2022-07-25 15:08:28,209 INFO [train.py:914] (2/4) Training started
2022-07-25 15:08:28,209 INFO [train.py:924] (2/4) Device: cuda:2
2022-07-25 15:08:28,211 INFO [train.py:938] (2/4) {'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', 'torch-version': '1.10.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'streaming5', 'icefall-git-sha1': '245515f-dirty', 'icefall-git-date': 'Mon Jul 25 14:56:53 2022', 'icefall-path': '/ceph-kw/kangwei/code/icefall_streaming2', 'k2-path': '/ceph-hw/kangwei/code/k2_release/k2/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-hw/kangwei/dev_tools/anaconda3/envs/rnnt2/lib/python3.8/site-packages/lhotse-1.1.0-py3.8.egg/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-8-0616225538-6947466454-kvmw6', 'IP address': '10.48.32.138'}, 'world_size': 4, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 25, 'start_epoch': 1, 'start_batch': 0, 'exp_dir': PosixPath('pruned_transducer_stateless5/exp-L-nolinear'), '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': True, 'num_encoder_layers': 18, 'dim_feedforward': 2048, 'nhead': 8, 'encoder_dim': 512, 'decoder_dim': 512, 'joiner_dim': 512, 'dynamic_chunk_training': True, 'causal_convolution': True, 'short_chunk_size': 20, 'num_left_chunks': 4, '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, 'drop_last': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'input_strategy': 'PrecomputedFeatures', 'blank_id': 0, 'vocab_size': 500}
2022-07-25 15:08:28,211 INFO [train.py:940] (2/4) About to create model
2022-07-25 15:08:28,718 INFO [train.py:944] (2/4) Number of model parameters: 116553580
2022-07-25 15:08:35,439 INFO [train.py:959] (2/4) Using DDP
2022-07-25 15:08:35,647 INFO [asr_datamodule.py:409] (2/4) About to get train-clean-100 cuts
2022-07-25 15:08:35,656 INFO [asr_datamodule.py:416] (2/4) About to get train-clean-360 cuts
2022-07-25 15:08:35,662 INFO [asr_datamodule.py:423] (2/4) About to get train-other-500 cuts
2022-07-25 15:08:35,668 INFO [asr_datamodule.py:225] (2/4) Enable MUSAN
2022-07-25 15:08:35,668 INFO [asr_datamodule.py:226] (2/4) About to get Musan cuts
2022-07-25 15:08:37,839 INFO [asr_datamodule.py:254] (2/4) Enable SpecAugment
2022-07-25 15:08:37,839 INFO [asr_datamodule.py:255] (2/4) Time warp factor: 80
2022-07-25 15:08:37,839 INFO [asr_datamodule.py:267] (2/4) Num frame mask: 10
2022-07-25 15:08:37,839 INFO [asr_datamodule.py:280] (2/4) About to create train dataset
2022-07-25 15:08:37,839 INFO [asr_datamodule.py:309] (2/4) Using DynamicBucketingSampler.
2022-07-25 15:08:40,381 INFO [asr_datamodule.py:324] (2/4) About to create train dataloader
2022-07-25 15:08:40,382 INFO [asr_datamodule.py:430] (2/4) About to get dev-clean cuts
2022-07-25 15:08:40,384 INFO [asr_datamodule.py:437] (2/4) About to get dev-other cuts
2022-07-25 15:08:40,385 INFO [asr_datamodule.py:355] (2/4) About to create dev dataset
2022-07-25 15:08:40,674 INFO [asr_datamodule.py:374] (2/4) About to create dev dataloader
2022-07-25 15:08:40,675 INFO [train.py:1123] (2/4) Sanity check -- see if any of the batches in epoch 1 would cause OOM.
2022-07-25 15:11:14,887 INFO [distributed.py:874] (2/4) Reducer buckets have been rebuilt in this iteration.
2022-07-25 15:11:45,330 INFO [train.py:850] (2/4) Epoch 1, batch 0, loss[loss=0.843, simple_loss=1.686, pruned_loss=6.673, over 7322.00 frames.], tot_loss[loss=0.843, simple_loss=1.686, pruned_loss=6.673, over 7322.00 frames.], batch size: 18, lr: 3.00e-03
2022-07-25 15:12:28,179 INFO [train.py:850] (2/4) Epoch 1, batch 50, loss[loss=0.5439, simple_loss=1.088, pruned_loss=7.225, over 7378.00 frames.], tot_loss[loss=0.6547, simple_loss=1.309, pruned_loss=7.098, over 331147.18 frames.], batch size: 21, lr: 3.00e-03
2022-07-25 15:13:12,624 INFO [train.py:850] (2/4) Epoch 1, batch 100, loss[loss=0.4253, simple_loss=0.8506, pruned_loss=6.797, over 7414.00 frames.], tot_loss[loss=0.5594, simple_loss=1.119, pruned_loss=7.051, over 583202.74 frames.], batch size: 18, lr: 3.00e-03
2022-07-25 15:13:56,759 INFO [train.py:850] (2/4) Epoch 1, batch 150, loss[loss=0.4464, simple_loss=0.8929, pruned_loss=6.867, over 7179.00 frames.], tot_loss[loss=0.5099, simple_loss=1.02, pruned_loss=6.961, over 779169.25 frames.], batch size: 22, lr: 3.00e-03
2022-07-25 15:14:39,825 INFO [train.py:850] (2/4) Epoch 1, batch 200, loss[loss=0.4322, simple_loss=0.8643, pruned_loss=6.778, over 7359.00 frames.], tot_loss[loss=0.4774, simple_loss=0.9548, pruned_loss=6.892, over 931187.95 frames.], batch size: 39, lr: 3.00e-03
2022-07-25 15:15:25,132 INFO [train.py:850] (2/4) Epoch 1, batch 250, loss[loss=0.3601, simple_loss=0.7201, pruned_loss=6.65, over 7441.00 frames.], tot_loss[loss=0.4564, simple_loss=0.9129, pruned_loss=6.855, over 1049448.30 frames.], batch size: 18, lr: 3.00e-03
2022-07-25 15:16:08,011 INFO [train.py:850] (2/4) Epoch 1, batch 300, loss[loss=0.4037, simple_loss=0.8073, pruned_loss=6.825, over 7299.00 frames.], tot_loss[loss=0.4424, simple_loss=0.8849, pruned_loss=6.828, over 1142800.34 frames.], batch size: 22, lr: 3.00e-03
2022-07-25 15:16:51,411 INFO [train.py:850] (2/4) Epoch 1, batch 350, loss[loss=0.4151, simple_loss=0.8302, pruned_loss=6.79, over 7186.00 frames.], tot_loss[loss=0.4308, simple_loss=0.8616, pruned_loss=6.807, over 1214362.87 frames.], batch size: 22, lr: 3.00e-03
2022-07-25 15:17:36,683 INFO [train.py:850] (2/4) Epoch 1, batch 400, loss[loss=0.3848, simple_loss=0.7695, pruned_loss=6.661, over 7390.00 frames.], tot_loss[loss=0.4221, simple_loss=0.8442, pruned_loss=6.79, over 1269014.92 frames.], batch size: 19, lr: 3.00e-03
2022-07-25 15:18:19,417 INFO [train.py:850] (2/4) Epoch 1, batch 450, loss[loss=0.3793, simple_loss=0.7585, pruned_loss=6.737, over 7201.00 frames.], tot_loss[loss=0.4139, simple_loss=0.8278, pruned_loss=6.771, over 1311691.65 frames.], batch size: 20, lr: 2.99e-03
2022-07-25 15:19:05,009 INFO [train.py:850] (2/4) Epoch 1, batch 500, loss[loss=0.3862, simple_loss=0.7725, pruned_loss=6.666, over 7487.00 frames.], tot_loss[loss=0.4055, simple_loss=0.811, pruned_loss=6.742, over 1347350.54 frames.], batch size: 23, lr: 2.99e-03
2022-07-25 15:19:47,833 INFO [train.py:850] (2/4) Epoch 1, batch 550, loss[loss=0.393, simple_loss=0.786, pruned_loss=6.763, over 7400.00 frames.], tot_loss[loss=0.3965, simple_loss=0.7931, pruned_loss=6.726, over 1372215.75 frames.], batch size: 22, lr: 2.99e-03
2022-07-25 15:20:32,838 INFO [train.py:850] (2/4) Epoch 1, batch 600, loss[loss=0.3334, simple_loss=0.6669, pruned_loss=6.676, over 7203.00 frames.], tot_loss[loss=0.3855, simple_loss=0.771, pruned_loss=6.731, over 1392428.48 frames.], batch size: 20, lr: 2.99e-03
2022-07-25 15:21:16,594 INFO [train.py:850] (2/4) Epoch 1, batch 650, loss[loss=0.3304, simple_loss=0.6608, pruned_loss=6.752, over 7327.00 frames.], tot_loss[loss=0.3717, simple_loss=0.7434, pruned_loss=6.736, over 1408097.13 frames.], batch size: 27, lr: 2.99e-03
2022-07-25 15:22:00,373 INFO [train.py:850] (2/4) Epoch 1, batch 700, loss[loss=0.3066, simple_loss=0.6132, pruned_loss=6.709, over 7280.00 frames.], tot_loss[loss=0.3564, simple_loss=0.7128, pruned_loss=6.735, over 1420833.92 frames.], batch size: 21, lr: 2.99e-03
2022-07-25 15:22:44,590 INFO [train.py:850] (2/4) Epoch 1, batch 750, loss[loss=0.3339, simple_loss=0.6678, pruned_loss=6.741, over 7348.00 frames.], tot_loss[loss=0.3439, simple_loss=0.6879, pruned_loss=6.741, over 1430122.49 frames.], batch size: 23, lr: 2.98e-03
2022-07-25 15:23:27,658 INFO [train.py:850] (2/4) Epoch 1, batch 800, loss[loss=0.2945, simple_loss=0.589, pruned_loss=6.825, over 7389.00 frames.], tot_loss[loss=0.3318, simple_loss=0.6637, pruned_loss=6.744, over 1437953.52 frames.], batch size: 21, lr: 2.98e-03
2022-07-25 15:24:12,356 INFO [train.py:850] (2/4) Epoch 1, batch 850, loss[loss=0.3154, simple_loss=0.6308, pruned_loss=6.835, over 7366.00 frames.], tot_loss[loss=0.3213, simple_loss=0.6426, pruned_loss=6.748, over 1443994.98 frames.], batch size: 39, lr: 2.98e-03
2022-07-25 15:24:56,567 INFO [train.py:850] (2/4) Epoch 1, batch 900, loss[loss=0.3013, simple_loss=0.6025, pruned_loss=6.847, over 7294.00 frames.], tot_loss[loss=0.3115, simple_loss=0.623, pruned_loss=6.743, over 1448729.47 frames.], batch size: 21, lr: 2.98e-03
2022-07-25 15:25:39,904 INFO [train.py:850] (2/4) Epoch 1, batch 950, loss[loss=0.2792, simple_loss=0.5585, pruned_loss=6.746, over 7292.00 frames.], tot_loss[loss=0.3026, simple_loss=0.6052, pruned_loss=6.743, over 1452817.34 frames.], batch size: 27, lr: 2.97e-03
2022-07-25 15:26:24,480 INFO [train.py:850] (2/4) Epoch 1, batch 1000, loss[loss=0.291, simple_loss=0.582, pruned_loss=6.806, over 7342.00 frames.], tot_loss[loss=0.2941, simple_loss=0.5883, pruned_loss=6.739, over 1455960.34 frames.], batch size: 23, lr: 2.97e-03
2022-07-25 15:27:06,963 INFO [train.py:850] (2/4) Epoch 1, batch 1050, loss[loss=0.223, simple_loss=0.4459, pruned_loss=6.599, over 7194.00 frames.], tot_loss[loss=0.287, simple_loss=0.574, pruned_loss=6.746, over 1457235.20 frames.], batch size: 18, lr: 2.97e-03
2022-07-25 15:27:51,138 INFO [train.py:850] (2/4) Epoch 1, batch 1100, loss[loss=0.2629, simple_loss=0.5258, pruned_loss=6.713, over 7294.00 frames.], tot_loss[loss=0.2807, simple_loss=0.5613, pruned_loss=6.755, over 1460019.56 frames.], batch size: 19, lr: 2.96e-03
2022-07-25 15:28:35,144 INFO [train.py:850] (2/4) Epoch 1, batch 1150, loss[loss=0.2968, simple_loss=0.5935, pruned_loss=6.742, over 7443.00 frames.], tot_loss[loss=0.2761, simple_loss=0.5523, pruned_loss=6.765, over 1460322.60 frames.], batch size: 74, lr: 2.96e-03
2022-07-25 15:29:19,090 INFO [train.py:850] (2/4) Epoch 1, batch 1200, loss[loss=0.2716, simple_loss=0.5432, pruned_loss=6.882, over 7287.00 frames.], tot_loss[loss=0.2711, simple_loss=0.5422, pruned_loss=6.77, over 1461123.86 frames.], batch size: 20, lr: 2.96e-03
2022-07-25 15:30:04,063 INFO [train.py:850] (2/4) Epoch 1, batch 1250, loss[loss=0.2161, simple_loss=0.4321, pruned_loss=6.746, over 7293.00 frames.], tot_loss[loss=0.2653, simple_loss=0.5306, pruned_loss=6.773, over 1462042.31 frames.], batch size: 20, lr: 2.95e-03
2022-07-25 15:30:47,304 INFO [train.py:850] (2/4) Epoch 1, batch 1300, loss[loss=0.2409, simple_loss=0.4818, pruned_loss=6.77, over 7292.00 frames.], tot_loss[loss=0.2617, simple_loss=0.5234, pruned_loss=6.778, over 1461718.98 frames.], batch size: 20, lr: 2.95e-03
2022-07-25 15:31:31,882 INFO [train.py:850] (2/4) Epoch 1, batch 1350, loss[loss=0.2, simple_loss=0.3999, pruned_loss=6.488, over 7254.00 frames.], tot_loss[loss=0.2575, simple_loss=0.5149, pruned_loss=6.773, over 1462886.07 frames.], batch size: 16, lr: 2.95e-03
2022-07-25 15:32:15,704 INFO [train.py:850] (2/4) Epoch 1, batch 1400, loss[loss=0.206, simple_loss=0.412, pruned_loss=6.737, over 7209.00 frames.], tot_loss[loss=0.2543, simple_loss=0.5085, pruned_loss=6.777, over 1463943.35 frames.], batch size: 19, lr: 2.94e-03
2022-07-25 15:32:59,416 INFO [train.py:850] (2/4) Epoch 1, batch 1450, loss[loss=0.2445, simple_loss=0.489, pruned_loss=6.918, over 7295.00 frames.], tot_loss[loss=0.2504, simple_loss=0.5007, pruned_loss=6.778, over 1464468.61 frames.], batch size: 27, lr: 2.94e-03
2022-07-25 15:33:45,365 INFO [train.py:850] (2/4) Epoch 1, batch 1500, loss[loss=0.2176, simple_loss=0.4352, pruned_loss=6.667, over 7153.00 frames.], tot_loss[loss=0.2475, simple_loss=0.495, pruned_loss=6.775, over 1465609.99 frames.], batch size: 17, lr: 2.94e-03
2022-07-25 15:34:28,219 INFO [train.py:850] (2/4) Epoch 1, batch 1550, loss[loss=0.2273, simple_loss=0.4546, pruned_loss=6.741, over 7330.00 frames.], tot_loss[loss=0.2452, simple_loss=0.4904, pruned_loss=6.772, over 1465200.95 frames.], batch size: 16, lr: 2.93e-03
2022-07-25 15:35:14,898 INFO [train.py:850] (2/4) Epoch 1, batch 1600, loss[loss=0.2775, simple_loss=0.555, pruned_loss=6.872, over 7407.00 frames.], tot_loss[loss=0.2426, simple_loss=0.4852, pruned_loss=6.771, over 1465423.35 frames.], batch size: 22, lr: 2.93e-03
2022-07-25 15:35:58,616 INFO [train.py:850] (2/4) Epoch 1, batch 1650, loss[loss=0.2035, simple_loss=0.4069, pruned_loss=6.694, over 7206.00 frames.], tot_loss[loss=0.2395, simple_loss=0.479, pruned_loss=6.772, over 1464303.56 frames.], batch size: 19, lr: 2.92e-03
2022-07-25 15:36:43,270 INFO [train.py:850] (2/4) Epoch 1, batch 1700, loss[loss=0.2132, simple_loss=0.4264, pruned_loss=6.649, over 7445.00 frames.], tot_loss[loss=0.2369, simple_loss=0.4738, pruned_loss=6.771, over 1466053.66 frames.], batch size: 18, lr: 2.92e-03
2022-07-25 15:37:28,611 INFO [train.py:850] (2/4) Epoch 1, batch 1750, loss[loss=0.1874, simple_loss=0.3747, pruned_loss=6.606, over 7296.00 frames.], tot_loss[loss=0.2348, simple_loss=0.4696, pruned_loss=6.769, over 1465294.29 frames.], batch size: 16, lr: 2.91e-03
2022-07-25 15:38:15,194 INFO [train.py:850] (2/4) Epoch 1, batch 1800, loss[loss=0.2596, simple_loss=0.5191, pruned_loss=6.867, over 7382.00 frames.], tot_loss[loss=0.2334, simple_loss=0.4668, pruned_loss=6.772, over 1465832.90 frames.], batch size: 19, lr: 2.91e-03
2022-07-25 15:38:59,703 INFO [train.py:850] (2/4) Epoch 1, batch 1850, loss[loss=0.2592, simple_loss=0.5184, pruned_loss=6.897, over 7231.00 frames.], tot_loss[loss=0.231, simple_loss=0.4619, pruned_loss=6.774, over 1465423.81 frames.], batch size: 24, lr: 2.91e-03
2022-07-25 15:39:43,203 INFO [train.py:850] (2/4) Epoch 1, batch 1900, loss[loss=0.2313, simple_loss=0.4626, pruned_loss=6.679, over 7210.00 frames.], tot_loss[loss=0.2289, simple_loss=0.4579, pruned_loss=6.767, over 1465578.38 frames.], batch size: 19, lr: 2.90e-03
2022-07-25 15:40:27,104 INFO [train.py:850] (2/4) Epoch 1, batch 1950, loss[loss=0.2132, simple_loss=0.4263, pruned_loss=6.774, over 7300.00 frames.], tot_loss[loss=0.2276, simple_loss=0.4553, pruned_loss=6.76, over 1466310.56 frames.], batch size: 18, lr: 2.90e-03
2022-07-25 15:41:11,721 INFO [train.py:850] (2/4) Epoch 1, batch 2000, loss[loss=0.1977, simple_loss=0.3953, pruned_loss=6.656, over 7379.00 frames.], tot_loss[loss=0.2264, simple_loss=0.4528, pruned_loss=6.757, over 1467111.61 frames.], batch size: 20, lr: 2.89e-03
2022-07-25 15:41:54,653 INFO [train.py:850] (2/4) Epoch 1, batch 2050, loss[loss=0.1891, simple_loss=0.3781, pruned_loss=6.674, over 7149.00 frames.], tot_loss[loss=0.2255, simple_loss=0.4509, pruned_loss=6.757, over 1466293.82 frames.], batch size: 17, lr: 2.89e-03
2022-07-25 15:42:39,930 INFO [train.py:850] (2/4) Epoch 1, batch 2100, loss[loss=0.2229, simple_loss=0.4459, pruned_loss=6.812, over 7352.00 frames.], tot_loss[loss=0.2249, simple_loss=0.4499, pruned_loss=6.762, over 1467600.18 frames.], batch size: 31, lr: 2.88e-03
2022-07-25 15:43:22,395 INFO [train.py:850] (2/4) Epoch 1, batch 2150, loss[loss=0.2192, simple_loss=0.4384, pruned_loss=6.597, over 7452.00 frames.], tot_loss[loss=0.2249, simple_loss=0.4497, pruned_loss=6.768, over 1466141.81 frames.], batch size: 17, lr: 2.88e-03
2022-07-25 15:44:07,575 INFO [train.py:850] (2/4) Epoch 1, batch 2200, loss[loss=0.2114, simple_loss=0.4229, pruned_loss=6.821, over 7385.00 frames.], tot_loss[loss=0.2237, simple_loss=0.4474, pruned_loss=6.767, over 1467075.30 frames.], batch size: 19, lr: 2.87e-03
2022-07-25 15:44:50,902 INFO [train.py:850] (2/4) Epoch 1, batch 2250, loss[loss=0.2191, simple_loss=0.4383, pruned_loss=6.779, over 7388.00 frames.], tot_loss[loss=0.2219, simple_loss=0.4437, pruned_loss=6.767, over 1466470.11 frames.], batch size: 21, lr: 2.86e-03
2022-07-25 15:45:35,444 INFO [train.py:850] (2/4) Epoch 1, batch 2300, loss[loss=0.2312, simple_loss=0.4625, pruned_loss=6.799, over 7472.00 frames.], tot_loss[loss=0.2211, simple_loss=0.4422, pruned_loss=6.767, over 1467195.60 frames.], batch size: 31, lr: 2.86e-03
2022-07-25 15:46:19,730 INFO [train.py:850] (2/4) Epoch 1, batch 2350, loss[loss=0.2384, simple_loss=0.4768, pruned_loss=6.933, over 7183.00 frames.], tot_loss[loss=0.22, simple_loss=0.44, pruned_loss=6.768, over 1467968.89 frames.], batch size: 21, lr: 2.85e-03
2022-07-25 15:47:03,462 INFO [train.py:850] (2/4) Epoch 1, batch 2400, loss[loss=0.217, simple_loss=0.434, pruned_loss=6.791, over 7381.00 frames.], tot_loss[loss=0.2188, simple_loss=0.4376, pruned_loss=6.764, over 1467062.95 frames.], batch size: 20, lr: 2.85e-03
2022-07-25 15:47:47,834 INFO [train.py:850] (2/4) Epoch 1, batch 2450, loss[loss=0.2207, simple_loss=0.4414, pruned_loss=6.8, over 7477.00 frames.], tot_loss[loss=0.217, simple_loss=0.4341, pruned_loss=6.766, over 1465997.45 frames.], batch size: 24, lr: 2.84e-03
2022-07-25 15:48:31,715 INFO [train.py:850] (2/4) Epoch 1, batch 2500, loss[loss=0.1811, simple_loss=0.3622, pruned_loss=6.641, over 7264.00 frames.], tot_loss[loss=0.2166, simple_loss=0.4331, pruned_loss=6.767, over 1465501.41 frames.], batch size: 16, lr: 2.84e-03
2022-07-25 15:49:14,930 INFO [train.py:850] (2/4) Epoch 1, batch 2550, loss[loss=0.2137, simple_loss=0.4274, pruned_loss=6.792, over 7297.00 frames.], tot_loss[loss=0.2161, simple_loss=0.4322, pruned_loss=6.768, over 1464692.46 frames.], batch size: 20, lr: 2.83e-03
2022-07-25 15:49:59,482 INFO [train.py:850] (2/4) Epoch 1, batch 2600, loss[loss=0.194, simple_loss=0.3881, pruned_loss=6.74, over 7456.00 frames.], tot_loss[loss=0.2146, simple_loss=0.4292, pruned_loss=6.765, over 1464836.68 frames.], batch size: 17, lr: 2.83e-03
2022-07-25 15:50:44,172 INFO [train.py:850] (2/4) Epoch 1, batch 2650, loss[loss=0.206, simple_loss=0.4119, pruned_loss=6.695, over 7304.00 frames.], tot_loss[loss=0.2133, simple_loss=0.4267, pruned_loss=6.762, over 1463968.75 frames.], batch size: 16, lr: 2.82e-03
2022-07-25 15:51:30,663 INFO [train.py:850] (2/4) Epoch 1, batch 2700, loss[loss=0.2241, simple_loss=0.4481, pruned_loss=6.797, over 7240.00 frames.], tot_loss[loss=0.2141, simple_loss=0.4283, pruned_loss=6.768, over 1464024.90 frames.], batch size: 24, lr: 2.81e-03
2022-07-25 15:52:13,136 INFO [train.py:850] (2/4) Epoch 1, batch 2750, loss[loss=0.2155, simple_loss=0.431, pruned_loss=6.809, over 7200.00 frames.], tot_loss[loss=0.2136, simple_loss=0.4273, pruned_loss=6.769, over 1463615.54 frames.], batch size: 19, lr: 2.81e-03
2022-07-25 15:52:56,281 INFO [train.py:850] (2/4) Epoch 1, batch 2800, loss[loss=0.1949, simple_loss=0.3898, pruned_loss=6.716, over 7270.00 frames.], tot_loss[loss=0.2125, simple_loss=0.425, pruned_loss=6.773, over 1463269.10 frames.], batch size: 16, lr: 2.80e-03
2022-07-25 15:53:40,255 INFO [train.py:850] (2/4) Epoch 1, batch 2850, loss[loss=0.1923, simple_loss=0.3847, pruned_loss=6.692, over 7445.00 frames.], tot_loss[loss=0.2113, simple_loss=0.4225, pruned_loss=6.772, over 1464710.61 frames.], batch size: 17, lr: 2.80e-03
2022-07-25 15:54:24,243 INFO [train.py:850] (2/4) Epoch 1, batch 2900, loss[loss=0.1741, simple_loss=0.3482, pruned_loss=6.659, over 7447.00 frames.], tot_loss[loss=0.21, simple_loss=0.42, pruned_loss=6.769, over 1465158.86 frames.], batch size: 18, lr: 2.79e-03
2022-07-25 15:55:08,615 INFO [train.py:850] (2/4) Epoch 1, batch 2950, loss[loss=0.2265, simple_loss=0.453, pruned_loss=6.814, over 7474.00 frames.], tot_loss[loss=0.2092, simple_loss=0.4184, pruned_loss=6.758, over 1464058.54 frames.], batch size: 31, lr: 2.78e-03
2022-07-25 15:55:50,719 INFO [train.py:850] (2/4) Epoch 1, batch 3000, loss[loss=0.9154, simple_loss=0.4646, pruned_loss=6.832, over 7186.00 frames.], tot_loss[loss=0.2468, simple_loss=0.4181, pruned_loss=6.758, over 1462996.61 frames.], batch size: 22, lr: 2.78e-03
2022-07-25 15:55:50,720 INFO [train.py:870] (2/4) Computing validation loss
2022-07-25 15:56:13,614 INFO [train.py:879] (2/4) Epoch 1, validation: loss=2.416, simple_loss=0.3964, pruned_loss=2.218, over 924787.00 frames.
2022-07-25 15:56:58,293 INFO [train.py:850] (2/4) Epoch 1, batch 3050, loss[loss=0.3043, simple_loss=0.4426, pruned_loss=0.8298, over 7192.00 frames.], tot_loss[loss=0.2656, simple_loss=0.4197, pruned_loss=5.522, over 1463419.90 frames.], batch size: 19, lr: 2.77e-03
2022-07-25 15:57:42,091 INFO [train.py:850] (2/4) Epoch 1, batch 3100, loss[loss=0.3094, simple_loss=0.5073, pruned_loss=0.5572, over 7290.00 frames.], tot_loss[loss=0.2666, simple_loss=0.4203, pruned_loss=4.425, over 1464460.21 frames.], batch size: 21, lr: 2.77e-03
2022-07-25 15:58:25,495 INFO [train.py:850] (2/4) Epoch 1, batch 3150, loss[loss=0.2431, simple_loss=0.42, pruned_loss=0.3308, over 7191.00 frames.], tot_loss[loss=0.2617, simple_loss=0.419, pruned_loss=3.527, over 1464392.40 frames.], batch size: 18, lr: 2.76e-03
2022-07-25 15:59:08,957 INFO [train.py:850] (2/4) Epoch 1, batch 3200, loss[loss=0.1901, simple_loss=0.3377, pruned_loss=0.2121, over 7458.00 frames.], tot_loss[loss=0.2554, simple_loss=0.4163, pruned_loss=2.812, over 1464470.76 frames.], batch size: 18, lr: 2.75e-03
2022-07-25 15:59:52,677 INFO [train.py:850] (2/4) Epoch 1, batch 3250, loss[loss=0.2257, simple_loss=0.4037, pruned_loss=0.2386, over 7192.00 frames.], tot_loss[loss=0.2507, simple_loss=0.4156, pruned_loss=2.248, over 1465468.15 frames.], batch size: 21, lr: 2.75e-03
2022-07-25 16:00:37,439 INFO [train.py:850] (2/4) Epoch 1, batch 3300, loss[loss=0.2224, simple_loss=0.3976, pruned_loss=0.2356, over 7290.00 frames.], tot_loss[loss=0.2463, simple_loss=0.4146, pruned_loss=1.806, over 1465701.69 frames.], batch size: 20, lr: 2.74e-03
2022-07-25 16:01:20,594 INFO [train.py:850] (2/4) Epoch 1, batch 3350, loss[loss=0.2108, simple_loss=0.378, pruned_loss=0.218, over 7440.00 frames.], tot_loss[loss=0.2418, simple_loss=0.4123, pruned_loss=1.458, over 1465514.42 frames.], batch size: 18, lr: 2.73e-03
2022-07-25 16:02:05,697 INFO [train.py:850] (2/4) Epoch 1, batch 3400, loss[loss=0.1891, simple_loss=0.341, pruned_loss=0.1856, over 7302.00 frames.], tot_loss[loss=0.2382, simple_loss=0.4106, pruned_loss=1.188, over 1464591.06 frames.], batch size: 18, lr: 2.73e-03
2022-07-25 16:02:48,147 INFO [train.py:850] (2/4) Epoch 1, batch 3450, loss[loss=0.2481, simple_loss=0.445, pruned_loss=0.2564, over 7490.00 frames.], tot_loss[loss=0.236, simple_loss=0.4105, pruned_loss=0.975, over 1465963.26 frames.], batch size: 23, lr: 2.72e-03
2022-07-25 16:03:32,432 INFO [train.py:850] (2/4) Epoch 1, batch 3500, loss[loss=0.2431, simple_loss=0.4342, pruned_loss=0.2606, over 7180.00 frames.], tot_loss[loss=0.2346, simple_loss=0.4111, pruned_loss=0.8102, over 1465563.23 frames.], batch size: 22, lr: 2.72e-03
2022-07-25 16:04:16,032 INFO [train.py:850] (2/4) Epoch 1, batch 3550, loss[loss=0.247, simple_loss=0.4415, pruned_loss=0.2624, over 7477.00 frames.], tot_loss[loss=0.2326, simple_loss=0.4103, pruned_loss=0.6791, over 1465428.88 frames.], batch size: 24, lr: 2.71e-03
2022-07-25 16:04:59,618 INFO [train.py:850] (2/4) Epoch 1, batch 3600, loss[loss=0.2146, simple_loss=0.3905, pruned_loss=0.1934, over 7187.00 frames.], tot_loss[loss=0.2311, simple_loss=0.4097, pruned_loss=0.5778, over 1464324.72 frames.], batch size: 21, lr: 2.70e-03
2022-07-25 16:05:44,731 INFO [train.py:850] (2/4) Epoch 1, batch 3650, loss[loss=0.1923, simple_loss=0.3506, pruned_loss=0.1698, over 7200.00 frames.], tot_loss[loss=0.2286, simple_loss=0.4071, pruned_loss=0.4958, over 1463022.91 frames.], batch size: 18, lr: 2.70e-03
2022-07-25 16:06:28,246 INFO [train.py:850] (2/4) Epoch 1, batch 3700, loss[loss=0.2139, simple_loss=0.3929, pruned_loss=0.1747, over 7489.00 frames.], tot_loss[loss=0.2266, simple_loss=0.4051, pruned_loss=0.4311, over 1463071.15 frames.], batch size: 23, lr: 2.69e-03
2022-07-25 16:07:11,906 INFO [train.py:850] (2/4) Epoch 1, batch 3750, loss[loss=0.2143, simple_loss=0.3892, pruned_loss=0.1974, over 7197.00 frames.], tot_loss[loss=0.2244, simple_loss=0.4026, pruned_loss=0.3801, over 1463054.96 frames.], batch size: 18, lr: 2.68e-03
2022-07-25 16:07:55,930 INFO [train.py:850] (2/4) Epoch 1, batch 3800, loss[loss=0.2079, simple_loss=0.3786, pruned_loss=0.186, over 7284.00 frames.], tot_loss[loss=0.2232, simple_loss=0.4014, pruned_loss=0.34, over 1464601.54 frames.], batch size: 19, lr: 2.68e-03
2022-07-25 16:08:39,206 INFO [train.py:850] (2/4) Epoch 1, batch 3850, loss[loss=0.2256, simple_loss=0.4081, pruned_loss=0.2157, over 7202.00 frames.], tot_loss[loss=0.2221, simple_loss=0.4004, pruned_loss=0.3093, over 1465683.21 frames.], batch size: 20, lr: 2.67e-03
2022-07-25 16:09:25,414 INFO [train.py:850] (2/4) Epoch 1, batch 3900, loss[loss=0.2251, simple_loss=0.4088, pruned_loss=0.207, over 7386.00 frames.], tot_loss[loss=0.2213, simple_loss=0.3996, pruned_loss=0.2851, over 1465012.07 frames.], batch size: 20, lr: 2.66e-03
2022-07-25 16:10:08,409 INFO [train.py:850] (2/4) Epoch 1, batch 3950, loss[loss=0.2076, simple_loss=0.3804, pruned_loss=0.174, over 7467.00 frames.], tot_loss[loss=0.2212, simple_loss=0.4, pruned_loss=0.2664, over 1465127.55 frames.], batch size: 24, lr: 2.66e-03
2022-07-25 16:11:07,053 INFO [train.py:850] (2/4) Epoch 1, batch 4000, loss[loss=0.2452, simple_loss=0.4424, pruned_loss=0.2396, over 7487.00 frames.], tot_loss[loss=0.2207, simple_loss=0.3996, pruned_loss=0.2515, over 1465770.22 frames.], batch size: 24, lr: 2.65e-03
2022-07-25 16:11:50,831 INFO [train.py:850] (2/4) Epoch 1, batch 4050, loss[loss=0.2556, simple_loss=0.4602, pruned_loss=0.2547, over 7488.00 frames.], tot_loss[loss=0.2205, simple_loss=0.3996, pruned_loss=0.2396, over 1466279.74 frames.], batch size: 24, lr: 2.64e-03
2022-07-25 16:12:34,629 INFO [train.py:850] (2/4) Epoch 1, batch 4100, loss[loss=0.1958, simple_loss=0.3583, pruned_loss=0.1665, over 7161.00 frames.], tot_loss[loss=0.2213, simple_loss=0.4011, pruned_loss=0.2325, over 1466029.86 frames.], batch size: 17, lr: 2.64e-03
2022-07-25 16:13:19,520 INFO [train.py:850] (2/4) Epoch 1, batch 4150, loss[loss=0.2201, simple_loss=0.3962, pruned_loss=0.2194, over 7304.00 frames.], tot_loss[loss=0.2236, simple_loss=0.4051, pruned_loss=0.2307, over 1465789.70 frames.], batch size: 18, lr: 2.63e-03
2022-07-25 16:14:04,112 INFO [train.py:850] (2/4) Epoch 1, batch 4200, loss[loss=0.2327, simple_loss=0.4249, pruned_loss=0.2029, over 7173.00 frames.], tot_loss[loss=0.2255, simple_loss=0.4083, pruned_loss=0.2291, over 1464600.31 frames.], batch size: 22, lr: 2.63e-03
2022-07-25 16:14:48,306 INFO [train.py:850] (2/4) Epoch 1, batch 4250, loss[loss=0.2712, simple_loss=0.4851, pruned_loss=0.287, over 7240.00 frames.], tot_loss[loss=0.2271, simple_loss=0.4109, pruned_loss=0.2283, over 1465103.32 frames.], batch size: 24, lr: 2.62e-03
2022-07-25 16:15:31,741 INFO [train.py:850] (2/4) Epoch 1, batch 4300, loss[loss=0.2325, simple_loss=0.4224, pruned_loss=0.2133, over 7306.00 frames.], tot_loss[loss=0.2276, simple_loss=0.4116, pruned_loss=0.2272, over 1465317.96 frames.], batch size: 22, lr: 2.61e-03
2022-07-25 16:16:15,382 INFO [train.py:850] (2/4) Epoch 1, batch 4350, loss[loss=0.2471, simple_loss=0.4455, pruned_loss=0.2438, over 7173.00 frames.], tot_loss[loss=0.2296, simple_loss=0.4148, pruned_loss=0.2291, over 1465614.82 frames.], batch size: 22, lr: 2.61e-03
2022-07-25 16:16:59,520 INFO [train.py:850] (2/4) Epoch 1, batch 4400, loss[loss=0.214, simple_loss=0.3929, pruned_loss=0.1753, over 7307.00 frames.], tot_loss[loss=0.2309, simple_loss=0.417, pruned_loss=0.2299, over 1464438.56 frames.], batch size: 39, lr: 2.60e-03
2022-07-25 16:17:42,738 INFO [train.py:850] (2/4) Epoch 1, batch 4450, loss[loss=0.2838, simple_loss=0.5102, pruned_loss=0.2869, over 7371.00 frames.], tot_loss[loss=0.2325, simple_loss=0.4195, pruned_loss=0.2313, over 1464302.13 frames.], batch size: 38, lr: 2.59e-03
2022-07-25 16:18:27,650 INFO [train.py:850] (2/4) Epoch 1, batch 4500, loss[loss=0.2701, simple_loss=0.4808, pruned_loss=0.2977, over 7249.00 frames.], tot_loss[loss=0.2334, simple_loss=0.4211, pruned_loss=0.2325, over 1464153.35 frames.], batch size: 25, lr: 2.59e-03
2022-07-25 16:19:10,974 INFO [train.py:850] (2/4) Epoch 1, batch 4550, loss[loss=0.2436, simple_loss=0.4382, pruned_loss=0.2444, over 7259.00 frames.], tot_loss[loss=0.2335, simple_loss=0.4211, pruned_loss=0.2323, over 1463996.88 frames.], batch size: 31, lr: 2.58e-03
2022-07-25 16:19:56,172 INFO [train.py:850] (2/4) Epoch 1, batch 4600, loss[loss=0.2556, simple_loss=0.4528, pruned_loss=0.292, over 7375.00 frames.], tot_loss[loss=0.2332, simple_loss=0.4204, pruned_loss=0.232, over 1463854.42 frames.], batch size: 20, lr: 2.57e-03
2022-07-25 16:20:39,868 INFO [train.py:850] (2/4) Epoch 1, batch 4650, loss[loss=0.2511, simple_loss=0.4454, pruned_loss=0.2837, over 7437.00 frames.], tot_loss[loss=0.2325, simple_loss=0.4192, pruned_loss=0.2307, over 1464132.67 frames.], batch size: 18, lr: 2.57e-03
2022-07-25 16:21:23,216 INFO [train.py:850] (2/4) Epoch 1, batch 4700, loss[loss=0.206, simple_loss=0.3723, pruned_loss=0.1984, over 7439.00 frames.], tot_loss[loss=0.2331, simple_loss=0.4202, pruned_loss=0.2311, over 1463618.78 frames.], batch size: 18, lr: 2.56e-03
2022-07-25 16:22:07,828 INFO [train.py:850] (2/4) Epoch 1, batch 4750, loss[loss=0.2156, simple_loss=0.3933, pruned_loss=0.189, over 7277.00 frames.], tot_loss[loss=0.2336, simple_loss=0.421, pruned_loss=0.232, over 1464752.96 frames.], batch size: 21, lr: 2.55e-03
2022-07-25 16:22:52,157 INFO [train.py:850] (2/4) Epoch 1, batch 4800, loss[loss=0.2344, simple_loss=0.4254, pruned_loss=0.2169, over 7371.00 frames.], tot_loss[loss=0.2332, simple_loss=0.4205, pruned_loss=0.2309, over 1464319.70 frames.], batch size: 21, lr: 2.55e-03
2022-07-25 16:23:35,326 INFO [train.py:850] (2/4) Epoch 1, batch 4850, loss[loss=0.2257, simple_loss=0.4064, pruned_loss=0.2251, over 7303.00 frames.], tot_loss[loss=0.2334, simple_loss=0.4209, pruned_loss=0.2301, over 1464128.25 frames.], batch size: 17, lr: 2.54e-03
2022-07-25 16:24:20,125 INFO [train.py:850] (2/4) Epoch 1, batch 4900, loss[loss=0.1978, simple_loss=0.3584, pruned_loss=0.186, over 7289.00 frames.], tot_loss[loss=0.2336, simple_loss=0.4213, pruned_loss=0.2299, over 1465247.99 frames.], batch size: 16, lr: 2.54e-03
2022-07-25 16:25:03,354 INFO [train.py:850] (2/4) Epoch 1, batch 4950, loss[loss=0.227, simple_loss=0.4107, pruned_loss=0.2164, over 7224.00 frames.], tot_loss[loss=0.2322, simple_loss=0.4191, pruned_loss=0.2274, over 1465172.09 frames.], batch size: 25, lr: 2.53e-03
2022-07-25 16:25:47,476 INFO [train.py:850] (2/4) Epoch 1, batch 5000, loss[loss=0.2804, simple_loss=0.501, pruned_loss=0.2993, over 7443.00 frames.], tot_loss[loss=0.2332, simple_loss=0.4208, pruned_loss=0.2286, over 1464688.31 frames.], batch size: 39, lr: 2.52e-03
2022-07-25 16:26:30,229 INFO [train.py:850] (2/4) Epoch 1, batch 5050, loss[loss=0.1979, simple_loss=0.3614, pruned_loss=0.1714, over 7137.00 frames.], tot_loss[loss=0.2331, simple_loss=0.4207, pruned_loss=0.2276, over 1463900.79 frames.], batch size: 17, lr: 2.52e-03
2022-07-25 16:27:14,914 INFO [train.py:850] (2/4) Epoch 1, batch 5100, loss[loss=0.2025, simple_loss=0.3688, pruned_loss=0.1812, over 7298.00 frames.], tot_loss[loss=0.2314, simple_loss=0.418, pruned_loss=0.224, over 1463964.44 frames.], batch size: 19, lr: 2.51e-03
2022-07-25 16:27:59,036 INFO [train.py:850] (2/4) Epoch 1, batch 5150, loss[loss=0.2504, simple_loss=0.4473, pruned_loss=0.2678, over 7292.00 frames.], tot_loss[loss=0.2319, simple_loss=0.419, pruned_loss=0.2248, over 1464565.59 frames.], batch size: 21, lr: 2.50e-03
2022-07-25 16:28:43,380 INFO [train.py:850] (2/4) Epoch 1, batch 5200, loss[loss=0.2252, simple_loss=0.41, pruned_loss=0.2018, over 7490.00 frames.], tot_loss[loss=0.2309, simple_loss=0.4173, pruned_loss=0.2226, over 1464698.86 frames.], batch size: 23, lr: 2.50e-03
2022-07-25 16:29:28,944 INFO [train.py:850] (2/4) Epoch 1, batch 5250, loss[loss=0.2167, simple_loss=0.3937, pruned_loss=0.1983, over 7343.00 frames.], tot_loss[loss=0.2303, simple_loss=0.4163, pruned_loss=0.2218, over 1464783.19 frames.], batch size: 23, lr: 2.49e-03
2022-07-25 16:30:13,139 INFO [train.py:850] (2/4) Epoch 1, batch 5300, loss[loss=0.2076, simple_loss=0.3797, pruned_loss=0.1776, over 7188.00 frames.], tot_loss[loss=0.2298, simple_loss=0.4153, pruned_loss=0.2213, over 1464531.42 frames.], batch size: 19, lr: 2.49e-03
2022-07-25 16:30:56,386 INFO [train.py:850] (2/4) Epoch 1, batch 5350, loss[loss=0.2184, simple_loss=0.396, pruned_loss=0.2041, over 7480.00 frames.], tot_loss[loss=0.2282, simple_loss=0.4127, pruned_loss=0.2185, over 1463404.80 frames.], batch size: 20, lr: 2.48e-03
2022-07-25 16:31:40,440 INFO [train.py:850] (2/4) Epoch 1, batch 5400, loss[loss=0.1906, simple_loss=0.3512, pruned_loss=0.1495, over 7311.00 frames.], tot_loss[loss=0.2271, simple_loss=0.4109, pruned_loss=0.2166, over 1464331.90 frames.], batch size: 18, lr: 2.47e-03
2022-07-25 16:32:24,282 INFO [train.py:850] (2/4) Epoch 1, batch 5450, loss[loss=0.2153, simple_loss=0.3913, pruned_loss=0.1969, over 7491.00 frames.], tot_loss[loss=0.2259, simple_loss=0.409, pruned_loss=0.2142, over 1463486.44 frames.], batch size: 19, lr: 2.47e-03
2022-07-25 16:33:07,780 INFO [train.py:850] (2/4) Epoch 1, batch 5500, loss[loss=0.2126, simple_loss=0.3904, pruned_loss=0.1744, over 7474.00 frames.], tot_loss[loss=0.2256, simple_loss=0.4086, pruned_loss=0.2125, over 1463277.08 frames.], batch size: 31, lr: 2.46e-03
2022-07-25 16:33:50,563 INFO [train.py:850] (2/4) Epoch 1, batch 5550, loss[loss=0.2097, simple_loss=0.3819, pruned_loss=0.1876, over 7441.00 frames.], tot_loss[loss=0.2247, simple_loss=0.4072, pruned_loss=0.2113, over 1462791.56 frames.], batch size: 18, lr: 2.45e-03
2022-07-25 16:34:34,712 INFO [train.py:850] (2/4) Epoch 1, batch 5600, loss[loss=0.1923, simple_loss=0.3513, pruned_loss=0.1662, over 7294.00 frames.], tot_loss[loss=0.2253, simple_loss=0.4083, pruned_loss=0.2117, over 1463980.29 frames.], batch size: 16, lr: 2.45e-03
2022-07-25 16:35:17,936 INFO [train.py:850] (2/4) Epoch 1, batch 5650, loss[loss=0.2317, simple_loss=0.4196, pruned_loss=0.2191, over 7413.00 frames.], tot_loss[loss=0.2258, simple_loss=0.4093, pruned_loss=0.212, over 1463263.45 frames.], batch size: 22, lr: 2.44e-03
2022-07-25 16:36:02,332 INFO [train.py:850] (2/4) Epoch 1, batch 5700, loss[loss=0.1968, simple_loss=0.3613, pruned_loss=0.1614, over 7229.00 frames.], tot_loss[loss=0.2266, simple_loss=0.4104, pruned_loss=0.2133, over 1463432.86 frames.], batch size: 24, lr: 2.44e-03
2022-07-25 16:36:46,129 INFO [train.py:850] (2/4) Epoch 1, batch 5750, loss[loss=0.2221, simple_loss=0.4007, pruned_loss=0.2169, over 7308.00 frames.], tot_loss[loss=0.2265, simple_loss=0.4105, pruned_loss=0.2131, over 1464048.21 frames.], batch size: 19, lr: 2.43e-03
2022-07-25 16:37:30,377 INFO [train.py:850] (2/4) Epoch 1, batch 5800, loss[loss=0.2252, simple_loss=0.4068, pruned_loss=0.2183, over 7395.00 frames.], tot_loss[loss=0.2277, simple_loss=0.4124, pruned_loss=0.2148, over 1464289.42 frames.], batch size: 73, lr: 2.42e-03
2022-07-25 16:38:13,810 INFO [train.py:850] (2/4) Epoch 1, batch 5850, loss[loss=0.2342, simple_loss=0.4264, pruned_loss=0.2098, over 7412.00 frames.], tot_loss[loss=0.2277, simple_loss=0.4126, pruned_loss=0.2139, over 1465049.79 frames.], batch size: 22, lr: 2.42e-03
2022-07-25 16:38:59,007 INFO [train.py:850] (2/4) Epoch 1, batch 5900, loss[loss=0.229, simple_loss=0.41, pruned_loss=0.24, over 7439.00 frames.], tot_loss[loss=0.2268, simple_loss=0.411, pruned_loss=0.2128, over 1465979.77 frames.], batch size: 17, lr: 2.41e-03
2022-07-25 16:39:43,101 INFO [train.py:850] (2/4) Epoch 1, batch 5950, loss[loss=0.2007, simple_loss=0.3703, pruned_loss=0.1551, over 7481.00 frames.], tot_loss[loss=0.2257, simple_loss=0.4092, pruned_loss=0.2115, over 1467229.75 frames.], batch size: 19, lr: 2.41e-03
2022-07-25 16:40:26,485 INFO [train.py:850] (2/4) Epoch 1, batch 6000, loss[loss=0.4142, simple_loss=0.3995, pruned_loss=0.2144, over 7207.00 frames.], tot_loss[loss=0.2265, simple_loss=0.4077, pruned_loss=0.2087, over 1466570.05 frames.], batch size: 24, lr: 2.40e-03
2022-07-25 16:40:26,486 INFO [train.py:870] (2/4) Computing validation loss
2022-07-25 16:40:49,372 INFO [train.py:879] (2/4) Epoch 1, validation: loss=0.3753, simple_loss=0.3692, pruned_loss=0.1908, over 924787.00 frames.
