5, batch 2100, loss[loss=0.2003, simple_loss=0.285, pruned_loss=0.05781, over 7410.00 frames.], tot_loss[loss=0.215, simple_loss=0.2931, pruned_loss=0.06852, over 1432115.73 frames.], batch size: 18, lr: 1.33e-03
2022-05-14 02:07:23,975 INFO [train.py:812] (5/8) Epoch 5, batch 2150, loss[loss=0.1931, simple_loss=0.2798, pruned_loss=0.05316, over 7064.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2944, pruned_loss=0.06871, over 1431014.71 frames.], batch size: 18, lr: 1.33e-03
2022-05-14 02:08:21,805 INFO [train.py:812] (5/8) Epoch 5, batch 2200, loss[loss=0.2217, simple_loss=0.3062, pruned_loss=0.06864, over 7338.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2938, pruned_loss=0.06902, over 1433521.64 frames.], batch size: 22, lr: 1.33e-03
2022-05-14 02:09:20,847 INFO [train.py:812] (5/8) Epoch 5, batch 2250, loss[loss=0.247, simple_loss=0.3142, pruned_loss=0.08995, over 7390.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2938, pruned_loss=0.06962, over 1431421.71 frames.], batch size: 23, lr: 1.33e-03
2022-05-14 02:10:20,191 INFO [train.py:812] (5/8) Epoch 5, batch 2300, loss[loss=0.1585, simple_loss=0.2315, pruned_loss=0.04268, over 7258.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2934, pruned_loss=0.06973, over 1430302.81 frames.], batch size: 17, lr: 1.33e-03
2022-05-14 02:11:18,993 INFO [train.py:812] (5/8) Epoch 5, batch 2350, loss[loss=0.1798, simple_loss=0.2624, pruned_loss=0.04858, over 7399.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2942, pruned_loss=0.06957, over 1433371.19 frames.], batch size: 18, lr: 1.33e-03
2022-05-14 02:12:18,589 INFO [train.py:812] (5/8) Epoch 5, batch 2400, loss[loss=0.2052, simple_loss=0.2949, pruned_loss=0.05775, over 7206.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2938, pruned_loss=0.06956, over 1434921.23 frames.], batch size: 21, lr: 1.32e-03
2022-05-14 02:13:16,798 INFO [train.py:812] (5/8) Epoch 5, batch 2450, loss[loss=0.2043, simple_loss=0.2687, pruned_loss=0.06997, over 7283.00 frames.], tot_loss[loss=0.2178, simple_loss=0.2949, pruned_loss=0.0704, over 1435292.02 frames.], batch size: 18, lr: 1.32e-03
2022-05-14 02:14:14,131 INFO [train.py:812] (5/8) Epoch 5, batch 2500, loss[loss=0.264, simple_loss=0.3396, pruned_loss=0.09418, over 7206.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2946, pruned_loss=0.07022, over 1433130.63 frames.], batch size: 22, lr: 1.32e-03
2022-05-14 02:15:13,185 INFO [train.py:812] (5/8) Epoch 5, batch 2550, loss[loss=0.2476, simple_loss=0.3337, pruned_loss=0.08072, over 7144.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2945, pruned_loss=0.06997, over 1433677.21 frames.], batch size: 20, lr: 1.32e-03
2022-05-14 02:16:11,204 INFO [train.py:812] (5/8) Epoch 5, batch 2600, loss[loss=0.2557, simple_loss=0.336, pruned_loss=0.08775, over 7314.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2952, pruned_loss=0.07052, over 1431677.00 frames.], batch size: 21, lr: 1.32e-03
2022-05-14 02:17:10,909 INFO [train.py:812] (5/8) Epoch 5, batch 2650, loss[loss=0.1912, simple_loss=0.2671, pruned_loss=0.05764, over 7003.00 frames.], tot_loss[loss=0.2171, simple_loss=0.2948, pruned_loss=0.06968, over 1429762.62 frames.], batch size: 16, lr: 1.32e-03
2022-05-14 02:18:10,457 INFO [train.py:812] (5/8) Epoch 5, batch 2700, loss[loss=0.1765, simple_loss=0.2605, pruned_loss=0.04627, over 7287.00 frames.], tot_loss[loss=0.216, simple_loss=0.2939, pruned_loss=0.06908, over 1431672.28 frames.], batch size: 18, lr: 1.32e-03
2022-05-14 02:19:10,224 INFO [train.py:812] (5/8) Epoch 5, batch 2750, loss[loss=0.2261, simple_loss=0.3, pruned_loss=0.0761, over 7358.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2932, pruned_loss=0.06894, over 1432263.03 frames.], batch size: 19, lr: 1.31e-03
2022-05-14 02:20:09,508 INFO [train.py:812] (5/8) Epoch 5, batch 2800, loss[loss=0.177, simple_loss=0.2542, pruned_loss=0.04987, over 7124.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2917, pruned_loss=0.06802, over 1433239.29 frames.], batch size: 17, lr: 1.31e-03
2022-05-14 02:21:07,407 INFO [train.py:812] (5/8) Epoch 5, batch 2850, loss[loss=0.2232, simple_loss=0.307, pruned_loss=0.06971, over 6809.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2927, pruned_loss=0.06835, over 1430488.57 frames.], batch size: 31, lr: 1.31e-03
2022-05-14 02:22:06,259 INFO [train.py:812] (5/8) Epoch 5, batch 2900, loss[loss=0.245, simple_loss=0.3205, pruned_loss=0.0847, over 7284.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2932, pruned_loss=0.06829, over 1428562.04 frames.], batch size: 24, lr: 1.31e-03
2022-05-14 02:23:05,639 INFO [train.py:812] (5/8) Epoch 5, batch 2950, loss[loss=0.2155, simple_loss=0.3025, pruned_loss=0.06428, over 7346.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2921, pruned_loss=0.06817, over 1428943.10 frames.], batch size: 22, lr: 1.31e-03
2022-05-14 02:24:04,415 INFO [train.py:812] (5/8) Epoch 5, batch 3000, loss[loss=0.2005, simple_loss=0.2855, pruned_loss=0.05771, over 7171.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2926, pruned_loss=0.06877, over 1425418.94 frames.], batch size: 26, lr: 1.31e-03
2022-05-14 02:24:04,416 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 02:24:12,114 INFO [train.py:841] (5/8) Epoch 5, validation: loss=0.1705, simple_loss=0.2732, pruned_loss=0.03391, over 698248.00 frames.
2022-05-14 02:25:11,867 INFO [train.py:812] (5/8) Epoch 5, batch 3050, loss[loss=0.2133, simple_loss=0.297, pruned_loss=0.06484, over 7191.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2931, pruned_loss=0.06864, over 1428788.99 frames.], batch size: 22, lr: 1.31e-03
2022-05-14 02:26:09,566 INFO [train.py:812] (5/8) Epoch 5, batch 3100, loss[loss=0.205, simple_loss=0.2974, pruned_loss=0.05624, over 7230.00 frames.], tot_loss[loss=0.216, simple_loss=0.2938, pruned_loss=0.06908, over 1427209.55 frames.], batch size: 20, lr: 1.30e-03
2022-05-14 02:27:19,077 INFO [train.py:812] (5/8) Epoch 5, batch 3150, loss[loss=0.1904, simple_loss=0.2767, pruned_loss=0.05206, over 7287.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2937, pruned_loss=0.06862, over 1427749.75 frames.], batch size: 25, lr: 1.30e-03
2022-05-14 02:28:18,320 INFO [train.py:812] (5/8) Epoch 5, batch 3200, loss[loss=0.1815, simple_loss=0.2707, pruned_loss=0.04612, over 7355.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2937, pruned_loss=0.06897, over 1428998.24 frames.], batch size: 19, lr: 1.30e-03
2022-05-14 02:29:17,311 INFO [train.py:812] (5/8) Epoch 5, batch 3250, loss[loss=0.2176, simple_loss=0.2882, pruned_loss=0.07351, over 7156.00 frames.], tot_loss[loss=0.2145, simple_loss=0.2922, pruned_loss=0.06837, over 1427296.41 frames.], batch size: 18, lr: 1.30e-03
2022-05-14 02:30:15,407 INFO [train.py:812] (5/8) Epoch 5, batch 3300, loss[loss=0.2338, simple_loss=0.3205, pruned_loss=0.07359, over 7195.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2932, pruned_loss=0.069, over 1422999.04 frames.], batch size: 26, lr: 1.30e-03
2022-05-14 02:31:14,128 INFO [train.py:812] (5/8) Epoch 5, batch 3350, loss[loss=0.2367, simple_loss=0.3211, pruned_loss=0.07614, over 7110.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2929, pruned_loss=0.06883, over 1425416.98 frames.], batch size: 21, lr: 1.30e-03
2022-05-14 02:32:12,539 INFO [train.py:812] (5/8) Epoch 5, batch 3400, loss[loss=0.225, simple_loss=0.3084, pruned_loss=0.07085, over 7226.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2938, pruned_loss=0.06889, over 1427236.27 frames.], batch size: 20, lr: 1.30e-03
2022-05-14 02:33:11,746 INFO [train.py:812] (5/8) Epoch 5, batch 3450, loss[loss=0.2316, simple_loss=0.2959, pruned_loss=0.08365, over 7190.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2931, pruned_loss=0.06867, over 1426903.30 frames.], batch size: 23, lr: 1.29e-03
2022-05-14 02:34:10,770 INFO [train.py:812] (5/8) Epoch 5, batch 3500, loss[loss=0.2016, simple_loss=0.2751, pruned_loss=0.06412, over 7331.00 frames.], tot_loss[loss=0.2145, simple_loss=0.293, pruned_loss=0.06803, over 1429184.44 frames.], batch size: 20, lr: 1.29e-03
2022-05-14 02:35:38,312 INFO [train.py:812] (5/8) Epoch 5, batch 3550, loss[loss=0.1927, simple_loss=0.2789, pruned_loss=0.05322, over 7404.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2934, pruned_loss=0.06837, over 1423814.53 frames.], batch size: 21, lr: 1.29e-03
2022-05-14 02:36:46,047 INFO [train.py:812] (5/8) Epoch 5, batch 3600, loss[loss=0.1868, simple_loss=0.262, pruned_loss=0.05575, over 7258.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2926, pruned_loss=0.06811, over 1420329.02 frames.], batch size: 19, lr: 1.29e-03
2022-05-14 02:38:13,272 INFO [train.py:812] (5/8) Epoch 5, batch 3650, loss[loss=0.2341, simple_loss=0.3078, pruned_loss=0.08019, over 6807.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2926, pruned_loss=0.06796, over 1414920.62 frames.], batch size: 31, lr: 1.29e-03
2022-05-14 02:39:12,928 INFO [train.py:812] (5/8) Epoch 5, batch 3700, loss[loss=0.1794, simple_loss=0.2616, pruned_loss=0.04859, over 7163.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2907, pruned_loss=0.0671, over 1418532.78 frames.], batch size: 18, lr: 1.29e-03
2022-05-14 02:40:11,642 INFO [train.py:812] (5/8) Epoch 5, batch 3750, loss[loss=0.1809, simple_loss=0.2576, pruned_loss=0.05205, over 7203.00 frames.], tot_loss[loss=0.2135, simple_loss=0.292, pruned_loss=0.0675, over 1420213.42 frames.], batch size: 16, lr: 1.29e-03
2022-05-14 02:41:09,956 INFO [train.py:812] (5/8) Epoch 5, batch 3800, loss[loss=0.2084, simple_loss=0.278, pruned_loss=0.06944, over 7278.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2914, pruned_loss=0.0672, over 1421500.50 frames.], batch size: 18, lr: 1.28e-03
2022-05-14 02:42:07,678 INFO [train.py:812] (5/8) Epoch 5, batch 3850, loss[loss=0.214, simple_loss=0.3036, pruned_loss=0.06223, over 7414.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2914, pruned_loss=0.06751, over 1421028.01 frames.], batch size: 21, lr: 1.28e-03
2022-05-14 02:43:06,305 INFO [train.py:812] (5/8) Epoch 5, batch 3900, loss[loss=0.2076, simple_loss=0.283, pruned_loss=0.06608, over 7163.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2907, pruned_loss=0.06722, over 1417693.54 frames.], batch size: 18, lr: 1.28e-03
2022-05-14 02:44:04,305 INFO [train.py:812] (5/8) Epoch 5, batch 3950, loss[loss=0.2159, simple_loss=0.2997, pruned_loss=0.06601, over 7421.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2913, pruned_loss=0.06775, over 1414903.04 frames.], batch size: 21, lr: 1.28e-03
2022-05-14 02:45:02,166 INFO [train.py:812] (5/8) Epoch 5, batch 4000, loss[loss=0.1979, simple_loss=0.2872, pruned_loss=0.05427, over 7436.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2914, pruned_loss=0.06757, over 1418250.73 frames.], batch size: 20, lr: 1.28e-03
2022-05-14 02:46:01,689 INFO [train.py:812] (5/8) Epoch 5, batch 4050, loss[loss=0.2008, simple_loss=0.2956, pruned_loss=0.05305, over 7218.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2905, pruned_loss=0.06721, over 1420352.12 frames.], batch size: 21, lr: 1.28e-03
2022-05-14 02:46:59,616 INFO [train.py:812] (5/8) Epoch 5, batch 4100, loss[loss=0.2168, simple_loss=0.2937, pruned_loss=0.06993, over 7274.00 frames.], tot_loss[loss=0.2145, simple_loss=0.2926, pruned_loss=0.0682, over 1417532.87 frames.], batch size: 18, lr: 1.28e-03
2022-05-14 02:47:58,847 INFO [train.py:812] (5/8) Epoch 5, batch 4150, loss[loss=0.2594, simple_loss=0.3185, pruned_loss=0.1001, over 7193.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2935, pruned_loss=0.06893, over 1415569.25 frames.], batch size: 22, lr: 1.27e-03
2022-05-14 02:48:57,884 INFO [train.py:812] (5/8) Epoch 5, batch 4200, loss[loss=0.2365, simple_loss=0.2994, pruned_loss=0.08679, over 7133.00 frames.], tot_loss[loss=0.2162, simple_loss=0.294, pruned_loss=0.06919, over 1414276.40 frames.], batch size: 17, lr: 1.27e-03
2022-05-14 02:49:57,210 INFO [train.py:812] (5/8) Epoch 5, batch 4250, loss[loss=0.1869, simple_loss=0.2808, pruned_loss=0.04657, over 7067.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2933, pruned_loss=0.06861, over 1414643.45 frames.], batch size: 18, lr: 1.27e-03
2022-05-14 02:50:54,438 INFO [train.py:812] (5/8) Epoch 5, batch 4300, loss[loss=0.1698, simple_loss=0.2638, pruned_loss=0.03785, over 7150.00 frames.], tot_loss[loss=0.216, simple_loss=0.294, pruned_loss=0.06895, over 1415416.58 frames.], batch size: 20, lr: 1.27e-03
2022-05-14 02:51:52,655 INFO [train.py:812] (5/8) Epoch 5, batch 4350, loss[loss=0.2105, simple_loss=0.2912, pruned_loss=0.06486, over 7421.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2948, pruned_loss=0.06983, over 1414703.56 frames.], batch size: 21, lr: 1.27e-03
2022-05-14 02:52:52,053 INFO [train.py:812] (5/8) Epoch 5, batch 4400, loss[loss=0.2075, simple_loss=0.2847, pruned_loss=0.06512, over 7263.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2942, pruned_loss=0.06955, over 1410085.12 frames.], batch size: 19, lr: 1.27e-03
2022-05-14 02:53:51,756 INFO [train.py:812] (5/8) Epoch 5, batch 4450, loss[loss=0.2109, simple_loss=0.2927, pruned_loss=0.06459, over 6845.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2945, pruned_loss=0.06949, over 1403640.04 frames.], batch size: 31, lr: 1.27e-03
2022-05-14 02:54:49,525 INFO [train.py:812] (5/8) Epoch 5, batch 4500, loss[loss=0.2469, simple_loss=0.3108, pruned_loss=0.09147, over 5207.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2972, pruned_loss=0.07089, over 1394002.91 frames.], batch size: 53, lr: 1.27e-03
2022-05-14 02:55:48,804 INFO [train.py:812] (5/8) Epoch 5, batch 4550, loss[loss=0.2929, simple_loss=0.3419, pruned_loss=0.1219, over 5283.00 frames.], tot_loss[loss=0.2242, simple_loss=0.3002, pruned_loss=0.07412, over 1337773.26 frames.], batch size: 53, lr: 1.26e-03
2022-05-14 02:56:57,104 INFO [train.py:812] (5/8) Epoch 6, batch 0, loss[loss=0.1969, simple_loss=0.2741, pruned_loss=0.0598, over 7157.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2741, pruned_loss=0.0598, over 7157.00 frames.], batch size: 19, lr: 1.21e-03
2022-05-14 02:57:56,761 INFO [train.py:812] (5/8) Epoch 6, batch 50, loss[loss=0.2584, simple_loss=0.3184, pruned_loss=0.09923, over 5005.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2928, pruned_loss=0.06697, over 318596.10 frames.], batch size: 52, lr: 1.21e-03
2022-05-14 02:58:56,471 INFO [train.py:812] (5/8) Epoch 6, batch 100, loss[loss=0.238, simple_loss=0.3194, pruned_loss=0.07829, over 7144.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2952, pruned_loss=0.06873, over 562286.52 frames.], batch size: 20, lr: 1.21e-03
2022-05-14 02:59:55,391 INFO [train.py:812] (5/8) Epoch 6, batch 150, loss[loss=0.2233, simple_loss=0.3069, pruned_loss=0.06986, over 6668.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2932, pruned_loss=0.06716, over 750570.13 frames.], batch size: 31, lr: 1.21e-03
2022-05-14 03:00:54,857 INFO [train.py:812] (5/8) Epoch 6, batch 200, loss[loss=0.1956, simple_loss=0.2707, pruned_loss=0.06022, over 7408.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2925, pruned_loss=0.06684, over 899918.11 frames.], batch size: 18, lr: 1.21e-03
2022-05-14 03:01:54,420 INFO [train.py:812] (5/8) Epoch 6, batch 250, loss[loss=0.2391, simple_loss=0.3243, pruned_loss=0.07694, over 7318.00 frames.], tot_loss[loss=0.2122, simple_loss=0.292, pruned_loss=0.06622, over 1019885.66 frames.], batch size: 22, lr: 1.21e-03
2022-05-14 03:02:54,512 INFO [train.py:812] (5/8) Epoch 6, batch 300, loss[loss=0.175, simple_loss=0.265, pruned_loss=0.04252, over 7242.00 frames.], tot_loss[loss=0.21, simple_loss=0.29, pruned_loss=0.065, over 1112227.15 frames.], batch size: 20, lr: 1.21e-03
2022-05-14 03:03:51,875 INFO [train.py:812] (5/8) Epoch 6, batch 350, loss[loss=0.2041, simple_loss=0.2826, pruned_loss=0.06279, over 7320.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2888, pruned_loss=0.06473, over 1185241.17 frames.], batch size: 20, lr: 1.20e-03
2022-05-14 03:04:49,933 INFO [train.py:812] (5/8) Epoch 6, batch 400, loss[loss=0.2077, simple_loss=0.2956, pruned_loss=0.05993, over 7367.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2896, pruned_loss=0.06479, over 1236553.02 frames.], batch size: 23, lr: 1.20e-03
2022-05-14 03:05:47,794 INFO [train.py:812] (5/8) Epoch 6, batch 450, loss[loss=0.2286, simple_loss=0.3001, pruned_loss=0.07854, over 7243.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2893, pruned_loss=0.06464, over 1279300.57 frames.], batch size: 16, lr: 1.20e-03
2022-05-14 03:06:47,293 INFO [train.py:812] (5/8) Epoch 6, batch 500, loss[loss=0.2487, simple_loss=0.3186, pruned_loss=0.0894, over 5037.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2897, pruned_loss=0.0647, over 1307600.28 frames.], batch size: 52, lr: 1.20e-03
2022-05-14 03:07:45,162 INFO [train.py:812] (5/8) Epoch 6, batch 550, loss[loss=0.2708, simple_loss=0.3515, pruned_loss=0.09499, over 6502.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2901, pruned_loss=0.06472, over 1332207.39 frames.], batch size: 38, lr: 1.20e-03
2022-05-14 03:08:44,002 INFO [train.py:812] (5/8) Epoch 6, batch 600, loss[loss=0.1891, simple_loss=0.279, pruned_loss=0.04958, over 7154.00 frames.], tot_loss[loss=0.208, simple_loss=0.2883, pruned_loss=0.06387, over 1351357.41 frames.], batch size: 20, lr: 1.20e-03
2022-05-14 03:09:42,698 INFO [train.py:812] (5/8) Epoch 6, batch 650, loss[loss=0.2046, simple_loss=0.2936, pruned_loss=0.0578, over 7408.00 frames.], tot_loss[loss=0.2079, simple_loss=0.288, pruned_loss=0.06391, over 1365910.20 frames.], batch size: 21, lr: 1.20e-03
2022-05-14 03:10:42,150 INFO [train.py:812] (5/8) Epoch 6, batch 700, loss[loss=0.203, simple_loss=0.2862, pruned_loss=0.05984, over 6745.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2894, pruned_loss=0.0647, over 1377364.29 frames.], batch size: 15, lr: 1.20e-03
2022-05-14 03:11:41,181 INFO [train.py:812] (5/8) Epoch 6, batch 750, loss[loss=0.2029, simple_loss=0.2872, pruned_loss=0.05934, over 7219.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2902, pruned_loss=0.06521, over 1387970.94 frames.], batch size: 21, lr: 1.19e-03
2022-05-14 03:12:41,094 INFO [train.py:812] (5/8) Epoch 6, batch 800, loss[loss=0.229, simple_loss=0.3163, pruned_loss=0.07089, over 7221.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2884, pruned_loss=0.06397, over 1397808.45 frames.], batch size: 21, lr: 1.19e-03
2022-05-14 03:13:40,483 INFO [train.py:812] (5/8) Epoch 6, batch 850, loss[loss=0.2281, simple_loss=0.305, pruned_loss=0.0756, over 7201.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2888, pruned_loss=0.0643, over 1403018.32 frames.], batch size: 23, lr: 1.19e-03
2022-05-14 03:14:39,814 INFO [train.py:812] (5/8) Epoch 6, batch 900, loss[loss=0.272, simple_loss=0.3574, pruned_loss=0.09324, over 7416.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2902, pruned_loss=0.06517, over 1405730.65 frames.], batch size: 21, lr: 1.19e-03
2022-05-14 03:15:38,615 INFO [train.py:812] (5/8) Epoch 6, batch 950, loss[loss=0.1942, simple_loss=0.2667, pruned_loss=0.0608, over 7130.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2901, pruned_loss=0.06527, over 1406585.43 frames.], batch size: 17, lr: 1.19e-03
2022-05-14 03:16:37,953 INFO [train.py:812] (5/8) Epoch 6, batch 1000, loss[loss=0.2319, simple_loss=0.3187, pruned_loss=0.07255, over 7399.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2905, pruned_loss=0.06555, over 1408521.47 frames.], batch size: 21, lr: 1.19e-03
2022-05-14 03:17:36,233 INFO [train.py:812] (5/8) Epoch 6, batch 1050, loss[loss=0.199, simple_loss=0.2811, pruned_loss=0.0584, over 7332.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2904, pruned_loss=0.06561, over 1414154.52 frames.], batch size: 20, lr: 1.19e-03
2022-05-14 03:18:39,066 INFO [train.py:812] (5/8) Epoch 6, batch 1100, loss[loss=0.2466, simple_loss=0.3211, pruned_loss=0.08607, over 7313.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2911, pruned_loss=0.06615, over 1409083.77 frames.], batch size: 21, lr: 1.19e-03
2022-05-14 03:19:37,445 INFO [train.py:812] (5/8) Epoch 6, batch 1150, loss[loss=0.2023, simple_loss=0.2813, pruned_loss=0.06165, over 7145.00 frames.], tot_loss[loss=0.2115, simple_loss=0.291, pruned_loss=0.06601, over 1413375.48 frames.], batch size: 20, lr: 1.19e-03
2022-05-14 03:20:36,713 INFO [train.py:812] (5/8) Epoch 6, batch 1200, loss[loss=0.1896, simple_loss=0.2743, pruned_loss=0.05249, over 7192.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2893, pruned_loss=0.06491, over 1414138.69 frames.], batch size: 26, lr: 1.18e-03
2022-05-14 03:21:34,757 INFO [train.py:812] (5/8) Epoch 6, batch 1250, loss[loss=0.1955, simple_loss=0.2749, pruned_loss=0.058, over 7138.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2891, pruned_loss=0.06482, over 1414400.78 frames.], batch size: 20, lr: 1.18e-03
2022-05-14 03:22:34,579 INFO [train.py:812] (5/8) Epoch 6, batch 1300, loss[loss=0.2025, simple_loss=0.2821, pruned_loss=0.06147, over 7364.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2894, pruned_loss=0.06564, over 1411869.71 frames.], batch size: 19, lr: 1.18e-03
2022-05-14 03:23:33,467 INFO [train.py:812] (5/8) Epoch 6, batch 1350, loss[loss=0.223, simple_loss=0.3058, pruned_loss=0.07009, over 7122.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2891, pruned_loss=0.06564, over 1416394.13 frames.], batch size: 28, lr: 1.18e-03
2022-05-14 03:24:32,547 INFO [train.py:812] (5/8) Epoch 6, batch 1400, loss[loss=0.1798, simple_loss=0.2672, pruned_loss=0.04622, over 7321.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2886, pruned_loss=0.06478, over 1419966.17 frames.], batch size: 20, lr: 1.18e-03
2022-05-14 03:25:31,685 INFO [train.py:812] (5/8) Epoch 6, batch 1450, loss[loss=0.2055, simple_loss=0.291, pruned_loss=0.06001, over 7432.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2877, pruned_loss=0.06395, over 1421191.22 frames.], batch size: 20, lr: 1.18e-03
2022-05-14 03:26:31,222 INFO [train.py:812] (5/8) Epoch 6, batch 1500, loss[loss=0.2193, simple_loss=0.3019, pruned_loss=0.06833, over 7145.00 frames.], tot_loss[loss=0.2082, simple_loss=0.288, pruned_loss=0.06424, over 1421333.34 frames.], batch size: 20, lr: 1.18e-03
2022-05-14 03:27:30,234 INFO [train.py:812] (5/8) Epoch 6, batch 1550, loss[loss=0.1657, simple_loss=0.2497, pruned_loss=0.04084, over 7270.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2882, pruned_loss=0.06449, over 1422793.95 frames.], batch size: 17, lr: 1.18e-03
2022-05-14 03:28:29,753 INFO [train.py:812] (5/8) Epoch 6, batch 1600, loss[loss=0.1866, simple_loss=0.2682, pruned_loss=0.05248, over 7424.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2878, pruned_loss=0.06456, over 1417231.87 frames.], batch size: 20, lr: 1.17e-03
2022-05-14 03:29:29,245 INFO [train.py:812] (5/8) Epoch 6, batch 1650, loss[loss=0.2437, simple_loss=0.3181, pruned_loss=0.08461, over 7241.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2873, pruned_loss=0.06427, over 1416761.54 frames.], batch size: 25, lr: 1.17e-03
2022-05-14 03:30:27,821 INFO [train.py:812] (5/8) Epoch 6, batch 1700, loss[loss=0.2061, simple_loss=0.292, pruned_loss=0.06009, over 7199.00 frames.], tot_loss[loss=0.208, simple_loss=0.2873, pruned_loss=0.06436, over 1414570.52 frames.], batch size: 22, lr: 1.17e-03
2022-05-14 03:31:26,899 INFO [train.py:812] (5/8) Epoch 6, batch 1750, loss[loss=0.2156, simple_loss=0.2917, pruned_loss=0.06973, over 7269.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2889, pruned_loss=0.06551, over 1411916.56 frames.], batch size: 18, lr: 1.17e-03
2022-05-14 03:32:26,516 INFO [train.py:812] (5/8) Epoch 6, batch 1800, loss[loss=0.2656, simple_loss=0.3249, pruned_loss=0.1032, over 4941.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2887, pruned_loss=0.06551, over 1412448.83 frames.], batch size: 53, lr: 1.17e-03
2022-05-14 03:33:25,596 INFO [train.py:812] (5/8) Epoch 6, batch 1850, loss[loss=0.1567, simple_loss=0.2396, pruned_loss=0.03688, over 7158.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2877, pruned_loss=0.06469, over 1415870.50 frames.], batch size: 18, lr: 1.17e-03
2022-05-14 03:34:24,944 INFO [train.py:812] (5/8) Epoch 6, batch 1900, loss[loss=0.2049, simple_loss=0.2686, pruned_loss=0.07059, over 7149.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2882, pruned_loss=0.06506, over 1414928.69 frames.], batch size: 17, lr: 1.17e-03
2022-05-14 03:35:24,027 INFO [train.py:812] (5/8) Epoch 6, batch 1950, loss[loss=0.1841, simple_loss=0.2779, pruned_loss=0.04509, over 7106.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2881, pruned_loss=0.06453, over 1420237.22 frames.], batch size: 21, lr: 1.17e-03
2022-05-14 03:36:21,513 INFO [train.py:812] (5/8) Epoch 6, batch 2000, loss[loss=0.1614, simple_loss=0.2469, pruned_loss=0.03795, over 7282.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2882, pruned_loss=0.06442, over 1424016.88 frames.], batch size: 18, lr: 1.17e-03
2022-05-14 03:37:19,577 INFO [train.py:812] (5/8) Epoch 6, batch 2050, loss[loss=0.2145, simple_loss=0.2911, pruned_loss=0.06897, over 7057.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2885, pruned_loss=0.06418, over 1423912.19 frames.], batch size: 28, lr: 1.16e-03
2022-05-14 03:38:19,351 INFO [train.py:812] (5/8) Epoch 6, batch 2100, loss[loss=0.2334, simple_loss=0.3118, pruned_loss=0.07745, over 6415.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2878, pruned_loss=0.06384, over 1426215.29 frames.], batch size: 38, lr: 1.16e-03
2022-05-14 03:39:18,983 INFO [train.py:812] (5/8) Epoch 6, batch 2150, loss[loss=0.2146, simple_loss=0.2985, pruned_loss=0.06528, over 7154.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2873, pruned_loss=0.0629, over 1431077.64 frames.], batch size: 20, lr: 1.16e-03
2022-05-14 03:40:18,736 INFO [train.py:812] (5/8) Epoch 6, batch 2200, loss[loss=0.2039, simple_loss=0.2841, pruned_loss=0.06191, over 7152.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2866, pruned_loss=0.06278, over 1427429.40 frames.], batch size: 20, lr: 1.16e-03
2022-05-14 03:41:17,648 INFO [train.py:812] (5/8) Epoch 6, batch 2250, loss[loss=0.1913, simple_loss=0.2684, pruned_loss=0.0571, over 7355.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2862, pruned_loss=0.06247, over 1426135.11 frames.], batch size: 19, lr: 1.16e-03
2022-05-14 03:42:16,654 INFO [train.py:812] (5/8) Epoch 6, batch 2300, loss[loss=0.2672, simple_loss=0.3442, pruned_loss=0.09514, over 7277.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2875, pruned_loss=0.0636, over 1422781.89 frames.], batch size: 24, lr: 1.16e-03
2022-05-14 03:43:15,824 INFO [train.py:812] (5/8) Epoch 6, batch 2350, loss[loss=0.2221, simple_loss=0.3093, pruned_loss=0.06747, over 7219.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2872, pruned_loss=0.06375, over 1421976.45 frames.], batch size: 21, lr: 1.16e-03
2022-05-14 03:44:15,946 INFO [train.py:812] (5/8) Epoch 6, batch 2400, loss[loss=0.1933, simple_loss=0.2722, pruned_loss=0.05723, over 7323.00 frames.], tot_loss[loss=0.2065, simple_loss=0.286, pruned_loss=0.06354, over 1421653.64 frames.], batch size: 20, lr: 1.16e-03
2022-05-14 03:45:14,486 INFO [train.py:812] (5/8) Epoch 6, batch 2450, loss[loss=0.1807, simple_loss=0.2642, pruned_loss=0.04855, over 6843.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2848, pruned_loss=0.06295, over 1420975.04 frames.], batch size: 15, lr: 1.16e-03
2022-05-14 03:46:13,710 INFO [train.py:812] (5/8) Epoch 6, batch 2500, loss[loss=0.1965, simple_loss=0.2863, pruned_loss=0.05335, over 7326.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2864, pruned_loss=0.06367, over 1420288.65 frames.], batch size: 22, lr: 1.15e-03
2022-05-14 03:47:11,214 INFO [train.py:812] (5/8) Epoch 6, batch 2550, loss[loss=0.1941, simple_loss=0.2707, pruned_loss=0.05871, over 7240.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2869, pruned_loss=0.06435, over 1422161.49 frames.], batch size: 16, lr: 1.15e-03
2022-05-14 03:48:09,666 INFO [train.py:812] (5/8) Epoch 6, batch 2600, loss[loss=0.2369, simple_loss=0.3269, pruned_loss=0.07349, over 7314.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2872, pruned_loss=0.06388, over 1425463.92 frames.], batch size: 21, lr: 1.15e-03
2022-05-14 03:49:08,321 INFO [train.py:812] (5/8) Epoch 6, batch 2650, loss[loss=0.2285, simple_loss=0.3157, pruned_loss=0.07066, over 7279.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2875, pruned_loss=0.0637, over 1424942.91 frames.], batch size: 25, lr: 1.15e-03
2022-05-14 03:50:08,473 INFO [train.py:812] (5/8) Epoch 6, batch 2700, loss[loss=0.1809, simple_loss=0.261, pruned_loss=0.05044, over 6818.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2867, pruned_loss=0.06325, over 1426654.58 frames.], batch size: 15, lr: 1.15e-03
2022-05-14 03:51:06,469 INFO [train.py:812] (5/8) Epoch 6, batch 2750, loss[loss=0.1702, simple_loss=0.2659, pruned_loss=0.03726, over 7244.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2867, pruned_loss=0.0629, over 1424331.51 frames.], batch size: 20, lr: 1.15e-03
2022-05-14 03:52:05,465 INFO [train.py:812] (5/8) Epoch 6, batch 2800, loss[loss=0.1889, simple_loss=0.2708, pruned_loss=0.0535, over 7279.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2864, pruned_loss=0.06271, over 1421875.99 frames.], batch size: 18, lr: 1.15e-03
2022-05-14 03:53:03,378 INFO [train.py:812] (5/8) Epoch 6, batch 2850, loss[loss=0.1634, simple_loss=0.2488, pruned_loss=0.03903, over 7284.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2862, pruned_loss=0.06246, over 1418471.03 frames.], batch size: 17, lr: 1.15e-03
2022-05-14 03:54:00,911 INFO [train.py:812] (5/8) Epoch 6, batch 2900, loss[loss=0.2119, simple_loss=0.2932, pruned_loss=0.06525, over 6742.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2863, pruned_loss=0.06258, over 1420081.33 frames.], batch size: 31, lr: 1.15e-03
2022-05-14 03:54:58,707 INFO [train.py:812] (5/8) Epoch 6, batch 2950, loss[loss=0.2325, simple_loss=0.3149, pruned_loss=0.07502, over 7140.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2856, pruned_loss=0.0626, over 1420117.87 frames.], batch size: 20, lr: 1.14e-03
2022-05-14 03:55:55,713 INFO [train.py:812] (5/8) Epoch 6, batch 3000, loss[loss=0.1979, simple_loss=0.281, pruned_loss=0.05746, over 7234.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2859, pruned_loss=0.0626, over 1419500.39 frames.], batch size: 20, lr: 1.14e-03
2022-05-14 03:55:55,714 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 03:56:03,337 INFO [train.py:841] (5/8) Epoch 6, validation: loss=0.1668, simple_loss=0.2696, pruned_loss=0.03205, over 698248.00 frames.
2022-05-14 03:57:02,143 INFO [train.py:812] (5/8) Epoch 6, batch 3050, loss[loss=0.2278, simple_loss=0.3034, pruned_loss=0.07612, over 7212.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2848, pruned_loss=0.06182, over 1425292.91 frames.], batch size: 23, lr: 1.14e-03
2022-05-14 03:58:01,673 INFO [train.py:812] (5/8) Epoch 6, batch 3100, loss[loss=0.2335, simple_loss=0.3137, pruned_loss=0.07663, over 7343.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2838, pruned_loss=0.06172, over 1423575.39 frames.], batch size: 22, lr: 1.14e-03
2022-05-14 03:58:58,840 INFO [train.py:812] (5/8) Epoch 6, batch 3150, loss[loss=0.217, simple_loss=0.2914, pruned_loss=0.07127, over 7167.00 frames.], tot_loss[loss=0.2049, simple_loss=0.285, pruned_loss=0.06236, over 1423331.74 frames.], batch size: 23, lr: 1.14e-03
2022-05-14 03:59:57,535 INFO [train.py:812] (5/8) Epoch 6, batch 3200, loss[loss=0.2054, simple_loss=0.284, pruned_loss=0.06342, over 7233.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2859, pruned_loss=0.0627, over 1424137.68 frames.], batch size: 21, lr: 1.14e-03
2022-05-14 04:00:56,298 INFO [train.py:812] (5/8) Epoch 6, batch 3250, loss[loss=0.1877, simple_loss=0.2687, pruned_loss=0.0534, over 7361.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2869, pruned_loss=0.06281, over 1424149.24 frames.], batch size: 19, lr: 1.14e-03
2022-05-14 04:01:55,579 INFO [train.py:812] (5/8) Epoch 6, batch 3300, loss[loss=0.2244, simple_loss=0.3092, pruned_loss=0.06983, over 7200.00 frames.], tot_loss[loss=0.2076, simple_loss=0.288, pruned_loss=0.06355, over 1420077.15 frames.], batch size: 23, lr: 1.14e-03
2022-05-14 04:02:54,513 INFO [train.py:812] (5/8) Epoch 6, batch 3350, loss[loss=0.2132, simple_loss=0.2918, pruned_loss=0.06733, over 7256.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2872, pruned_loss=0.06321, over 1424422.18 frames.], batch size: 19, lr: 1.14e-03
2022-05-14 04:03:53,905 INFO [train.py:812] (5/8) Epoch 6, batch 3400, loss[loss=0.2142, simple_loss=0.3009, pruned_loss=0.06377, over 7292.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2863, pruned_loss=0.06273, over 1424336.50 frames.], batch size: 24, lr: 1.14e-03
2022-05-14 04:04:52,389 INFO [train.py:812] (5/8) Epoch 6, batch 3450, loss[loss=0.1964, simple_loss=0.2875, pruned_loss=0.0526, over 7406.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2875, pruned_loss=0.06304, over 1427203.05 frames.], batch size: 21, lr: 1.13e-03
2022-05-14 04:05:50,847 INFO [train.py:812] (5/8) Epoch 6, batch 3500, loss[loss=0.201, simple_loss=0.2903, pruned_loss=0.05589, over 7189.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2865, pruned_loss=0.06326, over 1424384.49 frames.], batch size: 22, lr: 1.13e-03
2022-05-14 04:06:49,087 INFO [train.py:812] (5/8) Epoch 6, batch 3550, loss[loss=0.2095, simple_loss=0.2947, pruned_loss=0.06217, over 7323.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2855, pruned_loss=0.06257, over 1427765.39 frames.], batch size: 21, lr: 1.13e-03
2022-05-14 04:07:47,621 INFO [train.py:812] (5/8) Epoch 6, batch 3600, loss[loss=0.1797, simple_loss=0.2505, pruned_loss=0.05445, over 7160.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2852, pruned_loss=0.06247, over 1428623.63 frames.], batch size: 18, lr: 1.13e-03
2022-05-14 04:08:46,807 INFO [train.py:812] (5/8) Epoch 6, batch 3650, loss[loss=0.2452, simple_loss=0.313, pruned_loss=0.08869, over 7423.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2857, pruned_loss=0.06286, over 1427834.96 frames.], batch size: 21, lr: 1.13e-03
2022-05-14 04:09:44,286 INFO [train.py:812] (5/8) Epoch 6, batch 3700, loss[loss=0.2042, simple_loss=0.2886, pruned_loss=0.05984, over 7235.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2862, pruned_loss=0.06313, over 1426144.72 frames.], batch size: 20, lr: 1.13e-03
2022-05-14 04:10:41,358 INFO [train.py:812] (5/8) Epoch 6, batch 3750, loss[loss=0.2363, simple_loss=0.3095, pruned_loss=0.08159, over 7377.00 frames.], tot_loss[loss=0.207, simple_loss=0.2865, pruned_loss=0.06371, over 1424343.42 frames.], batch size: 23, lr: 1.13e-03
2022-05-14 04:11:40,655 INFO [train.py:812] (5/8) Epoch 6, batch 3800, loss[loss=0.1665, simple_loss=0.2508, pruned_loss=0.04117, over 7232.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2862, pruned_loss=0.06341, over 1420731.19 frames.], batch size: 20, lr: 1.13e-03
2022-05-14 04:12:39,822 INFO [train.py:812] (5/8) Epoch 6, batch 3850, loss[loss=0.1787, simple_loss=0.2629, pruned_loss=0.04721, over 7419.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2874, pruned_loss=0.06413, over 1420898.97 frames.], batch size: 20, lr: 1.13e-03
2022-05-14 04:13:39,078 INFO [train.py:812] (5/8) Epoch 6, batch 3900, loss[loss=0.1916, simple_loss=0.2669, pruned_loss=0.0581, over 7404.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2871, pruned_loss=0.06368, over 1425503.22 frames.], batch size: 18, lr: 1.13e-03
2022-05-14 04:14:38,335 INFO [train.py:812] (5/8) Epoch 6, batch 3950, loss[loss=0.2077, simple_loss=0.2898, pruned_loss=0.06286, over 7318.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2864, pruned_loss=0.0634, over 1424766.07 frames.], batch size: 24, lr: 1.12e-03
2022-05-14 04:15:37,079 INFO [train.py:812] (5/8) Epoch 6, batch 4000, loss[loss=0.2104, simple_loss=0.2887, pruned_loss=0.06601, over 7195.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2872, pruned_loss=0.06373, over 1426621.41 frames.], batch size: 23, lr: 1.12e-03
2022-05-14 04:16:34,882 INFO [train.py:812] (5/8) Epoch 6, batch 4050, loss[loss=0.2652, simple_loss=0.3213, pruned_loss=0.1045, over 7281.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2863, pruned_loss=0.06345, over 1427222.47 frames.], batch size: 24, lr: 1.12e-03
2022-05-14 04:17:34,623 INFO [train.py:812] (5/8) Epoch 6, batch 4100, loss[loss=0.1797, simple_loss=0.2606, pruned_loss=0.04934, over 7414.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2857, pruned_loss=0.06363, over 1427763.65 frames.], batch size: 18, lr: 1.12e-03
2022-05-14 04:18:33,840 INFO [train.py:812] (5/8) Epoch 6, batch 4150, loss[loss=0.2362, simple_loss=0.3266, pruned_loss=0.07286, over 6628.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2855, pruned_loss=0.06336, over 1427888.34 frames.], batch size: 31, lr: 1.12e-03
2022-05-14 04:19:32,910 INFO [train.py:812] (5/8) Epoch 6, batch 4200, loss[loss=0.2328, simple_loss=0.3174, pruned_loss=0.07412, over 7117.00 frames.], tot_loss[loss=0.2045, simple_loss=0.284, pruned_loss=0.06248, over 1429314.29 frames.], batch size: 21, lr: 1.12e-03
2022-05-14 04:20:33,095 INFO [train.py:812] (5/8) Epoch 6, batch 4250, loss[loss=0.226, simple_loss=0.322, pruned_loss=0.06503, over 7373.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2846, pruned_loss=0.0625, over 1430352.43 frames.], batch size: 23, lr: 1.12e-03
2022-05-14 04:21:32,382 INFO [train.py:812] (5/8) Epoch 6, batch 4300, loss[loss=0.1573, simple_loss=0.2386, pruned_loss=0.03803, over 7074.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2847, pruned_loss=0.06298, over 1425367.74 frames.], batch size: 18, lr: 1.12e-03
2022-05-14 04:22:31,650 INFO [train.py:812] (5/8) Epoch 6, batch 4350, loss[loss=0.1903, simple_loss=0.275, pruned_loss=0.05278, over 7213.00 frames.], tot_loss[loss=0.2046, simple_loss=0.284, pruned_loss=0.06259, over 1424934.81 frames.], batch size: 21, lr: 1.12e-03
2022-05-14 04:23:31,492 INFO [train.py:812] (5/8) Epoch 6, batch 4400, loss[loss=0.1879, simple_loss=0.2722, pruned_loss=0.05177, over 7430.00 frames.], tot_loss[loss=0.2035, simple_loss=0.283, pruned_loss=0.06203, over 1423161.69 frames.], batch size: 20, lr: 1.12e-03
2022-05-14 04:24:30,628 INFO [train.py:812] (5/8) Epoch 6, batch 4450, loss[loss=0.1838, simple_loss=0.2569, pruned_loss=0.05536, over 7281.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2837, pruned_loss=0.06285, over 1409707.94 frames.], batch size: 17, lr: 1.11e-03
2022-05-14 04:25:38,564 INFO [train.py:812] (5/8) Epoch 6, batch 4500, loss[loss=0.1766, simple_loss=0.2674, pruned_loss=0.04287, over 7234.00 frames.], tot_loss[loss=0.202, simple_loss=0.2808, pruned_loss=0.0616, over 1409578.62 frames.], batch size: 20, lr: 1.11e-03
2022-05-14 04:26:36,426 INFO [train.py:812] (5/8) Epoch 6, batch 4550, loss[loss=0.2914, simple_loss=0.3526, pruned_loss=0.115, over 5110.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2845, pruned_loss=0.06466, over 1360227.44 frames.], batch size: 52, lr: 1.11e-03
2022-05-14 04:27:44,661 INFO [train.py:812] (5/8) Epoch 7, batch 0, loss[loss=0.2053, simple_loss=0.2857, pruned_loss=0.06243, over 7402.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2857, pruned_loss=0.06243, over 7402.00 frames.], batch size: 18, lr: 1.07e-03
2022-05-14 04:28:43,248 INFO [train.py:812] (5/8) Epoch 7, batch 50, loss[loss=0.1692, simple_loss=0.2459, pruned_loss=0.0462, over 7422.00 frames.], tot_loss[loss=0.2016, simple_loss=0.282, pruned_loss=0.06056, over 322748.78 frames.], batch size: 18, lr: 1.07e-03
2022-05-14 04:29:42,455 INFO [train.py:812] (5/8) Epoch 7, batch 100, loss[loss=0.2352, simple_loss=0.3103, pruned_loss=0.08008, over 7149.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2821, pruned_loss=0.06013, over 567106.69 frames.], batch size: 19, lr: 1.06e-03
2022-05-14 04:30:41,781 INFO [train.py:812] (5/8) Epoch 7, batch 150, loss[loss=0.2016, simple_loss=0.2789, pruned_loss=0.06212, over 7143.00 frames.], tot_loss[loss=0.2024, simple_loss=0.283, pruned_loss=0.06084, over 756220.48 frames.], batch size: 19, lr: 1.06e-03
2022-05-14 04:31:41,613 INFO [train.py:812] (5/8) Epoch 7, batch 200, loss[loss=0.2099, simple_loss=0.2921, pruned_loss=0.06389, over 7391.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2833, pruned_loss=0.06051, over 907156.55 frames.], batch size: 23, lr: 1.06e-03
2022-05-14 04:32:39,926 INFO [train.py:812] (5/8) Epoch 7, batch 250, loss[loss=0.2236, simple_loss=0.305, pruned_loss=0.07109, over 7147.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2838, pruned_loss=0.06058, over 1020927.74 frames.], batch size: 20, lr: 1.06e-03
2022-05-14 04:33:39,355 INFO [train.py:812] (5/8) Epoch 7, batch 300, loss[loss=0.1804, simple_loss=0.2586, pruned_loss=0.05115, over 7221.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2846, pruned_loss=0.06084, over 1107289.29 frames.], batch size: 16, lr: 1.06e-03
2022-05-14 04:34:57,040 INFO [train.py:812] (5/8) Epoch 7, batch 350, loss[loss=0.1653, simple_loss=0.2635, pruned_loss=0.03358, over 7110.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2837, pruned_loss=0.05992, over 1177894.80 frames.], batch size: 21, lr: 1.06e-03
2022-05-14 04:35:53,856 INFO [train.py:812] (5/8) Epoch 7, batch 400, loss[loss=0.1605, simple_loss=0.2344, pruned_loss=0.04334, over 7162.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2832, pruned_loss=0.0597, over 1230318.01 frames.], batch size: 18, lr: 1.06e-03
2022-05-14 04:37:20,598 INFO [train.py:812] (5/8) Epoch 7, batch 450, loss[loss=0.17, simple_loss=0.2496, pruned_loss=0.04522, over 7357.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2826, pruned_loss=0.05963, over 1276075.10 frames.], batch size: 19, lr: 1.06e-03
2022-05-14 04:38:43,151 INFO [train.py:812] (5/8) Epoch 7, batch 500, loss[loss=0.2085, simple_loss=0.2943, pruned_loss=0.06137, over 6524.00 frames.], tot_loss[loss=0.202, simple_loss=0.284, pruned_loss=0.06001, over 1305407.44 frames.], batch size: 37, lr: 1.06e-03
2022-05-14 04:39:42,050 INFO [train.py:812] (5/8) Epoch 7, batch 550, loss[loss=0.196, simple_loss=0.2811, pruned_loss=0.05546, over 7107.00 frames.], tot_loss[loss=0.201, simple_loss=0.283, pruned_loss=0.05954, over 1330586.70 frames.], batch size: 21, lr: 1.06e-03
2022-05-14 04:40:39,576 INFO [train.py:812] (5/8) Epoch 7, batch 600, loss[loss=0.2075, simple_loss=0.2915, pruned_loss=0.0618, over 7004.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2835, pruned_loss=0.05993, over 1348352.65 frames.], batch size: 28, lr: 1.06e-03
2022-05-14 04:41:38,951 INFO [train.py:812] (5/8) Epoch 7, batch 650, loss[loss=0.2427, simple_loss=0.3104, pruned_loss=0.08748, over 5099.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2817, pruned_loss=0.05904, over 1364114.89 frames.], batch size: 53, lr: 1.05e-03
2022-05-14 04:42:37,553 INFO [train.py:812] (5/8) Epoch 7, batch 700, loss[loss=0.1803, simple_loss=0.259, pruned_loss=0.05078, over 7160.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2807, pruned_loss=0.05873, over 1378207.15 frames.], batch size: 18, lr: 1.05e-03
2022-05-14 04:43:36,165 INFO [train.py:812] (5/8) Epoch 7, batch 750, loss[loss=0.2421, simple_loss=0.3135, pruned_loss=0.08542, over 6772.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2807, pruned_loss=0.0589, over 1391677.78 frames.], batch size: 31, lr: 1.05e-03
2022-05-14 04:44:33,655 INFO [train.py:812] (5/8) Epoch 7, batch 800, loss[loss=0.1995, simple_loss=0.272, pruned_loss=0.0635, over 7335.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2799, pruned_loss=0.05883, over 1391555.68 frames.], batch size: 20, lr: 1.05e-03
2022-05-14 04:45:32,992 INFO [train.py:812] (5/8) Epoch 7, batch 850, loss[loss=0.2025, simple_loss=0.2811, pruned_loss=0.062, over 7290.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2797, pruned_loss=0.0589, over 1397798.01 frames.], batch size: 24, lr: 1.05e-03
2022-05-14 04:46:32,279 INFO [train.py:812] (5/8) Epoch 7, batch 900, loss[loss=0.2262, simple_loss=0.31, pruned_loss=0.07119, over 7383.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2798, pruned_loss=0.05875, over 1403207.39 frames.], batch size: 23, lr: 1.05e-03
2022-05-14 04:47:31,113 INFO [train.py:812] (5/8) Epoch 7, batch 950, loss[loss=0.2266, simple_loss=0.312, pruned_loss=0.07056, over 7382.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2814, pruned_loss=0.05946, over 1407618.35 frames.], batch size: 23, lr: 1.05e-03
2022-05-14 04:48:29,684 INFO [train.py:812] (5/8) Epoch 7, batch 1000, loss[loss=0.1819, simple_loss=0.2632, pruned_loss=0.05027, over 7384.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2824, pruned_loss=0.06012, over 1408574.77 frames.], batch size: 23, lr: 1.05e-03
2022-05-14 04:49:29,141 INFO [train.py:812] (5/8) Epoch 7, batch 1050, loss[loss=0.1658, simple_loss=0.2578, pruned_loss=0.03689, over 7148.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2821, pruned_loss=0.06026, over 1415912.31 frames.], batch size: 19, lr: 1.05e-03
2022-05-14 04:50:29,063 INFO [train.py:812] (5/8) Epoch 7, batch 1100, loss[loss=0.2478, simple_loss=0.3431, pruned_loss=0.07624, over 7285.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2816, pruned_loss=0.05952, over 1418664.73 frames.], batch size: 25, lr: 1.05e-03
2022-05-14 04:51:28,378 INFO [train.py:812] (5/8) Epoch 7, batch 1150, loss[loss=0.1758, simple_loss=0.2565, pruned_loss=0.04755, over 7135.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2827, pruned_loss=0.05988, over 1417315.87 frames.], batch size: 17, lr: 1.05e-03
2022-05-14 04:52:28,289 INFO [train.py:812] (5/8) Epoch 7, batch 1200, loss[loss=0.1852, simple_loss=0.2674, pruned_loss=0.05153, over 6825.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2832, pruned_loss=0.06014, over 1412338.70 frames.], batch size: 15, lr: 1.04e-03
2022-05-14 04:53:27,875 INFO [train.py:812] (5/8) Epoch 7, batch 1250, loss[loss=0.2062, simple_loss=0.289, pruned_loss=0.06168, over 7234.00 frames.], tot_loss[loss=0.202, simple_loss=0.2827, pruned_loss=0.06064, over 1413548.52 frames.], batch size: 20, lr: 1.04e-03
2022-05-14 04:54:25,621 INFO [train.py:812] (5/8) Epoch 7, batch 1300, loss[loss=0.1805, simple_loss=0.2538, pruned_loss=0.05358, over 7273.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2825, pruned_loss=0.06049, over 1414917.83 frames.], batch size: 17, lr: 1.04e-03
2022-05-14 04:55:24,138 INFO [train.py:812] (5/8) Epoch 7, batch 1350, loss[loss=0.2006, simple_loss=0.2831, pruned_loss=0.05906, over 7416.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2824, pruned_loss=0.06019, over 1420650.88 frames.], batch size: 21, lr: 1.04e-03
2022-05-14 04:56:22,952 INFO [train.py:812] (5/8) Epoch 7, batch 1400, loss[loss=0.1759, simple_loss=0.2674, pruned_loss=0.04218, over 7163.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2839, pruned_loss=0.06067, over 1419134.63 frames.], batch size: 19, lr: 1.04e-03
2022-05-14 04:57:22,026 INFO [train.py:812] (5/8) Epoch 7, batch 1450, loss[loss=0.2258, simple_loss=0.3042, pruned_loss=0.07372, over 6746.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2842, pruned_loss=0.06111, over 1418922.70 frames.], batch size: 31, lr: 1.04e-03
2022-05-14 04:58:20,152 INFO [train.py:812] (5/8) Epoch 7, batch 1500, loss[loss=0.2142, simple_loss=0.2904, pruned_loss=0.06901, over 7419.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2834, pruned_loss=0.06042, over 1422864.33 frames.], batch size: 21, lr: 1.04e-03
2022-05-14 04:59:18,877 INFO [train.py:812] (5/8) Epoch 7, batch 1550, loss[loss=0.211, simple_loss=0.2924, pruned_loss=0.06479, over 7166.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2826, pruned_loss=0.05981, over 1417374.08 frames.], batch size: 26, lr: 1.04e-03
2022-05-14 05:00:18,911 INFO [train.py:812] (5/8) Epoch 7, batch 1600, loss[loss=0.2538, simple_loss=0.3346, pruned_loss=0.08649, over 7103.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2823, pruned_loss=0.05954, over 1424279.06 frames.], batch size: 21, lr: 1.04e-03
2022-05-14 05:01:18,245 INFO [train.py:812] (5/8) Epoch 7, batch 1650, loss[loss=0.1835, simple_loss=0.2671, pruned_loss=0.04996, over 7065.00 frames.], tot_loss[loss=0.201, simple_loss=0.2819, pruned_loss=0.06, over 1418493.09 frames.], batch size: 18, lr: 1.04e-03
2022-05-14 05:02:16,814 INFO [train.py:812] (5/8) Epoch 7, batch 1700, loss[loss=0.1943, simple_loss=0.283, pruned_loss=0.05287, over 7215.00 frames.], tot_loss[loss=0.2, simple_loss=0.2809, pruned_loss=0.0596, over 1416683.13 frames.], batch size: 22, lr: 1.04e-03
2022-05-14 05:03:16,049 INFO [train.py:812] (5/8) Epoch 7, batch 1750, loss[loss=0.2243, simple_loss=0.3078, pruned_loss=0.07043, over 7336.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2815, pruned_loss=0.05979, over 1411788.57 frames.], batch size: 22, lr: 1.04e-03
2022-05-14 05:04:14,637 INFO [train.py:812] (5/8) Epoch 7, batch 1800, loss[loss=0.2749, simple_loss=0.3365, pruned_loss=0.1067, over 7300.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2817, pruned_loss=0.05926, over 1414979.46 frames.], batch size: 25, lr: 1.03e-03
2022-05-14 05:05:13,136 INFO [train.py:812] (5/8) Epoch 7, batch 1850, loss[loss=0.1665, simple_loss=0.2369, pruned_loss=0.04803, over 7006.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2813, pruned_loss=0.05878, over 1417169.23 frames.], batch size: 16, lr: 1.03e-03
2022-05-14 05:06:10,514 INFO [train.py:812] (5/8) Epoch 7, batch 1900, loss[loss=0.1771, simple_loss=0.2641, pruned_loss=0.04509, over 7062.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2821, pruned_loss=0.05931, over 1413528.20 frames.], batch size: 18, lr: 1.03e-03
2022-05-14 05:07:08,603 INFO [train.py:812] (5/8) Epoch 7, batch 1950, loss[loss=0.2201, simple_loss=0.279, pruned_loss=0.0806, over 7276.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2807, pruned_loss=0.05896, over 1417363.41 frames.], batch size: 18, lr: 1.03e-03
2022-05-14 05:08:07,393 INFO [train.py:812] (5/8) Epoch 7, batch 2000, loss[loss=0.2134, simple_loss=0.2855, pruned_loss=0.07067, over 7278.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2805, pruned_loss=0.05924, over 1418096.44 frames.], batch size: 25, lr: 1.03e-03
2022-05-14 05:09:04,289 INFO [train.py:812] (5/8) Epoch 7, batch 2050, loss[loss=0.2048, simple_loss=0.2887, pruned_loss=0.06043, over 7296.00 frames.], tot_loss[loss=0.1995, simple_loss=0.281, pruned_loss=0.05899, over 1414897.26 frames.], batch size: 24, lr: 1.03e-03
2022-05-14 05:10:01,686 INFO [train.py:812] (5/8) Epoch 7, batch 2100, loss[loss=0.1563, simple_loss=0.2425, pruned_loss=0.03506, over 6994.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2817, pruned_loss=0.05975, over 1417765.30 frames.], batch size: 16, lr: 1.03e-03
2022-05-14 05:11:00,087 INFO [train.py:812] (5/8) Epoch 7, batch 2150, loss[loss=0.2134, simple_loss=0.3012, pruned_loss=0.06281, over 7407.00 frames.], tot_loss[loss=0.2003, simple_loss=0.282, pruned_loss=0.05928, over 1423364.52 frames.], batch size: 21, lr: 1.03e-03
2022-05-14 05:11:57,833 INFO [train.py:812] (5/8) Epoch 7, batch 2200, loss[loss=0.199, simple_loss=0.2726, pruned_loss=0.06268, over 7136.00 frames.], tot_loss[loss=0.2, simple_loss=0.2815, pruned_loss=0.05926, over 1421783.60 frames.], batch size: 17, lr: 1.03e-03
2022-05-14 05:12:56,732 INFO [train.py:812] (5/8) Epoch 7, batch 2250, loss[loss=0.1709, simple_loss=0.257, pruned_loss=0.04236, over 7274.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2817, pruned_loss=0.05933, over 1416104.40 frames.], batch size: 17, lr: 1.03e-03
2022-05-14 05:13:54,320 INFO [train.py:812] (5/8) Epoch 7, batch 2300, loss[loss=0.2406, simple_loss=0.3227, pruned_loss=0.07926, over 7172.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2813, pruned_loss=0.05923, over 1419276.94 frames.], batch size: 23, lr: 1.03e-03
2022-05-14 05:14:53,674 INFO [train.py:812] (5/8) Epoch 7, batch 2350, loss[loss=0.2184, simple_loss=0.3092, pruned_loss=0.06383, over 7405.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2814, pruned_loss=0.05897, over 1416555.55 frames.], batch size: 21, lr: 1.02e-03
2022-05-14 05:15:53,816 INFO [train.py:812] (5/8) Epoch 7, batch 2400, loss[loss=0.183, simple_loss=0.268, pruned_loss=0.04896, over 7278.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2814, pruned_loss=0.05873, over 1420972.13 frames.], batch size: 18, lr: 1.02e-03
2022-05-14 05:16:51,043 INFO [train.py:812] (5/8) Epoch 7, batch 2450, loss[loss=0.2382, simple_loss=0.3163, pruned_loss=0.08, over 7417.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2822, pruned_loss=0.05922, over 1417112.45 frames.], batch size: 21, lr: 1.02e-03
2022-05-14 05:17:49,490 INFO [train.py:812] (5/8) Epoch 7, batch 2500, loss[loss=0.2071, simple_loss=0.2976, pruned_loss=0.05828, over 7316.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2829, pruned_loss=0.05963, over 1417281.67 frames.], batch size: 21, lr: 1.02e-03
2022-05-14 05:18:48,478 INFO [train.py:812] (5/8) Epoch 7, batch 2550, loss[loss=0.2001, simple_loss=0.2786, pruned_loss=0.06081, over 7423.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2822, pruned_loss=0.05926, over 1423610.94 frames.], batch size: 20, lr: 1.02e-03
2022-05-14 05:19:47,245 INFO [train.py:812] (5/8) Epoch 7, batch 2600, loss[loss=0.17, simple_loss=0.2495, pruned_loss=0.04526, over 7160.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2818, pruned_loss=0.05929, over 1417943.08 frames.], batch size: 18, lr: 1.02e-03
2022-05-14 05:20:45,568 INFO [train.py:812] (5/8) Epoch 7, batch 2650, loss[loss=0.1765, simple_loss=0.257, pruned_loss=0.04804, over 7155.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2814, pruned_loss=0.05953, over 1417509.50 frames.], batch size: 18, lr: 1.02e-03
2022-05-14 05:21:44,763 INFO [train.py:812] (5/8) Epoch 7, batch 2700, loss[loss=0.1923, simple_loss=0.2661, pruned_loss=0.05931, over 6822.00 frames.], tot_loss[loss=0.2, simple_loss=0.2816, pruned_loss=0.05923, over 1418815.67 frames.], batch size: 15, lr: 1.02e-03
2022-05-14 05:22:44,402 INFO [train.py:812] (5/8) Epoch 7, batch 2750, loss[loss=0.2022, simple_loss=0.2771, pruned_loss=0.0636, over 7409.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2818, pruned_loss=0.05922, over 1419116.51 frames.], batch size: 18, lr: 1.02e-03
2022-05-14 05:23:44,351 INFO [train.py:812] (5/8) Epoch 7, batch 2800, loss[loss=0.168, simple_loss=0.2541, pruned_loss=0.04095, over 6991.00 frames.], tot_loss[loss=0.199, simple_loss=0.2805, pruned_loss=0.05874, over 1417992.12 frames.], batch size: 16, lr: 1.02e-03
2022-05-14 05:24:43,850 INFO [train.py:812] (5/8) Epoch 7, batch 2850, loss[loss=0.2075, simple_loss=0.2902, pruned_loss=0.06241, over 7311.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2786, pruned_loss=0.05796, over 1423311.04 frames.], batch size: 21, lr: 1.02e-03
2022-05-14 05:25:43,740 INFO [train.py:812] (5/8) Epoch 7, batch 2900, loss[loss=0.2284, simple_loss=0.2982, pruned_loss=0.07928, over 5202.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2796, pruned_loss=0.05807, over 1425753.94 frames.], batch size: 53, lr: 1.02e-03
2022-05-14 05:26:42,746 INFO [train.py:812] (5/8) Epoch 7, batch 2950, loss[loss=0.2218, simple_loss=0.2983, pruned_loss=0.07263, over 7305.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2809, pruned_loss=0.05842, over 1426057.65 frames.], batch size: 25, lr: 1.01e-03
2022-05-14 05:27:42,376 INFO [train.py:812] (5/8) Epoch 7, batch 3000, loss[loss=0.2204, simple_loss=0.2973, pruned_loss=0.07177, over 7175.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2812, pruned_loss=0.05864, over 1427634.85 frames.], batch size: 26, lr: 1.01e-03
2022-05-14 05:27:42,377 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 05:27:49,661 INFO [train.py:841] (5/8) Epoch 7, validation: loss=0.1637, simple_loss=0.2662, pruned_loss=0.03066, over 698248.00 frames.
2022-05-14 05:28:48,978 INFO [train.py:812] (5/8) Epoch 7, batch 3050, loss[loss=0.1884, simple_loss=0.274, pruned_loss=0.05144, over 7170.00 frames.], tot_loss[loss=0.1992, simple_loss=0.281, pruned_loss=0.05866, over 1427512.82 frames.], batch size: 26, lr: 1.01e-03
2022-05-14 05:29:48,811 INFO [train.py:812] (5/8) Epoch 7, batch 3100, loss[loss=0.2169, simple_loss=0.2846, pruned_loss=0.07462, over 7087.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2817, pruned_loss=0.05903, over 1425286.52 frames.], batch size: 26, lr: 1.01e-03
2022-05-14 05:30:48,417 INFO [train.py:812] (5/8) Epoch 7, batch 3150, loss[loss=0.1998, simple_loss=0.2853, pruned_loss=0.05712, over 7117.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2809, pruned_loss=0.05867, over 1428844.62 frames.], batch size: 28, lr: 1.01e-03
2022-05-14 05:31:47,452 INFO [train.py:812] (5/8) Epoch 7, batch 3200, loss[loss=0.2041, simple_loss=0.2924, pruned_loss=0.0579, over 7337.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2819, pruned_loss=0.05929, over 1425078.55 frames.], batch size: 22, lr: 1.01e-03
2022-05-14 05:32:46,884 INFO [train.py:812] (5/8) Epoch 7, batch 3250, loss[loss=0.2115, simple_loss=0.2931, pruned_loss=0.06495, over 7038.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2804, pruned_loss=0.05858, over 1424748.74 frames.], batch size: 28, lr: 1.01e-03
2022-05-14 05:33:46,255 INFO [train.py:812] (5/8) Epoch 7, batch 3300, loss[loss=0.1862, simple_loss=0.2788, pruned_loss=0.04678, over 7149.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2803, pruned_loss=0.05828, over 1419391.61 frames.], batch size: 20, lr: 1.01e-03
2022-05-14 05:34:45,876 INFO [train.py:812] (5/8) Epoch 7, batch 3350, loss[loss=0.1829, simple_loss=0.2707, pruned_loss=0.04752, over 7155.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2807, pruned_loss=0.05829, over 1420697.90 frames.], batch size: 19, lr: 1.01e-03
2022-05-14 05:35:44,956 INFO [train.py:812] (5/8) Epoch 7, batch 3400, loss[loss=0.1847, simple_loss=0.2698, pruned_loss=0.04983, over 7125.00 frames.], tot_loss[loss=0.1986, simple_loss=0.281, pruned_loss=0.05815, over 1422706.65 frames.], batch size: 21, lr: 1.01e-03
2022-05-14 05:36:43,525 INFO [train.py:812] (5/8) Epoch 7, batch 3450, loss[loss=0.2037, simple_loss=0.2955, pruned_loss=0.05595, over 7302.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2812, pruned_loss=0.05821, over 1420029.90 frames.], batch size: 24, lr: 1.01e-03
2022-05-14 05:37:43,011 INFO [train.py:812] (5/8) Epoch 7, batch 3500, loss[loss=0.1989, simple_loss=0.2905, pruned_loss=0.05367, over 7224.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2816, pruned_loss=0.0584, over 1421675.28 frames.], batch size: 21, lr: 1.01e-03
2022-05-14 05:38:41,455 INFO [train.py:812] (5/8) Epoch 7, batch 3550, loss[loss=0.2329, simple_loss=0.3113, pruned_loss=0.07725, over 7373.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2812, pruned_loss=0.05818, over 1423213.94 frames.], batch size: 23, lr: 1.01e-03
2022-05-14 05:39:40,629 INFO [train.py:812] (5/8) Epoch 7, batch 3600, loss[loss=0.2197, simple_loss=0.3056, pruned_loss=0.06686, over 7213.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2815, pruned_loss=0.05832, over 1424754.62 frames.], batch size: 21, lr: 1.00e-03
2022-05-14 05:40:39,015 INFO [train.py:812] (5/8) Epoch 7, batch 3650, loss[loss=0.2203, simple_loss=0.3053, pruned_loss=0.06768, over 7040.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2818, pruned_loss=0.05853, over 1421179.61 frames.], batch size: 28, lr: 1.00e-03
2022-05-14 05:41:38,741 INFO [train.py:812] (5/8) Epoch 7, batch 3700, loss[loss=0.1756, simple_loss=0.2569, pruned_loss=0.04713, over 7432.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2806, pruned_loss=0.0583, over 1422838.94 frames.], batch size: 20, lr: 1.00e-03
2022-05-14 05:42:37,955 INFO [train.py:812] (5/8) Epoch 7, batch 3750, loss[loss=0.2094, simple_loss=0.2899, pruned_loss=0.06446, over 5176.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2809, pruned_loss=0.05867, over 1423757.84 frames.], batch size: 55, lr: 1.00e-03
2022-05-14 05:43:37,507 INFO [train.py:812] (5/8) Epoch 7, batch 3800, loss[loss=0.1777, simple_loss=0.2644, pruned_loss=0.04549, over 7361.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2809, pruned_loss=0.05878, over 1420777.55 frames.], batch size: 19, lr: 1.00e-03
2022-05-14 05:44:35,592 INFO [train.py:812] (5/8) Epoch 7, batch 3850, loss[loss=0.1957, simple_loss=0.2693, pruned_loss=0.06105, over 7144.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2797, pruned_loss=0.05794, over 1423990.98 frames.], batch size: 17, lr: 1.00e-03
2022-05-14 05:45:34,805 INFO [train.py:812] (5/8) Epoch 7, batch 3900, loss[loss=0.1854, simple_loss=0.2656, pruned_loss=0.05258, over 7154.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2791, pruned_loss=0.05756, over 1424341.61 frames.], batch size: 18, lr: 1.00e-03
2022-05-14 05:46:31,677 INFO [train.py:812] (5/8) Epoch 7, batch 3950, loss[loss=0.2011, simple_loss=0.2871, pruned_loss=0.0575, over 7333.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2796, pruned_loss=0.05792, over 1425815.63 frames.], batch size: 22, lr: 9.99e-04
2022-05-14 05:47:30,569 INFO [train.py:812] (5/8) Epoch 7, batch 4000, loss[loss=0.1885, simple_loss=0.2718, pruned_loss=0.05262, over 6773.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2798, pruned_loss=0.05793, over 1430277.97 frames.], batch size: 31, lr: 9.98e-04
2022-05-14 05:48:29,675 INFO [train.py:812] (5/8) Epoch 7, batch 4050, loss[loss=0.2009, simple_loss=0.2842, pruned_loss=0.05886, over 7163.00 frames.], tot_loss[loss=0.1983, simple_loss=0.28, pruned_loss=0.05827, over 1428718.33 frames.], batch size: 18, lr: 9.98e-04
2022-05-14 05:49:28,783 INFO [train.py:812] (5/8) Epoch 7, batch 4100, loss[loss=0.208, simple_loss=0.2882, pruned_loss=0.06393, over 7103.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2797, pruned_loss=0.05828, over 1423857.25 frames.], batch size: 21, lr: 9.97e-04
2022-05-14 05:50:26,071 INFO [train.py:812] (5/8) Epoch 7, batch 4150, loss[loss=0.2241, simple_loss=0.3009, pruned_loss=0.0736, over 7192.00 frames.], tot_loss[loss=0.199, simple_loss=0.2798, pruned_loss=0.05903, over 1424548.99 frames.], batch size: 23, lr: 9.96e-04
2022-05-14 05:51:25,276 INFO [train.py:812] (5/8) Epoch 7, batch 4200, loss[loss=0.1758, simple_loss=0.2458, pruned_loss=0.05286, over 7280.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2792, pruned_loss=0.05859, over 1428029.00 frames.], batch size: 17, lr: 9.95e-04
2022-05-14 05:52:24,622 INFO [train.py:812] (5/8) Epoch 7, batch 4250, loss[loss=0.1732, simple_loss=0.2641, pruned_loss=0.04121, over 7422.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2802, pruned_loss=0.05882, over 1422272.76 frames.], batch size: 20, lr: 9.95e-04
2022-05-14 05:53:23,916 INFO [train.py:812] (5/8) Epoch 7, batch 4300, loss[loss=0.1874, simple_loss=0.2689, pruned_loss=0.05299, over 7239.00 frames.], tot_loss[loss=0.2007, simple_loss=0.282, pruned_loss=0.05968, over 1417517.32 frames.], batch size: 20, lr: 9.94e-04
2022-05-14 05:54:23,289 INFO [train.py:812] (5/8) Epoch 7, batch 4350, loss[loss=0.1882, simple_loss=0.2814, pruned_loss=0.04751, over 6452.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2813, pruned_loss=0.05893, over 1409728.28 frames.], batch size: 37, lr: 9.93e-04
2022-05-14 05:55:22,289 INFO [train.py:812] (5/8) Epoch 7, batch 4400, loss[loss=0.2306, simple_loss=0.3252, pruned_loss=0.06796, over 6788.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2811, pruned_loss=0.05912, over 1411502.53 frames.], batch size: 31, lr: 9.92e-04
2022-05-14 05:56:20,660 INFO [train.py:812] (5/8) Epoch 7, batch 4450, loss[loss=0.2071, simple_loss=0.29, pruned_loss=0.06208, over 7208.00 frames.], tot_loss[loss=0.2002, simple_loss=0.282, pruned_loss=0.05924, over 1406104.53 frames.], batch size: 22, lr: 9.92e-04
2022-05-14 05:57:24,430 INFO [train.py:812] (5/8) Epoch 7, batch 4500, loss[loss=0.2303, simple_loss=0.3146, pruned_loss=0.07297, over 7200.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2837, pruned_loss=0.06039, over 1404020.49 frames.], batch size: 22, lr: 9.91e-04
2022-05-14 05:58:22,211 INFO [train.py:812] (5/8) Epoch 7, batch 4550, loss[loss=0.3131, simple_loss=0.367, pruned_loss=0.1296, over 5156.00 frames.], tot_loss[loss=0.204, simple_loss=0.2857, pruned_loss=0.06116, over 1389023.21 frames.], batch size: 52, lr: 9.90e-04
2022-05-14 05:59:32,592 INFO [train.py:812] (5/8) Epoch 8, batch 0, loss[loss=0.2109, simple_loss=0.3006, pruned_loss=0.06054, over 7336.00 frames.], tot_loss[loss=0.2109, simple_loss=0.3006, pruned_loss=0.06054, over 7336.00 frames.], batch size: 22, lr: 9.49e-04
2022-05-14 06:00:31,158 INFO [train.py:812] (5/8) Epoch 8, batch 50, loss[loss=0.1877, simple_loss=0.2614, pruned_loss=0.05706, over 7145.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2865, pruned_loss=0.05918, over 320501.34 frames.], batch size: 17, lr: 9.48e-04
2022-05-14 06:01:30,396 INFO [train.py:812] (5/8) Epoch 8, batch 100, loss[loss=0.2462, simple_loss=0.3306, pruned_loss=0.08085, over 7264.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2835, pruned_loss=0.05745, over 569096.56 frames.], batch size: 25, lr: 9.48e-04
2022-05-14 06:02:29,675 INFO [train.py:812] (5/8) Epoch 8, batch 150, loss[loss=0.1576, simple_loss=0.2477, pruned_loss=0.03368, over 7114.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2796, pruned_loss=0.05674, over 758781.89 frames.], batch size: 21, lr: 9.47e-04
2022-05-14 06:03:26,756 INFO [train.py:812] (5/8) Epoch 8, batch 200, loss[loss=0.212, simple_loss=0.3058, pruned_loss=0.0591, over 7204.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2784, pruned_loss=0.05632, over 907317.16 frames.], batch size: 22, lr: 9.46e-04
2022-05-14 06:04:24,362 INFO [train.py:812] (5/8) Epoch 8, batch 250, loss[loss=0.1787, simple_loss=0.2792, pruned_loss=0.03914, over 7111.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2798, pruned_loss=0.05671, over 1020623.77 frames.], batch size: 21, lr: 9.46e-04
2022-05-14 06:05:21,317 INFO [train.py:812] (5/8) Epoch 8, batch 300, loss[loss=0.1702, simple_loss=0.2547, pruned_loss=0.04283, over 7070.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2796, pruned_loss=0.05667, over 1106526.31 frames.], batch size: 18, lr: 9.45e-04
2022-05-14 06:06:19,882 INFO [train.py:812] (5/8) Epoch 8, batch 350, loss[loss=0.2433, simple_loss=0.3294, pruned_loss=0.07858, over 7108.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2791, pruned_loss=0.05641, over 1178305.48 frames.], batch size: 21, lr: 9.44e-04
2022-05-14 06:07:19,498 INFO [train.py:812] (5/8) Epoch 8, batch 400, loss[loss=0.2499, simple_loss=0.31, pruned_loss=0.09491, over 4745.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2801, pruned_loss=0.05683, over 1230831.07 frames.], batch size: 52, lr: 9.43e-04
2022-05-14 06:08:18,859 INFO [train.py:812] (5/8) Epoch 8, batch 450, loss[loss=0.1523, simple_loss=0.2405, pruned_loss=0.03207, over 6790.00 frames.], tot_loss[loss=0.198, simple_loss=0.2805, pruned_loss=0.0577, over 1272136.36 frames.], batch size: 15, lr: 9.43e-04
2022-05-14 06:09:18,430 INFO [train.py:812] (5/8) Epoch 8, batch 500, loss[loss=0.2203, simple_loss=0.2996, pruned_loss=0.07048, over 7205.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2787, pruned_loss=0.05653, over 1304878.41 frames.], batch size: 23, lr: 9.42e-04
2022-05-14 06:10:16,962 INFO [train.py:812] (5/8) Epoch 8, batch 550, loss[loss=0.2169, simple_loss=0.3004, pruned_loss=0.06668, over 7205.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2795, pruned_loss=0.05699, over 1332634.06 frames.], batch size: 23, lr: 9.41e-04
2022-05-14 06:11:16,974 INFO [train.py:812] (5/8) Epoch 8, batch 600, loss[loss=0.1692, simple_loss=0.2643, pruned_loss=0.03707, over 7225.00 frames.], tot_loss[loss=0.1972, simple_loss=0.28, pruned_loss=0.05723, over 1353284.70 frames.], batch size: 21, lr: 9.41e-04
2022-05-14 06:12:15,261 INFO [train.py:812] (5/8) Epoch 8, batch 650, loss[loss=0.207, simple_loss=0.2886, pruned_loss=0.06273, over 7263.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2789, pruned_loss=0.05663, over 1368053.53 frames.], batch size: 19, lr: 9.40e-04
2022-05-14 06:13:14,183 INFO [train.py:812] (5/8) Epoch 8, batch 700, loss[loss=0.2167, simple_loss=0.2824, pruned_loss=0.07549, over 5294.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2796, pruned_loss=0.05706, over 1377064.87 frames.], batch size: 52, lr: 9.39e-04
2022-05-14 06:14:13,340 INFO [train.py:812] (5/8) Epoch 8, batch 750, loss[loss=0.1693, simple_loss=0.2596, pruned_loss=0.03946, over 7359.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2782, pruned_loss=0.05656, over 1384804.95 frames.], batch size: 19, lr: 9.39e-04
2022-05-14 06:15:12,810 INFO [train.py:812] (5/8) Epoch 8, batch 800, loss[loss=0.2135, simple_loss=0.2987, pruned_loss=0.0642, over 6396.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2801, pruned_loss=0.05704, over 1390141.65 frames.], batch size: 37, lr: 9.38e-04
2022-05-14 06:16:12,231 INFO [train.py:812] (5/8) Epoch 8, batch 850, loss[loss=0.2005, simple_loss=0.2721, pruned_loss=0.06451, over 7404.00 frames.], tot_loss[loss=0.195, simple_loss=0.2777, pruned_loss=0.05615, over 1399122.67 frames.], batch size: 18, lr: 9.37e-04
2022-05-14 06:17:11,306 INFO [train.py:812] (5/8) Epoch 8, batch 900, loss[loss=0.2183, simple_loss=0.2952, pruned_loss=0.07069, over 6745.00 frames.], tot_loss[loss=0.1962, simple_loss=0.279, pruned_loss=0.05669, over 1398991.63 frames.], batch size: 31, lr: 9.36e-04
2022-05-14 06:18:09,038 INFO [train.py:812] (5/8) Epoch 8, batch 950, loss[loss=0.1812, simple_loss=0.2709, pruned_loss=0.04577, over 7230.00 frames.], tot_loss[loss=0.197, simple_loss=0.2797, pruned_loss=0.05713, over 1405033.82 frames.], batch size: 20, lr: 9.36e-04
2022-05-14 06:19:08,132 INFO [train.py:812] (5/8) Epoch 8, batch 1000, loss[loss=0.2222, simple_loss=0.2966, pruned_loss=0.07391, over 7227.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2789, pruned_loss=0.05643, over 1409133.73 frames.], batch size: 21, lr: 9.35e-04
2022-05-14 06:20:06,224 INFO [train.py:812] (5/8) Epoch 8, batch 1050, loss[loss=0.1563, simple_loss=0.2386, pruned_loss=0.03699, over 7116.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2788, pruned_loss=0.05649, over 1407114.54 frames.], batch size: 17, lr: 9.34e-04
2022-05-14 06:21:04,764 INFO [train.py:812] (5/8) Epoch 8, batch 1100, loss[loss=0.1873, simple_loss=0.2661, pruned_loss=0.05422, over 7201.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2783, pruned_loss=0.05631, over 1412229.13 frames.], batch size: 22, lr: 9.34e-04
2022-05-14 06:22:02,864 INFO [train.py:812] (5/8) Epoch 8, batch 1150, loss[loss=0.2427, simple_loss=0.3092, pruned_loss=0.08805, over 4990.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2797, pruned_loss=0.05652, over 1417523.36 frames.], batch size: 52, lr: 9.33e-04
2022-05-14 06:23:10,854 INFO [train.py:812] (5/8) Epoch 8, batch 1200, loss[loss=0.1976, simple_loss=0.2822, pruned_loss=0.05653, over 7137.00 frames.], tot_loss[loss=0.196, simple_loss=0.2792, pruned_loss=0.05638, over 1420656.80 frames.], batch size: 20, lr: 9.32e-04
2022-05-14 06:24:10,075 INFO [train.py:812] (5/8) Epoch 8, batch 1250, loss[loss=0.145, simple_loss=0.2373, pruned_loss=0.02635, over 7290.00 frames.], tot_loss[loss=0.1965, simple_loss=0.279, pruned_loss=0.057, over 1419250.67 frames.], batch size: 18, lr: 9.32e-04
2022-05-14 06:25:09,380 INFO [train.py:812] (5/8) Epoch 8, batch 1300, loss[loss=0.1907, simple_loss=0.2807, pruned_loss=0.0503, over 7140.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2791, pruned_loss=0.057, over 1415536.29 frames.], batch size: 20, lr: 9.31e-04
2022-05-14 06:26:08,278 INFO [train.py:812] (5/8) Epoch 8, batch 1350, loss[loss=0.2069, simple_loss=0.2876, pruned_loss=0.06312, over 7164.00 frames.], tot_loss[loss=0.197, simple_loss=0.2794, pruned_loss=0.05733, over 1414372.71 frames.], batch size: 19, lr: 9.30e-04
2022-05-14 06:27:08,070 INFO [train.py:812] (5/8) Epoch 8, batch 1400, loss[loss=0.1734, simple_loss=0.2567, pruned_loss=0.04511, over 7280.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2791, pruned_loss=0.05668, over 1416453.60 frames.], batch size: 18, lr: 9.30e-04
2022-05-14 06:28:06,822 INFO [train.py:812] (5/8) Epoch 8, batch 1450, loss[loss=0.1967, simple_loss=0.2733, pruned_loss=0.06001, over 7158.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2791, pruned_loss=0.05657, over 1416113.09 frames.], batch size: 18, lr: 9.29e-04
2022-05-14 06:29:06,637 INFO [train.py:812] (5/8) Epoch 8, batch 1500, loss[loss=0.1803, simple_loss=0.2631, pruned_loss=0.04875, over 7417.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2785, pruned_loss=0.0567, over 1416194.28 frames.], batch size: 18, lr: 9.28e-04
2022-05-14 06:30:05,550 INFO [train.py:812] (5/8) Epoch 8, batch 1550, loss[loss=0.2092, simple_loss=0.2873, pruned_loss=0.0656, over 7202.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2779, pruned_loss=0.05618, over 1421046.72 frames.], batch size: 22, lr: 9.28e-04
2022-05-14 06:31:05,141 INFO [train.py:812] (5/8) Epoch 8, batch 1600, loss[loss=0.1963, simple_loss=0.2723, pruned_loss=0.06014, over 6485.00 frames.], tot_loss[loss=0.1964, simple_loss=0.279, pruned_loss=0.05694, over 1421111.67 frames.], batch size: 38, lr: 9.27e-04
2022-05-14 06:32:04,298 INFO [train.py:812] (5/8) Epoch 8, batch 1650, loss[loss=0.1695, simple_loss=0.2601, pruned_loss=0.03944, over 7309.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2789, pruned_loss=0.05668, over 1419934.10 frames.], batch size: 24, lr: 9.26e-04
2022-05-14 06:33:04,112 INFO [train.py:812] (5/8) Epoch 8, batch 1700, loss[loss=0.1872, simple_loss=0.2722, pruned_loss=0.05113, over 7314.00 frames.], tot_loss[loss=0.1947, simple_loss=0.278, pruned_loss=0.05576, over 1420197.26 frames.], batch size: 21, lr: 9.26e-04
2022-05-14 06:34:03,597 INFO [train.py:812] (5/8) Epoch 8, batch 1750, loss[loss=0.197, simple_loss=0.2902, pruned_loss=0.05183, over 7317.00 frames.], tot_loss[loss=0.195, simple_loss=0.2782, pruned_loss=0.05589, over 1420444.62 frames.], batch size: 22, lr: 9.25e-04
2022-05-14 06:35:12,517 INFO [train.py:812] (5/8) Epoch 8, batch 1800, loss[loss=0.1833, simple_loss=0.2853, pruned_loss=0.04067, over 7339.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2765, pruned_loss=0.05503, over 1421111.65 frames.], batch size: 22, lr: 9.24e-04
2022-05-14 06:36:21,369 INFO [train.py:812] (5/8) Epoch 8, batch 1850, loss[loss=0.1908, simple_loss=0.2825, pruned_loss=0.04955, over 7245.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2774, pruned_loss=0.05543, over 1422857.61 frames.], batch size: 20, lr: 9.24e-04
2022-05-14 06:37:30,718 INFO [train.py:812] (5/8) Epoch 8, batch 1900, loss[loss=0.224, simple_loss=0.3187, pruned_loss=0.06463, over 7295.00 frames.], tot_loss[loss=0.1941, simple_loss=0.277, pruned_loss=0.05563, over 1421778.53 frames.], batch size: 25, lr: 9.23e-04
2022-05-14 06:38:48,459 INFO [train.py:812] (5/8) Epoch 8, batch 1950, loss[loss=0.1605, simple_loss=0.2352, pruned_loss=0.0429, over 7002.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2769, pruned_loss=0.05536, over 1425843.97 frames.], batch size: 16, lr: 9.22e-04
2022-05-14 06:40:06,954 INFO [train.py:812] (5/8) Epoch 8, batch 2000, loss[loss=0.1952, simple_loss=0.2806, pruned_loss=0.05485, over 7105.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2768, pruned_loss=0.05531, over 1425873.74 frames.], batch size: 21, lr: 9.22e-04
2022-05-14 06:41:06,079 INFO [train.py:812] (5/8) Epoch 8, batch 2050, loss[loss=0.2335, simple_loss=0.3015, pruned_loss=0.08277, over 5077.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2783, pruned_loss=0.05613, over 1420229.23 frames.], batch size: 52, lr: 9.21e-04
2022-05-14 06:42:04,898 INFO [train.py:812] (5/8) Epoch 8, batch 2100, loss[loss=0.1949, simple_loss=0.2821, pruned_loss=0.05387, over 7240.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2788, pruned_loss=0.05648, over 1417604.44 frames.], batch size: 20, lr: 9.20e-04
2022-05-14 06:43:04,059 INFO [train.py:812] (5/8) Epoch 8, batch 2150, loss[loss=0.2094, simple_loss=0.2955, pruned_loss=0.06166, over 7190.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2781, pruned_loss=0.05635, over 1418468.85 frames.], batch size: 22, lr: 9.20e-04
2022-05-14 06:44:02,976 INFO [train.py:812] (5/8) Epoch 8, batch 2200, loss[loss=0.2306, simple_loss=0.3181, pruned_loss=0.07158, over 7298.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2771, pruned_loss=0.05587, over 1415795.54 frames.], batch size: 24, lr: 9.19e-04
2022-05-14 06:45:01,869 INFO [train.py:812] (5/8) Epoch 8, batch 2250, loss[loss=0.2099, simple_loss=0.2989, pruned_loss=0.06049, over 7200.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2764, pruned_loss=0.05571, over 1410615.20 frames.], batch size: 23, lr: 9.18e-04
2022-05-14 06:46:00,794 INFO [train.py:812] (5/8) Epoch 8, batch 2300, loss[loss=0.1787, simple_loss=0.2555, pruned_loss=0.05093, over 7420.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2768, pruned_loss=0.05581, over 1411248.00 frames.], batch size: 18, lr: 9.18e-04
2022-05-14 06:46:59,518 INFO [train.py:812] (5/8) Epoch 8, batch 2350, loss[loss=0.1812, simple_loss=0.2721, pruned_loss=0.04515, over 7067.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2782, pruned_loss=0.0565, over 1411249.15 frames.], batch size: 18, lr: 9.17e-04
2022-05-14 06:47:58,458 INFO [train.py:812] (5/8) Epoch 8, batch 2400, loss[loss=0.1632, simple_loss=0.2511, pruned_loss=0.03762, over 7251.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2778, pruned_loss=0.05624, over 1415401.21 frames.], batch size: 19, lr: 9.16e-04
2022-05-14 06:48:57,535 INFO [train.py:812] (5/8) Epoch 8, batch 2450, loss[loss=0.1922, simple_loss=0.285, pruned_loss=0.04969, over 7308.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2779, pruned_loss=0.05563, over 1422495.95 frames.], batch size: 24, lr: 9.16e-04
2022-05-14 06:49:57,000 INFO [train.py:812] (5/8) Epoch 8, batch 2500, loss[loss=0.1849, simple_loss=0.2838, pruned_loss=0.04297, over 7319.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2785, pruned_loss=0.05569, over 1420642.51 frames.], batch size: 21, lr: 9.15e-04
2022-05-14 06:50:55,697 INFO [train.py:812] (5/8) Epoch 8, batch 2550, loss[loss=0.221, simple_loss=0.2992, pruned_loss=0.07146, over 7357.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2772, pruned_loss=0.05523, over 1425009.77 frames.], batch size: 19, lr: 9.14e-04
2022-05-14 06:51:54,441 INFO [train.py:812] (5/8) Epoch 8, batch 2600, loss[loss=0.1622, simple_loss=0.2428, pruned_loss=0.04085, over 7226.00 frames.], tot_loss[loss=0.193, simple_loss=0.2765, pruned_loss=0.05478, over 1425540.41 frames.], batch size: 16, lr: 9.14e-04
2022-05-14 06:52:51,873 INFO [train.py:812] (5/8) Epoch 8, batch 2650, loss[loss=0.1773, simple_loss=0.2665, pruned_loss=0.04408, over 7115.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2759, pruned_loss=0.05453, over 1426477.11 frames.], batch size: 21, lr: 9.13e-04
2022-05-14 06:53:49,809 INFO [train.py:812] (5/8) Epoch 8, batch 2700, loss[loss=0.1843, simple_loss=0.2594, pruned_loss=0.05458, over 6767.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2747, pruned_loss=0.05416, over 1428610.68 frames.], batch size: 15, lr: 9.12e-04
2022-05-14 06:54:48,273 INFO [train.py:812] (5/8) Epoch 8, batch 2750, loss[loss=0.17, simple_loss=0.2441, pruned_loss=0.04795, over 7407.00 frames.], tot_loss[loss=0.191, simple_loss=0.274, pruned_loss=0.05394, over 1428382.63 frames.], batch size: 17, lr: 9.12e-04
2022-05-14 06:55:46,920 INFO [train.py:812] (5/8) Epoch 8, batch 2800, loss[loss=0.1613, simple_loss=0.2606, pruned_loss=0.03102, over 7145.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2751, pruned_loss=0.05418, over 1427995.25 frames.], batch size: 20, lr: 9.11e-04
2022-05-14 06:56:44,433 INFO [train.py:812] (5/8) Epoch 8, batch 2850, loss[loss=0.2104, simple_loss=0.2941, pruned_loss=0.06336, over 7214.00 frames.], tot_loss[loss=0.192, simple_loss=0.2754, pruned_loss=0.05433, over 1426393.31 frames.], batch size: 22, lr: 9.11e-04
2022-05-14 06:57:43,872 INFO [train.py:812] (5/8) Epoch 8, batch 2900, loss[loss=0.1855, simple_loss=0.2677, pruned_loss=0.05163, over 7152.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2766, pruned_loss=0.05464, over 1425758.79 frames.], batch size: 17, lr: 9.10e-04
2022-05-14 06:58:42,758 INFO [train.py:812] (5/8) Epoch 8, batch 2950, loss[loss=0.1887, simple_loss=0.2649, pruned_loss=0.05621, over 7054.00 frames.], tot_loss[loss=0.192, simple_loss=0.2752, pruned_loss=0.05437, over 1425143.58 frames.], batch size: 18, lr: 9.09e-04
2022-05-14 06:59:42,239 INFO [train.py:812] (5/8) Epoch 8, batch 3000, loss[loss=0.252, simple_loss=0.3095, pruned_loss=0.09722, over 5155.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2758, pruned_loss=0.05486, over 1421400.84 frames.], batch size: 52, lr: 9.09e-04
2022-05-14 06:59:42,240 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 06:59:50,551 INFO [train.py:841] (5/8) Epoch 8, validation: loss=0.1612, simple_loss=0.2635, pruned_loss=0.0294, over 698248.00 frames.
2022-05-14 07:00:48,454 INFO [train.py:812] (5/8) Epoch 8, batch 3050, loss[loss=0.191, simple_loss=0.2759, pruned_loss=0.05305, over 6306.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2759, pruned_loss=0.05532, over 1414982.72 frames.], batch size: 37, lr: 9.08e-04
2022-05-14 07:01:48,157 INFO [train.py:812] (5/8) Epoch 8, batch 3100, loss[loss=0.1629, simple_loss=0.2509, pruned_loss=0.03744, over 7258.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2762, pruned_loss=0.05553, over 1418914.14 frames.], batch size: 19, lr: 9.07e-04
2022-05-14 07:02:45,300 INFO [train.py:812] (5/8) Epoch 8, batch 3150, loss[loss=0.2063, simple_loss=0.2842, pruned_loss=0.06415, over 7433.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2756, pruned_loss=0.0555, over 1421203.86 frames.], batch size: 20, lr: 9.07e-04
2022-05-14 07:03:44,351 INFO [train.py:812] (5/8) Epoch 8, batch 3200, loss[loss=0.1778, simple_loss=0.2797, pruned_loss=0.03791, over 7438.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2756, pruned_loss=0.0549, over 1424047.29 frames.], batch size: 20, lr: 9.06e-04
2022-05-14 07:04:43,315 INFO [train.py:812] (5/8) Epoch 8, batch 3250, loss[loss=0.1832, simple_loss=0.2751, pruned_loss=0.04565, over 7030.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2762, pruned_loss=0.05497, over 1422675.68 frames.], batch size: 28, lr: 9.05e-04
2022-05-14 07:05:41,212 INFO [train.py:812] (5/8) Epoch 8, batch 3300, loss[loss=0.2248, simple_loss=0.3125, pruned_loss=0.06852, over 6779.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2755, pruned_loss=0.05473, over 1422688.06 frames.], batch size: 31, lr: 9.05e-04
2022-05-14 07:06:40,365 INFO [train.py:812] (5/8) Epoch 8, batch 3350, loss[loss=0.1632, simple_loss=0.2472, pruned_loss=0.03959, over 7432.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2767, pruned_loss=0.05574, over 1421126.02 frames.], batch size: 20, lr: 9.04e-04
2022-05-14 07:07:39,878 INFO [train.py:812] (5/8) Epoch 8, batch 3400, loss[loss=0.2096, simple_loss=0.2952, pruned_loss=0.06196, over 6831.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2759, pruned_loss=0.05535, over 1419149.11 frames.], batch size: 31, lr: 9.04e-04
2022-05-14 07:08:38,475 INFO [train.py:812] (5/8) Epoch 8, batch 3450, loss[loss=0.2118, simple_loss=0.2785, pruned_loss=0.07252, over 7418.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2775, pruned_loss=0.05645, over 1421884.05 frames.], batch size: 18, lr: 9.03e-04
2022-05-14 07:09:37,920 INFO [train.py:812] (5/8) Epoch 8, batch 3500, loss[loss=0.1918, simple_loss=0.2737, pruned_loss=0.05493, over 7380.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2772, pruned_loss=0.05586, over 1421635.27 frames.], batch size: 23, lr: 9.02e-04
2022-05-14 07:10:37,035 INFO [train.py:812] (5/8) Epoch 8, batch 3550, loss[loss=0.1725, simple_loss=0.261, pruned_loss=0.04199, over 7260.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2769, pruned_loss=0.05546, over 1422691.42 frames.], batch size: 19, lr: 9.02e-04
2022-05-14 07:11:36,656 INFO [train.py:812] (5/8) Epoch 8, batch 3600, loss[loss=0.1805, simple_loss=0.2506, pruned_loss=0.05519, over 7262.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2761, pruned_loss=0.05524, over 1421595.67 frames.], batch size: 17, lr: 9.01e-04
2022-05-14 07:12:33,683 INFO [train.py:812] (5/8) Epoch 8, batch 3650, loss[loss=0.203, simple_loss=0.2915, pruned_loss=0.05724, over 7407.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2775, pruned_loss=0.05594, over 1415718.87 frames.], batch size: 21, lr: 9.01e-04
2022-05-14 07:13:32,603 INFO [train.py:812] (5/8) Epoch 8, batch 3700, loss[loss=0.2098, simple_loss=0.2998, pruned_loss=0.05984, over 7220.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2766, pruned_loss=0.05502, over 1419355.87 frames.], batch size: 21, lr: 9.00e-04
2022-05-14 07:14:31,403 INFO [train.py:812] (5/8) Epoch 8, batch 3750, loss[loss=0.2032, simple_loss=0.2791, pruned_loss=0.0636, over 7159.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2756, pruned_loss=0.05464, over 1416351.81 frames.], batch size: 19, lr: 8.99e-04
2022-05-14 07:15:30,611 INFO [train.py:812] (5/8) Epoch 8, batch 3800, loss[loss=0.2106, simple_loss=0.3002, pruned_loss=0.06046, over 7273.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2762, pruned_loss=0.05457, over 1419963.89 frames.], batch size: 24, lr: 8.99e-04
2022-05-14 07:16:28,746 INFO [train.py:812] (5/8) Epoch 8, batch 3850, loss[loss=0.1945, simple_loss=0.2849, pruned_loss=0.05205, over 7219.00 frames.], tot_loss[loss=0.194, simple_loss=0.2776, pruned_loss=0.05521, over 1418068.26 frames.], batch size: 21, lr: 8.98e-04
2022-05-14 07:17:33,318 INFO [train.py:812] (5/8) Epoch 8, batch 3900, loss[loss=0.195, simple_loss=0.2776, pruned_loss=0.05623, over 7434.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2768, pruned_loss=0.05472, over 1421796.89 frames.], batch size: 20, lr: 8.97e-04
2022-05-14 07:18:32,411 INFO [train.py:812] (5/8) Epoch 8, batch 3950, loss[loss=0.1854, simple_loss=0.2552, pruned_loss=0.05778, over 6995.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2761, pruned_loss=0.0543, over 1425135.46 frames.], batch size: 16, lr: 8.97e-04
2022-05-14 07:19:31,323 INFO [train.py:812] (5/8) Epoch 8, batch 4000, loss[loss=0.1976, simple_loss=0.2838, pruned_loss=0.05568, over 7143.00 frames.], tot_loss[loss=0.193, simple_loss=0.2769, pruned_loss=0.05454, over 1423699.24 frames.], batch size: 20, lr: 8.96e-04
2022-05-14 07:20:29,695 INFO [train.py:812] (5/8) Epoch 8, batch 4050, loss[loss=0.2315, simple_loss=0.3273, pruned_loss=0.06779, over 7406.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2766, pruned_loss=0.05485, over 1425833.92 frames.], batch size: 21, lr: 8.96e-04
2022-05-14 07:21:29,473 INFO [train.py:812] (5/8) Epoch 8, batch 4100, loss[loss=0.1732, simple_loss=0.2503, pruned_loss=0.04805, over 7273.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2772, pruned_loss=0.05549, over 1419269.64 frames.], batch size: 17, lr: 8.95e-04
2022-05-14 07:22:28,430 INFO [train.py:812] (5/8) Epoch 8, batch 4150, loss[loss=0.22, simple_loss=0.301, pruned_loss=0.06949, over 7341.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2776, pruned_loss=0.05564, over 1412281.20 frames.], batch size: 22, lr: 8.94e-04
2022-05-14 07:23:28,037 INFO [train.py:812] (5/8) Epoch 8, batch 4200, loss[loss=0.2101, simple_loss=0.2945, pruned_loss=0.06286, over 7146.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2783, pruned_loss=0.05596, over 1415039.58 frames.], batch size: 20, lr: 8.94e-04
2022-05-14 07:24:27,288 INFO [train.py:812] (5/8) Epoch 8, batch 4250, loss[loss=0.2017, simple_loss=0.2911, pruned_loss=0.05614, over 7213.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2769, pruned_loss=0.05491, over 1419677.88 frames.], batch size: 22, lr: 8.93e-04
2022-05-14 07:25:26,307 INFO [train.py:812] (5/8) Epoch 8, batch 4300, loss[loss=0.1994, simple_loss=0.2806, pruned_loss=0.05909, over 7311.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2758, pruned_loss=0.05488, over 1418335.75 frames.], batch size: 21, lr: 8.93e-04
2022-05-14 07:26:25,344 INFO [train.py:812] (5/8) Epoch 8, batch 4350, loss[loss=0.2328, simple_loss=0.3209, pruned_loss=0.0724, over 7109.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2753, pruned_loss=0.0549, over 1414322.66 frames.], batch size: 21, lr: 8.92e-04
2022-05-14 07:27:24,388 INFO [train.py:812] (5/8) Epoch 8, batch 4400, loss[loss=0.2005, simple_loss=0.2815, pruned_loss=0.05975, over 7010.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2752, pruned_loss=0.05496, over 1416888.14 frames.], batch size: 28, lr: 8.91e-04
2022-05-14 07:28:23,663 INFO [train.py:812] (5/8) Epoch 8, batch 4450, loss[loss=0.2084, simple_loss=0.2861, pruned_loss=0.06537, over 7332.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2752, pruned_loss=0.05504, over 1417028.88 frames.], batch size: 20, lr: 8.91e-04
2022-05-14 07:29:23,595 INFO [train.py:812] (5/8) Epoch 8, batch 4500, loss[loss=0.1696, simple_loss=0.2545, pruned_loss=0.0424, over 7161.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2744, pruned_loss=0.05501, over 1414286.78 frames.], batch size: 18, lr: 8.90e-04
2022-05-14 07:30:22,906 INFO [train.py:812] (5/8) Epoch 8, batch 4550, loss[loss=0.1839, simple_loss=0.2594, pruned_loss=0.05421, over 7298.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2742, pruned_loss=0.05608, over 1398369.30 frames.], batch size: 17, lr: 8.90e-04
2022-05-14 07:31:33,245 INFO [train.py:812] (5/8) Epoch 9, batch 0, loss[loss=0.2425, simple_loss=0.3279, pruned_loss=0.07849, over 7204.00 frames.], tot_loss[loss=0.2425, simple_loss=0.3279, pruned_loss=0.07849, over 7204.00 frames.], batch size: 23, lr: 8.54e-04
2022-05-14 07:32:31,239 INFO [train.py:812] (5/8) Epoch 9, batch 50, loss[loss=0.2104, simple_loss=0.293, pruned_loss=0.06385, over 7093.00 frames.], tot_loss[loss=0.1962, simple_loss=0.28, pruned_loss=0.0562, over 319535.94 frames.], batch size: 28, lr: 8.53e-04
2022-05-14 07:33:31,084 INFO [train.py:812] (5/8) Epoch 9, batch 100, loss[loss=0.1731, simple_loss=0.26, pruned_loss=0.04309, over 7237.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2766, pruned_loss=0.0545, over 566721.51 frames.], batch size: 20, lr: 8.53e-04
2022-05-14 07:34:29,320 INFO [train.py:812] (5/8) Epoch 9, batch 150, loss[loss=0.2322, simple_loss=0.3053, pruned_loss=0.0796, over 5099.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2754, pruned_loss=0.05411, over 753259.72 frames.], batch size: 52, lr: 8.52e-04
2022-05-14 07:35:29,137 INFO [train.py:812] (5/8) Epoch 9, batch 200, loss[loss=0.1905, simple_loss=0.275, pruned_loss=0.05302, over 7178.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2754, pruned_loss=0.05313, over 902196.55 frames.], batch size: 22, lr: 8.51e-04
2022-05-14 07:36:28,013 INFO [train.py:812] (5/8) Epoch 9, batch 250, loss[loss=0.1904, simple_loss=0.2677, pruned_loss=0.0566, over 7438.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2742, pruned_loss=0.05262, over 1018488.57 frames.], batch size: 20, lr: 8.51e-04
2022-05-14 07:37:25,191 INFO [train.py:812] (5/8) Epoch 9, batch 300, loss[loss=0.1732, simple_loss=0.2695, pruned_loss=0.0384, over 7339.00 frames.], tot_loss[loss=0.191, simple_loss=0.2752, pruned_loss=0.05343, over 1105022.95 frames.], batch size: 22, lr: 8.50e-04
2022-05-14 07:38:24,952 INFO [train.py:812] (5/8) Epoch 9, batch 350, loss[loss=0.1591, simple_loss=0.248, pruned_loss=0.03506, over 7159.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2733, pruned_loss=0.05267, over 1178962.59 frames.], batch size: 19, lr: 8.50e-04
2022-05-14 07:39:24,187 INFO [train.py:812] (5/8) Epoch 9, batch 400, loss[loss=0.1659, simple_loss=0.2392, pruned_loss=0.04627, over 7143.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2729, pruned_loss=0.05245, over 1237942.69 frames.], batch size: 17, lr: 8.49e-04
2022-05-14 07:40:21,409 INFO [train.py:812] (5/8) Epoch 9, batch 450, loss[loss=0.1623, simple_loss=0.2519, pruned_loss=0.03634, over 7255.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2726, pruned_loss=0.05238, over 1278242.08 frames.], batch size: 19, lr: 8.49e-04
2022-05-14 07:41:19,854 INFO [train.py:812] (5/8) Epoch 9, batch 500, loss[loss=0.1723, simple_loss=0.259, pruned_loss=0.04281, over 7426.00 frames.], tot_loss[loss=0.191, simple_loss=0.2747, pruned_loss=0.05361, over 1311295.24 frames.], batch size: 18, lr: 8.48e-04
2022-05-14 07:42:19,094 INFO [train.py:812] (5/8) Epoch 9, batch 550, loss[loss=0.2078, simple_loss=0.2851, pruned_loss=0.0653, over 7073.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2737, pruned_loss=0.05279, over 1338692.40 frames.], batch size: 18, lr: 8.48e-04
2022-05-14 07:43:17,525 INFO [train.py:812] (5/8) Epoch 9, batch 600, loss[loss=0.1892, simple_loss=0.2749, pruned_loss=0.05177, over 7056.00 frames.], tot_loss[loss=0.1889, simple_loss=0.273, pruned_loss=0.05241, over 1360370.90 frames.], batch size: 18, lr: 8.47e-04
2022-05-14 07:44:16,710 INFO [train.py:812] (5/8) Epoch 9, batch 650, loss[loss=0.2076, simple_loss=0.2748, pruned_loss=0.07022, over 7355.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2729, pruned_loss=0.05203, over 1373748.00 frames.], batch size: 19, lr: 8.46e-04
2022-05-14 07:45:15,375 INFO [train.py:812] (5/8) Epoch 9, batch 700, loss[loss=0.2001, simple_loss=0.2754, pruned_loss=0.06245, over 7429.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2731, pruned_loss=0.052, over 1386542.20 frames.], batch size: 20, lr: 8.46e-04
2022-05-14 07:46:13,719 INFO [train.py:812] (5/8) Epoch 9, batch 750, loss[loss=0.1767, simple_loss=0.2542, pruned_loss=0.04964, over 7172.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2741, pruned_loss=0.05283, over 1390053.93 frames.], batch size: 18, lr: 8.45e-04
2022-05-14 07:47:13,054 INFO [train.py:812] (5/8) Epoch 9, batch 800, loss[loss=0.2201, simple_loss=0.3014, pruned_loss=0.06936, over 7390.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2743, pruned_loss=0.0532, over 1395777.54 frames.], batch size: 23, lr: 8.45e-04
2022-05-14 07:48:11,326 INFO [train.py:812] (5/8) Epoch 9, batch 850, loss[loss=0.1912, simple_loss=0.275, pruned_loss=0.05373, over 7314.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2746, pruned_loss=0.05331, over 1400327.16 frames.], batch size: 21, lr: 8.44e-04
2022-05-14 07:49:11,211 INFO [train.py:812] (5/8) Epoch 9, batch 900, loss[loss=0.1916, simple_loss=0.2756, pruned_loss=0.05381, over 7222.00 frames.], tot_loss[loss=0.1896, simple_loss=0.274, pruned_loss=0.05264, over 1410320.92 frames.], batch size: 21, lr: 8.44e-04
2022-05-14 07:50:10,501 INFO [train.py:812] (5/8) Epoch 9, batch 950, loss[loss=0.1671, simple_loss=0.258, pruned_loss=0.03814, over 7351.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2743, pruned_loss=0.05345, over 1409274.99 frames.], batch size: 20, lr: 8.43e-04
2022-05-14 07:51:10,498 INFO [train.py:812] (5/8) Epoch 9, batch 1000, loss[loss=0.1823, simple_loss=0.2657, pruned_loss=0.04943, over 7426.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2733, pruned_loss=0.05291, over 1413116.86 frames.], batch size: 20, lr: 8.43e-04
2022-05-14 07:52:09,028 INFO [train.py:812] (5/8) Epoch 9, batch 1050, loss[loss=0.203, simple_loss=0.2839, pruned_loss=0.06107, over 7254.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2736, pruned_loss=0.05308, over 1418689.65 frames.], batch size: 19, lr: 8.42e-04
2022-05-14 07:53:07,740 INFO [train.py:812] (5/8) Epoch 9, batch 1100, loss[loss=0.143, simple_loss=0.2242, pruned_loss=0.03089, over 7273.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2743, pruned_loss=0.05316, over 1421811.43 frames.], batch size: 17, lr: 8.41e-04
2022-05-14 07:54:04,861 INFO [train.py:812] (5/8) Epoch 9, batch 1150, loss[loss=0.185, simple_loss=0.2748, pruned_loss=0.0476, over 7297.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2744, pruned_loss=0.05334, over 1421888.35 frames.], batch size: 25, lr: 8.41e-04
2022-05-14 07:55:04,924 INFO [train.py:812] (5/8) Epoch 9, batch 1200, loss[loss=0.157, simple_loss=0.2438, pruned_loss=0.03513, over 7432.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2732, pruned_loss=0.05231, over 1422137.67 frames.], batch size: 20, lr: 8.40e-04
2022-05-14 07:56:02,845 INFO [train.py:812] (5/8) Epoch 9, batch 1250, loss[loss=0.1751, simple_loss=0.2489, pruned_loss=0.05071, over 6847.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2719, pruned_loss=0.05201, over 1418702.66 frames.], batch size: 15, lr: 8.40e-04
2022-05-14 07:57:02,146 INFO [train.py:812] (5/8) Epoch 9, batch 1300, loss[loss=0.2155, simple_loss=0.306, pruned_loss=0.06246, over 7148.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2723, pruned_loss=0.05227, over 1415018.45 frames.], batch size: 19, lr: 8.39e-04
2022-05-14 07:58:01,345 INFO [train.py:812] (5/8) Epoch 9, batch 1350, loss[loss=0.2006, simple_loss=0.2779, pruned_loss=0.06167, over 7436.00 frames.], tot_loss[loss=0.19, simple_loss=0.2736, pruned_loss=0.05315, over 1419216.48 frames.], batch size: 20, lr: 8.39e-04
2022-05-14 07:59:00,868 INFO [train.py:812] (5/8) Epoch 9, batch 1400, loss[loss=0.2127, simple_loss=0.2999, pruned_loss=0.06276, over 7225.00 frames.], tot_loss[loss=0.19, simple_loss=0.2735, pruned_loss=0.05318, over 1415675.17 frames.], batch size: 21, lr: 8.38e-04
2022-05-14 07:59:57,891 INFO [train.py:812] (5/8) Epoch 9, batch 1450, loss[loss=0.2237, simple_loss=0.3005, pruned_loss=0.07345, over 7319.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2721, pruned_loss=0.0523, over 1420424.39 frames.], batch size: 21, lr: 8.38e-04
2022-05-14 08:00:55,531 INFO [train.py:812] (5/8) Epoch 9, batch 1500, loss[loss=0.1849, simple_loss=0.2768, pruned_loss=0.04653, over 7226.00 frames.], tot_loss[loss=0.188, simple_loss=0.2721, pruned_loss=0.05198, over 1423017.14 frames.], batch size: 20, lr: 8.37e-04
2022-05-14 08:01:53,872 INFO [train.py:812] (5/8) Epoch 9, batch 1550, loss[loss=0.2145, simple_loss=0.3066, pruned_loss=0.06125, over 7217.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2722, pruned_loss=0.05233, over 1422711.25 frames.], batch size: 22, lr: 8.37e-04
2022-05-14 08:02:51,999 INFO [train.py:812] (5/8) Epoch 9, batch 1600, loss[loss=0.1743, simple_loss=0.257, pruned_loss=0.04578, over 7067.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2726, pruned_loss=0.05229, over 1420581.42 frames.], batch size: 18, lr: 8.36e-04
2022-05-14 08:03:49,506 INFO [train.py:812] (5/8) Epoch 9, batch 1650, loss[loss=0.1954, simple_loss=0.288, pruned_loss=0.05139, over 7113.00 frames.], tot_loss[loss=0.1891, simple_loss=0.273, pruned_loss=0.05258, over 1422373.10 frames.], batch size: 21, lr: 8.35e-04
2022-05-14 08:04:47,903 INFO [train.py:812] (5/8) Epoch 9, batch 1700, loss[loss=0.2005, simple_loss=0.2897, pruned_loss=0.05565, over 7150.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2738, pruned_loss=0.05247, over 1421083.84 frames.], batch size: 20, lr: 8.35e-04
2022-05-14 08:05:46,543 INFO [train.py:812] (5/8) Epoch 9, batch 1750, loss[loss=0.2007, simple_loss=0.287, pruned_loss=0.05716, over 7322.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2731, pruned_loss=0.05222, over 1422607.94 frames.], batch size: 21, lr: 8.34e-04
2022-05-14 08:06:45,596 INFO [train.py:812] (5/8) Epoch 9, batch 1800, loss[loss=0.1643, simple_loss=0.2559, pruned_loss=0.03636, over 7226.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2738, pruned_loss=0.05273, over 1419302.74 frames.], batch size: 20, lr: 8.34e-04
2022-05-14 08:07:44,987 INFO [train.py:812] (5/8) Epoch 9, batch 1850, loss[loss=0.1909, simple_loss=0.2763, pruned_loss=0.05275, over 7221.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2749, pruned_loss=0.05308, over 1422131.71 frames.], batch size: 20, lr: 8.33e-04
2022-05-14 08:08:44,854 INFO [train.py:812] (5/8) Epoch 9, batch 1900, loss[loss=0.1894, simple_loss=0.2677, pruned_loss=0.05554, over 7158.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2753, pruned_loss=0.05301, over 1420533.42 frames.], batch size: 19, lr: 8.33e-04
2022-05-14 08:09:44,221 INFO [train.py:812] (5/8) Epoch 9, batch 1950, loss[loss=0.1841, simple_loss=0.2781, pruned_loss=0.04504, over 7125.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2739, pruned_loss=0.0526, over 1422003.45 frames.], batch size: 21, lr: 8.32e-04
2022-05-14 08:10:44,117 INFO [train.py:812] (5/8) Epoch 9, batch 2000, loss[loss=0.1988, simple_loss=0.2797, pruned_loss=0.05895, over 7318.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2733, pruned_loss=0.0523, over 1422447.09 frames.], batch size: 24, lr: 8.32e-04
2022-05-14 08:11:43,576 INFO [train.py:812] (5/8) Epoch 9, batch 2050, loss[loss=0.1509, simple_loss=0.2285, pruned_loss=0.03662, over 7284.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2743, pruned_loss=0.05327, over 1421899.70 frames.], batch size: 17, lr: 8.31e-04
2022-05-14 08:12:43,234 INFO [train.py:812] (5/8) Epoch 9, batch 2100, loss[loss=0.2135, simple_loss=0.2942, pruned_loss=0.06644, over 7253.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2738, pruned_loss=0.05293, over 1423297.56 frames.], batch size: 19, lr: 8.31e-04
2022-05-14 08:13:42,061 INFO [train.py:812] (5/8) Epoch 9, batch 2150, loss[loss=0.1917, simple_loss=0.2668, pruned_loss=0.05829, over 7073.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2735, pruned_loss=0.05254, over 1425250.99 frames.], batch size: 18, lr: 8.30e-04
2022-05-14 08:14:40,834 INFO [train.py:812] (5/8) Epoch 9, batch 2200, loss[loss=0.1669, simple_loss=0.25, pruned_loss=0.04195, over 7255.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2734, pruned_loss=0.05252, over 1423444.97 frames.], batch size: 17, lr: 8.30e-04
2022-05-14 08:15:40,320 INFO [train.py:812] (5/8) Epoch 9, batch 2250, loss[loss=0.1755, simple_loss=0.2548, pruned_loss=0.04808, over 7155.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2725, pruned_loss=0.05213, over 1423782.32 frames.], batch size: 18, lr: 8.29e-04
2022-05-14 08:16:40,192 INFO [train.py:812] (5/8) Epoch 9, batch 2300, loss[loss=0.1744, simple_loss=0.2518, pruned_loss=0.04852, over 7155.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2733, pruned_loss=0.0525, over 1424989.65 frames.], batch size: 20, lr: 8.29e-04
2022-05-14 08:17:37,465 INFO [train.py:812] (5/8) Epoch 9, batch 2350, loss[loss=0.194, simple_loss=0.2769, pruned_loss=0.05553, over 6811.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2745, pruned_loss=0.05337, over 1422920.30 frames.], batch size: 31, lr: 8.28e-04
2022-05-14 08:18:37,026 INFO [train.py:812] (5/8) Epoch 9, batch 2400, loss[loss=0.1581, simple_loss=0.2442, pruned_loss=0.03595, over 7276.00 frames.], tot_loss[loss=0.19, simple_loss=0.2742, pruned_loss=0.05295, over 1424216.25 frames.], batch size: 18, lr: 8.28e-04
2022-05-14 08:19:36,157 INFO [train.py:812] (5/8) Epoch 9, batch 2450, loss[loss=0.1685, simple_loss=0.2564, pruned_loss=0.04034, over 7410.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2738, pruned_loss=0.05274, over 1425282.90 frames.], batch size: 18, lr: 8.27e-04
2022-05-14 08:20:34,801 INFO [train.py:812] (5/8) Epoch 9, batch 2500, loss[loss=0.182, simple_loss=0.2606, pruned_loss=0.05168, over 7205.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2734, pruned_loss=0.0524, over 1423608.83 frames.], batch size: 22, lr: 8.27e-04
2022-05-14 08:21:43,990 INFO [train.py:812] (5/8) Epoch 9, batch 2550, loss[loss=0.1427, simple_loss=0.224, pruned_loss=0.03072, over 7136.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2719, pruned_loss=0.05158, over 1421232.81 frames.], batch size: 17, lr: 8.26e-04
2022-05-14 08:22:42,477 INFO [train.py:812] (5/8) Epoch 9, batch 2600, loss[loss=0.2475, simple_loss=0.3195, pruned_loss=0.08775, over 7371.00 frames.], tot_loss[loss=0.188, simple_loss=0.2726, pruned_loss=0.05165, over 1418884.30 frames.], batch size: 23, lr: 8.25e-04
2022-05-14 08:23:41,183 INFO [train.py:812] (5/8) Epoch 9, batch 2650, loss[loss=0.2472, simple_loss=0.3085, pruned_loss=0.09291, over 5323.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2719, pruned_loss=0.05132, over 1417758.30 frames.], batch size: 53, lr: 8.25e-04
2022-05-14 08:24:39,378 INFO [train.py:812] (5/8) Epoch 9, batch 2700, loss[loss=0.181, simple_loss=0.2709, pruned_loss=0.04553, over 7336.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2723, pruned_loss=0.05142, over 1419372.24 frames.], batch size: 22, lr: 8.24e-04
2022-05-14 08:25:38,209 INFO [train.py:812] (5/8) Epoch 9, batch 2750, loss[loss=0.164, simple_loss=0.2475, pruned_loss=0.04021, over 7318.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2724, pruned_loss=0.05148, over 1423740.92 frames.], batch size: 20, lr: 8.24e-04
2022-05-14 08:26:37,731 INFO [train.py:812] (5/8) Epoch 9, batch 2800, loss[loss=0.1988, simple_loss=0.2883, pruned_loss=0.05463, over 7205.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2731, pruned_loss=0.05185, over 1426948.90 frames.], batch size: 22, lr: 8.23e-04
2022-05-14 08:27:35,906 INFO [train.py:812] (5/8) Epoch 9, batch 2850, loss[loss=0.1989, simple_loss=0.2783, pruned_loss=0.05978, over 7163.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2727, pruned_loss=0.0519, over 1429315.22 frames.], batch size: 19, lr: 8.23e-04
2022-05-14 08:28:33,951 INFO [train.py:812] (5/8) Epoch 9, batch 2900, loss[loss=0.1969, simple_loss=0.2919, pruned_loss=0.0509, over 7313.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2723, pruned_loss=0.05137, over 1428351.75 frames.], batch size: 21, lr: 8.22e-04
2022-05-14 08:29:31,242 INFO [train.py:812] (5/8) Epoch 9, batch 2950, loss[loss=0.1696, simple_loss=0.2457, pruned_loss=0.04673, over 7303.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2723, pruned_loss=0.05148, over 1425056.39 frames.], batch size: 18, lr: 8.22e-04
2022-05-14 08:30:30,194 INFO [train.py:812] (5/8) Epoch 9, batch 3000, loss[loss=0.2025, simple_loss=0.2894, pruned_loss=0.05781, over 7312.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2712, pruned_loss=0.05095, over 1423246.56 frames.], batch size: 24, lr: 8.21e-04
2022-05-14 08:30:30,195 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 08:30:38,337 INFO [train.py:841] (5/8) Epoch 9, validation: loss=0.1602, simple_loss=0.262, pruned_loss=0.0292, over 698248.00 frames.