2022-07-25 16:41:33,384 INFO [train.py:850] (2/4) Epoch 1, batch 6050, loss[loss=0.4413, simple_loss=0.4392, pruned_loss=0.2217, over 7410.00 frames.], tot_loss[loss=0.2747, simple_loss=0.411, pruned_loss=0.214, over 1467240.37 frames.], batch size: 22, lr: 2.39e-03
2022-07-25 16:42:17,250 INFO [train.py:850] (2/4) Epoch 1, batch 6100, loss[loss=0.4782, simple_loss=0.4417, pruned_loss=0.2573, over 7195.00 frames.], tot_loss[loss=0.3061, simple_loss=0.4114, pruned_loss=0.2131, over 1466290.51 frames.], batch size: 18, lr: 2.39e-03
2022-07-25 16:43:02,448 INFO [train.py:850] (2/4) Epoch 1, batch 6150, loss[loss=0.5142, simple_loss=0.4697, pruned_loss=0.2793, over 7178.00 frames.], tot_loss[loss=0.3346, simple_loss=0.4143, pruned_loss=0.2151, over 1467021.16 frames.], batch size: 21, lr: 2.38e-03
2022-07-25 16:43:47,358 INFO [train.py:850] (2/4) Epoch 1, batch 6200, loss[loss=0.4184, simple_loss=0.4152, pruned_loss=0.2108, over 7482.00 frames.], tot_loss[loss=0.3507, simple_loss=0.4131, pruned_loss=0.2124, over 1466724.75 frames.], batch size: 23, lr: 2.38e-03
2022-07-25 16:44:31,402 INFO [train.py:850] (2/4) Epoch 1, batch 6250, loss[loss=0.4746, simple_loss=0.4647, pruned_loss=0.2423, over 7186.00 frames.], tot_loss[loss=0.3634, simple_loss=0.4127, pruned_loss=0.2102, over 1466588.69 frames.], batch size: 21, lr: 2.37e-03
2022-07-25 16:45:15,529 INFO [train.py:850] (2/4) Epoch 1, batch 6300, loss[loss=0.4859, simple_loss=0.4627, pruned_loss=0.2545, over 7287.00 frames.], tot_loss[loss=0.3753, simple_loss=0.4139, pruned_loss=0.2098, over 1466106.91 frames.], batch size: 21, lr: 2.37e-03
2022-07-25 16:45:59,722 INFO [train.py:850] (2/4) Epoch 1, batch 6350, loss[loss=0.3885, simple_loss=0.3956, pruned_loss=0.1906, over 7389.00 frames.], tot_loss[loss=0.3817, simple_loss=0.4122, pruned_loss=0.2078, over 1466113.49 frames.], batch size: 19, lr: 2.36e-03
2022-07-25 16:46:44,034 INFO [train.py:850] (2/4) Epoch 1, batch 6400, loss[loss=0.306, simple_loss=0.3436, pruned_loss=0.1343, over 7390.00 frames.], tot_loss[loss=0.3855, simple_loss=0.4107, pruned_loss=0.2052, over 1466241.56 frames.], batch size: 19, lr: 2.35e-03
2022-07-25 16:47:27,697 INFO [train.py:850] (2/4) Epoch 1, batch 6450, loss[loss=0.3443, simple_loss=0.3533, pruned_loss=0.1677, over 7276.00 frames.], tot_loss[loss=0.3881, simple_loss=0.4089, pruned_loss=0.2031, over 1465336.08 frames.], batch size: 16, lr: 2.35e-03
2022-07-25 16:48:11,262 INFO [train.py:850] (2/4) Epoch 1, batch 6500, loss[loss=0.3705, simple_loss=0.3643, pruned_loss=0.1884, over 7271.00 frames.], tot_loss[loss=0.3922, simple_loss=0.4094, pruned_loss=0.2027, over 1464268.12 frames.], batch size: 16, lr: 2.34e-03
2022-07-25 16:48:55,020 INFO [train.py:850] (2/4) Epoch 1, batch 6550, loss[loss=0.3378, simple_loss=0.3526, pruned_loss=0.1615, over 7158.00 frames.], tot_loss[loss=0.3923, simple_loss=0.4077, pruned_loss=0.2003, over 1464189.79 frames.], batch size: 17, lr: 2.34e-03
2022-07-25 16:49:40,224 INFO [train.py:850] (2/4) Epoch 1, batch 6600, loss[loss=0.4163, simple_loss=0.4084, pruned_loss=0.2121, over 7191.00 frames.], tot_loss[loss=0.393, simple_loss=0.4068, pruned_loss=0.1988, over 1465254.76 frames.], batch size: 18, lr: 2.33e-03
2022-07-25 16:50:23,077 INFO [train.py:850] (2/4) Epoch 1, batch 6650, loss[loss=0.369, simple_loss=0.4039, pruned_loss=0.1671, over 7303.00 frames.], tot_loss[loss=0.3939, simple_loss=0.4065, pruned_loss=0.1979, over 1465385.64 frames.], batch size: 22, lr: 2.33e-03
2022-07-25 16:51:07,151 INFO [train.py:850] (2/4) Epoch 1, batch 6700, loss[loss=0.4154, simple_loss=0.4269, pruned_loss=0.2019, over 7260.00 frames.], tot_loss[loss=0.3965, simple_loss=0.4077, pruned_loss=0.1983, over 1465708.91 frames.], batch size: 24, lr: 2.32e-03
2022-07-25 16:51:50,801 INFO [train.py:850] (2/4) Epoch 1, batch 6750, loss[loss=0.4202, simple_loss=0.4417, pruned_loss=0.1994, over 7291.00 frames.], tot_loss[loss=0.3948, simple_loss=0.4064, pruned_loss=0.1959, over 1464960.01 frames.], batch size: 22, lr: 2.31e-03
2022-07-25 16:52:35,371 INFO [train.py:850] (2/4) Epoch 1, batch 6800, loss[loss=0.3731, simple_loss=0.3678, pruned_loss=0.1892, over 7443.00 frames.], tot_loss[loss=0.3943, simple_loss=0.4056, pruned_loss=0.1949, over 1465013.45 frames.], batch size: 17, lr: 2.31e-03
2022-07-25 16:53:19,269 INFO [train.py:850] (2/4) Epoch 1, batch 6850, loss[loss=0.3015, simple_loss=0.3366, pruned_loss=0.1332, over 7276.00 frames.], tot_loss[loss=0.3965, simple_loss=0.4073, pruned_loss=0.1955, over 1466871.84 frames.], batch size: 16, lr: 2.30e-03
2022-07-25 16:54:03,204 INFO [train.py:850] (2/4) Epoch 1, batch 6900, loss[loss=0.4633, simple_loss=0.4566, pruned_loss=0.235, over 7280.00 frames.], tot_loss[loss=0.3975, simple_loss=0.4078, pruned_loss=0.1956, over 1467014.33 frames.], batch size: 21, lr: 2.30e-03
2022-07-25 16:54:47,699 INFO [train.py:850] (2/4) Epoch 1, batch 6950, loss[loss=0.4343, simple_loss=0.435, pruned_loss=0.2167, over 7459.00 frames.], tot_loss[loss=0.3966, simple_loss=0.4075, pruned_loss=0.1944, over 1466902.43 frames.], batch size: 24, lr: 2.29e-03
2022-07-25 16:55:31,939 INFO [train.py:850] (2/4) Epoch 1, batch 7000, loss[loss=0.4707, simple_loss=0.4493, pruned_loss=0.246, over 7409.00 frames.], tot_loss[loss=0.3958, simple_loss=0.4068, pruned_loss=0.1937, over 1467003.59 frames.], batch size: 73, lr: 2.29e-03
2022-07-25 16:56:15,907 INFO [train.py:850] (2/4) Epoch 1, batch 7050, loss[loss=0.4146, simple_loss=0.4268, pruned_loss=0.2012, over 7277.00 frames.], tot_loss[loss=0.3967, simple_loss=0.4069, pruned_loss=0.1942, over 1467143.86 frames.], batch size: 27, lr: 2.28e-03
2022-07-25 16:57:00,734 INFO [train.py:850] (2/4) Epoch 1, batch 7100, loss[loss=0.4254, simple_loss=0.4311, pruned_loss=0.2099, over 7290.00 frames.], tot_loss[loss=0.3961, simple_loss=0.4073, pruned_loss=0.1932, over 1466639.87 frames.], batch size: 27, lr: 2.28e-03
2022-07-25 16:57:44,591 INFO [train.py:850] (2/4) Epoch 1, batch 7150, loss[loss=0.3531, simple_loss=0.3851, pruned_loss=0.1606, over 7482.00 frames.], tot_loss[loss=0.3931, simple_loss=0.4055, pruned_loss=0.191, over 1466744.04 frames.], batch size: 21, lr: 2.27e-03
2022-07-25 16:58:29,118 INFO [train.py:850] (2/4) Epoch 1, batch 7200, loss[loss=0.3867, simple_loss=0.4073, pruned_loss=0.183, over 7350.00 frames.], tot_loss[loss=0.3919, simple_loss=0.4049, pruned_loss=0.1899, over 1466651.07 frames.], batch size: 31, lr: 2.27e-03
2022-07-25 16:59:12,332 INFO [train.py:850] (2/4) Epoch 1, batch 7250, loss[loss=0.4235, simple_loss=0.4232, pruned_loss=0.2119, over 7457.00 frames.], tot_loss[loss=0.3911, simple_loss=0.404, pruned_loss=0.1894, over 1465908.43 frames.], batch size: 78, lr: 2.26e-03
2022-07-25 16:59:57,207 INFO [train.py:850] (2/4) Epoch 1, batch 7300, loss[loss=0.4046, simple_loss=0.4247, pruned_loss=0.1922, over 7357.00 frames.], tot_loss[loss=0.389, simple_loss=0.4029, pruned_loss=0.1878, over 1465213.93 frames.], batch size: 23, lr: 2.26e-03
2022-07-25 17:00:41,086 INFO [train.py:850] (2/4) Epoch 1, batch 7350, loss[loss=0.3666, simple_loss=0.3933, pruned_loss=0.1699, over 7482.00 frames.], tot_loss[loss=0.3877, simple_loss=0.4019, pruned_loss=0.1869, over 1465170.08 frames.], batch size: 31, lr: 2.25e-03
2022-07-25 17:01:24,981 INFO [train.py:850] (2/4) Epoch 1, batch 7400, loss[loss=0.4675, simple_loss=0.4657, pruned_loss=0.2346, over 7241.00 frames.], tot_loss[loss=0.3883, simple_loss=0.4025, pruned_loss=0.1872, over 1464735.58 frames.], batch size: 25, lr: 2.24e-03
2022-07-25 17:02:09,195 INFO [train.py:850] (2/4) Epoch 1, batch 7450, loss[loss=0.413, simple_loss=0.4299, pruned_loss=0.1981, over 7227.00 frames.], tot_loss[loss=0.3867, simple_loss=0.4019, pruned_loss=0.1859, over 1465799.01 frames.], batch size: 25, lr: 2.24e-03
2022-07-25 17:02:53,566 INFO [train.py:850] (2/4) Epoch 1, batch 7500, loss[loss=0.4251, simple_loss=0.4182, pruned_loss=0.216, over 7428.00 frames.], tot_loss[loss=0.3858, simple_loss=0.4013, pruned_loss=0.1852, over 1465713.88 frames.], batch size: 72, lr: 2.23e-03
2022-07-25 17:03:37,791 INFO [train.py:850] (2/4) Epoch 1, batch 7550, loss[loss=0.4202, simple_loss=0.4401, pruned_loss=0.2002, over 7173.00 frames.], tot_loss[loss=0.3872, simple_loss=0.4026, pruned_loss=0.186, over 1465976.88 frames.], batch size: 22, lr: 2.23e-03
2022-07-25 17:04:22,235 INFO [train.py:850] (2/4) Epoch 1, batch 7600, loss[loss=0.3174, simple_loss=0.3396, pruned_loss=0.1475, over 7219.00 frames.], tot_loss[loss=0.3871, simple_loss=0.4024, pruned_loss=0.186, over 1465261.32 frames.], batch size: 16, lr: 2.22e-03
2022-07-25 17:05:05,723 INFO [train.py:850] (2/4) Epoch 1, batch 7650, loss[loss=0.4196, simple_loss=0.4164, pruned_loss=0.2114, over 7373.00 frames.], tot_loss[loss=0.3861, simple_loss=0.4014, pruned_loss=0.1854, over 1466373.69 frames.], batch size: 21, lr: 2.22e-03
2022-07-25 17:05:50,391 INFO [train.py:850] (2/4) Epoch 1, batch 7700, loss[loss=0.3478, simple_loss=0.3629, pruned_loss=0.1664, over 7439.00 frames.], tot_loss[loss=0.3871, simple_loss=0.4025, pruned_loss=0.1859, over 1467348.61 frames.], batch size: 17, lr: 2.21e-03
2022-07-25 17:06:34,274 INFO [train.py:850] (2/4) Epoch 1, batch 7750, loss[loss=0.335, simple_loss=0.3706, pruned_loss=0.1497, over 7386.00 frames.], tot_loss[loss=0.386, simple_loss=0.4023, pruned_loss=0.1849, over 1466604.76 frames.], batch size: 19, lr: 2.21e-03
2022-07-25 17:07:18,577 INFO [train.py:850] (2/4) Epoch 1, batch 7800, loss[loss=0.3114, simple_loss=0.3574, pruned_loss=0.1327, over 7388.00 frames.], tot_loss[loss=0.3853, simple_loss=0.4019, pruned_loss=0.1844, over 1467136.79 frames.], batch size: 19, lr: 2.20e-03
2022-07-25 17:08:02,843 INFO [train.py:850] (2/4) Epoch 1, batch 7850, loss[loss=0.3937, simple_loss=0.414, pruned_loss=0.1868, over 7289.00 frames.], tot_loss[loss=0.3865, simple_loss=0.4026, pruned_loss=0.1852, over 1466367.20 frames.], batch size: 27, lr: 2.20e-03
2022-07-25 17:08:48,606 INFO [train.py:850] (2/4) Epoch 1, batch 7900, loss[loss=0.4177, simple_loss=0.4248, pruned_loss=0.2053, over 7452.00 frames.], tot_loss[loss=0.3854, simple_loss=0.4021, pruned_loss=0.1843, over 1466402.13 frames.], batch size: 31, lr: 2.19e-03
2022-07-25 17:09:32,398 INFO [train.py:850] (2/4) Epoch 1, batch 7950, loss[loss=0.3548, simple_loss=0.3865, pruned_loss=0.1616, over 7207.00 frames.], tot_loss[loss=0.3845, simple_loss=0.4022, pruned_loss=0.1834, over 1465412.07 frames.], batch size: 20, lr: 2.19e-03
2022-07-25 17:10:33,220 INFO [train.py:850] (2/4) Epoch 1, batch 8000, loss[loss=0.383, simple_loss=0.4115, pruned_loss=0.1773, over 7344.00 frames.], tot_loss[loss=0.3828, simple_loss=0.4006, pruned_loss=0.1825, over 1466013.40 frames.], batch size: 38, lr: 2.18e-03
2022-07-25 17:11:17,317 INFO [train.py:850] (2/4) Epoch 1, batch 8050, loss[loss=0.3649, simple_loss=0.387, pruned_loss=0.1715, over 7484.00 frames.], tot_loss[loss=0.3827, simple_loss=0.4006, pruned_loss=0.1824, over 1465112.67 frames.], batch size: 20, lr: 2.18e-03
2022-07-25 17:12:01,904 INFO [train.py:850] (2/4) Epoch 1, batch 8100, loss[loss=0.3556, simple_loss=0.3939, pruned_loss=0.1586, over 7357.00 frames.], tot_loss[loss=0.3811, simple_loss=0.3995, pruned_loss=0.1814, over 1464953.15 frames.], batch size: 23, lr: 2.17e-03
2022-07-25 17:12:46,117 INFO [train.py:850] (2/4) Epoch 1, batch 8150, loss[loss=0.4426, simple_loss=0.4526, pruned_loss=0.2163, over 7290.00 frames.], tot_loss[loss=0.3819, simple_loss=0.4003, pruned_loss=0.1817, over 1464795.99 frames.], batch size: 20, lr: 2.17e-03
2022-07-25 17:13:30,324 INFO [train.py:850] (2/4) Epoch 1, batch 8200, loss[loss=0.4115, simple_loss=0.4264, pruned_loss=0.1983, over 7479.00 frames.], tot_loss[loss=0.3792, simple_loss=0.3985, pruned_loss=0.1799, over 1464774.73 frames.], batch size: 40, lr: 2.16e-03
2022-07-25 17:14:14,134 INFO [train.py:850] (2/4) Epoch 1, batch 8250, loss[loss=0.403, simple_loss=0.4243, pruned_loss=0.1908, over 7287.00 frames.], tot_loss[loss=0.3796, simple_loss=0.3989, pruned_loss=0.1802, over 1464912.37 frames.], batch size: 39, lr: 2.16e-03
2022-07-25 17:14:58,669 INFO [train.py:850] (2/4) Epoch 1, batch 8300, loss[loss=0.335, simple_loss=0.3611, pruned_loss=0.1545, over 7168.00 frames.], tot_loss[loss=0.3773, simple_loss=0.3977, pruned_loss=0.1784, over 1465540.19 frames.], batch size: 17, lr: 2.15e-03
2022-07-25 17:15:43,355 INFO [train.py:850] (2/4) Epoch 1, batch 8350, loss[loss=0.3258, simple_loss=0.3712, pruned_loss=0.1402, over 7475.00 frames.], tot_loss[loss=0.3759, simple_loss=0.3968, pruned_loss=0.1774, over 1465449.30 frames.], batch size: 21, lr: 2.15e-03
2022-07-25 17:16:28,177 INFO [train.py:850] (2/4) Epoch 1, batch 8400, loss[loss=0.3845, simple_loss=0.4087, pruned_loss=0.1801, over 7284.00 frames.], tot_loss[loss=0.3753, simple_loss=0.3966, pruned_loss=0.177, over 1465788.85 frames.], batch size: 27, lr: 2.15e-03
2022-07-25 17:17:11,073 INFO [train.py:850] (2/4) Epoch 1, batch 8450, loss[loss=0.4194, simple_loss=0.4399, pruned_loss=0.1994, over 7288.00 frames.], tot_loss[loss=0.3762, simple_loss=0.3976, pruned_loss=0.1774, over 1467360.51 frames.], batch size: 20, lr: 2.14e-03
2022-07-25 17:17:55,258 INFO [train.py:850] (2/4) Epoch 1, batch 8500, loss[loss=0.3271, simple_loss=0.3669, pruned_loss=0.1436, over 7286.00 frames.], tot_loss[loss=0.3751, simple_loss=0.3968, pruned_loss=0.1767, over 1467687.90 frames.], batch size: 21, lr: 2.14e-03
2022-07-25 17:18:38,652 INFO [train.py:850] (2/4) Epoch 1, batch 8550, loss[loss=0.4437, simple_loss=0.4487, pruned_loss=0.2194, over 7474.00 frames.], tot_loss[loss=0.377, simple_loss=0.3983, pruned_loss=0.1778, over 1466523.34 frames.], batch size: 70, lr: 2.13e-03
2022-07-25 17:19:23,366 INFO [train.py:850] (2/4) Epoch 1, batch 8600, loss[loss=0.3691, simple_loss=0.4029, pruned_loss=0.1677, over 7385.00 frames.], tot_loss[loss=0.3762, simple_loss=0.3983, pruned_loss=0.177, over 1467050.25 frames.], batch size: 21, lr: 2.13e-03
2022-07-25 17:20:06,412 INFO [train.py:850] (2/4) Epoch 1, batch 8650, loss[loss=0.2965, simple_loss=0.3318, pruned_loss=0.1306, over 7195.00 frames.], tot_loss[loss=0.3746, simple_loss=0.397, pruned_loss=0.176, over 1466987.54 frames.], batch size: 18, lr: 2.12e-03
2022-07-25 17:20:50,021 INFO [train.py:850] (2/4) Epoch 1, batch 8700, loss[loss=0.4149, simple_loss=0.4256, pruned_loss=0.2021, over 7422.00 frames.], tot_loss[loss=0.3745, simple_loss=0.397, pruned_loss=0.176, over 1466806.83 frames.], batch size: 71, lr: 2.12e-03
2022-07-25 17:21:32,237 INFO [train.py:850] (2/4) Epoch 1, batch 8750, loss[loss=0.3634, simple_loss=0.4058, pruned_loss=0.1605, over 7368.00 frames.], tot_loss[loss=0.3723, simple_loss=0.3956, pruned_loss=0.1745, over 1465770.05 frames.], batch size: 39, lr: 2.11e-03
2022-07-25 17:22:15,756 INFO [train.py:850] (2/4) Epoch 1, batch 8800, loss[loss=0.3504, simple_loss=0.3947, pruned_loss=0.153, over 7336.00 frames.], tot_loss[loss=0.3711, simple_loss=0.395, pruned_loss=0.1737, over 1465100.76 frames.], batch size: 23, lr: 2.11e-03
2022-07-25 17:22:58,765 INFO [train.py:850] (2/4) Epoch 1, batch 8850, loss[loss=0.3547, simple_loss=0.3888, pruned_loss=0.1603, over 7461.00 frames.], tot_loss[loss=0.3688, simple_loss=0.3936, pruned_loss=0.172, over 1465556.82 frames.], batch size: 31, lr: 2.10e-03
2022-07-25 17:24:39,863 INFO [train.py:850] (2/4) Epoch 2, batch 0, loss[loss=0.3065, simple_loss=0.3585, pruned_loss=0.1273, over 7280.00 frames.], tot_loss[loss=0.3065, simple_loss=0.3585, pruned_loss=0.1273, over 7280.00 frames.], batch size: 17, lr: 2.09e-03
2022-07-25 17:25:23,669 INFO [train.py:850] (2/4) Epoch 2, batch 50, loss[loss=0.2912, simple_loss=0.355, pruned_loss=0.1137, over 7187.00 frames.], tot_loss[loss=0.3309, simple_loss=0.3775, pruned_loss=0.1421, over 330691.06 frames.], batch size: 19, lr: 2.08e-03
2022-07-25 17:26:08,817 INFO [train.py:850] (2/4) Epoch 2, batch 100, loss[loss=0.323, simple_loss=0.3609, pruned_loss=0.1425, over 7387.00 frames.], tot_loss[loss=0.3301, simple_loss=0.377, pruned_loss=0.1416, over 582862.43 frames.], batch size: 20, lr: 2.08e-03
2022-07-25 17:26:52,132 INFO [train.py:850] (2/4) Epoch 2, batch 150, loss[loss=0.2918, simple_loss=0.331, pruned_loss=0.1263, over 7309.00 frames.], tot_loss[loss=0.3305, simple_loss=0.3772, pruned_loss=0.142, over 778908.76 frames.], batch size: 17, lr: 2.07e-03
2022-07-25 17:27:35,796 INFO [train.py:850] (2/4) Epoch 2, batch 200, loss[loss=0.337, simple_loss=0.3845, pruned_loss=0.1447, over 7191.00 frames.], tot_loss[loss=0.3245, simple_loss=0.3726, pruned_loss=0.1382, over 930951.05 frames.], batch size: 21, lr: 2.07e-03
2022-07-25 17:28:19,459 INFO [train.py:850] (2/4) Epoch 2, batch 250, loss[loss=0.3039, simple_loss=0.3565, pruned_loss=0.1256, over 7193.00 frames.], tot_loss[loss=0.3234, simple_loss=0.372, pruned_loss=0.1374, over 1048793.44 frames.], batch size: 18, lr: 2.06e-03
2022-07-25 17:29:01,756 INFO [train.py:850] (2/4) Epoch 2, batch 300, loss[loss=0.295, simple_loss=0.3472, pruned_loss=0.1214, over 7492.00 frames.], tot_loss[loss=0.3217, simple_loss=0.371, pruned_loss=0.1362, over 1141087.49 frames.], batch size: 19, lr: 2.06e-03
2022-07-25 17:29:46,174 INFO [train.py:850] (2/4) Epoch 2, batch 350, loss[loss=0.318, simple_loss=0.3741, pruned_loss=0.131, over 7312.00 frames.], tot_loss[loss=0.3195, simple_loss=0.3692, pruned_loss=0.1349, over 1212674.97 frames.], batch size: 27, lr: 2.06e-03
2022-07-25 17:30:29,816 INFO [train.py:850] (2/4) Epoch 2, batch 400, loss[loss=0.2713, simple_loss=0.328, pruned_loss=0.1073, over 7314.00 frames.], tot_loss[loss=0.317, simple_loss=0.3678, pruned_loss=0.1331, over 1269271.09 frames.], batch size: 18, lr: 2.05e-03
2022-07-25 17:31:15,239 INFO [train.py:850] (2/4) Epoch 2, batch 450, loss[loss=0.3216, simple_loss=0.3873, pruned_loss=0.1279, over 7253.00 frames.], tot_loss[loss=0.3152, simple_loss=0.3662, pruned_loss=0.132, over 1313560.48 frames.], batch size: 27, lr: 2.05e-03
2022-07-25 17:31:58,611 INFO [train.py:850] (2/4) Epoch 2, batch 500, loss[loss=0.2824, simple_loss=0.3165, pruned_loss=0.1241, over 7282.00 frames.], tot_loss[loss=0.3136, simple_loss=0.3648, pruned_loss=0.1312, over 1347764.24 frames.], batch size: 16, lr: 2.04e-03
2022-07-25 17:32:42,754 INFO [train.py:850] (2/4) Epoch 2, batch 550, loss[loss=0.4096, simple_loss=0.4304, pruned_loss=0.1944, over 7350.00 frames.], tot_loss[loss=0.3103, simple_loss=0.3623, pruned_loss=0.1292, over 1372743.98 frames.], batch size: 23, lr: 2.04e-03
2022-07-25 17:33:26,270 INFO [train.py:850] (2/4) Epoch 2, batch 600, loss[loss=0.3208, simple_loss=0.3721, pruned_loss=0.1347, over 7200.00 frames.], tot_loss[loss=0.3101, simple_loss=0.3619, pruned_loss=0.1292, over 1393414.96 frames.], batch size: 19, lr: 2.03e-03
2022-07-25 17:34:11,445 INFO [train.py:850] (2/4) Epoch 2, batch 650, loss[loss=0.3136, simple_loss=0.3731, pruned_loss=0.127, over 7453.00 frames.], tot_loss[loss=0.309, simple_loss=0.3615, pruned_loss=0.1282, over 1409214.64 frames.], batch size: 24, lr: 2.03e-03
2022-07-25 17:34:56,509 INFO [train.py:850] (2/4) Epoch 2, batch 700, loss[loss=0.3399, simple_loss=0.3928, pruned_loss=0.1435, over 7382.00 frames.], tot_loss[loss=0.3065, simple_loss=0.3596, pruned_loss=0.1268, over 1421623.38 frames.], batch size: 21, lr: 2.03e-03
2022-07-25 17:35:40,200 INFO [train.py:850] (2/4) Epoch 2, batch 750, loss[loss=0.268, simple_loss=0.3333, pruned_loss=0.1014, over 7198.00 frames.], tot_loss[loss=0.3073, simple_loss=0.3598, pruned_loss=0.1273, over 1432198.00 frames.], batch size: 19, lr: 2.02e-03
2022-07-25 17:36:24,962 INFO [train.py:850] (2/4) Epoch 2, batch 800, loss[loss=0.3266, simple_loss=0.3796, pruned_loss=0.1368, over 7350.00 frames.], tot_loss[loss=0.3086, simple_loss=0.361, pruned_loss=0.1281, over 1440084.61 frames.], batch size: 40, lr: 2.02e-03
2022-07-25 17:37:07,508 INFO [train.py:850] (2/4) Epoch 2, batch 850, loss[loss=0.3108, simple_loss=0.3784, pruned_loss=0.1216, over 7481.00 frames.], tot_loss[loss=0.3105, simple_loss=0.3632, pruned_loss=0.1289, over 1444984.57 frames.], batch size: 24, lr: 2.01e-03
2022-07-25 17:37:50,553 INFO [train.py:850] (2/4) Epoch 2, batch 900, loss[loss=0.2733, simple_loss=0.3223, pruned_loss=0.1122, over 7176.00 frames.], tot_loss[loss=0.3135, simple_loss=0.3653, pruned_loss=0.1309, over 1449317.25 frames.], batch size: 17, lr: 2.01e-03
2022-07-25 17:38:34,573 INFO [train.py:850] (2/4) Epoch 2, batch 950, loss[loss=0.3477, simple_loss=0.4109, pruned_loss=0.1422, over 7237.00 frames.], tot_loss[loss=0.3174, simple_loss=0.3685, pruned_loss=0.1332, over 1454034.70 frames.], batch size: 25, lr: 2.00e-03
2022-07-25 17:39:17,930 INFO [train.py:850] (2/4) Epoch 2, batch 1000, loss[loss=0.356, simple_loss=0.3845, pruned_loss=0.1637, over 7384.00 frames.], tot_loss[loss=0.3192, simple_loss=0.3693, pruned_loss=0.1346, over 1455348.40 frames.], batch size: 19, lr: 2.00e-03
2022-07-25 17:40:03,108 INFO [train.py:850] (2/4) Epoch 2, batch 1050, loss[loss=0.3409, simple_loss=0.3849, pruned_loss=0.1485, over 7286.00 frames.], tot_loss[loss=0.3215, simple_loss=0.3713, pruned_loss=0.1359, over 1457229.81 frames.], batch size: 21, lr: 2.00e-03
2022-07-25 17:40:45,766 INFO [train.py:850] (2/4) Epoch 2, batch 1100, loss[loss=0.357, simple_loss=0.4056, pruned_loss=0.1542, over 7302.00 frames.], tot_loss[loss=0.3218, simple_loss=0.372, pruned_loss=0.1358, over 1459442.68 frames.], batch size: 19, lr: 1.99e-03
2022-07-25 17:41:29,207 INFO [train.py:850] (2/4) Epoch 2, batch 1150, loss[loss=0.3036, simple_loss=0.3551, pruned_loss=0.1261, over 7487.00 frames.], tot_loss[loss=0.3236, simple_loss=0.3733, pruned_loss=0.1369, over 1461026.30 frames.], batch size: 20, lr: 1.99e-03
2022-07-25 17:42:13,494 INFO [train.py:850] (2/4) Epoch 2, batch 1200, loss[loss=0.2388, simple_loss=0.301, pruned_loss=0.08828, over 7459.00 frames.], tot_loss[loss=0.3253, simple_loss=0.3749, pruned_loss=0.1378, over 1462651.21 frames.], batch size: 17, lr: 1.98e-03
2022-07-25 17:42:57,330 INFO [train.py:850] (2/4) Epoch 2, batch 1250, loss[loss=0.2495, simple_loss=0.3078, pruned_loss=0.09566, over 7454.00 frames.], tot_loss[loss=0.326, simple_loss=0.3754, pruned_loss=0.1383, over 1463524.27 frames.], batch size: 18, lr: 1.98e-03
2022-07-25 17:43:41,695 INFO [train.py:850] (2/4) Epoch 2, batch 1300, loss[loss=0.3834, simple_loss=0.4097, pruned_loss=0.1785, over 7188.00 frames.], tot_loss[loss=0.3262, simple_loss=0.3754, pruned_loss=0.1385, over 1463434.01 frames.], batch size: 18, lr: 1.98e-03
2022-07-25 17:44:25,452 INFO [train.py:850] (2/4) Epoch 2, batch 1350, loss[loss=0.3455, simple_loss=0.3996, pruned_loss=0.1457, over 7175.00 frames.], tot_loss[loss=0.3262, simple_loss=0.375, pruned_loss=0.1387, over 1463031.93 frames.], batch size: 22, lr: 1.97e-03
2022-07-25 17:45:09,326 INFO [train.py:850] (2/4) Epoch 2, batch 1400, loss[loss=0.3449, simple_loss=0.3993, pruned_loss=0.1453, over 7488.00 frames.], tot_loss[loss=0.3254, simple_loss=0.3744, pruned_loss=0.1382, over 1462722.85 frames.], batch size: 23, lr: 1.97e-03
2022-07-25 17:45:53,033 INFO [train.py:850] (2/4) Epoch 2, batch 1450, loss[loss=0.2989, simple_loss=0.3645, pruned_loss=0.1166, over 7376.00 frames.], tot_loss[loss=0.326, simple_loss=0.3745, pruned_loss=0.1387, over 1463476.74 frames.], batch size: 21, lr: 1.97e-03
2022-07-25 17:46:36,022 INFO [train.py:850] (2/4) Epoch 2, batch 1500, loss[loss=0.3003, simple_loss=0.3699, pruned_loss=0.1153, over 7412.00 frames.], tot_loss[loss=0.3237, simple_loss=0.3729, pruned_loss=0.1372, over 1464169.00 frames.], batch size: 22, lr: 1.96e-03
2022-07-25 17:47:20,514 INFO [train.py:850] (2/4) Epoch 2, batch 1550, loss[loss=0.3268, simple_loss=0.3745, pruned_loss=0.1396, over 7291.00 frames.], tot_loss[loss=0.324, simple_loss=0.3737, pruned_loss=0.1372, over 1464814.14 frames.], batch size: 20, lr: 1.96e-03
2022-07-25 17:48:03,457 INFO [train.py:850] (2/4) Epoch 2, batch 1600, loss[loss=0.3332, simple_loss=0.3805, pruned_loss=0.1429, over 7379.00 frames.], tot_loss[loss=0.3213, simple_loss=0.3718, pruned_loss=0.1354, over 1464065.46 frames.], batch size: 21, lr: 1.95e-03
2022-07-25 17:48:47,742 INFO [train.py:850] (2/4) Epoch 2, batch 1650, loss[loss=0.3003, simple_loss=0.3524, pruned_loss=0.1241, over 7174.00 frames.], tot_loss[loss=0.3226, simple_loss=0.3731, pruned_loss=0.136, over 1464844.52 frames.], batch size: 17, lr: 1.95e-03
2022-07-25 17:49:30,620 INFO [train.py:850] (2/4) Epoch 2, batch 1700, loss[loss=0.2965, simple_loss=0.3634, pruned_loss=0.1148, over 7381.00 frames.], tot_loss[loss=0.3202, simple_loss=0.3711, pruned_loss=0.1347, over 1465236.29 frames.], batch size: 20, lr: 1.95e-03
2022-07-25 17:50:14,751 INFO [train.py:850] (2/4) Epoch 2, batch 1750, loss[loss=0.3236, simple_loss=0.3894, pruned_loss=0.1289, over 7281.00 frames.], tot_loss[loss=0.3222, simple_loss=0.3728, pruned_loss=0.1358, over 1465413.88 frames.], batch size: 21, lr: 1.94e-03
2022-07-25 17:50:59,534 INFO [train.py:850] (2/4) Epoch 2, batch 1800, loss[loss=0.2897, simple_loss=0.3502, pruned_loss=0.1146, over 7477.00 frames.], tot_loss[loss=0.3217, simple_loss=0.3726, pruned_loss=0.1354, over 1465786.42 frames.], batch size: 20, lr: 1.94e-03
2022-07-25 17:51:42,947 INFO [train.py:850] (2/4) Epoch 2, batch 1850, loss[loss=0.3087, simple_loss=0.3562, pruned_loss=0.1305, over 7490.00 frames.], tot_loss[loss=0.3187, simple_loss=0.3705, pruned_loss=0.1335, over 1465947.46 frames.], batch size: 19, lr: 1.94e-03
2022-07-25 17:52:26,819 INFO [train.py:850] (2/4) Epoch 2, batch 1900, loss[loss=0.3671, simple_loss=0.4075, pruned_loss=0.1633, over 7212.00 frames.], tot_loss[loss=0.3181, simple_loss=0.3701, pruned_loss=0.1331, over 1465691.62 frames.], batch size: 19, lr: 1.93e-03
2022-07-25 17:53:09,862 INFO [train.py:850] (2/4) Epoch 2, batch 1950, loss[loss=0.2725, simple_loss=0.3223, pruned_loss=0.1114, over 7325.00 frames.], tot_loss[loss=0.318, simple_loss=0.3703, pruned_loss=0.1329, over 1466427.77 frames.], batch size: 17, lr: 1.93e-03
2022-07-25 17:53:54,247 INFO [train.py:850] (2/4) Epoch 2, batch 2000, loss[loss=0.2673, simple_loss=0.3353, pruned_loss=0.09959, over 7305.00 frames.], tot_loss[loss=0.3162, simple_loss=0.369, pruned_loss=0.1317, over 1466339.41 frames.], batch size: 18, lr: 1.92e-03
2022-07-25 17:54:39,088 INFO [train.py:850] (2/4) Epoch 2, batch 2050, loss[loss=0.3486, simple_loss=0.3966, pruned_loss=0.1503, over 7291.00 frames.], tot_loss[loss=0.3161, simple_loss=0.3691, pruned_loss=0.1316, over 1466371.36 frames.], batch size: 27, lr: 1.92e-03
2022-07-25 17:55:23,495 INFO [train.py:850] (2/4) Epoch 2, batch 2100, loss[loss=0.2398, simple_loss=0.3057, pruned_loss=0.08694, over 7223.00 frames.], tot_loss[loss=0.3147, simple_loss=0.3678, pruned_loss=0.1308, over 1465877.38 frames.], batch size: 16, lr: 1.92e-03
2022-07-25 17:56:10,272 INFO [train.py:850] (2/4) Epoch 2, batch 2150, loss[loss=0.3191, simple_loss=0.3736, pruned_loss=0.1323, over 7422.00 frames.], tot_loss[loss=0.3136, simple_loss=0.3668, pruned_loss=0.1302, over 1465696.48 frames.], batch size: 22, lr: 1.91e-03
2022-07-25 17:56:53,607 INFO [train.py:850] (2/4) Epoch 2, batch 2200, loss[loss=0.2747, simple_loss=0.3228, pruned_loss=0.1133, over 7447.00 frames.], tot_loss[loss=0.315, simple_loss=0.3682, pruned_loss=0.1309, over 1465183.29 frames.], batch size: 17, lr: 1.91e-03
2022-07-25 17:57:36,617 INFO [train.py:850] (2/4) Epoch 2, batch 2250, loss[loss=0.2627, simple_loss=0.3374, pruned_loss=0.09398, over 7485.00 frames.], tot_loss[loss=0.3118, simple_loss=0.3658, pruned_loss=0.1289, over 1465583.40 frames.], batch size: 20, lr: 1.91e-03
2022-07-25 17:58:20,880 INFO [train.py:850] (2/4) Epoch 2, batch 2300, loss[loss=0.3667, simple_loss=0.4136, pruned_loss=0.1599, over 7454.00 frames.], tot_loss[loss=0.3126, simple_loss=0.3667, pruned_loss=0.1293, over 1464462.67 frames.], batch size: 24, lr: 1.90e-03
2022-07-25 17:59:04,604 INFO [train.py:850] (2/4) Epoch 2, batch 2350, loss[loss=0.2891, simple_loss=0.3599, pruned_loss=0.1091, over 7477.00 frames.], tot_loss[loss=0.3126, simple_loss=0.3668, pruned_loss=0.1292, over 1463742.67 frames.], batch size: 21, lr: 1.90e-03
2022-07-25 17:59:49,178 INFO [train.py:850] (2/4) Epoch 2, batch 2400, loss[loss=0.4113, simple_loss=0.4207, pruned_loss=0.201, over 7195.00 frames.], tot_loss[loss=0.3135, simple_loss=0.3678, pruned_loss=0.1296, over 1463719.88 frames.], batch size: 18, lr: 1.90e-03
2022-07-25 18:00:32,376 INFO [train.py:850] (2/4) Epoch 2, batch 2450, loss[loss=0.2778, simple_loss=0.3415, pruned_loss=0.107, over 7175.00 frames.], tot_loss[loss=0.3119, simple_loss=0.3666, pruned_loss=0.1286, over 1464103.88 frames.], batch size: 17, lr: 1.89e-03
2022-07-25 18:01:16,182 INFO [train.py:850] (2/4) Epoch 2, batch 2500, loss[loss=0.2789, simple_loss=0.3451, pruned_loss=0.1064, over 7193.00 frames.], tot_loss[loss=0.313, simple_loss=0.3674, pruned_loss=0.1293, over 1464314.78 frames.], batch size: 19, lr: 1.89e-03
2022-07-25 18:02:00,037 INFO [train.py:850] (2/4) Epoch 2, batch 2550, loss[loss=0.3619, simple_loss=0.3911, pruned_loss=0.1664, over 7419.00 frames.], tot_loss[loss=0.3131, simple_loss=0.3674, pruned_loss=0.1294, over 1464410.03 frames.], batch size: 66, lr: 1.89e-03
2022-07-25 18:02:43,035 INFO [train.py:850] (2/4) Epoch 2, batch 2600, loss[loss=0.2778, simple_loss=0.3389, pruned_loss=0.1083, over 7194.00 frames.], tot_loss[loss=0.3125, simple_loss=0.367, pruned_loss=0.1291, over 1465033.05 frames.], batch size: 19, lr: 1.88e-03
2022-07-25 18:03:27,184 INFO [train.py:850] (2/4) Epoch 2, batch 2650, loss[loss=0.3231, simple_loss=0.3742, pruned_loss=0.136, over 7481.00 frames.], tot_loss[loss=0.3105, simple_loss=0.3651, pruned_loss=0.1279, over 1466031.97 frames.], batch size: 24, lr: 1.88e-03
2022-07-25 18:04:10,599 INFO [train.py:850] (2/4) Epoch 2, batch 2700, loss[loss=0.3045, simple_loss=0.3789, pruned_loss=0.1151, over 7264.00 frames.], tot_loss[loss=0.3099, simple_loss=0.3652, pruned_loss=0.1273, over 1465840.75 frames.], batch size: 27, lr: 1.87e-03
2022-07-25 18:04:54,931 INFO [train.py:850] (2/4) Epoch 2, batch 2750, loss[loss=0.2703, simple_loss=0.3192, pruned_loss=0.1107, over 7160.00 frames.], tot_loss[loss=0.3098, simple_loss=0.3648, pruned_loss=0.1273, over 1466003.60 frames.], batch size: 17, lr: 1.87e-03
2022-07-25 18:05:38,145 INFO [train.py:850] (2/4) Epoch 2, batch 2800, loss[loss=0.2958, simple_loss=0.3601, pruned_loss=0.1157, over 7189.00 frames.], tot_loss[loss=0.3095, simple_loss=0.3651, pruned_loss=0.127, over 1466454.10 frames.], batch size: 20, lr: 1.87e-03
2022-07-25 18:06:21,596 INFO [train.py:850] (2/4) Epoch 2, batch 2850, loss[loss=0.3627, simple_loss=0.3978, pruned_loss=0.1638, over 7370.00 frames.], tot_loss[loss=0.3079, simple_loss=0.3633, pruned_loss=0.1262, over 1465563.44 frames.], batch size: 70, lr: 1.86e-03
2022-07-25 18:07:06,193 INFO [train.py:850] (2/4) Epoch 2, batch 2900, loss[loss=0.3465, simple_loss=0.4015, pruned_loss=0.1457, over 7450.00 frames.], tot_loss[loss=0.3078, simple_loss=0.3631, pruned_loss=0.1262, over 1465265.98 frames.], batch size: 67, lr: 1.86e-03
2022-07-25 18:07:50,091 INFO [train.py:850] (2/4) Epoch 2, batch 2950, loss[loss=0.3362, simple_loss=0.384, pruned_loss=0.1442, over 7223.00 frames.], tot_loss[loss=0.3076, simple_loss=0.3629, pruned_loss=0.1262, over 1465241.65 frames.], batch size: 24, lr: 1.86e-03
2022-07-25 18:08:33,321 INFO [train.py:850] (2/4) Epoch 2, batch 3000, loss[loss=0.2667, simple_loss=0.3199, pruned_loss=0.1067, over 7228.00 frames.], tot_loss[loss=0.3058, simple_loss=0.3614, pruned_loss=0.1251, over 1464676.98 frames.], batch size: 16, lr: 1.85e-03
2022-07-25 18:08:33,322 INFO [train.py:870] (2/4) Computing validation loss
2022-07-25 18:08:56,217 INFO [train.py:879] (2/4) Epoch 2, validation: loss=0.2709, simple_loss=0.3481, pruned_loss=0.0969, over 924787.00 frames.
2022-07-25 18:09:40,215 INFO [train.py:850] (2/4) Epoch 2, batch 3050, loss[loss=0.2716, simple_loss=0.3363, pruned_loss=0.1035, over 7199.00 frames.], tot_loss[loss=0.307, simple_loss=0.3623, pruned_loss=0.1258, over 1464171.93 frames.], batch size: 20, lr: 1.85e-03
2022-07-25 18:10:24,260 INFO [train.py:850] (2/4) Epoch 2, batch 3100, loss[loss=0.2686, simple_loss=0.3279, pruned_loss=0.1047, over 7312.00 frames.], tot_loss[loss=0.307, simple_loss=0.3625, pruned_loss=0.1258, over 1464286.87 frames.], batch size: 18, lr: 1.85e-03
2022-07-25 18:11:23,410 INFO [train.py:850] (2/4) Epoch 2, batch 3150, loss[loss=0.3441, simple_loss=0.3804, pruned_loss=0.1539, over 7383.00 frames.], tot_loss[loss=0.3059, simple_loss=0.3617, pruned_loss=0.1251, over 1464289.08 frames.], batch size: 21, lr: 1.84e-03
2022-07-25 18:12:07,105 INFO [train.py:850] (2/4) Epoch 2, batch 3200, loss[loss=0.2618, simple_loss=0.3201, pruned_loss=0.1017, over 7448.00 frames.], tot_loss[loss=0.3067, simple_loss=0.3623, pruned_loss=0.1255, over 1465414.60 frames.], batch size: 17, lr: 1.84e-03
2022-07-25 18:12:50,806 INFO [train.py:850] (2/4) Epoch 2, batch 3250, loss[loss=0.3086, simple_loss=0.3652, pruned_loss=0.126, over 7294.00 frames.], tot_loss[loss=0.3067, simple_loss=0.3627, pruned_loss=0.1254, over 1465803.71 frames.], batch size: 19, lr: 1.84e-03
2022-07-25 18:13:33,832 INFO [train.py:850] (2/4) Epoch 2, batch 3300, loss[loss=0.3878, simple_loss=0.4146, pruned_loss=0.1805, over 7175.00 frames.], tot_loss[loss=0.3069, simple_loss=0.3629, pruned_loss=0.1255, over 1465671.65 frames.], batch size: 21, lr: 1.84e-03
2022-07-25 18:14:19,364 INFO [train.py:850] (2/4) Epoch 2, batch 3350, loss[loss=0.251, simple_loss=0.3211, pruned_loss=0.09045, over 7398.00 frames.], tot_loss[loss=0.3062, simple_loss=0.3624, pruned_loss=0.125, over 1466272.20 frames.], batch size: 19, lr: 1.83e-03
2022-07-25 18:15:03,599 INFO [train.py:850] (2/4) Epoch 2, batch 3400, loss[loss=0.3157, simple_loss=0.3818, pruned_loss=0.1248, over 7242.00 frames.], tot_loss[loss=0.307, simple_loss=0.363, pruned_loss=0.1255, over 1465941.77 frames.], batch size: 24, lr: 1.83e-03
2022-07-25 18:15:47,150 INFO [train.py:850] (2/4) Epoch 2, batch 3450, loss[loss=0.3012, simple_loss=0.3635, pruned_loss=0.1194, over 7427.00 frames.], tot_loss[loss=0.3051, simple_loss=0.3618, pruned_loss=0.1242, over 1466638.10 frames.], batch size: 22, lr: 1.83e-03
2022-07-25 18:16:30,738 INFO [train.py:850] (2/4) Epoch 2, batch 3500, loss[loss=0.2635, simple_loss=0.316, pruned_loss=0.1056, over 7291.00 frames.], tot_loss[loss=0.3073, simple_loss=0.3635, pruned_loss=0.1255, over 1466401.46 frames.], batch size: 17, lr: 1.82e-03
2022-07-25 18:17:14,754 INFO [train.py:850] (2/4) Epoch 2, batch 3550, loss[loss=0.2533, simple_loss=0.3143, pruned_loss=0.09614, over 7297.00 frames.], tot_loss[loss=0.3058, simple_loss=0.3624, pruned_loss=0.1246, over 1467144.60 frames.], batch size: 17, lr: 1.82e-03
2022-07-25 18:17:58,829 INFO [train.py:850] (2/4) Epoch 2, batch 3600, loss[loss=0.3076, simple_loss=0.3558, pruned_loss=0.1297, over 7381.00 frames.], tot_loss[loss=0.307, simple_loss=0.3634, pruned_loss=0.1253, over 1466855.90 frames.], batch size: 20, lr: 1.82e-03
2022-07-25 18:18:42,677 INFO [train.py:850] (2/4) Epoch 2, batch 3650, loss[loss=0.285, simple_loss=0.3586, pruned_loss=0.1057, over 7177.00 frames.], tot_loss[loss=0.3064, simple_loss=0.3633, pruned_loss=0.1247, over 1466840.62 frames.], batch size: 22, lr: 1.81e-03
2022-07-25 18:19:25,833 INFO [train.py:850] (2/4) Epoch 2, batch 3700, loss[loss=0.3734, simple_loss=0.4057, pruned_loss=0.1706, over 7477.00 frames.], tot_loss[loss=0.3072, simple_loss=0.3636, pruned_loss=0.1254, over 1467496.18 frames.], batch size: 24, lr: 1.81e-03
2022-07-25 18:20:09,292 INFO [train.py:850] (2/4) Epoch 2, batch 3750, loss[loss=0.2289, simple_loss=0.2945, pruned_loss=0.08165, over 7172.00 frames.], tot_loss[loss=0.3045, simple_loss=0.3613, pruned_loss=0.1239, over 1466556.23 frames.], batch size: 17, lr: 1.81e-03
2022-07-25 18:20:52,885 INFO [train.py:850] (2/4) Epoch 2, batch 3800, loss[loss=0.3213, simple_loss=0.3846, pruned_loss=0.129, over 7469.00 frames.], tot_loss[loss=0.3039, simple_loss=0.361, pruned_loss=0.1234, over 1466970.52 frames.], batch size: 39, lr: 1.80e-03
2022-07-25 18:21:37,140 INFO [train.py:850] (2/4) Epoch 2, batch 3850, loss[loss=0.3161, simple_loss=0.3476, pruned_loss=0.1423, over 7287.00 frames.], tot_loss[loss=0.3021, simple_loss=0.3595, pruned_loss=0.1223, over 1467564.75 frames.], batch size: 16, lr: 1.80e-03
2022-07-25 18:22:20,577 INFO [train.py:850] (2/4) Epoch 2, batch 3900, loss[loss=0.3004, simple_loss=0.3625, pruned_loss=0.1192, over 7477.00 frames.], tot_loss[loss=0.2998, simple_loss=0.3577, pruned_loss=0.1209, over 1466485.02 frames.], batch size: 20, lr: 1.80e-03
2022-07-25 18:23:04,163 INFO [train.py:850] (2/4) Epoch 2, batch 3950, loss[loss=0.284, simple_loss=0.3497, pruned_loss=0.1092, over 7199.00 frames.], tot_loss[loss=0.2996, simple_loss=0.3576, pruned_loss=0.1208, over 1466571.89 frames.], batch size: 18, lr: 1.79e-03
2022-07-25 18:23:47,401 INFO [train.py:850] (2/4) Epoch 2, batch 4000, loss[loss=0.3443, simple_loss=0.3987, pruned_loss=0.1449, over 7202.00 frames.], tot_loss[loss=0.2999, simple_loss=0.3578, pruned_loss=0.121, over 1466019.97 frames.], batch size: 20, lr: 1.79e-03
2022-07-25 18:24:31,473 INFO [train.py:850] (2/4) Epoch 2, batch 4050, loss[loss=0.3825, simple_loss=0.4264, pruned_loss=0.1693, over 7288.00 frames.], tot_loss[loss=0.302, simple_loss=0.3593, pruned_loss=0.1224, over 1465789.36 frames.], batch size: 20, lr: 1.79e-03
2022-07-25 18:25:15,364 INFO [train.py:850] (2/4) Epoch 2, batch 4100, loss[loss=0.3546, simple_loss=0.4009, pruned_loss=0.1542, over 7467.00 frames.], tot_loss[loss=0.3056, simple_loss=0.3617, pruned_loss=0.1247, over 1467429.81 frames.], batch size: 21, lr: 1.79e-03
2022-07-25 18:25:59,338 INFO [train.py:850] (2/4) Epoch 2, batch 4150, loss[loss=0.3608, simple_loss=0.4055, pruned_loss=0.1581, over 7384.00 frames.], tot_loss[loss=0.3105, simple_loss=0.3644, pruned_loss=0.1283, over 1468157.84 frames.], batch size: 20, lr: 1.78e-03
2022-07-25 18:26:43,183 INFO [train.py:850] (2/4) Epoch 2, batch 4200, loss[loss=0.3773, simple_loss=0.4163, pruned_loss=0.1692, over 7364.00 frames.], tot_loss[loss=0.3144, simple_loss=0.3667, pruned_loss=0.1311, over 1469039.07 frames.], batch size: 31, lr: 1.78e-03
2022-07-25 18:27:27,170 INFO [train.py:850] (2/4) Epoch 2, batch 4250, loss[loss=0.3747, simple_loss=0.4017, pruned_loss=0.1738, over 7295.00 frames.], tot_loss[loss=0.3167, simple_loss=0.3674, pruned_loss=0.133, over 1468311.56 frames.], batch size: 19, lr: 1.78e-03
2022-07-25 18:28:11,910 INFO [train.py:850] (2/4) Epoch 2, batch 4300, loss[loss=0.3141, simple_loss=0.3644, pruned_loss=0.1319, over 7380.00 frames.], tot_loss[loss=0.3211, simple_loss=0.3697, pruned_loss=0.1363, over 1467416.68 frames.], batch size: 21, lr: 1.77e-03
2022-07-25 18:28:56,354 INFO [train.py:850] (2/4) Epoch 2, batch 4350, loss[loss=0.3348, simple_loss=0.3668, pruned_loss=0.1514, over 7444.00 frames.], tot_loss[loss=0.3254, simple_loss=0.3718, pruned_loss=0.1396, over 1466858.64 frames.], batch size: 18, lr: 1.77e-03
2022-07-25 18:29:40,788 INFO [train.py:850] (2/4) Epoch 2, batch 4400, loss[loss=0.2946, simple_loss=0.3371, pruned_loss=0.1261, over 7163.00 frames.], tot_loss[loss=0.3261, simple_loss=0.3709, pruned_loss=0.1406, over 1466622.13 frames.], batch size: 17, lr: 1.77e-03
2022-07-25 18:30:25,462 INFO [train.py:850] (2/4) Epoch 2, batch 4450, loss[loss=0.3254, simple_loss=0.3553, pruned_loss=0.1477, over 7448.00 frames.], tot_loss[loss=0.332, simple_loss=0.374, pruned_loss=0.1449, over 1467092.76 frames.], batch size: 17, lr: 1.76e-03
2022-07-25 18:31:08,731 INFO [train.py:850] (2/4) Epoch 2, batch 4500, loss[loss=0.3515, simple_loss=0.3872, pruned_loss=0.1579, over 7362.00 frames.], tot_loss[loss=0.3331, simple_loss=0.3744, pruned_loss=0.1459, over 1465522.57 frames.], batch size: 23, lr: 1.76e-03
2022-07-25 18:31:53,673 INFO [train.py:850] (2/4) Epoch 2, batch 4550, loss[loss=0.3852, simple_loss=0.3881, pruned_loss=0.1912, over 7302.00 frames.], tot_loss[loss=0.3382, simple_loss=0.3777, pruned_loss=0.1493, over 1465565.17 frames.], batch size: 17, lr: 1.76e-03
2022-07-25 18:32:37,821 INFO [train.py:850] (2/4) Epoch 2, batch 4600, loss[loss=0.3746, simple_loss=0.4101, pruned_loss=0.1695, over 7324.00 frames.], tot_loss[loss=0.3411, simple_loss=0.3796, pruned_loss=0.1513, over 1465897.50 frames.], batch size: 38, lr: 1.76e-03
2022-07-25 18:33:22,684 INFO [train.py:850] (2/4) Epoch 2, batch 4650, loss[loss=0.3298, simple_loss=0.3796, pruned_loss=0.14, over 7199.00 frames.], tot_loss[loss=0.3428, simple_loss=0.3805, pruned_loss=0.1525, over 1465710.66 frames.], batch size: 20, lr: 1.75e-03
2022-07-25 18:34:05,325 INFO [train.py:850] (2/4) Epoch 2, batch 4700, loss[loss=0.4437, simple_loss=0.4625, pruned_loss=0.2125, over 7295.00 frames.], tot_loss[loss=0.3458, simple_loss=0.3826, pruned_loss=0.1545, over 1465813.81 frames.], batch size: 20, lr: 1.75e-03
2022-07-25 18:34:50,493 INFO [train.py:850] (2/4) Epoch 2, batch 4750, loss[loss=0.354, simple_loss=0.3945, pruned_loss=0.1567, over 7407.00 frames.], tot_loss[loss=0.347, simple_loss=0.383, pruned_loss=0.1555, over 1466452.21 frames.], batch size: 22, lr: 1.75e-03
2022-07-25 18:35:34,257 INFO [train.py:850] (2/4) Epoch 2, batch 4800, loss[loss=0.3123, simple_loss=0.3578, pruned_loss=0.1333, over 7188.00 frames.], tot_loss[loss=0.3468, simple_loss=0.3822, pruned_loss=0.1557, over 1466446.61 frames.], batch size: 18, lr: 1.74e-03
2022-07-25 18:36:18,390 INFO [train.py:850] (2/4) Epoch 2, batch 4850, loss[loss=0.3484, simple_loss=0.3658, pruned_loss=0.1655, over 7487.00 frames.], tot_loss[loss=0.3459, simple_loss=0.3817, pruned_loss=0.1551, over 1465891.57 frames.], batch size: 19, lr: 1.74e-03
2022-07-25 18:37:01,765 INFO [train.py:850] (2/4) Epoch 2, batch 4900, loss[loss=0.4172, simple_loss=0.4246, pruned_loss=0.2049, over 7203.00 frames.], tot_loss[loss=0.3458, simple_loss=0.3816, pruned_loss=0.155, over 1464746.52 frames.], batch size: 25, lr: 1.74e-03
2022-07-25 18:37:45,412 INFO [train.py:850] (2/4) Epoch 2, batch 4950, loss[loss=0.3757, simple_loss=0.3986, pruned_loss=0.1764, over 7282.00 frames.], tot_loss[loss=0.3461, simple_loss=0.3812, pruned_loss=0.1554, over 1464846.08 frames.], batch size: 21, lr: 1.74e-03
2022-07-25 18:38:29,199 INFO [train.py:850] (2/4) Epoch 2, batch 5000, loss[loss=0.3048, simple_loss=0.3684, pruned_loss=0.1205, over 7375.00 frames.], tot_loss[loss=0.3466, simple_loss=0.3818, pruned_loss=0.1557, over 1464662.43 frames.], batch size: 21, lr: 1.73e-03
2022-07-25 18:39:13,773 INFO [train.py:850] (2/4) Epoch 2, batch 5050, loss[loss=0.269, simple_loss=0.3174, pruned_loss=0.1102, over 7158.00 frames.], tot_loss[loss=0.3457, simple_loss=0.381, pruned_loss=0.1551, over 1465045.83 frames.], batch size: 17, lr: 1.73e-03
2022-07-25 18:39:56,479 INFO [train.py:850] (2/4) Epoch 2, batch 5100, loss[loss=0.3349, simple_loss=0.3879, pruned_loss=0.141, over 7307.00 frames.], tot_loss[loss=0.3479, simple_loss=0.3826, pruned_loss=0.1565, over 1464977.05 frames.], batch size: 22, lr: 1.73e-03
2022-07-25 18:40:41,758 INFO [train.py:850] (2/4) Epoch 2, batch 5150, loss[loss=0.3342, simple_loss=0.368, pruned_loss=0.1502, over 7205.00 frames.], tot_loss[loss=0.3462, simple_loss=0.3807, pruned_loss=0.1558, over 1465241.06 frames.], batch size: 19, lr: 1.73e-03
2022-07-25 18:41:26,307 INFO [train.py:850] (2/4) Epoch 2, batch 5200, loss[loss=0.3139, simple_loss=0.3464, pruned_loss=0.1407, over 7495.00 frames.], tot_loss[loss=0.3462, simple_loss=0.3805, pruned_loss=0.1559, over 1464672.15 frames.], batch size: 19, lr: 1.72e-03
2022-07-25 18:42:10,742 INFO [train.py:850] (2/4) Epoch 2, batch 5250, loss[loss=0.3528, simple_loss=0.3878, pruned_loss=0.1589, over 7231.00 frames.], tot_loss[loss=0.3454, simple_loss=0.3801, pruned_loss=0.1554, over 1464595.02 frames.], batch size: 25, lr: 1.72e-03
2022-07-25 18:42:54,582 INFO [train.py:850] (2/4) Epoch 2, batch 5300, loss[loss=0.3101, simple_loss=0.3422, pruned_loss=0.139, over 7306.00 frames.], tot_loss[loss=0.3459, simple_loss=0.3808, pruned_loss=0.1555, over 1464092.54 frames.], batch size: 17, lr: 1.72e-03
2022-07-25 18:43:39,191 INFO [train.py:850] (2/4) Epoch 2, batch 5350, loss[loss=0.3178, simple_loss=0.3501, pruned_loss=0.1427, over 7197.00 frames.], tot_loss[loss=0.3452, simple_loss=0.3802, pruned_loss=0.1551, over 1464176.28 frames.], batch size: 18, lr: 1.71e-03
2022-07-25 18:44:22,599 INFO [train.py:850] (2/4) Epoch 2, batch 5400, loss[loss=0.3091, simple_loss=0.3504, pruned_loss=0.1339, over 7312.00 frames.], tot_loss[loss=0.3393, simple_loss=0.3756, pruned_loss=0.1515, over 1463655.80 frames.], batch size: 18, lr: 1.71e-03
2022-07-25 18:45:07,737 INFO [train.py:850] (2/4) Epoch 2, batch 5450, loss[loss=0.3, simple_loss=0.3291, pruned_loss=0.1355, over 7353.00 frames.], tot_loss[loss=0.339, simple_loss=0.3751, pruned_loss=0.1514, over 1465126.21 frames.], batch size: 16, lr: 1.71e-03
2022-07-25 18:45:51,385 INFO [train.py:850] (2/4) Epoch 2, batch 5500, loss[loss=0.3246, simple_loss=0.3677, pruned_loss=0.1408, over 7192.00 frames.], tot_loss[loss=0.3426, simple_loss=0.3779, pruned_loss=0.1537, over 1465689.67 frames.], batch size: 20, lr: 1.71e-03
2022-07-25 18:46:34,897 INFO [train.py:850] (2/4) Epoch 2, batch 5550, loss[loss=0.3334, simple_loss=0.3765, pruned_loss=0.1452, over 7199.00 frames.], tot_loss[loss=0.3421, simple_loss=0.3783, pruned_loss=0.153, over 1466017.04 frames.], batch size: 20, lr: 1.70e-03
2022-07-25 18:47:18,346 INFO [train.py:850] (2/4) Epoch 2, batch 5600, loss[loss=0.3977, simple_loss=0.4257, pruned_loss=0.1848, over 7220.00 frames.], tot_loss[loss=0.342, simple_loss=0.3783, pruned_loss=0.1529, over 1465618.27 frames.], batch size: 24, lr: 1.70e-03
2022-07-25 18:48:02,378 INFO [train.py:850] (2/4) Epoch 2, batch 5650, loss[loss=0.2782, simple_loss=0.3178, pruned_loss=0.1193, over 7103.00 frames.], tot_loss[loss=0.3413, simple_loss=0.3781, pruned_loss=0.1523, over 1465609.88 frames.], batch size: 18, lr: 1.70e-03
2022-07-25 18:48:45,452 INFO [train.py:850] (2/4) Epoch 2, batch 5700, loss[loss=0.3045, simple_loss=0.346, pruned_loss=0.1315, over 7105.00 frames.], tot_loss[loss=0.3396, simple_loss=0.3769, pruned_loss=0.1511, over 1465293.19 frames.], batch size: 18, lr: 1.70e-03
2022-07-25 18:49:29,704 INFO [train.py:850] (2/4) Epoch 2, batch 5750, loss[loss=0.4113, simple_loss=0.423, pruned_loss=0.1998, over 7386.00 frames.], tot_loss[loss=0.3404, simple_loss=0.3779, pruned_loss=0.1514, over 1465233.61 frames.], batch size: 19, lr: 1.69e-03
2022-07-25 18:50:14,194 INFO [train.py:850] (2/4) Epoch 2, batch 5800, loss[loss=0.3095, simple_loss=0.3625, pruned_loss=0.1283, over 7386.00 frames.], tot_loss[loss=0.3389, simple_loss=0.3766, pruned_loss=0.1506, over 1465813.64 frames.], batch size: 39, lr: 1.69e-03
2022-07-25 18:50:59,503 INFO [train.py:850] (2/4) Epoch 2, batch 5850, loss[loss=0.3226, simple_loss=0.3713, pruned_loss=0.1369, over 7152.00 frames.], tot_loss[loss=0.3381, simple_loss=0.3765, pruned_loss=0.1499, over 1465971.23 frames.], batch size: 17, lr: 1.69e-03
2022-07-25 18:51:42,731 INFO [train.py:850] (2/4) Epoch 2, batch 5900, loss[loss=0.3082, simple_loss=0.3633, pruned_loss=0.1266, over 7368.00 frames.], tot_loss[loss=0.3347, simple_loss=0.3737, pruned_loss=0.1479, over 1465919.94 frames.], batch size: 21, lr: 1.69e-03
2022-07-25 18:52:27,424 INFO [train.py:850] (2/4) Epoch 2, batch 5950, loss[loss=0.3125, simple_loss=0.3671, pruned_loss=0.129, over 7361.00 frames.], tot_loss[loss=0.3321, simple_loss=0.372, pruned_loss=0.1461, over 1465648.11 frames.], batch size: 23, lr: 1.68e-03
2022-07-25 18:53:11,521 INFO [train.py:850] (2/4) Epoch 2, batch 6000, loss[loss=0.3461, simple_loss=0.3906, pruned_loss=0.1508, over 7226.00 frames.], tot_loss[loss=0.3316, simple_loss=0.3717, pruned_loss=0.1458, over 1465113.79 frames.], batch size: 24, lr: 1.68e-03
2022-07-25 18:53:11,522 INFO [train.py:870] (2/4) Computing validation loss
2022-07-25 18:53:34,728 INFO [train.py:879] (2/4) Epoch 2, validation: loss=0.2454, simple_loss=0.3332, pruned_loss=0.07878, over 924787.00 frames.
2022-07-25 18:54:19,589 INFO [train.py:850] (2/4) Epoch 2, batch 6050, loss[loss=0.2498, simple_loss=0.3029, pruned_loss=0.09832, over 7459.00 frames.], tot_loss[loss=0.332, simple_loss=0.3714, pruned_loss=0.1463, over 1465735.69 frames.], batch size: 17, lr: 1.68e-03
2022-07-25 18:55:02,867 INFO [train.py:850] (2/4) Epoch 2, batch 6100, loss[loss=0.3236, simple_loss=0.3798, pruned_loss=0.1337, over 7366.00 frames.], tot_loss[loss=0.3323, simple_loss=0.3722, pruned_loss=0.1462, over 1465892.84 frames.], batch size: 23, lr: 1.68e-03
2022-07-25 18:55:48,388 INFO [train.py:850] (2/4) Epoch 2, batch 6150, loss[loss=0.3565, simple_loss=0.3888, pruned_loss=0.1621, over 7453.00 frames.], tot_loss[loss=0.3336, simple_loss=0.373, pruned_loss=0.1471, over 1465644.43 frames.], batch size: 71, lr: 1.67e-03
2022-07-25 18:56:33,576 INFO [train.py:850] (2/4) Epoch 2, batch 6200, loss[loss=0.3705, simple_loss=0.3963, pruned_loss=0.1724, over 7403.00 frames.], tot_loss[loss=0.3355, simple_loss=0.3746, pruned_loss=0.1482, over 1465836.76 frames.], batch size: 31, lr: 1.67e-03
2022-07-25 18:57:18,790 INFO [train.py:850] (2/4) Epoch 2, batch 6250, loss[loss=0.3154, simple_loss=0.3551, pruned_loss=0.1378, over 7396.00 frames.], tot_loss[loss=0.334, simple_loss=0.3737, pruned_loss=0.1472, over 1465484.71 frames.], batch size: 19, lr: 1.67e-03
2022-07-25 18:58:03,114 INFO [train.py:850] (2/4) Epoch 2, batch 6300, loss[loss=0.3151, simple_loss=0.3483, pruned_loss=0.141, over 7326.00 frames.], tot_loss[loss=0.3341, simple_loss=0.3739, pruned_loss=0.1471, over 1465215.63 frames.], batch size: 18, lr: 1.67e-03
2022-07-25 18:58:47,547 INFO [train.py:850] (2/4) Epoch 2, batch 6350, loss[loss=0.3087, simple_loss=0.3662, pruned_loss=0.1256, over 7480.00 frames.], tot_loss[loss=0.3348, simple_loss=0.375, pruned_loss=0.1473, over 1465556.31 frames.], batch size: 24, lr: 1.66e-03
2022-07-25 18:59:31,287 INFO [train.py:850] (2/4) Epoch 2, batch 6400, loss[loss=0.3331, simple_loss=0.3818, pruned_loss=0.1422, over 7177.00 frames.], tot_loss[loss=0.3317, simple_loss=0.373, pruned_loss=0.1452, over 1465349.20 frames.], batch size: 22, lr: 1.66e-03
2022-07-25 19:00:14,840 INFO [train.py:850] (2/4) Epoch 2, batch 6450, loss[loss=0.3244, simple_loss=0.3732, pruned_loss=0.1378, over 7180.00 frames.], tot_loss[loss=0.3304, simple_loss=0.372, pruned_loss=0.1444, over 1466118.22 frames.], batch size: 21, lr: 1.66e-03
2022-07-25 19:00:58,295 INFO [train.py:850] (2/4) Epoch 2, batch 6500, loss[loss=0.3174, simple_loss=0.3586, pruned_loss=0.1381, over 7192.00 frames.], tot_loss[loss=0.3306, simple_loss=0.3721, pruned_loss=0.1445, over 1466038.69 frames.], batch size: 19, lr: 1.66e-03
2022-07-25 19:01:44,511 INFO [train.py:850] (2/4) Epoch 2, batch 6550, loss[loss=0.3105, simple_loss=0.3535, pruned_loss=0.1337, over 7306.00 frames.], tot_loss[loss=0.3304, simple_loss=0.3723, pruned_loss=0.1443, over 1466509.84 frames.], batch size: 18, lr: 1.65e-03
2022-07-25 19:02:27,638 INFO [train.py:850] (2/4) Epoch 2, batch 6600, loss[loss=0.3274, simple_loss=0.3692, pruned_loss=0.1428, over 7286.00 frames.], tot_loss[loss=0.3303, simple_loss=0.3724, pruned_loss=0.1441, over 1466305.24 frames.], batch size: 20, lr: 1.65e-03
2022-07-25 19:03:12,825 INFO [train.py:850] (2/4) Epoch 2, batch 6650, loss[loss=0.3172, simple_loss=0.3669, pruned_loss=0.1338, over 7454.00 frames.], tot_loss[loss=0.331, simple_loss=0.3726, pruned_loss=0.1447, over 1465928.88 frames.], batch size: 39, lr: 1.65e-03
2022-07-25 19:03:56,495 INFO [train.py:850] (2/4) Epoch 2, batch 6700, loss[loss=0.2784, simple_loss=0.3214, pruned_loss=0.1177, over 7328.00 frames.], tot_loss[loss=0.3317, simple_loss=0.3733, pruned_loss=0.1451, over 1465057.96 frames.], batch size: 18, lr: 1.65e-03
2022-07-25 19:04:40,597 INFO [train.py:850] (2/4) Epoch 2, batch 6750, loss[loss=0.2844, simple_loss=0.3298, pruned_loss=0.1195, over 7445.00 frames.], tot_loss[loss=0.3331, simple_loss=0.374, pruned_loss=0.1461, over 1465114.84 frames.], batch size: 17, lr: 1.64e-03
2022-07-25 19:05:24,038 INFO [train.py:850] (2/4) Epoch 2, batch 6800, loss[loss=0.2778, simple_loss=0.3428, pruned_loss=0.1064, over 7475.00 frames.], tot_loss[loss=0.3328, simple_loss=0.3739, pruned_loss=0.1459, over 1465468.38 frames.], batch size: 21, lr: 1.64e-03
2022-07-25 19:06:08,013 INFO [train.py:850] (2/4) Epoch 2, batch 6850, loss[loss=0.3248, simple_loss=0.3739, pruned_loss=0.1378, over 7175.00 frames.], tot_loss[loss=0.3316, simple_loss=0.3731, pruned_loss=0.145, over 1466045.29 frames.], batch size: 21, lr: 1.64e-03
2022-07-25 19:06:52,771 INFO [train.py:850] (2/4) Epoch 2, batch 6900, loss[loss=0.3434, simple_loss=0.3858, pruned_loss=0.1505, over 7329.00 frames.], tot_loss[loss=0.3316, simple_loss=0.3734, pruned_loss=0.1449, over 1465858.93 frames.], batch size: 39, lr: 1.64e-03
2022-07-25 19:07:36,842 INFO [train.py:850] (2/4) Epoch 2, batch 6950, loss[loss=0.3119, simple_loss=0.3735, pruned_loss=0.1252, over 7272.00 frames.], tot_loss[loss=0.3314, simple_loss=0.3731, pruned_loss=0.1449, over 1466390.65 frames.], batch size: 27, lr: 1.63e-03
2022-07-25 19:08:20,651 INFO [train.py:850] (2/4) Epoch 2, batch 7000, loss[loss=0.3702, simple_loss=0.4018, pruned_loss=0.1693, over 7260.00 frames.], tot_loss[loss=0.3335, simple_loss=0.3742, pruned_loss=0.1464, over 1466006.46 frames.], batch size: 27, lr: 1.63e-03
2022-07-25 19:09:05,594 INFO [train.py:850] (2/4) Epoch 2, batch 7050, loss[loss=0.2615, simple_loss=0.3039, pruned_loss=0.1095, over 7303.00 frames.], tot_loss[loss=0.3319, simple_loss=0.3728, pruned_loss=0.1455, over 1465476.64 frames.], batch size: 16, lr: 1.63e-03
2022-07-25 19:09:49,099 INFO [train.py:850] (2/4) Epoch 2, batch 7100, loss[loss=0.3012, simple_loss=0.3559, pruned_loss=0.1233, over 7477.00 frames.], tot_loss[loss=0.3295, simple_loss=0.372, pruned_loss=0.1435, over 1466549.90 frames.], batch size: 21, lr: 1.63e-03
2022-07-25 19:10:48,443 INFO [train.py:850] (2/4) Epoch 2, batch 7150, loss[loss=0.335, simple_loss=0.3798, pruned_loss=0.1451, over 7476.00 frames.], tot_loss[loss=0.3289, simple_loss=0.3717, pruned_loss=0.143, over 1465902.23 frames.], batch size: 26, lr: 1.63e-03
2022-07-25 19:11:32,160 INFO [train.py:850] (2/4) Epoch 2, batch 7200, loss[loss=0.37, simple_loss=0.4071, pruned_loss=0.1665, over 7214.00 frames.], tot_loss[loss=0.3279, simple_loss=0.3711, pruned_loss=0.1424, over 1466814.55 frames.], batch size: 24, lr: 1.62e-03
2022-07-25 19:12:16,970 INFO [train.py:850] (2/4) Epoch 2, batch 7250, loss[loss=0.301, simple_loss=0.3593, pruned_loss=0.1213, over 7387.00 frames.], tot_loss[loss=0.3256, simple_loss=0.3693, pruned_loss=0.141, over 1466511.95 frames.], batch size: 21, lr: 1.62e-03
2022-07-25 19:13:01,413 INFO [train.py:850] (2/4) Epoch 2, batch 7300, loss[loss=0.2933, simple_loss=0.3371, pruned_loss=0.1247, over 7228.00 frames.], tot_loss[loss=0.3258, simple_loss=0.3692, pruned_loss=0.1412, over 1467729.45 frames.], batch size: 16, lr: 1.62e-03
2022-07-25 19:13:46,646 INFO [train.py:850] (2/4) Epoch 2, batch 7350, loss[loss=0.3177, simple_loss=0.3739, pruned_loss=0.1307, over 7471.00 frames.], tot_loss[loss=0.3237, simple_loss=0.3678, pruned_loss=0.1398, over 1467242.72 frames.], batch size: 21, lr: 1.62e-03
2022-07-25 19:14:29,876 INFO [train.py:850] (2/4) Epoch 2, batch 7400, loss[loss=0.3733, simple_loss=0.409, pruned_loss=0.1689, over 7206.00 frames.], tot_loss[loss=0.328, simple_loss=0.371, pruned_loss=0.1425, over 1467553.18 frames.], batch size: 20, lr: 1.61e-03
2022-07-25 19:15:14,855 INFO [train.py:850] (2/4) Epoch 2, batch 7450, loss[loss=0.3153, simple_loss=0.3729, pruned_loss=0.1289, over 7306.00 frames.], tot_loss[loss=0.328, simple_loss=0.371, pruned_loss=0.1425, over 1466546.30 frames.], batch size: 22, lr: 1.61e-03
2022-07-25 19:15:58,384 INFO [train.py:850] (2/4) Epoch 2, batch 7500, loss[loss=0.278, simple_loss=0.3326, pruned_loss=0.1117, over 7293.00 frames.], tot_loss[loss=0.327, simple_loss=0.3701, pruned_loss=0.1419, over 1465847.13 frames.], batch size: 19, lr: 1.61e-03
2022-07-25 19:16:42,920 INFO [train.py:850] (2/4) Epoch 2, batch 7550, loss[loss=0.2862, simple_loss=0.3436, pruned_loss=0.1144, over 7479.00 frames.], tot_loss[loss=0.3254, simple_loss=0.3686, pruned_loss=0.1411, over 1466545.77 frames.], batch size: 24, lr: 1.61e-03
2022-07-25 19:17:27,735 INFO [train.py:850] (2/4) Epoch 2, batch 7600, loss[loss=0.2651, simple_loss=0.3201, pruned_loss=0.105, over 7273.00 frames.], tot_loss[loss=0.3264, simple_loss=0.3696, pruned_loss=0.1416, over 1465124.03 frames.], batch size: 16, lr: 1.60e-03
2022-07-25 19:18:12,161 INFO [train.py:850] (2/4) Epoch 2, batch 7650, loss[loss=0.4118, simple_loss=0.433, pruned_loss=0.1953, over 7196.00 frames.], tot_loss[loss=0.3247, simple_loss=0.3681, pruned_loss=0.1406, over 1465204.73 frames.], batch size: 24, lr: 1.60e-03
2022-07-25 19:18:55,759 INFO [train.py:850] (2/4) Epoch 2, batch 7700, loss[loss=0.3122, simple_loss=0.3478, pruned_loss=0.1383, over 7395.00 frames.], tot_loss[loss=0.3249, simple_loss=0.3686, pruned_loss=0.1406, over 1465877.99 frames.], batch size: 19, lr: 1.60e-03
2022-07-25 19:19:39,606 INFO [train.py:850] (2/4) Epoch 2, batch 7750, loss[loss=0.3529, simple_loss=0.3867, pruned_loss=0.1595, over 7272.00 frames.], tot_loss[loss=0.3268, simple_loss=0.3695, pruned_loss=0.1421, over 1465822.38 frames.], batch size: 38, lr: 1.60e-03
2022-07-25 19:20:23,670 INFO [train.py:850] (2/4) Epoch 2, batch 7800, loss[loss=0.2962, simple_loss=0.3434, pruned_loss=0.1245, over 7388.00 frames.], tot_loss[loss=0.3258, simple_loss=0.3683, pruned_loss=0.1417, over 1466872.83 frames.], batch size: 19, lr: 1.60e-03
2022-07-25 19:21:07,986 INFO [train.py:850] (2/4) Epoch 2, batch 7850, loss[loss=0.3446, simple_loss=0.3882, pruned_loss=0.1505, over 7340.00 frames.], tot_loss[loss=0.3277, simple_loss=0.3697, pruned_loss=0.1428, over 1466326.77 frames.], batch size: 23, lr: 1.59e-03
2022-07-25 19:21:51,635 INFO [train.py:850] (2/4) Epoch 2, batch 7900, loss[loss=0.4275, simple_loss=0.4203, pruned_loss=0.2173, over 7298.00 frames.], tot_loss[loss=0.3272, simple_loss=0.3696, pruned_loss=0.1424, over 1466080.05 frames.], batch size: 19, lr: 1.59e-03
2022-07-25 19:22:36,265 INFO [train.py:850] (2/4) Epoch 2, batch 7950, loss[loss=0.2752, simple_loss=0.3436, pruned_loss=0.1034, over 7314.00 frames.], tot_loss[loss=0.3257, simple_loss=0.3689, pruned_loss=0.1413, over 1466221.89 frames.], batch size: 39, lr: 1.59e-03
2022-07-25 19:23:19,747 INFO [train.py:850] (2/4) Epoch 2, batch 8000, loss[loss=0.2345, simple_loss=0.2958, pruned_loss=0.08662, over 7429.00 frames.], tot_loss[loss=0.3237, simple_loss=0.3679, pruned_loss=0.1397, over 1466696.42 frames.], batch size: 18, lr: 1.59e-03
2022-07-25 19:24:04,366 INFO [train.py:850] (2/4) Epoch 2, batch 8050, loss[loss=0.2356, simple_loss=0.2986, pruned_loss=0.08624, over 7312.00 frames.], tot_loss[loss=0.3205, simple_loss=0.3651, pruned_loss=0.138, over 1466993.35 frames.], batch size: 17, lr: 1.59e-03
2022-07-25 19:24:47,573 INFO [train.py:850] (2/4) Epoch 2, batch 8100, loss[loss=0.286, simple_loss=0.3426, pruned_loss=0.1147, over 7453.00 frames.], tot_loss[loss=0.3178, simple_loss=0.3633, pruned_loss=0.1361, over 1465612.90 frames.], batch size: 17, lr: 1.58e-03
2022-07-25 19:25:32,301 INFO [train.py:850] (2/4) Epoch 2, batch 8150, loss[loss=0.265, simple_loss=0.3156, pruned_loss=0.1072, over 7297.00 frames.], tot_loss[loss=0.3187, simple_loss=0.3639, pruned_loss=0.1367, over 1467352.20 frames.], batch size: 19, lr: 1.58e-03
2022-07-25 19:26:16,434 INFO [train.py:850] (2/4) Epoch 2, batch 8200, loss[loss=0.2983, simple_loss=0.3378, pruned_loss=0.1294, over 7206.00 frames.], tot_loss[loss=0.3203, simple_loss=0.3649, pruned_loss=0.1378, over 1467487.21 frames.], batch size: 18, lr: 1.58e-03
2022-07-25 19:27:00,678 INFO [train.py:850] (2/4) Epoch 2, batch 8250, loss[loss=0.2771, simple_loss=0.3378, pruned_loss=0.1082, over 7493.00 frames.], tot_loss[loss=0.3209, simple_loss=0.3656, pruned_loss=0.1381, over 1467426.98 frames.], batch size: 19, lr: 1.58e-03
2022-07-25 19:27:44,835 INFO [train.py:850] (2/4) Epoch 2, batch 8300, loss[loss=0.3311, simple_loss=0.3648, pruned_loss=0.1487, over 7320.00 frames.], tot_loss[loss=0.3228, simple_loss=0.3668, pruned_loss=0.1394, over 1467047.96 frames.], batch size: 17, lr: 1.57e-03
2022-07-25 19:28:28,631 INFO [train.py:850] (2/4) Epoch 2, batch 8350, loss[loss=0.2913, simple_loss=0.3332, pruned_loss=0.1247, over 7329.00 frames.], tot_loss[loss=0.3206, simple_loss=0.3653, pruned_loss=0.1379, over 1466111.77 frames.], batch size: 17, lr: 1.57e-03
2022-07-25 19:29:12,924 INFO [train.py:850] (2/4) Epoch 2, batch 8400, loss[loss=0.4027, simple_loss=0.4267, pruned_loss=0.1893, over 7307.00 frames.], tot_loss[loss=0.3202, simple_loss=0.3653, pruned_loss=0.1376, over 1465869.87 frames.], batch size: 27, lr: 1.57e-03
2022-07-25 19:29:58,079 INFO [train.py:850] (2/4) Epoch 2, batch 8450, loss[loss=0.298, simple_loss=0.3643, pruned_loss=0.1158, over 7458.00 frames.], tot_loss[loss=0.3195, simple_loss=0.3647, pruned_loss=0.1372, over 1466748.54 frames.], batch size: 26, lr: 1.57e-03
2022-07-25 19:30:43,540 INFO [train.py:850] (2/4) Epoch 2, batch 8500, loss[loss=0.3748, simple_loss=0.4138, pruned_loss=0.1679, over 7364.00 frames.], tot_loss[loss=0.321, simple_loss=0.3657, pruned_loss=0.1382, over 1466633.16 frames.], batch size: 21, lr: 1.57e-03
2022-07-25 19:31:27,930 INFO [train.py:850] (2/4) Epoch 2, batch 8550, loss[loss=0.392, simple_loss=0.4209, pruned_loss=0.1816, over 7346.00 frames.], tot_loss[loss=0.3225, simple_loss=0.3666, pruned_loss=0.1392, over 1466129.81 frames.], batch size: 39, lr: 1.56e-03
2022-07-25 19:32:11,545 INFO [train.py:850] (2/4) Epoch 2, batch 8600, loss[loss=0.2502, simple_loss=0.3093, pruned_loss=0.09553, over 7432.00 frames.], tot_loss[loss=0.3208, simple_loss=0.3653, pruned_loss=0.1382, over 1466172.21 frames.], batch size: 18, lr: 1.56e-03
2022-07-25 19:32:56,198 INFO [train.py:850] (2/4) Epoch 2, batch 8650, loss[loss=0.3923, simple_loss=0.3966, pruned_loss=0.194, over 7101.00 frames.], tot_loss[loss=0.3227, simple_loss=0.3674, pruned_loss=0.139, over 1465805.31 frames.], batch size: 18, lr: 1.56e-03
2022-07-25 19:33:39,764 INFO [train.py:850] (2/4) Epoch 2, batch 8700, loss[loss=0.3702, simple_loss=0.4002, pruned_loss=0.1701, over 7297.00 frames.], tot_loss[loss=0.3217, simple_loss=0.3668, pruned_loss=0.1383, over 1466557.09 frames.], batch size: 22, lr: 1.56e-03
2022-07-25 19:34:23,187 INFO [train.py:850] (2/4) Epoch 2, batch 8750, loss[loss=0.2936, simple_loss=0.3475, pruned_loss=0.1198, over 7383.00 frames.], tot_loss[loss=0.319, simple_loss=0.3649, pruned_loss=0.1365, over 1466375.86 frames.], batch size: 20, lr: 1.56e-03
2022-07-25 19:35:05,512 INFO [train.py:850] (2/4) Epoch 2, batch 8800, loss[loss=0.3626, simple_loss=0.4046, pruned_loss=0.1604, over 7340.00 frames.], tot_loss[loss=0.3197, simple_loss=0.3657, pruned_loss=0.1368, over 1466033.10 frames.], batch size: 23, lr: 1.55e-03
2022-07-25 19:35:48,754 INFO [train.py:850] (2/4) Epoch 2, batch 8850, loss[loss=0.343, simple_loss=0.3901, pruned_loss=0.148, over 7475.00 frames.], tot_loss[loss=0.3211, simple_loss=0.3667, pruned_loss=0.1377, over 1466822.25 frames.], batch size: 21, lr: 1.55e-03
2022-07-25 19:37:29,385 INFO [train.py:850] (2/4) Epoch 3, batch 0, loss[loss=0.3456, simple_loss=0.3963, pruned_loss=0.1474, over 7475.00 frames.], tot_loss[loss=0.3456, simple_loss=0.3963, pruned_loss=0.1474, over 7475.00 frames.], batch size: 39, lr: 1.52e-03
2022-07-25 19:38:13,208 INFO [train.py:850] (2/4) Epoch 3, batch 50, loss[loss=0.3541, simple_loss=0.3923, pruned_loss=0.158, over 7168.00 frames.], tot_loss[loss=0.292, simple_loss=0.3518, pruned_loss=0.1161, over 329895.90 frames.], batch size: 21, lr: 1.52e-03
2022-07-25 19:38:57,438 INFO [train.py:850] (2/4) Epoch 3, batch 100, loss[loss=0.3368, simple_loss=0.3909, pruned_loss=0.1414, over 7481.00 frames.], tot_loss[loss=0.2886, simple_loss=0.3504, pruned_loss=0.1134, over 582396.33 frames.], batch size: 20, lr: 1.52e-03