2022-05-14 08:31:37,162 INFO [train.py:812] (5/8) Epoch 9, batch 3050, loss[loss=0.1782, simple_loss=0.2624, pruned_loss=0.04696, over 7322.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2718, pruned_loss=0.05158, over 1419708.54 frames.], batch size: 20, lr: 8.21e-04
2022-05-14 08:32:34,696 INFO [train.py:812] (5/8) Epoch 9, batch 3100, loss[loss=0.233, simple_loss=0.311, pruned_loss=0.07755, over 6761.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2739, pruned_loss=0.05264, over 1414463.77 frames.], batch size: 31, lr: 8.20e-04
2022-05-14 08:33:32,675 INFO [train.py:812] (5/8) Epoch 9, batch 3150, loss[loss=0.1983, simple_loss=0.2882, pruned_loss=0.05424, over 7156.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2737, pruned_loss=0.05288, over 1417967.79 frames.], batch size: 19, lr: 8.20e-04
2022-05-14 08:34:32,499 INFO [train.py:812] (5/8) Epoch 9, batch 3200, loss[loss=0.1808, simple_loss=0.2703, pruned_loss=0.04568, over 7145.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2735, pruned_loss=0.05279, over 1422255.56 frames.], batch size: 20, lr: 8.19e-04
2022-05-14 08:35:31,362 INFO [train.py:812] (5/8) Epoch 9, batch 3250, loss[loss=0.2442, simple_loss=0.3124, pruned_loss=0.08798, over 4953.00 frames.], tot_loss[loss=0.19, simple_loss=0.2737, pruned_loss=0.05317, over 1420142.90 frames.], batch size: 52, lr: 8.19e-04
2022-05-14 08:36:46,154 INFO [train.py:812] (5/8) Epoch 9, batch 3300, loss[loss=0.2035, simple_loss=0.2883, pruned_loss=0.05929, over 7193.00 frames.], tot_loss[loss=0.189, simple_loss=0.2727, pruned_loss=0.05262, over 1420635.33 frames.], batch size: 22, lr: 8.18e-04
2022-05-14 08:37:52,678 INFO [train.py:812] (5/8) Epoch 9, batch 3350, loss[loss=0.1748, simple_loss=0.2622, pruned_loss=0.04366, over 7264.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2722, pruned_loss=0.05209, over 1424018.98 frames.], batch size: 19, lr: 8.18e-04
2022-05-14 08:38:51,542 INFO [train.py:812] (5/8) Epoch 9, batch 3400, loss[loss=0.1832, simple_loss=0.2767, pruned_loss=0.04485, over 6791.00 frames.], tot_loss[loss=0.188, simple_loss=0.2723, pruned_loss=0.05183, over 1421601.68 frames.], batch size: 31, lr: 8.17e-04
2022-05-14 08:39:59,372 INFO [train.py:812] (5/8) Epoch 9, batch 3450, loss[loss=0.1535, simple_loss=0.2322, pruned_loss=0.03743, over 7426.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2727, pruned_loss=0.0518, over 1423764.47 frames.], batch size: 18, lr: 8.17e-04
2022-05-14 08:41:27,454 INFO [train.py:812] (5/8) Epoch 9, batch 3500, loss[loss=0.1623, simple_loss=0.2469, pruned_loss=0.03888, over 7151.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2718, pruned_loss=0.05125, over 1423878.46 frames.], batch size: 19, lr: 8.16e-04
2022-05-14 08:42:35,742 INFO [train.py:812] (5/8) Epoch 9, batch 3550, loss[loss=0.1439, simple_loss=0.2296, pruned_loss=0.02908, over 7172.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2712, pruned_loss=0.05093, over 1425890.23 frames.], batch size: 18, lr: 8.16e-04
2022-05-14 08:43:34,793 INFO [train.py:812] (5/8) Epoch 9, batch 3600, loss[loss=0.1801, simple_loss=0.2579, pruned_loss=0.05118, over 7278.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2718, pruned_loss=0.051, over 1423997.62 frames.], batch size: 18, lr: 8.15e-04
2022-05-14 08:44:32,173 INFO [train.py:812] (5/8) Epoch 9, batch 3650, loss[loss=0.1661, simple_loss=0.2441, pruned_loss=0.04401, over 7138.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2713, pruned_loss=0.05079, over 1425292.41 frames.], batch size: 17, lr: 8.15e-04
2022-05-14 08:45:31,306 INFO [train.py:812] (5/8) Epoch 9, batch 3700, loss[loss=0.168, simple_loss=0.2527, pruned_loss=0.04158, over 7282.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2716, pruned_loss=0.05074, over 1426375.02 frames.], batch size: 25, lr: 8.14e-04
2022-05-14 08:46:29,961 INFO [train.py:812] (5/8) Epoch 9, batch 3750, loss[loss=0.1779, simple_loss=0.2635, pruned_loss=0.04617, over 7440.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2725, pruned_loss=0.05144, over 1425267.85 frames.], batch size: 20, lr: 8.14e-04
2022-05-14 08:47:28,938 INFO [train.py:812] (5/8) Epoch 9, batch 3800, loss[loss=0.1765, simple_loss=0.2538, pruned_loss=0.04963, over 7407.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2726, pruned_loss=0.05123, over 1426885.23 frames.], batch size: 18, lr: 8.13e-04
2022-05-14 08:48:27,800 INFO [train.py:812] (5/8) Epoch 9, batch 3850, loss[loss=0.1639, simple_loss=0.2465, pruned_loss=0.0407, over 7263.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2721, pruned_loss=0.05088, over 1429185.43 frames.], batch size: 17, lr: 8.13e-04
2022-05-14 08:49:26,812 INFO [train.py:812] (5/8) Epoch 9, batch 3900, loss[loss=0.2635, simple_loss=0.3327, pruned_loss=0.09715, over 5383.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2736, pruned_loss=0.05186, over 1427566.14 frames.], batch size: 52, lr: 8.12e-04
2022-05-14 08:50:26,268 INFO [train.py:812] (5/8) Epoch 9, batch 3950, loss[loss=0.1893, simple_loss=0.2774, pruned_loss=0.05065, over 6813.00 frames.], tot_loss[loss=0.1881, simple_loss=0.273, pruned_loss=0.05165, over 1428878.52 frames.], batch size: 31, lr: 8.12e-04
2022-05-14 08:51:25,796 INFO [train.py:812] (5/8) Epoch 9, batch 4000, loss[loss=0.1713, simple_loss=0.2586, pruned_loss=0.04204, over 7232.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2732, pruned_loss=0.05152, over 1428500.58 frames.], batch size: 21, lr: 8.11e-04
2022-05-14 08:52:25,214 INFO [train.py:812] (5/8) Epoch 9, batch 4050, loss[loss=0.1672, simple_loss=0.2532, pruned_loss=0.04061, over 7396.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2723, pruned_loss=0.05148, over 1427411.91 frames.], batch size: 18, lr: 8.11e-04
2022-05-14 08:53:24,992 INFO [train.py:812] (5/8) Epoch 9, batch 4100, loss[loss=0.1937, simple_loss=0.2641, pruned_loss=0.0616, over 7154.00 frames.], tot_loss[loss=0.1883, simple_loss=0.273, pruned_loss=0.0518, over 1428022.76 frames.], batch size: 17, lr: 8.10e-04
2022-05-14 08:54:24,680 INFO [train.py:812] (5/8) Epoch 9, batch 4150, loss[loss=0.2326, simple_loss=0.3286, pruned_loss=0.06824, over 7062.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2731, pruned_loss=0.05204, over 1422918.00 frames.], batch size: 28, lr: 8.10e-04
2022-05-14 08:55:24,383 INFO [train.py:812] (5/8) Epoch 9, batch 4200, loss[loss=0.1666, simple_loss=0.2589, pruned_loss=0.03717, over 7322.00 frames.], tot_loss[loss=0.187, simple_loss=0.2714, pruned_loss=0.0513, over 1423842.38 frames.], batch size: 20, lr: 8.09e-04
2022-05-14 08:56:23,008 INFO [train.py:812] (5/8) Epoch 9, batch 4250, loss[loss=0.1653, simple_loss=0.2436, pruned_loss=0.04354, over 7120.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2708, pruned_loss=0.0513, over 1419519.24 frames.], batch size: 17, lr: 8.09e-04
2022-05-14 08:57:22,985 INFO [train.py:812] (5/8) Epoch 9, batch 4300, loss[loss=0.1828, simple_loss=0.2694, pruned_loss=0.04811, over 7422.00 frames.], tot_loss[loss=0.1862, simple_loss=0.27, pruned_loss=0.05123, over 1416426.58 frames.], batch size: 21, lr: 8.08e-04
2022-05-14 08:58:21,481 INFO [train.py:812] (5/8) Epoch 9, batch 4350, loss[loss=0.1514, simple_loss=0.2363, pruned_loss=0.03322, over 7280.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2686, pruned_loss=0.05017, over 1422223.02 frames.], batch size: 17, lr: 8.08e-04
2022-05-14 08:59:21,260 INFO [train.py:812] (5/8) Epoch 9, batch 4400, loss[loss=0.1834, simple_loss=0.2741, pruned_loss=0.04639, over 7068.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2682, pruned_loss=0.05043, over 1418618.70 frames.], batch size: 28, lr: 8.07e-04
2022-05-14 09:00:19,267 INFO [train.py:812] (5/8) Epoch 9, batch 4450, loss[loss=0.1826, simple_loss=0.272, pruned_loss=0.04661, over 7036.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2677, pruned_loss=0.0504, over 1413932.52 frames.], batch size: 28, lr: 8.07e-04
2022-05-14 09:01:19,085 INFO [train.py:812] (5/8) Epoch 9, batch 4500, loss[loss=0.1804, simple_loss=0.2757, pruned_loss=0.04254, over 7044.00 frames.], tot_loss[loss=0.186, simple_loss=0.2692, pruned_loss=0.05137, over 1397229.12 frames.], batch size: 28, lr: 8.07e-04
2022-05-14 09:02:17,094 INFO [train.py:812] (5/8) Epoch 9, batch 4550, loss[loss=0.1917, simple_loss=0.2746, pruned_loss=0.05441, over 6445.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2732, pruned_loss=0.05347, over 1356932.32 frames.], batch size: 38, lr: 8.06e-04
2022-05-14 09:03:24,798 INFO [train.py:812] (5/8) Epoch 10, batch 0, loss[loss=0.1737, simple_loss=0.2608, pruned_loss=0.0433, over 7420.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2608, pruned_loss=0.0433, over 7420.00 frames.], batch size: 21, lr: 7.75e-04
2022-05-14 09:04:24,075 INFO [train.py:812] (5/8) Epoch 10, batch 50, loss[loss=0.2075, simple_loss=0.3003, pruned_loss=0.05732, over 7234.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2716, pruned_loss=0.05085, over 321451.96 frames.], batch size: 23, lr: 7.74e-04
2022-05-14 09:05:23,157 INFO [train.py:812] (5/8) Epoch 10, batch 100, loss[loss=0.2325, simple_loss=0.2985, pruned_loss=0.08326, over 4669.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2698, pruned_loss=0.05069, over 556955.02 frames.], batch size: 52, lr: 7.74e-04
2022-05-14 09:06:22,301 INFO [train.py:812] (5/8) Epoch 10, batch 150, loss[loss=0.1949, simple_loss=0.2728, pruned_loss=0.05854, over 7430.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2701, pruned_loss=0.05038, over 751043.89 frames.], batch size: 20, lr: 7.73e-04
2022-05-14 09:07:20,627 INFO [train.py:812] (5/8) Epoch 10, batch 200, loss[loss=0.1903, simple_loss=0.281, pruned_loss=0.04981, over 7432.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2704, pruned_loss=0.05073, over 898198.72 frames.], batch size: 20, lr: 7.73e-04
2022-05-14 09:08:19,894 INFO [train.py:812] (5/8) Epoch 10, batch 250, loss[loss=0.1657, simple_loss=0.2545, pruned_loss=0.0384, over 7163.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2713, pruned_loss=0.05043, over 1010392.88 frames.], batch size: 18, lr: 7.72e-04
2022-05-14 09:09:19,083 INFO [train.py:812] (5/8) Epoch 10, batch 300, loss[loss=0.1504, simple_loss=0.2372, pruned_loss=0.03178, over 7329.00 frames.], tot_loss[loss=0.1857, simple_loss=0.271, pruned_loss=0.05024, over 1104368.38 frames.], batch size: 20, lr: 7.72e-04
2022-05-14 09:10:16,407 INFO [train.py:812] (5/8) Epoch 10, batch 350, loss[loss=0.1957, simple_loss=0.2936, pruned_loss=0.04888, over 7197.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2706, pruned_loss=0.05012, over 1173290.06 frames.], batch size: 23, lr: 7.71e-04
2022-05-14 09:11:15,121 INFO [train.py:812] (5/8) Epoch 10, batch 400, loss[loss=0.1907, simple_loss=0.2815, pruned_loss=0.04999, over 7176.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2719, pruned_loss=0.0505, over 1223193.14 frames.], batch size: 26, lr: 7.71e-04
2022-05-14 09:12:14,067 INFO [train.py:812] (5/8) Epoch 10, batch 450, loss[loss=0.1954, simple_loss=0.2785, pruned_loss=0.05615, over 6257.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2725, pruned_loss=0.05083, over 1261722.40 frames.], batch size: 37, lr: 7.71e-04
2022-05-14 09:13:13,637 INFO [train.py:812] (5/8) Epoch 10, batch 500, loss[loss=0.1769, simple_loss=0.2712, pruned_loss=0.04126, over 7158.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2727, pruned_loss=0.05102, over 1297047.67 frames.], batch size: 19, lr: 7.70e-04
2022-05-14 09:14:12,266 INFO [train.py:812] (5/8) Epoch 10, batch 550, loss[loss=0.1584, simple_loss=0.2364, pruned_loss=0.04022, over 7134.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2726, pruned_loss=0.05091, over 1325509.23 frames.], batch size: 17, lr: 7.70e-04
2022-05-14 09:15:10,135 INFO [train.py:812] (5/8) Epoch 10, batch 600, loss[loss=0.1707, simple_loss=0.2474, pruned_loss=0.04703, over 7270.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2724, pruned_loss=0.05068, over 1347221.94 frames.], batch size: 18, lr: 7.69e-04
2022-05-14 09:16:08,343 INFO [train.py:812] (5/8) Epoch 10, batch 650, loss[loss=0.1935, simple_loss=0.2738, pruned_loss=0.05657, over 7143.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2719, pruned_loss=0.05021, over 1363688.33 frames.], batch size: 26, lr: 7.69e-04
2022-05-14 09:17:07,946 INFO [train.py:812] (5/8) Epoch 10, batch 700, loss[loss=0.1726, simple_loss=0.2666, pruned_loss=0.03931, over 7255.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2711, pruned_loss=0.0499, over 1377416.37 frames.], batch size: 25, lr: 7.68e-04
2022-05-14 09:18:07,537 INFO [train.py:812] (5/8) Epoch 10, batch 750, loss[loss=0.1873, simple_loss=0.2733, pruned_loss=0.05063, over 7431.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2711, pruned_loss=0.04997, over 1387402.73 frames.], batch size: 20, lr: 7.68e-04
2022-05-14 09:19:06,538 INFO [train.py:812] (5/8) Epoch 10, batch 800, loss[loss=0.1716, simple_loss=0.2684, pruned_loss=0.03735, over 7259.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2708, pruned_loss=0.05002, over 1394684.72 frames.], batch size: 24, lr: 7.67e-04
2022-05-14 09:20:05,997 INFO [train.py:812] (5/8) Epoch 10, batch 850, loss[loss=0.1964, simple_loss=0.283, pruned_loss=0.05486, over 6503.00 frames.], tot_loss[loss=0.185, simple_loss=0.2704, pruned_loss=0.04978, over 1396900.47 frames.], batch size: 38, lr: 7.67e-04
2022-05-14 09:21:05,076 INFO [train.py:812] (5/8) Epoch 10, batch 900, loss[loss=0.1838, simple_loss=0.2729, pruned_loss=0.04738, over 7317.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2712, pruned_loss=0.05019, over 1406504.75 frames.], batch size: 21, lr: 7.66e-04
2022-05-14 09:22:03,782 INFO [train.py:812] (5/8) Epoch 10, batch 950, loss[loss=0.1849, simple_loss=0.2728, pruned_loss=0.04856, over 7144.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2718, pruned_loss=0.05075, over 1406208.46 frames.], batch size: 26, lr: 7.66e-04
2022-05-14 09:23:02,563 INFO [train.py:812] (5/8) Epoch 10, batch 1000, loss[loss=0.1699, simple_loss=0.2474, pruned_loss=0.0462, over 7336.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2716, pruned_loss=0.05074, over 1413787.04 frames.], batch size: 20, lr: 7.66e-04
2022-05-14 09:24:00,832 INFO [train.py:812] (5/8) Epoch 10, batch 1050, loss[loss=0.1949, simple_loss=0.28, pruned_loss=0.05486, over 7025.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2715, pruned_loss=0.0506, over 1417042.12 frames.], batch size: 28, lr: 7.65e-04
2022-05-14 09:24:59,373 INFO [train.py:812] (5/8) Epoch 10, batch 1100, loss[loss=0.1807, simple_loss=0.2787, pruned_loss=0.04135, over 7008.00 frames.], tot_loss[loss=0.187, simple_loss=0.2721, pruned_loss=0.05095, over 1418483.49 frames.], batch size: 28, lr: 7.65e-04
2022-05-14 09:25:57,291 INFO [train.py:812] (5/8) Epoch 10, batch 1150, loss[loss=0.1794, simple_loss=0.268, pruned_loss=0.04536, over 7339.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2716, pruned_loss=0.05046, over 1422343.91 frames.], batch size: 20, lr: 7.64e-04
2022-05-14 09:26:55,705 INFO [train.py:812] (5/8) Epoch 10, batch 1200, loss[loss=0.1965, simple_loss=0.2919, pruned_loss=0.05056, over 7211.00 frames.], tot_loss[loss=0.187, simple_loss=0.2727, pruned_loss=0.05069, over 1421191.96 frames.], batch size: 23, lr: 7.64e-04
2022-05-14 09:27:55,413 INFO [train.py:812] (5/8) Epoch 10, batch 1250, loss[loss=0.1648, simple_loss=0.2473, pruned_loss=0.04117, over 7283.00 frames.], tot_loss[loss=0.1875, simple_loss=0.273, pruned_loss=0.05098, over 1419139.84 frames.], batch size: 17, lr: 7.63e-04
2022-05-14 09:28:54,699 INFO [train.py:812] (5/8) Epoch 10, batch 1300, loss[loss=0.1613, simple_loss=0.2336, pruned_loss=0.04448, over 6997.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2717, pruned_loss=0.051, over 1416103.86 frames.], batch size: 16, lr: 7.63e-04
2022-05-14 09:29:54,192 INFO [train.py:812] (5/8) Epoch 10, batch 1350, loss[loss=0.1765, simple_loss=0.2724, pruned_loss=0.04037, over 7319.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2714, pruned_loss=0.05078, over 1416081.46 frames.], batch size: 21, lr: 7.62e-04
2022-05-14 09:30:53,017 INFO [train.py:812] (5/8) Epoch 10, batch 1400, loss[loss=0.1957, simple_loss=0.2888, pruned_loss=0.05135, over 7128.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2727, pruned_loss=0.05109, over 1419022.54 frames.], batch size: 21, lr: 7.62e-04
2022-05-14 09:31:52,538 INFO [train.py:812] (5/8) Epoch 10, batch 1450, loss[loss=0.2027, simple_loss=0.2853, pruned_loss=0.06004, over 7317.00 frames.], tot_loss[loss=0.186, simple_loss=0.2712, pruned_loss=0.05043, over 1420563.87 frames.], batch size: 25, lr: 7.62e-04
2022-05-14 09:32:51,546 INFO [train.py:812] (5/8) Epoch 10, batch 1500, loss[loss=0.2357, simple_loss=0.3041, pruned_loss=0.08369, over 5189.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2717, pruned_loss=0.0504, over 1417273.02 frames.], batch size: 53, lr: 7.61e-04
2022-05-14 09:33:51,501 INFO [train.py:812] (5/8) Epoch 10, batch 1550, loss[loss=0.2074, simple_loss=0.2863, pruned_loss=0.06429, over 7353.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2714, pruned_loss=0.04996, over 1421243.99 frames.], batch size: 19, lr: 7.61e-04
2022-05-14 09:34:49,172 INFO [train.py:812] (5/8) Epoch 10, batch 1600, loss[loss=0.1944, simple_loss=0.2727, pruned_loss=0.0581, over 7259.00 frames.], tot_loss[loss=0.186, simple_loss=0.272, pruned_loss=0.05001, over 1419919.38 frames.], batch size: 19, lr: 7.60e-04
2022-05-14 09:35:46,384 INFO [train.py:812] (5/8) Epoch 10, batch 1650, loss[loss=0.1528, simple_loss=0.2438, pruned_loss=0.03089, over 7413.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2706, pruned_loss=0.0494, over 1417857.07 frames.], batch size: 21, lr: 7.60e-04
2022-05-14 09:36:44,418 INFO [train.py:812] (5/8) Epoch 10, batch 1700, loss[loss=0.2117, simple_loss=0.315, pruned_loss=0.05422, over 7295.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2705, pruned_loss=0.0495, over 1415369.63 frames.], batch size: 24, lr: 7.59e-04
2022-05-14 09:37:43,560 INFO [train.py:812] (5/8) Epoch 10, batch 1750, loss[loss=0.1753, simple_loss=0.2479, pruned_loss=0.05131, over 6822.00 frames.], tot_loss[loss=0.1869, simple_loss=0.272, pruned_loss=0.05088, over 1406609.81 frames.], batch size: 15, lr: 7.59e-04
2022-05-14 09:38:41,644 INFO [train.py:812] (5/8) Epoch 10, batch 1800, loss[loss=0.1761, simple_loss=0.2726, pruned_loss=0.03985, over 7369.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2713, pruned_loss=0.05072, over 1410700.18 frames.], batch size: 19, lr: 7.59e-04
2022-05-14 09:39:39,848 INFO [train.py:812] (5/8) Epoch 10, batch 1850, loss[loss=0.1557, simple_loss=0.2385, pruned_loss=0.03642, over 7360.00 frames.], tot_loss[loss=0.1868, simple_loss=0.272, pruned_loss=0.05083, over 1411891.89 frames.], batch size: 19, lr: 7.58e-04
2022-05-14 09:40:38,497 INFO [train.py:812] (5/8) Epoch 10, batch 1900, loss[loss=0.1701, simple_loss=0.2546, pruned_loss=0.04276, over 7269.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2704, pruned_loss=0.05, over 1416739.19 frames.], batch size: 18, lr: 7.58e-04
2022-05-14 09:41:37,214 INFO [train.py:812] (5/8) Epoch 10, batch 1950, loss[loss=0.2094, simple_loss=0.2915, pruned_loss=0.06366, over 7200.00 frames.], tot_loss[loss=0.1863, simple_loss=0.271, pruned_loss=0.05083, over 1416095.03 frames.], batch size: 23, lr: 7.57e-04
2022-05-14 09:42:35,053 INFO [train.py:812] (5/8) Epoch 10, batch 2000, loss[loss=0.1784, simple_loss=0.2713, pruned_loss=0.04277, over 7237.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2705, pruned_loss=0.05044, over 1419113.69 frames.], batch size: 20, lr: 7.57e-04
2022-05-14 09:43:34,858 INFO [train.py:812] (5/8) Epoch 10, batch 2050, loss[loss=0.1972, simple_loss=0.2836, pruned_loss=0.05543, over 7220.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2703, pruned_loss=0.05055, over 1420760.12 frames.], batch size: 23, lr: 7.56e-04
2022-05-14 09:44:34,079 INFO [train.py:812] (5/8) Epoch 10, batch 2100, loss[loss=0.1918, simple_loss=0.2952, pruned_loss=0.04422, over 7150.00 frames.], tot_loss[loss=0.1851, simple_loss=0.27, pruned_loss=0.05009, over 1424971.70 frames.], batch size: 20, lr: 7.56e-04
2022-05-14 09:45:31,443 INFO [train.py:812] (5/8) Epoch 10, batch 2150, loss[loss=0.1667, simple_loss=0.2444, pruned_loss=0.04453, over 7413.00 frames.], tot_loss[loss=0.183, simple_loss=0.268, pruned_loss=0.049, over 1426711.65 frames.], batch size: 18, lr: 7.56e-04
2022-05-14 09:46:28,721 INFO [train.py:812] (5/8) Epoch 10, batch 2200, loss[loss=0.1927, simple_loss=0.2807, pruned_loss=0.05231, over 6421.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2688, pruned_loss=0.04929, over 1427185.15 frames.], batch size: 38, lr: 7.55e-04
2022-05-14 09:47:27,363 INFO [train.py:812] (5/8) Epoch 10, batch 2250, loss[loss=0.1692, simple_loss=0.258, pruned_loss=0.04018, over 7318.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2688, pruned_loss=0.04906, over 1429453.04 frames.], batch size: 21, lr: 7.55e-04
2022-05-14 09:48:25,580 INFO [train.py:812] (5/8) Epoch 10, batch 2300, loss[loss=0.1702, simple_loss=0.2659, pruned_loss=0.03729, over 7157.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2695, pruned_loss=0.04911, over 1426468.74 frames.], batch size: 20, lr: 7.54e-04
2022-05-14 09:49:24,919 INFO [train.py:812] (5/8) Epoch 10, batch 2350, loss[loss=0.2039, simple_loss=0.2905, pruned_loss=0.05861, over 7214.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2688, pruned_loss=0.04898, over 1424427.79 frames.], batch size: 22, lr: 7.54e-04
2022-05-14 09:50:22,135 INFO [train.py:812] (5/8) Epoch 10, batch 2400, loss[loss=0.1963, simple_loss=0.2713, pruned_loss=0.06069, over 7282.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2683, pruned_loss=0.04843, over 1426820.83 frames.], batch size: 18, lr: 7.53e-04
2022-05-14 09:51:20,801 INFO [train.py:812] (5/8) Epoch 10, batch 2450, loss[loss=0.1985, simple_loss=0.2762, pruned_loss=0.06038, over 7073.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2678, pruned_loss=0.04835, over 1430094.79 frames.], batch size: 18, lr: 7.53e-04
2022-05-14 09:52:18,420 INFO [train.py:812] (5/8) Epoch 10, batch 2500, loss[loss=0.1829, simple_loss=0.2693, pruned_loss=0.04826, over 7310.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2682, pruned_loss=0.04838, over 1428556.05 frames.], batch size: 21, lr: 7.53e-04
2022-05-14 09:53:18,331 INFO [train.py:812] (5/8) Epoch 10, batch 2550, loss[loss=0.1721, simple_loss=0.2635, pruned_loss=0.04036, over 7207.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2687, pruned_loss=0.04882, over 1425842.29 frames.], batch size: 21, lr: 7.52e-04
2022-05-14 09:54:18,074 INFO [train.py:812] (5/8) Epoch 10, batch 2600, loss[loss=0.2018, simple_loss=0.3018, pruned_loss=0.05096, over 7137.00 frames.], tot_loss[loss=0.1848, simple_loss=0.27, pruned_loss=0.04981, over 1428494.93 frames.], batch size: 26, lr: 7.52e-04
2022-05-14 09:55:17,728 INFO [train.py:812] (5/8) Epoch 10, batch 2650, loss[loss=0.2045, simple_loss=0.2832, pruned_loss=0.06288, over 7352.00 frames.], tot_loss[loss=0.185, simple_loss=0.2703, pruned_loss=0.04981, over 1425320.11 frames.], batch size: 22, lr: 7.51e-04
2022-05-14 09:56:16,889 INFO [train.py:812] (5/8) Epoch 10, batch 2700, loss[loss=0.1803, simple_loss=0.2685, pruned_loss=0.046, over 6780.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2697, pruned_loss=0.04951, over 1426894.84 frames.], batch size: 31, lr: 7.51e-04
2022-05-14 09:57:23,632 INFO [train.py:812] (5/8) Epoch 10, batch 2750, loss[loss=0.2086, simple_loss=0.289, pruned_loss=0.06406, over 6805.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2699, pruned_loss=0.04971, over 1424112.43 frames.], batch size: 31, lr: 7.50e-04
2022-05-14 09:58:22,150 INFO [train.py:812] (5/8) Epoch 10, batch 2800, loss[loss=0.1934, simple_loss=0.2742, pruned_loss=0.05635, over 7377.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2693, pruned_loss=0.04946, over 1429474.31 frames.], batch size: 23, lr: 7.50e-04
2022-05-14 09:59:21,337 INFO [train.py:812] (5/8) Epoch 10, batch 2850, loss[loss=0.1783, simple_loss=0.2651, pruned_loss=0.04571, over 7346.00 frames.], tot_loss[loss=0.184, simple_loss=0.2693, pruned_loss=0.04937, over 1427439.50 frames.], batch size: 22, lr: 7.50e-04
2022-05-14 10:00:20,870 INFO [train.py:812] (5/8) Epoch 10, batch 2900, loss[loss=0.2087, simple_loss=0.292, pruned_loss=0.06265, over 7121.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2699, pruned_loss=0.04956, over 1426362.68 frames.], batch size: 21, lr: 7.49e-04
2022-05-14 10:01:19,225 INFO [train.py:812] (5/8) Epoch 10, batch 2950, loss[loss=0.1479, simple_loss=0.2356, pruned_loss=0.03011, over 7300.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2696, pruned_loss=0.04945, over 1425740.55 frames.], batch size: 18, lr: 7.49e-04
2022-05-14 10:02:18,290 INFO [train.py:812] (5/8) Epoch 10, batch 3000, loss[loss=0.1311, simple_loss=0.2123, pruned_loss=0.02502, over 7277.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2688, pruned_loss=0.04914, over 1425858.87 frames.], batch size: 17, lr: 7.48e-04
2022-05-14 10:02:18,291 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 10:02:25,810 INFO [train.py:841] (5/8) Epoch 10, validation: loss=0.1584, simple_loss=0.26, pruned_loss=0.0284, over 698248.00 frames.
2022-05-14 10:03:25,411 INFO [train.py:812] (5/8) Epoch 10, batch 3050, loss[loss=0.1831, simple_loss=0.2734, pruned_loss=0.04644, over 7165.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2683, pruned_loss=0.04875, over 1426033.90 frames.], batch size: 19, lr: 7.48e-04
2022-05-14 10:04:24,554 INFO [train.py:812] (5/8) Epoch 10, batch 3100, loss[loss=0.1742, simple_loss=0.263, pruned_loss=0.04269, over 7123.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2682, pruned_loss=0.0481, over 1429212.30 frames.], batch size: 21, lr: 7.47e-04
2022-05-14 10:05:24,313 INFO [train.py:812] (5/8) Epoch 10, batch 3150, loss[loss=0.1849, simple_loss=0.2774, pruned_loss=0.04616, over 7313.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2684, pruned_loss=0.0489, over 1425577.96 frames.], batch size: 21, lr: 7.47e-04
2022-05-14 10:06:23,642 INFO [train.py:812] (5/8) Epoch 10, batch 3200, loss[loss=0.1831, simple_loss=0.264, pruned_loss=0.05112, over 7242.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2667, pruned_loss=0.04815, over 1425962.70 frames.], batch size: 20, lr: 7.47e-04
2022-05-14 10:07:23,028 INFO [train.py:812] (5/8) Epoch 10, batch 3250, loss[loss=0.1996, simple_loss=0.2915, pruned_loss=0.05387, over 7419.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2679, pruned_loss=0.04875, over 1426619.61 frames.], batch size: 21, lr: 7.46e-04
2022-05-14 10:08:22,216 INFO [train.py:812] (5/8) Epoch 10, batch 3300, loss[loss=0.1714, simple_loss=0.272, pruned_loss=0.0354, over 7216.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2679, pruned_loss=0.04855, over 1427545.29 frames.], batch size: 22, lr: 7.46e-04
2022-05-14 10:09:21,719 INFO [train.py:812] (5/8) Epoch 10, batch 3350, loss[loss=0.1943, simple_loss=0.2821, pruned_loss=0.05324, over 7207.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2687, pruned_loss=0.04883, over 1428644.17 frames.], batch size: 23, lr: 7.45e-04
2022-05-14 10:10:20,694 INFO [train.py:812] (5/8) Epoch 10, batch 3400, loss[loss=0.1702, simple_loss=0.2427, pruned_loss=0.04885, over 7281.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2687, pruned_loss=0.04924, over 1425165.08 frames.], batch size: 17, lr: 7.45e-04
2022-05-14 10:11:20,096 INFO [train.py:812] (5/8) Epoch 10, batch 3450, loss[loss=0.2117, simple_loss=0.2948, pruned_loss=0.06427, over 7291.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2694, pruned_loss=0.04975, over 1424458.80 frames.], batch size: 24, lr: 7.45e-04
2022-05-14 10:12:19,077 INFO [train.py:812] (5/8) Epoch 10, batch 3500, loss[loss=0.1998, simple_loss=0.3001, pruned_loss=0.04978, over 7414.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2698, pruned_loss=0.04951, over 1424417.62 frames.], batch size: 21, lr: 7.44e-04
2022-05-14 10:13:18,782 INFO [train.py:812] (5/8) Epoch 10, batch 3550, loss[loss=0.1985, simple_loss=0.2832, pruned_loss=0.05692, over 7111.00 frames.], tot_loss[loss=0.1837, simple_loss=0.269, pruned_loss=0.04919, over 1427397.89 frames.], batch size: 28, lr: 7.44e-04
2022-05-14 10:14:16,938 INFO [train.py:812] (5/8) Epoch 10, batch 3600, loss[loss=0.2049, simple_loss=0.2869, pruned_loss=0.06149, over 7038.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2692, pruned_loss=0.04912, over 1427498.92 frames.], batch size: 28, lr: 7.43e-04
2022-05-14 10:15:16,457 INFO [train.py:812] (5/8) Epoch 10, batch 3650, loss[loss=0.1498, simple_loss=0.2433, pruned_loss=0.02816, over 7075.00 frames.], tot_loss[loss=0.184, simple_loss=0.2697, pruned_loss=0.04912, over 1423545.01 frames.], batch size: 18, lr: 7.43e-04
2022-05-14 10:16:15,505 INFO [train.py:812] (5/8) Epoch 10, batch 3700, loss[loss=0.1588, simple_loss=0.2429, pruned_loss=0.03737, over 7279.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2696, pruned_loss=0.04871, over 1425360.83 frames.], batch size: 17, lr: 7.43e-04
2022-05-14 10:17:15,197 INFO [train.py:812] (5/8) Epoch 10, batch 3750, loss[loss=0.1829, simple_loss=0.261, pruned_loss=0.05235, over 7142.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2697, pruned_loss=0.0485, over 1428091.54 frames.], batch size: 19, lr: 7.42e-04
2022-05-14 10:18:14,385 INFO [train.py:812] (5/8) Epoch 10, batch 3800, loss[loss=0.1772, simple_loss=0.2549, pruned_loss=0.04979, over 7441.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2696, pruned_loss=0.0488, over 1425591.99 frames.], batch size: 20, lr: 7.42e-04
2022-05-14 10:19:13,023 INFO [train.py:812] (5/8) Epoch 10, batch 3850, loss[loss=0.1981, simple_loss=0.2758, pruned_loss=0.06016, over 7067.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2706, pruned_loss=0.04922, over 1425195.16 frames.], batch size: 18, lr: 7.41e-04
2022-05-14 10:20:21,830 INFO [train.py:812] (5/8) Epoch 10, batch 3900, loss[loss=0.1596, simple_loss=0.2426, pruned_loss=0.03825, over 7167.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2698, pruned_loss=0.04883, over 1426857.65 frames.], batch size: 19, lr: 7.41e-04
2022-05-14 10:21:21,391 INFO [train.py:812] (5/8) Epoch 10, batch 3950, loss[loss=0.2127, simple_loss=0.2896, pruned_loss=0.06793, over 5273.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2695, pruned_loss=0.04888, over 1421306.37 frames.], batch size: 52, lr: 7.41e-04
2022-05-14 10:22:19,903 INFO [train.py:812] (5/8) Epoch 10, batch 4000, loss[loss=0.1579, simple_loss=0.2427, pruned_loss=0.03655, over 7257.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2696, pruned_loss=0.04906, over 1421748.06 frames.], batch size: 19, lr: 7.40e-04
2022-05-14 10:23:18,816 INFO [train.py:812] (5/8) Epoch 10, batch 4050, loss[loss=0.1723, simple_loss=0.2458, pruned_loss=0.04934, over 7131.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2692, pruned_loss=0.04896, over 1422389.44 frames.], batch size: 17, lr: 7.40e-04
2022-05-14 10:24:17,050 INFO [train.py:812] (5/8) Epoch 10, batch 4100, loss[loss=0.1525, simple_loss=0.2467, pruned_loss=0.02916, over 7330.00 frames.], tot_loss[loss=0.183, simple_loss=0.2688, pruned_loss=0.0486, over 1424914.41 frames.], batch size: 21, lr: 7.39e-04
2022-05-14 10:25:16,647 INFO [train.py:812] (5/8) Epoch 10, batch 4150, loss[loss=0.1735, simple_loss=0.246, pruned_loss=0.05053, over 7408.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2693, pruned_loss=0.04878, over 1424963.65 frames.], batch size: 18, lr: 7.39e-04
2022-05-14 10:26:14,805 INFO [train.py:812] (5/8) Epoch 10, batch 4200, loss[loss=0.1818, simple_loss=0.2645, pruned_loss=0.0496, over 7278.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2693, pruned_loss=0.04878, over 1426793.48 frames.], batch size: 24, lr: 7.39e-04
2022-05-14 10:27:13,945 INFO [train.py:812] (5/8) Epoch 10, batch 4250, loss[loss=0.1724, simple_loss=0.2503, pruned_loss=0.04727, over 7287.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2702, pruned_loss=0.04898, over 1422588.70 frames.], batch size: 17, lr: 7.38e-04
2022-05-14 10:28:13,108 INFO [train.py:812] (5/8) Epoch 10, batch 4300, loss[loss=0.2196, simple_loss=0.3117, pruned_loss=0.06372, over 7313.00 frames.], tot_loss[loss=0.185, simple_loss=0.271, pruned_loss=0.04953, over 1417464.20 frames.], batch size: 24, lr: 7.38e-04
2022-05-14 10:29:10,974 INFO [train.py:812] (5/8) Epoch 10, batch 4350, loss[loss=0.2211, simple_loss=0.3029, pruned_loss=0.06971, over 4968.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2727, pruned_loss=0.05014, over 1407209.68 frames.], batch size: 54, lr: 7.37e-04
2022-05-14 10:30:10,256 INFO [train.py:812] (5/8) Epoch 10, batch 4400, loss[loss=0.1853, simple_loss=0.2707, pruned_loss=0.04997, over 7205.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2732, pruned_loss=0.05084, over 1410153.03 frames.], batch size: 22, lr: 7.37e-04
2022-05-14 10:31:10,029 INFO [train.py:812] (5/8) Epoch 10, batch 4450, loss[loss=0.24, simple_loss=0.3083, pruned_loss=0.08585, over 4531.00 frames.], tot_loss[loss=0.1886, simple_loss=0.274, pruned_loss=0.05158, over 1395247.33 frames.], batch size: 52, lr: 7.37e-04
2022-05-14 10:32:09,138 INFO [train.py:812] (5/8) Epoch 10, batch 4500, loss[loss=0.2, simple_loss=0.2952, pruned_loss=0.05243, over 7145.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2733, pruned_loss=0.05165, over 1392058.08 frames.], batch size: 20, lr: 7.36e-04
2022-05-14 10:33:08,607 INFO [train.py:812] (5/8) Epoch 10, batch 4550, loss[loss=0.2069, simple_loss=0.29, pruned_loss=0.06189, over 7175.00 frames.], tot_loss[loss=0.1888, simple_loss=0.273, pruned_loss=0.05234, over 1372424.69 frames.], batch size: 26, lr: 7.36e-04
2022-05-14 10:34:22,339 INFO [train.py:812] (5/8) Epoch 11, batch 0, loss[loss=0.1892, simple_loss=0.2651, pruned_loss=0.0567, over 7432.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2651, pruned_loss=0.0567, over 7432.00 frames.], batch size: 20, lr: 7.08e-04
2022-05-14 10:35:21,209 INFO [train.py:812] (5/8) Epoch 11, batch 50, loss[loss=0.1559, simple_loss=0.2371, pruned_loss=0.03731, over 7438.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2709, pruned_loss=0.04893, over 322211.17 frames.], batch size: 20, lr: 7.08e-04
2022-05-14 10:36:19,916 INFO [train.py:812] (5/8) Epoch 11, batch 100, loss[loss=0.1771, simple_loss=0.2516, pruned_loss=0.05128, over 7279.00 frames.], tot_loss[loss=0.1834, simple_loss=0.269, pruned_loss=0.04884, over 566285.89 frames.], batch size: 18, lr: 7.08e-04
2022-05-14 10:37:28,462 INFO [train.py:812] (5/8) Epoch 11, batch 150, loss[loss=0.188, simple_loss=0.2649, pruned_loss=0.05557, over 6816.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2722, pruned_loss=0.05027, over 759293.75 frames.], batch size: 15, lr: 7.07e-04
2022-05-14 10:38:36,337 INFO [train.py:812] (5/8) Epoch 11, batch 200, loss[loss=0.1548, simple_loss=0.2416, pruned_loss=0.03405, over 7403.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2695, pruned_loss=0.04832, over 907326.76 frames.], batch size: 18, lr: 7.07e-04
2022-05-14 10:39:34,526 INFO [train.py:812] (5/8) Epoch 11, batch 250, loss[loss=0.2107, simple_loss=0.292, pruned_loss=0.06474, over 6424.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2688, pruned_loss=0.04818, over 1022683.39 frames.], batch size: 37, lr: 7.06e-04
2022-05-14 10:40:50,470 INFO [train.py:812] (5/8) Epoch 11, batch 300, loss[loss=0.2118, simple_loss=0.282, pruned_loss=0.07081, over 5041.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2676, pruned_loss=0.04749, over 1114362.81 frames.], batch size: 52, lr: 7.06e-04
2022-05-14 10:41:47,783 INFO [train.py:812] (5/8) Epoch 11, batch 350, loss[loss=0.2112, simple_loss=0.2911, pruned_loss=0.06558, over 6668.00 frames.], tot_loss[loss=0.182, simple_loss=0.268, pruned_loss=0.04798, over 1186592.32 frames.], batch size: 31, lr: 7.06e-04
2022-05-14 10:43:03,915 INFO [train.py:812] (5/8) Epoch 11, batch 400, loss[loss=0.1717, simple_loss=0.2559, pruned_loss=0.04374, over 7415.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2674, pruned_loss=0.0476, over 1240760.78 frames.], batch size: 20, lr: 7.05e-04
2022-05-14 10:44:13,169 INFO [train.py:812] (5/8) Epoch 11, batch 450, loss[loss=0.1926, simple_loss=0.2793, pruned_loss=0.05294, over 7231.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2659, pruned_loss=0.0474, over 1280348.42 frames.], batch size: 20, lr: 7.05e-04
2022-05-14 10:45:12,581 INFO [train.py:812] (5/8) Epoch 11, batch 500, loss[loss=0.196, simple_loss=0.2874, pruned_loss=0.05231, over 7317.00 frames.], tot_loss[loss=0.18, simple_loss=0.2659, pruned_loss=0.04706, over 1315439.40 frames.], batch size: 20, lr: 7.04e-04
2022-05-14 10:46:12,006 INFO [train.py:812] (5/8) Epoch 11, batch 550, loss[loss=0.1847, simple_loss=0.265, pruned_loss=0.05218, over 7067.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2663, pruned_loss=0.04698, over 1341127.37 frames.], batch size: 18, lr: 7.04e-04
2022-05-14 10:47:11,312 INFO [train.py:812] (5/8) Epoch 11, batch 600, loss[loss=0.1593, simple_loss=0.2345, pruned_loss=0.04202, over 7015.00 frames.], tot_loss[loss=0.1809, simple_loss=0.267, pruned_loss=0.04739, over 1359954.83 frames.], batch size: 16, lr: 7.04e-04
2022-05-14 10:48:09,765 INFO [train.py:812] (5/8) Epoch 11, batch 650, loss[loss=0.1844, simple_loss=0.2593, pruned_loss=0.05473, over 7131.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2673, pruned_loss=0.04764, over 1366079.78 frames.], batch size: 17, lr: 7.03e-04
2022-05-14 10:49:08,403 INFO [train.py:812] (5/8) Epoch 11, batch 700, loss[loss=0.1729, simple_loss=0.2541, pruned_loss=0.04588, over 7243.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2685, pruned_loss=0.04802, over 1376150.64 frames.], batch size: 16, lr: 7.03e-04
2022-05-14 10:50:07,687 INFO [train.py:812] (5/8) Epoch 11, batch 750, loss[loss=0.208, simple_loss=0.2937, pruned_loss=0.06118, over 7147.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2684, pruned_loss=0.04819, over 1382875.26 frames.], batch size: 20, lr: 7.03e-04
2022-05-14 10:51:05,912 INFO [train.py:812] (5/8) Epoch 11, batch 800, loss[loss=0.2009, simple_loss=0.2986, pruned_loss=0.05163, over 7179.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2683, pruned_loss=0.04795, over 1394522.20 frames.], batch size: 26, lr: 7.02e-04
2022-05-14 10:52:03,626 INFO [train.py:812] (5/8) Epoch 11, batch 850, loss[loss=0.1576, simple_loss=0.2485, pruned_loss=0.03337, over 7323.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2686, pruned_loss=0.04804, over 1399247.09 frames.], batch size: 20, lr: 7.02e-04
2022-05-14 10:53:01,755 INFO [train.py:812] (5/8) Epoch 11, batch 900, loss[loss=0.1807, simple_loss=0.2647, pruned_loss=0.04833, over 7433.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2684, pruned_loss=0.04818, over 1407740.89 frames.], batch size: 20, lr: 7.02e-04
2022-05-14 10:54:00,384 INFO [train.py:812] (5/8) Epoch 11, batch 950, loss[loss=0.178, simple_loss=0.2568, pruned_loss=0.04959, over 6997.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2678, pruned_loss=0.04799, over 1409647.59 frames.], batch size: 16, lr: 7.01e-04
2022-05-14 10:54:58,953 INFO [train.py:812] (5/8) Epoch 11, batch 1000, loss[loss=0.1911, simple_loss=0.2848, pruned_loss=0.04872, over 7329.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2671, pruned_loss=0.04767, over 1414001.97 frames.], batch size: 25, lr: 7.01e-04
2022-05-14 10:55:58,119 INFO [train.py:812] (5/8) Epoch 11, batch 1050, loss[loss=0.1815, simple_loss=0.2591, pruned_loss=0.05199, over 7259.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2692, pruned_loss=0.04869, over 1409345.92 frames.], batch size: 19, lr: 7.00e-04
2022-05-14 10:56:57,225 INFO [train.py:812] (5/8) Epoch 11, batch 1100, loss[loss=0.1795, simple_loss=0.2542, pruned_loss=0.05237, over 7168.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2681, pruned_loss=0.04812, over 1414198.85 frames.], batch size: 18, lr: 7.00e-04
2022-05-14 10:57:56,862 INFO [train.py:812] (5/8) Epoch 11, batch 1150, loss[loss=0.2201, simple_loss=0.2882, pruned_loss=0.07599, over 7068.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2678, pruned_loss=0.04824, over 1417767.20 frames.], batch size: 18, lr: 7.00e-04
2022-05-14 10:58:55,493 INFO [train.py:812] (5/8) Epoch 11, batch 1200, loss[loss=0.1503, simple_loss=0.2334, pruned_loss=0.03355, over 7235.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2667, pruned_loss=0.04788, over 1420492.88 frames.], batch size: 16, lr: 6.99e-04
2022-05-14 10:59:53,786 INFO [train.py:812] (5/8) Epoch 11, batch 1250, loss[loss=0.1462, simple_loss=0.2234, pruned_loss=0.03453, over 7125.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2667, pruned_loss=0.04789, over 1424864.21 frames.], batch size: 17, lr: 6.99e-04
2022-05-14 11:00:50,424 INFO [train.py:812] (5/8) Epoch 11, batch 1300, loss[loss=0.1708, simple_loss=0.2669, pruned_loss=0.03729, over 7322.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2657, pruned_loss=0.04724, over 1420651.60 frames.], batch size: 21, lr: 6.99e-04
2022-05-14 11:01:49,327 INFO [train.py:812] (5/8) Epoch 11, batch 1350, loss[loss=0.1752, simple_loss=0.2683, pruned_loss=0.04103, over 7328.00 frames.], tot_loss[loss=0.18, simple_loss=0.2659, pruned_loss=0.04703, over 1424435.17 frames.], batch size: 21, lr: 6.98e-04
2022-05-14 11:02:46,390 INFO [train.py:812] (5/8) Epoch 11, batch 1400, loss[loss=0.1696, simple_loss=0.2595, pruned_loss=0.03991, over 7162.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2661, pruned_loss=0.04702, over 1427676.74 frames.], batch size: 19, lr: 6.98e-04
2022-05-14 11:03:44,651 INFO [train.py:812] (5/8) Epoch 11, batch 1450, loss[loss=0.1741, simple_loss=0.2566, pruned_loss=0.04574, over 7277.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2671, pruned_loss=0.04742, over 1427626.34 frames.], batch size: 17, lr: 6.97e-04
2022-05-14 11:04:41,552 INFO [train.py:812] (5/8) Epoch 11, batch 1500, loss[loss=0.1959, simple_loss=0.2737, pruned_loss=0.0591, over 7058.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2678, pruned_loss=0.04776, over 1425211.93 frames.], batch size: 28, lr: 6.97e-04
2022-05-14 11:05:41,358 INFO [train.py:812] (5/8) Epoch 11, batch 1550, loss[loss=0.1635, simple_loss=0.2434, pruned_loss=0.04184, over 7424.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2676, pruned_loss=0.04744, over 1423686.10 frames.], batch size: 20, lr: 6.97e-04
2022-05-14 11:06:38,922 INFO [train.py:812] (5/8) Epoch 11, batch 1600, loss[loss=0.1839, simple_loss=0.2745, pruned_loss=0.04666, over 6686.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2672, pruned_loss=0.04771, over 1417953.66 frames.], batch size: 31, lr: 6.96e-04
2022-05-14 11:07:38,269 INFO [train.py:812] (5/8) Epoch 11, batch 1650, loss[loss=0.147, simple_loss=0.2241, pruned_loss=0.03498, over 7255.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2673, pruned_loss=0.04793, over 1417409.83 frames.], batch size: 16, lr: 6.96e-04
2022-05-14 11:08:37,006 INFO [train.py:812] (5/8) Epoch 11, batch 1700, loss[loss=0.1517, simple_loss=0.2385, pruned_loss=0.03248, over 6830.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2664, pruned_loss=0.04735, over 1416706.28 frames.], batch size: 15, lr: 6.96e-04
2022-05-14 11:09:36,881 INFO [train.py:812] (5/8) Epoch 11, batch 1750, loss[loss=0.1853, simple_loss=0.2787, pruned_loss=0.04596, over 7115.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2655, pruned_loss=0.0471, over 1412587.65 frames.], batch size: 21, lr: 6.95e-04
2022-05-14 11:10:35,674 INFO [train.py:812] (5/8) Epoch 11, batch 1800, loss[loss=0.2288, simple_loss=0.3017, pruned_loss=0.07794, over 5503.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2659, pruned_loss=0.04717, over 1413014.62 frames.], batch size: 52, lr: 6.95e-04
2022-05-14 11:11:35,351 INFO [train.py:812] (5/8) Epoch 11, batch 1850, loss[loss=0.1688, simple_loss=0.2528, pruned_loss=0.0424, over 6444.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2656, pruned_loss=0.04693, over 1417565.58 frames.], batch size: 37, lr: 6.95e-04
2022-05-14 11:12:33,304 INFO [train.py:812] (5/8) Epoch 11, batch 1900, loss[loss=0.2067, simple_loss=0.3024, pruned_loss=0.05546, over 7326.00 frames.], tot_loss[loss=0.1799, simple_loss=0.266, pruned_loss=0.04692, over 1421829.04 frames.], batch size: 21, lr: 6.94e-04
2022-05-14 11:13:33,015 INFO [train.py:812] (5/8) Epoch 11, batch 1950, loss[loss=0.1939, simple_loss=0.2877, pruned_loss=0.05001, over 7355.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2658, pruned_loss=0.04686, over 1421159.63 frames.], batch size: 19, lr: 6.94e-04
2022-05-14 11:14:32,018 INFO [train.py:812] (5/8) Epoch 11, batch 2000, loss[loss=0.1367, simple_loss=0.2193, pruned_loss=0.02705, over 7181.00 frames.], tot_loss[loss=0.179, simple_loss=0.2655, pruned_loss=0.04623, over 1422966.17 frames.], batch size: 18, lr: 6.93e-04
2022-05-14 11:15:30,886 INFO [train.py:812] (5/8) Epoch 11, batch 2050, loss[loss=0.1586, simple_loss=0.2403, pruned_loss=0.03851, over 7277.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2659, pruned_loss=0.04656, over 1425170.58 frames.], batch size: 17, lr: 6.93e-04
2022-05-14 11:16:30,462 INFO [train.py:812] (5/8) Epoch 11, batch 2100, loss[loss=0.1905, simple_loss=0.2757, pruned_loss=0.05266, over 7368.00 frames.], tot_loss[loss=0.18, simple_loss=0.2665, pruned_loss=0.04673, over 1425014.92 frames.], batch size: 23, lr: 6.93e-04
2022-05-14 11:17:37,593 INFO [train.py:812] (5/8) Epoch 11, batch 2150, loss[loss=0.153, simple_loss=0.2354, pruned_loss=0.03534, over 7177.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2661, pruned_loss=0.04677, over 1425982.32 frames.], batch size: 18, lr: 6.92e-04
2022-05-14 11:18:36,033 INFO [train.py:812] (5/8) Epoch 11, batch 2200, loss[loss=0.1784, simple_loss=0.265, pruned_loss=0.0459, over 7226.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2657, pruned_loss=0.04681, over 1424369.34 frames.], batch size: 20, lr: 6.92e-04
2022-05-14 11:19:35,020 INFO [train.py:812] (5/8) Epoch 11, batch 2250, loss[loss=0.1591, simple_loss=0.2453, pruned_loss=0.0365, over 7342.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2668, pruned_loss=0.04714, over 1427074.98 frames.], batch size: 22, lr: 6.92e-04
2022-05-14 11:20:34,379 INFO [train.py:812] (5/8) Epoch 11, batch 2300, loss[loss=0.1698, simple_loss=0.2665, pruned_loss=0.03654, over 7153.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2662, pruned_loss=0.04713, over 1427291.44 frames.], batch size: 26, lr: 6.91e-04
2022-05-14 11:21:33,360 INFO [train.py:812] (5/8) Epoch 11, batch 2350, loss[loss=0.1798, simple_loss=0.2666, pruned_loss=0.04646, over 6737.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2655, pruned_loss=0.04657, over 1429709.16 frames.], batch size: 31, lr: 6.91e-04
2022-05-14 11:22:32,005 INFO [train.py:812] (5/8) Epoch 11, batch 2400, loss[loss=0.1729, simple_loss=0.2666, pruned_loss=0.0396, over 7307.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2654, pruned_loss=0.04675, over 1423473.13 frames.], batch size: 21, lr: 6.91e-04
2022-05-14 11:23:31,118 INFO [train.py:812] (5/8) Epoch 11, batch 2450, loss[loss=0.1867, simple_loss=0.2706, pruned_loss=0.05138, over 7002.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2646, pruned_loss=0.04661, over 1423387.22 frames.], batch size: 16, lr: 6.90e-04
2022-05-14 11:24:30,208 INFO [train.py:812] (5/8) Epoch 11, batch 2500, loss[loss=0.1666, simple_loss=0.2593, pruned_loss=0.03695, over 7147.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2648, pruned_loss=0.04626, over 1422876.33 frames.], batch size: 19, lr: 6.90e-04
2022-05-14 11:25:29,382 INFO [train.py:812] (5/8) Epoch 11, batch 2550, loss[loss=0.1763, simple_loss=0.2646, pruned_loss=0.04403, over 6811.00 frames.], tot_loss[loss=0.178, simple_loss=0.2647, pruned_loss=0.04565, over 1426639.89 frames.], batch size: 15, lr: 6.90e-04
2022-05-14 11:26:27,799 INFO [train.py:812] (5/8) Epoch 11, batch 2600, loss[loss=0.168, simple_loss=0.2612, pruned_loss=0.03736, over 7398.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2654, pruned_loss=0.04619, over 1428404.69 frames.], batch size: 23, lr: 6.89e-04
2022-05-14 11:27:26,089 INFO [train.py:812] (5/8) Epoch 11, batch 2650, loss[loss=0.1603, simple_loss=0.2424, pruned_loss=0.0391, over 7001.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2664, pruned_loss=0.04645, over 1424063.68 frames.], batch size: 16, lr: 6.89e-04
2022-05-14 11:28:23,549 INFO [train.py:812] (5/8) Epoch 11, batch 2700, loss[loss=0.1919, simple_loss=0.282, pruned_loss=0.05084, over 7409.00 frames.], tot_loss[loss=0.18, simple_loss=0.2669, pruned_loss=0.04658, over 1426925.26 frames.], batch size: 21, lr: 6.89e-04
2022-05-14 11:29:20,994 INFO [train.py:812] (5/8) Epoch 11, batch 2750, loss[loss=0.1703, simple_loss=0.2494, pruned_loss=0.04557, over 7265.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2659, pruned_loss=0.04661, over 1426028.72 frames.], batch size: 18, lr: 6.88e-04
2022-05-14 11:30:17,967 INFO [train.py:812] (5/8) Epoch 11, batch 2800, loss[loss=0.1735, simple_loss=0.2771, pruned_loss=0.03498, over 7150.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2662, pruned_loss=0.04651, over 1424378.92 frames.], batch size: 19, lr: 6.88e-04
2022-05-14 11:31:17,634 INFO [train.py:812] (5/8) Epoch 11, batch 2850, loss[loss=0.192, simple_loss=0.2798, pruned_loss=0.05211, over 7321.00 frames.], tot_loss[loss=0.179, simple_loss=0.2653, pruned_loss=0.04628, over 1424858.35 frames.], batch size: 21, lr: 6.87e-04
2022-05-14 11:32:14,484 INFO [train.py:812] (5/8) Epoch 11, batch 2900, loss[loss=0.2073, simple_loss=0.2903, pruned_loss=0.06214, over 7193.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2656, pruned_loss=0.04645, over 1427606.82 frames.], batch size: 23, lr: 6.87e-04
2022-05-14 11:33:13,345 INFO [train.py:812] (5/8) Epoch 11, batch 2950, loss[loss=0.1768, simple_loss=0.2698, pruned_loss=0.04188, over 7194.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2661, pruned_loss=0.04629, over 1425292.86 frames.], batch size: 22, lr: 6.87e-04
2022-05-14 11:34:12,257 INFO [train.py:812] (5/8) Epoch 11, batch 3000, loss[loss=0.159, simple_loss=0.2386, pruned_loss=0.0397, over 7163.00 frames.], tot_loss[loss=0.179, simple_loss=0.2661, pruned_loss=0.04599, over 1424549.16 frames.], batch size: 18, lr: 6.86e-04
2022-05-14 11:34:12,258 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 11:34:19,823 INFO [train.py:841] (5/8) Epoch 11, validation: loss=0.1564, simple_loss=0.2581, pruned_loss=0.02737, over 698248.00 frames.
2022-05-14 11:35:18,262 INFO [train.py:812] (5/8) Epoch 11, batch 3050, loss[loss=0.1864, simple_loss=0.2718, pruned_loss=0.05046, over 7155.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2656, pruned_loss=0.04597, over 1428429.22 frames.], batch size: 26, lr: 6.86e-04
2022-05-14 11:36:16,719 INFO [train.py:812] (5/8) Epoch 11, batch 3100, loss[loss=0.1693, simple_loss=0.2527, pruned_loss=0.04292, over 7424.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2665, pruned_loss=0.04686, over 1425830.93 frames.], batch size: 18, lr: 6.86e-04
2022-05-14 11:37:16,184 INFO [train.py:812] (5/8) Epoch 11, batch 3150, loss[loss=0.146, simple_loss=0.2313, pruned_loss=0.03034, over 7306.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2654, pruned_loss=0.04643, over 1427870.95 frames.], batch size: 18, lr: 6.85e-04
2022-05-14 11:38:15,165 INFO [train.py:812] (5/8) Epoch 11, batch 3200, loss[loss=0.1546, simple_loss=0.2346, pruned_loss=0.03731, over 7172.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2647, pruned_loss=0.04616, over 1429467.35 frames.], batch size: 18, lr: 6.85e-04
2022-05-14 11:39:14,891 INFO [train.py:812] (5/8) Epoch 11, batch 3250, loss[loss=0.1709, simple_loss=0.2519, pruned_loss=0.04496, over 7065.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2641, pruned_loss=0.04602, over 1430822.36 frames.], batch size: 18, lr: 6.85e-04
2022-05-14 11:40:14,266 INFO [train.py:812] (5/8) Epoch 11, batch 3300, loss[loss=0.1962, simple_loss=0.2779, pruned_loss=0.0573, over 6246.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2645, pruned_loss=0.0462, over 1429603.72 frames.], batch size: 37, lr: 6.84e-04
2022-05-14 11:41:13,840 INFO [train.py:812] (5/8) Epoch 11, batch 3350, loss[loss=0.1874, simple_loss=0.2789, pruned_loss=0.04796, over 7099.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2656, pruned_loss=0.04668, over 1423655.13 frames.], batch size: 21, lr: 6.84e-04
2022-05-14 11:42:12,398 INFO [train.py:812] (5/8) Epoch 11, batch 3400, loss[loss=0.1459, simple_loss=0.2241, pruned_loss=0.03384, over 7016.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2655, pruned_loss=0.04642, over 1420811.74 frames.], batch size: 16, lr: 6.84e-04
2022-05-14 11:43:11,481 INFO [train.py:812] (5/8) Epoch 11, batch 3450, loss[loss=0.1902, simple_loss=0.2766, pruned_loss=0.0519, over 7115.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2656, pruned_loss=0.0465, over 1423336.83 frames.], batch size: 21, lr: 6.83e-04
2022-05-14 11:44:10,158 INFO [train.py:812] (5/8) Epoch 11, batch 3500, loss[loss=0.1849, simple_loss=0.2544, pruned_loss=0.05767, over 7428.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2657, pruned_loss=0.04682, over 1424648.14 frames.], batch size: 18, lr: 6.83e-04
2022-05-14 11:45:10,020 INFO [train.py:812] (5/8) Epoch 11, batch 3550, loss[loss=0.1858, simple_loss=0.2811, pruned_loss=0.04525, over 6347.00 frames.], tot_loss[loss=0.18, simple_loss=0.2657, pruned_loss=0.04718, over 1423526.34 frames.], batch size: 37, lr: 6.83e-04
2022-05-14 11:46:08,746 INFO [train.py:812] (5/8) Epoch 11, batch 3600, loss[loss=0.1751, simple_loss=0.2713, pruned_loss=0.03947, over 6486.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2673, pruned_loss=0.04767, over 1419325.05 frames.], batch size: 37, lr: 6.82e-04
2022-05-14 11:47:07,756 INFO [train.py:812] (5/8) Epoch 11, batch 3650, loss[loss=0.1876, simple_loss=0.2709, pruned_loss=0.05219, over 7115.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2677, pruned_loss=0.04749, over 1421455.48 frames.], batch size: 21, lr: 6.82e-04
2022-05-14 11:48:06,833 INFO [train.py:812] (5/8) Epoch 11, batch 3700, loss[loss=0.1974, simple_loss=0.2876, pruned_loss=0.05357, over 7115.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2677, pruned_loss=0.04772, over 1418118.80 frames.], batch size: 21, lr: 6.82e-04
2022-05-14 11:49:06,468 INFO [train.py:812] (5/8) Epoch 11, batch 3750, loss[loss=0.1375, simple_loss=0.2302, pruned_loss=0.02238, over 7425.00 frames.], tot_loss[loss=0.1802, simple_loss=0.267, pruned_loss=0.04668, over 1424364.68 frames.], batch size: 20, lr: 6.81e-04
2022-05-14 11:50:05,387 INFO [train.py:812] (5/8) Epoch 11, batch 3800, loss[loss=0.1801, simple_loss=0.2631, pruned_loss=0.04852, over 7295.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2671, pruned_loss=0.04712, over 1422685.85 frames.], batch size: 24, lr: 6.81e-04
2022-05-14 11:51:04,549 INFO [train.py:812] (5/8) Epoch 11, batch 3850, loss[loss=0.1856, simple_loss=0.2728, pruned_loss=0.04922, over 7221.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2666, pruned_loss=0.04685, over 1426768.86 frames.], batch size: 22, lr: 6.81e-04
2022-05-14 11:52:01,417 INFO [train.py:812] (5/8) Epoch 11, batch 3900, loss[loss=0.2076, simple_loss=0.2952, pruned_loss=0.06002, over 7363.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2665, pruned_loss=0.04699, over 1427218.94 frames.], batch size: 23, lr: 6.80e-04
2022-05-14 11:53:00,850 INFO [train.py:812] (5/8) Epoch 11, batch 3950, loss[loss=0.1743, simple_loss=0.2605, pruned_loss=0.044, over 7440.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2655, pruned_loss=0.04652, over 1425867.16 frames.], batch size: 20, lr: 6.80e-04
2022-05-14 11:53:59,461 INFO [train.py:812] (5/8) Epoch 11, batch 4000, loss[loss=0.187, simple_loss=0.2771, pruned_loss=0.04844, over 7217.00 frames.], tot_loss[loss=0.1795, simple_loss=0.266, pruned_loss=0.04653, over 1417434.08 frames.], batch size: 21, lr: 6.80e-04
2022-05-14 11:54:58,914 INFO [train.py:812] (5/8) Epoch 11, batch 4050, loss[loss=0.1761, simple_loss=0.2685, pruned_loss=0.04187, over 7211.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2661, pruned_loss=0.04662, over 1416954.09 frames.], batch size: 22, lr: 6.79e-04
2022-05-14 11:55:58,040 INFO [train.py:812] (5/8) Epoch 11, batch 4100, loss[loss=0.2069, simple_loss=0.2867, pruned_loss=0.06355, over 7200.00 frames.], tot_loss[loss=0.1794, simple_loss=0.266, pruned_loss=0.04637, over 1416728.05 frames.], batch size: 22, lr: 6.79e-04
2022-05-14 11:56:56,025 INFO [train.py:812] (5/8) Epoch 11, batch 4150, loss[loss=0.2273, simple_loss=0.3022, pruned_loss=0.07626, over 6790.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2672, pruned_loss=0.04713, over 1414718.66 frames.], batch size: 31, lr: 6.79e-04
2022-05-14 11:57:54,921 INFO [train.py:812] (5/8) Epoch 11, batch 4200, loss[loss=0.1734, simple_loss=0.2599, pruned_loss=0.04343, over 7098.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2677, pruned_loss=0.04729, over 1415957.72 frames.], batch size: 28, lr: 6.78e-04
2022-05-14 11:58:54,424 INFO [train.py:812] (5/8) Epoch 11, batch 4250, loss[loss=0.2339, simple_loss=0.317, pruned_loss=0.07542, over 4854.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2671, pruned_loss=0.04689, over 1415387.32 frames.], batch size: 52, lr: 6.78e-04
2022-05-14 11:59:53,055 INFO [train.py:812] (5/8) Epoch 11, batch 4300, loss[loss=0.2462, simple_loss=0.3222, pruned_loss=0.0851, over 4795.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2675, pruned_loss=0.04735, over 1410529.07 frames.], batch size: 52, lr: 6.78e-04
2022-05-14 12:00:52,214 INFO [train.py:812] (5/8) Epoch 11, batch 4350, loss[loss=0.1481, simple_loss=0.2356, pruned_loss=0.03033, over 7233.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2671, pruned_loss=0.0472, over 1409288.02 frames.], batch size: 20, lr: 6.77e-04
2022-05-14 12:01:50,109 INFO [train.py:812] (5/8) Epoch 11, batch 4400, loss[loss=0.1746, simple_loss=0.2582, pruned_loss=0.04547, over 7195.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2682, pruned_loss=0.04769, over 1415064.51 frames.], batch size: 22, lr: 6.77e-04
2022-05-14 12:02:49,054 INFO [train.py:812] (5/8) Epoch 11, batch 4450, loss[loss=0.172, simple_loss=0.2617, pruned_loss=0.04119, over 7228.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2711, pruned_loss=0.04909, over 1418012.31 frames.], batch size: 20, lr: 6.77e-04
2022-05-14 12:03:48,062 INFO [train.py:812] (5/8) Epoch 11, batch 4500, loss[loss=0.214, simple_loss=0.2852, pruned_loss=0.0714, over 5099.00 frames.], tot_loss[loss=0.185, simple_loss=0.2714, pruned_loss=0.04934, over 1409083.17 frames.], batch size: 52, lr: 6.76e-04
2022-05-14 12:04:46,852 INFO [train.py:812] (5/8) Epoch 11, batch 4550, loss[loss=0.2056, simple_loss=0.2767, pruned_loss=0.06729, over 5061.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2738, pruned_loss=0.05181, over 1347203.66 frames.], batch size: 52, lr: 6.76e-04
2022-05-14 12:05:54,958 INFO [train.py:812] (5/8) Epoch 12, batch 0, loss[loss=0.1818, simple_loss=0.2733, pruned_loss=0.0452, over 7411.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2733, pruned_loss=0.0452, over 7411.00 frames.], batch size: 21, lr: 6.52e-04
2022-05-14 12:06:54,742 INFO [train.py:812] (5/8) Epoch 12, batch 50, loss[loss=0.1997, simple_loss=0.2734, pruned_loss=0.06298, over 4644.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2679, pruned_loss=0.0474, over 317966.78 frames.], batch size: 53, lr: 6.52e-04
2022-05-14 12:07:53,963 INFO [train.py:812] (5/8) Epoch 12, batch 100, loss[loss=0.159, simple_loss=0.2431, pruned_loss=0.03742, over 6301.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2683, pruned_loss=0.04751, over 557264.69 frames.], batch size: 37, lr: 6.51e-04
2022-05-14 12:08:53,460 INFO [train.py:812] (5/8) Epoch 12, batch 150, loss[loss=0.1532, simple_loss=0.2258, pruned_loss=0.0403, over 7288.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2682, pruned_loss=0.04739, over 747823.16 frames.], batch size: 17, lr: 6.51e-04
2022-05-14 12:09:52,490 INFO [train.py:812] (5/8) Epoch 12, batch 200, loss[loss=0.2135, simple_loss=0.2992, pruned_loss=0.06387, over 7204.00 frames.], tot_loss[loss=0.181, simple_loss=0.268, pruned_loss=0.04702, over 895746.78 frames.], batch size: 22, lr: 6.51e-04
2022-05-14 12:10:51,857 INFO [train.py:812] (5/8) Epoch 12, batch 250, loss[loss=0.1935, simple_loss=0.281, pruned_loss=0.05299, over 6959.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2683, pruned_loss=0.04696, over 1013794.01 frames.], batch size: 32, lr: 6.50e-04
2022-05-14 12:11:51,034 INFO [train.py:812] (5/8) Epoch 12, batch 300, loss[loss=0.1432, simple_loss=0.2329, pruned_loss=0.02677, over 7192.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2683, pruned_loss=0.04696, over 1098637.92 frames.], batch size: 22, lr: 6.50e-04
2022-05-14 12:12:50,779 INFO [train.py:812] (5/8) Epoch 12, batch 350, loss[loss=0.1687, simple_loss=0.2563, pruned_loss=0.04056, over 7330.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2667, pruned_loss=0.04614, over 1164838.69 frames.], batch size: 22, lr: 6.50e-04
2022-05-14 12:13:50,257 INFO [train.py:812] (5/8) Epoch 12, batch 400, loss[loss=0.1729, simple_loss=0.2662, pruned_loss=0.03978, over 7323.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2661, pruned_loss=0.046, over 1220366.71 frames.], batch size: 22, lr: 6.49e-04
2022-05-14 12:14:48,358 INFO [train.py:812] (5/8) Epoch 12, batch 450, loss[loss=0.1555, simple_loss=0.239, pruned_loss=0.03603, over 7151.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2655, pruned_loss=0.0459, over 1268478.67 frames.], batch size: 19, lr: 6.49e-04
2022-05-14 12:15:47,355 INFO [train.py:812] (5/8) Epoch 12, batch 500, loss[loss=0.2213, simple_loss=0.3052, pruned_loss=0.06871, over 7396.00 frames.], tot_loss[loss=0.178, simple_loss=0.2652, pruned_loss=0.04536, over 1302397.09 frames.], batch size: 23, lr: 6.49e-04
2022-05-14 12:16:45,612 INFO [train.py:812] (5/8) Epoch 12, batch 550, loss[loss=0.1856, simple_loss=0.2873, pruned_loss=0.04192, over 7419.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2652, pruned_loss=0.04574, over 1329362.66 frames.], batch size: 21, lr: 6.48e-04
2022-05-14 12:17:43,501 INFO [train.py:812] (5/8) Epoch 12, batch 600, loss[loss=0.2207, simple_loss=0.3063, pruned_loss=0.0675, over 7330.00 frames.], tot_loss[loss=0.178, simple_loss=0.2649, pruned_loss=0.04549, over 1348699.09 frames.], batch size: 22, lr: 6.48e-04
2022-05-14 12:18:41,729 INFO [train.py:812] (5/8) Epoch 12, batch 650, loss[loss=0.1687, simple_loss=0.2651, pruned_loss=0.0362, over 7384.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2636, pruned_loss=0.04454, over 1369876.98 frames.], batch size: 23, lr: 6.48e-04
2022-05-14 12:19:49,854 INFO [train.py:812] (5/8) Epoch 12, batch 700, loss[loss=0.1834, simple_loss=0.267, pruned_loss=0.0499, over 7297.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2641, pruned_loss=0.04444, over 1380076.58 frames.], batch size: 24, lr: 6.47e-04
2022-05-14 12:20:48,658 INFO [train.py:812] (5/8) Epoch 12, batch 750, loss[loss=0.1619, simple_loss=0.2497, pruned_loss=0.03707, over 7325.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2642, pruned_loss=0.04482, over 1385539.16 frames.], batch size: 20, lr: 6.47e-04
2022-05-14 12:21:47,952 INFO [train.py:812] (5/8) Epoch 12, batch 800, loss[loss=0.1419, simple_loss=0.2253, pruned_loss=0.02926, over 7403.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2643, pruned_loss=0.04531, over 1398439.69 frames.], batch size: 18, lr: 6.47e-04
2022-05-14 12:22:46,125 INFO [train.py:812] (5/8) Epoch 12, batch 850, loss[loss=0.2102, simple_loss=0.2857, pruned_loss=0.06736, over 6726.00 frames.], tot_loss[loss=0.1782, simple_loss=0.265, pruned_loss=0.0457, over 1402991.64 frames.], batch size: 31, lr: 6.46e-04
2022-05-14 12:23:43,975 INFO [train.py:812] (5/8) Epoch 12, batch 900, loss[loss=0.1548, simple_loss=0.2558, pruned_loss=0.02691, over 7325.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2645, pruned_loss=0.04546, over 1407737.40 frames.], batch size: 22, lr: 6.46e-04
2022-05-14 12:24:43,683 INFO [train.py:812] (5/8) Epoch 12, batch 950, loss[loss=0.1592, simple_loss=0.2441, pruned_loss=0.03716, over 7432.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2644, pruned_loss=0.04537, over 1413211.71 frames.], batch size: 20, lr: 6.46e-04
2022-05-14 12:25:42,164 INFO [train.py:812] (5/8) Epoch 12, batch 1000, loss[loss=0.1702, simple_loss=0.266, pruned_loss=0.03716, over 7165.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2658, pruned_loss=0.04588, over 1416229.31 frames.], batch size: 19, lr: 6.46e-04
2022-05-14 12:26:41,750 INFO [train.py:812] (5/8) Epoch 12, batch 1050, loss[loss=0.1571, simple_loss=0.2427, pruned_loss=0.03578, over 7004.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2656, pruned_loss=0.0455, over 1416590.54 frames.], batch size: 16, lr: 6.45e-04
2022-05-14 12:27:40,782 INFO [train.py:812] (5/8) Epoch 12, batch 1100, loss[loss=0.1708, simple_loss=0.2581, pruned_loss=0.04175, over 7166.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2666, pruned_loss=0.04586, over 1419804.79 frames.], batch size: 19, lr: 6.45e-04
2022-05-14 12:28:40,316 INFO [train.py:812] (5/8) Epoch 12, batch 1150, loss[loss=0.2185, simple_loss=0.2953, pruned_loss=0.07082, over 4963.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2657, pruned_loss=0.0455, over 1421924.92 frames.], batch size: 52, lr: 6.45e-04
2022-05-14 12:29:38,122 INFO [train.py:812] (5/8) Epoch 12, batch 1200, loss[loss=0.2084, simple_loss=0.2927, pruned_loss=0.062, over 7109.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2657, pruned_loss=0.04532, over 1424972.98 frames.], batch size: 21, lr: 6.44e-04
2022-05-14 12:30:37,017 INFO [train.py:812] (5/8) Epoch 12, batch 1250, loss[loss=0.172, simple_loss=0.2464, pruned_loss=0.04876, over 7007.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2648, pruned_loss=0.04502, over 1425911.61 frames.], batch size: 16, lr: 6.44e-04
2022-05-14 12:31:36,633 INFO [train.py:812] (5/8) Epoch 12, batch 1300, loss[loss=0.1748, simple_loss=0.2618, pruned_loss=0.0439, over 7323.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2649, pruned_loss=0.04522, over 1427660.39 frames.], batch size: 20, lr: 6.44e-04
2022-05-14 12:32:34,812 INFO [train.py:812] (5/8) Epoch 12, batch 1350, loss[loss=0.1967, simple_loss=0.2757, pruned_loss=0.05888, over 7325.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2654, pruned_loss=0.04603, over 1424668.01 frames.], batch size: 21, lr: 6.43e-04
2022-05-14 12:33:34,086 INFO [train.py:812] (5/8) Epoch 12, batch 1400, loss[loss=0.1822, simple_loss=0.2838, pruned_loss=0.04024, over 7316.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2646, pruned_loss=0.04556, over 1421662.07 frames.], batch size: 21, lr: 6.43e-04
2022-05-14 12:34:33,343 INFO [train.py:812] (5/8) Epoch 12, batch 1450, loss[loss=0.1529, simple_loss=0.2356, pruned_loss=0.03516, over 7068.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2645, pruned_loss=0.04553, over 1421977.37 frames.], batch size: 18, lr: 6.43e-04
2022-05-14 12:35:32,092 INFO [train.py:812] (5/8) Epoch 12, batch 1500, loss[loss=0.2031, simple_loss=0.2891, pruned_loss=0.0586, over 7223.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2648, pruned_loss=0.04631, over 1425355.76 frames.], batch size: 23, lr: 6.42e-04
2022-05-14 12:36:36,801 INFO [train.py:812] (5/8) Epoch 12, batch 1550, loss[loss=0.2135, simple_loss=0.2907, pruned_loss=0.06817, over 7227.00 frames.], tot_loss[loss=0.178, simple_loss=0.2641, pruned_loss=0.04597, over 1424862.59 frames.], batch size: 20, lr: 6.42e-04
2022-05-14 12:37:35,928 INFO [train.py:812] (5/8) Epoch 12, batch 1600, loss[loss=0.1639, simple_loss=0.2526, pruned_loss=0.03757, over 7349.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2658, pruned_loss=0.04628, over 1425554.89 frames.], batch size: 19, lr: 6.42e-04
2022-05-14 12:38:44,922 INFO [train.py:812] (5/8) Epoch 12, batch 1650, loss[loss=0.1624, simple_loss=0.2613, pruned_loss=0.0317, over 7380.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2654, pruned_loss=0.04611, over 1426357.06 frames.], batch size: 23, lr: 6.42e-04
2022-05-14 12:39:52,047 INFO [train.py:812] (5/8) Epoch 12, batch 1700, loss[loss=0.2139, simple_loss=0.2967, pruned_loss=0.06561, over 7215.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2657, pruned_loss=0.04571, over 1427547.44 frames.], batch size: 21, lr: 6.41e-04
2022-05-14 12:40:51,344 INFO [train.py:812] (5/8) Epoch 12, batch 1750, loss[loss=0.1942, simple_loss=0.2785, pruned_loss=0.05492, over 7155.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2665, pruned_loss=0.04617, over 1428263.99 frames.], batch size: 26, lr: 6.41e-04
2022-05-14 12:41:58,739 INFO [train.py:812] (5/8) Epoch 12, batch 1800, loss[loss=0.1629, simple_loss=0.2442, pruned_loss=0.04076, over 6999.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2663, pruned_loss=0.04613, over 1428783.87 frames.], batch size: 16, lr: 6.41e-04
2022-05-14 12:43:07,989 INFO [train.py:812] (5/8) Epoch 12, batch 1850, loss[loss=0.1819, simple_loss=0.2737, pruned_loss=0.04503, over 7188.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2656, pruned_loss=0.04582, over 1426964.53 frames.], batch size: 26, lr: 6.40e-04
2022-05-14 12:44:16,794 INFO [train.py:812] (5/8) Epoch 12, batch 1900, loss[loss=0.1622, simple_loss=0.2484, pruned_loss=0.03796, over 7423.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2652, pruned_loss=0.04581, over 1429675.03 frames.], batch size: 20, lr: 6.40e-04
2022-05-14 12:45:34,900 INFO [train.py:812] (5/8) Epoch 12, batch 1950, loss[loss=0.192, simple_loss=0.2681, pruned_loss=0.05799, over 7009.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2656, pruned_loss=0.04626, over 1428441.75 frames.], batch size: 16, lr: 6.40e-04
2022-05-14 12:46:34,649 INFO [train.py:812] (5/8) Epoch 12, batch 2000, loss[loss=0.1771, simple_loss=0.2659, pruned_loss=0.0441, over 6533.00 frames.], tot_loss[loss=0.1794, simple_loss=0.266, pruned_loss=0.04637, over 1427330.19 frames.], batch size: 38, lr: 6.39e-04
2022-05-14 12:47:34,769 INFO [train.py:812] (5/8) Epoch 12, batch 2050, loss[loss=0.1727, simple_loss=0.2596, pruned_loss=0.04287, over 7364.00 frames.], tot_loss[loss=0.178, simple_loss=0.2642, pruned_loss=0.04589, over 1424886.33 frames.], batch size: 23, lr: 6.39e-04
2022-05-14 12:48:34,236 INFO [train.py:812] (5/8) Epoch 12, batch 2100, loss[loss=0.1839, simple_loss=0.2689, pruned_loss=0.04947, over 6729.00 frames.], tot_loss[loss=0.178, simple_loss=0.2644, pruned_loss=0.04584, over 1428331.52 frames.], batch size: 31, lr: 6.39e-04
2022-05-14 12:49:34,267 INFO [train.py:812] (5/8) Epoch 12, batch 2150, loss[loss=0.1592, simple_loss=0.2395, pruned_loss=0.03942, over 6809.00 frames.], tot_loss[loss=0.178, simple_loss=0.2645, pruned_loss=0.04572, over 1422479.98 frames.], batch size: 15, lr: 6.38e-04
2022-05-14 12:50:33,579 INFO [train.py:812] (5/8) Epoch 12, batch 2200, loss[loss=0.162, simple_loss=0.2524, pruned_loss=0.03583, over 7426.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2637, pruned_loss=0.04534, over 1426632.50 frames.], batch size: 20, lr: 6.38e-04
2022-05-14 12:51:31,628 INFO [train.py:812] (5/8) Epoch 12, batch 2250, loss[loss=0.1851, simple_loss=0.2681, pruned_loss=0.05109, over 7152.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2639, pruned_loss=0.04553, over 1426200.16 frames.], batch size: 17, lr: 6.38e-04
2022-05-14 12:52:29,470 INFO [train.py:812] (5/8) Epoch 12, batch 2300, loss[loss=0.1459, simple_loss=0.2401, pruned_loss=0.02585, over 7359.00 frames.], tot_loss[loss=0.178, simple_loss=0.265, pruned_loss=0.04547, over 1424363.87 frames.], batch size: 19, lr: 6.38e-04
2022-05-14 12:53:28,554 INFO [train.py:812] (5/8) Epoch 12, batch 2350, loss[loss=0.183, simple_loss=0.2823, pruned_loss=0.04182, over 7281.00 frames.], tot_loss[loss=0.1781, simple_loss=0.265, pruned_loss=0.04561, over 1426075.16 frames.], batch size: 24, lr: 6.37e-04
2022-05-14 12:54:27,723 INFO [train.py:812] (5/8) Epoch 12, batch 2400, loss[loss=0.1709, simple_loss=0.2524, pruned_loss=0.04465, over 7114.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2655, pruned_loss=0.0455, over 1428363.91 frames.], batch size: 21, lr: 6.37e-04
2022-05-14 12:55:26,370 INFO [train.py:812] (5/8) Epoch 12, batch 2450, loss[loss=0.1898, simple_loss=0.2818, pruned_loss=0.0489, over 7241.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2656, pruned_loss=0.04532, over 1426870.19 frames.], batch size: 20, lr: 6.37e-04
2022-05-14 12:56:25,362 INFO [train.py:812] (5/8) Epoch 12, batch 2500, loss[loss=0.1634, simple_loss=0.2458, pruned_loss=0.04049, over 7068.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2647, pruned_loss=0.04518, over 1426531.57 frames.], batch size: 18, lr: 6.36e-04
2022-05-14 12:57:24,971 INFO [train.py:812] (5/8) Epoch 12, batch 2550, loss[loss=0.1703, simple_loss=0.2447, pruned_loss=0.04788, over 7267.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2651, pruned_loss=0.04532, over 1428526.33 frames.], batch size: 17, lr: 6.36e-04
2022-05-14 12:58:23,637 INFO [train.py:812] (5/8) Epoch 12, batch 2600, loss[loss=0.2066, simple_loss=0.2989, pruned_loss=0.05715, over 7271.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2654, pruned_loss=0.04557, over 1423189.40 frames.], batch size: 24, lr: 6.36e-04
2022-05-14 12:59:22,470 INFO [train.py:812] (5/8) Epoch 12, batch 2650, loss[loss=0.1658, simple_loss=0.2527, pruned_loss=0.03946, over 7261.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2657, pruned_loss=0.04575, over 1420003.94 frames.], batch size: 19, lr: 6.36e-04
2022-05-14 13:00:21,654 INFO [train.py:812] (5/8) Epoch 12, batch 2700, loss[loss=0.1682, simple_loss=0.2605, pruned_loss=0.03799, over 7300.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2646, pruned_loss=0.04537, over 1423269.62 frames.], batch size: 25, lr: 6.35e-04
2022-05-14 13:01:21,316 INFO [train.py:812] (5/8) Epoch 12, batch 2750, loss[loss=0.1604, simple_loss=0.25, pruned_loss=0.03541, over 7431.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2643, pruned_loss=0.04534, over 1425939.39 frames.], batch size: 20, lr: 6.35e-04
2022-05-14 13:02:20,427 INFO [train.py:812] (5/8) Epoch 12, batch 2800, loss[loss=0.199, simple_loss=0.2858, pruned_loss=0.05608, over 7119.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2642, pruned_loss=0.04552, over 1427158.74 frames.], batch size: 21, lr: 6.35e-04
2022-05-14 13:03:19,804 INFO [train.py:812] (5/8) Epoch 12, batch 2850, loss[loss=0.1642, simple_loss=0.2594, pruned_loss=0.03447, over 7323.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2631, pruned_loss=0.0447, over 1429226.71 frames.], batch size: 21, lr: 6.34e-04
2022-05-14 13:04:18,950 INFO [train.py:812] (5/8) Epoch 12, batch 2900, loss[loss=0.1675, simple_loss=0.2601, pruned_loss=0.03742, over 7302.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2647, pruned_loss=0.04538, over 1424752.02 frames.], batch size: 24, lr: 6.34e-04
2022-05-14 13:05:18,606 INFO [train.py:812] (5/8) Epoch 12, batch 2950, loss[loss=0.1571, simple_loss=0.2457, pruned_loss=0.03429, over 7219.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2652, pruned_loss=0.04573, over 1421074.07 frames.], batch size: 21, lr: 6.34e-04
2022-05-14 13:06:17,604 INFO [train.py:812] (5/8) Epoch 12, batch 3000, loss[loss=0.1852, simple_loss=0.2673, pruned_loss=0.05152, over 7325.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2647, pruned_loss=0.0454, over 1422639.33 frames.], batch size: 25, lr: 6.33e-04
2022-05-14 13:06:17,604 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 13:06:26,032 INFO [train.py:841] (5/8) Epoch 12, validation: loss=0.1553, simple_loss=0.2571, pruned_loss=0.02678, over 698248.00 frames.
2022-05-14 13:07:25,164 INFO [train.py:812] (5/8) Epoch 12, batch 3050, loss[loss=0.2048, simple_loss=0.2981, pruned_loss=0.05573, over 7367.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2665, pruned_loss=0.04631, over 1420874.40 frames.], batch size: 23, lr: 6.33e-04
2022-05-14 13:08:24,600 INFO [train.py:812] (5/8) Epoch 12, batch 3100, loss[loss=0.1717, simple_loss=0.257, pruned_loss=0.04323, over 7330.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2659, pruned_loss=0.04614, over 1422921.99 frames.], batch size: 20, lr: 6.33e-04
2022-05-14 13:09:23,898 INFO [train.py:812] (5/8) Epoch 12, batch 3150, loss[loss=0.1878, simple_loss=0.2639, pruned_loss=0.0558, over 7369.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2651, pruned_loss=0.04563, over 1424728.89 frames.], batch size: 23, lr: 6.33e-04
2022-05-14 13:10:22,788 INFO [train.py:812] (5/8) Epoch 12, batch 3200, loss[loss=0.1741, simple_loss=0.2561, pruned_loss=0.04603, over 7102.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2645, pruned_loss=0.04548, over 1424764.50 frames.], batch size: 21, lr: 6.32e-04
2022-05-14 13:11:22,011 INFO [train.py:812] (5/8) Epoch 12, batch 3250, loss[loss=0.1794, simple_loss=0.2691, pruned_loss=0.04489, over 7415.00 frames.], tot_loss[loss=0.178, simple_loss=0.265, pruned_loss=0.04556, over 1425786.83 frames.], batch size: 21, lr: 6.32e-04
2022-05-14 13:12:21,183 INFO [train.py:812] (5/8) Epoch 12, batch 3300, loss[loss=0.1592, simple_loss=0.2395, pruned_loss=0.03942, over 6994.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2653, pruned_loss=0.04558, over 1426059.92 frames.], batch size: 16, lr: 6.32e-04
2022-05-14 13:13:18,551 INFO [train.py:812] (5/8) Epoch 12, batch 3350, loss[loss=0.1811, simple_loss=0.2594, pruned_loss=0.05142, over 7283.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2657, pruned_loss=0.04554, over 1426315.63 frames.], batch size: 18, lr: 6.31e-04
2022-05-14 13:14:17,027 INFO [train.py:812] (5/8) Epoch 12, batch 3400, loss[loss=0.1798, simple_loss=0.2644, pruned_loss=0.04755, over 6449.00 frames.], tot_loss[loss=0.178, simple_loss=0.2654, pruned_loss=0.04527, over 1420994.05 frames.], batch size: 37, lr: 6.31e-04
2022-05-14 13:15:16,590 INFO [train.py:812] (5/8) Epoch 12, batch 3450, loss[loss=0.165, simple_loss=0.251, pruned_loss=0.03955, over 7119.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2651, pruned_loss=0.04572, over 1418523.54 frames.], batch size: 21, lr: 6.31e-04
2022-05-14 13:16:15,021 INFO [train.py:812] (5/8) Epoch 12, batch 3500, loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.04223, over 7318.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2657, pruned_loss=0.04583, over 1424412.85 frames.], batch size: 21, lr: 6.31e-04
2022-05-14 13:17:13,770 INFO [train.py:812] (5/8) Epoch 12, batch 3550, loss[loss=0.1743, simple_loss=0.2503, pruned_loss=0.04913, over 7005.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2653, pruned_loss=0.04561, over 1423556.99 frames.], batch size: 16, lr: 6.30e-04
2022-05-14 13:18:12,689 INFO [train.py:812] (5/8) Epoch 12, batch 3600, loss[loss=0.1601, simple_loss=0.2466, pruned_loss=0.03681, over 7233.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2655, pruned_loss=0.04535, over 1425495.35 frames.], batch size: 20, lr: 6.30e-04
2022-05-14 13:19:11,476 INFO [train.py:812] (5/8) Epoch 12, batch 3650, loss[loss=0.1846, simple_loss=0.2805, pruned_loss=0.04429, over 7436.00 frames.], tot_loss[loss=0.1775, simple_loss=0.265, pruned_loss=0.045, over 1424499.95 frames.], batch size: 20, lr: 6.30e-04
2022-05-14 13:20:08,350 INFO [train.py:812] (5/8) Epoch 12, batch 3700, loss[loss=0.1746, simple_loss=0.2708, pruned_loss=0.03922, over 6868.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2643, pruned_loss=0.04544, over 1421481.39 frames.], batch size: 31, lr: 6.29e-04
2022-05-14 13:21:06,285 INFO [train.py:812] (5/8) Epoch 12, batch 3750, loss[loss=0.172, simple_loss=0.261, pruned_loss=0.04147, over 7393.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2636, pruned_loss=0.04483, over 1425804.73 frames.], batch size: 23, lr: 6.29e-04
2022-05-14 13:22:05,721 INFO [train.py:812] (5/8) Epoch 12, batch 3800, loss[loss=0.1986, simple_loss=0.2979, pruned_loss=0.04963, over 7194.00 frames.], tot_loss[loss=0.1768, simple_loss=0.264, pruned_loss=0.04475, over 1428427.35 frames.], batch size: 26, lr: 6.29e-04
2022-05-14 13:23:04,539 INFO [train.py:812] (5/8) Epoch 12, batch 3850, loss[loss=0.1866, simple_loss=0.2711, pruned_loss=0.05107, over 7111.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2642, pruned_loss=0.04472, over 1428675.91 frames.], batch size: 21, lr: 6.29e-04
2022-05-14 13:24:03,543 INFO [train.py:812] (5/8) Epoch 12, batch 3900, loss[loss=0.1818, simple_loss=0.269, pruned_loss=0.04727, over 7434.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2649, pruned_loss=0.04515, over 1429428.52 frames.], batch size: 20, lr: 6.28e-04
2022-05-14 13:25:02,802 INFO [train.py:812] (5/8) Epoch 12, batch 3950, loss[loss=0.1919, simple_loss=0.2853, pruned_loss=0.04922, over 7227.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2645, pruned_loss=0.04524, over 1431216.83 frames.], batch size: 20, lr: 6.28e-04
2022-05-14 13:26:01,742 INFO [train.py:812] (5/8) Epoch 12, batch 4000, loss[loss=0.1954, simple_loss=0.2909, pruned_loss=0.05, over 7408.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2644, pruned_loss=0.04498, over 1426312.30 frames.], batch size: 21, lr: 6.28e-04
2022-05-14 13:27:01,232 INFO [train.py:812] (5/8) Epoch 12, batch 4050, loss[loss=0.1859, simple_loss=0.2822, pruned_loss=0.04482, over 7442.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2647, pruned_loss=0.04532, over 1424409.55 frames.], batch size: 20, lr: 6.27e-04
2022-05-14 13:28:00,391 INFO [train.py:812] (5/8) Epoch 12, batch 4100, loss[loss=0.1843, simple_loss=0.2746, pruned_loss=0.04699, over 7318.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2646, pruned_loss=0.04535, over 1420655.71 frames.], batch size: 20, lr: 6.27e-04
2022-05-14 13:28:59,922 INFO [train.py:812] (5/8) Epoch 12, batch 4150, loss[loss=0.1745, simple_loss=0.262, pruned_loss=0.04354, over 7230.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2652, pruned_loss=0.04591, over 1421449.15 frames.], batch size: 20, lr: 6.27e-04
2022-05-14 13:29:59,290 INFO [train.py:812] (5/8) Epoch 12, batch 4200, loss[loss=0.1836, simple_loss=0.2754, pruned_loss=0.04584, over 7339.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2653, pruned_loss=0.0458, over 1420615.19 frames.], batch size: 22, lr: 6.27e-04
2022-05-14 13:30:59,178 INFO [train.py:812] (5/8) Epoch 12, batch 4250, loss[loss=0.1729, simple_loss=0.251, pruned_loss=0.04738, over 7420.00 frames.], tot_loss[loss=0.1783, simple_loss=0.265, pruned_loss=0.04576, over 1424041.87 frames.], batch size: 18, lr: 6.26e-04
2022-05-14 13:31:58,484 INFO [train.py:812] (5/8) Epoch 12, batch 4300, loss[loss=0.1554, simple_loss=0.2476, pruned_loss=0.03157, over 7226.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2646, pruned_loss=0.04586, over 1417495.90 frames.], batch size: 20, lr: 6.26e-04
2022-05-14 13:32:57,530 INFO [train.py:812] (5/8) Epoch 12, batch 4350, loss[loss=0.1899, simple_loss=0.2626, pruned_loss=0.05864, over 7201.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2631, pruned_loss=0.04556, over 1419620.59 frames.], batch size: 22, lr: 6.26e-04
2022-05-14 13:33:56,601 INFO [train.py:812] (5/8) Epoch 12, batch 4400, loss[loss=0.1744, simple_loss=0.2658, pruned_loss=0.04155, over 7318.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2625, pruned_loss=0.0451, over 1418175.25 frames.], batch size: 21, lr: 6.25e-04
2022-05-14 13:34:56,718 INFO [train.py:812] (5/8) Epoch 12, batch 4450, loss[loss=0.1868, simple_loss=0.2825, pruned_loss=0.04556, over 6527.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2616, pruned_loss=0.04504, over 1406381.03 frames.], batch size: 37, lr: 6.25e-04
2022-05-14 13:35:55,755 INFO [train.py:812] (5/8) Epoch 12, batch 4500, loss[loss=0.1591, simple_loss=0.2638, pruned_loss=0.02719, over 6431.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2609, pruned_loss=0.04525, over 1390259.12 frames.], batch size: 38, lr: 6.25e-04
2022-05-14 13:36:54,582 INFO [train.py:812] (5/8) Epoch 12, batch 4550, loss[loss=0.1949, simple_loss=0.2723, pruned_loss=0.05878, over 5108.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2641, pruned_loss=0.04752, over 1351239.20 frames.], batch size: 52, lr: 6.25e-04
2022-05-14 13:38:08,551 INFO [train.py:812] (5/8) Epoch 13, batch 0, loss[loss=0.1885, simple_loss=0.2768, pruned_loss=0.05006, over 7144.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2768, pruned_loss=0.05006, over 7144.00 frames.], batch size: 20, lr: 6.03e-04
2022-05-14 13:39:08,079 INFO [train.py:812] (5/8) Epoch 13, batch 50, loss[loss=0.1611, simple_loss=0.2546, pruned_loss=0.03382, over 7235.00 frames.], tot_loss[loss=0.175, simple_loss=0.2634, pruned_loss=0.04326, over 318515.53 frames.], batch size: 20, lr: 6.03e-04
2022-05-14 13:40:06,194 INFO [train.py:812] (5/8) Epoch 13, batch 100, loss[loss=0.2356, simple_loss=0.3115, pruned_loss=0.07987, over 7178.00 frames.], tot_loss[loss=0.178, simple_loss=0.2658, pruned_loss=0.04506, over 564575.58 frames.], batch size: 23, lr: 6.03e-04
2022-05-14 13:41:05,023 INFO [train.py:812] (5/8) Epoch 13, batch 150, loss[loss=0.1825, simple_loss=0.2779, pruned_loss=0.04361, over 7146.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2658, pruned_loss=0.04426, over 753649.32 frames.], batch size: 20, lr: 6.03e-04
2022-05-14 13:42:04,237 INFO [train.py:812] (5/8) Epoch 13, batch 200, loss[loss=0.1829, simple_loss=0.2769, pruned_loss=0.04444, over 7155.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2643, pruned_loss=0.04399, over 900958.48 frames.], batch size: 20, lr: 6.02e-04
2022-05-14 13:43:03,743 INFO [train.py:812] (5/8) Epoch 13, batch 250, loss[loss=0.1598, simple_loss=0.2407, pruned_loss=0.03944, over 6789.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2641, pruned_loss=0.04426, over 1013902.42 frames.], batch size: 15, lr: 6.02e-04
2022-05-14 13:44:02,522 INFO [train.py:812] (5/8) Epoch 13, batch 300, loss[loss=0.1934, simple_loss=0.2727, pruned_loss=0.05707, over 7145.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2628, pruned_loss=0.04381, over 1103407.41 frames.], batch size: 20, lr: 6.02e-04
2022-05-14 13:45:01,886 INFO [train.py:812] (5/8) Epoch 13, batch 350, loss[loss=0.1751, simple_loss=0.2684, pruned_loss=0.04095, over 7008.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2635, pruned_loss=0.04382, over 1175895.03 frames.], batch size: 28, lr: 6.01e-04
2022-05-14 13:46:00,662 INFO [train.py:812] (5/8) Epoch 13, batch 400, loss[loss=0.1596, simple_loss=0.2505, pruned_loss=0.03437, over 7357.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2637, pruned_loss=0.04371, over 1233299.90 frames.], batch size: 19, lr: 6.01e-04
2022-05-14 13:46:57,910 INFO [train.py:812] (5/8) Epoch 13, batch 450, loss[loss=0.1777, simple_loss=0.278, pruned_loss=0.03869, over 7315.00 frames.], tot_loss[loss=0.175, simple_loss=0.2628, pruned_loss=0.04357, over 1276945.01 frames.], batch size: 21, lr: 6.01e-04
2022-05-14 13:47:55,557 INFO [train.py:812] (5/8) Epoch 13, batch 500, loss[loss=0.1748, simple_loss=0.2641, pruned_loss=0.0427, over 6636.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2613, pruned_loss=0.04324, over 1310416.82 frames.], batch size: 38, lr: 6.01e-04
2022-05-14 13:48:55,156 INFO [train.py:812] (5/8) Epoch 13, batch 550, loss[loss=0.179, simple_loss=0.2623, pruned_loss=0.04789, over 7392.00 frames.], tot_loss[loss=0.175, simple_loss=0.2623, pruned_loss=0.04383, over 1332894.18 frames.], batch size: 23, lr: 6.00e-04
2022-05-14 13:49:53,968 INFO [train.py:812] (5/8) Epoch 13, batch 600, loss[loss=0.143, simple_loss=0.2237, pruned_loss=0.03114, over 7212.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2615, pruned_loss=0.04405, over 1347466.69 frames.], batch size: 16, lr: 6.00e-04
2022-05-14 13:50:53,007 INFO [train.py:812] (5/8) Epoch 13, batch 650, loss[loss=0.1581, simple_loss=0.2461, pruned_loss=0.03509, over 7296.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2625, pruned_loss=0.04395, over 1366758.93 frames.], batch size: 18, lr: 6.00e-04
2022-05-14 13:51:52,322 INFO [train.py:812] (5/8) Epoch 13, batch 700, loss[loss=0.1482, simple_loss=0.2396, pruned_loss=0.02843, over 6816.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2639, pruned_loss=0.04391, over 1384045.16 frames.], batch size: 15, lr: 6.00e-04
2022-05-14 13:52:51,779 INFO [train.py:812] (5/8) Epoch 13, batch 750, loss[loss=0.2041, simple_loss=0.2933, pruned_loss=0.05747, over 7204.00 frames.], tot_loss[loss=0.1759, simple_loss=0.264, pruned_loss=0.04393, over 1396329.94 frames.], batch size: 23, lr: 5.99e-04
2022-05-14 13:53:50,403 INFO [train.py:812] (5/8) Epoch 13, batch 800, loss[loss=0.1683, simple_loss=0.2659, pruned_loss=0.03537, over 7205.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2643, pruned_loss=0.04441, over 1405032.29 frames.], batch size: 22, lr: 5.99e-04
2022-05-14 13:54:49,210 INFO [train.py:812] (5/8) Epoch 13, batch 850, loss[loss=0.1499, simple_loss=0.2353, pruned_loss=0.03224, over 7158.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2639, pruned_loss=0.044, over 1410935.81 frames.], batch size: 17, lr: 5.99e-04
2022-05-14 13:55:48,209 INFO [train.py:812] (5/8) Epoch 13, batch 900, loss[loss=0.1688, simple_loss=0.255, pruned_loss=0.04126, over 7336.00 frames.], tot_loss[loss=0.1744, simple_loss=0.262, pruned_loss=0.04343, over 1413901.88 frames.], batch size: 20, lr: 5.99e-04
2022-05-14 13:56:53,001 INFO [train.py:812] (5/8) Epoch 13, batch 950, loss[loss=0.1891, simple_loss=0.2755, pruned_loss=0.05136, over 7166.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2622, pruned_loss=0.04394, over 1414766.87 frames.], batch size: 26, lr: 5.98e-04
2022-05-14 13:57:52,236 INFO [train.py:812] (5/8) Epoch 13, batch 1000, loss[loss=0.1886, simple_loss=0.2785, pruned_loss=0.04937, over 6603.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2628, pruned_loss=0.04422, over 1415167.55 frames.], batch size: 38, lr: 5.98e-04
2022-05-14 13:58:51,870 INFO [train.py:812] (5/8) Epoch 13, batch 1050, loss[loss=0.1508, simple_loss=0.2381, pruned_loss=0.03179, over 7249.00 frames.], tot_loss[loss=0.1752, simple_loss=0.262, pruned_loss=0.04416, over 1416342.25 frames.], batch size: 19, lr: 5.98e-04
2022-05-14 13:59:49,629 INFO [train.py:812] (5/8) Epoch 13, batch 1100, loss[loss=0.1849, simple_loss=0.272, pruned_loss=0.04894, over 7385.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2626, pruned_loss=0.04401, over 1422472.14 frames.], batch size: 23, lr: 5.97e-04
2022-05-14 14:00:49,332 INFO [train.py:812] (5/8) Epoch 13, batch 1150, loss[loss=0.1665, simple_loss=0.2555, pruned_loss=0.03874, over 7318.00 frames.], tot_loss[loss=0.1749, simple_loss=0.262, pruned_loss=0.0439, over 1425324.13 frames.], batch size: 20, lr: 5.97e-04
2022-05-14 14:01:48,719 INFO [train.py:812] (5/8) Epoch 13, batch 1200, loss[loss=0.1801, simple_loss=0.2659, pruned_loss=0.04708, over 5108.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2617, pruned_loss=0.04372, over 1421971.86 frames.], batch size: 53, lr: 5.97e-04
2022-05-14 14:02:48,328 INFO [train.py:812] (5/8) Epoch 13, batch 1250, loss[loss=0.1766, simple_loss=0.2633, pruned_loss=0.04492, over 7152.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2624, pruned_loss=0.04404, over 1419435.27 frames.], batch size: 19, lr: 5.97e-04
2022-05-14 14:03:47,377 INFO [train.py:812] (5/8) Epoch 13, batch 1300, loss[loss=0.1678, simple_loss=0.2538, pruned_loss=0.04094, over 7068.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2624, pruned_loss=0.04404, over 1419525.16 frames.], batch size: 18, lr: 5.96e-04
2022-05-14 14:04:46,645 INFO [train.py:812] (5/8) Epoch 13, batch 1350, loss[loss=0.2662, simple_loss=0.3217, pruned_loss=0.1054, over 5079.00 frames.], tot_loss[loss=0.1767, simple_loss=0.264, pruned_loss=0.04471, over 1416425.86 frames.], batch size: 53, lr: 5.96e-04
2022-05-14 14:05:45,497 INFO [train.py:812] (5/8) Epoch 13, batch 1400, loss[loss=0.174, simple_loss=0.2588, pruned_loss=0.04458, over 7327.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2646, pruned_loss=0.0451, over 1415780.50 frames.], batch size: 25, lr: 5.96e-04
2022-05-14 14:06:43,975 INFO [train.py:812] (5/8) Epoch 13, batch 1450, loss[loss=0.1592, simple_loss=0.2572, pruned_loss=0.03062, over 7329.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2642, pruned_loss=0.04475, over 1414511.72 frames.], batch size: 21, lr: 5.96e-04
2022-05-14 14:07:42,553 INFO [train.py:812] (5/8) Epoch 13, batch 1500, loss[loss=0.1726, simple_loss=0.2682, pruned_loss=0.03847, over 7190.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2635, pruned_loss=0.0445, over 1418138.12 frames.], batch size: 23, lr: 5.95e-04
2022-05-14 14:08:42,622 INFO [train.py:812] (5/8) Epoch 13, batch 1550, loss[loss=0.2183, simple_loss=0.3168, pruned_loss=0.05992, over 6989.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2629, pruned_loss=0.04413, over 1419593.77 frames.], batch size: 28, lr: 5.95e-04
2022-05-14 14:09:41,283 INFO [train.py:812] (5/8) Epoch 13, batch 1600, loss[loss=0.1706, simple_loss=0.263, pruned_loss=0.03909, over 7302.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2633, pruned_loss=0.04427, over 1419078.31 frames.], batch size: 25, lr: 5.95e-04
2022-05-14 14:10:39,360 INFO [train.py:812] (5/8) Epoch 13, batch 1650, loss[loss=0.1903, simple_loss=0.2781, pruned_loss=0.05129, over 7301.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2626, pruned_loss=0.04409, over 1422413.06 frames.], batch size: 24, lr: 5.95e-04
2022-05-14 14:11:36,467 INFO [train.py:812] (5/8) Epoch 13, batch 1700, loss[loss=0.1536, simple_loss=0.2373, pruned_loss=0.03498, over 7128.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2622, pruned_loss=0.04406, over 1418990.18 frames.], batch size: 17, lr: 5.94e-04
2022-05-14 14:12:34,846 INFO [train.py:812] (5/8) Epoch 13, batch 1750, loss[loss=0.1767, simple_loss=0.2663, pruned_loss=0.04357, over 7158.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2613, pruned_loss=0.04396, over 1422356.87 frames.], batch size: 26, lr: 5.94e-04
2022-05-14 14:13:34,199 INFO [train.py:812] (5/8) Epoch 13, batch 1800, loss[loss=0.1459, simple_loss=0.2215, pruned_loss=0.03518, over 7010.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2612, pruned_loss=0.044, over 1427623.46 frames.], batch size: 16, lr: 5.94e-04
2022-05-14 14:14:33,811 INFO [train.py:812] (5/8) Epoch 13, batch 1850, loss[loss=0.1805, simple_loss=0.2747, pruned_loss=0.04317, over 7333.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2617, pruned_loss=0.04366, over 1428351.43 frames.], batch size: 22, lr: 5.94e-04
2022-05-14 14:15:33,203 INFO [train.py:812] (5/8) Epoch 13, batch 1900, loss[loss=0.1783, simple_loss=0.2701, pruned_loss=0.04329, over 7234.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2614, pruned_loss=0.04359, over 1428715.13 frames.], batch size: 20, lr: 5.93e-04
2022-05-14 14:16:32,309 INFO [train.py:812] (5/8) Epoch 13, batch 1950, loss[loss=0.1647, simple_loss=0.2478, pruned_loss=0.04073, over 7283.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2614, pruned_loss=0.04359, over 1428868.34 frames.], batch size: 17, lr: 5.93e-04
2022-05-14 14:17:31,536 INFO [train.py:812] (5/8) Epoch 13, batch 2000, loss[loss=0.1452, simple_loss=0.2213, pruned_loss=0.0345, over 7005.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2596, pruned_loss=0.04284, over 1428231.60 frames.], batch size: 16, lr: 5.93e-04
2022-05-14 14:18:40,071 INFO [train.py:812] (5/8) Epoch 13, batch 2050, loss[loss=0.1872, simple_loss=0.2747, pruned_loss=0.04983, over 7153.00 frames.], tot_loss[loss=0.173, simple_loss=0.2597, pruned_loss=0.04315, over 1421286.55 frames.], batch size: 19, lr: 5.93e-04
2022-05-14 14:19:39,661 INFO [train.py:812] (5/8) Epoch 13, batch 2100, loss[loss=0.1556, simple_loss=0.2389, pruned_loss=0.03613, over 7155.00 frames.], tot_loss[loss=0.1731, simple_loss=0.26, pruned_loss=0.04316, over 1421316.16 frames.], batch size: 19, lr: 5.92e-04
2022-05-14 14:20:39,433 INFO [train.py:812] (5/8) Epoch 13, batch 2150, loss[loss=0.1667, simple_loss=0.2614, pruned_loss=0.03599, over 7272.00 frames.], tot_loss[loss=0.1738, simple_loss=0.261, pruned_loss=0.04333, over 1421498.84 frames.], batch size: 18, lr: 5.92e-04
2022-05-14 14:21:36,885 INFO [train.py:812] (5/8) Epoch 13, batch 2200, loss[loss=0.1675, simple_loss=0.2606, pruned_loss=0.03723, over 7318.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2603, pruned_loss=0.04319, over 1422455.99 frames.], batch size: 20, lr: 5.92e-04
2022-05-14 14:22:35,523 INFO [train.py:812] (5/8) Epoch 13, batch 2250, loss[loss=0.1745, simple_loss=0.2694, pruned_loss=0.03985, over 7052.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2604, pruned_loss=0.04298, over 1421334.89 frames.], batch size: 28, lr: 5.91e-04
2022-05-14 14:23:34,261 INFO [train.py:812] (5/8) Epoch 13, batch 2300, loss[loss=0.2033, simple_loss=0.2807, pruned_loss=0.06292, over 7116.00 frames.], tot_loss[loss=0.174, simple_loss=0.261, pruned_loss=0.04349, over 1424914.38 frames.], batch size: 21, lr: 5.91e-04
2022-05-14 14:24:34,072 INFO [train.py:812] (5/8) Epoch 13, batch 2350, loss[loss=0.1888, simple_loss=0.2686, pruned_loss=0.05448, over 7163.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2619, pruned_loss=0.04396, over 1426375.69 frames.], batch size: 19, lr: 5.91e-04
2022-05-14 14:25:33,549 INFO [train.py:812] (5/8) Epoch 13, batch 2400, loss[loss=0.1564, simple_loss=0.2339, pruned_loss=0.03943, over 7149.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2613, pruned_loss=0.04369, over 1427093.62 frames.], batch size: 17, lr: 5.91e-04
2022-05-14 14:26:31,971 INFO [train.py:812] (5/8) Epoch 13, batch 2450, loss[loss=0.169, simple_loss=0.2603, pruned_loss=0.03884, over 7218.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2607, pruned_loss=0.04308, over 1426739.18 frames.], batch size: 21, lr: 5.90e-04
2022-05-14 14:27:30,822 INFO [train.py:812] (5/8) Epoch 13, batch 2500, loss[loss=0.166, simple_loss=0.2522, pruned_loss=0.03989, over 7293.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2622, pruned_loss=0.04363, over 1426957.30 frames.], batch size: 18, lr: 5.90e-04
2022-05-14 14:28:30,495 INFO [train.py:812] (5/8) Epoch 13, batch 2550, loss[loss=0.1722, simple_loss=0.2518, pruned_loss=0.04624, over 7242.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2628, pruned_loss=0.04389, over 1428425.31 frames.], batch size: 16, lr: 5.90e-04
2022-05-14 14:29:29,637 INFO [train.py:812] (5/8) Epoch 13, batch 2600, loss[loss=0.1724, simple_loss=0.2607, pruned_loss=0.04205, over 7244.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2629, pruned_loss=0.04418, over 1424762.82 frames.], batch size: 16, lr: 5.90e-04
2022-05-14 14:30:29,042 INFO [train.py:812] (5/8) Epoch 13, batch 2650, loss[loss=0.1642, simple_loss=0.246, pruned_loss=0.04117, over 6976.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2631, pruned_loss=0.04413, over 1423140.38 frames.], batch size: 16, lr: 5.89e-04
2022-05-14 14:31:27,710 INFO [train.py:812] (5/8) Epoch 13, batch 2700, loss[loss=0.1568, simple_loss=0.2369, pruned_loss=0.03832, over 6990.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2629, pruned_loss=0.04385, over 1424368.31 frames.], batch size: 16, lr: 5.89e-04
2022-05-14 14:32:27,067 INFO [train.py:812] (5/8) Epoch 13, batch 2750, loss[loss=0.2039, simple_loss=0.2932, pruned_loss=0.05727, over 7116.00 frames.], tot_loss[loss=0.1754, simple_loss=0.263, pruned_loss=0.0439, over 1421682.76 frames.], batch size: 21, lr: 5.89e-04
2022-05-14 14:33:24,881 INFO [train.py:812] (5/8) Epoch 13, batch 2800, loss[loss=0.1445, simple_loss=0.2264, pruned_loss=0.0313, over 7121.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2629, pruned_loss=0.04373, over 1420891.06 frames.], batch size: 17, lr: 5.89e-04
2022-05-14 14:34:24,904 INFO [train.py:812] (5/8) Epoch 13, batch 2850, loss[loss=0.1931, simple_loss=0.2786, pruned_loss=0.05377, over 7394.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2633, pruned_loss=0.04362, over 1427052.47 frames.], batch size: 23, lr: 5.88e-04
2022-05-14 14:35:22,655 INFO [train.py:812] (5/8) Epoch 13, batch 2900, loss[loss=0.1437, simple_loss=0.2315, pruned_loss=0.02791, over 7352.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2644, pruned_loss=0.04396, over 1424693.30 frames.], batch size: 19, lr: 5.88e-04
2022-05-14 14:36:21,963 INFO [train.py:812] (5/8) Epoch 13, batch 2950, loss[loss=0.2042, simple_loss=0.2872, pruned_loss=0.06061, over 7111.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2637, pruned_loss=0.04409, over 1426194.61 frames.], batch size: 21, lr: 5.88e-04
2022-05-14 14:37:20,734 INFO [train.py:812] (5/8) Epoch 13, batch 3000, loss[loss=0.1511, simple_loss=0.2307, pruned_loss=0.03573, over 7278.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2645, pruned_loss=0.0447, over 1426406.70 frames.], batch size: 17, lr: 5.88e-04
2022-05-14 14:37:20,735 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 14:37:28,226 INFO [train.py:841] (5/8) Epoch 13, validation: loss=0.1549, simple_loss=0.2559, pruned_loss=0.02694, over 698248.00 frames.
2022-05-14 14:38:28,327 INFO [train.py:812] (5/8) Epoch 13, batch 3050, loss[loss=0.1352, simple_loss=0.2119, pruned_loss=0.02926, over 7143.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2633, pruned_loss=0.04375, over 1427850.66 frames.], batch size: 17, lr: 5.87e-04
2022-05-14 14:39:27,852 INFO [train.py:812] (5/8) Epoch 13, batch 3100, loss[loss=0.1571, simple_loss=0.2461, pruned_loss=0.03407, over 7106.00 frames.], tot_loss[loss=0.175, simple_loss=0.2623, pruned_loss=0.04383, over 1427119.24 frames.], batch size: 21, lr: 5.87e-04
2022-05-14 14:40:36,515 INFO [train.py:812] (5/8) Epoch 13, batch 3150, loss[loss=0.2127, simple_loss=0.3002, pruned_loss=0.06261, over 7336.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2631, pruned_loss=0.044, over 1424623.21 frames.], batch size: 25, lr: 5.87e-04
2022-05-14 14:41:35,524 INFO [train.py:812] (5/8) Epoch 13, batch 3200, loss[loss=0.2436, simple_loss=0.306, pruned_loss=0.09062, over 5015.00 frames.], tot_loss[loss=0.1757, simple_loss=0.263, pruned_loss=0.04416, over 1425607.04 frames.], batch size: 52, lr: 5.87e-04
2022-05-14 14:42:44,505 INFO [train.py:812] (5/8) Epoch 13, batch 3250, loss[loss=0.1456, simple_loss=0.2197, pruned_loss=0.03577, over 7279.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2621, pruned_loss=0.0436, over 1429210.90 frames.], batch size: 17, lr: 5.86e-04
2022-05-14 14:43:53,099 INFO [train.py:812] (5/8) Epoch 13, batch 3300, loss[loss=0.1788, simple_loss=0.2665, pruned_loss=0.04553, over 7321.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2623, pruned_loss=0.04378, over 1428500.48 frames.], batch size: 20, lr: 5.86e-04
2022-05-14 14:44:51,604 INFO [train.py:812] (5/8) Epoch 13, batch 3350, loss[loss=0.1469, simple_loss=0.2323, pruned_loss=0.03071, over 6996.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2622, pruned_loss=0.04372, over 1421023.31 frames.], batch size: 16, lr: 5.86e-04
2022-05-14 14:46:18,984 INFO [train.py:812] (5/8) Epoch 13, batch 3400, loss[loss=0.1686, simple_loss=0.2647, pruned_loss=0.03625, over 7363.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2628, pruned_loss=0.04395, over 1424688.42 frames.], batch size: 23, lr: 5.86e-04
2022-05-14 14:47:27,728 INFO [train.py:812] (5/8) Epoch 13, batch 3450, loss[loss=0.1442, simple_loss=0.2327, pruned_loss=0.02785, over 7406.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2631, pruned_loss=0.04403, over 1413864.20 frames.], batch size: 18, lr: 5.85e-04
2022-05-14 14:48:26,508 INFO [train.py:812] (5/8) Epoch 13, batch 3500, loss[loss=0.1818, simple_loss=0.2718, pruned_loss=0.04591, over 6777.00 frames.], tot_loss[loss=0.1756, simple_loss=0.263, pruned_loss=0.04408, over 1415985.82 frames.], batch size: 31, lr: 5.85e-04
2022-05-14 14:49:26,107 INFO [train.py:812] (5/8) Epoch 13, batch 3550, loss[loss=0.1397, simple_loss=0.22, pruned_loss=0.02969, over 7016.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2626, pruned_loss=0.04384, over 1422400.46 frames.], batch size: 16, lr: 5.85e-04
2022-05-14 14:50:24,070 INFO [train.py:812] (5/8) Epoch 13, batch 3600, loss[loss=0.1593, simple_loss=0.2492, pruned_loss=0.03467, over 7291.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2625, pruned_loss=0.04385, over 1422534.39 frames.], batch size: 18, lr: 5.85e-04
2022-05-14 14:51:22,131 INFO [train.py:812] (5/8) Epoch 13, batch 3650, loss[loss=0.1795, simple_loss=0.2706, pruned_loss=0.04419, over 7413.00 frames.], tot_loss[loss=0.175, simple_loss=0.2624, pruned_loss=0.04377, over 1425410.93 frames.], batch size: 21, lr: 5.84e-04
2022-05-14 14:52:20,918 INFO [train.py:812] (5/8) Epoch 13, batch 3700, loss[loss=0.1485, simple_loss=0.2376, pruned_loss=0.02966, over 7252.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2619, pruned_loss=0.04375, over 1426419.17 frames.], batch size: 19, lr: 5.84e-04
2022-05-14 14:53:20,284 INFO [train.py:812] (5/8) Epoch 13, batch 3750, loss[loss=0.1709, simple_loss=0.2691, pruned_loss=0.03638, over 7412.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2619, pruned_loss=0.04366, over 1426890.60 frames.], batch size: 21, lr: 5.84e-04
2022-05-14 14:54:19,193 INFO [train.py:812] (5/8) Epoch 13, batch 3800, loss[loss=0.1866, simple_loss=0.2742, pruned_loss=0.04951, over 7015.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2628, pruned_loss=0.04402, over 1430279.23 frames.], batch size: 28, lr: 5.84e-04
2022-05-14 14:55:18,384 INFO [train.py:812] (5/8) Epoch 13, batch 3850, loss[loss=0.1872, simple_loss=0.2753, pruned_loss=0.0496, over 7207.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2635, pruned_loss=0.04409, over 1426780.14 frames.], batch size: 22, lr: 5.83e-04
2022-05-14 14:56:16,995 INFO [train.py:812] (5/8) Epoch 13, batch 3900, loss[loss=0.2014, simple_loss=0.2879, pruned_loss=0.05745, over 7307.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2629, pruned_loss=0.04378, over 1425379.62 frames.], batch size: 24, lr: 5.83e-04
2022-05-14 14:57:16,825 INFO [train.py:812] (5/8) Epoch 13, batch 3950, loss[loss=0.1804, simple_loss=0.262, pruned_loss=0.04943, over 7197.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2631, pruned_loss=0.04385, over 1424836.19 frames.], batch size: 23, lr: 5.83e-04
2022-05-14 14:58:15,141 INFO [train.py:812] (5/8) Epoch 13, batch 4000, loss[loss=0.1493, simple_loss=0.2355, pruned_loss=0.03155, over 7133.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2625, pruned_loss=0.04342, over 1424084.18 frames.], batch size: 17, lr: 5.83e-04
2022-05-14 14:59:14,566 INFO [train.py:812] (5/8) Epoch 13, batch 4050, loss[loss=0.2032, simple_loss=0.287, pruned_loss=0.05972, over 7238.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2622, pruned_loss=0.04346, over 1425656.86 frames.], batch size: 20, lr: 5.82e-04
2022-05-14 15:00:14,147 INFO [train.py:812] (5/8) Epoch 13, batch 4100, loss[loss=0.2186, simple_loss=0.3001, pruned_loss=0.06855, over 7141.00 frames.], tot_loss[loss=0.174, simple_loss=0.2617, pruned_loss=0.04322, over 1425239.85 frames.], batch size: 20, lr: 5.82e-04
2022-05-14 15:01:13,269 INFO [train.py:812] (5/8) Epoch 13, batch 4150, loss[loss=0.1529, simple_loss=0.247, pruned_loss=0.02944, over 7427.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2634, pruned_loss=0.04377, over 1420381.51 frames.], batch size: 20, lr: 5.82e-04
2022-05-14 15:02:11,342 INFO [train.py:812] (5/8) Epoch 13, batch 4200, loss[loss=0.1881, simple_loss=0.2753, pruned_loss=0.05046, over 7152.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2631, pruned_loss=0.04411, over 1422117.14 frames.], batch size: 20, lr: 5.82e-04
2022-05-14 15:03:10,128 INFO [train.py:812] (5/8) Epoch 13, batch 4250, loss[loss=0.194, simple_loss=0.2868, pruned_loss=0.05059, over 7121.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2624, pruned_loss=0.04396, over 1418632.34 frames.], batch size: 26, lr: 5.81e-04
2022-05-14 15:04:08,256 INFO [train.py:812] (5/8) Epoch 13, batch 4300, loss[loss=0.1936, simple_loss=0.2665, pruned_loss=0.06036, over 7427.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2632, pruned_loss=0.04485, over 1415181.97 frames.], batch size: 20, lr: 5.81e-04
2022-05-14 15:05:06,770 INFO [train.py:812] (5/8) Epoch 13, batch 4350, loss[loss=0.1418, simple_loss=0.2263, pruned_loss=0.02866, over 7018.00 frames.], tot_loss[loss=0.176, simple_loss=0.2627, pruned_loss=0.04463, over 1410502.69 frames.], batch size: 16, lr: 5.81e-04
2022-05-14 15:06:06,045 INFO [train.py:812] (5/8) Epoch 13, batch 4400, loss[loss=0.2183, simple_loss=0.2809, pruned_loss=0.07783, over 5132.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2611, pruned_loss=0.04404, over 1409251.83 frames.], batch size: 52, lr: 5.81e-04
2022-05-14 15:07:05,009 INFO [train.py:812] (5/8) Epoch 13, batch 4450, loss[loss=0.1897, simple_loss=0.2779, pruned_loss=0.05073, over 7295.00 frames.], tot_loss[loss=0.174, simple_loss=0.2602, pruned_loss=0.04387, over 1406720.50 frames.], batch size: 24, lr: 5.81e-04
2022-05-14 15:08:03,343 INFO [train.py:812] (5/8) Epoch 13, batch 4500, loss[loss=0.1555, simple_loss=0.2548, pruned_loss=0.02814, over 7415.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2618, pruned_loss=0.04448, over 1387959.65 frames.], batch size: 21, lr: 5.80e-04
2022-05-14 15:09:01,452 INFO [train.py:812] (5/8) Epoch 13, batch 4550, loss[loss=0.1891, simple_loss=0.2702, pruned_loss=0.05406, over 5416.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2644, pruned_loss=0.04573, over 1354103.88 frames.], batch size: 52, lr: 5.80e-04
2022-05-14 15:10:14,173 INFO [train.py:812] (5/8) Epoch 14, batch 0, loss[loss=0.1772, simple_loss=0.2617, pruned_loss=0.04638, over 7375.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2617, pruned_loss=0.04638, over 7375.00 frames.], batch size: 23, lr: 5.61e-04
2022-05-14 15:11:14,040 INFO [train.py:812] (5/8) Epoch 14, batch 50, loss[loss=0.1714, simple_loss=0.2658, pruned_loss=0.03846, over 7125.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2579, pruned_loss=0.04324, over 322597.83 frames.], batch size: 21, lr: 5.61e-04
2022-05-14 15:12:13,749 INFO [train.py:812] (5/8) Epoch 14, batch 100, loss[loss=0.1738, simple_loss=0.2655, pruned_loss=0.04109, over 7154.00 frames.], tot_loss[loss=0.1714, simple_loss=0.259, pruned_loss=0.04196, over 572452.04 frames.], batch size: 20, lr: 5.61e-04
2022-05-14 15:13:13,198 INFO [train.py:812] (5/8) Epoch 14, batch 150, loss[loss=0.153, simple_loss=0.2301, pruned_loss=0.03794, over 7001.00 frames.], tot_loss[loss=0.1717, simple_loss=0.259, pruned_loss=0.04224, over 763100.54 frames.], batch size: 16, lr: 5.61e-04
2022-05-14 15:14:11,609 INFO [train.py:812] (5/8) Epoch 14, batch 200, loss[loss=0.1813, simple_loss=0.2843, pruned_loss=0.0391, over 7192.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2595, pruned_loss=0.04191, over 909723.24 frames.], batch size: 22, lr: 5.60e-04
2022-05-14 15:15:09,287 INFO [train.py:812] (5/8) Epoch 14, batch 250, loss[loss=0.1895, simple_loss=0.2818, pruned_loss=0.04857, over 7214.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2592, pruned_loss=0.04132, over 1025788.65 frames.], batch size: 22, lr: 5.60e-04
2022-05-14 15:16:07,602 INFO [train.py:812] (5/8) Epoch 14, batch 300, loss[loss=0.1899, simple_loss=0.2795, pruned_loss=0.05016, over 7412.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2614, pruned_loss=0.04238, over 1112702.12 frames.], batch size: 21, lr: 5.60e-04
2022-05-14 15:17:06,820 INFO [train.py:812] (5/8) Epoch 14, batch 350, loss[loss=0.1593, simple_loss=0.2462, pruned_loss=0.03617, over 7427.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2598, pruned_loss=0.04204, over 1181351.30 frames.], batch size: 20, lr: 5.60e-04
2022-05-14 15:18:11,718 INFO [train.py:812] (5/8) Epoch 14, batch 400, loss[loss=0.1761, simple_loss=0.2649, pruned_loss=0.0437, over 7049.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2599, pruned_loss=0.04226, over 1231219.16 frames.], batch size: 28, lr: 5.59e-04
2022-05-14 15:19:10,236 INFO [train.py:812] (5/8) Epoch 14, batch 450, loss[loss=0.2149, simple_loss=0.3036, pruned_loss=0.0631, over 6537.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2605, pruned_loss=0.04244, over 1273026.25 frames.], batch size: 38, lr: 5.59e-04
2022-05-14 15:20:09,677 INFO [train.py:812] (5/8) Epoch 14, batch 500, loss[loss=0.1763, simple_loss=0.2625, pruned_loss=0.04501, over 7027.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2599, pruned_loss=0.04244, over 1300265.79 frames.], batch size: 28, lr: 5.59e-04
2022-05-14 15:21:08,775 INFO [train.py:812] (5/8) Epoch 14, batch 550, loss[loss=0.1754, simple_loss=0.2631, pruned_loss=0.04382, over 6518.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2604, pruned_loss=0.04261, over 1325708.29 frames.], batch size: 38, lr: 5.59e-04
2022-05-14 15:22:08,317 INFO [train.py:812] (5/8) Epoch 14, batch 600, loss[loss=0.1806, simple_loss=0.2666, pruned_loss=0.04728, over 7323.00 frames.], tot_loss[loss=0.172, simple_loss=0.2598, pruned_loss=0.04213, over 1348035.62 frames.], batch size: 21, lr: 5.59e-04
2022-05-14 15:23:07,100 INFO [train.py:812] (5/8) Epoch 14, batch 650, loss[loss=0.1795, simple_loss=0.2651, pruned_loss=0.04693, over 7053.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2612, pruned_loss=0.04254, over 1360493.83 frames.], batch size: 18, lr: 5.58e-04
2022-05-14 15:24:06,554 INFO [train.py:812] (5/8) Epoch 14, batch 700, loss[loss=0.1527, simple_loss=0.2457, pruned_loss=0.02982, over 7265.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2604, pruned_loss=0.04192, over 1376604.35 frames.], batch size: 18, lr: 5.58e-04
2022-05-14 15:25:05,440 INFO [train.py:812] (5/8) Epoch 14, batch 750, loss[loss=0.2017, simple_loss=0.2811, pruned_loss=0.06119, over 7189.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2598, pruned_loss=0.04168, over 1383481.27 frames.], batch size: 23, lr: 5.58e-04
2022-05-14 15:26:04,527 INFO [train.py:812] (5/8) Epoch 14, batch 800, loss[loss=0.2188, simple_loss=0.3084, pruned_loss=0.06457, over 7278.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2609, pruned_loss=0.04207, over 1392481.91 frames.], batch size: 25, lr: 5.58e-04
2022-05-14 15:27:03,660 INFO [train.py:812] (5/8) Epoch 14, batch 850, loss[loss=0.157, simple_loss=0.2588, pruned_loss=0.02762, over 7219.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2605, pruned_loss=0.04209, over 1401026.23 frames.], batch size: 21, lr: 5.57e-04
2022-05-14 15:28:02,924 INFO [train.py:812] (5/8) Epoch 14, batch 900, loss[loss=0.1527, simple_loss=0.2348, pruned_loss=0.0353, over 7163.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2604, pruned_loss=0.04203, over 1403634.00 frames.], batch size: 18, lr: 5.57e-04
2022-05-14 15:29:01,733 INFO [train.py:812] (5/8) Epoch 14, batch 950, loss[loss=0.1618, simple_loss=0.2489, pruned_loss=0.03739, over 7227.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2613, pruned_loss=0.04247, over 1403780.18 frames.], batch size: 21, lr: 5.57e-04
2022-05-14 15:30:01,403 INFO [train.py:812] (5/8) Epoch 14, batch 1000, loss[loss=0.1791, simple_loss=0.2738, pruned_loss=0.04217, over 7204.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2616, pruned_loss=0.04269, over 1410999.90 frames.], batch size: 22, lr: 5.57e-04
2022-05-14 15:31:00,115 INFO [train.py:812] (5/8) Epoch 14, batch 1050, loss[loss=0.1889, simple_loss=0.2771, pruned_loss=0.05029, over 7425.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2606, pruned_loss=0.04245, over 1410996.32 frames.], batch size: 21, lr: 5.56e-04
2022-05-14 15:31:57,358 INFO [train.py:812] (5/8) Epoch 14, batch 1100, loss[loss=0.2125, simple_loss=0.3071, pruned_loss=0.0589, over 6774.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2613, pruned_loss=0.04267, over 1410299.57 frames.], batch size: 31, lr: 5.56e-04
2022-05-14 15:32:55,102 INFO [train.py:812] (5/8) Epoch 14, batch 1150, loss[loss=0.1912, simple_loss=0.2744, pruned_loss=0.05406, over 7329.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2616, pruned_loss=0.04258, over 1410557.84 frames.], batch size: 22, lr: 5.56e-04
2022-05-14 15:33:54,525 INFO [train.py:812] (5/8) Epoch 14, batch 1200, loss[loss=0.2336, simple_loss=0.2982, pruned_loss=0.08445, over 4819.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2614, pruned_loss=0.04263, over 1409524.97 frames.], batch size: 52, lr: 5.56e-04
2022-05-14 15:34:52,740 INFO [train.py:812] (5/8) Epoch 14, batch 1250, loss[loss=0.1521, simple_loss=0.2363, pruned_loss=0.03393, over 7437.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2622, pruned_loss=0.04308, over 1413856.71 frames.], batch size: 20, lr: 5.56e-04
2022-05-14 15:35:51,072 INFO [train.py:812] (5/8) Epoch 14, batch 1300, loss[loss=0.175, simple_loss=0.2722, pruned_loss=0.0389, over 7251.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2617, pruned_loss=0.04268, over 1417522.79 frames.], batch size: 19, lr: 5.55e-04
2022-05-14 15:36:49,466 INFO [train.py:812] (5/8) Epoch 14, batch 1350, loss[loss=0.1562, simple_loss=0.2504, pruned_loss=0.03104, over 7270.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2598, pruned_loss=0.04171, over 1421335.52 frames.], batch size: 18, lr: 5.55e-04
2022-05-14 15:37:48,220 INFO [train.py:812] (5/8) Epoch 14, batch 1400, loss[loss=0.1345, simple_loss=0.2278, pruned_loss=0.02057, over 7158.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2609, pruned_loss=0.04221, over 1417143.10 frames.], batch size: 18, lr: 5.55e-04
2022-05-14 15:38:45,037 INFO [train.py:812] (5/8) Epoch 14, batch 1450, loss[loss=0.1392, simple_loss=0.2271, pruned_loss=0.02568, over 7275.00 frames.], tot_loss[loss=0.1737, simple_loss=0.262, pruned_loss=0.0427, over 1420868.77 frames.], batch size: 17, lr: 5.55e-04
2022-05-14 15:39:43,869 INFO [train.py:812] (5/8) Epoch 14, batch 1500, loss[loss=0.1322, simple_loss=0.2151, pruned_loss=0.02463, over 7289.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2607, pruned_loss=0.0422, over 1422348.58 frames.], batch size: 17, lr: 5.54e-04
2022-05-14 15:40:41,990 INFO [train.py:812] (5/8) Epoch 14, batch 1550, loss[loss=0.1597, simple_loss=0.2632, pruned_loss=0.02804, over 6268.00 frames.], tot_loss[loss=0.173, simple_loss=0.261, pruned_loss=0.04254, over 1417941.79 frames.], batch size: 37, lr: 5.54e-04
2022-05-14 15:41:40,134 INFO [train.py:812] (5/8) Epoch 14, batch 1600, loss[loss=0.1542, simple_loss=0.2485, pruned_loss=0.02995, over 7421.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2613, pruned_loss=0.04267, over 1417625.05 frames.], batch size: 21, lr: 5.54e-04
2022-05-14 15:42:38,931 INFO [train.py:812] (5/8) Epoch 14, batch 1650, loss[loss=0.1587, simple_loss=0.2434, pruned_loss=0.03697, over 7247.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2621, pruned_loss=0.04302, over 1419337.26 frames.], batch size: 20, lr: 5.54e-04
2022-05-14 15:43:38,137 INFO [train.py:812] (5/8) Epoch 14, batch 1700, loss[loss=0.1896, simple_loss=0.2786, pruned_loss=0.05029, over 6437.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2617, pruned_loss=0.04269, over 1418647.10 frames.], batch size: 37, lr: 5.54e-04
2022-05-14 15:44:37,146 INFO [train.py:812] (5/8) Epoch 14, batch 1750, loss[loss=0.14, simple_loss=0.2258, pruned_loss=0.02713, over 7286.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2608, pruned_loss=0.04203, over 1420998.82 frames.], batch size: 17, lr: 5.53e-04
2022-05-14 15:45:37,326 INFO [train.py:812] (5/8) Epoch 14, batch 1800, loss[loss=0.1753, simple_loss=0.2748, pruned_loss=0.03786, over 7139.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2606, pruned_loss=0.04204, over 1425364.38 frames.], batch size: 20, lr: 5.53e-04
2022-05-14 15:46:35,091 INFO [train.py:812] (5/8) Epoch 14, batch 1850, loss[loss=0.1816, simple_loss=0.2713, pruned_loss=0.04589, over 7272.00 frames.], tot_loss[loss=0.173, simple_loss=0.2612, pruned_loss=0.04244, over 1425597.59 frames.], batch size: 25, lr: 5.53e-04
2022-05-14 15:47:33,778 INFO [train.py:812] (5/8) Epoch 14, batch 1900, loss[loss=0.1722, simple_loss=0.2594, pruned_loss=0.0425, over 6148.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2616, pruned_loss=0.04245, over 1420454.11 frames.], batch size: 37, lr: 5.53e-04
2022-05-14 15:48:32,629 INFO [train.py:812] (5/8) Epoch 14, batch 1950, loss[loss=0.1583, simple_loss=0.2517, pruned_loss=0.0325, over 7268.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2624, pruned_loss=0.04254, over 1421814.00 frames.], batch size: 19, lr: 5.52e-04
2022-05-14 15:49:32,416 INFO [train.py:812] (5/8) Epoch 14, batch 2000, loss[loss=0.1709, simple_loss=0.2649, pruned_loss=0.03845, over 7341.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2627, pruned_loss=0.0424, over 1423460.86 frames.], batch size: 22, lr: 5.52e-04
2022-05-14 15:50:31,355 INFO [train.py:812] (5/8) Epoch 14, batch 2050, loss[loss=0.1727, simple_loss=0.2702, pruned_loss=0.03759, over 7374.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2622, pruned_loss=0.04256, over 1425785.47 frames.], batch size: 23, lr: 5.52e-04
2022-05-14 15:51:31,088 INFO [train.py:812] (5/8) Epoch 14, batch 2100, loss[loss=0.1634, simple_loss=0.2603, pruned_loss=0.03326, over 7230.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2634, pruned_loss=0.04282, over 1425619.99 frames.], batch size: 20, lr: 5.52e-04
2022-05-14 15:52:30,557 INFO [train.py:812] (5/8) Epoch 14, batch 2150, loss[loss=0.1711, simple_loss=0.2644, pruned_loss=0.0389, over 7178.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2618, pruned_loss=0.042, over 1428101.10 frames.], batch size: 26, lr: 5.52e-04
2022-05-14 15:53:29,904 INFO [train.py:812] (5/8) Epoch 14, batch 2200, loss[loss=0.1754, simple_loss=0.263, pruned_loss=0.0439, over 7433.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2619, pruned_loss=0.04251, over 1426733.76 frames.], batch size: 20, lr: 5.51e-04
2022-05-14 15:54:28,288 INFO [train.py:812] (5/8) Epoch 14, batch 2250, loss[loss=0.1597, simple_loss=0.2508, pruned_loss=0.03434, over 7226.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2616, pruned_loss=0.04246, over 1427737.05 frames.], batch size: 20, lr: 5.51e-04
2022-05-14 15:55:26,896 INFO [train.py:812] (5/8) Epoch 14, batch 2300, loss[loss=0.1952, simple_loss=0.2755, pruned_loss=0.05751, over 7064.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2594, pruned_loss=0.04172, over 1428416.30 frames.], batch size: 28, lr: 5.51e-04
2022-05-14 15:56:25,007 INFO [train.py:812] (5/8) Epoch 14, batch 2350, loss[loss=0.2457, simple_loss=0.3184, pruned_loss=0.08656, over 5219.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2599, pruned_loss=0.04217, over 1427758.92 frames.], batch size: 52, lr: 5.51e-04
2022-05-14 15:57:24,248 INFO [train.py:812] (5/8) Epoch 14, batch 2400, loss[loss=0.1393, simple_loss=0.2176, pruned_loss=0.03047, over 7284.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2598, pruned_loss=0.04203, over 1428605.03 frames.], batch size: 17, lr: 5.50e-04
2022-05-14 15:58:23,282 INFO [train.py:812] (5/8) Epoch 14, batch 2450, loss[loss=0.1891, simple_loss=0.2777, pruned_loss=0.05028, over 6774.00 frames.], tot_loss[loss=0.1725, simple_loss=0.26, pruned_loss=0.04246, over 1430555.12 frames.], batch size: 31, lr: 5.50e-04
2022-05-14 15:59:21,594 INFO [train.py:812] (5/8) Epoch 14, batch 2500, loss[loss=0.1333, simple_loss=0.2122, pruned_loss=0.02717, over 7288.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2601, pruned_loss=0.04242, over 1427579.61 frames.], batch size: 17, lr: 5.50e-04
2022-05-14 16:00:19,960 INFO [train.py:812] (5/8) Epoch 14, batch 2550, loss[loss=0.1834, simple_loss=0.2671, pruned_loss=0.04984, over 7295.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2604, pruned_loss=0.04274, over 1424151.45 frames.], batch size: 25, lr: 5.50e-04
2022-05-14 16:01:19,287 INFO [train.py:812] (5/8) Epoch 14, batch 2600, loss[loss=0.1736, simple_loss=0.2626, pruned_loss=0.04229, over 7408.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2602, pruned_loss=0.04274, over 1420738.61 frames.], batch size: 21, lr: 5.50e-04
2022-05-14 16:02:16,349 INFO [train.py:812] (5/8) Epoch 14, batch 2650, loss[loss=0.1558, simple_loss=0.2501, pruned_loss=0.03077, over 7122.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2603, pruned_loss=0.04248, over 1418006.26 frames.], batch size: 21, lr: 5.49e-04
2022-05-14 16:03:15,391 INFO [train.py:812] (5/8) Epoch 14, batch 2700, loss[loss=0.1629, simple_loss=0.2381, pruned_loss=0.04387, over 7424.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2597, pruned_loss=0.04191, over 1423945.24 frames.], batch size: 17, lr: 5.49e-04
2022-05-14 16:04:13,415 INFO [train.py:812] (5/8) Epoch 14, batch 2750, loss[loss=0.1965, simple_loss=0.2848, pruned_loss=0.05411, over 7280.00 frames.], tot_loss[loss=0.1722, simple_loss=0.26, pruned_loss=0.04218, over 1428284.75 frames.], batch size: 24, lr: 5.49e-04
2022-05-14 16:05:11,583 INFO [train.py:812] (5/8) Epoch 14, batch 2800, loss[loss=0.1501, simple_loss=0.2298, pruned_loss=0.03517, over 7118.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2598, pruned_loss=0.04184, over 1426647.69 frames.], batch size: 17, lr: 5.49e-04
2022-05-14 16:06:10,651 INFO [train.py:812] (5/8) Epoch 14, batch 2850, loss[loss=0.1739, simple_loss=0.2655, pruned_loss=0.04112, over 7411.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2593, pruned_loss=0.0418, over 1428019.67 frames.], batch size: 21, lr: 5.48e-04
2022-05-14 16:07:10,186 INFO [train.py:812] (5/8) Epoch 14, batch 2900, loss[loss=0.1384, simple_loss=0.232, pruned_loss=0.02236, over 7109.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2598, pruned_loss=0.0419, over 1429100.43 frames.], batch size: 21, lr: 5.48e-04
2022-05-14 16:08:08,878 INFO [train.py:812] (5/8) Epoch 14, batch 2950, loss[loss=0.1914, simple_loss=0.2794, pruned_loss=0.05167, over 7210.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2607, pruned_loss=0.04206, over 1430369.15 frames.], batch size: 23, lr: 5.48e-04
2022-05-14 16:09:07,578 INFO [train.py:812] (5/8) Epoch 14, batch 3000, loss[loss=0.181, simple_loss=0.2762, pruned_loss=0.04291, over 7291.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2595, pruned_loss=0.04181, over 1430096.63 frames.], batch size: 24, lr: 5.48e-04
2022-05-14 16:09:07,579 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 16:09:15,054 INFO [train.py:841] (5/8) Epoch 14, validation: loss=0.1549, simple_loss=0.2556, pruned_loss=0.02713, over 698248.00 frames.
2022-05-14 16:10:14,206 INFO [train.py:812] (5/8) Epoch 14, batch 3050, loss[loss=0.1618, simple_loss=0.2466, pruned_loss=0.03853, over 7271.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2599, pruned_loss=0.04194, over 1430131.21 frames.], batch size: 17, lr: 5.48e-04
2022-05-14 16:11:13,757 INFO [train.py:812] (5/8) Epoch 14, batch 3100, loss[loss=0.2009, simple_loss=0.2895, pruned_loss=0.05613, over 7183.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2605, pruned_loss=0.04218, over 1431374.29 frames.], batch size: 23, lr: 5.47e-04
2022-05-14 16:12:13,365 INFO [train.py:812] (5/8) Epoch 14, batch 3150, loss[loss=0.1897, simple_loss=0.2696, pruned_loss=0.05492, over 5272.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2596, pruned_loss=0.042, over 1429729.83 frames.], batch size: 52, lr: 5.47e-04
2022-05-14 16:13:13,717 INFO [train.py:812] (5/8) Epoch 14, batch 3200, loss[loss=0.1651, simple_loss=0.26, pruned_loss=0.0351, over 7335.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2593, pruned_loss=0.04179, over 1430186.81 frames.], batch size: 22, lr: 5.47e-04
2022-05-14 16:14:11,597 INFO [train.py:812] (5/8) Epoch 14, batch 3250, loss[loss=0.1971, simple_loss=0.2814, pruned_loss=0.05643, over 7124.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2593, pruned_loss=0.04171, over 1426971.96 frames.], batch size: 26, lr: 5.47e-04
2022-05-14 16:15:10,525 INFO [train.py:812] (5/8) Epoch 14, batch 3300, loss[loss=0.1409, simple_loss=0.2243, pruned_loss=0.0288, over 7173.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2589, pruned_loss=0.04176, over 1424618.58 frames.], batch size: 18, lr: 5.46e-04
2022-05-14 16:16:09,532 INFO [train.py:812] (5/8) Epoch 14, batch 3350, loss[loss=0.1389, simple_loss=0.2309, pruned_loss=0.02339, over 7386.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2597, pruned_loss=0.04229, over 1426181.41 frames.], batch size: 18, lr: 5.46e-04
2022-05-14 16:17:08,393 INFO [train.py:812] (5/8) Epoch 14, batch 3400, loss[loss=0.1732, simple_loss=0.2597, pruned_loss=0.04335, over 7165.00 frames.], tot_loss[loss=0.1726, simple_loss=0.26, pruned_loss=0.04264, over 1427179.03 frames.], batch size: 18, lr: 5.46e-04
2022-05-14 16:18:17,667 INFO [train.py:812] (5/8) Epoch 14, batch 3450, loss[loss=0.1718, simple_loss=0.2713, pruned_loss=0.03613, over 7116.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2613, pruned_loss=0.04305, over 1426505.19 frames.], batch size: 21, lr: 5.46e-04
2022-05-14 16:19:16,718 INFO [train.py:812] (5/8) Epoch 14, batch 3500, loss[loss=0.1904, simple_loss=0.2785, pruned_loss=0.05111, over 7350.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2596, pruned_loss=0.04261, over 1427674.55 frames.], batch size: 22, lr: 5.46e-04
2022-05-14 16:20:15,509 INFO [train.py:812] (5/8) Epoch 14, batch 3550, loss[loss=0.1752, simple_loss=0.2727, pruned_loss=0.03888, over 7324.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2599, pruned_loss=0.04235, over 1428043.69 frames.], batch size: 21, lr: 5.45e-04
2022-05-14 16:21:14,255 INFO [train.py:812] (5/8) Epoch 14, batch 3600, loss[loss=0.1665, simple_loss=0.2543, pruned_loss=0.03939, over 7360.00 frames.], tot_loss[loss=0.171, simple_loss=0.2584, pruned_loss=0.0418, over 1430585.18 frames.], batch size: 19, lr: 5.45e-04
2022-05-14 16:22:13,109 INFO [train.py:812] (5/8) Epoch 14, batch 3650, loss[loss=0.1734, simple_loss=0.269, pruned_loss=0.03888, over 7234.00 frames.], tot_loss[loss=0.171, simple_loss=0.2582, pruned_loss=0.04188, over 1430804.26 frames.], batch size: 20, lr: 5.45e-04
2022-05-14 16:23:12,541 INFO [train.py:812] (5/8) Epoch 14, batch 3700, loss[loss=0.1997, simple_loss=0.2872, pruned_loss=0.05609, over 7251.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2593, pruned_loss=0.04231, over 1421682.63 frames.], batch size: 24, lr: 5.45e-04
2022-05-14 16:24:11,504 INFO [train.py:812] (5/8) Epoch 14, batch 3750, loss[loss=0.2087, simple_loss=0.2832, pruned_loss=0.06713, over 5113.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2601, pruned_loss=0.04256, over 1421068.43 frames.], batch size: 53, lr: 5.45e-04
2022-05-14 16:25:11,055 INFO [train.py:812] (5/8) Epoch 14, batch 3800, loss[loss=0.1527, simple_loss=0.2376, pruned_loss=0.03391, over 6991.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2604, pruned_loss=0.04253, over 1420320.39 frames.], batch size: 16, lr: 5.44e-04
2022-05-14 16:26:09,754 INFO [train.py:812] (5/8) Epoch 14, batch 3850, loss[loss=0.1864, simple_loss=0.277, pruned_loss=0.04792, over 7212.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2604, pruned_loss=0.04249, over 1420590.48 frames.], batch size: 22, lr: 5.44e-04
2022-05-14 16:27:08,434 INFO [train.py:812] (5/8) Epoch 14, batch 3900, loss[loss=0.1593, simple_loss=0.2455, pruned_loss=0.03657, over 7320.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2607, pruned_loss=0.0424, over 1422431.74 frames.], batch size: 21, lr: 5.44e-04
2022-05-14 16:28:07,613 INFO [train.py:812] (5/8) Epoch 14, batch 3950, loss[loss=0.2248, simple_loss=0.2865, pruned_loss=0.08159, over 4964.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2595, pruned_loss=0.04169, over 1421005.44 frames.], batch size: 52, lr: 5.44e-04
2022-05-14 16:29:06,379 INFO [train.py:812] (5/8) Epoch 14, batch 4000, loss[loss=0.2054, simple_loss=0.2997, pruned_loss=0.05558, over 7328.00 frames.], tot_loss[loss=0.1716, simple_loss=0.26, pruned_loss=0.04158, over 1422853.87 frames.], batch size: 22, lr: 5.43e-04
2022-05-14 16:30:03,964 INFO [train.py:812] (5/8) Epoch 14, batch 4050, loss[loss=0.1376, simple_loss=0.2204, pruned_loss=0.02742, over 6847.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2593, pruned_loss=0.04142, over 1424374.15 frames.], batch size: 15, lr: 5.43e-04
2022-05-14 16:31:03,488 INFO [train.py:812] (5/8) Epoch 14, batch 4100, loss[loss=0.181, simple_loss=0.2721, pruned_loss=0.04496, over 6760.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2593, pruned_loss=0.04152, over 1422019.32 frames.], batch size: 31, lr: 5.43e-04
2022-05-14 16:32:02,259 INFO [train.py:812] (5/8) Epoch 14, batch 4150, loss[loss=0.1644, simple_loss=0.2541, pruned_loss=0.03736, over 7210.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2591, pruned_loss=0.04162, over 1420992.20 frames.], batch size: 21, lr: 5.43e-04
2022-05-14 16:33:01,711 INFO [train.py:812] (5/8) Epoch 14, batch 4200, loss[loss=0.1694, simple_loss=0.2493, pruned_loss=0.04468, over 7289.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2581, pruned_loss=0.04182, over 1422522.46 frames.], batch size: 17, lr: 5.43e-04
2022-05-14 16:34:00,228 INFO [train.py:812] (5/8) Epoch 14, batch 4250, loss[loss=0.1857, simple_loss=0.2713, pruned_loss=0.05005, over 6348.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2585, pruned_loss=0.04201, over 1416965.23 frames.], batch size: 37, lr: 5.42e-04
2022-05-14 16:34:59,089 INFO [train.py:812] (5/8) Epoch 14, batch 4300, loss[loss=0.1595, simple_loss=0.2532, pruned_loss=0.03285, over 7227.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2595, pruned_loss=0.0422, over 1412186.35 frames.], batch size: 21, lr: 5.42e-04
2022-05-14 16:35:56,840 INFO [train.py:812] (5/8) Epoch 14, batch 4350, loss[loss=0.1522, simple_loss=0.2288, pruned_loss=0.03785, over 6820.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2591, pruned_loss=0.04199, over 1408011.17 frames.], batch size: 15, lr: 5.42e-04
2022-05-14 16:37:01,590 INFO [train.py:812] (5/8) Epoch 14, batch 4400, loss[loss=0.1697, simple_loss=0.2618, pruned_loss=0.03882, over 7151.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2588, pruned_loss=0.04208, over 1401799.94 frames.], batch size: 20, lr: 5.42e-04
2022-05-14 16:38:00,538 INFO [train.py:812] (5/8) Epoch 14, batch 4450, loss[loss=0.1961, simple_loss=0.2712, pruned_loss=0.06047, over 5246.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2601, pruned_loss=0.04254, over 1392847.04 frames.], batch size: 52, lr: 5.42e-04
2022-05-14 16:38:59,688 INFO [train.py:812] (5/8) Epoch 14, batch 4500, loss[loss=0.2166, simple_loss=0.296, pruned_loss=0.06865, over 5101.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2612, pruned_loss=0.04324, over 1377119.03 frames.], batch size: 53, lr: 5.41e-04
2022-05-14 16:40:07,884 INFO [train.py:812] (5/8) Epoch 14, batch 4550, loss[loss=0.2078, simple_loss=0.292, pruned_loss=0.06179, over 6680.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2625, pruned_loss=0.04426, over 1367497.19 frames.], batch size: 31, lr: 5.41e-04
2022-05-14 16:41:16,660 INFO [train.py:812] (5/8) Epoch 15, batch 0, loss[loss=0.1557, simple_loss=0.2504, pruned_loss=0.03045, over 7076.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2504, pruned_loss=0.03045, over 7076.00 frames.], batch size: 28, lr: 5.25e-04
2022-05-14 16:42:15,476 INFO [train.py:812] (5/8) Epoch 15, batch 50, loss[loss=0.2007, simple_loss=0.2787, pruned_loss=0.06131, over 5128.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2605, pruned_loss=0.04137, over 322684.78 frames.], batch size: 53, lr: 5.24e-04
2022-05-14 16:43:15,411 INFO [train.py:812] (5/8) Epoch 15, batch 100, loss[loss=0.1774, simple_loss=0.2563, pruned_loss=0.04924, over 7164.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2615, pruned_loss=0.04201, over 569635.90 frames.], batch size: 18, lr: 5.24e-04
2022-05-14 16:44:31,097 INFO [train.py:812] (5/8) Epoch 15, batch 150, loss[loss=0.1701, simple_loss=0.2617, pruned_loss=0.03923, over 7116.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2616, pruned_loss=0.04151, over 759194.12 frames.], batch size: 21, lr: 5.24e-04
2022-05-14 16:45:30,978 INFO [train.py:812] (5/8) Epoch 15, batch 200, loss[loss=0.1682, simple_loss=0.2622, pruned_loss=0.03708, over 7314.00 frames.], tot_loss[loss=0.173, simple_loss=0.262, pruned_loss=0.04205, over 903349.67 frames.], batch size: 20, lr: 5.24e-04
2022-05-14 16:46:49,169 INFO [train.py:812] (5/8) Epoch 15, batch 250, loss[loss=0.1659, simple_loss=0.2623, pruned_loss=0.0347, over 6609.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2608, pruned_loss=0.04171, over 1020943.19 frames.], batch size: 38, lr: 5.24e-04
2022-05-14 16:48:07,558 INFO [train.py:812] (5/8) Epoch 15, batch 300, loss[loss=0.1633, simple_loss=0.243, pruned_loss=0.04179, over 7137.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2586, pruned_loss=0.04116, over 1110395.72 frames.], batch size: 17, lr: 5.23e-04
2022-05-14 16:49:06,740 INFO [train.py:812] (5/8) Epoch 15, batch 350, loss[loss=0.1711, simple_loss=0.2436, pruned_loss=0.04932, over 6791.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2588, pruned_loss=0.04145, over 1172371.52 frames.], batch size: 15, lr: 5.23e-04
2022-05-14 16:50:06,786 INFO [train.py:812] (5/8) Epoch 15, batch 400, loss[loss=0.1513, simple_loss=0.2487, pruned_loss=0.0269, over 7142.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2586, pruned_loss=0.04161, over 1226725.10 frames.], batch size: 20, lr: 5.23e-04
2022-05-14 16:51:05,890 INFO [train.py:812] (5/8) Epoch 15, batch 450, loss[loss=0.1794, simple_loss=0.272, pruned_loss=0.04341, over 7159.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2581, pruned_loss=0.04157, over 1271048.51 frames.], batch size: 19, lr: 5.23e-04
2022-05-14 16:52:05,390 INFO [train.py:812] (5/8) Epoch 15, batch 500, loss[loss=0.1493, simple_loss=0.2375, pruned_loss=0.03058, over 7433.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2585, pruned_loss=0.04144, over 1303170.77 frames.], batch size: 20, lr: 5.23e-04
2022-05-14 16:53:04,826 INFO [train.py:812] (5/8) Epoch 15, batch 550, loss[loss=0.1523, simple_loss=0.2384, pruned_loss=0.0331, over 7275.00 frames.], tot_loss[loss=0.1701, simple_loss=0.258, pruned_loss=0.04109, over 1331962.05 frames.], batch size: 18, lr: 5.22e-04
2022-05-14 16:54:04,513 INFO [train.py:812] (5/8) Epoch 15, batch 600, loss[loss=0.1738, simple_loss=0.2582, pruned_loss=0.04464, over 7235.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2576, pruned_loss=0.04055, over 1354962.17 frames.], batch size: 20, lr: 5.22e-04
2022-05-14 16:55:03,724 INFO [train.py:812] (5/8) Epoch 15, batch 650, loss[loss=0.2023, simple_loss=0.2859, pruned_loss=0.05934, over 7332.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2574, pruned_loss=0.04019, over 1369173.84 frames.], batch size: 22, lr: 5.22e-04
2022-05-14 16:56:03,039 INFO [train.py:812] (5/8) Epoch 15, batch 700, loss[loss=0.1805, simple_loss=0.2632, pruned_loss=0.04892, over 7343.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2584, pruned_loss=0.04048, over 1382549.95 frames.], batch size: 20, lr: 5.22e-04
2022-05-14 16:57:02,317 INFO [train.py:812] (5/8) Epoch 15, batch 750, loss[loss=0.1524, simple_loss=0.2541, pruned_loss=0.02531, over 7336.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2582, pruned_loss=0.04027, over 1391468.30 frames.], batch size: 22, lr: 5.22e-04
2022-05-14 16:58:01,727 INFO [train.py:812] (5/8) Epoch 15, batch 800, loss[loss=0.1687, simple_loss=0.2585, pruned_loss=0.03939, over 7342.00 frames.], tot_loss[loss=0.1702, simple_loss=0.259, pruned_loss=0.04073, over 1398893.82 frames.], batch size: 22, lr: 5.21e-04
2022-05-14 16:59:00,989 INFO [train.py:812] (5/8) Epoch 15, batch 850, loss[loss=0.1388, simple_loss=0.2197, pruned_loss=0.02899, over 7139.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2589, pruned_loss=0.04091, over 1402287.50 frames.], batch size: 17, lr: 5.21e-04
2022-05-14 17:00:00,603 INFO [train.py:812] (5/8) Epoch 15, batch 900, loss[loss=0.181, simple_loss=0.2674, pruned_loss=0.04727, over 7253.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2598, pruned_loss=0.04132, over 1398352.73 frames.], batch size: 19, lr: 5.21e-04
2022-05-14 17:00:59,820 INFO [train.py:812] (5/8) Epoch 15, batch 950, loss[loss=0.1779, simple_loss=0.2706, pruned_loss=0.04259, over 7340.00 frames.], tot_loss[loss=0.1713, simple_loss=0.26, pruned_loss=0.04125, over 1406770.99 frames.], batch size: 22, lr: 5.21e-04
2022-05-14 17:01:59,775 INFO [train.py:812] (5/8) Epoch 15, batch 1000, loss[loss=0.1812, simple_loss=0.2732, pruned_loss=0.04464, over 7060.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2598, pruned_loss=0.04095, over 1408201.76 frames.], batch size: 28, lr: 5.21e-04
2022-05-14 17:02:57,915 INFO [train.py:812] (5/8) Epoch 15, batch 1050, loss[loss=0.1562, simple_loss=0.2452, pruned_loss=0.03356, over 7286.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2599, pruned_loss=0.04114, over 1413467.45 frames.], batch size: 18, lr: 5.20e-04
2022-05-14 17:03:56,821 INFO [train.py:812] (5/8) Epoch 15, batch 1100, loss[loss=0.1651, simple_loss=0.251, pruned_loss=0.03964, over 7294.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2598, pruned_loss=0.04126, over 1417501.67 frames.], batch size: 17, lr: 5.20e-04
2022-05-14 17:04:54,405 INFO [train.py:812] (5/8) Epoch 15, batch 1150, loss[loss=0.1608, simple_loss=0.2596, pruned_loss=0.031, over 7413.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2584, pruned_loss=0.04057, over 1421702.07 frames.], batch size: 21, lr: 5.20e-04
2022-05-14 17:05:54,141 INFO [train.py:812] (5/8) Epoch 15, batch 1200, loss[loss=0.1493, simple_loss=0.2431, pruned_loss=0.0278, over 7435.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2578, pruned_loss=0.0405, over 1423238.72 frames.], batch size: 20, lr: 5.20e-04
2022-05-14 17:06:52,028 INFO [train.py:812] (5/8) Epoch 15, batch 1250, loss[loss=0.1606, simple_loss=0.2419, pruned_loss=0.03962, over 7355.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2583, pruned_loss=0.04098, over 1425826.19 frames.], batch size: 19, lr: 5.20e-04
2022-05-14 17:07:51,283 INFO [train.py:812] (5/8) Epoch 15, batch 1300, loss[loss=0.1673, simple_loss=0.2471, pruned_loss=0.04377, over 6313.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2586, pruned_loss=0.04136, over 1419730.79 frames.], batch size: 38, lr: 5.19e-04
2022-05-14 17:08:51,292 INFO [train.py:812] (5/8) Epoch 15, batch 1350, loss[loss=0.1574, simple_loss=0.2354, pruned_loss=0.03968, over 6985.00 frames.], tot_loss[loss=0.172, simple_loss=0.26, pruned_loss=0.04203, over 1421107.69 frames.], batch size: 16, lr: 5.19e-04
2022-05-14 17:09:50,451 INFO [train.py:812] (5/8) Epoch 15, batch 1400, loss[loss=0.2032, simple_loss=0.2854, pruned_loss=0.06046, over 7282.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2595, pruned_loss=0.04164, over 1421005.89 frames.], batch size: 24, lr: 5.19e-04
2022-05-14 17:10:49,134 INFO [train.py:812] (5/8) Epoch 15, batch 1450, loss[loss=0.1877, simple_loss=0.2836, pruned_loss=0.04595, over 7374.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2598, pruned_loss=0.04177, over 1417887.53 frames.], batch size: 23, lr: 5.19e-04
2022-05-14 17:11:46,393 INFO [train.py:812] (5/8) Epoch 15, batch 1500, loss[loss=0.1626, simple_loss=0.2517, pruned_loss=0.03671, over 7142.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2604, pruned_loss=0.04215, over 1411643.65 frames.], batch size: 20, lr: 5.19e-04
2022-05-14 17:12:45,403 INFO [train.py:812] (5/8) Epoch 15, batch 1550, loss[loss=0.1747, simple_loss=0.2572, pruned_loss=0.04608, over 7115.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2594, pruned_loss=0.04152, over 1416305.60 frames.], batch size: 21, lr: 5.18e-04
2022-05-14 17:13:44,515 INFO [train.py:812] (5/8) Epoch 15, batch 1600, loss[loss=0.1592, simple_loss=0.2634, pruned_loss=0.02751, over 7401.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2592, pruned_loss=0.04134, over 1418380.25 frames.], batch size: 21, lr: 5.18e-04
2022-05-14 17:14:43,360 INFO [train.py:812] (5/8) Epoch 15, batch 1650, loss[loss=0.2005, simple_loss=0.2922, pruned_loss=0.05437, over 7208.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2595, pruned_loss=0.04136, over 1423482.94 frames.], batch size: 23, lr: 5.18e-04
2022-05-14 17:15:42,310 INFO [train.py:812] (5/8) Epoch 15, batch 1700, loss[loss=0.1824, simple_loss=0.2641, pruned_loss=0.05038, over 7298.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2585, pruned_loss=0.04108, over 1427239.47 frames.], batch size: 25, lr: 5.18e-04
2022-05-14 17:16:41,856 INFO [train.py:812] (5/8) Epoch 15, batch 1750, loss[loss=0.1821, simple_loss=0.2778, pruned_loss=0.04323, over 7076.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2587, pruned_loss=0.04106, over 1430996.81 frames.], batch size: 28, lr: 5.18e-04
2022-05-14 17:17:41,416 INFO [train.py:812] (5/8) Epoch 15, batch 1800, loss[loss=0.1449, simple_loss=0.2292, pruned_loss=0.03032, over 7269.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2586, pruned_loss=0.04095, over 1427616.66 frames.], batch size: 17, lr: 5.17e-04
2022-05-14 17:18:41,018 INFO [train.py:812] (5/8) Epoch 15, batch 1850, loss[loss=0.152, simple_loss=0.2441, pruned_loss=0.03001, over 7163.00 frames.], tot_loss[loss=0.17, simple_loss=0.2587, pruned_loss=0.04065, over 1431335.82 frames.], batch size: 18, lr: 5.17e-04
2022-05-14 17:19:40,979 INFO [train.py:812] (5/8) Epoch 15, batch 1900, loss[loss=0.1704, simple_loss=0.2609, pruned_loss=0.0399, over 7116.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2582, pruned_loss=0.04083, over 1431288.40 frames.], batch size: 21, lr: 5.17e-04
2022-05-14 17:20:40,324 INFO [train.py:812] (5/8) Epoch 15, batch 1950, loss[loss=0.164, simple_loss=0.2569, pruned_loss=0.03552, over 7276.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2574, pruned_loss=0.04039, over 1431112.27 frames.], batch size: 18, lr: 5.17e-04
2022-05-14 17:21:39,011 INFO [train.py:812] (5/8) Epoch 15, batch 2000, loss[loss=0.1855, simple_loss=0.2784, pruned_loss=0.04631, over 6351.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2572, pruned_loss=0.04025, over 1427258.46 frames.], batch size: 37, lr: 5.17e-04
2022-05-14 17:22:38,286 INFO [train.py:812] (5/8) Epoch 15, batch 2050, loss[loss=0.1517, simple_loss=0.2476, pruned_loss=0.02793, over 7289.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2584, pruned_loss=0.04069, over 1428086.13 frames.], batch size: 25, lr: 5.16e-04
2022-05-14 17:23:37,389 INFO [train.py:812] (5/8) Epoch 15, batch 2100, loss[loss=0.1414, simple_loss=0.2258, pruned_loss=0.0285, over 7423.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2578, pruned_loss=0.04088, over 1421523.49 frames.], batch size: 18, lr: 5.16e-04
2022-05-14 17:24:36,164 INFO [train.py:812] (5/8) Epoch 15, batch 2150, loss[loss=0.192, simple_loss=0.2903, pruned_loss=0.04682, over 7207.00 frames.], tot_loss[loss=0.17, simple_loss=0.2582, pruned_loss=0.04088, over 1419846.46 frames.], batch size: 22, lr: 5.16e-04
2022-05-14 17:25:35,458 INFO [train.py:812] (5/8) Epoch 15, batch 2200, loss[loss=0.1538, simple_loss=0.2459, pruned_loss=0.03084, over 7435.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2588, pruned_loss=0.0407, over 1419492.78 frames.], batch size: 20, lr: 5.16e-04
2022-05-14 17:26:33,939 INFO [train.py:812] (5/8) Epoch 15, batch 2250, loss[loss=0.1879, simple_loss=0.2824, pruned_loss=0.04667, over 7056.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2593, pruned_loss=0.04088, over 1421550.84 frames.], batch size: 28, lr: 5.16e-04
2022-05-14 17:27:32,326 INFO [train.py:812] (5/8) Epoch 15, batch 2300, loss[loss=0.1536, simple_loss=0.2364, pruned_loss=0.03547, over 6825.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2591, pruned_loss=0.04103, over 1420886.93 frames.], batch size: 15, lr: 5.15e-04
2022-05-14 17:28:30,786 INFO [train.py:812] (5/8) Epoch 15, batch 2350, loss[loss=0.1558, simple_loss=0.2434, pruned_loss=0.03413, over 7407.00 frames.], tot_loss[loss=0.17, simple_loss=0.2586, pruned_loss=0.0407, over 1423895.52 frames.], batch size: 18, lr: 5.15e-04
2022-05-14 17:29:30,943 INFO [train.py:812] (5/8) Epoch 15, batch 2400, loss[loss=0.1691, simple_loss=0.2424, pruned_loss=0.04789, over 7426.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2598, pruned_loss=0.04138, over 1421444.82 frames.], batch size: 18, lr: 5.15e-04
2022-05-14 17:30:30,168 INFO [train.py:812] (5/8) Epoch 15, batch 2450, loss[loss=0.172, simple_loss=0.2696, pruned_loss=0.03721, over 7417.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2604, pruned_loss=0.04153, over 1422935.03 frames.], batch size: 21, lr: 5.15e-04
2022-05-14 17:31:29,562 INFO [train.py:812] (5/8) Epoch 15, batch 2500, loss[loss=0.1563, simple_loss=0.2516, pruned_loss=0.03046, over 7322.00 frames.], tot_loss[loss=0.171, simple_loss=0.2598, pruned_loss=0.04112, over 1424112.67 frames.], batch size: 21, lr: 5.15e-04
2022-05-14 17:32:27,885 INFO [train.py:812] (5/8) Epoch 15, batch 2550, loss[loss=0.149, simple_loss=0.2351, pruned_loss=0.03142, over 7161.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2602, pruned_loss=0.04119, over 1426819.27 frames.], batch size: 18, lr: 5.14e-04
2022-05-14 17:33:27,552 INFO [train.py:812] (5/8) Epoch 15, batch 2600, loss[loss=0.1657, simple_loss=0.2637, pruned_loss=0.03388, over 7228.00 frames.], tot_loss[loss=0.172, simple_loss=0.2607, pruned_loss=0.04168, over 1421913.92 frames.], batch size: 23, lr: 5.14e-04
2022-05-14 17:34:25,780 INFO [train.py:812] (5/8) Epoch 15, batch 2650, loss[loss=0.1759, simple_loss=0.2731, pruned_loss=0.03931, over 7312.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2603, pruned_loss=0.0415, over 1423135.50 frames.], batch size: 25, lr: 5.14e-04
2022-05-14 17:35:25,131 INFO [train.py:812] (5/8) Epoch 15, batch 2700, loss[loss=0.1845, simple_loss=0.2792, pruned_loss=0.04486, over 7317.00 frames.], tot_loss[loss=0.171, simple_loss=0.2601, pruned_loss=0.04098, over 1425133.26 frames.], batch size: 21, lr: 5.14e-04
2022-05-14 17:36:24,195 INFO [train.py:812] (5/8) Epoch 15, batch 2750, loss[loss=0.179, simple_loss=0.2715, pruned_loss=0.04329, over 7291.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2601, pruned_loss=0.04107, over 1425277.61 frames.], batch size: 24, lr: 5.14e-04
2022-05-14 17:37:23,465 INFO [train.py:812] (5/8) Epoch 15, batch 2800, loss[loss=0.1875, simple_loss=0.2781, pruned_loss=0.04846, over 7142.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2593, pruned_loss=0.04066, over 1428454.46 frames.], batch size: 20, lr: 5.14e-04
2022-05-14 17:38:20,798 INFO [train.py:812] (5/8) Epoch 15, batch 2850, loss[loss=0.1754, simple_loss=0.2536, pruned_loss=0.04862, over 7204.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2605, pruned_loss=0.04132, over 1429414.06 frames.], batch size: 16, lr: 5.13e-04
2022-05-14 17:39:21,077 INFO [train.py:812] (5/8) Epoch 15, batch 2900, loss[loss=0.167, simple_loss=0.2547, pruned_loss=0.03962, over 7391.00 frames.], tot_loss[loss=0.172, simple_loss=0.2608, pruned_loss=0.04159, over 1424333.32 frames.], batch size: 23, lr: 5.13e-04
2022-05-14 17:40:19,997 INFO [train.py:812] (5/8) Epoch 15, batch 2950, loss[loss=0.1616, simple_loss=0.2554, pruned_loss=0.03394, over 7412.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2605, pruned_loss=0.04143, over 1425197.18 frames.], batch size: 20, lr: 5.13e-04
2022-05-14 17:41:19,222 INFO [train.py:812] (5/8) Epoch 15, batch 3000, loss[loss=0.1913, simple_loss=0.2838, pruned_loss=0.04942, over 7162.00 frames.], tot_loss[loss=0.171, simple_loss=0.2596, pruned_loss=0.04115, over 1422561.39 frames.], batch size: 19, lr: 5.13e-04
2022-05-14 17:41:19,223 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 17:41:26,768 INFO [train.py:841] (5/8) Epoch 15, validation: loss=0.1543, simple_loss=0.2544, pruned_loss=0.02713, over 698248.00 frames.
2022-05-14 17:42:25,631 INFO [train.py:812] (5/8) Epoch 15, batch 3050, loss[loss=0.1476, simple_loss=0.2305, pruned_loss=0.03234, over 6782.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2597, pruned_loss=0.04126, over 1425014.04 frames.], batch size: 15, lr: 5.13e-04
2022-05-14 17:43:23,111 INFO [train.py:812] (5/8) Epoch 15, batch 3100, loss[loss=0.1804, simple_loss=0.2545, pruned_loss=0.05318, over 7329.00 frames.], tot_loss[loss=0.1717, simple_loss=0.26, pruned_loss=0.04166, over 1421871.98 frames.], batch size: 20, lr: 5.12e-04
2022-05-14 17:44:21,941 INFO [train.py:812] (5/8) Epoch 15, batch 3150, loss[loss=0.1615, simple_loss=0.2426, pruned_loss=0.0402, over 7275.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2589, pruned_loss=0.04123, over 1426409.26 frames.], batch size: 17, lr: 5.12e-04
2022-05-14 17:45:20,570 INFO [train.py:812] (5/8) Epoch 15, batch 3200, loss[loss=0.1703, simple_loss=0.2596, pruned_loss=0.04047, over 7068.00 frames.], tot_loss[loss=0.1699, simple_loss=0.258, pruned_loss=0.04087, over 1427869.34 frames.], batch size: 28, lr: 5.12e-04
2022-05-14 17:46:20,194 INFO [train.py:812] (5/8) Epoch 15, batch 3250, loss[loss=0.1575, simple_loss=0.2414, pruned_loss=0.03677, over 7063.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2577, pruned_loss=0.04101, over 1428308.38 frames.], batch size: 18, lr: 5.12e-04
2022-05-14 17:47:18,742 INFO [train.py:812] (5/8) Epoch 15, batch 3300, loss[loss=0.1476, simple_loss=0.2211, pruned_loss=0.03708, over 7267.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2567, pruned_loss=0.04055, over 1426958.96 frames.], batch size: 17, lr: 5.12e-04
2022-05-14 17:48:17,417 INFO [train.py:812] (5/8) Epoch 15, batch 3350, loss[loss=0.1731, simple_loss=0.2658, pruned_loss=0.04014, over 7194.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2587, pruned_loss=0.04093, over 1426333.70 frames.], batch size: 23, lr: 5.11e-04
2022-05-14 17:49:14,694 INFO [train.py:812] (5/8) Epoch 15, batch 3400, loss[loss=0.1284, simple_loss=0.2171, pruned_loss=0.01991, over 7213.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2593, pruned_loss=0.04097, over 1422874.92 frames.], batch size: 21, lr: 5.11e-04
2022-05-14 17:50:13,356 INFO [train.py:812] (5/8) Epoch 15, batch 3450, loss[loss=0.1767, simple_loss=0.2653, pruned_loss=0.04406, over 7046.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2598, pruned_loss=0.04141, over 1420300.91 frames.], batch size: 28, lr: 5.11e-04
2022-05-14 17:51:13,182 INFO [train.py:812] (5/8) Epoch 15, batch 3500, loss[loss=0.1785, simple_loss=0.2692, pruned_loss=0.04384, over 7180.00 frames.], tot_loss[loss=0.17, simple_loss=0.2587, pruned_loss=0.04068, over 1425838.12 frames.], batch size: 26, lr: 5.11e-04
2022-05-14 17:52:12,822 INFO [train.py:812] (5/8) Epoch 15, batch 3550, loss[loss=0.1471, simple_loss=0.2318, pruned_loss=0.03124, over 7228.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2598, pruned_loss=0.04119, over 1427325.36 frames.], batch size: 20, lr: 5.11e-04
2022-05-14 17:53:11,371 INFO [train.py:812] (5/8) Epoch 15, batch 3600, loss[loss=0.171, simple_loss=0.264, pruned_loss=0.03898, over 7321.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2593, pruned_loss=0.04096, over 1423855.21 frames.], batch size: 21, lr: 5.11e-04
2022-05-14 17:54:10,557 INFO [train.py:812] (5/8) Epoch 15, batch 3650, loss[loss=0.1589, simple_loss=0.2549, pruned_loss=0.03141, over 7249.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2592, pruned_loss=0.041, over 1424899.32 frames.], batch size: 19, lr: 5.10e-04
2022-05-14 17:55:10,254 INFO [train.py:812] (5/8) Epoch 15, batch 3700, loss[loss=0.1496, simple_loss=0.2396, pruned_loss=0.0298, over 7429.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2598, pruned_loss=0.0414, over 1421981.51 frames.], batch size: 20, lr: 5.10e-04
2022-05-14 17:56:09,470 INFO [train.py:812] (5/8) Epoch 15, batch 3750, loss[loss=0.2523, simple_loss=0.3196, pruned_loss=0.09247, over 4764.00 frames.], tot_loss[loss=0.171, simple_loss=0.2593, pruned_loss=0.04135, over 1423578.14 frames.], batch size: 53, lr: 5.10e-04
2022-05-14 17:57:14,309 INFO [train.py:812] (5/8) Epoch 15, batch 3800, loss[loss=0.1552, simple_loss=0.2369, pruned_loss=0.03672, over 7070.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2591, pruned_loss=0.041, over 1425403.19 frames.], batch size: 18, lr: 5.10e-04
2022-05-14 17:58:12,036 INFO [train.py:812] (5/8) Epoch 15, batch 3850, loss[loss=0.1871, simple_loss=0.2781, pruned_loss=0.04802, over 7229.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2599, pruned_loss=0.04092, over 1428489.94 frames.], batch size: 20, lr: 5.10e-04
2022-05-14 17:59:11,792 INFO [train.py:812] (5/8) Epoch 15, batch 3900, loss[loss=0.1443, simple_loss=0.2302, pruned_loss=0.02922, over 7250.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2585, pruned_loss=0.04052, over 1425538.66 frames.], batch size: 19, lr: 5.09e-04
2022-05-14 18:00:10,973 INFO [train.py:812] (5/8) Epoch 15, batch 3950, loss[loss=0.1875, simple_loss=0.2781, pruned_loss=0.04845, over 7351.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2585, pruned_loss=0.04085, over 1421096.82 frames.], batch size: 19, lr: 5.09e-04
2022-05-14 18:01:10,514 INFO [train.py:812] (5/8) Epoch 15, batch 4000, loss[loss=0.1827, simple_loss=0.2768, pruned_loss=0.04428, over 7214.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2584, pruned_loss=0.04071, over 1422013.74 frames.], batch size: 21, lr: 5.09e-04
2022-05-14 18:02:09,515 INFO [train.py:812] (5/8) Epoch 15, batch 4050, loss[loss=0.1816, simple_loss=0.2693, pruned_loss=0.04697, over 7218.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2584, pruned_loss=0.04038, over 1427206.34 frames.], batch size: 21, lr: 5.09e-04
2022-05-14 18:03:08,715 INFO [train.py:812] (5/8) Epoch 15, batch 4100, loss[loss=0.1858, simple_loss=0.2744, pruned_loss=0.04862, over 7204.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2585, pruned_loss=0.04062, over 1418367.05 frames.], batch size: 23, lr: 5.09e-04
2022-05-14 18:04:07,535 INFO [train.py:812] (5/8) Epoch 15, batch 4150, loss[loss=0.2243, simple_loss=0.2955, pruned_loss=0.07655, over 5122.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2593, pruned_loss=0.0416, over 1412526.18 frames.], batch size: 52, lr: 5.08e-04
2022-05-14 18:05:07,008 INFO [train.py:812] (5/8) Epoch 15, batch 4200, loss[loss=0.1566, simple_loss=0.238, pruned_loss=0.03759, over 7239.00 frames.], tot_loss[loss=0.1703, simple_loss=0.258, pruned_loss=0.04129, over 1409843.47 frames.], batch size: 20, lr: 5.08e-04
2022-05-14 18:06:05,951 INFO [train.py:812] (5/8) Epoch 15, batch 4250, loss[loss=0.1403, simple_loss=0.2309, pruned_loss=0.02485, over 7065.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2575, pruned_loss=0.04051, over 1408400.22 frames.], batch size: 18, lr: 5.08e-04
2022-05-14 18:07:05,135 INFO [train.py:812] (5/8) Epoch 15, batch 4300, loss[loss=0.1593, simple_loss=0.2397, pruned_loss=0.03944, over 6779.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2584, pruned_loss=0.04088, over 1404414.68 frames.], batch size: 15, lr: 5.08e-04
2022-05-14 18:08:04,063 INFO [train.py:812] (5/8) Epoch 15, batch 4350, loss[loss=0.1764, simple_loss=0.2602, pruned_loss=0.04634, over 7324.00 frames.], tot_loss[loss=0.17, simple_loss=0.2587, pruned_loss=0.04066, over 1408724.50 frames.], batch size: 21, lr: 5.08e-04
2022-05-14 18:09:03,499 INFO [train.py:812] (5/8) Epoch 15, batch 4400, loss[loss=0.169, simple_loss=0.2523, pruned_loss=0.04282, over 7167.00 frames.], tot_loss[loss=0.1692, simple_loss=0.258, pruned_loss=0.04024, over 1410438.50 frames.], batch size: 19, lr: 5.08e-04
2022-05-14 18:10:02,431 INFO [train.py:812] (5/8) Epoch 15, batch 4450, loss[loss=0.1419, simple_loss=0.2316, pruned_loss=0.02617, over 7152.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2568, pruned_loss=0.04, over 1403186.57 frames.], batch size: 18, lr: 5.07e-04
2022-05-14 18:11:01,297 INFO [train.py:812] (5/8) Epoch 15, batch 4500, loss[loss=0.1503, simple_loss=0.2461, pruned_loss=0.02725, over 7061.00 frames.], tot_loss[loss=0.1687, simple_loss=0.257, pruned_loss=0.04023, over 1394969.88 frames.], batch size: 18, lr: 5.07e-04
2022-05-14 18:11:59,582 INFO [train.py:812] (5/8) Epoch 15, batch 4550, loss[loss=0.2128, simple_loss=0.2907, pruned_loss=0.06747, over 5429.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2589, pruned_loss=0.04136, over 1366900.80 frames.], batch size: 52, lr: 5.07e-04
2022-05-14 18:13:08,753 INFO [train.py:812] (5/8) Epoch 16, batch 0, loss[loss=0.191, simple_loss=0.2839, pruned_loss=0.04909, over 7302.00 frames.], tot_loss[loss=0.191, simple_loss=0.2839, pruned_loss=0.04909, over 7302.00 frames.], batch size: 24, lr: 4.92e-04
2022-05-14 18:14:07,985 INFO [train.py:812] (5/8) Epoch 16, batch 50, loss[loss=0.1596, simple_loss=0.2443, pruned_loss=0.03746, over 7406.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2595, pruned_loss=0.04142, over 322327.26 frames.], batch size: 18, lr: 4.92e-04
2022-05-14 18:15:07,119 INFO [train.py:812] (5/8) Epoch 16, batch 100, loss[loss=0.1652, simple_loss=0.2596, pruned_loss=0.03543, over 7337.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2573, pruned_loss=0.03958, over 565802.32 frames.], batch size: 20, lr: 4.92e-04
2022-05-14 18:16:06,271 INFO [train.py:812] (5/8) Epoch 16, batch 150, loss[loss=0.1769, simple_loss=0.2658, pruned_loss=0.04402, over 7141.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2564, pruned_loss=0.03917, over 755767.41 frames.], batch size: 20, lr: 4.92e-04
2022-05-14 18:17:15,045 INFO [train.py:812] (5/8) Epoch 16, batch 200, loss[loss=0.1937, simple_loss=0.2747, pruned_loss=0.05633, over 7117.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2558, pruned_loss=0.03954, over 898824.96 frames.], batch size: 21, lr: 4.91e-04
2022-05-14 18:18:13,069 INFO [train.py:812] (5/8) Epoch 16, batch 250, loss[loss=0.1794, simple_loss=0.2717, pruned_loss=0.04351, over 7158.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2566, pruned_loss=0.03984, over 1014970.40 frames.], batch size: 19, lr: 4.91e-04
2022-05-14 18:19:12,324 INFO [train.py:812] (5/8) Epoch 16, batch 300, loss[loss=0.1528, simple_loss=0.2453, pruned_loss=0.03015, over 7154.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2558, pruned_loss=0.03948, over 1109522.50 frames.], batch size: 19, lr: 4.91e-04
2022-05-14 18:20:11,383 INFO [train.py:812] (5/8) Epoch 16, batch 350, loss[loss=0.1421, simple_loss=0.2294, pruned_loss=0.02742, over 7289.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2554, pruned_loss=0.03881, over 1180542.60 frames.], batch size: 18, lr: 4.91e-04
2022-05-14 18:21:11,293 INFO [train.py:812] (5/8) Epoch 16, batch 400, loss[loss=0.1722, simple_loss=0.2638, pruned_loss=0.04032, over 7258.00 frames.], tot_loss[loss=0.1679, simple_loss=0.257, pruned_loss=0.03941, over 1234213.30 frames.], batch size: 19, lr: 4.91e-04
2022-05-14 18:22:10,132 INFO [train.py:812] (5/8) Epoch 16, batch 450, loss[loss=0.1355, simple_loss=0.227, pruned_loss=0.02199, over 7438.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2578, pruned_loss=0.03963, over 1281022.66 frames.], batch size: 20, lr: 4.91e-04
2022-05-14 18:23:09,257 INFO [train.py:812] (5/8) Epoch 16, batch 500, loss[loss=0.2051, simple_loss=0.2936, pruned_loss=0.05825, over 7207.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2593, pruned_loss=0.04026, over 1317712.08 frames.], batch size: 23, lr: 4.90e-04
2022-05-14 18:24:07,723 INFO [train.py:812] (5/8) Epoch 16, batch 550, loss[loss=0.1484, simple_loss=0.2304, pruned_loss=0.03322, over 7276.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2578, pruned_loss=0.03968, over 1345020.48 frames.], batch size: 18, lr: 4.90e-04
2022-05-14 18:25:07,648 INFO [train.py:812] (5/8) Epoch 16, batch 600, loss[loss=0.1449, simple_loss=0.2324, pruned_loss=0.02868, over 7162.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2563, pruned_loss=0.03914, over 1361040.25 frames.], batch size: 19, lr: 4.90e-04
2022-05-14 18:26:06,741 INFO [train.py:812] (5/8) Epoch 16, batch 650, loss[loss=0.1634, simple_loss=0.2607, pruned_loss=0.03307, over 6471.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2568, pruned_loss=0.03963, over 1374048.05 frames.], batch size: 38, lr: 4.90e-04
2022-05-14 18:27:05,465 INFO [train.py:812] (5/8) Epoch 16, batch 700, loss[loss=0.1788, simple_loss=0.2706, pruned_loss=0.04347, over 7026.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2564, pruned_loss=0.03931, over 1385581.25 frames.], batch size: 28, lr: 4.90e-04
2022-05-14 18:28:04,418 INFO [train.py:812] (5/8) Epoch 16, batch 750, loss[loss=0.1498, simple_loss=0.2347, pruned_loss=0.03241, over 7153.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2565, pruned_loss=0.03942, over 1394939.92 frames.], batch size: 19, lr: 4.89e-04
2022-05-14 18:29:03,805 INFO [train.py:812] (5/8) Epoch 16, batch 800, loss[loss=0.1859, simple_loss=0.2643, pruned_loss=0.05378, over 7252.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2566, pruned_loss=0.03958, over 1402008.94 frames.], batch size: 19, lr: 4.89e-04
2022-05-14 18:30:02,508 INFO [train.py:812] (5/8) Epoch 16, batch 850, loss[loss=0.1598, simple_loss=0.2465, pruned_loss=0.03649, over 7144.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2569, pruned_loss=0.03983, over 1404105.68 frames.], batch size: 20, lr: 4.89e-04
2022-05-14 18:31:02,366 INFO [train.py:812] (5/8) Epoch 16, batch 900, loss[loss=0.1837, simple_loss=0.264, pruned_loss=0.05166, over 7368.00 frames.], tot_loss[loss=0.168, simple_loss=0.256, pruned_loss=0.03998, over 1402424.27 frames.], batch size: 19, lr: 4.89e-04
2022-05-14 18:32:01,910 INFO [train.py:812] (5/8) Epoch 16, batch 950, loss[loss=0.1572, simple_loss=0.2592, pruned_loss=0.02759, over 7439.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2556, pruned_loss=0.03979, over 1405885.02 frames.], batch size: 20, lr: 4.89e-04
2022-05-14 18:33:00,787 INFO [train.py:812] (5/8) Epoch 16, batch 1000, loss[loss=0.1888, simple_loss=0.2833, pruned_loss=0.04713, over 7297.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2555, pruned_loss=0.03957, over 1412454.77 frames.], batch size: 25, lr: 4.89e-04
2022-05-14 18:33:59,606 INFO [train.py:812] (5/8) Epoch 16, batch 1050, loss[loss=0.1689, simple_loss=0.2549, pruned_loss=0.04145, over 7328.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2564, pruned_loss=0.04001, over 1418162.86 frames.], batch size: 20, lr: 4.88e-04
2022-05-14 18:34:59,563 INFO [train.py:812] (5/8) Epoch 16, batch 1100, loss[loss=0.1694, simple_loss=0.256, pruned_loss=0.04138, over 7353.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2568, pruned_loss=0.03995, over 1421244.97 frames.], batch size: 19, lr: 4.88e-04
2022-05-14 18:35:59,309 INFO [train.py:812] (5/8) Epoch 16, batch 1150, loss[loss=0.2111, simple_loss=0.2856, pruned_loss=0.06824, over 4907.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2563, pruned_loss=0.03945, over 1421581.88 frames.], batch size: 53, lr: 4.88e-04
2022-05-14 18:36:59,225 INFO [train.py:812] (5/8) Epoch 16, batch 1200, loss[loss=0.1616, simple_loss=0.2476, pruned_loss=0.03781, over 7094.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2562, pruned_loss=0.03948, over 1419322.43 frames.], batch size: 21, lr: 4.88e-04
2022-05-14 18:37:58,849 INFO [train.py:812] (5/8) Epoch 16, batch 1250, loss[loss=0.1553, simple_loss=0.2393, pruned_loss=0.0357, over 6766.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2559, pruned_loss=0.03957, over 1418520.64 frames.], batch size: 15, lr: 4.88e-04
2022-05-14 18:38:58,780 INFO [train.py:812] (5/8) Epoch 16, batch 1300, loss[loss=0.1719, simple_loss=0.2641, pruned_loss=0.03986, over 7196.00 frames.], tot_loss[loss=0.1685, simple_loss=0.257, pruned_loss=0.03996, over 1424648.26 frames.], batch size: 22, lr: 4.88e-04
2022-05-14 18:39:58,302 INFO [train.py:812] (5/8) Epoch 16, batch 1350, loss[loss=0.1338, simple_loss=0.2254, pruned_loss=0.02109, over 7161.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2571, pruned_loss=0.03996, over 1417316.74 frames.], batch size: 19, lr: 4.87e-04
2022-05-14 18:40:58,008 INFO [train.py:812] (5/8) Epoch 16, batch 1400, loss[loss=0.186, simple_loss=0.278, pruned_loss=0.04698, over 7343.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2579, pruned_loss=0.0403, over 1416319.72 frames.], batch size: 22, lr: 4.87e-04
2022-05-14 18:41:57,513 INFO [train.py:812] (5/8) Epoch 16, batch 1450, loss[loss=0.1937, simple_loss=0.2912, pruned_loss=0.04813, over 7407.00 frames.], tot_loss[loss=0.169, simple_loss=0.258, pruned_loss=0.03999, over 1421862.21 frames.], batch size: 21, lr: 4.87e-04
2022-05-14 18:43:06,575 INFO [train.py:812] (5/8) Epoch 16, batch 1500, loss[loss=0.19, simple_loss=0.2781, pruned_loss=0.05093, over 7228.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2578, pruned_loss=0.03991, over 1421871.67 frames.], batch size: 23, lr: 4.87e-04
2022-05-14 18:44:06,053 INFO [train.py:812] (5/8) Epoch 16, batch 1550, loss[loss=0.1393, simple_loss=0.211, pruned_loss=0.03377, over 7226.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2576, pruned_loss=0.04028, over 1420564.25 frames.], batch size: 16, lr: 4.87e-04
2022-05-14 18:45:05,969 INFO [train.py:812] (5/8) Epoch 16, batch 1600, loss[loss=0.1476, simple_loss=0.2352, pruned_loss=0.02996, over 6805.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2582, pruned_loss=0.04062, over 1422667.18 frames.], batch size: 15, lr: 4.87e-04
2022-05-14 18:46:05,458 INFO [train.py:812] (5/8) Epoch 16, batch 1650, loss[loss=0.1621, simple_loss=0.2607, pruned_loss=0.0318, over 7147.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2581, pruned_loss=0.04077, over 1424554.77 frames.], batch size: 20, lr: 4.86e-04
2022-05-14 18:47:14,895 INFO [train.py:812] (5/8) Epoch 16, batch 1700, loss[loss=0.134, simple_loss=0.2274, pruned_loss=0.02033, over 7418.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2567, pruned_loss=0.04039, over 1424939.97 frames.], batch size: 18, lr: 4.86e-04
2022-05-14 18:48:31,544 INFO [train.py:812] (5/8) Epoch 16, batch 1750, loss[loss=0.1781, simple_loss=0.2723, pruned_loss=0.04195, over 7381.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2572, pruned_loss=0.04056, over 1424734.62 frames.], batch size: 23, lr: 4.86e-04
2022-05-14 18:49:49,411 INFO [train.py:812] (5/8) Epoch 16, batch 1800, loss[loss=0.1551, simple_loss=0.2299, pruned_loss=0.04016, over 7353.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2568, pruned_loss=0.04024, over 1423328.42 frames.], batch size: 19, lr: 4.86e-04
2022-05-14 18:50:57,728 INFO [train.py:812] (5/8) Epoch 16, batch 1850, loss[loss=0.1831, simple_loss=0.2843, pruned_loss=0.04092, over 7148.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2557, pruned_loss=0.0397, over 1425379.25 frames.], batch size: 20, lr: 4.86e-04
2022-05-14 18:51:57,516 INFO [train.py:812] (5/8) Epoch 16, batch 1900, loss[loss=0.1863, simple_loss=0.2883, pruned_loss=0.04218, over 7316.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2561, pruned_loss=0.03953, over 1429304.19 frames.], batch size: 25, lr: 4.86e-04
2022-05-14 18:52:55,168 INFO [train.py:812] (5/8) Epoch 16, batch 1950, loss[loss=0.2135, simple_loss=0.3139, pruned_loss=0.0565, over 7186.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2575, pruned_loss=0.04003, over 1430108.44 frames.], batch size: 23, lr: 4.85e-04
2022-05-14 18:53:54,470 INFO [train.py:812] (5/8) Epoch 16, batch 2000, loss[loss=0.1966, simple_loss=0.271, pruned_loss=0.0611, over 5089.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2579, pruned_loss=0.03982, over 1424120.82 frames.], batch size: 52, lr: 4.85e-04
2022-05-14 18:54:53,416 INFO [train.py:812] (5/8) Epoch 16, batch 2050, loss[loss=0.1623, simple_loss=0.2535, pruned_loss=0.03559, over 6098.00 frames.], tot_loss[loss=0.1689, simple_loss=0.258, pruned_loss=0.03993, over 1423044.35 frames.], batch size: 37, lr: 4.85e-04
2022-05-14 18:55:52,776 INFO [train.py:812] (5/8) Epoch 16, batch 2100, loss[loss=0.1615, simple_loss=0.2624, pruned_loss=0.0303, over 7122.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2586, pruned_loss=0.04034, over 1423861.73 frames.], batch size: 21, lr: 4.85e-04
2022-05-14 18:56:51,728 INFO [train.py:812] (5/8) Epoch 16, batch 2150, loss[loss=0.1338, simple_loss=0.2282, pruned_loss=0.01964, over 7257.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2583, pruned_loss=0.04011, over 1419066.18 frames.], batch size: 19, lr: 4.85e-04
2022-05-14 18:57:51,051 INFO [train.py:812] (5/8) Epoch 16, batch 2200, loss[loss=0.1971, simple_loss=0.2848, pruned_loss=0.05468, over 7206.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2582, pruned_loss=0.04041, over 1415986.39 frames.], batch size: 22, lr: 4.84e-04
2022-05-14 18:58:50,245 INFO [train.py:812] (5/8) Epoch 16, batch 2250, loss[loss=0.1729, simple_loss=0.2648, pruned_loss=0.04052, over 7416.00 frames.], tot_loss[loss=0.169, simple_loss=0.2574, pruned_loss=0.04026, over 1417657.69 frames.], batch size: 21, lr: 4.84e-04
2022-05-14 18:59:49,623 INFO [train.py:812] (5/8) Epoch 16, batch 2300, loss[loss=0.1713, simple_loss=0.265, pruned_loss=0.03884, over 7200.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2579, pruned_loss=0.04009, over 1419902.27 frames.], batch size: 23, lr: 4.84e-04
2022-05-14 19:00:48,757 INFO [train.py:812] (5/8) Epoch 16, batch 2350, loss[loss=0.2225, simple_loss=0.3021, pruned_loss=0.07144, over 7283.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2574, pruned_loss=0.03997, over 1422347.70 frames.], batch size: 25, lr: 4.84e-04
2022-05-14 19:01:48,408 INFO [train.py:812] (5/8) Epoch 16, batch 2400, loss[loss=0.1948, simple_loss=0.2841, pruned_loss=0.05277, over 7275.00 frames.], tot_loss[loss=0.1683, simple_loss=0.257, pruned_loss=0.03974, over 1425059.15 frames.], batch size: 25, lr: 4.84e-04
2022-05-14 19:02:47,313 INFO [train.py:812] (5/8) Epoch 16, batch 2450, loss[loss=0.1819, simple_loss=0.2808, pruned_loss=0.04147, over 6734.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2579, pruned_loss=0.0399, over 1424638.37 frames.], batch size: 31, lr: 4.84e-04
2022-05-14 19:03:46,896 INFO [train.py:812] (5/8) Epoch 16, batch 2500, loss[loss=0.1677, simple_loss=0.2642, pruned_loss=0.0356, over 7218.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2568, pruned_loss=0.03929, over 1426827.53 frames.], batch size: 21, lr: 4.83e-04
2022-05-14 19:04:46,173 INFO [train.py:812] (5/8) Epoch 16, batch 2550, loss[loss=0.1807, simple_loss=0.27, pruned_loss=0.04576, over 7140.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2556, pruned_loss=0.03881, over 1423095.02 frames.], batch size: 20, lr: 4.83e-04
2022-05-14 19:05:45,578 INFO [train.py:812] (5/8) Epoch 16, batch 2600, loss[loss=0.1186, simple_loss=0.2111, pruned_loss=0.01308, over 7362.00 frames.], tot_loss[loss=0.167, simple_loss=0.256, pruned_loss=0.03898, over 1421609.76 frames.], batch size: 19, lr: 4.83e-04
2022-05-14 19:06:45,272 INFO [train.py:812] (5/8) Epoch 16, batch 2650, loss[loss=0.1757, simple_loss=0.2671, pruned_loss=0.04212, over 7386.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2562, pruned_loss=0.03936, over 1422494.68 frames.], batch size: 23, lr: 4.83e-04
2022-05-14 19:07:45,168 INFO [train.py:812] (5/8) Epoch 16, batch 2700, loss[loss=0.1799, simple_loss=0.2766, pruned_loss=0.04165, over 7106.00 frames.], tot_loss[loss=0.168, simple_loss=0.2567, pruned_loss=0.03961, over 1420168.93 frames.], batch size: 26, lr: 4.83e-04
2022-05-14 19:08:44,237 INFO [train.py:812] (5/8) Epoch 16, batch 2750, loss[loss=0.1408, simple_loss=0.2241, pruned_loss=0.02879, over 7271.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2571, pruned_loss=0.03958, over 1424174.90 frames.], batch size: 18, lr: 4.83e-04
2022-05-14 19:09:44,115 INFO [train.py:812] (5/8) Epoch 16, batch 2800, loss[loss=0.1672, simple_loss=0.2571, pruned_loss=0.03862, over 7225.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2576, pruned_loss=0.04011, over 1426056.64 frames.], batch size: 21, lr: 4.82e-04
2022-05-14 19:10:43,440 INFO [train.py:812] (5/8) Epoch 16, batch 2850, loss[loss=0.1803, simple_loss=0.2543, pruned_loss=0.05311, over 7163.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2582, pruned_loss=0.041, over 1424972.35 frames.], batch size: 18, lr: 4.82e-04
2022-05-14 19:11:42,889 INFO [train.py:812] (5/8) Epoch 16, batch 2900, loss[loss=0.1636, simple_loss=0.2533, pruned_loss=0.03692, over 7170.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2583, pruned_loss=0.04089, over 1428041.12 frames.], batch size: 18, lr: 4.82e-04
2022-05-14 19:12:41,624 INFO [train.py:812] (5/8) Epoch 16, batch 2950, loss[loss=0.1656, simple_loss=0.2562, pruned_loss=0.03747, over 7343.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2582, pruned_loss=0.04067, over 1424381.04 frames.], batch size: 22, lr: 4.82e-04
2022-05-14 19:13:40,839 INFO [train.py:812] (5/8) Epoch 16, batch 3000, loss[loss=0.1711, simple_loss=0.2588, pruned_loss=0.04164, over 7416.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2584, pruned_loss=0.04092, over 1428405.88 frames.], batch size: 21, lr: 4.82e-04
2022-05-14 19:13:40,840 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 19:13:48,992 INFO [train.py:841] (5/8) Epoch 16, validation: loss=0.1537, simple_loss=0.2535, pruned_loss=0.02695, over 698248.00 frames.
2022-05-14 19:14:47,138 INFO [train.py:812] (5/8) Epoch 16, batch 3050, loss[loss=0.1486, simple_loss=0.2232, pruned_loss=0.03701, over 7420.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2573, pruned_loss=0.04039, over 1426675.21 frames.], batch size: 18, lr: 4.82e-04
2022-05-14 19:15:46,672 INFO [train.py:812] (5/8) Epoch 16, batch 3100, loss[loss=0.2032, simple_loss=0.2945, pruned_loss=0.05597, over 7186.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2572, pruned_loss=0.04033, over 1426485.66 frames.], batch size: 23, lr: 4.81e-04
2022-05-14 19:16:44,970 INFO [train.py:812] (5/8) Epoch 16, batch 3150, loss[loss=0.1419, simple_loss=0.2297, pruned_loss=0.02702, over 7164.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2576, pruned_loss=0.04043, over 1423665.40 frames.], batch size: 18, lr: 4.81e-04
2022-05-14 19:17:47,854 INFO [train.py:812] (5/8) Epoch 16, batch 3200, loss[loss=0.17, simple_loss=0.2612, pruned_loss=0.03941, over 7292.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2588, pruned_loss=0.04092, over 1423871.79 frames.], batch size: 24, lr: 4.81e-04
2022-05-14 19:18:47,164 INFO [train.py:812] (5/8) Epoch 16, batch 3250, loss[loss=0.17, simple_loss=0.267, pruned_loss=0.03653, over 7315.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2568, pruned_loss=0.04005, over 1426010.88 frames.], batch size: 21, lr: 4.81e-04
2022-05-14 19:19:45,407 INFO [train.py:812] (5/8) Epoch 16, batch 3300, loss[loss=0.1658, simple_loss=0.2638, pruned_loss=0.03397, over 7319.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2573, pruned_loss=0.03941, over 1429780.42 frames.], batch size: 25, lr: 4.81e-04
2022-05-14 19:20:42,557 INFO [train.py:812] (5/8) Epoch 16, batch 3350, loss[loss=0.1527, simple_loss=0.247, pruned_loss=0.02925, over 7244.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2565, pruned_loss=0.03893, over 1432113.10 frames.], batch size: 20, lr: 4.81e-04
2022-05-14 19:21:41,187 INFO [train.py:812] (5/8) Epoch 16, batch 3400, loss[loss=0.1645, simple_loss=0.2518, pruned_loss=0.03863, over 7139.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2572, pruned_loss=0.03955, over 1430244.78 frames.], batch size: 28, lr: 4.80e-04
2022-05-14 19:22:40,323 INFO [train.py:812] (5/8) Epoch 16, batch 3450, loss[loss=0.1437, simple_loss=0.2318, pruned_loss=0.02775, over 7352.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2578, pruned_loss=0.03992, over 1430646.54 frames.], batch size: 19, lr: 4.80e-04
2022-05-14 19:23:40,273 INFO [train.py:812] (5/8) Epoch 16, batch 3500, loss[loss=0.2058, simple_loss=0.291, pruned_loss=0.06034, over 7306.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2581, pruned_loss=0.04005, over 1429293.65 frames.], batch size: 21, lr: 4.80e-04
2022-05-14 19:24:39,224 INFO [train.py:812] (5/8) Epoch 16, batch 3550, loss[loss=0.1986, simple_loss=0.2812, pruned_loss=0.05797, over 7172.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2586, pruned_loss=0.04029, over 1424591.54 frames.], batch size: 26, lr: 4.80e-04
2022-05-14 19:25:38,818 INFO [train.py:812] (5/8) Epoch 16, batch 3600, loss[loss=0.1933, simple_loss=0.2866, pruned_loss=0.05, over 7330.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2585, pruned_loss=0.04045, over 1426900.73 frames.], batch size: 21, lr: 4.80e-04
2022-05-14 19:26:37,996 INFO [train.py:812] (5/8) Epoch 16, batch 3650, loss[loss=0.1471, simple_loss=0.2206, pruned_loss=0.03681, over 7283.00 frames.], tot_loss[loss=0.1695, simple_loss=0.258, pruned_loss=0.04048, over 1426875.01 frames.], batch size: 18, lr: 4.80e-04
2022-05-14 19:27:36,126 INFO [train.py:812] (5/8) Epoch 16, batch 3700, loss[loss=0.1417, simple_loss=0.2258, pruned_loss=0.02881, over 6876.00 frames.], tot_loss[loss=0.1684, simple_loss=0.257, pruned_loss=0.03987, over 1424196.32 frames.], batch size: 15, lr: 4.79e-04
2022-05-14 19:28:35,309 INFO [train.py:812] (5/8) Epoch 16, batch 3750, loss[loss=0.1892, simple_loss=0.2929, pruned_loss=0.04272, over 7258.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2576, pruned_loss=0.04027, over 1421758.68 frames.], batch size: 25, lr: 4.79e-04
2022-05-14 19:29:33,341 INFO [train.py:812] (5/8) Epoch 16, batch 3800, loss[loss=0.1798, simple_loss=0.2548, pruned_loss=0.05239, over 7137.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2578, pruned_loss=0.0404, over 1425894.81 frames.], batch size: 17, lr: 4.79e-04
2022-05-14 19:30:31,488 INFO [train.py:812] (5/8) Epoch 16, batch 3850, loss[loss=0.1553, simple_loss=0.2493, pruned_loss=0.03063, over 7279.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2578, pruned_loss=0.04036, over 1422115.80 frames.], batch size: 18, lr: 4.79e-04
2022-05-14 19:31:29,702 INFO [train.py:812] (5/8) Epoch 16, batch 3900, loss[loss=0.1753, simple_loss=0.2627, pruned_loss=0.04394, over 7230.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2573, pruned_loss=0.04, over 1423881.63 frames.], batch size: 21, lr: 4.79e-04
2022-05-14 19:32:28,903 INFO [train.py:812] (5/8) Epoch 16, batch 3950, loss[loss=0.1443, simple_loss=0.2441, pruned_loss=0.02223, over 7232.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2575, pruned_loss=0.0397, over 1422303.94 frames.], batch size: 20, lr: 4.79e-04
2022-05-14 19:33:27,629 INFO [train.py:812] (5/8) Epoch 16, batch 4000, loss[loss=0.1705, simple_loss=0.2698, pruned_loss=0.03556, over 7317.00 frames.], tot_loss[loss=0.168, simple_loss=0.2572, pruned_loss=0.03942, over 1419438.94 frames.], batch size: 21, lr: 4.79e-04
2022-05-14 19:34:27,159 INFO [train.py:812] (5/8) Epoch 16, batch 4050, loss[loss=0.1341, simple_loss=0.2284, pruned_loss=0.01992, over 7165.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2577, pruned_loss=0.03952, over 1418094.74 frames.], batch size: 18, lr: 4.78e-04
2022-05-14 19:35:27,342 INFO [train.py:812] (5/8) Epoch 16, batch 4100, loss[loss=0.1508, simple_loss=0.2448, pruned_loss=0.02843, over 7164.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2574, pruned_loss=0.03972, over 1423864.24 frames.], batch size: 18, lr: 4.78e-04
2022-05-14 19:36:26,282 INFO [train.py:812] (5/8) Epoch 16, batch 4150, loss[loss=0.1628, simple_loss=0.2597, pruned_loss=0.03294, over 7042.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2572, pruned_loss=0.03959, over 1418086.60 frames.], batch size: 28, lr: 4.78e-04
2022-05-14 19:37:25,121 INFO [train.py:812] (5/8) Epoch 16, batch 4200, loss[loss=0.17, simple_loss=0.2458, pruned_loss=0.04709, over 7001.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2564, pruned_loss=0.03961, over 1418310.94 frames.], batch size: 16, lr: 4.78e-04
2022-05-14 19:38:24,440 INFO [train.py:812] (5/8) Epoch 16, batch 4250, loss[loss=0.1582, simple_loss=0.2368, pruned_loss=0.03982, over 7170.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2559, pruned_loss=0.0397, over 1417844.11 frames.], batch size: 18, lr: 4.78e-04
2022-05-14 19:39:23,835 INFO [train.py:812] (5/8) Epoch 16, batch 4300, loss[loss=0.1459, simple_loss=0.2333, pruned_loss=0.02929, over 6768.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2557, pruned_loss=0.03989, over 1413421.26 frames.], batch size: 31, lr: 4.78e-04
2022-05-14 19:40:22,730 INFO [train.py:812] (5/8) Epoch 16, batch 4350, loss[loss=0.1457, simple_loss=0.2301, pruned_loss=0.03065, over 7171.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2554, pruned_loss=0.0392, over 1416466.72 frames.], batch size: 18, lr: 4.77e-04
2022-05-14 19:41:21,972 INFO [train.py:812] (5/8) Epoch 16, batch 4400, loss[loss=0.1647, simple_loss=0.262, pruned_loss=0.03369, over 7116.00 frames.], tot_loss[loss=0.1664, simple_loss=0.255, pruned_loss=0.03893, over 1416068.76 frames.], batch size: 21, lr: 4.77e-04
2022-05-14 19:42:18,617 INFO [train.py:812] (5/8) Epoch 16, batch 4450, loss[loss=0.1922, simple_loss=0.2939, pruned_loss=0.0452, over 7203.00 frames.], tot_loss[loss=0.167, simple_loss=0.2553, pruned_loss=0.03932, over 1410668.14 frames.], batch size: 22, lr: 4.77e-04
2022-05-14 19:43:16,028 INFO [train.py:812] (5/8) Epoch 16, batch 4500, loss[loss=0.1352, simple_loss=0.224, pruned_loss=0.0232, over 7131.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2546, pruned_loss=0.03881, over 1399857.92 frames.], batch size: 17, lr: 4.77e-04
2022-05-14 19:44:12,838 INFO [train.py:812] (5/8) Epoch 16, batch 4550, loss[loss=0.1858, simple_loss=0.2626, pruned_loss=0.0545, over 5218.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2573, pruned_loss=0.0407, over 1349676.01 frames.], batch size: 52, lr: 4.77e-04
2022-05-14 19:45:27,029 INFO [train.py:812] (5/8) Epoch 17, batch 0, loss[loss=0.1646, simple_loss=0.2516, pruned_loss=0.03882, over 7113.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2516, pruned_loss=0.03882, over 7113.00 frames.], batch size: 21, lr: 4.63e-04
2022-05-14 19:46:26,097 INFO [train.py:812] (5/8) Epoch 17, batch 50, loss[loss=0.1927, simple_loss=0.2938, pruned_loss=0.04577, over 7313.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2617, pruned_loss=0.04225, over 316874.88 frames.], batch size: 21, lr: 4.63e-04
2022-05-14 19:47:25,010 INFO [train.py:812] (5/8) Epoch 17, batch 100, loss[loss=0.1383, simple_loss=0.2274, pruned_loss=0.02462, over 7144.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2583, pruned_loss=0.04016, over 558285.75 frames.], batch size: 20, lr: 4.63e-04
2022-05-14 19:48:23,529 INFO [train.py:812] (5/8) Epoch 17, batch 150, loss[loss=0.1345, simple_loss=0.2174, pruned_loss=0.02577, over 6998.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2553, pruned_loss=0.03918, over 746869.25 frames.], batch size: 16, lr: 4.63e-04
2022-05-14 19:49:23,005 INFO [train.py:812] (5/8) Epoch 17, batch 200, loss[loss=0.166, simple_loss=0.2363, pruned_loss=0.04788, over 7140.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2574, pruned_loss=0.03979, over 895508.77 frames.], batch size: 17, lr: 4.63e-04
2022-05-14 19:50:21,371 INFO [train.py:812] (5/8) Epoch 17, batch 250, loss[loss=0.1566, simple_loss=0.2477, pruned_loss=0.03276, over 7251.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2568, pruned_loss=0.03922, over 1015197.57 frames.], batch size: 19, lr: 4.63e-04
2022-05-14 19:51:20,295 INFO [train.py:812] (5/8) Epoch 17, batch 300, loss[loss=0.1586, simple_loss=0.2385, pruned_loss=0.0394, over 7064.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2571, pruned_loss=0.0395, over 1101379.05 frames.], batch size: 18, lr: 4.62e-04
2022-05-14 19:52:19,497 INFO [train.py:812] (5/8) Epoch 17, batch 350, loss[loss=0.1746, simple_loss=0.2464, pruned_loss=0.05139, over 6811.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2563, pruned_loss=0.03895, over 1171921.88 frames.], batch size: 15, lr: 4.62e-04
2022-05-14 19:53:18,621 INFO [train.py:812] (5/8) Epoch 17, batch 400, loss[loss=0.2166, simple_loss=0.2943, pruned_loss=0.06946, over 4991.00 frames.], tot_loss[loss=0.1675, simple_loss=0.257, pruned_loss=0.03899, over 1227567.47 frames.], batch size: 52, lr: 4.62e-04
2022-05-14 19:54:16,257 INFO [train.py:812] (5/8) Epoch 17, batch 450, loss[loss=0.1553, simple_loss=0.2401, pruned_loss=0.03525, over 7355.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2569, pruned_loss=0.03878, over 1267744.17 frames.], batch size: 19, lr: 4.62e-04
2022-05-14 19:55:14,901 INFO [train.py:812] (5/8) Epoch 17, batch 500, loss[loss=0.1533, simple_loss=0.2386, pruned_loss=0.03402, over 7159.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2565, pruned_loss=0.03851, over 1301651.73 frames.], batch size: 18, lr: 4.62e-04
2022-05-14 19:56:13,696 INFO [train.py:812] (5/8) Epoch 17, batch 550, loss[loss=0.148, simple_loss=0.2282, pruned_loss=0.03392, over 7132.00 frames.], tot_loss[loss=0.1664, simple_loss=0.256, pruned_loss=0.03843, over 1327296.38 frames.], batch size: 17, lr: 4.62e-04
2022-05-14 19:57:12,589 INFO [train.py:812] (5/8) Epoch 17, batch 600, loss[loss=0.1615, simple_loss=0.2638, pruned_loss=0.02962, over 7002.00 frames.], tot_loss[loss=0.167, simple_loss=0.2565, pruned_loss=0.03878, over 1342121.39 frames.], batch size: 28, lr: 4.62e-04
2022-05-14 19:58:11,558 INFO [train.py:812] (5/8) Epoch 17, batch 650, loss[loss=0.1645, simple_loss=0.2506, pruned_loss=0.03922, over 7321.00 frames.], tot_loss[loss=0.168, simple_loss=0.2575, pruned_loss=0.03928, over 1361234.78 frames.], batch size: 20, lr: 4.61e-04
2022-05-14 19:59:10,281 INFO [train.py:812] (5/8) Epoch 17, batch 700, loss[loss=0.1583, simple_loss=0.2344, pruned_loss=0.04106, over 7262.00 frames.], tot_loss[loss=0.1686, simple_loss=0.258, pruned_loss=0.03961, over 1368042.29 frames.], batch size: 19, lr: 4.61e-04
2022-05-14 20:00:09,350 INFO [train.py:812] (5/8) Epoch 17, batch 750, loss[loss=0.1739, simple_loss=0.2667, pruned_loss=0.04059, over 7151.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2579, pruned_loss=0.03945, over 1377003.84 frames.], batch size: 20, lr: 4.61e-04
2022-05-14 20:01:08,201 INFO [train.py:812] (5/8) Epoch 17, batch 800, loss[loss=0.1387, simple_loss=0.226, pruned_loss=0.02574, over 7153.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2574, pruned_loss=0.03912, over 1387628.20 frames.], batch size: 19, lr: 4.61e-04
2022-05-14 20:02:07,229 INFO [train.py:812] (5/8) Epoch 17, batch 850, loss[loss=0.1669, simple_loss=0.2546, pruned_loss=0.03955, over 6419.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2567, pruned_loss=0.03936, over 1396461.66 frames.], batch size: 38, lr: 4.61e-04
2022-05-14 20:03:05,141 INFO [train.py:812] (5/8) Epoch 17, batch 900, loss[loss=0.1534, simple_loss=0.2469, pruned_loss=0.02993, over 7334.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2562, pruned_loss=0.03901, over 1408659.23 frames.], batch size: 20, lr: 4.61e-04
2022-05-14 20:04:03,144 INFO [train.py:812] (5/8) Epoch 17, batch 950, loss[loss=0.1422, simple_loss=0.2243, pruned_loss=0.02998, over 7147.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2562, pruned_loss=0.03878, over 1413655.76 frames.], batch size: 17, lr: 4.60e-04
2022-05-14 20:05:01,745 INFO [train.py:812] (5/8) Epoch 17, batch 1000, loss[loss=0.1944, simple_loss=0.2794, pruned_loss=0.05474, over 7119.00 frames.], tot_loss[loss=0.167, simple_loss=0.2563, pruned_loss=0.03881, over 1417612.79 frames.], batch size: 21, lr: 4.60e-04
2022-05-14 20:06:00,354 INFO [train.py:812] (5/8) Epoch 17, batch 1050, loss[loss=0.1894, simple_loss=0.2836, pruned_loss=0.04756, over 7322.00 frames.], tot_loss[loss=0.166, simple_loss=0.2554, pruned_loss=0.03826, over 1420960.85 frames.], batch size: 22, lr: 4.60e-04
2022-05-14 20:06:59,572 INFO [train.py:812] (5/8) Epoch 17, batch 1100, loss[loss=0.1757, simple_loss=0.2643, pruned_loss=0.04359, over 7310.00 frames.], tot_loss[loss=0.1658, simple_loss=0.255, pruned_loss=0.03827, over 1422042.73 frames.], batch size: 24, lr: 4.60e-04
2022-05-14 20:07:58,269 INFO [train.py:812] (5/8) Epoch 17, batch 1150, loss[loss=0.1845, simple_loss=0.2674, pruned_loss=0.05081, over 7299.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2557, pruned_loss=0.03832, over 1422647.19 frames.], batch size: 24, lr: 4.60e-04
2022-05-14 20:08:57,695 INFO [train.py:812] (5/8) Epoch 17, batch 1200, loss[loss=0.2247, simple_loss=0.3198, pruned_loss=0.06479, over 7319.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2556, pruned_loss=0.0387, over 1419650.45 frames.], batch size: 25, lr: 4.60e-04
2022-05-14 20:09:55,619 INFO [train.py:812] (5/8) Epoch 17, batch 1250, loss[loss=0.1558, simple_loss=0.24, pruned_loss=0.03577, over 7270.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2559, pruned_loss=0.03876, over 1415488.26 frames.], batch size: 18, lr: 4.60e-04
2022-05-14 20:10:53,531 INFO [train.py:812] (5/8) Epoch 17, batch 1300, loss[loss=0.1848, simple_loss=0.2721, pruned_loss=0.04871, over 7328.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2556, pruned_loss=0.03888, over 1412733.13 frames.], batch size: 22, lr: 4.59e-04
2022-05-14 20:11:51,656 INFO [train.py:812] (5/8) Epoch 17, batch 1350, loss[loss=0.1761, simple_loss=0.2539, pruned_loss=0.0491, over 6985.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2556, pruned_loss=0.03889, over 1418288.95 frames.], batch size: 16, lr: 4.59e-04
2022-05-14 20:12:51,099 INFO [train.py:812] (5/8) Epoch 17, batch 1400, loss[loss=0.1495, simple_loss=0.2394, pruned_loss=0.02975, over 7148.00 frames.], tot_loss[loss=0.1665, simple_loss=0.255, pruned_loss=0.03896, over 1419860.74 frames.], batch size: 20, lr: 4.59e-04
2022-05-14 20:13:49,587 INFO [train.py:812] (5/8) Epoch 17, batch 1450, loss[loss=0.19, simple_loss=0.286, pruned_loss=0.04699, over 7333.00 frames.], tot_loss[loss=0.1669, simple_loss=0.256, pruned_loss=0.03894, over 1418856.30 frames.], batch size: 22, lr: 4.59e-04
2022-05-14 20:14:48,951 INFO [train.py:812] (5/8) Epoch 17, batch 1500, loss[loss=0.1589, simple_loss=0.2433, pruned_loss=0.03728, over 7254.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2545, pruned_loss=0.03839, over 1425369.54 frames.], batch size: 19, lr: 4.59e-04
2022-05-14 20:15:57,354 INFO [train.py:812] (5/8) Epoch 17, batch 1550, loss[loss=0.1494, simple_loss=0.2468, pruned_loss=0.02602, over 7219.00 frames.], tot_loss[loss=0.166, simple_loss=0.255, pruned_loss=0.03847, over 1422944.14 frames.], batch size: 21, lr: 4.59e-04
2022-05-14 20:16:56,751 INFO [train.py:812] (5/8) Epoch 17, batch 1600, loss[loss=0.138, simple_loss=0.2317, pruned_loss=0.02213, over 7438.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2549, pruned_loss=0.03833, over 1427657.80 frames.], batch size: 20, lr: 4.58e-04
2022-05-14 20:17:55,350 INFO [train.py:812] (5/8) Epoch 17, batch 1650, loss[loss=0.1592, simple_loss=0.2563, pruned_loss=0.03102, over 7410.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2553, pruned_loss=0.03822, over 1429820.11 frames.], batch size: 21, lr: 4.58e-04
2022-05-14 20:18:53,695 INFO [train.py:812] (5/8) Epoch 17, batch 1700, loss[loss=0.2169, simple_loss=0.2865, pruned_loss=0.07372, over 5478.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2557, pruned_loss=0.03841, over 1423794.04 frames.], batch size: 56, lr: 4.58e-04
2022-05-14 20:19:52,467 INFO [train.py:812] (5/8) Epoch 17, batch 1750, loss[loss=0.179, simple_loss=0.269, pruned_loss=0.0445, over 7371.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2567, pruned_loss=0.03873, over 1415371.51 frames.], batch size: 23, lr: 4.58e-04
2022-05-14 20:20:51,546 INFO [train.py:812] (5/8) Epoch 17, batch 1800, loss[loss=0.1616, simple_loss=0.2559, pruned_loss=0.03366, over 7193.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2567, pruned_loss=0.03855, over 1416468.74 frames.], batch size: 23, lr: 4.58e-04
2022-05-14 20:21:48,756 INFO [train.py:812] (5/8) Epoch 17, batch 1850, loss[loss=0.1733, simple_loss=0.2549, pruned_loss=0.04582, over 6539.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2568, pruned_loss=0.03865, over 1417696.74 frames.], batch size: 38, lr: 4.58e-04
2022-05-14 20:22:47,441 INFO [train.py:812] (5/8) Epoch 17, batch 1900, loss[loss=0.1501, simple_loss=0.2418, pruned_loss=0.02913, over 7426.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2561, pruned_loss=0.03837, over 1422014.00 frames.], batch size: 20, lr: 4.58e-04
2022-05-14 20:23:46,068 INFO [train.py:812] (5/8) Epoch 17, batch 1950, loss[loss=0.1517, simple_loss=0.2445, pruned_loss=0.02942, over 7323.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2558, pruned_loss=0.03828, over 1424312.94 frames.], batch size: 21, lr: 4.57e-04
2022-05-14 20:24:44,623 INFO [train.py:812] (5/8) Epoch 17, batch 2000, loss[loss=0.1644, simple_loss=0.2473, pruned_loss=0.04072, over 7259.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2559, pruned_loss=0.03835, over 1426052.85 frames.], batch size: 19, lr: 4.57e-04
2022-05-14 20:25:43,659 INFO [train.py:812] (5/8) Epoch 17, batch 2050, loss[loss=0.1485, simple_loss=0.2229, pruned_loss=0.03703, over 7409.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2546, pruned_loss=0.0382, over 1429274.08 frames.], batch size: 18, lr: 4.57e-04
2022-05-14 20:26:43,362 INFO [train.py:812] (5/8) Epoch 17, batch 2100, loss[loss=0.1772, simple_loss=0.2656, pruned_loss=0.04444, over 7405.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2549, pruned_loss=0.03807, over 1429538.46 frames.], batch size: 21, lr: 4.57e-04
2022-05-14 20:27:42,662 INFO [train.py:812] (5/8) Epoch 17, batch 2150, loss[loss=0.1584, simple_loss=0.2562, pruned_loss=0.03036, over 7366.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2552, pruned_loss=0.03804, over 1425340.01 frames.], batch size: 19, lr: 4.57e-04
2022-05-14 20:28:40,072 INFO [train.py:812] (5/8) Epoch 17, batch 2200, loss[loss=0.1784, simple_loss=0.2746, pruned_loss=0.04107, over 7325.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2548, pruned_loss=0.03814, over 1422234.06 frames.], batch size: 22, lr: 4.57e-04
2022-05-14 20:29:39,212 INFO [train.py:812] (5/8) Epoch 17, batch 2250, loss[loss=0.1428, simple_loss=0.2413, pruned_loss=0.02218, over 7425.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2557, pruned_loss=0.03848, over 1423613.03 frames.], batch size: 21, lr: 4.56e-04
2022-05-14 20:30:37,972 INFO [train.py:812] (5/8) Epoch 17, batch 2300, loss[loss=0.1804, simple_loss=0.2777, pruned_loss=0.04149, over 7290.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2549, pruned_loss=0.03796, over 1422314.59 frames.], batch size: 24, lr: 4.56e-04
2022-05-14 20:31:36,713 INFO [train.py:812] (5/8) Epoch 17, batch 2350, loss[loss=0.1825, simple_loss=0.2671, pruned_loss=0.04898, over 7395.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2548, pruned_loss=0.0381, over 1425675.71 frames.], batch size: 23, lr: 4.56e-04
2022-05-14 20:32:36,102 INFO [train.py:812] (5/8) Epoch 17, batch 2400, loss[loss=0.1382, simple_loss=0.2192, pruned_loss=0.02858, over 7013.00 frames.], tot_loss[loss=0.165, simple_loss=0.2543, pruned_loss=0.03785, over 1424005.97 frames.], batch size: 16, lr: 4.56e-04
2022-05-14 20:33:34,531 INFO [train.py:812] (5/8) Epoch 17, batch 2450, loss[loss=0.1651, simple_loss=0.2587, pruned_loss=0.03572, over 7329.00 frames.], tot_loss[loss=0.165, simple_loss=0.2544, pruned_loss=0.03777, over 1423952.29 frames.], batch size: 22, lr: 4.56e-04
2022-05-14 20:34:34,261 INFO [train.py:812] (5/8) Epoch 17, batch 2500, loss[loss=0.201, simple_loss=0.2846, pruned_loss=0.05868, over 7222.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2533, pruned_loss=0.03757, over 1423616.96 frames.], batch size: 21, lr: 4.56e-04
2022-05-14 20:35:31,561 INFO [train.py:812] (5/8) Epoch 17, batch 2550, loss[loss=0.1525, simple_loss=0.2445, pruned_loss=0.03028, over 7208.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2533, pruned_loss=0.03759, over 1420242.76 frames.], batch size: 21, lr: 4.56e-04
2022-05-14 20:36:37,559 INFO [train.py:812] (5/8) Epoch 17, batch 2600, loss[loss=0.1672, simple_loss=0.258, pruned_loss=0.03814, over 7105.00 frames.], tot_loss[loss=0.166, simple_loss=0.2552, pruned_loss=0.03838, over 1422334.53 frames.], batch size: 28, lr: 4.55e-04
2022-05-14 20:37:36,696 INFO [train.py:812] (5/8) Epoch 17, batch 2650, loss[loss=0.1628, simple_loss=0.253, pruned_loss=0.03629, over 7367.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2557, pruned_loss=0.03878, over 1420500.40 frames.], batch size: 19, lr: 4.55e-04
2022-05-14 20:38:34,780 INFO [train.py:812] (5/8) Epoch 17, batch 2700, loss[loss=0.1845, simple_loss=0.2772, pruned_loss=0.04587, over 7337.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2545, pruned_loss=0.03852, over 1424008.51 frames.], batch size: 22, lr: 4.55e-04
2022-05-14 20:39:32,877 INFO [train.py:812] (5/8) Epoch 17, batch 2750, loss[loss=0.1579, simple_loss=0.2419, pruned_loss=0.03696, over 7150.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2547, pruned_loss=0.03854, over 1422838.76 frames.], batch size: 19, lr: 4.55e-04
2022-05-14 20:40:31,881 INFO [train.py:812] (5/8) Epoch 17, batch 2800, loss[loss=0.2434, simple_loss=0.3082, pruned_loss=0.08927, over 5004.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2541, pruned_loss=0.03825, over 1422141.55 frames.], batch size: 52, lr: 4.55e-04
2022-05-14 20:41:30,552 INFO [train.py:812] (5/8) Epoch 17, batch 2850, loss[loss=0.1762, simple_loss=0.2678, pruned_loss=0.04227, over 7316.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2556, pruned_loss=0.03866, over 1422405.99 frames.], batch size: 21, lr: 4.55e-04
2022-05-14 20:42:28,954 INFO [train.py:812] (5/8) Epoch 17, batch 2900, loss[loss=0.1578, simple_loss=0.2431, pruned_loss=0.03629, over 7228.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2559, pruned_loss=0.03901, over 1418325.49 frames.], batch size: 20, lr: 4.55e-04
2022-05-14 20:43:27,755 INFO [train.py:812] (5/8) Epoch 17, batch 2950, loss[loss=0.1543, simple_loss=0.2389, pruned_loss=0.0348, over 7257.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2563, pruned_loss=0.0391, over 1418814.93 frames.], batch size: 18, lr: 4.54e-04
2022-05-14 20:44:36,227 INFO [train.py:812] (5/8) Epoch 17, batch 3000, loss[loss=0.1886, simple_loss=0.2865, pruned_loss=0.04537, over 7139.00 frames.], tot_loss[loss=0.1674, simple_loss=0.257, pruned_loss=0.03895, over 1423943.00 frames.], batch size: 20, lr: 4.54e-04
2022-05-14 20:44:36,228 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 20:44:43,902 INFO [train.py:841] (5/8) Epoch 17, validation: loss=0.1538, simple_loss=0.2534, pruned_loss=0.02708, over 698248.00 frames.
2022-05-14 20:45:42,837 INFO [train.py:812] (5/8) Epoch 17, batch 3050, loss[loss=0.1878, simple_loss=0.2713, pruned_loss=0.05213, over 6160.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2573, pruned_loss=0.03914, over 1423143.35 frames.], batch size: 37, lr: 4.54e-04
2022-05-14 20:46:41,072 INFO [train.py:812] (5/8) Epoch 17, batch 3100, loss[loss=0.1855, simple_loss=0.2771, pruned_loss=0.04688, over 7309.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2575, pruned_loss=0.0396, over 1419463.50 frames.], batch size: 25, lr: 4.54e-04
2022-05-14 20:47:58,625 INFO [train.py:812] (5/8) Epoch 17, batch 3150, loss[loss=0.1668, simple_loss=0.2541, pruned_loss=0.03977, over 7334.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2573, pruned_loss=0.03969, over 1418679.56 frames.], batch size: 20, lr: 4.54e-04
2022-05-14 20:49:07,276 INFO [train.py:812] (5/8) Epoch 17, batch 3200, loss[loss=0.1606, simple_loss=0.2546, pruned_loss=0.03333, over 7353.00 frames.], tot_loss[loss=0.168, simple_loss=0.2569, pruned_loss=0.03954, over 1419001.62 frames.], batch size: 19, lr: 4.54e-04
2022-05-14 20:50:25,593 INFO [train.py:812] (5/8) Epoch 17, batch 3250, loss[loss=0.1493, simple_loss=0.2338, pruned_loss=0.03244, over 7070.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2564, pruned_loss=0.03901, over 1424250.21 frames.], batch size: 18, lr: 4.54e-04
2022-05-14 20:51:34,392 INFO [train.py:812] (5/8) Epoch 17, batch 3300, loss[loss=0.1959, simple_loss=0.289, pruned_loss=0.05142, over 7155.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2572, pruned_loss=0.03912, over 1425355.22 frames.], batch size: 19, lr: 4.53e-04
2022-05-14 20:52:33,382 INFO [train.py:812] (5/8) Epoch 17, batch 3350, loss[loss=0.1735, simple_loss=0.2688, pruned_loss=0.03913, over 7331.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2567, pruned_loss=0.03856, over 1426852.92 frames.], batch size: 22, lr: 4.53e-04
2022-05-14 20:53:32,423 INFO [train.py:812] (5/8) Epoch 17, batch 3400, loss[loss=0.1563, simple_loss=0.2442, pruned_loss=0.03421, over 7150.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2562, pruned_loss=0.03857, over 1423826.47 frames.], batch size: 20, lr: 4.53e-04
2022-05-14 20:54:31,686 INFO [train.py:812] (5/8) Epoch 17, batch 3450, loss[loss=0.1579, simple_loss=0.2539, pruned_loss=0.03098, over 7333.00 frames.], tot_loss[loss=0.1663, simple_loss=0.255, pruned_loss=0.03878, over 1424267.70 frames.], batch size: 20, lr: 4.53e-04
2022-05-14 20:55:30,405 INFO [train.py:812] (5/8) Epoch 17, batch 3500, loss[loss=0.1673, simple_loss=0.2593, pruned_loss=0.03766, over 7206.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2543, pruned_loss=0.03852, over 1423589.31 frames.], batch size: 22, lr: 4.53e-04
2022-05-14 20:56:29,357 INFO [train.py:812] (5/8) Epoch 17, batch 3550, loss[loss=0.1599, simple_loss=0.2625, pruned_loss=0.02861, over 7117.00 frames.], tot_loss[loss=0.165, simple_loss=0.254, pruned_loss=0.03796, over 1425652.14 frames.], batch size: 21, lr: 4.53e-04
2022-05-14 20:57:28,882 INFO [train.py:812] (5/8) Epoch 17, batch 3600, loss[loss=0.1289, simple_loss=0.2127, pruned_loss=0.02259, over 7263.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2548, pruned_loss=0.03795, over 1426947.67 frames.], batch size: 18, lr: 4.52e-04
2022-05-14 20:58:27,778 INFO [train.py:812] (5/8) Epoch 17, batch 3650, loss[loss=0.1491, simple_loss=0.2388, pruned_loss=0.02971, over 7317.00 frames.], tot_loss[loss=0.165, simple_loss=0.2542, pruned_loss=0.03792, over 1430117.72 frames.], batch size: 21, lr: 4.52e-04
2022-05-14 20:59:27,702 INFO [train.py:812] (5/8) Epoch 17, batch 3700, loss[loss=0.1561, simple_loss=0.2504, pruned_loss=0.03088, over 7152.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2544, pruned_loss=0.0381, over 1429447.12 frames.], batch size: 20, lr: 4.52e-04
2022-05-14 21:00:26,358 INFO [train.py:812] (5/8) Epoch 17, batch 3750, loss[loss=0.2041, simple_loss=0.2959, pruned_loss=0.05613, over 6190.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2551, pruned_loss=0.0383, over 1427113.54 frames.], batch size: 37, lr: 4.52e-04
2022-05-14 21:01:24,374 INFO [train.py:812] (5/8) Epoch 17, batch 3800, loss[loss=0.1454, simple_loss=0.2399, pruned_loss=0.02544, over 6433.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2554, pruned_loss=0.0379, over 1426347.67 frames.], batch size: 38, lr: 4.52e-04
2022-05-14 21:02:23,090 INFO [train.py:812] (5/8) Epoch 17, batch 3850, loss[loss=0.1449, simple_loss=0.2288, pruned_loss=0.03046, over 6991.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2552, pruned_loss=0.03764, over 1425919.55 frames.], batch size: 16, lr: 4.52e-04
2022-05-14 21:03:22,484 INFO [train.py:812] (5/8) Epoch 17, batch 3900, loss[loss=0.1705, simple_loss=0.2626, pruned_loss=0.03918, over 7208.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2548, pruned_loss=0.03777, over 1427992.67 frames.], batch size: 22, lr: 4.52e-04
2022-05-14 21:04:21,489 INFO [train.py:812] (5/8) Epoch 17, batch 3950, loss[loss=0.1902, simple_loss=0.2821, pruned_loss=0.04909, over 7199.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2564, pruned_loss=0.03859, over 1427838.05 frames.], batch size: 23, lr: 4.51e-04
2022-05-14 21:05:20,907 INFO [train.py:812] (5/8) Epoch 17, batch 4000, loss[loss=0.1342, simple_loss=0.2251, pruned_loss=0.02167, over 7283.00 frames.], tot_loss[loss=0.1667, simple_loss=0.256, pruned_loss=0.03867, over 1429161.27 frames.], batch size: 18, lr: 4.51e-04
2022-05-14 21:06:19,930 INFO [train.py:812] (5/8) Epoch 17, batch 4050, loss[loss=0.177, simple_loss=0.2745, pruned_loss=0.03975, over 6753.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2557, pruned_loss=0.03889, over 1424979.70 frames.], batch size: 31, lr: 4.51e-04
2022-05-14 21:07:18,995 INFO [train.py:812] (5/8) Epoch 17, batch 4100, loss[loss=0.1928, simple_loss=0.285, pruned_loss=0.05029, over 6522.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2575, pruned_loss=0.03939, over 1424785.98 frames.], batch size: 37, lr: 4.51e-04
2022-05-14 21:08:18,285 INFO [train.py:812] (5/8) Epoch 17, batch 4150, loss[loss=0.1452, simple_loss=0.2284, pruned_loss=0.03097, over 7136.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2564, pruned_loss=0.03942, over 1423581.32 frames.], batch size: 17, lr: 4.51e-04
2022-05-14 21:09:17,054 INFO [train.py:812] (5/8) Epoch 17, batch 4200, loss[loss=0.1651, simple_loss=0.2628, pruned_loss=0.03376, over 7168.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2567, pruned_loss=0.03953, over 1423740.07 frames.], batch size: 26, lr: 4.51e-04
2022-05-14 21:10:16,226 INFO [train.py:812] (5/8) Epoch 17, batch 4250, loss[loss=0.1474, simple_loss=0.2455, pruned_loss=0.0246, over 7276.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2571, pruned_loss=0.03932, over 1424276.31 frames.], batch size: 18, lr: 4.51e-04
2022-05-14 21:11:15,267 INFO [train.py:812] (5/8) Epoch 17, batch 4300, loss[loss=0.1396, simple_loss=0.231, pruned_loss=0.02409, over 7074.00 frames.], tot_loss[loss=0.1668, simple_loss=0.256, pruned_loss=0.03882, over 1423220.89 frames.], batch size: 18, lr: 4.50e-04
2022-05-14 21:12:14,086 INFO [train.py:812] (5/8) Epoch 17, batch 4350, loss[loss=0.1493, simple_loss=0.24, pruned_loss=0.02925, over 7172.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2552, pruned_loss=0.03853, over 1422416.62 frames.], batch size: 18, lr: 4.50e-04
2022-05-14 21:13:12,862 INFO [train.py:812] (5/8) Epoch 17, batch 4400, loss[loss=0.1643, simple_loss=0.2553, pruned_loss=0.03669, over 7219.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2556, pruned_loss=0.03893, over 1420868.74 frames.], batch size: 21, lr: 4.50e-04
2022-05-14 21:14:12,277 INFO [train.py:812] (5/8) Epoch 17, batch 4450, loss[loss=0.1548, simple_loss=0.233, pruned_loss=0.03829, over 7115.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2574, pruned_loss=0.03996, over 1416813.29 frames.], batch size: 17, lr: 4.50e-04
2022-05-14 21:15:12,242 INFO [train.py:812] (5/8) Epoch 17, batch 4500, loss[loss=0.154, simple_loss=0.2511, pruned_loss=0.02847, over 7237.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2563, pruned_loss=0.0393, over 1415913.45 frames.], batch size: 20, lr: 4.50e-04
2022-05-14 21:16:11,539 INFO [train.py:812] (5/8) Epoch 17, batch 4550, loss[loss=0.1654, simple_loss=0.2633, pruned_loss=0.03377, over 4507.00 frames.], tot_loss[loss=0.1669, simple_loss=0.255, pruned_loss=0.03936, over 1381413.81 frames.], batch size: 53, lr: 4.50e-04
2022-05-14 21:17:18,364 INFO [train.py:812] (5/8) Epoch 18, batch 0, loss[loss=0.1684, simple_loss=0.2635, pruned_loss=0.03662, over 7235.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2635, pruned_loss=0.03662, over 7235.00 frames.], batch size: 20, lr: 4.38e-04
2022-05-14 21:18:18,236 INFO [train.py:812] (5/8) Epoch 18, batch 50, loss[loss=0.1441, simple_loss=0.2209, pruned_loss=0.03361, over 6979.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2531, pruned_loss=0.03715, over 323304.74 frames.], batch size: 16, lr: 4.38e-04
2022-05-14 21:19:17,369 INFO [train.py:812] (5/8) Epoch 18, batch 100, loss[loss=0.1558, simple_loss=0.2371, pruned_loss=0.03721, over 7143.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2545, pruned_loss=0.03752, over 565521.03 frames.], batch size: 18, lr: 4.37e-04
2022-05-14 21:20:15,723 INFO [train.py:812] (5/8) Epoch 18, batch 150, loss[loss=0.1551, simple_loss=0.2441, pruned_loss=0.03302, over 7141.00 frames.], tot_loss[loss=0.1663, simple_loss=0.256, pruned_loss=0.0383, over 753070.49 frames.], batch size: 20, lr: 4.37e-04
2022-05-14 21:21:13,512 INFO [train.py:812] (5/8) Epoch 18, batch 200, loss[loss=0.1739, simple_loss=0.2587, pruned_loss=0.04456, over 7169.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2559, pruned_loss=0.03797, over 904455.88 frames.], batch size: 18, lr: 4.37e-04
2022-05-14 21:22:12,908 INFO [train.py:812] (5/8) Epoch 18, batch 250, loss[loss=0.141, simple_loss=0.2361, pruned_loss=0.02295, over 6725.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2562, pruned_loss=0.03818, over 1021677.30 frames.], batch size: 31, lr: 4.37e-04
2022-05-14 21:23:11,925 INFO [train.py:812] (5/8) Epoch 18, batch 300, loss[loss=0.1578, simple_loss=0.248, pruned_loss=0.03379, over 7037.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2568, pruned_loss=0.03871, over 1104951.66 frames.], batch size: 28, lr: 4.37e-04
2022-05-14 21:24:11,084 INFO [train.py:812] (5/8) Epoch 18, batch 350, loss[loss=0.1544, simple_loss=0.2498, pruned_loss=0.02952, over 7325.00 frames.], tot_loss[loss=0.166, simple_loss=0.2552, pruned_loss=0.03834, over 1172914.16 frames.], batch size: 22, lr: 4.37e-04
2022-05-14 21:25:08,901 INFO [train.py:812] (5/8) Epoch 18, batch 400, loss[loss=0.1707, simple_loss=0.2497, pruned_loss=0.04586, over 6832.00 frames.], tot_loss[loss=0.166, simple_loss=0.2561, pruned_loss=0.03796, over 1232706.01 frames.], batch size: 15, lr: 4.37e-04
2022-05-14 21:26:06,614 INFO [train.py:812] (5/8) Epoch 18, batch 450, loss[loss=0.1917, simple_loss=0.2886, pruned_loss=0.04733, over 7222.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2568, pruned_loss=0.03799, over 1275845.18 frames.], batch size: 22, lr: 4.36e-04
2022-05-14 21:27:06,220 INFO [train.py:812] (5/8) Epoch 18, batch 500, loss[loss=0.1596, simple_loss=0.2576, pruned_loss=0.03079, over 7336.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2558, pruned_loss=0.03776, over 1313313.75 frames.], batch size: 22, lr: 4.36e-04
2022-05-14 21:28:04,625 INFO [train.py:812] (5/8) Epoch 18, batch 550, loss[loss=0.1407, simple_loss=0.2281, pruned_loss=0.02668, over 7134.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2553, pruned_loss=0.03763, over 1340413.68 frames.], batch size: 17, lr: 4.36e-04
2022-05-14 21:29:02,266 INFO [train.py:812] (5/8) Epoch 18, batch 600, loss[loss=0.1568, simple_loss=0.2517, pruned_loss=0.03095, over 6597.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2568, pruned_loss=0.03839, over 1358173.32 frames.], batch size: 38, lr: 4.36e-04
2022-05-14 21:30:01,247 INFO [train.py:812] (5/8) Epoch 18, batch 650, loss[loss=0.1776, simple_loss=0.2608, pruned_loss=0.0472, over 5066.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2567, pruned_loss=0.03819, over 1370348.55 frames.], batch size: 54, lr: 4.36e-04
2022-05-14 21:30:59,624 INFO [train.py:812] (5/8) Epoch 18, batch 700, loss[loss=0.1501, simple_loss=0.2413, pruned_loss=0.02945, over 7323.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2565, pruned_loss=0.03828, over 1381889.58 frames.], batch size: 21, lr: 4.36e-04
2022-05-14 21:31:59,628 INFO [train.py:812] (5/8) Epoch 18, batch 750, loss[loss=0.1341, simple_loss=0.221, pruned_loss=0.02359, over 7418.00 frames.], tot_loss[loss=0.1652, simple_loss=0.255, pruned_loss=0.03776, over 1391963.32 frames.], batch size: 18, lr: 4.36e-04
2022-05-14 21:32:57,573 INFO [train.py:812] (5/8) Epoch 18, batch 800, loss[loss=0.1622, simple_loss=0.2542, pruned_loss=0.03509, over 7308.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2547, pruned_loss=0.03745, over 1403675.74 frames.], batch size: 21, lr: 4.36e-04
2022-05-14 21:33:57,262 INFO [train.py:812] (5/8) Epoch 18, batch 850, loss[loss=0.1667, simple_loss=0.2581, pruned_loss=0.0377, over 7418.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2545, pruned_loss=0.03759, over 1406533.28 frames.], batch size: 21, lr: 4.35e-04
2022-05-14 21:34:56,171 INFO [train.py:812] (5/8) Epoch 18, batch 900, loss[loss=0.1908, simple_loss=0.2814, pruned_loss=0.05015, over 7210.00 frames.], tot_loss[loss=0.1663, simple_loss=0.256, pruned_loss=0.03828, over 1406491.65 frames.], batch size: 22, lr: 4.35e-04
2022-05-14 21:35:54,608 INFO [train.py:812] (5/8) Epoch 18, batch 950, loss[loss=0.1468, simple_loss=0.2382, pruned_loss=0.02773, over 7272.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2562, pruned_loss=0.03825, over 1409012.41 frames.], batch size: 19, lr: 4.35e-04
2022-05-14 21:36:52,265 INFO [train.py:812] (5/8) Epoch 18, batch 1000, loss[loss=0.1767, simple_loss=0.2673, pruned_loss=0.04305, over 7298.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2554, pruned_loss=0.03784, over 1413899.28 frames.], batch size: 24, lr: 4.35e-04
2022-05-14 21:37:51,922 INFO [train.py:812] (5/8) Epoch 18, batch 1050, loss[loss=0.1544, simple_loss=0.2396, pruned_loss=0.03458, over 7261.00 frames.], tot_loss[loss=0.165, simple_loss=0.2546, pruned_loss=0.03767, over 1416102.07 frames.], batch size: 17, lr: 4.35e-04
2022-05-14 21:38:50,481 INFO [train.py:812] (5/8) Epoch 18, batch 1100, loss[loss=0.1785, simple_loss=0.2685, pruned_loss=0.04431, over 7297.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2552, pruned_loss=0.0382, over 1419841.85 frames.], batch size: 25, lr: 4.35e-04
2022-05-14 21:39:48,088 INFO [train.py:812] (5/8) Epoch 18, batch 1150, loss[loss=0.1824, simple_loss=0.265, pruned_loss=0.04995, over 7381.00 frames.], tot_loss[loss=0.1655, simple_loss=0.255, pruned_loss=0.03801, over 1418367.81 frames.], batch size: 23, lr: 4.35e-04
2022-05-14 21:40:45,339 INFO [train.py:812] (5/8) Epoch 18, batch 1200, loss[loss=0.1744, simple_loss=0.2605, pruned_loss=0.04416, over 7281.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2555, pruned_loss=0.0384, over 1416350.10 frames.], batch size: 18, lr: 4.34e-04
2022-05-14 21:41:44,608 INFO [train.py:812] (5/8) Epoch 18, batch 1250, loss[loss=0.1725, simple_loss=0.2634, pruned_loss=0.04081, over 7416.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2553, pruned_loss=0.03811, over 1418453.48 frames.], batch size: 21, lr: 4.34e-04
2022-05-14 21:42:42,166 INFO [train.py:812] (5/8) Epoch 18, batch 1300, loss[loss=0.168, simple_loss=0.2565, pruned_loss=0.03973, over 7156.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2548, pruned_loss=0.03808, over 1419674.97 frames.], batch size: 26, lr: 4.34e-04
2022-05-14 21:43:41,337 INFO [train.py:812] (5/8) Epoch 18, batch 1350, loss[loss=0.1556, simple_loss=0.2318, pruned_loss=0.03971, over 6985.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2553, pruned_loss=0.03799, over 1423101.88 frames.], batch size: 16, lr: 4.34e-04
2022-05-14 21:44:39,587 INFO [train.py:812] (5/8) Epoch 18, batch 1400, loss[loss=0.1609, simple_loss=0.2602, pruned_loss=0.03081, over 7110.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2558, pruned_loss=0.03795, over 1424262.88 frames.], batch size: 21, lr: 4.34e-04
2022-05-14 21:45:38,270 INFO [train.py:812] (5/8) Epoch 18, batch 1450, loss[loss=0.1456, simple_loss=0.2414, pruned_loss=0.02489, over 7152.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2554, pruned_loss=0.03798, over 1421948.09 frames.], batch size: 20, lr: 4.34e-04
2022-05-14 21:46:36,907 INFO [train.py:812] (5/8) Epoch 18, batch 1500, loss[loss=0.1965, simple_loss=0.2722, pruned_loss=0.06036, over 7302.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2564, pruned_loss=0.03844, over 1413874.46 frames.], batch size: 25, lr: 4.34e-04
2022-05-14 21:47:35,825 INFO [train.py:812] (5/8) Epoch 18, batch 1550, loss[loss=0.172, simple_loss=0.2638, pruned_loss=0.04005, over 7159.00 frames.], tot_loss[loss=0.166, simple_loss=0.2557, pruned_loss=0.03818, over 1420773.87 frames.], batch size: 19, lr: 4.33e-04
2022-05-14 21:48:33,671 INFO [train.py:812] (5/8) Epoch 18, batch 1600, loss[loss=0.17, simple_loss=0.2625, pruned_loss=0.03877, over 7430.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2561, pruned_loss=0.03853, over 1422711.90 frames.], batch size: 20, lr: 4.33e-04
2022-05-14 21:49:33,284 INFO [train.py:812] (5/8) Epoch 18, batch 1650, loss[loss=0.13, simple_loss=0.2172, pruned_loss=0.02137, over 7277.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2563, pruned_loss=0.0386, over 1421433.60 frames.], batch size: 17, lr: 4.33e-04
2022-05-14 21:50:30,801 INFO [train.py:812] (5/8) Epoch 18, batch 1700, loss[loss=0.1493, simple_loss=0.2354, pruned_loss=0.03156, over 7359.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2556, pruned_loss=0.03812, over 1424348.12 frames.], batch size: 19, lr: 4.33e-04
2022-05-14 21:51:29,691 INFO [train.py:812] (5/8) Epoch 18, batch 1750, loss[loss=0.151, simple_loss=0.2534, pruned_loss=0.02426, over 7321.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2552, pruned_loss=0.03788, over 1424430.38 frames.], batch size: 21, lr: 4.33e-04
2022-05-14 21:52:27,477 INFO [train.py:812] (5/8) Epoch 18, batch 1800, loss[loss=0.1705, simple_loss=0.2558, pruned_loss=0.04264, over 7229.00 frames.], tot_loss[loss=0.1652, simple_loss=0.255, pruned_loss=0.03768, over 1428427.33 frames.], batch size: 20, lr: 4.33e-04
2022-05-14 21:53:27,314 INFO [train.py:812] (5/8) Epoch 18, batch 1850, loss[loss=0.1982, simple_loss=0.2817, pruned_loss=0.05732, over 5059.00 frames.], tot_loss[loss=0.164, simple_loss=0.2537, pruned_loss=0.03716, over 1426275.75 frames.], batch size: 52, lr: 4.33e-04
2022-05-14 21:54:25,905 INFO [train.py:812] (5/8) Epoch 18, batch 1900, loss[loss=0.2014, simple_loss=0.2989, pruned_loss=0.05192, over 7321.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2556, pruned_loss=0.03757, over 1426574.96 frames.], batch size: 21, lr: 4.33e-04
2022-05-14 21:55:25,268 INFO [train.py:812] (5/8) Epoch 18, batch 1950, loss[loss=0.1495, simple_loss=0.2449, pruned_loss=0.02703, over 7312.00 frames.], tot_loss[loss=0.166, simple_loss=0.2563, pruned_loss=0.03788, over 1422621.71 frames.], batch size: 21, lr: 4.32e-04
2022-05-14 21:56:23,567 INFO [train.py:812] (5/8) Epoch 18, batch 2000, loss[loss=0.1869, simple_loss=0.2709, pruned_loss=0.05147, over 5207.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2556, pruned_loss=0.03802, over 1424016.39 frames.], batch size: 52, lr: 4.32e-04
2022-05-14 21:57:27,250 INFO [train.py:812] (5/8) Epoch 18, batch 2050, loss[loss=0.172, simple_loss=0.2696, pruned_loss=0.03724, over 7103.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2554, pruned_loss=0.03822, over 1419618.14 frames.], batch size: 21, lr: 4.32e-04
2022-05-14 21:58:25,555 INFO [train.py:812] (5/8) Epoch 18, batch 2100, loss[loss=0.2436, simple_loss=0.3202, pruned_loss=0.08348, over 6841.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2556, pruned_loss=0.03869, over 1415817.53 frames.], batch size: 31, lr: 4.32e-04
2022-05-14 21:59:24,672 INFO [train.py:812] (5/8) Epoch 18, batch 2150, loss[loss=0.1593, simple_loss=0.2653, pruned_loss=0.02663, over 7216.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2552, pruned_loss=0.03823, over 1417578.46 frames.], batch size: 21, lr: 4.32e-04
2022-05-14 22:00:22,622 INFO [train.py:812] (5/8) Epoch 18, batch 2200, loss[loss=0.1534, simple_loss=0.233, pruned_loss=0.03692, over 6800.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2549, pruned_loss=0.03801, over 1420302.10 frames.], batch size: 15, lr: 4.32e-04
2022-05-14 22:01:21,992 INFO [train.py:812] (5/8) Epoch 18, batch 2250, loss[loss=0.1563, simple_loss=0.2286, pruned_loss=0.042, over 6974.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2539, pruned_loss=0.03788, over 1423542.71 frames.], batch size: 16, lr: 4.32e-04
2022-05-14 22:02:21,441 INFO [train.py:812] (5/8) Epoch 18, batch 2300, loss[loss=0.1253, simple_loss=0.2169, pruned_loss=0.0168, over 7150.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2544, pruned_loss=0.03809, over 1426206.39 frames.], batch size: 20, lr: 4.31e-04
2022-05-14 22:03:21,214 INFO [train.py:812] (5/8) Epoch 18, batch 2350, loss[loss=0.1696, simple_loss=0.2736, pruned_loss=0.03278, over 7185.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2543, pruned_loss=0.03801, over 1426154.12 frames.], batch size: 26, lr: 4.31e-04
2022-05-14 22:04:20,398 INFO [train.py:812] (5/8) Epoch 18, batch 2400, loss[loss=0.2059, simple_loss=0.2861, pruned_loss=0.06287, over 6570.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2556, pruned_loss=0.03859, over 1424444.29 frames.], batch size: 38, lr: 4.31e-04
2022-05-14 22:05:18,840 INFO [train.py:812] (5/8) Epoch 18, batch 2450, loss[loss=0.1577, simple_loss=0.2448, pruned_loss=0.03527, over 7158.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2547, pruned_loss=0.03792, over 1425722.13 frames.], batch size: 19, lr: 4.31e-04
2022-05-14 22:06:16,636 INFO [train.py:812] (5/8) Epoch 18, batch 2500, loss[loss=0.1707, simple_loss=0.2653, pruned_loss=0.03811, over 7116.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2564, pruned_loss=0.03899, over 1419012.53 frames.], batch size: 21, lr: 4.31e-04
2022-05-14 22:07:15,270 INFO [train.py:812] (5/8) Epoch 18, batch 2550, loss[loss=0.1719, simple_loss=0.2665, pruned_loss=0.03867, over 7329.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2554, pruned_loss=0.03866, over 1420240.57 frames.], batch size: 21, lr: 4.31e-04
2022-05-14 22:08:14,570 INFO [train.py:812] (5/8) Epoch 18, batch 2600, loss[loss=0.1287, simple_loss=0.2107, pruned_loss=0.02337, over 7200.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2559, pruned_loss=0.03866, over 1419647.11 frames.], batch size: 16, lr: 4.31e-04
2022-05-14 22:09:14,551 INFO [train.py:812] (5/8) Epoch 18, batch 2650, loss[loss=0.1631, simple_loss=0.2485, pruned_loss=0.0388, over 7356.00 frames.], tot_loss[loss=0.166, simple_loss=0.2555, pruned_loss=0.03832, over 1419614.87 frames.], batch size: 19, lr: 4.31e-04
2022-05-14 22:10:13,351 INFO [train.py:812] (5/8) Epoch 18, batch 2700, loss[loss=0.1577, simple_loss=0.2404, pruned_loss=0.03752, over 7262.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2549, pruned_loss=0.03811, over 1419644.58 frames.], batch size: 18, lr: 4.30e-04
2022-05-14 22:11:12,897 INFO [train.py:812] (5/8) Epoch 18, batch 2750, loss[loss=0.1689, simple_loss=0.2705, pruned_loss=0.03362, over 7148.00 frames.], tot_loss[loss=0.166, simple_loss=0.2549, pruned_loss=0.0385, over 1417316.19 frames.], batch size: 20, lr: 4.30e-04
2022-05-14 22:12:10,425 INFO [train.py:812] (5/8) Epoch 18, batch 2800, loss[loss=0.1632, simple_loss=0.2531, pruned_loss=0.03667, over 7319.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2543, pruned_loss=0.03836, over 1417023.84 frames.], batch size: 21, lr: 4.30e-04
2022-05-14 22:13:09,208 INFO [train.py:812] (5/8) Epoch 18, batch 2850, loss[loss=0.1757, simple_loss=0.2713, pruned_loss=0.04003, over 7282.00 frames.], tot_loss[loss=0.165, simple_loss=0.2542, pruned_loss=0.03791, over 1419799.14 frames.], batch size: 25, lr: 4.30e-04
2022-05-14 22:14:17,880 INFO [train.py:812] (5/8) Epoch 18, batch 2900, loss[loss=0.1922, simple_loss=0.2781, pruned_loss=0.0531, over 7200.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2551, pruned_loss=0.03835, over 1422991.55 frames.], batch size: 22, lr: 4.30e-04
2022-05-14 22:15:17,291 INFO [train.py:812] (5/8) Epoch 18, batch 2950, loss[loss=0.1763, simple_loss=0.2792, pruned_loss=0.03674, over 6413.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2553, pruned_loss=0.03852, over 1419451.76 frames.], batch size: 38, lr: 4.30e-04
2022-05-14 22:16:16,270 INFO [train.py:812] (5/8) Epoch 18, batch 3000, loss[loss=0.1882, simple_loss=0.2718, pruned_loss=0.05225, over 7300.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2552, pruned_loss=0.03829, over 1418648.30 frames.], batch size: 25, lr: 4.30e-04
2022-05-14 22:16:16,271 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 22:16:23,834 INFO [train.py:841] (5/8) Epoch 18, validation: loss=0.153, simple_loss=0.2523, pruned_loss=0.02686, over 698248.00 frames.