2022-07-25 19:39:40,950 INFO [train.py:850] (2/4) Epoch 3, batch 150, loss[loss=0.3217, simple_loss=0.386, pruned_loss=0.1287, over 7321.00 frames.], tot_loss[loss=0.2825, simple_loss=0.3465, pruned_loss=0.1092, over 778166.78 frames.], batch size: 27, lr: 1.51e-03
2022-07-25 19:40:25,034 INFO [train.py:850] (2/4) Epoch 3, batch 200, loss[loss=0.2638, simple_loss=0.3161, pruned_loss=0.1057, over 7444.00 frames.], tot_loss[loss=0.2805, simple_loss=0.3443, pruned_loss=0.1083, over 930973.51 frames.], batch size: 18, lr: 1.51e-03
2022-07-25 19:41:08,683 INFO [train.py:850] (2/4) Epoch 3, batch 250, loss[loss=0.2881, simple_loss=0.3576, pruned_loss=0.1093, over 7484.00 frames.], tot_loss[loss=0.2791, simple_loss=0.3442, pruned_loss=0.107, over 1049594.08 frames.], batch size: 26, lr: 1.51e-03
2022-07-25 19:41:52,027 INFO [train.py:850] (2/4) Epoch 3, batch 300, loss[loss=0.3097, simple_loss=0.3671, pruned_loss=0.1262, over 7479.00 frames.], tot_loss[loss=0.2806, simple_loss=0.345, pruned_loss=0.1081, over 1142155.75 frames.], batch size: 21, lr: 1.51e-03
2022-07-25 19:42:36,205 INFO [train.py:850] (2/4) Epoch 3, batch 350, loss[loss=0.2683, simple_loss=0.337, pruned_loss=0.09981, over 7326.00 frames.], tot_loss[loss=0.2787, simple_loss=0.3441, pruned_loss=0.1067, over 1214438.74 frames.], batch size: 27, lr: 1.51e-03
2022-07-25 19:43:19,366 INFO [train.py:850] (2/4) Epoch 3, batch 400, loss[loss=0.2136, simple_loss=0.2901, pruned_loss=0.0686, over 7307.00 frames.], tot_loss[loss=0.2767, simple_loss=0.3425, pruned_loss=0.1054, over 1269972.47 frames.], batch size: 18, lr: 1.50e-03
2022-07-25 19:44:04,442 INFO [train.py:850] (2/4) Epoch 3, batch 450, loss[loss=0.2783, simple_loss=0.354, pruned_loss=0.1013, over 7213.00 frames.], tot_loss[loss=0.2757, simple_loss=0.3415, pruned_loss=0.105, over 1313379.76 frames.], batch size: 25, lr: 1.50e-03
2022-07-25 19:44:47,663 INFO [train.py:850] (2/4) Epoch 3, batch 500, loss[loss=0.2425, simple_loss=0.3311, pruned_loss=0.07691, over 7274.00 frames.], tot_loss[loss=0.2736, simple_loss=0.3399, pruned_loss=0.1036, over 1346582.99 frames.], batch size: 21, lr: 1.50e-03
2022-07-25 19:45:31,761 INFO [train.py:850] (2/4) Epoch 3, batch 550, loss[loss=0.2836, simple_loss=0.3601, pruned_loss=0.1035, over 7452.00 frames.], tot_loss[loss=0.2726, simple_loss=0.3396, pruned_loss=0.1028, over 1373394.40 frames.], batch size: 24, lr: 1.50e-03
2022-07-25 19:46:16,675 INFO [train.py:850] (2/4) Epoch 3, batch 600, loss[loss=0.2488, simple_loss=0.3241, pruned_loss=0.08679, over 7375.00 frames.], tot_loss[loss=0.2722, simple_loss=0.3391, pruned_loss=0.1027, over 1395191.57 frames.], batch size: 21, lr: 1.50e-03
2022-07-25 19:47:01,165 INFO [train.py:850] (2/4) Epoch 3, batch 650, loss[loss=0.258, simple_loss=0.3321, pruned_loss=0.09198, over 7190.00 frames.], tot_loss[loss=0.272, simple_loss=0.3385, pruned_loss=0.1028, over 1411666.73 frames.], batch size: 18, lr: 1.49e-03
2022-07-25 19:47:46,554 INFO [train.py:850] (2/4) Epoch 3, batch 700, loss[loss=0.2819, simple_loss=0.3494, pruned_loss=0.1072, over 7168.00 frames.], tot_loss[loss=0.2722, simple_loss=0.3387, pruned_loss=0.1028, over 1423832.54 frames.], batch size: 22, lr: 1.49e-03
2022-07-25 19:48:30,572 INFO [train.py:850] (2/4) Epoch 3, batch 750, loss[loss=0.2402, simple_loss=0.329, pruned_loss=0.07567, over 7292.00 frames.], tot_loss[loss=0.2708, simple_loss=0.3373, pruned_loss=0.1021, over 1433838.40 frames.], batch size: 20, lr: 1.49e-03
2022-07-25 19:49:15,023 INFO [train.py:850] (2/4) Epoch 3, batch 800, loss[loss=0.2766, simple_loss=0.332, pruned_loss=0.1107, over 7191.00 frames.], tot_loss[loss=0.2719, simple_loss=0.338, pruned_loss=0.1029, over 1440526.57 frames.], batch size: 18, lr: 1.49e-03
2022-07-25 19:49:58,600 INFO [train.py:850] (2/4) Epoch 3, batch 850, loss[loss=0.2942, simple_loss=0.3424, pruned_loss=0.123, over 7139.00 frames.], tot_loss[loss=0.2745, simple_loss=0.3402, pruned_loss=0.1044, over 1444097.62 frames.], batch size: 17, lr: 1.49e-03
2022-07-25 19:50:43,049 INFO [train.py:850] (2/4) Epoch 3, batch 900, loss[loss=0.2991, simple_loss=0.3652, pruned_loss=0.1165, over 7283.00 frames.], tot_loss[loss=0.2762, simple_loss=0.3411, pruned_loss=0.1057, over 1448514.71 frames.], batch size: 20, lr: 1.49e-03
2022-07-25 19:51:27,479 INFO [train.py:850] (2/4) Epoch 3, batch 950, loss[loss=0.2812, simple_loss=0.3472, pruned_loss=0.1076, over 7178.00 frames.], tot_loss[loss=0.276, simple_loss=0.3411, pruned_loss=0.1055, over 1452691.15 frames.], batch size: 22, lr: 1.48e-03
2022-07-25 19:52:11,370 INFO [train.py:850] (2/4) Epoch 3, batch 1000, loss[loss=0.2502, simple_loss=0.3138, pruned_loss=0.09334, over 7460.00 frames.], tot_loss[loss=0.2773, simple_loss=0.3422, pruned_loss=0.1062, over 1456354.65 frames.], batch size: 17, lr: 1.48e-03
2022-07-25 19:52:55,762 INFO [train.py:850] (2/4) Epoch 3, batch 1050, loss[loss=0.3133, simple_loss=0.3892, pruned_loss=0.1187, over 7463.00 frames.], tot_loss[loss=0.2802, simple_loss=0.3446, pruned_loss=0.1079, over 1458604.50 frames.], batch size: 31, lr: 1.48e-03
2022-07-25 19:53:38,628 INFO [train.py:850] (2/4) Epoch 3, batch 1100, loss[loss=0.3079, simple_loss=0.3675, pruned_loss=0.1242, over 7204.00 frames.], tot_loss[loss=0.2805, simple_loss=0.3448, pruned_loss=0.1081, over 1460299.52 frames.], batch size: 20, lr: 1.48e-03
2022-07-25 19:54:22,171 INFO [train.py:850] (2/4) Epoch 3, batch 1150, loss[loss=0.2742, simple_loss=0.3404, pruned_loss=0.104, over 7193.00 frames.], tot_loss[loss=0.281, simple_loss=0.3454, pruned_loss=0.1083, over 1461869.92 frames.], batch size: 18, lr: 1.48e-03
2022-07-25 19:55:06,349 INFO [train.py:850] (2/4) Epoch 3, batch 1200, loss[loss=0.3576, simple_loss=0.4127, pruned_loss=0.1513, over 7322.00 frames.], tot_loss[loss=0.2823, simple_loss=0.3471, pruned_loss=0.1087, over 1462316.17 frames.], batch size: 39, lr: 1.47e-03
2022-07-25 19:55:49,878 INFO [train.py:850] (2/4) Epoch 3, batch 1250, loss[loss=0.3672, simple_loss=0.4166, pruned_loss=0.1589, over 7469.00 frames.], tot_loss[loss=0.2831, simple_loss=0.3477, pruned_loss=0.1093, over 1463784.83 frames.], batch size: 31, lr: 1.47e-03
2022-07-25 19:56:34,683 INFO [train.py:850] (2/4) Epoch 3, batch 1300, loss[loss=0.3293, simple_loss=0.3881, pruned_loss=0.1352, over 7298.00 frames.], tot_loss[loss=0.2858, simple_loss=0.3501, pruned_loss=0.1107, over 1464699.94 frames.], batch size: 22, lr: 1.47e-03
2022-07-25 19:57:18,677 INFO [train.py:850] (2/4) Epoch 3, batch 1350, loss[loss=0.3529, simple_loss=0.4005, pruned_loss=0.1527, over 7242.00 frames.], tot_loss[loss=0.2871, simple_loss=0.351, pruned_loss=0.1116, over 1464347.40 frames.], batch size: 25, lr: 1.47e-03
2022-07-25 19:58:01,682 INFO [train.py:850] (2/4) Epoch 3, batch 1400, loss[loss=0.2847, simple_loss=0.3597, pruned_loss=0.1049, over 7236.00 frames.], tot_loss[loss=0.2852, simple_loss=0.3505, pruned_loss=0.11, over 1464292.69 frames.], batch size: 25, lr: 1.47e-03
2022-07-25 19:58:46,133 INFO [train.py:850] (2/4) Epoch 3, batch 1450, loss[loss=0.257, simple_loss=0.3134, pruned_loss=0.1003, over 7289.00 frames.], tot_loss[loss=0.2854, simple_loss=0.3501, pruned_loss=0.1103, over 1464577.56 frames.], batch size: 17, lr: 1.47e-03
2022-07-25 19:59:29,094 INFO [train.py:850] (2/4) Epoch 3, batch 1500, loss[loss=0.3514, simple_loss=0.3984, pruned_loss=0.1522, over 7357.00 frames.], tot_loss[loss=0.282, simple_loss=0.3468, pruned_loss=0.1086, over 1465067.82 frames.], batch size: 66, lr: 1.46e-03
2022-07-25 20:00:14,300 INFO [train.py:850] (2/4) Epoch 3, batch 1550, loss[loss=0.3056, simple_loss=0.3603, pruned_loss=0.1255, over 7477.00 frames.], tot_loss[loss=0.2825, simple_loss=0.3468, pruned_loss=0.109, over 1465623.61 frames.], batch size: 21, lr: 1.46e-03
2022-07-25 20:00:57,562 INFO [train.py:850] (2/4) Epoch 3, batch 1600, loss[loss=0.3206, simple_loss=0.3705, pruned_loss=0.1354, over 7467.00 frames.], tot_loss[loss=0.2834, simple_loss=0.3473, pruned_loss=0.1097, over 1465172.43 frames.], batch size: 72, lr: 1.46e-03
2022-07-25 20:01:41,878 INFO [train.py:850] (2/4) Epoch 3, batch 1650, loss[loss=0.2276, simple_loss=0.3046, pruned_loss=0.07528, over 7205.00 frames.], tot_loss[loss=0.2825, simple_loss=0.3469, pruned_loss=0.1091, over 1465707.39 frames.], batch size: 18, lr: 1.46e-03
2022-07-25 20:02:24,967 INFO [train.py:850] (2/4) Epoch 3, batch 1700, loss[loss=0.2289, simple_loss=0.3136, pruned_loss=0.07206, over 7297.00 frames.], tot_loss[loss=0.2813, simple_loss=0.3463, pruned_loss=0.1081, over 1465248.73 frames.], batch size: 20, lr: 1.46e-03
2022-07-25 20:03:09,423 INFO [train.py:850] (2/4) Epoch 3, batch 1750, loss[loss=0.2758, simple_loss=0.3462, pruned_loss=0.1026, over 7229.00 frames.], tot_loss[loss=0.2837, simple_loss=0.3481, pruned_loss=0.1097, over 1464822.69 frames.], batch size: 25, lr: 1.45e-03
2022-07-25 20:03:53,213 INFO [train.py:850] (2/4) Epoch 3, batch 1800, loss[loss=0.2361, simple_loss=0.3015, pruned_loss=0.08531, over 7448.00 frames.], tot_loss[loss=0.282, simple_loss=0.3474, pruned_loss=0.1083, over 1464595.54 frames.], batch size: 18, lr: 1.45e-03
2022-07-25 20:04:36,556 INFO [train.py:850] (2/4) Epoch 3, batch 1850, loss[loss=0.2758, simple_loss=0.3546, pruned_loss=0.09853, over 7186.00 frames.], tot_loss[loss=0.2802, simple_loss=0.3459, pruned_loss=0.1073, over 1464068.15 frames.], batch size: 19, lr: 1.45e-03
2022-07-25 20:05:21,304 INFO [train.py:850] (2/4) Epoch 3, batch 1900, loss[loss=0.2505, simple_loss=0.3323, pruned_loss=0.08434, over 7172.00 frames.], tot_loss[loss=0.2802, simple_loss=0.3453, pruned_loss=0.1076, over 1463786.03 frames.], batch size: 21, lr: 1.45e-03
2022-07-25 20:06:04,633 INFO [train.py:850] (2/4) Epoch 3, batch 1950, loss[loss=0.2019, simple_loss=0.2801, pruned_loss=0.06182, over 7296.00 frames.], tot_loss[loss=0.2798, simple_loss=0.3443, pruned_loss=0.1077, over 1463498.66 frames.], batch size: 19, lr: 1.45e-03
2022-07-25 20:06:47,546 INFO [train.py:850] (2/4) Epoch 3, batch 2000, loss[loss=0.2951, simple_loss=0.3541, pruned_loss=0.1181, over 7275.00 frames.], tot_loss[loss=0.2792, simple_loss=0.3442, pruned_loss=0.1071, over 1464551.85 frames.], batch size: 21, lr: 1.45e-03
2022-07-25 20:07:32,359 INFO [train.py:850] (2/4) Epoch 3, batch 2050, loss[loss=0.2361, simple_loss=0.3202, pruned_loss=0.076, over 7182.00 frames.], tot_loss[loss=0.2786, simple_loss=0.344, pruned_loss=0.1066, over 1464556.54 frames.], batch size: 21, lr: 1.44e-03
2022-07-25 20:08:15,752 INFO [train.py:850] (2/4) Epoch 3, batch 2100, loss[loss=0.2829, simple_loss=0.3567, pruned_loss=0.1045, over 7349.00 frames.], tot_loss[loss=0.2783, simple_loss=0.3442, pruned_loss=0.1063, over 1464822.80 frames.], batch size: 23, lr: 1.44e-03
2022-07-25 20:09:00,579 INFO [train.py:850] (2/4) Epoch 3, batch 2150, loss[loss=0.2414, simple_loss=0.3166, pruned_loss=0.08314, over 7210.00 frames.], tot_loss[loss=0.2774, simple_loss=0.3434, pruned_loss=0.1057, over 1464207.17 frames.], batch size: 18, lr: 1.44e-03
2022-07-25 20:09:43,784 INFO [train.py:850] (2/4) Epoch 3, batch 2200, loss[loss=0.2902, simple_loss=0.3622, pruned_loss=0.1091, over 7256.00 frames.], tot_loss[loss=0.2783, simple_loss=0.3441, pruned_loss=0.1063, over 1464993.02 frames.], batch size: 27, lr: 1.44e-03
2022-07-25 20:10:43,594 INFO [train.py:850] (2/4) Epoch 3, batch 2250, loss[loss=0.2208, simple_loss=0.2893, pruned_loss=0.07616, over 7177.00 frames.], tot_loss[loss=0.2779, simple_loss=0.3434, pruned_loss=0.1062, over 1464407.45 frames.], batch size: 17, lr: 1.44e-03
2022-07-25 20:11:28,385 INFO [train.py:850] (2/4) Epoch 3, batch 2300, loss[loss=0.2329, simple_loss=0.31, pruned_loss=0.07795, over 7202.00 frames.], tot_loss[loss=0.2773, simple_loss=0.3431, pruned_loss=0.1058, over 1463836.21 frames.], batch size: 19, lr: 1.44e-03
2022-07-25 20:12:11,722 INFO [train.py:850] (2/4) Epoch 3, batch 2350, loss[loss=0.2499, simple_loss=0.3101, pruned_loss=0.09481, over 7300.00 frames.], tot_loss[loss=0.2776, simple_loss=0.3438, pruned_loss=0.1056, over 1464036.41 frames.], batch size: 17, lr: 1.43e-03
2022-07-25 20:12:55,862 INFO [train.py:850] (2/4) Epoch 3, batch 2400, loss[loss=0.2475, simple_loss=0.3239, pruned_loss=0.08552, over 7406.00 frames.], tot_loss[loss=0.2757, simple_loss=0.3424, pruned_loss=0.1045, over 1465141.54 frames.], batch size: 19, lr: 1.43e-03
2022-07-25 20:13:39,106 INFO [train.py:850] (2/4) Epoch 3, batch 2450, loss[loss=0.2453, simple_loss=0.3067, pruned_loss=0.09194, over 7276.00 frames.], tot_loss[loss=0.2755, simple_loss=0.3424, pruned_loss=0.1043, over 1465536.75 frames.], batch size: 16, lr: 1.43e-03
2022-07-25 20:14:23,350 INFO [train.py:850] (2/4) Epoch 3, batch 2500, loss[loss=0.2955, simple_loss=0.3469, pruned_loss=0.122, over 7311.00 frames.], tot_loss[loss=0.2764, simple_loss=0.3433, pruned_loss=0.1047, over 1465541.52 frames.], batch size: 18, lr: 1.43e-03
2022-07-25 20:15:07,340 INFO [train.py:850] (2/4) Epoch 3, batch 2550, loss[loss=0.3, simple_loss=0.375, pruned_loss=0.1125, over 7335.00 frames.], tot_loss[loss=0.2768, simple_loss=0.3436, pruned_loss=0.105, over 1465027.46 frames.], batch size: 23, lr: 1.43e-03
2022-07-25 20:15:51,174 INFO [train.py:850] (2/4) Epoch 3, batch 2600, loss[loss=0.2908, simple_loss=0.3539, pruned_loss=0.1138, over 7227.00 frames.], tot_loss[loss=0.2764, simple_loss=0.3435, pruned_loss=0.1046, over 1465605.55 frames.], batch size: 25, lr: 1.43e-03
2022-07-25 20:16:34,996 INFO [train.py:850] (2/4) Epoch 3, batch 2650, loss[loss=0.2829, simple_loss=0.3597, pruned_loss=0.103, over 7279.00 frames.], tot_loss[loss=0.275, simple_loss=0.3426, pruned_loss=0.1037, over 1466055.22 frames.], batch size: 21, lr: 1.42e-03
2022-07-25 20:17:18,254 INFO [train.py:850] (2/4) Epoch 3, batch 2700, loss[loss=0.2339, simple_loss=0.3114, pruned_loss=0.07823, over 7310.00 frames.], tot_loss[loss=0.2737, simple_loss=0.3417, pruned_loss=0.1029, over 1467237.98 frames.], batch size: 17, lr: 1.42e-03
2022-07-25 20:18:02,683 INFO [train.py:850] (2/4) Epoch 3, batch 2750, loss[loss=0.2185, simple_loss=0.2869, pruned_loss=0.07504, over 7329.00 frames.], tot_loss[loss=0.2737, simple_loss=0.3415, pruned_loss=0.1029, over 1465837.32 frames.], batch size: 18, lr: 1.42e-03
2022-07-25 20:18:46,209 INFO [train.py:850] (2/4) Epoch 3, batch 2800, loss[loss=0.2281, simple_loss=0.3021, pruned_loss=0.07703, over 7206.00 frames.], tot_loss[loss=0.2735, simple_loss=0.3411, pruned_loss=0.1029, over 1465291.22 frames.], batch size: 18, lr: 1.42e-03
2022-07-25 20:19:29,155 INFO [train.py:850] (2/4) Epoch 3, batch 2850, loss[loss=0.229, simple_loss=0.2924, pruned_loss=0.08276, over 7255.00 frames.], tot_loss[loss=0.2716, simple_loss=0.3395, pruned_loss=0.1019, over 1464530.05 frames.], batch size: 16, lr: 1.42e-03
2022-07-25 20:20:13,936 INFO [train.py:850] (2/4) Epoch 3, batch 2900, loss[loss=0.2886, simple_loss=0.3568, pruned_loss=0.1102, over 7337.00 frames.], tot_loss[loss=0.2705, simple_loss=0.3387, pruned_loss=0.1011, over 1465099.56 frames.], batch size: 69, lr: 1.42e-03
2022-07-25 20:20:57,502 INFO [train.py:850] (2/4) Epoch 3, batch 2950, loss[loss=0.265, simple_loss=0.3429, pruned_loss=0.09359, over 7168.00 frames.], tot_loss[loss=0.27, simple_loss=0.3389, pruned_loss=0.1006, over 1465505.77 frames.], batch size: 22, lr: 1.41e-03
2022-07-25 20:21:42,774 INFO [train.py:850] (2/4) Epoch 3, batch 3000, loss[loss=0.2766, simple_loss=0.3357, pruned_loss=0.1088, over 7195.00 frames.], tot_loss[loss=0.2691, simple_loss=0.3378, pruned_loss=0.1002, over 1465924.61 frames.], batch size: 18, lr: 1.41e-03
2022-07-25 20:21:42,775 INFO [train.py:870] (2/4) Computing validation loss
2022-07-25 20:22:05,510 INFO [train.py:879] (2/4) Epoch 3, validation: loss=0.2401, simple_loss=0.3273, pruned_loss=0.07647, over 924787.00 frames.
2022-07-25 20:22:48,632 INFO [train.py:850] (2/4) Epoch 3, batch 3050, loss[loss=0.245, simple_loss=0.3143, pruned_loss=0.08784, over 7459.00 frames.], tot_loss[loss=0.2714, simple_loss=0.3397, pruned_loss=0.1015, over 1465991.15 frames.], batch size: 17, lr: 1.41e-03
2022-07-25 20:23:32,859 INFO [train.py:850] (2/4) Epoch 3, batch 3100, loss[loss=0.3908, simple_loss=0.422, pruned_loss=0.1798, over 7432.00 frames.], tot_loss[loss=0.271, simple_loss=0.3392, pruned_loss=0.1014, over 1466503.20 frames.], batch size: 69, lr: 1.41e-03
2022-07-25 20:24:16,304 INFO [train.py:850] (2/4) Epoch 3, batch 3150, loss[loss=0.2507, simple_loss=0.3319, pruned_loss=0.08477, over 7403.00 frames.], tot_loss[loss=0.2722, simple_loss=0.3398, pruned_loss=0.1024, over 1465720.79 frames.], batch size: 22, lr: 1.41e-03
2022-07-25 20:25:01,120 INFO [train.py:850] (2/4) Epoch 3, batch 3200, loss[loss=0.2744, simple_loss=0.3551, pruned_loss=0.09689, over 7226.00 frames.], tot_loss[loss=0.2749, simple_loss=0.3419, pruned_loss=0.1039, over 1466182.74 frames.], batch size: 24, lr: 1.41e-03
2022-07-25 20:25:45,323 INFO [train.py:850] (2/4) Epoch 3, batch 3250, loss[loss=0.3308, simple_loss=0.392, pruned_loss=0.1348, over 7309.00 frames.], tot_loss[loss=0.2782, simple_loss=0.3445, pruned_loss=0.1059, over 1467193.61 frames.], batch size: 38, lr: 1.41e-03
2022-07-25 20:26:28,431 INFO [train.py:850] (2/4) Epoch 3, batch 3300, loss[loss=0.2705, simple_loss=0.3459, pruned_loss=0.09757, over 7386.00 frames.], tot_loss[loss=0.2778, simple_loss=0.3443, pruned_loss=0.1057, over 1466653.25 frames.], batch size: 21, lr: 1.40e-03
2022-07-25 20:27:13,463 INFO [train.py:850] (2/4) Epoch 3, batch 3350, loss[loss=0.231, simple_loss=0.2898, pruned_loss=0.08611, over 7441.00 frames.], tot_loss[loss=0.2755, simple_loss=0.3427, pruned_loss=0.1042, over 1467002.52 frames.], batch size: 17, lr: 1.40e-03
2022-07-25 20:27:56,796 INFO [train.py:850] (2/4) Epoch 3, batch 3400, loss[loss=0.3065, simple_loss=0.3686, pruned_loss=0.1222, over 7294.00 frames.], tot_loss[loss=0.277, simple_loss=0.3435, pruned_loss=0.1052, over 1466712.40 frames.], batch size: 20, lr: 1.40e-03
2022-07-25 20:28:41,620 INFO [train.py:850] (2/4) Epoch 3, batch 3450, loss[loss=0.2464, simple_loss=0.3164, pruned_loss=0.08818, over 7480.00 frames.], tot_loss[loss=0.276, simple_loss=0.3428, pruned_loss=0.1046, over 1466678.76 frames.], batch size: 20, lr: 1.40e-03
2022-07-25 20:29:24,484 INFO [train.py:850] (2/4) Epoch 3, batch 3500, loss[loss=0.2362, simple_loss=0.3137, pruned_loss=0.07938, over 7386.00 frames.], tot_loss[loss=0.2743, simple_loss=0.3415, pruned_loss=0.1036, over 1467101.89 frames.], batch size: 20, lr: 1.40e-03
2022-07-25 20:30:09,397 INFO [train.py:850] (2/4) Epoch 3, batch 3550, loss[loss=0.274, simple_loss=0.3479, pruned_loss=0.1001, over 7386.00 frames.], tot_loss[loss=0.2763, simple_loss=0.3431, pruned_loss=0.1047, over 1466129.55 frames.], batch size: 20, lr: 1.40e-03
2022-07-25 20:30:51,907 INFO [train.py:850] (2/4) Epoch 3, batch 3600, loss[loss=0.2458, simple_loss=0.3137, pruned_loss=0.08894, over 7439.00 frames.], tot_loss[loss=0.2731, simple_loss=0.341, pruned_loss=0.1026, over 1466272.07 frames.], batch size: 17, lr: 1.39e-03
2022-07-25 20:31:36,066 INFO [train.py:850] (2/4) Epoch 3, batch 3650, loss[loss=0.2375, simple_loss=0.3085, pruned_loss=0.08321, over 7236.00 frames.], tot_loss[loss=0.2733, simple_loss=0.3409, pruned_loss=0.1029, over 1466897.46 frames.], batch size: 16, lr: 1.39e-03
2022-07-25 20:32:19,867 INFO [train.py:850] (2/4) Epoch 3, batch 3700, loss[loss=0.2321, simple_loss=0.3091, pruned_loss=0.07753, over 7472.00 frames.], tot_loss[loss=0.2733, simple_loss=0.3414, pruned_loss=0.1026, over 1466457.52 frames.], batch size: 20, lr: 1.39e-03
2022-07-25 20:33:04,018 INFO [train.py:850] (2/4) Epoch 3, batch 3750, loss[loss=0.2182, simple_loss=0.2882, pruned_loss=0.07408, over 7182.00 frames.], tot_loss[loss=0.2737, simple_loss=0.3418, pruned_loss=0.1028, over 1466793.21 frames.], batch size: 17, lr: 1.39e-03
2022-07-25 20:33:47,283 INFO [train.py:850] (2/4) Epoch 3, batch 3800, loss[loss=0.2941, simple_loss=0.3596, pruned_loss=0.1143, over 7308.00 frames.], tot_loss[loss=0.2722, simple_loss=0.3404, pruned_loss=0.102, over 1466520.39 frames.], batch size: 22, lr: 1.39e-03
2022-07-25 20:34:31,052 INFO [train.py:850] (2/4) Epoch 3, batch 3850, loss[loss=0.244, simple_loss=0.3174, pruned_loss=0.08535, over 7476.00 frames.], tot_loss[loss=0.2695, simple_loss=0.3381, pruned_loss=0.1005, over 1465669.27 frames.], batch size: 21, lr: 1.39e-03
2022-07-25 20:35:14,157 INFO [train.py:850] (2/4) Epoch 3, batch 3900, loss[loss=0.2597, simple_loss=0.3343, pruned_loss=0.09253, over 7231.00 frames.], tot_loss[loss=0.2694, simple_loss=0.3384, pruned_loss=0.1002, over 1464374.70 frames.], batch size: 24, lr: 1.38e-03
2022-07-25 20:35:58,699 INFO [train.py:850] (2/4) Epoch 3, batch 3950, loss[loss=0.2841, simple_loss=0.3613, pruned_loss=0.1035, over 7479.00 frames.], tot_loss[loss=0.2689, simple_loss=0.3383, pruned_loss=0.09971, over 1465749.01 frames.], batch size: 26, lr: 1.38e-03
2022-07-25 20:36:43,635 INFO [train.py:850] (2/4) Epoch 3, batch 4000, loss[loss=0.3102, simple_loss=0.3694, pruned_loss=0.1255, over 7334.00 frames.], tot_loss[loss=0.2704, simple_loss=0.3398, pruned_loss=0.1005, over 1466017.03 frames.], batch size: 27, lr: 1.38e-03
2022-07-25 20:37:28,419 INFO [train.py:850] (2/4) Epoch 3, batch 4050, loss[loss=0.2496, simple_loss=0.3123, pruned_loss=0.09348, over 7448.00 frames.], tot_loss[loss=0.2721, simple_loss=0.3412, pruned_loss=0.1015, over 1466358.32 frames.], batch size: 17, lr: 1.38e-03
2022-07-25 20:38:11,571 INFO [train.py:850] (2/4) Epoch 3, batch 4100, loss[loss=0.2723, simple_loss=0.337, pruned_loss=0.1038, over 7295.00 frames.], tot_loss[loss=0.2739, simple_loss=0.3419, pruned_loss=0.103, over 1466221.58 frames.], batch size: 19, lr: 1.38e-03
2022-07-25 20:38:54,717 INFO [train.py:850] (2/4) Epoch 3, batch 4150, loss[loss=0.2909, simple_loss=0.3501, pruned_loss=0.1158, over 7172.00 frames.], tot_loss[loss=0.2746, simple_loss=0.3416, pruned_loss=0.1038, over 1465712.75 frames.], batch size: 21, lr: 1.38e-03
2022-07-25 20:39:39,650 INFO [train.py:850] (2/4) Epoch 3, batch 4200, loss[loss=0.2612, simple_loss=0.3301, pruned_loss=0.09617, over 7452.00 frames.], tot_loss[loss=0.2796, simple_loss=0.3447, pruned_loss=0.1072, over 1465816.79 frames.], batch size: 18, lr: 1.38e-03
2022-07-25 20:40:23,169 INFO [train.py:850] (2/4) Epoch 3, batch 4250, loss[loss=0.2756, simple_loss=0.3577, pruned_loss=0.09672, over 7290.00 frames.], tot_loss[loss=0.2829, simple_loss=0.3461, pruned_loss=0.1099, over 1464590.63 frames.], batch size: 21, lr: 1.37e-03
2022-07-25 20:41:07,547 INFO [train.py:850] (2/4) Epoch 3, batch 4300, loss[loss=0.3146, simple_loss=0.3544, pruned_loss=0.1374, over 7191.00 frames.], tot_loss[loss=0.2878, simple_loss=0.3486, pruned_loss=0.1134, over 1464203.29 frames.], batch size: 18, lr: 1.37e-03
2022-07-25 20:41:51,339 INFO [train.py:850] (2/4) Epoch 3, batch 4350, loss[loss=0.2855, simple_loss=0.3301, pruned_loss=0.1204, over 7233.00 frames.], tot_loss[loss=0.2926, simple_loss=0.351, pruned_loss=0.1171, over 1465917.04 frames.], batch size: 16, lr: 1.37e-03
2022-07-25 20:42:35,927 INFO [train.py:850] (2/4) Epoch 3, batch 4400, loss[loss=0.2552, simple_loss=0.3212, pruned_loss=0.09461, over 7179.00 frames.], tot_loss[loss=0.2947, simple_loss=0.352, pruned_loss=0.1187, over 1465288.91 frames.], batch size: 21, lr: 1.37e-03
2022-07-25 20:43:20,327 INFO [train.py:850] (2/4) Epoch 3, batch 4450, loss[loss=0.339, simple_loss=0.3845, pruned_loss=0.1468, over 7360.00 frames.], tot_loss[loss=0.2971, simple_loss=0.3529, pruned_loss=0.1207, over 1464901.64 frames.], batch size: 39, lr: 1.37e-03
2022-07-25 20:44:03,672 INFO [train.py:850] (2/4) Epoch 3, batch 4500, loss[loss=0.3659, simple_loss=0.3823, pruned_loss=0.1748, over 7313.00 frames.], tot_loss[loss=0.3004, simple_loss=0.3547, pruned_loss=0.1231, over 1464960.66 frames.], batch size: 18, lr: 1.37e-03
2022-07-25 20:44:48,777 INFO [train.py:850] (2/4) Epoch 3, batch 4550, loss[loss=0.3233, simple_loss=0.3701, pruned_loss=0.1382, over 7243.00 frames.], tot_loss[loss=0.3025, simple_loss=0.3556, pruned_loss=0.1247, over 1464773.44 frames.], batch size: 25, lr: 1.37e-03
2022-07-25 20:45:31,887 INFO [train.py:850] (2/4) Epoch 3, batch 4600, loss[loss=0.3481, simple_loss=0.3979, pruned_loss=0.1492, over 7460.00 frames.], tot_loss[loss=0.3067, simple_loss=0.3587, pruned_loss=0.1273, over 1465692.07 frames.], batch size: 39, lr: 1.36e-03
2022-07-25 20:46:17,726 INFO [train.py:850] (2/4) Epoch 3, batch 4650, loss[loss=0.3181, simple_loss=0.3759, pruned_loss=0.1302, over 7283.00 frames.], tot_loss[loss=0.3061, simple_loss=0.3581, pruned_loss=0.1271, over 1465472.30 frames.], batch size: 27, lr: 1.36e-03
2022-07-25 20:46:59,989 INFO [train.py:850] (2/4) Epoch 3, batch 4700, loss[loss=0.2715, simple_loss=0.3292, pruned_loss=0.1069, over 7101.00 frames.], tot_loss[loss=0.3043, simple_loss=0.3563, pruned_loss=0.1262, over 1465181.74 frames.], batch size: 18, lr: 1.36e-03
2022-07-25 20:47:43,330 INFO [train.py:850] (2/4) Epoch 3, batch 4750, loss[loss=0.2934, simple_loss=0.3517, pruned_loss=0.1175, over 7279.00 frames.], tot_loss[loss=0.3071, simple_loss=0.3582, pruned_loss=0.128, over 1465042.29 frames.], batch size: 27, lr: 1.36e-03
2022-07-25 20:48:27,046 INFO [train.py:850] (2/4) Epoch 3, batch 4800, loss[loss=0.2849, simple_loss=0.3191, pruned_loss=0.1254, over 7291.00 frames.], tot_loss[loss=0.3079, simple_loss=0.3584, pruned_loss=0.1287, over 1465352.01 frames.], batch size: 17, lr: 1.36e-03
2022-07-25 20:49:10,354 INFO [train.py:850] (2/4) Epoch 3, batch 4850, loss[loss=0.3976, simple_loss=0.422, pruned_loss=0.1866, over 7228.00 frames.], tot_loss[loss=0.3086, simple_loss=0.3582, pruned_loss=0.1295, over 1465752.75 frames.], batch size: 24, lr: 1.36e-03
2022-07-25 20:49:54,974 INFO [train.py:850] (2/4) Epoch 3, batch 4900, loss[loss=0.3632, simple_loss=0.3991, pruned_loss=0.1636, over 7181.00 frames.], tot_loss[loss=0.308, simple_loss=0.3581, pruned_loss=0.129, over 1466251.01 frames.], batch size: 21, lr: 1.36e-03
2022-07-25 20:50:38,382 INFO [train.py:850] (2/4) Epoch 3, batch 4950, loss[loss=0.3164, simple_loss=0.3575, pruned_loss=0.1377, over 7438.00 frames.], tot_loss[loss=0.3073, simple_loss=0.3572, pruned_loss=0.1287, over 1466355.93 frames.], batch size: 18, lr: 1.35e-03
2022-07-25 20:51:21,588 INFO [train.py:850] (2/4) Epoch 3, batch 5000, loss[loss=0.2628, simple_loss=0.3214, pruned_loss=0.1021, over 7199.00 frames.], tot_loss[loss=0.3074, simple_loss=0.357, pruned_loss=0.1289, over 1465688.14 frames.], batch size: 19, lr: 1.35e-03
2022-07-25 20:52:05,556 INFO [train.py:850] (2/4) Epoch 3, batch 5050, loss[loss=0.2995, simple_loss=0.3399, pruned_loss=0.1295, over 7401.00 frames.], tot_loss[loss=0.3065, simple_loss=0.3563, pruned_loss=0.1283, over 1466191.50 frames.], batch size: 19, lr: 1.35e-03
2022-07-25 20:52:48,564 INFO [train.py:850] (2/4) Epoch 3, batch 5100, loss[loss=0.3559, simple_loss=0.3973, pruned_loss=0.1572, over 7490.00 frames.], tot_loss[loss=0.3063, simple_loss=0.3562, pruned_loss=0.1282, over 1465779.23 frames.], batch size: 26, lr: 1.35e-03
2022-07-25 20:53:33,214 INFO [train.py:850] (2/4) Epoch 3, batch 5150, loss[loss=0.2947, simple_loss=0.3614, pruned_loss=0.114, over 7179.00 frames.], tot_loss[loss=0.3071, simple_loss=0.3569, pruned_loss=0.1286, over 1465585.37 frames.], batch size: 21, lr: 1.35e-03
2022-07-25 20:54:16,917 INFO [train.py:850] (2/4) Epoch 3, batch 5200, loss[loss=0.2868, simple_loss=0.3558, pruned_loss=0.1089, over 7293.00 frames.], tot_loss[loss=0.3077, simple_loss=0.3577, pruned_loss=0.1289, over 1464807.63 frames.], batch size: 20, lr: 1.35e-03
2022-07-25 20:55:01,699 INFO [train.py:850] (2/4) Epoch 3, batch 5250, loss[loss=0.2428, simple_loss=0.3066, pruned_loss=0.08949, over 7200.00 frames.], tot_loss[loss=0.3066, simple_loss=0.3566, pruned_loss=0.1283, over 1465909.37 frames.], batch size: 19, lr: 1.35e-03
2022-07-25 20:55:45,758 INFO [train.py:850] (2/4) Epoch 3, batch 5300, loss[loss=0.2378, simple_loss=0.2957, pruned_loss=0.08996, over 7228.00 frames.], tot_loss[loss=0.3075, simple_loss=0.3574, pruned_loss=0.1288, over 1464999.82 frames.], batch size: 16, lr: 1.34e-03
2022-07-25 20:56:30,582 INFO [train.py:850] (2/4) Epoch 3, batch 5350, loss[loss=0.326, simple_loss=0.3679, pruned_loss=0.1421, over 7258.00 frames.], tot_loss[loss=0.3076, simple_loss=0.3574, pruned_loss=0.1289, over 1464690.31 frames.], batch size: 30, lr: 1.34e-03
2022-07-25 20:57:14,966 INFO [train.py:850] (2/4) Epoch 3, batch 5400, loss[loss=0.2832, simple_loss=0.3532, pruned_loss=0.1066, over 7341.00 frames.], tot_loss[loss=0.3073, simple_loss=0.3572, pruned_loss=0.1287, over 1464658.83 frames.], batch size: 23, lr: 1.34e-03
2022-07-25 20:57:58,849 INFO [train.py:850] (2/4) Epoch 3, batch 5450, loss[loss=0.2814, simple_loss=0.35, pruned_loss=0.1064, over 7483.00 frames.], tot_loss[loss=0.3073, simple_loss=0.3576, pruned_loss=0.1285, over 1465057.29 frames.], batch size: 23, lr: 1.34e-03
2022-07-25 20:58:44,083 INFO [train.py:850] (2/4) Epoch 3, batch 5500, loss[loss=0.2946, simple_loss=0.3523, pruned_loss=0.1184, over 7385.00 frames.], tot_loss[loss=0.3077, simple_loss=0.3579, pruned_loss=0.1287, over 1466523.77 frames.], batch size: 39, lr: 1.34e-03
2022-07-25 20:59:27,932 INFO [train.py:850] (2/4) Epoch 3, batch 5550, loss[loss=0.316, simple_loss=0.3599, pruned_loss=0.136, over 7176.00 frames.], tot_loss[loss=0.3084, simple_loss=0.358, pruned_loss=0.1294, over 1465761.40 frames.], batch size: 22, lr: 1.34e-03
2022-07-25 21:00:12,688 INFO [train.py:850] (2/4) Epoch 3, batch 5600, loss[loss=0.2943, simple_loss=0.3495, pruned_loss=0.1196, over 7491.00 frames.], tot_loss[loss=0.3075, simple_loss=0.3574, pruned_loss=0.1289, over 1465793.86 frames.], batch size: 19, lr: 1.34e-03
2022-07-25 21:00:56,522 INFO [train.py:850] (2/4) Epoch 3, batch 5650, loss[loss=0.3516, simple_loss=0.396, pruned_loss=0.1536, over 7360.00 frames.], tot_loss[loss=0.3074, simple_loss=0.3573, pruned_loss=0.1287, over 1465354.73 frames.], batch size: 39, lr: 1.33e-03
2022-07-25 21:01:39,014 INFO [train.py:850] (2/4) Epoch 3, batch 5700, loss[loss=0.3875, simple_loss=0.4079, pruned_loss=0.1836, over 7310.00 frames.], tot_loss[loss=0.3083, simple_loss=0.3581, pruned_loss=0.1293, over 1464797.17 frames.], batch size: 22, lr: 1.33e-03
2022-07-25 21:02:23,772 INFO [train.py:850] (2/4) Epoch 3, batch 5750, loss[loss=0.3047, simple_loss=0.3553, pruned_loss=0.127, over 7282.00 frames.], tot_loss[loss=0.3061, simple_loss=0.3569, pruned_loss=0.1277, over 1464752.93 frames.], batch size: 38, lr: 1.33e-03
2022-07-25 21:03:07,752 INFO [train.py:850] (2/4) Epoch 3, batch 5800, loss[loss=0.2589, simple_loss=0.3165, pruned_loss=0.1007, over 7319.00 frames.], tot_loss[loss=0.3064, simple_loss=0.3568, pruned_loss=0.128, over 1465442.63 frames.], batch size: 18, lr: 1.33e-03
2022-07-25 21:03:52,620 INFO [train.py:850] (2/4) Epoch 3, batch 5850, loss[loss=0.3364, simple_loss=0.3646, pruned_loss=0.1541, over 7424.00 frames.], tot_loss[loss=0.3059, simple_loss=0.356, pruned_loss=0.1279, over 1466257.77 frames.], batch size: 74, lr: 1.33e-03
2022-07-25 21:04:35,932 INFO [train.py:850] (2/4) Epoch 3, batch 5900, loss[loss=0.3287, simple_loss=0.384, pruned_loss=0.1367, over 7383.00 frames.], tot_loss[loss=0.3044, simple_loss=0.3549, pruned_loss=0.127, over 1466226.90 frames.], batch size: 21, lr: 1.33e-03
2022-07-25 21:05:20,632 INFO [train.py:850] (2/4) Epoch 3, batch 5950, loss[loss=0.2343, simple_loss=0.2948, pruned_loss=0.08694, over 7214.00 frames.], tot_loss[loss=0.3026, simple_loss=0.3531, pruned_loss=0.1261, over 1465830.85 frames.], batch size: 18, lr: 1.33e-03
2022-07-25 21:06:03,879 INFO [train.py:850] (2/4) Epoch 3, batch 6000, loss[loss=0.3002, simple_loss=0.3553, pruned_loss=0.1225, over 7292.00 frames.], tot_loss[loss=0.3012, simple_loss=0.3525, pruned_loss=0.125, over 1464946.49 frames.], batch size: 20, lr: 1.33e-03
2022-07-25 21:06:03,880 INFO [train.py:870] (2/4) Computing validation loss
2022-07-25 21:06:26,735 INFO [train.py:879] (2/4) Epoch 3, validation: loss=0.229, simple_loss=0.3219, pruned_loss=0.06803, over 924787.00 frames.
2022-07-25 21:07:11,911 INFO [train.py:850] (2/4) Epoch 3, batch 6050, loss[loss=0.3565, simple_loss=0.3891, pruned_loss=0.1619, over 7306.00 frames.], tot_loss[loss=0.3027, simple_loss=0.3536, pruned_loss=0.1259, over 1465444.90 frames.], batch size: 27, lr: 1.32e-03
2022-07-25 21:07:56,599 INFO [train.py:850] (2/4) Epoch 3, batch 6100, loss[loss=0.4419, simple_loss=0.4501, pruned_loss=0.2169, over 7394.00 frames.], tot_loss[loss=0.3029, simple_loss=0.3537, pruned_loss=0.126, over 1465979.67 frames.], batch size: 68, lr: 1.32e-03
2022-07-25 21:08:41,431 INFO [train.py:850] (2/4) Epoch 3, batch 6150, loss[loss=0.3113, simple_loss=0.3678, pruned_loss=0.1274, over 7187.00 frames.], tot_loss[loss=0.3017, simple_loss=0.3529, pruned_loss=0.1253, over 1464869.32 frames.], batch size: 21, lr: 1.32e-03
2022-07-25 21:09:25,221 INFO [train.py:850] (2/4) Epoch 3, batch 6200, loss[loss=0.3514, simple_loss=0.3803, pruned_loss=0.1612, over 7452.00 frames.], tot_loss[loss=0.3002, simple_loss=0.3521, pruned_loss=0.1242, over 1464575.99 frames.], batch size: 68, lr: 1.32e-03
2022-07-25 21:10:24,198 INFO [train.py:850] (2/4) Epoch 3, batch 6250, loss[loss=0.3033, simple_loss=0.3429, pruned_loss=0.1318, over 7305.00 frames.], tot_loss[loss=0.3037, simple_loss=0.3546, pruned_loss=0.1264, over 1464157.01 frames.], batch size: 17, lr: 1.32e-03
2022-07-25 21:11:08,902 INFO [train.py:850] (2/4) Epoch 3, batch 6300, loss[loss=0.3001, simple_loss=0.3661, pruned_loss=0.1171, over 7484.00 frames.], tot_loss[loss=0.3033, simple_loss=0.3548, pruned_loss=0.1259, over 1464187.46 frames.], batch size: 26, lr: 1.32e-03
2022-07-25 21:11:53,177 INFO [train.py:850] (2/4) Epoch 3, batch 6350, loss[loss=0.227, simple_loss=0.2923, pruned_loss=0.08086, over 7447.00 frames.], tot_loss[loss=0.3008, simple_loss=0.3527, pruned_loss=0.1245, over 1465509.86 frames.], batch size: 17, lr: 1.32e-03
2022-07-25 21:12:37,632 INFO [train.py:850] (2/4) Epoch 3, batch 6400, loss[loss=0.3524, simple_loss=0.3804, pruned_loss=0.1622, over 7476.00 frames.], tot_loss[loss=0.301, simple_loss=0.3532, pruned_loss=0.1244, over 1465833.09 frames.], batch size: 21, lr: 1.31e-03
2022-07-25 21:13:21,270 INFO [train.py:850] (2/4) Epoch 3, batch 6450, loss[loss=0.3121, simple_loss=0.3709, pruned_loss=0.1266, over 7207.00 frames.], tot_loss[loss=0.3004, simple_loss=0.3522, pruned_loss=0.1243, over 1465077.82 frames.], batch size: 24, lr: 1.31e-03
2022-07-25 21:14:04,013 INFO [train.py:850] (2/4) Epoch 3, batch 6500, loss[loss=0.3198, simple_loss=0.3729, pruned_loss=0.1334, over 7471.00 frames.], tot_loss[loss=0.3014, simple_loss=0.3534, pruned_loss=0.1247, over 1465628.94 frames.], batch size: 24, lr: 1.31e-03
2022-07-25 21:14:49,649 INFO [train.py:850] (2/4) Epoch 3, batch 6550, loss[loss=0.347, simple_loss=0.3859, pruned_loss=0.154, over 7359.00 frames.], tot_loss[loss=0.2997, simple_loss=0.3518, pruned_loss=0.1238, over 1465240.63 frames.], batch size: 23, lr: 1.31e-03
2022-07-25 21:15:35,700 INFO [train.py:850] (2/4) Epoch 3, batch 6600, loss[loss=0.2949, simple_loss=0.3479, pruned_loss=0.1209, over 7440.00 frames.], tot_loss[loss=0.3006, simple_loss=0.3528, pruned_loss=0.1242, over 1465752.93 frames.], batch size: 39, lr: 1.31e-03
2022-07-25 21:16:22,332 INFO [train.py:850] (2/4) Epoch 3, batch 6650, loss[loss=0.2634, simple_loss=0.3276, pruned_loss=0.09957, over 7151.00 frames.], tot_loss[loss=0.2999, simple_loss=0.352, pruned_loss=0.1239, over 1465212.89 frames.], batch size: 17, lr: 1.31e-03
2022-07-25 21:17:05,036 INFO [train.py:850] (2/4) Epoch 3, batch 6700, loss[loss=0.3101, simple_loss=0.3712, pruned_loss=0.1245, over 7413.00 frames.], tot_loss[loss=0.2992, simple_loss=0.3516, pruned_loss=0.1234, over 1464535.66 frames.], batch size: 22, lr: 1.31e-03
2022-07-25 21:17:49,476 INFO [train.py:850] (2/4) Epoch 3, batch 6750, loss[loss=0.2698, simple_loss=0.3149, pruned_loss=0.1123, over 7310.00 frames.], tot_loss[loss=0.2984, simple_loss=0.3513, pruned_loss=0.1228, over 1464306.64 frames.], batch size: 17, lr: 1.31e-03
2022-07-25 21:18:33,238 INFO [train.py:850] (2/4) Epoch 3, batch 6800, loss[loss=0.3119, simple_loss=0.3592, pruned_loss=0.1323, over 7376.00 frames.], tot_loss[loss=0.2989, simple_loss=0.3514, pruned_loss=0.1232, over 1464581.88 frames.], batch size: 21, lr: 1.30e-03
2022-07-25 21:19:16,724 INFO [train.py:850] (2/4) Epoch 3, batch 6850, loss[loss=0.259, simple_loss=0.3322, pruned_loss=0.09294, over 7280.00 frames.], tot_loss[loss=0.2974, simple_loss=0.3502, pruned_loss=0.1223, over 1465929.84 frames.], batch size: 30, lr: 1.30e-03
2022-07-25 21:20:00,469 INFO [train.py:850] (2/4) Epoch 3, batch 6900, loss[loss=0.2787, simple_loss=0.3366, pruned_loss=0.1104, over 7203.00 frames.], tot_loss[loss=0.2968, simple_loss=0.3502, pruned_loss=0.1217, over 1465852.67 frames.], batch size: 20, lr: 1.30e-03
2022-07-25 21:20:44,504 INFO [train.py:850] (2/4) Epoch 3, batch 6950, loss[loss=0.2236, simple_loss=0.2947, pruned_loss=0.07621, over 7210.00 frames.], tot_loss[loss=0.2978, simple_loss=0.3511, pruned_loss=0.1222, over 1465012.09 frames.], batch size: 18, lr: 1.30e-03
2022-07-25 21:21:29,191 INFO [train.py:850] (2/4) Epoch 3, batch 7000, loss[loss=0.2826, simple_loss=0.3371, pruned_loss=0.1141, over 7292.00 frames.], tot_loss[loss=0.2975, simple_loss=0.3511, pruned_loss=0.122, over 1465138.69 frames.], batch size: 21, lr: 1.30e-03
2022-07-25 21:22:12,932 INFO [train.py:850] (2/4) Epoch 3, batch 7050, loss[loss=0.2877, simple_loss=0.3424, pruned_loss=0.1165, over 7208.00 frames.], tot_loss[loss=0.2967, simple_loss=0.3507, pruned_loss=0.1213, over 1465414.13 frames.], batch size: 20, lr: 1.30e-03
2022-07-25 21:22:58,229 INFO [train.py:850] (2/4) Epoch 3, batch 7100, loss[loss=0.2991, simple_loss=0.36, pruned_loss=0.1191, over 7389.00 frames.], tot_loss[loss=0.2949, simple_loss=0.3493, pruned_loss=0.1202, over 1466286.76 frames.], batch size: 21, lr: 1.30e-03
2022-07-25 21:23:42,800 INFO [train.py:850] (2/4) Epoch 3, batch 7150, loss[loss=0.2487, simple_loss=0.3061, pruned_loss=0.0957, over 7435.00 frames.], tot_loss[loss=0.2944, simple_loss=0.3487, pruned_loss=0.1201, over 1466053.14 frames.], batch size: 18, lr: 1.30e-03
2022-07-25 21:24:26,632 INFO [train.py:850] (2/4) Epoch 3, batch 7200, loss[loss=0.3185, simple_loss=0.3648, pruned_loss=0.1361, over 7180.00 frames.], tot_loss[loss=0.2941, simple_loss=0.3483, pruned_loss=0.1199, over 1465525.98 frames.], batch size: 21, lr: 1.29e-03
2022-07-25 21:25:11,178 INFO [train.py:850] (2/4) Epoch 3, batch 7250, loss[loss=0.2568, simple_loss=0.3205, pruned_loss=0.09658, over 7161.00 frames.], tot_loss[loss=0.2955, simple_loss=0.3493, pruned_loss=0.1209, over 1466097.62 frames.], batch size: 17, lr: 1.29e-03
2022-07-25 21:25:54,593 INFO [train.py:850] (2/4) Epoch 3, batch 7300, loss[loss=0.3604, simple_loss=0.3922, pruned_loss=0.1643, over 7358.00 frames.], tot_loss[loss=0.2943, simple_loss=0.3482, pruned_loss=0.1202, over 1466089.34 frames.], batch size: 73, lr: 1.29e-03
2022-07-25 21:26:40,219 INFO [train.py:850] (2/4) Epoch 3, batch 7350, loss[loss=0.3373, simple_loss=0.3958, pruned_loss=0.1394, over 7309.00 frames.], tot_loss[loss=0.2966, simple_loss=0.3503, pruned_loss=0.1214, over 1465356.12 frames.], batch size: 22, lr: 1.29e-03
2022-07-25 21:27:23,719 INFO [train.py:850] (2/4) Epoch 3, batch 7400, loss[loss=0.2652, simple_loss=0.3213, pruned_loss=0.1045, over 7475.00 frames.], tot_loss[loss=0.2989, simple_loss=0.3522, pruned_loss=0.1228, over 1465313.48 frames.], batch size: 20, lr: 1.29e-03
2022-07-25 21:28:08,151 INFO [train.py:850] (2/4) Epoch 3, batch 7450, loss[loss=0.2734, simple_loss=0.33, pruned_loss=0.1084, over 7197.00 frames.], tot_loss[loss=0.2977, simple_loss=0.3513, pruned_loss=0.122, over 1464972.70 frames.], batch size: 18, lr: 1.29e-03
2022-07-25 21:28:51,140 INFO [train.py:850] (2/4) Epoch 3, batch 7500, loss[loss=0.3304, simple_loss=0.3594, pruned_loss=0.1507, over 7296.00 frames.], tot_loss[loss=0.2999, simple_loss=0.3527, pruned_loss=0.1236, over 1464760.05 frames.], batch size: 20, lr: 1.29e-03
2022-07-25 21:29:34,288 INFO [train.py:850] (2/4) Epoch 3, batch 7550, loss[loss=0.3075, simple_loss=0.3621, pruned_loss=0.1265, over 7460.00 frames.], tot_loss[loss=0.2986, simple_loss=0.3522, pruned_loss=0.1225, over 1464089.06 frames.], batch size: 31, lr: 1.29e-03
2022-07-25 21:30:17,715 INFO [train.py:850] (2/4) Epoch 3, batch 7600, loss[loss=0.2676, simple_loss=0.3281, pruned_loss=0.1036, over 7208.00 frames.], tot_loss[loss=0.2986, simple_loss=0.3519, pruned_loss=0.1227, over 1464916.73 frames.], batch size: 20, lr: 1.28e-03
2022-07-25 21:31:01,579 INFO [train.py:850] (2/4) Epoch 3, batch 7650, loss[loss=0.3258, simple_loss=0.3805, pruned_loss=0.1356, over 7303.00 frames.], tot_loss[loss=0.2981, simple_loss=0.3517, pruned_loss=0.1223, over 1464937.37 frames.], batch size: 20, lr: 1.28e-03
2022-07-25 21:31:45,558 INFO [train.py:850] (2/4) Epoch 3, batch 7700, loss[loss=0.2959, simple_loss=0.3485, pruned_loss=0.1216, over 7350.00 frames.], tot_loss[loss=0.2962, simple_loss=0.3503, pruned_loss=0.121, over 1464259.43 frames.], batch size: 23, lr: 1.28e-03
2022-07-25 21:32:29,066 INFO [train.py:850] (2/4) Epoch 3, batch 7750, loss[loss=0.2616, simple_loss=0.3342, pruned_loss=0.0945, over 7289.00 frames.], tot_loss[loss=0.2952, simple_loss=0.3498, pruned_loss=0.1203, over 1465354.85 frames.], batch size: 20, lr: 1.28e-03
2022-07-25 21:33:11,757 INFO [train.py:850] (2/4) Epoch 3, batch 7800, loss[loss=0.2935, simple_loss=0.3605, pruned_loss=0.1132, over 7191.00 frames.], tot_loss[loss=0.2958, simple_loss=0.3499, pruned_loss=0.1209, over 1465128.78 frames.], batch size: 21, lr: 1.28e-03
2022-07-25 21:33:56,697 INFO [train.py:850] (2/4) Epoch 3, batch 7850, loss[loss=0.2769, simple_loss=0.3362, pruned_loss=0.1089, over 7389.00 frames.], tot_loss[loss=0.2944, simple_loss=0.3492, pruned_loss=0.1198, over 1465806.79 frames.], batch size: 20, lr: 1.28e-03
2022-07-25 21:34:40,242 INFO [train.py:850] (2/4) Epoch 3, batch 7900, loss[loss=0.2796, simple_loss=0.3308, pruned_loss=0.1142, over 7397.00 frames.], tot_loss[loss=0.2944, simple_loss=0.3495, pruned_loss=0.1197, over 1465325.34 frames.], batch size: 19, lr: 1.28e-03
2022-07-25 21:35:25,283 INFO [train.py:850] (2/4) Epoch 3, batch 7950, loss[loss=0.3558, simple_loss=0.4138, pruned_loss=0.1489, over 7476.00 frames.], tot_loss[loss=0.2957, simple_loss=0.3506, pruned_loss=0.1204, over 1465494.90 frames.], batch size: 21, lr: 1.28e-03
2022-07-25 21:36:07,837 INFO [train.py:850] (2/4) Epoch 3, batch 8000, loss[loss=0.3346, simple_loss=0.3748, pruned_loss=0.1472, over 7467.00 frames.], tot_loss[loss=0.298, simple_loss=0.3521, pruned_loss=0.1219, over 1465109.83 frames.], batch size: 21, lr: 1.27e-03
2022-07-25 21:36:52,792 INFO [train.py:850] (2/4) Epoch 3, batch 8050, loss[loss=0.2662, simple_loss=0.3418, pruned_loss=0.09526, over 7425.00 frames.], tot_loss[loss=0.296, simple_loss=0.3508, pruned_loss=0.1207, over 1465213.62 frames.], batch size: 22, lr: 1.27e-03
2022-07-25 21:37:36,199 INFO [train.py:850] (2/4) Epoch 3, batch 8100, loss[loss=0.3249, simple_loss=0.3822, pruned_loss=0.1339, over 7475.00 frames.], tot_loss[loss=0.2953, simple_loss=0.3503, pruned_loss=0.1201, over 1465967.24 frames.], batch size: 21, lr: 1.27e-03
2022-07-25 21:38:19,730 INFO [train.py:850] (2/4) Epoch 3, batch 8150, loss[loss=0.2927, simple_loss=0.3403, pruned_loss=0.1225, over 7437.00 frames.], tot_loss[loss=0.2938, simple_loss=0.349, pruned_loss=0.1193, over 1465629.37 frames.], batch size: 17, lr: 1.27e-03
2022-07-25 21:39:04,092 INFO [train.py:850] (2/4) Epoch 3, batch 8200, loss[loss=0.2355, simple_loss=0.2946, pruned_loss=0.08824, over 7441.00 frames.], tot_loss[loss=0.2955, simple_loss=0.35, pruned_loss=0.1205, over 1465773.05 frames.], batch size: 18, lr: 1.27e-03
2022-07-25 21:39:48,130 INFO [train.py:850] (2/4) Epoch 3, batch 8250, loss[loss=0.299, simple_loss=0.3266, pruned_loss=0.1357, over 7307.00 frames.], tot_loss[loss=0.2958, simple_loss=0.3505, pruned_loss=0.1205, over 1466655.93 frames.], batch size: 17, lr: 1.27e-03
2022-07-25 21:40:31,543 INFO [train.py:850] (2/4) Epoch 3, batch 8300, loss[loss=0.2574, simple_loss=0.3371, pruned_loss=0.08889, over 7381.00 frames.], tot_loss[loss=0.2923, simple_loss=0.3477, pruned_loss=0.1184, over 1466081.72 frames.], batch size: 21, lr: 1.27e-03
2022-07-25 21:41:15,366 INFO [train.py:850] (2/4) Epoch 3, batch 8350, loss[loss=0.3448, simple_loss=0.3942, pruned_loss=0.1477, over 7299.00 frames.], tot_loss[loss=0.2941, simple_loss=0.349, pruned_loss=0.1196, over 1465832.98 frames.], batch size: 22, lr: 1.27e-03
2022-07-25 21:41:59,511 INFO [train.py:850] (2/4) Epoch 3, batch 8400, loss[loss=0.2588, simple_loss=0.3341, pruned_loss=0.09173, over 7426.00 frames.], tot_loss[loss=0.2932, simple_loss=0.3484, pruned_loss=0.1191, over 1464845.06 frames.], batch size: 22, lr: 1.27e-03
2022-07-25 21:42:43,550 INFO [train.py:850] (2/4) Epoch 3, batch 8450, loss[loss=0.2397, simple_loss=0.3027, pruned_loss=0.08839, over 7296.00 frames.], tot_loss[loss=0.2926, simple_loss=0.3478, pruned_loss=0.1187, over 1465774.22 frames.], batch size: 17, lr: 1.26e-03
2022-07-25 21:43:26,446 INFO [train.py:850] (2/4) Epoch 3, batch 8500, loss[loss=0.2658, simple_loss=0.3325, pruned_loss=0.09949, over 7181.00 frames.], tot_loss[loss=0.292, simple_loss=0.3471, pruned_loss=0.1184, over 1464915.45 frames.], batch size: 23, lr: 1.26e-03
2022-07-25 21:44:10,726 INFO [train.py:850] (2/4) Epoch 3, batch 8550, loss[loss=0.213, simple_loss=0.2813, pruned_loss=0.07233, over 7437.00 frames.], tot_loss[loss=0.2921, simple_loss=0.3475, pruned_loss=0.1183, over 1465541.68 frames.], batch size: 18, lr: 1.26e-03
2022-07-25 21:44:54,658 INFO [train.py:850] (2/4) Epoch 3, batch 8600, loss[loss=0.3223, simple_loss=0.3787, pruned_loss=0.133, over 7187.00 frames.], tot_loss[loss=0.2904, simple_loss=0.3461, pruned_loss=0.1173, over 1466363.60 frames.], batch size: 21, lr: 1.26e-03
2022-07-25 21:45:38,538 INFO [train.py:850] (2/4) Epoch 3, batch 8650, loss[loss=0.2744, simple_loss=0.3406, pruned_loss=0.1041, over 7291.00 frames.], tot_loss[loss=0.2913, simple_loss=0.3464, pruned_loss=0.118, over 1466675.96 frames.], batch size: 21, lr: 1.26e-03
2022-07-25 21:46:21,988 INFO [train.py:850] (2/4) Epoch 3, batch 8700, loss[loss=0.2924, simple_loss=0.3461, pruned_loss=0.1193, over 7346.00 frames.], tot_loss[loss=0.2916, simple_loss=0.3469, pruned_loss=0.1182, over 1467304.48 frames.], batch size: 23, lr: 1.26e-03
2022-07-25 21:47:05,362 INFO [train.py:850] (2/4) Epoch 3, batch 8750, loss[loss=0.3031, simple_loss=0.3514, pruned_loss=0.1274, over 7477.00 frames.], tot_loss[loss=0.2892, simple_loss=0.3453, pruned_loss=0.1166, over 1467203.93 frames.], batch size: 21, lr: 1.26e-03
2022-07-25 21:47:49,371 INFO [train.py:850] (2/4) Epoch 3, batch 8800, loss[loss=0.3488, simple_loss=0.3879, pruned_loss=0.1548, over 7453.00 frames.], tot_loss[loss=0.2877, simple_loss=0.3446, pruned_loss=0.1154, over 1467041.34 frames.], batch size: 72, lr: 1.26e-03
2022-07-25 21:48:33,454 INFO [train.py:850] (2/4) Epoch 3, batch 8850, loss[loss=0.2963, simple_loss=0.3593, pruned_loss=0.1166, over 7203.00 frames.], tot_loss[loss=0.2869, simple_loss=0.344, pruned_loss=0.1149, over 1466800.26 frames.], batch size: 20, lr: 1.26e-03
2022-07-25 21:50:13,808 INFO [train.py:850] (2/4) Epoch 4, batch 0, loss[loss=0.2436, simple_loss=0.3163, pruned_loss=0.08549, over 7382.00 frames.], tot_loss[loss=0.2436, simple_loss=0.3163, pruned_loss=0.08549, over 7382.00 frames.], batch size: 20, lr: 1.22e-03
2022-07-25 21:50:57,445 INFO [train.py:850] (2/4) Epoch 4, batch 50, loss[loss=0.2621, simple_loss=0.3452, pruned_loss=0.08948, over 7298.00 frames.], tot_loss[loss=0.2658, simple_loss=0.3369, pruned_loss=0.09732, over 330321.40 frames.], batch size: 22, lr: 1.22e-03
2022-07-25 21:51:41,067 INFO [train.py:850] (2/4) Epoch 4, batch 100, loss[loss=0.3458, simple_loss=0.3967, pruned_loss=0.1474, over 7370.00 frames.], tot_loss[loss=0.2669, simple_loss=0.3364, pruned_loss=0.0987, over 582347.08 frames.], batch size: 21, lr: 1.22e-03
2022-07-25 21:52:24,648 INFO [train.py:850] (2/4) Epoch 4, batch 150, loss[loss=0.2385, simple_loss=0.3202, pruned_loss=0.07845, over 7476.00 frames.], tot_loss[loss=0.264, simple_loss=0.3334, pruned_loss=0.09731, over 779048.84 frames.], batch size: 20, lr: 1.21e-03
2022-07-25 21:53:07,962 INFO [train.py:850] (2/4) Epoch 4, batch 200, loss[loss=0.2207, simple_loss=0.3108, pruned_loss=0.06526, over 7206.00 frames.], tot_loss[loss=0.2623, simple_loss=0.3329, pruned_loss=0.0958, over 931319.15 frames.], batch size: 20, lr: 1.21e-03
2022-07-25 21:53:51,693 INFO [train.py:850] (2/4) Epoch 4, batch 250, loss[loss=0.2486, simple_loss=0.3309, pruned_loss=0.08316, over 7212.00 frames.], tot_loss[loss=0.2605, simple_loss=0.3313, pruned_loss=0.09483, over 1049590.09 frames.], batch size: 24, lr: 1.21e-03
2022-07-25 21:54:34,794 INFO [train.py:850] (2/4) Epoch 4, batch 300, loss[loss=0.2997, simple_loss=0.366, pruned_loss=0.1168, over 7333.00 frames.], tot_loss[loss=0.2587, simple_loss=0.3303, pruned_loss=0.09359, over 1141123.40 frames.], batch size: 27, lr: 1.21e-03
2022-07-25 21:55:18,741 INFO [train.py:850] (2/4) Epoch 4, batch 350, loss[loss=0.22, simple_loss=0.3094, pruned_loss=0.06529, over 7290.00 frames.], tot_loss[loss=0.2563, simple_loss=0.3286, pruned_loss=0.09205, over 1213398.74 frames.], batch size: 21, lr: 1.21e-03
2022-07-25 21:56:02,684 INFO [train.py:850] (2/4) Epoch 4, batch 400, loss[loss=0.2877, simple_loss=0.3516, pruned_loss=0.112, over 7351.00 frames.], tot_loss[loss=0.2564, simple_loss=0.3287, pruned_loss=0.09203, over 1268800.64 frames.], batch size: 23, lr: 1.21e-03
2022-07-25 21:56:46,908 INFO [train.py:850] (2/4) Epoch 4, batch 450, loss[loss=0.2585, simple_loss=0.3368, pruned_loss=0.09005, over 7492.00 frames.], tot_loss[loss=0.2548, simple_loss=0.3276, pruned_loss=0.09098, over 1312036.49 frames.], batch size: 26, lr: 1.21e-03
2022-07-25 21:57:30,121 INFO [train.py:850] (2/4) Epoch 4, batch 500, loss[loss=0.2952, simple_loss=0.3583, pruned_loss=0.1161, over 7443.00 frames.], tot_loss[loss=0.2528, simple_loss=0.3263, pruned_loss=0.08968, over 1345395.72 frames.], batch size: 24, lr: 1.21e-03
2022-07-25 21:58:13,764 INFO [train.py:850] (2/4) Epoch 4, batch 550, loss[loss=0.2461, simple_loss=0.3323, pruned_loss=0.08001, over 7397.00 frames.], tot_loss[loss=0.2511, simple_loss=0.325, pruned_loss=0.08863, over 1372175.98 frames.], batch size: 38, lr: 1.21e-03
2022-07-25 21:58:56,515 INFO [train.py:850] (2/4) Epoch 4, batch 600, loss[loss=0.2652, simple_loss=0.3492, pruned_loss=0.09057, over 7182.00 frames.], tot_loss[loss=0.2508, simple_loss=0.3252, pruned_loss=0.08819, over 1393067.17 frames.], batch size: 22, lr: 1.20e-03
2022-07-25 21:59:40,180 INFO [train.py:850] (2/4) Epoch 4, batch 650, loss[loss=0.224, simple_loss=0.2876, pruned_loss=0.08017, over 7438.00 frames.], tot_loss[loss=0.25, simple_loss=0.3242, pruned_loss=0.0879, over 1409264.50 frames.], batch size: 18, lr: 1.20e-03
2022-07-25 22:00:23,156 INFO [train.py:850] (2/4) Epoch 4, batch 700, loss[loss=0.2789, simple_loss=0.3451, pruned_loss=0.1064, over 7454.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3237, pruned_loss=0.08682, over 1421121.12 frames.], batch size: 66, lr: 1.20e-03
2022-07-25 22:01:07,260 INFO [train.py:850] (2/4) Epoch 4, batch 750, loss[loss=0.3154, simple_loss=0.3739, pruned_loss=0.1285, over 7253.00 frames.], tot_loss[loss=0.2496, simple_loss=0.3244, pruned_loss=0.08737, over 1431015.14 frames.], batch size: 24, lr: 1.20e-03
2022-07-25 22:01:50,823 INFO [train.py:850] (2/4) Epoch 4, batch 800, loss[loss=0.2342, simple_loss=0.319, pruned_loss=0.07468, over 7348.00 frames.], tot_loss[loss=0.2505, simple_loss=0.3248, pruned_loss=0.08814, over 1438778.20 frames.], batch size: 23, lr: 1.20e-03
2022-07-25 22:02:35,072 INFO [train.py:850] (2/4) Epoch 4, batch 850, loss[loss=0.2386, simple_loss=0.3074, pruned_loss=0.08491, over 7442.00 frames.], tot_loss[loss=0.2509, simple_loss=0.325, pruned_loss=0.08846, over 1444282.17 frames.], batch size: 18, lr: 1.20e-03
2022-07-25 22:03:18,299 INFO [train.py:850] (2/4) Epoch 4, batch 900, loss[loss=0.2029, simple_loss=0.2779, pruned_loss=0.06398, over 7484.00 frames.], tot_loss[loss=0.2544, simple_loss=0.3274, pruned_loss=0.09067, over 1449692.10 frames.], batch size: 19, lr: 1.20e-03
2022-07-25 22:04:01,905 INFO [train.py:850] (2/4) Epoch 4, batch 950, loss[loss=0.2406, simple_loss=0.3114, pruned_loss=0.08487, over 7391.00 frames.], tot_loss[loss=0.2569, simple_loss=0.3292, pruned_loss=0.09235, over 1453868.18 frames.], batch size: 19, lr: 1.20e-03
2022-07-25 22:04:46,006 INFO [train.py:850] (2/4) Epoch 4, batch 1000, loss[loss=0.2887, simple_loss=0.3555, pruned_loss=0.1109, over 7234.00 frames.], tot_loss[loss=0.258, simple_loss=0.3302, pruned_loss=0.09293, over 1456460.40 frames.], batch size: 24, lr: 1.20e-03
2022-07-25 22:05:29,754 INFO [train.py:850] (2/4) Epoch 4, batch 1050, loss[loss=0.2247, simple_loss=0.3197, pruned_loss=0.06488, over 7313.00 frames.], tot_loss[loss=0.2584, simple_loss=0.3308, pruned_loss=0.09303, over 1457953.10 frames.], batch size: 22, lr: 1.20e-03
2022-07-25 22:06:13,366 INFO [train.py:850] (2/4) Epoch 4, batch 1100, loss[loss=0.2861, simple_loss=0.3397, pruned_loss=0.1162, over 7186.00 frames.], tot_loss[loss=0.2593, simple_loss=0.3313, pruned_loss=0.09363, over 1458620.16 frames.], batch size: 18, lr: 1.19e-03
2022-07-25 22:06:56,844 INFO [train.py:850] (2/4) Epoch 4, batch 1150, loss[loss=0.2801, simple_loss=0.3537, pruned_loss=0.1033, over 7175.00 frames.], tot_loss[loss=0.2587, simple_loss=0.3303, pruned_loss=0.09351, over 1459398.86 frames.], batch size: 22, lr: 1.19e-03
2022-07-25 22:07:40,487 INFO [train.py:850] (2/4) Epoch 4, batch 1200, loss[loss=0.306, simple_loss=0.3695, pruned_loss=0.1212, over 7492.00 frames.], tot_loss[loss=0.258, simple_loss=0.33, pruned_loss=0.09301, over 1461378.81 frames.], batch size: 23, lr: 1.19e-03
2022-07-25 22:08:23,937 INFO [train.py:850] (2/4) Epoch 4, batch 1250, loss[loss=0.2871, simple_loss=0.3607, pruned_loss=0.1067, over 7265.00 frames.], tot_loss[loss=0.2607, simple_loss=0.3323, pruned_loss=0.09451, over 1462289.06 frames.], batch size: 30, lr: 1.19e-03
2022-07-25 22:09:07,296 INFO [train.py:850] (2/4) Epoch 4, batch 1300, loss[loss=0.2362, simple_loss=0.3258, pruned_loss=0.07326, over 7200.00 frames.], tot_loss[loss=0.2618, simple_loss=0.3334, pruned_loss=0.09516, over 1462288.49 frames.], batch size: 20, lr: 1.19e-03
2022-07-25 22:10:06,132 INFO [train.py:850] (2/4) Epoch 4, batch 1350, loss[loss=0.2463, simple_loss=0.3265, pruned_loss=0.08304, over 7309.00 frames.], tot_loss[loss=0.263, simple_loss=0.3341, pruned_loss=0.09599, over 1462251.09 frames.], batch size: 22, lr: 1.19e-03
2022-07-25 22:10:49,679 INFO [train.py:850] (2/4) Epoch 4, batch 1400, loss[loss=0.2707, simple_loss=0.3494, pruned_loss=0.09599, over 7310.00 frames.], tot_loss[loss=0.2632, simple_loss=0.3343, pruned_loss=0.09607, over 1462714.10 frames.], batch size: 39, lr: 1.19e-03
2022-07-25 22:11:33,752 INFO [train.py:850] (2/4) Epoch 4, batch 1450, loss[loss=0.3261, simple_loss=0.3721, pruned_loss=0.1401, over 7467.00 frames.], tot_loss[loss=0.2641, simple_loss=0.335, pruned_loss=0.09664, over 1465135.54 frames.], batch size: 24, lr: 1.19e-03
2022-07-25 22:12:17,174 INFO [train.py:850] (2/4) Epoch 4, batch 1500, loss[loss=0.2249, simple_loss=0.3024, pruned_loss=0.07368, over 7206.00 frames.], tot_loss[loss=0.2634, simple_loss=0.3346, pruned_loss=0.09614, over 1464769.96 frames.], batch size: 19, lr: 1.19e-03
2022-07-25 22:13:01,078 INFO [train.py:850] (2/4) Epoch 4, batch 1550, loss[loss=0.2827, simple_loss=0.3629, pruned_loss=0.1013, over 7260.00 frames.], tot_loss[loss=0.2628, simple_loss=0.3343, pruned_loss=0.0957, over 1465915.49 frames.], batch size: 27, lr: 1.18e-03
2022-07-25 22:13:44,739 INFO [train.py:850] (2/4) Epoch 4, batch 1600, loss[loss=0.2515, simple_loss=0.3149, pruned_loss=0.09401, over 7296.00 frames.], tot_loss[loss=0.2621, simple_loss=0.3336, pruned_loss=0.09533, over 1465664.84 frames.], batch size: 19, lr: 1.18e-03
2022-07-25 22:14:28,904 INFO [train.py:850] (2/4) Epoch 4, batch 1650, loss[loss=0.2841, simple_loss=0.3584, pruned_loss=0.1049, over 7276.00 frames.], tot_loss[loss=0.2613, simple_loss=0.3328, pruned_loss=0.09488, over 1465954.89 frames.], batch size: 30, lr: 1.18e-03
2022-07-25 22:15:12,244 INFO [train.py:850] (2/4) Epoch 4, batch 1700, loss[loss=0.22, simple_loss=0.3018, pruned_loss=0.06912, over 7284.00 frames.], tot_loss[loss=0.261, simple_loss=0.3324, pruned_loss=0.09475, over 1464743.55 frames.], batch size: 20, lr: 1.18e-03
2022-07-25 22:15:56,959 INFO [train.py:850] (2/4) Epoch 4, batch 1750, loss[loss=0.3288, simple_loss=0.396, pruned_loss=0.1308, over 7385.00 frames.], tot_loss[loss=0.2622, simple_loss=0.3335, pruned_loss=0.0954, over 1465434.31 frames.], batch size: 21, lr: 1.18e-03
2022-07-25 22:16:40,328 INFO [train.py:850] (2/4) Epoch 4, batch 1800, loss[loss=0.2835, simple_loss=0.3387, pruned_loss=0.1141, over 7175.00 frames.], tot_loss[loss=0.2605, simple_loss=0.3319, pruned_loss=0.09458, over 1465277.10 frames.], batch size: 21, lr: 1.18e-03
2022-07-25 22:17:24,260 INFO [train.py:850] (2/4) Epoch 4, batch 1850, loss[loss=0.2413, simple_loss=0.3095, pruned_loss=0.08651, over 7297.00 frames.], tot_loss[loss=0.2609, simple_loss=0.3325, pruned_loss=0.09462, over 1465087.75 frames.], batch size: 17, lr: 1.18e-03
2022-07-25 22:18:07,893 INFO [train.py:850] (2/4) Epoch 4, batch 1900, loss[loss=0.2754, simple_loss=0.3525, pruned_loss=0.09911, over 7284.00 frames.], tot_loss[loss=0.2596, simple_loss=0.3313, pruned_loss=0.094, over 1465567.99 frames.], batch size: 20, lr: 1.18e-03
2022-07-25 22:18:51,205 INFO [train.py:850] (2/4) Epoch 4, batch 1950, loss[loss=0.2316, simple_loss=0.3076, pruned_loss=0.07781, over 7105.00 frames.], tot_loss[loss=0.258, simple_loss=0.3298, pruned_loss=0.09309, over 1465525.20 frames.], batch size: 18, lr: 1.18e-03
2022-07-25 22:19:34,249 INFO [train.py:850] (2/4) Epoch 4, batch 2000, loss[loss=0.2026, simple_loss=0.2798, pruned_loss=0.06272, over 7203.00 frames.], tot_loss[loss=0.2578, simple_loss=0.3304, pruned_loss=0.09259, over 1463230.15 frames.], batch size: 19, lr: 1.18e-03
2022-07-25 22:20:18,664 INFO [train.py:850] (2/4) Epoch 4, batch 2050, loss[loss=0.3059, simple_loss=0.3508, pruned_loss=0.1305, over 7283.00 frames.], tot_loss[loss=0.2584, simple_loss=0.3311, pruned_loss=0.09286, over 1464084.64 frames.], batch size: 17, lr: 1.17e-03
2022-07-25 22:21:01,686 INFO [train.py:850] (2/4) Epoch 4, batch 2100, loss[loss=0.3154, simple_loss=0.3644, pruned_loss=0.1332, over 7487.00 frames.], tot_loss[loss=0.2601, simple_loss=0.3324, pruned_loss=0.09389, over 1463793.25 frames.], batch size: 19, lr: 1.17e-03
2022-07-25 22:21:45,799 INFO [train.py:850] (2/4) Epoch 4, batch 2150, loss[loss=0.2792, simple_loss=0.3422, pruned_loss=0.1081, over 7486.00 frames.], tot_loss[loss=0.2585, simple_loss=0.3312, pruned_loss=0.09289, over 1462866.44 frames.], batch size: 20, lr: 1.17e-03
2022-07-25 22:22:30,198 INFO [train.py:850] (2/4) Epoch 4, batch 2200, loss[loss=0.2052, simple_loss=0.3027, pruned_loss=0.05379, over 7189.00 frames.], tot_loss[loss=0.2577, simple_loss=0.3305, pruned_loss=0.09249, over 1462882.32 frames.], batch size: 21, lr: 1.17e-03
2022-07-25 22:23:13,755 INFO [train.py:850] (2/4) Epoch 4, batch 2250, loss[loss=0.2318, simple_loss=0.3136, pruned_loss=0.07498, over 7345.00 frames.], tot_loss[loss=0.2569, simple_loss=0.3301, pruned_loss=0.09186, over 1463643.35 frames.], batch size: 23, lr: 1.17e-03
2022-07-25 22:23:57,402 INFO [train.py:850] (2/4) Epoch 4, batch 2300, loss[loss=0.2525, simple_loss=0.3384, pruned_loss=0.08327, over 7394.00 frames.], tot_loss[loss=0.2558, simple_loss=0.3289, pruned_loss=0.09133, over 1465357.73 frames.], batch size: 21, lr: 1.17e-03
2022-07-25 22:24:40,602 INFO [train.py:850] (2/4) Epoch 4, batch 2350, loss[loss=0.2998, simple_loss=0.3785, pruned_loss=0.1105, over 7307.00 frames.], tot_loss[loss=0.2578, simple_loss=0.3305, pruned_loss=0.09256, over 1465850.37 frames.], batch size: 22, lr: 1.17e-03
2022-07-25 22:25:24,987 INFO [train.py:850] (2/4) Epoch 4, batch 2400, loss[loss=0.2198, simple_loss=0.3051, pruned_loss=0.06725, over 7390.00 frames.], tot_loss[loss=0.2583, simple_loss=0.3308, pruned_loss=0.09292, over 1465538.16 frames.], batch size: 21, lr: 1.17e-03
2022-07-25 22:26:09,162 INFO [train.py:850] (2/4) Epoch 4, batch 2450, loss[loss=0.2461, simple_loss=0.3277, pruned_loss=0.08219, over 7291.00 frames.], tot_loss[loss=0.2588, simple_loss=0.3311, pruned_loss=0.09321, over 1465798.47 frames.], batch size: 20, lr: 1.17e-03
2022-07-25 22:26:52,927 INFO [train.py:850] (2/4) Epoch 4, batch 2500, loss[loss=0.2868, simple_loss=0.35, pruned_loss=0.1118, over 7410.00 frames.], tot_loss[loss=0.2599, simple_loss=0.3319, pruned_loss=0.09395, over 1465718.34 frames.], batch size: 39, lr: 1.17e-03
2022-07-25 22:27:39,501 INFO [train.py:850] (2/4) Epoch 4, batch 2550, loss[loss=0.2829, simple_loss=0.3543, pruned_loss=0.1058, over 7381.00 frames.], tot_loss[loss=0.2617, simple_loss=0.3334, pruned_loss=0.09506, over 1466123.86 frames.], batch size: 21, lr: 1.16e-03
2022-07-25 22:28:22,473 INFO [train.py:850] (2/4) Epoch 4, batch 2600, loss[loss=0.2432, simple_loss=0.3136, pruned_loss=0.08637, over 7300.00 frames.], tot_loss[loss=0.2615, simple_loss=0.3329, pruned_loss=0.09507, over 1466496.34 frames.], batch size: 19, lr: 1.16e-03
2022-07-25 22:29:06,884 INFO [train.py:850] (2/4) Epoch 4, batch 2650, loss[loss=0.2379, simple_loss=0.3099, pruned_loss=0.08292, over 7399.00 frames.], tot_loss[loss=0.2591, simple_loss=0.3307, pruned_loss=0.09374, over 1465741.02 frames.], batch size: 19, lr: 1.16e-03
2022-07-25 22:29:49,478 INFO [train.py:850] (2/4) Epoch 4, batch 2700, loss[loss=0.2273, simple_loss=0.314, pruned_loss=0.07029, over 7468.00 frames.], tot_loss[loss=0.259, simple_loss=0.331, pruned_loss=0.09351, over 1465540.46 frames.], batch size: 21, lr: 1.16e-03
2022-07-25 22:30:33,955 INFO [train.py:850] (2/4) Epoch 4, batch 2750, loss[loss=0.2423, simple_loss=0.3086, pruned_loss=0.08798, over 7290.00 frames.], tot_loss[loss=0.2582, simple_loss=0.3305, pruned_loss=0.09296, over 1466257.99 frames.], batch size: 17, lr: 1.16e-03
2022-07-25 22:31:16,933 INFO [train.py:850] (2/4) Epoch 4, batch 2800, loss[loss=0.2595, simple_loss=0.3412, pruned_loss=0.08893, over 7291.00 frames.], tot_loss[loss=0.2598, simple_loss=0.3319, pruned_loss=0.09388, over 1464547.83 frames.], batch size: 27, lr: 1.16e-03
2022-07-25 22:32:00,801 INFO [train.py:850] (2/4) Epoch 4, batch 2850, loss[loss=0.2522, simple_loss=0.3375, pruned_loss=0.08347, over 7304.00 frames.], tot_loss[loss=0.259, simple_loss=0.3314, pruned_loss=0.09335, over 1465147.63 frames.], batch size: 22, lr: 1.16e-03
2022-07-25 22:32:44,568 INFO [train.py:850] (2/4) Epoch 4, batch 2900, loss[loss=0.2395, simple_loss=0.3062, pruned_loss=0.08641, over 7443.00 frames.], tot_loss[loss=0.2558, simple_loss=0.3284, pruned_loss=0.09154, over 1465056.32 frames.], batch size: 17, lr: 1.16e-03
2022-07-25 22:33:27,935 INFO [train.py:850] (2/4) Epoch 4, batch 2950, loss[loss=0.1906, simple_loss=0.2731, pruned_loss=0.05404, over 7200.00 frames.], tot_loss[loss=0.2555, simple_loss=0.3284, pruned_loss=0.09128, over 1464476.08 frames.], batch size: 18, lr: 1.16e-03
2022-07-25 22:34:12,389 INFO [train.py:850] (2/4) Epoch 4, batch 3000, loss[loss=0.2559, simple_loss=0.3277, pruned_loss=0.09202, over 7290.00 frames.], tot_loss[loss=0.2559, simple_loss=0.3286, pruned_loss=0.09162, over 1464818.40 frames.], batch size: 21, lr: 1.16e-03
2022-07-25 22:34:12,390 INFO [train.py:870] (2/4) Computing validation loss
2022-07-25 22:34:36,299 INFO [train.py:879] (2/4) Epoch 4, validation: loss=0.2311, simple_loss=0.3226, pruned_loss=0.06982, over 924787.00 frames.
2022-07-25 22:35:20,183 INFO [train.py:850] (2/4) Epoch 4, batch 3050, loss[loss=0.2738, simple_loss=0.3433, pruned_loss=0.1021, over 7301.00 frames.], tot_loss[loss=0.2563, simple_loss=0.3286, pruned_loss=0.09199, over 1465283.56 frames.], batch size: 22, lr: 1.16e-03
2022-07-25 22:36:04,488 INFO [train.py:850] (2/4) Epoch 4, batch 3100, loss[loss=0.2252, simple_loss=0.3065, pruned_loss=0.07193, over 7182.00 frames.], tot_loss[loss=0.2552, simple_loss=0.328, pruned_loss=0.09113, over 1465746.11 frames.], batch size: 18, lr: 1.15e-03
2022-07-25 22:36:49,957 INFO [train.py:850] (2/4) Epoch 4, batch 3150, loss[loss=0.3538, simple_loss=0.4135, pruned_loss=0.147, over 7425.00 frames.], tot_loss[loss=0.2537, simple_loss=0.3267, pruned_loss=0.09034, over 1465711.99 frames.], batch size: 69, lr: 1.15e-03
2022-07-25 22:37:32,855 INFO [train.py:850] (2/4) Epoch 4, batch 3200, loss[loss=0.2131, simple_loss=0.2877, pruned_loss=0.06926, over 7446.00 frames.], tot_loss[loss=0.2536, simple_loss=0.3269, pruned_loss=0.0901, over 1465216.87 frames.], batch size: 18, lr: 1.15e-03
2022-07-25 22:38:18,630 INFO [train.py:850] (2/4) Epoch 4, batch 3250, loss[loss=0.2439, simple_loss=0.3163, pruned_loss=0.08579, over 7294.00 frames.], tot_loss[loss=0.2518, simple_loss=0.3256, pruned_loss=0.08902, over 1465956.57 frames.], batch size: 19, lr: 1.15e-03
2022-07-25 22:39:03,015 INFO [train.py:850] (2/4) Epoch 4, batch 3300, loss[loss=0.2493, simple_loss=0.3164, pruned_loss=0.09106, over 7433.00 frames.], tot_loss[loss=0.2527, simple_loss=0.3264, pruned_loss=0.08949, over 1465281.45 frames.], batch size: 18, lr: 1.15e-03
2022-07-25 22:39:47,819 INFO [train.py:850] (2/4) Epoch 4, batch 3350, loss[loss=0.194, simple_loss=0.2721, pruned_loss=0.05792, over 7460.00 frames.], tot_loss[loss=0.2526, simple_loss=0.3264, pruned_loss=0.08945, over 1464671.18 frames.], batch size: 17, lr: 1.15e-03
2022-07-25 22:40:31,663 INFO [train.py:850] (2/4) Epoch 4, batch 3400, loss[loss=0.2315, simple_loss=0.3082, pruned_loss=0.07742, over 7391.00 frames.], tot_loss[loss=0.2533, simple_loss=0.3269, pruned_loss=0.08984, over 1464368.15 frames.], batch size: 19, lr: 1.15e-03
2022-07-25 22:41:14,964 INFO [train.py:850] (2/4) Epoch 4, batch 3450, loss[loss=0.2454, simple_loss=0.3167, pruned_loss=0.08703, over 7282.00 frames.], tot_loss[loss=0.2505, simple_loss=0.3248, pruned_loss=0.08808, over 1463867.49 frames.], batch size: 20, lr: 1.15e-03
2022-07-25 22:41:58,845 INFO [train.py:850] (2/4) Epoch 4, batch 3500, loss[loss=0.2343, simple_loss=0.3021, pruned_loss=0.08329, over 7316.00 frames.], tot_loss[loss=0.2506, simple_loss=0.3246, pruned_loss=0.08827, over 1463620.92 frames.], batch size: 17, lr: 1.15e-03
2022-07-25 22:42:42,851 INFO [train.py:850] (2/4) Epoch 4, batch 3550, loss[loss=0.2617, simple_loss=0.3526, pruned_loss=0.0854, over 7484.00 frames.], tot_loss[loss=0.2508, simple_loss=0.3248, pruned_loss=0.08834, over 1464345.82 frames.], batch size: 21, lr: 1.15e-03
2022-07-25 22:43:26,653 INFO [train.py:850] (2/4) Epoch 4, batch 3600, loss[loss=0.25, simple_loss=0.3087, pruned_loss=0.09564, over 7317.00 frames.], tot_loss[loss=0.2519, simple_loss=0.3253, pruned_loss=0.08925, over 1464570.69 frames.], batch size: 18, lr: 1.15e-03
2022-07-25 22:44:10,108 INFO [train.py:850] (2/4) Epoch 4, batch 3650, loss[loss=0.2555, simple_loss=0.3148, pruned_loss=0.09804, over 7481.00 frames.], tot_loss[loss=0.2518, simple_loss=0.3257, pruned_loss=0.08895, over 1464362.07 frames.], batch size: 19, lr: 1.14e-03
2022-07-25 22:44:53,261 INFO [train.py:850] (2/4) Epoch 4, batch 3700, loss[loss=0.268, simple_loss=0.3445, pruned_loss=0.09574, over 7359.00 frames.], tot_loss[loss=0.2526, simple_loss=0.3266, pruned_loss=0.0893, over 1464239.95 frames.], batch size: 23, lr: 1.14e-03
2022-07-25 22:45:37,259 INFO [train.py:850] (2/4) Epoch 4, batch 3750, loss[loss=0.2543, simple_loss=0.3242, pruned_loss=0.09217, over 7494.00 frames.], tot_loss[loss=0.2517, simple_loss=0.3264, pruned_loss=0.08845, over 1465087.71 frames.], batch size: 19, lr: 1.14e-03
2022-07-25 22:46:20,684 INFO [train.py:850] (2/4) Epoch 4, batch 3800, loss[loss=0.2494, simple_loss=0.3211, pruned_loss=0.0889, over 7194.00 frames.], tot_loss[loss=0.2519, simple_loss=0.3269, pruned_loss=0.08844, over 1465268.68 frames.], batch size: 20, lr: 1.14e-03
2022-07-25 22:47:05,374 INFO [train.py:850] (2/4) Epoch 4, batch 3850, loss[loss=0.2694, simple_loss=0.3441, pruned_loss=0.09735, over 7476.00 frames.], tot_loss[loss=0.2519, simple_loss=0.3268, pruned_loss=0.08853, over 1463984.90 frames.], batch size: 23, lr: 1.14e-03
2022-07-25 22:47:48,444 INFO [train.py:850] (2/4) Epoch 4, batch 3900, loss[loss=0.3018, simple_loss=0.3609, pruned_loss=0.1214, over 7179.00 frames.], tot_loss[loss=0.2518, simple_loss=0.3271, pruned_loss=0.08829, over 1463762.84 frames.], batch size: 22, lr: 1.14e-03
2022-07-25 22:48:32,044 INFO [train.py:850] (2/4) Epoch 4, batch 3950, loss[loss=0.2691, simple_loss=0.344, pruned_loss=0.09715, over 7489.00 frames.], tot_loss[loss=0.252, simple_loss=0.3273, pruned_loss=0.08835, over 1463750.47 frames.], batch size: 23, lr: 1.14e-03
2022-07-25 22:49:16,007 INFO [train.py:850] (2/4) Epoch 4, batch 4000, loss[loss=0.2901, simple_loss=0.3453, pruned_loss=0.1174, over 7284.00 frames.], tot_loss[loss=0.2522, simple_loss=0.3273, pruned_loss=0.08851, over 1463881.09 frames.], batch size: 19, lr: 1.14e-03
2022-07-25 22:50:01,336 INFO [train.py:850] (2/4) Epoch 4, batch 4050, loss[loss=0.2541, simple_loss=0.3385, pruned_loss=0.08483, over 7296.00 frames.], tot_loss[loss=0.253, simple_loss=0.3277, pruned_loss=0.08916, over 1463467.82 frames.], batch size: 22, lr: 1.14e-03
2022-07-25 22:50:45,522 INFO [train.py:850] (2/4) Epoch 4, batch 4100, loss[loss=0.2633, simple_loss=0.3354, pruned_loss=0.09558, over 7210.00 frames.], tot_loss[loss=0.2548, simple_loss=0.3286, pruned_loss=0.09049, over 1463552.93 frames.], batch size: 24, lr: 1.14e-03
2022-07-25 22:51:28,769 INFO [train.py:850] (2/4) Epoch 4, batch 4150, loss[loss=0.2306, simple_loss=0.3097, pruned_loss=0.07577, over 7195.00 frames.], tot_loss[loss=0.2576, simple_loss=0.33, pruned_loss=0.09255, over 1463216.24 frames.], batch size: 18, lr: 1.14e-03
2022-07-25 22:52:13,733 INFO [train.py:850] (2/4) Epoch 4, batch 4200, loss[loss=0.3341, simple_loss=0.379, pruned_loss=0.1446, over 7310.00 frames.], tot_loss[loss=0.261, simple_loss=0.3317, pruned_loss=0.09517, over 1464445.27 frames.], batch size: 22, lr: 1.13e-03
2022-07-25 22:52:58,251 INFO [train.py:850] (2/4) Epoch 4, batch 4250, loss[loss=0.2539, simple_loss=0.3217, pruned_loss=0.09305, over 7432.00 frames.], tot_loss[loss=0.264, simple_loss=0.333, pruned_loss=0.09749, over 1463942.28 frames.], batch size: 18, lr: 1.13e-03
2022-07-25 22:53:42,721 INFO [train.py:850] (2/4) Epoch 4, batch 4300, loss[loss=0.2655, simple_loss=0.3204, pruned_loss=0.1053, over 7448.00 frames.], tot_loss[loss=0.2689, simple_loss=0.3361, pruned_loss=0.1008, over 1465377.76 frames.], batch size: 17, lr: 1.13e-03
2022-07-25 22:54:27,629 INFO [train.py:850] (2/4) Epoch 4, batch 4350, loss[loss=0.2563, simple_loss=0.3059, pruned_loss=0.1033, over 7144.00 frames.], tot_loss[loss=0.2729, simple_loss=0.3382, pruned_loss=0.1039, over 1465269.28 frames.], batch size: 17, lr: 1.13e-03
2022-07-25 22:55:10,886 INFO [train.py:850] (2/4) Epoch 4, batch 4400, loss[loss=0.4137, simple_loss=0.419, pruned_loss=0.2042, over 7404.00 frames.], tot_loss[loss=0.2757, simple_loss=0.3399, pruned_loss=0.1057, over 1464544.89 frames.], batch size: 73, lr: 1.13e-03
2022-07-25 22:55:54,655 INFO [train.py:850] (2/4) Epoch 4, batch 4450, loss[loss=0.264, simple_loss=0.3368, pruned_loss=0.09554, over 7472.00 frames.], tot_loss[loss=0.2803, simple_loss=0.3425, pruned_loss=0.109, over 1465958.85 frames.], batch size: 24, lr: 1.13e-03
2022-07-25 22:56:38,724 INFO [train.py:850] (2/4) Epoch 4, batch 4500, loss[loss=0.2419, simple_loss=0.3102, pruned_loss=0.08676, over 7477.00 frames.], tot_loss[loss=0.283, simple_loss=0.3441, pruned_loss=0.111, over 1466515.39 frames.], batch size: 17, lr: 1.13e-03
2022-07-25 22:57:22,799 INFO [train.py:850] (2/4) Epoch 4, batch 4550, loss[loss=0.2828, simple_loss=0.335, pruned_loss=0.1153, over 7396.00 frames.], tot_loss[loss=0.2859, simple_loss=0.3452, pruned_loss=0.1133, over 1465424.07 frames.], batch size: 19, lr: 1.13e-03
2022-07-25 22:58:07,550 INFO [train.py:850] (2/4) Epoch 4, batch 4600, loss[loss=0.2416, simple_loss=0.309, pruned_loss=0.08707, over 7293.00 frames.], tot_loss[loss=0.2843, simple_loss=0.3432, pruned_loss=0.1126, over 1465928.30 frames.], batch size: 20, lr: 1.13e-03
2022-07-25 22:58:51,271 INFO [train.py:850] (2/4) Epoch 4, batch 4650, loss[loss=0.2942, simple_loss=0.3435, pruned_loss=0.1224, over 7492.00 frames.], tot_loss[loss=0.2838, simple_loss=0.3427, pruned_loss=0.1124, over 1465252.08 frames.], batch size: 19, lr: 1.13e-03
2022-07-25 22:59:35,249 INFO [train.py:850] (2/4) Epoch 4, batch 4700, loss[loss=0.3027, simple_loss=0.361, pruned_loss=0.1222, over 7390.00 frames.], tot_loss[loss=0.2856, simple_loss=0.344, pruned_loss=0.1136, over 1465862.71 frames.], batch size: 21, lr: 1.13e-03
2022-07-25 23:00:18,121 INFO [train.py:850] (2/4) Epoch 4, batch 4750, loss[loss=0.2368, simple_loss=0.2952, pruned_loss=0.08918, over 7319.00 frames.], tot_loss[loss=0.2869, simple_loss=0.3442, pruned_loss=0.1147, over 1463967.52 frames.], batch size: 18, lr: 1.12e-03
2022-07-25 23:01:02,841 INFO [train.py:850] (2/4) Epoch 4, batch 4800, loss[loss=0.3011, simple_loss=0.371, pruned_loss=0.1156, over 7233.00 frames.], tot_loss[loss=0.2869, simple_loss=0.3437, pruned_loss=0.115, over 1464425.68 frames.], batch size: 25, lr: 1.12e-03
2022-07-25 23:01:47,270 INFO [train.py:850] (2/4) Epoch 4, batch 4850, loss[loss=0.303, simple_loss=0.3456, pruned_loss=0.1302, over 7491.00 frames.], tot_loss[loss=0.2887, simple_loss=0.3444, pruned_loss=0.1165, over 1463930.33 frames.], batch size: 19, lr: 1.12e-03
2022-07-25 23:02:30,609 INFO [train.py:850] (2/4) Epoch 4, batch 4900, loss[loss=0.2974, simple_loss=0.3559, pruned_loss=0.1195, over 7475.00 frames.], tot_loss[loss=0.2898, simple_loss=0.346, pruned_loss=0.1167, over 1463892.70 frames.], batch size: 21, lr: 1.12e-03
2022-07-25 23:03:15,022 INFO [train.py:850] (2/4) Epoch 4, batch 4950, loss[loss=0.2395, simple_loss=0.3112, pruned_loss=0.08384, over 7470.00 frames.], tot_loss[loss=0.2885, simple_loss=0.3452, pruned_loss=0.116, over 1464948.46 frames.], batch size: 20, lr: 1.12e-03
2022-07-25 23:03:58,194 INFO [train.py:850] (2/4) Epoch 4, batch 5000, loss[loss=0.2559, simple_loss=0.3269, pruned_loss=0.09249, over 7185.00 frames.], tot_loss[loss=0.2891, simple_loss=0.3462, pruned_loss=0.116, over 1464995.05 frames.], batch size: 21, lr: 1.12e-03
2022-07-25 23:04:42,031 INFO [train.py:850] (2/4) Epoch 4, batch 5050, loss[loss=0.2611, simple_loss=0.3343, pruned_loss=0.09394, over 7191.00 frames.], tot_loss[loss=0.2889, simple_loss=0.3459, pruned_loss=0.1159, over 1465105.98 frames.], batch size: 20, lr: 1.12e-03
2022-07-25 23:05:25,327 INFO [train.py:850] (2/4) Epoch 4, batch 5100, loss[loss=0.3071, simple_loss=0.3532, pruned_loss=0.1305, over 7487.00 frames.], tot_loss[loss=0.287, simple_loss=0.3441, pruned_loss=0.1149, over 1466707.25 frames.], batch size: 40, lr: 1.12e-03
2022-07-25 23:06:09,501 INFO [train.py:850] (2/4) Epoch 4, batch 5150, loss[loss=0.2588, simple_loss=0.3154, pruned_loss=0.1011, over 7199.00 frames.], tot_loss[loss=0.285, simple_loss=0.3428, pruned_loss=0.1136, over 1465885.97 frames.], batch size: 18, lr: 1.12e-03
2022-07-25 23:06:53,100 INFO [train.py:850] (2/4) Epoch 4, batch 5200, loss[loss=0.2801, simple_loss=0.3346, pruned_loss=0.1128, over 7386.00 frames.], tot_loss[loss=0.2857, simple_loss=0.343, pruned_loss=0.1142, over 1465764.65 frames.], batch size: 20, lr: 1.12e-03
2022-07-25 23:07:36,685 INFO [train.py:850] (2/4) Epoch 4, batch 5250, loss[loss=0.2436, simple_loss=0.3039, pruned_loss=0.09172, over 7294.00 frames.], tot_loss[loss=0.2845, simple_loss=0.3423, pruned_loss=0.1133, over 1465768.63 frames.], batch size: 16, lr: 1.12e-03
2022-07-25 23:08:20,926 INFO [train.py:850] (2/4) Epoch 4, batch 5300, loss[loss=0.3731, simple_loss=0.4217, pruned_loss=0.1623, over 7483.00 frames.], tot_loss[loss=0.2863, simple_loss=0.3434, pruned_loss=0.1146, over 1465675.64 frames.], batch size: 21, lr: 1.12e-03
2022-07-25 23:09:20,283 INFO [train.py:850] (2/4) Epoch 4, batch 5350, loss[loss=0.3233, simple_loss=0.3733, pruned_loss=0.1366, over 7309.00 frames.], tot_loss[loss=0.2869, simple_loss=0.3439, pruned_loss=0.115, over 1465012.78 frames.], batch size: 39, lr: 1.11e-03
2022-07-25 23:10:06,359 INFO [train.py:850] (2/4) Epoch 4, batch 5400, loss[loss=0.2943, simple_loss=0.3481, pruned_loss=0.1202, over 7397.00 frames.], tot_loss[loss=0.2828, simple_loss=0.3407, pruned_loss=0.1125, over 1464967.00 frames.], batch size: 19, lr: 1.11e-03
2022-07-25 23:10:50,418 INFO [train.py:850] (2/4) Epoch 4, batch 5450, loss[loss=0.321, simple_loss=0.3769, pruned_loss=0.1325, over 7281.00 frames.], tot_loss[loss=0.284, simple_loss=0.3416, pruned_loss=0.1131, over 1464525.61 frames.], batch size: 21, lr: 1.11e-03
2022-07-25 23:11:33,414 INFO [train.py:850] (2/4) Epoch 4, batch 5500, loss[loss=0.2726, simple_loss=0.3232, pruned_loss=0.111, over 7218.00 frames.], tot_loss[loss=0.2857, simple_loss=0.3429, pruned_loss=0.1142, over 1464945.30 frames.], batch size: 18, lr: 1.11e-03
2022-07-25 23:12:18,297 INFO [train.py:850] (2/4) Epoch 4, batch 5550, loss[loss=0.2986, simple_loss=0.3634, pruned_loss=0.1169, over 7243.00 frames.], tot_loss[loss=0.2862, simple_loss=0.3433, pruned_loss=0.1145, over 1464445.15 frames.], batch size: 27, lr: 1.11e-03
2022-07-25 23:13:02,111 INFO [train.py:850] (2/4) Epoch 4, batch 5600, loss[loss=0.2814, simple_loss=0.3505, pruned_loss=0.1062, over 7281.00 frames.], tot_loss[loss=0.2861, simple_loss=0.3433, pruned_loss=0.1145, over 1464552.35 frames.], batch size: 21, lr: 1.11e-03
2022-07-25 23:13:46,731 INFO [train.py:850] (2/4) Epoch 4, batch 5650, loss[loss=0.2682, simple_loss=0.3285, pruned_loss=0.1039, over 7487.00 frames.], tot_loss[loss=0.2865, simple_loss=0.3435, pruned_loss=0.1147, over 1465666.10 frames.], batch size: 19, lr: 1.11e-03
2022-07-25 23:14:29,871 INFO [train.py:850] (2/4) Epoch 4, batch 5700, loss[loss=0.2486, simple_loss=0.3156, pruned_loss=0.09082, over 7429.00 frames.], tot_loss[loss=0.285, simple_loss=0.3424, pruned_loss=0.1138, over 1465431.51 frames.], batch size: 18, lr: 1.11e-03
2022-07-25 23:15:14,718 INFO [train.py:850] (2/4) Epoch 4, batch 5750, loss[loss=0.3625, simple_loss=0.3981, pruned_loss=0.1635, over 7298.00 frames.], tot_loss[loss=0.2847, simple_loss=0.342, pruned_loss=0.1137, over 1465162.98 frames.], batch size: 20, lr: 1.11e-03
2022-07-25 23:15:57,804 INFO [train.py:850] (2/4) Epoch 4, batch 5800, loss[loss=0.3117, simple_loss=0.3597, pruned_loss=0.1318, over 7471.00 frames.], tot_loss[loss=0.2847, simple_loss=0.3425, pruned_loss=0.1135, over 1465534.69 frames.], batch size: 21, lr: 1.11e-03
2022-07-25 23:16:41,617 INFO [train.py:850] (2/4) Epoch 4, batch 5850, loss[loss=0.2677, simple_loss=0.3472, pruned_loss=0.09409, over 7297.00 frames.], tot_loss[loss=0.2835, simple_loss=0.3418, pruned_loss=0.1126, over 1465521.18 frames.], batch size: 20, lr: 1.11e-03
2022-07-25 23:17:25,966 INFO [train.py:850] (2/4) Epoch 4, batch 5900, loss[loss=0.2701, simple_loss=0.3335, pruned_loss=0.1033, over 7285.00 frames.], tot_loss[loss=0.2849, simple_loss=0.3429, pruned_loss=0.1135, over 1465227.82 frames.], batch size: 19, lr: 1.10e-03
2022-07-25 23:18:09,507 INFO [train.py:850] (2/4) Epoch 4, batch 5950, loss[loss=0.2913, simple_loss=0.3428, pruned_loss=0.1199, over 7178.00 frames.], tot_loss[loss=0.285, simple_loss=0.3429, pruned_loss=0.1135, over 1465510.65 frames.], batch size: 21, lr: 1.10e-03
2022-07-25 23:18:54,270 INFO [train.py:850] (2/4) Epoch 4, batch 6000, loss[loss=0.2634, simple_loss=0.3073, pruned_loss=0.1097, over 7312.00 frames.], tot_loss[loss=0.2834, simple_loss=0.3415, pruned_loss=0.1126, over 1465216.62 frames.], batch size: 18, lr: 1.10e-03
2022-07-25 23:18:54,271 INFO [train.py:870] (2/4) Computing validation loss
2022-07-25 23:19:19,088 INFO [train.py:879] (2/4) Epoch 4, validation: loss=0.2133, simple_loss=0.3092, pruned_loss=0.05874, over 924787.00 frames.
2022-07-25 23:20:02,472 INFO [train.py:850] (2/4) Epoch 4, batch 6050, loss[loss=0.2802, simple_loss=0.3368, pruned_loss=0.1118, over 7284.00 frames.], tot_loss[loss=0.2841, simple_loss=0.3419, pruned_loss=0.1132, over 1464638.88 frames.], batch size: 21, lr: 1.10e-03
2022-07-25 23:20:46,949 INFO [train.py:850] (2/4) Epoch 4, batch 6100, loss[loss=0.2947, simple_loss=0.3506, pruned_loss=0.1194, over 7280.00 frames.], tot_loss[loss=0.2832, simple_loss=0.3412, pruned_loss=0.1126, over 1464552.13 frames.], batch size: 22, lr: 1.10e-03
2022-07-25 23:21:31,077 INFO [train.py:850] (2/4) Epoch 4, batch 6150, loss[loss=0.3015, simple_loss=0.3654, pruned_loss=0.1188, over 7297.00 frames.], tot_loss[loss=0.2821, simple_loss=0.3403, pruned_loss=0.112, over 1464015.17 frames.], batch size: 22, lr: 1.10e-03
2022-07-25 23:22:15,196 INFO [train.py:850] (2/4) Epoch 4, batch 6200, loss[loss=0.3579, simple_loss=0.3961, pruned_loss=0.1599, over 7458.00 frames.], tot_loss[loss=0.2835, simple_loss=0.3414, pruned_loss=0.1128, over 1464916.38 frames.], batch size: 70, lr: 1.10e-03
2022-07-25 23:22:58,450 INFO [train.py:850] (2/4) Epoch 4, batch 6250, loss[loss=0.2866, simple_loss=0.3574, pruned_loss=0.1079, over 7168.00 frames.], tot_loss[loss=0.2839, simple_loss=0.3415, pruned_loss=0.1132, over 1465578.05 frames.], batch size: 22, lr: 1.10e-03
2022-07-25 23:23:42,075 INFO [train.py:850] (2/4) Epoch 4, batch 6300, loss[loss=0.266, simple_loss=0.318, pruned_loss=0.107, over 7389.00 frames.], tot_loss[loss=0.2836, simple_loss=0.3418, pruned_loss=0.1127, over 1465205.16 frames.], batch size: 19, lr: 1.10e-03
2022-07-25 23:24:25,972 INFO [train.py:850] (2/4) Epoch 4, batch 6350, loss[loss=0.2657, simple_loss=0.3364, pruned_loss=0.0975, over 7488.00 frames.], tot_loss[loss=0.2831, simple_loss=0.3421, pruned_loss=0.1121, over 1466780.06 frames.], batch size: 24, lr: 1.10e-03
2022-07-25 23:25:09,952 INFO [train.py:850] (2/4) Epoch 4, batch 6400, loss[loss=0.2428, simple_loss=0.2998, pruned_loss=0.09291, over 7297.00 frames.], tot_loss[loss=0.2831, simple_loss=0.3417, pruned_loss=0.1123, over 1466358.20 frames.], batch size: 17, lr: 1.10e-03
2022-07-25 23:25:54,860 INFO [train.py:850] (2/4) Epoch 4, batch 6450, loss[loss=0.239, simple_loss=0.3049, pruned_loss=0.08654, over 7390.00 frames.], tot_loss[loss=0.283, simple_loss=0.3418, pruned_loss=0.1121, over 1466024.86 frames.], batch size: 20, lr: 1.10e-03
2022-07-25 23:26:38,257 INFO [train.py:850] (2/4) Epoch 4, batch 6500, loss[loss=0.3341, simple_loss=0.3902, pruned_loss=0.139, over 7195.00 frames.], tot_loss[loss=0.2822, simple_loss=0.341, pruned_loss=0.1117, over 1465544.00 frames.], batch size: 20, lr: 1.10e-03
2022-07-25 23:27:23,883 INFO [train.py:850] (2/4) Epoch 4, batch 6550, loss[loss=0.2371, simple_loss=0.3175, pruned_loss=0.07833, over 7381.00 frames.], tot_loss[loss=0.2835, simple_loss=0.3418, pruned_loss=0.1126, over 1466942.76 frames.], batch size: 21, lr: 1.09e-03
2022-07-25 23:28:06,924 INFO [train.py:850] (2/4) Epoch 4, batch 6600, loss[loss=0.2755, simple_loss=0.3337, pruned_loss=0.1086, over 7281.00 frames.], tot_loss[loss=0.284, simple_loss=0.3417, pruned_loss=0.1132, over 1466390.82 frames.], batch size: 27, lr: 1.09e-03
2022-07-25 23:28:53,659 INFO [train.py:850] (2/4) Epoch 4, batch 6650, loss[loss=0.3655, simple_loss=0.3956, pruned_loss=0.1678, over 7185.00 frames.], tot_loss[loss=0.2871, simple_loss=0.3442, pruned_loss=0.115, over 1467658.28 frames.], batch size: 21, lr: 1.09e-03
2022-07-25 23:29:37,097 INFO [train.py:850] (2/4) Epoch 4, batch 6700, loss[loss=0.2913, simple_loss=0.3468, pruned_loss=0.1179, over 7464.00 frames.], tot_loss[loss=0.2866, simple_loss=0.3438, pruned_loss=0.1147, over 1468376.03 frames.], batch size: 72, lr: 1.09e-03
2022-07-25 23:30:20,746 INFO [train.py:850] (2/4) Epoch 4, batch 6750, loss[loss=0.2611, simple_loss=0.3266, pruned_loss=0.09778, over 7433.00 frames.], tot_loss[loss=0.2858, simple_loss=0.3437, pruned_loss=0.1139, over 1468321.73 frames.], batch size: 39, lr: 1.09e-03
2022-07-25 23:31:05,255 INFO [train.py:850] (2/4) Epoch 4, batch 6800, loss[loss=0.2868, simple_loss=0.346, pruned_loss=0.1138, over 7308.00 frames.], tot_loss[loss=0.2862, simple_loss=0.3442, pruned_loss=0.1141, over 1468713.62 frames.], batch size: 22, lr: 1.09e-03
2022-07-25 23:31:48,297 INFO [train.py:850] (2/4) Epoch 4, batch 6850, loss[loss=0.2802, simple_loss=0.3544, pruned_loss=0.103, over 7480.00 frames.], tot_loss[loss=0.2844, simple_loss=0.3433, pruned_loss=0.1128, over 1467944.82 frames.], batch size: 23, lr: 1.09e-03
2022-07-25 23:32:34,814 INFO [train.py:850] (2/4) Epoch 4, batch 6900, loss[loss=0.2547, simple_loss=0.3234, pruned_loss=0.09305, over 7185.00 frames.], tot_loss[loss=0.2827, simple_loss=0.3418, pruned_loss=0.1118, over 1467963.26 frames.], batch size: 23, lr: 1.09e-03
2022-07-25 23:33:18,347 INFO [train.py:850] (2/4) Epoch 4, batch 6950, loss[loss=0.2046, simple_loss=0.2694, pruned_loss=0.06988, over 7146.00 frames.], tot_loss[loss=0.2801, simple_loss=0.3397, pruned_loss=0.1103, over 1467388.09 frames.], batch size: 17, lr: 1.09e-03
2022-07-25 23:34:02,357 INFO [train.py:850] (2/4) Epoch 4, batch 7000, loss[loss=0.2891, simple_loss=0.3557, pruned_loss=0.1112, over 7493.00 frames.], tot_loss[loss=0.2794, simple_loss=0.3391, pruned_loss=0.1098, over 1466773.15 frames.], batch size: 23, lr: 1.09e-03
2022-07-25 23:34:47,326 INFO [train.py:850] (2/4) Epoch 4, batch 7050, loss[loss=0.25, simple_loss=0.3153, pruned_loss=0.09236, over 7480.00 frames.], tot_loss[loss=0.2793, simple_loss=0.3387, pruned_loss=0.11, over 1466770.81 frames.], batch size: 20, lr: 1.09e-03
2022-07-25 23:35:30,828 INFO [train.py:850] (2/4) Epoch 4, batch 7100, loss[loss=0.3273, simple_loss=0.3807, pruned_loss=0.137, over 7171.00 frames.], tot_loss[loss=0.2793, simple_loss=0.3389, pruned_loss=0.1099, over 1466876.03 frames.], batch size: 22, lr: 1.09e-03
2022-07-25 23:36:15,649 INFO [train.py:850] (2/4) Epoch 4, batch 7150, loss[loss=0.2507, simple_loss=0.3127, pruned_loss=0.09436, over 7102.00 frames.], tot_loss[loss=0.2803, simple_loss=0.3397, pruned_loss=0.1104, over 1465534.66 frames.], batch size: 18, lr: 1.08e-03
2022-07-25 23:36:59,826 INFO [train.py:850] (2/4) Epoch 4, batch 7200, loss[loss=0.269, simple_loss=0.337, pruned_loss=0.1005, over 7479.00 frames.], tot_loss[loss=0.2809, simple_loss=0.3403, pruned_loss=0.1108, over 1465622.35 frames.], batch size: 21, lr: 1.08e-03
2022-07-25 23:37:44,846 INFO [train.py:850] (2/4) Epoch 4, batch 7250, loss[loss=0.2909, simple_loss=0.3444, pruned_loss=0.1186, over 7275.00 frames.], tot_loss[loss=0.2807, simple_loss=0.3403, pruned_loss=0.1105, over 1465574.18 frames.], batch size: 21, lr: 1.08e-03
2022-07-25 23:38:27,877 INFO [train.py:850] (2/4) Epoch 4, batch 7300, loss[loss=0.2261, simple_loss=0.29, pruned_loss=0.08112, over 7482.00 frames.], tot_loss[loss=0.2812, simple_loss=0.3404, pruned_loss=0.111, over 1466065.82 frames.], batch size: 20, lr: 1.08e-03
2022-07-25 23:39:11,911 INFO [train.py:850] (2/4) Epoch 4, batch 7350, loss[loss=0.2416, simple_loss=0.2982, pruned_loss=0.09252, over 7371.00 frames.], tot_loss[loss=0.2799, simple_loss=0.3398, pruned_loss=0.1101, over 1466227.46 frames.], batch size: 20, lr: 1.08e-03
2022-07-25 23:39:56,166 INFO [train.py:850] (2/4) Epoch 4, batch 7400, loss[loss=0.2466, simple_loss=0.3228, pruned_loss=0.08522, over 7420.00 frames.], tot_loss[loss=0.2803, simple_loss=0.3396, pruned_loss=0.1105, over 1465502.78 frames.], batch size: 22, lr: 1.08e-03
2022-07-25 23:40:40,265 INFO [train.py:850] (2/4) Epoch 4, batch 7450, loss[loss=0.2793, simple_loss=0.3513, pruned_loss=0.1036, over 7399.00 frames.], tot_loss[loss=0.2792, simple_loss=0.3391, pruned_loss=0.1096, over 1466090.96 frames.], batch size: 31, lr: 1.08e-03
2022-07-25 23:41:24,354 INFO [train.py:850] (2/4) Epoch 4, batch 7500, loss[loss=0.3639, simple_loss=0.3928, pruned_loss=0.1675, over 7477.00 frames.], tot_loss[loss=0.2796, simple_loss=0.3391, pruned_loss=0.11, over 1465973.44 frames.], batch size: 21, lr: 1.08e-03
2022-07-25 23:42:08,087 INFO [train.py:850] (2/4) Epoch 4, batch 7550, loss[loss=0.3006, simple_loss=0.3546, pruned_loss=0.1233, over 7383.00 frames.], tot_loss[loss=0.2808, simple_loss=0.34, pruned_loss=0.1108, over 1466205.79 frames.], batch size: 20, lr: 1.08e-03
2022-07-25 23:42:52,891 INFO [train.py:850] (2/4) Epoch 4, batch 7600, loss[loss=0.3568, simple_loss=0.3943, pruned_loss=0.1597, over 7296.00 frames.], tot_loss[loss=0.2803, simple_loss=0.3398, pruned_loss=0.1104, over 1464935.81 frames.], batch size: 20, lr: 1.08e-03
2022-07-25 23:43:36,699 INFO [train.py:850] (2/4) Epoch 4, batch 7650, loss[loss=0.3116, simple_loss=0.3674, pruned_loss=0.1279, over 7401.00 frames.], tot_loss[loss=0.2792, simple_loss=0.3393, pruned_loss=0.1096, over 1465711.22 frames.], batch size: 31, lr: 1.08e-03
2022-07-25 23:44:22,774 INFO [train.py:850] (2/4) Epoch 4, batch 7700, loss[loss=0.2722, simple_loss=0.3326, pruned_loss=0.1059, over 7198.00 frames.], tot_loss[loss=0.2797, simple_loss=0.3393, pruned_loss=0.1101, over 1465747.71 frames.], batch size: 18, lr: 1.08e-03
2022-07-25 23:45:06,446 INFO [train.py:850] (2/4) Epoch 4, batch 7750, loss[loss=0.2959, simple_loss=0.3585, pruned_loss=0.1166, over 7418.00 frames.], tot_loss[loss=0.278, simple_loss=0.3382, pruned_loss=0.1089, over 1465603.54 frames.], batch size: 31, lr: 1.08e-03
2022-07-25 23:45:50,288 INFO [train.py:850] (2/4) Epoch 4, batch 7800, loss[loss=0.2922, simple_loss=0.3519, pruned_loss=0.1162, over 7305.00 frames.], tot_loss[loss=0.2783, simple_loss=0.339, pruned_loss=0.1088, over 1465902.50 frames.], batch size: 22, lr: 1.07e-03
2022-07-25 23:46:34,996 INFO [train.py:850] (2/4) Epoch 4, batch 7850, loss[loss=0.2439, simple_loss=0.315, pruned_loss=0.08637, over 7335.00 frames.], tot_loss[loss=0.2779, simple_loss=0.3381, pruned_loss=0.1088, over 1465422.97 frames.], batch size: 23, lr: 1.07e-03
2022-07-25 23:47:18,131 INFO [train.py:850] (2/4) Epoch 4, batch 7900, loss[loss=0.3618, simple_loss=0.3994, pruned_loss=0.1621, over 7476.00 frames.], tot_loss[loss=0.2785, simple_loss=0.3387, pruned_loss=0.1092, over 1466123.68 frames.], batch size: 21, lr: 1.07e-03
2022-07-25 23:48:03,426 INFO [train.py:850] (2/4) Epoch 4, batch 7950, loss[loss=0.3025, simple_loss=0.355, pruned_loss=0.1249, over 7449.00 frames.], tot_loss[loss=0.2769, simple_loss=0.3377, pruned_loss=0.1081, over 1466769.71 frames.], batch size: 40, lr: 1.07e-03
2022-07-25 23:48:46,431 INFO [train.py:850] (2/4) Epoch 4, batch 8000, loss[loss=0.3145, simple_loss=0.3745, pruned_loss=0.1272, over 7198.00 frames.], tot_loss[loss=0.2754, simple_loss=0.3365, pruned_loss=0.1072, over 1465426.60 frames.], batch size: 24, lr: 1.07e-03
2022-07-25 23:49:31,811 INFO [train.py:850] (2/4) Epoch 4, batch 8050, loss[loss=0.2356, simple_loss=0.2994, pruned_loss=0.08594, over 7457.00 frames.], tot_loss[loss=0.2766, simple_loss=0.3375, pruned_loss=0.1078, over 1465823.29 frames.], batch size: 17, lr: 1.07e-03
2022-07-25 23:50:15,403 INFO [train.py:850] (2/4) Epoch 4, batch 8100, loss[loss=0.2292, simple_loss=0.312, pruned_loss=0.07324, over 7196.00 frames.], tot_loss[loss=0.2762, simple_loss=0.3371, pruned_loss=0.1077, over 1466245.00 frames.], batch size: 19, lr: 1.07e-03
2022-07-25 23:51:00,332 INFO [train.py:850] (2/4) Epoch 4, batch 8150, loss[loss=0.2785, simple_loss=0.3508, pruned_loss=0.103, over 7298.00 frames.], tot_loss[loss=0.2765, simple_loss=0.3375, pruned_loss=0.1078, over 1466409.42 frames.], batch size: 22, lr: 1.07e-03
2022-07-25 23:51:44,619 INFO [train.py:850] (2/4) Epoch 4, batch 8200, loss[loss=0.2759, simple_loss=0.3418, pruned_loss=0.105, over 7379.00 frames.], tot_loss[loss=0.2764, simple_loss=0.3374, pruned_loss=0.1077, over 1466103.40 frames.], batch size: 39, lr: 1.07e-03
2022-07-25 23:52:28,956 INFO [train.py:850] (2/4) Epoch 4, batch 8250, loss[loss=0.3063, simple_loss=0.3593, pruned_loss=0.1267, over 7318.00 frames.], tot_loss[loss=0.2763, simple_loss=0.3376, pruned_loss=0.1075, over 1465967.26 frames.], batch size: 39, lr: 1.07e-03
2022-07-25 23:53:14,198 INFO [train.py:850] (2/4) Epoch 4, batch 8300, loss[loss=0.2648, simple_loss=0.3392, pruned_loss=0.09516, over 7300.00 frames.], tot_loss[loss=0.2742, simple_loss=0.336, pruned_loss=0.1062, over 1466654.07 frames.], batch size: 22, lr: 1.07e-03
2022-07-25 23:53:57,853 INFO [train.py:850] (2/4) Epoch 4, batch 8350, loss[loss=0.3089, simple_loss=0.352, pruned_loss=0.1329, over 7379.00 frames.], tot_loss[loss=0.2761, simple_loss=0.3374, pruned_loss=0.1074, over 1466573.86 frames.], batch size: 19, lr: 1.07e-03
2022-07-25 23:54:41,409 INFO [train.py:850] (2/4) Epoch 4, batch 8400, loss[loss=0.2424, simple_loss=0.3177, pruned_loss=0.08359, over 7302.00 frames.], tot_loss[loss=0.2758, simple_loss=0.3372, pruned_loss=0.1072, over 1467153.05 frames.], batch size: 22, lr: 1.07e-03
2022-07-25 23:55:26,035 INFO [train.py:850] (2/4) Epoch 4, batch 8450, loss[loss=0.2307, simple_loss=0.3041, pruned_loss=0.07863, over 7205.00 frames.], tot_loss[loss=0.2745, simple_loss=0.336, pruned_loss=0.1066, over 1467572.10 frames.], batch size: 18, lr: 1.06e-03
2022-07-25 23:56:09,726 INFO [train.py:850] (2/4) Epoch 4, batch 8500, loss[loss=0.2809, simple_loss=0.3333, pruned_loss=0.1142, over 7290.00 frames.], tot_loss[loss=0.2751, simple_loss=0.3363, pruned_loss=0.107, over 1467391.23 frames.], batch size: 19, lr: 1.06e-03
2022-07-25 23:56:55,313 INFO [train.py:850] (2/4) Epoch 4, batch 8550, loss[loss=0.2452, simple_loss=0.3268, pruned_loss=0.08184, over 7203.00 frames.], tot_loss[loss=0.2751, simple_loss=0.3361, pruned_loss=0.107, over 1467751.80 frames.], batch size: 25, lr: 1.06e-03
2022-07-25 23:57:38,933 INFO [train.py:850] (2/4) Epoch 4, batch 8600, loss[loss=0.2377, simple_loss=0.3136, pruned_loss=0.08087, over 7208.00 frames.], tot_loss[loss=0.273, simple_loss=0.3343, pruned_loss=0.1059, over 1468238.67 frames.], batch size: 24, lr: 1.06e-03
2022-07-25 23:58:23,027 INFO [train.py:850] (2/4) Epoch 4, batch 8650, loss[loss=0.2992, simple_loss=0.359, pruned_loss=0.1197, over 7238.00 frames.], tot_loss[loss=0.2712, simple_loss=0.333, pruned_loss=0.1047, over 1467497.35 frames.], batch size: 24, lr: 1.06e-03
2022-07-25 23:59:07,053 INFO [train.py:850] (2/4) Epoch 4, batch 8700, loss[loss=0.2257, simple_loss=0.2788, pruned_loss=0.08628, over 7313.00 frames.], tot_loss[loss=0.2721, simple_loss=0.3334, pruned_loss=0.1054, over 1468110.33 frames.], batch size: 17, lr: 1.06e-03
2022-07-25 23:59:49,803 INFO [train.py:850] (2/4) Epoch 4, batch 8750, loss[loss=0.2802, simple_loss=0.3405, pruned_loss=0.1099, over 7492.00 frames.], tot_loss[loss=0.2734, simple_loss=0.3346, pruned_loss=0.1061, over 1467885.98 frames.], batch size: 19, lr: 1.06e-03
2022-07-26 00:00:32,906 INFO [train.py:850] (2/4) Epoch 4, batch 8800, loss[loss=0.2431, simple_loss=0.312, pruned_loss=0.08706, over 7378.00 frames.], tot_loss[loss=0.2743, simple_loss=0.3357, pruned_loss=0.1064, over 1468261.20 frames.], batch size: 21, lr: 1.06e-03
2022-07-26 00:01:16,692 INFO [train.py:850] (2/4) Epoch 4, batch 8850, loss[loss=0.3332, simple_loss=0.3832, pruned_loss=0.1415, over 7389.00 frames.], tot_loss[loss=0.2748, simple_loss=0.3357, pruned_loss=0.107, over 1468031.91 frames.], batch size: 31, lr: 1.06e-03
2022-07-26 00:02:56,891 INFO [train.py:850] (2/4) Epoch 5, batch 0, loss[loss=0.2772, simple_loss=0.3548, pruned_loss=0.09975, over 7255.00 frames.], tot_loss[loss=0.2772, simple_loss=0.3548, pruned_loss=0.09975, over 7255.00 frames.], batch size: 27, lr: 1.02e-03
2022-07-26 00:03:41,456 INFO [train.py:850] (2/4) Epoch 5, batch 50, loss[loss=0.2611, simple_loss=0.3335, pruned_loss=0.0944, over 7462.00 frames.], tot_loss[loss=0.2579, simple_loss=0.3329, pruned_loss=0.09145, over 330603.83 frames.], batch size: 26, lr: 1.02e-03
2022-07-26 00:04:25,399 INFO [train.py:850] (2/4) Epoch 5, batch 100, loss[loss=0.259, simple_loss=0.3413, pruned_loss=0.08835, over 7249.00 frames.], tot_loss[loss=0.2504, simple_loss=0.3256, pruned_loss=0.08764, over 582253.86 frames.], batch size: 27, lr: 1.02e-03
2022-07-26 00:05:10,736 INFO [train.py:850] (2/4) Epoch 5, batch 150, loss[loss=0.2603, simple_loss=0.3293, pruned_loss=0.09562, over 7472.00 frames.], tot_loss[loss=0.2474, simple_loss=0.3228, pruned_loss=0.08602, over 778829.15 frames.], batch size: 21, lr: 1.02e-03
2022-07-26 00:05:53,744 INFO [train.py:850] (2/4) Epoch 5, batch 200, loss[loss=0.2773, simple_loss=0.347, pruned_loss=0.1038, over 7174.00 frames.], tot_loss[loss=0.2464, simple_loss=0.3217, pruned_loss=0.08555, over 929084.54 frames.], batch size: 21, lr: 1.02e-03
2022-07-26 00:06:37,175 INFO [train.py:850] (2/4) Epoch 5, batch 250, loss[loss=0.3067, simple_loss=0.3704, pruned_loss=0.1215, over 7200.00 frames.], tot_loss[loss=0.2452, simple_loss=0.3209, pruned_loss=0.08475, over 1047075.41 frames.], batch size: 19, lr: 1.02e-03
2022-07-26 00:07:21,125 INFO [train.py:850] (2/4) Epoch 5, batch 300, loss[loss=0.255, simple_loss=0.3374, pruned_loss=0.08637, over 7360.00 frames.], tot_loss[loss=0.2437, simple_loss=0.3198, pruned_loss=0.08382, over 1139882.57 frames.], batch size: 31, lr: 1.02e-03
2022-07-26 00:08:05,040 INFO [train.py:850] (2/4) Epoch 5, batch 350, loss[loss=0.2419, simple_loss=0.3221, pruned_loss=0.08088, over 7478.00 frames.], tot_loss[loss=0.2423, simple_loss=0.3192, pruned_loss=0.0827, over 1211895.04 frames.], batch size: 20, lr: 1.02e-03
2022-07-26 00:08:50,574 INFO [train.py:850] (2/4) Epoch 5, batch 400, loss[loss=0.2394, simple_loss=0.309, pruned_loss=0.08488, over 7304.00 frames.], tot_loss[loss=0.2416, simple_loss=0.3189, pruned_loss=0.08213, over 1267848.35 frames.], batch size: 18, lr: 1.02e-03
2022-07-26 00:09:49,874 INFO [train.py:850] (2/4) Epoch 5, batch 450, loss[loss=0.1959, simple_loss=0.2807, pruned_loss=0.05553, over 7381.00 frames.], tot_loss[loss=0.2406, simple_loss=0.3177, pruned_loss=0.08171, over 1311447.47 frames.], batch size: 19, lr: 1.01e-03
2022-07-26 00:10:33,440 INFO [train.py:850] (2/4) Epoch 5, batch 500, loss[loss=0.2453, simple_loss=0.319, pruned_loss=0.08574, over 7239.00 frames.], tot_loss[loss=0.2379, simple_loss=0.3159, pruned_loss=0.07996, over 1345808.26 frames.], batch size: 24, lr: 1.01e-03
2022-07-26 00:11:16,888 INFO [train.py:850] (2/4) Epoch 5, batch 550, loss[loss=0.2845, simple_loss=0.3482, pruned_loss=0.1104, over 7388.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3165, pruned_loss=0.08034, over 1372416.47 frames.], batch size: 70, lr: 1.01e-03
2022-07-26 00:11:59,878 INFO [train.py:850] (2/4) Epoch 5, batch 600, loss[loss=0.2631, simple_loss=0.3402, pruned_loss=0.09298, over 7390.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3153, pruned_loss=0.07954, over 1392129.23 frames.], batch size: 69, lr: 1.01e-03
2022-07-26 00:12:46,024 INFO [train.py:850] (2/4) Epoch 5, batch 650, loss[loss=0.1873, simple_loss=0.2697, pruned_loss=0.05249, over 7481.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3138, pruned_loss=0.07859, over 1408696.90 frames.], batch size: 20, lr: 1.01e-03
2022-07-26 00:13:31,066 INFO [train.py:850] (2/4) Epoch 5, batch 700, loss[loss=0.3136, simple_loss=0.3702, pruned_loss=0.1285, over 7461.00 frames.], tot_loss[loss=0.2348, simple_loss=0.313, pruned_loss=0.07828, over 1420955.19 frames.], batch size: 70, lr: 1.01e-03
2022-07-26 00:14:15,211 INFO [train.py:850] (2/4) Epoch 5, batch 750, loss[loss=0.251, simple_loss=0.3142, pruned_loss=0.09387, over 7440.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3127, pruned_loss=0.07827, over 1431269.40 frames.], batch size: 18, lr: 1.01e-03
2022-07-26 00:14:58,717 INFO [train.py:850] (2/4) Epoch 5, batch 800, loss[loss=0.2502, simple_loss=0.3281, pruned_loss=0.0861, over 7483.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3126, pruned_loss=0.07826, over 1438255.16 frames.], batch size: 24, lr: 1.01e-03
2022-07-26 00:15:42,260 INFO [train.py:850] (2/4) Epoch 5, batch 850, loss[loss=0.222, simple_loss=0.3024, pruned_loss=0.0708, over 7475.00 frames.], tot_loss[loss=0.236, simple_loss=0.3143, pruned_loss=0.07882, over 1443612.69 frames.], batch size: 20, lr: 1.01e-03
2022-07-26 00:16:28,175 INFO [train.py:850] (2/4) Epoch 5, batch 900, loss[loss=0.3247, simple_loss=0.3698, pruned_loss=0.1398, over 7484.00 frames.], tot_loss[loss=0.2392, simple_loss=0.3168, pruned_loss=0.08079, over 1448098.54 frames.], batch size: 20, lr: 1.01e-03
2022-07-26 00:17:11,557 INFO [train.py:850] (2/4) Epoch 5, batch 950, loss[loss=0.2081, simple_loss=0.2823, pruned_loss=0.06693, over 7453.00 frames.], tot_loss[loss=0.2415, simple_loss=0.3186, pruned_loss=0.08219, over 1450727.25 frames.], batch size: 17, lr: 1.01e-03
2022-07-26 00:17:55,828 INFO [train.py:850] (2/4) Epoch 5, batch 1000, loss[loss=0.284, simple_loss=0.3581, pruned_loss=0.105, over 7275.00 frames.], tot_loss[loss=0.2423, simple_loss=0.3193, pruned_loss=0.08264, over 1455032.25 frames.], batch size: 21, lr: 1.01e-03
2022-07-26 00:18:41,031 INFO [train.py:850] (2/4) Epoch 5, batch 1050, loss[loss=0.2076, simple_loss=0.2765, pruned_loss=0.06931, over 7466.00 frames.], tot_loss[loss=0.2458, simple_loss=0.322, pruned_loss=0.08479, over 1458225.18 frames.], batch size: 17, lr: 1.01e-03
2022-07-26 00:19:24,232 INFO [train.py:850] (2/4) Epoch 5, batch 1100, loss[loss=0.278, simple_loss=0.3517, pruned_loss=0.1021, over 7183.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3221, pruned_loss=0.08471, over 1459696.07 frames.], batch size: 21, lr: 1.01e-03
2022-07-26 00:20:09,537 INFO [train.py:850] (2/4) Epoch 5, batch 1150, loss[loss=0.2177, simple_loss=0.3046, pruned_loss=0.06539, over 7473.00 frames.], tot_loss[loss=0.2478, simple_loss=0.3231, pruned_loss=0.08623, over 1461522.48 frames.], batch size: 20, lr: 1.01e-03
2022-07-26 00:20:52,921 INFO [train.py:850] (2/4) Epoch 5, batch 1200, loss[loss=0.2069, simple_loss=0.2711, pruned_loss=0.07132, over 7305.00 frames.], tot_loss[loss=0.2472, simple_loss=0.3225, pruned_loss=0.08598, over 1461490.25 frames.], batch size: 17, lr: 1.00e-03
2022-07-26 00:21:37,487 INFO [train.py:850] (2/4) Epoch 5, batch 1250, loss[loss=0.2221, simple_loss=0.2967, pruned_loss=0.07372, over 7455.00 frames.], tot_loss[loss=0.2498, simple_loss=0.325, pruned_loss=0.08729, over 1463432.67 frames.], batch size: 18, lr: 1.00e-03
2022-07-26 00:22:21,019 INFO [train.py:850] (2/4) Epoch 5, batch 1300, loss[loss=0.2461, simple_loss=0.313, pruned_loss=0.08965, over 7312.00 frames.], tot_loss[loss=0.2507, simple_loss=0.3255, pruned_loss=0.08794, over 1463967.90 frames.], batch size: 17, lr: 1.00e-03
2022-07-26 00:23:04,645 INFO [train.py:850] (2/4) Epoch 5, batch 1350, loss[loss=0.2205, simple_loss=0.3027, pruned_loss=0.06922, over 7106.00 frames.], tot_loss[loss=0.2493, simple_loss=0.3245, pruned_loss=0.08699, over 1463510.19 frames.], batch size: 18, lr: 1.00e-03
2022-07-26 00:23:49,439 INFO [train.py:850] (2/4) Epoch 5, batch 1400, loss[loss=0.2043, simple_loss=0.2831, pruned_loss=0.06272, over 7200.00 frames.], tot_loss[loss=0.249, simple_loss=0.3244, pruned_loss=0.0868, over 1464376.60 frames.], batch size: 18, lr: 1.00e-03
2022-07-26 00:24:32,818 INFO [train.py:850] (2/4) Epoch 5, batch 1450, loss[loss=0.2967, simple_loss=0.3704, pruned_loss=0.1114, over 7284.00 frames.], tot_loss[loss=0.2494, simple_loss=0.3246, pruned_loss=0.08711, over 1463071.64 frames.], batch size: 21, lr: 1.00e-03
2022-07-26 00:25:16,765 INFO [train.py:850] (2/4) Epoch 5, batch 1500, loss[loss=0.2119, simple_loss=0.2964, pruned_loss=0.06374, over 7193.00 frames.], tot_loss[loss=0.2481, simple_loss=0.3235, pruned_loss=0.08633, over 1464347.17 frames.], batch size: 18, lr: 1.00e-03
2022-07-26 00:25:59,811 INFO [train.py:850] (2/4) Epoch 5, batch 1550, loss[loss=0.3034, simple_loss=0.367, pruned_loss=0.1199, over 7175.00 frames.], tot_loss[loss=0.2475, simple_loss=0.3233, pruned_loss=0.0858, over 1464815.38 frames.], batch size: 23, lr: 1.00e-03
2022-07-26 00:26:43,993 INFO [train.py:850] (2/4) Epoch 5, batch 1600, loss[loss=0.2553, simple_loss=0.325, pruned_loss=0.0928, over 7391.00 frames.], tot_loss[loss=0.2475, simple_loss=0.3232, pruned_loss=0.08594, over 1464067.88 frames.], batch size: 19, lr: 9.99e-04
2022-07-26 00:27:28,945 INFO [train.py:850] (2/4) Epoch 5, batch 1650, loss[loss=0.2145, simple_loss=0.294, pruned_loss=0.06752, over 7319.00 frames.], tot_loss[loss=0.2477, simple_loss=0.3231, pruned_loss=0.0861, over 1465655.17 frames.], batch size: 18, lr: 9.98e-04
2022-07-26 00:28:11,544 INFO [train.py:850] (2/4) Epoch 5, batch 1700, loss[loss=0.236, simple_loss=0.303, pruned_loss=0.08454, over 7156.00 frames.], tot_loss[loss=0.2467, simple_loss=0.3225, pruned_loss=0.08543, over 1465285.68 frames.], batch size: 17, lr: 9.98e-04
2022-07-26 00:28:56,372 INFO [train.py:850] (2/4) Epoch 5, batch 1750, loss[loss=0.2589, simple_loss=0.3334, pruned_loss=0.09215, over 7468.00 frames.], tot_loss[loss=0.2462, simple_loss=0.3225, pruned_loss=0.08492, over 1464924.52 frames.], batch size: 21, lr: 9.97e-04
2022-07-26 00:29:39,003 INFO [train.py:850] (2/4) Epoch 5, batch 1800, loss[loss=0.1942, simple_loss=0.2825, pruned_loss=0.0529, over 7429.00 frames.], tot_loss[loss=0.246, simple_loss=0.3222, pruned_loss=0.08486, over 1464471.36 frames.], batch size: 18, lr: 9.96e-04
2022-07-26 00:30:23,821 INFO [train.py:850] (2/4) Epoch 5, batch 1850, loss[loss=0.2463, simple_loss=0.3183, pruned_loss=0.08717, over 7473.00 frames.], tot_loss[loss=0.2449, simple_loss=0.3218, pruned_loss=0.08404, over 1465926.89 frames.], batch size: 24, lr: 9.96e-04
2022-07-26 00:31:08,194 INFO [train.py:850] (2/4) Epoch 5, batch 1900, loss[loss=0.2481, simple_loss=0.3186, pruned_loss=0.08881, over 7465.00 frames.], tot_loss[loss=0.2447, simple_loss=0.3213, pruned_loss=0.08398, over 1466225.28 frames.], batch size: 39, lr: 9.95e-04
2022-07-26 00:31:51,754 INFO [train.py:850] (2/4) Epoch 5, batch 1950, loss[loss=0.2499, simple_loss=0.3423, pruned_loss=0.07879, over 7220.00 frames.], tot_loss[loss=0.2438, simple_loss=0.3207, pruned_loss=0.08342, over 1466946.93 frames.], batch size: 25, lr: 9.94e-04
2022-07-26 00:32:37,058 INFO [train.py:850] (2/4) Epoch 5, batch 2000, loss[loss=0.2526, simple_loss=0.3237, pruned_loss=0.09077, over 7197.00 frames.], tot_loss[loss=0.2441, simple_loss=0.3208, pruned_loss=0.08373, over 1466424.62 frames.], batch size: 20, lr: 9.94e-04
2022-07-26 00:33:21,057 INFO [train.py:850] (2/4) Epoch 5, batch 2050, loss[loss=0.2329, simple_loss=0.3204, pruned_loss=0.07266, over 7465.00 frames.], tot_loss[loss=0.2441, simple_loss=0.3212, pruned_loss=0.08346, over 1466246.89 frames.], batch size: 31, lr: 9.93e-04
2022-07-26 00:34:04,031 INFO [train.py:850] (2/4) Epoch 5, batch 2100, loss[loss=0.2448, simple_loss=0.3325, pruned_loss=0.07851, over 7286.00 frames.], tot_loss[loss=0.2426, simple_loss=0.3202, pruned_loss=0.08247, over 1466021.31 frames.], batch size: 21, lr: 9.93e-04
2022-07-26 00:34:48,377 INFO [train.py:850] (2/4) Epoch 5, batch 2150, loss[loss=0.2794, simple_loss=0.3601, pruned_loss=0.09937, over 7230.00 frames.], tot_loss[loss=0.2433, simple_loss=0.3207, pruned_loss=0.083, over 1464828.56 frames.], batch size: 24, lr: 9.92e-04
2022-07-26 00:35:31,663 INFO [train.py:850] (2/4) Epoch 5, batch 2200, loss[loss=0.2981, simple_loss=0.3601, pruned_loss=0.1181, over 7277.00 frames.], tot_loss[loss=0.2425, simple_loss=0.3197, pruned_loss=0.08259, over 1464039.36 frames.], batch size: 27, lr: 9.91e-04
2022-07-26 00:36:16,079 INFO [train.py:850] (2/4) Epoch 5, batch 2250, loss[loss=0.2472, simple_loss=0.3357, pruned_loss=0.07935, over 7278.00 frames.], tot_loss[loss=0.2438, simple_loss=0.3208, pruned_loss=0.08342, over 1464418.31 frames.], batch size: 27, lr: 9.91e-04
2022-07-26 00:36:59,458 INFO [train.py:850] (2/4) Epoch 5, batch 2300, loss[loss=0.2851, simple_loss=0.3295, pruned_loss=0.1204, over 7314.00 frames.], tot_loss[loss=0.244, simple_loss=0.3205, pruned_loss=0.0837, over 1463087.23 frames.], batch size: 17, lr: 9.90e-04
2022-07-26 00:37:43,921 INFO [train.py:850] (2/4) Epoch 5, batch 2350, loss[loss=0.3296, simple_loss=0.3818, pruned_loss=0.1387, over 7469.00 frames.], tot_loss[loss=0.2439, simple_loss=0.3205, pruned_loss=0.08369, over 1463480.53 frames.], batch size: 68, lr: 9.89e-04
2022-07-26 00:38:26,643 INFO [train.py:850] (2/4) Epoch 5, batch 2400, loss[loss=0.2263, simple_loss=0.3103, pruned_loss=0.07112, over 7285.00 frames.], tot_loss[loss=0.2434, simple_loss=0.32, pruned_loss=0.08342, over 1462781.07 frames.], batch size: 20, lr: 9.89e-04
2022-07-26 00:39:11,381 INFO [train.py:850] (2/4) Epoch 5, batch 2450, loss[loss=0.2727, simple_loss=0.3557, pruned_loss=0.09486, over 7338.00 frames.], tot_loss[loss=0.2415, simple_loss=0.3185, pruned_loss=0.08225, over 1463378.90 frames.], batch size: 27, lr: 9.88e-04
2022-07-26 00:39:55,758 INFO [train.py:850] (2/4) Epoch 5, batch 2500, loss[loss=0.2284, simple_loss=0.3143, pruned_loss=0.07118, over 7447.00 frames.], tot_loss[loss=0.2423, simple_loss=0.319, pruned_loss=0.08276, over 1463349.43 frames.], batch size: 24, lr: 9.87e-04
2022-07-26 00:40:39,364 INFO [train.py:850] (2/4) Epoch 5, batch 2550, loss[loss=0.1864, simple_loss=0.2747, pruned_loss=0.04904, over 7491.00 frames.], tot_loss[loss=0.2428, simple_loss=0.3195, pruned_loss=0.08304, over 1463252.58 frames.], batch size: 19, lr: 9.87e-04
2022-07-26 00:41:23,833 INFO [train.py:850] (2/4) Epoch 5, batch 2600, loss[loss=0.2369, simple_loss=0.3221, pruned_loss=0.07583, over 7385.00 frames.], tot_loss[loss=0.2419, simple_loss=0.3191, pruned_loss=0.08236, over 1463597.35 frames.], batch size: 21, lr: 9.86e-04
2022-07-26 00:42:06,354 INFO [train.py:850] (2/4) Epoch 5, batch 2650, loss[loss=0.2529, simple_loss=0.3417, pruned_loss=0.08209, over 7213.00 frames.], tot_loss[loss=0.2415, simple_loss=0.3187, pruned_loss=0.08221, over 1464726.90 frames.], batch size: 25, lr: 9.85e-04
2022-07-26 00:42:50,224 INFO [train.py:850] (2/4) Epoch 5, batch 2700, loss[loss=0.2285, simple_loss=0.3121, pruned_loss=0.07243, over 7208.00 frames.], tot_loss[loss=0.2421, simple_loss=0.3187, pruned_loss=0.08277, over 1464101.43 frames.], batch size: 20, lr: 9.85e-04
2022-07-26 00:43:34,526 INFO [train.py:850] (2/4) Epoch 5, batch 2750, loss[loss=0.2037, simple_loss=0.2947, pruned_loss=0.05631, over 7476.00 frames.], tot_loss[loss=0.2422, simple_loss=0.319, pruned_loss=0.08272, over 1464299.89 frames.], batch size: 20, lr: 9.84e-04
2022-07-26 00:44:17,617 INFO [train.py:850] (2/4) Epoch 5, batch 2800, loss[loss=0.2889, simple_loss=0.3586, pruned_loss=0.1096, over 7469.00 frames.], tot_loss[loss=0.2431, simple_loss=0.3199, pruned_loss=0.08312, over 1465683.20 frames.], batch size: 24, lr: 9.84e-04
2022-07-26 00:45:03,042 INFO [train.py:850] (2/4) Epoch 5, batch 2850, loss[loss=0.277, simple_loss=0.3391, pruned_loss=0.1074, over 7335.00 frames.], tot_loss[loss=0.2431, simple_loss=0.32, pruned_loss=0.08307, over 1465214.17 frames.], batch size: 23, lr: 9.83e-04
2022-07-26 00:45:46,343 INFO [train.py:850] (2/4) Epoch 5, batch 2900, loss[loss=0.1868, simple_loss=0.2695, pruned_loss=0.05201, over 7450.00 frames.], tot_loss[loss=0.2425, simple_loss=0.3197, pruned_loss=0.08268, over 1465067.36 frames.], batch size: 17, lr: 9.82e-04
2022-07-26 00:46:30,214 INFO [train.py:850] (2/4) Epoch 5, batch 2950, loss[loss=0.2444, simple_loss=0.3234, pruned_loss=0.08264, over 7342.00 frames.], tot_loss[loss=0.243, simple_loss=0.3205, pruned_loss=0.08276, over 1466064.90 frames.], batch size: 27, lr: 9.82e-04
2022-07-26 00:47:13,631 INFO [train.py:850] (2/4) Epoch 5, batch 3000, loss[loss=0.2423, simple_loss=0.3247, pruned_loss=0.0799, over 7197.00 frames.], tot_loss[loss=0.244, simple_loss=0.3215, pruned_loss=0.08321, over 1465943.73 frames.], batch size: 20, lr: 9.81e-04
2022-07-26 00:47:13,632 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 00:47:36,500 INFO [train.py:879] (2/4) Epoch 5, validation: loss=0.219, simple_loss=0.312, pruned_loss=0.06299, over 924787.00 frames.