2022-05-14 22:17:22,910 INFO [train.py:812] (5/8) Epoch 18, batch 3050, loss[loss=0.1759, simple_loss=0.2671, pruned_loss=0.04232, over 7111.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2553, pruned_loss=0.03829, over 1417591.90 frames.], batch size: 21, lr: 4.29e-04
2022-05-14 22:18:21,076 INFO [train.py:812] (5/8) Epoch 18, batch 3100, loss[loss=0.1594, simple_loss=0.2559, pruned_loss=0.03146, over 7228.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2558, pruned_loss=0.03861, over 1419100.99 frames.], batch size: 20, lr: 4.29e-04
2022-05-14 22:19:19,570 INFO [train.py:812] (5/8) Epoch 18, batch 3150, loss[loss=0.1569, simple_loss=0.2432, pruned_loss=0.03525, over 7256.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2558, pruned_loss=0.03842, over 1421485.23 frames.], batch size: 19, lr: 4.29e-04
2022-05-14 22:20:18,604 INFO [train.py:812] (5/8) Epoch 18, batch 3200, loss[loss=0.1622, simple_loss=0.258, pruned_loss=0.03315, over 6802.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2556, pruned_loss=0.03831, over 1419924.37 frames.], batch size: 31, lr: 4.29e-04
2022-05-14 22:21:17,366 INFO [train.py:812] (5/8) Epoch 18, batch 3250, loss[loss=0.1911, simple_loss=0.2809, pruned_loss=0.05061, over 7388.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2543, pruned_loss=0.03791, over 1422444.68 frames.], batch size: 23, lr: 4.29e-04
2022-05-14 22:22:16,157 INFO [train.py:812] (5/8) Epoch 18, batch 3300, loss[loss=0.1453, simple_loss=0.2216, pruned_loss=0.03453, over 7162.00 frames.], tot_loss[loss=0.164, simple_loss=0.2532, pruned_loss=0.03739, over 1427139.92 frames.], batch size: 18, lr: 4.29e-04
2022-05-14 22:23:15,276 INFO [train.py:812] (5/8) Epoch 18, batch 3350, loss[loss=0.1815, simple_loss=0.2657, pruned_loss=0.04865, over 7406.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2544, pruned_loss=0.03759, over 1426693.59 frames.], batch size: 18, lr: 4.29e-04
2022-05-14 22:24:13,565 INFO [train.py:812] (5/8) Epoch 18, batch 3400, loss[loss=0.1783, simple_loss=0.2687, pruned_loss=0.04401, over 7389.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2554, pruned_loss=0.03783, over 1430405.11 frames.], batch size: 23, lr: 4.29e-04
2022-05-14 22:25:13,426 INFO [train.py:812] (5/8) Epoch 18, batch 3450, loss[loss=0.1491, simple_loss=0.2327, pruned_loss=0.03274, over 7410.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2548, pruned_loss=0.0373, over 1430963.96 frames.], batch size: 18, lr: 4.28e-04
2022-05-14 22:26:12,106 INFO [train.py:812] (5/8) Epoch 18, batch 3500, loss[loss=0.1687, simple_loss=0.2597, pruned_loss=0.03885, over 6298.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2545, pruned_loss=0.03728, over 1433565.43 frames.], batch size: 37, lr: 4.28e-04
2022-05-14 22:27:09,547 INFO [train.py:812] (5/8) Epoch 18, batch 3550, loss[loss=0.1746, simple_loss=0.2695, pruned_loss=0.03985, over 7228.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2546, pruned_loss=0.0373, over 1431444.94 frames.], batch size: 23, lr: 4.28e-04
2022-05-14 22:28:09,240 INFO [train.py:812] (5/8) Epoch 18, batch 3600, loss[loss=0.1778, simple_loss=0.2752, pruned_loss=0.04021, over 7226.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2548, pruned_loss=0.03754, over 1432515.45 frames.], batch size: 21, lr: 4.28e-04
2022-05-14 22:29:08,066 INFO [train.py:812] (5/8) Epoch 18, batch 3650, loss[loss=0.1514, simple_loss=0.2506, pruned_loss=0.02613, over 7327.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2552, pruned_loss=0.03787, over 1423964.82 frames.], batch size: 22, lr: 4.28e-04
2022-05-14 22:30:06,374 INFO [train.py:812] (5/8) Epoch 18, batch 3700, loss[loss=0.1618, simple_loss=0.2508, pruned_loss=0.03641, over 6997.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2554, pruned_loss=0.03739, over 1424907.99 frames.], batch size: 16, lr: 4.28e-04
2022-05-14 22:31:03,706 INFO [train.py:812] (5/8) Epoch 18, batch 3750, loss[loss=0.1947, simple_loss=0.2729, pruned_loss=0.05824, over 7299.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2558, pruned_loss=0.03762, over 1426671.66 frames.], batch size: 25, lr: 4.28e-04
2022-05-14 22:32:02,180 INFO [train.py:812] (5/8) Epoch 18, batch 3800, loss[loss=0.1944, simple_loss=0.2773, pruned_loss=0.05578, over 7353.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2547, pruned_loss=0.03751, over 1426325.93 frames.], batch size: 19, lr: 4.28e-04
2022-05-14 22:33:02,002 INFO [train.py:812] (5/8) Epoch 18, batch 3850, loss[loss=0.1486, simple_loss=0.2458, pruned_loss=0.02572, over 7423.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2545, pruned_loss=0.03728, over 1424531.85 frames.], batch size: 18, lr: 4.27e-04
2022-05-14 22:34:00,983 INFO [train.py:812] (5/8) Epoch 18, batch 3900, loss[loss=0.1429, simple_loss=0.2334, pruned_loss=0.02621, over 7111.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2548, pruned_loss=0.03732, over 1420910.50 frames.], batch size: 21, lr: 4.27e-04
2022-05-14 22:35:00,683 INFO [train.py:812] (5/8) Epoch 18, batch 3950, loss[loss=0.1621, simple_loss=0.2523, pruned_loss=0.03593, over 7079.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2539, pruned_loss=0.03725, over 1422569.15 frames.], batch size: 28, lr: 4.27e-04
2022-05-14 22:35:58,137 INFO [train.py:812] (5/8) Epoch 18, batch 4000, loss[loss=0.1442, simple_loss=0.2258, pruned_loss=0.03125, over 6788.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2531, pruned_loss=0.0371, over 1423296.11 frames.], batch size: 15, lr: 4.27e-04
2022-05-14 22:36:56,541 INFO [train.py:812] (5/8) Epoch 18, batch 4050, loss[loss=0.1815, simple_loss=0.2692, pruned_loss=0.04692, over 7075.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2539, pruned_loss=0.03744, over 1426781.98 frames.], batch size: 28, lr: 4.27e-04
2022-05-14 22:37:55,335 INFO [train.py:812] (5/8) Epoch 18, batch 4100, loss[loss=0.1653, simple_loss=0.2576, pruned_loss=0.03649, over 7150.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2539, pruned_loss=0.03758, over 1423118.04 frames.], batch size: 20, lr: 4.27e-04
2022-05-14 22:38:54,562 INFO [train.py:812] (5/8) Epoch 18, batch 4150, loss[loss=0.1762, simple_loss=0.2658, pruned_loss=0.04328, over 7338.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2543, pruned_loss=0.03792, over 1421801.98 frames.], batch size: 20, lr: 4.27e-04
2022-05-14 22:39:53,787 INFO [train.py:812] (5/8) Epoch 18, batch 4200, loss[loss=0.1433, simple_loss=0.2345, pruned_loss=0.02606, over 7007.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2536, pruned_loss=0.03778, over 1422149.57 frames.], batch size: 16, lr: 4.26e-04
2022-05-14 22:40:53,078 INFO [train.py:812] (5/8) Epoch 18, batch 4250, loss[loss=0.1624, simple_loss=0.2631, pruned_loss=0.03088, over 7020.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2531, pruned_loss=0.03768, over 1417366.24 frames.], batch size: 32, lr: 4.26e-04
2022-05-14 22:41:52,051 INFO [train.py:812] (5/8) Epoch 18, batch 4300, loss[loss=0.1476, simple_loss=0.2275, pruned_loss=0.03389, over 7005.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2525, pruned_loss=0.03737, over 1418232.71 frames.], batch size: 16, lr: 4.26e-04
2022-05-14 22:42:51,537 INFO [train.py:812] (5/8) Epoch 18, batch 4350, loss[loss=0.1663, simple_loss=0.258, pruned_loss=0.03725, over 7221.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2533, pruned_loss=0.03784, over 1405219.09 frames.], batch size: 21, lr: 4.26e-04
2022-05-14 22:43:50,400 INFO [train.py:812] (5/8) Epoch 18, batch 4400, loss[loss=0.1303, simple_loss=0.2161, pruned_loss=0.02229, over 7061.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2543, pruned_loss=0.03797, over 1398939.36 frames.], batch size: 18, lr: 4.26e-04
2022-05-14 22:44:47,950 INFO [train.py:812] (5/8) Epoch 18, batch 4450, loss[loss=0.1637, simple_loss=0.2481, pruned_loss=0.03963, over 6286.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2552, pruned_loss=0.03829, over 1391704.55 frames.], batch size: 37, lr: 4.26e-04
2022-05-14 22:45:55,876 INFO [train.py:812] (5/8) Epoch 18, batch 4500, loss[loss=0.1533, simple_loss=0.2405, pruned_loss=0.03305, over 7014.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2561, pruned_loss=0.0388, over 1380115.76 frames.], batch size: 16, lr: 4.26e-04
2022-05-14 22:46:55,058 INFO [train.py:812] (5/8) Epoch 18, batch 4550, loss[loss=0.1675, simple_loss=0.2642, pruned_loss=0.03542, over 7156.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2553, pruned_loss=0.03858, over 1369490.68 frames.], batch size: 19, lr: 4.26e-04
2022-05-14 22:48:10,082 INFO [train.py:812] (5/8) Epoch 19, batch 0, loss[loss=0.1698, simple_loss=0.2577, pruned_loss=0.04095, over 7267.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2577, pruned_loss=0.04095, over 7267.00 frames.], batch size: 25, lr: 4.15e-04
2022-05-14 22:49:27,400 INFO [train.py:812] (5/8) Epoch 19, batch 50, loss[loss=0.1577, simple_loss=0.2503, pruned_loss=0.03253, over 7341.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2547, pruned_loss=0.03723, over 325650.61 frames.], batch size: 22, lr: 4.15e-04
2022-05-14 22:50:35,551 INFO [train.py:812] (5/8) Epoch 19, batch 100, loss[loss=0.1709, simple_loss=0.2625, pruned_loss=0.03965, over 7342.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2521, pruned_loss=0.03624, over 575347.68 frames.], batch size: 22, lr: 4.14e-04
2022-05-14 22:51:34,795 INFO [train.py:812] (5/8) Epoch 19, batch 150, loss[loss=0.1764, simple_loss=0.2706, pruned_loss=0.0411, over 7221.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2518, pruned_loss=0.03596, over 764437.60 frames.], batch size: 21, lr: 4.14e-04
2022-05-14 22:53:02,462 INFO [train.py:812] (5/8) Epoch 19, batch 200, loss[loss=0.1452, simple_loss=0.2267, pruned_loss=0.03183, over 7281.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2517, pruned_loss=0.03629, over 909630.04 frames.], batch size: 17, lr: 4.14e-04
2022-05-14 22:54:01,873 INFO [train.py:812] (5/8) Epoch 19, batch 250, loss[loss=0.1438, simple_loss=0.2374, pruned_loss=0.02514, over 6736.00 frames.], tot_loss[loss=0.163, simple_loss=0.252, pruned_loss=0.03704, over 1025581.79 frames.], batch size: 31, lr: 4.14e-04
2022-05-14 22:55:01,085 INFO [train.py:812] (5/8) Epoch 19, batch 300, loss[loss=0.1351, simple_loss=0.227, pruned_loss=0.02159, over 7240.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2523, pruned_loss=0.03699, over 1115688.13 frames.], batch size: 20, lr: 4.14e-04
2022-05-14 22:56:00,981 INFO [train.py:812] (5/8) Epoch 19, batch 350, loss[loss=0.1609, simple_loss=0.2537, pruned_loss=0.0341, over 6775.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2519, pruned_loss=0.03668, over 1183087.02 frames.], batch size: 31, lr: 4.14e-04
2022-05-14 22:56:59,181 INFO [train.py:812] (5/8) Epoch 19, batch 400, loss[loss=0.143, simple_loss=0.2246, pruned_loss=0.03065, over 7061.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2537, pruned_loss=0.0374, over 1235219.53 frames.], batch size: 18, lr: 4.14e-04
2022-05-14 22:57:58,718 INFO [train.py:812] (5/8) Epoch 19, batch 450, loss[loss=0.2, simple_loss=0.2906, pruned_loss=0.05467, over 7343.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2539, pruned_loss=0.03739, over 1276948.38 frames.], batch size: 22, lr: 4.14e-04
2022-05-14 22:58:57,744 INFO [train.py:812] (5/8) Epoch 19, batch 500, loss[loss=0.1437, simple_loss=0.216, pruned_loss=0.03573, over 7128.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2544, pruned_loss=0.03753, over 1307575.72 frames.], batch size: 17, lr: 4.13e-04
2022-05-14 22:59:57,486 INFO [train.py:812] (5/8) Epoch 19, batch 550, loss[loss=0.1564, simple_loss=0.2306, pruned_loss=0.04112, over 7285.00 frames.], tot_loss[loss=0.164, simple_loss=0.2537, pruned_loss=0.03712, over 1336827.86 frames.], batch size: 17, lr: 4.13e-04
2022-05-14 23:00:56,144 INFO [train.py:812] (5/8) Epoch 19, batch 600, loss[loss=0.1299, simple_loss=0.2222, pruned_loss=0.01878, over 7276.00 frames.], tot_loss[loss=0.164, simple_loss=0.2539, pruned_loss=0.03707, over 1357026.60 frames.], batch size: 18, lr: 4.13e-04
2022-05-14 23:01:55,591 INFO [train.py:812] (5/8) Epoch 19, batch 650, loss[loss=0.1677, simple_loss=0.2601, pruned_loss=0.03765, over 7119.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2534, pruned_loss=0.03699, over 1375536.61 frames.], batch size: 21, lr: 4.13e-04
2022-05-14 23:02:54,271 INFO [train.py:812] (5/8) Epoch 19, batch 700, loss[loss=0.1763, simple_loss=0.2568, pruned_loss=0.04788, over 4862.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2538, pruned_loss=0.03704, over 1385391.79 frames.], batch size: 53, lr: 4.13e-04
2022-05-14 23:03:53,345 INFO [train.py:812] (5/8) Epoch 19, batch 750, loss[loss=0.1428, simple_loss=0.2341, pruned_loss=0.02572, over 7167.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2533, pruned_loss=0.03677, over 1394071.74 frames.], batch size: 19, lr: 4.13e-04
2022-05-14 23:04:52,301 INFO [train.py:812] (5/8) Epoch 19, batch 800, loss[loss=0.1744, simple_loss=0.2705, pruned_loss=0.0391, over 6755.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2536, pruned_loss=0.03676, over 1396165.13 frames.], batch size: 31, lr: 4.13e-04
2022-05-14 23:05:50,873 INFO [train.py:812] (5/8) Epoch 19, batch 850, loss[loss=0.1612, simple_loss=0.252, pruned_loss=0.03523, over 7070.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2541, pruned_loss=0.03678, over 1403507.28 frames.], batch size: 18, lr: 4.13e-04
2022-05-14 23:06:49,951 INFO [train.py:812] (5/8) Epoch 19, batch 900, loss[loss=0.1622, simple_loss=0.2359, pruned_loss=0.04421, over 6846.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2553, pruned_loss=0.03754, over 1408818.69 frames.], batch size: 15, lr: 4.12e-04
2022-05-14 23:07:49,366 INFO [train.py:812] (5/8) Epoch 19, batch 950, loss[loss=0.1678, simple_loss=0.2656, pruned_loss=0.03496, over 7374.00 frames.], tot_loss[loss=0.1643, simple_loss=0.254, pruned_loss=0.0373, over 1412316.33 frames.], batch size: 23, lr: 4.12e-04
2022-05-14 23:08:48,630 INFO [train.py:812] (5/8) Epoch 19, batch 1000, loss[loss=0.1501, simple_loss=0.2478, pruned_loss=0.02615, over 7140.00 frames.], tot_loss[loss=0.164, simple_loss=0.2541, pruned_loss=0.03689, over 1419182.35 frames.], batch size: 20, lr: 4.12e-04
2022-05-14 23:09:47,736 INFO [train.py:812] (5/8) Epoch 19, batch 1050, loss[loss=0.1822, simple_loss=0.2773, pruned_loss=0.04352, over 7297.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2542, pruned_loss=0.03713, over 1417465.38 frames.], batch size: 25, lr: 4.12e-04
2022-05-14 23:10:45,902 INFO [train.py:812] (5/8) Epoch 19, batch 1100, loss[loss=0.1687, simple_loss=0.2598, pruned_loss=0.03883, over 7321.00 frames.], tot_loss[loss=0.1644, simple_loss=0.254, pruned_loss=0.0374, over 1417697.36 frames.], batch size: 20, lr: 4.12e-04
2022-05-14 23:11:43,619 INFO [train.py:812] (5/8) Epoch 19, batch 1150, loss[loss=0.1822, simple_loss=0.2726, pruned_loss=0.0459, over 7283.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2541, pruned_loss=0.03732, over 1418486.03 frames.], batch size: 24, lr: 4.12e-04
2022-05-14 23:12:42,330 INFO [train.py:812] (5/8) Epoch 19, batch 1200, loss[loss=0.1832, simple_loss=0.2765, pruned_loss=0.04498, over 4989.00 frames.], tot_loss[loss=0.1643, simple_loss=0.254, pruned_loss=0.03729, over 1412922.65 frames.], batch size: 52, lr: 4.12e-04
2022-05-14 23:13:40,374 INFO [train.py:812] (5/8) Epoch 19, batch 1250, loss[loss=0.1575, simple_loss=0.2589, pruned_loss=0.028, over 7110.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2535, pruned_loss=0.03694, over 1413290.20 frames.], batch size: 21, lr: 4.12e-04
2022-05-14 23:14:39,564 INFO [train.py:812] (5/8) Epoch 19, batch 1300, loss[loss=0.1417, simple_loss=0.2303, pruned_loss=0.02654, over 7157.00 frames.], tot_loss[loss=0.1638, simple_loss=0.254, pruned_loss=0.03678, over 1414569.70 frames.], batch size: 19, lr: 4.12e-04
2022-05-14 23:15:38,788 INFO [train.py:812] (5/8) Epoch 19, batch 1350, loss[loss=0.178, simple_loss=0.2684, pruned_loss=0.04382, over 6961.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2546, pruned_loss=0.03697, over 1411838.90 frames.], batch size: 28, lr: 4.11e-04
2022-05-14 23:16:38,072 INFO [train.py:812] (5/8) Epoch 19, batch 1400, loss[loss=0.143, simple_loss=0.2287, pruned_loss=0.02862, over 7051.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2538, pruned_loss=0.03702, over 1409802.95 frames.], batch size: 18, lr: 4.11e-04
2022-05-14 23:17:42,355 INFO [train.py:812] (5/8) Epoch 19, batch 1450, loss[loss=0.1668, simple_loss=0.2569, pruned_loss=0.03839, over 7317.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2533, pruned_loss=0.03628, over 1416838.47 frames.], batch size: 21, lr: 4.11e-04
2022-05-14 23:18:41,360 INFO [train.py:812] (5/8) Epoch 19, batch 1500, loss[loss=0.1359, simple_loss=0.2186, pruned_loss=0.02659, over 7262.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2532, pruned_loss=0.03623, over 1420695.16 frames.], batch size: 19, lr: 4.11e-04
2022-05-14 23:19:40,445 INFO [train.py:812] (5/8) Epoch 19, batch 1550, loss[loss=0.1742, simple_loss=0.2695, pruned_loss=0.03943, over 7413.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2532, pruned_loss=0.03652, over 1424314.82 frames.], batch size: 21, lr: 4.11e-04
2022-05-14 23:20:40,041 INFO [train.py:812] (5/8) Epoch 19, batch 1600, loss[loss=0.1846, simple_loss=0.2686, pruned_loss=0.0503, over 7209.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2535, pruned_loss=0.03694, over 1423491.03 frames.], batch size: 22, lr: 4.11e-04
2022-05-14 23:21:39,517 INFO [train.py:812] (5/8) Epoch 19, batch 1650, loss[loss=0.1579, simple_loss=0.2412, pruned_loss=0.03727, over 7165.00 frames.], tot_loss[loss=0.1632, simple_loss=0.253, pruned_loss=0.03666, over 1422735.65 frames.], batch size: 18, lr: 4.11e-04
2022-05-14 23:22:38,869 INFO [train.py:812] (5/8) Epoch 19, batch 1700, loss[loss=0.1425, simple_loss=0.2374, pruned_loss=0.02377, over 7152.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2537, pruned_loss=0.03683, over 1423407.34 frames.], batch size: 18, lr: 4.11e-04
2022-05-14 23:23:37,794 INFO [train.py:812] (5/8) Epoch 19, batch 1750, loss[loss=0.1545, simple_loss=0.2475, pruned_loss=0.03075, over 7146.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2542, pruned_loss=0.0371, over 1416290.30 frames.], batch size: 20, lr: 4.10e-04
2022-05-14 23:24:36,352 INFO [train.py:812] (5/8) Epoch 19, batch 1800, loss[loss=0.1605, simple_loss=0.2494, pruned_loss=0.03585, over 7266.00 frames.], tot_loss[loss=0.1648, simple_loss=0.255, pruned_loss=0.0373, over 1417321.44 frames.], batch size: 19, lr: 4.10e-04
2022-05-14 23:25:35,727 INFO [train.py:812] (5/8) Epoch 19, batch 1850, loss[loss=0.2002, simple_loss=0.2918, pruned_loss=0.05433, over 7315.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2547, pruned_loss=0.03688, over 1422753.38 frames.], batch size: 24, lr: 4.10e-04
2022-05-14 23:26:34,571 INFO [train.py:812] (5/8) Epoch 19, batch 1900, loss[loss=0.1617, simple_loss=0.2588, pruned_loss=0.03228, over 7151.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2555, pruned_loss=0.03741, over 1419871.55 frames.], batch size: 28, lr: 4.10e-04
2022-05-14 23:27:34,098 INFO [train.py:812] (5/8) Epoch 19, batch 1950, loss[loss=0.1473, simple_loss=0.2311, pruned_loss=0.03179, over 7006.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2558, pruned_loss=0.03758, over 1420833.12 frames.], batch size: 16, lr: 4.10e-04
2022-05-14 23:28:32,897 INFO [train.py:812] (5/8) Epoch 19, batch 2000, loss[loss=0.1845, simple_loss=0.2778, pruned_loss=0.04556, over 7141.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2552, pruned_loss=0.03748, over 1424284.80 frames.], batch size: 20, lr: 4.10e-04
2022-05-14 23:29:32,677 INFO [train.py:812] (5/8) Epoch 19, batch 2050, loss[loss=0.1716, simple_loss=0.2573, pruned_loss=0.04295, over 7329.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2546, pruned_loss=0.0376, over 1424938.32 frames.], batch size: 25, lr: 4.10e-04
2022-05-14 23:30:30,655 INFO [train.py:812] (5/8) Epoch 19, batch 2100, loss[loss=0.157, simple_loss=0.2509, pruned_loss=0.03158, over 7162.00 frames.], tot_loss[loss=0.165, simple_loss=0.2549, pruned_loss=0.03752, over 1425292.70 frames.], batch size: 19, lr: 4.10e-04
2022-05-14 23:31:30,577 INFO [train.py:812] (5/8) Epoch 19, batch 2150, loss[loss=0.1669, simple_loss=0.2645, pruned_loss=0.03468, over 7224.00 frames.], tot_loss[loss=0.1652, simple_loss=0.255, pruned_loss=0.03769, over 1422223.22 frames.], batch size: 21, lr: 4.09e-04
2022-05-14 23:32:29,960 INFO [train.py:812] (5/8) Epoch 19, batch 2200, loss[loss=0.1797, simple_loss=0.2821, pruned_loss=0.03862, over 7113.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2542, pruned_loss=0.03697, over 1425919.45 frames.], batch size: 21, lr: 4.09e-04
2022-05-14 23:33:29,284 INFO [train.py:812] (5/8) Epoch 19, batch 2250, loss[loss=0.1881, simple_loss=0.2706, pruned_loss=0.0528, over 6271.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2549, pruned_loss=0.0374, over 1425134.15 frames.], batch size: 37, lr: 4.09e-04
2022-05-14 23:34:27,799 INFO [train.py:812] (5/8) Epoch 19, batch 2300, loss[loss=0.1801, simple_loss=0.2653, pruned_loss=0.04745, over 7380.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2544, pruned_loss=0.03734, over 1426154.28 frames.], batch size: 23, lr: 4.09e-04
2022-05-14 23:35:25,969 INFO [train.py:812] (5/8) Epoch 19, batch 2350, loss[loss=0.137, simple_loss=0.2201, pruned_loss=0.02694, over 7277.00 frames.], tot_loss[loss=0.164, simple_loss=0.2539, pruned_loss=0.03704, over 1424074.00 frames.], batch size: 17, lr: 4.09e-04
2022-05-14 23:36:25,344 INFO [train.py:812] (5/8) Epoch 19, batch 2400, loss[loss=0.1575, simple_loss=0.2481, pruned_loss=0.03345, over 7145.00 frames.], tot_loss[loss=0.164, simple_loss=0.254, pruned_loss=0.03701, over 1420824.67 frames.], batch size: 20, lr: 4.09e-04
2022-05-14 23:37:24,272 INFO [train.py:812] (5/8) Epoch 19, batch 2450, loss[loss=0.2012, simple_loss=0.292, pruned_loss=0.05527, over 7141.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2535, pruned_loss=0.03668, over 1423146.29 frames.], batch size: 20, lr: 4.09e-04
2022-05-14 23:38:23,582 INFO [train.py:812] (5/8) Epoch 19, batch 2500, loss[loss=0.1692, simple_loss=0.2668, pruned_loss=0.03581, over 7125.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2534, pruned_loss=0.03691, over 1421643.66 frames.], batch size: 26, lr: 4.09e-04
2022-05-14 23:39:22,977 INFO [train.py:812] (5/8) Epoch 19, batch 2550, loss[loss=0.1847, simple_loss=0.2831, pruned_loss=0.04317, over 7287.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2529, pruned_loss=0.03681, over 1421871.33 frames.], batch size: 24, lr: 4.08e-04
2022-05-14 23:40:21,742 INFO [train.py:812] (5/8) Epoch 19, batch 2600, loss[loss=0.1459, simple_loss=0.2243, pruned_loss=0.03376, over 6997.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2542, pruned_loss=0.03677, over 1425767.65 frames.], batch size: 16, lr: 4.08e-04
2022-05-14 23:41:20,963 INFO [train.py:812] (5/8) Epoch 19, batch 2650, loss[loss=0.2267, simple_loss=0.299, pruned_loss=0.07719, over 7289.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2545, pruned_loss=0.03705, over 1427724.14 frames.], batch size: 24, lr: 4.08e-04
2022-05-14 23:42:20,838 INFO [train.py:812] (5/8) Epoch 19, batch 2700, loss[loss=0.1893, simple_loss=0.266, pruned_loss=0.05629, over 7314.00 frames.], tot_loss[loss=0.164, simple_loss=0.2539, pruned_loss=0.03703, over 1431632.81 frames.], batch size: 25, lr: 4.08e-04
2022-05-14 23:43:20,414 INFO [train.py:812] (5/8) Epoch 19, batch 2750, loss[loss=0.1866, simple_loss=0.2764, pruned_loss=0.04838, over 7409.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2546, pruned_loss=0.03711, over 1430529.70 frames.], batch size: 21, lr: 4.08e-04
2022-05-14 23:44:19,808 INFO [train.py:812] (5/8) Epoch 19, batch 2800, loss[loss=0.1869, simple_loss=0.293, pruned_loss=0.04036, over 7075.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2542, pruned_loss=0.03668, over 1431042.22 frames.], batch size: 18, lr: 4.08e-04
2022-05-14 23:45:18,634 INFO [train.py:812] (5/8) Epoch 19, batch 2850, loss[loss=0.1613, simple_loss=0.2559, pruned_loss=0.03335, over 7166.00 frames.], tot_loss[loss=0.164, simple_loss=0.2541, pruned_loss=0.03694, over 1427604.33 frames.], batch size: 19, lr: 4.08e-04
2022-05-14 23:46:17,219 INFO [train.py:812] (5/8) Epoch 19, batch 2900, loss[loss=0.2101, simple_loss=0.2933, pruned_loss=0.06344, over 7144.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2543, pruned_loss=0.03729, over 1424993.49 frames.], batch size: 26, lr: 4.08e-04
2022-05-14 23:47:15,879 INFO [train.py:812] (5/8) Epoch 19, batch 2950, loss[loss=0.1301, simple_loss=0.219, pruned_loss=0.0206, over 7271.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2539, pruned_loss=0.03715, over 1430175.52 frames.], batch size: 17, lr: 4.08e-04
2022-05-14 23:48:15,113 INFO [train.py:812] (5/8) Epoch 19, batch 3000, loss[loss=0.1731, simple_loss=0.2645, pruned_loss=0.04079, over 4807.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2535, pruned_loss=0.03682, over 1429350.63 frames.], batch size: 52, lr: 4.07e-04
2022-05-14 23:48:15,114 INFO [train.py:832] (5/8) Computing validation loss
2022-05-14 23:48:22,685 INFO [train.py:841] (5/8) Epoch 19, validation: loss=0.1531, simple_loss=0.2523, pruned_loss=0.02694, over 698248.00 frames.
2022-05-14 23:49:22,394 INFO [train.py:812] (5/8) Epoch 19, batch 3050, loss[loss=0.1785, simple_loss=0.2727, pruned_loss=0.04215, over 7184.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2532, pruned_loss=0.03647, over 1431007.58 frames.], batch size: 23, lr: 4.07e-04
2022-05-14 23:50:21,359 INFO [train.py:812] (5/8) Epoch 19, batch 3100, loss[loss=0.1855, simple_loss=0.2717, pruned_loss=0.04965, over 6437.00 frames.], tot_loss[loss=0.163, simple_loss=0.2528, pruned_loss=0.03655, over 1432241.68 frames.], batch size: 38, lr: 4.07e-04
2022-05-14 23:51:20,043 INFO [train.py:812] (5/8) Epoch 19, batch 3150, loss[loss=0.1647, simple_loss=0.2479, pruned_loss=0.04079, over 7273.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2535, pruned_loss=0.0371, over 1429899.33 frames.], batch size: 18, lr: 4.07e-04
2022-05-14 23:52:18,565 INFO [train.py:812] (5/8) Epoch 19, batch 3200, loss[loss=0.1606, simple_loss=0.2521, pruned_loss=0.03459, over 7172.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2534, pruned_loss=0.03716, over 1428768.32 frames.], batch size: 19, lr: 4.07e-04
2022-05-14 23:53:18,016 INFO [train.py:812] (5/8) Epoch 19, batch 3250, loss[loss=0.1384, simple_loss=0.2236, pruned_loss=0.02657, over 7359.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2547, pruned_loss=0.03757, over 1425659.00 frames.], batch size: 19, lr: 4.07e-04
2022-05-14 23:54:16,318 INFO [train.py:812] (5/8) Epoch 19, batch 3300, loss[loss=0.1523, simple_loss=0.2507, pruned_loss=0.02694, over 6334.00 frames.], tot_loss[loss=0.165, simple_loss=0.255, pruned_loss=0.03755, over 1425486.73 frames.], batch size: 38, lr: 4.07e-04
2022-05-14 23:55:15,319 INFO [train.py:812] (5/8) Epoch 19, batch 3350, loss[loss=0.1645, simple_loss=0.2635, pruned_loss=0.03277, over 7119.00 frames.], tot_loss[loss=0.165, simple_loss=0.2547, pruned_loss=0.03765, over 1424690.06 frames.], batch size: 21, lr: 4.07e-04
2022-05-14 23:56:14,421 INFO [train.py:812] (5/8) Epoch 19, batch 3400, loss[loss=0.1618, simple_loss=0.2478, pruned_loss=0.03791, over 7271.00 frames.], tot_loss[loss=0.165, simple_loss=0.2547, pruned_loss=0.03762, over 1424932.01 frames.], batch size: 18, lr: 4.06e-04
2022-05-14 23:57:14,013 INFO [train.py:812] (5/8) Epoch 19, batch 3450, loss[loss=0.1554, simple_loss=0.2393, pruned_loss=0.03571, over 7359.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2527, pruned_loss=0.03708, over 1420893.88 frames.], batch size: 19, lr: 4.06e-04
2022-05-14 23:58:13,013 INFO [train.py:812] (5/8) Epoch 19, batch 3500, loss[loss=0.1586, simple_loss=0.2435, pruned_loss=0.03688, over 7274.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2527, pruned_loss=0.03703, over 1423105.18 frames.], batch size: 18, lr: 4.06e-04
2022-05-14 23:59:12,615 INFO [train.py:812] (5/8) Epoch 19, batch 3550, loss[loss=0.1616, simple_loss=0.2369, pruned_loss=0.04317, over 7120.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2519, pruned_loss=0.0365, over 1423906.50 frames.], batch size: 17, lr: 4.06e-04
2022-05-15 00:00:11,603 INFO [train.py:812] (5/8) Epoch 19, batch 3600, loss[loss=0.2088, simple_loss=0.2854, pruned_loss=0.06609, over 7189.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2523, pruned_loss=0.03645, over 1420374.36 frames.], batch size: 23, lr: 4.06e-04
2022-05-15 00:01:10,998 INFO [train.py:812] (5/8) Epoch 19, batch 3650, loss[loss=0.1554, simple_loss=0.2621, pruned_loss=0.02437, over 7319.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2533, pruned_loss=0.03698, over 1414667.39 frames.], batch size: 20, lr: 4.06e-04
2022-05-15 00:02:10,010 INFO [train.py:812] (5/8) Epoch 19, batch 3700, loss[loss=0.1588, simple_loss=0.2598, pruned_loss=0.02886, over 7410.00 frames.], tot_loss[loss=0.165, simple_loss=0.2551, pruned_loss=0.0374, over 1417231.12 frames.], batch size: 21, lr: 4.06e-04
2022-05-15 00:03:09,354 INFO [train.py:812] (5/8) Epoch 19, batch 3750, loss[loss=0.1757, simple_loss=0.2671, pruned_loss=0.04213, over 7385.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2551, pruned_loss=0.03755, over 1413559.60 frames.], batch size: 23, lr: 4.06e-04
2022-05-15 00:04:08,154 INFO [train.py:812] (5/8) Epoch 19, batch 3800, loss[loss=0.1743, simple_loss=0.2565, pruned_loss=0.04604, over 7354.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2555, pruned_loss=0.03759, over 1418745.06 frames.], batch size: 19, lr: 4.06e-04
2022-05-15 00:05:06,751 INFO [train.py:812] (5/8) Epoch 19, batch 3850, loss[loss=0.1514, simple_loss=0.2316, pruned_loss=0.03555, over 7176.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2549, pruned_loss=0.03763, over 1417202.68 frames.], batch size: 18, lr: 4.05e-04
2022-05-15 00:06:04,372 INFO [train.py:812] (5/8) Epoch 19, batch 3900, loss[loss=0.1492, simple_loss=0.2447, pruned_loss=0.02682, over 7112.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2556, pruned_loss=0.03786, over 1414587.03 frames.], batch size: 21, lr: 4.05e-04
2022-05-15 00:07:04,141 INFO [train.py:812] (5/8) Epoch 19, batch 3950, loss[loss=0.2027, simple_loss=0.2996, pruned_loss=0.05284, over 7155.00 frames.], tot_loss[loss=0.166, simple_loss=0.2558, pruned_loss=0.03807, over 1416356.91 frames.], batch size: 18, lr: 4.05e-04
2022-05-15 00:08:03,273 INFO [train.py:812] (5/8) Epoch 19, batch 4000, loss[loss=0.1964, simple_loss=0.2826, pruned_loss=0.05515, over 5116.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2548, pruned_loss=0.03774, over 1418460.54 frames.], batch size: 52, lr: 4.05e-04
2022-05-15 00:09:00,795 INFO [train.py:812] (5/8) Epoch 19, batch 4050, loss[loss=0.1438, simple_loss=0.2219, pruned_loss=0.03284, over 6808.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2537, pruned_loss=0.03752, over 1416059.74 frames.], batch size: 15, lr: 4.05e-04
2022-05-15 00:09:59,476 INFO [train.py:812] (5/8) Epoch 19, batch 4100, loss[loss=0.1692, simple_loss=0.2599, pruned_loss=0.0392, over 4753.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2541, pruned_loss=0.0377, over 1415796.44 frames.], batch size: 52, lr: 4.05e-04
2022-05-15 00:10:57,148 INFO [train.py:812] (5/8) Epoch 19, batch 4150, loss[loss=0.1652, simple_loss=0.2634, pruned_loss=0.03344, over 7389.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2535, pruned_loss=0.03751, over 1421394.46 frames.], batch size: 23, lr: 4.05e-04
2022-05-15 00:11:56,901 INFO [train.py:812] (5/8) Epoch 19, batch 4200, loss[loss=0.1722, simple_loss=0.2555, pruned_loss=0.04448, over 7208.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2538, pruned_loss=0.03757, over 1419615.35 frames.], batch size: 23, lr: 4.05e-04
2022-05-15 00:12:56,154 INFO [train.py:812] (5/8) Epoch 19, batch 4250, loss[loss=0.1456, simple_loss=0.2292, pruned_loss=0.03093, over 6826.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2538, pruned_loss=0.03733, over 1419689.11 frames.], batch size: 15, lr: 4.04e-04
2022-05-15 00:14:05,108 INFO [train.py:812] (5/8) Epoch 19, batch 4300, loss[loss=0.1895, simple_loss=0.2779, pruned_loss=0.05058, over 7149.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2534, pruned_loss=0.03706, over 1418905.67 frames.], batch size: 26, lr: 4.04e-04
2022-05-15 00:15:04,942 INFO [train.py:812] (5/8) Epoch 19, batch 4350, loss[loss=0.1801, simple_loss=0.2696, pruned_loss=0.04525, over 7165.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2525, pruned_loss=0.03689, over 1416390.13 frames.], batch size: 18, lr: 4.04e-04
2022-05-15 00:16:03,374 INFO [train.py:812] (5/8) Epoch 19, batch 4400, loss[loss=0.1862, simple_loss=0.2726, pruned_loss=0.04985, over 6436.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2524, pruned_loss=0.03698, over 1411857.71 frames.], batch size: 38, lr: 4.04e-04
2022-05-15 00:17:02,487 INFO [train.py:812] (5/8) Epoch 19, batch 4450, loss[loss=0.1391, simple_loss=0.2236, pruned_loss=0.0273, over 6780.00 frames.], tot_loss[loss=0.1619, simple_loss=0.251, pruned_loss=0.03636, over 1406797.83 frames.], batch size: 15, lr: 4.04e-04
2022-05-15 00:18:02,041 INFO [train.py:812] (5/8) Epoch 19, batch 4500, loss[loss=0.1417, simple_loss=0.2353, pruned_loss=0.02403, over 7150.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2522, pruned_loss=0.03686, over 1394291.49 frames.], batch size: 20, lr: 4.04e-04
2022-05-15 00:19:01,074 INFO [train.py:812] (5/8) Epoch 19, batch 4550, loss[loss=0.1823, simple_loss=0.2701, pruned_loss=0.04728, over 6434.00 frames.], tot_loss[loss=0.163, simple_loss=0.2516, pruned_loss=0.03718, over 1368466.23 frames.], batch size: 38, lr: 4.04e-04
2022-05-15 00:20:09,399 INFO [train.py:812] (5/8) Epoch 20, batch 0, loss[loss=0.1394, simple_loss=0.2301, pruned_loss=0.02435, over 7356.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2301, pruned_loss=0.02435, over 7356.00 frames.], batch size: 19, lr: 3.94e-04
2022-05-15 00:21:09,534 INFO [train.py:812] (5/8) Epoch 20, batch 50, loss[loss=0.1266, simple_loss=0.2166, pruned_loss=0.01833, over 7285.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2505, pruned_loss=0.03528, over 320836.83 frames.], batch size: 18, lr: 3.94e-04
2022-05-15 00:22:08,825 INFO [train.py:812] (5/8) Epoch 20, batch 100, loss[loss=0.1846, simple_loss=0.2718, pruned_loss=0.04872, over 5212.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2508, pruned_loss=0.03496, over 566036.96 frames.], batch size: 53, lr: 3.94e-04
2022-05-15 00:23:08,548 INFO [train.py:812] (5/8) Epoch 20, batch 150, loss[loss=0.1609, simple_loss=0.2581, pruned_loss=0.03178, over 7318.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2545, pruned_loss=0.03622, over 756368.93 frames.], batch size: 21, lr: 3.94e-04
2022-05-15 00:24:07,747 INFO [train.py:812] (5/8) Epoch 20, batch 200, loss[loss=0.15, simple_loss=0.25, pruned_loss=0.02496, over 7321.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2542, pruned_loss=0.0361, over 904007.07 frames.], batch size: 22, lr: 3.93e-04
2022-05-15 00:25:08,003 INFO [train.py:812] (5/8) Epoch 20, batch 250, loss[loss=0.1643, simple_loss=0.2595, pruned_loss=0.03455, over 7329.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2523, pruned_loss=0.03525, over 1022811.94 frames.], batch size: 22, lr: 3.93e-04
2022-05-15 00:26:07,269 INFO [train.py:812] (5/8) Epoch 20, batch 300, loss[loss=0.2011, simple_loss=0.2914, pruned_loss=0.05545, over 7198.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2538, pruned_loss=0.03532, over 1112221.42 frames.], batch size: 23, lr: 3.93e-04
2022-05-15 00:27:07,174 INFO [train.py:812] (5/8) Epoch 20, batch 350, loss[loss=0.1786, simple_loss=0.2695, pruned_loss=0.04378, over 7149.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2541, pruned_loss=0.03564, over 1184928.82 frames.], batch size: 20, lr: 3.93e-04
2022-05-15 00:28:05,183 INFO [train.py:812] (5/8) Epoch 20, batch 400, loss[loss=0.1659, simple_loss=0.2564, pruned_loss=0.03772, over 7143.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2548, pruned_loss=0.03629, over 1237415.35 frames.], batch size: 20, lr: 3.93e-04
2022-05-15 00:29:03,654 INFO [train.py:812] (5/8) Epoch 20, batch 450, loss[loss=0.1705, simple_loss=0.2691, pruned_loss=0.036, over 7374.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2558, pruned_loss=0.03688, over 1274816.80 frames.], batch size: 23, lr: 3.93e-04
2022-05-15 00:30:01,851 INFO [train.py:812] (5/8) Epoch 20, batch 500, loss[loss=0.1534, simple_loss=0.2524, pruned_loss=0.02718, over 7228.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2554, pruned_loss=0.03697, over 1306332.45 frames.], batch size: 21, lr: 3.93e-04
2022-05-15 00:31:00,526 INFO [train.py:812] (5/8) Epoch 20, batch 550, loss[loss=0.1606, simple_loss=0.2565, pruned_loss=0.03239, over 6872.00 frames.], tot_loss[loss=0.164, simple_loss=0.2548, pruned_loss=0.03658, over 1332869.13 frames.], batch size: 31, lr: 3.93e-04
2022-05-15 00:32:00,101 INFO [train.py:812] (5/8) Epoch 20, batch 600, loss[loss=0.1327, simple_loss=0.222, pruned_loss=0.0217, over 7162.00 frames.], tot_loss[loss=0.1626, simple_loss=0.253, pruned_loss=0.03613, over 1354924.67 frames.], batch size: 18, lr: 3.93e-04
2022-05-15 00:32:59,173 INFO [train.py:812] (5/8) Epoch 20, batch 650, loss[loss=0.1457, simple_loss=0.2358, pruned_loss=0.02782, over 7172.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2531, pruned_loss=0.0361, over 1369270.96 frames.], batch size: 18, lr: 3.92e-04
2022-05-15 00:33:55,660 INFO [train.py:812] (5/8) Epoch 20, batch 700, loss[loss=0.1836, simple_loss=0.2712, pruned_loss=0.04794, over 7232.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2532, pruned_loss=0.03592, over 1383081.54 frames.], batch size: 20, lr: 3.92e-04
2022-05-15 00:34:54,547 INFO [train.py:812] (5/8) Epoch 20, batch 750, loss[loss=0.1602, simple_loss=0.2674, pruned_loss=0.02648, over 7294.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2525, pruned_loss=0.03561, over 1393384.73 frames.], batch size: 25, lr: 3.92e-04
2022-05-15 00:35:51,669 INFO [train.py:812] (5/8) Epoch 20, batch 800, loss[loss=0.1598, simple_loss=0.2378, pruned_loss=0.04088, over 7419.00 frames.], tot_loss[loss=0.162, simple_loss=0.2526, pruned_loss=0.03564, over 1402292.00 frames.], batch size: 18, lr: 3.92e-04
2022-05-15 00:36:56,563 INFO [train.py:812] (5/8) Epoch 20, batch 850, loss[loss=0.1729, simple_loss=0.2649, pruned_loss=0.04045, over 7089.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2535, pruned_loss=0.03603, over 1410189.01 frames.], batch size: 28, lr: 3.92e-04
2022-05-15 00:37:55,356 INFO [train.py:812] (5/8) Epoch 20, batch 900, loss[loss=0.1561, simple_loss=0.2514, pruned_loss=0.03043, over 7356.00 frames.], tot_loss[loss=0.162, simple_loss=0.2525, pruned_loss=0.03574, over 1415452.97 frames.], batch size: 19, lr: 3.92e-04
2022-05-15 00:38:53,707 INFO [train.py:812] (5/8) Epoch 20, batch 950, loss[loss=0.158, simple_loss=0.2544, pruned_loss=0.03076, over 7238.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2529, pruned_loss=0.03588, over 1419245.46 frames.], batch size: 20, lr: 3.92e-04
2022-05-15 00:39:52,438 INFO [train.py:812] (5/8) Epoch 20, batch 1000, loss[loss=0.1818, simple_loss=0.2823, pruned_loss=0.04061, over 7267.00 frames.], tot_loss[loss=0.162, simple_loss=0.2525, pruned_loss=0.03576, over 1419917.10 frames.], batch size: 24, lr: 3.92e-04
2022-05-15 00:40:51,832 INFO [train.py:812] (5/8) Epoch 20, batch 1050, loss[loss=0.1789, simple_loss=0.2591, pruned_loss=0.04932, over 7201.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2528, pruned_loss=0.0358, over 1419722.53 frames.], batch size: 22, lr: 3.92e-04
2022-05-15 00:41:50,553 INFO [train.py:812] (5/8) Epoch 20, batch 1100, loss[loss=0.1916, simple_loss=0.2787, pruned_loss=0.05228, over 7203.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2529, pruned_loss=0.03581, over 1417465.42 frames.], batch size: 22, lr: 3.91e-04
2022-05-15 00:42:49,017 INFO [train.py:812] (5/8) Epoch 20, batch 1150, loss[loss=0.2009, simple_loss=0.293, pruned_loss=0.05439, over 7257.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2539, pruned_loss=0.03592, over 1421160.70 frames.], batch size: 24, lr: 3.91e-04
2022-05-15 00:43:48,220 INFO [train.py:812] (5/8) Epoch 20, batch 1200, loss[loss=0.1615, simple_loss=0.2524, pruned_loss=0.03529, over 7345.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2524, pruned_loss=0.03558, over 1426500.82 frames.], batch size: 22, lr: 3.91e-04
2022-05-15 00:44:47,686 INFO [train.py:812] (5/8) Epoch 20, batch 1250, loss[loss=0.1257, simple_loss=0.2175, pruned_loss=0.01701, over 7129.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2521, pruned_loss=0.03569, over 1426824.62 frames.], batch size: 17, lr: 3.91e-04
2022-05-15 00:45:46,804 INFO [train.py:812] (5/8) Epoch 20, batch 1300, loss[loss=0.1737, simple_loss=0.2727, pruned_loss=0.03737, over 7113.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2518, pruned_loss=0.03583, over 1428450.36 frames.], batch size: 21, lr: 3.91e-04
2022-05-15 00:46:46,851 INFO [train.py:812] (5/8) Epoch 20, batch 1350, loss[loss=0.1967, simple_loss=0.2943, pruned_loss=0.04958, over 7209.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2528, pruned_loss=0.03633, over 1431141.07 frames.], batch size: 22, lr: 3.91e-04
2022-05-15 00:47:55,894 INFO [train.py:812] (5/8) Epoch 20, batch 1400, loss[loss=0.1583, simple_loss=0.2476, pruned_loss=0.03453, over 7199.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2529, pruned_loss=0.03639, over 1432571.84 frames.], batch size: 26, lr: 3.91e-04
2022-05-15 00:48:55,556 INFO [train.py:812] (5/8) Epoch 20, batch 1450, loss[loss=0.1886, simple_loss=0.278, pruned_loss=0.04957, over 7205.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2537, pruned_loss=0.03651, over 1430358.77 frames.], batch size: 26, lr: 3.91e-04
2022-05-15 00:49:54,734 INFO [train.py:812] (5/8) Epoch 20, batch 1500, loss[loss=0.1799, simple_loss=0.267, pruned_loss=0.04636, over 7372.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2548, pruned_loss=0.03697, over 1427966.63 frames.], batch size: 23, lr: 3.91e-04
2022-05-15 00:51:04,078 INFO [train.py:812] (5/8) Epoch 20, batch 1550, loss[loss=0.1307, simple_loss=0.2249, pruned_loss=0.01823, over 7433.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2544, pruned_loss=0.03646, over 1430467.68 frames.], batch size: 20, lr: 3.91e-04
2022-05-15 00:52:22,135 INFO [train.py:812] (5/8) Epoch 20, batch 1600, loss[loss=0.1575, simple_loss=0.2584, pruned_loss=0.02825, over 7346.00 frames.], tot_loss[loss=0.164, simple_loss=0.2548, pruned_loss=0.03655, over 1425458.52 frames.], batch size: 22, lr: 3.90e-04
2022-05-15 00:53:19,542 INFO [train.py:812] (5/8) Epoch 20, batch 1650, loss[loss=0.1676, simple_loss=0.2597, pruned_loss=0.0378, over 7197.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2549, pruned_loss=0.03679, over 1421665.75 frames.], batch size: 23, lr: 3.90e-04
2022-05-15 00:54:36,064 INFO [train.py:812] (5/8) Epoch 20, batch 1700, loss[loss=0.1649, simple_loss=0.2454, pruned_loss=0.04224, over 7160.00 frames.], tot_loss[loss=0.1634, simple_loss=0.254, pruned_loss=0.03643, over 1421118.67 frames.], batch size: 19, lr: 3.90e-04
2022-05-15 00:55:43,687 INFO [train.py:812] (5/8) Epoch 20, batch 1750, loss[loss=0.1951, simple_loss=0.2877, pruned_loss=0.0513, over 7336.00 frames.], tot_loss[loss=0.163, simple_loss=0.2536, pruned_loss=0.03622, over 1426280.29 frames.], batch size: 22, lr: 3.90e-04
2022-05-15 00:56:42,591 INFO [train.py:812] (5/8) Epoch 20, batch 1800, loss[loss=0.1751, simple_loss=0.27, pruned_loss=0.04013, over 7291.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2537, pruned_loss=0.03624, over 1425561.59 frames.], batch size: 25, lr: 3.90e-04
2022-05-15 00:57:42,325 INFO [train.py:812] (5/8) Epoch 20, batch 1850, loss[loss=0.1573, simple_loss=0.2416, pruned_loss=0.03649, over 7060.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2538, pruned_loss=0.03627, over 1428746.21 frames.], batch size: 18, lr: 3.90e-04
2022-05-15 00:58:41,677 INFO [train.py:812] (5/8) Epoch 20, batch 1900, loss[loss=0.1587, simple_loss=0.2515, pruned_loss=0.03291, over 7227.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2539, pruned_loss=0.03613, over 1429793.28 frames.], batch size: 20, lr: 3.90e-04
2022-05-15 00:59:40,058 INFO [train.py:812] (5/8) Epoch 20, batch 1950, loss[loss=0.1513, simple_loss=0.2473, pruned_loss=0.02766, over 6348.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2529, pruned_loss=0.03591, over 1429717.97 frames.], batch size: 38, lr: 3.90e-04
2022-05-15 01:00:37,499 INFO [train.py:812] (5/8) Epoch 20, batch 2000, loss[loss=0.1601, simple_loss=0.257, pruned_loss=0.03158, over 7233.00 frames.], tot_loss[loss=0.162, simple_loss=0.252, pruned_loss=0.03602, over 1430592.42 frames.], batch size: 20, lr: 3.90e-04
2022-05-15 01:01:35,455 INFO [train.py:812] (5/8) Epoch 20, batch 2050, loss[loss=0.1721, simple_loss=0.2635, pruned_loss=0.04039, over 7218.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2514, pruned_loss=0.03617, over 1429676.43 frames.], batch size: 21, lr: 3.89e-04
2022-05-15 01:02:33,057 INFO [train.py:812] (5/8) Epoch 20, batch 2100, loss[loss=0.1793, simple_loss=0.2685, pruned_loss=0.04504, over 7437.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2518, pruned_loss=0.03619, over 1432253.44 frames.], batch size: 20, lr: 3.89e-04
2022-05-15 01:03:30,908 INFO [train.py:812] (5/8) Epoch 20, batch 2150, loss[loss=0.1752, simple_loss=0.261, pruned_loss=0.04475, over 7198.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2519, pruned_loss=0.03654, over 1426741.30 frames.], batch size: 22, lr: 3.89e-04
2022-05-15 01:04:30,268 INFO [train.py:812] (5/8) Epoch 20, batch 2200, loss[loss=0.1449, simple_loss=0.2226, pruned_loss=0.03364, over 6805.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2521, pruned_loss=0.03659, over 1421729.30 frames.], batch size: 15, lr: 3.89e-04
2022-05-15 01:05:28,859 INFO [train.py:812] (5/8) Epoch 20, batch 2250, loss[loss=0.167, simple_loss=0.2532, pruned_loss=0.04036, over 7140.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2522, pruned_loss=0.03644, over 1422774.94 frames.], batch size: 20, lr: 3.89e-04
2022-05-15 01:06:27,813 INFO [train.py:812] (5/8) Epoch 20, batch 2300, loss[loss=0.1825, simple_loss=0.2727, pruned_loss=0.04612, over 7374.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2518, pruned_loss=0.03616, over 1423007.85 frames.], batch size: 23, lr: 3.89e-04
2022-05-15 01:07:25,470 INFO [train.py:812] (5/8) Epoch 20, batch 2350, loss[loss=0.1714, simple_loss=0.2669, pruned_loss=0.03794, over 7319.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2525, pruned_loss=0.03641, over 1422206.91 frames.], batch size: 21, lr: 3.89e-04
2022-05-15 01:08:24,204 INFO [train.py:812] (5/8) Epoch 20, batch 2400, loss[loss=0.1473, simple_loss=0.2314, pruned_loss=0.03159, over 7440.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2521, pruned_loss=0.03603, over 1423757.06 frames.], batch size: 20, lr: 3.89e-04
2022-05-15 01:09:23,901 INFO [train.py:812] (5/8) Epoch 20, batch 2450, loss[loss=0.1549, simple_loss=0.2545, pruned_loss=0.02763, over 7006.00 frames.], tot_loss[loss=0.1618, simple_loss=0.252, pruned_loss=0.03586, over 1426746.54 frames.], batch size: 28, lr: 3.89e-04
2022-05-15 01:10:23,008 INFO [train.py:812] (5/8) Epoch 20, batch 2500, loss[loss=0.1846, simple_loss=0.2772, pruned_loss=0.04599, over 7178.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2513, pruned_loss=0.03571, over 1426520.18 frames.], batch size: 26, lr: 3.88e-04
2022-05-15 01:11:22,817 INFO [train.py:812] (5/8) Epoch 20, batch 2550, loss[loss=0.193, simple_loss=0.2726, pruned_loss=0.05674, over 7329.00 frames.], tot_loss[loss=0.1622, simple_loss=0.252, pruned_loss=0.0362, over 1425574.73 frames.], batch size: 20, lr: 3.88e-04
2022-05-15 01:12:22,059 INFO [train.py:812] (5/8) Epoch 20, batch 2600, loss[loss=0.1734, simple_loss=0.2678, pruned_loss=0.03947, over 6909.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2526, pruned_loss=0.03621, over 1426092.15 frames.], batch size: 31, lr: 3.88e-04
2022-05-15 01:13:22,177 INFO [train.py:812] (5/8) Epoch 20, batch 2650, loss[loss=0.1387, simple_loss=0.2157, pruned_loss=0.03079, over 7010.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2517, pruned_loss=0.03586, over 1427625.02 frames.], batch size: 16, lr: 3.88e-04
2022-05-15 01:14:21,645 INFO [train.py:812] (5/8) Epoch 20, batch 2700, loss[loss=0.1745, simple_loss=0.2604, pruned_loss=0.04435, over 7374.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2513, pruned_loss=0.03623, over 1428456.97 frames.], batch size: 23, lr: 3.88e-04
2022-05-15 01:15:21,500 INFO [train.py:812] (5/8) Epoch 20, batch 2750, loss[loss=0.1785, simple_loss=0.2606, pruned_loss=0.04827, over 7206.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2514, pruned_loss=0.03618, over 1427158.43 frames.], batch size: 23, lr: 3.88e-04
2022-05-15 01:16:20,985 INFO [train.py:812] (5/8) Epoch 20, batch 2800, loss[loss=0.1433, simple_loss=0.2227, pruned_loss=0.03194, over 7159.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2514, pruned_loss=0.03599, over 1431321.28 frames.], batch size: 18, lr: 3.88e-04
2022-05-15 01:17:20,823 INFO [train.py:812] (5/8) Epoch 20, batch 2850, loss[loss=0.1633, simple_loss=0.2637, pruned_loss=0.03142, over 7410.00 frames.], tot_loss[loss=0.1613, simple_loss=0.251, pruned_loss=0.03579, over 1433280.12 frames.], batch size: 21, lr: 3.88e-04
2022-05-15 01:18:20,009 INFO [train.py:812] (5/8) Epoch 20, batch 2900, loss[loss=0.1886, simple_loss=0.268, pruned_loss=0.05456, over 7171.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2508, pruned_loss=0.0358, over 1428422.51 frames.], batch size: 26, lr: 3.88e-04
2022-05-15 01:19:19,543 INFO [train.py:812] (5/8) Epoch 20, batch 2950, loss[loss=0.1873, simple_loss=0.2799, pruned_loss=0.04737, over 7223.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2518, pruned_loss=0.03602, over 1433270.49 frames.], batch size: 20, lr: 3.87e-04
2022-05-15 01:20:18,530 INFO [train.py:812] (5/8) Epoch 20, batch 3000, loss[loss=0.2093, simple_loss=0.3018, pruned_loss=0.0584, over 7388.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2537, pruned_loss=0.0363, over 1431923.18 frames.], batch size: 23, lr: 3.87e-04
2022-05-15 01:20:18,531 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 01:20:27,133 INFO [train.py:841] (5/8) Epoch 20, validation: loss=0.1532, simple_loss=0.2519, pruned_loss=0.02723, over 698248.00 frames.
2022-05-15 01:21:26,362 INFO [train.py:812] (5/8) Epoch 20, batch 3050, loss[loss=0.1542, simple_loss=0.2386, pruned_loss=0.03494, over 7164.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2532, pruned_loss=0.03609, over 1433412.39 frames.], batch size: 19, lr: 3.87e-04
2022-05-15 01:22:25,315 INFO [train.py:812] (5/8) Epoch 20, batch 3100, loss[loss=0.1737, simple_loss=0.2629, pruned_loss=0.04228, over 7114.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2533, pruned_loss=0.03614, over 1432695.73 frames.], batch size: 21, lr: 3.87e-04
2022-05-15 01:23:24,536 INFO [train.py:812] (5/8) Epoch 20, batch 3150, loss[loss=0.1469, simple_loss=0.2347, pruned_loss=0.02955, over 7273.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2526, pruned_loss=0.03614, over 1432871.44 frames.], batch size: 18, lr: 3.87e-04
2022-05-15 01:24:21,329 INFO [train.py:812] (5/8) Epoch 20, batch 3200, loss[loss=0.1836, simple_loss=0.2812, pruned_loss=0.04298, over 6782.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2525, pruned_loss=0.03643, over 1433852.48 frames.], batch size: 31, lr: 3.87e-04
2022-05-15 01:25:18,743 INFO [train.py:812] (5/8) Epoch 20, batch 3250, loss[loss=0.1497, simple_loss=0.2257, pruned_loss=0.03688, over 7075.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2525, pruned_loss=0.03652, over 1429939.10 frames.], batch size: 18, lr: 3.87e-04
2022-05-15 01:26:16,473 INFO [train.py:812] (5/8) Epoch 20, batch 3300, loss[loss=0.1604, simple_loss=0.2392, pruned_loss=0.04086, over 7135.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2524, pruned_loss=0.03654, over 1428946.50 frames.], batch size: 17, lr: 3.87e-04
2022-05-15 01:27:14,060 INFO [train.py:812] (5/8) Epoch 20, batch 3350, loss[loss=0.1506, simple_loss=0.2487, pruned_loss=0.02632, over 7154.00 frames.], tot_loss[loss=0.1619, simple_loss=0.252, pruned_loss=0.03593, over 1428620.95 frames.], batch size: 20, lr: 3.87e-04
2022-05-15 01:28:13,188 INFO [train.py:812] (5/8) Epoch 20, batch 3400, loss[loss=0.1379, simple_loss=0.2181, pruned_loss=0.0288, over 7292.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2527, pruned_loss=0.03626, over 1427659.00 frames.], batch size: 17, lr: 3.87e-04
2022-05-15 01:29:12,303 INFO [train.py:812] (5/8) Epoch 20, batch 3450, loss[loss=0.1824, simple_loss=0.2757, pruned_loss=0.04455, over 7225.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2534, pruned_loss=0.03643, over 1426187.09 frames.], batch size: 20, lr: 3.86e-04
2022-05-15 01:30:11,783 INFO [train.py:812] (5/8) Epoch 20, batch 3500, loss[loss=0.1492, simple_loss=0.2459, pruned_loss=0.02629, over 7270.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2521, pruned_loss=0.03572, over 1423592.88 frames.], batch size: 19, lr: 3.86e-04
2022-05-15 01:31:11,499 INFO [train.py:812] (5/8) Epoch 20, batch 3550, loss[loss=0.152, simple_loss=0.2428, pruned_loss=0.0306, over 7112.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2521, pruned_loss=0.03544, over 1425916.53 frames.], batch size: 21, lr: 3.86e-04
2022-05-15 01:32:11,004 INFO [train.py:812] (5/8) Epoch 20, batch 3600, loss[loss=0.1953, simple_loss=0.2901, pruned_loss=0.05022, over 7182.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2518, pruned_loss=0.03567, over 1428766.03 frames.], batch size: 23, lr: 3.86e-04
2022-05-15 01:33:10,975 INFO [train.py:812] (5/8) Epoch 20, batch 3650, loss[loss=0.1743, simple_loss=0.2702, pruned_loss=0.03923, over 7318.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2517, pruned_loss=0.03592, over 1429992.11 frames.], batch size: 21, lr: 3.86e-04
2022-05-15 01:34:09,095 INFO [train.py:812] (5/8) Epoch 20, batch 3700, loss[loss=0.1432, simple_loss=0.2323, pruned_loss=0.02708, over 7175.00 frames.], tot_loss[loss=0.1617, simple_loss=0.252, pruned_loss=0.0357, over 1431672.66 frames.], batch size: 18, lr: 3.86e-04
2022-05-15 01:35:08,005 INFO [train.py:812] (5/8) Epoch 20, batch 3750, loss[loss=0.1858, simple_loss=0.2821, pruned_loss=0.04474, over 7022.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2516, pruned_loss=0.03563, over 1425935.40 frames.], batch size: 28, lr: 3.86e-04
2022-05-15 01:36:06,430 INFO [train.py:812] (5/8) Epoch 20, batch 3800, loss[loss=0.149, simple_loss=0.2375, pruned_loss=0.03031, over 7336.00 frames.], tot_loss[loss=0.1609, simple_loss=0.251, pruned_loss=0.03541, over 1421293.86 frames.], batch size: 20, lr: 3.86e-04
2022-05-15 01:37:04,478 INFO [train.py:812] (5/8) Epoch 20, batch 3850, loss[loss=0.1634, simple_loss=0.246, pruned_loss=0.04038, over 7272.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2514, pruned_loss=0.03589, over 1419500.09 frames.], batch size: 17, lr: 3.86e-04
2022-05-15 01:38:02,158 INFO [train.py:812] (5/8) Epoch 20, batch 3900, loss[loss=0.1695, simple_loss=0.2703, pruned_loss=0.03433, over 7105.00 frames.], tot_loss[loss=0.1617, simple_loss=0.252, pruned_loss=0.0357, over 1417019.40 frames.], batch size: 21, lr: 3.85e-04
2022-05-15 01:39:01,287 INFO [train.py:812] (5/8) Epoch 20, batch 3950, loss[loss=0.1553, simple_loss=0.2455, pruned_loss=0.03249, over 7328.00 frames.], tot_loss[loss=0.1622, simple_loss=0.252, pruned_loss=0.03622, over 1410842.89 frames.], batch size: 20, lr: 3.85e-04
2022-05-15 01:39:59,103 INFO [train.py:812] (5/8) Epoch 20, batch 4000, loss[loss=0.1526, simple_loss=0.2399, pruned_loss=0.03265, over 7161.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2519, pruned_loss=0.03648, over 1409068.16 frames.], batch size: 18, lr: 3.85e-04
2022-05-15 01:40:58,257 INFO [train.py:812] (5/8) Epoch 20, batch 4050, loss[loss=0.1566, simple_loss=0.2486, pruned_loss=0.03232, over 7327.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2514, pruned_loss=0.03638, over 1406538.55 frames.], batch size: 20, lr: 3.85e-04
2022-05-15 01:41:57,212 INFO [train.py:812] (5/8) Epoch 20, batch 4100, loss[loss=0.163, simple_loss=0.2426, pruned_loss=0.04176, over 7278.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2497, pruned_loss=0.03583, over 1407005.66 frames.], batch size: 18, lr: 3.85e-04
2022-05-15 01:42:56,561 INFO [train.py:812] (5/8) Epoch 20, batch 4150, loss[loss=0.1381, simple_loss=0.2245, pruned_loss=0.02587, over 7063.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2496, pruned_loss=0.03557, over 1410701.00 frames.], batch size: 18, lr: 3.85e-04
2022-05-15 01:43:53,665 INFO [train.py:812] (5/8) Epoch 20, batch 4200, loss[loss=0.1738, simple_loss=0.2447, pruned_loss=0.0514, over 7298.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2509, pruned_loss=0.03615, over 1405622.52 frames.], batch size: 16, lr: 3.85e-04
2022-05-15 01:44:52,592 INFO [train.py:812] (5/8) Epoch 20, batch 4250, loss[loss=0.1836, simple_loss=0.273, pruned_loss=0.04707, over 7194.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2504, pruned_loss=0.03599, over 1403391.48 frames.], batch size: 23, lr: 3.85e-04
2022-05-15 01:45:49,897 INFO [train.py:812] (5/8) Epoch 20, batch 4300, loss[loss=0.1827, simple_loss=0.2735, pruned_loss=0.046, over 7213.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2505, pruned_loss=0.03622, over 1401324.70 frames.], batch size: 21, lr: 3.85e-04
2022-05-15 01:46:48,975 INFO [train.py:812] (5/8) Epoch 20, batch 4350, loss[loss=0.183, simple_loss=0.2645, pruned_loss=0.05075, over 5057.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2493, pruned_loss=0.03568, over 1403475.12 frames.], batch size: 52, lr: 3.84e-04
2022-05-15 01:47:48,032 INFO [train.py:812] (5/8) Epoch 20, batch 4400, loss[loss=0.1767, simple_loss=0.2493, pruned_loss=0.052, over 7153.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2487, pruned_loss=0.03573, over 1398217.33 frames.], batch size: 19, lr: 3.84e-04
2022-05-15 01:48:47,107 INFO [train.py:812] (5/8) Epoch 20, batch 4450, loss[loss=0.1395, simple_loss=0.2234, pruned_loss=0.02774, over 7196.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2489, pruned_loss=0.0358, over 1390701.97 frames.], batch size: 16, lr: 3.84e-04
2022-05-15 01:49:45,786 INFO [train.py:812] (5/8) Epoch 20, batch 4500, loss[loss=0.1974, simple_loss=0.2861, pruned_loss=0.05435, over 7202.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2508, pruned_loss=0.03648, over 1384156.93 frames.], batch size: 23, lr: 3.84e-04
2022-05-15 01:50:44,393 INFO [train.py:812] (5/8) Epoch 20, batch 4550, loss[loss=0.1597, simple_loss=0.251, pruned_loss=0.03418, over 6454.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2534, pruned_loss=0.03801, over 1336703.80 frames.], batch size: 38, lr: 3.84e-04
2022-05-15 01:51:55,159 INFO [train.py:812] (5/8) Epoch 21, batch 0, loss[loss=0.148, simple_loss=0.236, pruned_loss=0.03002, over 6999.00 frames.], tot_loss[loss=0.148, simple_loss=0.236, pruned_loss=0.03002, over 6999.00 frames.], batch size: 16, lr: 3.75e-04
2022-05-15 01:52:54,966 INFO [train.py:812] (5/8) Epoch 21, batch 50, loss[loss=0.1492, simple_loss=0.2529, pruned_loss=0.0228, over 6379.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2505, pruned_loss=0.03499, over 322740.23 frames.], batch size: 38, lr: 3.75e-04
2022-05-15 01:53:53,848 INFO [train.py:812] (5/8) Epoch 21, batch 100, loss[loss=0.1581, simple_loss=0.2462, pruned_loss=0.03507, over 6819.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2514, pruned_loss=0.03589, over 565923.90 frames.], batch size: 15, lr: 3.75e-04
2022-05-15 01:54:52,692 INFO [train.py:812] (5/8) Epoch 21, batch 150, loss[loss=0.139, simple_loss=0.2319, pruned_loss=0.02298, over 7159.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2514, pruned_loss=0.03555, over 755881.57 frames.], batch size: 18, lr: 3.75e-04
2022-05-15 01:55:51,316 INFO [train.py:812] (5/8) Epoch 21, batch 200, loss[loss=0.1747, simple_loss=0.2702, pruned_loss=0.03957, over 6755.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2522, pruned_loss=0.03567, over 900475.17 frames.], batch size: 31, lr: 3.75e-04
2022-05-15 01:56:53,971 INFO [train.py:812] (5/8) Epoch 21, batch 250, loss[loss=0.1487, simple_loss=0.243, pruned_loss=0.02719, over 7160.00 frames.], tot_loss[loss=0.162, simple_loss=0.252, pruned_loss=0.03601, over 1012663.28 frames.], batch size: 19, lr: 3.75e-04
2022-05-15 01:57:52,813 INFO [train.py:812] (5/8) Epoch 21, batch 300, loss[loss=0.1435, simple_loss=0.2272, pruned_loss=0.02984, over 7278.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2523, pruned_loss=0.03632, over 1101276.77 frames.], batch size: 18, lr: 3.75e-04
2022-05-15 01:58:49,823 INFO [train.py:812] (5/8) Epoch 21, batch 350, loss[loss=0.1429, simple_loss=0.2399, pruned_loss=0.02298, over 7258.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2531, pruned_loss=0.03658, over 1168801.32 frames.], batch size: 19, lr: 3.74e-04
2022-05-15 01:59:47,391 INFO [train.py:812] (5/8) Epoch 21, batch 400, loss[loss=0.1701, simple_loss=0.2546, pruned_loss=0.0428, over 7071.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2516, pruned_loss=0.0359, over 1228146.76 frames.], batch size: 18, lr: 3.74e-04
2022-05-15 02:00:46,778 INFO [train.py:812] (5/8) Epoch 21, batch 450, loss[loss=0.1634, simple_loss=0.2489, pruned_loss=0.03899, over 7062.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2517, pruned_loss=0.03592, over 1271605.75 frames.], batch size: 18, lr: 3.74e-04
2022-05-15 02:01:45,944 INFO [train.py:812] (5/8) Epoch 21, batch 500, loss[loss=0.1859, simple_loss=0.2811, pruned_loss=0.04532, over 7038.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2521, pruned_loss=0.03614, over 1309913.09 frames.], batch size: 28, lr: 3.74e-04
2022-05-15 02:02:44,634 INFO [train.py:812] (5/8) Epoch 21, batch 550, loss[loss=0.1369, simple_loss=0.2257, pruned_loss=0.02409, over 7231.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2517, pruned_loss=0.03585, over 1336388.54 frames.], batch size: 16, lr: 3.74e-04
2022-05-15 02:03:42,717 INFO [train.py:812] (5/8) Epoch 21, batch 600, loss[loss=0.1674, simple_loss=0.263, pruned_loss=0.03588, over 7207.00 frames.], tot_loss[loss=0.162, simple_loss=0.2521, pruned_loss=0.03593, over 1354721.35 frames.], batch size: 22, lr: 3.74e-04
2022-05-15 02:04:42,216 INFO [train.py:812] (5/8) Epoch 21, batch 650, loss[loss=0.1323, simple_loss=0.2114, pruned_loss=0.02659, over 7142.00 frames.], tot_loss[loss=0.161, simple_loss=0.2505, pruned_loss=0.0357, over 1368785.37 frames.], batch size: 17, lr: 3.74e-04
2022-05-15 02:05:41,116 INFO [train.py:812] (5/8) Epoch 21, batch 700, loss[loss=0.1718, simple_loss=0.2576, pruned_loss=0.04301, over 7231.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2518, pruned_loss=0.03564, over 1378632.72 frames.], batch size: 20, lr: 3.74e-04
2022-05-15 02:06:40,199 INFO [train.py:812] (5/8) Epoch 21, batch 750, loss[loss=0.1366, simple_loss=0.2227, pruned_loss=0.0252, over 7400.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2525, pruned_loss=0.03599, over 1384482.26 frames.], batch size: 18, lr: 3.74e-04
2022-05-15 02:07:37,520 INFO [train.py:812] (5/8) Epoch 21, batch 800, loss[loss=0.1495, simple_loss=0.2489, pruned_loss=0.02506, over 7227.00 frames.], tot_loss[loss=0.162, simple_loss=0.2519, pruned_loss=0.03604, over 1383609.75 frames.], batch size: 20, lr: 3.73e-04
2022-05-15 02:08:37,254 INFO [train.py:812] (5/8) Epoch 21, batch 850, loss[loss=0.1625, simple_loss=0.2611, pruned_loss=0.03198, over 7289.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2508, pruned_loss=0.03546, over 1390928.16 frames.], batch size: 25, lr: 3.73e-04
2022-05-15 02:09:36,858 INFO [train.py:812] (5/8) Epoch 21, batch 900, loss[loss=0.1915, simple_loss=0.2756, pruned_loss=0.05369, over 7227.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2495, pruned_loss=0.03461, over 1399939.42 frames.], batch size: 20, lr: 3.73e-04
2022-05-15 02:10:36,703 INFO [train.py:812] (5/8) Epoch 21, batch 950, loss[loss=0.1638, simple_loss=0.2536, pruned_loss=0.037, over 7328.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2501, pruned_loss=0.03485, over 1406113.22 frames.], batch size: 22, lr: 3.73e-04
2022-05-15 02:11:34,898 INFO [train.py:812] (5/8) Epoch 21, batch 1000, loss[loss=0.1621, simple_loss=0.2591, pruned_loss=0.03253, over 7186.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2512, pruned_loss=0.0352, over 1405166.78 frames.], batch size: 23, lr: 3.73e-04
2022-05-15 02:12:42,504 INFO [train.py:812] (5/8) Epoch 21, batch 1050, loss[loss=0.1636, simple_loss=0.2613, pruned_loss=0.03295, over 7409.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2525, pruned_loss=0.0353, over 1406368.93 frames.], batch size: 21, lr: 3.73e-04
2022-05-15 02:13:41,815 INFO [train.py:812] (5/8) Epoch 21, batch 1100, loss[loss=0.1314, simple_loss=0.2169, pruned_loss=0.02289, over 6790.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2517, pruned_loss=0.03507, over 1407231.77 frames.], batch size: 15, lr: 3.73e-04
2022-05-15 02:14:40,540 INFO [train.py:812] (5/8) Epoch 21, batch 1150, loss[loss=0.1643, simple_loss=0.2504, pruned_loss=0.03912, over 7309.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2519, pruned_loss=0.03525, over 1412632.48 frames.], batch size: 24, lr: 3.73e-04
2022-05-15 02:15:37,791 INFO [train.py:812] (5/8) Epoch 21, batch 1200, loss[loss=0.1405, simple_loss=0.2245, pruned_loss=0.02825, over 7286.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2526, pruned_loss=0.03537, over 1415329.93 frames.], batch size: 18, lr: 3.73e-04
2022-05-15 02:16:37,262 INFO [train.py:812] (5/8) Epoch 21, batch 1250, loss[loss=0.1579, simple_loss=0.2611, pruned_loss=0.02736, over 7304.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2515, pruned_loss=0.03513, over 1416937.92 frames.], batch size: 24, lr: 3.73e-04
2022-05-15 02:17:36,464 INFO [train.py:812] (5/8) Epoch 21, batch 1300, loss[loss=0.1524, simple_loss=0.2406, pruned_loss=0.03206, over 7075.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2511, pruned_loss=0.03489, over 1416299.40 frames.], batch size: 18, lr: 3.72e-04
2022-05-15 02:18:34,028 INFO [train.py:812] (5/8) Epoch 21, batch 1350, loss[loss=0.1775, simple_loss=0.2674, pruned_loss=0.04382, over 7331.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2504, pruned_loss=0.0349, over 1423397.28 frames.], batch size: 22, lr: 3.72e-04
2022-05-15 02:19:32,906 INFO [train.py:812] (5/8) Epoch 21, batch 1400, loss[loss=0.1599, simple_loss=0.2561, pruned_loss=0.03182, over 7389.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2508, pruned_loss=0.03502, over 1425967.49 frames.], batch size: 23, lr: 3.72e-04
2022-05-15 02:20:31,801 INFO [train.py:812] (5/8) Epoch 21, batch 1450, loss[loss=0.1675, simple_loss=0.2618, pruned_loss=0.0366, over 4817.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2504, pruned_loss=0.03503, over 1419981.60 frames.], batch size: 52, lr: 3.72e-04
2022-05-15 02:21:30,166 INFO [train.py:812] (5/8) Epoch 21, batch 1500, loss[loss=0.1518, simple_loss=0.2416, pruned_loss=0.03103, over 7326.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2521, pruned_loss=0.03575, over 1418395.38 frames.], batch size: 22, lr: 3.72e-04
2022-05-15 02:22:29,839 INFO [train.py:812] (5/8) Epoch 21, batch 1550, loss[loss=0.1721, simple_loss=0.2719, pruned_loss=0.03611, over 6743.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2531, pruned_loss=0.03656, over 1420576.25 frames.], batch size: 31, lr: 3.72e-04
2022-05-15 02:23:26,745 INFO [train.py:812] (5/8) Epoch 21, batch 1600, loss[loss=0.163, simple_loss=0.2617, pruned_loss=0.03219, over 7331.00 frames.], tot_loss[loss=0.1626, simple_loss=0.253, pruned_loss=0.03609, over 1421729.69 frames.], batch size: 22, lr: 3.72e-04
2022-05-15 02:24:25,705 INFO [train.py:812] (5/8) Epoch 21, batch 1650, loss[loss=0.1572, simple_loss=0.2551, pruned_loss=0.02961, over 7337.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2527, pruned_loss=0.03574, over 1422566.33 frames.], batch size: 20, lr: 3.72e-04
2022-05-15 02:25:24,255 INFO [train.py:812] (5/8) Epoch 21, batch 1700, loss[loss=0.1844, simple_loss=0.2733, pruned_loss=0.04774, over 7329.00 frames.], tot_loss[loss=0.1615, simple_loss=0.252, pruned_loss=0.03548, over 1422417.93 frames.], batch size: 22, lr: 3.72e-04
2022-05-15 02:26:22,312 INFO [train.py:812] (5/8) Epoch 21, batch 1750, loss[loss=0.1356, simple_loss=0.2187, pruned_loss=0.0263, over 7406.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2519, pruned_loss=0.0354, over 1423553.01 frames.], batch size: 18, lr: 3.72e-04
2022-05-15 02:27:21,187 INFO [train.py:812] (5/8) Epoch 21, batch 1800, loss[loss=0.1715, simple_loss=0.2592, pruned_loss=0.04183, over 7176.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2515, pruned_loss=0.03517, over 1423906.36 frames.], batch size: 23, lr: 3.71e-04
2022-05-15 02:28:20,359 INFO [train.py:812] (5/8) Epoch 21, batch 1850, loss[loss=0.1352, simple_loss=0.2277, pruned_loss=0.02134, over 7414.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2515, pruned_loss=0.03512, over 1423143.60 frames.], batch size: 18, lr: 3.71e-04
2022-05-15 02:29:19,105 INFO [train.py:812] (5/8) Epoch 21, batch 1900, loss[loss=0.1712, simple_loss=0.2737, pruned_loss=0.03435, over 7157.00 frames.], tot_loss[loss=0.1616, simple_loss=0.252, pruned_loss=0.03559, over 1424631.43 frames.], batch size: 19, lr: 3.71e-04
2022-05-15 02:30:18,950 INFO [train.py:812] (5/8) Epoch 21, batch 1950, loss[loss=0.1599, simple_loss=0.2404, pruned_loss=0.03968, over 7256.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2514, pruned_loss=0.03541, over 1428147.52 frames.], batch size: 19, lr: 3.71e-04
2022-05-15 02:31:18,443 INFO [train.py:812] (5/8) Epoch 21, batch 2000, loss[loss=0.1628, simple_loss=0.2556, pruned_loss=0.035, over 6842.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2505, pruned_loss=0.03541, over 1424849.57 frames.], batch size: 31, lr: 3.71e-04
2022-05-15 02:32:18,145 INFO [train.py:812] (5/8) Epoch 21, batch 2050, loss[loss=0.1672, simple_loss=0.2694, pruned_loss=0.03256, over 7225.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2509, pruned_loss=0.03577, over 1424623.64 frames.], batch size: 21, lr: 3.71e-04
2022-05-15 02:33:17,363 INFO [train.py:812] (5/8) Epoch 21, batch 2100, loss[loss=0.1585, simple_loss=0.2448, pruned_loss=0.03609, over 7062.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2516, pruned_loss=0.03577, over 1423547.42 frames.], batch size: 18, lr: 3.71e-04
2022-05-15 02:34:16,890 INFO [train.py:812] (5/8) Epoch 21, batch 2150, loss[loss=0.1316, simple_loss=0.2172, pruned_loss=0.02301, over 7202.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2519, pruned_loss=0.03576, over 1422496.27 frames.], batch size: 16, lr: 3.71e-04
2022-05-15 02:35:14,498 INFO [train.py:812] (5/8) Epoch 21, batch 2200, loss[loss=0.2101, simple_loss=0.3108, pruned_loss=0.05471, over 7209.00 frames.], tot_loss[loss=0.16, simple_loss=0.25, pruned_loss=0.03501, over 1423765.19 frames.], batch size: 22, lr: 3.71e-04
2022-05-15 02:36:12,365 INFO [train.py:812] (5/8) Epoch 21, batch 2250, loss[loss=0.1628, simple_loss=0.261, pruned_loss=0.03234, over 7213.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2507, pruned_loss=0.03538, over 1424853.14 frames.], batch size: 22, lr: 3.71e-04
2022-05-15 02:37:12,526 INFO [train.py:812] (5/8) Epoch 21, batch 2300, loss[loss=0.2179, simple_loss=0.2957, pruned_loss=0.07004, over 5206.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2504, pruned_loss=0.03536, over 1422025.38 frames.], batch size: 52, lr: 3.71e-04
2022-05-15 02:38:11,392 INFO [train.py:812] (5/8) Epoch 21, batch 2350, loss[loss=0.1794, simple_loss=0.268, pruned_loss=0.04542, over 7269.00 frames.], tot_loss[loss=0.163, simple_loss=0.2528, pruned_loss=0.03658, over 1417106.26 frames.], batch size: 24, lr: 3.70e-04
2022-05-15 02:39:10,736 INFO [train.py:812] (5/8) Epoch 21, batch 2400, loss[loss=0.1685, simple_loss=0.2609, pruned_loss=0.03804, over 7199.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2519, pruned_loss=0.03616, over 1420539.57 frames.], batch size: 23, lr: 3.70e-04
2022-05-15 02:40:10,443 INFO [train.py:812] (5/8) Epoch 21, batch 2450, loss[loss=0.1726, simple_loss=0.253, pruned_loss=0.04608, over 7162.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2512, pruned_loss=0.03568, over 1421113.47 frames.], batch size: 19, lr: 3.70e-04
2022-05-15 02:41:09,424 INFO [train.py:812] (5/8) Epoch 21, batch 2500, loss[loss=0.1789, simple_loss=0.2698, pruned_loss=0.04403, over 7413.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2515, pruned_loss=0.03589, over 1422590.85 frames.], batch size: 21, lr: 3.70e-04
2022-05-15 02:42:07,845 INFO [train.py:812] (5/8) Epoch 21, batch 2550, loss[loss=0.1664, simple_loss=0.258, pruned_loss=0.03741, over 5244.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2524, pruned_loss=0.0361, over 1421130.51 frames.], batch size: 52, lr: 3.70e-04
2022-05-15 02:43:06,155 INFO [train.py:812] (5/8) Epoch 21, batch 2600, loss[loss=0.1367, simple_loss=0.2253, pruned_loss=0.02408, over 7070.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2527, pruned_loss=0.03621, over 1422298.56 frames.], batch size: 18, lr: 3.70e-04
2022-05-15 02:44:05,921 INFO [train.py:812] (5/8) Epoch 21, batch 2650, loss[loss=0.1612, simple_loss=0.2436, pruned_loss=0.03939, over 7330.00 frames.], tot_loss[loss=0.163, simple_loss=0.2527, pruned_loss=0.03658, over 1418349.82 frames.], batch size: 20, lr: 3.70e-04
2022-05-15 02:45:04,653 INFO [train.py:812] (5/8) Epoch 21, batch 2700, loss[loss=0.1353, simple_loss=0.2229, pruned_loss=0.0239, over 7410.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2525, pruned_loss=0.03641, over 1421163.54 frames.], batch size: 18, lr: 3.70e-04
2022-05-15 02:46:03,783 INFO [train.py:812] (5/8) Epoch 21, batch 2750, loss[loss=0.1527, simple_loss=0.2377, pruned_loss=0.03387, over 7145.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2528, pruned_loss=0.03669, over 1422333.74 frames.], batch size: 18, lr: 3.70e-04
2022-05-15 02:47:03,043 INFO [train.py:812] (5/8) Epoch 21, batch 2800, loss[loss=0.1722, simple_loss=0.2589, pruned_loss=0.04272, over 7373.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2518, pruned_loss=0.03596, over 1425736.44 frames.], batch size: 23, lr: 3.70e-04
2022-05-15 02:48:12,221 INFO [train.py:812] (5/8) Epoch 21, batch 2850, loss[loss=0.1936, simple_loss=0.2939, pruned_loss=0.04658, over 7213.00 frames.], tot_loss[loss=0.162, simple_loss=0.2518, pruned_loss=0.03605, over 1421398.87 frames.], batch size: 23, lr: 3.69e-04
2022-05-15 02:49:11,206 INFO [train.py:812] (5/8) Epoch 21, batch 2900, loss[loss=0.1655, simple_loss=0.2612, pruned_loss=0.03488, over 7130.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2514, pruned_loss=0.03552, over 1415873.35 frames.], batch size: 28, lr: 3.69e-04
2022-05-15 02:50:09,822 INFO [train.py:812] (5/8) Epoch 21, batch 2950, loss[loss=0.1652, simple_loss=0.2476, pruned_loss=0.04137, over 7364.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2512, pruned_loss=0.03514, over 1414546.41 frames.], batch size: 19, lr: 3.69e-04
2022-05-15 02:51:09,101 INFO [train.py:812] (5/8) Epoch 21, batch 3000, loss[loss=0.1611, simple_loss=0.2562, pruned_loss=0.03297, over 6754.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2508, pruned_loss=0.03492, over 1413541.74 frames.], batch size: 31, lr: 3.69e-04
2022-05-15 02:51:09,102 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 02:51:16,348 INFO [train.py:841] (5/8) Epoch 21, validation: loss=0.153, simple_loss=0.2519, pruned_loss=0.02704, over 698248.00 frames.
2022-05-15 02:52:35,441 INFO [train.py:812] (5/8) Epoch 21, batch 3050, loss[loss=0.1592, simple_loss=0.2449, pruned_loss=0.03677, over 7291.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2508, pruned_loss=0.03478, over 1414696.82 frames.], batch size: 18, lr: 3.69e-04
2022-05-15 02:53:32,966 INFO [train.py:812] (5/8) Epoch 21, batch 3100, loss[loss=0.1844, simple_loss=0.2753, pruned_loss=0.04677, over 7369.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2518, pruned_loss=0.0357, over 1413808.37 frames.], batch size: 23, lr: 3.69e-04
2022-05-15 02:55:01,531 INFO [train.py:812] (5/8) Epoch 21, batch 3150, loss[loss=0.1822, simple_loss=0.2694, pruned_loss=0.04753, over 7308.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2522, pruned_loss=0.03648, over 1419036.17 frames.], batch size: 24, lr: 3.69e-04
2022-05-15 02:56:00,660 INFO [train.py:812] (5/8) Epoch 21, batch 3200, loss[loss=0.1733, simple_loss=0.2549, pruned_loss=0.04587, over 7319.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2529, pruned_loss=0.03681, over 1423297.06 frames.], batch size: 21, lr: 3.69e-04
2022-05-15 02:57:00,415 INFO [train.py:812] (5/8) Epoch 21, batch 3250, loss[loss=0.1866, simple_loss=0.2758, pruned_loss=0.04867, over 7061.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2531, pruned_loss=0.03655, over 1421765.77 frames.], batch size: 18, lr: 3.69e-04
2022-05-15 02:58:08,764 INFO [train.py:812] (5/8) Epoch 21, batch 3300, loss[loss=0.1265, simple_loss=0.2114, pruned_loss=0.0208, over 7149.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2523, pruned_loss=0.03607, over 1422754.53 frames.], batch size: 17, lr: 3.69e-04
2022-05-15 02:59:08,376 INFO [train.py:812] (5/8) Epoch 21, batch 3350, loss[loss=0.1486, simple_loss=0.2497, pruned_loss=0.02378, over 7238.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2521, pruned_loss=0.036, over 1418702.89 frames.], batch size: 20, lr: 3.68e-04
2022-05-15 03:00:06,804 INFO [train.py:812] (5/8) Epoch 21, batch 3400, loss[loss=0.1895, simple_loss=0.2812, pruned_loss=0.04889, over 6437.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2532, pruned_loss=0.03662, over 1415876.82 frames.], batch size: 38, lr: 3.68e-04
2022-05-15 03:01:06,180 INFO [train.py:812] (5/8) Epoch 21, batch 3450, loss[loss=0.139, simple_loss=0.2306, pruned_loss=0.0237, over 7318.00 frames.], tot_loss[loss=0.1629, simple_loss=0.253, pruned_loss=0.03639, over 1414417.13 frames.], batch size: 21, lr: 3.68e-04
2022-05-15 03:02:05,065 INFO [train.py:812] (5/8) Epoch 21, batch 3500, loss[loss=0.1902, simple_loss=0.2752, pruned_loss=0.05257, over 7085.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2531, pruned_loss=0.03615, over 1409897.34 frames.], batch size: 28, lr: 3.68e-04
2022-05-15 03:03:04,126 INFO [train.py:812] (5/8) Epoch 21, batch 3550, loss[loss=0.1446, simple_loss=0.2336, pruned_loss=0.02777, over 7254.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2522, pruned_loss=0.03564, over 1414449.29 frames.], batch size: 17, lr: 3.68e-04
2022-05-15 03:04:02,913 INFO [train.py:812] (5/8) Epoch 21, batch 3600, loss[loss=0.1714, simple_loss=0.2613, pruned_loss=0.04078, over 7368.00 frames.], tot_loss[loss=0.1624, simple_loss=0.253, pruned_loss=0.03592, over 1411457.27 frames.], batch size: 23, lr: 3.68e-04
2022-05-15 03:05:02,878 INFO [train.py:812] (5/8) Epoch 21, batch 3650, loss[loss=0.1837, simple_loss=0.2628, pruned_loss=0.05231, over 7178.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2526, pruned_loss=0.03577, over 1413375.90 frames.], batch size: 26, lr: 3.68e-04
2022-05-15 03:06:01,336 INFO [train.py:812] (5/8) Epoch 21, batch 3700, loss[loss=0.1601, simple_loss=0.2548, pruned_loss=0.03272, over 7312.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2528, pruned_loss=0.03575, over 1414237.19 frames.], batch size: 21, lr: 3.68e-04
2022-05-15 03:07:01,119 INFO [train.py:812] (5/8) Epoch 21, batch 3750, loss[loss=0.1565, simple_loss=0.2445, pruned_loss=0.03424, over 7294.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2515, pruned_loss=0.03553, over 1418397.83 frames.], batch size: 25, lr: 3.68e-04
2022-05-15 03:07:59,609 INFO [train.py:812] (5/8) Epoch 21, batch 3800, loss[loss=0.1816, simple_loss=0.2642, pruned_loss=0.04952, over 7132.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2514, pruned_loss=0.03576, over 1418974.53 frames.], batch size: 26, lr: 3.68e-04
2022-05-15 03:08:58,689 INFO [train.py:812] (5/8) Epoch 21, batch 3850, loss[loss=0.1706, simple_loss=0.268, pruned_loss=0.03663, over 7328.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2519, pruned_loss=0.0354, over 1419247.52 frames.], batch size: 20, lr: 3.68e-04
2022-05-15 03:09:55,534 INFO [train.py:812] (5/8) Epoch 21, batch 3900, loss[loss=0.1498, simple_loss=0.2423, pruned_loss=0.02867, over 7258.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2517, pruned_loss=0.03505, over 1423209.97 frames.], batch size: 19, lr: 3.67e-04
2022-05-15 03:10:53,475 INFO [train.py:812] (5/8) Epoch 21, batch 3950, loss[loss=0.1471, simple_loss=0.2267, pruned_loss=0.03374, over 7421.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2525, pruned_loss=0.03518, over 1418653.72 frames.], batch size: 18, lr: 3.67e-04
2022-05-15 03:11:51,917 INFO [train.py:812] (5/8) Epoch 21, batch 4000, loss[loss=0.1544, simple_loss=0.2461, pruned_loss=0.03139, over 7361.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2528, pruned_loss=0.0353, over 1422460.68 frames.], batch size: 19, lr: 3.67e-04
2022-05-15 03:12:50,954 INFO [train.py:812] (5/8) Epoch 21, batch 4050, loss[loss=0.2255, simple_loss=0.3003, pruned_loss=0.07538, over 5199.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2519, pruned_loss=0.03498, over 1419529.57 frames.], batch size: 52, lr: 3.67e-04
2022-05-15 03:13:49,280 INFO [train.py:812] (5/8) Epoch 21, batch 4100, loss[loss=0.145, simple_loss=0.2386, pruned_loss=0.02568, over 7220.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2522, pruned_loss=0.03532, over 1411153.56 frames.], batch size: 21, lr: 3.67e-04
2022-05-15 03:14:46,159 INFO [train.py:812] (5/8) Epoch 21, batch 4150, loss[loss=0.1497, simple_loss=0.2424, pruned_loss=0.02854, over 7448.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2534, pruned_loss=0.03598, over 1413078.07 frames.], batch size: 19, lr: 3.67e-04
2022-05-15 03:15:43,926 INFO [train.py:812] (5/8) Epoch 21, batch 4200, loss[loss=0.1741, simple_loss=0.2636, pruned_loss=0.04226, over 6795.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2536, pruned_loss=0.03587, over 1412800.05 frames.], batch size: 31, lr: 3.67e-04
2022-05-15 03:16:47,806 INFO [train.py:812] (5/8) Epoch 21, batch 4250, loss[loss=0.1798, simple_loss=0.2675, pruned_loss=0.04601, over 7212.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2527, pruned_loss=0.03559, over 1416909.94 frames.], batch size: 21, lr: 3.67e-04
2022-05-15 03:17:46,892 INFO [train.py:812] (5/8) Epoch 21, batch 4300, loss[loss=0.1856, simple_loss=0.2764, pruned_loss=0.04739, over 7294.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2528, pruned_loss=0.03554, over 1418383.33 frames.], batch size: 24, lr: 3.67e-04
2022-05-15 03:18:45,848 INFO [train.py:812] (5/8) Epoch 21, batch 4350, loss[loss=0.1904, simple_loss=0.2855, pruned_loss=0.04762, over 7203.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2522, pruned_loss=0.03547, over 1417741.02 frames.], batch size: 21, lr: 3.67e-04
2022-05-15 03:19:43,037 INFO [train.py:812] (5/8) Epoch 21, batch 4400, loss[loss=0.1307, simple_loss=0.2206, pruned_loss=0.02045, over 7174.00 frames.], tot_loss[loss=0.161, simple_loss=0.2517, pruned_loss=0.03518, over 1416646.29 frames.], batch size: 18, lr: 3.66e-04
2022-05-15 03:20:42,012 INFO [train.py:812] (5/8) Epoch 21, batch 4450, loss[loss=0.1421, simple_loss=0.2304, pruned_loss=0.02688, over 7003.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2518, pruned_loss=0.0354, over 1409212.89 frames.], batch size: 16, lr: 3.66e-04
2022-05-15 03:21:40,282 INFO [train.py:812] (5/8) Epoch 21, batch 4500, loss[loss=0.124, simple_loss=0.2038, pruned_loss=0.02209, over 7013.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2518, pruned_loss=0.03525, over 1411142.16 frames.], batch size: 16, lr: 3.66e-04
2022-05-15 03:22:39,941 INFO [train.py:812] (5/8) Epoch 21, batch 4550, loss[loss=0.22, simple_loss=0.289, pruned_loss=0.07549, over 4926.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2508, pruned_loss=0.03549, over 1395056.89 frames.], batch size: 52, lr: 3.66e-04
2022-05-15 03:23:52,241 INFO [train.py:812] (5/8) Epoch 22, batch 0, loss[loss=0.1689, simple_loss=0.2671, pruned_loss=0.03533, over 7272.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2671, pruned_loss=0.03533, over 7272.00 frames.], batch size: 25, lr: 3.58e-04
2022-05-15 03:24:50,139 INFO [train.py:812] (5/8) Epoch 22, batch 50, loss[loss=0.1479, simple_loss=0.2351, pruned_loss=0.03038, over 7174.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2516, pruned_loss=0.03534, over 318286.49 frames.], batch size: 18, lr: 3.58e-04
2022-05-15 03:25:49,146 INFO [train.py:812] (5/8) Epoch 22, batch 100, loss[loss=0.1519, simple_loss=0.245, pruned_loss=0.02941, over 7117.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2504, pruned_loss=0.03502, over 565112.02 frames.], batch size: 21, lr: 3.58e-04
2022-05-15 03:26:47,246 INFO [train.py:812] (5/8) Epoch 22, batch 150, loss[loss=0.1579, simple_loss=0.2585, pruned_loss=0.02866, over 7325.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2506, pruned_loss=0.03516, over 755160.37 frames.], batch size: 21, lr: 3.58e-04
2022-05-15 03:27:46,006 INFO [train.py:812] (5/8) Epoch 22, batch 200, loss[loss=0.1524, simple_loss=0.259, pruned_loss=0.02289, over 7338.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2498, pruned_loss=0.03432, over 902788.53 frames.], batch size: 22, lr: 3.58e-04
2022-05-15 03:28:43,579 INFO [train.py:812] (5/8) Epoch 22, batch 250, loss[loss=0.1486, simple_loss=0.2423, pruned_loss=0.02747, over 7238.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2506, pruned_loss=0.03484, over 1016676.46 frames.], batch size: 19, lr: 3.57e-04
2022-05-15 03:29:41,562 INFO [train.py:812] (5/8) Epoch 22, batch 300, loss[loss=0.1469, simple_loss=0.2464, pruned_loss=0.0237, over 7241.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2501, pruned_loss=0.03464, over 1109172.37 frames.], batch size: 20, lr: 3.57e-04
2022-05-15 03:30:39,528 INFO [train.py:812] (5/8) Epoch 22, batch 350, loss[loss=0.1461, simple_loss=0.2256, pruned_loss=0.03329, over 7171.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2491, pruned_loss=0.03452, over 1179787.76 frames.], batch size: 19, lr: 3.57e-04
2022-05-15 03:31:38,292 INFO [train.py:812] (5/8) Epoch 22, batch 400, loss[loss=0.1736, simple_loss=0.2809, pruned_loss=0.03314, over 7223.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2498, pruned_loss=0.03476, over 1232297.92 frames.], batch size: 21, lr: 3.57e-04
2022-05-15 03:32:37,210 INFO [train.py:812] (5/8) Epoch 22, batch 450, loss[loss=0.2036, simple_loss=0.2834, pruned_loss=0.06184, over 5198.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2496, pruned_loss=0.03491, over 1274915.69 frames.], batch size: 52, lr: 3.57e-04
2022-05-15 03:33:36,427 INFO [train.py:812] (5/8) Epoch 22, batch 500, loss[loss=0.1678, simple_loss=0.2605, pruned_loss=0.03759, over 7262.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2502, pruned_loss=0.0351, over 1309899.38 frames.], batch size: 25, lr: 3.57e-04
2022-05-15 03:34:33,231 INFO [train.py:812] (5/8) Epoch 22, batch 550, loss[loss=0.1617, simple_loss=0.2523, pruned_loss=0.03559, over 7434.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2512, pruned_loss=0.03533, over 1333462.83 frames.], batch size: 20, lr: 3.57e-04
2022-05-15 03:35:32,153 INFO [train.py:812] (5/8) Epoch 22, batch 600, loss[loss=0.1449, simple_loss=0.2427, pruned_loss=0.02355, over 7357.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2501, pruned_loss=0.03481, over 1355118.95 frames.], batch size: 22, lr: 3.57e-04
2022-05-15 03:36:31,073 INFO [train.py:812] (5/8) Epoch 22, batch 650, loss[loss=0.1373, simple_loss=0.2364, pruned_loss=0.01905, over 7331.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2512, pruned_loss=0.03496, over 1370089.93 frames.], batch size: 22, lr: 3.57e-04
2022-05-15 03:37:30,545 INFO [train.py:812] (5/8) Epoch 22, batch 700, loss[loss=0.1914, simple_loss=0.2883, pruned_loss=0.04727, over 7320.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2517, pruned_loss=0.03542, over 1377909.11 frames.], batch size: 25, lr: 3.57e-04
2022-05-15 03:38:28,386 INFO [train.py:812] (5/8) Epoch 22, batch 750, loss[loss=0.1533, simple_loss=0.237, pruned_loss=0.03483, over 7154.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2506, pruned_loss=0.03481, over 1386862.56 frames.], batch size: 18, lr: 3.57e-04
2022-05-15 03:39:28,259 INFO [train.py:812] (5/8) Epoch 22, batch 800, loss[loss=0.1676, simple_loss=0.2611, pruned_loss=0.03704, over 7275.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2507, pruned_loss=0.0346, over 1399547.40 frames.], batch size: 25, lr: 3.56e-04
2022-05-15 03:40:27,749 INFO [train.py:812] (5/8) Epoch 22, batch 850, loss[loss=0.1603, simple_loss=0.2427, pruned_loss=0.03889, over 7400.00 frames.], tot_loss[loss=0.159, simple_loss=0.2499, pruned_loss=0.03408, over 1405507.05 frames.], batch size: 18, lr: 3.56e-04
2022-05-15 03:41:26,075 INFO [train.py:812] (5/8) Epoch 22, batch 900, loss[loss=0.1606, simple_loss=0.256, pruned_loss=0.03259, over 6509.00 frames.], tot_loss[loss=0.159, simple_loss=0.2497, pruned_loss=0.03422, over 1409526.33 frames.], batch size: 38, lr: 3.56e-04
2022-05-15 03:42:25,511 INFO [train.py:812] (5/8) Epoch 22, batch 950, loss[loss=0.148, simple_loss=0.2319, pruned_loss=0.03205, over 7299.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2493, pruned_loss=0.03427, over 1411503.21 frames.], batch size: 18, lr: 3.56e-04
2022-05-15 03:43:24,208 INFO [train.py:812] (5/8) Epoch 22, batch 1000, loss[loss=0.1511, simple_loss=0.25, pruned_loss=0.02609, over 7160.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2503, pruned_loss=0.03468, over 1412002.68 frames.], batch size: 19, lr: 3.56e-04
2022-05-15 03:44:23,446 INFO [train.py:812] (5/8) Epoch 22, batch 1050, loss[loss=0.1362, simple_loss=0.2382, pruned_loss=0.01707, over 7340.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2491, pruned_loss=0.03437, over 1415788.99 frames.], batch size: 22, lr: 3.56e-04
2022-05-15 03:45:23,003 INFO [train.py:812] (5/8) Epoch 22, batch 1100, loss[loss=0.1559, simple_loss=0.2423, pruned_loss=0.03468, over 6566.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2489, pruned_loss=0.03404, over 1419322.53 frames.], batch size: 37, lr: 3.56e-04
2022-05-15 03:46:20,331 INFO [train.py:812] (5/8) Epoch 22, batch 1150, loss[loss=0.1553, simple_loss=0.2392, pruned_loss=0.03573, over 7252.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2491, pruned_loss=0.03419, over 1420428.81 frames.], batch size: 19, lr: 3.56e-04
2022-05-15 03:47:19,434 INFO [train.py:812] (5/8) Epoch 22, batch 1200, loss[loss=0.157, simple_loss=0.2487, pruned_loss=0.03263, over 7304.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2495, pruned_loss=0.03485, over 1421254.38 frames.], batch size: 25, lr: 3.56e-04
2022-05-15 03:48:18,942 INFO [train.py:812] (5/8) Epoch 22, batch 1250, loss[loss=0.1371, simple_loss=0.2181, pruned_loss=0.02808, over 6989.00 frames.], tot_loss[loss=0.1605, simple_loss=0.25, pruned_loss=0.03554, over 1420223.52 frames.], batch size: 16, lr: 3.56e-04
2022-05-15 03:49:19,103 INFO [train.py:812] (5/8) Epoch 22, batch 1300, loss[loss=0.1643, simple_loss=0.2544, pruned_loss=0.03709, over 7154.00 frames.], tot_loss[loss=0.1605, simple_loss=0.25, pruned_loss=0.03555, over 1419430.77 frames.], batch size: 19, lr: 3.56e-04
2022-05-15 03:50:16,172 INFO [train.py:812] (5/8) Epoch 22, batch 1350, loss[loss=0.1923, simple_loss=0.2807, pruned_loss=0.05198, over 7409.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2498, pruned_loss=0.03559, over 1423721.90 frames.], batch size: 21, lr: 3.55e-04
2022-05-15 03:51:15,333 INFO [train.py:812] (5/8) Epoch 22, batch 1400, loss[loss=0.1767, simple_loss=0.2771, pruned_loss=0.03816, over 7202.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2491, pruned_loss=0.03533, over 1420116.34 frames.], batch size: 22, lr: 3.55e-04
2022-05-15 03:52:14,139 INFO [train.py:812] (5/8) Epoch 22, batch 1450, loss[loss=0.1711, simple_loss=0.2637, pruned_loss=0.03923, over 7410.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2506, pruned_loss=0.03529, over 1424311.30 frames.], batch size: 20, lr: 3.55e-04
2022-05-15 03:53:13,820 INFO [train.py:812] (5/8) Epoch 22, batch 1500, loss[loss=0.1546, simple_loss=0.2491, pruned_loss=0.03005, over 7237.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2506, pruned_loss=0.03515, over 1425879.23 frames.], batch size: 20, lr: 3.55e-04
2022-05-15 03:54:13,329 INFO [train.py:812] (5/8) Epoch 22, batch 1550, loss[loss=0.1842, simple_loss=0.2821, pruned_loss=0.04314, over 7237.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2502, pruned_loss=0.03497, over 1428412.22 frames.], batch size: 20, lr: 3.55e-04
2022-05-15 03:55:12,245 INFO [train.py:812] (5/8) Epoch 22, batch 1600, loss[loss=0.1288, simple_loss=0.2195, pruned_loss=0.01906, over 6824.00 frames.], tot_loss[loss=0.16, simple_loss=0.2503, pruned_loss=0.03484, over 1428834.06 frames.], batch size: 15, lr: 3.55e-04
2022-05-15 03:56:08,987 INFO [train.py:812] (5/8) Epoch 22, batch 1650, loss[loss=0.174, simple_loss=0.2645, pruned_loss=0.0418, over 6771.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2508, pruned_loss=0.03456, over 1430802.76 frames.], batch size: 31, lr: 3.55e-04
2022-05-15 03:57:06,968 INFO [train.py:812] (5/8) Epoch 22, batch 1700, loss[loss=0.1532, simple_loss=0.2577, pruned_loss=0.02439, over 7325.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2499, pruned_loss=0.03422, over 1432703.08 frames.], batch size: 22, lr: 3.55e-04
2022-05-15 03:58:03,876 INFO [train.py:812] (5/8) Epoch 22, batch 1750, loss[loss=0.1586, simple_loss=0.2553, pruned_loss=0.03099, over 7240.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2513, pruned_loss=0.03476, over 1431679.32 frames.], batch size: 20, lr: 3.55e-04
2022-05-15 03:59:03,629 INFO [train.py:812] (5/8) Epoch 22, batch 1800, loss[loss=0.1392, simple_loss=0.2205, pruned_loss=0.02891, over 7318.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2505, pruned_loss=0.03485, over 1430155.05 frames.], batch size: 17, lr: 3.55e-04
2022-05-15 04:00:02,106 INFO [train.py:812] (5/8) Epoch 22, batch 1850, loss[loss=0.1595, simple_loss=0.2528, pruned_loss=0.03308, over 6487.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2503, pruned_loss=0.03476, over 1426609.07 frames.], batch size: 38, lr: 3.55e-04
2022-05-15 04:01:00,864 INFO [train.py:812] (5/8) Epoch 22, batch 1900, loss[loss=0.1615, simple_loss=0.2576, pruned_loss=0.03275, over 5137.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2501, pruned_loss=0.03452, over 1424521.26 frames.], batch size: 52, lr: 3.54e-04
2022-05-15 04:02:00,140 INFO [train.py:812] (5/8) Epoch 22, batch 1950, loss[loss=0.1718, simple_loss=0.2426, pruned_loss=0.0505, over 7281.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2506, pruned_loss=0.03462, over 1425989.50 frames.], batch size: 17, lr: 3.54e-04
2022-05-15 04:02:59,567 INFO [train.py:812] (5/8) Epoch 22, batch 2000, loss[loss=0.1662, simple_loss=0.2623, pruned_loss=0.03501, over 7321.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2504, pruned_loss=0.03458, over 1427978.58 frames.], batch size: 20, lr: 3.54e-04
2022-05-15 04:03:58,564 INFO [train.py:812] (5/8) Epoch 22, batch 2050, loss[loss=0.1449, simple_loss=0.2248, pruned_loss=0.03255, over 7271.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2513, pruned_loss=0.03503, over 1428576.52 frames.], batch size: 17, lr: 3.54e-04
2022-05-15 04:04:58,107 INFO [train.py:812] (5/8) Epoch 22, batch 2100, loss[loss=0.1483, simple_loss=0.2317, pruned_loss=0.03243, over 7428.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2507, pruned_loss=0.03474, over 1427639.67 frames.], batch size: 18, lr: 3.54e-04
2022-05-15 04:05:56,575 INFO [train.py:812] (5/8) Epoch 22, batch 2150, loss[loss=0.1352, simple_loss=0.2242, pruned_loss=0.02313, over 7156.00 frames.], tot_loss[loss=0.1594, simple_loss=0.25, pruned_loss=0.03438, over 1423115.89 frames.], batch size: 18, lr: 3.54e-04
2022-05-15 04:06:55,007 INFO [train.py:812] (5/8) Epoch 22, batch 2200, loss[loss=0.1666, simple_loss=0.2578, pruned_loss=0.03768, over 7121.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2502, pruned_loss=0.03416, over 1425866.36 frames.], batch size: 21, lr: 3.54e-04
2022-05-15 04:07:52,614 INFO [train.py:812] (5/8) Epoch 22, batch 2250, loss[loss=0.1647, simple_loss=0.2351, pruned_loss=0.04713, over 7206.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2509, pruned_loss=0.03459, over 1424000.24 frames.], batch size: 16, lr: 3.54e-04
2022-05-15 04:08:49,582 INFO [train.py:812] (5/8) Epoch 22, batch 2300, loss[loss=0.2032, simple_loss=0.293, pruned_loss=0.05673, over 5229.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2511, pruned_loss=0.03456, over 1425642.64 frames.], batch size: 53, lr: 3.54e-04
2022-05-15 04:09:47,972 INFO [train.py:812] (5/8) Epoch 22, batch 2350, loss[loss=0.176, simple_loss=0.2707, pruned_loss=0.04065, over 6345.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2503, pruned_loss=0.03433, over 1427369.81 frames.], batch size: 38, lr: 3.54e-04
2022-05-15 04:10:57,206 INFO [train.py:812] (5/8) Epoch 22, batch 2400, loss[loss=0.1547, simple_loss=0.2322, pruned_loss=0.03859, over 7144.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2501, pruned_loss=0.03461, over 1426147.83 frames.], batch size: 17, lr: 3.54e-04
2022-05-15 04:11:56,421 INFO [train.py:812] (5/8) Epoch 22, batch 2450, loss[loss=0.1479, simple_loss=0.2258, pruned_loss=0.03503, over 7268.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2498, pruned_loss=0.03426, over 1424886.04 frames.], batch size: 17, lr: 3.54e-04
2022-05-15 04:12:56,108 INFO [train.py:812] (5/8) Epoch 22, batch 2500, loss[loss=0.1561, simple_loss=0.2541, pruned_loss=0.02905, over 7410.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2491, pruned_loss=0.034, over 1422885.63 frames.], batch size: 21, lr: 3.53e-04
2022-05-15 04:13:55,275 INFO [train.py:812] (5/8) Epoch 22, batch 2550, loss[loss=0.1974, simple_loss=0.2747, pruned_loss=0.06003, over 7065.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2498, pruned_loss=0.03441, over 1422587.53 frames.], batch size: 18, lr: 3.53e-04
2022-05-15 04:14:54,424 INFO [train.py:812] (5/8) Epoch 22, batch 2600, loss[loss=0.1494, simple_loss=0.2376, pruned_loss=0.0306, over 7155.00 frames.], tot_loss[loss=0.16, simple_loss=0.2507, pruned_loss=0.03463, over 1418204.70 frames.], batch size: 19, lr: 3.53e-04
2022-05-15 04:15:53,324 INFO [train.py:812] (5/8) Epoch 22, batch 2650, loss[loss=0.1505, simple_loss=0.2374, pruned_loss=0.03185, over 7257.00 frames.], tot_loss[loss=0.1595, simple_loss=0.25, pruned_loss=0.03447, over 1421726.76 frames.], batch size: 19, lr: 3.53e-04
2022-05-15 04:16:52,249 INFO [train.py:812] (5/8) Epoch 22, batch 2700, loss[loss=0.1546, simple_loss=0.241, pruned_loss=0.03409, over 7165.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2487, pruned_loss=0.03419, over 1419582.88 frames.], batch size: 18, lr: 3.53e-04
2022-05-15 04:17:51,073 INFO [train.py:812] (5/8) Epoch 22, batch 2750, loss[loss=0.1614, simple_loss=0.2507, pruned_loss=0.03608, over 7065.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2496, pruned_loss=0.03457, over 1419576.98 frames.], batch size: 18, lr: 3.53e-04
2022-05-15 04:18:49,888 INFO [train.py:812] (5/8) Epoch 22, batch 2800, loss[loss=0.1257, simple_loss=0.2188, pruned_loss=0.01631, over 7273.00 frames.], tot_loss[loss=0.1596, simple_loss=0.25, pruned_loss=0.03466, over 1420293.23 frames.], batch size: 18, lr: 3.53e-04
2022-05-15 04:19:48,550 INFO [train.py:812] (5/8) Epoch 22, batch 2850, loss[loss=0.1527, simple_loss=0.2383, pruned_loss=0.03355, over 7157.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2496, pruned_loss=0.0344, over 1418857.96 frames.], batch size: 19, lr: 3.53e-04
2022-05-15 04:20:47,922 INFO [train.py:812] (5/8) Epoch 22, batch 2900, loss[loss=0.1609, simple_loss=0.245, pruned_loss=0.03839, over 7158.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2497, pruned_loss=0.03423, over 1420750.92 frames.], batch size: 19, lr: 3.53e-04
2022-05-15 04:21:47,224 INFO [train.py:812] (5/8) Epoch 22, batch 2950, loss[loss=0.1451, simple_loss=0.2503, pruned_loss=0.01995, over 7413.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2494, pruned_loss=0.03423, over 1421543.91 frames.], batch size: 21, lr: 3.53e-04
2022-05-15 04:22:47,046 INFO [train.py:812] (5/8) Epoch 22, batch 3000, loss[loss=0.1578, simple_loss=0.2375, pruned_loss=0.03907, over 7171.00 frames.], tot_loss[loss=0.1584, simple_loss=0.249, pruned_loss=0.03392, over 1426041.07 frames.], batch size: 18, lr: 3.53e-04
2022-05-15 04:22:47,047 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 04:22:54,482 INFO [train.py:841] (5/8) Epoch 22, validation: loss=0.1529, simple_loss=0.2512, pruned_loss=0.02731, over 698248.00 frames.
2022-05-15 04:23:53,731 INFO [train.py:812] (5/8) Epoch 22, batch 3050, loss[loss=0.1853, simple_loss=0.2782, pruned_loss=0.04623, over 7081.00 frames.], tot_loss[loss=0.1593, simple_loss=0.25, pruned_loss=0.0343, over 1427568.93 frames.], batch size: 28, lr: 3.52e-04
2022-05-15 04:24:53,789 INFO [train.py:812] (5/8) Epoch 22, batch 3100, loss[loss=0.1649, simple_loss=0.2562, pruned_loss=0.03678, over 4970.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2499, pruned_loss=0.03432, over 1428033.25 frames.], batch size: 52, lr: 3.52e-04
2022-05-15 04:25:52,320 INFO [train.py:812] (5/8) Epoch 22, batch 3150, loss[loss=0.1615, simple_loss=0.262, pruned_loss=0.03053, over 7417.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2493, pruned_loss=0.03417, over 1425464.15 frames.], batch size: 21, lr: 3.52e-04
2022-05-15 04:26:51,016 INFO [train.py:812] (5/8) Epoch 22, batch 3200, loss[loss=0.1557, simple_loss=0.2546, pruned_loss=0.02843, over 7053.00 frames.], tot_loss[loss=0.1585, simple_loss=0.249, pruned_loss=0.03406, over 1426699.12 frames.], batch size: 18, lr: 3.52e-04
2022-05-15 04:27:50,208 INFO [train.py:812] (5/8) Epoch 22, batch 3250, loss[loss=0.1413, simple_loss=0.2233, pruned_loss=0.02964, over 6988.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2502, pruned_loss=0.0346, over 1428181.77 frames.], batch size: 16, lr: 3.52e-04
2022-05-15 04:28:47,777 INFO [train.py:812] (5/8) Epoch 22, batch 3300, loss[loss=0.159, simple_loss=0.2437, pruned_loss=0.03712, over 7447.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2511, pruned_loss=0.03456, over 1430559.86 frames.], batch size: 20, lr: 3.52e-04
2022-05-15 04:29:46,918 INFO [train.py:812] (5/8) Epoch 22, batch 3350, loss[loss=0.1736, simple_loss=0.2568, pruned_loss=0.04521, over 7351.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2516, pruned_loss=0.03468, over 1429494.79 frames.], batch size: 19, lr: 3.52e-04
2022-05-15 04:30:46,402 INFO [train.py:812] (5/8) Epoch 22, batch 3400, loss[loss=0.1656, simple_loss=0.2487, pruned_loss=0.04129, over 7137.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2518, pruned_loss=0.035, over 1425265.76 frames.], batch size: 17, lr: 3.52e-04
2022-05-15 04:31:45,611 INFO [train.py:812] (5/8) Epoch 22, batch 3450, loss[loss=0.1489, simple_loss=0.2511, pruned_loss=0.02333, over 7337.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2524, pruned_loss=0.03488, over 1426633.14 frames.], batch size: 22, lr: 3.52e-04
2022-05-15 04:32:45,116 INFO [train.py:812] (5/8) Epoch 22, batch 3500, loss[loss=0.1618, simple_loss=0.2533, pruned_loss=0.03516, over 7327.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2518, pruned_loss=0.03478, over 1429076.66 frames.], batch size: 22, lr: 3.52e-04
2022-05-15 04:33:44,160 INFO [train.py:812] (5/8) Epoch 22, batch 3550, loss[loss=0.1649, simple_loss=0.2539, pruned_loss=0.03794, over 6655.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2528, pruned_loss=0.03518, over 1427530.97 frames.], batch size: 31, lr: 3.52e-04
2022-05-15 04:34:43,565 INFO [train.py:812] (5/8) Epoch 22, batch 3600, loss[loss=0.1509, simple_loss=0.2328, pruned_loss=0.03454, over 7253.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2521, pruned_loss=0.03514, over 1422569.64 frames.], batch size: 17, lr: 3.51e-04
2022-05-15 04:35:42,320 INFO [train.py:812] (5/8) Epoch 22, batch 3650, loss[loss=0.1655, simple_loss=0.2563, pruned_loss=0.03733, over 7378.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2518, pruned_loss=0.0347, over 1424143.41 frames.], batch size: 23, lr: 3.51e-04
2022-05-15 04:36:47,193 INFO [train.py:812] (5/8) Epoch 22, batch 3700, loss[loss=0.1622, simple_loss=0.2579, pruned_loss=0.03327, over 7218.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2506, pruned_loss=0.03461, over 1426412.78 frames.], batch size: 21, lr: 3.51e-04
2022-05-15 04:37:46,496 INFO [train.py:812] (5/8) Epoch 22, batch 3750, loss[loss=0.1597, simple_loss=0.2325, pruned_loss=0.04349, over 6984.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2503, pruned_loss=0.03478, over 1430861.32 frames.], batch size: 16, lr: 3.51e-04
2022-05-15 04:38:46,121 INFO [train.py:812] (5/8) Epoch 22, batch 3800, loss[loss=0.1736, simple_loss=0.2494, pruned_loss=0.04885, over 4836.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2495, pruned_loss=0.03463, over 1424823.92 frames.], batch size: 52, lr: 3.51e-04
2022-05-15 04:39:44,010 INFO [train.py:812] (5/8) Epoch 22, batch 3850, loss[loss=0.2082, simple_loss=0.3048, pruned_loss=0.05579, over 7237.00 frames.], tot_loss[loss=0.16, simple_loss=0.2503, pruned_loss=0.03483, over 1427112.27 frames.], batch size: 20, lr: 3.51e-04
2022-05-15 04:40:43,467 INFO [train.py:812] (5/8) Epoch 22, batch 3900, loss[loss=0.1618, simple_loss=0.263, pruned_loss=0.03035, over 6523.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2501, pruned_loss=0.03439, over 1427466.26 frames.], batch size: 38, lr: 3.51e-04
2022-05-15 04:41:41,333 INFO [train.py:812] (5/8) Epoch 22, batch 3950, loss[loss=0.1459, simple_loss=0.2223, pruned_loss=0.03473, over 7294.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2496, pruned_loss=0.03431, over 1425818.85 frames.], batch size: 17, lr: 3.51e-04
2022-05-15 04:42:39,859 INFO [train.py:812] (5/8) Epoch 22, batch 4000, loss[loss=0.1683, simple_loss=0.2721, pruned_loss=0.03221, over 7318.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2505, pruned_loss=0.03446, over 1425492.49 frames.], batch size: 21, lr: 3.51e-04
2022-05-15 04:43:37,310 INFO [train.py:812] (5/8) Epoch 22, batch 4050, loss[loss=0.1665, simple_loss=0.2549, pruned_loss=0.03904, over 7375.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2501, pruned_loss=0.03426, over 1423693.90 frames.], batch size: 19, lr: 3.51e-04
2022-05-15 04:44:35,622 INFO [train.py:812] (5/8) Epoch 22, batch 4100, loss[loss=0.2013, simple_loss=0.2895, pruned_loss=0.05658, over 7327.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2503, pruned_loss=0.03427, over 1424278.81 frames.], batch size: 20, lr: 3.51e-04
2022-05-15 04:45:34,801 INFO [train.py:812] (5/8) Epoch 22, batch 4150, loss[loss=0.1621, simple_loss=0.243, pruned_loss=0.04059, over 7068.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2498, pruned_loss=0.03421, over 1420072.38 frames.], batch size: 18, lr: 3.51e-04
2022-05-15 04:46:33,507 INFO [train.py:812] (5/8) Epoch 22, batch 4200, loss[loss=0.173, simple_loss=0.2659, pruned_loss=0.04002, over 7136.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2503, pruned_loss=0.03471, over 1415432.29 frames.], batch size: 20, lr: 3.50e-04
2022-05-15 04:47:30,294 INFO [train.py:812] (5/8) Epoch 22, batch 4250, loss[loss=0.1408, simple_loss=0.2299, pruned_loss=0.02587, over 6747.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2508, pruned_loss=0.03534, over 1409384.01 frames.], batch size: 31, lr: 3.50e-04
2022-05-15 04:48:27,298 INFO [train.py:812] (5/8) Epoch 22, batch 4300, loss[loss=0.1794, simple_loss=0.2809, pruned_loss=0.03895, over 7303.00 frames.], tot_loss[loss=0.16, simple_loss=0.2504, pruned_loss=0.03485, over 1411729.71 frames.], batch size: 24, lr: 3.50e-04
2022-05-15 04:49:26,469 INFO [train.py:812] (5/8) Epoch 22, batch 4350, loss[loss=0.1564, simple_loss=0.2541, pruned_loss=0.02931, over 7349.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2513, pruned_loss=0.03478, over 1408261.90 frames.], batch size: 22, lr: 3.50e-04
2022-05-15 04:50:35,267 INFO [train.py:812] (5/8) Epoch 22, batch 4400, loss[loss=0.1391, simple_loss=0.2374, pruned_loss=0.02045, over 7110.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2519, pruned_loss=0.03508, over 1403319.24 frames.], batch size: 21, lr: 3.50e-04
2022-05-15 04:51:33,771 INFO [train.py:812] (5/8) Epoch 22, batch 4450, loss[loss=0.1487, simple_loss=0.2466, pruned_loss=0.02538, over 7343.00 frames.], tot_loss[loss=0.162, simple_loss=0.2532, pruned_loss=0.03542, over 1399812.24 frames.], batch size: 22, lr: 3.50e-04
2022-05-15 04:52:33,290 INFO [train.py:812] (5/8) Epoch 22, batch 4500, loss[loss=0.1784, simple_loss=0.2694, pruned_loss=0.04367, over 7042.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2548, pruned_loss=0.03631, over 1388274.84 frames.], batch size: 28, lr: 3.50e-04
2022-05-15 04:53:50,567 INFO [train.py:812] (5/8) Epoch 22, batch 4550, loss[loss=0.1883, simple_loss=0.271, pruned_loss=0.05283, over 4988.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2567, pruned_loss=0.03776, over 1347375.77 frames.], batch size: 53, lr: 3.50e-04
2022-05-15 04:55:29,959 INFO [train.py:812] (5/8) Epoch 23, batch 0, loss[loss=0.1318, simple_loss=0.2192, pruned_loss=0.02224, over 6815.00 frames.], tot_loss[loss=0.1318, simple_loss=0.2192, pruned_loss=0.02224, over 6815.00 frames.], batch size: 15, lr: 3.42e-04
2022-05-15 04:56:28,537 INFO [train.py:812] (5/8) Epoch 23, batch 50, loss[loss=0.142, simple_loss=0.245, pruned_loss=0.0195, over 7162.00 frames.], tot_loss[loss=0.1575, simple_loss=0.248, pruned_loss=0.03348, over 319926.30 frames.], batch size: 19, lr: 3.42e-04
2022-05-15 04:57:26,847 INFO [train.py:812] (5/8) Epoch 23, batch 100, loss[loss=0.1516, simple_loss=0.2373, pruned_loss=0.03292, over 7284.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2517, pruned_loss=0.03507, over 566744.76 frames.], batch size: 18, lr: 3.42e-04
2022-05-15 04:58:25,157 INFO [train.py:812] (5/8) Epoch 23, batch 150, loss[loss=0.1799, simple_loss=0.2708, pruned_loss=0.04446, over 7308.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2517, pruned_loss=0.03524, over 754100.89 frames.], batch size: 24, lr: 3.42e-04
2022-05-15 04:59:34,119 INFO [train.py:812] (5/8) Epoch 23, batch 200, loss[loss=0.1737, simple_loss=0.26, pruned_loss=0.04368, over 6483.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2502, pruned_loss=0.03419, over 902033.55 frames.], batch size: 38, lr: 3.42e-04
2022-05-15 05:00:33,211 INFO [train.py:812] (5/8) Epoch 23, batch 250, loss[loss=0.1755, simple_loss=0.2656, pruned_loss=0.04274, over 7190.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2511, pruned_loss=0.03428, over 1017321.36 frames.], batch size: 23, lr: 3.42e-04
2022-05-15 05:01:30,505 INFO [train.py:812] (5/8) Epoch 23, batch 300, loss[loss=0.1825, simple_loss=0.2607, pruned_loss=0.05216, over 7159.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2509, pruned_loss=0.03446, over 1103363.16 frames.], batch size: 19, lr: 3.42e-04
2022-05-15 05:02:29,194 INFO [train.py:812] (5/8) Epoch 23, batch 350, loss[loss=0.1488, simple_loss=0.2467, pruned_loss=0.02548, over 7326.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2499, pruned_loss=0.03394, over 1177181.41 frames.], batch size: 22, lr: 3.42e-04
2022-05-15 05:03:27,249 INFO [train.py:812] (5/8) Epoch 23, batch 400, loss[loss=0.1687, simple_loss=0.2706, pruned_loss=0.03342, over 7182.00 frames.], tot_loss[loss=0.1589, simple_loss=0.25, pruned_loss=0.03388, over 1229799.38 frames.], batch size: 23, lr: 3.42e-04
2022-05-15 05:04:26,527 INFO [train.py:812] (5/8) Epoch 23, batch 450, loss[loss=0.1864, simple_loss=0.28, pruned_loss=0.04642, over 7292.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2505, pruned_loss=0.0341, over 1271364.89 frames.], batch size: 24, lr: 3.42e-04
2022-05-15 05:05:24,821 INFO [train.py:812] (5/8) Epoch 23, batch 500, loss[loss=0.1331, simple_loss=0.2164, pruned_loss=0.02488, over 6776.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2512, pruned_loss=0.03395, over 1305959.78 frames.], batch size: 15, lr: 3.41e-04
2022-05-15 05:06:21,986 INFO [train.py:812] (5/8) Epoch 23, batch 550, loss[loss=0.1765, simple_loss=0.2688, pruned_loss=0.04214, over 7303.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2513, pruned_loss=0.03422, over 1335637.11 frames.], batch size: 24, lr: 3.41e-04
2022-05-15 05:07:20,808 INFO [train.py:812] (5/8) Epoch 23, batch 600, loss[loss=0.1797, simple_loss=0.2722, pruned_loss=0.04362, over 7118.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2516, pruned_loss=0.0344, over 1357883.09 frames.], batch size: 21, lr: 3.41e-04
2022-05-15 05:08:19,859 INFO [train.py:812] (5/8) Epoch 23, batch 650, loss[loss=0.1579, simple_loss=0.2521, pruned_loss=0.03188, over 6711.00 frames.], tot_loss[loss=0.1608, simple_loss=0.252, pruned_loss=0.03475, over 1373076.66 frames.], batch size: 31, lr: 3.41e-04
2022-05-15 05:09:19,427 INFO [train.py:812] (5/8) Epoch 23, batch 700, loss[loss=0.1972, simple_loss=0.2821, pruned_loss=0.05614, over 4801.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2517, pruned_loss=0.03461, over 1379460.99 frames.], batch size: 52, lr: 3.41e-04
2022-05-15 05:10:18,519 INFO [train.py:812] (5/8) Epoch 23, batch 750, loss[loss=0.1746, simple_loss=0.2724, pruned_loss=0.03842, over 7192.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2524, pruned_loss=0.0345, over 1391310.47 frames.], batch size: 23, lr: 3.41e-04
2022-05-15 05:11:17,827 INFO [train.py:812] (5/8) Epoch 23, batch 800, loss[loss=0.1385, simple_loss=0.2266, pruned_loss=0.02523, over 7361.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2519, pruned_loss=0.03444, over 1396139.26 frames.], batch size: 19, lr: 3.41e-04
2022-05-15 05:12:15,506 INFO [train.py:812] (5/8) Epoch 23, batch 850, loss[loss=0.1325, simple_loss=0.22, pruned_loss=0.02249, over 7423.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2503, pruned_loss=0.03392, over 1404384.57 frames.], batch size: 20, lr: 3.41e-04
2022-05-15 05:13:14,529 INFO [train.py:812] (5/8) Epoch 23, batch 900, loss[loss=0.1511, simple_loss=0.2355, pruned_loss=0.03337, over 7153.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2504, pruned_loss=0.0341, over 1408589.46 frames.], batch size: 19, lr: 3.41e-04
2022-05-15 05:14:13,173 INFO [train.py:812] (5/8) Epoch 23, batch 950, loss[loss=0.1964, simple_loss=0.2916, pruned_loss=0.05064, over 7055.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2505, pruned_loss=0.03397, over 1410716.42 frames.], batch size: 28, lr: 3.41e-04
2022-05-15 05:15:13,115 INFO [train.py:812] (5/8) Epoch 23, batch 1000, loss[loss=0.1523, simple_loss=0.2494, pruned_loss=0.02764, over 7356.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2504, pruned_loss=0.03373, over 1417854.55 frames.], batch size: 19, lr: 3.41e-04
2022-05-15 05:16:12,121 INFO [train.py:812] (5/8) Epoch 23, batch 1050, loss[loss=0.1832, simple_loss=0.2578, pruned_loss=0.05425, over 5081.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2505, pruned_loss=0.03424, over 1418515.93 frames.], batch size: 52, lr: 3.41e-04
2022-05-15 05:17:10,932 INFO [train.py:812] (5/8) Epoch 23, batch 1100, loss[loss=0.1404, simple_loss=0.2192, pruned_loss=0.03084, over 7279.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2509, pruned_loss=0.0341, over 1418407.64 frames.], batch size: 17, lr: 3.40e-04
2022-05-15 05:18:09,879 INFO [train.py:812] (5/8) Epoch 23, batch 1150, loss[loss=0.1407, simple_loss=0.2316, pruned_loss=0.02497, over 7423.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2512, pruned_loss=0.03399, over 1421901.81 frames.], batch size: 20, lr: 3.40e-04
2022-05-15 05:19:09,543 INFO [train.py:812] (5/8) Epoch 23, batch 1200, loss[loss=0.1586, simple_loss=0.2477, pruned_loss=0.03479, over 7271.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2507, pruned_loss=0.03411, over 1420537.13 frames.], batch size: 18, lr: 3.40e-04
2022-05-15 05:20:07,296 INFO [train.py:812] (5/8) Epoch 23, batch 1250, loss[loss=0.1381, simple_loss=0.2183, pruned_loss=0.02897, over 6781.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2496, pruned_loss=0.03375, over 1423863.23 frames.], batch size: 15, lr: 3.40e-04
2022-05-15 05:21:05,552 INFO [train.py:812] (5/8) Epoch 23, batch 1300, loss[loss=0.1645, simple_loss=0.2643, pruned_loss=0.03232, over 7216.00 frames.], tot_loss[loss=0.1592, simple_loss=0.25, pruned_loss=0.03421, over 1426469.60 frames.], batch size: 23, lr: 3.40e-04
2022-05-15 05:22:03,012 INFO [train.py:812] (5/8) Epoch 23, batch 1350, loss[loss=0.1329, simple_loss=0.2177, pruned_loss=0.02404, over 7279.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2497, pruned_loss=0.03443, over 1427794.30 frames.], batch size: 18, lr: 3.40e-04
2022-05-15 05:23:02,503 INFO [train.py:812] (5/8) Epoch 23, batch 1400, loss[loss=0.1554, simple_loss=0.2502, pruned_loss=0.03026, over 7450.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2499, pruned_loss=0.03438, over 1427678.21 frames.], batch size: 22, lr: 3.40e-04
2022-05-15 05:24:01,077 INFO [train.py:812] (5/8) Epoch 23, batch 1450, loss[loss=0.1462, simple_loss=0.2333, pruned_loss=0.02957, over 7419.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2493, pruned_loss=0.03442, over 1421964.87 frames.], batch size: 18, lr: 3.40e-04
2022-05-15 05:24:59,730 INFO [train.py:812] (5/8) Epoch 23, batch 1500, loss[loss=0.159, simple_loss=0.2506, pruned_loss=0.03369, over 7007.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2476, pruned_loss=0.0337, over 1423049.98 frames.], batch size: 28, lr: 3.40e-04
2022-05-15 05:25:58,409 INFO [train.py:812] (5/8) Epoch 23, batch 1550, loss[loss=0.1397, simple_loss=0.2296, pruned_loss=0.02485, over 7351.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2483, pruned_loss=0.03401, over 1414716.37 frames.], batch size: 19, lr: 3.40e-04
2022-05-15 05:26:57,171 INFO [train.py:812] (5/8) Epoch 23, batch 1600, loss[loss=0.1582, simple_loss=0.2543, pruned_loss=0.03106, over 7208.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2492, pruned_loss=0.03457, over 1412700.73 frames.], batch size: 21, lr: 3.40e-04
2022-05-15 05:27:55,178 INFO [train.py:812] (5/8) Epoch 23, batch 1650, loss[loss=0.161, simple_loss=0.251, pruned_loss=0.0355, over 7373.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2491, pruned_loss=0.03487, over 1415317.07 frames.], batch size: 23, lr: 3.40e-04
2022-05-15 05:28:54,099 INFO [train.py:812] (5/8) Epoch 23, batch 1700, loss[loss=0.1325, simple_loss=0.2213, pruned_loss=0.02191, over 7406.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2497, pruned_loss=0.03499, over 1416370.33 frames.], batch size: 18, lr: 3.39e-04
2022-05-15 05:29:50,561 INFO [train.py:812] (5/8) Epoch 23, batch 1750, loss[loss=0.1862, simple_loss=0.2823, pruned_loss=0.04503, over 7145.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2504, pruned_loss=0.03496, over 1414942.14 frames.], batch size: 26, lr: 3.39e-04
2022-05-15 05:30:48,707 INFO [train.py:812] (5/8) Epoch 23, batch 1800, loss[loss=0.2471, simple_loss=0.3293, pruned_loss=0.08241, over 5036.00 frames.], tot_loss[loss=0.1597, simple_loss=0.25, pruned_loss=0.03472, over 1411821.48 frames.], batch size: 52, lr: 3.39e-04
2022-05-15 05:31:46,088 INFO [train.py:812] (5/8) Epoch 23, batch 1850, loss[loss=0.161, simple_loss=0.2511, pruned_loss=0.03549, over 7433.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2494, pruned_loss=0.03403, over 1416745.34 frames.], batch size: 20, lr: 3.39e-04
2022-05-15 05:32:43,997 INFO [train.py:812] (5/8) Epoch 23, batch 1900, loss[loss=0.1972, simple_loss=0.2919, pruned_loss=0.05123, over 7139.00 frames.], tot_loss[loss=0.159, simple_loss=0.2495, pruned_loss=0.03423, over 1420937.73 frames.], batch size: 20, lr: 3.39e-04
2022-05-15 05:33:42,349 INFO [train.py:812] (5/8) Epoch 23, batch 1950, loss[loss=0.1877, simple_loss=0.2736, pruned_loss=0.05091, over 7153.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2503, pruned_loss=0.03479, over 1418141.29 frames.], batch size: 20, lr: 3.39e-04
2022-05-15 05:34:41,251 INFO [train.py:812] (5/8) Epoch 23, batch 2000, loss[loss=0.1455, simple_loss=0.2401, pruned_loss=0.0255, over 7251.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2511, pruned_loss=0.03471, over 1421596.66 frames.], batch size: 19, lr: 3.39e-04
2022-05-15 05:35:40,355 INFO [train.py:812] (5/8) Epoch 23, batch 2050, loss[loss=0.1732, simple_loss=0.2761, pruned_loss=0.03515, over 7229.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2511, pruned_loss=0.03467, over 1425702.21 frames.], batch size: 20, lr: 3.39e-04
2022-05-15 05:36:39,468 INFO [train.py:812] (5/8) Epoch 23, batch 2100, loss[loss=0.1799, simple_loss=0.2639, pruned_loss=0.04792, over 7207.00 frames.], tot_loss[loss=0.16, simple_loss=0.2507, pruned_loss=0.03466, over 1420461.03 frames.], batch size: 23, lr: 3.39e-04
2022-05-15 05:37:37,948 INFO [train.py:812] (5/8) Epoch 23, batch 2150, loss[loss=0.1315, simple_loss=0.2247, pruned_loss=0.01911, over 7156.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2504, pruned_loss=0.03429, over 1421171.77 frames.], batch size: 19, lr: 3.39e-04
2022-05-15 05:38:37,635 INFO [train.py:812] (5/8) Epoch 23, batch 2200, loss[loss=0.1572, simple_loss=0.2575, pruned_loss=0.02843, over 7150.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2508, pruned_loss=0.03443, over 1416298.09 frames.], batch size: 20, lr: 3.39e-04
2022-05-15 05:39:36,694 INFO [train.py:812] (5/8) Epoch 23, batch 2250, loss[loss=0.1626, simple_loss=0.248, pruned_loss=0.03861, over 7150.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2507, pruned_loss=0.03446, over 1411997.07 frames.], batch size: 19, lr: 3.39e-04
2022-05-15 05:40:35,581 INFO [train.py:812] (5/8) Epoch 23, batch 2300, loss[loss=0.1597, simple_loss=0.2504, pruned_loss=0.03448, over 7308.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2499, pruned_loss=0.03447, over 1414091.72 frames.], batch size: 21, lr: 3.38e-04
2022-05-15 05:41:34,383 INFO [train.py:812] (5/8) Epoch 23, batch 2350, loss[loss=0.1635, simple_loss=0.2657, pruned_loss=0.03061, over 7334.00 frames.], tot_loss[loss=0.1593, simple_loss=0.25, pruned_loss=0.03428, over 1415913.34 frames.], batch size: 22, lr: 3.38e-04
2022-05-15 05:42:33,212 INFO [train.py:812] (5/8) Epoch 23, batch 2400, loss[loss=0.1696, simple_loss=0.2557, pruned_loss=0.04175, over 7287.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2501, pruned_loss=0.03433, over 1418502.25 frames.], batch size: 24, lr: 3.38e-04
2022-05-15 05:43:31,286 INFO [train.py:812] (5/8) Epoch 23, batch 2450, loss[loss=0.1684, simple_loss=0.2611, pruned_loss=0.03785, over 7208.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2513, pruned_loss=0.03447, over 1422610.91 frames.], batch size: 22, lr: 3.38e-04
2022-05-15 05:44:30,333 INFO [train.py:812] (5/8) Epoch 23, batch 2500, loss[loss=0.1883, simple_loss=0.2795, pruned_loss=0.04852, over 6483.00 frames.], tot_loss[loss=0.1592, simple_loss=0.25, pruned_loss=0.03423, over 1421173.01 frames.], batch size: 37, lr: 3.38e-04
2022-05-15 05:45:29,330 INFO [train.py:812] (5/8) Epoch 23, batch 2550, loss[loss=0.1905, simple_loss=0.2812, pruned_loss=0.04993, over 7376.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2507, pruned_loss=0.0344, over 1422075.50 frames.], batch size: 23, lr: 3.38e-04
2022-05-15 05:46:26,772 INFO [train.py:812] (5/8) Epoch 23, batch 2600, loss[loss=0.1534, simple_loss=0.2519, pruned_loss=0.02743, over 7328.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2508, pruned_loss=0.03442, over 1426049.86 frames.], batch size: 22, lr: 3.38e-04
2022-05-15 05:47:25,315 INFO [train.py:812] (5/8) Epoch 23, batch 2650, loss[loss=0.2096, simple_loss=0.2823, pruned_loss=0.06849, over 7291.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2493, pruned_loss=0.03403, over 1423355.77 frames.], batch size: 25, lr: 3.38e-04
2022-05-15 05:48:25,319 INFO [train.py:812] (5/8) Epoch 23, batch 2700, loss[loss=0.1388, simple_loss=0.2266, pruned_loss=0.0255, over 7156.00 frames.], tot_loss[loss=0.159, simple_loss=0.2497, pruned_loss=0.03413, over 1422950.79 frames.], batch size: 19, lr: 3.38e-04
2022-05-15 05:49:24,346 INFO [train.py:812] (5/8) Epoch 23, batch 2750, loss[loss=0.1522, simple_loss=0.2414, pruned_loss=0.03149, over 7163.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2495, pruned_loss=0.03447, over 1421682.99 frames.], batch size: 18, lr: 3.38e-04
2022-05-15 05:50:23,720 INFO [train.py:812] (5/8) Epoch 23, batch 2800, loss[loss=0.1464, simple_loss=0.2257, pruned_loss=0.03353, over 7178.00 frames.], tot_loss[loss=0.159, simple_loss=0.2495, pruned_loss=0.03427, over 1420137.36 frames.], batch size: 18, lr: 3.38e-04
2022-05-15 05:51:22,630 INFO [train.py:812] (5/8) Epoch 23, batch 2850, loss[loss=0.1591, simple_loss=0.2662, pruned_loss=0.026, over 7049.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2493, pruned_loss=0.03397, over 1421609.03 frames.], batch size: 28, lr: 3.38e-04
2022-05-15 05:52:22,316 INFO [train.py:812] (5/8) Epoch 23, batch 2900, loss[loss=0.1812, simple_loss=0.2685, pruned_loss=0.04695, over 7288.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2491, pruned_loss=0.03388, over 1423955.92 frames.], batch size: 25, lr: 3.37e-04
2022-05-15 05:53:20,349 INFO [train.py:812] (5/8) Epoch 23, batch 2950, loss[loss=0.1705, simple_loss=0.2603, pruned_loss=0.04039, over 7202.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2491, pruned_loss=0.03392, over 1424513.82 frames.], batch size: 22, lr: 3.37e-04
2022-05-15 05:54:18,724 INFO [train.py:812] (5/8) Epoch 23, batch 3000, loss[loss=0.1348, simple_loss=0.2185, pruned_loss=0.0255, over 6991.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2492, pruned_loss=0.03364, over 1423412.56 frames.], batch size: 16, lr: 3.37e-04
2022-05-15 05:54:18,725 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 05:54:28,115 INFO [train.py:841] (5/8) Epoch 23, validation: loss=0.153, simple_loss=0.251, pruned_loss=0.02752, over 698248.00 frames.
2022-05-15 05:55:26,688 INFO [train.py:812] (5/8) Epoch 23, batch 3050, loss[loss=0.147, simple_loss=0.2315, pruned_loss=0.03122, over 7152.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2492, pruned_loss=0.03368, over 1425879.80 frames.], batch size: 19, lr: 3.37e-04
2022-05-15 05:56:31,533 INFO [train.py:812] (5/8) Epoch 23, batch 3100, loss[loss=0.1736, simple_loss=0.2674, pruned_loss=0.03988, over 7234.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2487, pruned_loss=0.03372, over 1424962.57 frames.], batch size: 20, lr: 3.37e-04
2022-05-15 05:57:31,005 INFO [train.py:812] (5/8) Epoch 23, batch 3150, loss[loss=0.1661, simple_loss=0.264, pruned_loss=0.03411, over 7329.00 frames.], tot_loss[loss=0.1586, simple_loss=0.249, pruned_loss=0.03413, over 1426190.37 frames.], batch size: 20, lr: 3.37e-04
2022-05-15 05:58:30,538 INFO [train.py:812] (5/8) Epoch 23, batch 3200, loss[loss=0.1424, simple_loss=0.2339, pruned_loss=0.02546, over 7108.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2492, pruned_loss=0.0343, over 1428398.18 frames.], batch size: 21, lr: 3.37e-04
2022-05-15 05:59:29,503 INFO [train.py:812] (5/8) Epoch 23, batch 3250, loss[loss=0.1496, simple_loss=0.243, pruned_loss=0.0281, over 6306.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2501, pruned_loss=0.03447, over 1422578.21 frames.], batch size: 37, lr: 3.37e-04
2022-05-15 06:00:29,701 INFO [train.py:812] (5/8) Epoch 23, batch 3300, loss[loss=0.1651, simple_loss=0.2563, pruned_loss=0.03696, over 7293.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2497, pruned_loss=0.03381, over 1422807.68 frames.], batch size: 24, lr: 3.37e-04
2022-05-15 06:01:29,024 INFO [train.py:812] (5/8) Epoch 23, batch 3350, loss[loss=0.1641, simple_loss=0.2613, pruned_loss=0.03342, over 7160.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2482, pruned_loss=0.03337, over 1428004.91 frames.], batch size: 26, lr: 3.37e-04
2022-05-15 06:02:28,575 INFO [train.py:812] (5/8) Epoch 23, batch 3400, loss[loss=0.1571, simple_loss=0.2476, pruned_loss=0.03335, over 7158.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2484, pruned_loss=0.03344, over 1429084.05 frames.], batch size: 19, lr: 3.37e-04
2022-05-15 06:03:27,790 INFO [train.py:812] (5/8) Epoch 23, batch 3450, loss[loss=0.1548, simple_loss=0.2423, pruned_loss=0.03364, over 6752.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2477, pruned_loss=0.0336, over 1429947.12 frames.], batch size: 15, lr: 3.37e-04
2022-05-15 06:04:27,364 INFO [train.py:812] (5/8) Epoch 23, batch 3500, loss[loss=0.1686, simple_loss=0.2459, pruned_loss=0.04569, over 6769.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2478, pruned_loss=0.0336, over 1430603.65 frames.], batch size: 15, lr: 3.37e-04
2022-05-15 06:05:25,961 INFO [train.py:812] (5/8) Epoch 23, batch 3550, loss[loss=0.1351, simple_loss=0.2272, pruned_loss=0.02151, over 7427.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2477, pruned_loss=0.03337, over 1430293.90 frames.], batch size: 18, lr: 3.36e-04
2022-05-15 06:06:25,111 INFO [train.py:812] (5/8) Epoch 23, batch 3600, loss[loss=0.1544, simple_loss=0.2315, pruned_loss=0.03869, over 7289.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2491, pruned_loss=0.03358, over 1431459.37 frames.], batch size: 17, lr: 3.36e-04
2022-05-15 06:07:24,188 INFO [train.py:812] (5/8) Epoch 23, batch 3650, loss[loss=0.1696, simple_loss=0.2661, pruned_loss=0.03655, over 6455.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2501, pruned_loss=0.03416, over 1431072.75 frames.], batch size: 37, lr: 3.36e-04
2022-05-15 06:08:33,526 INFO [train.py:812] (5/8) Epoch 23, batch 3700, loss[loss=0.1665, simple_loss=0.2507, pruned_loss=0.04116, over 7158.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2497, pruned_loss=0.03391, over 1429617.13 frames.], batch size: 19, lr: 3.36e-04
2022-05-15 06:09:32,186 INFO [train.py:812] (5/8) Epoch 23, batch 3750, loss[loss=0.1587, simple_loss=0.2413, pruned_loss=0.03804, over 7297.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2502, pruned_loss=0.03432, over 1427619.61 frames.], batch size: 17, lr: 3.36e-04
2022-05-15 06:10:31,404 INFO [train.py:812] (5/8) Epoch 23, batch 3800, loss[loss=0.145, simple_loss=0.236, pruned_loss=0.02696, over 7383.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2499, pruned_loss=0.03448, over 1429904.89 frames.], batch size: 23, lr: 3.36e-04
2022-05-15 06:11:30,121 INFO [train.py:812] (5/8) Epoch 23, batch 3850, loss[loss=0.1621, simple_loss=0.2603, pruned_loss=0.03196, over 7120.00 frames.], tot_loss[loss=0.1587, simple_loss=0.249, pruned_loss=0.03418, over 1431221.36 frames.], batch size: 28, lr: 3.36e-04
2022-05-15 06:12:28,293 INFO [train.py:812] (5/8) Epoch 23, batch 3900, loss[loss=0.1694, simple_loss=0.2661, pruned_loss=0.03636, over 7117.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2494, pruned_loss=0.03447, over 1430611.33 frames.], batch size: 21, lr: 3.36e-04
2022-05-15 06:13:25,762 INFO [train.py:812] (5/8) Epoch 23, batch 3950, loss[loss=0.1729, simple_loss=0.2673, pruned_loss=0.0393, over 7164.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2501, pruned_loss=0.03474, over 1430484.47 frames.], batch size: 19, lr: 3.36e-04
2022-05-15 06:14:22,991 INFO [train.py:812] (5/8) Epoch 23, batch 4000, loss[loss=0.1257, simple_loss=0.2099, pruned_loss=0.02072, over 7282.00 frames.], tot_loss[loss=0.1586, simple_loss=0.249, pruned_loss=0.0341, over 1426797.46 frames.], batch size: 17, lr: 3.36e-04
2022-05-15 06:15:21,466 INFO [train.py:812] (5/8) Epoch 23, batch 4050, loss[loss=0.1402, simple_loss=0.2148, pruned_loss=0.03283, over 6777.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2501, pruned_loss=0.0344, over 1421984.36 frames.], batch size: 15, lr: 3.36e-04
2022-05-15 06:16:21,790 INFO [train.py:812] (5/8) Epoch 23, batch 4100, loss[loss=0.1354, simple_loss=0.2196, pruned_loss=0.02559, over 6833.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2501, pruned_loss=0.03472, over 1418852.25 frames.], batch size: 15, lr: 3.36e-04
2022-05-15 06:17:19,463 INFO [train.py:812] (5/8) Epoch 23, batch 4150, loss[loss=0.1622, simple_loss=0.2622, pruned_loss=0.03108, over 7319.00 frames.], tot_loss[loss=0.16, simple_loss=0.2508, pruned_loss=0.03463, over 1417722.29 frames.], batch size: 21, lr: 3.35e-04
2022-05-15 06:18:18,887 INFO [train.py:812] (5/8) Epoch 23, batch 4200, loss[loss=0.1234, simple_loss=0.2119, pruned_loss=0.01748, over 6988.00 frames.], tot_loss[loss=0.1599, simple_loss=0.251, pruned_loss=0.0344, over 1422185.35 frames.], batch size: 16, lr: 3.35e-04
2022-05-15 06:19:17,839 INFO [train.py:812] (5/8) Epoch 23, batch 4250, loss[loss=0.1741, simple_loss=0.2603, pruned_loss=0.04399, over 7237.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2512, pruned_loss=0.03427, over 1423669.64 frames.], batch size: 20, lr: 3.35e-04
2022-05-15 06:20:16,265 INFO [train.py:812] (5/8) Epoch 23, batch 4300, loss[loss=0.1499, simple_loss=0.2294, pruned_loss=0.03524, over 7167.00 frames.], tot_loss[loss=0.1591, simple_loss=0.25, pruned_loss=0.03411, over 1421117.63 frames.], batch size: 18, lr: 3.35e-04
2022-05-15 06:21:15,782 INFO [train.py:812] (5/8) Epoch 23, batch 4350, loss[loss=0.1385, simple_loss=0.2243, pruned_loss=0.02632, over 6800.00 frames.], tot_loss[loss=0.159, simple_loss=0.2496, pruned_loss=0.03424, over 1422498.08 frames.], batch size: 15, lr: 3.35e-04
2022-05-15 06:22:15,637 INFO [train.py:812] (5/8) Epoch 23, batch 4400, loss[loss=0.1435, simple_loss=0.2331, pruned_loss=0.02691, over 7074.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2488, pruned_loss=0.03409, over 1419880.79 frames.], batch size: 18, lr: 3.35e-04
2022-05-15 06:23:14,928 INFO [train.py:812] (5/8) Epoch 23, batch 4450, loss[loss=0.1754, simple_loss=0.2576, pruned_loss=0.04664, over 4922.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2491, pruned_loss=0.03436, over 1414478.50 frames.], batch size: 52, lr: 3.35e-04
2022-05-15 06:24:12,956 INFO [train.py:812] (5/8) Epoch 23, batch 4500, loss[loss=0.1388, simple_loss=0.2291, pruned_loss=0.02428, over 7073.00 frames.], tot_loss[loss=0.159, simple_loss=0.2494, pruned_loss=0.03427, over 1413014.01 frames.], batch size: 18, lr: 3.35e-04
2022-05-15 06:25:11,067 INFO [train.py:812] (5/8) Epoch 23, batch 4550, loss[loss=0.2044, simple_loss=0.2909, pruned_loss=0.05898, over 4920.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2522, pruned_loss=0.03618, over 1357224.36 frames.], batch size: 53, lr: 3.35e-04
2022-05-15 06:26:16,418 INFO [train.py:812] (5/8) Epoch 24, batch 0, loss[loss=0.1502, simple_loss=0.2369, pruned_loss=0.03177, over 6814.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2369, pruned_loss=0.03177, over 6814.00 frames.], batch size: 15, lr: 3.28e-04
2022-05-15 06:27:14,048 INFO [train.py:812] (5/8) Epoch 24, batch 50, loss[loss=0.1427, simple_loss=0.2194, pruned_loss=0.03293, over 7294.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2485, pruned_loss=0.03297, over 317399.33 frames.], batch size: 17, lr: 3.28e-04
2022-05-15 06:28:13,398 INFO [train.py:812] (5/8) Epoch 24, batch 100, loss[loss=0.2033, simple_loss=0.289, pruned_loss=0.05882, over 7335.00 frames.], tot_loss[loss=0.1596, simple_loss=0.251, pruned_loss=0.0341, over 567492.02 frames.], batch size: 20, lr: 3.28e-04
2022-05-15 06:29:11,042 INFO [train.py:812] (5/8) Epoch 24, batch 150, loss[loss=0.1548, simple_loss=0.2603, pruned_loss=0.02465, over 7389.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2511, pruned_loss=0.03454, over 753080.38 frames.], batch size: 23, lr: 3.28e-04
2022-05-15 06:30:10,078 INFO [train.py:812] (5/8) Epoch 24, batch 200, loss[loss=0.1672, simple_loss=0.2646, pruned_loss=0.03489, over 7217.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2509, pruned_loss=0.03463, over 904568.95 frames.], batch size: 22, lr: 3.28e-04
2022-05-15 06:31:07,637 INFO [train.py:812] (5/8) Epoch 24, batch 250, loss[loss=0.1443, simple_loss=0.2399, pruned_loss=0.02438, over 7415.00 frames.], tot_loss[loss=0.16, simple_loss=0.2506, pruned_loss=0.03468, over 1016795.92 frames.], batch size: 21, lr: 3.28e-04
2022-05-15 06:32:07,185 INFO [train.py:812] (5/8) Epoch 24, batch 300, loss[loss=0.1643, simple_loss=0.2512, pruned_loss=0.03868, over 7140.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2501, pruned_loss=0.03406, over 1107583.06 frames.], batch size: 20, lr: 3.27e-04
2022-05-15 06:33:03,993 INFO [train.py:812] (5/8) Epoch 24, batch 350, loss[loss=0.1614, simple_loss=0.252, pruned_loss=0.03544, over 7290.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2489, pruned_loss=0.0333, over 1178780.60 frames.], batch size: 25, lr: 3.27e-04
2022-05-15 06:34:01,148 INFO [train.py:812] (5/8) Epoch 24, batch 400, loss[loss=0.1642, simple_loss=0.256, pruned_loss=0.03615, over 7269.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2483, pruned_loss=0.0333, over 1229380.80 frames.], batch size: 24, lr: 3.27e-04
2022-05-15 06:34:58,897 INFO [train.py:812] (5/8) Epoch 24, batch 450, loss[loss=0.1497, simple_loss=0.2415, pruned_loss=0.02899, over 7139.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2487, pruned_loss=0.03323, over 1275526.87 frames.], batch size: 20, lr: 3.27e-04
2022-05-15 06:35:57,371 INFO [train.py:812] (5/8) Epoch 24, batch 500, loss[loss=0.148, simple_loss=0.2366, pruned_loss=0.02966, over 7352.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2488, pruned_loss=0.03326, over 1307527.45 frames.], batch size: 19, lr: 3.27e-04
2022-05-15 06:36:55,954 INFO [train.py:812] (5/8) Epoch 24, batch 550, loss[loss=0.176, simple_loss=0.2712, pruned_loss=0.04042, over 7208.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2485, pruned_loss=0.0335, over 1335776.81 frames.], batch size: 22, lr: 3.27e-04
2022-05-15 06:37:55,358 INFO [train.py:812] (5/8) Epoch 24, batch 600, loss[loss=0.1374, simple_loss=0.2235, pruned_loss=0.02568, over 7361.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2478, pruned_loss=0.03303, over 1353062.07 frames.], batch size: 19, lr: 3.27e-04
2022-05-15 06:38:54,598 INFO [train.py:812] (5/8) Epoch 24, batch 650, loss[loss=0.1557, simple_loss=0.2453, pruned_loss=0.03307, over 7362.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2481, pruned_loss=0.03341, over 1362513.83 frames.], batch size: 19, lr: 3.27e-04
2022-05-15 06:39:54,716 INFO [train.py:812] (5/8) Epoch 24, batch 700, loss[loss=0.1712, simple_loss=0.2626, pruned_loss=0.03994, over 7109.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2465, pruned_loss=0.03293, over 1380268.52 frames.], batch size: 26, lr: 3.27e-04
2022-05-15 06:40:53,843 INFO [train.py:812] (5/8) Epoch 24, batch 750, loss[loss=0.1757, simple_loss=0.2459, pruned_loss=0.05275, over 6996.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2472, pruned_loss=0.03271, over 1391543.36 frames.], batch size: 16, lr: 3.27e-04
2022-05-15 06:41:53,030 INFO [train.py:812] (5/8) Epoch 24, batch 800, loss[loss=0.1366, simple_loss=0.2378, pruned_loss=0.01767, over 7250.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2468, pruned_loss=0.03275, over 1398473.98 frames.], batch size: 19, lr: 3.27e-04
2022-05-15 06:42:52,214 INFO [train.py:812] (5/8) Epoch 24, batch 850, loss[loss=0.1732, simple_loss=0.2572, pruned_loss=0.04455, over 6692.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2465, pruned_loss=0.03254, over 1404925.07 frames.], batch size: 31, lr: 3.27e-04
2022-05-15 06:43:51,469 INFO [train.py:812] (5/8) Epoch 24, batch 900, loss[loss=0.1435, simple_loss=0.2438, pruned_loss=0.02158, over 7427.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2466, pruned_loss=0.03247, over 1411123.10 frames.], batch size: 20, lr: 3.27e-04
2022-05-15 06:44:50,515 INFO [train.py:812] (5/8) Epoch 24, batch 950, loss[loss=0.1604, simple_loss=0.252, pruned_loss=0.03441, over 6463.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2459, pruned_loss=0.03225, over 1416237.26 frames.], batch size: 38, lr: 3.26e-04
2022-05-15 06:45:49,605 INFO [train.py:812] (5/8) Epoch 24, batch 1000, loss[loss=0.1504, simple_loss=0.2467, pruned_loss=0.02708, over 7310.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2452, pruned_loss=0.03231, over 1418364.36 frames.], batch size: 21, lr: 3.26e-04
2022-05-15 06:46:47,309 INFO [train.py:812] (5/8) Epoch 24, batch 1050, loss[loss=0.145, simple_loss=0.241, pruned_loss=0.02448, over 7229.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2465, pruned_loss=0.03285, over 1411927.05 frames.], batch size: 20, lr: 3.26e-04
2022-05-15 06:47:46,483 INFO [train.py:812] (5/8) Epoch 24, batch 1100, loss[loss=0.1705, simple_loss=0.2684, pruned_loss=0.03628, over 7148.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2467, pruned_loss=0.03297, over 1412014.93 frames.], batch size: 20, lr: 3.26e-04
2022-05-15 06:48:44,901 INFO [train.py:812] (5/8) Epoch 24, batch 1150, loss[loss=0.1583, simple_loss=0.2532, pruned_loss=0.03169, over 6626.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2463, pruned_loss=0.03292, over 1415190.20 frames.], batch size: 38, lr: 3.26e-04
2022-05-15 06:49:42,952 INFO [train.py:812] (5/8) Epoch 24, batch 1200, loss[loss=0.1569, simple_loss=0.2554, pruned_loss=0.02925, over 7166.00 frames.], tot_loss[loss=0.1575, simple_loss=0.248, pruned_loss=0.03354, over 1417747.34 frames.], batch size: 18, lr: 3.26e-04
2022-05-15 06:50:50,779 INFO [train.py:812] (5/8) Epoch 24, batch 1250, loss[loss=0.1579, simple_loss=0.2453, pruned_loss=0.03529, over 7338.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2479, pruned_loss=0.03361, over 1418488.91 frames.], batch size: 20, lr: 3.26e-04
2022-05-15 06:51:49,903 INFO [train.py:812] (5/8) Epoch 24, batch 1300, loss[loss=0.1721, simple_loss=0.2678, pruned_loss=0.03821, over 6648.00 frames.], tot_loss[loss=0.1576, simple_loss=0.248, pruned_loss=0.03362, over 1419687.52 frames.], batch size: 31, lr: 3.26e-04
2022-05-15 06:52:48,830 INFO [train.py:812] (5/8) Epoch 24, batch 1350, loss[loss=0.1364, simple_loss=0.2237, pruned_loss=0.02455, over 7414.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2491, pruned_loss=0.03394, over 1425211.54 frames.], batch size: 18, lr: 3.26e-04
2022-05-15 06:53:46,290 INFO [train.py:812] (5/8) Epoch 24, batch 1400, loss[loss=0.1702, simple_loss=0.2804, pruned_loss=0.03001, over 7190.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2494, pruned_loss=0.03413, over 1423724.08 frames.], batch size: 26, lr: 3.26e-04
2022-05-15 06:55:13,446 INFO [train.py:812] (5/8) Epoch 24, batch 1450, loss[loss=0.1657, simple_loss=0.2639, pruned_loss=0.03377, over 7148.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2495, pruned_loss=0.03416, over 1421692.72 frames.], batch size: 20, lr: 3.26e-04
2022-05-15 06:56:21,940 INFO [train.py:812] (5/8) Epoch 24, batch 1500, loss[loss=0.1615, simple_loss=0.2576, pruned_loss=0.03265, over 7144.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2496, pruned_loss=0.03444, over 1420352.76 frames.], batch size: 20, lr: 3.26e-04
2022-05-15 06:57:21,236 INFO [train.py:812] (5/8) Epoch 24, batch 1550, loss[loss=0.1821, simple_loss=0.2831, pruned_loss=0.04051, over 6785.00 frames.], tot_loss[loss=0.1587, simple_loss=0.249, pruned_loss=0.03424, over 1421269.63 frames.], batch size: 31, lr: 3.26e-04
2022-05-15 06:58:39,393 INFO [train.py:812] (5/8) Epoch 24, batch 1600, loss[loss=0.1532, simple_loss=0.2435, pruned_loss=0.03148, over 7311.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2495, pruned_loss=0.03413, over 1422627.89 frames.], batch size: 20, lr: 3.25e-04
2022-05-15 06:59:37,738 INFO [train.py:812] (5/8) Epoch 24, batch 1650, loss[loss=0.1267, simple_loss=0.2031, pruned_loss=0.02515, over 6762.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2493, pruned_loss=0.03408, over 1413666.82 frames.], batch size: 15, lr: 3.25e-04
2022-05-15 07:00:36,791 INFO [train.py:812] (5/8) Epoch 24, batch 1700, loss[loss=0.1634, simple_loss=0.2645, pruned_loss=0.03115, over 7311.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2483, pruned_loss=0.03348, over 1417769.34 frames.], batch size: 21, lr: 3.25e-04
2022-05-15 07:01:34,450 INFO [train.py:812] (5/8) Epoch 24, batch 1750, loss[loss=0.1622, simple_loss=0.2508, pruned_loss=0.03684, over 7063.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2492, pruned_loss=0.03383, over 1418729.61 frames.], batch size: 18, lr: 3.25e-04
2022-05-15 07:02:33,273 INFO [train.py:812] (5/8) Epoch 24, batch 1800, loss[loss=0.162, simple_loss=0.2531, pruned_loss=0.03545, over 7338.00 frames.], tot_loss[loss=0.1581, simple_loss=0.249, pruned_loss=0.03363, over 1419126.53 frames.], batch size: 22, lr: 3.25e-04
2022-05-15 07:03:31,321 INFO [train.py:812] (5/8) Epoch 24, batch 1850, loss[loss=0.155, simple_loss=0.2522, pruned_loss=0.02891, over 7300.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2489, pruned_loss=0.03343, over 1423681.43 frames.], batch size: 24, lr: 3.25e-04
2022-05-15 07:04:30,215 INFO [train.py:812] (5/8) Epoch 24, batch 1900, loss[loss=0.1586, simple_loss=0.2434, pruned_loss=0.03688, over 7026.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2491, pruned_loss=0.03384, over 1422252.37 frames.], batch size: 28, lr: 3.25e-04
2022-05-15 07:05:29,099 INFO [train.py:812] (5/8) Epoch 24, batch 1950, loss[loss=0.1545, simple_loss=0.2562, pruned_loss=0.02644, over 7110.00 frames.], tot_loss[loss=0.159, simple_loss=0.2499, pruned_loss=0.03404, over 1422660.34 frames.], batch size: 21, lr: 3.25e-04
2022-05-15 07:06:27,417 INFO [train.py:812] (5/8) Epoch 24, batch 2000, loss[loss=0.1681, simple_loss=0.2578, pruned_loss=0.03922, over 4962.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2502, pruned_loss=0.03418, over 1420839.13 frames.], batch size: 52, lr: 3.25e-04
2022-05-15 07:07:25,815 INFO [train.py:812] (5/8) Epoch 24, batch 2050, loss[loss=0.1429, simple_loss=0.2292, pruned_loss=0.02828, over 7436.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2506, pruned_loss=0.03435, over 1421369.32 frames.], batch size: 20, lr: 3.25e-04
2022-05-15 07:08:23,656 INFO [train.py:812] (5/8) Epoch 24, batch 2100, loss[loss=0.1584, simple_loss=0.2505, pruned_loss=0.03313, over 7002.00 frames.], tot_loss[loss=0.1591, simple_loss=0.25, pruned_loss=0.03412, over 1422751.66 frames.], batch size: 16, lr: 3.25e-04
2022-05-15 07:09:22,546 INFO [train.py:812] (5/8) Epoch 24, batch 2150, loss[loss=0.1932, simple_loss=0.2815, pruned_loss=0.05245, over 4955.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2495, pruned_loss=0.03403, over 1420033.12 frames.], batch size: 52, lr: 3.25e-04
2022-05-15 07:10:21,850 INFO [train.py:812] (5/8) Epoch 24, batch 2200, loss[loss=0.1383, simple_loss=0.2343, pruned_loss=0.02117, over 7138.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2489, pruned_loss=0.03362, over 1418833.87 frames.], batch size: 17, lr: 3.25e-04
2022-05-15 07:11:20,852 INFO [train.py:812] (5/8) Epoch 24, batch 2250, loss[loss=0.1766, simple_loss=0.2658, pruned_loss=0.04372, over 7309.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2495, pruned_loss=0.03378, over 1408074.36 frames.], batch size: 25, lr: 3.24e-04
2022-05-15 07:12:19,959 INFO [train.py:812] (5/8) Epoch 24, batch 2300, loss[loss=0.1447, simple_loss=0.229, pruned_loss=0.03023, over 7279.00 frames.], tot_loss[loss=0.158, simple_loss=0.2488, pruned_loss=0.03357, over 1415599.57 frames.], batch size: 17, lr: 3.24e-04
2022-05-15 07:13:18,774 INFO [train.py:812] (5/8) Epoch 24, batch 2350, loss[loss=0.1638, simple_loss=0.2582, pruned_loss=0.03469, over 7333.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2495, pruned_loss=0.0337, over 1417079.14 frames.], batch size: 22, lr: 3.24e-04
2022-05-15 07:14:18,393 INFO [train.py:812] (5/8) Epoch 24, batch 2400, loss[loss=0.1393, simple_loss=0.2218, pruned_loss=0.02842, over 6766.00 frames.], tot_loss[loss=0.1587, simple_loss=0.25, pruned_loss=0.03366, over 1419600.91 frames.], batch size: 15, lr: 3.24e-04
2022-05-15 07:15:15,757 INFO [train.py:812] (5/8) Epoch 24, batch 2450, loss[loss=0.1505, simple_loss=0.2385, pruned_loss=0.03128, over 7238.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2496, pruned_loss=0.03382, over 1416499.21 frames.], batch size: 20, lr: 3.24e-04
2022-05-15 07:16:21,374 INFO [train.py:812] (5/8) Epoch 24, batch 2500, loss[loss=0.1734, simple_loss=0.2818, pruned_loss=0.03248, over 7329.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2486, pruned_loss=0.03347, over 1417311.81 frames.], batch size: 21, lr: 3.24e-04
2022-05-15 07:17:19,926 INFO [train.py:812] (5/8) Epoch 24, batch 2550, loss[loss=0.2111, simple_loss=0.2897, pruned_loss=0.06628, over 5330.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2477, pruned_loss=0.03324, over 1413803.93 frames.], batch size: 52, lr: 3.24e-04
2022-05-15 07:18:18,700 INFO [train.py:812] (5/8) Epoch 24, batch 2600, loss[loss=0.154, simple_loss=0.2482, pruned_loss=0.02986, over 7283.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2491, pruned_loss=0.0332, over 1417106.02 frames.], batch size: 18, lr: 3.24e-04
2022-05-15 07:19:17,314 INFO [train.py:812] (5/8) Epoch 24, batch 2650, loss[loss=0.1494, simple_loss=0.2505, pruned_loss=0.0242, over 7326.00 frames.], tot_loss[loss=0.158, simple_loss=0.2492, pruned_loss=0.03335, over 1416005.02 frames.], batch size: 21, lr: 3.24e-04
2022-05-15 07:20:16,532 INFO [train.py:812] (5/8) Epoch 24, batch 2700, loss[loss=0.1613, simple_loss=0.2502, pruned_loss=0.03623, over 7328.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2488, pruned_loss=0.03333, over 1421305.40 frames.], batch size: 22, lr: 3.24e-04
2022-05-15 07:21:15,972 INFO [train.py:812] (5/8) Epoch 24, batch 2750, loss[loss=0.1487, simple_loss=0.2421, pruned_loss=0.02766, over 7422.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2489, pruned_loss=0.03301, over 1425548.89 frames.], batch size: 21, lr: 3.24e-04
2022-05-15 07:22:15,046 INFO [train.py:812] (5/8) Epoch 24, batch 2800, loss[loss=0.1803, simple_loss=0.2786, pruned_loss=0.04102, over 7234.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2499, pruned_loss=0.03378, over 1421908.76 frames.], batch size: 20, lr: 3.24e-04
2022-05-15 07:23:13,158 INFO [train.py:812] (5/8) Epoch 24, batch 2850, loss[loss=0.1824, simple_loss=0.2704, pruned_loss=0.04719, over 7360.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2508, pruned_loss=0.03422, over 1422582.92 frames.], batch size: 19, lr: 3.24e-04
2022-05-15 07:24:12,092 INFO [train.py:812] (5/8) Epoch 24, batch 2900, loss[loss=0.152, simple_loss=0.2536, pruned_loss=0.02525, over 7316.00 frames.], tot_loss[loss=0.16, simple_loss=0.2514, pruned_loss=0.03431, over 1422298.37 frames.], batch size: 25, lr: 3.24e-04
2022-05-15 07:25:09,934 INFO [train.py:812] (5/8) Epoch 24, batch 2950, loss[loss=0.1463, simple_loss=0.2288, pruned_loss=0.03191, over 7278.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2507, pruned_loss=0.03388, over 1426425.30 frames.], batch size: 17, lr: 3.23e-04
2022-05-15 07:26:08,035 INFO [train.py:812] (5/8) Epoch 24, batch 3000, loss[loss=0.1656, simple_loss=0.2543, pruned_loss=0.03842, over 7115.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2505, pruned_loss=0.03399, over 1421770.52 frames.], batch size: 21, lr: 3.23e-04
2022-05-15 07:26:08,036 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 07:26:15,600 INFO [train.py:841] (5/8) Epoch 24, validation: loss=0.1537, simple_loss=0.2513, pruned_loss=0.02802, over 698248.00 frames.
2022-05-15 07:27:15,000 INFO [train.py:812] (5/8) Epoch 24, batch 3050, loss[loss=0.1471, simple_loss=0.2372, pruned_loss=0.02856, over 7276.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2488, pruned_loss=0.03339, over 1416255.95 frames.], batch size: 18, lr: 3.23e-04
2022-05-15 07:28:13,636 INFO [train.py:812] (5/8) Epoch 24, batch 3100, loss[loss=0.1652, simple_loss=0.2592, pruned_loss=0.03565, over 6837.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2482, pruned_loss=0.03328, over 1419893.94 frames.], batch size: 31, lr: 3.23e-04
2022-05-15 07:29:12,198 INFO [train.py:812] (5/8) Epoch 24, batch 3150, loss[loss=0.1437, simple_loss=0.2264, pruned_loss=0.03048, over 7016.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2489, pruned_loss=0.03368, over 1421909.38 frames.], batch size: 16, lr: 3.23e-04
2022-05-15 07:30:11,684 INFO [train.py:812] (5/8) Epoch 24, batch 3200, loss[loss=0.1483, simple_loss=0.2391, pruned_loss=0.02874, over 7321.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2481, pruned_loss=0.03322, over 1426080.41 frames.], batch size: 21, lr: 3.23e-04
2022-05-15 07:31:10,166 INFO [train.py:812] (5/8) Epoch 24, batch 3250, loss[loss=0.1457, simple_loss=0.2344, pruned_loss=0.02855, over 7165.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2481, pruned_loss=0.03308, over 1427715.99 frames.], batch size: 18, lr: 3.23e-04
2022-05-15 07:32:09,037 INFO [train.py:812] (5/8) Epoch 24, batch 3300, loss[loss=0.2101, simple_loss=0.2991, pruned_loss=0.06049, over 7309.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2475, pruned_loss=0.03281, over 1427635.27 frames.], batch size: 24, lr: 3.23e-04
2022-05-15 07:33:06,610 INFO [train.py:812] (5/8) Epoch 24, batch 3350, loss[loss=0.1595, simple_loss=0.2573, pruned_loss=0.03086, over 7272.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2487, pruned_loss=0.03339, over 1424226.31 frames.], batch size: 24, lr: 3.23e-04
2022-05-15 07:34:04,970 INFO [train.py:812] (5/8) Epoch 24, batch 3400, loss[loss=0.1581, simple_loss=0.2502, pruned_loss=0.03295, over 7369.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2485, pruned_loss=0.03341, over 1428132.53 frames.], batch size: 19, lr: 3.23e-04
2022-05-15 07:35:03,120 INFO [train.py:812] (5/8) Epoch 24, batch 3450, loss[loss=0.1616, simple_loss=0.2614, pruned_loss=0.0309, over 7335.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2487, pruned_loss=0.03291, over 1423464.02 frames.], batch size: 22, lr: 3.23e-04
2022-05-15 07:36:01,792 INFO [train.py:812] (5/8) Epoch 24, batch 3500, loss[loss=0.1433, simple_loss=0.2254, pruned_loss=0.03058, over 7179.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2477, pruned_loss=0.03256, over 1421817.11 frames.], batch size: 16, lr: 3.23e-04
2022-05-15 07:37:00,357 INFO [train.py:812] (5/8) Epoch 24, batch 3550, loss[loss=0.176, simple_loss=0.2696, pruned_loss=0.04115, over 7110.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2478, pruned_loss=0.03266, over 1423212.65 frames.], batch size: 21, lr: 3.23e-04
2022-05-15 07:38:00,109 INFO [train.py:812] (5/8) Epoch 24, batch 3600, loss[loss=0.138, simple_loss=0.2231, pruned_loss=0.02649, over 7063.00 frames.], tot_loss[loss=0.157, simple_loss=0.2486, pruned_loss=0.0327, over 1423320.52 frames.], batch size: 18, lr: 3.22e-04
2022-05-15 07:38:57,461 INFO [train.py:812] (5/8) Epoch 24, batch 3650, loss[loss=0.1679, simple_loss=0.2574, pruned_loss=0.03915, over 7356.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2496, pruned_loss=0.03343, over 1423276.62 frames.], batch size: 19, lr: 3.22e-04
2022-05-15 07:39:55,857 INFO [train.py:812] (5/8) Epoch 24, batch 3700, loss[loss=0.1462, simple_loss=0.2475, pruned_loss=0.02245, over 6421.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2496, pruned_loss=0.03368, over 1420665.65 frames.], batch size: 38, lr: 3.22e-04
2022-05-15 07:40:52,807 INFO [train.py:812] (5/8) Epoch 24, batch 3750, loss[loss=0.1637, simple_loss=0.2426, pruned_loss=0.04235, over 7274.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2497, pruned_loss=0.03398, over 1422022.03 frames.], batch size: 18, lr: 3.22e-04
2022-05-15 07:41:51,842 INFO [train.py:812] (5/8) Epoch 24, batch 3800, loss[loss=0.1539, simple_loss=0.2467, pruned_loss=0.03054, over 7427.00 frames.], tot_loss[loss=0.1582, simple_loss=0.249, pruned_loss=0.03369, over 1423937.55 frames.], batch size: 20, lr: 3.22e-04
2022-05-15 07:42:51,149 INFO [train.py:812] (5/8) Epoch 24, batch 3850, loss[loss=0.1817, simple_loss=0.2675, pruned_loss=0.04796, over 5011.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2495, pruned_loss=0.03381, over 1420736.82 frames.], batch size: 53, lr: 3.22e-04
2022-05-15 07:43:50,673 INFO [train.py:812] (5/8) Epoch 24, batch 3900, loss[loss=0.165, simple_loss=0.2568, pruned_loss=0.03658, over 6779.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2493, pruned_loss=0.03419, over 1417820.09 frames.], batch size: 31, lr: 3.22e-04
2022-05-15 07:44:49,670 INFO [train.py:812] (5/8) Epoch 24, batch 3950, loss[loss=0.1273, simple_loss=0.21, pruned_loss=0.02233, over 7126.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2495, pruned_loss=0.03377, over 1417012.74 frames.], batch size: 17, lr: 3.22e-04
2022-05-15 07:45:48,782 INFO [train.py:812] (5/8) Epoch 24, batch 4000, loss[loss=0.143, simple_loss=0.2426, pruned_loss=0.02175, over 7201.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2499, pruned_loss=0.03384, over 1415760.78 frames.], batch size: 22, lr: 3.22e-04
2022-05-15 07:46:47,066 INFO [train.py:812] (5/8) Epoch 24, batch 4050, loss[loss=0.19, simple_loss=0.2789, pruned_loss=0.05058, over 4938.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2498, pruned_loss=0.03385, over 1417138.62 frames.], batch size: 52, lr: 3.22e-04
2022-05-15 07:47:46,724 INFO [train.py:812] (5/8) Epoch 24, batch 4100, loss[loss=0.1405, simple_loss=0.2306, pruned_loss=0.02515, over 7273.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2495, pruned_loss=0.03364, over 1417152.42 frames.], batch size: 18, lr: 3.22e-04
2022-05-15 07:48:45,769 INFO [train.py:812] (5/8) Epoch 24, batch 4150, loss[loss=0.1316, simple_loss=0.2131, pruned_loss=0.025, over 6994.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2492, pruned_loss=0.03347, over 1418386.58 frames.], batch size: 16, lr: 3.22e-04
2022-05-15 07:49:44,868 INFO [train.py:812] (5/8) Epoch 24, batch 4200, loss[loss=0.1519, simple_loss=0.2447, pruned_loss=0.02957, over 7284.00 frames.], tot_loss[loss=0.159, simple_loss=0.2505, pruned_loss=0.03377, over 1418653.83 frames.], batch size: 18, lr: 3.22e-04
2022-05-15 07:50:44,105 INFO [train.py:812] (5/8) Epoch 24, batch 4250, loss[loss=0.1756, simple_loss=0.2732, pruned_loss=0.03899, over 7383.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2493, pruned_loss=0.03326, over 1416373.17 frames.], batch size: 23, lr: 3.22e-04
2022-05-15 07:51:43,362 INFO [train.py:812] (5/8) Epoch 24, batch 4300, loss[loss=0.143, simple_loss=0.216, pruned_loss=0.03496, over 6769.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2483, pruned_loss=0.03312, over 1415474.09 frames.], batch size: 15, lr: 3.21e-04
2022-05-15 07:52:41,806 INFO [train.py:812] (5/8) Epoch 24, batch 4350, loss[loss=0.1862, simple_loss=0.273, pruned_loss=0.04968, over 6741.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2489, pruned_loss=0.03323, over 1413404.53 frames.], batch size: 31, lr: 3.21e-04
2022-05-15 07:53:40,603 INFO [train.py:812] (5/8) Epoch 24, batch 4400, loss[loss=0.164, simple_loss=0.2578, pruned_loss=0.03508, over 6339.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2495, pruned_loss=0.03386, over 1407761.68 frames.], batch size: 38, lr: 3.21e-04
2022-05-15 07:54:38,514 INFO [train.py:812] (5/8) Epoch 24, batch 4450, loss[loss=0.1823, simple_loss=0.2752, pruned_loss=0.04468, over 6400.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2488, pruned_loss=0.03385, over 1410087.65 frames.], batch size: 38, lr: 3.21e-04
2022-05-15 07:55:37,616 INFO [train.py:812] (5/8) Epoch 24, batch 4500, loss[loss=0.1575, simple_loss=0.2496, pruned_loss=0.03268, over 6403.00 frames.], tot_loss[loss=0.159, simple_loss=0.2493, pruned_loss=0.03428, over 1397344.48 frames.], batch size: 37, lr: 3.21e-04
2022-05-15 07:56:36,611 INFO [train.py:812] (5/8) Epoch 24, batch 4550, loss[loss=0.191, simple_loss=0.2715, pruned_loss=0.05531, over 7292.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2495, pruned_loss=0.03465, over 1386154.38 frames.], batch size: 24, lr: 3.21e-04
2022-05-15 07:57:47,756 INFO [train.py:812] (5/8) Epoch 25, batch 0, loss[loss=0.1596, simple_loss=0.2599, pruned_loss=0.0297, over 7065.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2599, pruned_loss=0.0297, over 7065.00 frames.], batch size: 18, lr: 3.15e-04
2022-05-15 07:58:47,071 INFO [train.py:812] (5/8) Epoch 25, batch 50, loss[loss=0.1584, simple_loss=0.2541, pruned_loss=0.03133, over 7259.00 frames.], tot_loss[loss=0.16, simple_loss=0.251, pruned_loss=0.03452, over 321851.17 frames.], batch size: 19, lr: 3.15e-04
2022-05-15 07:59:46,726 INFO [train.py:812] (5/8) Epoch 25, batch 100, loss[loss=0.1678, simple_loss=0.2649, pruned_loss=0.03532, over 7325.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2508, pruned_loss=0.03454, over 570288.27 frames.], batch size: 20, lr: 3.15e-04
2022-05-15 08:00:45,688 INFO [train.py:812] (5/8) Epoch 25, batch 150, loss[loss=0.159, simple_loss=0.2572, pruned_loss=0.03033, over 7326.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2489, pruned_loss=0.03364, over 761620.73 frames.], batch size: 21, lr: 3.14e-04
2022-05-15 08:01:45,472 INFO [train.py:812] (5/8) Epoch 25, batch 200, loss[loss=0.1378, simple_loss=0.2257, pruned_loss=0.02495, over 6753.00 frames.], tot_loss[loss=0.1564, simple_loss=0.247, pruned_loss=0.03286, over 906216.35 frames.], batch size: 15, lr: 3.14e-04
2022-05-15 08:02:44,394 INFO [train.py:812] (5/8) Epoch 25, batch 250, loss[loss=0.1517, simple_loss=0.2474, pruned_loss=0.02799, over 7235.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2471, pruned_loss=0.03292, over 1018538.54 frames.], batch size: 20, lr: 3.14e-04
2022-05-15 08:03:43,891 INFO [train.py:812] (5/8) Epoch 25, batch 300, loss[loss=0.1694, simple_loss=0.2582, pruned_loss=0.04028, over 7166.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2474, pruned_loss=0.03284, over 1112118.04 frames.], batch size: 19, lr: 3.14e-04
2022-05-15 08:04:42,793 INFO [train.py:812] (5/8) Epoch 25, batch 350, loss[loss=0.1806, simple_loss=0.2716, pruned_loss=0.04475, over 7225.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2475, pruned_loss=0.03299, over 1181180.60 frames.], batch size: 23, lr: 3.14e-04
2022-05-15 08:05:50,917 INFO [train.py:812] (5/8) Epoch 25, batch 400, loss[loss=0.1524, simple_loss=0.2419, pruned_loss=0.03143, over 7240.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2474, pruned_loss=0.03257, over 1236285.70 frames.], batch size: 20, lr: 3.14e-04
2022-05-15 08:06:49,139 INFO [train.py:812] (5/8) Epoch 25, batch 450, loss[loss=0.166, simple_loss=0.2656, pruned_loss=0.03322, over 7028.00 frames.], tot_loss[loss=0.1557, simple_loss=0.247, pruned_loss=0.03219, over 1277245.55 frames.], batch size: 28, lr: 3.14e-04
2022-05-15 08:07:48,542 INFO [train.py:812] (5/8) Epoch 25, batch 500, loss[loss=0.15, simple_loss=0.2386, pruned_loss=0.03076, over 7163.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2479, pruned_loss=0.03279, over 1312200.04 frames.], batch size: 18, lr: 3.14e-04
2022-05-15 08:08:47,656 INFO [train.py:812] (5/8) Epoch 25, batch 550, loss[loss=0.1296, simple_loss=0.227, pruned_loss=0.01607, over 7162.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2484, pruned_loss=0.03321, over 1339537.30 frames.], batch size: 18, lr: 3.14e-04
2022-05-15 08:09:45,620 INFO [train.py:812] (5/8) Epoch 25, batch 600, loss[loss=0.1581, simple_loss=0.2482, pruned_loss=0.034, over 7216.00 frames.], tot_loss[loss=0.157, simple_loss=0.2484, pruned_loss=0.03283, over 1358060.06 frames.], batch size: 23, lr: 3.14e-04
2022-05-15 08:10:45,001 INFO [train.py:812] (5/8) Epoch 25, batch 650, loss[loss=0.1322, simple_loss=0.211, pruned_loss=0.02669, over 7302.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2477, pruned_loss=0.03285, over 1371095.15 frames.], batch size: 17, lr: 3.14e-04
2022-05-15 08:11:43,788 INFO [train.py:812] (5/8) Epoch 25, batch 700, loss[loss=0.1427, simple_loss=0.2308, pruned_loss=0.02734, over 7204.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2474, pruned_loss=0.03291, over 1387786.19 frames.], batch size: 16, lr: 3.14e-04
2022-05-15 08:12:42,948 INFO [train.py:812] (5/8) Epoch 25, batch 750, loss[loss=0.1276, simple_loss=0.2268, pruned_loss=0.01417, over 7241.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2473, pruned_loss=0.03271, over 1398955.14 frames.], batch size: 20, lr: 3.14e-04
2022-05-15 08:13:42,680 INFO [train.py:812] (5/8) Epoch 25, batch 800, loss[loss=0.1812, simple_loss=0.2697, pruned_loss=0.04636, over 7416.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2485, pruned_loss=0.0331, over 1406358.59 frames.], batch size: 21, lr: 3.14e-04
2022-05-15 08:14:42,175 INFO [train.py:812] (5/8) Epoch 25, batch 850, loss[loss=0.1572, simple_loss=0.2512, pruned_loss=0.03159, over 7324.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2482, pruned_loss=0.03329, over 1407981.26 frames.], batch size: 21, lr: 3.13e-04
2022-05-15 08:15:39,801 INFO [train.py:812] (5/8) Epoch 25, batch 900, loss[loss=0.179, simple_loss=0.2747, pruned_loss=0.04167, over 7278.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2492, pruned_loss=0.03346, over 1410512.64 frames.], batch size: 25, lr: 3.13e-04
2022-05-15 08:16:38,337 INFO [train.py:812] (5/8) Epoch 25, batch 950, loss[loss=0.1609, simple_loss=0.2451, pruned_loss=0.03829, over 4831.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2482, pruned_loss=0.03349, over 1405036.29 frames.], batch size: 52, lr: 3.13e-04
2022-05-15 08:17:38,343 INFO [train.py:812] (5/8) Epoch 25, batch 1000, loss[loss=0.1738, simple_loss=0.2758, pruned_loss=0.03592, over 7416.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2483, pruned_loss=0.03321, over 1411793.66 frames.], batch size: 21, lr: 3.13e-04
2022-05-15 08:18:37,739 INFO [train.py:812] (5/8) Epoch 25, batch 1050, loss[loss=0.1535, simple_loss=0.2492, pruned_loss=0.0289, over 7333.00 frames.], tot_loss[loss=0.1578, simple_loss=0.249, pruned_loss=0.03334, over 1418545.48 frames.], batch size: 20, lr: 3.13e-04
2022-05-15 08:19:35,297 INFO [train.py:812] (5/8) Epoch 25, batch 1100, loss[loss=0.1711, simple_loss=0.2671, pruned_loss=0.0375, over 7321.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2483, pruned_loss=0.03309, over 1421286.79 frames.], batch size: 22, lr: 3.13e-04
2022-05-15 08:20:32,124 INFO [train.py:812] (5/8) Epoch 25, batch 1150, loss[loss=0.1795, simple_loss=0.2835, pruned_loss=0.03773, over 7207.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2482, pruned_loss=0.03307, over 1424164.77 frames.], batch size: 23, lr: 3.13e-04
2022-05-15 08:21:31,796 INFO [train.py:812] (5/8) Epoch 25, batch 1200, loss[loss=0.1899, simple_loss=0.2741, pruned_loss=0.05285, over 7386.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2478, pruned_loss=0.03299, over 1423490.61 frames.], batch size: 23, lr: 3.13e-04
2022-05-15 08:22:29,879 INFO [train.py:812] (5/8) Epoch 25, batch 1250, loss[loss=0.1489, simple_loss=0.2497, pruned_loss=0.02408, over 7148.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2477, pruned_loss=0.03322, over 1421112.85 frames.], batch size: 20, lr: 3.13e-04
2022-05-15 08:23:28,188 INFO [train.py:812] (5/8) Epoch 25, batch 1300, loss[loss=0.1621, simple_loss=0.2373, pruned_loss=0.04339, over 6797.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2478, pruned_loss=0.03316, over 1420538.16 frames.], batch size: 15, lr: 3.13e-04
2022-05-15 08:24:27,527 INFO [train.py:812] (5/8) Epoch 25, batch 1350, loss[loss=0.1706, simple_loss=0.2609, pruned_loss=0.04011, over 6467.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2489, pruned_loss=0.03333, over 1420792.68 frames.], batch size: 38, lr: 3.13e-04
2022-05-15 08:25:26,986 INFO [train.py:812] (5/8) Epoch 25, batch 1400, loss[loss=0.1226, simple_loss=0.211, pruned_loss=0.01711, over 7295.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2496, pruned_loss=0.03344, over 1425931.06 frames.], batch size: 17, lr: 3.13e-04
2022-05-15 08:26:26,002 INFO [train.py:812] (5/8) Epoch 25, batch 1450, loss[loss=0.1843, simple_loss=0.2847, pruned_loss=0.04196, over 7148.00 frames.], tot_loss[loss=0.158, simple_loss=0.2491, pruned_loss=0.03351, over 1422425.16 frames.], batch size: 20, lr: 3.13e-04
2022-05-15 08:27:24,392 INFO [train.py:812] (5/8) Epoch 25, batch 1500, loss[loss=0.146, simple_loss=0.2441, pruned_loss=0.02394, over 6719.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2487, pruned_loss=0.03349, over 1421525.55 frames.], batch size: 31, lr: 3.13e-04
2022-05-15 08:28:23,098 INFO [train.py:812] (5/8) Epoch 25, batch 1550, loss[loss=0.1447, simple_loss=0.2331, pruned_loss=0.02813, over 7281.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2498, pruned_loss=0.03378, over 1422026.30 frames.], batch size: 18, lr: 3.12e-04
2022-05-15 08:29:22,777 INFO [train.py:812] (5/8) Epoch 25, batch 1600, loss[loss=0.1455, simple_loss=0.2292, pruned_loss=0.03092, over 6852.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2494, pruned_loss=0.03368, over 1421748.14 frames.], batch size: 15, lr: 3.12e-04
2022-05-15 08:30:21,912 INFO [train.py:812] (5/8) Epoch 25, batch 1650, loss[loss=0.1647, simple_loss=0.2642, pruned_loss=0.03257, over 7217.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2489, pruned_loss=0.03367, over 1422830.36 frames.], batch size: 21, lr: 3.12e-04
2022-05-15 08:31:21,074 INFO [train.py:812] (5/8) Epoch 25, batch 1700, loss[loss=0.1644, simple_loss=0.2675, pruned_loss=0.03067, over 7388.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2488, pruned_loss=0.03376, over 1421015.39 frames.], batch size: 23, lr: 3.12e-04
2022-05-15 08:32:19,156 INFO [train.py:812] (5/8) Epoch 25, batch 1750, loss[loss=0.1513, simple_loss=0.2332, pruned_loss=0.03466, over 7122.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2485, pruned_loss=0.03324, over 1423398.71 frames.], batch size: 17, lr: 3.12e-04
2022-05-15 08:33:18,558 INFO [train.py:812] (5/8) Epoch 25, batch 1800, loss[loss=0.1369, simple_loss=0.2259, pruned_loss=0.02398, over 6983.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2484, pruned_loss=0.03309, over 1422941.20 frames.], batch size: 16, lr: 3.12e-04
2022-05-15 08:34:17,241 INFO [train.py:812] (5/8) Epoch 25, batch 1850, loss[loss=0.1229, simple_loss=0.2081, pruned_loss=0.01889, over 6799.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2472, pruned_loss=0.03293, over 1419590.74 frames.], batch size: 15, lr: 3.12e-04
2022-05-15 08:35:20,950 INFO [train.py:812] (5/8) Epoch 25, batch 1900, loss[loss=0.1607, simple_loss=0.2483, pruned_loss=0.03656, over 7294.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2482, pruned_loss=0.03351, over 1421493.21 frames.], batch size: 25, lr: 3.12e-04
2022-05-15 08:36:19,545 INFO [train.py:812] (5/8) Epoch 25, batch 1950, loss[loss=0.1405, simple_loss=0.2336, pruned_loss=0.02372, over 7257.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2483, pruned_loss=0.03327, over 1422971.30 frames.], batch size: 19, lr: 3.12e-04
2022-05-15 08:37:18,261 INFO [train.py:812] (5/8) Epoch 25, batch 2000, loss[loss=0.1561, simple_loss=0.2462, pruned_loss=0.03297, over 7158.00 frames.], tot_loss[loss=0.1571, simple_loss=0.248, pruned_loss=0.03309, over 1423574.19 frames.], batch size: 18, lr: 3.12e-04
2022-05-15 08:38:16,608 INFO [train.py:812] (5/8) Epoch 25, batch 2050, loss[loss=0.1777, simple_loss=0.2823, pruned_loss=0.03652, over 7319.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2474, pruned_loss=0.03286, over 1426540.49 frames.], batch size: 21, lr: 3.12e-04
2022-05-15 08:39:15,896 INFO [train.py:812] (5/8) Epoch 25, batch 2100, loss[loss=0.1512, simple_loss=0.2397, pruned_loss=0.03135, over 7261.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2475, pruned_loss=0.03266, over 1423532.32 frames.], batch size: 19, lr: 3.12e-04
2022-05-15 08:40:13,572 INFO [train.py:812] (5/8) Epoch 25, batch 2150, loss[loss=0.1401, simple_loss=0.2271, pruned_loss=0.02658, over 7419.00 frames.], tot_loss[loss=0.1568, simple_loss=0.248, pruned_loss=0.0328, over 1421399.98 frames.], batch size: 20, lr: 3.12e-04
2022-05-15 08:41:13,376 INFO [train.py:812] (5/8) Epoch 25, batch 2200, loss[loss=0.1342, simple_loss=0.2187, pruned_loss=0.02484, over 6777.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2469, pruned_loss=0.03242, over 1420615.51 frames.], batch size: 15, lr: 3.12e-04
2022-05-15 08:42:11,843 INFO [train.py:812] (5/8) Epoch 25, batch 2250, loss[loss=0.1597, simple_loss=0.2456, pruned_loss=0.03693, over 7061.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2473, pruned_loss=0.03248, over 1417675.19 frames.], batch size: 18, lr: 3.12e-04
2022-05-15 08:43:09,219 INFO [train.py:812] (5/8) Epoch 25, batch 2300, loss[loss=0.1531, simple_loss=0.2299, pruned_loss=0.03809, over 7219.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2467, pruned_loss=0.0321, over 1419165.40 frames.], batch size: 16, lr: 3.11e-04
2022-05-15 08:44:06,067 INFO [train.py:812] (5/8) Epoch 25, batch 2350, loss[loss=0.1572, simple_loss=0.251, pruned_loss=0.03167, over 7308.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2459, pruned_loss=0.03176, over 1419257.51 frames.], batch size: 21, lr: 3.11e-04
2022-05-15 08:45:05,434 INFO [train.py:812] (5/8) Epoch 25, batch 2400, loss[loss=0.1682, simple_loss=0.2468, pruned_loss=0.04479, over 7340.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2481, pruned_loss=0.03265, over 1423555.58 frames.], batch size: 19, lr: 3.11e-04
2022-05-15 08:46:04,717 INFO [train.py:812] (5/8) Epoch 25, batch 2450, loss[loss=0.1389, simple_loss=0.2225, pruned_loss=0.02763, over 7131.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2478, pruned_loss=0.03259, over 1422926.96 frames.], batch size: 17, lr: 3.11e-04
2022-05-15 08:47:04,374 INFO [train.py:812] (5/8) Epoch 25, batch 2500, loss[loss=0.1635, simple_loss=0.2716, pruned_loss=0.02772, over 7412.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2489, pruned_loss=0.03327, over 1422702.92 frames.], batch size: 21, lr: 3.11e-04
2022-05-15 08:48:03,390 INFO [train.py:812] (5/8) Epoch 25, batch 2550, loss[loss=0.1674, simple_loss=0.2579, pruned_loss=0.03839, over 7423.00 frames.], tot_loss[loss=0.158, simple_loss=0.2492, pruned_loss=0.03342, over 1423847.46 frames.], batch size: 20, lr: 3.11e-04
2022-05-15 08:49:03,054 INFO [train.py:812] (5/8) Epoch 25, batch 2600, loss[loss=0.1285, simple_loss=0.2159, pruned_loss=0.02054, over 7150.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2489, pruned_loss=0.03328, over 1420604.45 frames.], batch size: 17, lr: 3.11e-04
2022-05-15 08:50:01,831 INFO [train.py:812] (5/8) Epoch 25, batch 2650, loss[loss=0.1609, simple_loss=0.2526, pruned_loss=0.0346, over 7195.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2495, pruned_loss=0.03339, over 1422524.82 frames.], batch size: 22, lr: 3.11e-04
2022-05-15 08:51:09,483 INFO [train.py:812] (5/8) Epoch 25, batch 2700, loss[loss=0.1588, simple_loss=0.2465, pruned_loss=0.03559, over 7068.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2487, pruned_loss=0.03302, over 1424489.00 frames.], batch size: 18, lr: 3.11e-04
2022-05-15 08:52:06,906 INFO [train.py:812] (5/8) Epoch 25, batch 2750, loss[loss=0.16, simple_loss=0.2644, pruned_loss=0.02776, over 7139.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2478, pruned_loss=0.0328, over 1419024.87 frames.], batch size: 20, lr: 3.11e-04
2022-05-15 08:53:06,503 INFO [train.py:812] (5/8) Epoch 25, batch 2800, loss[loss=0.1636, simple_loss=0.2478, pruned_loss=0.0397, over 7261.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2479, pruned_loss=0.03269, over 1420239.25 frames.], batch size: 19, lr: 3.11e-04
2022-05-15 08:54:05,448 INFO [train.py:812] (5/8) Epoch 25, batch 2850, loss[loss=0.157, simple_loss=0.2451, pruned_loss=0.03445, over 7439.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2477, pruned_loss=0.03233, over 1419291.56 frames.], batch size: 20, lr: 3.11e-04
2022-05-15 08:55:04,574 INFO [train.py:812] (5/8) Epoch 25, batch 2900, loss[loss=0.1597, simple_loss=0.247, pruned_loss=0.03623, over 7192.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2485, pruned_loss=0.03223, over 1419772.03 frames.], batch size: 23, lr: 3.11e-04
2022-05-15 08:56:02,077 INFO [train.py:812] (5/8) Epoch 25, batch 2950, loss[loss=0.1431, simple_loss=0.2387, pruned_loss=0.02373, over 7105.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2485, pruned_loss=0.03224, over 1424971.97 frames.], batch size: 21, lr: 3.11e-04
2022-05-15 08:57:29,007 INFO [train.py:812] (5/8) Epoch 25, batch 3000, loss[loss=0.1386, simple_loss=0.2333, pruned_loss=0.02201, over 6703.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2474, pruned_loss=0.03239, over 1427992.74 frames.], batch size: 31, lr: 3.10e-04
2022-05-15 08:57:29,008 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 08:57:46,643 INFO [train.py:841] (5/8) Epoch 25, validation: loss=0.1532, simple_loss=0.2507, pruned_loss=0.02787, over 698248.00 frames.
2022-05-15 08:58:45,926 INFO [train.py:812] (5/8) Epoch 25, batch 3050, loss[loss=0.144, simple_loss=0.2366, pruned_loss=0.02569, over 7120.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2469, pruned_loss=0.03207, over 1427677.20 frames.], batch size: 21, lr: 3.10e-04
2022-05-15 08:59:53,879 INFO [train.py:812] (5/8) Epoch 25, batch 3100, loss[loss=0.1465, simple_loss=0.2264, pruned_loss=0.03331, over 7223.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2462, pruned_loss=0.03207, over 1429406.17 frames.], batch size: 16, lr: 3.10e-04
2022-05-15 09:01:01,450 INFO [train.py:812] (5/8) Epoch 25, batch 3150, loss[loss=0.142, simple_loss=0.234, pruned_loss=0.02505, over 7258.00 frames.], tot_loss[loss=0.156, simple_loss=0.2472, pruned_loss=0.03241, over 1430613.91 frames.], batch size: 19, lr: 3.10e-04
2022-05-15 09:02:01,437 INFO [train.py:812] (5/8) Epoch 25, batch 3200, loss[loss=0.1722, simple_loss=0.2626, pruned_loss=0.04094, over 5026.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2465, pruned_loss=0.03215, over 1429303.90 frames.], batch size: 52, lr: 3.10e-04
2022-05-15 09:03:00,349 INFO [train.py:812] (5/8) Epoch 25, batch 3250, loss[loss=0.1594, simple_loss=0.2495, pruned_loss=0.03465, over 7233.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2475, pruned_loss=0.0326, over 1427153.03 frames.], batch size: 20, lr: 3.10e-04
2022-05-15 09:03:59,274 INFO [train.py:812] (5/8) Epoch 25, batch 3300, loss[loss=0.1351, simple_loss=0.2307, pruned_loss=0.01981, over 7160.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2475, pruned_loss=0.03282, over 1426169.19 frames.], batch size: 19, lr: 3.10e-04
2022-05-15 09:04:58,416 INFO [train.py:812] (5/8) Epoch 25, batch 3350, loss[loss=0.1648, simple_loss=0.2503, pruned_loss=0.03966, over 7255.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2474, pruned_loss=0.03298, over 1423215.54 frames.], batch size: 19, lr: 3.10e-04
2022-05-15 09:05:57,546 INFO [train.py:812] (5/8) Epoch 25, batch 3400, loss[loss=0.1368, simple_loss=0.2181, pruned_loss=0.02771, over 7271.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2474, pruned_loss=0.033, over 1424954.20 frames.], batch size: 17, lr: 3.10e-04
2022-05-15 09:06:55,966 INFO [train.py:812] (5/8) Epoch 25, batch 3450, loss[loss=0.1514, simple_loss=0.2482, pruned_loss=0.02734, over 7216.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2476, pruned_loss=0.03316, over 1420437.44 frames.], batch size: 21, lr: 3.10e-04
2022-05-15 09:07:54,155 INFO [train.py:812] (5/8) Epoch 25, batch 3500, loss[loss=0.144, simple_loss=0.2193, pruned_loss=0.03433, over 7141.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2484, pruned_loss=0.03362, over 1422393.25 frames.], batch size: 17, lr: 3.10e-04
2022-05-15 09:08:53,538 INFO [train.py:812] (5/8) Epoch 25, batch 3550, loss[loss=0.1466, simple_loss=0.2401, pruned_loss=0.02659, over 7330.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2493, pruned_loss=0.03387, over 1423484.77 frames.], batch size: 20, lr: 3.10e-04
2022-05-15 09:09:52,738 INFO [train.py:812] (5/8) Epoch 25, batch 3600, loss[loss=0.1691, simple_loss=0.262, pruned_loss=0.03804, over 7206.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2493, pruned_loss=0.0338, over 1422149.23 frames.], batch size: 23, lr: 3.10e-04
2022-05-15 09:10:51,684 INFO [train.py:812] (5/8) Epoch 25, batch 3650, loss[loss=0.1663, simple_loss=0.2628, pruned_loss=0.0349, over 6299.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2488, pruned_loss=0.03372, over 1418364.36 frames.], batch size: 38, lr: 3.10e-04
2022-05-15 09:11:51,256 INFO [train.py:812] (5/8) Epoch 25, batch 3700, loss[loss=0.1475, simple_loss=0.2363, pruned_loss=0.02929, over 7435.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2474, pruned_loss=0.03309, over 1421558.84 frames.], batch size: 20, lr: 3.10e-04
2022-05-15 09:12:50,492 INFO [train.py:812] (5/8) Epoch 25, batch 3750, loss[loss=0.1645, simple_loss=0.2597, pruned_loss=0.03459, over 7387.00 frames.], tot_loss[loss=0.157, simple_loss=0.2475, pruned_loss=0.03324, over 1424209.82 frames.], batch size: 23, lr: 3.09e-04
2022-05-15 09:13:50,113 INFO [train.py:812] (5/8) Epoch 25, batch 3800, loss[loss=0.1999, simple_loss=0.2867, pruned_loss=0.05649, over 4868.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2475, pruned_loss=0.03312, over 1422862.21 frames.], batch size: 52, lr: 3.09e-04
2022-05-15 09:14:48,004 INFO [train.py:812] (5/8) Epoch 25, batch 3850, loss[loss=0.1422, simple_loss=0.2371, pruned_loss=0.02367, over 7276.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2476, pruned_loss=0.03331, over 1422174.79 frames.], batch size: 18, lr: 3.09e-04
2022-05-15 09:15:47,049 INFO [train.py:812] (5/8) Epoch 25, batch 3900, loss[loss=0.141, simple_loss=0.2287, pruned_loss=0.02663, over 7254.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2482, pruned_loss=0.03335, over 1421488.27 frames.], batch size: 19, lr: 3.09e-04
2022-05-15 09:16:44,710 INFO [train.py:812] (5/8) Epoch 25, batch 3950, loss[loss=0.1491, simple_loss=0.2339, pruned_loss=0.0322, over 7428.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2481, pruned_loss=0.03313, over 1423538.53 frames.], batch size: 18, lr: 3.09e-04
2022-05-15 09:17:43,592 INFO [train.py:812] (5/8) Epoch 25, batch 4000, loss[loss=0.1504, simple_loss=0.2427, pruned_loss=0.02904, over 7315.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2488, pruned_loss=0.03328, over 1422798.01 frames.], batch size: 21, lr: 3.09e-04
2022-05-15 09:18:42,702 INFO [train.py:812] (5/8) Epoch 25, batch 4050, loss[loss=0.1657, simple_loss=0.2575, pruned_loss=0.03695, over 7428.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2486, pruned_loss=0.0332, over 1421905.25 frames.], batch size: 20, lr: 3.09e-04
2022-05-15 09:19:42,002 INFO [train.py:812] (5/8) Epoch 25, batch 4100, loss[loss=0.1626, simple_loss=0.2607, pruned_loss=0.03222, over 6212.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2486, pruned_loss=0.03315, over 1421993.79 frames.], batch size: 37, lr: 3.09e-04
2022-05-15 09:20:41,043 INFO [train.py:812] (5/8) Epoch 25, batch 4150, loss[loss=0.1723, simple_loss=0.2667, pruned_loss=0.03894, over 7223.00 frames.], tot_loss[loss=0.157, simple_loss=0.2483, pruned_loss=0.03286, over 1418320.57 frames.], batch size: 21, lr: 3.09e-04
2022-05-15 09:21:39,842 INFO [train.py:812] (5/8) Epoch 25, batch 4200, loss[loss=0.1637, simple_loss=0.2572, pruned_loss=0.03511, over 7195.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2497, pruned_loss=0.03297, over 1419943.88 frames.], batch size: 23, lr: 3.09e-04
2022-05-15 09:22:38,428 INFO [train.py:812] (5/8) Epoch 25, batch 4250, loss[loss=0.1373, simple_loss=0.2374, pruned_loss=0.01863, over 6558.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2486, pruned_loss=0.0331, over 1414500.89 frames.], batch size: 38, lr: 3.09e-04
2022-05-15 09:23:37,090 INFO [train.py:812] (5/8) Epoch 25, batch 4300, loss[loss=0.1571, simple_loss=0.2441, pruned_loss=0.03506, over 7160.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2482, pruned_loss=0.03317, over 1414417.30 frames.], batch size: 19, lr: 3.09e-04
2022-05-15 09:24:36,164 INFO [train.py:812] (5/8) Epoch 25, batch 4350, loss[loss=0.1499, simple_loss=0.244, pruned_loss=0.02794, over 7325.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2468, pruned_loss=0.03272, over 1415511.02 frames.], batch size: 25, lr: 3.09e-04
2022-05-15 09:25:35,365 INFO [train.py:812] (5/8) Epoch 25, batch 4400, loss[loss=0.1916, simple_loss=0.2872, pruned_loss=0.04796, over 7327.00 frames.], tot_loss[loss=0.157, simple_loss=0.2479, pruned_loss=0.03302, over 1414393.31 frames.], batch size: 24, lr: 3.09e-04
2022-05-15 09:26:34,028 INFO [train.py:812] (5/8) Epoch 25, batch 4450, loss[loss=0.1802, simple_loss=0.2784, pruned_loss=0.04095, over 7294.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2494, pruned_loss=0.03356, over 1404960.37 frames.], batch size: 25, lr: 3.09e-04
2022-05-15 09:27:33,015 INFO [train.py:812] (5/8) Epoch 25, batch 4500, loss[loss=0.1954, simple_loss=0.2761, pruned_loss=0.0574, over 5162.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2506, pruned_loss=0.03411, over 1387718.05 frames.], batch size: 53, lr: 3.08e-04
2022-05-15 09:28:30,316 INFO [train.py:812] (5/8) Epoch 25, batch 4550, loss[loss=0.182, simple_loss=0.2689, pruned_loss=0.04757, over 5265.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2523, pruned_loss=0.03453, over 1351106.01 frames.], batch size: 54, lr: 3.08e-04
2022-05-15 09:29:36,546 INFO [train.py:812] (5/8) Epoch 26, batch 0, loss[loss=0.1777, simple_loss=0.2708, pruned_loss=0.04232, over 7226.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2708, pruned_loss=0.04232, over 7226.00 frames.], batch size: 21, lr: 3.02e-04
2022-05-15 09:30:35,914 INFO [train.py:812] (5/8) Epoch 26, batch 50, loss[loss=0.1828, simple_loss=0.269, pruned_loss=0.04831, over 7317.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2456, pruned_loss=0.03036, over 322726.17 frames.], batch size: 21, lr: 3.02e-04
2022-05-15 09:31:35,512 INFO [train.py:812] (5/8) Epoch 26, batch 100, loss[loss=0.1602, simple_loss=0.2499, pruned_loss=0.03526, over 5211.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2498, pruned_loss=0.03246, over 567029.23 frames.], batch size: 52, lr: 3.02e-04
2022-05-15 09:32:35,338 INFO [train.py:812] (5/8) Epoch 26, batch 150, loss[loss=0.1384, simple_loss=0.2201, pruned_loss=0.02832, over 7282.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2487, pruned_loss=0.03194, over 760865.24 frames.], batch size: 17, lr: 3.02e-04
2022-05-15 09:33:34,900 INFO [train.py:812] (5/8) Epoch 26, batch 200, loss[loss=0.1745, simple_loss=0.2696, pruned_loss=0.03968, over 7374.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2474, pruned_loss=0.03188, over 907524.56 frames.], batch size: 23, lr: 3.02e-04
2022-05-15 09:34:32,547 INFO [train.py:812] (5/8) Epoch 26, batch 250, loss[loss=0.1836, simple_loss=0.2649, pruned_loss=0.05121, over 7204.00 frames.], tot_loss[loss=0.156, simple_loss=0.2474, pruned_loss=0.03234, over 1020139.41 frames.], batch size: 22, lr: 3.02e-04
2022-05-15 09:35:31,927 INFO [train.py:812] (5/8) Epoch 26, batch 300, loss[loss=0.1557, simple_loss=0.2508, pruned_loss=0.03031, over 7324.00 frames.], tot_loss[loss=0.156, simple_loss=0.2475, pruned_loss=0.03223, over 1106106.93 frames.], batch size: 20, lr: 3.02e-04
2022-05-15 09:36:29,839 INFO [train.py:812] (5/8) Epoch 26, batch 350, loss[loss=0.1494, simple_loss=0.2407, pruned_loss=0.02908, over 7165.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2468, pruned_loss=0.03224, over 1176073.86 frames.], batch size: 18, lr: 3.02e-04
2022-05-15 09:37:29,638 INFO [train.py:812] (5/8) Epoch 26, batch 400, loss[loss=0.1496, simple_loss=0.2388, pruned_loss=0.03023, over 7415.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2481, pruned_loss=0.03274, over 1233990.88 frames.], batch size: 18, lr: 3.02e-04
2022-05-15 09:38:28,200 INFO [train.py:812] (5/8) Epoch 26, batch 450, loss[loss=0.1676, simple_loss=0.2615, pruned_loss=0.03683, over 7420.00 frames.], tot_loss[loss=0.1567, simple_loss=0.248, pruned_loss=0.0327, over 1274269.29 frames.], batch size: 21, lr: 3.02e-04
2022-05-15 09:39:25,635 INFO [train.py:812] (5/8) Epoch 26, batch 500, loss[loss=0.187, simple_loss=0.2847, pruned_loss=0.04464, over 7385.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2487, pruned_loss=0.03288, over 1301358.61 frames.], batch size: 23, lr: 3.02e-04
2022-05-15 09:40:22,334 INFO [train.py:812] (5/8) Epoch 26, batch 550, loss[loss=0.1692, simple_loss=0.2595, pruned_loss=0.03942, over 7239.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2482, pruned_loss=0.03281, over 1327775.66 frames.], batch size: 20, lr: 3.02e-04
2022-05-15 09:41:20,605 INFO [train.py:812] (5/8) Epoch 26, batch 600, loss[loss=0.1499, simple_loss=0.2459, pruned_loss=0.02692, over 7032.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2477, pruned_loss=0.03276, over 1346460.63 frames.], batch size: 28, lr: 3.02e-04
2022-05-15 09:42:19,341 INFO [train.py:812] (5/8) Epoch 26, batch 650, loss[loss=0.1524, simple_loss=0.2449, pruned_loss=0.02992, over 7337.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2467, pruned_loss=0.03254, over 1360668.07 frames.], batch size: 20, lr: 3.02e-04
2022-05-15 09:43:17,898 INFO [train.py:812] (5/8) Epoch 26, batch 700, loss[loss=0.1711, simple_loss=0.2617, pruned_loss=0.04025, over 7143.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2476, pruned_loss=0.03286, over 1374536.80 frames.], batch size: 20, lr: 3.02e-04
2022-05-15 09:44:17,490 INFO [train.py:812] (5/8) Epoch 26, batch 750, loss[loss=0.1516, simple_loss=0.2444, pruned_loss=0.0294, over 7433.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2475, pruned_loss=0.03265, over 1389858.39 frames.], batch size: 20, lr: 3.01e-04
2022-05-15 09:45:17,279 INFO [train.py:812] (5/8) Epoch 26, batch 800, loss[loss=0.1607, simple_loss=0.247, pruned_loss=0.03719, over 6751.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2475, pruned_loss=0.03265, over 1395703.84 frames.], batch size: 31, lr: 3.01e-04
2022-05-15 09:46:14,827 INFO [train.py:812] (5/8) Epoch 26, batch 850, loss[loss=0.1524, simple_loss=0.2509, pruned_loss=0.02693, over 7111.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2478, pruned_loss=0.03248, over 1406289.18 frames.], batch size: 21, lr: 3.01e-04
2022-05-15 09:47:13,158 INFO [train.py:812] (5/8) Epoch 26, batch 900, loss[loss=0.1223, simple_loss=0.2055, pruned_loss=0.01955, over 6805.00 frames.], tot_loss[loss=0.156, simple_loss=0.2472, pruned_loss=0.03239, over 1406170.08 frames.], batch size: 15, lr: 3.01e-04
2022-05-15 09:48:12,071 INFO [train.py:812] (5/8) Epoch 26, batch 950, loss[loss=0.1469, simple_loss=0.2291, pruned_loss=0.03236, over 7255.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2465, pruned_loss=0.03207, over 1412496.71 frames.], batch size: 17, lr: 3.01e-04
2022-05-15 09:49:11,023 INFO [train.py:812] (5/8) Epoch 26, batch 1000, loss[loss=0.1634, simple_loss=0.261, pruned_loss=0.03289, over 7106.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2471, pruned_loss=0.03225, over 1411885.57 frames.], batch size: 21, lr: 3.01e-04
2022-05-15 09:50:10,502 INFO [train.py:812] (5/8) Epoch 26, batch 1050, loss[loss=0.1943, simple_loss=0.2803, pruned_loss=0.05409, over 5268.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2477, pruned_loss=0.03233, over 1412743.85 frames.], batch size: 52, lr: 3.01e-04
2022-05-15 09:51:08,583 INFO [train.py:812] (5/8) Epoch 26, batch 1100, loss[loss=0.1522, simple_loss=0.2548, pruned_loss=0.0248, over 7109.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2482, pruned_loss=0.03264, over 1414544.12 frames.], batch size: 21, lr: 3.01e-04
2022-05-15 09:52:08,139 INFO [train.py:812] (5/8) Epoch 26, batch 1150, loss[loss=0.155, simple_loss=0.2476, pruned_loss=0.03117, over 7373.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2476, pruned_loss=0.03255, over 1418652.73 frames.], batch size: 23, lr: 3.01e-04
2022-05-15 09:53:08,256 INFO [train.py:812] (5/8) Epoch 26, batch 1200, loss[loss=0.1422, simple_loss=0.2302, pruned_loss=0.02714, over 7123.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2483, pruned_loss=0.033, over 1422330.86 frames.], batch size: 17, lr: 3.01e-04
2022-05-15 09:54:07,376 INFO [train.py:812] (5/8) Epoch 26, batch 1250, loss[loss=0.1545, simple_loss=0.252, pruned_loss=0.02851, over 7327.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2491, pruned_loss=0.03305, over 1424669.99 frames.], batch size: 21, lr: 3.01e-04
2022-05-15 09:55:11,131 INFO [train.py:812] (5/8) Epoch 26, batch 1300, loss[loss=0.149, simple_loss=0.2427, pruned_loss=0.02766, over 7431.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2492, pruned_loss=0.03321, over 1427853.89 frames.], batch size: 20, lr: 3.01e-04
2022-05-15 09:56:09,535 INFO [train.py:812] (5/8) Epoch 26, batch 1350, loss[loss=0.1664, simple_loss=0.2601, pruned_loss=0.03633, over 7330.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2499, pruned_loss=0.0334, over 1428023.59 frames.], batch size: 21, lr: 3.01e-04
2022-05-15 09:57:07,829 INFO [train.py:812] (5/8) Epoch 26, batch 1400, loss[loss=0.1793, simple_loss=0.2649, pruned_loss=0.04686, over 7335.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2501, pruned_loss=0.03334, over 1428204.70 frames.], batch size: 22, lr: 3.01e-04
2022-05-15 09:58:05,640 INFO [train.py:812] (5/8) Epoch 26, batch 1450, loss[loss=0.1362, simple_loss=0.2262, pruned_loss=0.02307, over 7011.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2494, pruned_loss=0.03297, over 1429762.90 frames.], batch size: 16, lr: 3.01e-04
2022-05-15 09:59:03,789 INFO [train.py:812] (5/8) Epoch 26, batch 1500, loss[loss=0.1453, simple_loss=0.2427, pruned_loss=0.02397, over 7224.00 frames.], tot_loss[loss=0.157, simple_loss=0.2487, pruned_loss=0.0327, over 1428554.32 frames.], batch size: 21, lr: 3.00e-04
2022-05-15 10:00:02,485 INFO [train.py:812] (5/8) Epoch 26, batch 1550, loss[loss=0.1572, simple_loss=0.2407, pruned_loss=0.03688, over 7132.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2481, pruned_loss=0.03255, over 1427369.76 frames.], batch size: 17, lr: 3.00e-04
2022-05-15 10:01:01,517 INFO [train.py:812] (5/8) Epoch 26, batch 1600, loss[loss=0.1586, simple_loss=0.2683, pruned_loss=0.02448, over 7148.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2494, pruned_loss=0.03287, over 1424000.15 frames.], batch size: 20, lr: 3.00e-04
2022-05-15 10:02:00,508 INFO [train.py:812] (5/8) Epoch 26, batch 1650, loss[loss=0.1861, simple_loss=0.2778, pruned_loss=0.04717, over 7085.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2481, pruned_loss=0.03248, over 1425220.44 frames.], batch size: 28, lr: 3.00e-04
2022-05-15 10:02:59,713 INFO [train.py:812] (5/8) Epoch 26, batch 1700, loss[loss=0.1317, simple_loss=0.2266, pruned_loss=0.01844, over 7319.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2476, pruned_loss=0.03237, over 1425328.93 frames.], batch size: 21, lr: 3.00e-04
2022-05-15 10:04:07,541 INFO [train.py:812] (5/8) Epoch 26, batch 1750, loss[loss=0.1542, simple_loss=0.2334, pruned_loss=0.03754, over 7154.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2477, pruned_loss=0.03263, over 1424466.84 frames.], batch size: 17, lr: 3.00e-04
2022-05-15 10:05:06,586 INFO [train.py:812] (5/8) Epoch 26, batch 1800, loss[loss=0.1839, simple_loss=0.2828, pruned_loss=0.04251, over 7139.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2473, pruned_loss=0.0324, over 1419947.59 frames.], batch size: 20, lr: 3.00e-04
2022-05-15 10:06:05,259 INFO [train.py:812] (5/8) Epoch 26, batch 1850, loss[loss=0.1791, simple_loss=0.2727, pruned_loss=0.04272, over 7421.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2476, pruned_loss=0.03255, over 1420942.87 frames.], batch size: 20, lr: 3.00e-04
2022-05-15 10:07:04,817 INFO [train.py:812] (5/8) Epoch 26, batch 1900, loss[loss=0.1297, simple_loss=0.2098, pruned_loss=0.02484, over 7140.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2477, pruned_loss=0.03258, over 1422347.30 frames.], batch size: 17, lr: 3.00e-04
2022-05-15 10:08:02,584 INFO [train.py:812] (5/8) Epoch 26, batch 1950, loss[loss=0.1918, simple_loss=0.2868, pruned_loss=0.04844, over 4860.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2473, pruned_loss=0.03294, over 1420862.40 frames.], batch size: 52, lr: 3.00e-04
2022-05-15 10:09:00,908 INFO [train.py:812] (5/8) Epoch 26, batch 2000, loss[loss=0.1468, simple_loss=0.2404, pruned_loss=0.02658, over 7162.00 frames.], tot_loss[loss=0.1574, simple_loss=0.248, pruned_loss=0.03339, over 1416686.62 frames.], batch size: 19, lr: 3.00e-04
2022-05-15 10:10:00,114 INFO [train.py:812] (5/8) Epoch 26, batch 2050, loss[loss=0.1562, simple_loss=0.2487, pruned_loss=0.0318, over 7333.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2481, pruned_loss=0.03367, over 1418520.66 frames.], batch size: 20, lr: 3.00e-04
2022-05-15 10:10:59,275 INFO [train.py:812] (5/8) Epoch 26, batch 2100, loss[loss=0.1751, simple_loss=0.2695, pruned_loss=0.04037, over 7205.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2495, pruned_loss=0.03406, over 1417263.37 frames.], batch size: 22, lr: 3.00e-04
2022-05-15 10:11:58,139 INFO [train.py:812] (5/8) Epoch 26, batch 2150, loss[loss=0.1327, simple_loss=0.2229, pruned_loss=0.02124, over 7173.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2497, pruned_loss=0.03371, over 1419742.29 frames.], batch size: 18, lr: 3.00e-04
2022-05-15 10:12:57,674 INFO [train.py:812] (5/8) Epoch 26, batch 2200, loss[loss=0.1699, simple_loss=0.2592, pruned_loss=0.04026, over 7058.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2496, pruned_loss=0.03344, over 1422783.77 frames.], batch size: 28, lr: 3.00e-04
2022-05-15 10:13:56,432 INFO [train.py:812] (5/8) Epoch 26, batch 2250, loss[loss=0.1471, simple_loss=0.2433, pruned_loss=0.02549, over 7362.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2481, pruned_loss=0.0327, over 1424965.62 frames.], batch size: 23, lr: 3.00e-04
2022-05-15 10:14:54,799 INFO [train.py:812] (5/8) Epoch 26, batch 2300, loss[loss=0.1456, simple_loss=0.2404, pruned_loss=0.02541, over 7069.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2485, pruned_loss=0.0326, over 1424882.96 frames.], batch size: 18, lr: 2.99e-04
2022-05-15 10:15:54,086 INFO [train.py:812] (5/8) Epoch 26, batch 2350, loss[loss=0.1487, simple_loss=0.2364, pruned_loss=0.0305, over 7259.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2481, pruned_loss=0.03279, over 1425700.29 frames.], batch size: 19, lr: 2.99e-04
2022-05-15 10:16:53,706 INFO [train.py:812] (5/8) Epoch 26, batch 2400, loss[loss=0.1746, simple_loss=0.2771, pruned_loss=0.03606, over 7381.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2477, pruned_loss=0.03288, over 1422755.22 frames.], batch size: 23, lr: 2.99e-04
2022-05-15 10:17:52,702 INFO [train.py:812] (5/8) Epoch 26, batch 2450, loss[loss=0.1637, simple_loss=0.2643, pruned_loss=0.03155, over 6728.00 frames.], tot_loss[loss=0.1572, simple_loss=0.248, pruned_loss=0.03314, over 1421491.33 frames.], batch size: 31, lr: 2.99e-04
2022-05-15 10:18:50,825 INFO [train.py:812] (5/8) Epoch 26, batch 2500, loss[loss=0.1535, simple_loss=0.2376, pruned_loss=0.03475, over 7347.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2472, pruned_loss=0.03268, over 1423303.40 frames.], batch size: 19, lr: 2.99e-04
2022-05-15 10:19:48,011 INFO [train.py:812] (5/8) Epoch 26, batch 2550, loss[loss=0.1578, simple_loss=0.2408, pruned_loss=0.03744, over 7401.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2472, pruned_loss=0.03251, over 1425974.36 frames.], batch size: 18, lr: 2.99e-04
2022-05-15 10:20:46,856 INFO [train.py:812] (5/8) Epoch 26, batch 2600, loss[loss=0.1605, simple_loss=0.2524, pruned_loss=0.03434, over 7158.00 frames.], tot_loss[loss=0.1563, simple_loss=0.247, pruned_loss=0.03277, over 1424761.22 frames.], batch size: 19, lr: 2.99e-04
2022-05-15 10:21:44,644 INFO [train.py:812] (5/8) Epoch 26, batch 2650, loss[loss=0.1566, simple_loss=0.2448, pruned_loss=0.03421, over 7071.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2477, pruned_loss=0.03274, over 1420986.53 frames.], batch size: 28, lr: 2.99e-04
2022-05-15 10:22:43,731 INFO [train.py:812] (5/8) Epoch 26, batch 2700, loss[loss=0.1435, simple_loss=0.2428, pruned_loss=0.02212, over 7266.00 frames.], tot_loss[loss=0.156, simple_loss=0.2475, pruned_loss=0.03222, over 1422157.12 frames.], batch size: 19, lr: 2.99e-04
2022-05-15 10:23:42,386 INFO [train.py:812] (5/8) Epoch 26, batch 2750, loss[loss=0.1885, simple_loss=0.2945, pruned_loss=0.04121, over 7276.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2484, pruned_loss=0.03258, over 1415514.29 frames.], batch size: 25, lr: 2.99e-04
2022-05-15 10:24:40,494 INFO [train.py:812] (5/8) Epoch 26, batch 2800, loss[loss=0.1653, simple_loss=0.2545, pruned_loss=0.03806, over 7258.00 frames.], tot_loss[loss=0.1564, simple_loss=0.248, pruned_loss=0.03239, over 1417323.79 frames.], batch size: 18, lr: 2.99e-04
2022-05-15 10:25:38,076 INFO [train.py:812] (5/8) Epoch 26, batch 2850, loss[loss=0.1719, simple_loss=0.2652, pruned_loss=0.03925, over 7402.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2469, pruned_loss=0.03243, over 1412458.98 frames.], batch size: 21, lr: 2.99e-04
2022-05-15 10:26:37,759 INFO [train.py:812] (5/8) Epoch 26, batch 2900, loss[loss=0.1526, simple_loss=0.2461, pruned_loss=0.02956, over 7143.00 frames.], tot_loss[loss=0.156, simple_loss=0.247, pruned_loss=0.03252, over 1417906.70 frames.], batch size: 20, lr: 2.99e-04
2022-05-15 10:27:35,279 INFO [train.py:812] (5/8) Epoch 26, batch 2950, loss[loss=0.1626, simple_loss=0.2486, pruned_loss=0.03826, over 7330.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2475, pruned_loss=0.0325, over 1417680.79 frames.], batch size: 20, lr: 2.99e-04
2022-05-15 10:28:33,131 INFO [train.py:812] (5/8) Epoch 26, batch 3000, loss[loss=0.1509, simple_loss=0.2408, pruned_loss=0.03054, over 6337.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2484, pruned_loss=0.03237, over 1422056.54 frames.], batch size: 37, lr: 2.99e-04
2022-05-15 10:28:33,132 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 10:28:40,783 INFO [train.py:841] (5/8) Epoch 26, validation: loss=0.1534, simple_loss=0.2507, pruned_loss=0.02805, over 698248.00 frames.
2022-05-15 10:29:38,757 INFO [train.py:812] (5/8) Epoch 26, batch 3050, loss[loss=0.1622, simple_loss=0.26, pruned_loss=0.03223, over 7338.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2495, pruned_loss=0.03264, over 1421333.73 frames.], batch size: 22, lr: 2.99e-04
2022-05-15 10:30:38,711 INFO [train.py:812] (5/8) Epoch 26, batch 3100, loss[loss=0.1313, simple_loss=0.2183, pruned_loss=0.02216, over 7262.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2497, pruned_loss=0.03323, over 1418865.00 frames.], batch size: 19, lr: 2.98e-04
2022-05-15 10:31:36,294 INFO [train.py:812] (5/8) Epoch 26, batch 3150, loss[loss=0.1568, simple_loss=0.2379, pruned_loss=0.03784, over 7131.00 frames.], tot_loss[loss=0.1578, simple_loss=0.249, pruned_loss=0.03328, over 1417488.95 frames.], batch size: 17, lr: 2.98e-04
2022-05-15 10:32:35,770 INFO [train.py:812] (5/8) Epoch 26, batch 3200, loss[loss=0.1315, simple_loss=0.2228, pruned_loss=0.02013, over 7157.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2488, pruned_loss=0.03315, over 1420997.19 frames.], batch size: 19, lr: 2.98e-04
2022-05-15 10:33:35,077 INFO [train.py:812] (5/8) Epoch 26, batch 3250, loss[loss=0.1494, simple_loss=0.2412, pruned_loss=0.02876, over 7277.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2474, pruned_loss=0.03261, over 1424009.31 frames.], batch size: 18, lr: 2.98e-04
2022-05-15 10:34:33,012 INFO [train.py:812] (5/8) Epoch 26, batch 3300, loss[loss=0.1758, simple_loss=0.2622, pruned_loss=0.04469, over 7168.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2474, pruned_loss=0.03278, over 1417018.34 frames.], batch size: 26, lr: 2.98e-04
2022-05-15 10:35:31,809 INFO [train.py:812] (5/8) Epoch 26, batch 3350, loss[loss=0.1741, simple_loss=0.2723, pruned_loss=0.03798, over 7312.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2473, pruned_loss=0.03257, over 1413959.59 frames.], batch size: 21, lr: 2.98e-04
2022-05-15 10:36:31,851 INFO [train.py:812] (5/8) Epoch 26, batch 3400, loss[loss=0.1579, simple_loss=0.2475, pruned_loss=0.03413, over 6379.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2463, pruned_loss=0.03241, over 1418772.68 frames.], batch size: 38, lr: 2.98e-04
2022-05-15 10:37:30,426 INFO [train.py:812] (5/8) Epoch 26, batch 3450, loss[loss=0.1442, simple_loss=0.2328, pruned_loss=0.02784, over 7159.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2465, pruned_loss=0.03231, over 1418977.27 frames.], batch size: 18, lr: 2.98e-04
2022-05-15 10:38:29,756 INFO [train.py:812] (5/8) Epoch 26, batch 3500, loss[loss=0.1655, simple_loss=0.2586, pruned_loss=0.03622, over 7386.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2475, pruned_loss=0.03246, over 1418433.46 frames.], batch size: 23, lr: 2.98e-04
2022-05-15 10:39:28,318 INFO [train.py:812] (5/8) Epoch 26, batch 3550, loss[loss=0.1561, simple_loss=0.2536, pruned_loss=0.02924, over 7413.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2469, pruned_loss=0.03222, over 1421058.75 frames.], batch size: 21, lr: 2.98e-04
2022-05-15 10:40:26,260 INFO [train.py:812] (5/8) Epoch 26, batch 3600, loss[loss=0.1716, simple_loss=0.258, pruned_loss=0.04258, over 7183.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2461, pruned_loss=0.03226, over 1425988.51 frames.], batch size: 23, lr: 2.98e-04
2022-05-15 10:41:25,802 INFO [train.py:812] (5/8) Epoch 26, batch 3650, loss[loss=0.1297, simple_loss=0.2137, pruned_loss=0.02282, over 7258.00 frames.], tot_loss[loss=0.155, simple_loss=0.2461, pruned_loss=0.0319, over 1427560.36 frames.], batch size: 19, lr: 2.98e-04
2022-05-15 10:42:23,889 INFO [train.py:812] (5/8) Epoch 26, batch 3700, loss[loss=0.1389, simple_loss=0.2213, pruned_loss=0.02822, over 7056.00 frames.], tot_loss[loss=0.1552, simple_loss=0.246, pruned_loss=0.03222, over 1424190.18 frames.], batch size: 18, lr: 2.98e-04
2022-05-15 10:43:22,966 INFO [train.py:812] (5/8) Epoch 26, batch 3750, loss[loss=0.1505, simple_loss=0.2464, pruned_loss=0.02729, over 7158.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2461, pruned_loss=0.03223, over 1422485.93 frames.], batch size: 19, lr: 2.98e-04
2022-05-15 10:44:21,245 INFO [train.py:812] (5/8) Epoch 26, batch 3800, loss[loss=0.1737, simple_loss=0.2645, pruned_loss=0.04146, over 6321.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2464, pruned_loss=0.03192, over 1421145.34 frames.], batch size: 38, lr: 2.98e-04
2022-05-15 10:45:20,406 INFO [train.py:812] (5/8) Epoch 26, batch 3850, loss[loss=0.159, simple_loss=0.2535, pruned_loss=0.03228, over 7144.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2465, pruned_loss=0.03188, over 1418429.83 frames.], batch size: 20, lr: 2.97e-04
2022-05-15 10:46:19,969 INFO [train.py:812] (5/8) Epoch 26, batch 3900, loss[loss=0.132, simple_loss=0.2178, pruned_loss=0.02314, over 7412.00 frames.], tot_loss[loss=0.1555, simple_loss=0.247, pruned_loss=0.03199, over 1420640.09 frames.], batch size: 18, lr: 2.97e-04
2022-05-15 10:47:17,421 INFO [train.py:812] (5/8) Epoch 26, batch 3950, loss[loss=0.1749, simple_loss=0.2719, pruned_loss=0.03893, over 7228.00 frames.], tot_loss[loss=0.1554, simple_loss=0.247, pruned_loss=0.0319, over 1425135.32 frames.], batch size: 20, lr: 2.97e-04
2022-05-15 10:48:16,820 INFO [train.py:812] (5/8) Epoch 26, batch 4000, loss[loss=0.1621, simple_loss=0.2454, pruned_loss=0.0394, over 7442.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2467, pruned_loss=0.03224, over 1418292.44 frames.], batch size: 20, lr: 2.97e-04
2022-05-15 10:49:15,494 INFO [train.py:812] (5/8) Epoch 26, batch 4050, loss[loss=0.1582, simple_loss=0.2548, pruned_loss=0.03076, over 7419.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2469, pruned_loss=0.03192, over 1419172.06 frames.], batch size: 21, lr: 2.97e-04
2022-05-15 10:50:14,945 INFO [train.py:812] (5/8) Epoch 26, batch 4100, loss[loss=0.1486, simple_loss=0.2372, pruned_loss=0.03, over 7407.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2477, pruned_loss=0.03233, over 1417746.87 frames.], batch size: 21, lr: 2.97e-04
2022-05-15 10:51:14,792 INFO [train.py:812] (5/8) Epoch 26, batch 4150, loss[loss=0.148, simple_loss=0.2372, pruned_loss=0.02942, over 7258.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2472, pruned_loss=0.03215, over 1422732.19 frames.], batch size: 19, lr: 2.97e-04
2022-05-15 10:52:13,186 INFO [train.py:812] (5/8) Epoch 26, batch 4200, loss[loss=0.1555, simple_loss=0.2508, pruned_loss=0.03013, over 7004.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2479, pruned_loss=0.03256, over 1419593.16 frames.], batch size: 28, lr: 2.97e-04
2022-05-15 10:53:19,317 INFO [train.py:812] (5/8) Epoch 26, batch 4250, loss[loss=0.1361, simple_loss=0.2204, pruned_loss=0.02584, over 7161.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2476, pruned_loss=0.03253, over 1419213.51 frames.], batch size: 18, lr: 2.97e-04
2022-05-15 10:54:17,957 INFO [train.py:812] (5/8) Epoch 26, batch 4300, loss[loss=0.1603, simple_loss=0.2506, pruned_loss=0.035, over 7140.00 frames.], tot_loss[loss=0.157, simple_loss=0.2482, pruned_loss=0.03288, over 1422392.30 frames.], batch size: 26, lr: 2.97e-04
2022-05-15 10:55:15,835 INFO [train.py:812] (5/8) Epoch 26, batch 4350, loss[loss=0.1571, simple_loss=0.2495, pruned_loss=0.03233, over 7217.00 frames.], tot_loss[loss=0.157, simple_loss=0.2483, pruned_loss=0.03284, over 1415664.57 frames.], batch size: 20, lr: 2.97e-04
2022-05-15 10:56:15,064 INFO [train.py:812] (5/8) Epoch 26, batch 4400, loss[loss=0.1478, simple_loss=0.2303, pruned_loss=0.03263, over 7065.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2486, pruned_loss=0.0328, over 1415161.70 frames.], batch size: 18, lr: 2.97e-04
2022-05-15 10:57:23,156 INFO [train.py:812] (5/8) Epoch 26, batch 4450, loss[loss=0.1657, simple_loss=0.265, pruned_loss=0.03321, over 7278.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2485, pruned_loss=0.03254, over 1414698.20 frames.], batch size: 24, lr: 2.97e-04
2022-05-15 10:58:40,622 INFO [train.py:812] (5/8) Epoch 26, batch 4500, loss[loss=0.1477, simple_loss=0.242, pruned_loss=0.02667, over 7328.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2488, pruned_loss=0.03317, over 1398748.29 frames.], batch size: 20, lr: 2.97e-04
2022-05-15 10:59:48,358 INFO [train.py:812] (5/8) Epoch 26, batch 4550, loss[loss=0.1925, simple_loss=0.2808, pruned_loss=0.05204, over 5162.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2484, pruned_loss=0.03301, over 1390142.69 frames.], batch size: 52, lr: 2.97e-04
2022-05-15 11:01:05,799 INFO [train.py:812] (5/8) Epoch 27, batch 0, loss[loss=0.1711, simple_loss=0.2579, pruned_loss=0.04214, over 7171.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2579, pruned_loss=0.04214, over 7171.00 frames.], batch size: 18, lr: 2.91e-04
2022-05-15 11:02:14,188 INFO [train.py:812] (5/8) Epoch 27, batch 50, loss[loss=0.1409, simple_loss=0.2281, pruned_loss=0.0268, over 7285.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2462, pruned_loss=0.03344, over 319059.38 frames.], batch size: 17, lr: 2.91e-04
2022-05-15 11:03:12,400 INFO [train.py:812] (5/8) Epoch 27, batch 100, loss[loss=0.1312, simple_loss=0.2176, pruned_loss=0.0224, over 7292.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2431, pruned_loss=0.03065, over 563358.21 frames.], batch size: 17, lr: 2.91e-04
2022-05-15 11:04:11,549 INFO [train.py:812] (5/8) Epoch 27, batch 150, loss[loss=0.1536, simple_loss=0.2454, pruned_loss=0.03088, over 6434.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2454, pruned_loss=0.0314, over 751269.41 frames.], batch size: 37, lr: 2.91e-04
2022-05-15 11:05:08,302 INFO [train.py:812] (5/8) Epoch 27, batch 200, loss[loss=0.179, simple_loss=0.2695, pruned_loss=0.04431, over 7174.00 frames.], tot_loss[loss=0.155, simple_loss=0.2459, pruned_loss=0.03201, over 894352.90 frames.], batch size: 26, lr: 2.91e-04
2022-05-15 11:06:06,625 INFO [train.py:812] (5/8) Epoch 27, batch 250, loss[loss=0.1365, simple_loss=0.2289, pruned_loss=0.02207, over 6414.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2469, pruned_loss=0.03192, over 1006147.86 frames.], batch size: 38, lr: 2.91e-04
2022-05-15 11:07:05,728 INFO [train.py:812] (5/8) Epoch 27, batch 300, loss[loss=0.164, simple_loss=0.2678, pruned_loss=0.03011, over 6166.00 frames.], tot_loss[loss=0.155, simple_loss=0.2465, pruned_loss=0.03176, over 1099954.58 frames.], batch size: 37, lr: 2.91e-04
2022-05-15 11:08:04,295 INFO [train.py:812] (5/8) Epoch 27, batch 350, loss[loss=0.1731, simple_loss=0.2692, pruned_loss=0.03855, over 6788.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2468, pruned_loss=0.03193, over 1167820.43 frames.], batch size: 31, lr: 2.91e-04
2022-05-15 11:09:03,334 INFO [train.py:812] (5/8) Epoch 27, batch 400, loss[loss=0.1457, simple_loss=0.2551, pruned_loss=0.01814, over 7150.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2472, pruned_loss=0.0323, over 1227807.65 frames.], batch size: 20, lr: 2.91e-04
2022-05-15 11:10:01,854 INFO [train.py:812] (5/8) Epoch 27, batch 450, loss[loss=0.1321, simple_loss=0.2217, pruned_loss=0.02123, over 7233.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2472, pruned_loss=0.03211, over 1275353.57 frames.], batch size: 20, lr: 2.91e-04
2022-05-15 11:10:59,676 INFO [train.py:812] (5/8) Epoch 27, batch 500, loss[loss=0.1727, simple_loss=0.2555, pruned_loss=0.04491, over 5080.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2462, pruned_loss=0.03205, over 1306869.44 frames.], batch size: 52, lr: 2.91e-04
2022-05-15 11:11:59,499 INFO [train.py:812] (5/8) Epoch 27, batch 550, loss[loss=0.1647, simple_loss=0.261, pruned_loss=0.03421, over 7214.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2463, pruned_loss=0.03191, over 1332467.34 frames.], batch size: 22, lr: 2.90e-04
2022-05-15 11:12:58,970 INFO [train.py:812] (5/8) Epoch 27, batch 600, loss[loss=0.1481, simple_loss=0.2367, pruned_loss=0.02974, over 7264.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2469, pruned_loss=0.03201, over 1355101.62 frames.], batch size: 19, lr: 2.90e-04
2022-05-15 11:13:58,659 INFO [train.py:812] (5/8) Epoch 27, batch 650, loss[loss=0.1476, simple_loss=0.234, pruned_loss=0.03057, over 7290.00 frames.], tot_loss[loss=0.1547, simple_loss=0.246, pruned_loss=0.03171, over 1371869.04 frames.], batch size: 18, lr: 2.90e-04
2022-05-15 11:14:57,647 INFO [train.py:812] (5/8) Epoch 27, batch 700, loss[loss=0.1619, simple_loss=0.2612, pruned_loss=0.03133, over 7442.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2478, pruned_loss=0.03227, over 1381352.59 frames.], batch size: 22, lr: 2.90e-04
2022-05-15 11:16:01,092 INFO [train.py:812] (5/8) Epoch 27, batch 750, loss[loss=0.1476, simple_loss=0.2452, pruned_loss=0.02497, over 7145.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2473, pruned_loss=0.03189, over 1389851.17 frames.], batch size: 20, lr: 2.90e-04
2022-05-15 11:17:00,043 INFO [train.py:812] (5/8) Epoch 27, batch 800, loss[loss=0.148, simple_loss=0.2406, pruned_loss=0.02772, over 7223.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2474, pruned_loss=0.03176, over 1395654.93 frames.], batch size: 20, lr: 2.90e-04
2022-05-15 11:17:59,418 INFO [train.py:812] (5/8) Epoch 27, batch 850, loss[loss=0.1509, simple_loss=0.2436, pruned_loss=0.02906, over 4726.00 frames.], tot_loss[loss=0.155, simple_loss=0.2469, pruned_loss=0.03158, over 1397565.99 frames.], batch size: 52, lr: 2.90e-04
2022-05-15 11:18:57,704 INFO [train.py:812] (5/8) Epoch 27, batch 900, loss[loss=0.1302, simple_loss=0.2166, pruned_loss=0.02192, over 7400.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2461, pruned_loss=0.0316, over 1406540.94 frames.], batch size: 18, lr: 2.90e-04
2022-05-15 11:19:56,351 INFO [train.py:812] (5/8) Epoch 27, batch 950, loss[loss=0.1328, simple_loss=0.2131, pruned_loss=0.02626, over 7236.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2463, pruned_loss=0.03126, over 1408806.12 frames.], batch size: 16, lr: 2.90e-04
2022-05-15 11:20:55,357 INFO [train.py:812] (5/8) Epoch 27, batch 1000, loss[loss=0.1864, simple_loss=0.273, pruned_loss=0.04993, over 7302.00 frames.], tot_loss[loss=0.1551, simple_loss=0.247, pruned_loss=0.03156, over 1411823.59 frames.], batch size: 24, lr: 2.90e-04
2022-05-15 11:21:53,179 INFO [train.py:812] (5/8) Epoch 27, batch 1050, loss[loss=0.1571, simple_loss=0.2558, pruned_loss=0.02925, over 7201.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2469, pruned_loss=0.03143, over 1418255.26 frames.], batch size: 23, lr: 2.90e-04
2022-05-15 11:22:52,377 INFO [train.py:812] (5/8) Epoch 27, batch 1100, loss[loss=0.1833, simple_loss=0.2756, pruned_loss=0.04556, over 7210.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2468, pruned_loss=0.03191, over 1421875.25 frames.], batch size: 22, lr: 2.90e-04
2022-05-15 11:23:52,071 INFO [train.py:812] (5/8) Epoch 27, batch 1150, loss[loss=0.1347, simple_loss=0.223, pruned_loss=0.02322, over 7153.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2467, pruned_loss=0.03208, over 1422855.52 frames.], batch size: 19, lr: 2.90e-04
2022-05-15 11:24:50,275 INFO [train.py:812] (5/8) Epoch 27, batch 1200, loss[loss=0.1512, simple_loss=0.2463, pruned_loss=0.02802, over 7271.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2466, pruned_loss=0.03198, over 1426639.81 frames.], batch size: 24, lr: 2.90e-04
2022-05-15 11:25:49,792 INFO [train.py:812] (5/8) Epoch 27, batch 1250, loss[loss=0.1757, simple_loss=0.2697, pruned_loss=0.04087, over 6428.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2462, pruned_loss=0.03179, over 1427103.95 frames.], batch size: 38, lr: 2.90e-04
2022-05-15 11:26:48,358 INFO [train.py:812] (5/8) Epoch 27, batch 1300, loss[loss=0.1599, simple_loss=0.2461, pruned_loss=0.03683, over 7282.00 frames.], tot_loss[loss=0.1553, simple_loss=0.247, pruned_loss=0.0318, over 1423787.72 frames.], batch size: 18, lr: 2.90e-04
2022-05-15 11:27:46,498 INFO [train.py:812] (5/8) Epoch 27, batch 1350, loss[loss=0.1484, simple_loss=0.2383, pruned_loss=0.02924, over 7426.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2447, pruned_loss=0.03105, over 1427157.15 frames.], batch size: 18, lr: 2.89e-04
2022-05-15 11:28:44,271 INFO [train.py:812] (5/8) Epoch 27, batch 1400, loss[loss=0.1574, simple_loss=0.2513, pruned_loss=0.03168, over 7199.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2447, pruned_loss=0.0311, over 1420435.29 frames.], batch size: 23, lr: 2.89e-04
2022-05-15 11:29:43,168 INFO [train.py:812] (5/8) Epoch 27, batch 1450, loss[loss=0.1267, simple_loss=0.2179, pruned_loss=0.01779, over 7282.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2452, pruned_loss=0.03162, over 1422537.63 frames.], batch size: 18, lr: 2.89e-04
2022-05-15 11:30:41,644 INFO [train.py:812] (5/8) Epoch 27, batch 1500, loss[loss=0.1796, simple_loss=0.2738, pruned_loss=0.04269, over 4966.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2452, pruned_loss=0.03159, over 1418545.12 frames.], batch size: 52, lr: 2.89e-04
2022-05-15 11:31:41,204 INFO [train.py:812] (5/8) Epoch 27, batch 1550, loss[loss=0.1627, simple_loss=0.257, pruned_loss=0.03423, over 7105.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2457, pruned_loss=0.03173, over 1421751.12 frames.], batch size: 21, lr: 2.89e-04
2022-05-15 11:32:40,455 INFO [train.py:812] (5/8) Epoch 27, batch 1600, loss[loss=0.1455, simple_loss=0.2348, pruned_loss=0.02813, over 7262.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2453, pruned_loss=0.03177, over 1425582.93 frames.], batch size: 19, lr: 2.89e-04
2022-05-15 11:33:39,688 INFO [train.py:812] (5/8) Epoch 27, batch 1650, loss[loss=0.1622, simple_loss=0.2708, pruned_loss=0.02681, over 7123.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2457, pruned_loss=0.03184, over 1429175.71 frames.], batch size: 26, lr: 2.89e-04
2022-05-15 11:34:38,049 INFO [train.py:812] (5/8) Epoch 27, batch 1700, loss[loss=0.1539, simple_loss=0.2516, pruned_loss=0.02804, over 7347.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2456, pruned_loss=0.03189, over 1430458.22 frames.], batch size: 22, lr: 2.89e-04
2022-05-15 11:35:35,781 INFO [train.py:812] (5/8) Epoch 27, batch 1750, loss[loss=0.1681, simple_loss=0.2568, pruned_loss=0.03974, over 7203.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2467, pruned_loss=0.0324, over 1431132.48 frames.], batch size: 26, lr: 2.89e-04
2022-05-15 11:36:34,351 INFO [train.py:812] (5/8) Epoch 27, batch 1800, loss[loss=0.1365, simple_loss=0.2334, pruned_loss=0.01981, over 7108.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2462, pruned_loss=0.03234, over 1428784.94 frames.], batch size: 21, lr: 2.89e-04
2022-05-15 11:37:32,436 INFO [train.py:812] (5/8) Epoch 27, batch 1850, loss[loss=0.1768, simple_loss=0.2653, pruned_loss=0.04419, over 4710.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2469, pruned_loss=0.03239, over 1428580.74 frames.], batch size: 53, lr: 2.89e-04
2022-05-15 11:38:30,734 INFO [train.py:812] (5/8) Epoch 27, batch 1900, loss[loss=0.1441, simple_loss=0.2388, pruned_loss=0.02477, over 7349.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2457, pruned_loss=0.03188, over 1427701.30 frames.], batch size: 19, lr: 2.89e-04
2022-05-15 11:39:30,107 INFO [train.py:812] (5/8) Epoch 27, batch 1950, loss[loss=0.1702, simple_loss=0.2684, pruned_loss=0.03596, over 6459.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2464, pruned_loss=0.03217, over 1424655.61 frames.], batch size: 37, lr: 2.89e-04
2022-05-15 11:40:29,417 INFO [train.py:812] (5/8) Epoch 27, batch 2000, loss[loss=0.1507, simple_loss=0.248, pruned_loss=0.02666, over 6737.00 frames.], tot_loss[loss=0.1551, simple_loss=0.246, pruned_loss=0.03211, over 1422731.02 frames.], batch size: 31, lr: 2.89e-04
2022-05-15 11:41:28,686 INFO [train.py:812] (5/8) Epoch 27, batch 2050, loss[loss=0.1715, simple_loss=0.2677, pruned_loss=0.03766, over 7225.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2472, pruned_loss=0.03228, over 1426332.09 frames.], batch size: 26, lr: 2.89e-04
2022-05-15 11:42:27,739 INFO [train.py:812] (5/8) Epoch 27, batch 2100, loss[loss=0.1927, simple_loss=0.2738, pruned_loss=0.05579, over 7212.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2473, pruned_loss=0.03255, over 1424545.38 frames.], batch size: 22, lr: 2.89e-04
2022-05-15 11:43:25,343 INFO [train.py:812] (5/8) Epoch 27, batch 2150, loss[loss=0.1706, simple_loss=0.2556, pruned_loss=0.04279, over 7322.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2484, pruned_loss=0.03306, over 1428173.68 frames.], batch size: 25, lr: 2.89e-04
2022-05-15 11:44:23,704 INFO [train.py:812] (5/8) Epoch 27, batch 2200, loss[loss=0.1465, simple_loss=0.2406, pruned_loss=0.0262, over 7244.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2481, pruned_loss=0.03281, over 1426722.84 frames.], batch size: 20, lr: 2.88e-04
2022-05-15 11:45:23,012 INFO [train.py:812] (5/8) Epoch 27, batch 2250, loss[loss=0.1336, simple_loss=0.2132, pruned_loss=0.02699, over 7011.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2479, pruned_loss=0.03266, over 1431648.25 frames.], batch size: 16, lr: 2.88e-04
2022-05-15 11:46:21,527 INFO [train.py:812] (5/8) Epoch 27, batch 2300, loss[loss=0.1281, simple_loss=0.2158, pruned_loss=0.02015, over 7143.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2474, pruned_loss=0.03219, over 1433038.12 frames.], batch size: 17, lr: 2.88e-04
2022-05-15 11:47:19,552 INFO [train.py:812] (5/8) Epoch 27, batch 2350, loss[loss=0.1682, simple_loss=0.2705, pruned_loss=0.03298, over 7143.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2484, pruned_loss=0.03264, over 1431605.12 frames.], batch size: 20, lr: 2.88e-04
2022-05-15 11:48:16,537 INFO [train.py:812] (5/8) Epoch 27, batch 2400, loss[loss=0.186, simple_loss=0.2765, pruned_loss=0.04774, over 7291.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2486, pruned_loss=0.03235, over 1432820.45 frames.], batch size: 24, lr: 2.88e-04
2022-05-15 11:49:16,229 INFO [train.py:812] (5/8) Epoch 27, batch 2450, loss[loss=0.1531, simple_loss=0.2453, pruned_loss=0.03046, over 7237.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2481, pruned_loss=0.03218, over 1435450.91 frames.], batch size: 20, lr: 2.88e-04
2022-05-15 11:50:15,237 INFO [train.py:812] (5/8) Epoch 27, batch 2500, loss[loss=0.1576, simple_loss=0.2553, pruned_loss=0.02994, over 7210.00 frames.], tot_loss[loss=0.1553, simple_loss=0.247, pruned_loss=0.03183, over 1436770.56 frames.], batch size: 21, lr: 2.88e-04
2022-05-15 11:51:13,614 INFO [train.py:812] (5/8) Epoch 27, batch 2550, loss[loss=0.1741, simple_loss=0.2584, pruned_loss=0.04487, over 6791.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2459, pruned_loss=0.03115, over 1433956.69 frames.], batch size: 31, lr: 2.88e-04
2022-05-15 11:52:12,754 INFO [train.py:812] (5/8) Epoch 27, batch 2600, loss[loss=0.1501, simple_loss=0.2308, pruned_loss=0.0347, over 6808.00 frames.], tot_loss[loss=0.1544, simple_loss=0.246, pruned_loss=0.03136, over 1433968.84 frames.], batch size: 15, lr: 2.88e-04
2022-05-15 11:53:12,238 INFO [train.py:812] (5/8) Epoch 27, batch 2650, loss[loss=0.1654, simple_loss=0.2649, pruned_loss=0.0329, over 7300.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2467, pruned_loss=0.0314, over 1430415.66 frames.], batch size: 24, lr: 2.88e-04
2022-05-15 11:54:11,663 INFO [train.py:812] (5/8) Epoch 27, batch 2700, loss[loss=0.1545, simple_loss=0.2454, pruned_loss=0.03181, over 7335.00 frames.], tot_loss[loss=0.155, simple_loss=0.2468, pruned_loss=0.0316, over 1428248.77 frames.], batch size: 22, lr: 2.88e-04
2022-05-15 11:55:10,411 INFO [train.py:812] (5/8) Epoch 27, batch 2750, loss[loss=0.1617, simple_loss=0.2434, pruned_loss=0.03994, over 7168.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2463, pruned_loss=0.03129, over 1427272.61 frames.], batch size: 19, lr: 2.88e-04
2022-05-15 11:56:08,581 INFO [train.py:812] (5/8) Epoch 27, batch 2800, loss[loss=0.1599, simple_loss=0.2472, pruned_loss=0.03631, over 7300.00 frames.], tot_loss[loss=0.155, simple_loss=0.2466, pruned_loss=0.03169, over 1426752.38 frames.], batch size: 25, lr: 2.88e-04
2022-05-15 11:57:08,039 INFO [train.py:812] (5/8) Epoch 27, batch 2850, loss[loss=0.1601, simple_loss=0.2401, pruned_loss=0.04007, over 7255.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2468, pruned_loss=0.03201, over 1426272.47 frames.], batch size: 19, lr: 2.88e-04
2022-05-15 11:58:06,922 INFO [train.py:812] (5/8) Epoch 27, batch 2900, loss[loss=0.1494, simple_loss=0.2441, pruned_loss=0.02734, over 7165.00 frames.], tot_loss[loss=0.155, simple_loss=0.2466, pruned_loss=0.03173, over 1425485.14 frames.], batch size: 19, lr: 2.88e-04
2022-05-15 11:59:06,485 INFO [train.py:812] (5/8) Epoch 27, batch 2950, loss[loss=0.1441, simple_loss=0.2454, pruned_loss=0.02138, over 7126.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2475, pruned_loss=0.0324, over 1419571.41 frames.], batch size: 21, lr: 2.88e-04
2022-05-15 12:00:05,424 INFO [train.py:812] (5/8) Epoch 27, batch 3000, loss[loss=0.1866, simple_loss=0.2771, pruned_loss=0.04803, over 7421.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2474, pruned_loss=0.03217, over 1418968.48 frames.], batch size: 21, lr: 2.88e-04
2022-05-15 12:00:05,425 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 12:00:12,945 INFO [train.py:841] (5/8) Epoch 27, validation: loss=0.1528, simple_loss=0.25, pruned_loss=0.02785, over 698248.00 frames.