2022-07-26 00:48:22,381 INFO [train.py:850] (2/4) Epoch 5, batch 3050, loss[loss=0.1959, simple_loss=0.283, pruned_loss=0.05437, over 7291.00 frames.], tot_loss[loss=0.2419, simple_loss=0.3199, pruned_loss=0.08198, over 1465884.84 frames.], batch size: 19, lr: 9.80e-04
2022-07-26 00:49:05,598 INFO [train.py:850] (2/4) Epoch 5, batch 3100, loss[loss=0.2128, simple_loss=0.2955, pruned_loss=0.06504, over 7450.00 frames.], tot_loss[loss=0.2414, simple_loss=0.3192, pruned_loss=0.08179, over 1466044.01 frames.], batch size: 18, lr: 9.80e-04
2022-07-26 00:49:50,483 INFO [train.py:850] (2/4) Epoch 5, batch 3150, loss[loss=0.2427, simple_loss=0.3304, pruned_loss=0.07753, over 7357.00 frames.], tot_loss[loss=0.2416, simple_loss=0.319, pruned_loss=0.08207, over 1465937.72 frames.], batch size: 23, lr: 9.79e-04
2022-07-26 00:50:35,446 INFO [train.py:850] (2/4) Epoch 5, batch 3200, loss[loss=0.2011, simple_loss=0.2733, pruned_loss=0.0644, over 7295.00 frames.], tot_loss[loss=0.2434, simple_loss=0.3206, pruned_loss=0.08308, over 1465093.82 frames.], batch size: 16, lr: 9.79e-04
2022-07-26 00:51:20,010 INFO [train.py:850] (2/4) Epoch 5, batch 3250, loss[loss=0.2481, simple_loss=0.333, pruned_loss=0.08158, over 7182.00 frames.], tot_loss[loss=0.2428, simple_loss=0.3204, pruned_loss=0.08259, over 1464703.10 frames.], batch size: 22, lr: 9.78e-04
2022-07-26 00:52:03,507 INFO [train.py:850] (2/4) Epoch 5, batch 3300, loss[loss=0.2234, simple_loss=0.3024, pruned_loss=0.07223, over 7470.00 frames.], tot_loss[loss=0.2428, simple_loss=0.3205, pruned_loss=0.08253, over 1465293.13 frames.], batch size: 21, lr: 9.77e-04
2022-07-26 00:52:47,777 INFO [train.py:850] (2/4) Epoch 5, batch 3350, loss[loss=0.2531, simple_loss=0.3395, pruned_loss=0.08335, over 7212.00 frames.], tot_loss[loss=0.2437, simple_loss=0.3211, pruned_loss=0.08315, over 1465149.02 frames.], batch size: 25, lr: 9.77e-04
2022-07-26 00:53:32,265 INFO [train.py:850] (2/4) Epoch 5, batch 3400, loss[loss=0.2236, simple_loss=0.2924, pruned_loss=0.07739, over 7315.00 frames.], tot_loss[loss=0.2424, simple_loss=0.3202, pruned_loss=0.08229, over 1465926.78 frames.], batch size: 18, lr: 9.76e-04
2022-07-26 00:54:16,827 INFO [train.py:850] (2/4) Epoch 5, batch 3450, loss[loss=0.2329, simple_loss=0.2995, pruned_loss=0.08315, over 7326.00 frames.], tot_loss[loss=0.2416, simple_loss=0.3194, pruned_loss=0.08196, over 1465178.81 frames.], batch size: 17, lr: 9.76e-04
2022-07-26 00:55:02,198 INFO [train.py:850] (2/4) Epoch 5, batch 3500, loss[loss=0.2459, simple_loss=0.3404, pruned_loss=0.0757, over 7349.00 frames.], tot_loss[loss=0.2405, simple_loss=0.3184, pruned_loss=0.08125, over 1465228.25 frames.], batch size: 23, lr: 9.75e-04
2022-07-26 00:55:46,212 INFO [train.py:850] (2/4) Epoch 5, batch 3550, loss[loss=0.2222, simple_loss=0.3148, pruned_loss=0.06479, over 7195.00 frames.], tot_loss[loss=0.2413, simple_loss=0.3194, pruned_loss=0.08159, over 1465766.89 frames.], batch size: 21, lr: 9.74e-04
2022-07-26 00:56:29,873 INFO [train.py:850] (2/4) Epoch 5, batch 3600, loss[loss=0.2494, simple_loss=0.3293, pruned_loss=0.08469, over 7404.00 frames.], tot_loss[loss=0.2401, simple_loss=0.319, pruned_loss=0.0806, over 1466927.35 frames.], batch size: 39, lr: 9.74e-04
2022-07-26 00:57:14,946 INFO [train.py:850] (2/4) Epoch 5, batch 3650, loss[loss=0.1942, simple_loss=0.279, pruned_loss=0.05467, over 7197.00 frames.], tot_loss[loss=0.2406, simple_loss=0.319, pruned_loss=0.08106, over 1466137.15 frames.], batch size: 18, lr: 9.73e-04
2022-07-26 00:57:58,796 INFO [train.py:850] (2/4) Epoch 5, batch 3700, loss[loss=0.2384, simple_loss=0.3238, pruned_loss=0.07645, over 7386.00 frames.], tot_loss[loss=0.2385, simple_loss=0.3172, pruned_loss=0.07987, over 1465510.16 frames.], batch size: 21, lr: 9.72e-04
2022-07-26 00:58:44,264 INFO [train.py:850] (2/4) Epoch 5, batch 3750, loss[loss=0.2272, simple_loss=0.3096, pruned_loss=0.07237, over 7293.00 frames.], tot_loss[loss=0.2398, simple_loss=0.3182, pruned_loss=0.08075, over 1465041.23 frames.], batch size: 22, lr: 9.72e-04
2022-07-26 00:59:28,373 INFO [train.py:850] (2/4) Epoch 5, batch 3800, loss[loss=0.247, simple_loss=0.3208, pruned_loss=0.08657, over 7473.00 frames.], tot_loss[loss=0.2412, simple_loss=0.319, pruned_loss=0.08171, over 1465377.09 frames.], batch size: 24, lr: 9.71e-04
2022-07-26 01:00:12,204 INFO [train.py:850] (2/4) Epoch 5, batch 3850, loss[loss=0.2457, simple_loss=0.326, pruned_loss=0.08277, over 7304.00 frames.], tot_loss[loss=0.2399, simple_loss=0.3187, pruned_loss=0.08059, over 1465243.71 frames.], batch size: 27, lr: 9.71e-04
2022-07-26 01:00:55,504 INFO [train.py:850] (2/4) Epoch 5, batch 3900, loss[loss=0.2255, simple_loss=0.315, pruned_loss=0.06794, over 7473.00 frames.], tot_loss[loss=0.2387, simple_loss=0.3176, pruned_loss=0.07992, over 1466453.41 frames.], batch size: 21, lr: 9.70e-04
2022-07-26 01:01:39,295 INFO [train.py:850] (2/4) Epoch 5, batch 3950, loss[loss=0.2408, simple_loss=0.3381, pruned_loss=0.07179, over 7468.00 frames.], tot_loss[loss=0.2389, simple_loss=0.3185, pruned_loss=0.0797, over 1466709.37 frames.], batch size: 21, lr: 9.69e-04
2022-07-26 01:02:23,975 INFO [train.py:850] (2/4) Epoch 5, batch 4000, loss[loss=0.2616, simple_loss=0.3196, pruned_loss=0.1018, over 7199.00 frames.], tot_loss[loss=0.2413, simple_loss=0.3199, pruned_loss=0.08133, over 1466031.99 frames.], batch size: 18, lr: 9.69e-04
2022-07-26 01:03:08,357 INFO [train.py:850] (2/4) Epoch 5, batch 4050, loss[loss=0.2412, simple_loss=0.3102, pruned_loss=0.0861, over 7374.00 frames.], tot_loss[loss=0.2415, simple_loss=0.3199, pruned_loss=0.08153, over 1465876.17 frames.], batch size: 20, lr: 9.68e-04
2022-07-26 01:03:52,685 INFO [train.py:850] (2/4) Epoch 5, batch 4100, loss[loss=0.2841, simple_loss=0.3536, pruned_loss=0.1073, over 7297.00 frames.], tot_loss[loss=0.2435, simple_loss=0.3211, pruned_loss=0.08301, over 1465309.35 frames.], batch size: 22, lr: 9.68e-04
2022-07-26 01:04:36,707 INFO [train.py:850] (2/4) Epoch 5, batch 4150, loss[loss=0.2525, simple_loss=0.3254, pruned_loss=0.08978, over 7491.00 frames.], tot_loss[loss=0.2442, simple_loss=0.321, pruned_loss=0.08371, over 1465235.06 frames.], batch size: 19, lr: 9.67e-04
2022-07-26 01:05:19,780 INFO [train.py:850] (2/4) Epoch 5, batch 4200, loss[loss=0.2031, simple_loss=0.2864, pruned_loss=0.05987, over 7209.00 frames.], tot_loss[loss=0.2484, simple_loss=0.3238, pruned_loss=0.08652, over 1465603.92 frames.], batch size: 19, lr: 9.66e-04
2022-07-26 01:06:03,326 INFO [train.py:850] (2/4) Epoch 5, batch 4250, loss[loss=0.2504, simple_loss=0.3265, pruned_loss=0.08715, over 7477.00 frames.], tot_loss[loss=0.2513, simple_loss=0.325, pruned_loss=0.08881, over 1466459.82 frames.], batch size: 24, lr: 9.66e-04
2022-07-26 01:06:47,646 INFO [train.py:850] (2/4) Epoch 5, batch 4300, loss[loss=0.3323, simple_loss=0.3504, pruned_loss=0.1571, over 7304.00 frames.], tot_loss[loss=0.2548, simple_loss=0.326, pruned_loss=0.09181, over 1466527.98 frames.], batch size: 17, lr: 9.65e-04
2022-07-26 01:07:31,612 INFO [train.py:850] (2/4) Epoch 5, batch 4350, loss[loss=0.2431, simple_loss=0.3231, pruned_loss=0.08154, over 7290.00 frames.], tot_loss[loss=0.2569, simple_loss=0.3267, pruned_loss=0.09354, over 1465416.76 frames.], batch size: 22, lr: 9.65e-04
2022-07-26 01:08:16,335 INFO [train.py:850] (2/4) Epoch 5, batch 4400, loss[loss=0.3122, simple_loss=0.3653, pruned_loss=0.1295, over 7217.00 frames.], tot_loss[loss=0.2592, simple_loss=0.3278, pruned_loss=0.0953, over 1464734.59 frames.], batch size: 25, lr: 9.64e-04
2022-07-26 01:09:15,736 INFO [train.py:850] (2/4) Epoch 5, batch 4450, loss[loss=0.2841, simple_loss=0.3379, pruned_loss=0.1152, over 7168.00 frames.], tot_loss[loss=0.261, simple_loss=0.3287, pruned_loss=0.09662, over 1465069.50 frames.], batch size: 17, lr: 9.63e-04
2022-07-26 01:09:58,716 INFO [train.py:850] (2/4) Epoch 5, batch 4500, loss[loss=0.2412, simple_loss=0.3175, pruned_loss=0.08242, over 7200.00 frames.], tot_loss[loss=0.2657, simple_loss=0.3313, pruned_loss=0.1, over 1464863.80 frames.], batch size: 18, lr: 9.63e-04
2022-07-26 01:10:45,211 INFO [train.py:850] (2/4) Epoch 5, batch 4550, loss[loss=0.2651, simple_loss=0.3299, pruned_loss=0.1001, over 7359.00 frames.], tot_loss[loss=0.2678, simple_loss=0.3322, pruned_loss=0.1017, over 1465024.64 frames.], batch size: 23, lr: 9.62e-04
2022-07-26 01:11:29,283 INFO [train.py:850] (2/4) Epoch 5, batch 4600, loss[loss=0.2737, simple_loss=0.321, pruned_loss=0.1132, over 7456.00 frames.], tot_loss[loss=0.2696, simple_loss=0.3334, pruned_loss=0.1029, over 1466536.11 frames.], batch size: 17, lr: 9.62e-04
2022-07-26 01:12:14,568 INFO [train.py:850] (2/4) Epoch 5, batch 4650, loss[loss=0.2858, simple_loss=0.3609, pruned_loss=0.1054, over 7421.00 frames.], tot_loss[loss=0.2687, simple_loss=0.3321, pruned_loss=0.1026, over 1467161.29 frames.], batch size: 22, lr: 9.61e-04
2022-07-26 01:12:58,306 INFO [train.py:850] (2/4) Epoch 5, batch 4700, loss[loss=0.2518, simple_loss=0.3184, pruned_loss=0.09261, over 7395.00 frames.], tot_loss[loss=0.2707, simple_loss=0.3336, pruned_loss=0.1039, over 1466277.51 frames.], batch size: 20, lr: 9.60e-04
2022-07-26 01:13:43,263 INFO [train.py:850] (2/4) Epoch 5, batch 4750, loss[loss=0.252, simple_loss=0.3173, pruned_loss=0.09336, over 7411.00 frames.], tot_loss[loss=0.2718, simple_loss=0.3342, pruned_loss=0.1048, over 1466820.87 frames.], batch size: 22, lr: 9.60e-04
2022-07-26 01:14:26,163 INFO [train.py:850] (2/4) Epoch 5, batch 4800, loss[loss=0.3119, simple_loss=0.3487, pruned_loss=0.1376, over 7196.00 frames.], tot_loss[loss=0.2726, simple_loss=0.3349, pruned_loss=0.1051, over 1466082.13 frames.], batch size: 19, lr: 9.59e-04
2022-07-26 01:15:10,119 INFO [train.py:850] (2/4) Epoch 5, batch 4850, loss[loss=0.2336, simple_loss=0.2956, pruned_loss=0.08578, over 7489.00 frames.], tot_loss[loss=0.2724, simple_loss=0.3347, pruned_loss=0.1051, over 1466790.74 frames.], batch size: 19, lr: 9.59e-04
2022-07-26 01:15:54,390 INFO [train.py:850] (2/4) Epoch 5, batch 4900, loss[loss=0.239, simple_loss=0.3088, pruned_loss=0.08463, over 7188.00 frames.], tot_loss[loss=0.2735, simple_loss=0.3352, pruned_loss=0.1059, over 1466247.36 frames.], batch size: 18, lr: 9.58e-04
2022-07-26 01:16:39,385 INFO [train.py:850] (2/4) Epoch 5, batch 4950, loss[loss=0.2339, simple_loss=0.3011, pruned_loss=0.08333, over 7196.00 frames.], tot_loss[loss=0.2726, simple_loss=0.3344, pruned_loss=0.1054, over 1465989.35 frames.], batch size: 19, lr: 9.58e-04
2022-07-26 01:17:22,819 INFO [train.py:850] (2/4) Epoch 5, batch 5000, loss[loss=0.3278, simple_loss=0.393, pruned_loss=0.1313, over 7271.00 frames.], tot_loss[loss=0.2722, simple_loss=0.334, pruned_loss=0.1052, over 1466084.10 frames.], batch size: 21, lr: 9.57e-04
2022-07-26 01:18:07,688 INFO [train.py:850] (2/4) Epoch 5, batch 5050, loss[loss=0.2702, simple_loss=0.338, pruned_loss=0.1012, over 7229.00 frames.], tot_loss[loss=0.273, simple_loss=0.3346, pruned_loss=0.1057, over 1466264.34 frames.], batch size: 25, lr: 9.56e-04
2022-07-26 01:18:51,367 INFO [train.py:850] (2/4) Epoch 5, batch 5100, loss[loss=0.2658, simple_loss=0.3388, pruned_loss=0.09645, over 7208.00 frames.], tot_loss[loss=0.2729, simple_loss=0.3345, pruned_loss=0.1056, over 1465442.41 frames.], batch size: 25, lr: 9.56e-04
2022-07-26 01:19:36,330 INFO [train.py:850] (2/4) Epoch 5, batch 5150, loss[loss=0.2754, simple_loss=0.3475, pruned_loss=0.1016, over 7410.00 frames.], tot_loss[loss=0.2727, simple_loss=0.3345, pruned_loss=0.1054, over 1465427.07 frames.], batch size: 22, lr: 9.55e-04
2022-07-26 01:20:22,205 INFO [train.py:850] (2/4) Epoch 5, batch 5200, loss[loss=0.331, simple_loss=0.3947, pruned_loss=0.1337, over 7244.00 frames.], tot_loss[loss=0.2735, simple_loss=0.3351, pruned_loss=0.106, over 1465737.22 frames.], batch size: 27, lr: 9.55e-04
2022-07-26 01:21:06,220 INFO [train.py:850] (2/4) Epoch 5, batch 5250, loss[loss=0.2984, simple_loss=0.3539, pruned_loss=0.1214, over 7435.00 frames.], tot_loss[loss=0.2717, simple_loss=0.3337, pruned_loss=0.1049, over 1465348.08 frames.], batch size: 24, lr: 9.54e-04
2022-07-26 01:21:50,740 INFO [train.py:850] (2/4) Epoch 5, batch 5300, loss[loss=0.2819, simple_loss=0.3627, pruned_loss=0.1006, over 7348.00 frames.], tot_loss[loss=0.2726, simple_loss=0.3346, pruned_loss=0.1053, over 1465133.96 frames.], batch size: 23, lr: 9.54e-04
2022-07-26 01:22:34,973 INFO [train.py:850] (2/4) Epoch 5, batch 5350, loss[loss=0.2032, simple_loss=0.2717, pruned_loss=0.06732, over 7153.00 frames.], tot_loss[loss=0.2714, simple_loss=0.3335, pruned_loss=0.1047, over 1464472.62 frames.], batch size: 17, lr: 9.53e-04
2022-07-26 01:23:19,306 INFO [train.py:850] (2/4) Epoch 5, batch 5400, loss[loss=0.2398, simple_loss=0.314, pruned_loss=0.08281, over 7376.00 frames.], tot_loss[loss=0.272, simple_loss=0.3338, pruned_loss=0.1051, over 1466237.80 frames.], batch size: 39, lr: 9.52e-04
2022-07-26 01:24:04,128 INFO [train.py:850] (2/4) Epoch 5, batch 5450, loss[loss=0.2516, simple_loss=0.3037, pruned_loss=0.09978, over 7303.00 frames.], tot_loss[loss=0.2702, simple_loss=0.3322, pruned_loss=0.1041, over 1465637.15 frames.], batch size: 17, lr: 9.52e-04
2022-07-26 01:24:47,314 INFO [train.py:850] (2/4) Epoch 5, batch 5500, loss[loss=0.2042, simple_loss=0.2819, pruned_loss=0.06325, over 7434.00 frames.], tot_loss[loss=0.2698, simple_loss=0.3322, pruned_loss=0.1037, over 1466075.34 frames.], batch size: 18, lr: 9.51e-04
2022-07-26 01:25:31,425 INFO [train.py:850] (2/4) Epoch 5, batch 5550, loss[loss=0.3309, simple_loss=0.3682, pruned_loss=0.1468, over 7377.00 frames.], tot_loss[loss=0.2693, simple_loss=0.3318, pruned_loss=0.1034, over 1466862.95 frames.], batch size: 19, lr: 9.51e-04
2022-07-26 01:26:15,899 INFO [train.py:850] (2/4) Epoch 5, batch 5600, loss[loss=0.2436, simple_loss=0.3287, pruned_loss=0.07919, over 7212.00 frames.], tot_loss[loss=0.2691, simple_loss=0.3321, pruned_loss=0.103, over 1467552.39 frames.], batch size: 19, lr: 9.50e-04
2022-07-26 01:27:00,888 INFO [train.py:850] (2/4) Epoch 5, batch 5650, loss[loss=0.2961, simple_loss=0.3528, pruned_loss=0.1197, over 7461.00 frames.], tot_loss[loss=0.2691, simple_loss=0.3321, pruned_loss=0.103, over 1467255.73 frames.], batch size: 69, lr: 9.50e-04
2022-07-26 01:27:46,093 INFO [train.py:850] (2/4) Epoch 5, batch 5700, loss[loss=0.2989, simple_loss=0.3623, pruned_loss=0.1178, over 7184.00 frames.], tot_loss[loss=0.2686, simple_loss=0.3317, pruned_loss=0.1028, over 1466494.84 frames.], batch size: 21, lr: 9.49e-04
2022-07-26 01:28:30,796 INFO [train.py:850] (2/4) Epoch 5, batch 5750, loss[loss=0.236, simple_loss=0.3114, pruned_loss=0.08025, over 7482.00 frames.], tot_loss[loss=0.2702, simple_loss=0.3328, pruned_loss=0.1038, over 1467481.10 frames.], batch size: 20, lr: 9.48e-04
2022-07-26 01:29:13,672 INFO [train.py:850] (2/4) Epoch 5, batch 5800, loss[loss=0.2583, simple_loss=0.3292, pruned_loss=0.09372, over 7202.00 frames.], tot_loss[loss=0.2682, simple_loss=0.3312, pruned_loss=0.1026, over 1466864.65 frames.], batch size: 20, lr: 9.48e-04
2022-07-26 01:29:57,779 INFO [train.py:850] (2/4) Epoch 5, batch 5850, loss[loss=0.2838, simple_loss=0.349, pruned_loss=0.1093, over 7440.00 frames.], tot_loss[loss=0.2676, simple_loss=0.3306, pruned_loss=0.1023, over 1465739.52 frames.], batch size: 40, lr: 9.47e-04
2022-07-26 01:30:41,776 INFO [train.py:850] (2/4) Epoch 5, batch 5900, loss[loss=0.3522, simple_loss=0.3933, pruned_loss=0.1555, over 7183.00 frames.], tot_loss[loss=0.2706, simple_loss=0.3328, pruned_loss=0.1042, over 1465424.49 frames.], batch size: 21, lr: 9.47e-04
2022-07-26 01:31:26,317 INFO [train.py:850] (2/4) Epoch 5, batch 5950, loss[loss=0.2565, simple_loss=0.3263, pruned_loss=0.09332, over 7468.00 frames.], tot_loss[loss=0.2691, simple_loss=0.3319, pruned_loss=0.1031, over 1464597.06 frames.], batch size: 21, lr: 9.46e-04
2022-07-26 01:32:11,554 INFO [train.py:850] (2/4) Epoch 5, batch 6000, loss[loss=0.2896, simple_loss=0.3414, pruned_loss=0.1189, over 7306.00 frames.], tot_loss[loss=0.2698, simple_loss=0.3324, pruned_loss=0.1036, over 1464218.72 frames.], batch size: 22, lr: 9.46e-04
2022-07-26 01:32:11,556 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 01:32:34,390 INFO [train.py:879] (2/4) Epoch 5, validation: loss=0.2045, simple_loss=0.3015, pruned_loss=0.05375, over 924787.00 frames.
2022-07-26 01:33:19,500 INFO [train.py:850] (2/4) Epoch 5, batch 6050, loss[loss=0.2295, simple_loss=0.3131, pruned_loss=0.07293, over 7278.00 frames.], tot_loss[loss=0.2699, simple_loss=0.3321, pruned_loss=0.1038, over 1464114.14 frames.], batch size: 30, lr: 9.45e-04
2022-07-26 01:34:03,429 INFO [train.py:850] (2/4) Epoch 5, batch 6100, loss[loss=0.2919, simple_loss=0.3472, pruned_loss=0.1183, over 7289.00 frames.], tot_loss[loss=0.2673, simple_loss=0.3299, pruned_loss=0.1024, over 1464495.86 frames.], batch size: 22, lr: 9.44e-04
2022-07-26 01:34:47,329 INFO [train.py:850] (2/4) Epoch 5, batch 6150, loss[loss=0.2991, simple_loss=0.3676, pruned_loss=0.1153, over 7431.00 frames.], tot_loss[loss=0.2665, simple_loss=0.3297, pruned_loss=0.1016, over 1463682.99 frames.], batch size: 31, lr: 9.44e-04
2022-07-26 01:35:31,144 INFO [train.py:850] (2/4) Epoch 5, batch 6200, loss[loss=0.2574, simple_loss=0.3267, pruned_loss=0.09408, over 7290.00 frames.], tot_loss[loss=0.266, simple_loss=0.3291, pruned_loss=0.1014, over 1463923.35 frames.], batch size: 20, lr: 9.43e-04
2022-07-26 01:36:15,711 INFO [train.py:850] (2/4) Epoch 5, batch 6250, loss[loss=0.2405, simple_loss=0.3068, pruned_loss=0.08713, over 7471.00 frames.], tot_loss[loss=0.2667, simple_loss=0.3303, pruned_loss=0.1016, over 1464429.57 frames.], batch size: 20, lr: 9.43e-04
2022-07-26 01:36:59,200 INFO [train.py:850] (2/4) Epoch 5, batch 6300, loss[loss=0.2616, simple_loss=0.3342, pruned_loss=0.09451, over 7388.00 frames.], tot_loss[loss=0.2655, simple_loss=0.329, pruned_loss=0.101, over 1465020.78 frames.], batch size: 21, lr: 9.42e-04
2022-07-26 01:37:43,135 INFO [train.py:850] (2/4) Epoch 5, batch 6350, loss[loss=0.219, simple_loss=0.2932, pruned_loss=0.07244, over 7490.00 frames.], tot_loss[loss=0.265, simple_loss=0.3288, pruned_loss=0.1006, over 1464681.45 frames.], batch size: 20, lr: 9.42e-04
2022-07-26 01:38:26,958 INFO [train.py:850] (2/4) Epoch 5, batch 6400, loss[loss=0.2319, simple_loss=0.3105, pruned_loss=0.07664, over 7293.00 frames.], tot_loss[loss=0.2644, simple_loss=0.3285, pruned_loss=0.1001, over 1464605.47 frames.], batch size: 27, lr: 9.41e-04
2022-07-26 01:39:12,439 INFO [train.py:850] (2/4) Epoch 5, batch 6450, loss[loss=0.2457, simple_loss=0.33, pruned_loss=0.08073, over 7300.00 frames.], tot_loss[loss=0.2626, simple_loss=0.3271, pruned_loss=0.09899, over 1464583.32 frames.], batch size: 22, lr: 9.41e-04
2022-07-26 01:39:57,408 INFO [train.py:850] (2/4) Epoch 5, batch 6500, loss[loss=0.2749, simple_loss=0.3458, pruned_loss=0.102, over 7492.00 frames.], tot_loss[loss=0.2633, simple_loss=0.328, pruned_loss=0.09933, over 1466149.30 frames.], batch size: 23, lr: 9.40e-04
2022-07-26 01:40:42,842 INFO [train.py:850] (2/4) Epoch 5, batch 6550, loss[loss=0.2753, simple_loss=0.3488, pruned_loss=0.1009, over 7422.00 frames.], tot_loss[loss=0.2629, simple_loss=0.3282, pruned_loss=0.0988, over 1464803.82 frames.], batch size: 22, lr: 9.39e-04
2022-07-26 01:41:26,597 INFO [train.py:850] (2/4) Epoch 5, batch 6600, loss[loss=0.2946, simple_loss=0.3618, pruned_loss=0.1137, over 7343.00 frames.], tot_loss[loss=0.2638, simple_loss=0.329, pruned_loss=0.09931, over 1465011.93 frames.], batch size: 23, lr: 9.39e-04
2022-07-26 01:42:11,543 INFO [train.py:850] (2/4) Epoch 5, batch 6650, loss[loss=0.2604, simple_loss=0.3145, pruned_loss=0.1032, over 7434.00 frames.], tot_loss[loss=0.2646, simple_loss=0.3291, pruned_loss=0.1001, over 1464372.74 frames.], batch size: 17, lr: 9.38e-04
2022-07-26 01:42:55,055 INFO [train.py:850] (2/4) Epoch 5, batch 6700, loss[loss=0.261, simple_loss=0.33, pruned_loss=0.096, over 7302.00 frames.], tot_loss[loss=0.2662, simple_loss=0.3302, pruned_loss=0.1011, over 1464789.51 frames.], batch size: 22, lr: 9.38e-04
2022-07-26 01:43:39,645 INFO [train.py:850] (2/4) Epoch 5, batch 6750, loss[loss=0.2158, simple_loss=0.2969, pruned_loss=0.06732, over 7301.00 frames.], tot_loss[loss=0.2669, simple_loss=0.3306, pruned_loss=0.1016, over 1465917.28 frames.], batch size: 19, lr: 9.37e-04
2022-07-26 01:44:23,916 INFO [train.py:850] (2/4) Epoch 5, batch 6800, loss[loss=0.2226, simple_loss=0.2867, pruned_loss=0.07923, over 7445.00 frames.], tot_loss[loss=0.266, simple_loss=0.3299, pruned_loss=0.101, over 1466693.38 frames.], batch size: 18, lr: 9.37e-04
2022-07-26 01:45:08,619 INFO [train.py:850] (2/4) Epoch 5, batch 6850, loss[loss=0.2445, simple_loss=0.3117, pruned_loss=0.08867, over 7287.00 frames.], tot_loss[loss=0.266, simple_loss=0.3302, pruned_loss=0.1009, over 1466459.40 frames.], batch size: 19, lr: 9.36e-04
2022-07-26 01:45:53,156 INFO [train.py:850] (2/4) Epoch 5, batch 6900, loss[loss=0.2496, simple_loss=0.3138, pruned_loss=0.09266, over 7314.00 frames.], tot_loss[loss=0.2648, simple_loss=0.329, pruned_loss=0.1003, over 1466223.46 frames.], batch size: 22, lr: 9.36e-04
2022-07-26 01:46:37,914 INFO [train.py:850] (2/4) Epoch 5, batch 6950, loss[loss=0.2425, simple_loss=0.2979, pruned_loss=0.09355, over 7317.00 frames.], tot_loss[loss=0.2645, simple_loss=0.3291, pruned_loss=0.09995, over 1465419.28 frames.], batch size: 17, lr: 9.35e-04
2022-07-26 01:47:22,264 INFO [train.py:850] (2/4) Epoch 5, batch 7000, loss[loss=0.2597, simple_loss=0.3129, pruned_loss=0.1033, over 7493.00 frames.], tot_loss[loss=0.2647, simple_loss=0.329, pruned_loss=0.1002, over 1465748.94 frames.], batch size: 19, lr: 9.35e-04
2022-07-26 01:48:05,860 INFO [train.py:850] (2/4) Epoch 5, batch 7050, loss[loss=0.2388, simple_loss=0.3085, pruned_loss=0.08457, over 7203.00 frames.], tot_loss[loss=0.2642, simple_loss=0.3287, pruned_loss=0.09981, over 1465588.95 frames.], batch size: 18, lr: 9.34e-04
2022-07-26 01:48:50,331 INFO [train.py:850] (2/4) Epoch 5, batch 7100, loss[loss=0.271, simple_loss=0.3276, pruned_loss=0.1072, over 7167.00 frames.], tot_loss[loss=0.265, simple_loss=0.3297, pruned_loss=0.1001, over 1466492.09 frames.], batch size: 17, lr: 9.33e-04
2022-07-26 01:49:34,573 INFO [train.py:850] (2/4) Epoch 5, batch 7150, loss[loss=0.2813, simple_loss=0.335, pruned_loss=0.1138, over 7345.00 frames.], tot_loss[loss=0.2646, simple_loss=0.3294, pruned_loss=0.09989, over 1466249.90 frames.], batch size: 23, lr: 9.33e-04
2022-07-26 01:50:19,167 INFO [train.py:850] (2/4) Epoch 5, batch 7200, loss[loss=0.2752, simple_loss=0.3408, pruned_loss=0.1048, over 7274.00 frames.], tot_loss[loss=0.2655, simple_loss=0.3307, pruned_loss=0.1002, over 1466012.27 frames.], batch size: 20, lr: 9.32e-04
2022-07-26 01:51:04,070 INFO [train.py:850] (2/4) Epoch 5, batch 7250, loss[loss=0.2771, simple_loss=0.3452, pruned_loss=0.1045, over 7173.00 frames.], tot_loss[loss=0.2636, simple_loss=0.3294, pruned_loss=0.09885, over 1467134.79 frames.], batch size: 22, lr: 9.32e-04
2022-07-26 01:51:47,573 INFO [train.py:850] (2/4) Epoch 5, batch 7300, loss[loss=0.2294, simple_loss=0.3096, pruned_loss=0.07462, over 7275.00 frames.], tot_loss[loss=0.2637, simple_loss=0.3297, pruned_loss=0.09888, over 1466048.06 frames.], batch size: 30, lr: 9.31e-04
2022-07-26 01:52:32,671 INFO [train.py:850] (2/4) Epoch 5, batch 7350, loss[loss=0.2529, simple_loss=0.3278, pruned_loss=0.08905, over 7481.00 frames.], tot_loss[loss=0.2652, simple_loss=0.3308, pruned_loss=0.09979, over 1466200.87 frames.], batch size: 21, lr: 9.31e-04
2022-07-26 01:53:17,904 INFO [train.py:850] (2/4) Epoch 5, batch 7400, loss[loss=0.247, simple_loss=0.3314, pruned_loss=0.08127, over 7227.00 frames.], tot_loss[loss=0.2641, simple_loss=0.3296, pruned_loss=0.09932, over 1465796.62 frames.], batch size: 24, lr: 9.30e-04
2022-07-26 01:54:02,505 INFO [train.py:850] (2/4) Epoch 5, batch 7450, loss[loss=0.2358, simple_loss=0.3114, pruned_loss=0.08005, over 7217.00 frames.], tot_loss[loss=0.2642, simple_loss=0.33, pruned_loss=0.09925, over 1465384.00 frames.], batch size: 24, lr: 9.30e-04
2022-07-26 01:54:46,898 INFO [train.py:850] (2/4) Epoch 5, batch 7500, loss[loss=0.3263, simple_loss=0.3751, pruned_loss=0.1387, over 7353.00 frames.], tot_loss[loss=0.2626, simple_loss=0.3286, pruned_loss=0.09833, over 1466330.03 frames.], batch size: 23, lr: 9.29e-04
2022-07-26 01:55:31,791 INFO [train.py:850] (2/4) Epoch 5, batch 7550, loss[loss=0.2047, simple_loss=0.2833, pruned_loss=0.06308, over 7303.00 frames.], tot_loss[loss=0.2611, simple_loss=0.3272, pruned_loss=0.09752, over 1465564.14 frames.], batch size: 19, lr: 9.29e-04
2022-07-26 01:56:16,118 INFO [train.py:850] (2/4) Epoch 5, batch 7600, loss[loss=0.3084, simple_loss=0.3601, pruned_loss=0.1284, over 7468.00 frames.], tot_loss[loss=0.2612, simple_loss=0.3267, pruned_loss=0.09789, over 1467127.92 frames.], batch size: 72, lr: 9.28e-04
2022-07-26 01:57:00,492 INFO [train.py:850] (2/4) Epoch 5, batch 7650, loss[loss=0.2507, simple_loss=0.3351, pruned_loss=0.0832, over 7229.00 frames.], tot_loss[loss=0.261, simple_loss=0.3267, pruned_loss=0.0976, over 1466363.99 frames.], batch size: 24, lr: 9.28e-04
2022-07-26 01:57:44,640 INFO [train.py:850] (2/4) Epoch 5, batch 7700, loss[loss=0.232, simple_loss=0.303, pruned_loss=0.08053, over 7246.00 frames.], tot_loss[loss=0.2599, simple_loss=0.3261, pruned_loss=0.09683, over 1466611.73 frames.], batch size: 24, lr: 9.27e-04
2022-07-26 01:58:29,140 INFO [train.py:850] (2/4) Epoch 5, batch 7750, loss[loss=0.2293, simple_loss=0.3236, pruned_loss=0.06751, over 7418.00 frames.], tot_loss[loss=0.2599, simple_loss=0.3261, pruned_loss=0.09685, over 1465998.48 frames.], batch size: 22, lr: 9.27e-04
2022-07-26 01:59:12,374 INFO [train.py:850] (2/4) Epoch 5, batch 7800, loss[loss=0.2995, simple_loss=0.3609, pruned_loss=0.119, over 7459.00 frames.], tot_loss[loss=0.2607, simple_loss=0.3265, pruned_loss=0.09746, over 1465330.78 frames.], batch size: 24, lr: 9.26e-04
2022-07-26 01:59:56,932 INFO [train.py:850] (2/4) Epoch 5, batch 7850, loss[loss=0.2532, simple_loss=0.3268, pruned_loss=0.08981, over 7205.00 frames.], tot_loss[loss=0.26, simple_loss=0.3257, pruned_loss=0.09716, over 1465634.18 frames.], batch size: 20, lr: 9.25e-04
2022-07-26 02:00:41,227 INFO [train.py:850] (2/4) Epoch 5, batch 7900, loss[loss=0.2902, simple_loss=0.3549, pruned_loss=0.1127, over 7425.00 frames.], tot_loss[loss=0.2605, simple_loss=0.326, pruned_loss=0.09747, over 1465787.86 frames.], batch size: 22, lr: 9.25e-04
2022-07-26 02:01:25,639 INFO [train.py:850] (2/4) Epoch 5, batch 7950, loss[loss=0.1908, simple_loss=0.2765, pruned_loss=0.05252, over 7291.00 frames.], tot_loss[loss=0.2597, simple_loss=0.3256, pruned_loss=0.09693, over 1465594.24 frames.], batch size: 19, lr: 9.24e-04
2022-07-26 02:02:10,288 INFO [train.py:850] (2/4) Epoch 5, batch 8000, loss[loss=0.2803, simple_loss=0.3423, pruned_loss=0.1092, over 7398.00 frames.], tot_loss[loss=0.2601, simple_loss=0.3261, pruned_loss=0.09706, over 1465680.19 frames.], batch size: 20, lr: 9.24e-04
2022-07-26 02:02:54,447 INFO [train.py:850] (2/4) Epoch 5, batch 8050, loss[loss=0.2665, simple_loss=0.3341, pruned_loss=0.09939, over 7204.00 frames.], tot_loss[loss=0.2602, simple_loss=0.3261, pruned_loss=0.09718, over 1464355.61 frames.], batch size: 19, lr: 9.23e-04
2022-07-26 02:03:37,915 INFO [train.py:850] (2/4) Epoch 5, batch 8100, loss[loss=0.2619, simple_loss=0.3294, pruned_loss=0.09719, over 7355.00 frames.], tot_loss[loss=0.2611, simple_loss=0.3271, pruned_loss=0.09753, over 1464915.06 frames.], batch size: 38, lr: 9.23e-04
2022-07-26 02:04:24,337 INFO [train.py:850] (2/4) Epoch 5, batch 8150, loss[loss=0.353, simple_loss=0.3945, pruned_loss=0.1557, over 7413.00 frames.], tot_loss[loss=0.2602, simple_loss=0.3265, pruned_loss=0.097, over 1464458.64 frames.], batch size: 77, lr: 9.22e-04
2022-07-26 02:05:09,999 INFO [train.py:850] (2/4) Epoch 5, batch 8200, loss[loss=0.3058, simple_loss=0.3699, pruned_loss=0.1209, over 7251.00 frames.], tot_loss[loss=0.2589, simple_loss=0.3254, pruned_loss=0.09618, over 1465780.07 frames.], batch size: 27, lr: 9.22e-04
2022-07-26 02:05:54,644 INFO [train.py:850] (2/4) Epoch 5, batch 8250, loss[loss=0.3481, simple_loss=0.4032, pruned_loss=0.1465, over 7312.00 frames.], tot_loss[loss=0.2611, simple_loss=0.3276, pruned_loss=0.09735, over 1464646.44 frames.], batch size: 22, lr: 9.21e-04
2022-07-26 02:06:38,610 INFO [train.py:850] (2/4) Epoch 5, batch 8300, loss[loss=0.2666, simple_loss=0.3134, pruned_loss=0.1099, over 7389.00 frames.], tot_loss[loss=0.2623, simple_loss=0.3278, pruned_loss=0.09838, over 1465732.86 frames.], batch size: 19, lr: 9.21e-04
2022-07-26 02:07:24,193 INFO [train.py:850] (2/4) Epoch 5, batch 8350, loss[loss=0.2483, simple_loss=0.3225, pruned_loss=0.08708, over 7472.00 frames.], tot_loss[loss=0.2626, simple_loss=0.3279, pruned_loss=0.09867, over 1466830.89 frames.], batch size: 21, lr: 9.20e-04
2022-07-26 02:08:09,001 INFO [train.py:850] (2/4) Epoch 5, batch 8400, loss[loss=0.2371, simple_loss=0.3122, pruned_loss=0.08097, over 7383.00 frames.], tot_loss[loss=0.2604, simple_loss=0.3261, pruned_loss=0.09734, over 1467054.06 frames.], batch size: 19, lr: 9.20e-04
2022-07-26 02:09:07,909 INFO [train.py:850] (2/4) Epoch 5, batch 8450, loss[loss=0.2524, simple_loss=0.3253, pruned_loss=0.08972, over 7342.00 frames.], tot_loss[loss=0.2621, simple_loss=0.3277, pruned_loss=0.09825, over 1467627.29 frames.], batch size: 31, lr: 9.19e-04
2022-07-26 02:09:52,138 INFO [train.py:850] (2/4) Epoch 5, batch 8500, loss[loss=0.2118, simple_loss=0.3027, pruned_loss=0.06045, over 7356.00 frames.], tot_loss[loss=0.263, simple_loss=0.3282, pruned_loss=0.09887, over 1467728.84 frames.], batch size: 23, lr: 9.19e-04
2022-07-26 02:10:37,717 INFO [train.py:850] (2/4) Epoch 5, batch 8550, loss[loss=0.2731, simple_loss=0.3349, pruned_loss=0.1056, over 7489.00 frames.], tot_loss[loss=0.263, simple_loss=0.3281, pruned_loss=0.09898, over 1467564.24 frames.], batch size: 24, lr: 9.18e-04
2022-07-26 02:11:22,193 INFO [train.py:850] (2/4) Epoch 5, batch 8600, loss[loss=0.2264, simple_loss=0.2897, pruned_loss=0.08154, over 7317.00 frames.], tot_loss[loss=0.2626, simple_loss=0.3276, pruned_loss=0.0988, over 1467457.93 frames.], batch size: 17, lr: 9.18e-04
2022-07-26 02:12:05,321 INFO [train.py:850] (2/4) Epoch 5, batch 8650, loss[loss=0.2729, simple_loss=0.3313, pruned_loss=0.1072, over 7238.00 frames.], tot_loss[loss=0.2624, simple_loss=0.3275, pruned_loss=0.09868, over 1466516.53 frames.], batch size: 30, lr: 9.17e-04
2022-07-26 02:12:49,299 INFO [train.py:850] (2/4) Epoch 5, batch 8700, loss[loss=0.3241, simple_loss=0.3739, pruned_loss=0.1372, over 7421.00 frames.], tot_loss[loss=0.2634, simple_loss=0.3284, pruned_loss=0.09917, over 1466335.58 frames.], batch size: 68, lr: 9.17e-04
2022-07-26 02:13:32,010 INFO [train.py:850] (2/4) Epoch 5, batch 8750, loss[loss=0.2223, simple_loss=0.3009, pruned_loss=0.07189, over 7288.00 frames.], tot_loss[loss=0.2613, simple_loss=0.3271, pruned_loss=0.09779, over 1466329.54 frames.], batch size: 21, lr: 9.16e-04
2022-07-26 02:14:16,123 INFO [train.py:850] (2/4) Epoch 5, batch 8800, loss[loss=0.2881, simple_loss=0.3455, pruned_loss=0.1153, over 7448.00 frames.], tot_loss[loss=0.2607, simple_loss=0.327, pruned_loss=0.09723, over 1466637.86 frames.], batch size: 31, lr: 9.16e-04
2022-07-26 02:15:00,522 INFO [train.py:850] (2/4) Epoch 5, batch 8850, loss[loss=0.3305, simple_loss=0.3711, pruned_loss=0.145, over 7423.00 frames.], tot_loss[loss=0.2615, simple_loss=0.3274, pruned_loss=0.09784, over 1465777.06 frames.], batch size: 69, lr: 9.15e-04
2022-07-26 02:16:40,044 INFO [train.py:850] (2/4) Epoch 6, batch 0, loss[loss=0.2594, simple_loss=0.3295, pruned_loss=0.09467, over 7207.00 frames.], tot_loss[loss=0.2594, simple_loss=0.3295, pruned_loss=0.09467, over 7207.00 frames.], batch size: 18, lr: 8.79e-04
2022-07-26 02:17:24,118 INFO [train.py:850] (2/4) Epoch 6, batch 50, loss[loss=0.2345, simple_loss=0.3066, pruned_loss=0.08126, over 7432.00 frames.], tot_loss[loss=0.244, simple_loss=0.3182, pruned_loss=0.08491, over 330541.24 frames.], batch size: 22, lr: 8.78e-04
2022-07-26 02:18:07,251 INFO [train.py:850] (2/4) Epoch 6, batch 100, loss[loss=0.1939, simple_loss=0.2848, pruned_loss=0.05154, over 7387.00 frames.], tot_loss[loss=0.2397, simple_loss=0.3154, pruned_loss=0.08202, over 581796.74 frames.], batch size: 21, lr: 8.78e-04
2022-07-26 02:18:51,652 INFO [train.py:850] (2/4) Epoch 6, batch 150, loss[loss=0.2206, simple_loss=0.3092, pruned_loss=0.06602, over 7201.00 frames.], tot_loss[loss=0.2385, simple_loss=0.3147, pruned_loss=0.08113, over 777601.49 frames.], batch size: 25, lr: 8.77e-04
2022-07-26 02:19:34,492 INFO [train.py:850] (2/4) Epoch 6, batch 200, loss[loss=0.2721, simple_loss=0.347, pruned_loss=0.09863, over 7365.00 frames.], tot_loss[loss=0.2377, simple_loss=0.3148, pruned_loss=0.08027, over 929851.80 frames.], batch size: 39, lr: 8.77e-04
2022-07-26 02:20:18,400 INFO [train.py:850] (2/4) Epoch 6, batch 250, loss[loss=0.2302, simple_loss=0.3203, pruned_loss=0.07001, over 7322.00 frames.], tot_loss[loss=0.2377, simple_loss=0.316, pruned_loss=0.0797, over 1048236.56 frames.], batch size: 31, lr: 8.76e-04
2022-07-26 02:21:02,109 INFO [train.py:850] (2/4) Epoch 6, batch 300, loss[loss=0.2203, simple_loss=0.3043, pruned_loss=0.06817, over 7283.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3152, pruned_loss=0.07961, over 1141193.22 frames.], batch size: 21, lr: 8.76e-04
2022-07-26 02:21:46,590 INFO [train.py:850] (2/4) Epoch 6, batch 350, loss[loss=0.1955, simple_loss=0.2867, pruned_loss=0.05218, over 7204.00 frames.], tot_loss[loss=0.2359, simple_loss=0.3143, pruned_loss=0.07873, over 1213448.74 frames.], batch size: 18, lr: 8.76e-04
2022-07-26 02:22:29,664 INFO [train.py:850] (2/4) Epoch 6, batch 400, loss[loss=0.2048, simple_loss=0.279, pruned_loss=0.0653, over 7312.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3115, pruned_loss=0.07693, over 1270068.73 frames.], batch size: 18, lr: 8.75e-04
2022-07-26 02:23:14,553 INFO [train.py:850] (2/4) Epoch 6, batch 450, loss[loss=0.282, simple_loss=0.3489, pruned_loss=0.1075, over 7416.00 frames.], tot_loss[loss=0.2311, simple_loss=0.3104, pruned_loss=0.07593, over 1313801.40 frames.], batch size: 22, lr: 8.75e-04
2022-07-26 02:23:57,880 INFO [train.py:850] (2/4) Epoch 6, batch 500, loss[loss=0.2314, simple_loss=0.298, pruned_loss=0.0824, over 7324.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3097, pruned_loss=0.07506, over 1347514.33 frames.], batch size: 18, lr: 8.74e-04
2022-07-26 02:24:41,525 INFO [train.py:850] (2/4) Epoch 6, batch 550, loss[loss=0.2152, simple_loss=0.2908, pruned_loss=0.06979, over 7194.00 frames.], tot_loss[loss=0.229, simple_loss=0.3087, pruned_loss=0.07459, over 1373935.75 frames.], batch size: 18, lr: 8.74e-04
2022-07-26 02:25:25,756 INFO [train.py:850] (2/4) Epoch 6, batch 600, loss[loss=0.1762, simple_loss=0.2619, pruned_loss=0.04529, over 7317.00 frames.], tot_loss[loss=0.2287, simple_loss=0.3081, pruned_loss=0.07468, over 1394518.00 frames.], batch size: 18, lr: 8.73e-04
2022-07-26 02:26:09,095 INFO [train.py:850] (2/4) Epoch 6, batch 650, loss[loss=0.2009, simple_loss=0.2753, pruned_loss=0.06324, over 7179.00 frames.], tot_loss[loss=0.2269, simple_loss=0.3069, pruned_loss=0.07348, over 1409278.23 frames.], batch size: 17, lr: 8.73e-04
2022-07-26 02:26:52,721 INFO [train.py:850] (2/4) Epoch 6, batch 700, loss[loss=0.2126, simple_loss=0.3039, pruned_loss=0.06063, over 7293.00 frames.], tot_loss[loss=0.2289, simple_loss=0.3087, pruned_loss=0.0746, over 1421572.68 frames.], batch size: 20, lr: 8.72e-04
2022-07-26 02:27:36,701 INFO [train.py:850] (2/4) Epoch 6, batch 750, loss[loss=0.2028, simple_loss=0.2849, pruned_loss=0.06036, over 7487.00 frames.], tot_loss[loss=0.228, simple_loss=0.3079, pruned_loss=0.07409, over 1430778.92 frames.], batch size: 19, lr: 8.72e-04
2022-07-26 02:28:21,110 INFO [train.py:850] (2/4) Epoch 6, batch 800, loss[loss=0.2777, simple_loss=0.3463, pruned_loss=0.1046, over 7279.00 frames.], tot_loss[loss=0.2301, simple_loss=0.31, pruned_loss=0.07512, over 1438072.18 frames.], batch size: 20, lr: 8.71e-04
2022-07-26 02:29:04,781 INFO [train.py:850] (2/4) Epoch 6, batch 850, loss[loss=0.2458, simple_loss=0.3354, pruned_loss=0.07804, over 7198.00 frames.], tot_loss[loss=0.2315, simple_loss=0.3116, pruned_loss=0.07577, over 1443490.63 frames.], batch size: 21, lr: 8.71e-04
2022-07-26 02:29:48,127 INFO [train.py:850] (2/4) Epoch 6, batch 900, loss[loss=0.2273, simple_loss=0.3125, pruned_loss=0.07106, over 7286.00 frames.], tot_loss[loss=0.2323, simple_loss=0.3116, pruned_loss=0.07648, over 1448107.79 frames.], batch size: 20, lr: 8.70e-04
2022-07-26 02:30:32,891 INFO [train.py:850] (2/4) Epoch 6, batch 950, loss[loss=0.2213, simple_loss=0.2904, pruned_loss=0.07613, over 7475.00 frames.], tot_loss[loss=0.2354, simple_loss=0.3138, pruned_loss=0.07847, over 1450987.99 frames.], batch size: 20, lr: 8.70e-04
2022-07-26 02:31:15,833 INFO [train.py:850] (2/4) Epoch 6, batch 1000, loss[loss=0.2346, simple_loss=0.3031, pruned_loss=0.08308, over 7203.00 frames.], tot_loss[loss=0.2368, simple_loss=0.3151, pruned_loss=0.07924, over 1453587.23 frames.], batch size: 18, lr: 8.69e-04
2022-07-26 02:32:00,233 INFO [train.py:850] (2/4) Epoch 6, batch 1050, loss[loss=0.2423, simple_loss=0.3237, pruned_loss=0.08048, over 7372.00 frames.], tot_loss[loss=0.2357, simple_loss=0.3149, pruned_loss=0.07826, over 1456522.62 frames.], batch size: 21, lr: 8.69e-04
2022-07-26 02:32:43,614 INFO [train.py:850] (2/4) Epoch 6, batch 1100, loss[loss=0.2374, simple_loss=0.3266, pruned_loss=0.07409, over 7482.00 frames.], tot_loss[loss=0.2364, simple_loss=0.3155, pruned_loss=0.0787, over 1459282.47 frames.], batch size: 23, lr: 8.68e-04
2022-07-26 02:33:27,359 INFO [train.py:850] (2/4) Epoch 6, batch 1150, loss[loss=0.2055, simple_loss=0.2741, pruned_loss=0.06848, over 7457.00 frames.], tot_loss[loss=0.2366, simple_loss=0.3156, pruned_loss=0.07881, over 1460187.38 frames.], batch size: 17, lr: 8.68e-04
2022-07-26 02:34:10,300 INFO [train.py:850] (2/4) Epoch 6, batch 1200, loss[loss=0.2441, simple_loss=0.311, pruned_loss=0.08856, over 7196.00 frames.], tot_loss[loss=0.2373, simple_loss=0.3157, pruned_loss=0.07945, over 1460900.93 frames.], batch size: 16, lr: 8.67e-04
2022-07-26 02:34:54,229 INFO [train.py:850] (2/4) Epoch 6, batch 1250, loss[loss=0.194, simple_loss=0.276, pruned_loss=0.05599, over 7174.00 frames.], tot_loss[loss=0.2377, simple_loss=0.316, pruned_loss=0.07971, over 1461396.80 frames.], batch size: 18, lr: 8.67e-04
2022-07-26 02:35:37,809 INFO [train.py:850] (2/4) Epoch 6, batch 1300, loss[loss=0.2289, simple_loss=0.3167, pruned_loss=0.07054, over 7300.00 frames.], tot_loss[loss=0.2369, simple_loss=0.315, pruned_loss=0.07936, over 1462925.68 frames.], batch size: 22, lr: 8.66e-04
2022-07-26 02:36:21,891 INFO [train.py:850] (2/4) Epoch 6, batch 1350, loss[loss=0.2312, simple_loss=0.3131, pruned_loss=0.07465, over 7296.00 frames.], tot_loss[loss=0.237, simple_loss=0.3151, pruned_loss=0.07951, over 1462702.13 frames.], batch size: 19, lr: 8.66e-04
2022-07-26 02:37:06,371 INFO [train.py:850] (2/4) Epoch 6, batch 1400, loss[loss=0.2228, simple_loss=0.3073, pruned_loss=0.06916, over 7240.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3153, pruned_loss=0.07955, over 1463081.85 frames.], batch size: 24, lr: 8.66e-04
2022-07-26 02:37:51,112 INFO [train.py:850] (2/4) Epoch 6, batch 1450, loss[loss=0.2119, simple_loss=0.3077, pruned_loss=0.05806, over 7179.00 frames.], tot_loss[loss=0.237, simple_loss=0.3154, pruned_loss=0.07933, over 1463338.97 frames.], batch size: 21, lr: 8.65e-04
2022-07-26 02:38:35,261 INFO [train.py:850] (2/4) Epoch 6, batch 1500, loss[loss=0.1956, simple_loss=0.2708, pruned_loss=0.06018, over 7442.00 frames.], tot_loss[loss=0.2373, simple_loss=0.3155, pruned_loss=0.07953, over 1463778.54 frames.], batch size: 18, lr: 8.65e-04
2022-07-26 02:39:20,486 INFO [train.py:850] (2/4) Epoch 6, batch 1550, loss[loss=0.2101, simple_loss=0.2804, pruned_loss=0.06985, over 7301.00 frames.], tot_loss[loss=0.2365, simple_loss=0.3149, pruned_loss=0.07903, over 1464167.23 frames.], batch size: 17, lr: 8.64e-04
2022-07-26 02:40:04,159 INFO [train.py:850] (2/4) Epoch 6, batch 1600, loss[loss=0.2531, simple_loss=0.3326, pruned_loss=0.0868, over 7302.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3152, pruned_loss=0.07957, over 1464414.77 frames.], batch size: 39, lr: 8.64e-04
2022-07-26 02:40:49,778 INFO [train.py:850] (2/4) Epoch 6, batch 1650, loss[loss=0.2892, simple_loss=0.3581, pruned_loss=0.1102, over 7415.00 frames.], tot_loss[loss=0.2387, simple_loss=0.3166, pruned_loss=0.08037, over 1464655.99 frames.], batch size: 31, lr: 8.63e-04
2022-07-26 02:41:33,801 INFO [train.py:850] (2/4) Epoch 6, batch 1700, loss[loss=0.2373, simple_loss=0.3278, pruned_loss=0.07343, over 7236.00 frames.], tot_loss[loss=0.2382, simple_loss=0.3166, pruned_loss=0.07991, over 1464398.19 frames.], batch size: 25, lr: 8.63e-04
2022-07-26 02:42:17,389 INFO [train.py:850] (2/4) Epoch 6, batch 1750, loss[loss=0.1895, simple_loss=0.2652, pruned_loss=0.0569, over 7293.00 frames.], tot_loss[loss=0.2375, simple_loss=0.3158, pruned_loss=0.07966, over 1464605.66 frames.], batch size: 17, lr: 8.62e-04
2022-07-26 02:43:01,248 INFO [train.py:850] (2/4) Epoch 6, batch 1800, loss[loss=0.2114, simple_loss=0.2847, pruned_loss=0.06905, over 7161.00 frames.], tot_loss[loss=0.2368, simple_loss=0.3152, pruned_loss=0.07923, over 1464840.82 frames.], batch size: 17, lr: 8.62e-04
2022-07-26 02:43:45,249 INFO [train.py:850] (2/4) Epoch 6, batch 1850, loss[loss=0.3142, simple_loss=0.3854, pruned_loss=0.1215, over 7484.00 frames.], tot_loss[loss=0.2377, simple_loss=0.3159, pruned_loss=0.07973, over 1465094.87 frames.], batch size: 23, lr: 8.61e-04
2022-07-26 02:44:29,160 INFO [train.py:850] (2/4) Epoch 6, batch 1900, loss[loss=0.2167, simple_loss=0.3011, pruned_loss=0.06614, over 7384.00 frames.], tot_loss[loss=0.2378, simple_loss=0.316, pruned_loss=0.0798, over 1465145.08 frames.], batch size: 31, lr: 8.61e-04
2022-07-26 02:45:13,392 INFO [train.py:850] (2/4) Epoch 6, batch 1950, loss[loss=0.3369, simple_loss=0.3842, pruned_loss=0.1449, over 7414.00 frames.], tot_loss[loss=0.2384, simple_loss=0.3164, pruned_loss=0.08015, over 1465725.84 frames.], batch size: 74, lr: 8.60e-04
2022-07-26 02:45:56,503 INFO [train.py:850] (2/4) Epoch 6, batch 2000, loss[loss=0.2687, simple_loss=0.3461, pruned_loss=0.09562, over 7380.00 frames.], tot_loss[loss=0.2391, simple_loss=0.3172, pruned_loss=0.08045, over 1466534.27 frames.], batch size: 21, lr: 8.60e-04
2022-07-26 02:46:40,458 INFO [train.py:850] (2/4) Epoch 6, batch 2050, loss[loss=0.2696, simple_loss=0.3329, pruned_loss=0.1032, over 7393.00 frames.], tot_loss[loss=0.239, simple_loss=0.3172, pruned_loss=0.08038, over 1465932.46 frames.], batch size: 19, lr: 8.60e-04
2022-07-26 02:47:24,220 INFO [train.py:850] (2/4) Epoch 6, batch 2100, loss[loss=0.2509, simple_loss=0.3265, pruned_loss=0.08764, over 7388.00 frames.], tot_loss[loss=0.2367, simple_loss=0.3158, pruned_loss=0.07883, over 1465624.41 frames.], batch size: 21, lr: 8.59e-04
2022-07-26 02:48:09,894 INFO [train.py:850] (2/4) Epoch 6, batch 2150, loss[loss=0.1971, simple_loss=0.2827, pruned_loss=0.05577, over 7180.00 frames.], tot_loss[loss=0.235, simple_loss=0.3148, pruned_loss=0.07758, over 1465224.15 frames.], batch size: 21, lr: 8.59e-04
2022-07-26 02:48:52,649 INFO [train.py:850] (2/4) Epoch 6, batch 2200, loss[loss=0.2022, simple_loss=0.2731, pruned_loss=0.06562, over 7308.00 frames.], tot_loss[loss=0.2348, simple_loss=0.3149, pruned_loss=0.0774, over 1464030.26 frames.], batch size: 18, lr: 8.58e-04
2022-07-26 02:49:36,899 INFO [train.py:850] (2/4) Epoch 6, batch 2250, loss[loss=0.2424, simple_loss=0.3207, pruned_loss=0.0821, over 7378.00 frames.], tot_loss[loss=0.2351, simple_loss=0.3149, pruned_loss=0.07761, over 1463620.02 frames.], batch size: 20, lr: 8.58e-04
2022-07-26 02:50:20,860 INFO [train.py:850] (2/4) Epoch 6, batch 2300, loss[loss=0.2135, simple_loss=0.3024, pruned_loss=0.06231, over 7277.00 frames.], tot_loss[loss=0.2351, simple_loss=0.3148, pruned_loss=0.07764, over 1463746.95 frames.], batch size: 19, lr: 8.57e-04
2022-07-26 02:51:04,940 INFO [train.py:850] (2/4) Epoch 6, batch 2350, loss[loss=0.2046, simple_loss=0.2951, pruned_loss=0.05701, over 7279.00 frames.], tot_loss[loss=0.2349, simple_loss=0.315, pruned_loss=0.07743, over 1464825.29 frames.], batch size: 19, lr: 8.57e-04
2022-07-26 02:51:48,711 INFO [train.py:850] (2/4) Epoch 6, batch 2400, loss[loss=0.2246, simple_loss=0.3173, pruned_loss=0.06593, over 7484.00 frames.], tot_loss[loss=0.2349, simple_loss=0.3149, pruned_loss=0.07745, over 1465640.38 frames.], batch size: 23, lr: 8.56e-04
2022-07-26 02:52:32,491 INFO [train.py:850] (2/4) Epoch 6, batch 2450, loss[loss=0.1886, simple_loss=0.2781, pruned_loss=0.04955, over 7293.00 frames.], tot_loss[loss=0.2341, simple_loss=0.314, pruned_loss=0.07711, over 1465703.62 frames.], batch size: 19, lr: 8.56e-04
2022-07-26 02:53:16,513 INFO [train.py:850] (2/4) Epoch 6, batch 2500, loss[loss=0.2193, simple_loss=0.3089, pruned_loss=0.06487, over 7484.00 frames.], tot_loss[loss=0.2316, simple_loss=0.312, pruned_loss=0.0756, over 1466169.65 frames.], batch size: 23, lr: 8.55e-04
2022-07-26 02:54:00,827 INFO [train.py:850] (2/4) Epoch 6, batch 2550, loss[loss=0.194, simple_loss=0.2739, pruned_loss=0.05701, over 7445.00 frames.], tot_loss[loss=0.2329, simple_loss=0.3132, pruned_loss=0.07627, over 1466776.29 frames.], batch size: 17, lr: 8.55e-04
2022-07-26 02:54:44,242 INFO [train.py:850] (2/4) Epoch 6, batch 2600, loss[loss=0.2373, simple_loss=0.3058, pruned_loss=0.08444, over 7460.00 frames.], tot_loss[loss=0.2325, simple_loss=0.3129, pruned_loss=0.07607, over 1465357.91 frames.], batch size: 17, lr: 8.55e-04
2022-07-26 02:55:28,071 INFO [train.py:850] (2/4) Epoch 6, batch 2650, loss[loss=0.2361, simple_loss=0.3288, pruned_loss=0.07165, over 7234.00 frames.], tot_loss[loss=0.2332, simple_loss=0.3132, pruned_loss=0.07662, over 1465552.75 frames.], batch size: 24, lr: 8.54e-04
2022-07-26 02:56:11,160 INFO [train.py:850] (2/4) Epoch 6, batch 2700, loss[loss=0.2273, simple_loss=0.3171, pruned_loss=0.06876, over 7475.00 frames.], tot_loss[loss=0.2331, simple_loss=0.313, pruned_loss=0.07656, over 1466134.32 frames.], batch size: 23, lr: 8.54e-04
2022-07-26 02:56:55,412 INFO [train.py:850] (2/4) Epoch 6, batch 2750, loss[loss=0.2422, simple_loss=0.3305, pruned_loss=0.07692, over 7418.00 frames.], tot_loss[loss=0.2336, simple_loss=0.3134, pruned_loss=0.07684, over 1465734.77 frames.], batch size: 22, lr: 8.53e-04
2022-07-26 02:57:39,703 INFO [train.py:850] (2/4) Epoch 6, batch 2800, loss[loss=0.2674, simple_loss=0.3397, pruned_loss=0.09756, over 7470.00 frames.], tot_loss[loss=0.2336, simple_loss=0.3132, pruned_loss=0.07693, over 1465819.11 frames.], batch size: 21, lr: 8.53e-04
2022-07-26 02:58:22,920 INFO [train.py:850] (2/4) Epoch 6, batch 2850, loss[loss=0.1791, simple_loss=0.2636, pruned_loss=0.0473, over 7490.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3116, pruned_loss=0.07597, over 1466202.63 frames.], batch size: 19, lr: 8.52e-04
2022-07-26 02:59:06,014 INFO [train.py:850] (2/4) Epoch 6, batch 2900, loss[loss=0.1956, simple_loss=0.2789, pruned_loss=0.05615, over 7383.00 frames.], tot_loss[loss=0.2325, simple_loss=0.3121, pruned_loss=0.07648, over 1465913.44 frames.], batch size: 20, lr: 8.52e-04
2022-07-26 02:59:49,768 INFO [train.py:850] (2/4) Epoch 6, batch 2950, loss[loss=0.2447, simple_loss=0.3251, pruned_loss=0.08217, over 7219.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3123, pruned_loss=0.07659, over 1465600.89 frames.], batch size: 24, lr: 8.51e-04
2022-07-26 03:00:33,396 INFO [train.py:850] (2/4) Epoch 6, batch 3000, loss[loss=0.2247, simple_loss=0.3109, pruned_loss=0.06927, over 7354.00 frames.], tot_loss[loss=0.2338, simple_loss=0.3135, pruned_loss=0.07709, over 1465780.17 frames.], batch size: 23, lr: 8.51e-04
2022-07-26 03:00:33,397 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 03:00:56,199 INFO [train.py:879] (2/4) Epoch 6, validation: loss=0.2174, simple_loss=0.3096, pruned_loss=0.06256, over 924787.00 frames.