2022-05-15 12:01:11,828 INFO [train.py:812] (5/8) Epoch 27, batch 3050, loss[loss=0.1664, simple_loss=0.2702, pruned_loss=0.03132, over 7129.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2459, pruned_loss=0.03194, over 1410535.78 frames.], batch size: 21, lr: 2.87e-04
2022-05-15 12:02:10,769 INFO [train.py:812] (5/8) Epoch 27, batch 3100, loss[loss=0.1639, simple_loss=0.2599, pruned_loss=0.03393, over 7317.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2471, pruned_loss=0.03228, over 1417229.25 frames.], batch size: 21, lr: 2.87e-04
2022-05-15 12:03:20,252 INFO [train.py:812] (5/8) Epoch 27, batch 3150, loss[loss=0.1815, simple_loss=0.2696, pruned_loss=0.04671, over 7206.00 frames.], tot_loss[loss=0.156, simple_loss=0.2473, pruned_loss=0.03237, over 1417499.64 frames.], batch size: 22, lr: 2.87e-04
2022-05-15 12:04:19,258 INFO [train.py:812] (5/8) Epoch 27, batch 3200, loss[loss=0.1731, simple_loss=0.2696, pruned_loss=0.03826, over 7197.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2469, pruned_loss=0.03224, over 1419422.33 frames.], batch size: 23, lr: 2.87e-04
2022-05-15 12:05:18,850 INFO [train.py:812] (5/8) Epoch 27, batch 3250, loss[loss=0.1454, simple_loss=0.2379, pruned_loss=0.02643, over 6223.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2464, pruned_loss=0.03225, over 1419626.14 frames.], batch size: 37, lr: 2.87e-04
2022-05-15 12:06:17,719 INFO [train.py:812] (5/8) Epoch 27, batch 3300, loss[loss=0.1622, simple_loss=0.2702, pruned_loss=0.02712, over 6849.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2474, pruned_loss=0.03259, over 1419183.39 frames.], batch size: 31, lr: 2.87e-04
2022-05-15 12:07:17,051 INFO [train.py:812] (5/8) Epoch 27, batch 3350, loss[loss=0.1636, simple_loss=0.262, pruned_loss=0.03258, over 7316.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2486, pruned_loss=0.03301, over 1420000.16 frames.], batch size: 22, lr: 2.87e-04
2022-05-15 12:08:16,179 INFO [train.py:812] (5/8) Epoch 27, batch 3400, loss[loss=0.1538, simple_loss=0.2444, pruned_loss=0.03157, over 7147.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2492, pruned_loss=0.03318, over 1417697.90 frames.], batch size: 20, lr: 2.87e-04
2022-05-15 12:09:14,979 INFO [train.py:812] (5/8) Epoch 27, batch 3450, loss[loss=0.1404, simple_loss=0.2414, pruned_loss=0.01973, over 7341.00 frames.], tot_loss[loss=0.157, simple_loss=0.2487, pruned_loss=0.03269, over 1421028.02 frames.], batch size: 22, lr: 2.87e-04
2022-05-15 12:10:13,393 INFO [train.py:812] (5/8) Epoch 27, batch 3500, loss[loss=0.1207, simple_loss=0.2061, pruned_loss=0.01764, over 6775.00 frames.], tot_loss[loss=0.156, simple_loss=0.2474, pruned_loss=0.03234, over 1423205.06 frames.], batch size: 15, lr: 2.87e-04
2022-05-15 12:11:13,076 INFO [train.py:812] (5/8) Epoch 27, batch 3550, loss[loss=0.1864, simple_loss=0.2626, pruned_loss=0.05509, over 5013.00 frames.], tot_loss[loss=0.1558, simple_loss=0.247, pruned_loss=0.03229, over 1415781.30 frames.], batch size: 52, lr: 2.87e-04
2022-05-15 12:12:10,922 INFO [train.py:812] (5/8) Epoch 27, batch 3600, loss[loss=0.1623, simple_loss=0.2577, pruned_loss=0.03344, over 7149.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2476, pruned_loss=0.0327, over 1413578.64 frames.], batch size: 19, lr: 2.87e-04
2022-05-15 12:13:10,379 INFO [train.py:812] (5/8) Epoch 27, batch 3650, loss[loss=0.1457, simple_loss=0.2307, pruned_loss=0.03039, over 7068.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2471, pruned_loss=0.03225, over 1412791.62 frames.], batch size: 18, lr: 2.87e-04
2022-05-15 12:14:09,326 INFO [train.py:812] (5/8) Epoch 27, batch 3700, loss[loss=0.1369, simple_loss=0.2101, pruned_loss=0.03186, over 7275.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2469, pruned_loss=0.0325, over 1412451.41 frames.], batch size: 18, lr: 2.87e-04
2022-05-15 12:15:08,329 INFO [train.py:812] (5/8) Epoch 27, batch 3750, loss[loss=0.1552, simple_loss=0.2467, pruned_loss=0.03184, over 7220.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2461, pruned_loss=0.03245, over 1415984.79 frames.], batch size: 21, lr: 2.87e-04
2022-05-15 12:16:08,056 INFO [train.py:812] (5/8) Epoch 27, batch 3800, loss[loss=0.1633, simple_loss=0.2536, pruned_loss=0.03647, over 7325.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2456, pruned_loss=0.03181, over 1420314.36 frames.], batch size: 20, lr: 2.87e-04
2022-05-15 12:17:07,772 INFO [train.py:812] (5/8) Epoch 27, batch 3850, loss[loss=0.1452, simple_loss=0.2316, pruned_loss=0.02942, over 7399.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2466, pruned_loss=0.03222, over 1413968.48 frames.], batch size: 18, lr: 2.87e-04
2022-05-15 12:18:06,242 INFO [train.py:812] (5/8) Epoch 27, batch 3900, loss[loss=0.157, simple_loss=0.2585, pruned_loss=0.02776, over 7113.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2463, pruned_loss=0.0321, over 1414904.74 frames.], batch size: 28, lr: 2.86e-04
2022-05-15 12:19:05,030 INFO [train.py:812] (5/8) Epoch 27, batch 3950, loss[loss=0.1542, simple_loss=0.2427, pruned_loss=0.03284, over 7367.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2467, pruned_loss=0.03192, over 1419475.86 frames.], batch size: 19, lr: 2.86e-04
2022-05-15 12:20:04,219 INFO [train.py:812] (5/8) Epoch 27, batch 4000, loss[loss=0.1584, simple_loss=0.2528, pruned_loss=0.03199, over 7054.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2461, pruned_loss=0.03186, over 1424635.96 frames.], batch size: 28, lr: 2.86e-04
2022-05-15 12:21:04,112 INFO [train.py:812] (5/8) Epoch 27, batch 4050, loss[loss=0.1654, simple_loss=0.2587, pruned_loss=0.03605, over 7326.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2477, pruned_loss=0.0325, over 1425938.71 frames.], batch size: 20, lr: 2.86e-04
2022-05-15 12:22:03,544 INFO [train.py:812] (5/8) Epoch 27, batch 4100, loss[loss=0.1358, simple_loss=0.2331, pruned_loss=0.01932, over 7330.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2471, pruned_loss=0.03216, over 1424671.74 frames.], batch size: 20, lr: 2.86e-04
2022-05-15 12:23:02,356 INFO [train.py:812] (5/8) Epoch 27, batch 4150, loss[loss=0.162, simple_loss=0.2616, pruned_loss=0.03115, over 7118.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2476, pruned_loss=0.03224, over 1422082.54 frames.], batch size: 21, lr: 2.86e-04
2022-05-15 12:23:59,502 INFO [train.py:812] (5/8) Epoch 27, batch 4200, loss[loss=0.1644, simple_loss=0.263, pruned_loss=0.03296, over 7344.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2471, pruned_loss=0.03201, over 1423721.67 frames.], batch size: 22, lr: 2.86e-04
2022-05-15 12:24:57,510 INFO [train.py:812] (5/8) Epoch 27, batch 4250, loss[loss=0.1654, simple_loss=0.2552, pruned_loss=0.03779, over 7409.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2485, pruned_loss=0.03238, over 1416402.78 frames.], batch size: 21, lr: 2.86e-04
2022-05-15 12:25:55,493 INFO [train.py:812] (5/8) Epoch 27, batch 4300, loss[loss=0.161, simple_loss=0.2635, pruned_loss=0.02929, over 6737.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2489, pruned_loss=0.03247, over 1414155.09 frames.], batch size: 31, lr: 2.86e-04
2022-05-15 12:26:54,775 INFO [train.py:812] (5/8) Epoch 27, batch 4350, loss[loss=0.1304, simple_loss=0.2259, pruned_loss=0.01743, over 7007.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2491, pruned_loss=0.03278, over 1414189.68 frames.], batch size: 16, lr: 2.86e-04
2022-05-15 12:27:53,340 INFO [train.py:812] (5/8) Epoch 27, batch 4400, loss[loss=0.1557, simple_loss=0.2591, pruned_loss=0.02613, over 6525.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2491, pruned_loss=0.03292, over 1402098.73 frames.], batch size: 38, lr: 2.86e-04
2022-05-15 12:28:51,331 INFO [train.py:812] (5/8) Epoch 27, batch 4450, loss[loss=0.1518, simple_loss=0.2496, pruned_loss=0.02701, over 7341.00 frames.], tot_loss[loss=0.157, simple_loss=0.2485, pruned_loss=0.03278, over 1397840.94 frames.], batch size: 22, lr: 2.86e-04
2022-05-15 12:29:50,411 INFO [train.py:812] (5/8) Epoch 27, batch 4500, loss[loss=0.1571, simple_loss=0.2513, pruned_loss=0.03142, over 7170.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2486, pruned_loss=0.0329, over 1387654.25 frames.], batch size: 18, lr: 2.86e-04
2022-05-15 12:30:49,292 INFO [train.py:812] (5/8) Epoch 27, batch 4550, loss[loss=0.1868, simple_loss=0.2664, pruned_loss=0.05358, over 5102.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2474, pruned_loss=0.03296, over 1370169.31 frames.], batch size: 52, lr: 2.86e-04
2022-05-15 12:32:00,143 INFO [train.py:812] (5/8) Epoch 28, batch 0, loss[loss=0.1236, simple_loss=0.2046, pruned_loss=0.02127, over 7256.00 frames.], tot_loss[loss=0.1236, simple_loss=0.2046, pruned_loss=0.02127, over 7256.00 frames.], batch size: 19, lr: 2.81e-04
2022-05-15 12:32:59,426 INFO [train.py:812] (5/8) Epoch 28, batch 50, loss[loss=0.1553, simple_loss=0.244, pruned_loss=0.03325, over 7256.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2467, pruned_loss=0.03382, over 321113.90 frames.], batch size: 19, lr: 2.81e-04
2022-05-15 12:33:58,599 INFO [train.py:812] (5/8) Epoch 28, batch 100, loss[loss=0.1534, simple_loss=0.2569, pruned_loss=0.025, over 7148.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2462, pruned_loss=0.03309, over 564337.81 frames.], batch size: 20, lr: 2.80e-04
2022-05-15 12:35:03,226 INFO [train.py:812] (5/8) Epoch 28, batch 150, loss[loss=0.1482, simple_loss=0.2511, pruned_loss=0.0226, over 6414.00 frames.], tot_loss[loss=0.157, simple_loss=0.2475, pruned_loss=0.03325, over 752912.57 frames.], batch size: 37, lr: 2.80e-04
2022-05-15 12:36:01,533 INFO [train.py:812] (5/8) Epoch 28, batch 200, loss[loss=0.1601, simple_loss=0.2433, pruned_loss=0.03848, over 7205.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2472, pruned_loss=0.0325, over 898764.84 frames.], batch size: 23, lr: 2.80e-04
2022-05-15 12:36:59,619 INFO [train.py:812] (5/8) Epoch 28, batch 250, loss[loss=0.1694, simple_loss=0.2589, pruned_loss=0.03997, over 7299.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2471, pruned_loss=0.03203, over 1015030.35 frames.], batch size: 24, lr: 2.80e-04
2022-05-15 12:37:58,305 INFO [train.py:812] (5/8) Epoch 28, batch 300, loss[loss=0.1529, simple_loss=0.2519, pruned_loss=0.02694, over 6822.00 frames.], tot_loss[loss=0.1553, simple_loss=0.247, pruned_loss=0.03176, over 1105266.80 frames.], batch size: 31, lr: 2.80e-04
2022-05-15 12:38:57,250 INFO [train.py:812] (5/8) Epoch 28, batch 350, loss[loss=0.1332, simple_loss=0.2228, pruned_loss=0.02182, over 7160.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2463, pruned_loss=0.03141, over 1178049.54 frames.], batch size: 19, lr: 2.80e-04
2022-05-15 12:39:55,232 INFO [train.py:812] (5/8) Epoch 28, batch 400, loss[loss=0.1704, simple_loss=0.2567, pruned_loss=0.04204, over 7135.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2476, pruned_loss=0.0321, over 1234037.19 frames.], batch size: 17, lr: 2.80e-04
2022-05-15 12:40:54,504 INFO [train.py:812] (5/8) Epoch 28, batch 450, loss[loss=0.1528, simple_loss=0.2522, pruned_loss=0.02676, over 7310.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2472, pruned_loss=0.03195, over 1270923.18 frames.], batch size: 25, lr: 2.80e-04
2022-05-15 12:41:53,061 INFO [train.py:812] (5/8) Epoch 28, batch 500, loss[loss=0.177, simple_loss=0.2753, pruned_loss=0.03934, over 7325.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2472, pruned_loss=0.03168, over 1308058.25 frames.], batch size: 21, lr: 2.80e-04
2022-05-15 12:42:52,284 INFO [train.py:812] (5/8) Epoch 28, batch 550, loss[loss=0.2057, simple_loss=0.2865, pruned_loss=0.06249, over 7065.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2467, pruned_loss=0.03174, over 1329906.99 frames.], batch size: 18, lr: 2.80e-04
2022-05-15 12:43:51,382 INFO [train.py:812] (5/8) Epoch 28, batch 600, loss[loss=0.1331, simple_loss=0.2232, pruned_loss=0.02155, over 7326.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2461, pruned_loss=0.03162, over 1347882.89 frames.], batch size: 20, lr: 2.80e-04
2022-05-15 12:44:49,182 INFO [train.py:812] (5/8) Epoch 28, batch 650, loss[loss=0.1911, simple_loss=0.2979, pruned_loss=0.0421, over 7112.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2466, pruned_loss=0.03159, over 1365478.58 frames.], batch size: 28, lr: 2.80e-04
2022-05-15 12:45:47,942 INFO [train.py:812] (5/8) Epoch 28, batch 700, loss[loss=0.1415, simple_loss=0.2282, pruned_loss=0.02746, over 7062.00 frames.], tot_loss[loss=0.1544, simple_loss=0.246, pruned_loss=0.0314, over 1379349.77 frames.], batch size: 18, lr: 2.80e-04
2022-05-15 12:46:48,085 INFO [train.py:812] (5/8) Epoch 28, batch 750, loss[loss=0.1643, simple_loss=0.2606, pruned_loss=0.03403, over 7224.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2448, pruned_loss=0.03082, over 1390680.92 frames.], batch size: 21, lr: 2.80e-04
2022-05-15 12:47:47,174 INFO [train.py:812] (5/8) Epoch 28, batch 800, loss[loss=0.1606, simple_loss=0.2501, pruned_loss=0.03555, over 7038.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2457, pruned_loss=0.03093, over 1398511.46 frames.], batch size: 28, lr: 2.80e-04
2022-05-15 12:48:46,799 INFO [train.py:812] (5/8) Epoch 28, batch 850, loss[loss=0.1466, simple_loss=0.2455, pruned_loss=0.02382, over 7295.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2455, pruned_loss=0.03069, over 1405666.07 frames.], batch size: 25, lr: 2.80e-04
2022-05-15 12:49:45,702 INFO [train.py:812] (5/8) Epoch 28, batch 900, loss[loss=0.1385, simple_loss=0.2164, pruned_loss=0.03028, over 6986.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2457, pruned_loss=0.03104, over 1408069.60 frames.], batch size: 16, lr: 2.80e-04
2022-05-15 12:50:45,069 INFO [train.py:812] (5/8) Epoch 28, batch 950, loss[loss=0.1461, simple_loss=0.2345, pruned_loss=0.02892, over 7174.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2461, pruned_loss=0.03158, over 1411143.89 frames.], batch size: 18, lr: 2.80e-04
2022-05-15 12:51:44,006 INFO [train.py:812] (5/8) Epoch 28, batch 1000, loss[loss=0.1488, simple_loss=0.2471, pruned_loss=0.02529, over 7431.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2461, pruned_loss=0.03151, over 1416818.89 frames.], batch size: 20, lr: 2.79e-04
2022-05-15 12:52:42,468 INFO [train.py:812] (5/8) Epoch 28, batch 1050, loss[loss=0.1325, simple_loss=0.2357, pruned_loss=0.01464, over 7413.00 frames.], tot_loss[loss=0.155, simple_loss=0.2467, pruned_loss=0.03165, over 1416699.12 frames.], batch size: 21, lr: 2.79e-04
2022-05-15 12:53:50,429 INFO [train.py:812] (5/8) Epoch 28, batch 1100, loss[loss=0.1414, simple_loss=0.2296, pruned_loss=0.02665, over 7064.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2468, pruned_loss=0.03148, over 1415676.90 frames.], batch size: 18, lr: 2.79e-04
2022-05-15 12:54:49,776 INFO [train.py:812] (5/8) Epoch 28, batch 1150, loss[loss=0.1557, simple_loss=0.2504, pruned_loss=0.03045, over 7189.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2463, pruned_loss=0.03132, over 1421010.01 frames.], batch size: 23, lr: 2.79e-04
2022-05-15 12:55:48,175 INFO [train.py:812] (5/8) Epoch 28, batch 1200, loss[loss=0.1738, simple_loss=0.2618, pruned_loss=0.04291, over 7142.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2461, pruned_loss=0.03107, over 1425760.85 frames.], batch size: 17, lr: 2.79e-04
2022-05-15 12:56:47,572 INFO [train.py:812] (5/8) Epoch 28, batch 1250, loss[loss=0.1401, simple_loss=0.2175, pruned_loss=0.03132, over 7120.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2467, pruned_loss=0.03147, over 1423860.93 frames.], batch size: 17, lr: 2.79e-04
2022-05-15 12:57:56,211 INFO [train.py:812] (5/8) Epoch 28, batch 1300, loss[loss=0.1359, simple_loss=0.2182, pruned_loss=0.02677, over 7271.00 frames.], tot_loss[loss=0.155, simple_loss=0.2467, pruned_loss=0.0317, over 1420035.08 frames.], batch size: 18, lr: 2.79e-04
2022-05-15 12:58:55,618 INFO [train.py:812] (5/8) Epoch 28, batch 1350, loss[loss=0.151, simple_loss=0.234, pruned_loss=0.03401, over 7358.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2461, pruned_loss=0.03156, over 1419435.42 frames.], batch size: 19, lr: 2.79e-04
2022-05-15 13:00:02,707 INFO [train.py:812] (5/8) Epoch 28, batch 1400, loss[loss=0.1627, simple_loss=0.2458, pruned_loss=0.03981, over 7065.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2451, pruned_loss=0.03093, over 1419537.20 frames.], batch size: 18, lr: 2.79e-04
2022-05-15 13:01:30,481 INFO [train.py:812] (5/8) Epoch 28, batch 1450, loss[loss=0.142, simple_loss=0.2321, pruned_loss=0.02589, over 7331.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2439, pruned_loss=0.03064, over 1421280.30 frames.], batch size: 20, lr: 2.79e-04
2022-05-15 13:02:27,758 INFO [train.py:812] (5/8) Epoch 28, batch 1500, loss[loss=0.1719, simple_loss=0.2708, pruned_loss=0.03646, over 7121.00 frames.], tot_loss[loss=0.153, simple_loss=0.2447, pruned_loss=0.03061, over 1423247.00 frames.], batch size: 21, lr: 2.79e-04
2022-05-15 13:03:25,156 INFO [train.py:812] (5/8) Epoch 28, batch 1550, loss[loss=0.1396, simple_loss=0.2255, pruned_loss=0.02684, over 7257.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2443, pruned_loss=0.03056, over 1421530.37 frames.], batch size: 16, lr: 2.79e-04
2022-05-15 13:04:33,677 INFO [train.py:812] (5/8) Epoch 28, batch 1600, loss[loss=0.1556, simple_loss=0.2426, pruned_loss=0.0343, over 7413.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2444, pruned_loss=0.03061, over 1425137.73 frames.], batch size: 21, lr: 2.79e-04
2022-05-15 13:05:32,117 INFO [train.py:812] (5/8) Epoch 28, batch 1650, loss[loss=0.135, simple_loss=0.2266, pruned_loss=0.0217, over 7073.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2449, pruned_loss=0.03122, over 1426097.07 frames.], batch size: 18, lr: 2.79e-04
2022-05-15 13:06:30,572 INFO [train.py:812] (5/8) Epoch 28, batch 1700, loss[loss=0.1442, simple_loss=0.2387, pruned_loss=0.02484, over 7346.00 frames.], tot_loss[loss=0.154, simple_loss=0.2456, pruned_loss=0.03125, over 1427641.09 frames.], batch size: 19, lr: 2.79e-04
2022-05-15 13:07:29,474 INFO [train.py:812] (5/8) Epoch 28, batch 1750, loss[loss=0.1702, simple_loss=0.2586, pruned_loss=0.04088, over 6719.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2459, pruned_loss=0.03131, over 1429239.83 frames.], batch size: 31, lr: 2.79e-04
2022-05-15 13:08:28,862 INFO [train.py:812] (5/8) Epoch 28, batch 1800, loss[loss=0.17, simple_loss=0.2554, pruned_loss=0.04232, over 7233.00 frames.], tot_loss[loss=0.155, simple_loss=0.2463, pruned_loss=0.03191, over 1427881.53 frames.], batch size: 20, lr: 2.79e-04
2022-05-15 13:09:27,163 INFO [train.py:812] (5/8) Epoch 28, batch 1850, loss[loss=0.1458, simple_loss=0.2294, pruned_loss=0.0311, over 7155.00 frames.], tot_loss[loss=0.155, simple_loss=0.2464, pruned_loss=0.03183, over 1430502.14 frames.], batch size: 19, lr: 2.79e-04
2022-05-15 13:10:26,310 INFO [train.py:812] (5/8) Epoch 28, batch 1900, loss[loss=0.1492, simple_loss=0.2413, pruned_loss=0.02855, over 7294.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2471, pruned_loss=0.0321, over 1430459.20 frames.], batch size: 17, lr: 2.78e-04
2022-05-15 13:11:24,508 INFO [train.py:812] (5/8) Epoch 28, batch 1950, loss[loss=0.1539, simple_loss=0.2458, pruned_loss=0.03101, over 6565.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2463, pruned_loss=0.03169, over 1426299.05 frames.], batch size: 38, lr: 2.78e-04
2022-05-15 13:12:23,403 INFO [train.py:812] (5/8) Epoch 28, batch 2000, loss[loss=0.1628, simple_loss=0.25, pruned_loss=0.03778, over 7224.00 frames.], tot_loss[loss=0.1547, simple_loss=0.246, pruned_loss=0.03173, over 1426083.81 frames.], batch size: 21, lr: 2.78e-04
2022-05-15 13:13:21,533 INFO [train.py:812] (5/8) Epoch 28, batch 2050, loss[loss=0.1804, simple_loss=0.2672, pruned_loss=0.04684, over 7223.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2473, pruned_loss=0.03282, over 1424553.78 frames.], batch size: 23, lr: 2.78e-04
2022-05-15 13:14:20,995 INFO [train.py:812] (5/8) Epoch 28, batch 2100, loss[loss=0.1837, simple_loss=0.2792, pruned_loss=0.04412, over 7332.00 frames.], tot_loss[loss=0.156, simple_loss=0.247, pruned_loss=0.03247, over 1424823.51 frames.], batch size: 25, lr: 2.78e-04
2022-05-15 13:15:20,652 INFO [train.py:812] (5/8) Epoch 28, batch 2150, loss[loss=0.1353, simple_loss=0.2157, pruned_loss=0.0275, over 7154.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2466, pruned_loss=0.03206, over 1423723.60 frames.], batch size: 17, lr: 2.78e-04
2022-05-15 13:16:19,134 INFO [train.py:812] (5/8) Epoch 28, batch 2200, loss[loss=0.1526, simple_loss=0.2435, pruned_loss=0.03086, over 7291.00 frames.], tot_loss[loss=0.1548, simple_loss=0.246, pruned_loss=0.03182, over 1422330.07 frames.], batch size: 24, lr: 2.78e-04
2022-05-15 13:17:18,234 INFO [train.py:812] (5/8) Epoch 28, batch 2250, loss[loss=0.146, simple_loss=0.2351, pruned_loss=0.02849, over 7329.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2455, pruned_loss=0.0317, over 1424571.81 frames.], batch size: 22, lr: 2.78e-04
2022-05-15 13:18:16,763 INFO [train.py:812] (5/8) Epoch 28, batch 2300, loss[loss=0.1661, simple_loss=0.2553, pruned_loss=0.03848, over 7147.00 frames.], tot_loss[loss=0.154, simple_loss=0.2451, pruned_loss=0.03148, over 1421693.56 frames.], batch size: 20, lr: 2.78e-04
2022-05-15 13:19:16,293 INFO [train.py:812] (5/8) Epoch 28, batch 2350, loss[loss=0.1474, simple_loss=0.2399, pruned_loss=0.02744, over 7164.00 frames.], tot_loss[loss=0.1547, simple_loss=0.246, pruned_loss=0.03174, over 1419384.37 frames.], batch size: 19, lr: 2.78e-04
2022-05-15 13:20:14,237 INFO [train.py:812] (5/8) Epoch 28, batch 2400, loss[loss=0.1653, simple_loss=0.256, pruned_loss=0.03731, over 7174.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2472, pruned_loss=0.03219, over 1422228.27 frames.], batch size: 23, lr: 2.78e-04
2022-05-15 13:21:14,080 INFO [train.py:812] (5/8) Epoch 28, batch 2450, loss[loss=0.1435, simple_loss=0.2438, pruned_loss=0.02157, over 6409.00 frames.], tot_loss[loss=0.1554, simple_loss=0.247, pruned_loss=0.03188, over 1423052.00 frames.], batch size: 37, lr: 2.78e-04
2022-05-15 13:22:13,016 INFO [train.py:812] (5/8) Epoch 28, batch 2500, loss[loss=0.1395, simple_loss=0.2273, pruned_loss=0.02582, over 6789.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2467, pruned_loss=0.03223, over 1420753.52 frames.], batch size: 15, lr: 2.78e-04
2022-05-15 13:23:12,417 INFO [train.py:812] (5/8) Epoch 28, batch 2550, loss[loss=0.1565, simple_loss=0.2562, pruned_loss=0.02838, over 7260.00 frames.], tot_loss[loss=0.1556, simple_loss=0.247, pruned_loss=0.03209, over 1420930.46 frames.], batch size: 19, lr: 2.78e-04
2022-05-15 13:24:10,657 INFO [train.py:812] (5/8) Epoch 28, batch 2600, loss[loss=0.1478, simple_loss=0.2484, pruned_loss=0.02361, over 7240.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2472, pruned_loss=0.03228, over 1420820.24 frames.], batch size: 20, lr: 2.78e-04
2022-05-15 13:25:09,878 INFO [train.py:812] (5/8) Epoch 28, batch 2650, loss[loss=0.1691, simple_loss=0.2562, pruned_loss=0.04098, over 7004.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2474, pruned_loss=0.03235, over 1419852.43 frames.], batch size: 16, lr: 2.78e-04
2022-05-15 13:26:08,936 INFO [train.py:812] (5/8) Epoch 28, batch 2700, loss[loss=0.1447, simple_loss=0.2379, pruned_loss=0.02577, over 7323.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2481, pruned_loss=0.03228, over 1421463.88 frames.], batch size: 21, lr: 2.78e-04
2022-05-15 13:27:07,537 INFO [train.py:812] (5/8) Epoch 28, batch 2750, loss[loss=0.1619, simple_loss=0.2585, pruned_loss=0.03262, over 7263.00 frames.], tot_loss[loss=0.156, simple_loss=0.2475, pruned_loss=0.0322, over 1419334.20 frames.], batch size: 19, lr: 2.78e-04
2022-05-15 13:28:05,902 INFO [train.py:812] (5/8) Epoch 28, batch 2800, loss[loss=0.1512, simple_loss=0.2555, pruned_loss=0.02347, over 7234.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2473, pruned_loss=0.03213, over 1415143.55 frames.], batch size: 20, lr: 2.77e-04
2022-05-15 13:29:05,087 INFO [train.py:812] (5/8) Epoch 28, batch 2850, loss[loss=0.1362, simple_loss=0.2156, pruned_loss=0.02843, over 7128.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2471, pruned_loss=0.03202, over 1420444.53 frames.], batch size: 17, lr: 2.77e-04
2022-05-15 13:30:03,004 INFO [train.py:812] (5/8) Epoch 28, batch 2900, loss[loss=0.1845, simple_loss=0.2845, pruned_loss=0.04222, over 7267.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2478, pruned_loss=0.03234, over 1418952.91 frames.], batch size: 25, lr: 2.77e-04
2022-05-15 13:31:01,402 INFO [train.py:812] (5/8) Epoch 28, batch 2950, loss[loss=0.1867, simple_loss=0.2767, pruned_loss=0.04836, over 7196.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2482, pruned_loss=0.03234, over 1422338.70 frames.], batch size: 23, lr: 2.77e-04
2022-05-15 13:32:00,614 INFO [train.py:812] (5/8) Epoch 28, batch 3000, loss[loss=0.1536, simple_loss=0.2511, pruned_loss=0.02804, over 7114.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2483, pruned_loss=0.03249, over 1424474.34 frames.], batch size: 28, lr: 2.77e-04
2022-05-15 13:32:00,614 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 13:32:08,090 INFO [train.py:841] (5/8) Epoch 28, validation: loss=0.1523, simple_loss=0.2496, pruned_loss=0.02748, over 698248.00 frames.
2022-05-15 13:33:05,903 INFO [train.py:812] (5/8) Epoch 28, batch 3050, loss[loss=0.1269, simple_loss=0.2191, pruned_loss=0.01731, over 7137.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2482, pruned_loss=0.0323, over 1426189.18 frames.], batch size: 17, lr: 2.77e-04
2022-05-15 13:34:04,095 INFO [train.py:812] (5/8) Epoch 28, batch 3100, loss[loss=0.1669, simple_loss=0.2673, pruned_loss=0.03323, over 7388.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2471, pruned_loss=0.03197, over 1425230.32 frames.], batch size: 23, lr: 2.77e-04
2022-05-15 13:35:03,611 INFO [train.py:812] (5/8) Epoch 28, batch 3150, loss[loss=0.1216, simple_loss=0.2042, pruned_loss=0.01947, over 7403.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2467, pruned_loss=0.03186, over 1423606.27 frames.], batch size: 18, lr: 2.77e-04
2022-05-15 13:36:02,626 INFO [train.py:812] (5/8) Epoch 28, batch 3200, loss[loss=0.1491, simple_loss=0.2418, pruned_loss=0.02817, over 7312.00 frames.], tot_loss[loss=0.155, simple_loss=0.2469, pruned_loss=0.03157, over 1424614.41 frames.], batch size: 21, lr: 2.77e-04
2022-05-15 13:37:02,640 INFO [train.py:812] (5/8) Epoch 28, batch 3250, loss[loss=0.1377, simple_loss=0.2255, pruned_loss=0.02497, over 7162.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2459, pruned_loss=0.03152, over 1424276.46 frames.], batch size: 18, lr: 2.77e-04
2022-05-15 13:37:59,648 INFO [train.py:812] (5/8) Epoch 28, batch 3300, loss[loss=0.1293, simple_loss=0.212, pruned_loss=0.02327, over 6990.00 frames.], tot_loss[loss=0.1543, simple_loss=0.246, pruned_loss=0.0313, over 1422808.75 frames.], batch size: 16, lr: 2.77e-04
2022-05-15 13:38:57,849 INFO [train.py:812] (5/8) Epoch 28, batch 3350, loss[loss=0.1738, simple_loss=0.2638, pruned_loss=0.04183, over 7362.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2461, pruned_loss=0.03165, over 1419711.05 frames.], batch size: 23, lr: 2.77e-04
2022-05-15 13:39:56,925 INFO [train.py:812] (5/8) Epoch 28, batch 3400, loss[loss=0.1642, simple_loss=0.2544, pruned_loss=0.03703, over 7330.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2467, pruned_loss=0.03201, over 1421722.29 frames.], batch size: 20, lr: 2.77e-04
2022-05-15 13:40:56,426 INFO [train.py:812] (5/8) Epoch 28, batch 3450, loss[loss=0.1611, simple_loss=0.2527, pruned_loss=0.03475, over 7205.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2472, pruned_loss=0.03219, over 1423057.59 frames.], batch size: 22, lr: 2.77e-04
2022-05-15 13:41:55,477 INFO [train.py:812] (5/8) Epoch 28, batch 3500, loss[loss=0.1399, simple_loss=0.2267, pruned_loss=0.02654, over 7453.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2467, pruned_loss=0.03192, over 1423043.18 frames.], batch size: 19, lr: 2.77e-04
2022-05-15 13:42:54,595 INFO [train.py:812] (5/8) Epoch 28, batch 3550, loss[loss=0.153, simple_loss=0.246, pruned_loss=0.03006, over 7328.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2469, pruned_loss=0.03174, over 1423550.19 frames.], batch size: 22, lr: 2.77e-04
2022-05-15 13:43:53,653 INFO [train.py:812] (5/8) Epoch 28, batch 3600, loss[loss=0.1515, simple_loss=0.2433, pruned_loss=0.02991, over 7058.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2473, pruned_loss=0.03169, over 1422639.39 frames.], batch size: 18, lr: 2.77e-04
2022-05-15 13:44:53,067 INFO [train.py:812] (5/8) Epoch 28, batch 3650, loss[loss=0.1754, simple_loss=0.2709, pruned_loss=0.03997, over 7403.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2468, pruned_loss=0.03168, over 1423080.23 frames.], batch size: 21, lr: 2.77e-04
2022-05-15 13:45:51,489 INFO [train.py:812] (5/8) Epoch 28, batch 3700, loss[loss=0.1616, simple_loss=0.2498, pruned_loss=0.03671, over 7432.00 frames.], tot_loss[loss=0.155, simple_loss=0.2467, pruned_loss=0.03169, over 1423341.52 frames.], batch size: 20, lr: 2.77e-04
2022-05-15 13:46:50,218 INFO [train.py:812] (5/8) Epoch 28, batch 3750, loss[loss=0.1758, simple_loss=0.2665, pruned_loss=0.04258, over 5049.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2466, pruned_loss=0.03184, over 1419113.30 frames.], batch size: 52, lr: 2.76e-04
2022-05-15 13:47:49,316 INFO [train.py:812] (5/8) Epoch 28, batch 3800, loss[loss=0.129, simple_loss=0.2132, pruned_loss=0.02233, over 7258.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2464, pruned_loss=0.03158, over 1421442.48 frames.], batch size: 17, lr: 2.76e-04
2022-05-15 13:48:48,420 INFO [train.py:812] (5/8) Epoch 28, batch 3850, loss[loss=0.1472, simple_loss=0.2508, pruned_loss=0.02179, over 7156.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2463, pruned_loss=0.03127, over 1425969.90 frames.], batch size: 19, lr: 2.76e-04
2022-05-15 13:49:47,454 INFO [train.py:812] (5/8) Epoch 28, batch 3900, loss[loss=0.162, simple_loss=0.2534, pruned_loss=0.03531, over 7211.00 frames.], tot_loss[loss=0.1543, simple_loss=0.246, pruned_loss=0.03131, over 1424830.33 frames.], batch size: 22, lr: 2.76e-04
2022-05-15 13:50:47,234 INFO [train.py:812] (5/8) Epoch 28, batch 3950, loss[loss=0.1637, simple_loss=0.2605, pruned_loss=0.03347, over 7201.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2457, pruned_loss=0.03107, over 1426167.25 frames.], batch size: 22, lr: 2.76e-04
2022-05-15 13:51:46,169 INFO [train.py:812] (5/8) Epoch 28, batch 4000, loss[loss=0.1759, simple_loss=0.2812, pruned_loss=0.03527, over 6870.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2451, pruned_loss=0.03105, over 1422788.10 frames.], batch size: 31, lr: 2.76e-04
2022-05-15 13:52:45,720 INFO [train.py:812] (5/8) Epoch 28, batch 4050, loss[loss=0.1852, simple_loss=0.266, pruned_loss=0.05224, over 5483.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2461, pruned_loss=0.03139, over 1416957.92 frames.], batch size: 52, lr: 2.76e-04
2022-05-15 13:53:44,809 INFO [train.py:812] (5/8) Epoch 28, batch 4100, loss[loss=0.1506, simple_loss=0.229, pruned_loss=0.03611, over 7150.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2458, pruned_loss=0.03165, over 1419254.50 frames.], batch size: 17, lr: 2.76e-04
2022-05-15 13:54:49,272 INFO [train.py:812] (5/8) Epoch 28, batch 4150, loss[loss=0.1522, simple_loss=0.2416, pruned_loss=0.03143, over 7150.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2462, pruned_loss=0.03194, over 1424358.59 frames.], batch size: 19, lr: 2.76e-04
2022-05-15 13:55:47,966 INFO [train.py:812] (5/8) Epoch 28, batch 4200, loss[loss=0.1832, simple_loss=0.2715, pruned_loss=0.0475, over 4858.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2474, pruned_loss=0.03222, over 1417974.06 frames.], batch size: 53, lr: 2.76e-04
2022-05-15 13:56:46,296 INFO [train.py:812] (5/8) Epoch 28, batch 4250, loss[loss=0.1355, simple_loss=0.229, pruned_loss=0.02096, over 7058.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2469, pruned_loss=0.03196, over 1415523.21 frames.], batch size: 18, lr: 2.76e-04
2022-05-15 13:57:45,184 INFO [train.py:812] (5/8) Epoch 28, batch 4300, loss[loss=0.1287, simple_loss=0.2142, pruned_loss=0.02158, over 7133.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2466, pruned_loss=0.03187, over 1416469.30 frames.], batch size: 17, lr: 2.76e-04
2022-05-15 13:58:44,137 INFO [train.py:812] (5/8) Epoch 28, batch 4350, loss[loss=0.166, simple_loss=0.2696, pruned_loss=0.03117, over 7214.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2469, pruned_loss=0.0317, over 1416271.61 frames.], batch size: 21, lr: 2.76e-04
2022-05-15 13:59:42,361 INFO [train.py:812] (5/8) Epoch 28, batch 4400, loss[loss=0.1513, simple_loss=0.2437, pruned_loss=0.02944, over 6305.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2466, pruned_loss=0.03138, over 1409030.91 frames.], batch size: 37, lr: 2.76e-04
2022-05-15 14:00:51,466 INFO [train.py:812] (5/8) Epoch 28, batch 4450, loss[loss=0.1351, simple_loss=0.2128, pruned_loss=0.02872, over 7221.00 frames.], tot_loss[loss=0.155, simple_loss=0.2466, pruned_loss=0.03173, over 1403907.43 frames.], batch size: 16, lr: 2.76e-04
2022-05-15 14:01:50,436 INFO [train.py:812] (5/8) Epoch 28, batch 4500, loss[loss=0.1416, simple_loss=0.2431, pruned_loss=0.02006, over 7226.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2474, pruned_loss=0.03197, over 1392079.09 frames.], batch size: 21, lr: 2.76e-04
2022-05-15 14:02:49,656 INFO [train.py:812] (5/8) Epoch 28, batch 4550, loss[loss=0.162, simple_loss=0.2649, pruned_loss=0.02958, over 6586.00 frames.], tot_loss[loss=0.1565, simple_loss=0.248, pruned_loss=0.03252, over 1361151.82 frames.], batch size: 38, lr: 2.76e-04
2022-05-15 14:04:01,612 INFO [train.py:812] (5/8) Epoch 29, batch 0, loss[loss=0.1546, simple_loss=0.2529, pruned_loss=0.02819, over 7004.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2529, pruned_loss=0.02819, over 7004.00 frames.], batch size: 28, lr: 2.71e-04
2022-05-15 14:05:00,861 INFO [train.py:812] (5/8) Epoch 29, batch 50, loss[loss=0.1549, simple_loss=0.2477, pruned_loss=0.03103, over 7291.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2487, pruned_loss=0.03172, over 324659.01 frames.], batch size: 24, lr: 2.71e-04
2022-05-15 14:05:59,920 INFO [train.py:812] (5/8) Epoch 29, batch 100, loss[loss=0.1578, simple_loss=0.2486, pruned_loss=0.03355, over 7328.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2486, pruned_loss=0.03213, over 570349.69 frames.], batch size: 21, lr: 2.71e-04
2022-05-15 14:06:58,557 INFO [train.py:812] (5/8) Epoch 29, batch 150, loss[loss=0.1475, simple_loss=0.2518, pruned_loss=0.02159, over 7228.00 frames.], tot_loss[loss=0.1564, simple_loss=0.249, pruned_loss=0.03195, over 760609.94 frames.], batch size: 20, lr: 2.71e-04
2022-05-15 14:07:56,897 INFO [train.py:812] (5/8) Epoch 29, batch 200, loss[loss=0.1453, simple_loss=0.2314, pruned_loss=0.02956, over 7067.00 frames.], tot_loss[loss=0.154, simple_loss=0.2463, pruned_loss=0.03085, over 909983.34 frames.], batch size: 18, lr: 2.71e-04
2022-05-15 14:08:56,150 INFO [train.py:812] (5/8) Epoch 29, batch 250, loss[loss=0.1636, simple_loss=0.2595, pruned_loss=0.03387, over 5267.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2468, pruned_loss=0.03088, over 1020487.28 frames.], batch size: 52, lr: 2.71e-04
2022-05-15 14:09:54,973 INFO [train.py:812] (5/8) Epoch 29, batch 300, loss[loss=0.1458, simple_loss=0.2356, pruned_loss=0.02803, over 7172.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2468, pruned_loss=0.03079, over 1110360.04 frames.], batch size: 18, lr: 2.70e-04
2022-05-15 14:10:53,105 INFO [train.py:812] (5/8) Epoch 29, batch 350, loss[loss=0.1448, simple_loss=0.2323, pruned_loss=0.02861, over 7067.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2471, pruned_loss=0.03068, over 1181530.99 frames.], batch size: 18, lr: 2.70e-04
2022-05-15 14:11:51,470 INFO [train.py:812] (5/8) Epoch 29, batch 400, loss[loss=0.1546, simple_loss=0.2413, pruned_loss=0.03401, over 7147.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2469, pruned_loss=0.03088, over 1237656.18 frames.], batch size: 20, lr: 2.70e-04
2022-05-15 14:12:49,828 INFO [train.py:812] (5/8) Epoch 29, batch 450, loss[loss=0.1613, simple_loss=0.2594, pruned_loss=0.03164, over 7128.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2468, pruned_loss=0.03123, over 1283304.76 frames.], batch size: 21, lr: 2.70e-04
2022-05-15 14:13:47,247 INFO [train.py:812] (5/8) Epoch 29, batch 500, loss[loss=0.1747, simple_loss=0.2795, pruned_loss=0.03493, over 5105.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2463, pruned_loss=0.0311, over 1310786.62 frames.], batch size: 52, lr: 2.70e-04
2022-05-15 14:14:46,075 INFO [train.py:812] (5/8) Epoch 29, batch 550, loss[loss=0.144, simple_loss=0.2422, pruned_loss=0.02295, over 7230.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2466, pruned_loss=0.03146, over 1332765.98 frames.], batch size: 21, lr: 2.70e-04
2022-05-15 14:15:44,266 INFO [train.py:812] (5/8) Epoch 29, batch 600, loss[loss=0.1245, simple_loss=0.2154, pruned_loss=0.01676, over 7261.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2458, pruned_loss=0.03124, over 1349451.10 frames.], batch size: 19, lr: 2.70e-04
2022-05-15 14:16:43,602 INFO [train.py:812] (5/8) Epoch 29, batch 650, loss[loss=0.1196, simple_loss=0.2151, pruned_loss=0.01201, over 7076.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2448, pruned_loss=0.03067, over 1367916.05 frames.], batch size: 18, lr: 2.70e-04
2022-05-15 14:17:43,339 INFO [train.py:812] (5/8) Epoch 29, batch 700, loss[loss=0.1709, simple_loss=0.2612, pruned_loss=0.04031, over 4940.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2453, pruned_loss=0.03112, over 1376045.98 frames.], batch size: 54, lr: 2.70e-04
2022-05-15 14:18:41,553 INFO [train.py:812] (5/8) Epoch 29, batch 750, loss[loss=0.1432, simple_loss=0.2398, pruned_loss=0.02337, over 7431.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2451, pruned_loss=0.03107, over 1382207.95 frames.], batch size: 20, lr: 2.70e-04
2022-05-15 14:19:40,278 INFO [train.py:812] (5/8) Epoch 29, batch 800, loss[loss=0.1655, simple_loss=0.2564, pruned_loss=0.03731, over 7120.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2457, pruned_loss=0.03128, over 1388180.58 frames.], batch size: 21, lr: 2.70e-04
2022-05-15 14:20:39,355 INFO [train.py:812] (5/8) Epoch 29, batch 850, loss[loss=0.1617, simple_loss=0.2532, pruned_loss=0.03507, over 6535.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2462, pruned_loss=0.03131, over 1392540.44 frames.], batch size: 38, lr: 2.70e-04
2022-05-15 14:21:38,037 INFO [train.py:812] (5/8) Epoch 29, batch 900, loss[loss=0.1612, simple_loss=0.2591, pruned_loss=0.0316, over 6787.00 frames.], tot_loss[loss=0.154, simple_loss=0.2459, pruned_loss=0.03106, over 1400067.88 frames.], batch size: 31, lr: 2.70e-04
2022-05-15 14:22:37,039 INFO [train.py:812] (5/8) Epoch 29, batch 950, loss[loss=0.1452, simple_loss=0.2373, pruned_loss=0.02652, over 7194.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2458, pruned_loss=0.03132, over 1409820.38 frames.], batch size: 22, lr: 2.70e-04
2022-05-15 14:23:36,644 INFO [train.py:812] (5/8) Epoch 29, batch 1000, loss[loss=0.1462, simple_loss=0.2291, pruned_loss=0.03161, over 6830.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2453, pruned_loss=0.03129, over 1415178.70 frames.], batch size: 15, lr: 2.70e-04
2022-05-15 14:24:36,126 INFO [train.py:812] (5/8) Epoch 29, batch 1050, loss[loss=0.1327, simple_loss=0.2349, pruned_loss=0.01521, over 7419.00 frames.], tot_loss[loss=0.154, simple_loss=0.2457, pruned_loss=0.03113, over 1420353.13 frames.], batch size: 21, lr: 2.70e-04
2022-05-15 14:25:35,352 INFO [train.py:812] (5/8) Epoch 29, batch 1100, loss[loss=0.1361, simple_loss=0.2208, pruned_loss=0.02574, over 7282.00 frames.], tot_loss[loss=0.154, simple_loss=0.2455, pruned_loss=0.0312, over 1422434.44 frames.], batch size: 17, lr: 2.70e-04
2022-05-15 14:26:34,875 INFO [train.py:812] (5/8) Epoch 29, batch 1150, loss[loss=0.1591, simple_loss=0.251, pruned_loss=0.03356, over 7060.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2461, pruned_loss=0.03145, over 1421213.19 frames.], batch size: 28, lr: 2.70e-04
2022-05-15 14:27:33,676 INFO [train.py:812] (5/8) Epoch 29, batch 1200, loss[loss=0.1638, simple_loss=0.2536, pruned_loss=0.03698, over 7053.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2467, pruned_loss=0.03104, over 1423564.86 frames.], batch size: 28, lr: 2.70e-04
2022-05-15 14:28:32,480 INFO [train.py:812] (5/8) Epoch 29, batch 1250, loss[loss=0.1644, simple_loss=0.2506, pruned_loss=0.03904, over 7225.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2464, pruned_loss=0.03111, over 1417512.91 frames.], batch size: 22, lr: 2.70e-04
2022-05-15 14:29:29,499 INFO [train.py:812] (5/8) Epoch 29, batch 1300, loss[loss=0.1449, simple_loss=0.2491, pruned_loss=0.02032, over 7153.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2461, pruned_loss=0.03126, over 1420478.88 frames.], batch size: 20, lr: 2.69e-04
2022-05-15 14:30:28,421 INFO [train.py:812] (5/8) Epoch 29, batch 1350, loss[loss=0.1781, simple_loss=0.2827, pruned_loss=0.03682, over 7105.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2457, pruned_loss=0.03101, over 1425653.28 frames.], batch size: 21, lr: 2.69e-04
2022-05-15 14:31:27,369 INFO [train.py:812] (5/8) Epoch 29, batch 1400, loss[loss=0.1473, simple_loss=0.2298, pruned_loss=0.03244, over 7282.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2455, pruned_loss=0.03104, over 1427958.33 frames.], batch size: 17, lr: 2.69e-04
2022-05-15 14:32:26,339 INFO [train.py:812] (5/8) Epoch 29, batch 1450, loss[loss=0.1467, simple_loss=0.2451, pruned_loss=0.02413, over 7284.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2458, pruned_loss=0.03125, over 1431571.46 frames.], batch size: 24, lr: 2.69e-04
2022-05-15 14:33:24,458 INFO [train.py:812] (5/8) Epoch 29, batch 1500, loss[loss=0.1427, simple_loss=0.2331, pruned_loss=0.02612, over 7327.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2464, pruned_loss=0.03164, over 1428514.17 frames.], batch size: 20, lr: 2.69e-04
2022-05-15 14:34:23,906 INFO [train.py:812] (5/8) Epoch 29, batch 1550, loss[loss=0.1691, simple_loss=0.2585, pruned_loss=0.0398, over 7224.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2465, pruned_loss=0.03154, over 1429899.60 frames.], batch size: 21, lr: 2.69e-04
2022-05-15 14:35:22,634 INFO [train.py:812] (5/8) Epoch 29, batch 1600, loss[loss=0.1499, simple_loss=0.2305, pruned_loss=0.03466, over 6822.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2468, pruned_loss=0.03168, over 1426500.58 frames.], batch size: 15, lr: 2.69e-04
2022-05-15 14:36:22,714 INFO [train.py:812] (5/8) Epoch 29, batch 1650, loss[loss=0.1234, simple_loss=0.2049, pruned_loss=0.02097, over 6736.00 frames.], tot_loss[loss=0.1544, simple_loss=0.246, pruned_loss=0.03144, over 1428174.65 frames.], batch size: 15, lr: 2.69e-04
2022-05-15 14:37:22,108 INFO [train.py:812] (5/8) Epoch 29, batch 1700, loss[loss=0.133, simple_loss=0.2239, pruned_loss=0.02112, over 7269.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2449, pruned_loss=0.03062, over 1431023.58 frames.], batch size: 19, lr: 2.69e-04
2022-05-15 14:38:21,726 INFO [train.py:812] (5/8) Epoch 29, batch 1750, loss[loss=0.1552, simple_loss=0.2523, pruned_loss=0.029, over 7109.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2452, pruned_loss=0.03079, over 1433292.00 frames.], batch size: 21, lr: 2.69e-04
2022-05-15 14:39:20,837 INFO [train.py:812] (5/8) Epoch 29, batch 1800, loss[loss=0.1519, simple_loss=0.2335, pruned_loss=0.03518, over 7003.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2448, pruned_loss=0.03086, over 1423216.27 frames.], batch size: 16, lr: 2.69e-04
2022-05-15 14:40:20,271 INFO [train.py:812] (5/8) Epoch 29, batch 1850, loss[loss=0.1416, simple_loss=0.2341, pruned_loss=0.02456, over 7419.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2451, pruned_loss=0.03095, over 1425482.36 frames.], batch size: 18, lr: 2.69e-04
2022-05-15 14:41:18,729 INFO [train.py:812] (5/8) Epoch 29, batch 1900, loss[loss=0.1887, simple_loss=0.2715, pruned_loss=0.05298, over 7190.00 frames.], tot_loss[loss=0.1534, simple_loss=0.245, pruned_loss=0.03091, over 1425992.47 frames.], batch size: 26, lr: 2.69e-04
2022-05-15 14:42:17,743 INFO [train.py:812] (5/8) Epoch 29, batch 1950, loss[loss=0.1701, simple_loss=0.2668, pruned_loss=0.03675, over 7271.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2449, pruned_loss=0.03085, over 1428528.59 frames.], batch size: 25, lr: 2.69e-04
2022-05-15 14:43:16,656 INFO [train.py:812] (5/8) Epoch 29, batch 2000, loss[loss=0.1487, simple_loss=0.2434, pruned_loss=0.02703, over 7196.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2443, pruned_loss=0.03049, over 1431806.92 frames.], batch size: 23, lr: 2.69e-04
2022-05-15 14:44:14,137 INFO [train.py:812] (5/8) Epoch 29, batch 2050, loss[loss=0.1407, simple_loss=0.2366, pruned_loss=0.02237, over 7317.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2447, pruned_loss=0.03071, over 1424932.16 frames.], batch size: 21, lr: 2.69e-04
2022-05-15 14:45:11,936 INFO [train.py:812] (5/8) Epoch 29, batch 2100, loss[loss=0.1406, simple_loss=0.2411, pruned_loss=0.02005, over 7288.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2434, pruned_loss=0.03037, over 1426046.03 frames.], batch size: 25, lr: 2.69e-04
2022-05-15 14:46:11,704 INFO [train.py:812] (5/8) Epoch 29, batch 2150, loss[loss=0.1653, simple_loss=0.2633, pruned_loss=0.03368, over 7229.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2438, pruned_loss=0.02993, over 1427077.07 frames.], batch size: 21, lr: 2.69e-04
2022-05-15 14:47:09,925 INFO [train.py:812] (5/8) Epoch 29, batch 2200, loss[loss=0.1664, simple_loss=0.2639, pruned_loss=0.03451, over 7283.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2443, pruned_loss=0.03035, over 1421535.32 frames.], batch size: 25, lr: 2.69e-04
2022-05-15 14:48:08,314 INFO [train.py:812] (5/8) Epoch 29, batch 2250, loss[loss=0.1639, simple_loss=0.2657, pruned_loss=0.03105, over 7115.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2452, pruned_loss=0.03063, over 1425635.36 frames.], batch size: 21, lr: 2.68e-04
2022-05-15 14:49:05,767 INFO [train.py:812] (5/8) Epoch 29, batch 2300, loss[loss=0.1808, simple_loss=0.2708, pruned_loss=0.04544, over 7287.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2449, pruned_loss=0.03088, over 1427743.55 frames.], batch size: 24, lr: 2.68e-04
2022-05-15 14:50:03,885 INFO [train.py:812] (5/8) Epoch 29, batch 2350, loss[loss=0.153, simple_loss=0.2434, pruned_loss=0.03129, over 7070.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2451, pruned_loss=0.0313, over 1425164.58 frames.], batch size: 18, lr: 2.68e-04
2022-05-15 14:51:02,203 INFO [train.py:812] (5/8) Epoch 29, batch 2400, loss[loss=0.1498, simple_loss=0.2405, pruned_loss=0.0295, over 7357.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2442, pruned_loss=0.03098, over 1426665.18 frames.], batch size: 19, lr: 2.68e-04
2022-05-15 14:51:59,577 INFO [train.py:812] (5/8) Epoch 29, batch 2450, loss[loss=0.1535, simple_loss=0.2482, pruned_loss=0.02934, over 7115.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2452, pruned_loss=0.03149, over 1417437.17 frames.], batch size: 21, lr: 2.68e-04
2022-05-15 14:52:57,611 INFO [train.py:812] (5/8) Epoch 29, batch 2500, loss[loss=0.1297, simple_loss=0.2199, pruned_loss=0.01979, over 7411.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2445, pruned_loss=0.03136, over 1420780.35 frames.], batch size: 18, lr: 2.68e-04
2022-05-15 14:53:56,700 INFO [train.py:812] (5/8) Epoch 29, batch 2550, loss[loss=0.1325, simple_loss=0.2204, pruned_loss=0.02234, over 7170.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2444, pruned_loss=0.03149, over 1418546.66 frames.], batch size: 18, lr: 2.68e-04
2022-05-15 14:54:55,377 INFO [train.py:812] (5/8) Epoch 29, batch 2600, loss[loss=0.1601, simple_loss=0.2593, pruned_loss=0.03045, over 7201.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2445, pruned_loss=0.03132, over 1415793.05 frames.], batch size: 23, lr: 2.68e-04
2022-05-15 14:56:04,284 INFO [train.py:812] (5/8) Epoch 29, batch 2650, loss[loss=0.1331, simple_loss=0.2208, pruned_loss=0.02268, over 7420.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2437, pruned_loss=0.03075, over 1418741.05 frames.], batch size: 18, lr: 2.68e-04
2022-05-15 14:57:02,551 INFO [train.py:812] (5/8) Epoch 29, batch 2700, loss[loss=0.1606, simple_loss=0.2507, pruned_loss=0.03522, over 4714.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2431, pruned_loss=0.03051, over 1418025.52 frames.], batch size: 52, lr: 2.68e-04
2022-05-15 14:58:00,029 INFO [train.py:812] (5/8) Epoch 29, batch 2750, loss[loss=0.1461, simple_loss=0.2405, pruned_loss=0.0258, over 7315.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2438, pruned_loss=0.03086, over 1414554.22 frames.], batch size: 21, lr: 2.68e-04
2022-05-15 14:59:07,969 INFO [train.py:812] (5/8) Epoch 29, batch 2800, loss[loss=0.1548, simple_loss=0.2452, pruned_loss=0.0322, over 7323.00 frames.], tot_loss[loss=0.1527, simple_loss=0.244, pruned_loss=0.03068, over 1417503.98 frames.], batch size: 22, lr: 2.68e-04
2022-05-15 15:00:06,411 INFO [train.py:812] (5/8) Epoch 29, batch 2850, loss[loss=0.1704, simple_loss=0.2581, pruned_loss=0.04138, over 7251.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2435, pruned_loss=0.03073, over 1418029.57 frames.], batch size: 19, lr: 2.68e-04
2022-05-15 15:01:14,249 INFO [train.py:812] (5/8) Epoch 29, batch 2900, loss[loss=0.1424, simple_loss=0.2252, pruned_loss=0.02981, over 7281.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2435, pruned_loss=0.03044, over 1416782.53 frames.], batch size: 17, lr: 2.68e-04
2022-05-15 15:02:42,658 INFO [train.py:812] (5/8) Epoch 29, batch 2950, loss[loss=0.1203, simple_loss=0.2087, pruned_loss=0.01595, over 7131.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2427, pruned_loss=0.03053, over 1417147.58 frames.], batch size: 17, lr: 2.68e-04
2022-05-15 15:03:40,335 INFO [train.py:812] (5/8) Epoch 29, batch 3000, loss[loss=0.1441, simple_loss=0.2436, pruned_loss=0.02231, over 7236.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2443, pruned_loss=0.031, over 1418073.94 frames.], batch size: 20, lr: 2.68e-04
2022-05-15 15:03:40,336 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 15:03:47,851 INFO [train.py:841] (5/8) Epoch 29, validation: loss=0.153, simple_loss=0.2498, pruned_loss=0.02809, over 698248.00 frames.
2022-05-15 15:04:46,861 INFO [train.py:812] (5/8) Epoch 29, batch 3050, loss[loss=0.1654, simple_loss=0.2489, pruned_loss=0.04091, over 7159.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2442, pruned_loss=0.03111, over 1421113.92 frames.], batch size: 18, lr: 2.68e-04
2022-05-15 15:05:54,598 INFO [train.py:812] (5/8) Epoch 29, batch 3100, loss[loss=0.1544, simple_loss=0.2317, pruned_loss=0.03851, over 7270.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2435, pruned_loss=0.03079, over 1417682.71 frames.], batch size: 18, lr: 2.68e-04
2022-05-15 15:06:53,590 INFO [train.py:812] (5/8) Epoch 29, batch 3150, loss[loss=0.1772, simple_loss=0.2781, pruned_loss=0.03816, over 7222.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2441, pruned_loss=0.0307, over 1422005.10 frames.], batch size: 21, lr: 2.68e-04
2022-05-15 15:07:52,384 INFO [train.py:812] (5/8) Epoch 29, batch 3200, loss[loss=0.1556, simple_loss=0.2478, pruned_loss=0.03171, over 7116.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2448, pruned_loss=0.03052, over 1421571.13 frames.], batch size: 21, lr: 2.68e-04
2022-05-15 15:08:52,059 INFO [train.py:812] (5/8) Epoch 29, batch 3250, loss[loss=0.1474, simple_loss=0.2325, pruned_loss=0.03117, over 6829.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2447, pruned_loss=0.03072, over 1421294.74 frames.], batch size: 15, lr: 2.67e-04
2022-05-15 15:09:50,369 INFO [train.py:812] (5/8) Epoch 29, batch 3300, loss[loss=0.1397, simple_loss=0.2426, pruned_loss=0.01841, over 7219.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2467, pruned_loss=0.03112, over 1421117.14 frames.], batch size: 21, lr: 2.67e-04
2022-05-15 15:10:48,349 INFO [train.py:812] (5/8) Epoch 29, batch 3350, loss[loss=0.1768, simple_loss=0.261, pruned_loss=0.04624, over 7049.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2466, pruned_loss=0.03156, over 1419053.87 frames.], batch size: 28, lr: 2.67e-04
2022-05-15 15:11:47,264 INFO [train.py:812] (5/8) Epoch 29, batch 3400, loss[loss=0.1383, simple_loss=0.2221, pruned_loss=0.02726, over 7065.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2464, pruned_loss=0.03188, over 1417584.69 frames.], batch size: 18, lr: 2.67e-04
2022-05-15 15:12:46,976 INFO [train.py:812] (5/8) Epoch 29, batch 3450, loss[loss=0.1427, simple_loss=0.2234, pruned_loss=0.03095, over 7282.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2461, pruned_loss=0.03187, over 1420696.51 frames.], batch size: 17, lr: 2.67e-04
2022-05-15 15:13:45,904 INFO [train.py:812] (5/8) Epoch 29, batch 3500, loss[loss=0.1588, simple_loss=0.2542, pruned_loss=0.0317, over 6861.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2459, pruned_loss=0.03148, over 1420206.90 frames.], batch size: 31, lr: 2.67e-04
2022-05-15 15:14:51,715 INFO [train.py:812] (5/8) Epoch 29, batch 3550, loss[loss=0.1309, simple_loss=0.2172, pruned_loss=0.02224, over 7284.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2447, pruned_loss=0.03084, over 1423080.73 frames.], batch size: 18, lr: 2.67e-04
2022-05-15 15:15:51,038 INFO [train.py:812] (5/8) Epoch 29, batch 3600, loss[loss=0.1236, simple_loss=0.2099, pruned_loss=0.01867, over 6770.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2449, pruned_loss=0.03103, over 1422990.34 frames.], batch size: 15, lr: 2.67e-04
2022-05-15 15:16:50,745 INFO [train.py:812] (5/8) Epoch 29, batch 3650, loss[loss=0.1459, simple_loss=0.2374, pruned_loss=0.0272, over 7338.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2449, pruned_loss=0.03084, over 1426548.76 frames.], batch size: 22, lr: 2.67e-04
2022-05-15 15:17:49,905 INFO [train.py:812] (5/8) Epoch 29, batch 3700, loss[loss=0.1748, simple_loss=0.2619, pruned_loss=0.04385, over 7186.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2454, pruned_loss=0.03118, over 1426678.13 frames.], batch size: 23, lr: 2.67e-04
2022-05-15 15:18:49,039 INFO [train.py:812] (5/8) Epoch 29, batch 3750, loss[loss=0.1898, simple_loss=0.2798, pruned_loss=0.04985, over 4847.00 frames.], tot_loss[loss=0.154, simple_loss=0.2459, pruned_loss=0.03107, over 1426638.20 frames.], batch size: 52, lr: 2.67e-04
2022-05-15 15:19:48,065 INFO [train.py:812] (5/8) Epoch 29, batch 3800, loss[loss=0.1364, simple_loss=0.2325, pruned_loss=0.02016, over 7422.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2462, pruned_loss=0.03101, over 1427003.05 frames.], batch size: 20, lr: 2.67e-04
2022-05-15 15:20:46,935 INFO [train.py:812] (5/8) Epoch 29, batch 3850, loss[loss=0.1716, simple_loss=0.2578, pruned_loss=0.04267, over 7360.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2463, pruned_loss=0.03116, over 1427841.70 frames.], batch size: 23, lr: 2.67e-04
2022-05-15 15:21:45,033 INFO [train.py:812] (5/8) Epoch 29, batch 3900, loss[loss=0.167, simple_loss=0.2558, pruned_loss=0.0391, over 7270.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2467, pruned_loss=0.03127, over 1430519.18 frames.], batch size: 24, lr: 2.67e-04
2022-05-15 15:22:44,235 INFO [train.py:812] (5/8) Epoch 29, batch 3950, loss[loss=0.1391, simple_loss=0.2186, pruned_loss=0.02984, over 7435.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2476, pruned_loss=0.03138, over 1431388.53 frames.], batch size: 18, lr: 2.67e-04
2022-05-15 15:23:43,014 INFO [train.py:812] (5/8) Epoch 29, batch 4000, loss[loss=0.144, simple_loss=0.2323, pruned_loss=0.02791, over 7352.00 frames.], tot_loss[loss=0.155, simple_loss=0.2473, pruned_loss=0.03137, over 1430592.61 frames.], batch size: 22, lr: 2.67e-04
2022-05-15 15:24:42,292 INFO [train.py:812] (5/8) Epoch 29, batch 4050, loss[loss=0.1378, simple_loss=0.2291, pruned_loss=0.02323, over 7281.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2476, pruned_loss=0.03159, over 1429709.93 frames.], batch size: 17, lr: 2.67e-04
2022-05-15 15:25:41,042 INFO [train.py:812] (5/8) Epoch 29, batch 4100, loss[loss=0.1501, simple_loss=0.2468, pruned_loss=0.02665, over 7326.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2473, pruned_loss=0.03124, over 1430432.07 frames.], batch size: 22, lr: 2.67e-04
2022-05-15 15:26:40,398 INFO [train.py:812] (5/8) Epoch 29, batch 4150, loss[loss=0.1498, simple_loss=0.2484, pruned_loss=0.02557, over 7325.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2463, pruned_loss=0.03097, over 1423972.87 frames.], batch size: 21, lr: 2.67e-04
2022-05-15 15:27:39,246 INFO [train.py:812] (5/8) Epoch 29, batch 4200, loss[loss=0.1208, simple_loss=0.2102, pruned_loss=0.01568, over 7273.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2468, pruned_loss=0.03094, over 1421401.62 frames.], batch size: 19, lr: 2.66e-04
2022-05-15 15:28:38,673 INFO [train.py:812] (5/8) Epoch 29, batch 4250, loss[loss=0.1691, simple_loss=0.2612, pruned_loss=0.0385, over 6813.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2457, pruned_loss=0.03065, over 1422292.71 frames.], batch size: 31, lr: 2.66e-04
2022-05-15 15:29:36,728 INFO [train.py:812] (5/8) Epoch 29, batch 4300, loss[loss=0.1296, simple_loss=0.2143, pruned_loss=0.02244, over 7159.00 frames.], tot_loss[loss=0.153, simple_loss=0.2452, pruned_loss=0.03037, over 1417964.60 frames.], batch size: 18, lr: 2.66e-04
2022-05-15 15:30:35,684 INFO [train.py:812] (5/8) Epoch 29, batch 4350, loss[loss=0.1425, simple_loss=0.2458, pruned_loss=0.01956, over 7320.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2442, pruned_loss=0.03005, over 1419462.04 frames.], batch size: 21, lr: 2.66e-04
2022-05-15 15:31:34,532 INFO [train.py:812] (5/8) Epoch 29, batch 4400, loss[loss=0.1837, simple_loss=0.2814, pruned_loss=0.043, over 7280.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2452, pruned_loss=0.03053, over 1411470.53 frames.], batch size: 24, lr: 2.66e-04
2022-05-15 15:32:33,463 INFO [train.py:812] (5/8) Epoch 29, batch 4450, loss[loss=0.144, simple_loss=0.2437, pruned_loss=0.02212, over 6503.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2453, pruned_loss=0.03091, over 1402852.70 frames.], batch size: 38, lr: 2.66e-04
2022-05-15 15:33:31,922 INFO [train.py:812] (5/8) Epoch 29, batch 4500, loss[loss=0.2087, simple_loss=0.3003, pruned_loss=0.05852, over 7226.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2468, pruned_loss=0.03167, over 1379566.88 frames.], batch size: 22, lr: 2.66e-04
2022-05-15 15:34:29,702 INFO [train.py:812] (5/8) Epoch 29, batch 4550, loss[loss=0.1962, simple_loss=0.2847, pruned_loss=0.05386, over 4989.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2487, pruned_loss=0.03238, over 1361986.30 frames.], batch size: 52, lr: 2.66e-04
2022-05-15 15:35:40,748 INFO [train.py:812] (5/8) Epoch 30, batch 0, loss[loss=0.1344, simple_loss=0.2345, pruned_loss=0.01719, over 7337.00 frames.], tot_loss[loss=0.1344, simple_loss=0.2345, pruned_loss=0.01719, over 7337.00 frames.], batch size: 20, lr: 2.62e-04
2022-05-15 15:36:39,952 INFO [train.py:812] (5/8) Epoch 30, batch 50, loss[loss=0.1376, simple_loss=0.2307, pruned_loss=0.02223, over 7292.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2465, pruned_loss=0.03003, over 323660.90 frames.], batch size: 18, lr: 2.62e-04
2022-05-15 15:37:39,029 INFO [train.py:812] (5/8) Epoch 30, batch 100, loss[loss=0.1377, simple_loss=0.2317, pruned_loss=0.02185, over 7287.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2452, pruned_loss=0.03075, over 571661.28 frames.], batch size: 17, lr: 2.62e-04
2022-05-15 15:38:38,752 INFO [train.py:812] (5/8) Epoch 30, batch 150, loss[loss=0.1515, simple_loss=0.2477, pruned_loss=0.02769, over 7283.00 frames.], tot_loss[loss=0.1541, simple_loss=0.246, pruned_loss=0.03106, over 749404.22 frames.], batch size: 24, lr: 2.62e-04
2022-05-15 15:39:36,261 INFO [train.py:812] (5/8) Epoch 30, batch 200, loss[loss=0.1487, simple_loss=0.2361, pruned_loss=0.03069, over 7357.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2465, pruned_loss=0.03156, over 899969.57 frames.], batch size: 19, lr: 2.61e-04
2022-05-15 15:40:35,793 INFO [train.py:812] (5/8) Epoch 30, batch 250, loss[loss=0.1345, simple_loss=0.2184, pruned_loss=0.02529, over 6818.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2468, pruned_loss=0.03141, over 1015752.63 frames.], batch size: 15, lr: 2.61e-04
2022-05-15 15:41:34,895 INFO [train.py:812] (5/8) Epoch 30, batch 300, loss[loss=0.1635, simple_loss=0.2501, pruned_loss=0.03842, over 7264.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2475, pruned_loss=0.03168, over 1107162.50 frames.], batch size: 18, lr: 2.61e-04
2022-05-15 15:42:33,923 INFO [train.py:812] (5/8) Epoch 30, batch 350, loss[loss=0.1544, simple_loss=0.2499, pruned_loss=0.02945, over 7331.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2459, pruned_loss=0.03154, over 1180521.54 frames.], batch size: 20, lr: 2.61e-04
2022-05-15 15:43:32,151 INFO [train.py:812] (5/8) Epoch 30, batch 400, loss[loss=0.1717, simple_loss=0.2631, pruned_loss=0.04013, over 7300.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2458, pruned_loss=0.03131, over 1236573.97 frames.], batch size: 24, lr: 2.61e-04
2022-05-15 15:44:30,884 INFO [train.py:812] (5/8) Epoch 30, batch 450, loss[loss=0.1449, simple_loss=0.2482, pruned_loss=0.02083, over 7404.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2447, pruned_loss=0.03114, over 1279296.51 frames.], batch size: 21, lr: 2.61e-04
2022-05-15 15:45:28,625 INFO [train.py:812] (5/8) Epoch 30, batch 500, loss[loss=0.1401, simple_loss=0.2332, pruned_loss=0.02355, over 7314.00 frames.], tot_loss[loss=0.1536, simple_loss=0.245, pruned_loss=0.03113, over 1307687.12 frames.], batch size: 20, lr: 2.61e-04
2022-05-15 15:46:27,331 INFO [train.py:812] (5/8) Epoch 30, batch 550, loss[loss=0.1515, simple_loss=0.2493, pruned_loss=0.0268, over 7289.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2454, pruned_loss=0.03099, over 1335826.89 frames.], batch size: 24, lr: 2.61e-04
2022-05-15 15:47:24,857 INFO [train.py:812] (5/8) Epoch 30, batch 600, loss[loss=0.1666, simple_loss=0.2546, pruned_loss=0.03925, over 7201.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2456, pruned_loss=0.03146, over 1352012.52 frames.], batch size: 22, lr: 2.61e-04
2022-05-15 15:48:22,462 INFO [train.py:812] (5/8) Epoch 30, batch 650, loss[loss=0.1486, simple_loss=0.2372, pruned_loss=0.02996, over 7058.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2458, pruned_loss=0.03165, over 1366955.25 frames.], batch size: 18, lr: 2.61e-04
2022-05-15 15:49:20,303 INFO [train.py:812] (5/8) Epoch 30, batch 700, loss[loss=0.1605, simple_loss=0.2512, pruned_loss=0.03489, over 7320.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2456, pruned_loss=0.0314, over 1375809.55 frames.], batch size: 20, lr: 2.61e-04
2022-05-15 15:50:18,861 INFO [train.py:812] (5/8) Epoch 30, batch 750, loss[loss=0.16, simple_loss=0.2568, pruned_loss=0.03158, over 7238.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2465, pruned_loss=0.03159, over 1382713.49 frames.], batch size: 20, lr: 2.61e-04
2022-05-15 15:51:17,426 INFO [train.py:812] (5/8) Epoch 30, batch 800, loss[loss=0.1355, simple_loss=0.2262, pruned_loss=0.0224, over 7332.00 frames.], tot_loss[loss=0.154, simple_loss=0.2455, pruned_loss=0.03125, over 1388650.88 frames.], batch size: 22, lr: 2.61e-04
2022-05-15 15:52:16,528 INFO [train.py:812] (5/8) Epoch 30, batch 850, loss[loss=0.1427, simple_loss=0.23, pruned_loss=0.0277, over 7062.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2447, pruned_loss=0.0311, over 1397806.94 frames.], batch size: 18, lr: 2.61e-04
2022-05-15 15:53:14,182 INFO [train.py:812] (5/8) Epoch 30, batch 900, loss[loss=0.1702, simple_loss=0.2621, pruned_loss=0.03915, over 7220.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2443, pruned_loss=0.03093, over 1401643.82 frames.], batch size: 21, lr: 2.61e-04
2022-05-15 15:54:13,184 INFO [train.py:812] (5/8) Epoch 30, batch 950, loss[loss=0.1497, simple_loss=0.2457, pruned_loss=0.02685, over 7107.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2453, pruned_loss=0.03121, over 1407461.76 frames.], batch size: 21, lr: 2.61e-04
2022-05-15 15:55:11,650 INFO [train.py:812] (5/8) Epoch 30, batch 1000, loss[loss=0.1624, simple_loss=0.2516, pruned_loss=0.03664, over 7149.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2464, pruned_loss=0.03149, over 1411344.24 frames.], batch size: 20, lr: 2.61e-04
2022-05-15 15:56:10,064 INFO [train.py:812] (5/8) Epoch 30, batch 1050, loss[loss=0.1267, simple_loss=0.2127, pruned_loss=0.0203, over 7289.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2461, pruned_loss=0.03153, over 1407759.69 frames.], batch size: 18, lr: 2.61e-04
2022-05-15 15:57:08,272 INFO [train.py:812] (5/8) Epoch 30, batch 1100, loss[loss=0.1487, simple_loss=0.2524, pruned_loss=0.02251, over 7335.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2476, pruned_loss=0.03185, over 1416903.76 frames.], batch size: 21, lr: 2.61e-04
2022-05-15 15:58:07,687 INFO [train.py:812] (5/8) Epoch 30, batch 1150, loss[loss=0.1387, simple_loss=0.22, pruned_loss=0.02871, over 6999.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2474, pruned_loss=0.03154, over 1417393.38 frames.], batch size: 16, lr: 2.61e-04
2022-05-15 15:59:06,096 INFO [train.py:812] (5/8) Epoch 30, batch 1200, loss[loss=0.1343, simple_loss=0.2203, pruned_loss=0.02415, over 7164.00 frames.], tot_loss[loss=0.1547, simple_loss=0.247, pruned_loss=0.03118, over 1422479.47 frames.], batch size: 19, lr: 2.61e-04
2022-05-15 16:00:14,951 INFO [train.py:812] (5/8) Epoch 30, batch 1250, loss[loss=0.1807, simple_loss=0.2752, pruned_loss=0.04312, over 5097.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2467, pruned_loss=0.03121, over 1417635.21 frames.], batch size: 52, lr: 2.60e-04
2022-05-15 16:01:13,722 INFO [train.py:812] (5/8) Epoch 30, batch 1300, loss[loss=0.1468, simple_loss=0.2424, pruned_loss=0.02563, over 7335.00 frames.], tot_loss[loss=0.154, simple_loss=0.2462, pruned_loss=0.03087, over 1418658.47 frames.], batch size: 22, lr: 2.60e-04
2022-05-15 16:02:13,352 INFO [train.py:812] (5/8) Epoch 30, batch 1350, loss[loss=0.1637, simple_loss=0.2629, pruned_loss=0.03225, over 6278.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2463, pruned_loss=0.03133, over 1419102.14 frames.], batch size: 37, lr: 2.60e-04
2022-05-15 16:03:12,427 INFO [train.py:812] (5/8) Epoch 30, batch 1400, loss[loss=0.1307, simple_loss=0.2137, pruned_loss=0.02382, over 6764.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2451, pruned_loss=0.03106, over 1419573.60 frames.], batch size: 15, lr: 2.60e-04
2022-05-15 16:04:10,802 INFO [train.py:812] (5/8) Epoch 30, batch 1450, loss[loss=0.1481, simple_loss=0.2414, pruned_loss=0.02744, over 7453.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2448, pruned_loss=0.03091, over 1419152.60 frames.], batch size: 22, lr: 2.60e-04
2022-05-15 16:05:09,032 INFO [train.py:812] (5/8) Epoch 30, batch 1500, loss[loss=0.1438, simple_loss=0.2431, pruned_loss=0.02221, over 7256.00 frames.], tot_loss[loss=0.1535, simple_loss=0.245, pruned_loss=0.03098, over 1417641.94 frames.], batch size: 19, lr: 2.60e-04
2022-05-15 16:06:06,397 INFO [train.py:812] (5/8) Epoch 30, batch 1550, loss[loss=0.1692, simple_loss=0.2623, pruned_loss=0.03806, over 7214.00 frames.], tot_loss[loss=0.1532, simple_loss=0.245, pruned_loss=0.0307, over 1418378.41 frames.], batch size: 23, lr: 2.60e-04
2022-05-15 16:07:03,132 INFO [train.py:812] (5/8) Epoch 30, batch 1600, loss[loss=0.1475, simple_loss=0.2498, pruned_loss=0.0226, over 7326.00 frames.], tot_loss[loss=0.154, simple_loss=0.2461, pruned_loss=0.03096, over 1419643.22 frames.], batch size: 21, lr: 2.60e-04
2022-05-15 16:08:02,728 INFO [train.py:812] (5/8) Epoch 30, batch 1650, loss[loss=0.1657, simple_loss=0.2595, pruned_loss=0.03595, over 7166.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2459, pruned_loss=0.03062, over 1423517.47 frames.], batch size: 26, lr: 2.60e-04
2022-05-15 16:09:00,129 INFO [train.py:812] (5/8) Epoch 30, batch 1700, loss[loss=0.1631, simple_loss=0.2528, pruned_loss=0.03668, over 7147.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2468, pruned_loss=0.03103, over 1426523.39 frames.], batch size: 17, lr: 2.60e-04
2022-05-15 16:09:58,742 INFO [train.py:812] (5/8) Epoch 30, batch 1750, loss[loss=0.1481, simple_loss=0.246, pruned_loss=0.02513, over 7147.00 frames.], tot_loss[loss=0.1535, simple_loss=0.246, pruned_loss=0.03054, over 1423688.57 frames.], batch size: 20, lr: 2.60e-04
2022-05-15 16:10:56,925 INFO [train.py:812] (5/8) Epoch 30, batch 1800, loss[loss=0.2088, simple_loss=0.2968, pruned_loss=0.06043, over 5172.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2457, pruned_loss=0.03064, over 1421262.69 frames.], batch size: 53, lr: 2.60e-04
2022-05-15 16:11:55,124 INFO [train.py:812] (5/8) Epoch 30, batch 1850, loss[loss=0.1696, simple_loss=0.2594, pruned_loss=0.03992, over 7116.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2447, pruned_loss=0.0302, over 1425025.96 frames.], batch size: 21, lr: 2.60e-04
2022-05-15 16:12:53,266 INFO [train.py:812] (5/8) Epoch 30, batch 1900, loss[loss=0.1502, simple_loss=0.2297, pruned_loss=0.03533, over 7250.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2453, pruned_loss=0.03082, over 1427599.46 frames.], batch size: 16, lr: 2.60e-04
2022-05-15 16:13:52,779 INFO [train.py:812] (5/8) Epoch 30, batch 1950, loss[loss=0.1463, simple_loss=0.2293, pruned_loss=0.03171, over 7256.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2453, pruned_loss=0.03096, over 1428905.50 frames.], batch size: 17, lr: 2.60e-04
2022-05-15 16:14:51,455 INFO [train.py:812] (5/8) Epoch 30, batch 2000, loss[loss=0.1383, simple_loss=0.2208, pruned_loss=0.02791, over 7332.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2451, pruned_loss=0.03078, over 1430104.46 frames.], batch size: 22, lr: 2.60e-04
2022-05-15 16:15:50,898 INFO [train.py:812] (5/8) Epoch 30, batch 2050, loss[loss=0.1943, simple_loss=0.2756, pruned_loss=0.05648, over 7204.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2453, pruned_loss=0.03077, over 1430383.05 frames.], batch size: 23, lr: 2.60e-04
2022-05-15 16:16:49,922 INFO [train.py:812] (5/8) Epoch 30, batch 2100, loss[loss=0.1374, simple_loss=0.2324, pruned_loss=0.02126, over 7145.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2452, pruned_loss=0.03106, over 1429155.41 frames.], batch size: 20, lr: 2.60e-04
2022-05-15 16:17:48,122 INFO [train.py:812] (5/8) Epoch 30, batch 2150, loss[loss=0.1542, simple_loss=0.2385, pruned_loss=0.03494, over 7122.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2453, pruned_loss=0.0308, over 1428162.38 frames.], batch size: 17, lr: 2.60e-04
2022-05-15 16:18:47,071 INFO [train.py:812] (5/8) Epoch 30, batch 2200, loss[loss=0.1537, simple_loss=0.2585, pruned_loss=0.02449, over 7279.00 frames.], tot_loss[loss=0.1532, simple_loss=0.245, pruned_loss=0.03074, over 1423780.69 frames.], batch size: 24, lr: 2.60e-04
2022-05-15 16:19:45,891 INFO [train.py:812] (5/8) Epoch 30, batch 2250, loss[loss=0.1896, simple_loss=0.2871, pruned_loss=0.04605, over 7161.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2458, pruned_loss=0.03101, over 1422259.44 frames.], batch size: 26, lr: 2.59e-04
2022-05-15 16:20:43,569 INFO [train.py:812] (5/8) Epoch 30, batch 2300, loss[loss=0.1339, simple_loss=0.2303, pruned_loss=0.01876, over 7318.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2464, pruned_loss=0.03119, over 1418819.14 frames.], batch size: 20, lr: 2.59e-04
2022-05-15 16:21:42,617 INFO [train.py:812] (5/8) Epoch 30, batch 2350, loss[loss=0.1673, simple_loss=0.2655, pruned_loss=0.03449, over 7337.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2458, pruned_loss=0.03096, over 1420371.24 frames.], batch size: 22, lr: 2.59e-04
2022-05-15 16:22:41,755 INFO [train.py:812] (5/8) Epoch 30, batch 2400, loss[loss=0.1766, simple_loss=0.2722, pruned_loss=0.04048, over 7278.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2464, pruned_loss=0.03123, over 1422368.84 frames.], batch size: 25, lr: 2.59e-04
2022-05-15 16:23:41,325 INFO [train.py:812] (5/8) Epoch 30, batch 2450, loss[loss=0.1698, simple_loss=0.2711, pruned_loss=0.0342, over 7150.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2452, pruned_loss=0.0308, over 1426338.99 frames.], batch size: 20, lr: 2.59e-04
2022-05-15 16:24:39,657 INFO [train.py:812] (5/8) Epoch 30, batch 2500, loss[loss=0.1394, simple_loss=0.2215, pruned_loss=0.02863, over 7244.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2448, pruned_loss=0.0308, over 1429869.24 frames.], batch size: 16, lr: 2.59e-04
2022-05-15 16:25:38,961 INFO [train.py:812] (5/8) Epoch 30, batch 2550, loss[loss=0.1381, simple_loss=0.2256, pruned_loss=0.02529, over 7425.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2445, pruned_loss=0.03088, over 1427273.20 frames.], batch size: 18, lr: 2.59e-04
2022-05-15 16:26:37,715 INFO [train.py:812] (5/8) Epoch 30, batch 2600, loss[loss=0.1456, simple_loss=0.2495, pruned_loss=0.02087, over 7123.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2446, pruned_loss=0.03054, over 1426819.15 frames.], batch size: 21, lr: 2.59e-04
2022-05-15 16:27:37,198 INFO [train.py:812] (5/8) Epoch 30, batch 2650, loss[loss=0.1086, simple_loss=0.2007, pruned_loss=0.008304, over 7135.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2439, pruned_loss=0.03032, over 1428895.45 frames.], batch size: 17, lr: 2.59e-04
2022-05-15 16:28:36,165 INFO [train.py:812] (5/8) Epoch 30, batch 2700, loss[loss=0.1528, simple_loss=0.2509, pruned_loss=0.02728, over 7118.00 frames.], tot_loss[loss=0.1531, simple_loss=0.245, pruned_loss=0.0306, over 1428919.43 frames.], batch size: 21, lr: 2.59e-04
2022-05-15 16:29:34,470 INFO [train.py:812] (5/8) Epoch 30, batch 2750, loss[loss=0.1493, simple_loss=0.2398, pruned_loss=0.02936, over 7234.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2457, pruned_loss=0.03085, over 1424477.95 frames.], batch size: 20, lr: 2.59e-04
2022-05-15 16:30:32,049 INFO [train.py:812] (5/8) Epoch 30, batch 2800, loss[loss=0.1503, simple_loss=0.2447, pruned_loss=0.02789, over 7332.00 frames.], tot_loss[loss=0.1541, simple_loss=0.246, pruned_loss=0.03115, over 1423644.33 frames.], batch size: 22, lr: 2.59e-04
2022-05-15 16:31:31,626 INFO [train.py:812] (5/8) Epoch 30, batch 2850, loss[loss=0.1425, simple_loss=0.2361, pruned_loss=0.02441, over 7231.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2444, pruned_loss=0.03061, over 1418106.37 frames.], batch size: 20, lr: 2.59e-04
2022-05-15 16:32:29,825 INFO [train.py:812] (5/8) Epoch 30, batch 2900, loss[loss=0.1359, simple_loss=0.2225, pruned_loss=0.02463, over 7014.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2436, pruned_loss=0.03, over 1420467.28 frames.], batch size: 16, lr: 2.59e-04
2022-05-15 16:33:36,450 INFO [train.py:812] (5/8) Epoch 30, batch 2950, loss[loss=0.1419, simple_loss=0.2411, pruned_loss=0.02132, over 6478.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2429, pruned_loss=0.02993, over 1421982.26 frames.], batch size: 37, lr: 2.59e-04
2022-05-15 16:34:35,494 INFO [train.py:812] (5/8) Epoch 30, batch 3000, loss[loss=0.1508, simple_loss=0.2509, pruned_loss=0.02528, over 7121.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2441, pruned_loss=0.03036, over 1424754.20 frames.], batch size: 21, lr: 2.59e-04
2022-05-15 16:34:35,495 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 16:34:43,056 INFO [train.py:841] (5/8) Epoch 30, validation: loss=0.1528, simple_loss=0.2494, pruned_loss=0.02809, over 698248.00 frames.
2022-05-15 16:35:41,794 INFO [train.py:812] (5/8) Epoch 30, batch 3050, loss[loss=0.1553, simple_loss=0.2551, pruned_loss=0.02772, over 7118.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2446, pruned_loss=0.03016, over 1426235.22 frames.], batch size: 21, lr: 2.59e-04
2022-05-15 16:36:40,844 INFO [train.py:812] (5/8) Epoch 30, batch 3100, loss[loss=0.1531, simple_loss=0.2482, pruned_loss=0.02903, over 7411.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2452, pruned_loss=0.03014, over 1426653.21 frames.], batch size: 21, lr: 2.59e-04
2022-05-15 16:37:40,499 INFO [train.py:812] (5/8) Epoch 30, batch 3150, loss[loss=0.1341, simple_loss=0.2163, pruned_loss=0.02593, over 7164.00 frames.], tot_loss[loss=0.152, simple_loss=0.2441, pruned_loss=0.03002, over 1422601.62 frames.], batch size: 18, lr: 2.59e-04
2022-05-15 16:38:39,680 INFO [train.py:812] (5/8) Epoch 30, batch 3200, loss[loss=0.1455, simple_loss=0.2362, pruned_loss=0.02737, over 7256.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2433, pruned_loss=0.03018, over 1425293.28 frames.], batch size: 19, lr: 2.59e-04
2022-05-15 16:39:38,941 INFO [train.py:812] (5/8) Epoch 30, batch 3250, loss[loss=0.1422, simple_loss=0.2415, pruned_loss=0.02139, over 7049.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2435, pruned_loss=0.03031, over 1420280.31 frames.], batch size: 28, lr: 2.59e-04
2022-05-15 16:40:36,541 INFO [train.py:812] (5/8) Epoch 30, batch 3300, loss[loss=0.1496, simple_loss=0.2516, pruned_loss=0.02376, over 7328.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2439, pruned_loss=0.03033, over 1423359.66 frames.], batch size: 20, lr: 2.58e-04
2022-05-15 16:41:35,373 INFO [train.py:812] (5/8) Epoch 30, batch 3350, loss[loss=0.1444, simple_loss=0.2261, pruned_loss=0.03132, over 7302.00 frames.], tot_loss[loss=0.152, simple_loss=0.2433, pruned_loss=0.03036, over 1427808.21 frames.], batch size: 17, lr: 2.58e-04
2022-05-15 16:42:33,348 INFO [train.py:812] (5/8) Epoch 30, batch 3400, loss[loss=0.1847, simple_loss=0.2691, pruned_loss=0.05019, over 5065.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2437, pruned_loss=0.03058, over 1423829.20 frames.], batch size: 52, lr: 2.58e-04
2022-05-15 16:43:31,881 INFO [train.py:812] (5/8) Epoch 30, batch 3450, loss[loss=0.1636, simple_loss=0.2572, pruned_loss=0.035, over 7318.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2434, pruned_loss=0.03055, over 1420752.46 frames.], batch size: 24, lr: 2.58e-04
2022-05-15 16:44:30,289 INFO [train.py:812] (5/8) Epoch 30, batch 3500, loss[loss=0.1788, simple_loss=0.2829, pruned_loss=0.03735, over 7170.00 frames.], tot_loss[loss=0.1527, simple_loss=0.244, pruned_loss=0.03064, over 1423237.23 frames.], batch size: 26, lr: 2.58e-04
2022-05-15 16:45:29,364 INFO [train.py:812] (5/8) Epoch 30, batch 3550, loss[loss=0.1308, simple_loss=0.2218, pruned_loss=0.01992, over 7164.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2442, pruned_loss=0.03065, over 1422267.76 frames.], batch size: 18, lr: 2.58e-04
2022-05-15 16:46:28,169 INFO [train.py:812] (5/8) Epoch 30, batch 3600, loss[loss=0.1265, simple_loss=0.214, pruned_loss=0.01946, over 7265.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2438, pruned_loss=0.03028, over 1426924.42 frames.], batch size: 19, lr: 2.58e-04
2022-05-15 16:47:27,447 INFO [train.py:812] (5/8) Epoch 30, batch 3650, loss[loss=0.1648, simple_loss=0.2659, pruned_loss=0.03183, over 6902.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2445, pruned_loss=0.03064, over 1428807.97 frames.], batch size: 31, lr: 2.58e-04
2022-05-15 16:48:25,013 INFO [train.py:812] (5/8) Epoch 30, batch 3700, loss[loss=0.1242, simple_loss=0.201, pruned_loss=0.02369, over 7284.00 frames.], tot_loss[loss=0.1524, simple_loss=0.244, pruned_loss=0.03043, over 1429819.41 frames.], batch size: 17, lr: 2.58e-04
2022-05-15 16:49:23,804 INFO [train.py:812] (5/8) Epoch 30, batch 3750, loss[loss=0.173, simple_loss=0.2644, pruned_loss=0.04084, over 7086.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2447, pruned_loss=0.03055, over 1432188.61 frames.], batch size: 28, lr: 2.58e-04
2022-05-15 16:50:21,201 INFO [train.py:812] (5/8) Epoch 30, batch 3800, loss[loss=0.1813, simple_loss=0.274, pruned_loss=0.04433, over 7204.00 frames.], tot_loss[loss=0.154, simple_loss=0.2458, pruned_loss=0.03112, over 1425052.86 frames.], batch size: 22, lr: 2.58e-04
2022-05-15 16:51:18,885 INFO [train.py:812] (5/8) Epoch 30, batch 3850, loss[loss=0.1379, simple_loss=0.2107, pruned_loss=0.03257, over 7166.00 frames.], tot_loss[loss=0.153, simple_loss=0.2447, pruned_loss=0.03066, over 1426136.39 frames.], batch size: 16, lr: 2.58e-04
2022-05-15 16:52:16,792 INFO [train.py:812] (5/8) Epoch 30, batch 3900, loss[loss=0.1278, simple_loss=0.2131, pruned_loss=0.02125, over 7142.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2454, pruned_loss=0.03086, over 1426301.58 frames.], batch size: 17, lr: 2.58e-04
2022-05-15 16:53:15,073 INFO [train.py:812] (5/8) Epoch 30, batch 3950, loss[loss=0.1493, simple_loss=0.2434, pruned_loss=0.02755, over 7389.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2457, pruned_loss=0.03071, over 1420414.49 frames.], batch size: 23, lr: 2.58e-04
2022-05-15 16:54:13,793 INFO [train.py:812] (5/8) Epoch 30, batch 4000, loss[loss=0.1283, simple_loss=0.2255, pruned_loss=0.01558, over 7294.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2469, pruned_loss=0.03093, over 1418693.36 frames.], batch size: 25, lr: 2.58e-04
2022-05-15 16:55:12,881 INFO [train.py:812] (5/8) Epoch 30, batch 4050, loss[loss=0.1558, simple_loss=0.2515, pruned_loss=0.03, over 7095.00 frames.], tot_loss[loss=0.154, simple_loss=0.2464, pruned_loss=0.03079, over 1418536.82 frames.], batch size: 28, lr: 2.58e-04
2022-05-15 16:56:10,894 INFO [train.py:812] (5/8) Epoch 30, batch 4100, loss[loss=0.1477, simple_loss=0.2504, pruned_loss=0.02248, over 7318.00 frames.], tot_loss[loss=0.153, simple_loss=0.2452, pruned_loss=0.03035, over 1420873.36 frames.], batch size: 21, lr: 2.58e-04
2022-05-15 16:57:19,274 INFO [train.py:812] (5/8) Epoch 30, batch 4150, loss[loss=0.1415, simple_loss=0.2394, pruned_loss=0.02178, over 7219.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2441, pruned_loss=0.0301, over 1422121.54 frames.], batch size: 21, lr: 2.58e-04
2022-05-15 16:58:17,988 INFO [train.py:812] (5/8) Epoch 30, batch 4200, loss[loss=0.1338, simple_loss=0.2266, pruned_loss=0.0205, over 7446.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2444, pruned_loss=0.03034, over 1422992.67 frames.], batch size: 20, lr: 2.58e-04
2022-05-15 16:59:24,875 INFO [train.py:812] (5/8) Epoch 30, batch 4250, loss[loss=0.1494, simple_loss=0.2467, pruned_loss=0.02609, over 7382.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2457, pruned_loss=0.03082, over 1417970.40 frames.], batch size: 23, lr: 2.58e-04
2022-05-15 17:00:23,118 INFO [train.py:812] (5/8) Epoch 30, batch 4300, loss[loss=0.1506, simple_loss=0.2341, pruned_loss=0.03353, over 7266.00 frames.], tot_loss[loss=0.1531, simple_loss=0.245, pruned_loss=0.03061, over 1421220.91 frames.], batch size: 17, lr: 2.58e-04
2022-05-15 17:01:31,689 INFO [train.py:812] (5/8) Epoch 30, batch 4350, loss[loss=0.1509, simple_loss=0.2539, pruned_loss=0.02397, over 7230.00 frames.], tot_loss[loss=0.1532, simple_loss=0.245, pruned_loss=0.03072, over 1423386.35 frames.], batch size: 20, lr: 2.58e-04
2022-05-15 17:02:30,808 INFO [train.py:812] (5/8) Epoch 30, batch 4400, loss[loss=0.14, simple_loss=0.2347, pruned_loss=0.02262, over 7232.00 frames.], tot_loss[loss=0.153, simple_loss=0.2447, pruned_loss=0.03065, over 1419089.66 frames.], batch size: 20, lr: 2.57e-04
2022-05-15 17:03:47,897 INFO [train.py:812] (5/8) Epoch 30, batch 4450, loss[loss=0.1677, simple_loss=0.2658, pruned_loss=0.03475, over 6444.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2442, pruned_loss=0.03035, over 1413068.17 frames.], batch size: 37, lr: 2.57e-04
2022-05-15 17:04:54,620 INFO [train.py:812] (5/8) Epoch 30, batch 4500, loss[loss=0.1933, simple_loss=0.2811, pruned_loss=0.05276, over 4984.00 frames.], tot_loss[loss=0.154, simple_loss=0.2456, pruned_loss=0.0312, over 1397904.05 frames.], batch size: 52, lr: 2.57e-04
2022-05-15 17:05:52,197 INFO [train.py:812] (5/8) Epoch 30, batch 4550, loss[loss=0.154, simple_loss=0.2444, pruned_loss=0.03176, over 5006.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2484, pruned_loss=0.03246, over 1357000.74 frames.], batch size: 52, lr: 2.57e-04
2022-05-15 17:07:08,081 INFO [train.py:812] (5/8) Epoch 31, batch 0, loss[loss=0.1282, simple_loss=0.2158, pruned_loss=0.02031, over 7333.00 frames.], tot_loss[loss=0.1282, simple_loss=0.2158, pruned_loss=0.02031, over 7333.00 frames.], batch size: 20, lr: 2.53e-04
2022-05-15 17:08:07,414 INFO [train.py:812] (5/8) Epoch 31, batch 50, loss[loss=0.16, simple_loss=0.267, pruned_loss=0.02653, over 7258.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2449, pruned_loss=0.03094, over 317315.77 frames.], batch size: 19, lr: 2.53e-04
2022-05-15 17:09:06,194 INFO [train.py:812] (5/8) Epoch 31, batch 100, loss[loss=0.164, simple_loss=0.2629, pruned_loss=0.03256, over 7374.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2467, pruned_loss=0.03089, over 561114.77 frames.], batch size: 23, lr: 2.53e-04
2022-05-15 17:10:05,005 INFO [train.py:812] (5/8) Epoch 31, batch 150, loss[loss=0.1465, simple_loss=0.2407, pruned_loss=0.02612, over 7194.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2449, pruned_loss=0.03094, over 756395.99 frames.], batch size: 22, lr: 2.53e-04
2022-05-15 17:11:03,882 INFO [train.py:812] (5/8) Epoch 31, batch 200, loss[loss=0.2032, simple_loss=0.2733, pruned_loss=0.06653, over 4915.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2444, pruned_loss=0.03072, over 900855.89 frames.], batch size: 54, lr: 2.53e-04
2022-05-15 17:12:02,392 INFO [train.py:812] (5/8) Epoch 31, batch 250, loss[loss=0.1715, simple_loss=0.2567, pruned_loss=0.04319, over 7261.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2464, pruned_loss=0.03089, over 1015583.93 frames.], batch size: 25, lr: 2.53e-04
2022-05-15 17:13:01,760 INFO [train.py:812] (5/8) Epoch 31, batch 300, loss[loss=0.1437, simple_loss=0.2432, pruned_loss=0.02209, over 7327.00 frames.], tot_loss[loss=0.1537, simple_loss=0.246, pruned_loss=0.03067, over 1106796.08 frames.], batch size: 21, lr: 2.53e-04
2022-05-15 17:13:59,732 INFO [train.py:812] (5/8) Epoch 31, batch 350, loss[loss=0.112, simple_loss=0.2029, pruned_loss=0.01054, over 7161.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2455, pruned_loss=0.03043, over 1173815.12 frames.], batch size: 18, lr: 2.53e-04
2022-05-15 17:14:57,244 INFO [train.py:812] (5/8) Epoch 31, batch 400, loss[loss=0.1521, simple_loss=0.2456, pruned_loss=0.02927, over 7220.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2453, pruned_loss=0.03055, over 1224881.96 frames.], batch size: 21, lr: 2.53e-04
2022-05-15 17:15:56,093 INFO [train.py:812] (5/8) Epoch 31, batch 450, loss[loss=0.1753, simple_loss=0.2744, pruned_loss=0.03808, over 7156.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2456, pruned_loss=0.03038, over 1266203.99 frames.], batch size: 26, lr: 2.53e-04
2022-05-15 17:16:55,560 INFO [train.py:812] (5/8) Epoch 31, batch 500, loss[loss=0.1354, simple_loss=0.2111, pruned_loss=0.02988, over 7265.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2452, pruned_loss=0.03053, over 1301214.02 frames.], batch size: 17, lr: 2.53e-04
2022-05-15 17:17:54,437 INFO [train.py:812] (5/8) Epoch 31, batch 550, loss[loss=0.1549, simple_loss=0.2673, pruned_loss=0.02129, over 7414.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2455, pruned_loss=0.03052, over 1328654.13 frames.], batch size: 21, lr: 2.53e-04
2022-05-15 17:18:53,061 INFO [train.py:812] (5/8) Epoch 31, batch 600, loss[loss=0.129, simple_loss=0.2173, pruned_loss=0.02036, over 7067.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2457, pruned_loss=0.0306, over 1348602.96 frames.], batch size: 18, lr: 2.53e-04
2022-05-15 17:19:50,581 INFO [train.py:812] (5/8) Epoch 31, batch 650, loss[loss=0.1563, simple_loss=0.256, pruned_loss=0.02825, over 7145.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2448, pruned_loss=0.03017, over 1370052.40 frames.], batch size: 20, lr: 2.53e-04
2022-05-15 17:20:49,361 INFO [train.py:812] (5/8) Epoch 31, batch 700, loss[loss=0.1259, simple_loss=0.2088, pruned_loss=0.0215, over 7204.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2443, pruned_loss=0.03024, over 1379967.24 frames.], batch size: 16, lr: 2.52e-04
2022-05-15 17:21:47,372 INFO [train.py:812] (5/8) Epoch 31, batch 750, loss[loss=0.1467, simple_loss=0.25, pruned_loss=0.02166, over 7234.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2444, pruned_loss=0.03008, over 1388245.11 frames.], batch size: 20, lr: 2.52e-04
2022-05-15 17:22:46,098 INFO [train.py:812] (5/8) Epoch 31, batch 800, loss[loss=0.1441, simple_loss=0.2365, pruned_loss=0.02583, over 7316.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2452, pruned_loss=0.03019, over 1396010.62 frames.], batch size: 20, lr: 2.52e-04
2022-05-15 17:23:44,734 INFO [train.py:812] (5/8) Epoch 31, batch 850, loss[loss=0.1317, simple_loss=0.2234, pruned_loss=0.01998, over 7440.00 frames.], tot_loss[loss=0.152, simple_loss=0.2441, pruned_loss=0.02997, over 1399183.88 frames.], batch size: 20, lr: 2.52e-04
2022-05-15 17:24:43,291 INFO [train.py:812] (5/8) Epoch 31, batch 900, loss[loss=0.1494, simple_loss=0.2321, pruned_loss=0.03335, over 6840.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2442, pruned_loss=0.03038, over 1403767.49 frames.], batch size: 15, lr: 2.52e-04
2022-05-15 17:25:42,274 INFO [train.py:812] (5/8) Epoch 31, batch 950, loss[loss=0.1882, simple_loss=0.2843, pruned_loss=0.04611, over 7061.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2447, pruned_loss=0.03095, over 1405447.90 frames.], batch size: 28, lr: 2.52e-04
2022-05-15 17:26:41,330 INFO [train.py:812] (5/8) Epoch 31, batch 1000, loss[loss=0.1606, simple_loss=0.2625, pruned_loss=0.02937, over 7340.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2449, pruned_loss=0.03119, over 1407604.68 frames.], batch size: 22, lr: 2.52e-04
2022-05-15 17:27:40,596 INFO [train.py:812] (5/8) Epoch 31, batch 1050, loss[loss=0.1789, simple_loss=0.2676, pruned_loss=0.04512, over 7043.00 frames.], tot_loss[loss=0.154, simple_loss=0.2452, pruned_loss=0.03139, over 1410858.19 frames.], batch size: 28, lr: 2.52e-04
2022-05-15 17:28:39,399 INFO [train.py:812] (5/8) Epoch 31, batch 1100, loss[loss=0.1488, simple_loss=0.2419, pruned_loss=0.02782, over 7061.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2449, pruned_loss=0.03112, over 1415003.92 frames.], batch size: 18, lr: 2.52e-04
2022-05-15 17:29:38,128 INFO [train.py:812] (5/8) Epoch 31, batch 1150, loss[loss=0.1242, simple_loss=0.2147, pruned_loss=0.01688, over 7457.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2446, pruned_loss=0.03109, over 1417315.40 frames.], batch size: 19, lr: 2.52e-04
2022-05-15 17:30:36,859 INFO [train.py:812] (5/8) Epoch 31, batch 1200, loss[loss=0.1637, simple_loss=0.2531, pruned_loss=0.03713, over 7196.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2453, pruned_loss=0.03093, over 1419218.87 frames.], batch size: 22, lr: 2.52e-04
2022-05-15 17:31:36,138 INFO [train.py:812] (5/8) Epoch 31, batch 1250, loss[loss=0.1361, simple_loss=0.2289, pruned_loss=0.0217, over 7404.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2459, pruned_loss=0.03141, over 1418608.42 frames.], batch size: 18, lr: 2.52e-04
2022-05-15 17:32:35,749 INFO [train.py:812] (5/8) Epoch 31, batch 1300, loss[loss=0.1853, simple_loss=0.2874, pruned_loss=0.04159, over 7203.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2461, pruned_loss=0.03133, over 1418174.89 frames.], batch size: 26, lr: 2.52e-04
2022-05-15 17:33:34,081 INFO [train.py:812] (5/8) Epoch 31, batch 1350, loss[loss=0.1184, simple_loss=0.2034, pruned_loss=0.01675, over 7133.00 frames.], tot_loss[loss=0.1547, simple_loss=0.247, pruned_loss=0.0312, over 1415153.26 frames.], batch size: 17, lr: 2.52e-04
2022-05-15 17:34:32,625 INFO [train.py:812] (5/8) Epoch 31, batch 1400, loss[loss=0.1596, simple_loss=0.2585, pruned_loss=0.03032, over 7348.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2467, pruned_loss=0.03106, over 1418926.83 frames.], batch size: 22, lr: 2.52e-04
2022-05-15 17:35:31,404 INFO [train.py:812] (5/8) Epoch 31, batch 1450, loss[loss=0.1465, simple_loss=0.243, pruned_loss=0.02506, over 7138.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2461, pruned_loss=0.03058, over 1419736.51 frames.], batch size: 20, lr: 2.52e-04
2022-05-15 17:36:30,346 INFO [train.py:812] (5/8) Epoch 31, batch 1500, loss[loss=0.1609, simple_loss=0.2598, pruned_loss=0.031, over 7304.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2467, pruned_loss=0.03078, over 1425535.53 frames.], batch size: 25, lr: 2.52e-04
2022-05-15 17:37:27,988 INFO [train.py:812] (5/8) Epoch 31, batch 1550, loss[loss=0.1518, simple_loss=0.2418, pruned_loss=0.03096, over 7277.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2452, pruned_loss=0.03019, over 1427171.71 frames.], batch size: 25, lr: 2.52e-04
2022-05-15 17:38:27,303 INFO [train.py:812] (5/8) Epoch 31, batch 1600, loss[loss=0.1499, simple_loss=0.2465, pruned_loss=0.02666, over 7265.00 frames.], tot_loss[loss=0.1527, simple_loss=0.245, pruned_loss=0.0302, over 1428283.72 frames.], batch size: 19, lr: 2.52e-04
2022-05-15 17:39:26,106 INFO [train.py:812] (5/8) Epoch 31, batch 1650, loss[loss=0.1547, simple_loss=0.2484, pruned_loss=0.03046, over 7113.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2466, pruned_loss=0.03103, over 1428306.53 frames.], batch size: 21, lr: 2.52e-04
2022-05-15 17:40:24,533 INFO [train.py:812] (5/8) Epoch 31, batch 1700, loss[loss=0.1346, simple_loss=0.235, pruned_loss=0.01709, over 7292.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2456, pruned_loss=0.03111, over 1425172.13 frames.], batch size: 24, lr: 2.52e-04
2022-05-15 17:41:22,579 INFO [train.py:812] (5/8) Epoch 31, batch 1750, loss[loss=0.1526, simple_loss=0.2458, pruned_loss=0.02967, over 7392.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2453, pruned_loss=0.03076, over 1427411.59 frames.], batch size: 23, lr: 2.52e-04
2022-05-15 17:42:21,640 INFO [train.py:812] (5/8) Epoch 31, batch 1800, loss[loss=0.15, simple_loss=0.2394, pruned_loss=0.03029, over 7430.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2441, pruned_loss=0.03061, over 1422986.38 frames.], batch size: 20, lr: 2.51e-04
2022-05-15 17:43:20,015 INFO [train.py:812] (5/8) Epoch 31, batch 1850, loss[loss=0.1234, simple_loss=0.2058, pruned_loss=0.02054, over 7144.00 frames.], tot_loss[loss=0.1525, simple_loss=0.244, pruned_loss=0.03047, over 1421183.57 frames.], batch size: 17, lr: 2.51e-04
2022-05-15 17:44:19,080 INFO [train.py:812] (5/8) Epoch 31, batch 1900, loss[loss=0.1542, simple_loss=0.2497, pruned_loss=0.02935, over 7326.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2449, pruned_loss=0.03075, over 1425036.44 frames.], batch size: 20, lr: 2.51e-04
2022-05-15 17:45:17,760 INFO [train.py:812] (5/8) Epoch 31, batch 1950, loss[loss=0.1646, simple_loss=0.2603, pruned_loss=0.03442, over 7384.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2447, pruned_loss=0.03074, over 1425380.03 frames.], batch size: 23, lr: 2.51e-04
2022-05-15 17:46:16,471 INFO [train.py:812] (5/8) Epoch 31, batch 2000, loss[loss=0.162, simple_loss=0.2602, pruned_loss=0.03195, over 7165.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2437, pruned_loss=0.03047, over 1427444.02 frames.], batch size: 18, lr: 2.51e-04
2022-05-15 17:47:15,233 INFO [train.py:812] (5/8) Epoch 31, batch 2050, loss[loss=0.1655, simple_loss=0.263, pruned_loss=0.034, over 7200.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2434, pruned_loss=0.03048, over 1424672.72 frames.], batch size: 22, lr: 2.51e-04
2022-05-15 17:48:13,877 INFO [train.py:812] (5/8) Epoch 31, batch 2100, loss[loss=0.1794, simple_loss=0.263, pruned_loss=0.04789, over 7152.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2439, pruned_loss=0.03052, over 1423022.18 frames.], batch size: 19, lr: 2.51e-04
2022-05-15 17:49:12,989 INFO [train.py:812] (5/8) Epoch 31, batch 2150, loss[loss=0.1373, simple_loss=0.2327, pruned_loss=0.02094, over 7166.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2429, pruned_loss=0.03009, over 1427134.13 frames.], batch size: 18, lr: 2.51e-04
2022-05-15 17:50:11,052 INFO [train.py:812] (5/8) Epoch 31, batch 2200, loss[loss=0.149, simple_loss=0.2321, pruned_loss=0.03296, over 7070.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2438, pruned_loss=0.03031, over 1428600.17 frames.], batch size: 18, lr: 2.51e-04
2022-05-15 17:51:08,670 INFO [train.py:812] (5/8) Epoch 31, batch 2250, loss[loss=0.1922, simple_loss=0.2817, pruned_loss=0.05131, over 7195.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2453, pruned_loss=0.0306, over 1428336.38 frames.], batch size: 23, lr: 2.51e-04
2022-05-15 17:52:08,177 INFO [train.py:812] (5/8) Epoch 31, batch 2300, loss[loss=0.1523, simple_loss=0.2421, pruned_loss=0.03119, over 7250.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2458, pruned_loss=0.03092, over 1430368.74 frames.], batch size: 19, lr: 2.51e-04
2022-05-15 17:53:06,338 INFO [train.py:812] (5/8) Epoch 31, batch 2350, loss[loss=0.1457, simple_loss=0.2354, pruned_loss=0.02801, over 7073.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2453, pruned_loss=0.03075, over 1429928.37 frames.], batch size: 18, lr: 2.51e-04
2022-05-15 17:54:10,959 INFO [train.py:812] (5/8) Epoch 31, batch 2400, loss[loss=0.1271, simple_loss=0.2197, pruned_loss=0.01726, over 7215.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2449, pruned_loss=0.03074, over 1428086.23 frames.], batch size: 21, lr: 2.51e-04
2022-05-15 17:55:08,400 INFO [train.py:812] (5/8) Epoch 31, batch 2450, loss[loss=0.1511, simple_loss=0.2434, pruned_loss=0.02942, over 7221.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2453, pruned_loss=0.03069, over 1423764.32 frames.], batch size: 21, lr: 2.51e-04
2022-05-15 17:56:07,144 INFO [train.py:812] (5/8) Epoch 31, batch 2500, loss[loss=0.1418, simple_loss=0.2455, pruned_loss=0.01902, over 7340.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2443, pruned_loss=0.03025, over 1426610.80 frames.], batch size: 22, lr: 2.51e-04
2022-05-15 17:57:05,822 INFO [train.py:812] (5/8) Epoch 31, batch 2550, loss[loss=0.1632, simple_loss=0.2695, pruned_loss=0.02843, over 7219.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2443, pruned_loss=0.03024, over 1428257.95 frames.], batch size: 23, lr: 2.51e-04
2022-05-15 17:58:14,112 INFO [train.py:812] (5/8) Epoch 31, batch 2600, loss[loss=0.1373, simple_loss=0.2211, pruned_loss=0.02677, over 7409.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2445, pruned_loss=0.03055, over 1427321.86 frames.], batch size: 18, lr: 2.51e-04
2022-05-15 17:59:11,629 INFO [train.py:812] (5/8) Epoch 31, batch 2650, loss[loss=0.1637, simple_loss=0.2652, pruned_loss=0.03112, over 7414.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2446, pruned_loss=0.03045, over 1424598.03 frames.], batch size: 21, lr: 2.51e-04
2022-05-15 18:00:10,464 INFO [train.py:812] (5/8) Epoch 31, batch 2700, loss[loss=0.1604, simple_loss=0.2561, pruned_loss=0.03233, over 7267.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2441, pruned_loss=0.0299, over 1418215.97 frames.], batch size: 25, lr: 2.51e-04
2022-05-15 18:01:09,680 INFO [train.py:812] (5/8) Epoch 31, batch 2750, loss[loss=0.1428, simple_loss=0.2325, pruned_loss=0.0265, over 7152.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2444, pruned_loss=0.0302, over 1418848.15 frames.], batch size: 20, lr: 2.51e-04
2022-05-15 18:02:08,942 INFO [train.py:812] (5/8) Epoch 31, batch 2800, loss[loss=0.1223, simple_loss=0.2156, pruned_loss=0.01453, over 7159.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2445, pruned_loss=0.03055, over 1421225.88 frames.], batch size: 18, lr: 2.51e-04
2022-05-15 18:03:06,853 INFO [train.py:812] (5/8) Epoch 31, batch 2850, loss[loss=0.1694, simple_loss=0.2641, pruned_loss=0.03732, over 7196.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2454, pruned_loss=0.03076, over 1419179.11 frames.], batch size: 22, lr: 2.51e-04
2022-05-15 18:04:06,620 INFO [train.py:812] (5/8) Epoch 31, batch 2900, loss[loss=0.1466, simple_loss=0.2507, pruned_loss=0.0212, over 7112.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2452, pruned_loss=0.03065, over 1423510.01 frames.], batch size: 21, lr: 2.51e-04
2022-05-15 18:05:04,889 INFO [train.py:812] (5/8) Epoch 31, batch 2950, loss[loss=0.145, simple_loss=0.2364, pruned_loss=0.02674, over 7247.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2451, pruned_loss=0.03023, over 1422458.75 frames.], batch size: 19, lr: 2.50e-04
2022-05-15 18:06:03,443 INFO [train.py:812] (5/8) Epoch 31, batch 3000, loss[loss=0.1488, simple_loss=0.2435, pruned_loss=0.0271, over 7330.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2445, pruned_loss=0.03053, over 1421760.09 frames.], batch size: 20, lr: 2.50e-04
2022-05-15 18:06:03,443 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 18:06:10,972 INFO [train.py:841] (5/8) Epoch 31, validation: loss=0.1541, simple_loss=0.25, pruned_loss=0.02913, over 698248.00 frames.
2022-05-15 18:07:09,530 INFO [train.py:812] (5/8) Epoch 31, batch 3050, loss[loss=0.1314, simple_loss=0.2118, pruned_loss=0.0255, over 6999.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2453, pruned_loss=0.03057, over 1421537.91 frames.], batch size: 16, lr: 2.50e-04
2022-05-15 18:08:09,152 INFO [train.py:812] (5/8) Epoch 31, batch 3100, loss[loss=0.1696, simple_loss=0.2621, pruned_loss=0.03858, over 7300.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2444, pruned_loss=0.03065, over 1425294.10 frames.], batch size: 25, lr: 2.50e-04
2022-05-15 18:09:08,138 INFO [train.py:812] (5/8) Epoch 31, batch 3150, loss[loss=0.139, simple_loss=0.2228, pruned_loss=0.02765, over 7001.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2448, pruned_loss=0.03093, over 1424588.08 frames.], batch size: 16, lr: 2.50e-04
2022-05-15 18:10:05,066 INFO [train.py:812] (5/8) Epoch 31, batch 3200, loss[loss=0.1865, simple_loss=0.2795, pruned_loss=0.0467, over 7203.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2446, pruned_loss=0.03096, over 1416862.53 frames.], batch size: 23, lr: 2.50e-04
2022-05-15 18:11:03,133 INFO [train.py:812] (5/8) Epoch 31, batch 3250, loss[loss=0.1824, simple_loss=0.2831, pruned_loss=0.04086, over 7154.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2453, pruned_loss=0.03111, over 1416306.72 frames.], batch size: 20, lr: 2.50e-04
2022-05-15 18:12:02,721 INFO [train.py:812] (5/8) Epoch 31, batch 3300, loss[loss=0.1423, simple_loss=0.2191, pruned_loss=0.03276, over 7289.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2444, pruned_loss=0.03061, over 1422454.55 frames.], batch size: 17, lr: 2.50e-04
2022-05-15 18:13:01,586 INFO [train.py:812] (5/8) Epoch 31, batch 3350, loss[loss=0.1534, simple_loss=0.2486, pruned_loss=0.02913, over 7213.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2438, pruned_loss=0.03026, over 1421969.02 frames.], batch size: 21, lr: 2.50e-04
2022-05-15 18:14:00,859 INFO [train.py:812] (5/8) Epoch 31, batch 3400, loss[loss=0.1556, simple_loss=0.2633, pruned_loss=0.0239, over 7320.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2432, pruned_loss=0.02974, over 1421056.22 frames.], batch size: 25, lr: 2.50e-04
2022-05-15 18:14:57,863 INFO [train.py:812] (5/8) Epoch 31, batch 3450, loss[loss=0.1579, simple_loss=0.2531, pruned_loss=0.03137, over 6476.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2447, pruned_loss=0.03017, over 1425120.63 frames.], batch size: 38, lr: 2.50e-04
2022-05-15 18:15:56,012 INFO [train.py:812] (5/8) Epoch 31, batch 3500, loss[loss=0.1762, simple_loss=0.275, pruned_loss=0.03874, over 7371.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2447, pruned_loss=0.03015, over 1427113.99 frames.], batch size: 23, lr: 2.50e-04
2022-05-15 18:16:54,979 INFO [train.py:812] (5/8) Epoch 31, batch 3550, loss[loss=0.1611, simple_loss=0.2401, pruned_loss=0.04103, over 7437.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2449, pruned_loss=0.03021, over 1428213.41 frames.], batch size: 20, lr: 2.50e-04
2022-05-15 18:17:52,433 INFO [train.py:812] (5/8) Epoch 31, batch 3600, loss[loss=0.1762, simple_loss=0.2592, pruned_loss=0.04659, over 7288.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2458, pruned_loss=0.03088, over 1422865.61 frames.], batch size: 24, lr: 2.50e-04
2022-05-15 18:18:51,312 INFO [train.py:812] (5/8) Epoch 31, batch 3650, loss[loss=0.1535, simple_loss=0.234, pruned_loss=0.03648, over 7127.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2451, pruned_loss=0.03075, over 1422516.53 frames.], batch size: 17, lr: 2.50e-04
2022-05-15 18:19:50,482 INFO [train.py:812] (5/8) Epoch 31, batch 3700, loss[loss=0.1392, simple_loss=0.222, pruned_loss=0.02825, over 7273.00 frames.], tot_loss[loss=0.1526, simple_loss=0.244, pruned_loss=0.03054, over 1425038.49 frames.], batch size: 17, lr: 2.50e-04
2022-05-15 18:20:49,312 INFO [train.py:812] (5/8) Epoch 31, batch 3750, loss[loss=0.1432, simple_loss=0.2338, pruned_loss=0.02629, over 7260.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2439, pruned_loss=0.03075, over 1423298.85 frames.], batch size: 19, lr: 2.50e-04
2022-05-15 18:21:49,287 INFO [train.py:812] (5/8) Epoch 31, batch 3800, loss[loss=0.1279, simple_loss=0.2125, pruned_loss=0.02163, over 7282.00 frames.], tot_loss[loss=0.152, simple_loss=0.2432, pruned_loss=0.03038, over 1424989.65 frames.], batch size: 18, lr: 2.50e-04
2022-05-15 18:22:47,392 INFO [train.py:812] (5/8) Epoch 31, batch 3850, loss[loss=0.1621, simple_loss=0.2495, pruned_loss=0.03731, over 7062.00 frames.], tot_loss[loss=0.152, simple_loss=0.2434, pruned_loss=0.03025, over 1424640.87 frames.], batch size: 18, lr: 2.50e-04
2022-05-15 18:23:45,717 INFO [train.py:812] (5/8) Epoch 31, batch 3900, loss[loss=0.1507, simple_loss=0.2499, pruned_loss=0.02572, over 7283.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2434, pruned_loss=0.03012, over 1428488.99 frames.], batch size: 24, lr: 2.50e-04
2022-05-15 18:24:43,601 INFO [train.py:812] (5/8) Epoch 31, batch 3950, loss[loss=0.1284, simple_loss=0.2153, pruned_loss=0.02074, over 7348.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2431, pruned_loss=0.03023, over 1428808.48 frames.], batch size: 19, lr: 2.50e-04
2022-05-15 18:25:41,726 INFO [train.py:812] (5/8) Epoch 31, batch 4000, loss[loss=0.1489, simple_loss=0.2303, pruned_loss=0.03372, over 7161.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2444, pruned_loss=0.03094, over 1425999.23 frames.], batch size: 18, lr: 2.50e-04
2022-05-15 18:26:41,003 INFO [train.py:812] (5/8) Epoch 31, batch 4050, loss[loss=0.1759, simple_loss=0.2611, pruned_loss=0.04532, over 7269.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2447, pruned_loss=0.03072, over 1425111.67 frames.], batch size: 24, lr: 2.49e-04
2022-05-15 18:27:40,595 INFO [train.py:812] (5/8) Epoch 31, batch 4100, loss[loss=0.1423, simple_loss=0.2387, pruned_loss=0.02294, over 7152.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2446, pruned_loss=0.03078, over 1427085.00 frames.], batch size: 19, lr: 2.49e-04
2022-05-15 18:28:39,533 INFO [train.py:812] (5/8) Epoch 31, batch 4150, loss[loss=0.1601, simple_loss=0.2669, pruned_loss=0.02659, over 7125.00 frames.], tot_loss[loss=0.153, simple_loss=0.2447, pruned_loss=0.03063, over 1428916.25 frames.], batch size: 21, lr: 2.49e-04
2022-05-15 18:29:38,570 INFO [train.py:812] (5/8) Epoch 31, batch 4200, loss[loss=0.133, simple_loss=0.2029, pruned_loss=0.03157, over 7266.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2442, pruned_loss=0.03062, over 1431404.21 frames.], batch size: 16, lr: 2.49e-04
2022-05-15 18:30:36,496 INFO [train.py:812] (5/8) Epoch 31, batch 4250, loss[loss=0.1665, simple_loss=0.2486, pruned_loss=0.04223, over 7195.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2443, pruned_loss=0.03066, over 1427657.99 frames.], batch size: 26, lr: 2.49e-04
2022-05-15 18:31:35,773 INFO [train.py:812] (5/8) Epoch 31, batch 4300, loss[loss=0.1699, simple_loss=0.2663, pruned_loss=0.03677, over 7291.00 frames.], tot_loss[loss=0.152, simple_loss=0.2434, pruned_loss=0.03028, over 1430548.62 frames.], batch size: 24, lr: 2.49e-04
2022-05-15 18:32:33,412 INFO [train.py:812] (5/8) Epoch 31, batch 4350, loss[loss=0.1331, simple_loss=0.2333, pruned_loss=0.01643, over 7108.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2442, pruned_loss=0.0304, over 1421240.47 frames.], batch size: 21, lr: 2.49e-04
2022-05-15 18:33:32,234 INFO [train.py:812] (5/8) Epoch 31, batch 4400, loss[loss=0.151, simple_loss=0.2496, pruned_loss=0.02615, over 7115.00 frames.], tot_loss[loss=0.1523, simple_loss=0.244, pruned_loss=0.03027, over 1410362.22 frames.], batch size: 21, lr: 2.49e-04
2022-05-15 18:34:30,891 INFO [train.py:812] (5/8) Epoch 31, batch 4450, loss[loss=0.1578, simple_loss=0.2473, pruned_loss=0.03415, over 6411.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2435, pruned_loss=0.0302, over 1409928.57 frames.], batch size: 37, lr: 2.49e-04
2022-05-15 18:35:30,064 INFO [train.py:812] (5/8) Epoch 31, batch 4500, loss[loss=0.1486, simple_loss=0.2473, pruned_loss=0.02494, over 6597.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2446, pruned_loss=0.03058, over 1385497.49 frames.], batch size: 38, lr: 2.49e-04
2022-05-15 18:36:28,998 INFO [train.py:812] (5/8) Epoch 31, batch 4550, loss[loss=0.1877, simple_loss=0.2727, pruned_loss=0.05139, over 4869.00 frames.], tot_loss[loss=0.155, simple_loss=0.2468, pruned_loss=0.0316, over 1355904.56 frames.], batch size: 52, lr: 2.49e-04
2022-05-15 18:37:36,643 INFO [train.py:812] (5/8) Epoch 32, batch 0, loss[loss=0.1496, simple_loss=0.236, pruned_loss=0.03164, over 5241.00 frames.], tot_loss[loss=0.1496, simple_loss=0.236, pruned_loss=0.03164, over 5241.00 frames.], batch size: 52, lr: 2.45e-04
2022-05-15 18:38:34,874 INFO [train.py:812] (5/8) Epoch 32, batch 50, loss[loss=0.1627, simple_loss=0.2603, pruned_loss=0.03254, over 6457.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2503, pruned_loss=0.03176, over 320267.00 frames.], batch size: 38, lr: 2.45e-04
2022-05-15 18:39:33,405 INFO [train.py:812] (5/8) Epoch 32, batch 100, loss[loss=0.1655, simple_loss=0.2608, pruned_loss=0.03512, over 7318.00 frames.], tot_loss[loss=0.1547, simple_loss=0.247, pruned_loss=0.03123, over 567413.84 frames.], batch size: 25, lr: 2.45e-04
2022-05-15 18:40:32,487 INFO [train.py:812] (5/8) Epoch 32, batch 150, loss[loss=0.1543, simple_loss=0.2572, pruned_loss=0.02567, over 7139.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2452, pruned_loss=0.03098, over 758517.68 frames.], batch size: 26, lr: 2.45e-04
2022-05-15 18:41:31,073 INFO [train.py:812] (5/8) Epoch 32, batch 200, loss[loss=0.1364, simple_loss=0.2188, pruned_loss=0.02695, over 6988.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2437, pruned_loss=0.02999, over 902747.34 frames.], batch size: 16, lr: 2.45e-04
2022-05-15 18:42:29,420 INFO [train.py:812] (5/8) Epoch 32, batch 250, loss[loss=0.1497, simple_loss=0.2465, pruned_loss=0.02645, over 7280.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2436, pruned_loss=0.02996, over 1022393.92 frames.], batch size: 24, lr: 2.45e-04
2022-05-15 18:43:28,924 INFO [train.py:812] (5/8) Epoch 32, batch 300, loss[loss=0.1865, simple_loss=0.2695, pruned_loss=0.05179, over 7318.00 frames.], tot_loss[loss=0.1529, simple_loss=0.245, pruned_loss=0.03036, over 1113150.31 frames.], batch size: 24, lr: 2.45e-04
2022-05-15 18:44:28,366 INFO [train.py:812] (5/8) Epoch 32, batch 350, loss[loss=0.162, simple_loss=0.2529, pruned_loss=0.03559, over 7059.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2445, pruned_loss=0.03043, over 1180952.66 frames.], batch size: 28, lr: 2.45e-04
2022-05-15 18:45:27,065 INFO [train.py:812] (5/8) Epoch 32, batch 400, loss[loss=0.1603, simple_loss=0.2553, pruned_loss=0.03268, over 7101.00 frames.], tot_loss[loss=0.153, simple_loss=0.2446, pruned_loss=0.03067, over 1236521.11 frames.], batch size: 26, lr: 2.45e-04
2022-05-15 18:46:25,909 INFO [train.py:812] (5/8) Epoch 32, batch 450, loss[loss=0.1592, simple_loss=0.2524, pruned_loss=0.03302, over 7325.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2437, pruned_loss=0.03041, over 1277304.03 frames.], batch size: 21, lr: 2.45e-04
2022-05-15 18:47:25,152 INFO [train.py:812] (5/8) Epoch 32, batch 500, loss[loss=0.1539, simple_loss=0.2518, pruned_loss=0.028, over 7336.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2428, pruned_loss=0.03, over 1313913.09 frames.], batch size: 22, lr: 2.45e-04
2022-05-15 18:48:23,088 INFO [train.py:812] (5/8) Epoch 32, batch 550, loss[loss=0.152, simple_loss=0.2535, pruned_loss=0.02519, over 7339.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2432, pruned_loss=0.02993, over 1342212.93 frames.], batch size: 22, lr: 2.45e-04
2022-05-15 18:49:22,846 INFO [train.py:812] (5/8) Epoch 32, batch 600, loss[loss=0.118, simple_loss=0.2099, pruned_loss=0.01307, over 7140.00 frames.], tot_loss[loss=0.1513, simple_loss=0.243, pruned_loss=0.02983, over 1364494.13 frames.], batch size: 17, lr: 2.45e-04
2022-05-15 18:50:21,230 INFO [train.py:812] (5/8) Epoch 32, batch 650, loss[loss=0.1553, simple_loss=0.2401, pruned_loss=0.0352, over 6975.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2429, pruned_loss=0.02975, over 1379297.14 frames.], batch size: 16, lr: 2.45e-04
2022-05-15 18:51:18,826 INFO [train.py:812] (5/8) Epoch 32, batch 700, loss[loss=0.1874, simple_loss=0.2756, pruned_loss=0.0496, over 7189.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2442, pruned_loss=0.03023, over 1387037.88 frames.], batch size: 23, lr: 2.45e-04
2022-05-15 18:52:17,784 INFO [train.py:812] (5/8) Epoch 32, batch 750, loss[loss=0.1559, simple_loss=0.2483, pruned_loss=0.03171, over 7113.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2445, pruned_loss=0.03023, over 1395081.63 frames.], batch size: 21, lr: 2.44e-04
2022-05-15 18:53:17,310 INFO [train.py:812] (5/8) Epoch 32, batch 800, loss[loss=0.1429, simple_loss=0.2258, pruned_loss=0.02996, over 7277.00 frames.], tot_loss[loss=0.1529, simple_loss=0.245, pruned_loss=0.03041, over 1399451.57 frames.], batch size: 18, lr: 2.44e-04
2022-05-15 18:54:15,839 INFO [train.py:812] (5/8) Epoch 32, batch 850, loss[loss=0.1657, simple_loss=0.2532, pruned_loss=0.03909, over 7308.00 frames.], tot_loss[loss=0.153, simple_loss=0.2454, pruned_loss=0.03031, over 1407162.12 frames.], batch size: 25, lr: 2.44e-04
2022-05-15 18:55:14,215 INFO [train.py:812] (5/8) Epoch 32, batch 900, loss[loss=0.1629, simple_loss=0.2517, pruned_loss=0.03702, over 7332.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2457, pruned_loss=0.03008, over 1410360.46 frames.], batch size: 22, lr: 2.44e-04
2022-05-15 18:56:22,070 INFO [train.py:812] (5/8) Epoch 32, batch 950, loss[loss=0.1393, simple_loss=0.2174, pruned_loss=0.03058, over 6828.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2441, pruned_loss=0.03015, over 1412126.82 frames.], batch size: 15, lr: 2.44e-04
2022-05-15 18:57:31,071 INFO [train.py:812] (5/8) Epoch 32, batch 1000, loss[loss=0.1348, simple_loss=0.2284, pruned_loss=0.02066, over 7421.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2432, pruned_loss=0.02955, over 1416210.29 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 18:58:30,358 INFO [train.py:812] (5/8) Epoch 32, batch 1050, loss[loss=0.1471, simple_loss=0.2361, pruned_loss=0.02902, over 7230.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2429, pruned_loss=0.02975, over 1420029.23 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 18:59:29,285 INFO [train.py:812] (5/8) Epoch 32, batch 1100, loss[loss=0.156, simple_loss=0.2482, pruned_loss=0.0319, over 7208.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2431, pruned_loss=0.03, over 1417988.90 frames.], batch size: 22, lr: 2.44e-04
2022-05-15 19:00:36,737 INFO [train.py:812] (5/8) Epoch 32, batch 1150, loss[loss=0.1336, simple_loss=0.2177, pruned_loss=0.0248, over 7139.00 frames.], tot_loss[loss=0.152, simple_loss=0.2439, pruned_loss=0.03, over 1421692.98 frames.], batch size: 17, lr: 2.44e-04
2022-05-15 19:01:36,497 INFO [train.py:812] (5/8) Epoch 32, batch 1200, loss[loss=0.1449, simple_loss=0.2401, pruned_loss=0.02484, over 7404.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2429, pruned_loss=0.02977, over 1424139.05 frames.], batch size: 21, lr: 2.44e-04
2022-05-15 19:02:45,246 INFO [train.py:812] (5/8) Epoch 32, batch 1250, loss[loss=0.1832, simple_loss=0.2835, pruned_loss=0.04147, over 7201.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2436, pruned_loss=0.03032, over 1416958.57 frames.], batch size: 23, lr: 2.44e-04
2022-05-15 19:03:53,725 INFO [train.py:812] (5/8) Epoch 32, batch 1300, loss[loss=0.1774, simple_loss=0.283, pruned_loss=0.03593, over 7150.00 frames.], tot_loss[loss=0.1526, simple_loss=0.244, pruned_loss=0.0306, over 1422231.01 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 19:05:00,945 INFO [train.py:812] (5/8) Epoch 32, batch 1350, loss[loss=0.1453, simple_loss=0.2308, pruned_loss=0.02989, over 7335.00 frames.], tot_loss[loss=0.1526, simple_loss=0.244, pruned_loss=0.0306, over 1420327.76 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 19:05:59,734 INFO [train.py:812] (5/8) Epoch 32, batch 1400, loss[loss=0.1465, simple_loss=0.2382, pruned_loss=0.02743, over 7231.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2432, pruned_loss=0.03029, over 1420546.55 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 19:06:57,258 INFO [train.py:812] (5/8) Epoch 32, batch 1450, loss[loss=0.1434, simple_loss=0.2288, pruned_loss=0.02904, over 7336.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2442, pruned_loss=0.03071, over 1422447.81 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 19:08:05,676 INFO [train.py:812] (5/8) Epoch 32, batch 1500, loss[loss=0.1854, simple_loss=0.2681, pruned_loss=0.05138, over 4880.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2441, pruned_loss=0.03027, over 1421108.36 frames.], batch size: 53, lr: 2.44e-04
2022-05-15 19:09:04,128 INFO [train.py:812] (5/8) Epoch 32, batch 1550, loss[loss=0.1546, simple_loss=0.2343, pruned_loss=0.03748, over 7401.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2434, pruned_loss=0.0301, over 1420411.27 frames.], batch size: 18, lr: 2.44e-04
2022-05-15 19:10:03,429 INFO [train.py:812] (5/8) Epoch 32, batch 1600, loss[loss=0.1681, simple_loss=0.2663, pruned_loss=0.03491, over 7205.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2435, pruned_loss=0.03011, over 1416653.55 frames.], batch size: 23, lr: 2.44e-04
2022-05-15 19:11:01,501 INFO [train.py:812] (5/8) Epoch 32, batch 1650, loss[loss=0.1494, simple_loss=0.2436, pruned_loss=0.02764, over 7412.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2444, pruned_loss=0.03038, over 1417018.93 frames.], batch size: 21, lr: 2.44e-04
2022-05-15 19:12:00,699 INFO [train.py:812] (5/8) Epoch 32, batch 1700, loss[loss=0.1655, simple_loss=0.2643, pruned_loss=0.03337, over 7114.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2446, pruned_loss=0.03076, over 1412354.88 frames.], batch size: 21, lr: 2.44e-04
2022-05-15 19:12:59,706 INFO [train.py:812] (5/8) Epoch 32, batch 1750, loss[loss=0.1864, simple_loss=0.2601, pruned_loss=0.05636, over 5341.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2442, pruned_loss=0.03036, over 1410137.28 frames.], batch size: 53, lr: 2.44e-04
2022-05-15 19:14:04,604 INFO [train.py:812] (5/8) Epoch 32, batch 1800, loss[loss=0.1368, simple_loss=0.2322, pruned_loss=0.02066, over 7231.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2448, pruned_loss=0.03046, over 1411165.10 frames.], batch size: 20, lr: 2.44e-04
2022-05-15 19:15:03,156 INFO [train.py:812] (5/8) Epoch 32, batch 1850, loss[loss=0.1308, simple_loss=0.2111, pruned_loss=0.02529, over 7002.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2451, pruned_loss=0.03077, over 1405609.12 frames.], batch size: 16, lr: 2.44e-04
2022-05-15 19:16:02,090 INFO [train.py:812] (5/8) Epoch 32, batch 1900, loss[loss=0.1169, simple_loss=0.2075, pruned_loss=0.01319, over 7347.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2439, pruned_loss=0.03031, over 1411485.79 frames.], batch size: 19, lr: 2.44e-04
2022-05-15 19:17:00,598 INFO [train.py:812] (5/8) Epoch 32, batch 1950, loss[loss=0.1614, simple_loss=0.2524, pruned_loss=0.03523, over 7355.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2437, pruned_loss=0.03009, over 1417346.37 frames.], batch size: 19, lr: 2.43e-04
2022-05-15 19:18:00,431 INFO [train.py:812] (5/8) Epoch 32, batch 2000, loss[loss=0.1245, simple_loss=0.215, pruned_loss=0.017, over 7273.00 frames.], tot_loss[loss=0.152, simple_loss=0.2436, pruned_loss=0.03018, over 1418971.18 frames.], batch size: 18, lr: 2.43e-04
2022-05-15 19:18:57,509 INFO [train.py:812] (5/8) Epoch 32, batch 2050, loss[loss=0.1544, simple_loss=0.2549, pruned_loss=0.02691, over 7147.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2437, pruned_loss=0.03038, over 1416710.30 frames.], batch size: 20, lr: 2.43e-04
2022-05-15 19:19:56,210 INFO [train.py:812] (5/8) Epoch 32, batch 2100, loss[loss=0.1582, simple_loss=0.2476, pruned_loss=0.03444, over 6777.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2449, pruned_loss=0.03083, over 1415933.67 frames.], batch size: 15, lr: 2.43e-04
2022-05-15 19:20:54,964 INFO [train.py:812] (5/8) Epoch 32, batch 2150, loss[loss=0.1514, simple_loss=0.2492, pruned_loss=0.02684, over 7223.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2446, pruned_loss=0.03053, over 1419394.32 frames.], batch size: 21, lr: 2.43e-04
2022-05-15 19:21:53,670 INFO [train.py:812] (5/8) Epoch 32, batch 2200, loss[loss=0.1465, simple_loss=0.2395, pruned_loss=0.02676, over 7180.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2442, pruned_loss=0.03036, over 1422343.75 frames.], batch size: 26, lr: 2.43e-04
2022-05-15 19:22:52,760 INFO [train.py:812] (5/8) Epoch 32, batch 2250, loss[loss=0.1336, simple_loss=0.2224, pruned_loss=0.02234, over 7057.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2442, pruned_loss=0.0302, over 1423855.95 frames.], batch size: 18, lr: 2.43e-04
2022-05-15 19:23:52,305 INFO [train.py:812] (5/8) Epoch 32, batch 2300, loss[loss=0.1425, simple_loss=0.2451, pruned_loss=0.01995, over 7327.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2443, pruned_loss=0.03039, over 1420008.91 frames.], batch size: 22, lr: 2.43e-04
2022-05-15 19:24:49,713 INFO [train.py:812] (5/8) Epoch 32, batch 2350, loss[loss=0.1242, simple_loss=0.2066, pruned_loss=0.02088, over 7273.00 frames.], tot_loss[loss=0.1538, simple_loss=0.246, pruned_loss=0.03081, over 1424203.80 frames.], batch size: 17, lr: 2.43e-04
2022-05-15 19:25:48,453 INFO [train.py:812] (5/8) Epoch 32, batch 2400, loss[loss=0.1647, simple_loss=0.2523, pruned_loss=0.03852, over 7321.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2467, pruned_loss=0.0315, over 1419929.21 frames.], batch size: 20, lr: 2.43e-04
2022-05-15 19:26:47,723 INFO [train.py:812] (5/8) Epoch 32, batch 2450, loss[loss=0.1758, simple_loss=0.2742, pruned_loss=0.03864, over 7135.00 frames.], tot_loss[loss=0.1542, simple_loss=0.246, pruned_loss=0.03118, over 1421431.13 frames.], batch size: 26, lr: 2.43e-04
2022-05-15 19:27:46,272 INFO [train.py:812] (5/8) Epoch 32, batch 2500, loss[loss=0.133, simple_loss=0.2089, pruned_loss=0.02858, over 7266.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2455, pruned_loss=0.03103, over 1424223.03 frames.], batch size: 17, lr: 2.43e-04
2022-05-15 19:28:44,154 INFO [train.py:812] (5/8) Epoch 32, batch 2550, loss[loss=0.159, simple_loss=0.2536, pruned_loss=0.03218, over 7323.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2451, pruned_loss=0.0306, over 1423757.47 frames.], batch size: 20, lr: 2.43e-04
2022-05-15 19:29:41,343 INFO [train.py:812] (5/8) Epoch 32, batch 2600, loss[loss=0.15, simple_loss=0.2332, pruned_loss=0.0334, over 7139.00 frames.], tot_loss[loss=0.153, simple_loss=0.2449, pruned_loss=0.03053, over 1422317.93 frames.], batch size: 17, lr: 2.43e-04
2022-05-15 19:30:39,821 INFO [train.py:812] (5/8) Epoch 32, batch 2650, loss[loss=0.1592, simple_loss=0.2641, pruned_loss=0.02717, over 7129.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2445, pruned_loss=0.03042, over 1424793.70 frames.], batch size: 26, lr: 2.43e-04
2022-05-15 19:31:39,432 INFO [train.py:812] (5/8) Epoch 32, batch 2700, loss[loss=0.1602, simple_loss=0.2567, pruned_loss=0.03183, over 7327.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2438, pruned_loss=0.03026, over 1423735.00 frames.], batch size: 20, lr: 2.43e-04
2022-05-15 19:32:37,364 INFO [train.py:812] (5/8) Epoch 32, batch 2750, loss[loss=0.154, simple_loss=0.2499, pruned_loss=0.02908, over 7104.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2444, pruned_loss=0.03026, over 1425096.85 frames.], batch size: 28, lr: 2.43e-04
2022-05-15 19:33:35,467 INFO [train.py:812] (5/8) Epoch 32, batch 2800, loss[loss=0.1524, simple_loss=0.2364, pruned_loss=0.03414, over 7400.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2437, pruned_loss=0.02986, over 1424380.01 frames.], batch size: 18, lr: 2.43e-04
2022-05-15 19:34:34,363 INFO [train.py:812] (5/8) Epoch 32, batch 2850, loss[loss=0.1622, simple_loss=0.2654, pruned_loss=0.02945, over 6184.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2428, pruned_loss=0.02942, over 1420527.45 frames.], batch size: 37, lr: 2.43e-04
2022-05-15 19:35:32,669 INFO [train.py:812] (5/8) Epoch 32, batch 2900, loss[loss=0.1331, simple_loss=0.2232, pruned_loss=0.02154, over 7237.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2439, pruned_loss=0.02977, over 1425036.32 frames.], batch size: 20, lr: 2.43e-04
2022-05-15 19:36:30,938 INFO [train.py:812] (5/8) Epoch 32, batch 2950, loss[loss=0.1503, simple_loss=0.2455, pruned_loss=0.0275, over 7200.00 frames.], tot_loss[loss=0.152, simple_loss=0.2441, pruned_loss=0.0299, over 1417919.37 frames.], batch size: 23, lr: 2.43e-04
2022-05-15 19:37:29,687 INFO [train.py:812] (5/8) Epoch 32, batch 3000, loss[loss=0.1765, simple_loss=0.2746, pruned_loss=0.03924, over 7438.00 frames.], tot_loss[loss=0.1528, simple_loss=0.245, pruned_loss=0.0303, over 1419326.44 frames.], batch size: 20, lr: 2.43e-04
2022-05-15 19:37:29,688 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 19:37:37,094 INFO [train.py:841] (5/8) Epoch 32, validation: loss=0.1532, simple_loss=0.2494, pruned_loss=0.02852, over 698248.00 frames.
2022-05-15 19:38:35,483 INFO [train.py:812] (5/8) Epoch 32, batch 3050, loss[loss=0.1604, simple_loss=0.2419, pruned_loss=0.03943, over 7302.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2445, pruned_loss=0.03045, over 1423050.02 frames.], batch size: 25, lr: 2.43e-04
2022-05-15 19:39:34,741 INFO [train.py:812] (5/8) Epoch 32, batch 3100, loss[loss=0.1524, simple_loss=0.2429, pruned_loss=0.03096, over 7148.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2442, pruned_loss=0.03046, over 1426752.50 frames.], batch size: 28, lr: 2.42e-04
2022-05-15 19:40:34,133 INFO [train.py:812] (5/8) Epoch 32, batch 3150, loss[loss=0.1274, simple_loss=0.2135, pruned_loss=0.02064, over 7283.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2441, pruned_loss=0.03051, over 1424286.20 frames.], batch size: 17, lr: 2.42e-04
2022-05-15 19:41:32,598 INFO [train.py:812] (5/8) Epoch 32, batch 3200, loss[loss=0.1547, simple_loss=0.2536, pruned_loss=0.02787, over 7114.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2447, pruned_loss=0.03025, over 1426772.75 frames.], batch size: 21, lr: 2.42e-04
2022-05-15 19:42:31,640 INFO [train.py:812] (5/8) Epoch 32, batch 3250, loss[loss=0.1604, simple_loss=0.2545, pruned_loss=0.0332, over 7341.00 frames.], tot_loss[loss=0.1527, simple_loss=0.245, pruned_loss=0.03023, over 1427297.34 frames.], batch size: 22, lr: 2.42e-04
2022-05-15 19:43:31,260 INFO [train.py:812] (5/8) Epoch 32, batch 3300, loss[loss=0.1451, simple_loss=0.2399, pruned_loss=0.02511, over 7440.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2437, pruned_loss=0.02988, over 1422943.69 frames.], batch size: 20, lr: 2.42e-04
2022-05-15 19:44:30,446 INFO [train.py:812] (5/8) Epoch 32, batch 3350, loss[loss=0.1417, simple_loss=0.2492, pruned_loss=0.01712, over 7312.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2431, pruned_loss=0.02998, over 1424930.37 frames.], batch size: 21, lr: 2.42e-04
2022-05-15 19:45:29,622 INFO [train.py:812] (5/8) Epoch 32, batch 3400, loss[loss=0.1394, simple_loss=0.2335, pruned_loss=0.02266, over 7326.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2435, pruned_loss=0.02991, over 1422168.54 frames.], batch size: 20, lr: 2.42e-04
2022-05-15 19:46:27,573 INFO [train.py:812] (5/8) Epoch 32, batch 3450, loss[loss=0.1676, simple_loss=0.252, pruned_loss=0.04167, over 7195.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2445, pruned_loss=0.03006, over 1425097.96 frames.], batch size: 22, lr: 2.42e-04
2022-05-15 19:47:26,410 INFO [train.py:812] (5/8) Epoch 32, batch 3500, loss[loss=0.1625, simple_loss=0.259, pruned_loss=0.03296, over 7281.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2447, pruned_loss=0.02987, over 1427749.63 frames.], batch size: 24, lr: 2.42e-04
2022-05-15 19:48:25,211 INFO [train.py:812] (5/8) Epoch 32, batch 3550, loss[loss=0.1658, simple_loss=0.2597, pruned_loss=0.03601, over 7370.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2443, pruned_loss=0.02999, over 1430728.82 frames.], batch size: 23, lr: 2.42e-04
2022-05-15 19:49:24,688 INFO [train.py:812] (5/8) Epoch 32, batch 3600, loss[loss=0.135, simple_loss=0.2399, pruned_loss=0.015, over 6332.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2445, pruned_loss=0.03017, over 1427895.56 frames.], batch size: 37, lr: 2.42e-04
2022-05-15 19:50:24,031 INFO [train.py:812] (5/8) Epoch 32, batch 3650, loss[loss=0.1537, simple_loss=0.2485, pruned_loss=0.0294, over 7229.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2443, pruned_loss=0.03004, over 1427794.11 frames.], batch size: 20, lr: 2.42e-04
2022-05-15 19:51:24,137 INFO [train.py:812] (5/8) Epoch 32, batch 3700, loss[loss=0.127, simple_loss=0.2171, pruned_loss=0.01846, over 7129.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2432, pruned_loss=0.02953, over 1429985.13 frames.], batch size: 17, lr: 2.42e-04
2022-05-15 19:52:22,863 INFO [train.py:812] (5/8) Epoch 32, batch 3750, loss[loss=0.1582, simple_loss=0.2576, pruned_loss=0.02941, over 7205.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2443, pruned_loss=0.02999, over 1424129.32 frames.], batch size: 23, lr: 2.42e-04
2022-05-15 19:53:21,632 INFO [train.py:812] (5/8) Epoch 32, batch 3800, loss[loss=0.1434, simple_loss=0.2392, pruned_loss=0.02378, over 7379.00 frames.], tot_loss[loss=0.1519, simple_loss=0.244, pruned_loss=0.0299, over 1426075.71 frames.], batch size: 23, lr: 2.42e-04
2022-05-15 19:54:19,348 INFO [train.py:812] (5/8) Epoch 32, batch 3850, loss[loss=0.1489, simple_loss=0.2443, pruned_loss=0.02676, over 7439.00 frames.], tot_loss[loss=0.152, simple_loss=0.244, pruned_loss=0.03003, over 1427847.55 frames.], batch size: 20, lr: 2.42e-04
2022-05-15 19:55:27,956 INFO [train.py:812] (5/8) Epoch 32, batch 3900, loss[loss=0.1376, simple_loss=0.2285, pruned_loss=0.02333, over 7162.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2441, pruned_loss=0.03, over 1428605.31 frames.], batch size: 18, lr: 2.42e-04
2022-05-15 19:56:25,324 INFO [train.py:812] (5/8) Epoch 32, batch 3950, loss[loss=0.1921, simple_loss=0.2878, pruned_loss=0.04817, over 7220.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2442, pruned_loss=0.03023, over 1423659.56 frames.], batch size: 21, lr: 2.42e-04
2022-05-15 19:57:24,548 INFO [train.py:812] (5/8) Epoch 32, batch 4000, loss[loss=0.1201, simple_loss=0.2066, pruned_loss=0.0168, over 7405.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2434, pruned_loss=0.03043, over 1420368.02 frames.], batch size: 18, lr: 2.42e-04
2022-05-15 19:58:22,808 INFO [train.py:812] (5/8) Epoch 32, batch 4050, loss[loss=0.1987, simple_loss=0.2947, pruned_loss=0.05132, over 7385.00 frames.], tot_loss[loss=0.1526, simple_loss=0.244, pruned_loss=0.03062, over 1418635.24 frames.], batch size: 23, lr: 2.42e-04
2022-05-15 19:59:20,915 INFO [train.py:812] (5/8) Epoch 32, batch 4100, loss[loss=0.1726, simple_loss=0.2557, pruned_loss=0.0448, over 7212.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2445, pruned_loss=0.03071, over 1417715.51 frames.], batch size: 22, lr: 2.42e-04
2022-05-15 20:00:19,815 INFO [train.py:812] (5/8) Epoch 32, batch 4150, loss[loss=0.1655, simple_loss=0.2576, pruned_loss=0.03669, over 7218.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2443, pruned_loss=0.03052, over 1421951.75 frames.], batch size: 21, lr: 2.42e-04
2022-05-15 20:01:19,536 INFO [train.py:812] (5/8) Epoch 32, batch 4200, loss[loss=0.1416, simple_loss=0.2358, pruned_loss=0.02371, over 7326.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2435, pruned_loss=0.03017, over 1421921.46 frames.], batch size: 20, lr: 2.42e-04
2022-05-15 20:02:17,831 INFO [train.py:812] (5/8) Epoch 32, batch 4250, loss[loss=0.1219, simple_loss=0.2177, pruned_loss=0.01304, over 7263.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2444, pruned_loss=0.03058, over 1420673.79 frames.], batch size: 19, lr: 2.42e-04
2022-05-15 20:03:17,410 INFO [train.py:812] (5/8) Epoch 32, batch 4300, loss[loss=0.1255, simple_loss=0.2093, pruned_loss=0.02086, over 7405.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2434, pruned_loss=0.03041, over 1421061.73 frames.], batch size: 18, lr: 2.42e-04
2022-05-15 20:04:16,082 INFO [train.py:812] (5/8) Epoch 32, batch 4350, loss[loss=0.14, simple_loss=0.2243, pruned_loss=0.02782, over 7174.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2443, pruned_loss=0.03063, over 1421425.35 frames.], batch size: 18, lr: 2.41e-04
2022-05-15 20:05:14,954 INFO [train.py:812] (5/8) Epoch 32, batch 4400, loss[loss=0.16, simple_loss=0.2531, pruned_loss=0.03344, over 7308.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2452, pruned_loss=0.03101, over 1407454.26 frames.], batch size: 25, lr: 2.41e-04
2022-05-15 20:06:12,626 INFO [train.py:812] (5/8) Epoch 32, batch 4450, loss[loss=0.132, simple_loss=0.219, pruned_loss=0.02249, over 6797.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2454, pruned_loss=0.03087, over 1404175.79 frames.], batch size: 15, lr: 2.41e-04
2022-05-15 20:07:11,376 INFO [train.py:812] (5/8) Epoch 32, batch 4500, loss[loss=0.1588, simple_loss=0.2556, pruned_loss=0.03097, over 6680.00 frames.], tot_loss[loss=0.1553, simple_loss=0.247, pruned_loss=0.03179, over 1396339.50 frames.], batch size: 31, lr: 2.41e-04
2022-05-15 20:08:09,886 INFO [train.py:812] (5/8) Epoch 32, batch 4550, loss[loss=0.1857, simple_loss=0.2778, pruned_loss=0.04677, over 5016.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2472, pruned_loss=0.0325, over 1357929.29 frames.], batch size: 53, lr: 2.41e-04
2022-05-15 20:09:17,619 INFO [train.py:812] (5/8) Epoch 33, batch 0, loss[loss=0.1509, simple_loss=0.2388, pruned_loss=0.03152, over 6753.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2388, pruned_loss=0.03152, over 6753.00 frames.], batch size: 31, lr: 2.38e-04
2022-05-15 20:10:15,653 INFO [train.py:812] (5/8) Epoch 33, batch 50, loss[loss=0.1663, simple_loss=0.2591, pruned_loss=0.03675, over 5158.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2465, pruned_loss=0.03012, over 313853.92 frames.], batch size: 52, lr: 2.38e-04
2022-05-15 20:11:14,516 INFO [train.py:812] (5/8) Epoch 33, batch 100, loss[loss=0.1391, simple_loss=0.238, pruned_loss=0.02009, over 6258.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2467, pruned_loss=0.03036, over 558781.67 frames.], batch size: 37, lr: 2.38e-04
2022-05-15 20:12:13,165 INFO [train.py:812] (5/8) Epoch 33, batch 150, loss[loss=0.1555, simple_loss=0.2474, pruned_loss=0.0318, over 7198.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2471, pruned_loss=0.03022, over 751765.47 frames.], batch size: 23, lr: 2.37e-04
2022-05-15 20:13:12,825 INFO [train.py:812] (5/8) Epoch 33, batch 200, loss[loss=0.1297, simple_loss=0.2115, pruned_loss=0.02394, over 6991.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2453, pruned_loss=0.03013, over 894740.21 frames.], batch size: 16, lr: 2.37e-04
2022-05-15 20:14:10,197 INFO [train.py:812] (5/8) Epoch 33, batch 250, loss[loss=0.1336, simple_loss=0.229, pruned_loss=0.01907, over 7224.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2456, pruned_loss=0.0301, over 1009667.74 frames.], batch size: 20, lr: 2.37e-04
2022-05-15 20:15:08,984 INFO [train.py:812] (5/8) Epoch 33, batch 300, loss[loss=0.1751, simple_loss=0.2804, pruned_loss=0.03491, over 6727.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2469, pruned_loss=0.03093, over 1092768.98 frames.], batch size: 31, lr: 2.37e-04
2022-05-15 20:16:07,540 INFO [train.py:812] (5/8) Epoch 33, batch 350, loss[loss=0.1359, simple_loss=0.2167, pruned_loss=0.02755, over 7403.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2461, pruned_loss=0.03069, over 1163556.05 frames.], batch size: 18, lr: 2.37e-04
2022-05-15 20:17:07,054 INFO [train.py:812] (5/8) Epoch 33, batch 400, loss[loss=0.1488, simple_loss=0.2474, pruned_loss=0.02503, over 7430.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2446, pruned_loss=0.03036, over 1220023.62 frames.], batch size: 20, lr: 2.37e-04
2022-05-15 20:18:06,465 INFO [train.py:812] (5/8) Epoch 33, batch 450, loss[loss=0.1711, simple_loss=0.2648, pruned_loss=0.03871, over 6758.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2441, pruned_loss=0.03025, over 1262640.46 frames.], batch size: 31, lr: 2.37e-04
2022-05-15 20:19:06,079 INFO [train.py:812] (5/8) Epoch 33, batch 500, loss[loss=0.1807, simple_loss=0.2682, pruned_loss=0.0466, over 7187.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2448, pruned_loss=0.03055, over 1300189.50 frames.], batch size: 23, lr: 2.37e-04
2022-05-15 20:20:04,286 INFO [train.py:812] (5/8) Epoch 33, batch 550, loss[loss=0.1581, simple_loss=0.2542, pruned_loss=0.031, over 7313.00 frames.], tot_loss[loss=0.1537, simple_loss=0.246, pruned_loss=0.03067, over 1329030.84 frames.], batch size: 21, lr: 2.37e-04
2022-05-15 20:21:03,110 INFO [train.py:812] (5/8) Epoch 33, batch 600, loss[loss=0.1587, simple_loss=0.2535, pruned_loss=0.03201, over 7287.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2458, pruned_loss=0.03052, over 1347254.72 frames.], batch size: 24, lr: 2.37e-04
2022-05-15 20:22:00,729 INFO [train.py:812] (5/8) Epoch 33, batch 650, loss[loss=0.1575, simple_loss=0.2516, pruned_loss=0.03174, over 7160.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2459, pruned_loss=0.03041, over 1363664.51 frames.], batch size: 26, lr: 2.37e-04
2022-05-15 20:23:00,262 INFO [train.py:812] (5/8) Epoch 33, batch 700, loss[loss=0.1567, simple_loss=0.2406, pruned_loss=0.03638, over 7133.00 frames.], tot_loss[loss=0.1535, simple_loss=0.246, pruned_loss=0.03049, over 1374011.78 frames.], batch size: 17, lr: 2.37e-04
2022-05-15 20:23:58,719 INFO [train.py:812] (5/8) Epoch 33, batch 750, loss[loss=0.1437, simple_loss=0.2393, pruned_loss=0.02401, over 7216.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2455, pruned_loss=0.03036, over 1379961.40 frames.], batch size: 21, lr: 2.37e-04
2022-05-15 20:24:57,925 INFO [train.py:812] (5/8) Epoch 33, batch 800, loss[loss=0.157, simple_loss=0.245, pruned_loss=0.03448, over 7433.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2449, pruned_loss=0.0307, over 1390916.50 frames.], batch size: 20, lr: 2.37e-04
2022-05-15 20:25:55,899 INFO [train.py:812] (5/8) Epoch 33, batch 850, loss[loss=0.1677, simple_loss=0.265, pruned_loss=0.03524, over 7376.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2444, pruned_loss=0.03027, over 1398595.37 frames.], batch size: 23, lr: 2.37e-04
2022-05-15 20:26:54,544 INFO [train.py:812] (5/8) Epoch 33, batch 900, loss[loss=0.1521, simple_loss=0.2422, pruned_loss=0.03099, over 7219.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2433, pruned_loss=0.02986, over 1407852.63 frames.], batch size: 23, lr: 2.37e-04
2022-05-15 20:27:51,772 INFO [train.py:812] (5/8) Epoch 33, batch 950, loss[loss=0.1541, simple_loss=0.2413, pruned_loss=0.03344, over 7437.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2438, pruned_loss=0.02982, over 1412630.26 frames.], batch size: 20, lr: 2.37e-04
2022-05-15 20:28:51,353 INFO [train.py:812] (5/8) Epoch 33, batch 1000, loss[loss=0.1618, simple_loss=0.2684, pruned_loss=0.02764, over 7205.00 frames.], tot_loss[loss=0.1511, simple_loss=0.243, pruned_loss=0.02962, over 1412917.41 frames.], batch size: 23, lr: 2.37e-04
2022-05-15 20:29:49,398 INFO [train.py:812] (5/8) Epoch 33, batch 1050, loss[loss=0.1738, simple_loss=0.2647, pruned_loss=0.04149, over 7115.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2434, pruned_loss=0.02982, over 1412408.27 frames.], batch size: 28, lr: 2.37e-04
2022-05-15 20:30:48,555 INFO [train.py:812] (5/8) Epoch 33, batch 1100, loss[loss=0.153, simple_loss=0.2583, pruned_loss=0.0239, over 7295.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2437, pruned_loss=0.03, over 1417243.20 frames.], batch size: 24, lr: 2.37e-04
2022-05-15 20:31:47,029 INFO [train.py:812] (5/8) Epoch 33, batch 1150, loss[loss=0.1561, simple_loss=0.2546, pruned_loss=0.02877, over 7179.00 frames.], tot_loss[loss=0.1517, simple_loss=0.244, pruned_loss=0.02973, over 1418777.21 frames.], batch size: 23, lr: 2.37e-04
2022-05-15 20:32:51,511 INFO [train.py:812] (5/8) Epoch 33, batch 1200, loss[loss=0.1913, simple_loss=0.288, pruned_loss=0.04731, over 7160.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2442, pruned_loss=0.02952, over 1421360.67 frames.], batch size: 26, lr: 2.37e-04
2022-05-15 20:33:50,441 INFO [train.py:812] (5/8) Epoch 33, batch 1250, loss[loss=0.1727, simple_loss=0.2639, pruned_loss=0.0408, over 6362.00 frames.], tot_loss[loss=0.152, simple_loss=0.2446, pruned_loss=0.02968, over 1419797.76 frames.], batch size: 38, lr: 2.37e-04
2022-05-15 20:34:50,210 INFO [train.py:812] (5/8) Epoch 33, batch 1300, loss[loss=0.1586, simple_loss=0.2621, pruned_loss=0.02751, over 7222.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2441, pruned_loss=0.02984, over 1420196.24 frames.], batch size: 21, lr: 2.37e-04
2022-05-15 20:35:49,519 INFO [train.py:812] (5/8) Epoch 33, batch 1350, loss[loss=0.14, simple_loss=0.2194, pruned_loss=0.03026, over 7277.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2439, pruned_loss=0.03022, over 1419430.20 frames.], batch size: 17, lr: 2.37e-04
2022-05-15 20:36:48,921 INFO [train.py:812] (5/8) Epoch 33, batch 1400, loss[loss=0.1576, simple_loss=0.2613, pruned_loss=0.02697, over 7144.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2437, pruned_loss=0.03001, over 1421798.93 frames.], batch size: 20, lr: 2.36e-04
2022-05-15 20:37:47,487 INFO [train.py:812] (5/8) Epoch 33, batch 1450, loss[loss=0.1667, simple_loss=0.2664, pruned_loss=0.03353, over 6671.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2444, pruned_loss=0.03025, over 1424756.09 frames.], batch size: 31, lr: 2.36e-04
2022-05-15 20:38:46,318 INFO [train.py:812] (5/8) Epoch 33, batch 1500, loss[loss=0.1828, simple_loss=0.2716, pruned_loss=0.04699, over 4956.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2443, pruned_loss=0.03041, over 1422056.94 frames.], batch size: 52, lr: 2.36e-04
2022-05-15 20:39:44,933 INFO [train.py:812] (5/8) Epoch 33, batch 1550, loss[loss=0.1661, simple_loss=0.2561, pruned_loss=0.03808, over 7231.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2446, pruned_loss=0.03055, over 1418496.88 frames.], batch size: 21, lr: 2.36e-04
2022-05-15 20:40:43,837 INFO [train.py:812] (5/8) Epoch 33, batch 1600, loss[loss=0.1564, simple_loss=0.2626, pruned_loss=0.02504, over 7424.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2445, pruned_loss=0.03052, over 1419640.28 frames.], batch size: 21, lr: 2.36e-04
2022-05-15 20:41:42,723 INFO [train.py:812] (5/8) Epoch 33, batch 1650, loss[loss=0.1447, simple_loss=0.2378, pruned_loss=0.02579, over 7212.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2444, pruned_loss=0.03054, over 1420702.75 frames.], batch size: 21, lr: 2.36e-04
2022-05-15 20:42:41,749 INFO [train.py:812] (5/8) Epoch 33, batch 1700, loss[loss=0.1722, simple_loss=0.2697, pruned_loss=0.0373, over 7307.00 frames.], tot_loss[loss=0.1523, simple_loss=0.244, pruned_loss=0.03034, over 1423664.02 frames.], batch size: 24, lr: 2.36e-04
2022-05-15 20:43:40,818 INFO [train.py:812] (5/8) Epoch 33, batch 1750, loss[loss=0.1387, simple_loss=0.2394, pruned_loss=0.01903, over 7027.00 frames.], tot_loss[loss=0.1532, simple_loss=0.245, pruned_loss=0.03066, over 1416788.26 frames.], batch size: 28, lr: 2.36e-04
2022-05-15 20:44:39,996 INFO [train.py:812] (5/8) Epoch 33, batch 1800, loss[loss=0.1418, simple_loss=0.2272, pruned_loss=0.02824, over 7253.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2445, pruned_loss=0.03068, over 1421012.16 frames.], batch size: 19, lr: 2.36e-04
2022-05-15 20:45:38,874 INFO [train.py:812] (5/8) Epoch 33, batch 1850, loss[loss=0.1264, simple_loss=0.2195, pruned_loss=0.0166, over 7312.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2444, pruned_loss=0.03074, over 1423185.96 frames.], batch size: 21, lr: 2.36e-04
2022-05-15 20:46:37,340 INFO [train.py:812] (5/8) Epoch 33, batch 1900, loss[loss=0.1932, simple_loss=0.2815, pruned_loss=0.05243, over 7383.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2439, pruned_loss=0.03018, over 1425335.01 frames.], batch size: 23, lr: 2.36e-04
2022-05-15 20:47:35,891 INFO [train.py:812] (5/8) Epoch 33, batch 1950, loss[loss=0.1632, simple_loss=0.254, pruned_loss=0.03624, over 7267.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2444, pruned_loss=0.03012, over 1423904.80 frames.], batch size: 24, lr: 2.36e-04
2022-05-15 20:48:34,897 INFO [train.py:812] (5/8) Epoch 33, batch 2000, loss[loss=0.1441, simple_loss=0.2401, pruned_loss=0.02404, over 6434.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2445, pruned_loss=0.03018, over 1425530.87 frames.], batch size: 38, lr: 2.36e-04
2022-05-15 20:49:32,706 INFO [train.py:812] (5/8) Epoch 33, batch 2050, loss[loss=0.1254, simple_loss=0.2126, pruned_loss=0.01908, over 7163.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2441, pruned_loss=0.02973, over 1426589.99 frames.], batch size: 18, lr: 2.36e-04
2022-05-15 20:50:32,320 INFO [train.py:812] (5/8) Epoch 33, batch 2100, loss[loss=0.1464, simple_loss=0.2356, pruned_loss=0.02859, over 7152.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2433, pruned_loss=0.02967, over 1427743.91 frames.], batch size: 19, lr: 2.36e-04
2022-05-15 20:51:30,236 INFO [train.py:812] (5/8) Epoch 33, batch 2150, loss[loss=0.1275, simple_loss=0.2207, pruned_loss=0.01713, over 7405.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2434, pruned_loss=0.02948, over 1428843.20 frames.], batch size: 18, lr: 2.36e-04
2022-05-15 20:52:28,375 INFO [train.py:812] (5/8) Epoch 33, batch 2200, loss[loss=0.1983, simple_loss=0.2943, pruned_loss=0.05109, over 5040.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2436, pruned_loss=0.02978, over 1423622.08 frames.], batch size: 52, lr: 2.36e-04
2022-05-15 20:53:26,612 INFO [train.py:812] (5/8) Epoch 33, batch 2250, loss[loss=0.1779, simple_loss=0.2681, pruned_loss=0.04387, over 7183.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2431, pruned_loss=0.0297, over 1421011.17 frames.], batch size: 26, lr: 2.36e-04
2022-05-15 20:54:25,513 INFO [train.py:812] (5/8) Epoch 33, batch 2300, loss[loss=0.1663, simple_loss=0.2578, pruned_loss=0.03743, over 7213.00 frames.], tot_loss[loss=0.151, simple_loss=0.2425, pruned_loss=0.02977, over 1419998.04 frames.], batch size: 22, lr: 2.36e-04
2022-05-15 20:55:24,370 INFO [train.py:812] (5/8) Epoch 33, batch 2350, loss[loss=0.137, simple_loss=0.2303, pruned_loss=0.02186, over 6810.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2427, pruned_loss=0.03014, over 1422268.93 frames.], batch size: 15, lr: 2.36e-04
2022-05-15 20:56:22,959 INFO [train.py:812] (5/8) Epoch 33, batch 2400, loss[loss=0.1636, simple_loss=0.2583, pruned_loss=0.03451, over 7423.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2427, pruned_loss=0.0301, over 1423450.31 frames.], batch size: 20, lr: 2.36e-04
2022-05-15 20:57:40,433 INFO [train.py:812] (5/8) Epoch 33, batch 2450, loss[loss=0.1456, simple_loss=0.2373, pruned_loss=0.02697, over 7270.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2422, pruned_loss=0.02998, over 1425977.04 frames.], batch size: 19, lr: 2.36e-04
2022-05-15 20:58:40,008 INFO [train.py:812] (5/8) Epoch 33, batch 2500, loss[loss=0.1608, simple_loss=0.2634, pruned_loss=0.02915, over 7315.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2422, pruned_loss=0.02964, over 1428191.55 frames.], batch size: 21, lr: 2.36e-04
2022-05-15 20:59:48,284 INFO [train.py:812] (5/8) Epoch 33, batch 2550, loss[loss=0.1918, simple_loss=0.2757, pruned_loss=0.05389, over 7375.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2423, pruned_loss=0.02996, over 1427895.60 frames.], batch size: 23, lr: 2.36e-04
2022-05-15 21:00:46,739 INFO [train.py:812] (5/8) Epoch 33, batch 2600, loss[loss=0.1536, simple_loss=0.2529, pruned_loss=0.02719, over 7205.00 frames.], tot_loss[loss=0.151, simple_loss=0.2422, pruned_loss=0.0299, over 1428948.09 frames.], batch size: 23, lr: 2.36e-04
2022-05-15 21:01:44,972 INFO [train.py:812] (5/8) Epoch 33, batch 2650, loss[loss=0.1244, simple_loss=0.2047, pruned_loss=0.02203, over 6756.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2425, pruned_loss=0.02968, over 1424340.45 frames.], batch size: 15, lr: 2.35e-04
2022-05-15 21:02:52,790 INFO [train.py:812] (5/8) Epoch 33, batch 2700, loss[loss=0.1403, simple_loss=0.2321, pruned_loss=0.02423, over 7421.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2426, pruned_loss=0.02946, over 1425302.50 frames.], batch size: 20, lr: 2.35e-04
2022-05-15 21:04:10,603 INFO [train.py:812] (5/8) Epoch 33, batch 2750, loss[loss=0.1454, simple_loss=0.2362, pruned_loss=0.02735, over 7274.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2423, pruned_loss=0.02944, over 1426201.25 frames.], batch size: 18, lr: 2.35e-04
2022-05-15 21:05:09,525 INFO [train.py:812] (5/8) Epoch 33, batch 2800, loss[loss=0.1762, simple_loss=0.2705, pruned_loss=0.04094, over 7184.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2424, pruned_loss=0.02942, over 1425363.96 frames.], batch size: 23, lr: 2.35e-04
2022-05-15 21:06:07,210 INFO [train.py:812] (5/8) Epoch 33, batch 2850, loss[loss=0.1453, simple_loss=0.2331, pruned_loss=0.0287, over 7331.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2422, pruned_loss=0.02934, over 1427051.18 frames.], batch size: 21, lr: 2.35e-04
2022-05-15 21:07:06,356 INFO [train.py:812] (5/8) Epoch 33, batch 2900, loss[loss=0.1469, simple_loss=0.2467, pruned_loss=0.02348, over 7299.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2423, pruned_loss=0.02934, over 1425839.15 frames.], batch size: 25, lr: 2.35e-04
2022-05-15 21:08:04,491 INFO [train.py:812] (5/8) Epoch 33, batch 2950, loss[loss=0.1499, simple_loss=0.2419, pruned_loss=0.02896, over 7427.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2431, pruned_loss=0.02929, over 1427820.31 frames.], batch size: 20, lr: 2.35e-04
2022-05-15 21:09:12,189 INFO [train.py:812] (5/8) Epoch 33, batch 3000, loss[loss=0.1279, simple_loss=0.2127, pruned_loss=0.02156, over 7067.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2423, pruned_loss=0.02908, over 1427187.25 frames.], batch size: 18, lr: 2.35e-04
2022-05-15 21:09:12,190 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 21:09:19,691 INFO [train.py:841] (5/8) Epoch 33, validation: loss=0.1535, simple_loss=0.2493, pruned_loss=0.02886, over 698248.00 frames.
2022-05-15 21:10:18,072 INFO [train.py:812] (5/8) Epoch 33, batch 3050, loss[loss=0.1557, simple_loss=0.2568, pruned_loss=0.02729, over 6368.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2417, pruned_loss=0.02892, over 1423043.61 frames.], batch size: 38, lr: 2.35e-04
2022-05-15 21:11:15,930 INFO [train.py:812] (5/8) Epoch 33, batch 3100, loss[loss=0.1779, simple_loss=0.2729, pruned_loss=0.0414, over 7376.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2426, pruned_loss=0.02921, over 1423016.45 frames.], batch size: 23, lr: 2.35e-04
2022-05-15 21:12:14,883 INFO [train.py:812] (5/8) Epoch 33, batch 3150, loss[loss=0.1271, simple_loss=0.2168, pruned_loss=0.01869, over 7063.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2423, pruned_loss=0.02902, over 1420438.39 frames.], batch size: 18, lr: 2.35e-04
2022-05-15 21:13:13,020 INFO [train.py:812] (5/8) Epoch 33, batch 3200, loss[loss=0.1317, simple_loss=0.2218, pruned_loss=0.02085, over 7204.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2429, pruned_loss=0.02915, over 1421541.91 frames.], batch size: 16, lr: 2.35e-04
2022-05-15 21:14:11,706 INFO [train.py:812] (5/8) Epoch 33, batch 3250, loss[loss=0.1279, simple_loss=0.2147, pruned_loss=0.0205, over 7258.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2431, pruned_loss=0.02964, over 1418304.67 frames.], batch size: 18, lr: 2.35e-04
2022-05-15 21:15:11,675 INFO [train.py:812] (5/8) Epoch 33, batch 3300, loss[loss=0.1703, simple_loss=0.267, pruned_loss=0.03678, over 7232.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2427, pruned_loss=0.02922, over 1423759.01 frames.], batch size: 20, lr: 2.35e-04
2022-05-15 21:16:10,446 INFO [train.py:812] (5/8) Epoch 33, batch 3350, loss[loss=0.1598, simple_loss=0.2495, pruned_loss=0.035, over 7316.00 frames.], tot_loss[loss=0.1508, simple_loss=0.243, pruned_loss=0.02929, over 1427387.66 frames.], batch size: 21, lr: 2.35e-04
2022-05-15 21:17:09,944 INFO [train.py:812] (5/8) Epoch 33, batch 3400, loss[loss=0.1474, simple_loss=0.2378, pruned_loss=0.02843, over 7277.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2426, pruned_loss=0.02931, over 1427667.53 frames.], batch size: 18, lr: 2.35e-04
2022-05-15 21:18:09,761 INFO [train.py:812] (5/8) Epoch 33, batch 3450, loss[loss=0.1339, simple_loss=0.2318, pruned_loss=0.01799, over 7337.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2431, pruned_loss=0.0296, over 1431553.52 frames.], batch size: 20, lr: 2.35e-04
2022-05-15 21:19:07,582 INFO [train.py:812] (5/8) Epoch 33, batch 3500, loss[loss=0.154, simple_loss=0.257, pruned_loss=0.02551, over 7385.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2441, pruned_loss=0.0298, over 1427723.37 frames.], batch size: 23, lr: 2.35e-04
2022-05-15 21:20:05,726 INFO [train.py:812] (5/8) Epoch 33, batch 3550, loss[loss=0.1428, simple_loss=0.2278, pruned_loss=0.0289, over 7404.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2437, pruned_loss=0.02975, over 1427269.50 frames.], batch size: 18, lr: 2.35e-04
2022-05-15 21:21:04,470 INFO [train.py:812] (5/8) Epoch 33, batch 3600, loss[loss=0.1415, simple_loss=0.2281, pruned_loss=0.02745, over 7334.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2439, pruned_loss=0.02986, over 1423309.89 frames.], batch size: 20, lr: 2.35e-04
2022-05-15 21:22:03,594 INFO [train.py:812] (5/8) Epoch 33, batch 3650, loss[loss=0.1552, simple_loss=0.2445, pruned_loss=0.03295, over 7330.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2435, pruned_loss=0.02973, over 1422784.71 frames.], batch size: 20, lr: 2.35e-04
2022-05-15 21:23:02,594 INFO [train.py:812] (5/8) Epoch 33, batch 3700, loss[loss=0.1385, simple_loss=0.2254, pruned_loss=0.02577, over 7291.00 frames.], tot_loss[loss=0.152, simple_loss=0.244, pruned_loss=0.03003, over 1426452.05 frames.], batch size: 17, lr: 2.35e-04
2022-05-15 21:24:01,234 INFO [train.py:812] (5/8) Epoch 33, batch 3750, loss[loss=0.1738, simple_loss=0.2663, pruned_loss=0.04063, over 7226.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2435, pruned_loss=0.02995, over 1426895.41 frames.], batch size: 21, lr: 2.35e-04
2022-05-15 21:25:00,726 INFO [train.py:812] (5/8) Epoch 33, batch 3800, loss[loss=0.1647, simple_loss=0.263, pruned_loss=0.0332, over 7211.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2426, pruned_loss=0.02983, over 1427179.26 frames.], batch size: 23, lr: 2.35e-04
2022-05-15 21:25:58,508 INFO [train.py:812] (5/8) Epoch 33, batch 3850, loss[loss=0.1606, simple_loss=0.2582, pruned_loss=0.0315, over 7311.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2423, pruned_loss=0.02939, over 1428549.23 frames.], batch size: 21, lr: 2.35e-04
2022-05-15 21:26:57,076 INFO [train.py:812] (5/8) Epoch 33, batch 3900, loss[loss=0.131, simple_loss=0.2197, pruned_loss=0.02111, over 6789.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2434, pruned_loss=0.02948, over 1428459.63 frames.], batch size: 15, lr: 2.35e-04
2022-05-15 21:27:55,703 INFO [train.py:812] (5/8) Epoch 33, batch 3950, loss[loss=0.1338, simple_loss=0.2193, pruned_loss=0.02419, over 7420.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2445, pruned_loss=0.02984, over 1430754.05 frames.], batch size: 18, lr: 2.34e-04
2022-05-15 21:28:55,478 INFO [train.py:812] (5/8) Epoch 33, batch 4000, loss[loss=0.1528, simple_loss=0.2544, pruned_loss=0.02567, over 6404.00 frames.], tot_loss[loss=0.151, simple_loss=0.2433, pruned_loss=0.02933, over 1431113.73 frames.], batch size: 38, lr: 2.34e-04
2022-05-15 21:29:54,322 INFO [train.py:812] (5/8) Epoch 33, batch 4050, loss[loss=0.1401, simple_loss=0.2261, pruned_loss=0.02704, over 7267.00 frames.], tot_loss[loss=0.151, simple_loss=0.2433, pruned_loss=0.02933, over 1426944.49 frames.], batch size: 18, lr: 2.34e-04
2022-05-15 21:30:52,667 INFO [train.py:812] (5/8) Epoch 33, batch 4100, loss[loss=0.1624, simple_loss=0.2537, pruned_loss=0.03552, over 7158.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2433, pruned_loss=0.02974, over 1421147.91 frames.], batch size: 26, lr: 2.34e-04
2022-05-15 21:31:50,547 INFO [train.py:812] (5/8) Epoch 33, batch 4150, loss[loss=0.1318, simple_loss=0.2191, pruned_loss=0.02222, over 6804.00 frames.], tot_loss[loss=0.1516, simple_loss=0.244, pruned_loss=0.02964, over 1421236.31 frames.], batch size: 15, lr: 2.34e-04
2022-05-15 21:32:49,101 INFO [train.py:812] (5/8) Epoch 33, batch 4200, loss[loss=0.1473, simple_loss=0.2417, pruned_loss=0.02643, over 7267.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2436, pruned_loss=0.02971, over 1419384.59 frames.], batch size: 19, lr: 2.34e-04
2022-05-15 21:33:48,271 INFO [train.py:812] (5/8) Epoch 33, batch 4250, loss[loss=0.1228, simple_loss=0.2142, pruned_loss=0.01569, over 7430.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2431, pruned_loss=0.0293, over 1420749.76 frames.], batch size: 20, lr: 2.34e-04
2022-05-15 21:34:46,556 INFO [train.py:812] (5/8) Epoch 33, batch 4300, loss[loss=0.1396, simple_loss=0.2319, pruned_loss=0.02365, over 6667.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2426, pruned_loss=0.02885, over 1419887.34 frames.], batch size: 31, lr: 2.34e-04
2022-05-15 21:35:44,805 INFO [train.py:812] (5/8) Epoch 33, batch 4350, loss[loss=0.1582, simple_loss=0.2498, pruned_loss=0.03333, over 7221.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2421, pruned_loss=0.02868, over 1415905.02 frames.], batch size: 21, lr: 2.34e-04
2022-05-15 21:36:43,620 INFO [train.py:812] (5/8) Epoch 33, batch 4400, loss[loss=0.1423, simple_loss=0.2352, pruned_loss=0.02475, over 7155.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2417, pruned_loss=0.02864, over 1414948.41 frames.], batch size: 20, lr: 2.34e-04
2022-05-15 21:37:42,097 INFO [train.py:812] (5/8) Epoch 33, batch 4450, loss[loss=0.1646, simple_loss=0.2629, pruned_loss=0.03316, over 7335.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2423, pruned_loss=0.02925, over 1407492.10 frames.], batch size: 22, lr: 2.34e-04
2022-05-15 21:38:41,150 INFO [train.py:812] (5/8) Epoch 33, batch 4500, loss[loss=0.1442, simple_loss=0.2374, pruned_loss=0.02548, over 7139.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2423, pruned_loss=0.02894, over 1397940.62 frames.], batch size: 20, lr: 2.34e-04
2022-05-15 21:39:39,850 INFO [train.py:812] (5/8) Epoch 33, batch 4550, loss[loss=0.1355, simple_loss=0.2312, pruned_loss=0.01994, over 5186.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2434, pruned_loss=0.0291, over 1375383.58 frames.], batch size: 54, lr: 2.34e-04
2022-05-15 21:40:52,122 INFO [train.py:812] (5/8) Epoch 34, batch 0, loss[loss=0.1518, simple_loss=0.2357, pruned_loss=0.03393, over 7424.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2357, pruned_loss=0.03393, over 7424.00 frames.], batch size: 20, lr: 2.31e-04
2022-05-15 21:41:51,333 INFO [train.py:812] (5/8) Epoch 34, batch 50, loss[loss=0.14, simple_loss=0.2333, pruned_loss=0.0233, over 7119.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2389, pruned_loss=0.02847, over 324830.75 frames.], batch size: 28, lr: 2.30e-04
2022-05-15 21:42:51,066 INFO [train.py:812] (5/8) Epoch 34, batch 100, loss[loss=0.1765, simple_loss=0.2831, pruned_loss=0.03489, over 7115.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2447, pruned_loss=0.0303, over 565641.47 frames.], batch size: 21, lr: 2.30e-04
2022-05-15 21:43:50,303 INFO [train.py:812] (5/8) Epoch 34, batch 150, loss[loss=0.1564, simple_loss=0.2421, pruned_loss=0.03534, over 7068.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2426, pruned_loss=0.02952, over 755189.77 frames.], batch size: 18, lr: 2.30e-04
2022-05-15 21:44:49,630 INFO [train.py:812] (5/8) Epoch 34, batch 200, loss[loss=0.1255, simple_loss=0.2136, pruned_loss=0.01872, over 7276.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2417, pruned_loss=0.02959, over 904967.05 frames.], batch size: 17, lr: 2.30e-04
2022-05-15 21:45:48,776 INFO [train.py:812] (5/8) Epoch 34, batch 250, loss[loss=0.1525, simple_loss=0.2526, pruned_loss=0.02619, over 5241.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2412, pruned_loss=0.02925, over 1011995.82 frames.], batch size: 52, lr: 2.30e-04
2022-05-15 21:46:48,822 INFO [train.py:812] (5/8) Epoch 34, batch 300, loss[loss=0.1603, simple_loss=0.2735, pruned_loss=0.02349, over 7367.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2426, pruned_loss=0.02982, over 1102438.89 frames.], batch size: 23, lr: 2.30e-04
2022-05-15 21:47:46,232 INFO [train.py:812] (5/8) Epoch 34, batch 350, loss[loss=0.1303, simple_loss=0.2214, pruned_loss=0.01955, over 7132.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2441, pruned_loss=0.03016, over 1167343.53 frames.], batch size: 17, lr: 2.30e-04
2022-05-15 21:48:46,226 INFO [train.py:812] (5/8) Epoch 34, batch 400, loss[loss=0.1595, simple_loss=0.2658, pruned_loss=0.02661, over 7413.00 frames.], tot_loss[loss=0.152, simple_loss=0.2439, pruned_loss=0.03007, over 1228044.52 frames.], batch size: 21, lr: 2.30e-04
2022-05-15 21:49:44,735 INFO [train.py:812] (5/8) Epoch 34, batch 450, loss[loss=0.1286, simple_loss=0.2044, pruned_loss=0.02634, over 7412.00 frames.], tot_loss[loss=0.152, simple_loss=0.2439, pruned_loss=0.03008, over 1273318.51 frames.], batch size: 18, lr: 2.30e-04
2022-05-15 21:50:44,144 INFO [train.py:812] (5/8) Epoch 34, batch 500, loss[loss=0.1597, simple_loss=0.2455, pruned_loss=0.03697, over 7289.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2443, pruned_loss=0.03039, over 1306338.97 frames.], batch size: 24, lr: 2.30e-04
2022-05-15 21:51:42,501 INFO [train.py:812] (5/8) Epoch 34, batch 550, loss[loss=0.1583, simple_loss=0.2533, pruned_loss=0.0317, over 6427.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2434, pruned_loss=0.03012, over 1329970.51 frames.], batch size: 37, lr: 2.30e-04
2022-05-15 21:52:57,440 INFO [train.py:812] (5/8) Epoch 34, batch 600, loss[loss=0.1481, simple_loss=0.2502, pruned_loss=0.02303, over 7322.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2435, pruned_loss=0.02987, over 1352119.45 frames.], batch size: 25, lr: 2.30e-04
2022-05-15 21:53:55,925 INFO [train.py:812] (5/8) Epoch 34, batch 650, loss[loss=0.1437, simple_loss=0.2399, pruned_loss=0.02376, over 7166.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2436, pruned_loss=0.02976, over 1370491.59 frames.], batch size: 18, lr: 2.30e-04
2022-05-15 21:54:54,860 INFO [train.py:812] (5/8) Epoch 34, batch 700, loss[loss=0.133, simple_loss=0.2155, pruned_loss=0.02527, over 7126.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2427, pruned_loss=0.02938, over 1377324.81 frames.], batch size: 17, lr: 2.30e-04
2022-05-15 21:55:51,393 INFO [train.py:812] (5/8) Epoch 34, batch 750, loss[loss=0.1647, simple_loss=0.2551, pruned_loss=0.03721, over 7211.00 frames.], tot_loss[loss=0.151, simple_loss=0.2433, pruned_loss=0.02938, over 1389598.67 frames.], batch size: 23, lr: 2.30e-04
2022-05-15 21:56:50,463 INFO [train.py:812] (5/8) Epoch 34, batch 800, loss[loss=0.1475, simple_loss=0.2326, pruned_loss=0.03121, over 7269.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2436, pruned_loss=0.02956, over 1395145.86 frames.], batch size: 18, lr: 2.30e-04
2022-05-15 21:57:49,837 INFO [train.py:812] (5/8) Epoch 34, batch 850, loss[loss=0.1532, simple_loss=0.2414, pruned_loss=0.0325, over 6428.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2439, pruned_loss=0.02975, over 1404926.39 frames.], batch size: 37, lr: 2.30e-04
2022-05-15 21:58:48,165 INFO [train.py:812] (5/8) Epoch 34, batch 900, loss[loss=0.1874, simple_loss=0.2686, pruned_loss=0.05305, over 4913.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2431, pruned_loss=0.02991, over 1409698.48 frames.], batch size: 52, lr: 2.30e-04
2022-05-15 21:59:45,322 INFO [train.py:812] (5/8) Epoch 34, batch 950, loss[loss=0.1506, simple_loss=0.2324, pruned_loss=0.03436, over 7285.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2426, pruned_loss=0.03002, over 1407877.23 frames.], batch size: 18, lr: 2.30e-04
2022-05-15 22:00:43,712 INFO [train.py:812] (5/8) Epoch 34, batch 1000, loss[loss=0.1399, simple_loss=0.2331, pruned_loss=0.02336, over 7434.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2421, pruned_loss=0.02951, over 1409301.72 frames.], batch size: 20, lr: 2.30e-04
2022-05-15 22:01:41,758 INFO [train.py:812] (5/8) Epoch 34, batch 1050, loss[loss=0.1368, simple_loss=0.2246, pruned_loss=0.02447, over 7156.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2419, pruned_loss=0.02913, over 1415047.26 frames.], batch size: 19, lr: 2.30e-04
2022-05-15 22:02:40,789 INFO [train.py:812] (5/8) Epoch 34, batch 1100, loss[loss=0.1374, simple_loss=0.2334, pruned_loss=0.02069, over 6513.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2422, pruned_loss=0.02915, over 1413148.90 frames.], batch size: 38, lr: 2.30e-04
2022-05-15 22:03:39,411 INFO [train.py:812] (5/8) Epoch 34, batch 1150, loss[loss=0.1595, simple_loss=0.2534, pruned_loss=0.03278, over 7440.00 frames.], tot_loss[loss=0.15, simple_loss=0.2418, pruned_loss=0.02908, over 1416254.14 frames.], batch size: 20, lr: 2.30e-04
2022-05-15 22:04:38,145 INFO [train.py:812] (5/8) Epoch 34, batch 1200, loss[loss=0.1807, simple_loss=0.2635, pruned_loss=0.04896, over 7221.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2412, pruned_loss=0.02886, over 1420813.28 frames.], batch size: 23, lr: 2.30e-04
2022-05-15 22:05:35,694 INFO [train.py:812] (5/8) Epoch 34, batch 1250, loss[loss=0.1541, simple_loss=0.2531, pruned_loss=0.02757, over 7334.00 frames.], tot_loss[loss=0.1503, simple_loss=0.242, pruned_loss=0.02929, over 1417362.09 frames.], batch size: 22, lr: 2.30e-04
2022-05-15 22:06:34,725 INFO [train.py:812] (5/8) Epoch 34, batch 1300, loss[loss=0.1671, simple_loss=0.2556, pruned_loss=0.03931, over 7104.00 frames.], tot_loss[loss=0.15, simple_loss=0.2415, pruned_loss=0.02925, over 1417588.81 frames.], batch size: 26, lr: 2.30e-04
2022-05-15 22:07:33,161 INFO [train.py:812] (5/8) Epoch 34, batch 1350, loss[loss=0.1489, simple_loss=0.2438, pruned_loss=0.02706, over 7221.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2411, pruned_loss=0.02907, over 1419619.34 frames.], batch size: 21, lr: 2.29e-04
2022-05-15 22:08:32,137 INFO [train.py:812] (5/8) Epoch 34, batch 1400, loss[loss=0.1453, simple_loss=0.2369, pruned_loss=0.02685, over 7263.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2415, pruned_loss=0.02951, over 1422664.55 frames.], batch size: 19, lr: 2.29e-04
2022-05-15 22:09:31,082 INFO [train.py:812] (5/8) Epoch 34, batch 1450, loss[loss=0.1638, simple_loss=0.2667, pruned_loss=0.03048, over 7420.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2416, pruned_loss=0.02946, over 1425807.99 frames.], batch size: 21, lr: 2.29e-04
2022-05-15 22:10:29,312 INFO [train.py:812] (5/8) Epoch 34, batch 1500, loss[loss=0.1616, simple_loss=0.2534, pruned_loss=0.03486, over 7390.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2419, pruned_loss=0.02966, over 1424082.80 frames.], batch size: 23, lr: 2.29e-04
2022-05-15 22:11:28,511 INFO [train.py:812] (5/8) Epoch 34, batch 1550, loss[loss=0.1587, simple_loss=0.2634, pruned_loss=0.02696, over 7273.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2432, pruned_loss=0.03002, over 1421459.75 frames.], batch size: 24, lr: 2.29e-04
2022-05-15 22:12:27,920 INFO [train.py:812] (5/8) Epoch 34, batch 1600, loss[loss=0.1522, simple_loss=0.252, pruned_loss=0.02618, over 7332.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2428, pruned_loss=0.02987, over 1422845.96 frames.], batch size: 20, lr: 2.29e-04
2022-05-15 22:13:26,069 INFO [train.py:812] (5/8) Epoch 34, batch 1650, loss[loss=0.1474, simple_loss=0.2432, pruned_loss=0.02578, over 7195.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2438, pruned_loss=0.03024, over 1422393.43 frames.], batch size: 22, lr: 2.29e-04
2022-05-15 22:14:25,162 INFO [train.py:812] (5/8) Epoch 34, batch 1700, loss[loss=0.166, simple_loss=0.2544, pruned_loss=0.03875, over 7376.00 frames.], tot_loss[loss=0.1522, simple_loss=0.244, pruned_loss=0.03017, over 1426690.15 frames.], batch size: 23, lr: 2.29e-04
2022-05-15 22:15:24,014 INFO [train.py:812] (5/8) Epoch 34, batch 1750, loss[loss=0.1537, simple_loss=0.2415, pruned_loss=0.03297, over 7134.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2439, pruned_loss=0.03026, over 1421746.60 frames.], batch size: 28, lr: 2.29e-04
2022-05-15 22:16:22,692 INFO [train.py:812] (5/8) Epoch 34, batch 1800, loss[loss=0.1302, simple_loss=0.2123, pruned_loss=0.02406, over 7281.00 frames.], tot_loss[loss=0.1523, simple_loss=0.244, pruned_loss=0.03029, over 1423554.37 frames.], batch size: 17, lr: 2.29e-04
2022-05-15 22:17:21,667 INFO [train.py:812] (5/8) Epoch 34, batch 1850, loss[loss=0.1471, simple_loss=0.2359, pruned_loss=0.02911, over 7321.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2441, pruned_loss=0.03044, over 1416031.21 frames.], batch size: 21, lr: 2.29e-04
2022-05-15 22:18:20,739 INFO [train.py:812] (5/8) Epoch 34, batch 1900, loss[loss=0.1442, simple_loss=0.251, pruned_loss=0.01869, over 6735.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2445, pruned_loss=0.0304, over 1411082.44 frames.], batch size: 31, lr: 2.29e-04
2022-05-15 22:19:17,931 INFO [train.py:812] (5/8) Epoch 34, batch 1950, loss[loss=0.143, simple_loss=0.2342, pruned_loss=0.02594, over 6983.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2448, pruned_loss=0.03048, over 1416933.93 frames.], batch size: 16, lr: 2.29e-04
2022-05-15 22:20:16,841 INFO [train.py:812] (5/8) Epoch 34, batch 2000, loss[loss=0.1312, simple_loss=0.2121, pruned_loss=0.02521, over 7427.00 frames.], tot_loss[loss=0.1519, simple_loss=0.244, pruned_loss=0.02994, over 1422363.99 frames.], batch size: 18, lr: 2.29e-04
2022-05-15 22:21:15,725 INFO [train.py:812] (5/8) Epoch 34, batch 2050, loss[loss=0.1578, simple_loss=0.2486, pruned_loss=0.03346, over 7198.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2427, pruned_loss=0.02928, over 1422233.79 frames.], batch size: 26, lr: 2.29e-04
2022-05-15 22:22:14,799 INFO [train.py:812] (5/8) Epoch 34, batch 2100, loss[loss=0.1693, simple_loss=0.2672, pruned_loss=0.03574, over 7193.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2438, pruned_loss=0.02947, over 1424858.25 frames.], batch size: 23, lr: 2.29e-04
2022-05-15 22:23:12,293 INFO [train.py:812] (5/8) Epoch 34, batch 2150, loss[loss=0.1523, simple_loss=0.2445, pruned_loss=0.03, over 7291.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2431, pruned_loss=0.02912, over 1424177.05 frames.], batch size: 24, lr: 2.29e-04
2022-05-15 22:24:11,538 INFO [train.py:812] (5/8) Epoch 34, batch 2200, loss[loss=0.1422, simple_loss=0.2412, pruned_loss=0.02157, over 7309.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2438, pruned_loss=0.02919, over 1427033.32 frames.], batch size: 21, lr: 2.29e-04
2022-05-15 22:25:10,881 INFO [train.py:812] (5/8) Epoch 34, batch 2250, loss[loss=0.1316, simple_loss=0.2168, pruned_loss=0.02314, over 7271.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2442, pruned_loss=0.02966, over 1423379.70 frames.], batch size: 18, lr: 2.29e-04
2022-05-15 22:26:09,573 INFO [train.py:812] (5/8) Epoch 34, batch 2300, loss[loss=0.1441, simple_loss=0.237, pruned_loss=0.02558, over 7153.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2444, pruned_loss=0.02946, over 1423970.53 frames.], batch size: 19, lr: 2.29e-04
2022-05-15 22:27:08,006 INFO [train.py:812] (5/8) Epoch 34, batch 2350, loss[loss=0.1499, simple_loss=0.239, pruned_loss=0.0304, over 7155.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2435, pruned_loss=0.0291, over 1424741.46 frames.], batch size: 19, lr: 2.29e-04
2022-05-15 22:28:06,466 INFO [train.py:812] (5/8) Epoch 34, batch 2400, loss[loss=0.1889, simple_loss=0.2838, pruned_loss=0.047, over 7353.00 frames.], tot_loss[loss=0.151, simple_loss=0.2433, pruned_loss=0.02937, over 1425161.29 frames.], batch size: 23, lr: 2.29e-04
2022-05-15 22:29:04,650 INFO [train.py:812] (5/8) Epoch 34, batch 2450, loss[loss=0.1588, simple_loss=0.2668, pruned_loss=0.02534, over 7221.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2438, pruned_loss=0.02944, over 1419545.12 frames.], batch size: 21, lr: 2.29e-04
2022-05-15 22:30:04,442 INFO [train.py:812] (5/8) Epoch 34, batch 2500, loss[loss=0.1302, simple_loss=0.212, pruned_loss=0.02426, over 7005.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2436, pruned_loss=0.02939, over 1418331.09 frames.], batch size: 16, lr: 2.29e-04
2022-05-15 22:31:02,269 INFO [train.py:812] (5/8) Epoch 34, batch 2550, loss[loss=0.1548, simple_loss=0.2482, pruned_loss=0.0307, over 7323.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2433, pruned_loss=0.02931, over 1419983.44 frames.], batch size: 22, lr: 2.29e-04
2022-05-15 22:32:00,047 INFO [train.py:812] (5/8) Epoch 34, batch 2600, loss[loss=0.1545, simple_loss=0.2456, pruned_loss=0.03172, over 7045.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2436, pruned_loss=0.02947, over 1419582.16 frames.], batch size: 18, lr: 2.29e-04
2022-05-15 22:32:58,086 INFO [train.py:812] (5/8) Epoch 34, batch 2650, loss[loss=0.1547, simple_loss=0.2569, pruned_loss=0.02623, over 7339.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2429, pruned_loss=0.02946, over 1420797.26 frames.], batch size: 22, lr: 2.29e-04
2022-05-15 22:33:56,977 INFO [train.py:812] (5/8) Epoch 34, batch 2700, loss[loss=0.1354, simple_loss=0.2205, pruned_loss=0.02519, over 7267.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2428, pruned_loss=0.02928, over 1425449.32 frames.], batch size: 18, lr: 2.28e-04
2022-05-15 22:34:55,301 INFO [train.py:812] (5/8) Epoch 34, batch 2750, loss[loss=0.1484, simple_loss=0.2323, pruned_loss=0.0322, over 7320.00 frames.], tot_loss[loss=0.151, simple_loss=0.2431, pruned_loss=0.02942, over 1424031.95 frames.], batch size: 21, lr: 2.28e-04
2022-05-15 22:35:54,125 INFO [train.py:812] (5/8) Epoch 34, batch 2800, loss[loss=0.1331, simple_loss=0.2225, pruned_loss=0.02184, over 7400.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2439, pruned_loss=0.02951, over 1429227.86 frames.], batch size: 18, lr: 2.28e-04
2022-05-15 22:36:52,773 INFO [train.py:812] (5/8) Epoch 34, batch 2850, loss[loss=0.1498, simple_loss=0.2383, pruned_loss=0.03066, over 7188.00 frames.], tot_loss[loss=0.1514, simple_loss=0.244, pruned_loss=0.02939, over 1431111.80 frames.], batch size: 23, lr: 2.28e-04
2022-05-15 22:37:50,506 INFO [train.py:812] (5/8) Epoch 34, batch 2900, loss[loss=0.1526, simple_loss=0.2474, pruned_loss=0.02889, over 7146.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2442, pruned_loss=0.02951, over 1427914.69 frames.], batch size: 20, lr: 2.28e-04
2022-05-15 22:38:49,695 INFO [train.py:812] (5/8) Epoch 34, batch 2950, loss[loss=0.1336, simple_loss=0.2226, pruned_loss=0.02234, over 7151.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2425, pruned_loss=0.02915, over 1428076.88 frames.], batch size: 20, lr: 2.28e-04
2022-05-15 22:39:49,324 INFO [train.py:812] (5/8) Epoch 34, batch 3000, loss[loss=0.1582, simple_loss=0.2457, pruned_loss=0.03532, over 7353.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2424, pruned_loss=0.02908, over 1427750.87 frames.], batch size: 19, lr: 2.28e-04
2022-05-15 22:39:49,325 INFO [train.py:832] (5/8) Computing validation loss
2022-05-15 22:39:56,835 INFO [train.py:841] (5/8) Epoch 34, validation: loss=0.1534, simple_loss=0.2492, pruned_loss=0.02878, over 698248.00 frames.
2022-05-15 22:40:55,230 INFO [train.py:812] (5/8) Epoch 34, batch 3050, loss[loss=0.178, simple_loss=0.2659, pruned_loss=0.04503, over 7358.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2438, pruned_loss=0.0293, over 1428224.82 frames.], batch size: 19, lr: 2.28e-04
2022-05-15 22:41:53,784 INFO [train.py:812] (5/8) Epoch 34, batch 3100, loss[loss=0.1284, simple_loss=0.212, pruned_loss=0.02247, over 6785.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2442, pruned_loss=0.02966, over 1429089.58 frames.], batch size: 15, lr: 2.28e-04
2022-05-15 22:42:52,710 INFO [train.py:812] (5/8) Epoch 34, batch 3150, loss[loss=0.1452, simple_loss=0.227, pruned_loss=0.03172, over 7276.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2437, pruned_loss=0.0297, over 1429372.81 frames.], batch size: 17, lr: 2.28e-04
2022-05-15 22:43:51,460 INFO [train.py:812] (5/8) Epoch 34, batch 3200, loss[loss=0.1776, simple_loss=0.2684, pruned_loss=0.04343, over 5047.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2434, pruned_loss=0.02961, over 1425292.88 frames.], batch size: 53, lr: 2.28e-04
2022-05-15 22:44:49,525 INFO [train.py:812] (5/8) Epoch 34, batch 3250, loss[loss=0.1423, simple_loss=0.236, pruned_loss=0.02431, over 7135.00 frames.], tot_loss[loss=0.151, simple_loss=0.2432, pruned_loss=0.02938, over 1423076.15 frames.], batch size: 17, lr: 2.28e-04
2022-05-15 22:45:48,015 INFO [train.py:812] (5/8) Epoch 34, batch 3300, loss[loss=0.1489, simple_loss=0.2527, pruned_loss=0.02256, over 7054.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2432, pruned_loss=0.02953, over 1419763.90 frames.], batch size: 28, lr: 2.28e-04
2022-05-15 22:46:47,348 INFO [train.py:812] (5/8) Epoch 34, batch 3350, loss[loss=0.1479, simple_loss=0.2443, pruned_loss=0.0257, over 7140.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2427, pruned_loss=0.02971, over 1422299.66 frames.], batch size: 20, lr: 2.28e-04
2022-05-15 22:47:45,297 INFO [train.py:812] (5/8) Epoch 34, batch 3400, loss[loss=0.1599, simple_loss=0.2501, pruned_loss=0.03479, over 7199.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2429, pruned_loss=0.02975, over 1422403.76 frames.], batch size: 23, lr: 2.28e-04
2022-05-15 22:48:43,911 INFO [train.py:812] (5/8) Epoch 34, batch 3450, loss[loss=0.1531, simple_loss=0.246, pruned_loss=0.03007, over 7013.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2432, pruned_loss=0.02982, over 1428017.15 frames.], batch size: 16, lr: 2.28e-04
2022-05-15 22:49:41,433 INFO [train.py:812] (5/8) Epoch 34, batch 3500, loss[loss=0.1576, simple_loss=0.257, pruned_loss=0.02912, over 7187.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2446, pruned_loss=0.03017, over 1429609.90 frames.], batch size: 23, lr: 2.28e-04
2022-05-15 22:50:38,734 INFO [train.py:812] (5/8) Epoch 34, batch 3550, loss[loss=0.1256, simple_loss=0.2081, pruned_loss=0.02155, over 7290.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2432, pruned_loss=0.02955, over 1431388.33 frames.], batch size: 17, lr: 2.28e-04
2022-05-15 22:51:37,801 INFO [train.py:812] (5/8) Epoch 34, batch 3600, loss[loss=0.1385, simple_loss=0.2384, pruned_loss=0.0193, over 7322.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2433, pruned_loss=0.02953, over 1432792.41 frames.], batch size: 21, lr: 2.28e-04
2022-05-15 22:52:35,167 INFO [train.py:812] (5/8) Epoch 34, batch 3650, loss[loss=0.1475, simple_loss=0.2382, pruned_loss=0.02838, over 6639.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2439, pruned_loss=0.02981, over 1428012.53 frames.], batch size: 38, lr: 2.28e-04
2022-05-15 22:53:34,803 INFO [train.py:812] (5/8) Epoch 34, batch 3700, loss[loss=0.1399, simple_loss=0.2336, pruned_loss=0.02307, over 7244.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2424, pruned_loss=0.02951, over 1423430.85 frames.], batch size: 20, lr: 2.28e-04
2022-05-15 22:54:33,343 INFO [train.py:812] (5/8) Epoch 34, batch 3750, loss[loss=0.1574, simple_loss=0.2501, pruned_loss=0.03241, over 7293.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2419, pruned_loss=0.02916, over 1421062.96 frames.], batch size: 24, lr: 2.28e-04
2022-05-15 22:55:32,477 INFO [train.py:812] (5/8) Epoch 34, batch 3800, loss[loss=0.1537, simple_loss=0.2544, pruned_loss=0.02647, over 7147.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2431, pruned_loss=0.02958, over 1424945.94 frames.], batch size: 20, lr: 2.28e-04
2022-05-15 22:56:31,633 INFO [train.py:812] (5/8) Epoch 34, batch 3850, loss[loss=0.1777, simple_loss=0.2693, pruned_loss=0.04308, over 7186.00 frames.], tot_loss[loss=0.1511, simple_loss=0.243, pruned_loss=0.02966, over 1427145.79 frames.], batch size: 23, lr: 2.28e-04
2022-05-15 22:57:28,720 INFO [train.py:812] (5/8) Epoch 34, batch 3900, loss[loss=0.1597, simple_loss=0.2563, pruned_loss=0.03155, over 7223.00 frames.], tot_loss[loss=0.1512, simple_loss=0.243, pruned_loss=0.0297, over 1426116.11 frames.], batch size: 23, lr: 2.28e-04
2022-05-15 22:58:46,452 INFO [train.py:812] (5/8) Epoch 34, batch 3950, loss[loss=0.1715, simple_loss=0.261, pruned_loss=0.04098, over 7331.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2435, pruned_loss=0.03012, over 1423237.32 frames.], batch size: 20, lr: 2.28e-04
2022-05-15 22:59:45,591 INFO [train.py:812] (5/8) Epoch 34, batch 4000, loss[loss=0.1504, simple_loss=0.239, pruned_loss=0.03086, over 7059.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2434, pruned_loss=0.02994, over 1423189.21 frames.], batch size: 18, lr: 2.28e-04
2022-05-15 23:00:53,097 INFO [train.py:812] (5/8) Epoch 34, batch 4050, loss[loss=0.1394, simple_loss=0.2356, pruned_loss=0.02159, over 7159.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2447, pruned_loss=0.03047, over 1418058.41 frames.], batch size: 26, lr: 2.27e-04
2022-05-15 23:01:51,479 INFO [train.py:812] (5/8) Epoch 34, batch 4100, loss[loss=0.1597, simple_loss=0.2547, pruned_loss=0.03237, over 6501.00 frames.], tot_loss[loss=0.153, simple_loss=0.2451, pruned_loss=0.03047, over 1418941.59 frames.], batch size: 38, lr: 2.27e-04
2022-05-15 23:02:49,332 INFO [train.py:812] (5/8) Epoch 34, batch 4150, loss[loss=0.1305, simple_loss=0.2141, pruned_loss=0.02343, over 7413.00 frames.], tot_loss[loss=0.153, simple_loss=0.2448, pruned_loss=0.03059, over 1418185.07 frames.], batch size: 18, lr: 2.27e-04
2022-05-15 23:03:57,825 INFO [train.py:812] (5/8) Epoch 34, batch 4200, loss[loss=0.1708, simple_loss=0.2687, pruned_loss=0.03642, over 7242.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2447, pruned_loss=0.03031, over 1420168.98 frames.], batch size: 20, lr: 2.27e-04
2022-05-15 23:05:06,366 INFO [train.py:812] (5/8) Epoch 34, batch 4250, loss[loss=0.1482, simple_loss=0.2261, pruned_loss=0.0351, over 7141.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2444, pruned_loss=0.03011, over 1420289.14 frames.], batch size: 17, lr: 2.27e-04
2022-05-15 23:06:05,065 INFO [train.py:812] (5/8) Epoch 34, batch 4300, loss[loss=0.1407, simple_loss=0.2254, pruned_loss=0.02802, over 6993.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2449, pruned_loss=0.0303, over 1420847.82 frames.], batch size: 16, lr: 2.27e-04
2022-05-15 23:07:13,189 INFO [train.py:812] (5/8) Epoch 34, batch 4350, loss[loss=0.1172, simple_loss=0.2026, pruned_loss=0.0159, over 6735.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2449, pruned_loss=0.03023, over 1415527.11 frames.], batch size: 15, lr: 2.27e-04
2022-05-15 23:08:12,768 INFO [train.py:812] (5/8) Epoch 34, batch 4400, loss[loss=0.1526, simple_loss=0.2421, pruned_loss=0.03157, over 7161.00 frames.], tot_loss[loss=0.1521, simple_loss=0.244, pruned_loss=0.03008, over 1416260.39 frames.], batch size: 18, lr: 2.27e-04
2022-05-15 23:09:11,154 INFO [train.py:812] (5/8) Epoch 34, batch 4450, loss[loss=0.1807, simple_loss=0.2592, pruned_loss=0.05113, over 7190.00 frames.], tot_loss[loss=0.153, simple_loss=0.2449, pruned_loss=0.03058, over 1400886.15 frames.], batch size: 23, lr: 2.27e-04
2022-05-15 23:10:19,457 INFO [train.py:812] (5/8) Epoch 34, batch 4500, loss[loss=0.2167, simple_loss=0.2989, pruned_loss=0.06723, over 4947.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2449, pruned_loss=0.03079, over 1391693.78 frames.], batch size: 52, lr: 2.27e-04
2022-05-15 23:11:16,018 INFO [train.py:812] (5/8) Epoch 34, batch 4550, loss[loss=0.1746, simple_loss=0.263, pruned_loss=0.04307, over 5229.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2473, pruned_loss=0.03164, over 1351848.91 frames.], batch size: 52, lr: 2.27e-04
2022-05-15 23:12:20,601 INFO [train.py:812] (5/8) Epoch 35, batch 0, loss[loss=0.156, simple_loss=0.2461, pruned_loss=0.03294, over 7238.00 frames.], tot_loss[loss=0.156, simple_loss=0.2461, pruned_loss=0.03294, over 7238.00 frames.], batch size: 20, lr: 2.24e-04
2022-05-15 23:13:24,532 INFO [train.py:812] (5/8) Epoch 35, batch 50, loss[loss=0.1558, simple_loss=0.26, pruned_loss=0.02582, over 7270.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2458, pruned_loss=0.03205, over 318314.87 frames.], batch size: 24, lr: 2.24e-04
2022-05-15 23:14:23,038 INFO [train.py:812] (5/8) Epoch 35, batch 100, loss[loss=0.1493, simple_loss=0.2477, pruned_loss=0.02542, over 7185.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2436, pruned_loss=0.02998, over 567692.98 frames.], batch size: 26, lr: 2.24e-04
2022-05-15 23:15:22,489 INFO [train.py:812] (5/8) Epoch 35, batch 150, loss[loss=0.1582, simple_loss=0.2551, pruned_loss=0.03069, over 7379.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2429, pruned_loss=0.02909, over 760247.62 frames.], batch size: 23, lr: 2.24e-04
2022-05-15 23:16:21,259 INFO [train.py:812] (5/8) Epoch 35, batch 200, loss[loss=0.141, simple_loss=0.2322, pruned_loss=0.02492, over 7064.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2429, pruned_loss=0.02945, over 909943.73 frames.], batch size: 18, lr: 2.24e-04
2022-05-15 23:17:21,131 INFO [train.py:812] (5/8) Epoch 35, batch 250, loss[loss=0.1561, simple_loss=0.2481, pruned_loss=0.03205, over 7232.00 frames.], tot_loss[loss=0.15, simple_loss=0.2418, pruned_loss=0.02905, over 1027202.16 frames.], batch size: 20, lr: 2.24e-04
2022-05-15 23:18:18,839 INFO [train.py:812] (5/8) Epoch 35, batch 300, loss[loss=0.1214, simple_loss=0.2121, pruned_loss=0.01529, over 7159.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2423, pruned_loss=0.02931, over 1113925.51 frames.], batch size: 19, lr: 2.24e-04
2022-05-15 23:19:18,449 INFO [train.py:812] (5/8) Epoch 35, batch 350, loss[loss=0.1567, simple_loss=0.2536, pruned_loss=0.02993, over 7186.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2423, pruned_loss=0.02939, over 1185860.37 frames.], batch size: 23, lr: 2.24e-04
2022-05-15 23:20:16,871 INFO [train.py:812] (5/8) Epoch 35, batch 400, loss[loss=0.1359, simple_loss=0.2261, pruned_loss=0.02291, over 7330.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2424, pruned_loss=0.02952, over 1240266.69 frames.], batch size: 20, lr: 2.24e-04
2022-05-15 23:21:15,122 INFO [train.py:812] (5/8) Epoch 35, batch 450, loss[loss=0.1779, simple_loss=0.2829, pruned_loss=0.03643, over 6772.00 frames.], tot_loss[loss=0.151, simple_loss=0.2426, pruned_loss=0.02965, over 1284744.82 frames.], batch size: 31, lr: 2.24e-04
2022-05-15 23:22:13,109 INFO [train.py:812] (5/8) Epoch 35, batch 500, loss[loss=0.1554, simple_loss=0.2536, pruned_loss=0.02859, over 7331.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2423, pruned_loss=0.02975, over 1313953.06 frames.], batch size: 20, lr: 2.23e-04
2022-05-15 23:23:12,691 INFO [train.py:812] (5/8) Epoch 35, batch 550, loss[loss=0.1356, simple_loss=0.2251, pruned_loss=0.02307, over 7065.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2413, pruned_loss=0.0291, over 1335020.89 frames.], batch size: 18, lr: 2.23e-04
2022-05-15 23:24:10,889 INFO [train.py:812] (5/8) Epoch 35, batch 600, loss[loss=0.1425, simple_loss=0.2366, pruned_loss=0.02414, over 7319.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2429, pruned_loss=0.02942, over 1353910.53 frames.], batch size: 22, lr: 2.23e-04
2022-05-15 23:25:10,089 INFO [train.py:812] (5/8) Epoch 35, batch 650, loss[loss=0.1427, simple_loss=0.2329, pruned_loss=0.02623, over 7176.00 frames.], tot_loss[loss=0.151, simple_loss=0.2435, pruned_loss=0.02925, over 1372852.52 frames.], batch size: 18, lr: 2.23e-04
2022-05-15 23:26:08,915 INFO [train.py:812] (5/8) Epoch 35, batch 700, loss[loss=0.1591, simple_loss=0.2448, pruned_loss=0.03671, over 7266.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2436, pruned_loss=0.02944, over 1387406.92 frames.], batch size: 17, lr: 2.23e-04
2022-05-15 23:27:08,857 INFO [train.py:812] (5/8) Epoch 35, batch 750, loss[loss=0.1511, simple_loss=0.2402, pruned_loss=0.03104, over 7254.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2431, pruned_loss=0.02957, over 1393723.92 frames.], batch size: 19, lr: 2.23e-04
2022-05-15 23:28:07,087 INFO [train.py:812] (5/8) Epoch 35, batch 800, loss[loss=0.1537, simple_loss=0.2608, pruned_loss=0.02328, over 7224.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2434, pruned_loss=0.0294, over 1402583.80 frames.], batch size: 21, lr: 2.23e-04
2022-05-15 23:29:06,738 INFO [train.py:812] (5/8) Epoch 35, batch 850, loss[loss=0.1728, simple_loss=0.2755, pruned_loss=0.03502, over 7287.00 frames.], tot_loss[loss=0.1514, simple_loss=0.244, pruned_loss=0.02938, over 1402573.98 frames.], batch size: 24, lr: 2.23e-04
2022-05-15 23:30:05,616 INFO [train.py:812] (5/8) Epoch 35, batch 900, loss[loss=0.1633, simple_loss=0.2481, pruned_loss=0.03928, over 5331.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2438, pruned_loss=0.02922, over 1406237.96 frames.], batch size: 52, lr: 2.23e-04
2022-05-15 23:31:04,507 INFO [train.py:812] (5/8) Epoch 35, batch 950, loss[loss=0.1438, simple_loss=0.2319, pruned_loss=0.02782, over 7251.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2433, pruned_loss=0.02905, over 1409400.00 frames.], batch size: 19, lr: 2.23e-04
2022-05-15 23:32:02,581 INFO [train.py:812] (5/8) Epoch 35, batch 1000, loss[loss=0.1422, simple_loss=0.2348, pruned_loss=0.02478, over 6708.00 frames.], tot_loss[loss=0.151, simple_loss=0.2435, pruned_loss=0.02926, over 1410533.78 frames.], batch size: 31, lr: 2.23e-04
2022-05-15 23:33:01,145 INFO [train.py:812] (5/8) Epoch 35, batch 1050, loss[loss=0.1546, simple_loss=0.2436, pruned_loss=0.03281, over 7412.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2428, pruned_loss=0.02925, over 1415230.18 frames.], batch size: 21, lr: 2.23e-04
2022-05-15 23:33:59,683 INFO [train.py:812] (5/8) Epoch 35, batch 1100, loss[loss=0.1523, simple_loss=0.2408, pruned_loss=0.03186, over 7366.00 frames.], tot_loss[loss=0.151, simple_loss=0.2429, pruned_loss=0.02953, over 1419853.79 frames.], batch size: 19, lr: 2.23e-04
2022-05-15 23:34:58,667 INFO [train.py:812] (5/8) Epoch 35, batch 1150, loss[loss=0.1689, simple_loss=0.2605, pruned_loss=0.0387, over 7186.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2425, pruned_loss=0.02927, over 1421333.59 frames.], batch size: 23, lr: 2.23e-04
2022-05-15 23:35:56,576 INFO [train.py:812] (5/8) Epoch 35, batch 1200, loss[loss=0.1419, simple_loss=0.2291, pruned_loss=0.02735, over 7267.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2429, pruned_loss=0.02937, over 1424747.65 frames.], batch size: 18, lr: 2.23e-04
2022-05-15 23:36:54,992 INFO [train.py:812] (5/8) Epoch 35, batch 1250, loss[loss=0.1704, simple_loss=0.2679, pruned_loss=0.03642, over 7336.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2437, pruned_loss=0.02974, over 1423709.63 frames.], batch size: 22, lr: 2.23e-04
2022-05-15 23:37:53,435 INFO [train.py:812] (5/8) Epoch 35, batch 1300, loss[loss=0.153, simple_loss=0.247, pruned_loss=0.02953, over 7082.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2437, pruned_loss=0.03002, over 1420443.37 frames.], batch size: 28, lr: 2.23e-04
2022-05-15 23:38:52,777 INFO [train.py:812] (5/8) Epoch 35, batch 1350, loss[loss=0.1667, simple_loss=0.2599, pruned_loss=0.0368, over 7092.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2437, pruned_loss=0.03, over 1423135.88 frames.], batch size: 28, lr: 2.23e-04
2022-05-15 23:39:51,279 INFO [train.py:812] (5/8) Epoch 35, batch 1400, loss[loss=0.1388, simple_loss=0.2317, pruned_loss=0.02293, over 7332.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2436, pruned_loss=0.02988, over 1421708.17 frames.], batch size: 20, lr: 2.23e-04
2022-05-15 23:40:50,701 INFO [train.py:812] (5/8) Epoch 35, batch 1450, loss[loss=0.1437, simple_loss=0.2308, pruned_loss=0.02835, over 7265.00 frames.], tot_loss[loss=0.1515, simple_loss=0.243, pruned_loss=0.02996, over 1420190.46 frames.], batch size: 19, lr: 2.23e-04
2022-05-15 23:41:50,030 INFO [train.py:812] (5/8) Epoch 35, batch 1500, loss[loss=0.131, simple_loss=0.2164, pruned_loss=0.02281, over 7127.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2428, pruned_loss=0.02963, over 1420708.28 frames.], batch size: 17, lr: 2.23e-04
2022-05-15 23:42:48,877 INFO [train.py:812] (5/8) Epoch 35, batch 1550, loss[loss=0.1716, simple_loss=0.2764, pruned_loss=0.03345, over 7230.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2432, pruned_loss=0.0299, over 1420336.96 frames.], batch size: 21, lr: 2.23e-04
2022-05-15 23:43:47,342 INFO [train.py:812] (5/8) Epoch 35, batch 1600, loss[loss=0.1591, simple_loss=0.2487, pruned_loss=0.0348, over 7031.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2433, pruned_loss=0.02972, over 1421799.75 frames.], batch size: 28, lr: 2.23e-04
2022-05-15 23:44:46,505 INFO [train.py:812] (5/8) Epoch 35, batch 1650, loss[loss=0.138, simple_loss=0.2334, pruned_loss=0.02125, over 7413.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2427, pruned_loss=0.02978, over 1426838.66 frames.], batch size: 18, lr: 2.23e-04
2022-05-15 23:45:45,293 INFO [train.py:812] (5/8) Epoch 35, batch 1700, loss[loss=0.1619, simple_loss=0.246, pruned_loss=0.03885, over 5176.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2426, pruned_loss=0.0296, over 1425654.28 frames.], batch size: 52, lr: 2.23e-04
2022-05-15 23:46:45,280 INFO [train.py:812] (5/8) Epoch 35, batch 1750, loss[loss=0.1312, simple_loss=0.2284, pruned_loss=0.01695, over 7176.00 frames.], tot_loss[loss=0.15, simple_loss=0.2417, pruned_loss=0.02917, over 1425247.60 frames.], batch size: 18, lr: 2.23e-04
2022-05-15 23:47:44,616 INFO [train.py:812] (5/8) Epoch 35, batch 1800, loss[loss=0.1648, simple_loss=0.2652, pruned_loss=0.03221, over 7359.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2407, pruned_loss=0.02851, over 1428942.83 frames.], batch size: 25, lr: 2.23e-04
2022-05-15 23:48:43,671 INFO [train.py:812] (5/8) Epoch 35, batch 1850, loss[loss=0.1536, simple_loss=0.2421, pruned_loss=0.03249, over 7061.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2404, pruned_loss=0.02854, over 1426641.72 frames.], batch size: 18, lr: 2.23e-04
2022-05-15 23:49:42,134 INFO [train.py:812] (5/8) Epoch 35, batch 1900, loss[loss=0.1535, simple_loss=0.2457, pruned_loss=0.03067, over 7374.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2404, pruned_loss=0.02845, over 1426341.19 frames.], batch size: 23, lr: 2.22e-04
2022-05-15 23:50:50,954 INFO [train.py:812] (5/8) Epoch 35, batch 1950, loss[loss=0.1325, simple_loss=0.2244, pruned_loss=0.0203, over 7146.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2412, pruned_loss=0.02888, over 1424661.48 frames.], batch size: 18, lr: 2.22e-04
2022-05-15 23:51:48,108 INFO [train.py:812] (5/8) Epoch 35, batch 2000, loss[loss=0.1462, simple_loss=0.2486, pruned_loss=0.02191, over 6544.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2411, pruned_loss=0.02884, over 1419740.09 frames.], batch size: 38, lr: 2.22e-04
2022-05-15 23:52:46,837 INFO [train.py:812] (5/8) Epoch 35, batch 2050, loss[loss=0.1583, simple_loss=0.2549, pruned_loss=0.03082, over 7111.00 frames.], tot_loss[loss=0.1499, simple_loss=0.242, pruned_loss=0.02885, over 1421237.18 frames.], batch size: 21, lr: 2.22e-04
2022-05-15 23:53:45,607 INFO [train.py:812] (5/8) Epoch 35, batch 2100, loss[loss=0.1684, simple_loss=0.2596, pruned_loss=0.03861, over 7409.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2427, pruned_loss=0.02889, over 1424101.13 frames.], batch size: 21, lr: 2.22e-04
2022-05-15 23:54:43,308 INFO [train.py:812] (5/8) Epoch 35, batch 2150, loss[loss=0.153, simple_loss=0.2435, pruned_loss=0.03125, over 6469.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2423, pruned_loss=0.02865, over 1427333.79 frames.], batch size: 38, lr: 2.22e-04
2022-05-15 23:55:40,402 INFO [train.py:812] (5/8) Epoch 35, batch 2200, loss[loss=0.1425, simple_loss=0.2339, pruned_loss=0.02559, over 7426.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2419, pruned_loss=0.02854, over 1423836.83 frames.], batch size: 20, lr: 2.22e-04
2022-05-15 23:56:39,582 INFO [train.py:812] (5/8) Epoch 35, batch 2250, loss[loss=0.1349, simple_loss=0.2198, pruned_loss=0.02504, over 7283.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2425, pruned_loss=0.0287, over 1421503.90 frames.], batch size: 18, lr: 2.22e-04
2022-05-15 23:57:38,192 INFO [train.py:812] (5/8) Epoch 35, batch 2300, loss[loss=0.1574, simple_loss=0.2496, pruned_loss=0.03255, over 7206.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2429, pruned_loss=0.02925, over 1418293.98 frames.], batch size: 26, lr: 2.22e-04
2022-05-15 23:58:36,520 INFO [train.py:812] (5/8) Epoch 35, batch 2350, loss[loss=0.1356, simple_loss=0.2312, pruned_loss=0.02, over 7129.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2423, pruned_loss=0.02875, over 1416395.83 frames.], batch size: 28, lr: 2.22e-04
2022-05-15 23:59:34,358 INFO [train.py:812] (5/8) Epoch 35, batch 2400, loss[loss=0.1302, simple_loss=0.2083, pruned_loss=0.02607, over 6982.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2415, pruned_loss=0.02835, over 1422274.43 frames.], batch size: 16, lr: 2.22e-04
2022-05-16 00:00:32,000 INFO [train.py:812] (5/8) Epoch 35, batch 2450, loss[loss=0.1421, simple_loss=0.2327, pruned_loss=0.0258, over 7434.00 frames.], tot_loss[loss=0.1495, simple_loss=0.242, pruned_loss=0.02853, over 1422417.90 frames.], batch size: 20, lr: 2.22e-04
2022-05-16 00:01:31,443 INFO [train.py:812] (5/8) Epoch 35, batch 2500, loss[loss=0.1847, simple_loss=0.2829, pruned_loss=0.04331, over 6543.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2423, pruned_loss=0.02851, over 1423947.59 frames.], batch size: 38, lr: 2.22e-04
2022-05-16 00:02:30,461 INFO [train.py:812] (5/8) Epoch 35, batch 2550, loss[loss=0.1497, simple_loss=0.2425, pruned_loss=0.02845, over 7117.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2424, pruned_loss=0.02847, over 1423677.82 frames.], batch size: 21, lr: 2.22e-04
2022-05-16 00:03:28,736 INFO [train.py:812] (5/8) Epoch 35, batch 2600, loss[loss=0.1776, simple_loss=0.2732, pruned_loss=0.04099, over 7199.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2422, pruned_loss=0.02863, over 1423860.64 frames.], batch size: 22, lr: 2.22e-04
2022-05-16 00:04:26,533 INFO [train.py:812] (5/8) Epoch 35, batch 2650, loss[loss=0.1532, simple_loss=0.2519, pruned_loss=0.02727, over 7205.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2421, pruned_loss=0.02881, over 1423320.80 frames.], batch size: 23, lr: 2.22e-04
2022-05-16 00:05:25,292 INFO [train.py:812] (5/8) Epoch 35, batch 2700, loss[loss=0.174, simple_loss=0.2676, pruned_loss=0.04015, over 7133.00 frames.], tot_loss[loss=0.15, simple_loss=0.2422, pruned_loss=0.02892, over 1424847.99 frames.], batch size: 21, lr: 2.22e-04
2022-05-16 00:06:24,289 INFO [train.py:812] (5/8) Epoch 35, batch 2750, loss[loss=0.1613, simple_loss=0.2572, pruned_loss=0.03267, over 7327.00 frames.], tot_loss[loss=0.15, simple_loss=0.2422, pruned_loss=0.02893, over 1424393.19 frames.], batch size: 21, lr: 2.22e-04
2022-05-16 00:07:23,046 INFO [train.py:812] (5/8) Epoch 35, batch 2800, loss[loss=0.1472, simple_loss=0.2358, pruned_loss=0.02924, over 7331.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2431, pruned_loss=0.02931, over 1426047.26 frames.], batch size: 20, lr: 2.22e-04
2022-05-16 00:08:20,728 INFO [train.py:812] (5/8) Epoch 35, batch 2850, loss[loss=0.1677, simple_loss=0.2632, pruned_loss=0.03608, over 7154.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2435, pruned_loss=0.02938, over 1423818.45 frames.], batch size: 19, lr: 2.22e-04
2022-05-16 00:09:20,168 INFO [train.py:812] (5/8) Epoch 35, batch 2900, loss[loss=0.1927, simple_loss=0.2878, pruned_loss=0.0488, over 6326.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2431, pruned_loss=0.02914, over 1422825.44 frames.], batch size: 37, lr: 2.22e-04
2022-05-16 00:10:18,321 INFO [train.py:812] (5/8) Epoch 35, batch 2950, loss[loss=0.1393, simple_loss=0.2166, pruned_loss=0.03106, over 7199.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2436, pruned_loss=0.02944, over 1416487.50 frames.], batch size: 16, lr: 2.22e-04
2022-05-16 00:11:17,546 INFO [train.py:812] (5/8) Epoch 35, batch 3000, loss[loss=0.1621, simple_loss=0.2599, pruned_loss=0.03217, over 7387.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2431, pruned_loss=0.02922, over 1420557.20 frames.], batch size: 23, lr: 2.22e-04
2022-05-16 00:11:17,547 INFO [train.py:832] (5/8) Computing validation loss
2022-05-16 00:11:25,088 INFO [train.py:841] (5/8) Epoch 35, validation: loss=0.1528, simple_loss=0.2485, pruned_loss=0.02851, over 698248.00 frames.
2022-05-16 00:12:24,388 INFO [train.py:812] (5/8) Epoch 35, batch 3050, loss[loss=0.1543, simple_loss=0.2546, pruned_loss=0.02695, over 7234.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2431, pruned_loss=0.0288, over 1423533.88 frames.], batch size: 20, lr: 2.22e-04
2022-05-16 00:13:22,717 INFO [train.py:812] (5/8) Epoch 35, batch 3100, loss[loss=0.1593, simple_loss=0.2538, pruned_loss=0.03239, over 7376.00 frames.], tot_loss[loss=0.1504, simple_loss=0.243, pruned_loss=0.02888, over 1420236.33 frames.], batch size: 23, lr: 2.22e-04
2022-05-16 00:14:22,591 INFO [train.py:812] (5/8) Epoch 35, batch 3150, loss[loss=0.1922, simple_loss=0.2826, pruned_loss=0.05091, over 7192.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2421, pruned_loss=0.02881, over 1422800.89 frames.], batch size: 22, lr: 2.22e-04
2022-05-16 00:15:21,749 INFO [train.py:812] (5/8) Epoch 35, batch 3200, loss[loss=0.1703, simple_loss=0.2621, pruned_loss=0.03926, over 7205.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2433, pruned_loss=0.02921, over 1427332.91 frames.], batch size: 22, lr: 2.22e-04
2022-05-16 00:16:21,593 INFO [train.py:812] (5/8) Epoch 35, batch 3250, loss[loss=0.1302, simple_loss=0.2229, pruned_loss=0.01875, over 7433.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2429, pruned_loss=0.02919, over 1425649.21 frames.], batch size: 20, lr: 2.22e-04
2022-05-16 00:17:21,154 INFO [train.py:812] (5/8) Epoch 35, batch 3300, loss[loss=0.1271, simple_loss=0.2149, pruned_loss=0.01966, over 7429.00 frames.], tot_loss[loss=0.151, simple_loss=0.2435, pruned_loss=0.02931, over 1426216.78 frames.], batch size: 20, lr: 2.22e-04
2022-05-16 00:18:19,928 INFO [train.py:812] (5/8) Epoch 35, batch 3350, loss[loss=0.1492, simple_loss=0.2429, pruned_loss=0.02774, over 7428.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2436, pruned_loss=0.02959, over 1429738.09 frames.], batch size: 20, lr: 2.21e-04
2022-05-16 00:19:17,053 INFO [train.py:812] (5/8) Epoch 35, batch 3400, loss[loss=0.1285, simple_loss=0.2143, pruned_loss=0.0213, over 7279.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2434, pruned_loss=0.02973, over 1426075.67 frames.], batch size: 18, lr: 2.21e-04
2022-05-16 00:20:15,913 INFO [train.py:812] (5/8) Epoch 35, batch 3450, loss[loss=0.1248, simple_loss=0.2062, pruned_loss=0.02174, over 6997.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2436, pruned_loss=0.0301, over 1429071.10 frames.], batch size: 16, lr: 2.21e-04
2022-05-16 00:21:14,723 INFO [train.py:812] (5/8) Epoch 35, batch 3500, loss[loss=0.1334, simple_loss=0.2325, pruned_loss=0.01718, over 7320.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2439, pruned_loss=0.03011, over 1427984.18 frames.], batch size: 22, lr: 2.21e-04
2022-05-16 00:22:12,886 INFO [train.py:812] (5/8) Epoch 35, batch 3550, loss[loss=0.1631, simple_loss=0.254, pruned_loss=0.03617, over 6715.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2441, pruned_loss=0.03027, over 1420785.15 frames.], batch size: 31, lr: 2.21e-04
2022-05-16 00:23:10,730 INFO [train.py:812] (5/8) Epoch 35, batch 3600, loss[loss=0.1624, simple_loss=0.2508, pruned_loss=0.03699, over 7197.00 frames.], tot_loss[loss=0.1519, simple_loss=0.244, pruned_loss=0.02995, over 1419226.22 frames.], batch size: 22, lr: 2.21e-04
2022-05-16 00:24:08,621 INFO [train.py:812] (5/8) Epoch 35, batch 3650, loss[loss=0.1481, simple_loss=0.2494, pruned_loss=0.02333, over 7294.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2441, pruned_loss=0.0298, over 1420584.07 frames.], batch size: 25, lr: 2.21e-04
2022-05-16 00:25:06,929 INFO [train.py:812] (5/8) Epoch 35, batch 3700, loss[loss=0.1582, simple_loss=0.2539, pruned_loss=0.03123, over 6473.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2438, pruned_loss=0.02976, over 1420117.20 frames.], batch size: 38, lr: 2.21e-04
2022-05-16 00:26:05,758 INFO [train.py:812] (5/8) Epoch 35, batch 3750, loss[loss=0.1566, simple_loss=0.2487, pruned_loss=0.03221, over 5316.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2434, pruned_loss=0.0295, over 1417315.14 frames.], batch size: 52, lr: 2.21e-04
2022-05-16 00:27:04,264 INFO [train.py:812] (5/8) Epoch 35, batch 3800, loss[loss=0.1641, simple_loss=0.2569, pruned_loss=0.03564, over 6742.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2441, pruned_loss=0.02966, over 1418170.99 frames.], batch size: 31, lr: 2.21e-04
2022-05-16 00:28:02,173 INFO [train.py:812] (5/8) Epoch 35, batch 3850, loss[loss=0.1553, simple_loss=0.2426, pruned_loss=0.03402, over 7280.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2443, pruned_loss=0.03001, over 1421347.34 frames.], batch size: 24, lr: 2.21e-04
2022-05-16 00:29:00,965 INFO [train.py:812] (5/8) Epoch 35, batch 3900, loss[loss=0.1444, simple_loss=0.2187, pruned_loss=0.03505, over 6763.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2444, pruned_loss=0.03007, over 1418360.16 frames.], batch size: 15, lr: 2.21e-04
2022-05-16 00:30:00,133 INFO [train.py:812] (5/8) Epoch 35, batch 3950, loss[loss=0.1388, simple_loss=0.2282, pruned_loss=0.02466, over 7144.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2438, pruned_loss=0.03001, over 1418804.24 frames.], batch size: 17, lr: 2.21e-04
2022-05-16 00:30:58,300 INFO [train.py:812] (5/8) Epoch 35, batch 4000, loss[loss=0.173, simple_loss=0.2479, pruned_loss=0.04901, over 7017.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2435, pruned_loss=0.02985, over 1417924.46 frames.], batch size: 16, lr: 2.21e-04
2022-05-16 00:32:02,098 INFO [train.py:812] (5/8) Epoch 35, batch 4050, loss[loss=0.1616, simple_loss=0.2547, pruned_loss=0.03426, over 6342.00 frames.], tot_loss[loss=0.1518, simple_loss=0.244, pruned_loss=0.02978, over 1420836.27 frames.], batch size: 37, lr: 2.21e-04
2022-05-16 00:33:00,944 INFO [train.py:812] (5/8) Epoch 35, batch 4100, loss[loss=0.1543, simple_loss=0.2516, pruned_loss=0.0285, over 7224.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2434, pruned_loss=0.02984, over 1425905.20 frames.], batch size: 21, lr: 2.21e-04
2022-05-16 00:33:59,512 INFO [train.py:812] (5/8) Epoch 35, batch 4150, loss[loss=0.1562, simple_loss=0.2577, pruned_loss=0.02732, over 7324.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2428, pruned_loss=0.02942, over 1424479.27 frames.], batch size: 21, lr: 2.21e-04
2022-05-16 00:34:58,346 INFO [train.py:812] (5/8) Epoch 35, batch 4200, loss[loss=0.1711, simple_loss=0.2673, pruned_loss=0.03745, over 7310.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2438, pruned_loss=0.02979, over 1422117.98 frames.], batch size: 21, lr: 2.21e-04
2022-05-16 00:35:57,136 INFO [train.py:812] (5/8) Epoch 35, batch 4250, loss[loss=0.1438, simple_loss=0.223, pruned_loss=0.0323, over 7287.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2431, pruned_loss=0.02955, over 1427077.81 frames.], batch size: 17, lr: 2.21e-04
2022-05-16 00:36:55,257 INFO [train.py:812] (5/8) Epoch 35, batch 4300, loss[loss=0.1476, simple_loss=0.2354, pruned_loss=0.02986, over 7158.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2425, pruned_loss=0.02944, over 1419108.88 frames.], batch size: 26, lr: 2.21e-04
2022-05-16 00:37:53,305 INFO [train.py:812] (5/8) Epoch 35, batch 4350, loss[loss=0.1474, simple_loss=0.2414, pruned_loss=0.02665, over 7285.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2433, pruned_loss=0.02977, over 1415151.44 frames.], batch size: 24, lr: 2.21e-04
2022-05-16 00:38:52,105 INFO [train.py:812] (5/8) Epoch 35, batch 4400, loss[loss=0.1513, simple_loss=0.2444, pruned_loss=0.02913, over 7163.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2439, pruned_loss=0.02979, over 1410817.26 frames.], batch size: 19, lr: 2.21e-04
2022-05-16 00:39:50,114 INFO [train.py:812] (5/8) Epoch 35, batch 4450, loss[loss=0.1453, simple_loss=0.2382, pruned_loss=0.02619, over 6710.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2446, pruned_loss=0.02996, over 1394914.29 frames.], batch size: 31, lr: 2.21e-04
2022-05-16 00:40:48,504 INFO [train.py:812] (5/8) Epoch 35, batch 4500, loss[loss=0.1851, simple_loss=0.2689, pruned_loss=0.05067, over 7163.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2448, pruned_loss=0.03005, over 1380510.70 frames.], batch size: 26, lr: 2.21e-04
2022-05-16 00:41:45,650 INFO [train.py:812] (5/8) Epoch 35, batch 4550, loss[loss=0.1802, simple_loss=0.2696, pruned_loss=0.04546, over 5467.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2464, pruned_loss=0.03111, over 1354792.11 frames.], batch size: 53, lr: 2.21e-04
2022-05-16 00:42:50,920 INFO [train.py:812] (5/8) Epoch 36, batch 0, loss[loss=0.1307, simple_loss=0.2247, pruned_loss=0.01832, over 7333.00 frames.], tot_loss[loss=0.1307, simple_loss=0.2247, pruned_loss=0.01832, over 7333.00 frames.], batch size: 20, lr: 2.18e-04
2022-05-16 00:43:50,521 INFO [train.py:812] (5/8) Epoch 36, batch 50, loss[loss=0.1525, simple_loss=0.2455, pruned_loss=0.02979, over 7424.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2443, pruned_loss=0.03022, over 315939.48 frames.], batch size: 20, lr: 2.18e-04
2022-05-16 00:44:48,841 INFO [train.py:812] (5/8) Epoch 36, batch 100, loss[loss=0.156, simple_loss=0.2561, pruned_loss=0.02796, over 4741.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2425, pruned_loss=0.02823, over 561176.66 frames.], batch size: 52, lr: 2.17e-04
2022-05-16 00:45:47,223 INFO [train.py:812] (5/8) Epoch 36, batch 150, loss[loss=0.1405, simple_loss=0.2418, pruned_loss=0.01962, over 7227.00 frames.], tot_loss[loss=0.149, simple_loss=0.2406, pruned_loss=0.02875, over 750495.04 frames.], batch size: 20, lr: 2.17e-04
2022-05-16 00:46:46,269 INFO [train.py:812] (5/8) Epoch 36, batch 200, loss[loss=0.1445, simple_loss=0.2417, pruned_loss=0.02362, over 7317.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2426, pruned_loss=0.02889, over 901035.98 frames.], batch size: 21, lr: 2.17e-04
2022-05-16 00:47:45,329 INFO [train.py:812] (5/8) Epoch 36, batch 250, loss[loss=0.1284, simple_loss=0.2212, pruned_loss=0.01782, over 7158.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2418, pruned_loss=0.02887, over 1020609.24 frames.], batch size: 19, lr: 2.17e-04
2022-05-16 00:48:43,708 INFO [train.py:812] (5/8) Epoch 36, batch 300, loss[loss=0.1653, simple_loss=0.2564, pruned_loss=0.03717, over 7139.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2426, pruned_loss=0.0291, over 1105924.12 frames.], batch size: 26, lr: 2.17e-04
2022-05-16 00:49:42,210 INFO [train.py:812] (5/8) Epoch 36, batch 350, loss[loss=0.1676, simple_loss=0.2676, pruned_loss=0.03379, over 6835.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2442, pruned_loss=0.02935, over 1174962.49 frames.], batch size: 31, lr: 2.17e-04
2022-05-16 00:50:40,184 INFO [train.py:812] (5/8) Epoch 36, batch 400, loss[loss=0.1737, simple_loss=0.2643, pruned_loss=0.04162, over 7213.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2448, pruned_loss=0.02939, over 1230988.29 frames.], batch size: 22, lr: 2.17e-04
2022-05-16 00:51:39,731 INFO [train.py:812] (5/8) Epoch 36, batch 450, loss[loss=0.1461, simple_loss=0.2381, pruned_loss=0.02702, over 7201.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2443, pruned_loss=0.02935, over 1278935.93 frames.], batch size: 26, lr: 2.17e-04
2022-05-16 00:52:38,602 INFO [train.py:812] (5/8) Epoch 36, batch 500, loss[loss=0.1423, simple_loss=0.2434, pruned_loss=0.0206, over 7206.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2439, pruned_loss=0.02915, over 1310569.42 frames.], batch size: 23, lr: 2.17e-04
2022-05-16 00:53:37,488 INFO [train.py:812] (5/8) Epoch 36, batch 550, loss[loss=0.1412, simple_loss=0.2331, pruned_loss=0.02469, over 7433.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2443, pruned_loss=0.02897, over 1336911.45 frames.], batch size: 20, lr: 2.17e-04
2022-05-16 00:54:35,748 INFO [train.py:812] (5/8) Epoch 36, batch 600, loss[loss=0.1683, simple_loss=0.2532, pruned_loss=0.04171, over 7190.00 frames.], tot_loss[loss=0.1501, simple_loss=0.243, pruned_loss=0.02858, over 1359299.60 frames.], batch size: 23, lr: 2.17e-04
2022-05-16 00:55:34,851 INFO [train.py:812] (5/8) Epoch 36, batch 650, loss[loss=0.148, simple_loss=0.2385, pruned_loss=0.02873, over 7157.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2415, pruned_loss=0.02798, over 1374809.30 frames.], batch size: 19, lr: 2.17e-04
2022-05-16 00:56:33,797 INFO [train.py:812] (5/8) Epoch 36, batch 700, loss[loss=0.1387, simple_loss=0.227, pruned_loss=0.02521, over 7269.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2422, pruned_loss=0.02858, over 1386471.35 frames.], batch size: 19, lr: 2.17e-04
2022-05-16 00:57:42,565 INFO [train.py:812] (5/8) Epoch 36, batch 750, loss[loss=0.1419, simple_loss=0.2274, pruned_loss=0.02821, over 7329.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2424, pruned_loss=0.02902, over 1385611.56 frames.], batch size: 20, lr: 2.17e-04
2022-05-16 00:58:59,873 INFO [train.py:812] (5/8) Epoch 36, batch 800, loss[loss=0.1635, simple_loss=0.2592, pruned_loss=0.03389, over 7402.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2429, pruned_loss=0.02903, over 1394090.93 frames.], batch size: 21, lr: 2.17e-04
2022-05-16 00:59:58,269 INFO [train.py:812] (5/8) Epoch 36, batch 850, loss[loss=0.129, simple_loss=0.2236, pruned_loss=0.01725, over 7226.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2432, pruned_loss=0.02912, over 1395373.10 frames.], batch size: 21, lr: 2.17e-04
2022-05-16 01:00:57,354 INFO [train.py:812] (5/8) Epoch 36, batch 900, loss[loss=0.1424, simple_loss=0.2455, pruned_loss=0.01962, over 6700.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2429, pruned_loss=0.029, over 1401794.02 frames.], batch size: 31, lr: 2.17e-04
2022-05-16 01:01:55,207 INFO [train.py:812] (5/8) Epoch 36, batch 950, loss[loss=0.1359, simple_loss=0.2122, pruned_loss=0.0298, over 7004.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2437, pruned_loss=0.02922, over 1405655.41 frames.], batch size: 16, lr: 2.17e-04
2022-05-16 01:03:03,137 INFO [train.py:812] (5/8) Epoch 36, batch 1000, loss[loss=0.1231, simple_loss=0.2173, pruned_loss=0.01439, over 7281.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2429, pruned_loss=0.02883, over 1407233.29 frames.], batch size: 17, lr: 2.17e-04
2022-05-16 01:04:02,085 INFO [train.py:812] (5/8) Epoch 36, batch 1050, loss[loss=0.1332, simple_loss=0.2231, pruned_loss=0.02163, over 7351.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2424, pruned_loss=0.02893, over 1408369.79 frames.], batch size: 19, lr: 2.17e-04
2022-05-16 01:05:09,919 INFO [train.py:812] (5/8) Epoch 36, batch 1100, loss[loss=0.1432, simple_loss=0.2372, pruned_loss=0.02454, over 7207.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2428, pruned_loss=0.02921, over 1409025.81 frames.], batch size: 22, lr: 2.17e-04
2022-05-16 01:06:19,081 INFO [train.py:812] (5/8) Epoch 36, batch 1150, loss[loss=0.143, simple_loss=0.2364, pruned_loss=0.0248, over 7291.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2427, pruned_loss=0.02936, over 1414401.98 frames.], batch size: 24, lr: 2.17e-04
2022-05-16 01:07:18,003 INFO [train.py:812] (5/8) Epoch 36, batch 1200, loss[loss=0.1351, simple_loss=0.217, pruned_loss=0.02658, over 7259.00 frames.], tot_loss[loss=0.1519, simple_loss=0.244, pruned_loss=0.02993, over 1409657.08 frames.], batch size: 17, lr: 2.17e-04
2022-05-16 01:08:16,994 INFO [train.py:812] (5/8) Epoch 36, batch 1250, loss[loss=0.1286, simple_loss=0.2097, pruned_loss=0.02375, over 7028.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2434, pruned_loss=0.02957, over 1411701.72 frames.], batch size: 16, lr: 2.17e-04
2022-05-16 01:09:23,813 INFO [train.py:812] (5/8) Epoch 36, batch 1300, loss[loss=0.1474, simple_loss=0.2258, pruned_loss=0.03453, over 7133.00 frames.], tot_loss[loss=0.151, simple_loss=0.2433, pruned_loss=0.02938, over 1415461.50 frames.], batch size: 17, lr: 2.17e-04
2022-05-16 01:10:23,379 INFO [train.py:812] (5/8) Epoch 36, batch 1350, loss[loss=0.1434, simple_loss=0.2392, pruned_loss=0.02382, over 7257.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2425, pruned_loss=0.02889, over 1419904.27 frames.], batch size: 19, lr: 2.17e-04
2022-05-16 01:11:21,651 INFO [train.py:812] (5/8) Epoch 36, batch 1400, loss[loss=0.1219, simple_loss=0.2065, pruned_loss=0.01858, over 7005.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2435, pruned_loss=0.02939, over 1418255.92 frames.], batch size: 16, lr: 2.17e-04
2022-05-16 01:12:20,388 INFO [train.py:812] (5/8) Epoch 36, batch 1450, loss[loss=0.1151, simple_loss=0.1973, pruned_loss=0.01643, over 6832.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2429, pruned_loss=0.0294, over 1414712.60 frames.], batch size: 15, lr: 2.17e-04
2022-05-16 01:13:19,126 INFO [train.py:812] (5/8) Epoch 36, batch 1500, loss[loss=0.1396, simple_loss=0.229, pruned_loss=0.02514, over 7320.00 frames.], tot_loss[loss=0.151, simple_loss=0.2433, pruned_loss=0.02936, over 1418972.43 frames.], batch size: 21, lr: 2.17e-04
2022-05-16 01:14:17,194 INFO [train.py:812] (5/8) Epoch 36, batch 1550, loss[loss=0.1467, simple_loss=0.2449, pruned_loss=0.02427, over 7242.00 frames.], tot_loss[loss=0.1505, simple_loss=0.243, pruned_loss=0.02902, over 1420265.14 frames.], batch size: 20, lr: 2.17e-04
2022-05-16 01:15:14,902 INFO [train.py:812] (5/8) Epoch 36, batch 1600, loss[loss=0.159, simple_loss=0.2459, pruned_loss=0.03608, over 7363.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2426, pruned_loss=0.02921, over 1419918.34 frames.], batch size: 23, lr: 2.16e-04
2022-05-16 01:16:13,261 INFO [train.py:812] (5/8) Epoch 36, batch 1650, loss[loss=0.1383, simple_loss=0.2354, pruned_loss=0.02053, over 7163.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2432, pruned_loss=0.02914, over 1420938.22 frames.], batch size: 19, lr: 2.16e-04
2022-05-16 01:17:10,682 INFO [train.py:812] (5/8) Epoch 36, batch 1700, loss[loss=0.1306, simple_loss=0.2172, pruned_loss=0.02203, over 7296.00 frames.], tot_loss[loss=0.151, simple_loss=0.2433, pruned_loss=0.02934, over 1423401.27 frames.], batch size: 25, lr: 2.16e-04
2022-05-16 01:18:09,643 INFO [train.py:812] (5/8) Epoch 36, batch 1750, loss[loss=0.1268, simple_loss=0.2204, pruned_loss=0.01657, over 7280.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2433, pruned_loss=0.0296, over 1419128.49 frames.], batch size: 18, lr: 2.16e-04
2022-05-16 01:19:07,112 INFO [train.py:812] (5/8) Epoch 36, batch 1800, loss[loss=0.2021, simple_loss=0.2841, pruned_loss=0.06003, over 7220.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2435, pruned_loss=0.02968, over 1421349.82 frames.], batch size: 23, lr: 2.16e-04
2022-05-16 01:20:05,636 INFO [train.py:812] (5/8) Epoch 36, batch 1850, loss[loss=0.1535, simple_loss=0.2553, pruned_loss=0.02589, over 7116.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2427, pruned_loss=0.02926, over 1424111.30 frames.], batch size: 21, lr: 2.16e-04
2022-05-16 01:21:04,227 INFO [train.py:812] (5/8) Epoch 36, batch 1900, loss[loss=0.15, simple_loss=0.2504, pruned_loss=0.02481, over 6806.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2434, pruned_loss=0.02962, over 1425680.64 frames.], batch size: 31, lr: 2.16e-04
2022-05-16 01:22:03,010 INFO [train.py:812] (5/8) Epoch 36, batch 1950, loss[loss=0.1414, simple_loss=0.2381, pruned_loss=0.0223, over 7234.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2431, pruned_loss=0.02964, over 1422245.60 frames.], batch size: 20, lr: 2.16e-04
2022-05-16 01:23:01,525 INFO [train.py:812] (5/8) Epoch 36, batch 2000, loss[loss=0.1316, simple_loss=0.2098, pruned_loss=0.02668, over 7012.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2438, pruned_loss=0.02958, over 1419653.16 frames.], batch size: 16, lr: 2.16e-04
2022-05-16 01:24:00,260 INFO [train.py:812] (5/8) Epoch 36, batch 2050, loss[loss=0.1935, simple_loss=0.2903, pruned_loss=0.0484, over 7309.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2439, pruned_loss=0.02949, over 1423962.84 frames.], batch size: 21, lr: 2.16e-04
2022-05-16 01:24:59,287 INFO [train.py:812] (5/8) Epoch 36, batch 2100, loss[loss=0.1562, simple_loss=0.2561, pruned_loss=0.02812, over 7405.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2435, pruned_loss=0.02961, over 1422716.33 frames.], batch size: 21, lr: 2.16e-04
2022-05-16 01:25:59,143 INFO [train.py:812] (5/8) Epoch 36, batch 2150, loss[loss=0.1363, simple_loss=0.2168, pruned_loss=0.02787, over 7254.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2421, pruned_loss=0.02913, over 1425151.47 frames.], batch size: 19, lr: 2.16e-04
2022-05-16 01:26:58,702 INFO [train.py:812] (5/8) Epoch 36, batch 2200, loss[loss=0.1272, simple_loss=0.2074, pruned_loss=0.02344, over 7421.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2416, pruned_loss=0.02872, over 1424414.93 frames.], batch size: 18, lr: 2.16e-04
2022-05-16 01:27:57,406 INFO [train.py:812] (5/8) Epoch 36, batch 2250, loss[loss=0.1538, simple_loss=0.2513, pruned_loss=0.02818, over 7328.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2423, pruned_loss=0.02875, over 1421862.84 frames.], batch size: 22, lr: 2.16e-04
2022-05-16 01:28:55,635 INFO [train.py:812] (5/8) Epoch 36, batch 2300, loss[loss=0.1185, simple_loss=0.1939, pruned_loss=0.02157, over 7128.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2409, pruned_loss=0.02829, over 1425307.84 frames.], batch size: 17, lr: 2.16e-04
2022-05-16 01:29:55,114 INFO [train.py:812] (5/8) Epoch 36, batch 2350, loss[loss=0.1672, simple_loss=0.256, pruned_loss=0.03915, over 5335.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2422, pruned_loss=0.02855, over 1423984.15 frames.], batch size: 52, lr: 2.16e-04
2022-05-16 01:30:54,427 INFO [train.py:812] (5/8) Epoch 36, batch 2400, loss[loss=0.1456, simple_loss=0.2412, pruned_loss=0.02495, over 7421.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2422, pruned_loss=0.02834, over 1427435.84 frames.], batch size: 18, lr: 2.16e-04
2022-05-16 01:31:54,039 INFO [train.py:812] (5/8) Epoch 36, batch 2450, loss[loss=0.1539, simple_loss=0.2377, pruned_loss=0.0351, over 7154.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2421, pruned_loss=0.02868, over 1423366.45 frames.], batch size: 18, lr: 2.16e-04
2022-05-16 01:32:52,229 INFO [train.py:812] (5/8) Epoch 36, batch 2500, loss[loss=0.1488, simple_loss=0.2423, pruned_loss=0.02767, over 7148.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2413, pruned_loss=0.02841, over 1426478.41 frames.], batch size: 20, lr: 2.16e-04
2022-05-16 01:33:51,379 INFO [train.py:812] (5/8) Epoch 36, batch 2550, loss[loss=0.1406, simple_loss=0.2344, pruned_loss=0.02344, over 7352.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2418, pruned_loss=0.02864, over 1423495.63 frames.], batch size: 19, lr: 2.16e-04
2022-05-16 01:34:50,030 INFO [train.py:812] (5/8) Epoch 36, batch 2600, loss[loss=0.1406, simple_loss=0.2367, pruned_loss=0.0223, over 7160.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2415, pruned_loss=0.02838, over 1424702.51 frames.], batch size: 19, lr: 2.16e-04
2022-05-16 01:35:48,650 INFO [train.py:812] (5/8) Epoch 36, batch 2650, loss[loss=0.2021, simple_loss=0.2943, pruned_loss=0.0549, over 5304.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2416, pruned_loss=0.02856, over 1424153.44 frames.], batch size: 52, lr: 2.16e-04
2022-05-16 01:36:46,987 INFO [train.py:812] (5/8) Epoch 36, batch 2700, loss[loss=0.146, simple_loss=0.2404, pruned_loss=0.02581, over 7316.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2421, pruned_loss=0.02865, over 1424461.27 frames.], batch size: 21, lr: 2.16e-04
2022-05-16 01:37:45,793 INFO [train.py:812] (5/8) Epoch 36, batch 2750, loss[loss=0.1576, simple_loss=0.2554, pruned_loss=0.02991, over 7116.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2419, pruned_loss=0.02888, over 1426333.51 frames.], batch size: 21, lr: 2.16e-04
2022-05-16 01:38:44,964 INFO [train.py:812] (5/8) Epoch 36, batch 2800, loss[loss=0.1862, simple_loss=0.2831, pruned_loss=0.04466, over 7204.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2421, pruned_loss=0.02924, over 1427269.61 frames.], batch size: 22, lr: 2.16e-04
2022-05-16 01:39:44,891 INFO [train.py:812] (5/8) Epoch 36, batch 2850, loss[loss=0.1223, simple_loss=0.2051, pruned_loss=0.01975, over 7288.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2411, pruned_loss=0.02901, over 1428405.42 frames.], batch size: 17, lr: 2.16e-04
2022-05-16 01:40:43,906 INFO [train.py:812] (5/8) Epoch 36, batch 2900, loss[loss=0.1296, simple_loss=0.2248, pruned_loss=0.01718, over 7251.00 frames.], tot_loss[loss=0.1495, simple_loss=0.241, pruned_loss=0.02902, over 1427112.14 frames.], batch size: 19, lr: 2.16e-04
2022-05-16 01:41:42,648 INFO [train.py:812] (5/8) Epoch 36, batch 2950, loss[loss=0.1328, simple_loss=0.2226, pruned_loss=0.02143, over 7168.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2426, pruned_loss=0.02935, over 1425401.54 frames.], batch size: 18, lr: 2.16e-04
2022-05-16 01:42:41,256 INFO [train.py:812] (5/8) Epoch 36, batch 3000, loss[loss=0.1351, simple_loss=0.2286, pruned_loss=0.02083, over 7176.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2433, pruned_loss=0.02944, over 1422119.48 frames.], batch size: 19, lr: 2.16e-04
2022-05-16 01:42:41,257 INFO [train.py:832] (5/8) Computing validation loss
2022-05-16 01:42:48,527 INFO [train.py:841] (5/8) Epoch 36, validation: loss=0.1533, simple_loss=0.2487, pruned_loss=0.02893, over 698248.00 frames.