2022-07-26 03:01:39,999 INFO [train.py:850] (2/4) Epoch 6, batch 3050, loss[loss=0.1755, simple_loss=0.2507, pruned_loss=0.05019, over 7206.00 frames.], tot_loss[loss=0.2333, simple_loss=0.3126, pruned_loss=0.07698, over 1465700.37 frames.], batch size: 18, lr: 8.51e-04
2022-07-26 03:02:23,521 INFO [train.py:850] (2/4) Epoch 6, batch 3100, loss[loss=0.2509, simple_loss=0.3285, pruned_loss=0.08666, over 7432.00 frames.], tot_loss[loss=0.234, simple_loss=0.3134, pruned_loss=0.07735, over 1465115.47 frames.], batch size: 26, lr: 8.50e-04
2022-07-26 03:03:07,684 INFO [train.py:850] (2/4) Epoch 6, batch 3150, loss[loss=0.2268, simple_loss=0.2954, pruned_loss=0.07913, over 7236.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3137, pruned_loss=0.07708, over 1464630.94 frames.], batch size: 16, lr: 8.50e-04
2022-07-26 03:03:51,209 INFO [train.py:850] (2/4) Epoch 6, batch 3200, loss[loss=0.2528, simple_loss=0.3394, pruned_loss=0.0831, over 7341.00 frames.], tot_loss[loss=0.2348, simple_loss=0.3147, pruned_loss=0.07744, over 1465801.85 frames.], batch size: 23, lr: 8.49e-04
2022-07-26 03:04:35,431 INFO [train.py:850] (2/4) Epoch 6, batch 3250, loss[loss=0.217, simple_loss=0.3048, pruned_loss=0.06464, over 7177.00 frames.], tot_loss[loss=0.2345, simple_loss=0.3143, pruned_loss=0.07733, over 1466567.79 frames.], batch size: 22, lr: 8.49e-04
2022-07-26 03:05:18,877 INFO [train.py:850] (2/4) Epoch 6, batch 3300, loss[loss=0.2597, simple_loss=0.3426, pruned_loss=0.08841, over 7294.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3117, pruned_loss=0.07589, over 1465803.08 frames.], batch size: 21, lr: 8.48e-04
2022-07-26 03:06:03,276 INFO [train.py:850] (2/4) Epoch 6, batch 3350, loss[loss=0.2647, simple_loss=0.3472, pruned_loss=0.09104, over 7313.00 frames.], tot_loss[loss=0.2319, simple_loss=0.3124, pruned_loss=0.07571, over 1465829.31 frames.], batch size: 27, lr: 8.48e-04
2022-07-26 03:06:45,996 INFO [train.py:850] (2/4) Epoch 6, batch 3400, loss[loss=0.2792, simple_loss=0.3344, pruned_loss=0.112, over 7191.00 frames.], tot_loss[loss=0.2315, simple_loss=0.3117, pruned_loss=0.07569, over 1464602.28 frames.], batch size: 18, lr: 8.47e-04
2022-07-26 03:07:29,903 INFO [train.py:850] (2/4) Epoch 6, batch 3450, loss[loss=0.2202, simple_loss=0.2893, pruned_loss=0.07555, over 7303.00 frames.], tot_loss[loss=0.2318, simple_loss=0.312, pruned_loss=0.07586, over 1464899.13 frames.], batch size: 17, lr: 8.47e-04
2022-07-26 03:08:13,876 INFO [train.py:850] (2/4) Epoch 6, batch 3500, loss[loss=0.251, simple_loss=0.3227, pruned_loss=0.08962, over 7380.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3126, pruned_loss=0.07583, over 1465294.25 frames.], batch size: 70, lr: 8.47e-04
2022-07-26 03:09:12,990 INFO [train.py:850] (2/4) Epoch 6, batch 3550, loss[loss=0.2247, simple_loss=0.3019, pruned_loss=0.07379, over 7223.00 frames.], tot_loss[loss=0.2306, simple_loss=0.3117, pruned_loss=0.07479, over 1464528.42 frames.], batch size: 24, lr: 8.46e-04
2022-07-26 03:09:56,261 INFO [train.py:850] (2/4) Epoch 6, batch 3600, loss[loss=0.2484, simple_loss=0.3257, pruned_loss=0.08554, over 7347.00 frames.], tot_loss[loss=0.2305, simple_loss=0.312, pruned_loss=0.07448, over 1464909.11 frames.], batch size: 23, lr: 8.46e-04
2022-07-26 03:10:40,467 INFO [train.py:850] (2/4) Epoch 6, batch 3650, loss[loss=0.2214, simple_loss=0.3068, pruned_loss=0.068, over 7369.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3121, pruned_loss=0.07464, over 1465242.24 frames.], batch size: 21, lr: 8.45e-04
2022-07-26 03:11:23,570 INFO [train.py:850] (2/4) Epoch 6, batch 3700, loss[loss=0.1962, simple_loss=0.2674, pruned_loss=0.06254, over 7256.00 frames.], tot_loss[loss=0.229, simple_loss=0.31, pruned_loss=0.07402, over 1465774.37 frames.], batch size: 16, lr: 8.45e-04
2022-07-26 03:12:07,766 INFO [train.py:850] (2/4) Epoch 6, batch 3750, loss[loss=0.2368, simple_loss=0.321, pruned_loss=0.07629, over 7417.00 frames.], tot_loss[loss=0.2286, simple_loss=0.3097, pruned_loss=0.07379, over 1466355.88 frames.], batch size: 22, lr: 8.44e-04
2022-07-26 03:12:51,902 INFO [train.py:850] (2/4) Epoch 6, batch 3800, loss[loss=0.2847, simple_loss=0.3549, pruned_loss=0.1072, over 7377.00 frames.], tot_loss[loss=0.2285, simple_loss=0.3101, pruned_loss=0.07347, over 1465275.12 frames.], batch size: 21, lr: 8.44e-04
2022-07-26 03:13:36,282 INFO [train.py:850] (2/4) Epoch 6, batch 3850, loss[loss=0.2274, simple_loss=0.2942, pruned_loss=0.08027, over 7437.00 frames.], tot_loss[loss=0.2294, simple_loss=0.3103, pruned_loss=0.07427, over 1465486.05 frames.], batch size: 17, lr: 8.44e-04
2022-07-26 03:14:19,489 INFO [train.py:850] (2/4) Epoch 6, batch 3900, loss[loss=0.2359, simple_loss=0.3086, pruned_loss=0.08165, over 7441.00 frames.], tot_loss[loss=0.2294, simple_loss=0.3099, pruned_loss=0.07441, over 1465016.83 frames.], batch size: 18, lr: 8.43e-04
2022-07-26 03:15:03,945 INFO [train.py:850] (2/4) Epoch 6, batch 3950, loss[loss=0.1969, simple_loss=0.2864, pruned_loss=0.05365, over 7203.00 frames.], tot_loss[loss=0.2281, simple_loss=0.309, pruned_loss=0.07365, over 1465166.30 frames.], batch size: 20, lr: 8.43e-04
2022-07-26 03:15:48,576 INFO [train.py:850] (2/4) Epoch 6, batch 4000, loss[loss=0.208, simple_loss=0.2882, pruned_loss=0.06389, over 7433.00 frames.], tot_loss[loss=0.2277, simple_loss=0.3086, pruned_loss=0.07341, over 1465329.72 frames.], batch size: 18, lr: 8.42e-04
2022-07-26 03:16:32,833 INFO [train.py:850] (2/4) Epoch 6, batch 4050, loss[loss=0.2461, simple_loss=0.3011, pruned_loss=0.09551, over 7195.00 frames.], tot_loss[loss=0.2286, simple_loss=0.309, pruned_loss=0.0741, over 1465647.64 frames.], batch size: 16, lr: 8.42e-04
2022-07-26 03:17:17,209 INFO [train.py:850] (2/4) Epoch 6, batch 4100, loss[loss=0.2791, simple_loss=0.3562, pruned_loss=0.101, over 7183.00 frames.], tot_loss[loss=0.233, simple_loss=0.3126, pruned_loss=0.07667, over 1466264.91 frames.], batch size: 21, lr: 8.41e-04
2022-07-26 03:18:00,228 INFO [train.py:850] (2/4) Epoch 6, batch 4150, loss[loss=0.2272, simple_loss=0.2827, pruned_loss=0.08583, over 7473.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3136, pruned_loss=0.07775, over 1467196.25 frames.], batch size: 17, lr: 8.41e-04
2022-07-26 03:18:44,960 INFO [train.py:850] (2/4) Epoch 6, batch 4200, loss[loss=0.2455, simple_loss=0.3307, pruned_loss=0.08015, over 7269.00 frames.], tot_loss[loss=0.237, simple_loss=0.3148, pruned_loss=0.07959, over 1467070.44 frames.], batch size: 27, lr: 8.41e-04
2022-07-26 03:19:28,306 INFO [train.py:850] (2/4) Epoch 6, batch 4250, loss[loss=0.2124, simple_loss=0.2861, pruned_loss=0.06934, over 7159.00 frames.], tot_loss[loss=0.2395, simple_loss=0.3156, pruned_loss=0.0817, over 1466545.97 frames.], batch size: 17, lr: 8.40e-04
2022-07-26 03:20:10,908 INFO [train.py:850] (2/4) Epoch 6, batch 4300, loss[loss=0.3321, simple_loss=0.3788, pruned_loss=0.1427, over 7404.00 frames.], tot_loss[loss=0.2422, simple_loss=0.3169, pruned_loss=0.08371, over 1465184.32 frames.], batch size: 22, lr: 8.40e-04
2022-07-26 03:20:54,546 INFO [train.py:850] (2/4) Epoch 6, batch 4350, loss[loss=0.275, simple_loss=0.3425, pruned_loss=0.1038, over 7196.00 frames.], tot_loss[loss=0.2461, simple_loss=0.3187, pruned_loss=0.08673, over 1465081.17 frames.], batch size: 20, lr: 8.39e-04
2022-07-26 03:21:37,262 INFO [train.py:850] (2/4) Epoch 6, batch 4400, loss[loss=0.1984, simple_loss=0.2782, pruned_loss=0.05937, over 7200.00 frames.], tot_loss[loss=0.2481, simple_loss=0.3198, pruned_loss=0.08819, over 1464758.06 frames.], batch size: 18, lr: 8.39e-04
2022-07-26 03:22:21,422 INFO [train.py:850] (2/4) Epoch 6, batch 4450, loss[loss=0.2944, simple_loss=0.3443, pruned_loss=0.1223, over 7305.00 frames.], tot_loss[loss=0.2511, simple_loss=0.3215, pruned_loss=0.0904, over 1464831.41 frames.], batch size: 20, lr: 8.38e-04
2022-07-26 03:23:05,160 INFO [train.py:850] (2/4) Epoch 6, batch 4500, loss[loss=0.2329, simple_loss=0.2997, pruned_loss=0.08305, over 7478.00 frames.], tot_loss[loss=0.2533, simple_loss=0.3227, pruned_loss=0.09194, over 1465142.83 frames.], batch size: 20, lr: 8.38e-04
2022-07-26 03:23:49,663 INFO [train.py:850] (2/4) Epoch 6, batch 4550, loss[loss=0.2683, simple_loss=0.3312, pruned_loss=0.1028, over 7424.00 frames.], tot_loss[loss=0.2566, simple_loss=0.3251, pruned_loss=0.0941, over 1463881.12 frames.], batch size: 31, lr: 8.38e-04
2022-07-26 03:24:33,054 INFO [train.py:850] (2/4) Epoch 6, batch 4600, loss[loss=0.2755, simple_loss=0.3307, pruned_loss=0.1102, over 7183.00 frames.], tot_loss[loss=0.2583, simple_loss=0.3258, pruned_loss=0.09545, over 1464713.27 frames.], batch size: 18, lr: 8.37e-04
2022-07-26 03:25:16,828 INFO [train.py:850] (2/4) Epoch 6, batch 4650, loss[loss=0.2854, simple_loss=0.3494, pruned_loss=0.1107, over 7467.00 frames.], tot_loss[loss=0.2589, simple_loss=0.3262, pruned_loss=0.09579, over 1464993.42 frames.], batch size: 21, lr: 8.37e-04
2022-07-26 03:26:01,148 INFO [train.py:850] (2/4) Epoch 6, batch 4700, loss[loss=0.2471, simple_loss=0.3216, pruned_loss=0.08627, over 7345.00 frames.], tot_loss[loss=0.2602, simple_loss=0.3273, pruned_loss=0.09654, over 1464969.28 frames.], batch size: 23, lr: 8.36e-04
2022-07-26 03:26:45,967 INFO [train.py:850] (2/4) Epoch 6, batch 4750, loss[loss=0.2339, simple_loss=0.3103, pruned_loss=0.07878, over 7460.00 frames.], tot_loss[loss=0.2617, simple_loss=0.3283, pruned_loss=0.09759, over 1466310.40 frames.], batch size: 21, lr: 8.36e-04
2022-07-26 03:27:29,395 INFO [train.py:850] (2/4) Epoch 6, batch 4800, loss[loss=0.3207, simple_loss=0.3661, pruned_loss=0.1376, over 7414.00 frames.], tot_loss[loss=0.2636, simple_loss=0.3294, pruned_loss=0.09895, over 1465533.24 frames.], batch size: 22, lr: 8.35e-04
2022-07-26 03:28:13,556 INFO [train.py:850] (2/4) Epoch 6, batch 4850, loss[loss=0.3166, simple_loss=0.3576, pruned_loss=0.1378, over 7384.00 frames.], tot_loss[loss=0.2657, simple_loss=0.3304, pruned_loss=0.1005, over 1465314.88 frames.], batch size: 20, lr: 8.35e-04
2022-07-26 03:28:57,463 INFO [train.py:850] (2/4) Epoch 6, batch 4900, loss[loss=0.2399, simple_loss=0.3166, pruned_loss=0.08159, over 7344.00 frames.], tot_loss[loss=0.2651, simple_loss=0.3297, pruned_loss=0.1002, over 1466942.33 frames.], batch size: 23, lr: 8.35e-04
2022-07-26 03:29:41,708 INFO [train.py:850] (2/4) Epoch 6, batch 4950, loss[loss=0.267, simple_loss=0.3223, pruned_loss=0.1059, over 7163.00 frames.], tot_loss[loss=0.265, simple_loss=0.3293, pruned_loss=0.1004, over 1466109.71 frames.], batch size: 17, lr: 8.34e-04
2022-07-26 03:30:25,020 INFO [train.py:850] (2/4) Epoch 6, batch 5000, loss[loss=0.279, simple_loss=0.3357, pruned_loss=0.1111, over 7286.00 frames.], tot_loss[loss=0.2644, simple_loss=0.3292, pruned_loss=0.09985, over 1466082.52 frames.], batch size: 20, lr: 8.34e-04
2022-07-26 03:31:09,908 INFO [train.py:850] (2/4) Epoch 6, batch 5050, loss[loss=0.2267, simple_loss=0.2853, pruned_loss=0.08401, over 7441.00 frames.], tot_loss[loss=0.2642, simple_loss=0.329, pruned_loss=0.09964, over 1466070.21 frames.], batch size: 17, lr: 8.33e-04
2022-07-26 03:31:54,085 INFO [train.py:850] (2/4) Epoch 6, batch 5100, loss[loss=0.2113, simple_loss=0.291, pruned_loss=0.06578, over 7491.00 frames.], tot_loss[loss=0.263, simple_loss=0.3279, pruned_loss=0.09907, over 1465657.63 frames.], batch size: 19, lr: 8.33e-04
2022-07-26 03:32:38,944 INFO [train.py:850] (2/4) Epoch 6, batch 5150, loss[loss=0.2428, simple_loss=0.3159, pruned_loss=0.08486, over 7479.00 frames.], tot_loss[loss=0.2621, simple_loss=0.3271, pruned_loss=0.09855, over 1465505.26 frames.], batch size: 20, lr: 8.33e-04
2022-07-26 03:33:23,106 INFO [train.py:850] (2/4) Epoch 6, batch 5200, loss[loss=0.2881, simple_loss=0.3574, pruned_loss=0.1094, over 7415.00 frames.], tot_loss[loss=0.2624, simple_loss=0.3272, pruned_loss=0.09879, over 1466131.54 frames.], batch size: 22, lr: 8.32e-04
2022-07-26 03:34:07,334 INFO [train.py:850] (2/4) Epoch 6, batch 5250, loss[loss=0.2711, simple_loss=0.3435, pruned_loss=0.09934, over 7367.00 frames.], tot_loss[loss=0.2619, simple_loss=0.327, pruned_loss=0.09837, over 1464275.19 frames.], batch size: 21, lr: 8.32e-04
2022-07-26 03:34:51,427 INFO [train.py:850] (2/4) Epoch 6, batch 5300, loss[loss=0.2396, simple_loss=0.2979, pruned_loss=0.09061, over 7158.00 frames.], tot_loss[loss=0.2595, simple_loss=0.3252, pruned_loss=0.09693, over 1464634.37 frames.], batch size: 17, lr: 8.31e-04
2022-07-26 03:35:34,879 INFO [train.py:850] (2/4) Epoch 6, batch 5350, loss[loss=0.2433, simple_loss=0.3128, pruned_loss=0.08688, over 7328.00 frames.], tot_loss[loss=0.2598, simple_loss=0.3253, pruned_loss=0.09712, over 1464425.24 frames.], batch size: 18, lr: 8.31e-04
2022-07-26 03:36:17,963 INFO [train.py:850] (2/4) Epoch 6, batch 5400, loss[loss=0.2251, simple_loss=0.301, pruned_loss=0.07461, over 7167.00 frames.], tot_loss[loss=0.2588, simple_loss=0.3246, pruned_loss=0.09646, over 1465080.90 frames.], batch size: 17, lr: 8.30e-04
2022-07-26 03:37:02,246 INFO [train.py:850] (2/4) Epoch 6, batch 5450, loss[loss=0.236, simple_loss=0.3098, pruned_loss=0.08109, over 7217.00 frames.], tot_loss[loss=0.2574, simple_loss=0.3241, pruned_loss=0.09534, over 1464736.73 frames.], batch size: 25, lr: 8.30e-04
2022-07-26 03:37:45,874 INFO [train.py:850] (2/4) Epoch 6, batch 5500, loss[loss=0.2553, simple_loss=0.3256, pruned_loss=0.09251, over 7291.00 frames.], tot_loss[loss=0.2571, simple_loss=0.3236, pruned_loss=0.09535, over 1465546.87 frames.], batch size: 20, lr: 8.30e-04
2022-07-26 03:38:29,827 INFO [train.py:850] (2/4) Epoch 6, batch 5550, loss[loss=0.2727, simple_loss=0.3271, pruned_loss=0.1092, over 7173.00 frames.], tot_loss[loss=0.256, simple_loss=0.3225, pruned_loss=0.09476, over 1466410.80 frames.], batch size: 21, lr: 8.29e-04
2022-07-26 03:39:13,222 INFO [train.py:850] (2/4) Epoch 6, batch 5600, loss[loss=0.2659, simple_loss=0.3236, pruned_loss=0.1041, over 7153.00 frames.], tot_loss[loss=0.2562, simple_loss=0.3227, pruned_loss=0.09485, over 1466519.01 frames.], batch size: 17, lr: 8.29e-04
2022-07-26 03:39:57,459 INFO [train.py:850] (2/4) Epoch 6, batch 5650, loss[loss=0.2106, simple_loss=0.281, pruned_loss=0.07006, over 7488.00 frames.], tot_loss[loss=0.2557, simple_loss=0.3226, pruned_loss=0.0944, over 1466260.35 frames.], batch size: 20, lr: 8.28e-04
2022-07-26 03:40:40,896 INFO [train.py:850] (2/4) Epoch 6, batch 5700, loss[loss=0.3314, simple_loss=0.3856, pruned_loss=0.1386, over 7384.00 frames.], tot_loss[loss=0.2553, simple_loss=0.3219, pruned_loss=0.09437, over 1465795.66 frames.], batch size: 21, lr: 8.28e-04
2022-07-26 03:41:24,274 INFO [train.py:850] (2/4) Epoch 6, batch 5750, loss[loss=0.2385, simple_loss=0.3025, pruned_loss=0.08729, over 7331.00 frames.], tot_loss[loss=0.2562, simple_loss=0.3221, pruned_loss=0.09514, over 1465915.19 frames.], batch size: 16, lr: 8.28e-04
2022-07-26 03:42:07,629 INFO [train.py:850] (2/4) Epoch 6, batch 5800, loss[loss=0.3818, simple_loss=0.4078, pruned_loss=0.1779, over 7473.00 frames.], tot_loss[loss=0.2571, simple_loss=0.323, pruned_loss=0.09561, over 1466447.90 frames.], batch size: 72, lr: 8.27e-04
2022-07-26 03:42:52,156 INFO [train.py:850] (2/4) Epoch 6, batch 5850, loss[loss=0.2139, simple_loss=0.282, pruned_loss=0.07293, over 7165.00 frames.], tot_loss[loss=0.2574, simple_loss=0.3235, pruned_loss=0.09561, over 1465872.10 frames.], batch size: 17, lr: 8.27e-04
2022-07-26 03:43:36,903 INFO [train.py:850] (2/4) Epoch 6, batch 5900, loss[loss=0.284, simple_loss=0.3434, pruned_loss=0.1123, over 7293.00 frames.], tot_loss[loss=0.2586, simple_loss=0.3246, pruned_loss=0.09633, over 1464497.33 frames.], batch size: 19, lr: 8.26e-04
2022-07-26 03:44:20,577 INFO [train.py:850] (2/4) Epoch 6, batch 5950, loss[loss=0.2409, simple_loss=0.3187, pruned_loss=0.08157, over 7377.00 frames.], tot_loss[loss=0.2581, simple_loss=0.3244, pruned_loss=0.09592, over 1465325.22 frames.], batch size: 21, lr: 8.26e-04
2022-07-26 03:45:05,918 INFO [train.py:850] (2/4) Epoch 6, batch 6000, loss[loss=0.2571, simple_loss=0.3355, pruned_loss=0.08929, over 7200.00 frames.], tot_loss[loss=0.2597, simple_loss=0.3258, pruned_loss=0.0968, over 1464874.18 frames.], batch size: 20, lr: 8.26e-04
2022-07-26 03:45:05,919 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 03:45:28,830 INFO [train.py:879] (2/4) Epoch 6, validation: loss=0.2002, simple_loss=0.2972, pruned_loss=0.05165, over 924787.00 frames.
2022-07-26 03:46:13,148 INFO [train.py:850] (2/4) Epoch 6, batch 6050, loss[loss=0.2488, simple_loss=0.3053, pruned_loss=0.09615, over 7318.00 frames.], tot_loss[loss=0.2591, simple_loss=0.3256, pruned_loss=0.09634, over 1464515.56 frames.], batch size: 17, lr: 8.25e-04
2022-07-26 03:46:56,522 INFO [train.py:850] (2/4) Epoch 6, batch 6100, loss[loss=0.2774, simple_loss=0.3352, pruned_loss=0.1098, over 7362.00 frames.], tot_loss[loss=0.2586, simple_loss=0.3251, pruned_loss=0.096, over 1464809.23 frames.], batch size: 38, lr: 8.25e-04
2022-07-26 03:47:41,419 INFO [train.py:850] (2/4) Epoch 6, batch 6150, loss[loss=0.2338, simple_loss=0.3005, pruned_loss=0.08355, over 7371.00 frames.], tot_loss[loss=0.2563, simple_loss=0.3232, pruned_loss=0.09467, over 1464218.12 frames.], batch size: 21, lr: 8.24e-04
2022-07-26 03:48:24,768 INFO [train.py:850] (2/4) Epoch 6, batch 6200, loss[loss=0.2641, simple_loss=0.329, pruned_loss=0.09964, over 7378.00 frames.], tot_loss[loss=0.2559, simple_loss=0.3228, pruned_loss=0.0945, over 1464461.56 frames.], batch size: 20, lr: 8.24e-04
2022-07-26 03:49:09,200 INFO [train.py:850] (2/4) Epoch 6, batch 6250, loss[loss=0.284, simple_loss=0.3465, pruned_loss=0.1107, over 7186.00 frames.], tot_loss[loss=0.2555, simple_loss=0.3227, pruned_loss=0.09417, over 1465603.60 frames.], batch size: 22, lr: 8.24e-04
2022-07-26 03:49:53,046 INFO [train.py:850] (2/4) Epoch 6, batch 6300, loss[loss=0.2718, simple_loss=0.3391, pruned_loss=0.1022, over 7297.00 frames.], tot_loss[loss=0.2565, simple_loss=0.3236, pruned_loss=0.09467, over 1465670.85 frames.], batch size: 21, lr: 8.23e-04
2022-07-26 03:50:38,103 INFO [train.py:850] (2/4) Epoch 6, batch 6350, loss[loss=0.2746, simple_loss=0.3366, pruned_loss=0.1063, over 7241.00 frames.], tot_loss[loss=0.2541, simple_loss=0.3214, pruned_loss=0.09341, over 1465699.77 frames.], batch size: 24, lr: 8.23e-04
2022-07-26 03:51:22,275 INFO [train.py:850] (2/4) Epoch 6, batch 6400, loss[loss=0.2757, simple_loss=0.3383, pruned_loss=0.1066, over 7206.00 frames.], tot_loss[loss=0.2561, simple_loss=0.3232, pruned_loss=0.09448, over 1465026.03 frames.], batch size: 20, lr: 8.22e-04
2022-07-26 03:52:06,853 INFO [train.py:850] (2/4) Epoch 6, batch 6450, loss[loss=0.2259, simple_loss=0.3098, pruned_loss=0.071, over 7204.00 frames.], tot_loss[loss=0.2563, simple_loss=0.324, pruned_loss=0.09432, over 1465270.60 frames.], batch size: 20, lr: 8.22e-04
2022-07-26 03:52:52,118 INFO [train.py:850] (2/4) Epoch 6, batch 6500, loss[loss=0.2747, simple_loss=0.3418, pruned_loss=0.1038, over 7308.00 frames.], tot_loss[loss=0.2556, simple_loss=0.3228, pruned_loss=0.0942, over 1465370.77 frames.], batch size: 30, lr: 8.22e-04
2022-07-26 03:53:37,341 INFO [train.py:850] (2/4) Epoch 6, batch 6550, loss[loss=0.2486, simple_loss=0.3163, pruned_loss=0.09045, over 7384.00 frames.], tot_loss[loss=0.2568, simple_loss=0.3236, pruned_loss=0.09496, over 1465916.37 frames.], batch size: 20, lr: 8.21e-04
2022-07-26 03:54:20,715 INFO [train.py:850] (2/4) Epoch 6, batch 6600, loss[loss=0.272, simple_loss=0.3491, pruned_loss=0.09745, over 7484.00 frames.], tot_loss[loss=0.2547, simple_loss=0.3223, pruned_loss=0.09356, over 1465345.93 frames.], batch size: 23, lr: 8.21e-04
2022-07-26 03:55:04,869 INFO [train.py:850] (2/4) Epoch 6, batch 6650, loss[loss=0.2531, simple_loss=0.3337, pruned_loss=0.0862, over 7292.00 frames.], tot_loss[loss=0.2546, simple_loss=0.3221, pruned_loss=0.09354, over 1465337.82 frames.], batch size: 27, lr: 8.20e-04
2022-07-26 03:55:48,182 INFO [train.py:850] (2/4) Epoch 6, batch 6700, loss[loss=0.3153, simple_loss=0.3677, pruned_loss=0.1315, over 7488.00 frames.], tot_loss[loss=0.2561, simple_loss=0.3227, pruned_loss=0.09474, over 1465377.81 frames.], batch size: 26, lr: 8.20e-04
2022-07-26 03:56:32,435 INFO [train.py:850] (2/4) Epoch 6, batch 6750, loss[loss=0.2294, simple_loss=0.3053, pruned_loss=0.07673, over 7374.00 frames.], tot_loss[loss=0.2551, simple_loss=0.3223, pruned_loss=0.09397, over 1466262.99 frames.], batch size: 20, lr: 8.20e-04
2022-07-26 03:57:17,002 INFO [train.py:850] (2/4) Epoch 6, batch 6800, loss[loss=0.2293, simple_loss=0.2988, pruned_loss=0.07993, over 7323.00 frames.], tot_loss[loss=0.2557, simple_loss=0.3227, pruned_loss=0.09435, over 1466550.50 frames.], batch size: 18, lr: 8.19e-04
2022-07-26 03:58:00,206 INFO [train.py:850] (2/4) Epoch 6, batch 6850, loss[loss=0.2715, simple_loss=0.3406, pruned_loss=0.1012, over 7411.00 frames.], tot_loss[loss=0.253, simple_loss=0.3211, pruned_loss=0.09244, over 1466417.96 frames.], batch size: 22, lr: 8.19e-04
2022-07-26 03:58:44,248 INFO [train.py:850] (2/4) Epoch 6, batch 6900, loss[loss=0.2979, simple_loss=0.3578, pruned_loss=0.119, over 7362.00 frames.], tot_loss[loss=0.2544, simple_loss=0.3216, pruned_loss=0.09358, over 1465542.37 frames.], batch size: 23, lr: 8.18e-04
2022-07-26 03:59:27,691 INFO [train.py:850] (2/4) Epoch 6, batch 6950, loss[loss=0.2923, simple_loss=0.3531, pruned_loss=0.1158, over 7243.00 frames.], tot_loss[loss=0.2545, simple_loss=0.3215, pruned_loss=0.09375, over 1466561.40 frames.], batch size: 24, lr: 8.18e-04
2022-07-26 04:00:11,880 INFO [train.py:850] (2/4) Epoch 6, batch 7000, loss[loss=0.2744, simple_loss=0.3366, pruned_loss=0.1061, over 7291.00 frames.], tot_loss[loss=0.2526, simple_loss=0.3206, pruned_loss=0.09226, over 1465614.32 frames.], batch size: 21, lr: 8.18e-04
2022-07-26 04:00:56,645 INFO [train.py:850] (2/4) Epoch 6, batch 7050, loss[loss=0.2688, simple_loss=0.325, pruned_loss=0.1063, over 7178.00 frames.], tot_loss[loss=0.2523, simple_loss=0.3202, pruned_loss=0.09218, over 1464461.43 frames.], batch size: 23, lr: 8.17e-04
2022-07-26 04:01:40,908 INFO [train.py:850] (2/4) Epoch 6, batch 7100, loss[loss=0.2607, simple_loss=0.3261, pruned_loss=0.0977, over 7474.00 frames.], tot_loss[loss=0.2535, simple_loss=0.3211, pruned_loss=0.093, over 1465275.07 frames.], batch size: 24, lr: 8.17e-04
2022-07-26 04:02:26,267 INFO [train.py:850] (2/4) Epoch 6, batch 7150, loss[loss=0.2491, simple_loss=0.3052, pruned_loss=0.09654, over 7490.00 frames.], tot_loss[loss=0.2537, simple_loss=0.3216, pruned_loss=0.09291, over 1464769.38 frames.], batch size: 19, lr: 8.16e-04
2022-07-26 04:03:10,437 INFO [train.py:850] (2/4) Epoch 6, batch 7200, loss[loss=0.2191, simple_loss=0.3048, pruned_loss=0.06671, over 7196.00 frames.], tot_loss[loss=0.2527, simple_loss=0.3208, pruned_loss=0.09231, over 1464241.42 frames.], batch size: 21, lr: 8.16e-04
2022-07-26 04:03:53,244 INFO [train.py:850] (2/4) Epoch 6, batch 7250, loss[loss=0.2402, simple_loss=0.3005, pruned_loss=0.08997, over 7437.00 frames.], tot_loss[loss=0.2524, simple_loss=0.3204, pruned_loss=0.09217, over 1463771.74 frames.], batch size: 18, lr: 8.16e-04
2022-07-26 04:04:37,304 INFO [train.py:850] (2/4) Epoch 6, batch 7300, loss[loss=0.2643, simple_loss=0.346, pruned_loss=0.0913, over 7212.00 frames.], tot_loss[loss=0.2535, simple_loss=0.3214, pruned_loss=0.09277, over 1464170.16 frames.], batch size: 25, lr: 8.15e-04
2022-07-26 04:05:21,896 INFO [train.py:850] (2/4) Epoch 6, batch 7350, loss[loss=0.2833, simple_loss=0.3625, pruned_loss=0.1021, over 7289.00 frames.], tot_loss[loss=0.2539, simple_loss=0.322, pruned_loss=0.09292, over 1464873.50 frames.], batch size: 21, lr: 8.15e-04
2022-07-26 04:06:05,948 INFO [train.py:850] (2/4) Epoch 6, batch 7400, loss[loss=0.2202, simple_loss=0.288, pruned_loss=0.07623, over 7274.00 frames.], tot_loss[loss=0.255, simple_loss=0.3229, pruned_loss=0.09352, over 1465125.87 frames.], batch size: 16, lr: 8.14e-04
2022-07-26 04:06:50,421 INFO [train.py:850] (2/4) Epoch 6, batch 7450, loss[loss=0.2563, simple_loss=0.335, pruned_loss=0.08883, over 7294.00 frames.], tot_loss[loss=0.2542, simple_loss=0.3219, pruned_loss=0.09328, over 1465137.45 frames.], batch size: 21, lr: 8.14e-04
2022-07-26 04:07:34,337 INFO [train.py:850] (2/4) Epoch 6, batch 7500, loss[loss=0.2607, simple_loss=0.3162, pruned_loss=0.1026, over 7198.00 frames.], tot_loss[loss=0.2543, simple_loss=0.322, pruned_loss=0.09333, over 1465427.43 frames.], batch size: 18, lr: 8.14e-04
2022-07-26 04:08:33,507 INFO [train.py:850] (2/4) Epoch 6, batch 7550, loss[loss=0.2057, simple_loss=0.2663, pruned_loss=0.07262, over 7441.00 frames.], tot_loss[loss=0.2523, simple_loss=0.3202, pruned_loss=0.09221, over 1465175.83 frames.], batch size: 18, lr: 8.13e-04
2022-07-26 04:09:16,235 INFO [train.py:850] (2/4) Epoch 6, batch 7600, loss[loss=0.2738, simple_loss=0.324, pruned_loss=0.1118, over 7312.00 frames.], tot_loss[loss=0.251, simple_loss=0.3195, pruned_loss=0.09122, over 1465754.58 frames.], batch size: 18, lr: 8.13e-04
2022-07-26 04:10:01,144 INFO [train.py:850] (2/4) Epoch 6, batch 7650, loss[loss=0.3155, simple_loss=0.3637, pruned_loss=0.1336, over 7261.00 frames.], tot_loss[loss=0.2535, simple_loss=0.3216, pruned_loss=0.0927, over 1467330.90 frames.], batch size: 27, lr: 8.13e-04
2022-07-26 04:10:44,187 INFO [train.py:850] (2/4) Epoch 6, batch 7700, loss[loss=0.2377, simple_loss=0.3044, pruned_loss=0.0855, over 7188.00 frames.], tot_loss[loss=0.2543, simple_loss=0.3226, pruned_loss=0.09302, over 1467327.31 frames.], batch size: 18, lr: 8.12e-04
2022-07-26 04:11:28,550 INFO [train.py:850] (2/4) Epoch 6, batch 7750, loss[loss=0.2896, simple_loss=0.3501, pruned_loss=0.1145, over 7198.00 frames.], tot_loss[loss=0.2539, simple_loss=0.3225, pruned_loss=0.09262, over 1466710.71 frames.], batch size: 19, lr: 8.12e-04
2022-07-26 04:12:12,717 INFO [train.py:850] (2/4) Epoch 6, batch 7800, loss[loss=0.2812, simple_loss=0.3326, pruned_loss=0.1149, over 7107.00 frames.], tot_loss[loss=0.2532, simple_loss=0.3219, pruned_loss=0.09224, over 1466192.13 frames.], batch size: 18, lr: 8.11e-04
2022-07-26 04:12:57,201 INFO [train.py:850] (2/4) Epoch 6, batch 7850, loss[loss=0.2918, simple_loss=0.3573, pruned_loss=0.1131, over 7248.00 frames.], tot_loss[loss=0.2529, simple_loss=0.3215, pruned_loss=0.09219, over 1466934.39 frames.], batch size: 27, lr: 8.11e-04
2022-07-26 04:13:40,613 INFO [train.py:850] (2/4) Epoch 6, batch 7900, loss[loss=0.2629, simple_loss=0.3337, pruned_loss=0.09606, over 7189.00 frames.], tot_loss[loss=0.2518, simple_loss=0.3208, pruned_loss=0.09135, over 1467907.57 frames.], batch size: 21, lr: 8.11e-04
2022-07-26 04:14:25,373 INFO [train.py:850] (2/4) Epoch 6, batch 7950, loss[loss=0.2881, simple_loss=0.3587, pruned_loss=0.1088, over 7182.00 frames.], tot_loss[loss=0.2527, simple_loss=0.3213, pruned_loss=0.09207, over 1467300.16 frames.], batch size: 23, lr: 8.10e-04
2022-07-26 04:15:08,254 INFO [train.py:850] (2/4) Epoch 6, batch 8000, loss[loss=0.2764, simple_loss=0.3307, pruned_loss=0.1111, over 7197.00 frames.], tot_loss[loss=0.2521, simple_loss=0.3206, pruned_loss=0.09179, over 1466171.50 frames.], batch size: 19, lr: 8.10e-04
2022-07-26 04:15:52,829 INFO [train.py:850] (2/4) Epoch 6, batch 8050, loss[loss=0.2344, simple_loss=0.2982, pruned_loss=0.08529, over 7451.00 frames.], tot_loss[loss=0.2537, simple_loss=0.3222, pruned_loss=0.09255, over 1467114.66 frames.], batch size: 17, lr: 8.09e-04
2022-07-26 04:16:36,698 INFO [train.py:850] (2/4) Epoch 6, batch 8100, loss[loss=0.1872, simple_loss=0.2667, pruned_loss=0.05385, over 7485.00 frames.], tot_loss[loss=0.2524, simple_loss=0.3211, pruned_loss=0.09188, over 1467597.05 frames.], batch size: 19, lr: 8.09e-04
2022-07-26 04:17:20,774 INFO [train.py:850] (2/4) Epoch 6, batch 8150, loss[loss=0.3141, simple_loss=0.3546, pruned_loss=0.1368, over 7390.00 frames.], tot_loss[loss=0.2529, simple_loss=0.3217, pruned_loss=0.092, over 1466633.66 frames.], batch size: 74, lr: 8.09e-04
2022-07-26 04:18:05,653 INFO [train.py:850] (2/4) Epoch 6, batch 8200, loss[loss=0.2431, simple_loss=0.3207, pruned_loss=0.08269, over 7342.00 frames.], tot_loss[loss=0.2519, simple_loss=0.3208, pruned_loss=0.09145, over 1466450.34 frames.], batch size: 31, lr: 8.08e-04
2022-07-26 04:18:50,989 INFO [train.py:850] (2/4) Epoch 6, batch 8250, loss[loss=0.2409, simple_loss=0.3069, pruned_loss=0.08744, over 7288.00 frames.], tot_loss[loss=0.2499, simple_loss=0.3191, pruned_loss=0.09038, over 1466100.81 frames.], batch size: 21, lr: 8.08e-04
2022-07-26 04:19:36,421 INFO [train.py:850] (2/4) Epoch 6, batch 8300, loss[loss=0.2385, simple_loss=0.313, pruned_loss=0.08197, over 7195.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3181, pruned_loss=0.08954, over 1465857.60 frames.], batch size: 19, lr: 8.08e-04
2022-07-26 04:20:22,403 INFO [train.py:850] (2/4) Epoch 6, batch 8350, loss[loss=0.2828, simple_loss=0.3567, pruned_loss=0.1045, over 7407.00 frames.], tot_loss[loss=0.2494, simple_loss=0.3189, pruned_loss=0.08995, over 1465429.77 frames.], batch size: 38, lr: 8.07e-04
2022-07-26 04:21:05,809 INFO [train.py:850] (2/4) Epoch 6, batch 8400, loss[loss=0.1995, simple_loss=0.2769, pruned_loss=0.061, over 7200.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3173, pruned_loss=0.08869, over 1465057.06 frames.], batch size: 18, lr: 8.07e-04
2022-07-26 04:21:50,265 INFO [train.py:850] (2/4) Epoch 6, batch 8450, loss[loss=0.2531, simple_loss=0.3205, pruned_loss=0.0928, over 7386.00 frames.], tot_loss[loss=0.2498, simple_loss=0.3196, pruned_loss=0.09006, over 1465043.88 frames.], batch size: 21, lr: 8.06e-04
2022-07-26 04:22:35,026 INFO [train.py:850] (2/4) Epoch 6, batch 8500, loss[loss=0.3072, simple_loss=0.3759, pruned_loss=0.1192, over 7409.00 frames.], tot_loss[loss=0.2488, simple_loss=0.3185, pruned_loss=0.08959, over 1466318.98 frames.], batch size: 22, lr: 8.06e-04
2022-07-26 04:23:20,545 INFO [train.py:850] (2/4) Epoch 6, batch 8550, loss[loss=0.2686, simple_loss=0.3403, pruned_loss=0.09844, over 7382.00 frames.], tot_loss[loss=0.2487, simple_loss=0.3185, pruned_loss=0.08944, over 1465806.14 frames.], batch size: 21, lr: 8.06e-04
2022-07-26 04:24:04,414 INFO [train.py:850] (2/4) Epoch 6, batch 8600, loss[loss=0.2563, simple_loss=0.3172, pruned_loss=0.09765, over 7386.00 frames.], tot_loss[loss=0.2477, simple_loss=0.3169, pruned_loss=0.08922, over 1465928.43 frames.], batch size: 20, lr: 8.05e-04
2022-07-26 04:24:47,921 INFO [train.py:850] (2/4) Epoch 6, batch 8650, loss[loss=0.2808, simple_loss=0.3439, pruned_loss=0.1089, over 7487.00 frames.], tot_loss[loss=0.2478, simple_loss=0.3169, pruned_loss=0.08931, over 1465836.08 frames.], batch size: 28, lr: 8.05e-04
2022-07-26 04:25:30,947 INFO [train.py:850] (2/4) Epoch 6, batch 8700, loss[loss=0.2803, simple_loss=0.3248, pruned_loss=0.1179, over 7446.00 frames.], tot_loss[loss=0.2487, simple_loss=0.3179, pruned_loss=0.08972, over 1465357.02 frames.], batch size: 18, lr: 8.05e-04
2022-07-26 04:26:15,032 INFO [train.py:850] (2/4) Epoch 6, batch 8750, loss[loss=0.2874, simple_loss=0.3499, pruned_loss=0.1125, over 7211.00 frames.], tot_loss[loss=0.2487, simple_loss=0.3179, pruned_loss=0.08975, over 1466670.60 frames.], batch size: 25, lr: 8.04e-04
2022-07-26 04:26:57,808 INFO [train.py:850] (2/4) Epoch 6, batch 8800, loss[loss=0.213, simple_loss=0.2786, pruned_loss=0.07374, over 7176.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3165, pruned_loss=0.08901, over 1466479.05 frames.], batch size: 17, lr: 8.04e-04
2022-07-26 04:27:40,638 INFO [train.py:850] (2/4) Epoch 6, batch 8850, loss[loss=0.1852, simple_loss=0.2668, pruned_loss=0.05187, over 7205.00 frames.], tot_loss[loss=0.2457, simple_loss=0.3156, pruned_loss=0.08791, over 1465115.50 frames.], batch size: 18, lr: 8.03e-04
2022-07-26 04:29:22,082 INFO [train.py:850] (2/4) Epoch 7, batch 0, loss[loss=0.2081, simple_loss=0.2873, pruned_loss=0.06444, over 7150.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2873, pruned_loss=0.06444, over 7150.00 frames.], batch size: 17, lr: 7.71e-04
2022-07-26 04:30:06,152 INFO [train.py:850] (2/4) Epoch 7, batch 50, loss[loss=0.236, simple_loss=0.3113, pruned_loss=0.08041, over 7203.00 frames.], tot_loss[loss=0.2353, simple_loss=0.3128, pruned_loss=0.07891, over 330365.84 frames.], batch size: 20, lr: 7.70e-04
2022-07-26 04:30:49,624 INFO [train.py:850] (2/4) Epoch 7, batch 100, loss[loss=0.232, simple_loss=0.3097, pruned_loss=0.07719, over 7284.00 frames.], tot_loss[loss=0.2317, simple_loss=0.3111, pruned_loss=0.07618, over 581431.62 frames.], batch size: 20, lr: 7.70e-04
2022-07-26 04:31:33,709 INFO [train.py:850] (2/4) Epoch 7, batch 150, loss[loss=0.252, simple_loss=0.3426, pruned_loss=0.08073, over 7220.00 frames.], tot_loss[loss=0.23, simple_loss=0.31, pruned_loss=0.07507, over 778153.07 frames.], batch size: 24, lr: 7.69e-04
2022-07-26 04:32:17,591 INFO [train.py:850] (2/4) Epoch 7, batch 200, loss[loss=0.222, simple_loss=0.3033, pruned_loss=0.0704, over 7484.00 frames.], tot_loss[loss=0.2286, simple_loss=0.3088, pruned_loss=0.0742, over 930276.67 frames.], batch size: 23, lr: 7.69e-04
2022-07-26 04:33:01,474 INFO [train.py:850] (2/4) Epoch 7, batch 250, loss[loss=0.2203, simple_loss=0.3004, pruned_loss=0.0701, over 7476.00 frames.], tot_loss[loss=0.2298, simple_loss=0.3098, pruned_loss=0.07486, over 1048036.12 frames.], batch size: 24, lr: 7.69e-04
2022-07-26 04:33:45,352 INFO [train.py:850] (2/4) Epoch 7, batch 300, loss[loss=0.1726, simple_loss=0.2533, pruned_loss=0.04597, over 7297.00 frames.], tot_loss[loss=0.2267, simple_loss=0.3069, pruned_loss=0.07322, over 1140379.67 frames.], batch size: 17, lr: 7.68e-04
2022-07-26 04:34:29,483 INFO [train.py:850] (2/4) Epoch 7, batch 350, loss[loss=0.2286, simple_loss=0.31, pruned_loss=0.07355, over 7360.00 frames.], tot_loss[loss=0.2257, simple_loss=0.3064, pruned_loss=0.0725, over 1212494.07 frames.], batch size: 71, lr: 7.68e-04
2022-07-26 04:35:12,470 INFO [train.py:850] (2/4) Epoch 7, batch 400, loss[loss=0.2199, simple_loss=0.291, pruned_loss=0.07435, over 7296.00 frames.], tot_loss[loss=0.2247, simple_loss=0.3056, pruned_loss=0.07196, over 1269181.10 frames.], batch size: 17, lr: 7.68e-04
2022-07-26 04:35:58,141 INFO [train.py:850] (2/4) Epoch 7, batch 450, loss[loss=0.1996, simple_loss=0.2847, pruned_loss=0.0572, over 7493.00 frames.], tot_loss[loss=0.2247, simple_loss=0.3062, pruned_loss=0.07165, over 1312008.24 frames.], batch size: 19, lr: 7.67e-04
2022-07-26 04:36:41,794 INFO [train.py:850] (2/4) Epoch 7, batch 500, loss[loss=0.2294, simple_loss=0.3108, pruned_loss=0.074, over 7352.00 frames.], tot_loss[loss=0.2225, simple_loss=0.3035, pruned_loss=0.07076, over 1345462.55 frames.], batch size: 23, lr: 7.67e-04
2022-07-26 04:37:25,605 INFO [train.py:850] (2/4) Epoch 7, batch 550, loss[loss=0.2062, simple_loss=0.2979, pruned_loss=0.05726, over 7294.00 frames.], tot_loss[loss=0.2222, simple_loss=0.3036, pruned_loss=0.07043, over 1373012.77 frames.], batch size: 21, lr: 7.67e-04
2022-07-26 04:38:08,857 INFO [train.py:850] (2/4) Epoch 7, batch 600, loss[loss=0.2047, simple_loss=0.2857, pruned_loss=0.06191, over 7490.00 frames.], tot_loss[loss=0.2219, simple_loss=0.3036, pruned_loss=0.07014, over 1393277.28 frames.], batch size: 19, lr: 7.66e-04
2022-07-26 04:38:52,930 INFO [train.py:850] (2/4) Epoch 7, batch 650, loss[loss=0.211, simple_loss=0.2799, pruned_loss=0.07104, over 7455.00 frames.], tot_loss[loss=0.2206, simple_loss=0.3026, pruned_loss=0.06929, over 1409029.97 frames.], batch size: 17, lr: 7.66e-04
2022-07-26 04:39:36,470 INFO [train.py:850] (2/4) Epoch 7, batch 700, loss[loss=0.1919, simple_loss=0.2728, pruned_loss=0.05551, over 7389.00 frames.], tot_loss[loss=0.2183, simple_loss=0.301, pruned_loss=0.06783, over 1421992.35 frames.], batch size: 19, lr: 7.66e-04
2022-07-26 04:40:19,608 INFO [train.py:850] (2/4) Epoch 7, batch 750, loss[loss=0.2232, simple_loss=0.3065, pruned_loss=0.06996, over 7101.00 frames.], tot_loss[loss=0.218, simple_loss=0.3009, pruned_loss=0.06761, over 1431325.01 frames.], batch size: 18, lr: 7.65e-04
2022-07-26 04:41:03,266 INFO [train.py:850] (2/4) Epoch 7, batch 800, loss[loss=0.246, simple_loss=0.3262, pruned_loss=0.08292, over 7371.00 frames.], tot_loss[loss=0.2189, simple_loss=0.302, pruned_loss=0.06789, over 1438953.18 frames.], batch size: 39, lr: 7.65e-04
2022-07-26 04:41:47,551 INFO [train.py:850] (2/4) Epoch 7, batch 850, loss[loss=0.2191, simple_loss=0.3003, pruned_loss=0.06894, over 7381.00 frames.], tot_loss[loss=0.2201, simple_loss=0.3027, pruned_loss=0.06871, over 1444129.63 frames.], batch size: 20, lr: 7.64e-04
2022-07-26 04:42:31,524 INFO [train.py:850] (2/4) Epoch 7, batch 900, loss[loss=0.2014, simple_loss=0.274, pruned_loss=0.06438, over 7440.00 frames.], tot_loss[loss=0.2213, simple_loss=0.3038, pruned_loss=0.06944, over 1449942.54 frames.], batch size: 17, lr: 7.64e-04
2022-07-26 04:43:15,338 INFO [train.py:850] (2/4) Epoch 7, batch 950, loss[loss=0.208, simple_loss=0.2822, pruned_loss=0.06688, over 7490.00 frames.], tot_loss[loss=0.2225, simple_loss=0.3046, pruned_loss=0.07023, over 1453102.92 frames.], batch size: 19, lr: 7.64e-04
2022-07-26 04:43:58,330 INFO [train.py:850] (2/4) Epoch 7, batch 1000, loss[loss=0.2041, simple_loss=0.2927, pruned_loss=0.05775, over 7292.00 frames.], tot_loss[loss=0.2222, simple_loss=0.3043, pruned_loss=0.07004, over 1455807.22 frames.], batch size: 19, lr: 7.63e-04
2022-07-26 04:44:42,306 INFO [train.py:850] (2/4) Epoch 7, batch 1050, loss[loss=0.2328, simple_loss=0.3053, pruned_loss=0.08015, over 7322.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3057, pruned_loss=0.07069, over 1457164.78 frames.], batch size: 18, lr: 7.63e-04
2022-07-26 04:45:26,271 INFO [train.py:850] (2/4) Epoch 7, batch 1100, loss[loss=0.2753, simple_loss=0.351, pruned_loss=0.09978, over 7449.00 frames.], tot_loss[loss=0.2239, simple_loss=0.3057, pruned_loss=0.07106, over 1459761.66 frames.], batch size: 39, lr: 7.63e-04
2022-07-26 04:46:11,409 INFO [train.py:850] (2/4) Epoch 7, batch 1150, loss[loss=0.2134, simple_loss=0.3063, pruned_loss=0.06021, over 7226.00 frames.], tot_loss[loss=0.2257, simple_loss=0.307, pruned_loss=0.07216, over 1460512.15 frames.], batch size: 24, lr: 7.62e-04
2022-07-26 04:46:54,908 INFO [train.py:850] (2/4) Epoch 7, batch 1200, loss[loss=0.2469, simple_loss=0.317, pruned_loss=0.08844, over 7291.00 frames.], tot_loss[loss=0.2276, simple_loss=0.3085, pruned_loss=0.07333, over 1461862.76 frames.], batch size: 19, lr: 7.62e-04
2022-07-26 04:47:38,899 INFO [train.py:850] (2/4) Epoch 7, batch 1250, loss[loss=0.2152, simple_loss=0.2928, pruned_loss=0.06883, over 7486.00 frames.], tot_loss[loss=0.2288, simple_loss=0.3091, pruned_loss=0.0743, over 1462298.88 frames.], batch size: 19, lr: 7.62e-04
2022-07-26 04:48:22,659 INFO [train.py:850] (2/4) Epoch 7, batch 1300, loss[loss=0.2031, simple_loss=0.2768, pruned_loss=0.06468, over 7170.00 frames.], tot_loss[loss=0.2299, simple_loss=0.31, pruned_loss=0.07491, over 1463403.40 frames.], batch size: 17, lr: 7.61e-04
2022-07-26 04:49:07,035 INFO [train.py:850] (2/4) Epoch 7, batch 1350, loss[loss=0.245, simple_loss=0.3218, pruned_loss=0.08413, over 7436.00 frames.], tot_loss[loss=0.2316, simple_loss=0.3114, pruned_loss=0.07585, over 1463123.38 frames.], batch size: 69, lr: 7.61e-04
2022-07-26 04:49:50,901 INFO [train.py:850] (2/4) Epoch 7, batch 1400, loss[loss=0.2179, simple_loss=0.302, pruned_loss=0.06685, over 7379.00 frames.], tot_loss[loss=0.2291, simple_loss=0.3093, pruned_loss=0.07447, over 1463391.86 frames.], batch size: 21, lr: 7.61e-04
2022-07-26 04:50:35,175 INFO [train.py:850] (2/4) Epoch 7, batch 1450, loss[loss=0.2461, simple_loss=0.3248, pruned_loss=0.08374, over 7478.00 frames.], tot_loss[loss=0.23, simple_loss=0.3104, pruned_loss=0.07487, over 1464781.56 frames.], batch size: 24, lr: 7.60e-04
2022-07-26 04:51:19,335 INFO [train.py:850] (2/4) Epoch 7, batch 1500, loss[loss=0.2402, simple_loss=0.3244, pruned_loss=0.07797, over 7467.00 frames.], tot_loss[loss=0.231, simple_loss=0.3115, pruned_loss=0.07527, over 1465900.21 frames.], batch size: 21, lr: 7.60e-04
2022-07-26 04:52:03,970 INFO [train.py:850] (2/4) Epoch 7, batch 1550, loss[loss=0.312, simple_loss=0.3767, pruned_loss=0.1237, over 7420.00 frames.], tot_loss[loss=0.2293, simple_loss=0.3103, pruned_loss=0.07415, over 1466562.47 frames.], batch size: 74, lr: 7.60e-04
2022-07-26 04:52:46,990 INFO [train.py:850] (2/4) Epoch 7, batch 1600, loss[loss=0.2003, simple_loss=0.2912, pruned_loss=0.05471, over 7377.00 frames.], tot_loss[loss=0.2291, simple_loss=0.3099, pruned_loss=0.07411, over 1466719.97 frames.], batch size: 20, lr: 7.59e-04
2022-07-26 04:53:30,960 INFO [train.py:850] (2/4) Epoch 7, batch 1650, loss[loss=0.2454, simple_loss=0.3257, pruned_loss=0.0826, over 7489.00 frames.], tot_loss[loss=0.2285, simple_loss=0.3098, pruned_loss=0.07359, over 1465568.22 frames.], batch size: 24, lr: 7.59e-04
2022-07-26 04:54:14,880 INFO [train.py:850] (2/4) Epoch 7, batch 1700, loss[loss=0.2115, simple_loss=0.3066, pruned_loss=0.05822, over 7468.00 frames.], tot_loss[loss=0.2292, simple_loss=0.3106, pruned_loss=0.07392, over 1466721.39 frames.], batch size: 21, lr: 7.59e-04
2022-07-26 04:54:58,970 INFO [train.py:850] (2/4) Epoch 7, batch 1750, loss[loss=0.198, simple_loss=0.276, pruned_loss=0.05997, over 7110.00 frames.], tot_loss[loss=0.2294, simple_loss=0.3107, pruned_loss=0.07403, over 1465893.01 frames.], batch size: 18, lr: 7.58e-04
2022-07-26 04:55:42,623 INFO [train.py:850] (2/4) Epoch 7, batch 1800, loss[loss=0.2186, simple_loss=0.2856, pruned_loss=0.07581, over 7320.00 frames.], tot_loss[loss=0.2302, simple_loss=0.311, pruned_loss=0.07467, over 1465803.70 frames.], batch size: 17, lr: 7.58e-04
2022-07-26 04:56:26,473 INFO [train.py:850] (2/4) Epoch 7, batch 1850, loss[loss=0.3382, simple_loss=0.3944, pruned_loss=0.1409, over 7330.00 frames.], tot_loss[loss=0.2311, simple_loss=0.3117, pruned_loss=0.0753, over 1464841.17 frames.], batch size: 30, lr: 7.58e-04
2022-07-26 04:57:10,997 INFO [train.py:850] (2/4) Epoch 7, batch 1900, loss[loss=0.229, simple_loss=0.3112, pruned_loss=0.07334, over 7474.00 frames.], tot_loss[loss=0.231, simple_loss=0.3118, pruned_loss=0.07514, over 1465939.50 frames.], batch size: 24, lr: 7.57e-04
2022-07-26 04:57:55,398 INFO [train.py:850] (2/4) Epoch 7, batch 1950, loss[loss=0.2103, simple_loss=0.2853, pruned_loss=0.06766, over 7303.00 frames.], tot_loss[loss=0.2309, simple_loss=0.3119, pruned_loss=0.07494, over 1466072.82 frames.], batch size: 17, lr: 7.57e-04
2022-07-26 04:58:38,901 INFO [train.py:850] (2/4) Epoch 7, batch 2000, loss[loss=0.2078, simple_loss=0.2928, pruned_loss=0.06144, over 7441.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3117, pruned_loss=0.07485, over 1465333.71 frames.], batch size: 18, lr: 7.57e-04
2022-07-26 04:59:23,204 INFO [train.py:850] (2/4) Epoch 7, batch 2050, loss[loss=0.227, simple_loss=0.3169, pruned_loss=0.06854, over 7468.00 frames.], tot_loss[loss=0.2297, simple_loss=0.3105, pruned_loss=0.07444, over 1465601.70 frames.], batch size: 21, lr: 7.56e-04
2022-07-26 05:00:06,000 INFO [train.py:850] (2/4) Epoch 7, batch 2100, loss[loss=0.2372, simple_loss=0.3205, pruned_loss=0.0769, over 7180.00 frames.], tot_loss[loss=0.2285, simple_loss=0.3097, pruned_loss=0.07367, over 1465286.03 frames.], batch size: 22, lr: 7.56e-04
2022-07-26 05:00:50,149 INFO [train.py:850] (2/4) Epoch 7, batch 2150, loss[loss=0.2129, simple_loss=0.2782, pruned_loss=0.07373, over 7473.00 frames.], tot_loss[loss=0.2287, simple_loss=0.3095, pruned_loss=0.07396, over 1464641.96 frames.], batch size: 17, lr: 7.56e-04
2022-07-26 05:01:33,021 INFO [train.py:850] (2/4) Epoch 7, batch 2200, loss[loss=0.2504, simple_loss=0.3297, pruned_loss=0.08558, over 7417.00 frames.], tot_loss[loss=0.2271, simple_loss=0.3081, pruned_loss=0.07301, over 1464753.90 frames.], batch size: 40, lr: 7.55e-04
2022-07-26 05:02:17,011 INFO [train.py:850] (2/4) Epoch 7, batch 2250, loss[loss=0.2149, simple_loss=0.3061, pruned_loss=0.06182, over 7279.00 frames.], tot_loss[loss=0.2268, simple_loss=0.308, pruned_loss=0.07283, over 1463997.23 frames.], batch size: 21, lr: 7.55e-04
2022-07-26 05:03:00,807 INFO [train.py:850] (2/4) Epoch 7, batch 2300, loss[loss=0.3177, simple_loss=0.3644, pruned_loss=0.1355, over 7386.00 frames.], tot_loss[loss=0.2259, simple_loss=0.3074, pruned_loss=0.07216, over 1463789.89 frames.], batch size: 70, lr: 7.55e-04
2022-07-26 05:03:45,110 INFO [train.py:850] (2/4) Epoch 7, batch 2350, loss[loss=0.1802, simple_loss=0.2581, pruned_loss=0.05121, over 7451.00 frames.], tot_loss[loss=0.2256, simple_loss=0.307, pruned_loss=0.07206, over 1463981.92 frames.], batch size: 17, lr: 7.54e-04
2022-07-26 05:04:28,023 INFO [train.py:850] (2/4) Epoch 7, batch 2400, loss[loss=0.2321, simple_loss=0.3292, pruned_loss=0.06749, over 7461.00 frames.], tot_loss[loss=0.2254, simple_loss=0.3073, pruned_loss=0.07179, over 1464644.95 frames.], batch size: 26, lr: 7.54e-04
2022-07-26 05:05:12,884 INFO [train.py:850] (2/4) Epoch 7, batch 2450, loss[loss=0.2052, simple_loss=0.3046, pruned_loss=0.05287, over 7393.00 frames.], tot_loss[loss=0.2259, simple_loss=0.3079, pruned_loss=0.07194, over 1465162.74 frames.], batch size: 19, lr: 7.54e-04
2022-07-26 05:05:57,245 INFO [train.py:850] (2/4) Epoch 7, batch 2500, loss[loss=0.1902, simple_loss=0.281, pruned_loss=0.04966, over 7283.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3075, pruned_loss=0.0718, over 1465051.02 frames.], batch size: 20, lr: 7.53e-04
2022-07-26 05:06:42,996 INFO [train.py:850] (2/4) Epoch 7, batch 2550, loss[loss=0.2622, simple_loss=0.3469, pruned_loss=0.08871, over 7322.00 frames.], tot_loss[loss=0.2268, simple_loss=0.309, pruned_loss=0.07229, over 1464923.80 frames.], batch size: 27, lr: 7.53e-04
2022-07-26 05:07:27,501 INFO [train.py:850] (2/4) Epoch 7, batch 2600, loss[loss=0.2667, simple_loss=0.343, pruned_loss=0.09518, over 7388.00 frames.], tot_loss[loss=0.2263, simple_loss=0.3087, pruned_loss=0.07192, over 1464854.24 frames.], batch size: 31, lr: 7.53e-04
2022-07-26 05:08:26,155 INFO [train.py:850] (2/4) Epoch 7, batch 2650, loss[loss=0.1822, simple_loss=0.2651, pruned_loss=0.04965, over 7289.00 frames.], tot_loss[loss=0.2263, simple_loss=0.3085, pruned_loss=0.07208, over 1464691.10 frames.], batch size: 17, lr: 7.52e-04
2022-07-26 05:09:09,711 INFO [train.py:850] (2/4) Epoch 7, batch 2700, loss[loss=0.255, simple_loss=0.3358, pruned_loss=0.08714, over 7417.00 frames.], tot_loss[loss=0.2265, simple_loss=0.3084, pruned_loss=0.07229, over 1464392.71 frames.], batch size: 31, lr: 7.52e-04
2022-07-26 05:09:54,273 INFO [train.py:850] (2/4) Epoch 7, batch 2750, loss[loss=0.1779, simple_loss=0.2602, pruned_loss=0.04773, over 7454.00 frames.], tot_loss[loss=0.2266, simple_loss=0.3089, pruned_loss=0.07211, over 1466523.27 frames.], batch size: 17, lr: 7.52e-04
2022-07-26 05:10:37,230 INFO [train.py:850] (2/4) Epoch 7, batch 2800, loss[loss=0.1957, simple_loss=0.2707, pruned_loss=0.06039, over 7447.00 frames.], tot_loss[loss=0.2268, simple_loss=0.3094, pruned_loss=0.07209, over 1465845.35 frames.], batch size: 18, lr: 7.51e-04
2022-07-26 05:11:20,967 INFO [train.py:850] (2/4) Epoch 7, batch 2850, loss[loss=0.2255, simple_loss=0.3255, pruned_loss=0.06276, over 7417.00 frames.], tot_loss[loss=0.2252, simple_loss=0.3079, pruned_loss=0.07125, over 1466743.39 frames.], batch size: 22, lr: 7.51e-04
2022-07-26 05:12:04,435 INFO [train.py:850] (2/4) Epoch 7, batch 2900, loss[loss=0.2147, simple_loss=0.3004, pruned_loss=0.06453, over 7294.00 frames.], tot_loss[loss=0.2251, simple_loss=0.3076, pruned_loss=0.07128, over 1466161.35 frames.], batch size: 20, lr: 7.51e-04
2022-07-26 05:12:47,769 INFO [train.py:850] (2/4) Epoch 7, batch 2950, loss[loss=0.2315, simple_loss=0.3083, pruned_loss=0.0774, over 7179.00 frames.], tot_loss[loss=0.2253, simple_loss=0.3076, pruned_loss=0.07147, over 1465772.19 frames.], batch size: 21, lr: 7.50e-04
2022-07-26 05:13:31,659 INFO [train.py:850] (2/4) Epoch 7, batch 3000, loss[loss=0.1978, simple_loss=0.283, pruned_loss=0.05628, over 7201.00 frames.], tot_loss[loss=0.2263, simple_loss=0.3086, pruned_loss=0.07197, over 1465871.66 frames.], batch size: 20, lr: 7.50e-04
2022-07-26 05:13:31,661 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 05:13:54,736 INFO [train.py:879] (2/4) Epoch 7, validation: loss=0.2057, simple_loss=0.2995, pruned_loss=0.05599, over 924787.00 frames.
2022-07-26 05:14:38,978 INFO [train.py:850] (2/4) Epoch 7, batch 3050, loss[loss=0.1779, simple_loss=0.2614, pruned_loss=0.04724, over 7299.00 frames.], tot_loss[loss=0.2257, simple_loss=0.3077, pruned_loss=0.0718, over 1465358.12 frames.], batch size: 17, lr: 7.50e-04
2022-07-26 05:15:23,150 INFO [train.py:850] (2/4) Epoch 7, batch 3100, loss[loss=0.1885, simple_loss=0.2698, pruned_loss=0.05357, over 7148.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3061, pruned_loss=0.07135, over 1465139.12 frames.], batch size: 17, lr: 7.49e-04
2022-07-26 05:16:07,529 INFO [train.py:850] (2/4) Epoch 7, batch 3150, loss[loss=0.2465, simple_loss=0.3251, pruned_loss=0.08398, over 7279.00 frames.], tot_loss[loss=0.2225, simple_loss=0.3043, pruned_loss=0.07036, over 1463280.33 frames.], batch size: 21, lr: 7.49e-04
2022-07-26 05:16:53,804 INFO [train.py:850] (2/4) Epoch 7, batch 3200, loss[loss=0.2327, simple_loss=0.3262, pruned_loss=0.06965, over 7415.00 frames.], tot_loss[loss=0.2221, simple_loss=0.3044, pruned_loss=0.06996, over 1465346.17 frames.], batch size: 22, lr: 7.49e-04
2022-07-26 05:17:38,283 INFO [train.py:850] (2/4) Epoch 7, batch 3250, loss[loss=0.2166, simple_loss=0.2897, pruned_loss=0.07175, over 7293.00 frames.], tot_loss[loss=0.2228, simple_loss=0.3054, pruned_loss=0.07009, over 1465329.01 frames.], batch size: 19, lr: 7.48e-04
2022-07-26 05:18:21,767 INFO [train.py:850] (2/4) Epoch 7, batch 3300, loss[loss=0.2113, simple_loss=0.2951, pruned_loss=0.06369, over 7476.00 frames.], tot_loss[loss=0.2216, simple_loss=0.3044, pruned_loss=0.06938, over 1464438.53 frames.], batch size: 21, lr: 7.48e-04
2022-07-26 05:19:06,202 INFO [train.py:850] (2/4) Epoch 7, batch 3350, loss[loss=0.2303, simple_loss=0.3016, pruned_loss=0.07946, over 7473.00 frames.], tot_loss[loss=0.2208, simple_loss=0.3037, pruned_loss=0.06893, over 1464179.70 frames.], batch size: 20, lr: 7.48e-04
2022-07-26 05:19:49,126 INFO [train.py:850] (2/4) Epoch 7, batch 3400, loss[loss=0.2607, simple_loss=0.3395, pruned_loss=0.09096, over 7277.00 frames.], tot_loss[loss=0.2214, simple_loss=0.3045, pruned_loss=0.06917, over 1464545.83 frames.], batch size: 21, lr: 7.47e-04
2022-07-26 05:20:33,479 INFO [train.py:850] (2/4) Epoch 7, batch 3450, loss[loss=0.1676, simple_loss=0.2556, pruned_loss=0.03977, over 7446.00 frames.], tot_loss[loss=0.2207, simple_loss=0.3042, pruned_loss=0.06862, over 1464703.41 frames.], batch size: 18, lr: 7.47e-04
2022-07-26 05:21:16,670 INFO [train.py:850] (2/4) Epoch 7, batch 3500, loss[loss=0.194, simple_loss=0.283, pruned_loss=0.05253, over 7291.00 frames.], tot_loss[loss=0.2208, simple_loss=0.3042, pruned_loss=0.06869, over 1463874.43 frames.], batch size: 22, lr: 7.47e-04
2022-07-26 05:22:00,446 INFO [train.py:850] (2/4) Epoch 7, batch 3550, loss[loss=0.1871, simple_loss=0.275, pruned_loss=0.04963, over 7288.00 frames.], tot_loss[loss=0.2207, simple_loss=0.3041, pruned_loss=0.06859, over 1463053.44 frames.], batch size: 19, lr: 7.46e-04
2022-07-26 05:22:44,305 INFO [train.py:850] (2/4) Epoch 7, batch 3600, loss[loss=0.2176, simple_loss=0.3002, pruned_loss=0.06751, over 7208.00 frames.], tot_loss[loss=0.2205, simple_loss=0.3039, pruned_loss=0.06858, over 1464078.30 frames.], batch size: 20, lr: 7.46e-04
2022-07-26 05:23:28,410 INFO [train.py:850] (2/4) Epoch 7, batch 3650, loss[loss=0.2221, simple_loss=0.3164, pruned_loss=0.06391, over 7279.00 frames.], tot_loss[loss=0.2202, simple_loss=0.3035, pruned_loss=0.06841, over 1463796.32 frames.], batch size: 20, lr: 7.46e-04
2022-07-26 05:24:11,679 INFO [train.py:850] (2/4) Epoch 7, batch 3700, loss[loss=0.2134, simple_loss=0.307, pruned_loss=0.05991, over 7293.00 frames.], tot_loss[loss=0.2216, simple_loss=0.3052, pruned_loss=0.069, over 1464084.77 frames.], batch size: 20, lr: 7.45e-04
2022-07-26 05:24:54,836 INFO [train.py:850] (2/4) Epoch 7, batch 3750, loss[loss=0.302, simple_loss=0.3658, pruned_loss=0.1192, over 7247.00 frames.], tot_loss[loss=0.222, simple_loss=0.3055, pruned_loss=0.06923, over 1464799.54 frames.], batch size: 27, lr: 7.45e-04
2022-07-26 05:25:38,616 INFO [train.py:850] (2/4) Epoch 7, batch 3800, loss[loss=0.2572, simple_loss=0.3498, pruned_loss=0.08231, over 7249.00 frames.], tot_loss[loss=0.2223, simple_loss=0.3055, pruned_loss=0.06952, over 1465793.05 frames.], batch size: 24, lr: 7.45e-04
2022-07-26 05:26:22,620 INFO [train.py:850] (2/4) Epoch 7, batch 3850, loss[loss=0.2354, simple_loss=0.3183, pruned_loss=0.07627, over 7305.00 frames.], tot_loss[loss=0.2229, simple_loss=0.3058, pruned_loss=0.06997, over 1465355.79 frames.], batch size: 19, lr: 7.44e-04
2022-07-26 05:27:06,466 INFO [train.py:850] (2/4) Epoch 7, batch 3900, loss[loss=0.2688, simple_loss=0.3345, pruned_loss=0.1016, over 7174.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3062, pruned_loss=0.07097, over 1464827.83 frames.], batch size: 22, lr: 7.44e-04
2022-07-26 05:27:50,762 INFO [train.py:850] (2/4) Epoch 7, batch 3950, loss[loss=0.2101, simple_loss=0.2998, pruned_loss=0.06025, over 7487.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3063, pruned_loss=0.07095, over 1465243.40 frames.], batch size: 20, lr: 7.44e-04
2022-07-26 05:28:35,001 INFO [train.py:850] (2/4) Epoch 7, batch 4000, loss[loss=0.2241, simple_loss=0.3166, pruned_loss=0.06576, over 7467.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3066, pruned_loss=0.07108, over 1465811.00 frames.], batch size: 21, lr: 7.43e-04
2022-07-26 05:29:18,337 INFO [train.py:850] (2/4) Epoch 7, batch 4050, loss[loss=0.2182, simple_loss=0.2881, pruned_loss=0.07417, over 7192.00 frames.], tot_loss[loss=0.2245, simple_loss=0.3065, pruned_loss=0.07121, over 1465418.36 frames.], batch size: 18, lr: 7.43e-04
2022-07-26 05:30:02,562 INFO [train.py:850] (2/4) Epoch 7, batch 4100, loss[loss=0.1895, simple_loss=0.2895, pruned_loss=0.04482, over 7183.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3072, pruned_loss=0.07197, over 1465689.15 frames.], batch size: 21, lr: 7.43e-04
2022-07-26 05:30:46,837 INFO [train.py:850] (2/4) Epoch 7, batch 4150, loss[loss=0.2748, simple_loss=0.3348, pruned_loss=0.1074, over 7474.00 frames.], tot_loss[loss=0.2279, simple_loss=0.3087, pruned_loss=0.07353, over 1466018.22 frames.], batch size: 23, lr: 7.42e-04
2022-07-26 05:31:30,362 INFO [train.py:850] (2/4) Epoch 7, batch 4200, loss[loss=0.2674, simple_loss=0.3342, pruned_loss=0.1003, over 7476.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3098, pruned_loss=0.07495, over 1466510.10 frames.], batch size: 20, lr: 7.42e-04
2022-07-26 05:32:13,140 INFO [train.py:850] (2/4) Epoch 7, batch 4250, loss[loss=0.3081, simple_loss=0.3823, pruned_loss=0.1169, over 7334.00 frames.], tot_loss[loss=0.233, simple_loss=0.3117, pruned_loss=0.07718, over 1466850.03 frames.], batch size: 38, lr: 7.42e-04
2022-07-26 05:32:56,980 INFO [train.py:850] (2/4) Epoch 7, batch 4300, loss[loss=0.2353, simple_loss=0.3115, pruned_loss=0.07961, over 7293.00 frames.], tot_loss[loss=0.2361, simple_loss=0.3134, pruned_loss=0.0794, over 1465670.26 frames.], batch size: 19, lr: 7.41e-04
2022-07-26 05:33:40,624 INFO [train.py:850] (2/4) Epoch 7, batch 4350, loss[loss=0.2959, simple_loss=0.3584, pruned_loss=0.1167, over 7345.00 frames.], tot_loss[loss=0.2378, simple_loss=0.3144, pruned_loss=0.08063, over 1466265.16 frames.], batch size: 23, lr: 7.41e-04
2022-07-26 05:34:24,671 INFO [train.py:850] (2/4) Epoch 7, batch 4400, loss[loss=0.2501, simple_loss=0.322, pruned_loss=0.08907, over 7386.00 frames.], tot_loss[loss=0.24, simple_loss=0.3152, pruned_loss=0.08246, over 1466308.76 frames.], batch size: 20, lr: 7.41e-04
2022-07-26 05:35:08,863 INFO [train.py:850] (2/4) Epoch 7, batch 4450, loss[loss=0.2541, simple_loss=0.3259, pruned_loss=0.09113, over 7254.00 frames.], tot_loss[loss=0.2434, simple_loss=0.3175, pruned_loss=0.08461, over 1465501.92 frames.], batch size: 27, lr: 7.40e-04
2022-07-26 05:35:52,111 INFO [train.py:850] (2/4) Epoch 7, batch 4500, loss[loss=0.2527, simple_loss=0.3257, pruned_loss=0.08987, over 7240.00 frames.], tot_loss[loss=0.246, simple_loss=0.3188, pruned_loss=0.08658, over 1464362.70 frames.], batch size: 24, lr: 7.40e-04
2022-07-26 05:36:36,842 INFO [train.py:850] (2/4) Epoch 7, batch 4550, loss[loss=0.2569, simple_loss=0.3312, pruned_loss=0.09126, over 7365.00 frames.], tot_loss[loss=0.2487, simple_loss=0.32, pruned_loss=0.08868, over 1464448.39 frames.], batch size: 39, lr: 7.40e-04
2022-07-26 05:37:20,622 INFO [train.py:850] (2/4) Epoch 7, batch 4600, loss[loss=0.2224, simple_loss=0.2967, pruned_loss=0.07407, over 7301.00 frames.], tot_loss[loss=0.2498, simple_loss=0.32, pruned_loss=0.08978, over 1464655.51 frames.], batch size: 19, lr: 7.40e-04
2022-07-26 05:38:04,633 INFO [train.py:850] (2/4) Epoch 7, batch 4650, loss[loss=0.2301, simple_loss=0.2941, pruned_loss=0.08305, over 7317.00 frames.], tot_loss[loss=0.2502, simple_loss=0.32, pruned_loss=0.09017, over 1463774.01 frames.], batch size: 18, lr: 7.39e-04
2022-07-26 05:38:48,311 INFO [train.py:850] (2/4) Epoch 7, batch 4700, loss[loss=0.2218, simple_loss=0.29, pruned_loss=0.07681, over 7292.00 frames.], tot_loss[loss=0.2497, simple_loss=0.3193, pruned_loss=0.09, over 1462028.25 frames.], batch size: 17, lr: 7.39e-04
2022-07-26 05:39:32,815 INFO [train.py:850] (2/4) Epoch 7, batch 4750, loss[loss=0.2301, simple_loss=0.3037, pruned_loss=0.07822, over 7317.00 frames.], tot_loss[loss=0.2489, simple_loss=0.3185, pruned_loss=0.08968, over 1462904.14 frames.], batch size: 30, lr: 7.39e-04
2022-07-26 05:40:17,075 INFO [train.py:850] (2/4) Epoch 7, batch 4800, loss[loss=0.213, simple_loss=0.2793, pruned_loss=0.07333, over 7442.00 frames.], tot_loss[loss=0.249, simple_loss=0.3184, pruned_loss=0.08974, over 1464138.48 frames.], batch size: 17, lr: 7.38e-04
2022-07-26 05:41:01,783 INFO [train.py:850] (2/4) Epoch 7, batch 4850, loss[loss=0.2311, simple_loss=0.2938, pruned_loss=0.08422, over 7380.00 frames.], tot_loss[loss=0.249, simple_loss=0.3183, pruned_loss=0.0898, over 1463732.99 frames.], batch size: 20, lr: 7.38e-04
2022-07-26 05:41:45,867 INFO [train.py:850] (2/4) Epoch 7, batch 4900, loss[loss=0.2556, simple_loss=0.3151, pruned_loss=0.09802, over 7386.00 frames.], tot_loss[loss=0.2478, simple_loss=0.3174, pruned_loss=0.08916, over 1463377.18 frames.], batch size: 20, lr: 7.38e-04
2022-07-26 05:42:29,151 INFO [train.py:850] (2/4) Epoch 7, batch 4950, loss[loss=0.3009, simple_loss=0.3597, pruned_loss=0.121, over 7189.00 frames.], tot_loss[loss=0.2487, simple_loss=0.318, pruned_loss=0.08973, over 1463056.28 frames.], batch size: 23, lr: 7.37e-04
2022-07-26 05:43:12,396 INFO [train.py:850] (2/4) Epoch 7, batch 5000, loss[loss=0.2176, simple_loss=0.2848, pruned_loss=0.07518, over 7435.00 frames.], tot_loss[loss=0.2481, simple_loss=0.3174, pruned_loss=0.0894, over 1463809.53 frames.], batch size: 18, lr: 7.37e-04
2022-07-26 05:43:57,802 INFO [train.py:850] (2/4) Epoch 7, batch 5050, loss[loss=0.2089, simple_loss=0.2763, pruned_loss=0.0708, over 7274.00 frames.], tot_loss[loss=0.2495, simple_loss=0.3182, pruned_loss=0.09043, over 1464770.59 frames.], batch size: 16, lr: 7.37e-04
2022-07-26 05:44:42,319 INFO [train.py:850] (2/4) Epoch 7, batch 5100, loss[loss=0.2581, simple_loss=0.3165, pruned_loss=0.09989, over 7395.00 frames.], tot_loss[loss=0.2493, simple_loss=0.3179, pruned_loss=0.09032, over 1464835.21 frames.], batch size: 19, lr: 7.36e-04
2022-07-26 05:45:27,336 INFO [train.py:850] (2/4) Epoch 7, batch 5150, loss[loss=0.2111, simple_loss=0.2834, pruned_loss=0.06939, over 7183.00 frames.], tot_loss[loss=0.2485, simple_loss=0.3174, pruned_loss=0.08974, over 1464012.84 frames.], batch size: 18, lr: 7.36e-04
2022-07-26 05:46:11,010 INFO [train.py:850] (2/4) Epoch 7, batch 5200, loss[loss=0.2454, simple_loss=0.3107, pruned_loss=0.09009, over 7348.00 frames.], tot_loss[loss=0.2497, simple_loss=0.3183, pruned_loss=0.0905, over 1465064.32 frames.], batch size: 39, lr: 7.36e-04
2022-07-26 05:46:55,525 INFO [train.py:850] (2/4) Epoch 7, batch 5250, loss[loss=0.2116, simple_loss=0.2738, pruned_loss=0.07473, over 7293.00 frames.], tot_loss[loss=0.2494, simple_loss=0.3183, pruned_loss=0.09023, over 1465817.84 frames.], batch size: 17, lr: 7.35e-04
2022-07-26 05:47:38,821 INFO [train.py:850] (2/4) Epoch 7, batch 5300, loss[loss=0.2572, simple_loss=0.3273, pruned_loss=0.09354, over 7422.00 frames.], tot_loss[loss=0.2511, simple_loss=0.3195, pruned_loss=0.09135, over 1465459.10 frames.], batch size: 22, lr: 7.35e-04
2022-07-26 05:48:23,482 INFO [train.py:850] (2/4) Epoch 7, batch 5350, loss[loss=0.2346, simple_loss=0.302, pruned_loss=0.08358, over 7191.00 frames.], tot_loss[loss=0.2492, simple_loss=0.3178, pruned_loss=0.09028, over 1465284.85 frames.], batch size: 18, lr: 7.35e-04
2022-07-26 05:49:07,989 INFO [train.py:850] (2/4) Epoch 7, batch 5400, loss[loss=0.261, simple_loss=0.3176, pruned_loss=0.1022, over 7436.00 frames.], tot_loss[loss=0.2508, simple_loss=0.3194, pruned_loss=0.09111, over 1464612.30 frames.], batch size: 17, lr: 7.35e-04
2022-07-26 05:49:51,287 INFO [train.py:850] (2/4) Epoch 7, batch 5450, loss[loss=0.2163, simple_loss=0.2948, pruned_loss=0.06894, over 7487.00 frames.], tot_loss[loss=0.2493, simple_loss=0.3187, pruned_loss=0.08996, over 1464432.21 frames.], batch size: 19, lr: 7.34e-04
2022-07-26 05:50:35,693 INFO [train.py:850] (2/4) Epoch 7, batch 5500, loss[loss=0.3073, simple_loss=0.3416, pruned_loss=0.1365, over 7102.00 frames.], tot_loss[loss=0.2484, simple_loss=0.3183, pruned_loss=0.08925, over 1464510.79 frames.], batch size: 18, lr: 7.34e-04
2022-07-26 05:51:19,412 INFO [train.py:850] (2/4) Epoch 7, batch 5550, loss[loss=0.2122, simple_loss=0.2807, pruned_loss=0.07184, over 7394.00 frames.], tot_loss[loss=0.2488, simple_loss=0.3185, pruned_loss=0.0896, over 1464135.35 frames.], batch size: 19, lr: 7.34e-04
2022-07-26 05:52:01,952 INFO [train.py:850] (2/4) Epoch 7, batch 5600, loss[loss=0.2705, simple_loss=0.3292, pruned_loss=0.1059, over 7372.00 frames.], tot_loss[loss=0.2498, simple_loss=0.3191, pruned_loss=0.09027, over 1464570.71 frames.], batch size: 20, lr: 7.33e-04
2022-07-26 05:52:47,293 INFO [train.py:850] (2/4) Epoch 7, batch 5650, loss[loss=0.2414, simple_loss=0.3152, pruned_loss=0.08376, over 7249.00 frames.], tot_loss[loss=0.2497, simple_loss=0.3191, pruned_loss=0.09014, over 1464986.56 frames.], batch size: 24, lr: 7.33e-04
2022-07-26 05:53:32,065 INFO [train.py:850] (2/4) Epoch 7, batch 5700, loss[loss=0.2684, simple_loss=0.3502, pruned_loss=0.09328, over 7331.00 frames.], tot_loss[loss=0.2495, simple_loss=0.3188, pruned_loss=0.09008, over 1465971.36 frames.], batch size: 23, lr: 7.33e-04
2022-07-26 05:54:16,701 INFO [train.py:850] (2/4) Epoch 7, batch 5750, loss[loss=0.2244, simple_loss=0.2893, pruned_loss=0.07979, over 7308.00 frames.], tot_loss[loss=0.25, simple_loss=0.3191, pruned_loss=0.09044, over 1466653.10 frames.], batch size: 18, lr: 7.32e-04
2022-07-26 05:55:00,337 INFO [train.py:850] (2/4) Epoch 7, batch 5800, loss[loss=0.2743, simple_loss=0.339, pruned_loss=0.1048, over 7178.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3182, pruned_loss=0.08946, over 1466420.79 frames.], batch size: 21, lr: 7.32e-04
2022-07-26 05:55:46,126 INFO [train.py:850] (2/4) Epoch 7, batch 5850, loss[loss=0.2453, simple_loss=0.3149, pruned_loss=0.08785, over 7229.00 frames.], tot_loss[loss=0.248, simple_loss=0.3177, pruned_loss=0.08914, over 1466333.44 frames.], batch size: 24, lr: 7.32e-04
2022-07-26 05:56:30,048 INFO [train.py:850] (2/4) Epoch 7, batch 5900, loss[loss=0.2808, simple_loss=0.3414, pruned_loss=0.1101, over 7474.00 frames.], tot_loss[loss=0.2454, simple_loss=0.3156, pruned_loss=0.08763, over 1466254.10 frames.], batch size: 21, lr: 7.31e-04
2022-07-26 05:57:13,734 INFO [train.py:850] (2/4) Epoch 7, batch 5950, loss[loss=0.269, simple_loss=0.3318, pruned_loss=0.1031, over 7277.00 frames.], tot_loss[loss=0.2464, simple_loss=0.316, pruned_loss=0.08846, over 1466055.81 frames.], batch size: 21, lr: 7.31e-04
2022-07-26 05:57:57,470 INFO [train.py:850] (2/4) Epoch 7, batch 6000, loss[loss=0.1882, simple_loss=0.266, pruned_loss=0.05519, over 7159.00 frames.], tot_loss[loss=0.247, simple_loss=0.3166, pruned_loss=0.08869, over 1466074.39 frames.], batch size: 17, lr: 7.31e-04
2022-07-26 05:57:57,472 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 05:58:20,305 INFO [train.py:879] (2/4) Epoch 7, validation: loss=0.1996, simple_loss=0.2979, pruned_loss=0.05067, over 924787.00 frames.
2022-07-26 05:59:05,578 INFO [train.py:850] (2/4) Epoch 7, batch 6050, loss[loss=0.283, simple_loss=0.3541, pruned_loss=0.106, over 7188.00 frames.], tot_loss[loss=0.2483, simple_loss=0.3181, pruned_loss=0.0893, over 1465121.74 frames.], batch size: 21, lr: 7.31e-04
2022-07-26 05:59:50,450 INFO [train.py:850] (2/4) Epoch 7, batch 6100, loss[loss=0.2278, simple_loss=0.3003, pruned_loss=0.07763, over 7194.00 frames.], tot_loss[loss=0.2482, simple_loss=0.318, pruned_loss=0.08916, over 1465067.98 frames.], batch size: 18, lr: 7.30e-04
2022-07-26 06:00:35,057 INFO [train.py:850] (2/4) Epoch 7, batch 6150, loss[loss=0.2529, simple_loss=0.321, pruned_loss=0.09246, over 7468.00 frames.], tot_loss[loss=0.2488, simple_loss=0.3183, pruned_loss=0.0896, over 1465597.79 frames.], batch size: 24, lr: 7.30e-04
2022-07-26 06:01:19,317 INFO [train.py:850] (2/4) Epoch 7, batch 6200, loss[loss=0.2249, simple_loss=0.2868, pruned_loss=0.08147, over 7258.00 frames.], tot_loss[loss=0.2499, simple_loss=0.3192, pruned_loss=0.0903, over 1467363.85 frames.], batch size: 16, lr: 7.30e-04
2022-07-26 06:02:03,952 INFO [train.py:850] (2/4) Epoch 7, batch 6250, loss[loss=0.2147, simple_loss=0.2734, pruned_loss=0.07796, over 7474.00 frames.], tot_loss[loss=0.2496, simple_loss=0.3189, pruned_loss=0.09013, over 1467721.13 frames.], batch size: 17, lr: 7.29e-04
2022-07-26 06:02:47,863 INFO [train.py:850] (2/4) Epoch 7, batch 6300, loss[loss=0.2362, simple_loss=0.3106, pruned_loss=0.0809, over 7292.00 frames.], tot_loss[loss=0.2491, simple_loss=0.3189, pruned_loss=0.08966, over 1467100.48 frames.], batch size: 21, lr: 7.29e-04
2022-07-26 06:03:31,633 INFO [train.py:850] (2/4) Epoch 7, batch 6350, loss[loss=0.2347, simple_loss=0.3055, pruned_loss=0.08195, over 7484.00 frames.], tot_loss[loss=0.2494, simple_loss=0.3195, pruned_loss=0.08967, over 1467642.61 frames.], batch size: 19, lr: 7.29e-04
2022-07-26 06:04:15,185 INFO [train.py:850] (2/4) Epoch 7, batch 6400, loss[loss=0.3161, simple_loss=0.3656, pruned_loss=0.1333, over 7442.00 frames.], tot_loss[loss=0.2491, simple_loss=0.3192, pruned_loss=0.08951, over 1467043.10 frames.], batch size: 26, lr: 7.28e-04
2022-07-26 06:04:59,873 INFO [train.py:850] (2/4) Epoch 7, batch 6450, loss[loss=0.244, simple_loss=0.307, pruned_loss=0.09049, over 7483.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3182, pruned_loss=0.08953, over 1467041.66 frames.], batch size: 20, lr: 7.28e-04
2022-07-26 06:05:42,827 INFO [train.py:850] (2/4) Epoch 7, batch 6500, loss[loss=0.2629, simple_loss=0.3263, pruned_loss=0.09973, over 7326.00 frames.], tot_loss[loss=0.2499, simple_loss=0.3188, pruned_loss=0.09048, over 1466644.39 frames.], batch size: 31, lr: 7.28e-04
2022-07-26 06:06:29,027 INFO [train.py:850] (2/4) Epoch 7, batch 6550, loss[loss=0.2412, simple_loss=0.3105, pruned_loss=0.08597, over 7414.00 frames.], tot_loss[loss=0.2464, simple_loss=0.3158, pruned_loss=0.08847, over 1466546.13 frames.], batch size: 65, lr: 7.27e-04
2022-07-26 06:07:14,798 INFO [train.py:850] (2/4) Epoch 7, batch 6600, loss[loss=0.2935, simple_loss=0.3422, pruned_loss=0.1224, over 7198.00 frames.], tot_loss[loss=0.2463, simple_loss=0.316, pruned_loss=0.08829, over 1466920.70 frames.], batch size: 18, lr: 7.27e-04
2022-07-26 06:08:14,691 INFO [train.py:850] (2/4) Epoch 7, batch 6650, loss[loss=0.2313, simple_loss=0.3141, pruned_loss=0.07426, over 7411.00 frames.], tot_loss[loss=0.2458, simple_loss=0.3159, pruned_loss=0.08781, over 1467133.74 frames.], batch size: 31, lr: 7.27e-04
2022-07-26 06:08:57,815 INFO [train.py:850] (2/4) Epoch 7, batch 6700, loss[loss=0.2115, simple_loss=0.2811, pruned_loss=0.07093, over 7153.00 frames.], tot_loss[loss=0.2469, simple_loss=0.3166, pruned_loss=0.08854, over 1467726.08 frames.], batch size: 17, lr: 7.27e-04
2022-07-26 06:09:42,140 INFO [train.py:850] (2/4) Epoch 7, batch 6750, loss[loss=0.2724, simple_loss=0.3339, pruned_loss=0.1054, over 7487.00 frames.], tot_loss[loss=0.2469, simple_loss=0.3168, pruned_loss=0.08853, over 1467591.50 frames.], batch size: 21, lr: 7.26e-04
2022-07-26 06:10:25,614 INFO [train.py:850] (2/4) Epoch 7, batch 6800, loss[loss=0.2226, simple_loss=0.3038, pruned_loss=0.07072, over 7472.00 frames.], tot_loss[loss=0.2468, simple_loss=0.3173, pruned_loss=0.08817, over 1467879.70 frames.], batch size: 21, lr: 7.26e-04
2022-07-26 06:11:09,075 INFO [train.py:850] (2/4) Epoch 7, batch 6850, loss[loss=0.2803, simple_loss=0.3527, pruned_loss=0.104, over 7181.00 frames.], tot_loss[loss=0.2465, simple_loss=0.3165, pruned_loss=0.08824, over 1467706.53 frames.], batch size: 22, lr: 7.26e-04
2022-07-26 06:11:52,432 INFO [train.py:850] (2/4) Epoch 7, batch 6900, loss[loss=0.231, simple_loss=0.3149, pruned_loss=0.0736, over 7234.00 frames.], tot_loss[loss=0.2474, simple_loss=0.317, pruned_loss=0.08886, over 1468095.58 frames.], batch size: 24, lr: 7.25e-04
2022-07-26 06:12:35,986 INFO [train.py:850] (2/4) Epoch 7, batch 6950, loss[loss=0.225, simple_loss=0.2967, pruned_loss=0.07666, over 7307.00 frames.], tot_loss[loss=0.2468, simple_loss=0.3169, pruned_loss=0.08835, over 1466580.53 frames.], batch size: 18, lr: 7.25e-04
2022-07-26 06:13:20,118 INFO [train.py:850] (2/4) Epoch 7, batch 7000, loss[loss=0.2541, simple_loss=0.3348, pruned_loss=0.08669, over 7417.00 frames.], tot_loss[loss=0.2457, simple_loss=0.3162, pruned_loss=0.08761, over 1466587.54 frames.], batch size: 22, lr: 7.25e-04
2022-07-26 06:14:03,107 INFO [train.py:850] (2/4) Epoch 7, batch 7050, loss[loss=0.2445, simple_loss=0.3261, pruned_loss=0.08148, over 7328.00 frames.], tot_loss[loss=0.2449, simple_loss=0.3158, pruned_loss=0.08696, over 1467098.41 frames.], batch size: 31, lr: 7.25e-04
2022-07-26 06:14:46,605 INFO [train.py:850] (2/4) Epoch 7, batch 7100, loss[loss=0.2504, simple_loss=0.322, pruned_loss=0.08944, over 7420.00 frames.], tot_loss[loss=0.2438, simple_loss=0.3145, pruned_loss=0.08657, over 1467130.19 frames.], batch size: 22, lr: 7.24e-04
2022-07-26 06:15:31,290 INFO [train.py:850] (2/4) Epoch 7, batch 7150, loss[loss=0.258, simple_loss=0.3244, pruned_loss=0.0958, over 7482.00 frames.], tot_loss[loss=0.2443, simple_loss=0.3144, pruned_loss=0.08714, over 1467367.36 frames.], batch size: 21, lr: 7.24e-04
2022-07-26 06:16:15,593 INFO [train.py:850] (2/4) Epoch 7, batch 7200, loss[loss=0.2306, simple_loss=0.3056, pruned_loss=0.07779, over 7106.00 frames.], tot_loss[loss=0.245, simple_loss=0.3151, pruned_loss=0.08746, over 1466649.79 frames.], batch size: 18, lr: 7.24e-04
2022-07-26 06:16:59,861 INFO [train.py:850] (2/4) Epoch 7, batch 7250, loss[loss=0.2766, simple_loss=0.3364, pruned_loss=0.1085, over 7476.00 frames.], tot_loss[loss=0.2445, simple_loss=0.3148, pruned_loss=0.0871, over 1466389.50 frames.], batch size: 71, lr: 7.23e-04
2022-07-26 06:17:43,449 INFO [train.py:850] (2/4) Epoch 7, batch 7300, loss[loss=0.2337, simple_loss=0.31, pruned_loss=0.07871, over 7398.00 frames.], tot_loss[loss=0.2437, simple_loss=0.3146, pruned_loss=0.08638, over 1466254.61 frames.], batch size: 38, lr: 7.23e-04
2022-07-26 06:18:29,730 INFO [train.py:850] (2/4) Epoch 7, batch 7350, loss[loss=0.2658, simple_loss=0.3365, pruned_loss=0.09755, over 7429.00 frames.], tot_loss[loss=0.2435, simple_loss=0.3142, pruned_loss=0.08638, over 1466048.69 frames.], batch size: 40, lr: 7.23e-04
2022-07-26 06:19:13,413 INFO [train.py:850] (2/4) Epoch 7, batch 7400, loss[loss=0.2572, simple_loss=0.3259, pruned_loss=0.09424, over 7293.00 frames.], tot_loss[loss=0.2424, simple_loss=0.3135, pruned_loss=0.08568, over 1465098.60 frames.], batch size: 19, lr: 7.22e-04
2022-07-26 06:19:59,061 INFO [train.py:850] (2/4) Epoch 7, batch 7450, loss[loss=0.2708, simple_loss=0.3183, pruned_loss=0.1117, over 7450.00 frames.], tot_loss[loss=0.2419, simple_loss=0.3135, pruned_loss=0.08516, over 1466695.27 frames.], batch size: 17, lr: 7.22e-04
2022-07-26 06:20:42,256 INFO [train.py:850] (2/4) Epoch 7, batch 7500, loss[loss=0.2437, simple_loss=0.3316, pruned_loss=0.07789, over 7486.00 frames.], tot_loss[loss=0.2433, simple_loss=0.3147, pruned_loss=0.08597, over 1466918.34 frames.], batch size: 26, lr: 7.22e-04
2022-07-26 06:21:26,109 INFO [train.py:850] (2/4) Epoch 7, batch 7550, loss[loss=0.2049, simple_loss=0.2936, pruned_loss=0.05806, over 7281.00 frames.], tot_loss[loss=0.2423, simple_loss=0.314, pruned_loss=0.08528, over 1465846.93 frames.], batch size: 20, lr: 7.22e-04
2022-07-26 06:22:09,902 INFO [train.py:850] (2/4) Epoch 7, batch 7600, loss[loss=0.2994, simple_loss=0.3618, pruned_loss=0.1185, over 7432.00 frames.], tot_loss[loss=0.2429, simple_loss=0.3145, pruned_loss=0.08568, over 1465597.02 frames.], batch size: 31, lr: 7.21e-04
2022-07-26 06:22:53,450 INFO [train.py:850] (2/4) Epoch 7, batch 7650, loss[loss=0.2189, simple_loss=0.3026, pruned_loss=0.06765, over 7192.00 frames.], tot_loss[loss=0.2429, simple_loss=0.3142, pruned_loss=0.08581, over 1464692.18 frames.], batch size: 18, lr: 7.21e-04
2022-07-26 06:23:38,567 INFO [train.py:850] (2/4) Epoch 7, batch 7700, loss[loss=0.222, simple_loss=0.2995, pruned_loss=0.07225, over 7305.00 frames.], tot_loss[loss=0.2435, simple_loss=0.3148, pruned_loss=0.08611, over 1464307.82 frames.], batch size: 20, lr: 7.21e-04
2022-07-26 06:24:22,864 INFO [train.py:850] (2/4) Epoch 7, batch 7750, loss[loss=0.2476, simple_loss=0.3084, pruned_loss=0.09341, over 7296.00 frames.], tot_loss[loss=0.2434, simple_loss=0.3152, pruned_loss=0.08582, over 1464836.14 frames.], batch size: 18, lr: 7.20e-04
2022-07-26 06:25:07,214 INFO [train.py:850] (2/4) Epoch 7, batch 7800, loss[loss=0.2442, simple_loss=0.3124, pruned_loss=0.08804, over 7298.00 frames.], tot_loss[loss=0.2428, simple_loss=0.3148, pruned_loss=0.08536, over 1465547.95 frames.], batch size: 19, lr: 7.20e-04
2022-07-26 06:25:51,522 INFO [train.py:850] (2/4) Epoch 7, batch 7850, loss[loss=0.2203, simple_loss=0.2926, pruned_loss=0.074, over 7205.00 frames.], tot_loss[loss=0.2436, simple_loss=0.3152, pruned_loss=0.08603, over 1466050.54 frames.], batch size: 19, lr: 7.20e-04
2022-07-26 06:26:35,002 INFO [train.py:850] (2/4) Epoch 7, batch 7900, loss[loss=0.2724, simple_loss=0.3434, pruned_loss=0.1007, over 7263.00 frames.], tot_loss[loss=0.2412, simple_loss=0.3129, pruned_loss=0.08471, over 1465060.09 frames.], batch size: 27, lr: 7.19e-04
2022-07-26 06:27:19,428 INFO [train.py:850] (2/4) Epoch 7, batch 7950, loss[loss=0.2205, simple_loss=0.2966, pruned_loss=0.07221, over 7298.00 frames.], tot_loss[loss=0.2411, simple_loss=0.3125, pruned_loss=0.08484, over 1465514.38 frames.], batch size: 27, lr: 7.19e-04
2022-07-26 06:28:03,376 INFO [train.py:850] (2/4) Epoch 7, batch 8000, loss[loss=0.2532, simple_loss=0.303, pruned_loss=0.1017, over 7304.00 frames.], tot_loss[loss=0.2392, simple_loss=0.3105, pruned_loss=0.08394, over 1464479.15 frames.], batch size: 17, lr: 7.19e-04
2022-07-26 06:28:47,920 INFO [train.py:850] (2/4) Epoch 7, batch 8050, loss[loss=0.2438, simple_loss=0.323, pruned_loss=0.08231, over 7254.00 frames.], tot_loss[loss=0.2408, simple_loss=0.3123, pruned_loss=0.08466, over 1464509.36 frames.], batch size: 27, lr: 7.19e-04
2022-07-26 06:29:31,086 INFO [train.py:850] (2/4) Epoch 7, batch 8100, loss[loss=0.2638, simple_loss=0.3376, pruned_loss=0.09495, over 7182.00 frames.], tot_loss[loss=0.2401, simple_loss=0.3116, pruned_loss=0.08432, over 1464521.21 frames.], batch size: 21, lr: 7.18e-04
2022-07-26 06:30:16,086 INFO [train.py:850] (2/4) Epoch 7, batch 8150, loss[loss=0.2285, simple_loss=0.3097, pruned_loss=0.07365, over 7410.00 frames.], tot_loss[loss=0.2401, simple_loss=0.3115, pruned_loss=0.0844, over 1465346.20 frames.], batch size: 22, lr: 7.18e-04
2022-07-26 06:31:01,359 INFO [train.py:850] (2/4) Epoch 7, batch 8200, loss[loss=0.2716, simple_loss=0.3296, pruned_loss=0.1068, over 7281.00 frames.], tot_loss[loss=0.2407, simple_loss=0.312, pruned_loss=0.08469, over 1466327.92 frames.], batch size: 17, lr: 7.18e-04
2022-07-26 06:31:46,129 INFO [train.py:850] (2/4) Epoch 7, batch 8250, loss[loss=0.2168, simple_loss=0.2942, pruned_loss=0.06976, over 7478.00 frames.], tot_loss[loss=0.2403, simple_loss=0.3116, pruned_loss=0.08449, over 1466251.76 frames.], batch size: 20, lr: 7.17e-04
2022-07-26 06:32:30,137 INFO [train.py:850] (2/4) Epoch 7, batch 8300, loss[loss=0.268, simple_loss=0.342, pruned_loss=0.09695, over 7207.00 frames.], tot_loss[loss=0.241, simple_loss=0.312, pruned_loss=0.08506, over 1466090.36 frames.], batch size: 24, lr: 7.17e-04
2022-07-26 06:33:14,307 INFO [train.py:850] (2/4) Epoch 7, batch 8350, loss[loss=0.2529, simple_loss=0.3296, pruned_loss=0.0881, over 7280.00 frames.], tot_loss[loss=0.241, simple_loss=0.3124, pruned_loss=0.08478, over 1465591.95 frames.], batch size: 21, lr: 7.17e-04
2022-07-26 06:33:58,407 INFO [train.py:850] (2/4) Epoch 7, batch 8400, loss[loss=0.2764, simple_loss=0.3462, pruned_loss=0.1033, over 7285.00 frames.], tot_loss[loss=0.2398, simple_loss=0.3118, pruned_loss=0.08391, over 1466076.41 frames.], batch size: 21, lr: 7.17e-04
2022-07-26 06:34:42,516 INFO [train.py:850] (2/4) Epoch 7, batch 8450, loss[loss=0.1885, simple_loss=0.2609, pruned_loss=0.05806, over 7458.00 frames.], tot_loss[loss=0.2388, simple_loss=0.3111, pruned_loss=0.08325, over 1466921.36 frames.], batch size: 17, lr: 7.16e-04
2022-07-26 06:35:26,217 INFO [train.py:850] (2/4) Epoch 7, batch 8500, loss[loss=0.2595, simple_loss=0.3412, pruned_loss=0.08893, over 7191.00 frames.], tot_loss[loss=0.2404, simple_loss=0.3125, pruned_loss=0.08415, over 1466142.47 frames.], batch size: 21, lr: 7.16e-04
2022-07-26 06:36:10,867 INFO [train.py:850] (2/4) Epoch 7, batch 8550, loss[loss=0.2551, simple_loss=0.3314, pruned_loss=0.08936, over 7302.00 frames.], tot_loss[loss=0.2426, simple_loss=0.3137, pruned_loss=0.08575, over 1466353.33 frames.], batch size: 22, lr: 7.16e-04
2022-07-26 06:36:54,780 INFO [train.py:850] (2/4) Epoch 7, batch 8600, loss[loss=0.2206, simple_loss=0.2888, pruned_loss=0.07616, over 7253.00 frames.], tot_loss[loss=0.2442, simple_loss=0.3146, pruned_loss=0.08685, over 1466975.67 frames.], batch size: 16, lr: 7.15e-04
2022-07-26 06:37:39,733 INFO [train.py:850] (2/4) Epoch 7, batch 8650, loss[loss=0.2568, simple_loss=0.3361, pruned_loss=0.08869, over 7407.00 frames.], tot_loss[loss=0.2434, simple_loss=0.314, pruned_loss=0.08635, over 1466654.77 frames.], batch size: 31, lr: 7.15e-04
2022-07-26 06:38:22,428 INFO [train.py:850] (2/4) Epoch 7, batch 8700, loss[loss=0.2519, simple_loss=0.3278, pruned_loss=0.08802, over 7486.00 frames.], tot_loss[loss=0.2434, simple_loss=0.3142, pruned_loss=0.08629, over 1466851.46 frames.], batch size: 26, lr: 7.15e-04
2022-07-26 06:39:05,884 INFO [train.py:850] (2/4) Epoch 7, batch 8750, loss[loss=0.183, simple_loss=0.2628, pruned_loss=0.05163, over 7275.00 frames.], tot_loss[loss=0.2414, simple_loss=0.3126, pruned_loss=0.0851, over 1465876.71 frames.], batch size: 16, lr: 7.15e-04
2022-07-26 06:39:48,479 INFO [train.py:850] (2/4) Epoch 7, batch 8800, loss[loss=0.2136, simple_loss=0.2812, pruned_loss=0.07294, over 7430.00 frames.], tot_loss[loss=0.2413, simple_loss=0.3125, pruned_loss=0.08499, over 1465817.85 frames.], batch size: 18, lr: 7.14e-04
2022-07-26 06:40:32,258 INFO [train.py:850] (2/4) Epoch 7, batch 8850, loss[loss=0.2179, simple_loss=0.2817, pruned_loss=0.07708, over 7160.00 frames.], tot_loss[loss=0.2397, simple_loss=0.3112, pruned_loss=0.08405, over 1466339.10 frames.], batch size: 17, lr: 7.14e-04
2022-07-26 06:42:13,763 INFO [train.py:850] (2/4) Epoch 8, batch 0, loss[loss=0.2447, simple_loss=0.3393, pruned_loss=0.07504, over 7276.00 frames.], tot_loss[loss=0.2447, simple_loss=0.3393, pruned_loss=0.07504, over 7276.00 frames.], batch size: 21, lr: 6.85e-04
2022-07-26 06:42:59,014 INFO [train.py:850] (2/4) Epoch 8, batch 50, loss[loss=0.1953, simple_loss=0.2823, pruned_loss=0.05416, over 7220.00 frames.], tot_loss[loss=0.2375, simple_loss=0.3163, pruned_loss=0.0793, over 331303.65 frames.], batch size: 25, lr: 6.84e-04
2022-07-26 06:43:43,114 INFO [train.py:850] (2/4) Epoch 8, batch 100, loss[loss=0.1832, simple_loss=0.2713, pruned_loss=0.0475, over 7484.00 frames.], tot_loss[loss=0.2257, simple_loss=0.3065, pruned_loss=0.07244, over 582574.42 frames.], batch size: 20, lr: 6.84e-04
2022-07-26 06:44:27,713 INFO [train.py:850] (2/4) Epoch 8, batch 150, loss[loss=0.2133, simple_loss=0.3018, pruned_loss=0.06236, over 7282.00 frames.], tot_loss[loss=0.2218, simple_loss=0.3031, pruned_loss=0.07022, over 778585.51 frames.], batch size: 20, lr: 6.84e-04
2022-07-26 06:45:11,420 INFO [train.py:850] (2/4) Epoch 8, batch 200, loss[loss=0.2749, simple_loss=0.3412, pruned_loss=0.1043, over 7407.00 frames.], tot_loss[loss=0.2205, simple_loss=0.3031, pruned_loss=0.069, over 931051.87 frames.], batch size: 67, lr: 6.84e-04
2022-07-26 06:45:55,814 INFO [train.py:850] (2/4) Epoch 8, batch 250, loss[loss=0.2309, simple_loss=0.2993, pruned_loss=0.08129, over 7459.00 frames.], tot_loss[loss=0.2186, simple_loss=0.3013, pruned_loss=0.06791, over 1049921.93 frames.], batch size: 24, lr: 6.83e-04
2022-07-26 06:46:39,029 INFO [train.py:850] (2/4) Epoch 8, batch 300, loss[loss=0.1977, simple_loss=0.2857, pruned_loss=0.05482, over 7194.00 frames.], tot_loss[loss=0.2183, simple_loss=0.301, pruned_loss=0.06778, over 1143038.23 frames.], batch size: 20, lr: 6.83e-04
2022-07-26 06:47:23,733 INFO [train.py:850] (2/4) Epoch 8, batch 350, loss[loss=0.2102, simple_loss=0.3114, pruned_loss=0.05447, over 7187.00 frames.], tot_loss[loss=0.2177, simple_loss=0.3008, pruned_loss=0.06724, over 1214678.34 frames.], batch size: 21, lr: 6.83e-04
2022-07-26 06:48:06,813 INFO [train.py:850] (2/4) Epoch 8, batch 400, loss[loss=0.2281, simple_loss=0.3214, pruned_loss=0.0674, over 7296.00 frames.], tot_loss[loss=0.2167, simple_loss=0.3003, pruned_loss=0.06661, over 1270517.35 frames.], batch size: 27, lr: 6.83e-04
2022-07-26 06:48:50,001 INFO [train.py:850] (2/4) Epoch 8, batch 450, loss[loss=0.2215, simple_loss=0.3097, pruned_loss=0.06666, over 7464.00 frames.], tot_loss[loss=0.216, simple_loss=0.2998, pruned_loss=0.06616, over 1313783.86 frames.], batch size: 21, lr: 6.82e-04
2022-07-26 06:49:33,946 INFO [train.py:850] (2/4) Epoch 8, batch 500, loss[loss=0.2039, simple_loss=0.2984, pruned_loss=0.05471, over 7474.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2991, pruned_loss=0.06591, over 1347238.19 frames.], batch size: 20, lr: 6.82e-04
2022-07-26 06:50:17,665 INFO [train.py:850] (2/4) Epoch 8, batch 550, loss[loss=0.1779, simple_loss=0.2653, pruned_loss=0.04521, over 7100.00 frames.], tot_loss[loss=0.2145, simple_loss=0.298, pruned_loss=0.06549, over 1372878.99 frames.], batch size: 18, lr: 6.82e-04
2022-07-26 06:51:01,581 INFO [train.py:850] (2/4) Epoch 8, batch 600, loss[loss=0.1905, simple_loss=0.2762, pruned_loss=0.05244, over 7296.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2976, pruned_loss=0.06508, over 1394147.06 frames.], batch size: 19, lr: 6.81e-04
2022-07-26 06:51:46,247 INFO [train.py:850] (2/4) Epoch 8, batch 650, loss[loss=0.2632, simple_loss=0.3321, pruned_loss=0.09717, over 7283.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2967, pruned_loss=0.0646, over 1409985.34 frames.], batch size: 20, lr: 6.81e-04
2022-07-26 06:52:31,451 INFO [train.py:850] (2/4) Epoch 8, batch 700, loss[loss=0.2197, simple_loss=0.3118, pruned_loss=0.06378, over 7212.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2965, pruned_loss=0.06396, over 1422898.59 frames.], batch size: 25, lr: 6.81e-04
2022-07-26 06:53:17,565 INFO [train.py:850] (2/4) Epoch 8, batch 750, loss[loss=0.2249, simple_loss=0.3149, pruned_loss=0.06746, over 7193.00 frames.], tot_loss[loss=0.2124, simple_loss=0.297, pruned_loss=0.06392, over 1432469.04 frames.], batch size: 18, lr: 6.81e-04
2022-07-26 06:53:59,953 INFO [train.py:850] (2/4) Epoch 8, batch 800, loss[loss=0.2887, simple_loss=0.3513, pruned_loss=0.1131, over 7467.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2969, pruned_loss=0.06428, over 1439289.69 frames.], batch size: 66, lr: 6.80e-04
2022-07-26 06:54:44,703 INFO [train.py:850] (2/4) Epoch 8, batch 850, loss[loss=0.2062, simple_loss=0.2882, pruned_loss=0.06207, over 7408.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2983, pruned_loss=0.06497, over 1445685.85 frames.], batch size: 39, lr: 6.80e-04
2022-07-26 06:55:30,209 INFO [train.py:850] (2/4) Epoch 8, batch 900, loss[loss=0.2356, simple_loss=0.3254, pruned_loss=0.07284, over 7170.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2991, pruned_loss=0.06557, over 1450505.01 frames.], batch size: 22, lr: 6.80e-04
2022-07-26 06:56:16,288 INFO [train.py:850] (2/4) Epoch 8, batch 950, loss[loss=0.2985, simple_loss=0.3634, pruned_loss=0.1168, over 7474.00 frames.], tot_loss[loss=0.217, simple_loss=0.3005, pruned_loss=0.06668, over 1454498.77 frames.], batch size: 72, lr: 6.80e-04
2022-07-26 06:57:00,817 INFO [train.py:850] (2/4) Epoch 8, batch 1000, loss[loss=0.2467, simple_loss=0.3376, pruned_loss=0.07788, over 7308.00 frames.], tot_loss[loss=0.216, simple_loss=0.2998, pruned_loss=0.06607, over 1457119.91 frames.], batch size: 22, lr: 6.79e-04
2022-07-26 06:57:45,279 INFO [train.py:850] (2/4) Epoch 8, batch 1050, loss[loss=0.208, simple_loss=0.2935, pruned_loss=0.0613, over 7361.00 frames.], tot_loss[loss=0.2178, simple_loss=0.3012, pruned_loss=0.06717, over 1459248.15 frames.], batch size: 20, lr: 6.79e-04
2022-07-26 06:58:28,955 INFO [train.py:850] (2/4) Epoch 8, batch 1100, loss[loss=0.2088, simple_loss=0.2967, pruned_loss=0.06048, over 7384.00 frames.], tot_loss[loss=0.218, simple_loss=0.3011, pruned_loss=0.06746, over 1460944.98 frames.], batch size: 31, lr: 6.79e-04
2022-07-26 06:59:12,355 INFO [train.py:850] (2/4) Epoch 8, batch 1150, loss[loss=0.2134, simple_loss=0.3038, pruned_loss=0.06146, over 7397.00 frames.], tot_loss[loss=0.22, simple_loss=0.3028, pruned_loss=0.06864, over 1462377.10 frames.], batch size: 39, lr: 6.79e-04
2022-07-26 06:59:55,784 INFO [train.py:850] (2/4) Epoch 8, batch 1200, loss[loss=0.2527, simple_loss=0.3379, pruned_loss=0.08373, over 7291.00 frames.], tot_loss[loss=0.2213, simple_loss=0.3034, pruned_loss=0.06955, over 1462374.34 frames.], batch size: 21, lr: 6.78e-04
2022-07-26 07:00:40,264 INFO [train.py:850] (2/4) Epoch 8, batch 1250, loss[loss=0.2025, simple_loss=0.3023, pruned_loss=0.0513, over 7420.00 frames.], tot_loss[loss=0.2216, simple_loss=0.3037, pruned_loss=0.06974, over 1462504.43 frames.], batch size: 22, lr: 6.78e-04
2022-07-26 07:01:24,197 INFO [train.py:850] (2/4) Epoch 8, batch 1300, loss[loss=0.1814, simple_loss=0.2566, pruned_loss=0.05311, over 7313.00 frames.], tot_loss[loss=0.2227, simple_loss=0.3046, pruned_loss=0.07036, over 1462713.90 frames.], batch size: 18, lr: 6.78e-04
2022-07-26 07:02:09,124 INFO [train.py:850] (2/4) Epoch 8, batch 1350, loss[loss=0.2267, simple_loss=0.3085, pruned_loss=0.07246, over 7303.00 frames.], tot_loss[loss=0.2245, simple_loss=0.3062, pruned_loss=0.07141, over 1462776.37 frames.], batch size: 19, lr: 6.77e-04
2022-07-26 07:02:51,690 INFO [train.py:850] (2/4) Epoch 8, batch 1400, loss[loss=0.1741, simple_loss=0.2712, pruned_loss=0.0385, over 7191.00 frames.], tot_loss[loss=0.2243, simple_loss=0.3062, pruned_loss=0.07126, over 1463632.01 frames.], batch size: 21, lr: 6.77e-04
2022-07-26 07:03:37,262 INFO [train.py:850] (2/4) Epoch 8, batch 1450, loss[loss=0.2697, simple_loss=0.3461, pruned_loss=0.09665, over 7218.00 frames.], tot_loss[loss=0.2237, simple_loss=0.3057, pruned_loss=0.07083, over 1464113.32 frames.], batch size: 25, lr: 6.77e-04
2022-07-26 07:04:21,454 INFO [train.py:850] (2/4) Epoch 8, batch 1500, loss[loss=0.238, simple_loss=0.3232, pruned_loss=0.07634, over 7328.00 frames.], tot_loss[loss=0.2228, simple_loss=0.3051, pruned_loss=0.07027, over 1463562.55 frames.], batch size: 39, lr: 6.77e-04
2022-07-26 07:05:04,446 INFO [train.py:850] (2/4) Epoch 8, batch 1550, loss[loss=0.2136, simple_loss=0.2966, pruned_loss=0.06534, over 7103.00 frames.], tot_loss[loss=0.2232, simple_loss=0.3054, pruned_loss=0.07047, over 1462951.28 frames.], batch size: 18, lr: 6.76e-04
2022-07-26 07:05:48,332 INFO [train.py:850] (2/4) Epoch 8, batch 1600, loss[loss=0.1909, simple_loss=0.2693, pruned_loss=0.05627, over 7284.00 frames.], tot_loss[loss=0.2201, simple_loss=0.3025, pruned_loss=0.06882, over 1464071.28 frames.], batch size: 16, lr: 6.76e-04
2022-07-26 07:06:31,104 INFO [train.py:850] (2/4) Epoch 8, batch 1650, loss[loss=0.2427, simple_loss=0.3072, pruned_loss=0.08904, over 7307.00 frames.], tot_loss[loss=0.2197, simple_loss=0.3021, pruned_loss=0.06859, over 1465915.28 frames.], batch size: 17, lr: 6.76e-04
2022-07-26 07:07:15,242 INFO [train.py:850] (2/4) Epoch 8, batch 1700, loss[loss=0.2573, simple_loss=0.3487, pruned_loss=0.08289, over 7243.00 frames.], tot_loss[loss=0.2208, simple_loss=0.303, pruned_loss=0.06927, over 1465527.30 frames.], batch size: 24, lr: 6.76e-04
2022-07-26 07:08:14,485 INFO [train.py:850] (2/4) Epoch 8, batch 1750, loss[loss=0.2337, simple_loss=0.2989, pruned_loss=0.08426, over 7430.00 frames.], tot_loss[loss=0.2223, simple_loss=0.3039, pruned_loss=0.07035, over 1464291.09 frames.], batch size: 17, lr: 6.75e-04
2022-07-26 07:08:58,101 INFO [train.py:850] (2/4) Epoch 8, batch 1800, loss[loss=0.3127, simple_loss=0.3768, pruned_loss=0.1244, over 7434.00 frames.], tot_loss[loss=0.2219, simple_loss=0.3041, pruned_loss=0.06987, over 1464761.69 frames.], batch size: 70, lr: 6.75e-04
2022-07-26 07:09:43,309 INFO [train.py:850] (2/4) Epoch 8, batch 1850, loss[loss=0.2447, simple_loss=0.3201, pruned_loss=0.08465, over 7200.00 frames.], tot_loss[loss=0.2217, simple_loss=0.3043, pruned_loss=0.06952, over 1464500.07 frames.], batch size: 19, lr: 6.75e-04
2022-07-26 07:10:25,744 INFO [train.py:850] (2/4) Epoch 8, batch 1900, loss[loss=0.1995, simple_loss=0.2905, pruned_loss=0.05424, over 7389.00 frames.], tot_loss[loss=0.2222, simple_loss=0.3051, pruned_loss=0.06966, over 1464986.30 frames.], batch size: 19, lr: 6.75e-04
2022-07-26 07:11:10,444 INFO [train.py:850] (2/4) Epoch 8, batch 1950, loss[loss=0.178, simple_loss=0.2588, pruned_loss=0.04854, over 7456.00 frames.], tot_loss[loss=0.2211, simple_loss=0.3046, pruned_loss=0.06879, over 1464986.77 frames.], batch size: 18, lr: 6.74e-04
2022-07-26 07:11:54,017 INFO [train.py:850] (2/4) Epoch 8, batch 2000, loss[loss=0.2054, simple_loss=0.2845, pruned_loss=0.06318, over 7307.00 frames.], tot_loss[loss=0.2225, simple_loss=0.3062, pruned_loss=0.06936, over 1464185.74 frames.], batch size: 17, lr: 6.74e-04
2022-07-26 07:12:37,087 INFO [train.py:850] (2/4) Epoch 8, batch 2050, loss[loss=0.1977, simple_loss=0.2859, pruned_loss=0.05472, over 7185.00 frames.], tot_loss[loss=0.2229, simple_loss=0.3063, pruned_loss=0.06974, over 1464911.95 frames.], batch size: 18, lr: 6.74e-04
2022-07-26 07:13:21,565 INFO [train.py:850] (2/4) Epoch 8, batch 2100, loss[loss=0.2291, simple_loss=0.3081, pruned_loss=0.07501, over 7482.00 frames.], tot_loss[loss=0.2228, simple_loss=0.3062, pruned_loss=0.0697, over 1464919.19 frames.], batch size: 20, lr: 6.74e-04
2022-07-26 07:14:06,252 INFO [train.py:850] (2/4) Epoch 8, batch 2150, loss[loss=0.1874, simple_loss=0.2791, pruned_loss=0.04782, over 7385.00 frames.], tot_loss[loss=0.2223, simple_loss=0.3057, pruned_loss=0.06939, over 1465867.40 frames.], batch size: 20, lr: 6.73e-04
2022-07-26 07:14:52,272 INFO [train.py:850] (2/4) Epoch 8, batch 2200, loss[loss=0.1927, simple_loss=0.2821, pruned_loss=0.05164, over 7382.00 frames.], tot_loss[loss=0.2222, simple_loss=0.3059, pruned_loss=0.06924, over 1465881.95 frames.], batch size: 19, lr: 6.73e-04
2022-07-26 07:15:36,775 INFO [train.py:850] (2/4) Epoch 8, batch 2250, loss[loss=0.2083, simple_loss=0.3012, pruned_loss=0.05774, over 7170.00 frames.], tot_loss[loss=0.2206, simple_loss=0.3044, pruned_loss=0.06842, over 1465302.92 frames.], batch size: 22, lr: 6.73e-04
2022-07-26 07:16:20,405 INFO [train.py:850] (2/4) Epoch 8, batch 2300, loss[loss=0.2398, simple_loss=0.311, pruned_loss=0.08435, over 7458.00 frames.], tot_loss[loss=0.2234, simple_loss=0.307, pruned_loss=0.0699, over 1466174.28 frames.], batch size: 17, lr: 6.73e-04
2022-07-26 07:17:04,105 INFO [train.py:850] (2/4) Epoch 8, batch 2350, loss[loss=0.1793, simple_loss=0.265, pruned_loss=0.04678, over 7410.00 frames.], tot_loss[loss=0.2224, simple_loss=0.3062, pruned_loss=0.0693, over 1466657.11 frames.], batch size: 19, lr: 6.72e-04
2022-07-26 07:17:46,561 INFO [train.py:850] (2/4) Epoch 8, batch 2400, loss[loss=0.1861, simple_loss=0.2621, pruned_loss=0.0551, over 7450.00 frames.], tot_loss[loss=0.2222, simple_loss=0.3059, pruned_loss=0.06922, over 1467147.85 frames.], batch size: 18, lr: 6.72e-04
2022-07-26 07:18:31,059 INFO [train.py:850] (2/4) Epoch 8, batch 2450, loss[loss=0.1901, simple_loss=0.2805, pruned_loss=0.04989, over 7209.00 frames.], tot_loss[loss=0.2222, simple_loss=0.3061, pruned_loss=0.06915, over 1466064.94 frames.], batch size: 20, lr: 6.72e-04
2022-07-26 07:19:13,557 INFO [train.py:850] (2/4) Epoch 8, batch 2500, loss[loss=0.2063, simple_loss=0.2967, pruned_loss=0.05795, over 7377.00 frames.], tot_loss[loss=0.2206, simple_loss=0.3045, pruned_loss=0.06836, over 1464955.04 frames.], batch size: 20, lr: 6.71e-04
2022-07-26 07:19:58,114 INFO [train.py:850] (2/4) Epoch 8, batch 2550, loss[loss=0.2092, simple_loss=0.3028, pruned_loss=0.05778, over 7472.00 frames.], tot_loss[loss=0.2193, simple_loss=0.3034, pruned_loss=0.06756, over 1465337.55 frames.], batch size: 21, lr: 6.71e-04
2022-07-26 07:20:41,123 INFO [train.py:850] (2/4) Epoch 8, batch 2600, loss[loss=0.1721, simple_loss=0.2577, pruned_loss=0.04325, over 7195.00 frames.], tot_loss[loss=0.2196, simple_loss=0.3035, pruned_loss=0.06787, over 1466053.03 frames.], batch size: 19, lr: 6.71e-04
2022-07-26 07:21:23,909 INFO [train.py:850] (2/4) Epoch 8, batch 2650, loss[loss=0.1844, simple_loss=0.2687, pruned_loss=0.05006, over 7194.00 frames.], tot_loss[loss=0.2187, simple_loss=0.3026, pruned_loss=0.06742, over 1465315.90 frames.], batch size: 18, lr: 6.71e-04
2022-07-26 07:22:07,992 INFO [train.py:850] (2/4) Epoch 8, batch 2700, loss[loss=0.2282, simple_loss=0.3156, pruned_loss=0.07038, over 7487.00 frames.], tot_loss[loss=0.2173, simple_loss=0.3012, pruned_loss=0.06671, over 1465432.41 frames.], batch size: 23, lr: 6.70e-04
2022-07-26 07:22:51,234 INFO [train.py:850] (2/4) Epoch 8, batch 2750, loss[loss=0.2438, simple_loss=0.3169, pruned_loss=0.08535, over 7342.00 frames.], tot_loss[loss=0.2182, simple_loss=0.302, pruned_loss=0.0672, over 1466027.59 frames.], batch size: 23, lr: 6.70e-04
2022-07-26 07:23:35,772 INFO [train.py:850] (2/4) Epoch 8, batch 2800, loss[loss=0.2292, simple_loss=0.3251, pruned_loss=0.06662, over 7187.00 frames.], tot_loss[loss=0.2179, simple_loss=0.3018, pruned_loss=0.06701, over 1465395.82 frames.], batch size: 22, lr: 6.70e-04
2022-07-26 07:24:19,015 INFO [train.py:850] (2/4) Epoch 8, batch 2850, loss[loss=0.1949, simple_loss=0.2717, pruned_loss=0.05905, over 7290.00 frames.], tot_loss[loss=0.2178, simple_loss=0.3019, pruned_loss=0.06687, over 1465327.54 frames.], batch size: 18, lr: 6.70e-04
2022-07-26 07:25:02,413 INFO [train.py:850] (2/4) Epoch 8, batch 2900, loss[loss=0.2644, simple_loss=0.3413, pruned_loss=0.09373, over 7316.00 frames.], tot_loss[loss=0.2195, simple_loss=0.3035, pruned_loss=0.06773, over 1463995.34 frames.], batch size: 27, lr: 6.69e-04
2022-07-26 07:25:46,465 INFO [train.py:850] (2/4) Epoch 8, batch 2950, loss[loss=0.2092, simple_loss=0.2844, pruned_loss=0.06698, over 7385.00 frames.], tot_loss[loss=0.2183, simple_loss=0.3029, pruned_loss=0.06687, over 1464846.73 frames.], batch size: 19, lr: 6.69e-04
2022-07-26 07:26:29,166 INFO [train.py:850] (2/4) Epoch 8, batch 3000, loss[loss=0.2094, simple_loss=0.2876, pruned_loss=0.06561, over 7157.00 frames.], tot_loss[loss=0.2173, simple_loss=0.302, pruned_loss=0.06632, over 1465479.03 frames.], batch size: 17, lr: 6.69e-04
2022-07-26 07:26:29,168 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 07:26:51,923 INFO [train.py:879] (2/4) Epoch 8, validation: loss=0.2041, simple_loss=0.3005, pruned_loss=0.05384, over 924787.00 frames.