2022-05-16 01:43:48,422 INFO [train.py:812] (5/8) Epoch 36, batch 3050, loss[loss=0.1595, simple_loss=0.2445, pruned_loss=0.0372, over 7280.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2437, pruned_loss=0.02959, over 1424057.18 frames.], batch size: 24, lr: 2.16e-04
2022-05-16 01:44:47,698 INFO [train.py:812] (5/8) Epoch 36, batch 3100, loss[loss=0.1445, simple_loss=0.2415, pruned_loss=0.02377, over 7280.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2442, pruned_loss=0.02955, over 1428475.54 frames.], batch size: 25, lr: 2.15e-04
2022-05-16 01:45:47,511 INFO [train.py:812] (5/8) Epoch 36, batch 3150, loss[loss=0.1633, simple_loss=0.2553, pruned_loss=0.0357, over 7377.00 frames.], tot_loss[loss=0.1517, simple_loss=0.244, pruned_loss=0.02969, over 1426782.73 frames.], batch size: 23, lr: 2.15e-04
2022-05-16 01:46:46,125 INFO [train.py:812] (5/8) Epoch 36, batch 3200, loss[loss=0.1318, simple_loss=0.2133, pruned_loss=0.02514, over 7134.00 frames.], tot_loss[loss=0.152, simple_loss=0.2443, pruned_loss=0.0299, over 1420410.01 frames.], batch size: 17, lr: 2.15e-04
2022-05-16 01:47:45,903 INFO [train.py:812] (5/8) Epoch 36, batch 3250, loss[loss=0.1637, simple_loss=0.2544, pruned_loss=0.03648, over 5079.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2441, pruned_loss=0.02977, over 1417920.70 frames.], batch size: 53, lr: 2.15e-04
2022-05-16 01:48:53,282 INFO [train.py:812] (5/8) Epoch 36, batch 3300, loss[loss=0.1743, simple_loss=0.2632, pruned_loss=0.04271, over 7208.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2451, pruned_loss=0.03014, over 1421478.88 frames.], batch size: 23, lr: 2.15e-04
2022-05-16 01:49:52,245 INFO [train.py:812] (5/8) Epoch 36, batch 3350, loss[loss=0.1545, simple_loss=0.2503, pruned_loss=0.0294, over 7193.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2449, pruned_loss=0.02976, over 1425595.94 frames.], batch size: 23, lr: 2.15e-04
2022-05-16 01:50:50,246 INFO [train.py:812] (5/8) Epoch 36, batch 3400, loss[loss=0.1548, simple_loss=0.2521, pruned_loss=0.02874, over 7267.00 frames.], tot_loss[loss=0.151, simple_loss=0.2433, pruned_loss=0.02936, over 1424596.29 frames.], batch size: 19, lr: 2.15e-04
2022-05-16 01:51:53,909 INFO [train.py:812] (5/8) Epoch 36, batch 3450, loss[loss=0.1316, simple_loss=0.2159, pruned_loss=0.02366, over 7279.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2436, pruned_loss=0.02932, over 1422474.72 frames.], batch size: 17, lr: 2.15e-04
2022-05-16 01:52:52,271 INFO [train.py:812] (5/8) Epoch 36, batch 3500, loss[loss=0.1465, simple_loss=0.2437, pruned_loss=0.02467, over 7405.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2434, pruned_loss=0.02923, over 1419529.21 frames.], batch size: 21, lr: 2.15e-04
2022-05-16 01:53:51,032 INFO [train.py:812] (5/8) Epoch 36, batch 3550, loss[loss=0.1573, simple_loss=0.2502, pruned_loss=0.03226, over 7102.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2434, pruned_loss=0.02909, over 1423079.58 frames.], batch size: 28, lr: 2.15e-04
2022-05-16 01:54:49,072 INFO [train.py:812] (5/8) Epoch 36, batch 3600, loss[loss=0.1625, simple_loss=0.2572, pruned_loss=0.03388, over 7265.00 frames.], tot_loss[loss=0.1514, simple_loss=0.244, pruned_loss=0.02943, over 1421897.21 frames.], batch size: 25, lr: 2.15e-04
2022-05-16 01:55:48,147 INFO [train.py:812] (5/8) Epoch 36, batch 3650, loss[loss=0.1593, simple_loss=0.2555, pruned_loss=0.03154, over 7288.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2438, pruned_loss=0.02947, over 1423292.50 frames.], batch size: 24, lr: 2.15e-04
2022-05-16 01:56:46,026 INFO [train.py:812] (5/8) Epoch 36, batch 3700, loss[loss=0.159, simple_loss=0.2635, pruned_loss=0.0273, over 7103.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2436, pruned_loss=0.02959, over 1426267.99 frames.], batch size: 21, lr: 2.15e-04
2022-05-16 01:57:44,807 INFO [train.py:812] (5/8) Epoch 36, batch 3750, loss[loss=0.1534, simple_loss=0.2528, pruned_loss=0.02698, over 7329.00 frames.], tot_loss[loss=0.151, simple_loss=0.2436, pruned_loss=0.02923, over 1425606.06 frames.], batch size: 22, lr: 2.15e-04
2022-05-16 01:58:43,665 INFO [train.py:812] (5/8) Epoch 36, batch 3800, loss[loss=0.1245, simple_loss=0.2157, pruned_loss=0.0167, over 7348.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2448, pruned_loss=0.02977, over 1427885.63 frames.], batch size: 19, lr: 2.15e-04
2022-05-16 01:59:42,777 INFO [train.py:812] (5/8) Epoch 36, batch 3850, loss[loss=0.1223, simple_loss=0.2094, pruned_loss=0.01767, over 6993.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2449, pruned_loss=0.02964, over 1424525.20 frames.], batch size: 16, lr: 2.15e-04
2022-05-16 02:00:41,785 INFO [train.py:812] (5/8) Epoch 36, batch 3900, loss[loss=0.1623, simple_loss=0.2539, pruned_loss=0.03537, over 7179.00 frames.], tot_loss[loss=0.152, simple_loss=0.2444, pruned_loss=0.02986, over 1426521.37 frames.], batch size: 23, lr: 2.15e-04
2022-05-16 02:01:40,058 INFO [train.py:812] (5/8) Epoch 36, batch 3950, loss[loss=0.1452, simple_loss=0.2446, pruned_loss=0.02294, over 6820.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2446, pruned_loss=0.02988, over 1424236.38 frames.], batch size: 31, lr: 2.15e-04
2022-05-16 02:02:38,468 INFO [train.py:812] (5/8) Epoch 36, batch 4000, loss[loss=0.1597, simple_loss=0.258, pruned_loss=0.03064, over 7135.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2448, pruned_loss=0.02983, over 1423394.61 frames.], batch size: 28, lr: 2.15e-04
2022-05-16 02:03:36,268 INFO [train.py:812] (5/8) Epoch 36, batch 4050, loss[loss=0.1475, simple_loss=0.2455, pruned_loss=0.02472, over 7217.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2445, pruned_loss=0.02954, over 1425812.46 frames.], batch size: 21, lr: 2.15e-04
2022-05-16 02:04:34,900 INFO [train.py:812] (5/8) Epoch 36, batch 4100, loss[loss=0.1317, simple_loss=0.2094, pruned_loss=0.02707, over 7149.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2438, pruned_loss=0.02943, over 1426110.03 frames.], batch size: 17, lr: 2.15e-04
2022-05-16 02:05:34,472 INFO [train.py:812] (5/8) Epoch 36, batch 4150, loss[loss=0.1465, simple_loss=0.2425, pruned_loss=0.02529, over 7196.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2429, pruned_loss=0.02933, over 1418597.72 frames.], batch size: 23, lr: 2.15e-04
2022-05-16 02:06:32,906 INFO [train.py:812] (5/8) Epoch 36, batch 4200, loss[loss=0.1584, simple_loss=0.2565, pruned_loss=0.03022, over 7231.00 frames.], tot_loss[loss=0.151, simple_loss=0.2428, pruned_loss=0.02955, over 1416140.18 frames.], batch size: 20, lr: 2.15e-04
2022-05-16 02:07:31,842 INFO [train.py:812] (5/8) Epoch 36, batch 4250, loss[loss=0.1534, simple_loss=0.2492, pruned_loss=0.02876, over 7217.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2426, pruned_loss=0.02957, over 1415849.46 frames.], batch size: 22, lr: 2.15e-04
2022-05-16 02:08:30,998 INFO [train.py:812] (5/8) Epoch 36, batch 4300, loss[loss=0.1585, simple_loss=0.2429, pruned_loss=0.03711, over 7190.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2415, pruned_loss=0.02906, over 1412487.29 frames.], batch size: 22, lr: 2.15e-04
2022-05-16 02:09:30,641 INFO [train.py:812] (5/8) Epoch 36, batch 4350, loss[loss=0.1441, simple_loss=0.2379, pruned_loss=0.02508, over 7422.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2407, pruned_loss=0.02876, over 1410413.80 frames.], batch size: 20, lr: 2.15e-04
2022-05-16 02:10:29,631 INFO [train.py:812] (5/8) Epoch 36, batch 4400, loss[loss=0.1341, simple_loss=0.2307, pruned_loss=0.01877, over 7348.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2405, pruned_loss=0.02849, over 1415289.35 frames.], batch size: 19, lr: 2.15e-04
2022-05-16 02:11:29,737 INFO [train.py:812] (5/8) Epoch 36, batch 4450, loss[loss=0.132, simple_loss=0.2224, pruned_loss=0.0208, over 7211.00 frames.], tot_loss[loss=0.1486, simple_loss=0.24, pruned_loss=0.02858, over 1405889.21 frames.], batch size: 21, lr: 2.15e-04
2022-05-16 02:12:28,140 INFO [train.py:812] (5/8) Epoch 36, batch 4500, loss[loss=0.1634, simple_loss=0.2641, pruned_loss=0.03136, over 7227.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2401, pruned_loss=0.0286, over 1393554.74 frames.], batch size: 21, lr: 2.15e-04
2022-05-16 02:13:26,412 INFO [train.py:812] (5/8) Epoch 36, batch 4550, loss[loss=0.1469, simple_loss=0.2436, pruned_loss=0.02515, over 7255.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2418, pruned_loss=0.03003, over 1353666.43 frames.], batch size: 19, lr: 2.15e-04
2022-05-16 02:14:35,970 INFO [train.py:812] (5/8) Epoch 37, batch 0, loss[loss=0.1424, simple_loss=0.2406, pruned_loss=0.0221, over 7326.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2406, pruned_loss=0.0221, over 7326.00 frames.], batch size: 22, lr: 2.12e-04
2022-05-16 02:15:34,999 INFO [train.py:812] (5/8) Epoch 37, batch 50, loss[loss=0.1394, simple_loss=0.2169, pruned_loss=0.03088, over 7064.00 frames.], tot_loss[loss=0.1527, simple_loss=0.244, pruned_loss=0.03074, over 320494.87 frames.], batch size: 18, lr: 2.12e-04
2022-05-16 02:16:33,775 INFO [train.py:812] (5/8) Epoch 37, batch 100, loss[loss=0.1509, simple_loss=0.2505, pruned_loss=0.02565, over 7337.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2441, pruned_loss=0.03037, over 566561.18 frames.], batch size: 20, lr: 2.12e-04
2022-05-16 02:17:32,744 INFO [train.py:812] (5/8) Epoch 37, batch 150, loss[loss=0.1459, simple_loss=0.2503, pruned_loss=0.02076, over 7097.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2434, pruned_loss=0.0298, over 753770.75 frames.], batch size: 28, lr: 2.11e-04
2022-05-16 02:18:31,177 INFO [train.py:812] (5/8) Epoch 37, batch 200, loss[loss=0.1585, simple_loss=0.2619, pruned_loss=0.02756, over 7323.00 frames.], tot_loss[loss=0.1528, simple_loss=0.246, pruned_loss=0.02983, over 905759.55 frames.], batch size: 21, lr: 2.11e-04
2022-05-16 02:19:29,649 INFO [train.py:812] (5/8) Epoch 37, batch 250, loss[loss=0.1511, simple_loss=0.2377, pruned_loss=0.03229, over 7263.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2451, pruned_loss=0.02931, over 1018530.21 frames.], batch size: 19, lr: 2.11e-04
2022-05-16 02:20:28,583 INFO [train.py:812] (5/8) Epoch 37, batch 300, loss[loss=0.1507, simple_loss=0.2495, pruned_loss=0.02597, over 7340.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2437, pruned_loss=0.02928, over 1105677.91 frames.], batch size: 22, lr: 2.11e-04
2022-05-16 02:21:27,108 INFO [train.py:812] (5/8) Epoch 37, batch 350, loss[loss=0.1206, simple_loss=0.2117, pruned_loss=0.0147, over 7168.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2426, pruned_loss=0.02906, over 1174133.88 frames.], batch size: 18, lr: 2.11e-04
2022-05-16 02:22:25,667 INFO [train.py:812] (5/8) Epoch 37, batch 400, loss[loss=0.1525, simple_loss=0.2438, pruned_loss=0.03063, over 7231.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2425, pruned_loss=0.02892, over 1233146.04 frames.], batch size: 20, lr: 2.11e-04
2022-05-16 02:23:24,539 INFO [train.py:812] (5/8) Epoch 37, batch 450, loss[loss=0.1453, simple_loss=0.2518, pruned_loss=0.0194, over 7145.00 frames.], tot_loss[loss=0.1503, simple_loss=0.243, pruned_loss=0.02883, over 1277607.60 frames.], batch size: 20, lr: 2.11e-04
2022-05-16 02:24:21,844 INFO [train.py:812] (5/8) Epoch 37, batch 500, loss[loss=0.1375, simple_loss=0.2338, pruned_loss=0.02058, over 7236.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2413, pruned_loss=0.02809, over 1306405.97 frames.], batch size: 20, lr: 2.11e-04
2022-05-16 02:25:21,112 INFO [train.py:812] (5/8) Epoch 37, batch 550, loss[loss=0.1329, simple_loss=0.2282, pruned_loss=0.01875, over 7066.00 frames.], tot_loss[loss=0.1494, simple_loss=0.242, pruned_loss=0.02837, over 1322916.30 frames.], batch size: 18, lr: 2.11e-04
2022-05-16 02:26:19,457 INFO [train.py:812] (5/8) Epoch 37, batch 600, loss[loss=0.1455, simple_loss=0.2276, pruned_loss=0.0317, over 7436.00 frames.], tot_loss[loss=0.149, simple_loss=0.2413, pruned_loss=0.02839, over 1347805.30 frames.], batch size: 20, lr: 2.11e-04
2022-05-16 02:27:18,102 INFO [train.py:812] (5/8) Epoch 37, batch 650, loss[loss=0.1129, simple_loss=0.2018, pruned_loss=0.01199, over 7144.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2403, pruned_loss=0.0284, over 1367169.72 frames.], batch size: 17, lr: 2.11e-04
2022-05-16 02:28:16,741 INFO [train.py:812] (5/8) Epoch 37, batch 700, loss[loss=0.1304, simple_loss=0.237, pruned_loss=0.01186, over 7224.00 frames.], tot_loss[loss=0.1478, simple_loss=0.24, pruned_loss=0.02775, over 1380590.43 frames.], batch size: 20, lr: 2.11e-04
2022-05-16 02:29:16,754 INFO [train.py:812] (5/8) Epoch 37, batch 750, loss[loss=0.1492, simple_loss=0.235, pruned_loss=0.03167, over 7162.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2397, pruned_loss=0.02786, over 1388663.77 frames.], batch size: 19, lr: 2.11e-04
2022-05-16 02:30:15,258 INFO [train.py:812] (5/8) Epoch 37, batch 800, loss[loss=0.1545, simple_loss=0.2389, pruned_loss=0.03499, over 7423.00 frames.], tot_loss[loss=0.1472, simple_loss=0.239, pruned_loss=0.02768, over 1398660.69 frames.], batch size: 18, lr: 2.11e-04
2022-05-16 02:31:14,043 INFO [train.py:812] (5/8) Epoch 37, batch 850, loss[loss=0.144, simple_loss=0.2312, pruned_loss=0.02844, over 7256.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2401, pruned_loss=0.02834, over 1397538.81 frames.], batch size: 19, lr: 2.11e-04
2022-05-16 02:32:12,861 INFO [train.py:812] (5/8) Epoch 37, batch 900, loss[loss=0.1402, simple_loss=0.2397, pruned_loss=0.02036, over 7064.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2399, pruned_loss=0.0281, over 1406194.02 frames.], batch size: 18, lr: 2.11e-04
2022-05-16 02:33:11,831 INFO [train.py:812] (5/8) Epoch 37, batch 950, loss[loss=0.1171, simple_loss=0.2022, pruned_loss=0.01594, over 7269.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2402, pruned_loss=0.02831, over 1409757.84 frames.], batch size: 17, lr: 2.11e-04
2022-05-16 02:34:09,790 INFO [train.py:812] (5/8) Epoch 37, batch 1000, loss[loss=0.1712, simple_loss=0.2628, pruned_loss=0.03976, over 6776.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2398, pruned_loss=0.02789, over 1412937.60 frames.], batch size: 31, lr: 2.11e-04
2022-05-16 02:35:08,649 INFO [train.py:812] (5/8) Epoch 37, batch 1050, loss[loss=0.167, simple_loss=0.2604, pruned_loss=0.03678, over 7403.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2401, pruned_loss=0.02802, over 1417918.08 frames.], batch size: 23, lr: 2.11e-04
2022-05-16 02:36:07,857 INFO [train.py:812] (5/8) Epoch 37, batch 1100, loss[loss=0.1644, simple_loss=0.2802, pruned_loss=0.02431, over 7238.00 frames.], tot_loss[loss=0.1479, simple_loss=0.24, pruned_loss=0.02789, over 1418275.33 frames.], batch size: 21, lr: 2.11e-04
2022-05-16 02:37:06,610 INFO [train.py:812] (5/8) Epoch 37, batch 1150, loss[loss=0.1705, simple_loss=0.2593, pruned_loss=0.04088, over 5189.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2409, pruned_loss=0.02838, over 1417333.32 frames.], batch size: 52, lr: 2.11e-04
2022-05-16 02:38:04,369 INFO [train.py:812] (5/8) Epoch 37, batch 1200, loss[loss=0.1576, simple_loss=0.2524, pruned_loss=0.0314, over 7144.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2423, pruned_loss=0.0286, over 1419810.69 frames.], batch size: 20, lr: 2.11e-04
2022-05-16 02:39:03,400 INFO [train.py:812] (5/8) Epoch 37, batch 1250, loss[loss=0.1654, simple_loss=0.2635, pruned_loss=0.03365, over 7216.00 frames.], tot_loss[loss=0.15, simple_loss=0.2429, pruned_loss=0.0286, over 1420038.44 frames.], batch size: 22, lr: 2.11e-04
2022-05-16 02:40:01,881 INFO [train.py:812] (5/8) Epoch 37, batch 1300, loss[loss=0.1455, simple_loss=0.2189, pruned_loss=0.03604, over 7147.00 frames.], tot_loss[loss=0.1501, simple_loss=0.243, pruned_loss=0.0286, over 1422250.63 frames.], batch size: 17, lr: 2.11e-04
2022-05-16 02:41:00,868 INFO [train.py:812] (5/8) Epoch 37, batch 1350, loss[loss=0.1443, simple_loss=0.2346, pruned_loss=0.02699, over 7068.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2428, pruned_loss=0.0287, over 1418510.50 frames.], batch size: 18, lr: 2.11e-04
2022-05-16 02:41:59,960 INFO [train.py:812] (5/8) Epoch 37, batch 1400, loss[loss=0.1147, simple_loss=0.1967, pruned_loss=0.01631, over 7008.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2424, pruned_loss=0.02885, over 1417903.67 frames.], batch size: 16, lr: 2.11e-04
2022-05-16 02:42:58,483 INFO [train.py:812] (5/8) Epoch 37, batch 1450, loss[loss=0.1713, simple_loss=0.2624, pruned_loss=0.04014, over 7267.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2426, pruned_loss=0.02883, over 1418699.77 frames.], batch size: 24, lr: 2.11e-04
2022-05-16 02:43:56,626 INFO [train.py:812] (5/8) Epoch 37, batch 1500, loss[loss=0.1787, simple_loss=0.2807, pruned_loss=0.03836, over 7289.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2432, pruned_loss=0.02903, over 1415324.37 frames.], batch size: 24, lr: 2.11e-04
2022-05-16 02:44:55,808 INFO [train.py:812] (5/8) Epoch 37, batch 1550, loss[loss=0.1632, simple_loss=0.2547, pruned_loss=0.0359, over 6799.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2434, pruned_loss=0.02941, over 1410349.87 frames.], batch size: 31, lr: 2.11e-04
2022-05-16 02:45:54,063 INFO [train.py:812] (5/8) Epoch 37, batch 1600, loss[loss=0.162, simple_loss=0.2526, pruned_loss=0.03571, over 7376.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2424, pruned_loss=0.02922, over 1410774.13 frames.], batch size: 23, lr: 2.11e-04
2022-05-16 02:46:52,088 INFO [train.py:812] (5/8) Epoch 37, batch 1650, loss[loss=0.1908, simple_loss=0.2853, pruned_loss=0.0482, over 7191.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2418, pruned_loss=0.02859, over 1414124.33 frames.], batch size: 22, lr: 2.11e-04
2022-05-16 02:47:50,657 INFO [train.py:812] (5/8) Epoch 37, batch 1700, loss[loss=0.1372, simple_loss=0.231, pruned_loss=0.0217, over 7167.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2421, pruned_loss=0.02875, over 1413325.25 frames.], batch size: 19, lr: 2.11e-04
2022-05-16 02:48:48,765 INFO [train.py:812] (5/8) Epoch 37, batch 1750, loss[loss=0.1524, simple_loss=0.2474, pruned_loss=0.02865, over 7356.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2418, pruned_loss=0.02878, over 1407824.13 frames.], batch size: 19, lr: 2.10e-04
2022-05-16 02:49:47,274 INFO [train.py:812] (5/8) Epoch 37, batch 1800, loss[loss=0.1779, simple_loss=0.271, pruned_loss=0.04244, over 7314.00 frames.], tot_loss[loss=0.15, simple_loss=0.2423, pruned_loss=0.02884, over 1409746.40 frames.], batch size: 24, lr: 2.10e-04
2022-05-16 02:50:46,344 INFO [train.py:812] (5/8) Epoch 37, batch 1850, loss[loss=0.1454, simple_loss=0.2335, pruned_loss=0.0286, over 7252.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2421, pruned_loss=0.0289, over 1410254.62 frames.], batch size: 19, lr: 2.10e-04
2022-05-16 02:51:45,111 INFO [train.py:812] (5/8) Epoch 37, batch 1900, loss[loss=0.1527, simple_loss=0.2461, pruned_loss=0.02968, over 6847.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2434, pruned_loss=0.02949, over 1416407.23 frames.], batch size: 31, lr: 2.10e-04
2022-05-16 02:52:44,114 INFO [train.py:812] (5/8) Epoch 37, batch 1950, loss[loss=0.1493, simple_loss=0.245, pruned_loss=0.02683, over 7221.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2429, pruned_loss=0.02926, over 1420007.74 frames.], batch size: 21, lr: 2.10e-04
2022-05-16 02:53:42,337 INFO [train.py:812] (5/8) Epoch 37, batch 2000, loss[loss=0.159, simple_loss=0.255, pruned_loss=0.03146, over 7419.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2427, pruned_loss=0.02907, over 1417545.99 frames.], batch size: 21, lr: 2.10e-04
2022-05-16 02:54:41,771 INFO [train.py:812] (5/8) Epoch 37, batch 2050, loss[loss=0.1453, simple_loss=0.2307, pruned_loss=0.03, over 7237.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2415, pruned_loss=0.0288, over 1420006.25 frames.], batch size: 20, lr: 2.10e-04
2022-05-16 02:55:38,569 INFO [train.py:812] (5/8) Epoch 37, batch 2100, loss[loss=0.1417, simple_loss=0.2336, pruned_loss=0.0249, over 7145.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2409, pruned_loss=0.02843, over 1420733.94 frames.], batch size: 20, lr: 2.10e-04
2022-05-16 02:56:46,821 INFO [train.py:812] (5/8) Epoch 37, batch 2150, loss[loss=0.1398, simple_loss=0.2391, pruned_loss=0.02023, over 7410.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2418, pruned_loss=0.02878, over 1418247.40 frames.], batch size: 21, lr: 2.10e-04
2022-05-16 02:57:45,118 INFO [train.py:812] (5/8) Epoch 37, batch 2200, loss[loss=0.1311, simple_loss=0.2248, pruned_loss=0.01869, over 7257.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2418, pruned_loss=0.02867, over 1419740.65 frames.], batch size: 19, lr: 2.10e-04
2022-05-16 02:58:53,434 INFO [train.py:812] (5/8) Epoch 37, batch 2250, loss[loss=0.1449, simple_loss=0.2532, pruned_loss=0.01829, over 7144.00 frames.], tot_loss[loss=0.15, simple_loss=0.2425, pruned_loss=0.02878, over 1420469.95 frames.], batch size: 20, lr: 2.10e-04
2022-05-16 03:00:01,336 INFO [train.py:812] (5/8) Epoch 37, batch 2300, loss[loss=0.1734, simple_loss=0.2683, pruned_loss=0.03926, over 7210.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2431, pruned_loss=0.02885, over 1420157.68 frames.], batch size: 23, lr: 2.10e-04
2022-05-16 03:01:00,996 INFO [train.py:812] (5/8) Epoch 37, batch 2350, loss[loss=0.1265, simple_loss=0.2097, pruned_loss=0.02162, over 7288.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2426, pruned_loss=0.02857, over 1413773.40 frames.], batch size: 17, lr: 2.10e-04
2022-05-16 03:01:59,215 INFO [train.py:812] (5/8) Epoch 37, batch 2400, loss[loss=0.1556, simple_loss=0.2496, pruned_loss=0.03079, over 7306.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2424, pruned_loss=0.02857, over 1419506.81 frames.], batch size: 25, lr: 2.10e-04
2022-05-16 03:02:57,104 INFO [train.py:812] (5/8) Epoch 37, batch 2450, loss[loss=0.1541, simple_loss=0.246, pruned_loss=0.03111, over 7182.00 frames.], tot_loss[loss=0.1495, simple_loss=0.242, pruned_loss=0.02854, over 1424838.33 frames.], batch size: 26, lr: 2.10e-04
2022-05-16 03:04:04,663 INFO [train.py:812] (5/8) Epoch 37, batch 2500, loss[loss=0.1451, simple_loss=0.2375, pruned_loss=0.02638, over 7154.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2417, pruned_loss=0.02891, over 1427579.73 frames.], batch size: 19, lr: 2.10e-04
2022-05-16 03:05:04,449 INFO [train.py:812] (5/8) Epoch 37, batch 2550, loss[loss=0.1754, simple_loss=0.2587, pruned_loss=0.04605, over 7289.00 frames.], tot_loss[loss=0.1501, simple_loss=0.242, pruned_loss=0.02909, over 1428704.25 frames.], batch size: 24, lr: 2.10e-04
2022-05-16 03:06:02,644 INFO [train.py:812] (5/8) Epoch 37, batch 2600, loss[loss=0.146, simple_loss=0.2325, pruned_loss=0.02974, over 6826.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2426, pruned_loss=0.02934, over 1424804.53 frames.], batch size: 15, lr: 2.10e-04
2022-05-16 03:07:21,585 INFO [train.py:812] (5/8) Epoch 37, batch 2650, loss[loss=0.1611, simple_loss=0.2548, pruned_loss=0.03376, over 7214.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2427, pruned_loss=0.02936, over 1427452.24 frames.], batch size: 22, lr: 2.10e-04
2022-05-16 03:08:19,628 INFO [train.py:812] (5/8) Epoch 37, batch 2700, loss[loss=0.1431, simple_loss=0.2318, pruned_loss=0.02725, over 6407.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2433, pruned_loss=0.02979, over 1424268.16 frames.], batch size: 38, lr: 2.10e-04
2022-05-16 03:09:18,861 INFO [train.py:812] (5/8) Epoch 37, batch 2750, loss[loss=0.1957, simple_loss=0.2764, pruned_loss=0.05753, over 4679.00 frames.], tot_loss[loss=0.151, simple_loss=0.2432, pruned_loss=0.02944, over 1424329.30 frames.], batch size: 53, lr: 2.10e-04
2022-05-16 03:10:17,062 INFO [train.py:812] (5/8) Epoch 37, batch 2800, loss[loss=0.1401, simple_loss=0.2308, pruned_loss=0.02472, over 7276.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2423, pruned_loss=0.0293, over 1428750.11 frames.], batch size: 18, lr: 2.10e-04
2022-05-16 03:11:34,311 INFO [train.py:812] (5/8) Epoch 37, batch 2850, loss[loss=0.1619, simple_loss=0.2627, pruned_loss=0.03057, over 6471.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2425, pruned_loss=0.02909, over 1427540.18 frames.], batch size: 38, lr: 2.10e-04
2022-05-16 03:12:32,630 INFO [train.py:812] (5/8) Epoch 37, batch 2900, loss[loss=0.134, simple_loss=0.2152, pruned_loss=0.02635, over 7012.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2428, pruned_loss=0.02915, over 1427987.33 frames.], batch size: 16, lr: 2.10e-04
2022-05-16 03:13:31,837 INFO [train.py:812] (5/8) Epoch 37, batch 2950, loss[loss=0.1513, simple_loss=0.2413, pruned_loss=0.03069, over 7425.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2425, pruned_loss=0.02927, over 1423574.66 frames.], batch size: 20, lr: 2.10e-04
2022-05-16 03:14:30,565 INFO [train.py:812] (5/8) Epoch 37, batch 3000, loss[loss=0.169, simple_loss=0.2663, pruned_loss=0.03581, over 7217.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2424, pruned_loss=0.02904, over 1420451.28 frames.], batch size: 21, lr: 2.10e-04
2022-05-16 03:14:30,566 INFO [train.py:832] (5/8) Computing validation loss
2022-05-16 03:14:38,087 INFO [train.py:841] (5/8) Epoch 37, validation: loss=0.1539, simple_loss=0.2491, pruned_loss=0.02931, over 698248.00 frames.
2022-05-16 03:15:37,672 INFO [train.py:812] (5/8) Epoch 37, batch 3050, loss[loss=0.1396, simple_loss=0.2176, pruned_loss=0.03076, over 6823.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2427, pruned_loss=0.02936, over 1419798.69 frames.], batch size: 15, lr: 2.10e-04
2022-05-16 03:16:36,467 INFO [train.py:812] (5/8) Epoch 37, batch 3100, loss[loss=0.1316, simple_loss=0.2186, pruned_loss=0.02234, over 7449.00 frames.], tot_loss[loss=0.1502, simple_loss=0.242, pruned_loss=0.02925, over 1419114.56 frames.], batch size: 19, lr: 2.10e-04
2022-05-16 03:17:34,872 INFO [train.py:812] (5/8) Epoch 37, batch 3150, loss[loss=0.1254, simple_loss=0.2137, pruned_loss=0.01856, over 7003.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2413, pruned_loss=0.02882, over 1418507.87 frames.], batch size: 16, lr: 2.10e-04
2022-05-16 03:18:33,958 INFO [train.py:812] (5/8) Epoch 37, batch 3200, loss[loss=0.1918, simple_loss=0.273, pruned_loss=0.05527, over 5084.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2411, pruned_loss=0.02889, over 1418690.76 frames.], batch size: 53, lr: 2.10e-04
2022-05-16 03:19:33,512 INFO [train.py:812] (5/8) Epoch 37, batch 3250, loss[loss=0.1616, simple_loss=0.2581, pruned_loss=0.03253, over 7204.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2419, pruned_loss=0.02927, over 1418167.45 frames.], batch size: 22, lr: 2.10e-04
2022-05-16 03:20:31,422 INFO [train.py:812] (5/8) Epoch 37, batch 3300, loss[loss=0.1702, simple_loss=0.2607, pruned_loss=0.03979, over 7411.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2423, pruned_loss=0.02935, over 1415644.53 frames.], batch size: 21, lr: 2.10e-04
2022-05-16 03:21:29,305 INFO [train.py:812] (5/8) Epoch 37, batch 3350, loss[loss=0.1733, simple_loss=0.2704, pruned_loss=0.03806, over 7373.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2436, pruned_loss=0.02944, over 1411286.28 frames.], batch size: 23, lr: 2.09e-04
2022-05-16 03:22:27,822 INFO [train.py:812] (5/8) Epoch 37, batch 3400, loss[loss=0.1232, simple_loss=0.2162, pruned_loss=0.01508, over 7130.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2426, pruned_loss=0.0292, over 1415896.69 frames.], batch size: 17, lr: 2.09e-04
2022-05-16 03:23:27,190 INFO [train.py:812] (5/8) Epoch 37, batch 3450, loss[loss=0.1359, simple_loss=0.225, pruned_loss=0.02342, over 7272.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2411, pruned_loss=0.02856, over 1418680.53 frames.], batch size: 17, lr: 2.09e-04
2022-05-16 03:24:25,214 INFO [train.py:812] (5/8) Epoch 37, batch 3500, loss[loss=0.1344, simple_loss=0.2278, pruned_loss=0.0205, over 7355.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2414, pruned_loss=0.02867, over 1416336.15 frames.], batch size: 19, lr: 2.09e-04
2022-05-16 03:25:24,378 INFO [train.py:812] (5/8) Epoch 37, batch 3550, loss[loss=0.1249, simple_loss=0.1992, pruned_loss=0.02534, over 6795.00 frames.], tot_loss[loss=0.149, simple_loss=0.2409, pruned_loss=0.02854, over 1413280.68 frames.], batch size: 15, lr: 2.09e-04
2022-05-16 03:26:23,178 INFO [train.py:812] (5/8) Epoch 37, batch 3600, loss[loss=0.1306, simple_loss=0.2159, pruned_loss=0.02264, over 6998.00 frames.], tot_loss[loss=0.1482, simple_loss=0.24, pruned_loss=0.02822, over 1419756.52 frames.], batch size: 16, lr: 2.09e-04
2022-05-16 03:27:22,031 INFO [train.py:812] (5/8) Epoch 37, batch 3650, loss[loss=0.1509, simple_loss=0.2418, pruned_loss=0.03, over 7158.00 frames.], tot_loss[loss=0.1485, simple_loss=0.24, pruned_loss=0.02844, over 1422368.55 frames.], batch size: 19, lr: 2.09e-04
2022-05-16 03:28:20,566 INFO [train.py:812] (5/8) Epoch 37, batch 3700, loss[loss=0.1678, simple_loss=0.258, pruned_loss=0.03882, over 7228.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2404, pruned_loss=0.02847, over 1426169.63 frames.], batch size: 20, lr: 2.09e-04
2022-05-16 03:29:19,672 INFO [train.py:812] (5/8) Epoch 37, batch 3750, loss[loss=0.1711, simple_loss=0.2661, pruned_loss=0.03807, over 7289.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2409, pruned_loss=0.02844, over 1422413.73 frames.], batch size: 24, lr: 2.09e-04
2022-05-16 03:30:17,095 INFO [train.py:812] (5/8) Epoch 37, batch 3800, loss[loss=0.1397, simple_loss=0.2198, pruned_loss=0.02981, over 7286.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2404, pruned_loss=0.02854, over 1424402.30 frames.], batch size: 17, lr: 2.09e-04
2022-05-16 03:31:15,848 INFO [train.py:812] (5/8) Epoch 37, batch 3850, loss[loss=0.1708, simple_loss=0.2582, pruned_loss=0.04172, over 5063.00 frames.], tot_loss[loss=0.149, simple_loss=0.2407, pruned_loss=0.0286, over 1423495.93 frames.], batch size: 52, lr: 2.09e-04
2022-05-16 03:32:12,580 INFO [train.py:812] (5/8) Epoch 37, batch 3900, loss[loss=0.1428, simple_loss=0.2362, pruned_loss=0.02473, over 7333.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2408, pruned_loss=0.02872, over 1425269.88 frames.], batch size: 20, lr: 2.09e-04
2022-05-16 03:33:11,475 INFO [train.py:812] (5/8) Epoch 37, batch 3950, loss[loss=0.1561, simple_loss=0.2412, pruned_loss=0.03556, over 7278.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2414, pruned_loss=0.02885, over 1426526.87 frames.], batch size: 18, lr: 2.09e-04
2022-05-16 03:34:09,776 INFO [train.py:812] (5/8) Epoch 37, batch 4000, loss[loss=0.1418, simple_loss=0.2342, pruned_loss=0.02472, over 7146.00 frames.], tot_loss[loss=0.149, simple_loss=0.241, pruned_loss=0.02848, over 1427132.51 frames.], batch size: 20, lr: 2.09e-04
2022-05-16 03:35:09,207 INFO [train.py:812] (5/8) Epoch 37, batch 4050, loss[loss=0.1474, simple_loss=0.2559, pruned_loss=0.01943, over 7143.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2411, pruned_loss=0.02838, over 1426451.01 frames.], batch size: 20, lr: 2.09e-04
2022-05-16 03:36:06,878 INFO [train.py:812] (5/8) Epoch 37, batch 4100, loss[loss=0.1551, simple_loss=0.2503, pruned_loss=0.02995, over 7321.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2418, pruned_loss=0.0287, over 1424875.15 frames.], batch size: 25, lr: 2.09e-04
2022-05-16 03:37:05,670 INFO [train.py:812] (5/8) Epoch 37, batch 4150, loss[loss=0.1488, simple_loss=0.2454, pruned_loss=0.02611, over 7230.00 frames.], tot_loss[loss=0.149, simple_loss=0.2413, pruned_loss=0.02833, over 1426843.29 frames.], batch size: 21, lr: 2.09e-04
2022-05-16 03:38:02,988 INFO [train.py:812] (5/8) Epoch 37, batch 4200, loss[loss=0.1568, simple_loss=0.2524, pruned_loss=0.0306, over 7335.00 frames.], tot_loss[loss=0.1487, simple_loss=0.241, pruned_loss=0.0282, over 1428839.85 frames.], batch size: 22, lr: 2.09e-04
2022-05-16 03:39:02,447 INFO [train.py:812] (5/8) Epoch 37, batch 4250, loss[loss=0.1528, simple_loss=0.2465, pruned_loss=0.0296, over 7205.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2406, pruned_loss=0.02785, over 1431835.35 frames.], batch size: 22, lr: 2.09e-04
2022-05-16 03:40:00,849 INFO [train.py:812] (5/8) Epoch 37, batch 4300, loss[loss=0.1502, simple_loss=0.2394, pruned_loss=0.03044, over 7321.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2414, pruned_loss=0.02859, over 1426277.41 frames.], batch size: 20, lr: 2.09e-04
2022-05-16 03:41:00,621 INFO [train.py:812] (5/8) Epoch 37, batch 4350, loss[loss=0.1813, simple_loss=0.28, pruned_loss=0.04129, over 7328.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2412, pruned_loss=0.02829, over 1430513.63 frames.], batch size: 22, lr: 2.09e-04
2022-05-16 03:41:59,219 INFO [train.py:812] (5/8) Epoch 37, batch 4400, loss[loss=0.1478, simple_loss=0.2426, pruned_loss=0.02646, over 7333.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2411, pruned_loss=0.028, over 1423276.78 frames.], batch size: 22, lr: 2.09e-04
2022-05-16 03:42:59,061 INFO [train.py:812] (5/8) Epoch 37, batch 4450, loss[loss=0.1278, simple_loss=0.2104, pruned_loss=0.02262, over 7419.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2422, pruned_loss=0.02827, over 1421936.94 frames.], batch size: 18, lr: 2.09e-04
2022-05-16 03:43:58,082 INFO [train.py:812] (5/8) Epoch 37, batch 4500, loss[loss=0.1354, simple_loss=0.2182, pruned_loss=0.02625, over 7270.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2418, pruned_loss=0.02816, over 1416045.75 frames.], batch size: 18, lr: 2.09e-04
2022-05-16 03:44:56,291 INFO [train.py:812] (5/8) Epoch 37, batch 4550, loss[loss=0.1497, simple_loss=0.2414, pruned_loss=0.02898, over 6601.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2434, pruned_loss=0.02901, over 1391560.24 frames.], batch size: 38, lr: 2.09e-04
2022-05-16 03:46:01,553 INFO [train.py:812] (5/8) Epoch 38, batch 0, loss[loss=0.1443, simple_loss=0.2394, pruned_loss=0.02461, over 7368.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2394, pruned_loss=0.02461, over 7368.00 frames.], batch size: 19, lr: 2.06e-04
2022-05-16 03:47:10,792 INFO [train.py:812] (5/8) Epoch 38, batch 50, loss[loss=0.1526, simple_loss=0.2537, pruned_loss=0.02572, over 6421.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2365, pruned_loss=0.02619, over 322412.92 frames.], batch size: 38, lr: 2.06e-04
2022-05-16 03:48:09,435 INFO [train.py:812] (5/8) Epoch 38, batch 100, loss[loss=0.146, simple_loss=0.2409, pruned_loss=0.02556, over 7252.00 frames.], tot_loss[loss=0.148, simple_loss=0.2403, pruned_loss=0.02786, over 559546.20 frames.], batch size: 19, lr: 2.06e-04
2022-05-16 03:49:08,236 INFO [train.py:812] (5/8) Epoch 38, batch 150, loss[loss=0.1747, simple_loss=0.2648, pruned_loss=0.04229, over 7376.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2419, pruned_loss=0.0282, over 747657.97 frames.], batch size: 23, lr: 2.06e-04
2022-05-16 03:50:07,478 INFO [train.py:812] (5/8) Epoch 38, batch 200, loss[loss=0.1387, simple_loss=0.2463, pruned_loss=0.01557, over 7419.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2409, pruned_loss=0.02818, over 896069.54 frames.], batch size: 21, lr: 2.06e-04
2022-05-16 03:51:06,650 INFO [train.py:812] (5/8) Epoch 38, batch 250, loss[loss=0.125, simple_loss=0.2217, pruned_loss=0.01421, over 7359.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2403, pruned_loss=0.028, over 1014187.96 frames.], batch size: 19, lr: 2.06e-04
2022-05-16 03:52:05,086 INFO [train.py:812] (5/8) Epoch 38, batch 300, loss[loss=0.1564, simple_loss=0.2537, pruned_loss=0.02953, over 7237.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2415, pruned_loss=0.02853, over 1104953.71 frames.], batch size: 20, lr: 2.06e-04
2022-05-16 03:53:04,632 INFO [train.py:812] (5/8) Epoch 38, batch 350, loss[loss=0.1295, simple_loss=0.2187, pruned_loss=0.02013, over 7264.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2414, pruned_loss=0.02838, over 1173054.04 frames.], batch size: 19, lr: 2.06e-04
2022-05-16 03:54:02,516 INFO [train.py:812] (5/8) Epoch 38, batch 400, loss[loss=0.1352, simple_loss=0.218, pruned_loss=0.0262, over 7275.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2409, pruned_loss=0.02812, over 1232699.62 frames.], batch size: 17, lr: 2.06e-04
2022-05-16 03:55:02,009 INFO [train.py:812] (5/8) Epoch 38, batch 450, loss[loss=0.1468, simple_loss=0.2458, pruned_loss=0.02392, over 7104.00 frames.], tot_loss[loss=0.149, simple_loss=0.2412, pruned_loss=0.02838, over 1275969.21 frames.], batch size: 21, lr: 2.06e-04
2022-05-16 03:56:00,722 INFO [train.py:812] (5/8) Epoch 38, batch 500, loss[loss=0.1294, simple_loss=0.2153, pruned_loss=0.02174, over 7288.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2408, pruned_loss=0.02854, over 1311651.23 frames.], batch size: 18, lr: 2.06e-04
2022-05-16 03:56:58,584 INFO [train.py:812] (5/8) Epoch 38, batch 550, loss[loss=0.1323, simple_loss=0.2283, pruned_loss=0.01812, over 7322.00 frames.], tot_loss[loss=0.1489, simple_loss=0.241, pruned_loss=0.02841, over 1336757.43 frames.], batch size: 20, lr: 2.06e-04
2022-05-16 03:57:56,238 INFO [train.py:812] (5/8) Epoch 38, batch 600, loss[loss=0.1597, simple_loss=0.2585, pruned_loss=0.03044, over 7368.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2416, pruned_loss=0.02858, over 1357757.81 frames.], batch size: 23, lr: 2.06e-04
2022-05-16 03:58:54,204 INFO [train.py:812] (5/8) Epoch 38, batch 650, loss[loss=0.1521, simple_loss=0.2507, pruned_loss=0.02674, over 7338.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2417, pruned_loss=0.02846, over 1373582.35 frames.], batch size: 22, lr: 2.06e-04
2022-05-16 03:59:53,350 INFO [train.py:812] (5/8) Epoch 38, batch 700, loss[loss=0.1363, simple_loss=0.2339, pruned_loss=0.01935, over 7165.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2424, pruned_loss=0.02826, over 1386470.75 frames.], batch size: 18, lr: 2.06e-04
2022-05-16 04:00:52,139 INFO [train.py:812] (5/8) Epoch 38, batch 750, loss[loss=0.1583, simple_loss=0.2483, pruned_loss=0.03411, over 7368.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2423, pruned_loss=0.02819, over 1400868.81 frames.], batch size: 23, lr: 2.05e-04
2022-05-16 04:01:50,296 INFO [train.py:812] (5/8) Epoch 38, batch 800, loss[loss=0.1315, simple_loss=0.2206, pruned_loss=0.02121, over 7414.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2427, pruned_loss=0.02851, over 1408783.79 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:02:49,188 INFO [train.py:812] (5/8) Epoch 38, batch 850, loss[loss=0.1216, simple_loss=0.2109, pruned_loss=0.01612, over 7355.00 frames.], tot_loss[loss=0.149, simple_loss=0.2418, pruned_loss=0.02812, over 1410621.80 frames.], batch size: 19, lr: 2.05e-04
2022-05-16 04:03:47,712 INFO [train.py:812] (5/8) Epoch 38, batch 900, loss[loss=0.1464, simple_loss=0.2334, pruned_loss=0.02967, over 7286.00 frames.], tot_loss[loss=0.1486, simple_loss=0.241, pruned_loss=0.02808, over 1412358.96 frames.], batch size: 24, lr: 2.05e-04
2022-05-16 04:04:46,166 INFO [train.py:812] (5/8) Epoch 38, batch 950, loss[loss=0.1356, simple_loss=0.2313, pruned_loss=0.01992, over 7254.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2419, pruned_loss=0.02859, over 1418068.27 frames.], batch size: 19, lr: 2.05e-04
2022-05-16 04:05:44,603 INFO [train.py:812] (5/8) Epoch 38, batch 1000, loss[loss=0.1696, simple_loss=0.2579, pruned_loss=0.0407, over 7220.00 frames.], tot_loss[loss=0.15, simple_loss=0.2422, pruned_loss=0.02883, over 1420825.44 frames.], batch size: 22, lr: 2.05e-04
2022-05-16 04:06:43,927 INFO [train.py:812] (5/8) Epoch 38, batch 1050, loss[loss=0.1503, simple_loss=0.2407, pruned_loss=0.03, over 7333.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2425, pruned_loss=0.02889, over 1420595.43 frames.], batch size: 20, lr: 2.05e-04
2022-05-16 04:07:41,755 INFO [train.py:812] (5/8) Epoch 38, batch 1100, loss[loss=0.1472, simple_loss=0.2262, pruned_loss=0.03409, over 6784.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2421, pruned_loss=0.02874, over 1423482.22 frames.], batch size: 15, lr: 2.05e-04
2022-05-16 04:08:41,050 INFO [train.py:812] (5/8) Epoch 38, batch 1150, loss[loss=0.1305, simple_loss=0.2133, pruned_loss=0.02386, over 7273.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2416, pruned_loss=0.02836, over 1420698.53 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:09:40,709 INFO [train.py:812] (5/8) Epoch 38, batch 1200, loss[loss=0.149, simple_loss=0.2441, pruned_loss=0.02692, over 7162.00 frames.], tot_loss[loss=0.15, simple_loss=0.2427, pruned_loss=0.02866, over 1422938.52 frames.], batch size: 26, lr: 2.05e-04
2022-05-16 04:10:39,676 INFO [train.py:812] (5/8) Epoch 38, batch 1250, loss[loss=0.1432, simple_loss=0.2398, pruned_loss=0.02325, over 6283.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2423, pruned_loss=0.02852, over 1426242.23 frames.], batch size: 37, lr: 2.05e-04
2022-05-16 04:11:38,486 INFO [train.py:812] (5/8) Epoch 38, batch 1300, loss[loss=0.132, simple_loss=0.2106, pruned_loss=0.02675, over 7264.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2432, pruned_loss=0.02876, over 1425843.57 frames.], batch size: 17, lr: 2.05e-04
2022-05-16 04:12:36,175 INFO [train.py:812] (5/8) Epoch 38, batch 1350, loss[loss=0.1527, simple_loss=0.2558, pruned_loss=0.0248, over 7130.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2431, pruned_loss=0.0287, over 1419009.03 frames.], batch size: 21, lr: 2.05e-04
2022-05-16 04:13:33,879 INFO [train.py:812] (5/8) Epoch 38, batch 1400, loss[loss=0.1568, simple_loss=0.2609, pruned_loss=0.02634, over 7274.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2421, pruned_loss=0.02839, over 1419521.09 frames.], batch size: 24, lr: 2.05e-04
2022-05-16 04:14:32,882 INFO [train.py:812] (5/8) Epoch 38, batch 1450, loss[loss=0.1682, simple_loss=0.2614, pruned_loss=0.03745, over 7195.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2429, pruned_loss=0.02864, over 1424083.23 frames.], batch size: 22, lr: 2.05e-04
2022-05-16 04:15:31,391 INFO [train.py:812] (5/8) Epoch 38, batch 1500, loss[loss=0.1727, simple_loss=0.2635, pruned_loss=0.04098, over 7320.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2435, pruned_loss=0.02889, over 1424472.67 frames.], batch size: 25, lr: 2.05e-04
2022-05-16 04:16:30,119 INFO [train.py:812] (5/8) Epoch 38, batch 1550, loss[loss=0.1468, simple_loss=0.2369, pruned_loss=0.02835, over 7244.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2431, pruned_loss=0.02896, over 1421472.41 frames.], batch size: 20, lr: 2.05e-04
2022-05-16 04:17:27,388 INFO [train.py:812] (5/8) Epoch 38, batch 1600, loss[loss=0.1405, simple_loss=0.2365, pruned_loss=0.02229, over 7250.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2433, pruned_loss=0.02893, over 1424178.04 frames.], batch size: 19, lr: 2.05e-04
2022-05-16 04:18:25,538 INFO [train.py:812] (5/8) Epoch 38, batch 1650, loss[loss=0.1752, simple_loss=0.2802, pruned_loss=0.03512, over 7001.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2432, pruned_loss=0.02881, over 1423767.43 frames.], batch size: 28, lr: 2.05e-04
2022-05-16 04:19:24,095 INFO [train.py:812] (5/8) Epoch 38, batch 1700, loss[loss=0.1391, simple_loss=0.2221, pruned_loss=0.02807, over 7163.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2422, pruned_loss=0.02871, over 1422198.27 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:20:24,540 INFO [train.py:812] (5/8) Epoch 38, batch 1750, loss[loss=0.1944, simple_loss=0.2702, pruned_loss=0.05931, over 4526.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2426, pruned_loss=0.02882, over 1420785.59 frames.], batch size: 52, lr: 2.05e-04
2022-05-16 04:21:23,194 INFO [train.py:812] (5/8) Epoch 38, batch 1800, loss[loss=0.1431, simple_loss=0.2393, pruned_loss=0.02348, over 7332.00 frames.], tot_loss[loss=0.1496, simple_loss=0.242, pruned_loss=0.02857, over 1419214.67 frames.], batch size: 20, lr: 2.05e-04
2022-05-16 04:22:21,206 INFO [train.py:812] (5/8) Epoch 38, batch 1850, loss[loss=0.1415, simple_loss=0.228, pruned_loss=0.02751, over 7286.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2418, pruned_loss=0.02831, over 1421058.43 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:23:20,146 INFO [train.py:812] (5/8) Epoch 38, batch 1900, loss[loss=0.1133, simple_loss=0.2, pruned_loss=0.01331, over 7207.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2419, pruned_loss=0.02817, over 1424332.23 frames.], batch size: 16, lr: 2.05e-04
2022-05-16 04:24:18,829 INFO [train.py:812] (5/8) Epoch 38, batch 1950, loss[loss=0.1593, simple_loss=0.2446, pruned_loss=0.03703, over 7255.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2426, pruned_loss=0.02831, over 1426864.87 frames.], batch size: 19, lr: 2.05e-04
2022-05-16 04:25:17,619 INFO [train.py:812] (5/8) Epoch 38, batch 2000, loss[loss=0.1127, simple_loss=0.201, pruned_loss=0.01214, over 7395.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2423, pruned_loss=0.02849, over 1425377.02 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:26:16,372 INFO [train.py:812] (5/8) Epoch 38, batch 2050, loss[loss=0.1397, simple_loss=0.2366, pruned_loss=0.02139, over 7263.00 frames.], tot_loss[loss=0.15, simple_loss=0.2427, pruned_loss=0.02864, over 1422980.91 frames.], batch size: 19, lr: 2.05e-04
2022-05-16 04:27:14,047 INFO [train.py:812] (5/8) Epoch 38, batch 2100, loss[loss=0.173, simple_loss=0.2695, pruned_loss=0.03823, over 7157.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2429, pruned_loss=0.02889, over 1417162.52 frames.], batch size: 26, lr: 2.05e-04
2022-05-16 04:28:12,415 INFO [train.py:812] (5/8) Epoch 38, batch 2150, loss[loss=0.1406, simple_loss=0.2284, pruned_loss=0.02644, over 7068.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2431, pruned_loss=0.02928, over 1417411.70 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:29:11,058 INFO [train.py:812] (5/8) Epoch 38, batch 2200, loss[loss=0.1268, simple_loss=0.2177, pruned_loss=0.01795, over 7067.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2441, pruned_loss=0.0292, over 1418772.86 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:30:15,081 INFO [train.py:812] (5/8) Epoch 38, batch 2250, loss[loss=0.1495, simple_loss=0.2446, pruned_loss=0.02715, over 6595.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2445, pruned_loss=0.02943, over 1417954.84 frames.], batch size: 38, lr: 2.05e-04
2022-05-16 04:31:14,133 INFO [train.py:812] (5/8) Epoch 38, batch 2300, loss[loss=0.1491, simple_loss=0.2397, pruned_loss=0.02927, over 7059.00 frames.], tot_loss[loss=0.1513, simple_loss=0.244, pruned_loss=0.02928, over 1421508.85 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:32:13,290 INFO [train.py:812] (5/8) Epoch 38, batch 2350, loss[loss=0.1543, simple_loss=0.2421, pruned_loss=0.03325, over 7317.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2441, pruned_loss=0.02959, over 1419881.33 frames.], batch size: 20, lr: 2.05e-04
2022-05-16 04:33:12,138 INFO [train.py:812] (5/8) Epoch 38, batch 2400, loss[loss=0.131, simple_loss=0.2134, pruned_loss=0.02425, over 7403.00 frames.], tot_loss[loss=0.1507, simple_loss=0.243, pruned_loss=0.02921, over 1425391.84 frames.], batch size: 18, lr: 2.05e-04
2022-05-16 04:34:10,711 INFO [train.py:812] (5/8) Epoch 38, batch 2450, loss[loss=0.1526, simple_loss=0.2448, pruned_loss=0.03014, over 7336.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2429, pruned_loss=0.02873, over 1427401.91 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:35:08,861 INFO [train.py:812] (5/8) Epoch 38, batch 2500, loss[loss=0.1432, simple_loss=0.2301, pruned_loss=0.02809, over 7163.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2428, pruned_loss=0.02893, over 1426852.65 frames.], batch size: 18, lr: 2.04e-04
2022-05-16 04:36:06,678 INFO [train.py:812] (5/8) Epoch 38, batch 2550, loss[loss=0.1329, simple_loss=0.2166, pruned_loss=0.02462, over 7155.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2426, pruned_loss=0.02896, over 1424080.23 frames.], batch size: 18, lr: 2.04e-04
2022-05-16 04:37:05,273 INFO [train.py:812] (5/8) Epoch 38, batch 2600, loss[loss=0.1356, simple_loss=0.2292, pruned_loss=0.02096, over 7433.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2423, pruned_loss=0.0284, over 1423485.44 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:38:03,416 INFO [train.py:812] (5/8) Epoch 38, batch 2650, loss[loss=0.1601, simple_loss=0.26, pruned_loss=0.03011, over 7216.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2424, pruned_loss=0.02872, over 1424708.72 frames.], batch size: 23, lr: 2.04e-04
2022-05-16 04:39:01,016 INFO [train.py:812] (5/8) Epoch 38, batch 2700, loss[loss=0.1563, simple_loss=0.2572, pruned_loss=0.0277, over 7228.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2421, pruned_loss=0.02836, over 1423356.27 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:39:59,905 INFO [train.py:812] (5/8) Epoch 38, batch 2750, loss[loss=0.1501, simple_loss=0.2449, pruned_loss=0.02768, over 7356.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2422, pruned_loss=0.02832, over 1425340.38 frames.], batch size: 19, lr: 2.04e-04
2022-05-16 04:40:57,545 INFO [train.py:812] (5/8) Epoch 38, batch 2800, loss[loss=0.1493, simple_loss=0.2453, pruned_loss=0.0266, over 7270.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2418, pruned_loss=0.02847, over 1423849.83 frames.], batch size: 24, lr: 2.04e-04
2022-05-16 04:41:55,561 INFO [train.py:812] (5/8) Epoch 38, batch 2850, loss[loss=0.1424, simple_loss=0.2372, pruned_loss=0.02381, over 7408.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2419, pruned_loss=0.02873, over 1423456.75 frames.], batch size: 21, lr: 2.04e-04
2022-05-16 04:42:54,108 INFO [train.py:812] (5/8) Epoch 38, batch 2900, loss[loss=0.1238, simple_loss=0.213, pruned_loss=0.01726, over 7117.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2419, pruned_loss=0.029, over 1424097.65 frames.], batch size: 17, lr: 2.04e-04
2022-05-16 04:43:53,095 INFO [train.py:812] (5/8) Epoch 38, batch 2950, loss[loss=0.1175, simple_loss=0.2033, pruned_loss=0.01585, over 7413.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2419, pruned_loss=0.02876, over 1428535.85 frames.], batch size: 18, lr: 2.04e-04
2022-05-16 04:44:52,012 INFO [train.py:812] (5/8) Epoch 38, batch 3000, loss[loss=0.1614, simple_loss=0.2583, pruned_loss=0.03222, over 7198.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2421, pruned_loss=0.02851, over 1428427.25 frames.], batch size: 23, lr: 2.04e-04
2022-05-16 04:44:52,013 INFO [train.py:832] (5/8) Computing validation loss
2022-05-16 04:44:59,415 INFO [train.py:841] (5/8) Epoch 38, validation: loss=0.1532, simple_loss=0.2484, pruned_loss=0.02898, over 698248.00 frames.
2022-05-16 04:45:58,532 INFO [train.py:812] (5/8) Epoch 38, batch 3050, loss[loss=0.1276, simple_loss=0.2125, pruned_loss=0.0214, over 7157.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2427, pruned_loss=0.02871, over 1429162.68 frames.], batch size: 18, lr: 2.04e-04
2022-05-16 04:46:56,194 INFO [train.py:812] (5/8) Epoch 38, batch 3100, loss[loss=0.1622, simple_loss=0.2533, pruned_loss=0.03557, over 7207.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2432, pruned_loss=0.02926, over 1422224.78 frames.], batch size: 22, lr: 2.04e-04
2022-05-16 04:47:54,519 INFO [train.py:812] (5/8) Epoch 38, batch 3150, loss[loss=0.1554, simple_loss=0.2438, pruned_loss=0.03345, over 7384.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2423, pruned_loss=0.02902, over 1420743.07 frames.], batch size: 23, lr: 2.04e-04
2022-05-16 04:48:52,447 INFO [train.py:812] (5/8) Epoch 38, batch 3200, loss[loss=0.1598, simple_loss=0.2524, pruned_loss=0.03366, over 7110.00 frames.], tot_loss[loss=0.15, simple_loss=0.2421, pruned_loss=0.02892, over 1425588.07 frames.], batch size: 21, lr: 2.04e-04
2022-05-16 04:49:51,324 INFO [train.py:812] (5/8) Epoch 38, batch 3250, loss[loss=0.1396, simple_loss=0.223, pruned_loss=0.02804, over 7261.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2411, pruned_loss=0.02862, over 1427335.73 frames.], batch size: 18, lr: 2.04e-04
2022-05-16 04:50:49,190 INFO [train.py:812] (5/8) Epoch 38, batch 3300, loss[loss=0.1448, simple_loss=0.2427, pruned_loss=0.02343, over 7237.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2407, pruned_loss=0.02845, over 1426684.14 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:51:47,399 INFO [train.py:812] (5/8) Epoch 38, batch 3350, loss[loss=0.1701, simple_loss=0.2642, pruned_loss=0.03801, over 7198.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2423, pruned_loss=0.02854, over 1427490.45 frames.], batch size: 22, lr: 2.04e-04
2022-05-16 04:52:45,594 INFO [train.py:812] (5/8) Epoch 38, batch 3400, loss[loss=0.1581, simple_loss=0.2522, pruned_loss=0.03203, over 6720.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2423, pruned_loss=0.02855, over 1431031.25 frames.], batch size: 31, lr: 2.04e-04
2022-05-16 04:53:45,197 INFO [train.py:812] (5/8) Epoch 38, batch 3450, loss[loss=0.1407, simple_loss=0.2317, pruned_loss=0.02486, over 7426.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2426, pruned_loss=0.02834, over 1432130.61 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:54:43,555 INFO [train.py:812] (5/8) Epoch 38, batch 3500, loss[loss=0.1447, simple_loss=0.2382, pruned_loss=0.02562, over 7246.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2425, pruned_loss=0.02841, over 1430173.89 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:55:41,786 INFO [train.py:812] (5/8) Epoch 38, batch 3550, loss[loss=0.1586, simple_loss=0.2479, pruned_loss=0.03463, over 7155.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2433, pruned_loss=0.02855, over 1430383.64 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 04:56:49,631 INFO [train.py:812] (5/8) Epoch 38, batch 3600, loss[loss=0.1529, simple_loss=0.2432, pruned_loss=0.03131, over 6746.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2433, pruned_loss=0.02872, over 1427755.52 frames.], batch size: 31, lr: 2.04e-04
2022-05-16 04:57:48,339 INFO [train.py:812] (5/8) Epoch 38, batch 3650, loss[loss=0.152, simple_loss=0.2475, pruned_loss=0.02827, over 7082.00 frames.], tot_loss[loss=0.1493, simple_loss=0.242, pruned_loss=0.02824, over 1430489.64 frames.], batch size: 28, lr: 2.04e-04
2022-05-16 04:58:46,155 INFO [train.py:812] (5/8) Epoch 38, batch 3700, loss[loss=0.1525, simple_loss=0.2592, pruned_loss=0.02295, over 7264.00 frames.], tot_loss[loss=0.149, simple_loss=0.2415, pruned_loss=0.02826, over 1421822.28 frames.], batch size: 24, lr: 2.04e-04
2022-05-16 05:00:03,392 INFO [train.py:812] (5/8) Epoch 38, batch 3750, loss[loss=0.1548, simple_loss=0.2427, pruned_loss=0.0335, over 7155.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2423, pruned_loss=0.02856, over 1417161.30 frames.], batch size: 19, lr: 2.04e-04
2022-05-16 05:01:01,850 INFO [train.py:812] (5/8) Epoch 38, batch 3800, loss[loss=0.1375, simple_loss=0.2385, pruned_loss=0.01823, over 7384.00 frames.], tot_loss[loss=0.149, simple_loss=0.2414, pruned_loss=0.02834, over 1417103.49 frames.], batch size: 23, lr: 2.04e-04
2022-05-16 05:02:01,298 INFO [train.py:812] (5/8) Epoch 38, batch 3850, loss[loss=0.1439, simple_loss=0.2398, pruned_loss=0.02395, over 7117.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2414, pruned_loss=0.02811, over 1420187.39 frames.], batch size: 21, lr: 2.04e-04
2022-05-16 05:03:01,094 INFO [train.py:812] (5/8) Epoch 38, batch 3900, loss[loss=0.1389, simple_loss=0.2391, pruned_loss=0.01942, over 7325.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2411, pruned_loss=0.02836, over 1422007.73 frames.], batch size: 20, lr: 2.04e-04
2022-05-16 05:03:59,290 INFO [train.py:812] (5/8) Epoch 38, batch 3950, loss[loss=0.1623, simple_loss=0.2603, pruned_loss=0.03219, over 7211.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2414, pruned_loss=0.02838, over 1416864.05 frames.], batch size: 22, lr: 2.04e-04
2022-05-16 05:04:56,834 INFO [train.py:812] (5/8) Epoch 38, batch 4000, loss[loss=0.126, simple_loss=0.2209, pruned_loss=0.01554, over 7156.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2418, pruned_loss=0.02881, over 1417971.47 frames.], batch size: 19, lr: 2.04e-04
2022-05-16 05:06:06,116 INFO [train.py:812] (5/8) Epoch 38, batch 4050, loss[loss=0.1296, simple_loss=0.2145, pruned_loss=0.02238, over 7281.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2423, pruned_loss=0.02914, over 1410575.92 frames.], batch size: 17, lr: 2.04e-04
2022-05-16 05:07:14,545 INFO [train.py:812] (5/8) Epoch 38, batch 4100, loss[loss=0.1426, simple_loss=0.2397, pruned_loss=0.02277, over 7215.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2429, pruned_loss=0.02903, over 1412175.85 frames.], batch size: 21, lr: 2.04e-04
2022-05-16 05:08:13,923 INFO [train.py:812] (5/8) Epoch 38, batch 4150, loss[loss=0.13, simple_loss=0.2232, pruned_loss=0.01836, over 7250.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2418, pruned_loss=0.02884, over 1411258.53 frames.], batch size: 19, lr: 2.03e-04
2022-05-16 05:09:21,210 INFO [train.py:812] (5/8) Epoch 38, batch 4200, loss[loss=0.1394, simple_loss=0.2361, pruned_loss=0.02129, over 7288.00 frames.], tot_loss[loss=0.1487, simple_loss=0.241, pruned_loss=0.02817, over 1411879.26 frames.], batch size: 24, lr: 2.03e-04
2022-05-16 05:10:29,479 INFO [train.py:812] (5/8) Epoch 38, batch 4250, loss[loss=0.1542, simple_loss=0.2412, pruned_loss=0.03362, over 7229.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2418, pruned_loss=0.0289, over 1413067.90 frames.], batch size: 20, lr: 2.03e-04
2022-05-16 05:11:27,923 INFO [train.py:812] (5/8) Epoch 38, batch 4300, loss[loss=0.1901, simple_loss=0.2658, pruned_loss=0.05715, over 5065.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2403, pruned_loss=0.02858, over 1410244.49 frames.], batch size: 53, lr: 2.03e-04
2022-05-16 05:12:26,581 INFO [train.py:812] (5/8) Epoch 38, batch 4350, loss[loss=0.1343, simple_loss=0.2127, pruned_loss=0.02791, over 6989.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2389, pruned_loss=0.02842, over 1413558.76 frames.], batch size: 16, lr: 2.03e-04
2022-05-16 05:13:26,094 INFO [train.py:812] (5/8) Epoch 38, batch 4400, loss[loss=0.16, simple_loss=0.2368, pruned_loss=0.04162, over 7191.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2388, pruned_loss=0.0284, over 1414414.83 frames.], batch size: 16, lr: 2.03e-04
2022-05-16 05:14:25,868 INFO [train.py:812] (5/8) Epoch 38, batch 4450, loss[loss=0.1388, simple_loss=0.2284, pruned_loss=0.02463, over 7259.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2383, pruned_loss=0.02842, over 1407224.66 frames.], batch size: 16, lr: 2.03e-04
2022-05-16 05:15:24,260 INFO [train.py:812] (5/8) Epoch 38, batch 4500, loss[loss=0.1716, simple_loss=0.2576, pruned_loss=0.04284, over 6310.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2389, pruned_loss=0.02904, over 1383022.36 frames.], batch size: 38, lr: 2.03e-04
2022-05-16 05:16:23,033 INFO [train.py:812] (5/8) Epoch 38, batch 4550, loss[loss=0.1799, simple_loss=0.2614, pruned_loss=0.04922, over 4979.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2383, pruned_loss=0.02898, over 1355267.99 frames.], batch size: 52, lr: 2.03e-04
2022-05-16 05:17:28,542 INFO [train.py:812] (5/8) Epoch 39, batch 0, loss[loss=0.1378, simple_loss=0.2415, pruned_loss=0.01703, over 7261.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2415, pruned_loss=0.01703, over 7261.00 frames.], batch size: 19, lr: 2.01e-04
2022-05-16 05:18:26,970 INFO [train.py:812] (5/8) Epoch 39, batch 50, loss[loss=0.1327, simple_loss=0.2339, pruned_loss=0.01571, over 7150.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2405, pruned_loss=0.02642, over 319783.62 frames.], batch size: 20, lr: 2.01e-04
2022-05-16 05:19:25,799 INFO [train.py:812] (5/8) Epoch 39, batch 100, loss[loss=0.1628, simple_loss=0.2582, pruned_loss=0.03375, over 6784.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2411, pruned_loss=0.02729, over 565702.61 frames.], batch size: 31, lr: 2.01e-04
2022-05-16 05:20:24,079 INFO [train.py:812] (5/8) Epoch 39, batch 150, loss[loss=0.1551, simple_loss=0.2391, pruned_loss=0.03551, over 7166.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2405, pruned_loss=0.02808, over 755877.05 frames.], batch size: 18, lr: 2.01e-04
2022-05-16 05:21:22,570 INFO [train.py:812] (5/8) Epoch 39, batch 200, loss[loss=0.1446, simple_loss=0.2365, pruned_loss=0.02629, over 7420.00 frames.], tot_loss[loss=0.15, simple_loss=0.2425, pruned_loss=0.02877, over 902153.24 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:22:20,441 INFO [train.py:812] (5/8) Epoch 39, batch 250, loss[loss=0.1417, simple_loss=0.2407, pruned_loss=0.02137, over 6569.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2428, pruned_loss=0.02897, over 1018674.99 frames.], batch size: 38, lr: 2.00e-04
2022-05-16 05:23:19,077 INFO [train.py:812] (5/8) Epoch 39, batch 300, loss[loss=0.1664, simple_loss=0.2615, pruned_loss=0.03566, over 7430.00 frames.], tot_loss[loss=0.1492, simple_loss=0.242, pruned_loss=0.02825, over 1113268.74 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:24:17,710 INFO [train.py:812] (5/8) Epoch 39, batch 350, loss[loss=0.1447, simple_loss=0.2366, pruned_loss=0.02639, over 7312.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2412, pruned_loss=0.02823, over 1179853.47 frames.], batch size: 24, lr: 2.00e-04
2022-05-16 05:25:17,235 INFO [train.py:812] (5/8) Epoch 39, batch 400, loss[loss=0.147, simple_loss=0.247, pruned_loss=0.02352, over 7233.00 frames.], tot_loss[loss=0.149, simple_loss=0.2413, pruned_loss=0.02836, over 1229262.83 frames.], batch size: 21, lr: 2.00e-04
2022-05-16 05:26:16,331 INFO [train.py:812] (5/8) Epoch 39, batch 450, loss[loss=0.1634, simple_loss=0.2588, pruned_loss=0.03399, over 7198.00 frames.], tot_loss[loss=0.15, simple_loss=0.2423, pruned_loss=0.02882, over 1274849.42 frames.], batch size: 23, lr: 2.00e-04
2022-05-16 05:27:15,051 INFO [train.py:812] (5/8) Epoch 39, batch 500, loss[loss=0.1538, simple_loss=0.2584, pruned_loss=0.02459, over 7147.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2419, pruned_loss=0.02873, over 1301567.09 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:28:14,643 INFO [train.py:812] (5/8) Epoch 39, batch 550, loss[loss=0.1605, simple_loss=0.2607, pruned_loss=0.03017, over 7417.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2424, pruned_loss=0.02912, over 1327091.87 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:29:14,821 INFO [train.py:812] (5/8) Epoch 39, batch 600, loss[loss=0.145, simple_loss=0.2228, pruned_loss=0.03354, over 7176.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2418, pruned_loss=0.02887, over 1345679.62 frames.], batch size: 18, lr: 2.00e-04
2022-05-16 05:30:14,573 INFO [train.py:812] (5/8) Epoch 39, batch 650, loss[loss=0.1432, simple_loss=0.2189, pruned_loss=0.03376, over 7285.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2417, pruned_loss=0.02885, over 1364745.43 frames.], batch size: 17, lr: 2.00e-04
2022-05-16 05:31:13,748 INFO [train.py:812] (5/8) Epoch 39, batch 700, loss[loss=0.1139, simple_loss=0.2045, pruned_loss=0.01165, over 6816.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2407, pruned_loss=0.02841, over 1377882.90 frames.], batch size: 15, lr: 2.00e-04
2022-05-16 05:32:12,655 INFO [train.py:812] (5/8) Epoch 39, batch 750, loss[loss=0.1589, simple_loss=0.2539, pruned_loss=0.03198, over 6365.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2397, pruned_loss=0.02783, over 1386571.00 frames.], batch size: 38, lr: 2.00e-04
2022-05-16 05:33:12,259 INFO [train.py:812] (5/8) Epoch 39, batch 800, loss[loss=0.1462, simple_loss=0.2388, pruned_loss=0.02681, over 7246.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2396, pruned_loss=0.02755, over 1399296.83 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:34:10,572 INFO [train.py:812] (5/8) Epoch 39, batch 850, loss[loss=0.1626, simple_loss=0.2562, pruned_loss=0.03445, over 7052.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2393, pruned_loss=0.0276, over 1405131.99 frames.], batch size: 28, lr: 2.00e-04
2022-05-16 05:35:08,857 INFO [train.py:812] (5/8) Epoch 39, batch 900, loss[loss=0.1578, simple_loss=0.2612, pruned_loss=0.02727, over 7416.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2399, pruned_loss=0.02788, over 1404474.66 frames.], batch size: 21, lr: 2.00e-04
2022-05-16 05:36:07,910 INFO [train.py:812] (5/8) Epoch 39, batch 950, loss[loss=0.1201, simple_loss=0.202, pruned_loss=0.01906, over 7146.00 frames.], tot_loss[loss=0.149, simple_loss=0.2414, pruned_loss=0.02832, over 1405156.08 frames.], batch size: 17, lr: 2.00e-04
2022-05-16 05:37:07,597 INFO [train.py:812] (5/8) Epoch 39, batch 1000, loss[loss=0.1246, simple_loss=0.2138, pruned_loss=0.01772, over 7348.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2418, pruned_loss=0.02844, over 1408089.46 frames.], batch size: 19, lr: 2.00e-04
2022-05-16 05:38:06,524 INFO [train.py:812] (5/8) Epoch 39, batch 1050, loss[loss=0.1393, simple_loss=0.2464, pruned_loss=0.0161, over 6919.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2417, pruned_loss=0.02833, over 1410787.99 frames.], batch size: 31, lr: 2.00e-04
2022-05-16 05:39:05,049 INFO [train.py:812] (5/8) Epoch 39, batch 1100, loss[loss=0.1751, simple_loss=0.2624, pruned_loss=0.0439, over 7376.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2409, pruned_loss=0.02821, over 1415247.35 frames.], batch size: 23, lr: 2.00e-04
2022-05-16 05:40:03,909 INFO [train.py:812] (5/8) Epoch 39, batch 1150, loss[loss=0.1365, simple_loss=0.2204, pruned_loss=0.02637, over 7273.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2404, pruned_loss=0.02808, over 1419403.56 frames.], batch size: 18, lr: 2.00e-04
2022-05-16 05:41:02,322 INFO [train.py:812] (5/8) Epoch 39, batch 1200, loss[loss=0.141, simple_loss=0.242, pruned_loss=0.02004, over 6809.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2402, pruned_loss=0.02811, over 1420400.36 frames.], batch size: 31, lr: 2.00e-04
2022-05-16 05:42:00,508 INFO [train.py:812] (5/8) Epoch 39, batch 1250, loss[loss=0.1469, simple_loss=0.2386, pruned_loss=0.02763, over 7434.00 frames.], tot_loss[loss=0.1478, simple_loss=0.24, pruned_loss=0.02784, over 1421597.20 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:42:59,384 INFO [train.py:812] (5/8) Epoch 39, batch 1300, loss[loss=0.12, simple_loss=0.1993, pruned_loss=0.0204, over 7282.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2394, pruned_loss=0.02778, over 1425245.80 frames.], batch size: 17, lr: 2.00e-04
2022-05-16 05:43:56,594 INFO [train.py:812] (5/8) Epoch 39, batch 1350, loss[loss=0.1497, simple_loss=0.2412, pruned_loss=0.02916, over 7323.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2397, pruned_loss=0.02784, over 1425880.32 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:45:05,759 INFO [train.py:812] (5/8) Epoch 39, batch 1400, loss[loss=0.1245, simple_loss=0.2233, pruned_loss=0.01285, over 7151.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2392, pruned_loss=0.02764, over 1424734.46 frames.], batch size: 19, lr: 2.00e-04
2022-05-16 05:46:03,927 INFO [train.py:812] (5/8) Epoch 39, batch 1450, loss[loss=0.1564, simple_loss=0.2446, pruned_loss=0.03408, over 7296.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2403, pruned_loss=0.02797, over 1425158.96 frames.], batch size: 25, lr: 2.00e-04
2022-05-16 05:47:01,603 INFO [train.py:812] (5/8) Epoch 39, batch 1500, loss[loss=0.1978, simple_loss=0.3033, pruned_loss=0.04618, over 7099.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2408, pruned_loss=0.02815, over 1423634.72 frames.], batch size: 21, lr: 2.00e-04
2022-05-16 05:48:00,116 INFO [train.py:812] (5/8) Epoch 39, batch 1550, loss[loss=0.1682, simple_loss=0.2505, pruned_loss=0.04295, over 7211.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2398, pruned_loss=0.02765, over 1423800.29 frames.], batch size: 22, lr: 2.00e-04
2022-05-16 05:48:59,842 INFO [train.py:812] (5/8) Epoch 39, batch 1600, loss[loss=0.1596, simple_loss=0.2592, pruned_loss=0.03003, over 6703.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2397, pruned_loss=0.0277, over 1426009.78 frames.], batch size: 31, lr: 2.00e-04
2022-05-16 05:49:57,796 INFO [train.py:812] (5/8) Epoch 39, batch 1650, loss[loss=0.148, simple_loss=0.2426, pruned_loss=0.02668, over 7213.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2403, pruned_loss=0.02799, over 1425309.76 frames.], batch size: 21, lr: 2.00e-04
2022-05-16 05:51:01,156 INFO [train.py:812] (5/8) Epoch 39, batch 1700, loss[loss=0.1399, simple_loss=0.2323, pruned_loss=0.02369, over 7106.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2416, pruned_loss=0.02829, over 1427129.57 frames.], batch size: 28, lr: 2.00e-04
2022-05-16 05:51:59,350 INFO [train.py:812] (5/8) Epoch 39, batch 1750, loss[loss=0.1324, simple_loss=0.229, pruned_loss=0.01784, over 7426.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2418, pruned_loss=0.02804, over 1426128.35 frames.], batch size: 20, lr: 2.00e-04
2022-05-16 05:52:58,517 INFO [train.py:812] (5/8) Epoch 39, batch 1800, loss[loss=0.153, simple_loss=0.2498, pruned_loss=0.02808, over 7205.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2418, pruned_loss=0.02799, over 1423220.84 frames.], batch size: 23, lr: 2.00e-04
2022-05-16 05:53:57,499 INFO [train.py:812] (5/8) Epoch 39, batch 1850, loss[loss=0.1446, simple_loss=0.2366, pruned_loss=0.02634, over 7159.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2418, pruned_loss=0.02793, over 1420683.56 frames.], batch size: 19, lr: 2.00e-04
2022-05-16 05:54:55,986 INFO [train.py:812] (5/8) Epoch 39, batch 1900, loss[loss=0.145, simple_loss=0.232, pruned_loss=0.02897, over 7270.00 frames.], tot_loss[loss=0.149, simple_loss=0.2413, pruned_loss=0.02828, over 1424182.44 frames.], batch size: 18, lr: 2.00e-04
2022-05-16 05:55:54,010 INFO [train.py:812] (5/8) Epoch 39, batch 1950, loss[loss=0.1284, simple_loss=0.2272, pruned_loss=0.01479, over 7318.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2414, pruned_loss=0.02813, over 1424045.53 frames.], batch size: 21, lr: 1.99e-04
2022-05-16 05:56:52,302 INFO [train.py:812] (5/8) Epoch 39, batch 2000, loss[loss=0.1365, simple_loss=0.2275, pruned_loss=0.02282, over 7261.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2411, pruned_loss=0.02813, over 1423462.11 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 05:57:50,316 INFO [train.py:812] (5/8) Epoch 39, batch 2050, loss[loss=0.1475, simple_loss=0.2356, pruned_loss=0.02971, over 7331.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2416, pruned_loss=0.02827, over 1422096.75 frames.], batch size: 20, lr: 1.99e-04
2022-05-16 05:58:49,540 INFO [train.py:812] (5/8) Epoch 39, batch 2100, loss[loss=0.1296, simple_loss=0.2207, pruned_loss=0.01928, over 7218.00 frames.], tot_loss[loss=0.1487, simple_loss=0.241, pruned_loss=0.02818, over 1423744.22 frames.], batch size: 16, lr: 1.99e-04
2022-05-16 05:59:47,760 INFO [train.py:812] (5/8) Epoch 39, batch 2150, loss[loss=0.1425, simple_loss=0.2344, pruned_loss=0.02524, over 7263.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2417, pruned_loss=0.02873, over 1421395.33 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 06:00:46,890 INFO [train.py:812] (5/8) Epoch 39, batch 2200, loss[loss=0.1496, simple_loss=0.2429, pruned_loss=0.02813, over 7207.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2417, pruned_loss=0.02866, over 1421512.96 frames.], batch size: 22, lr: 1.99e-04
2022-05-16 06:01:45,957 INFO [train.py:812] (5/8) Epoch 39, batch 2250, loss[loss=0.1637, simple_loss=0.256, pruned_loss=0.03563, over 7145.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2408, pruned_loss=0.02849, over 1424641.12 frames.], batch size: 20, lr: 1.99e-04
2022-05-16 06:02:45,396 INFO [train.py:812] (5/8) Epoch 39, batch 2300, loss[loss=0.1394, simple_loss=0.2269, pruned_loss=0.0259, over 7150.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2412, pruned_loss=0.02845, over 1424179.76 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 06:03:45,422 INFO [train.py:812] (5/8) Epoch 39, batch 2350, loss[loss=0.1225, simple_loss=0.2223, pruned_loss=0.01137, over 7230.00 frames.], tot_loss[loss=0.148, simple_loss=0.2402, pruned_loss=0.0279, over 1425327.84 frames.], batch size: 20, lr: 1.99e-04
2022-05-16 06:04:43,830 INFO [train.py:812] (5/8) Epoch 39, batch 2400, loss[loss=0.1592, simple_loss=0.2541, pruned_loss=0.03217, over 7147.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2405, pruned_loss=0.02816, over 1427732.75 frames.], batch size: 20, lr: 1.99e-04
2022-05-16 06:05:41,845 INFO [train.py:812] (5/8) Epoch 39, batch 2450, loss[loss=0.1206, simple_loss=0.2044, pruned_loss=0.01844, over 7414.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2399, pruned_loss=0.02787, over 1428904.37 frames.], batch size: 18, lr: 1.99e-04
2022-05-16 06:06:40,886 INFO [train.py:812] (5/8) Epoch 39, batch 2500, loss[loss=0.1601, simple_loss=0.2489, pruned_loss=0.03569, over 7418.00 frames.], tot_loss[loss=0.147, simple_loss=0.2392, pruned_loss=0.02746, over 1426875.56 frames.], batch size: 18, lr: 1.99e-04
2022-05-16 06:07:38,132 INFO [train.py:812] (5/8) Epoch 39, batch 2550, loss[loss=0.1356, simple_loss=0.2308, pruned_loss=0.02017, over 7432.00 frames.], tot_loss[loss=0.147, simple_loss=0.2392, pruned_loss=0.02744, over 1431362.61 frames.], batch size: 20, lr: 1.99e-04
2022-05-16 06:08:37,349 INFO [train.py:812] (5/8) Epoch 39, batch 2600, loss[loss=0.1701, simple_loss=0.2576, pruned_loss=0.04131, over 7201.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2398, pruned_loss=0.0276, over 1429039.11 frames.], batch size: 26, lr: 1.99e-04
2022-05-16 06:09:36,144 INFO [train.py:812] (5/8) Epoch 39, batch 2650, loss[loss=0.1627, simple_loss=0.265, pruned_loss=0.03017, over 7073.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2405, pruned_loss=0.02765, over 1429346.24 frames.], batch size: 28, lr: 1.99e-04
2022-05-16 06:10:34,086 INFO [train.py:812] (5/8) Epoch 39, batch 2700, loss[loss=0.176, simple_loss=0.2776, pruned_loss=0.03722, over 7294.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2408, pruned_loss=0.02824, over 1427041.28 frames.], batch size: 25, lr: 1.99e-04
2022-05-16 06:11:32,678 INFO [train.py:812] (5/8) Epoch 39, batch 2750, loss[loss=0.1326, simple_loss=0.2213, pruned_loss=0.02196, over 7157.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2409, pruned_loss=0.02835, over 1427307.78 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 06:12:31,334 INFO [train.py:812] (5/8) Epoch 39, batch 2800, loss[loss=0.1458, simple_loss=0.2426, pruned_loss=0.02445, over 7344.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2414, pruned_loss=0.02872, over 1424499.56 frames.], batch size: 22, lr: 1.99e-04
2022-05-16 06:13:29,182 INFO [train.py:812] (5/8) Epoch 39, batch 2850, loss[loss=0.1449, simple_loss=0.2498, pruned_loss=0.01998, over 6389.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2418, pruned_loss=0.02869, over 1425442.63 frames.], batch size: 38, lr: 1.99e-04
2022-05-16 06:14:28,565 INFO [train.py:812] (5/8) Epoch 39, batch 2900, loss[loss=0.1556, simple_loss=0.2512, pruned_loss=0.02997, over 7322.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2418, pruned_loss=0.02853, over 1424514.34 frames.], batch size: 21, lr: 1.99e-04
2022-05-16 06:15:27,557 INFO [train.py:812] (5/8) Epoch 39, batch 2950, loss[loss=0.1455, simple_loss=0.2427, pruned_loss=0.0241, over 7340.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2412, pruned_loss=0.02857, over 1428196.94 frames.], batch size: 22, lr: 1.99e-04
2022-05-16 06:16:26,919 INFO [train.py:812] (5/8) Epoch 39, batch 3000, loss[loss=0.1433, simple_loss=0.2364, pruned_loss=0.02504, over 7232.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2415, pruned_loss=0.02883, over 1429536.21 frames.], batch size: 20, lr: 1.99e-04
2022-05-16 06:16:26,920 INFO [train.py:832] (5/8) Computing validation loss
2022-05-16 06:16:34,436 INFO [train.py:841] (5/8) Epoch 39, validation: loss=0.153, simple_loss=0.2484, pruned_loss=0.02885, over 698248.00 frames.
2022-05-16 06:17:33,436 INFO [train.py:812] (5/8) Epoch 39, batch 3050, loss[loss=0.1376, simple_loss=0.2271, pruned_loss=0.02403, over 7141.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2414, pruned_loss=0.02877, over 1426025.49 frames.], batch size: 17, lr: 1.99e-04
2022-05-16 06:18:32,168 INFO [train.py:812] (5/8) Epoch 39, batch 3100, loss[loss=0.1508, simple_loss=0.2502, pruned_loss=0.02563, over 6204.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2413, pruned_loss=0.0285, over 1418129.54 frames.], batch size: 37, lr: 1.99e-04
2022-05-16 06:19:30,255 INFO [train.py:812] (5/8) Epoch 39, batch 3150, loss[loss=0.148, simple_loss=0.2354, pruned_loss=0.03033, over 7412.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2418, pruned_loss=0.02852, over 1423277.59 frames.], batch size: 21, lr: 1.99e-04
2022-05-16 06:20:28,868 INFO [train.py:812] (5/8) Epoch 39, batch 3200, loss[loss=0.1378, simple_loss=0.2361, pruned_loss=0.01975, over 6368.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2415, pruned_loss=0.02804, over 1423851.72 frames.], batch size: 37, lr: 1.99e-04
2022-05-16 06:21:26,201 INFO [train.py:812] (5/8) Epoch 39, batch 3250, loss[loss=0.1376, simple_loss=0.2315, pruned_loss=0.02187, over 6591.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2418, pruned_loss=0.02818, over 1424253.43 frames.], batch size: 38, lr: 1.99e-04
2022-05-16 06:22:25,449 INFO [train.py:812] (5/8) Epoch 39, batch 3300, loss[loss=0.1322, simple_loss=0.2229, pruned_loss=0.02072, over 7160.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2412, pruned_loss=0.02792, over 1424585.33 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 06:23:24,291 INFO [train.py:812] (5/8) Epoch 39, batch 3350, loss[loss=0.1348, simple_loss=0.2208, pruned_loss=0.02433, over 7141.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2406, pruned_loss=0.0276, over 1425683.15 frames.], batch size: 17, lr: 1.99e-04
2022-05-16 06:24:23,078 INFO [train.py:812] (5/8) Epoch 39, batch 3400, loss[loss=0.1535, simple_loss=0.2476, pruned_loss=0.02965, over 7368.00 frames.], tot_loss[loss=0.148, simple_loss=0.2406, pruned_loss=0.02768, over 1426144.53 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 06:25:22,168 INFO [train.py:812] (5/8) Epoch 39, batch 3450, loss[loss=0.1557, simple_loss=0.2499, pruned_loss=0.03072, over 7206.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2413, pruned_loss=0.02859, over 1418279.54 frames.], batch size: 23, lr: 1.99e-04
2022-05-16 06:26:21,429 INFO [train.py:812] (5/8) Epoch 39, batch 3500, loss[loss=0.1399, simple_loss=0.2309, pruned_loss=0.02443, over 7154.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2416, pruned_loss=0.02857, over 1419996.06 frames.], batch size: 19, lr: 1.99e-04
2022-05-16 06:27:20,247 INFO [train.py:812] (5/8) Epoch 39, batch 3550, loss[loss=0.1458, simple_loss=0.2439, pruned_loss=0.02385, over 7333.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2413, pruned_loss=0.02871, over 1421889.60 frames.], batch size: 22, lr: 1.99e-04
2022-05-16 06:28:19,600 INFO [train.py:812] (5/8) Epoch 39, batch 3600, loss[loss=0.1313, simple_loss=0.2173, pruned_loss=0.0227, over 7296.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2423, pruned_loss=0.02877, over 1422694.74 frames.], batch size: 18, lr: 1.99e-04
2022-05-16 06:29:17,988 INFO [train.py:812] (5/8) Epoch 39, batch 3650, loss[loss=0.1707, simple_loss=0.268, pruned_loss=0.03668, over 7160.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2427, pruned_loss=0.02881, over 1424622.93 frames.], batch size: 28, lr: 1.99e-04
2022-05-16 06:30:16,881 INFO [train.py:812] (5/8) Epoch 39, batch 3700, loss[loss=0.1526, simple_loss=0.2458, pruned_loss=0.02965, over 6416.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2409, pruned_loss=0.02823, over 1421460.56 frames.], batch size: 38, lr: 1.99e-04
2022-05-16 06:31:16,257 INFO [train.py:812] (5/8) Epoch 39, batch 3750, loss[loss=0.1541, simple_loss=0.2466, pruned_loss=0.03083, over 7207.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2403, pruned_loss=0.02819, over 1414450.25 frames.], batch size: 23, lr: 1.98e-04
2022-05-16 06:32:15,545 INFO [train.py:812] (5/8) Epoch 39, batch 3800, loss[loss=0.1409, simple_loss=0.2346, pruned_loss=0.02363, over 7360.00 frames.], tot_loss[loss=0.148, simple_loss=0.2401, pruned_loss=0.02799, over 1422000.18 frames.], batch size: 19, lr: 1.98e-04
2022-05-16 06:33:12,748 INFO [train.py:812] (5/8) Epoch 39, batch 3850, loss[loss=0.176, simple_loss=0.2582, pruned_loss=0.04694, over 5121.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2407, pruned_loss=0.02812, over 1418841.07 frames.], batch size: 53, lr: 1.98e-04
2022-05-16 06:34:10,748 INFO [train.py:812] (5/8) Epoch 39, batch 3900, loss[loss=0.1416, simple_loss=0.239, pruned_loss=0.02212, over 7073.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2413, pruned_loss=0.02853, over 1420786.80 frames.], batch size: 28, lr: 1.98e-04
2022-05-16 06:35:09,061 INFO [train.py:812] (5/8) Epoch 39, batch 3950, loss[loss=0.1651, simple_loss=0.2586, pruned_loss=0.03585, over 7296.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2409, pruned_loss=0.028, over 1422868.81 frames.], batch size: 25, lr: 1.98e-04
2022-05-16 06:36:07,261 INFO [train.py:812] (5/8) Epoch 39, batch 4000, loss[loss=0.1382, simple_loss=0.2348, pruned_loss=0.02082, over 6866.00 frames.], tot_loss[loss=0.148, simple_loss=0.2406, pruned_loss=0.02771, over 1425575.56 frames.], batch size: 31, lr: 1.98e-04
2022-05-16 06:37:03,577 INFO [train.py:812] (5/8) Epoch 39, batch 4050, loss[loss=0.158, simple_loss=0.2517, pruned_loss=0.03217, over 6818.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2413, pruned_loss=0.02808, over 1424517.95 frames.], batch size: 31, lr: 1.98e-04
2022-05-16 06:38:02,733 INFO [train.py:812] (5/8) Epoch 39, batch 4100, loss[loss=0.1363, simple_loss=0.2401, pruned_loss=0.01621, over 7211.00 frames.], tot_loss[loss=0.1486, simple_loss=0.241, pruned_loss=0.02806, over 1422555.33 frames.], batch size: 21, lr: 1.98e-04
2022-05-16 06:39:01,683 INFO [train.py:812] (5/8) Epoch 39, batch 4150, loss[loss=0.1637, simple_loss=0.2626, pruned_loss=0.03239, over 7218.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2411, pruned_loss=0.02786, over 1420253.44 frames.], batch size: 21, lr: 1.98e-04
2022-05-16 06:40:00,308 INFO [train.py:812] (5/8) Epoch 39, batch 4200, loss[loss=0.1523, simple_loss=0.2539, pruned_loss=0.02541, over 6779.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2413, pruned_loss=0.02809, over 1420144.27 frames.], batch size: 31, lr: 1.98e-04
2022-05-16 06:40:58,787 INFO [train.py:812] (5/8) Epoch 39, batch 4250, loss[loss=0.1241, simple_loss=0.2101, pruned_loss=0.01909, over 7144.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2409, pruned_loss=0.02796, over 1417690.39 frames.], batch size: 17, lr: 1.98e-04
2022-05-16 06:41:58,202 INFO [train.py:812] (5/8) Epoch 39, batch 4300, loss[loss=0.1579, simple_loss=0.252, pruned_loss=0.03191, over 7283.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2417, pruned_loss=0.02804, over 1418209.73 frames.], batch size: 25, lr: 1.98e-04
2022-05-16 06:42:56,997 INFO [train.py:812] (5/8) Epoch 39, batch 4350, loss[loss=0.1431, simple_loss=0.234, pruned_loss=0.0261, over 7443.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2432, pruned_loss=0.02913, over 1413972.92 frames.], batch size: 20, lr: 1.98e-04
2022-05-16 06:43:56,255 INFO [train.py:812] (5/8) Epoch 39, batch 4400, loss[loss=0.1339, simple_loss=0.235, pruned_loss=0.01639, over 7335.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2442, pruned_loss=0.02924, over 1410908.40 frames.], batch size: 22, lr: 1.98e-04
2022-05-16 06:44:54,172 INFO [train.py:812] (5/8) Epoch 39, batch 4450, loss[loss=0.1463, simple_loss=0.2241, pruned_loss=0.03429, over 7010.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2448, pruned_loss=0.02935, over 1399230.92 frames.], batch size: 16, lr: 1.98e-04
2022-05-16 06:45:52,381 INFO [train.py:812] (5/8) Epoch 39, batch 4500, loss[loss=0.1317, simple_loss=0.2127, pruned_loss=0.02537, over 7162.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2447, pruned_loss=0.02928, over 1388317.92 frames.], batch size: 18, lr: 1.98e-04
2022-05-16 06:46:49,711 INFO [train.py:812] (5/8) Epoch 39, batch 4550, loss[loss=0.1962, simple_loss=0.2828, pruned_loss=0.05478, over 4949.00 frames.], tot_loss[loss=0.1542, simple_loss=0.247, pruned_loss=0.0307, over 1349082.18 frames.], batch size: 53, lr: 1.98e-04
2022-05-16 06:47:54,894 INFO [train.py:812] (5/8) Epoch 40, batch 0, loss[loss=0.2028, simple_loss=0.2947, pruned_loss=0.05549, over 7290.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2947, pruned_loss=0.05549, over 7290.00 frames.], batch size: 24, lr: 1.96e-04
2022-05-16 06:48:53,190 INFO [train.py:812] (5/8) Epoch 40, batch 50, loss[loss=0.1204, simple_loss=0.2116, pruned_loss=0.01463, over 7289.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2458, pruned_loss=0.02958, over 316829.92 frames.], batch size: 17, lr: 1.95e-04
2022-05-16 06:49:52,147 INFO [train.py:812] (5/8) Epoch 40, batch 100, loss[loss=0.1579, simple_loss=0.2593, pruned_loss=0.02823, over 7360.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2427, pruned_loss=0.02796, over 561548.00 frames.], batch size: 19, lr: 1.95e-04
2022-05-16 06:50:51,441 INFO [train.py:812] (5/8) Epoch 40, batch 150, loss[loss=0.1362, simple_loss=0.2313, pruned_loss=0.02057, over 7233.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2402, pruned_loss=0.02829, over 753925.72 frames.], batch size: 20, lr: 1.95e-04
2022-05-16 06:51:50,277 INFO [train.py:812] (5/8) Epoch 40, batch 200, loss[loss=0.1329, simple_loss=0.2173, pruned_loss=0.02429, over 7393.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2422, pruned_loss=0.02869, over 902181.20 frames.], batch size: 18, lr: 1.95e-04
2022-05-16 06:52:48,878 INFO [train.py:812] (5/8) Epoch 40, batch 250, loss[loss=0.144, simple_loss=0.2439, pruned_loss=0.02207, over 7111.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2415, pruned_loss=0.0283, over 1014886.03 frames.], batch size: 21, lr: 1.95e-04
2022-05-16 06:53:47,893 INFO [train.py:812] (5/8) Epoch 40, batch 300, loss[loss=0.1573, simple_loss=0.2604, pruned_loss=0.02711, over 7285.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2425, pruned_loss=0.02866, over 1105345.84 frames.], batch size: 24, lr: 1.95e-04
2022-05-16 06:54:46,893 INFO [train.py:812] (5/8) Epoch 40, batch 350, loss[loss=0.1403, simple_loss=0.2358, pruned_loss=0.02245, over 7152.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2415, pruned_loss=0.02875, over 1171150.07 frames.], batch size: 20, lr: 1.95e-04
2022-05-16 06:55:45,288 INFO [train.py:812] (5/8) Epoch 40, batch 400, loss[loss=0.1564, simple_loss=0.248, pruned_loss=0.03242, over 7207.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2419, pruned_loss=0.02877, over 1228259.63 frames.], batch size: 26, lr: 1.95e-04
2022-05-16 06:56:53,564 INFO [train.py:812] (5/8) Epoch 40, batch 450, loss[loss=0.1913, simple_loss=0.2856, pruned_loss=0.04848, over 7280.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2406, pruned_loss=0.02805, over 1272549.28 frames.], batch size: 25, lr: 1.95e-04
2022-05-16 06:57:52,467 INFO [train.py:812] (5/8) Epoch 40, batch 500, loss[loss=0.1425, simple_loss=0.2484, pruned_loss=0.01827, over 7317.00 frames.], tot_loss[loss=0.1478, simple_loss=0.24, pruned_loss=0.0278, over 1305016.27 frames.], batch size: 21, lr: 1.95e-04
2022-05-16 06:58:59,577 INFO [train.py:812] (5/8) Epoch 40, batch 550, loss[loss=0.1584, simple_loss=0.2454, pruned_loss=0.03574, over 7231.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2412, pruned_loss=0.02856, over 1326454.86 frames.], batch size: 20, lr: 1.95e-04
2022-05-16 06:59:58,449 INFO [train.py:812] (5/8) Epoch 40, batch 600, loss[loss=0.1265, simple_loss=0.2123, pruned_loss=0.02035, over 7259.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2403, pruned_loss=0.0284, over 1348307.32 frames.], batch size: 19, lr: 1.95e-04
2022-05-16 07:01:07,483 INFO [train.py:812] (5/8) Epoch 40, batch 650, loss[loss=0.1572, simple_loss=0.2509, pruned_loss=0.03173, over 7233.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2399, pruned_loss=0.02811, over 1368003.01 frames.], batch size: 20, lr: 1.95e-04
2022-05-16 07:02:07,015 INFO [train.py:812] (5/8) Epoch 40, batch 700, loss[loss=0.1233, simple_loss=0.2067, pruned_loss=0.01998, over 7276.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2412, pruned_loss=0.02869, over 1380720.89 frames.], batch size: 18, lr: 1.95e-04
2022-05-16 07:03:06,180 INFO [train.py:812] (5/8) Epoch 40, batch 750, loss[loss=0.1407, simple_loss=0.2307, pruned_loss=0.02539, over 7356.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2408, pruned_loss=0.02825, over 1386714.13 frames.], batch size: 19, lr: 1.95e-04
2022-05-16 07:04:05,438 INFO [train.py:812] (5/8) Epoch 40, batch 800, loss[loss=0.1484, simple_loss=0.2469, pruned_loss=0.02497, over 7110.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2404, pruned_loss=0.02811, over 1395900.36 frames.], batch size: 21, lr: 1.95e-04
2022-05-16 07:05:03,686 INFO [train.py:812] (5/8) Epoch 40, batch 850, loss[loss=0.14, simple_loss=0.2356, pruned_loss=0.02217, over 7136.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2411, pruned_loss=0.02833, over 1402678.56 frames.], batch size: 17, lr: 1.95e-04
2022-05-16 07:06:12,405 INFO [train.py:812] (5/8) Epoch 40, batch 900, loss[loss=0.1496, simple_loss=0.2386, pruned_loss=0.03026, over 7187.00 frames.], tot_loss[loss=0.149, simple_loss=0.2416, pruned_loss=0.02816, over 1408324.72 frames.], batch size: 23, lr: 1.95e-04
2022-05-16 07:07:10,694 INFO [train.py:812] (5/8) Epoch 40, batch 950, loss[loss=0.1662, simple_loss=0.2509, pruned_loss=0.04079, over 5043.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2416, pruned_loss=0.02804, over 1411782.12 frames.], batch size: 53, lr: 1.95e-04
2022-05-16 07:08:20,193 INFO [train.py:812] (5/8) Epoch 40, batch 1000, loss[loss=0.1455, simple_loss=0.2435, pruned_loss=0.02373, over 7104.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2415, pruned_loss=0.02789, over 1410683.96 frames.], batch size: 21, lr: 1.95e-04
2022-05-16 07:09:19,145 INFO [train.py:812] (5/8) Epoch 40, batch 1050, loss[loss=0.1323, simple_loss=0.2278, pruned_loss=0.01845, over 7218.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2415, pruned_loss=0.02785, over 1409781.18 frames.], batch size: 21, lr: 1.95e-04
2022-05-16 07:10:42,476 INFO [train.py:812] (5/8) Epoch 40, batch 1100, loss[loss=0.1438, simple_loss=0.238, pruned_loss=0.0248, over 7158.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2413, pruned_loss=0.02808, over 1407969.32 frames.], batch size: 18, lr: 1.95e-04
2022-05-16 07:11:40,912 INFO [train.py:812] (5/8) Epoch 40, batch 1150, loss[loss=0.1535, simple_loss=0.2549, pruned_loss=0.02604, over 6767.00 frames.], tot_loss[loss=0.1482, simple_loss=0.241, pruned_loss=0.02769, over 1415368.90 frames.], batch size: 31, lr: 1.95e-04
2022-05-16 07:12:38,507 INFO [train.py:812] (5/8) Epoch 40, batch 1200, loss[loss=0.1518, simple_loss=0.2409, pruned_loss=0.03136, over 6567.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2419, pruned_loss=0.02796, over 1418192.73 frames.], batch size: 38, lr: 1.95e-04
2022-05-16 07:13:37,099 INFO [train.py:812] (5/8) Epoch 40, batch 1250, loss[loss=0.153, simple_loss=0.2509, pruned_loss=0.02754, over 7279.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2414, pruned_loss=0.02813, over 1422432.34 frames.], batch size: 25, lr: 1.95e-04
2022-05-16 07:14:35,218 INFO [train.py:812] (5/8) Epoch 40, batch 1300, loss[loss=0.1652, simple_loss=0.2671, pruned_loss=0.0316, over 7424.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2416, pruned_loss=0.02837, over 1422653.20 frames.], batch size: 20, lr: 1.95e-04
2022-05-16 07:15:33,948 INFO [train.py:812] (5/8) Epoch 40, batch 1350, loss[loss=0.1594, simple_loss=0.2519, pruned_loss=0.03345, over 6164.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2414, pruned_loss=0.02851, over 1422034.82 frames.], batch size: 37, lr: 1.95e-04
2022-05-16 07:16:32,338 INFO [train.py:812] (5/8) Epoch 40, batch 1400, loss[loss=0.1274, simple_loss=0.2173, pruned_loss=0.0187, over 6511.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2417, pruned_loss=0.02837, over 1423812.16 frames.], batch size: 38, lr: 1.95e-04
2022-05-16 07:17:30,653 INFO [train.py:812] (5/8) Epoch 40, batch 1450, loss[loss=0.1705, simple_loss=0.2681, pruned_loss=0.03643, over 7199.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2412, pruned_loss=0.02813, over 1424718.51 frames.], batch size: 23, lr: 1.95e-04
2022-05-16 07:18:29,817 INFO [train.py:812] (5/8) Epoch 40, batch 1500, loss[loss=0.1337, simple_loss=0.2154, pruned_loss=0.02604, over 7140.00 frames.], tot_loss[loss=0.149, simple_loss=0.2415, pruned_loss=0.0282, over 1425732.81 frames.], batch size: 17, lr: 1.95e-04
2022-05-16 07:19:28,048 INFO [train.py:812] (5/8) Epoch 40, batch 1550, loss[loss=0.1845, simple_loss=0.2641, pruned_loss=0.05239, over 7184.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2409, pruned_loss=0.02806, over 1423855.62 frames.], batch size: 23, lr: 1.95e-04
2022-05-16 07:20:27,053 INFO [train.py:812] (5/8) Epoch 40, batch 1600, loss[loss=0.1745, simple_loss=0.267, pruned_loss=0.04095, over 7105.00 frames.], tot_loss[loss=0.15, simple_loss=0.2423, pruned_loss=0.02878, over 1426680.76 frames.], batch size: 28, lr: 1.95e-04
2022-05-16 07:21:25,474 INFO [train.py:812] (5/8) Epoch 40, batch 1650, loss[loss=0.1678, simple_loss=0.2512, pruned_loss=0.04223, over 4937.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2424, pruned_loss=0.02859, over 1419255.67 frames.], batch size: 53, lr: 1.95e-04
2022-05-16 07:22:23,878 INFO [train.py:812] (5/8) Epoch 40, batch 1700, loss[loss=0.1295, simple_loss=0.2107, pruned_loss=0.02413, over 6996.00 frames.], tot_loss[loss=0.15, simple_loss=0.2424, pruned_loss=0.02878, over 1411993.07 frames.], batch size: 16, lr: 1.95e-04
2022-05-16 07:23:23,259 INFO [train.py:812] (5/8) Epoch 40, batch 1750, loss[loss=0.158, simple_loss=0.2575, pruned_loss=0.02926, over 7326.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2421, pruned_loss=0.02866, over 1413111.22 frames.], batch size: 21, lr: 1.95e-04
2022-05-16 07:24:22,403 INFO [train.py:812] (5/8) Epoch 40, batch 1800, loss[loss=0.1661, simple_loss=0.2594, pruned_loss=0.03642, over 7337.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2429, pruned_loss=0.02911, over 1415560.48 frames.], batch size: 22, lr: 1.95e-04
2022-05-16 07:25:21,110 INFO [train.py:812] (5/8) Epoch 40, batch 1850, loss[loss=0.1195, simple_loss=0.2061, pruned_loss=0.01639, over 7063.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2426, pruned_loss=0.02895, over 1418560.14 frames.], batch size: 18, lr: 1.95e-04
2022-05-16 07:26:20,225 INFO [train.py:812] (5/8) Epoch 40, batch 1900, loss[loss=0.1508, simple_loss=0.2385, pruned_loss=0.03156, over 7159.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2432, pruned_loss=0.02902, over 1422968.11 frames.], batch size: 19, lr: 1.94e-04
2022-05-16 07:27:17,886 INFO [train.py:812] (5/8) Epoch 40, batch 1950, loss[loss=0.1632, simple_loss=0.2496, pruned_loss=0.03836, over 5065.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2426, pruned_loss=0.02888, over 1417046.53 frames.], batch size: 52, lr: 1.94e-04
2022-05-16 07:28:16,406 INFO [train.py:812] (5/8) Epoch 40, batch 2000, loss[loss=0.134, simple_loss=0.224, pruned_loss=0.02198, over 7069.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2414, pruned_loss=0.02851, over 1420980.52 frames.], batch size: 18, lr: 1.94e-04
2022-05-16 07:29:15,091 INFO [train.py:812] (5/8) Epoch 40, batch 2050, loss[loss=0.1566, simple_loss=0.2398, pruned_loss=0.03676, over 7432.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2416, pruned_loss=0.02887, over 1425196.28 frames.], batch size: 20, lr: 1.94e-04
2022-05-16 07:30:14,445 INFO [train.py:812] (5/8) Epoch 40, batch 2100, loss[loss=0.1486, simple_loss=0.2399, pruned_loss=0.02859, over 7415.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2414, pruned_loss=0.02867, over 1424080.05 frames.], batch size: 18, lr: 1.94e-04
2022-05-16 07:31:12,643 INFO [train.py:812] (5/8) Epoch 40, batch 2150, loss[loss=0.1567, simple_loss=0.265, pruned_loss=0.02418, over 7152.00 frames.], tot_loss[loss=0.1487, simple_loss=0.241, pruned_loss=0.02816, over 1428851.35 frames.], batch size: 20, lr: 1.94e-04
2022-05-16 07:32:11,372 INFO [train.py:812] (5/8) Epoch 40, batch 2200, loss[loss=0.1708, simple_loss=0.2611, pruned_loss=0.04024, over 7224.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2412, pruned_loss=0.02817, over 1431277.78 frames.], batch size: 20, lr: 1.94e-04
2022-05-16 07:33:10,313 INFO [train.py:812] (5/8) Epoch 40, batch 2250, loss[loss=0.1763, simple_loss=0.2581, pruned_loss=0.04724, over 7208.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2415, pruned_loss=0.02845, over 1429040.73 frames.], batch size: 22, lr: 1.94e-04
2022-05-16 07:34:08,434 INFO [train.py:812] (5/8) Epoch 40, batch 2300, loss[loss=0.1365, simple_loss=0.2362, pruned_loss=0.01844, over 7428.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2407, pruned_loss=0.0281, over 1426292.93 frames.], batch size: 20, lr: 1.94e-04
2022-05-16 07:35:07,169 INFO [train.py:812] (5/8) Epoch 40, batch 2350, loss[loss=0.1433, simple_loss=0.2465, pruned_loss=0.02006, over 7331.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2395, pruned_loss=0.0274, over 1426089.59 frames.], batch size: 22, lr: 1.94e-04
2022-05-16 07:36:06,695 INFO [train.py:812] (5/8) Epoch 40, batch 2400, loss[loss=0.1607, simple_loss=0.2584, pruned_loss=0.03156, over 7202.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2395, pruned_loss=0.02755, over 1426872.47 frames.], batch size: 22, lr: 1.94e-04
2022-05-16 07:37:04,706 INFO [train.py:812] (5/8) Epoch 40, batch 2450, loss[loss=0.1557, simple_loss=0.2524, pruned_loss=0.02952, over 7092.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2404, pruned_loss=0.02793, over 1422843.41 frames.], batch size: 28, lr: 1.94e-04
2022-05-16 07:38:03,661 INFO [train.py:812] (5/8) Epoch 40, batch 2500, loss[loss=0.1317, simple_loss=0.2305, pruned_loss=0.01644, over 7409.00 frames.], tot_loss[loss=0.148, simple_loss=0.2402, pruned_loss=0.0279, over 1419554.69 frames.], batch size: 21, lr: 1.94e-04
2022-05-16 07:39:02,693 INFO [train.py:812] (5/8) Epoch 40, batch 2550, loss[loss=0.1654, simple_loss=0.2616, pruned_loss=0.03454, over 7069.00 frames.], tot_loss[loss=0.149, simple_loss=0.2413, pruned_loss=0.02832, over 1419513.84 frames.], batch size: 28, lr: 1.94e-04
2022-05-16 07:40:02,328 INFO [train.py:812] (5/8) Epoch 40, batch 2600, loss[loss=0.1653, simple_loss=0.2537, pruned_loss=0.03843, over 7333.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2407, pruned_loss=0.02827, over 1419025.40 frames.], batch size: 22, lr: 1.94e-04
2022-05-16 07:40:59,582 INFO [train.py:812] (5/8) Epoch 40, batch 2650, loss[loss=0.1293, simple_loss=0.2245, pruned_loss=0.01709, over 7147.00 frames.], tot_loss[loss=0.149, simple_loss=0.2413, pruned_loss=0.02834, over 1421761.28 frames.], batch size: 18, lr: 1.94e-04
2022-05-16 07:42:08,094 INFO [train.py:812] (5/8) Epoch 40, batch 2700, loss[loss=0.1491, simple_loss=0.2455, pruned_loss=0.02635, over 7164.00 frames.], tot_loss[loss=0.149, simple_loss=0.2411, pruned_loss=0.02845, over 1422786.30 frames.], batch size: 26, lr: 1.94e-04
2022-05-16 07:43:06,177 INFO [train.py:812] (5/8) Epoch 40, batch 2750, loss[loss=0.174, simple_loss=0.2686, pruned_loss=0.03974, over 7294.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2415, pruned_loss=0.0285, over 1425891.30 frames.], batch size: 24, lr: 1.94e-04
2022-05-16 07:44:05,717 INFO [train.py:812] (5/8) Epoch 40, batch 2800, loss[loss=0.1321, simple_loss=0.2244, pruned_loss=0.01991, over 7060.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2417, pruned_loss=0.02833, over 1422061.20 frames.], batch size: 18, lr: 1.94e-04
2022-05-16 07:45:02,946 INFO [train.py:812] (5/8) Epoch 40, batch 2850, loss[loss=0.1481, simple_loss=0.2373, pruned_loss=0.02943, over 6510.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2416, pruned_loss=0.02836, over 1418765.95 frames.], batch size: 38, lr: 1.94e-04
2022-05-16 07:46:01,093 INFO [train.py:812] (5/8) Epoch 40, batch 2900, loss[loss=0.1334, simple_loss=0.224, pruned_loss=0.0214, over 7059.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2411, pruned_loss=0.02793, over 1419296.55 frames.], batch size: 18, lr: 1.94e-04
2022-05-16 07:46:58,671 INFO [train.py:812] (5/8) Epoch 40, batch 2950, loss[loss=0.1586, simple_loss=0.25, pruned_loss=0.03361, over 7308.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2423, pruned_loss=0.0282, over 1418752.69 frames.], batch size: 24, lr: 1.94e-04
2022-05-16 07:47:56,497 INFO [train.py:812] (5/8) Epoch 40, batch 3000, loss[loss=0.1538, simple_loss=0.2538, pruned_loss=0.02694, over 7330.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2427, pruned_loss=0.0285, over 1412907.24 frames.], batch size: 22, lr: 1.94e-04
2022-05-16 07:47:56,498 INFO [train.py:832] (5/8) Computing validation loss
2022-05-16 07:48:04,107 INFO [train.py:841] (5/8) Epoch 40, validation: loss=0.1534, simple_loss=0.2485, pruned_loss=0.02916, over 698248.00 frames.
2022-05-16 07:49:02,647 INFO [train.py:812] (5/8) Epoch 40, batch 3050, loss[loss=0.1304, simple_loss=0.2205, pruned_loss=0.02015, over 7363.00 frames.], tot_loss[loss=0.1494, simple_loss=0.242, pruned_loss=0.02835, over 1414320.80 frames.], batch size: 19, lr: 1.94e-04
2022-05-16 07:50:01,831 INFO [train.py:812] (5/8) Epoch 40, batch 3100, loss[loss=0.1634, simple_loss=0.2609, pruned_loss=0.03291, over 7131.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2421, pruned_loss=0.02832, over 1416298.63 frames.], batch size: 26, lr: 1.94e-04
2022-05-16 07:51:00,383 INFO [train.py:812] (5/8) Epoch 40, batch 3150, loss[loss=0.1608, simple_loss=0.2655, pruned_loss=0.02809, over 7139.00 frames.], tot_loss[loss=0.15, simple_loss=0.2429, pruned_loss=0.0285, over 1420185.33 frames.], batch size: 20, lr: 1.94e-04
2022-05-16 07:51:59,395 INFO [train.py:812] (5/8) Epoch 40, batch 3200, loss[loss=0.183, simple_loss=0.2732, pruned_loss=0.04639, over 4646.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2429, pruned_loss=0.02833, over 1420304.85 frames.], batch size: 52, lr: 1.94e-04
2022-05-16 07:52:57,278 INFO [train.py:812] (5/8) Epoch 40, batch 3250, loss[loss=0.1488, simple_loss=0.245, pruned_loss=0.02632, over 7374.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2433, pruned_loss=0.02829, over 1418789.10 frames.], batch size: 23, lr: 1.94e-04
2022-05-16 07:53:57,052 INFO [train.py:812] (5/8) Epoch 40, batch 3300, loss[loss=0.1437, simple_loss=0.2375, pruned_loss=0.02493, over 7124.00 frames.], tot_loss[loss=0.149, simple_loss=0.2419, pruned_loss=0.02803, over 1418181.85 frames.], batch size: 21, lr: 1.94e-04
2022-05-16 07:54:55,978 INFO [train.py:812] (5/8) Epoch 40, batch 3350, loss[loss=0.1548, simple_loss=0.2556, pruned_loss=0.02699, over 7106.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2419, pruned_loss=0.02821, over 1415730.72 frames.], batch size: 21, lr: 1.94e-04
2022-05-16 07:55:55,669 INFO [train.py:812] (5/8) Epoch 40, batch 3400, loss[loss=0.1445, simple_loss=0.2355, pruned_loss=0.02677, over 7158.00 frames.], tot_loss[loss=0.148, simple_loss=0.2405, pruned_loss=0.02776, over 1416938.53 frames.], batch size: 19, lr: 1.94e-04
2022-05-16 07:56:54,694 INFO [train.py:812] (5/8) Epoch 40, batch 3450, loss[loss=0.1293, simple_loss=0.2124, pruned_loss=0.02313, over 7290.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2416, pruned_loss=0.0283, over 1416410.43 frames.], batch size: 17, lr: 1.94e-04
2022-05-16 07:57:54,427 INFO [train.py:812] (5/8) Epoch 40, batch 3500, loss[loss=0.1592, simple_loss=0.2599, pruned_loss=0.02919, over 7318.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2421, pruned_loss=0.02836, over 1418146.73 frames.], batch size: 21, lr: 1.94e-04
2022-05-16 07:58:53,143 INFO [train.py:812] (5/8) Epoch 40, batch 3550, loss[loss=0.1335, simple_loss=0.2173, pruned_loss=0.02485, over 7066.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2408, pruned_loss=0.02792, over 1419931.21 frames.], batch size: 18, lr: 1.94e-04
2022-05-16 07:59:51,355 INFO [train.py:812] (5/8) Epoch 40, batch 3600, loss[loss=0.1908, simple_loss=0.2699, pruned_loss=0.0559, over 5041.00 frames.], tot_loss[loss=0.149, simple_loss=0.2414, pruned_loss=0.02829, over 1417095.41 frames.], batch size: 53, lr: 1.94e-04
2022-05-16 08:00:51,209 INFO [train.py:812] (5/8) Epoch 40, batch 3650, loss[loss=0.1662, simple_loss=0.2555, pruned_loss=0.03843, over 6449.00 frames.], tot_loss[loss=0.149, simple_loss=0.2412, pruned_loss=0.0284, over 1418631.10 frames.], batch size: 37, lr: 1.94e-04
2022-05-16 08:01:49,904 INFO [train.py:812] (5/8) Epoch 40, batch 3700, loss[loss=0.15, simple_loss=0.2362, pruned_loss=0.03189, over 7131.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2413, pruned_loss=0.02846, over 1422342.82 frames.], batch size: 17, lr: 1.94e-04
2022-05-16 08:02:47,055 INFO [train.py:812] (5/8) Epoch 40, batch 3750, loss[loss=0.1337, simple_loss=0.2272, pruned_loss=0.02007, over 7348.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2421, pruned_loss=0.02858, over 1419573.41 frames.], batch size: 19, lr: 1.93e-04
2022-05-16 08:03:45,478 INFO [train.py:812] (5/8) Epoch 40, batch 3800, loss[loss=0.1343, simple_loss=0.2172, pruned_loss=0.02572, over 7013.00 frames.], tot_loss[loss=0.15, simple_loss=0.2426, pruned_loss=0.02866, over 1423544.66 frames.], batch size: 16, lr: 1.93e-04
2022-05-16 08:04:42,346 INFO [train.py:812] (5/8) Epoch 40, batch 3850, loss[loss=0.1628, simple_loss=0.2621, pruned_loss=0.03169, over 7403.00 frames.], tot_loss[loss=0.1495, simple_loss=0.242, pruned_loss=0.02847, over 1419698.78 frames.], batch size: 21, lr: 1.93e-04
2022-05-16 08:05:41,371 INFO [train.py:812] (5/8) Epoch 40, batch 3900, loss[loss=0.1704, simple_loss=0.2675, pruned_loss=0.03668, over 7197.00 frames.], tot_loss[loss=0.1493, simple_loss=0.242, pruned_loss=0.02834, over 1420920.94 frames.], batch size: 23, lr: 1.93e-04
2022-05-16 08:06:40,225 INFO [train.py:812] (5/8) Epoch 40, batch 3950, loss[loss=0.1407, simple_loss=0.2359, pruned_loss=0.02279, over 7071.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2408, pruned_loss=0.02807, over 1415226.81 frames.], batch size: 18, lr: 1.93e-04
2022-05-16 08:07:38,723 INFO [train.py:812] (5/8) Epoch 40, batch 4000, loss[loss=0.135, simple_loss=0.2233, pruned_loss=0.02335, over 7140.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2408, pruned_loss=0.02823, over 1415001.66 frames.], batch size: 17, lr: 1.93e-04
2022-05-16 08:08:36,081 INFO [train.py:812] (5/8) Epoch 40, batch 4050, loss[loss=0.1552, simple_loss=0.2526, pruned_loss=0.02893, over 7193.00 frames.], tot_loss[loss=0.149, simple_loss=0.2413, pruned_loss=0.02831, over 1420577.80 frames.], batch size: 22, lr: 1.93e-04
2022-05-16 08:09:35,714 INFO [train.py:812] (5/8) Epoch 40, batch 4100, loss[loss=0.1295, simple_loss=0.2331, pruned_loss=0.01294, over 7240.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2412, pruned_loss=0.02816, over 1420381.36 frames.], batch size: 20, lr: 1.93e-04
2022-05-16 08:10:34,174 INFO [train.py:812] (5/8) Epoch 40, batch 4150, loss[loss=0.1391, simple_loss=0.222, pruned_loss=0.02809, over 7272.00 frames.], tot_loss[loss=0.149, simple_loss=0.2412, pruned_loss=0.02837, over 1422375.11 frames.], batch size: 18, lr: 1.93e-04
2022-05-16 08:11:32,959 INFO [train.py:812] (5/8) Epoch 40, batch 4200, loss[loss=0.1355, simple_loss=0.2284, pruned_loss=0.02127, over 7154.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2412, pruned_loss=0.02827, over 1423831.73 frames.], batch size: 18, lr: 1.93e-04
2022-05-16 08:12:31,933 INFO [train.py:812] (5/8) Epoch 40, batch 4250, loss[loss=0.1463, simple_loss=0.2458, pruned_loss=0.0234, over 7306.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2407, pruned_loss=0.028, over 1418745.09 frames.], batch size: 21, lr: 1.93e-04
2022-05-16 08:13:30,180 INFO [train.py:812] (5/8) Epoch 40, batch 4300, loss[loss=0.1173, simple_loss=0.2025, pruned_loss=0.01606, over 7167.00 frames.], tot_loss[loss=0.149, simple_loss=0.2412, pruned_loss=0.02839, over 1418956.71 frames.], batch size: 18, lr: 1.93e-04
2022-05-16 08:14:29,466 INFO [train.py:812] (5/8) Epoch 40, batch 4350, loss[loss=0.1565, simple_loss=0.2542, pruned_loss=0.02938, over 7337.00 frames.], tot_loss[loss=0.149, simple_loss=0.2416, pruned_loss=0.02821, over 1420336.20 frames.], batch size: 20, lr: 1.93e-04
2022-05-16 08:15:29,001 INFO [train.py:812] (5/8) Epoch 40, batch 4400, loss[loss=0.1836, simple_loss=0.2732, pruned_loss=0.04705, over 6874.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2419, pruned_loss=0.02844, over 1421200.77 frames.], batch size: 31, lr: 1.93e-04
2022-05-16 08:16:26,673 INFO [train.py:812] (5/8) Epoch 40, batch 4450, loss[loss=0.1346, simple_loss=0.2204, pruned_loss=0.02437, over 7175.00 frames.], tot_loss[loss=0.1496, simple_loss=0.242, pruned_loss=0.02864, over 1408466.45 frames.], batch size: 18, lr: 1.93e-04
2022-05-16 08:17:25,845 INFO [train.py:812] (5/8) Epoch 40, batch 4500, loss[loss=0.1313, simple_loss=0.2294, pruned_loss=0.01659, over 7226.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2424, pruned_loss=0.02861, over 1400583.68 frames.], batch size: 21, lr: 1.93e-04
2022-05-16 08:18:25,885 INFO [train.py:812] (5/8) Epoch 40, batch 4550, loss[loss=0.1511, simple_loss=0.2285, pruned_loss=0.03681, over 7208.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2394, pruned_loss=0.02823, over 1391183.23 frames.], batch size: 16, lr: 1.93e-04
2022-05-16 08:19:10,457 INFO [train.py:1030] (5/8) Done!