2022-07-26 07:27:35,467 INFO [train.py:850] (2/4) Epoch 8, batch 3050, loss[loss=0.2047, simple_loss=0.2924, pruned_loss=0.05849, over 7193.00 frames.], tot_loss[loss=0.2179, simple_loss=0.302, pruned_loss=0.06688, over 1464796.17 frames.], batch size: 19, lr: 6.69e-04
2022-07-26 07:28:19,470 INFO [train.py:850] (2/4) Epoch 8, batch 3100, loss[loss=0.1697, simple_loss=0.2625, pruned_loss=0.0384, over 7202.00 frames.], tot_loss[loss=0.2164, simple_loss=0.3008, pruned_loss=0.066, over 1465033.70 frames.], batch size: 18, lr: 6.68e-04
2022-07-26 07:29:06,835 INFO [train.py:850] (2/4) Epoch 8, batch 3150, loss[loss=0.202, simple_loss=0.2961, pruned_loss=0.05393, over 7423.00 frames.], tot_loss[loss=0.2176, simple_loss=0.3017, pruned_loss=0.06669, over 1465402.77 frames.], batch size: 22, lr: 6.68e-04
2022-07-26 07:29:51,606 INFO [train.py:850] (2/4) Epoch 8, batch 3200, loss[loss=0.208, simple_loss=0.2982, pruned_loss=0.05889, over 7187.00 frames.], tot_loss[loss=0.2186, simple_loss=0.3029, pruned_loss=0.06718, over 1464900.28 frames.], batch size: 21, lr: 6.68e-04
2022-07-26 07:30:36,730 INFO [train.py:850] (2/4) Epoch 8, batch 3250, loss[loss=0.1799, simple_loss=0.2561, pruned_loss=0.05184, over 7173.00 frames.], tot_loss[loss=0.2204, simple_loss=0.3039, pruned_loss=0.06845, over 1464237.25 frames.], batch size: 17, lr: 6.68e-04
2022-07-26 07:31:20,387 INFO [train.py:850] (2/4) Epoch 8, batch 3300, loss[loss=0.2057, simple_loss=0.2925, pruned_loss=0.0595, over 7178.00 frames.], tot_loss[loss=0.2183, simple_loss=0.3022, pruned_loss=0.06719, over 1463584.79 frames.], batch size: 21, lr: 6.67e-04
2022-07-26 07:32:03,619 INFO [train.py:850] (2/4) Epoch 8, batch 3350, loss[loss=0.2186, simple_loss=0.2981, pruned_loss=0.0696, over 7293.00 frames.], tot_loss[loss=0.2177, simple_loss=0.3018, pruned_loss=0.06683, over 1463917.05 frames.], batch size: 19, lr: 6.67e-04
2022-07-26 07:32:47,522 INFO [train.py:850] (2/4) Epoch 8, batch 3400, loss[loss=0.2026, simple_loss=0.2838, pruned_loss=0.06068, over 7196.00 frames.], tot_loss[loss=0.2167, simple_loss=0.3009, pruned_loss=0.06625, over 1463356.62 frames.], batch size: 18, lr: 6.67e-04
2022-07-26 07:33:31,236 INFO [train.py:850] (2/4) Epoch 8, batch 3450, loss[loss=0.216, simple_loss=0.3047, pruned_loss=0.06363, over 7287.00 frames.], tot_loss[loss=0.2175, simple_loss=0.3019, pruned_loss=0.06655, over 1464748.53 frames.], batch size: 20, lr: 6.67e-04
2022-07-26 07:34:15,759 INFO [train.py:850] (2/4) Epoch 8, batch 3500, loss[loss=0.1991, simple_loss=0.2823, pruned_loss=0.05795, over 7394.00 frames.], tot_loss[loss=0.2159, simple_loss=0.3006, pruned_loss=0.06557, over 1464107.35 frames.], batch size: 19, lr: 6.66e-04
2022-07-26 07:34:59,082 INFO [train.py:850] (2/4) Epoch 8, batch 3550, loss[loss=0.1865, simple_loss=0.264, pruned_loss=0.05452, over 7449.00 frames.], tot_loss[loss=0.2164, simple_loss=0.3006, pruned_loss=0.0661, over 1463986.35 frames.], batch size: 17, lr: 6.66e-04
2022-07-26 07:35:41,613 INFO [train.py:850] (2/4) Epoch 8, batch 3600, loss[loss=0.1851, simple_loss=0.273, pruned_loss=0.04858, over 7386.00 frames.], tot_loss[loss=0.216, simple_loss=0.3004, pruned_loss=0.0658, over 1464224.36 frames.], batch size: 20, lr: 6.66e-04
2022-07-26 07:36:26,086 INFO [train.py:850] (2/4) Epoch 8, batch 3650, loss[loss=0.2773, simple_loss=0.3559, pruned_loss=0.0994, over 7382.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3, pruned_loss=0.06549, over 1464421.71 frames.], batch size: 20, lr: 6.66e-04
2022-07-26 07:37:08,831 INFO [train.py:850] (2/4) Epoch 8, batch 3700, loss[loss=0.2107, simple_loss=0.2826, pruned_loss=0.06939, over 7445.00 frames.], tot_loss[loss=0.2172, simple_loss=0.3013, pruned_loss=0.06655, over 1463584.95 frames.], batch size: 17, lr: 6.65e-04
2022-07-26 07:37:53,793 INFO [train.py:850] (2/4) Epoch 8, batch 3750, loss[loss=0.1853, simple_loss=0.2623, pruned_loss=0.05414, over 7468.00 frames.], tot_loss[loss=0.2166, simple_loss=0.3009, pruned_loss=0.06611, over 1463526.71 frames.], batch size: 17, lr: 6.65e-04
2022-07-26 07:38:36,980 INFO [train.py:850] (2/4) Epoch 8, batch 3800, loss[loss=0.2335, simple_loss=0.3213, pruned_loss=0.07286, over 7171.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2999, pruned_loss=0.06557, over 1464683.40 frames.], batch size: 22, lr: 6.65e-04
2022-07-26 07:39:21,051 INFO [train.py:850] (2/4) Epoch 8, batch 3850, loss[loss=0.2071, simple_loss=0.2989, pruned_loss=0.05766, over 7375.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2983, pruned_loss=0.06458, over 1464139.15 frames.], batch size: 20, lr: 6.65e-04
2022-07-26 07:40:05,180 INFO [train.py:850] (2/4) Epoch 8, batch 3900, loss[loss=0.2116, simple_loss=0.3096, pruned_loss=0.05682, over 7403.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2995, pruned_loss=0.06463, over 1465234.20 frames.], batch size: 39, lr: 6.64e-04
2022-07-26 07:40:48,603 INFO [train.py:850] (2/4) Epoch 8, batch 3950, loss[loss=0.2455, simple_loss=0.3275, pruned_loss=0.08178, over 7199.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3002, pruned_loss=0.0654, over 1465266.40 frames.], batch size: 20, lr: 6.64e-04
2022-07-26 07:41:32,598 INFO [train.py:850] (2/4) Epoch 8, batch 4000, loss[loss=0.234, simple_loss=0.3221, pruned_loss=0.07294, over 7487.00 frames.], tot_loss[loss=0.2161, simple_loss=0.301, pruned_loss=0.06557, over 1465253.37 frames.], batch size: 23, lr: 6.64e-04
2022-07-26 07:42:16,452 INFO [train.py:850] (2/4) Epoch 8, batch 4050, loss[loss=0.206, simple_loss=0.2927, pruned_loss=0.0597, over 7478.00 frames.], tot_loss[loss=0.2173, simple_loss=0.302, pruned_loss=0.06633, over 1465058.28 frames.], batch size: 21, lr: 6.64e-04
2022-07-26 07:43:01,156 INFO [train.py:850] (2/4) Epoch 8, batch 4100, loss[loss=0.1896, simple_loss=0.2697, pruned_loss=0.0548, over 7296.00 frames.], tot_loss[loss=0.2179, simple_loss=0.3021, pruned_loss=0.06681, over 1463976.14 frames.], batch size: 18, lr: 6.63e-04
2022-07-26 07:43:44,702 INFO [train.py:850] (2/4) Epoch 8, batch 4150, loss[loss=0.2148, simple_loss=0.3065, pruned_loss=0.06162, over 7294.00 frames.], tot_loss[loss=0.2191, simple_loss=0.3029, pruned_loss=0.06763, over 1464909.43 frames.], batch size: 22, lr: 6.63e-04
2022-07-26 07:44:27,249 INFO [train.py:850] (2/4) Epoch 8, batch 4200, loss[loss=0.2178, simple_loss=0.3137, pruned_loss=0.06098, over 7490.00 frames.], tot_loss[loss=0.2208, simple_loss=0.3032, pruned_loss=0.06916, over 1465189.52 frames.], batch size: 23, lr: 6.63e-04
2022-07-26 07:45:11,430 INFO [train.py:850] (2/4) Epoch 8, batch 4250, loss[loss=0.1819, simple_loss=0.2539, pruned_loss=0.055, over 7314.00 frames.], tot_loss[loss=0.2221, simple_loss=0.3034, pruned_loss=0.07042, over 1465678.22 frames.], batch size: 18, lr: 6.63e-04
2022-07-26 07:45:54,419 INFO [train.py:850] (2/4) Epoch 8, batch 4300, loss[loss=0.2441, simple_loss=0.3237, pruned_loss=0.08224, over 7209.00 frames.], tot_loss[loss=0.2238, simple_loss=0.3036, pruned_loss=0.07201, over 1465343.18 frames.], batch size: 24, lr: 6.62e-04
2022-07-26 07:46:40,948 INFO [train.py:850] (2/4) Epoch 8, batch 4350, loss[loss=0.1891, simple_loss=0.2772, pruned_loss=0.05056, over 7167.00 frames.], tot_loss[loss=0.2274, simple_loss=0.3058, pruned_loss=0.07452, over 1465532.46 frames.], batch size: 22, lr: 6.62e-04
2022-07-26 07:47:24,801 INFO [train.py:850] (2/4) Epoch 8, batch 4400, loss[loss=0.2161, simple_loss=0.3027, pruned_loss=0.06473, over 7279.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3082, pruned_loss=0.07673, over 1466354.87 frames.], batch size: 27, lr: 6.62e-04
2022-07-26 07:48:08,715 INFO [train.py:850] (2/4) Epoch 8, batch 4450, loss[loss=0.2706, simple_loss=0.3358, pruned_loss=0.1027, over 7192.00 frames.], tot_loss[loss=0.2331, simple_loss=0.3095, pruned_loss=0.07834, over 1467033.04 frames.], batch size: 18, lr: 6.62e-04
2022-07-26 07:48:53,439 INFO [train.py:850] (2/4) Epoch 8, batch 4500, loss[loss=0.244, simple_loss=0.313, pruned_loss=0.08754, over 7474.00 frames.], tot_loss[loss=0.2336, simple_loss=0.3095, pruned_loss=0.07883, over 1466252.08 frames.], batch size: 21, lr: 6.61e-04
2022-07-26 07:49:36,859 INFO [train.py:850] (2/4) Epoch 8, batch 4550, loss[loss=0.244, simple_loss=0.3114, pruned_loss=0.08828, over 7286.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3097, pruned_loss=0.07971, over 1465644.25 frames.], batch size: 19, lr: 6.61e-04
2022-07-26 07:50:21,888 INFO [train.py:850] (2/4) Epoch 8, batch 4600, loss[loss=0.2621, simple_loss=0.3002, pruned_loss=0.112, over 7460.00 frames.], tot_loss[loss=0.2367, simple_loss=0.311, pruned_loss=0.0812, over 1465200.90 frames.], batch size: 17, lr: 6.61e-04
2022-07-26 07:51:04,815 INFO [train.py:850] (2/4) Epoch 8, batch 4650, loss[loss=0.2787, simple_loss=0.3432, pruned_loss=0.1071, over 7470.00 frames.], tot_loss[loss=0.2384, simple_loss=0.3119, pruned_loss=0.08242, over 1465484.21 frames.], batch size: 31, lr: 6.61e-04
2022-07-26 07:51:48,131 INFO [train.py:850] (2/4) Epoch 8, batch 4700, loss[loss=0.2576, simple_loss=0.3359, pruned_loss=0.08967, over 7382.00 frames.], tot_loss[loss=0.2382, simple_loss=0.3117, pruned_loss=0.0824, over 1465113.68 frames.], batch size: 21, lr: 6.60e-04
2022-07-26 07:52:32,162 INFO [train.py:850] (2/4) Epoch 8, batch 4750, loss[loss=0.2851, simple_loss=0.3598, pruned_loss=0.1052, over 7230.00 frames.], tot_loss[loss=0.2379, simple_loss=0.3111, pruned_loss=0.08237, over 1465033.73 frames.], batch size: 25, lr: 6.60e-04
2022-07-26 07:53:15,028 INFO [train.py:850] (2/4) Epoch 8, batch 4800, loss[loss=0.2013, simple_loss=0.2779, pruned_loss=0.06232, over 7200.00 frames.], tot_loss[loss=0.2402, simple_loss=0.3129, pruned_loss=0.08376, over 1464886.61 frames.], batch size: 18, lr: 6.60e-04
2022-07-26 07:53:59,676 INFO [train.py:850] (2/4) Epoch 8, batch 4850, loss[loss=0.2216, simple_loss=0.2932, pruned_loss=0.07499, over 7450.00 frames.], tot_loss[loss=0.2406, simple_loss=0.313, pruned_loss=0.08412, over 1464855.88 frames.], batch size: 18, lr: 6.60e-04
2022-07-26 07:54:43,046 INFO [train.py:850] (2/4) Epoch 8, batch 4900, loss[loss=0.2796, simple_loss=0.3384, pruned_loss=0.1104, over 7256.00 frames.], tot_loss[loss=0.2408, simple_loss=0.3128, pruned_loss=0.08443, over 1464693.93 frames.], batch size: 27, lr: 6.59e-04
2022-07-26 07:55:27,526 INFO [train.py:850] (2/4) Epoch 8, batch 4950, loss[loss=0.2894, simple_loss=0.349, pruned_loss=0.1149, over 7348.00 frames.], tot_loss[loss=0.2418, simple_loss=0.3136, pruned_loss=0.08497, over 1464576.93 frames.], batch size: 66, lr: 6.59e-04
2022-07-26 07:56:10,761 INFO [train.py:850] (2/4) Epoch 8, batch 5000, loss[loss=0.2667, simple_loss=0.3202, pruned_loss=0.1066, over 7202.00 frames.], tot_loss[loss=0.2421, simple_loss=0.3136, pruned_loss=0.08523, over 1464170.03 frames.], batch size: 20, lr: 6.59e-04
2022-07-26 07:56:54,966 INFO [train.py:850] (2/4) Epoch 8, batch 5050, loss[loss=0.2305, simple_loss=0.3037, pruned_loss=0.07862, over 7418.00 frames.], tot_loss[loss=0.2415, simple_loss=0.313, pruned_loss=0.085, over 1464183.09 frames.], batch size: 39, lr: 6.59e-04
2022-07-26 07:57:38,689 INFO [train.py:850] (2/4) Epoch 8, batch 5100, loss[loss=0.2551, simple_loss=0.3289, pruned_loss=0.09063, over 7197.00 frames.], tot_loss[loss=0.2412, simple_loss=0.3128, pruned_loss=0.08479, over 1464180.23 frames.], batch size: 20, lr: 6.58e-04
2022-07-26 07:58:22,033 INFO [train.py:850] (2/4) Epoch 8, batch 5150, loss[loss=0.2431, simple_loss=0.3162, pruned_loss=0.08495, over 7335.00 frames.], tot_loss[loss=0.2416, simple_loss=0.3131, pruned_loss=0.08504, over 1463749.90 frames.], batch size: 67, lr: 6.58e-04
2022-07-26 07:59:06,524 INFO [train.py:850] (2/4) Epoch 8, batch 5200, loss[loss=0.279, simple_loss=0.3343, pruned_loss=0.1118, over 7469.00 frames.], tot_loss[loss=0.2422, simple_loss=0.3134, pruned_loss=0.08549, over 1464386.34 frames.], batch size: 21, lr: 6.58e-04
2022-07-26 07:59:50,905 INFO [train.py:850] (2/4) Epoch 8, batch 5250, loss[loss=0.2797, simple_loss=0.3353, pruned_loss=0.112, over 7172.00 frames.], tot_loss[loss=0.2424, simple_loss=0.3137, pruned_loss=0.08554, over 1464998.93 frames.], batch size: 22, lr: 6.58e-04
2022-07-26 08:00:35,676 INFO [train.py:850] (2/4) Epoch 8, batch 5300, loss[loss=0.2054, simple_loss=0.2907, pruned_loss=0.06005, over 7202.00 frames.], tot_loss[loss=0.2422, simple_loss=0.3134, pruned_loss=0.08552, over 1465567.14 frames.], batch size: 19, lr: 6.57e-04
2022-07-26 08:01:19,944 INFO [train.py:850] (2/4) Epoch 8, batch 5350, loss[loss=0.3052, simple_loss=0.3406, pruned_loss=0.1349, over 7461.00 frames.], tot_loss[loss=0.2408, simple_loss=0.3124, pruned_loss=0.08461, over 1465113.84 frames.], batch size: 17, lr: 6.57e-04
2022-07-26 08:02:03,020 INFO [train.py:850] (2/4) Epoch 8, batch 5400, loss[loss=0.2384, simple_loss=0.3107, pruned_loss=0.08304, over 7299.00 frames.], tot_loss[loss=0.2401, simple_loss=0.312, pruned_loss=0.08412, over 1465904.84 frames.], batch size: 22, lr: 6.57e-04
2022-07-26 08:02:46,986 INFO [train.py:850] (2/4) Epoch 8, batch 5450, loss[loss=0.232, simple_loss=0.3086, pruned_loss=0.07775, over 7376.00 frames.], tot_loss[loss=0.2394, simple_loss=0.3113, pruned_loss=0.08375, over 1465783.29 frames.], batch size: 21, lr: 6.57e-04
2022-07-26 08:03:30,583 INFO [train.py:850] (2/4) Epoch 8, batch 5500, loss[loss=0.2246, simple_loss=0.2933, pruned_loss=0.07792, over 7274.00 frames.], tot_loss[loss=0.2414, simple_loss=0.3128, pruned_loss=0.08507, over 1465974.24 frames.], batch size: 16, lr: 6.57e-04
2022-07-26 08:04:15,063 INFO [train.py:850] (2/4) Epoch 8, batch 5550, loss[loss=0.1751, simple_loss=0.2466, pruned_loss=0.05177, over 7150.00 frames.], tot_loss[loss=0.2424, simple_loss=0.3139, pruned_loss=0.08545, over 1466003.47 frames.], batch size: 17, lr: 6.56e-04
2022-07-26 08:04:58,664 INFO [train.py:850] (2/4) Epoch 8, batch 5600, loss[loss=0.2542, simple_loss=0.3252, pruned_loss=0.09154, over 7417.00 frames.], tot_loss[loss=0.2414, simple_loss=0.3132, pruned_loss=0.08479, over 1466011.51 frames.], batch size: 22, lr: 6.56e-04
2022-07-26 08:05:42,529 INFO [train.py:850] (2/4) Epoch 8, batch 5650, loss[loss=0.2517, simple_loss=0.3273, pruned_loss=0.08801, over 7289.00 frames.], tot_loss[loss=0.2413, simple_loss=0.3132, pruned_loss=0.0847, over 1466433.19 frames.], batch size: 21, lr: 6.56e-04
2022-07-26 08:06:27,413 INFO [train.py:850] (2/4) Epoch 8, batch 5700, loss[loss=0.2322, simple_loss=0.3108, pruned_loss=0.07681, over 7312.00 frames.], tot_loss[loss=0.2422, simple_loss=0.3138, pruned_loss=0.08525, over 1466097.93 frames.], batch size: 31, lr: 6.56e-04
2022-07-26 08:07:25,795 INFO [train.py:850] (2/4) Epoch 8, batch 5750, loss[loss=0.2317, simple_loss=0.288, pruned_loss=0.08771, over 7456.00 frames.], tot_loss[loss=0.2428, simple_loss=0.3143, pruned_loss=0.08562, over 1466175.19 frames.], batch size: 17, lr: 6.55e-04
2022-07-26 08:08:10,399 INFO [train.py:850] (2/4) Epoch 8, batch 5800, loss[loss=0.3317, simple_loss=0.3758, pruned_loss=0.1438, over 7468.00 frames.], tot_loss[loss=0.2439, simple_loss=0.3152, pruned_loss=0.08631, over 1465996.57 frames.], batch size: 24, lr: 6.55e-04
2022-07-26 08:08:53,843 INFO [train.py:850] (2/4) Epoch 8, batch 5850, loss[loss=0.2674, simple_loss=0.3331, pruned_loss=0.1009, over 7384.00 frames.], tot_loss[loss=0.2426, simple_loss=0.3133, pruned_loss=0.08591, over 1465341.70 frames.], batch size: 21, lr: 6.55e-04
2022-07-26 08:09:39,137 INFO [train.py:850] (2/4) Epoch 8, batch 5900, loss[loss=0.2291, simple_loss=0.2999, pruned_loss=0.07919, over 7198.00 frames.], tot_loss[loss=0.2405, simple_loss=0.3117, pruned_loss=0.08468, over 1464008.17 frames.], batch size: 19, lr: 6.55e-04
2022-07-26 08:10:23,241 INFO [train.py:850] (2/4) Epoch 8, batch 5950, loss[loss=0.1768, simple_loss=0.2503, pruned_loss=0.05166, over 7301.00 frames.], tot_loss[loss=0.2384, simple_loss=0.3101, pruned_loss=0.08338, over 1464539.09 frames.], batch size: 17, lr: 6.54e-04
2022-07-26 08:11:06,968 INFO [train.py:850] (2/4) Epoch 8, batch 6000, loss[loss=0.2146, simple_loss=0.296, pruned_loss=0.0666, over 7294.00 frames.], tot_loss[loss=0.239, simple_loss=0.311, pruned_loss=0.08351, over 1464102.82 frames.], batch size: 20, lr: 6.54e-04
2022-07-26 08:11:06,969 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 08:11:29,807 INFO [train.py:879] (2/4) Epoch 8, validation: loss=0.1918, simple_loss=0.2904, pruned_loss=0.04664, over 924787.00 frames.
2022-07-26 08:12:13,859 INFO [train.py:850] (2/4) Epoch 8, batch 6050, loss[loss=0.1921, simple_loss=0.2792, pruned_loss=0.05249, over 7492.00 frames.], tot_loss[loss=0.2398, simple_loss=0.3119, pruned_loss=0.08385, over 1464683.91 frames.], batch size: 20, lr: 6.54e-04
2022-07-26 08:12:58,257 INFO [train.py:850] (2/4) Epoch 8, batch 6100, loss[loss=0.1967, simple_loss=0.2754, pruned_loss=0.05904, over 7445.00 frames.], tot_loss[loss=0.2383, simple_loss=0.31, pruned_loss=0.08325, over 1464110.67 frames.], batch size: 18, lr: 6.54e-04
2022-07-26 08:13:42,403 INFO [train.py:850] (2/4) Epoch 8, batch 6150, loss[loss=0.2293, simple_loss=0.3102, pruned_loss=0.07425, over 7281.00 frames.], tot_loss[loss=0.2404, simple_loss=0.312, pruned_loss=0.08438, over 1464195.81 frames.], batch size: 19, lr: 6.53e-04
2022-07-26 08:14:26,039 INFO [train.py:850] (2/4) Epoch 8, batch 6200, loss[loss=0.2497, simple_loss=0.3276, pruned_loss=0.08596, over 7225.00 frames.], tot_loss[loss=0.2412, simple_loss=0.3128, pruned_loss=0.08477, over 1465448.14 frames.], batch size: 24, lr: 6.53e-04
2022-07-26 08:15:10,408 INFO [train.py:850] (2/4) Epoch 8, batch 6250, loss[loss=0.239, simple_loss=0.3071, pruned_loss=0.08542, over 7470.00 frames.], tot_loss[loss=0.2393, simple_loss=0.3113, pruned_loss=0.08365, over 1465338.86 frames.], batch size: 21, lr: 6.53e-04
2022-07-26 08:15:53,314 INFO [train.py:850] (2/4) Epoch 8, batch 6300, loss[loss=0.305, simple_loss=0.3715, pruned_loss=0.1193, over 7486.00 frames.], tot_loss[loss=0.2402, simple_loss=0.312, pruned_loss=0.08418, over 1465172.48 frames.], batch size: 23, lr: 6.53e-04
2022-07-26 08:16:38,290 INFO [train.py:850] (2/4) Epoch 8, batch 6350, loss[loss=0.2311, simple_loss=0.2916, pruned_loss=0.08532, over 7307.00 frames.], tot_loss[loss=0.2404, simple_loss=0.3122, pruned_loss=0.08425, over 1465117.21 frames.], batch size: 17, lr: 6.52e-04
2022-07-26 08:17:21,820 INFO [train.py:850] (2/4) Epoch 8, batch 6400, loss[loss=0.2526, simple_loss=0.3229, pruned_loss=0.0912, over 7376.00 frames.], tot_loss[loss=0.2406, simple_loss=0.3124, pruned_loss=0.0844, over 1464918.85 frames.], batch size: 74, lr: 6.52e-04
2022-07-26 08:18:07,088 INFO [train.py:850] (2/4) Epoch 8, batch 6450, loss[loss=0.2701, simple_loss=0.3228, pruned_loss=0.1087, over 7488.00 frames.], tot_loss[loss=0.2395, simple_loss=0.3113, pruned_loss=0.08382, over 1466324.15 frames.], batch size: 19, lr: 6.52e-04
2022-07-26 08:18:51,074 INFO [train.py:850] (2/4) Epoch 8, batch 6500, loss[loss=0.2189, simple_loss=0.2912, pruned_loss=0.07331, over 7208.00 frames.], tot_loss[loss=0.2399, simple_loss=0.3121, pruned_loss=0.08382, over 1465435.70 frames.], batch size: 18, lr: 6.52e-04
2022-07-26 08:19:34,692 INFO [train.py:850] (2/4) Epoch 8, batch 6550, loss[loss=0.2706, simple_loss=0.3345, pruned_loss=0.1033, over 7195.00 frames.], tot_loss[loss=0.2401, simple_loss=0.3123, pruned_loss=0.08392, over 1465757.26 frames.], batch size: 20, lr: 6.52e-04
2022-07-26 08:20:19,385 INFO [train.py:850] (2/4) Epoch 8, batch 6600, loss[loss=0.2237, simple_loss=0.2966, pruned_loss=0.07538, over 7403.00 frames.], tot_loss[loss=0.2395, simple_loss=0.312, pruned_loss=0.08356, over 1465667.76 frames.], batch size: 19, lr: 6.51e-04
2022-07-26 08:21:03,061 INFO [train.py:850] (2/4) Epoch 8, batch 6650, loss[loss=0.2229, simple_loss=0.3149, pruned_loss=0.06549, over 7281.00 frames.], tot_loss[loss=0.2391, simple_loss=0.3116, pruned_loss=0.08333, over 1466356.52 frames.], batch size: 21, lr: 6.51e-04
2022-07-26 08:21:46,829 INFO [train.py:850] (2/4) Epoch 8, batch 6700, loss[loss=0.2416, simple_loss=0.3143, pruned_loss=0.08442, over 7369.00 frames.], tot_loss[loss=0.2401, simple_loss=0.3123, pruned_loss=0.08397, over 1466198.66 frames.], batch size: 39, lr: 6.51e-04
2022-07-26 08:22:30,739 INFO [train.py:850] (2/4) Epoch 8, batch 6750, loss[loss=0.2855, simple_loss=0.3627, pruned_loss=0.1041, over 7312.00 frames.], tot_loss[loss=0.2383, simple_loss=0.3106, pruned_loss=0.083, over 1465906.29 frames.], batch size: 22, lr: 6.51e-04
2022-07-26 08:23:15,346 INFO [train.py:850] (2/4) Epoch 8, batch 6800, loss[loss=0.187, simple_loss=0.2817, pruned_loss=0.04609, over 7184.00 frames.], tot_loss[loss=0.2373, simple_loss=0.3102, pruned_loss=0.08214, over 1465253.49 frames.], batch size: 21, lr: 6.50e-04
2022-07-26 08:23:59,269 INFO [train.py:850] (2/4) Epoch 8, batch 6850, loss[loss=0.2431, simple_loss=0.318, pruned_loss=0.08413, over 7241.00 frames.], tot_loss[loss=0.2378, simple_loss=0.311, pruned_loss=0.0823, over 1465121.98 frames.], batch size: 25, lr: 6.50e-04
2022-07-26 08:24:42,059 INFO [train.py:850] (2/4) Epoch 8, batch 6900, loss[loss=0.2364, simple_loss=0.3225, pruned_loss=0.07511, over 7284.00 frames.], tot_loss[loss=0.2362, simple_loss=0.3099, pruned_loss=0.08126, over 1465067.16 frames.], batch size: 20, lr: 6.50e-04
2022-07-26 08:25:26,489 INFO [train.py:850] (2/4) Epoch 8, batch 6950, loss[loss=0.2267, simple_loss=0.2962, pruned_loss=0.07862, over 7405.00 frames.], tot_loss[loss=0.2375, simple_loss=0.3105, pruned_loss=0.08225, over 1465173.35 frames.], batch size: 19, lr: 6.50e-04
2022-07-26 08:26:10,065 INFO [train.py:850] (2/4) Epoch 8, batch 7000, loss[loss=0.2869, simple_loss=0.3401, pruned_loss=0.1168, over 7474.00 frames.], tot_loss[loss=0.2353, simple_loss=0.3088, pruned_loss=0.08085, over 1465831.65 frames.], batch size: 69, lr: 6.49e-04
2022-07-26 08:26:55,166 INFO [train.py:850] (2/4) Epoch 8, batch 7050, loss[loss=0.2777, simple_loss=0.3297, pruned_loss=0.1129, over 7324.00 frames.], tot_loss[loss=0.2357, simple_loss=0.3091, pruned_loss=0.08111, over 1465987.54 frames.], batch size: 18, lr: 6.49e-04
2022-07-26 08:27:38,337 INFO [train.py:850] (2/4) Epoch 8, batch 7100, loss[loss=0.2305, simple_loss=0.3098, pruned_loss=0.0756, over 7203.00 frames.], tot_loss[loss=0.2354, simple_loss=0.3085, pruned_loss=0.08112, over 1465313.28 frames.], batch size: 19, lr: 6.49e-04
2022-07-26 08:28:23,228 INFO [train.py:850] (2/4) Epoch 8, batch 7150, loss[loss=0.2287, simple_loss=0.3099, pruned_loss=0.07374, over 7188.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3083, pruned_loss=0.08046, over 1464825.06 frames.], batch size: 19, lr: 6.49e-04
2022-07-26 08:29:06,437 INFO [train.py:850] (2/4) Epoch 8, batch 7200, loss[loss=0.1753, simple_loss=0.2666, pruned_loss=0.04193, over 7490.00 frames.], tot_loss[loss=0.2336, simple_loss=0.3077, pruned_loss=0.07975, over 1464512.94 frames.], batch size: 19, lr: 6.48e-04
2022-07-26 08:29:49,302 INFO [train.py:850] (2/4) Epoch 8, batch 7250, loss[loss=0.2376, simple_loss=0.3076, pruned_loss=0.08377, over 7287.00 frames.], tot_loss[loss=0.2335, simple_loss=0.3076, pruned_loss=0.07975, over 1464526.26 frames.], batch size: 27, lr: 6.48e-04
2022-07-26 08:30:34,593 INFO [train.py:850] (2/4) Epoch 8, batch 7300, loss[loss=0.2679, simple_loss=0.3331, pruned_loss=0.1014, over 7197.00 frames.], tot_loss[loss=0.2344, simple_loss=0.308, pruned_loss=0.08036, over 1464952.65 frames.], batch size: 21, lr: 6.48e-04
2022-07-26 08:31:19,753 INFO [train.py:850] (2/4) Epoch 8, batch 7350, loss[loss=0.2079, simple_loss=0.2754, pruned_loss=0.07024, over 7303.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3087, pruned_loss=0.08029, over 1465311.31 frames.], batch size: 17, lr: 6.48e-04
2022-07-26 08:32:05,172 INFO [train.py:850] (2/4) Epoch 8, batch 7400, loss[loss=0.2375, simple_loss=0.3172, pruned_loss=0.07889, over 7430.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3086, pruned_loss=0.08031, over 1465693.35 frames.], batch size: 38, lr: 6.48e-04
2022-07-26 08:32:49,778 INFO [train.py:850] (2/4) Epoch 8, batch 7450, loss[loss=0.2475, simple_loss=0.3391, pruned_loss=0.07794, over 7214.00 frames.], tot_loss[loss=0.2358, simple_loss=0.3099, pruned_loss=0.08087, over 1465297.88 frames.], batch size: 24, lr: 6.47e-04
2022-07-26 08:33:33,528 INFO [train.py:850] (2/4) Epoch 8, batch 7500, loss[loss=0.2471, simple_loss=0.3298, pruned_loss=0.08218, over 7203.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3111, pruned_loss=0.08169, over 1465291.26 frames.], batch size: 20, lr: 6.47e-04
2022-07-26 08:34:18,906 INFO [train.py:850] (2/4) Epoch 8, batch 7550, loss[loss=0.24, simple_loss=0.3096, pruned_loss=0.08524, over 7483.00 frames.], tot_loss[loss=0.236, simple_loss=0.3093, pruned_loss=0.08134, over 1465611.00 frames.], batch size: 39, lr: 6.47e-04
2022-07-26 08:35:02,517 INFO [train.py:850] (2/4) Epoch 8, batch 7600, loss[loss=0.2272, simple_loss=0.2984, pruned_loss=0.07804, over 7433.00 frames.], tot_loss[loss=0.2349, simple_loss=0.3088, pruned_loss=0.08046, over 1465907.02 frames.], batch size: 18, lr: 6.47e-04
2022-07-26 08:35:47,827 INFO [train.py:850] (2/4) Epoch 8, batch 7650, loss[loss=0.3035, simple_loss=0.368, pruned_loss=0.1194, over 7474.00 frames.], tot_loss[loss=0.2343, simple_loss=0.308, pruned_loss=0.08029, over 1466533.33 frames.], batch size: 24, lr: 6.46e-04
2022-07-26 08:36:30,531 INFO [train.py:850] (2/4) Epoch 8, batch 7700, loss[loss=0.2162, simple_loss=0.2994, pruned_loss=0.06649, over 7327.00 frames.], tot_loss[loss=0.235, simple_loss=0.3086, pruned_loss=0.08068, over 1466259.36 frames.], batch size: 38, lr: 6.46e-04
2022-07-26 08:37:15,188 INFO [train.py:850] (2/4) Epoch 8, batch 7750, loss[loss=0.3034, simple_loss=0.3306, pruned_loss=0.1381, over 7212.00 frames.], tot_loss[loss=0.2365, simple_loss=0.3097, pruned_loss=0.08163, over 1465866.58 frames.], batch size: 16, lr: 6.46e-04
2022-07-26 08:37:58,481 INFO [train.py:850] (2/4) Epoch 8, batch 7800, loss[loss=0.2478, simple_loss=0.3191, pruned_loss=0.08828, over 7422.00 frames.], tot_loss[loss=0.2359, simple_loss=0.3093, pruned_loss=0.0812, over 1465917.47 frames.], batch size: 70, lr: 6.46e-04
2022-07-26 08:38:42,599 INFO [train.py:850] (2/4) Epoch 8, batch 7850, loss[loss=0.1793, simple_loss=0.2561, pruned_loss=0.05121, over 7162.00 frames.], tot_loss[loss=0.2347, simple_loss=0.3088, pruned_loss=0.0803, over 1465901.43 frames.], batch size: 17, lr: 6.45e-04
2022-07-26 08:39:28,719 INFO [train.py:850] (2/4) Epoch 8, batch 7900, loss[loss=0.2338, simple_loss=0.3003, pruned_loss=0.08364, over 7108.00 frames.], tot_loss[loss=0.2365, simple_loss=0.31, pruned_loss=0.08153, over 1466114.40 frames.], batch size: 18, lr: 6.45e-04
2022-07-26 08:40:14,285 INFO [train.py:850] (2/4) Epoch 8, batch 7950, loss[loss=0.1836, simple_loss=0.2584, pruned_loss=0.05435, over 7171.00 frames.], tot_loss[loss=0.2379, simple_loss=0.3116, pruned_loss=0.08209, over 1465305.27 frames.], batch size: 17, lr: 6.45e-04
2022-07-26 08:40:59,223 INFO [train.py:850] (2/4) Epoch 8, batch 8000, loss[loss=0.2363, simple_loss=0.3044, pruned_loss=0.08408, over 7197.00 frames.], tot_loss[loss=0.2382, simple_loss=0.3115, pruned_loss=0.08249, over 1466347.77 frames.], batch size: 19, lr: 6.45e-04
2022-07-26 08:41:42,542 INFO [train.py:850] (2/4) Epoch 8, batch 8050, loss[loss=0.2659, simple_loss=0.3383, pruned_loss=0.09675, over 7277.00 frames.], tot_loss[loss=0.2367, simple_loss=0.3108, pruned_loss=0.08133, over 1465982.75 frames.], batch size: 21, lr: 6.45e-04
2022-07-26 08:42:27,179 INFO [train.py:850] (2/4) Epoch 8, batch 8100, loss[loss=0.1817, simple_loss=0.2705, pruned_loss=0.04644, over 7100.00 frames.], tot_loss[loss=0.2342, simple_loss=0.3087, pruned_loss=0.07978, over 1465686.02 frames.], batch size: 18, lr: 6.44e-04
2022-07-26 08:43:11,281 INFO [train.py:850] (2/4) Epoch 8, batch 8150, loss[loss=0.1849, simple_loss=0.2527, pruned_loss=0.05857, over 7307.00 frames.], tot_loss[loss=0.2343, simple_loss=0.3087, pruned_loss=0.07996, over 1464710.09 frames.], batch size: 17, lr: 6.44e-04
2022-07-26 08:43:56,319 INFO [train.py:850] (2/4) Epoch 8, batch 8200, loss[loss=0.2167, simple_loss=0.2932, pruned_loss=0.07006, over 7226.00 frames.], tot_loss[loss=0.234, simple_loss=0.308, pruned_loss=0.07993, over 1465191.98 frames.], batch size: 16, lr: 6.44e-04
2022-07-26 08:44:40,833 INFO [train.py:850] (2/4) Epoch 8, batch 8250, loss[loss=0.2428, simple_loss=0.3264, pruned_loss=0.07957, over 7420.00 frames.], tot_loss[loss=0.2344, simple_loss=0.3082, pruned_loss=0.08028, over 1464912.10 frames.], batch size: 22, lr: 6.44e-04
2022-07-26 08:45:23,752 INFO [train.py:850] (2/4) Epoch 8, batch 8300, loss[loss=0.304, simple_loss=0.3623, pruned_loss=0.1228, over 7257.00 frames.], tot_loss[loss=0.2344, simple_loss=0.308, pruned_loss=0.08035, over 1464975.43 frames.], batch size: 27, lr: 6.43e-04
2022-07-26 08:46:09,255 INFO [train.py:850] (2/4) Epoch 8, batch 8350, loss[loss=0.1917, simple_loss=0.277, pruned_loss=0.05319, over 7441.00 frames.], tot_loss[loss=0.2351, simple_loss=0.3084, pruned_loss=0.08089, over 1464506.33 frames.], batch size: 18, lr: 6.43e-04
2022-07-26 08:46:52,280 INFO [train.py:850] (2/4) Epoch 8, batch 8400, loss[loss=0.234, simple_loss=0.3115, pruned_loss=0.07825, over 7393.00 frames.], tot_loss[loss=0.2357, simple_loss=0.3088, pruned_loss=0.08128, over 1464550.38 frames.], batch size: 40, lr: 6.43e-04
2022-07-26 08:47:37,791 INFO [train.py:850] (2/4) Epoch 8, batch 8450, loss[loss=0.2157, simple_loss=0.2922, pruned_loss=0.06954, over 7298.00 frames.], tot_loss[loss=0.2347, simple_loss=0.3081, pruned_loss=0.08065, over 1465559.40 frames.], batch size: 18, lr: 6.43e-04
2022-07-26 08:48:21,585 INFO [train.py:850] (2/4) Epoch 8, batch 8500, loss[loss=0.2349, simple_loss=0.3168, pruned_loss=0.07651, over 7345.00 frames.], tot_loss[loss=0.2348, simple_loss=0.3084, pruned_loss=0.08061, over 1466283.68 frames.], batch size: 27, lr: 6.43e-04
2022-07-26 08:49:05,658 INFO [train.py:850] (2/4) Epoch 8, batch 8550, loss[loss=0.2324, simple_loss=0.2915, pruned_loss=0.08666, over 7401.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3076, pruned_loss=0.08011, over 1465820.00 frames.], batch size: 19, lr: 6.42e-04
2022-07-26 08:49:50,066 INFO [train.py:850] (2/4) Epoch 8, batch 8600, loss[loss=0.189, simple_loss=0.2724, pruned_loss=0.05276, over 7417.00 frames.], tot_loss[loss=0.2362, simple_loss=0.3094, pruned_loss=0.08151, over 1465200.42 frames.], batch size: 22, lr: 6.42e-04
2022-07-26 08:50:33,094 INFO [train.py:850] (2/4) Epoch 8, batch 8650, loss[loss=0.1996, simple_loss=0.2689, pruned_loss=0.0651, over 7309.00 frames.], tot_loss[loss=0.2349, simple_loss=0.3078, pruned_loss=0.08094, over 1464890.23 frames.], batch size: 16, lr: 6.42e-04
2022-07-26 08:51:16,908 INFO [train.py:850] (2/4) Epoch 8, batch 8700, loss[loss=0.2047, simple_loss=0.2858, pruned_loss=0.06181, over 7201.00 frames.], tot_loss[loss=0.234, simple_loss=0.3075, pruned_loss=0.08024, over 1465208.12 frames.], batch size: 19, lr: 6.42e-04
2022-07-26 08:51:59,714 INFO [train.py:850] (2/4) Epoch 8, batch 8750, loss[loss=0.2029, simple_loss=0.2846, pruned_loss=0.06061, over 7300.00 frames.], tot_loss[loss=0.233, simple_loss=0.3068, pruned_loss=0.07958, over 1464762.30 frames.], batch size: 19, lr: 6.41e-04
2022-07-26 08:52:43,167 INFO [train.py:850] (2/4) Epoch 8, batch 8800, loss[loss=0.2023, simple_loss=0.2908, pruned_loss=0.05693, over 7295.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3061, pruned_loss=0.07963, over 1464740.56 frames.], batch size: 19, lr: 6.41e-04
2022-07-26 08:53:26,839 INFO [train.py:850] (2/4) Epoch 8, batch 8850, loss[loss=0.3136, simple_loss=0.3699, pruned_loss=0.1286, over 7444.00 frames.], tot_loss[loss=0.2337, simple_loss=0.3076, pruned_loss=0.07989, over 1464975.10 frames.], batch size: 74, lr: 6.41e-04
2022-07-26 08:55:08,330 INFO [train.py:850] (2/4) Epoch 9, batch 0, loss[loss=0.2382, simple_loss=0.3278, pruned_loss=0.07428, over 7288.00 frames.], tot_loss[loss=0.2382, simple_loss=0.3278, pruned_loss=0.07428, over 7288.00 frames.], batch size: 21, lr: 6.15e-04
2022-07-26 08:55:54,233 INFO [train.py:850] (2/4) Epoch 9, batch 50, loss[loss=0.2068, simple_loss=0.3021, pruned_loss=0.05572, over 7423.00 frames.], tot_loss[loss=0.2258, simple_loss=0.3085, pruned_loss=0.0716, over 331327.24 frames.], batch size: 22, lr: 6.15e-04
2022-07-26 08:56:38,479 INFO [train.py:850] (2/4) Epoch 9, batch 100, loss[loss=0.2158, simple_loss=0.3028, pruned_loss=0.06441, over 7198.00 frames.], tot_loss[loss=0.2181, simple_loss=0.3013, pruned_loss=0.06748, over 582533.48 frames.], batch size: 21, lr: 6.15e-04
2022-07-26 08:57:21,674 INFO [train.py:850] (2/4) Epoch 9, batch 150, loss[loss=0.2135, simple_loss=0.2952, pruned_loss=0.06588, over 7197.00 frames.], tot_loss[loss=0.216, simple_loss=0.2998, pruned_loss=0.06615, over 777462.86 frames.], batch size: 19, lr: 6.15e-04
2022-07-26 08:58:05,624 INFO [train.py:850] (2/4) Epoch 9, batch 200, loss[loss=0.1784, simple_loss=0.2659, pruned_loss=0.04542, over 7485.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2987, pruned_loss=0.06582, over 930044.09 frames.], batch size: 19, lr: 6.14e-04
2022-07-26 08:58:49,347 INFO [train.py:850] (2/4) Epoch 9, batch 250, loss[loss=0.1928, simple_loss=0.2828, pruned_loss=0.05138, over 7201.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2982, pruned_loss=0.06519, over 1048136.21 frames.], batch size: 19, lr: 6.14e-04
2022-07-26 08:59:35,415 INFO [train.py:850] (2/4) Epoch 9, batch 300, loss[loss=0.2027, simple_loss=0.2816, pruned_loss=0.0619, over 7274.00 frames.], tot_loss[loss=0.2139, simple_loss=0.299, pruned_loss=0.06441, over 1139539.59 frames.], batch size: 16, lr: 6.14e-04
2022-07-26 09:00:19,869 INFO [train.py:850] (2/4) Epoch 9, batch 350, loss[loss=0.1685, simple_loss=0.2599, pruned_loss=0.03853, over 7203.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2979, pruned_loss=0.06444, over 1212002.78 frames.], batch size: 18, lr: 6.14e-04
2022-07-26 09:01:02,474 INFO [train.py:850] (2/4) Epoch 9, batch 400, loss[loss=0.162, simple_loss=0.2493, pruned_loss=0.03734, over 7284.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2991, pruned_loss=0.06512, over 1268210.44 frames.], batch size: 17, lr: 6.14e-04
2022-07-26 09:01:47,032 INFO [train.py:850] (2/4) Epoch 9, batch 450, loss[loss=0.1967, simple_loss=0.2883, pruned_loss=0.05257, over 7300.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2984, pruned_loss=0.06429, over 1312374.67 frames.], batch size: 22, lr: 6.13e-04
2022-07-26 09:02:29,780 INFO [train.py:850] (2/4) Epoch 9, batch 500, loss[loss=0.2116, simple_loss=0.2955, pruned_loss=0.06385, over 7381.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2984, pruned_loss=0.06417, over 1345795.25 frames.], batch size: 20, lr: 6.13e-04
2022-07-26 09:03:14,237 INFO [train.py:850] (2/4) Epoch 9, batch 550, loss[loss=0.2256, simple_loss=0.3018, pruned_loss=0.07465, over 7396.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2972, pruned_loss=0.06305, over 1373391.79 frames.], batch size: 21, lr: 6.13e-04
2022-07-26 09:03:57,363 INFO [train.py:850] (2/4) Epoch 9, batch 600, loss[loss=0.1879, simple_loss=0.2851, pruned_loss=0.04539, over 7432.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2953, pruned_loss=0.06254, over 1393147.64 frames.], batch size: 40, lr: 6.13e-04
2022-07-26 09:04:41,498 INFO [train.py:850] (2/4) Epoch 9, batch 650, loss[loss=0.2122, simple_loss=0.3082, pruned_loss=0.05815, over 7385.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2945, pruned_loss=0.0621, over 1408977.08 frames.], batch size: 39, lr: 6.12e-04
2022-07-26 09:05:25,069 INFO [train.py:850] (2/4) Epoch 9, batch 700, loss[loss=0.1791, simple_loss=0.2735, pruned_loss=0.04235, over 7206.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2946, pruned_loss=0.06217, over 1421070.90 frames.], batch size: 19, lr: 6.12e-04
2022-07-26 09:06:08,991 INFO [train.py:850] (2/4) Epoch 9, batch 750, loss[loss=0.2055, simple_loss=0.2879, pruned_loss=0.06157, over 7176.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2943, pruned_loss=0.0623, over 1431153.77 frames.], batch size: 22, lr: 6.12e-04
2022-07-26 09:06:53,821 INFO [train.py:850] (2/4) Epoch 9, batch 800, loss[loss=0.1971, simple_loss=0.2912, pruned_loss=0.05153, over 7392.00 frames.], tot_loss[loss=0.212, simple_loss=0.2969, pruned_loss=0.06358, over 1439960.67 frames.], batch size: 21, lr: 6.12e-04
2022-07-26 09:07:53,280 INFO [train.py:850] (2/4) Epoch 9, batch 850, loss[loss=0.2013, simple_loss=0.2773, pruned_loss=0.06267, over 7196.00 frames.], tot_loss[loss=0.2116, simple_loss=0.2966, pruned_loss=0.06326, over 1445759.20 frames.], batch size: 18, lr: 6.12e-04
2022-07-26 09:08:36,835 INFO [train.py:850] (2/4) Epoch 9, batch 900, loss[loss=0.2471, simple_loss=0.3266, pruned_loss=0.08378, over 7293.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2961, pruned_loss=0.06336, over 1450538.58 frames.], batch size: 27, lr: 6.11e-04
2022-07-26 09:09:20,677 INFO [train.py:850] (2/4) Epoch 9, batch 950, loss[loss=0.2461, simple_loss=0.3321, pruned_loss=0.08004, over 7279.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2965, pruned_loss=0.06317, over 1453276.21 frames.], batch size: 27, lr: 6.11e-04
2022-07-26 09:10:03,609 INFO [train.py:850] (2/4) Epoch 9, batch 1000, loss[loss=0.2112, simple_loss=0.3, pruned_loss=0.06117, over 7178.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2994, pruned_loss=0.06461, over 1455629.47 frames.], batch size: 22, lr: 6.11e-04
2022-07-26 09:10:47,765 INFO [train.py:850] (2/4) Epoch 9, batch 1050, loss[loss=0.1781, simple_loss=0.2599, pruned_loss=0.04816, over 7442.00 frames.], tot_loss[loss=0.2156, simple_loss=0.3, pruned_loss=0.06559, over 1457832.27 frames.], batch size: 17, lr: 6.11e-04
2022-07-26 09:11:30,994 INFO [train.py:850] (2/4) Epoch 9, batch 1100, loss[loss=0.1888, simple_loss=0.2605, pruned_loss=0.05853, over 7313.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2993, pruned_loss=0.06552, over 1458770.41 frames.], batch size: 17, lr: 6.11e-04
2022-07-26 09:12:15,830 INFO [train.py:850] (2/4) Epoch 9, batch 1150, loss[loss=0.226, simple_loss=0.3204, pruned_loss=0.06582, over 7305.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2995, pruned_loss=0.06559, over 1460298.26 frames.], batch size: 22, lr: 6.10e-04
2022-07-26 09:12:59,066 INFO [train.py:850] (2/4) Epoch 9, batch 1200, loss[loss=0.2105, simple_loss=0.3016, pruned_loss=0.05967, over 7284.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2999, pruned_loss=0.06588, over 1461663.87 frames.], batch size: 20, lr: 6.10e-04
2022-07-26 09:13:44,090 INFO [train.py:850] (2/4) Epoch 9, batch 1250, loss[loss=0.2834, simple_loss=0.3384, pruned_loss=0.1142, over 7187.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2998, pruned_loss=0.06622, over 1462548.88 frames.], batch size: 23, lr: 6.10e-04
2022-07-26 09:14:27,560 INFO [train.py:850] (2/4) Epoch 9, batch 1300, loss[loss=0.2121, simple_loss=0.3056, pruned_loss=0.05928, over 7167.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2984, pruned_loss=0.06516, over 1462935.18 frames.], batch size: 22, lr: 6.10e-04
2022-07-26 09:15:11,867 INFO [train.py:850] (2/4) Epoch 9, batch 1350, loss[loss=0.2087, simple_loss=0.2912, pruned_loss=0.0631, over 7483.00 frames.], tot_loss[loss=0.2169, simple_loss=0.3009, pruned_loss=0.06649, over 1463294.45 frames.], batch size: 20, lr: 6.10e-04
2022-07-26 09:15:56,274 INFO [train.py:850] (2/4) Epoch 9, batch 1400, loss[loss=0.2905, simple_loss=0.3708, pruned_loss=0.1051, over 7453.00 frames.], tot_loss[loss=0.2166, simple_loss=0.3006, pruned_loss=0.06627, over 1464925.33 frames.], batch size: 26, lr: 6.09e-04
2022-07-26 09:16:39,857 INFO [train.py:850] (2/4) Epoch 9, batch 1450, loss[loss=0.1633, simple_loss=0.2448, pruned_loss=0.0409, over 7489.00 frames.], tot_loss[loss=0.2173, simple_loss=0.3012, pruned_loss=0.06673, over 1465629.79 frames.], batch size: 19, lr: 6.09e-04
2022-07-26 09:17:23,947 INFO [train.py:850] (2/4) Epoch 9, batch 1500, loss[loss=0.192, simple_loss=0.2821, pruned_loss=0.05094, over 7485.00 frames.], tot_loss[loss=0.2162, simple_loss=0.3003, pruned_loss=0.06605, over 1465124.44 frames.], batch size: 23, lr: 6.09e-04
2022-07-26 09:18:07,992 INFO [train.py:850] (2/4) Epoch 9, batch 1550, loss[loss=0.1801, simple_loss=0.2626, pruned_loss=0.04875, over 7252.00 frames.], tot_loss[loss=0.216, simple_loss=0.3003, pruned_loss=0.06587, over 1464336.40 frames.], batch size: 16, lr: 6.09e-04
2022-07-26 09:18:51,169 INFO [train.py:850] (2/4) Epoch 9, batch 1600, loss[loss=0.1984, simple_loss=0.2863, pruned_loss=0.0552, over 7199.00 frames.], tot_loss[loss=0.2159, simple_loss=0.3002, pruned_loss=0.06584, over 1465189.45 frames.], batch size: 18, lr: 6.08e-04
2022-07-26 09:19:35,516 INFO [train.py:850] (2/4) Epoch 9, batch 1650, loss[loss=0.2044, simple_loss=0.2897, pruned_loss=0.05951, over 7204.00 frames.], tot_loss[loss=0.215, simple_loss=0.2995, pruned_loss=0.06521, over 1465529.25 frames.], batch size: 18, lr: 6.08e-04
2022-07-26 09:20:18,559 INFO [train.py:850] (2/4) Epoch 9, batch 1700, loss[loss=0.1979, simple_loss=0.2691, pruned_loss=0.06335, over 7164.00 frames.], tot_loss[loss=0.2156, simple_loss=0.3002, pruned_loss=0.06555, over 1466052.73 frames.], batch size: 17, lr: 6.08e-04
2022-07-26 09:21:02,862 INFO [train.py:850] (2/4) Epoch 9, batch 1750, loss[loss=0.2109, simple_loss=0.3028, pruned_loss=0.05946, over 7300.00 frames.], tot_loss[loss=0.2159, simple_loss=0.3005, pruned_loss=0.06563, over 1465878.70 frames.], batch size: 22, lr: 6.08e-04
2022-07-26 09:21:46,169 INFO [train.py:850] (2/4) Epoch 9, batch 1800, loss[loss=0.217, simple_loss=0.289, pruned_loss=0.07251, over 7307.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3002, pruned_loss=0.0654, over 1464693.25 frames.], batch size: 18, lr: 6.08e-04
2022-07-26 09:22:30,460 INFO [train.py:850] (2/4) Epoch 9, batch 1850, loss[loss=0.2061, simple_loss=0.2898, pruned_loss=0.06121, over 7486.00 frames.], tot_loss[loss=0.2165, simple_loss=0.3014, pruned_loss=0.06582, over 1464606.40 frames.], batch size: 19, lr: 6.07e-04
2022-07-26 09:23:13,815 INFO [train.py:850] (2/4) Epoch 9, batch 1900, loss[loss=0.2572, simple_loss=0.3295, pruned_loss=0.09243, over 7300.00 frames.], tot_loss[loss=0.2161, simple_loss=0.3008, pruned_loss=0.06572, over 1464866.35 frames.], batch size: 22, lr: 6.07e-04
2022-07-26 09:23:57,016 INFO [train.py:850] (2/4) Epoch 9, batch 1950, loss[loss=0.257, simple_loss=0.3207, pruned_loss=0.09661, over 7197.00 frames.], tot_loss[loss=0.2162, simple_loss=0.3006, pruned_loss=0.06584, over 1464804.60 frames.], batch size: 18, lr: 6.07e-04
2022-07-26 09:24:40,795 INFO [train.py:850] (2/4) Epoch 9, batch 2000, loss[loss=0.1856, simple_loss=0.2722, pruned_loss=0.04948, over 7306.00 frames.], tot_loss[loss=0.2164, simple_loss=0.3011, pruned_loss=0.06586, over 1464574.63 frames.], batch size: 17, lr: 6.07e-04
2022-07-26 09:25:25,480 INFO [train.py:850] (2/4) Epoch 9, batch 2050, loss[loss=0.2114, simple_loss=0.2872, pruned_loss=0.0678, over 7264.00 frames.], tot_loss[loss=0.2166, simple_loss=0.3015, pruned_loss=0.06587, over 1463672.94 frames.], batch size: 16, lr: 6.07e-04
2022-07-26 09:26:10,197 INFO [train.py:850] (2/4) Epoch 9, batch 2100, loss[loss=0.174, simple_loss=0.2618, pruned_loss=0.04308, over 7432.00 frames.], tot_loss[loss=0.2158, simple_loss=0.3013, pruned_loss=0.06517, over 1465124.29 frames.], batch size: 18, lr: 6.06e-04
2022-07-26 09:26:53,157 INFO [train.py:850] (2/4) Epoch 9, batch 2150, loss[loss=0.1927, simple_loss=0.2701, pruned_loss=0.05762, over 7327.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2996, pruned_loss=0.06432, over 1464716.82 frames.], batch size: 18, lr: 6.06e-04
2022-07-26 09:27:36,498 INFO [train.py:850] (2/4) Epoch 9, batch 2200, loss[loss=0.2335, simple_loss=0.3177, pruned_loss=0.07462, over 7462.00 frames.], tot_loss[loss=0.2154, simple_loss=0.3009, pruned_loss=0.06491, over 1464866.80 frames.], batch size: 39, lr: 6.06e-04
2022-07-26 09:28:21,011 INFO [train.py:850] (2/4) Epoch 9, batch 2250, loss[loss=0.2184, simple_loss=0.3028, pruned_loss=0.06695, over 7196.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3004, pruned_loss=0.06531, over 1464807.48 frames.], batch size: 18, lr: 6.06e-04
2022-07-26 09:29:04,923 INFO [train.py:850] (2/4) Epoch 9, batch 2300, loss[loss=0.1938, simple_loss=0.2758, pruned_loss=0.05588, over 7318.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2998, pruned_loss=0.06484, over 1464751.92 frames.], batch size: 18, lr: 6.06e-04
2022-07-26 09:29:48,596 INFO [train.py:850] (2/4) Epoch 9, batch 2350, loss[loss=0.1826, simple_loss=0.2615, pruned_loss=0.05191, over 7464.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2986, pruned_loss=0.06398, over 1464777.34 frames.], batch size: 17, lr: 6.05e-04
2022-07-26 09:30:32,046 INFO [train.py:850] (2/4) Epoch 9, batch 2400, loss[loss=0.1891, simple_loss=0.277, pruned_loss=0.05057, over 7299.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2995, pruned_loss=0.06486, over 1464448.59 frames.], batch size: 19, lr: 6.05e-04
2022-07-26 09:31:15,471 INFO [train.py:850] (2/4) Epoch 9, batch 2450, loss[loss=0.1986, simple_loss=0.2897, pruned_loss=0.05375, over 7475.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2988, pruned_loss=0.06456, over 1464300.08 frames.], batch size: 20, lr: 6.05e-04
2022-07-26 09:31:59,773 INFO [train.py:850] (2/4) Epoch 9, batch 2500, loss[loss=0.221, simple_loss=0.3101, pruned_loss=0.06592, over 7473.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2987, pruned_loss=0.06422, over 1464374.55 frames.], batch size: 21, lr: 6.05e-04
2022-07-26 09:32:43,382 INFO [train.py:850] (2/4) Epoch 9, batch 2550, loss[loss=0.2176, simple_loss=0.3069, pruned_loss=0.06416, over 7198.00 frames.], tot_loss[loss=0.2157, simple_loss=0.3008, pruned_loss=0.06528, over 1465037.53 frames.], batch size: 25, lr: 6.05e-04
2022-07-26 09:33:27,323 INFO [train.py:850] (2/4) Epoch 9, batch 2600, loss[loss=0.2386, simple_loss=0.3219, pruned_loss=0.07765, over 7180.00 frames.], tot_loss[loss=0.2168, simple_loss=0.3023, pruned_loss=0.06566, over 1464438.49 frames.], batch size: 22, lr: 6.04e-04
2022-07-26 09:34:11,113 INFO [train.py:850] (2/4) Epoch 9, batch 2650, loss[loss=0.2545, simple_loss=0.3289, pruned_loss=0.09002, over 7380.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2997, pruned_loss=0.06454, over 1464342.90 frames.], batch size: 69, lr: 6.04e-04
2022-07-26 09:34:54,824 INFO [train.py:850] (2/4) Epoch 9, batch 2700, loss[loss=0.2352, simple_loss=0.3169, pruned_loss=0.07673, over 7200.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2997, pruned_loss=0.06452, over 1463899.44 frames.], batch size: 20, lr: 6.04e-04
2022-07-26 09:35:38,844 INFO [train.py:850] (2/4) Epoch 9, batch 2750, loss[loss=0.2016, simple_loss=0.2784, pruned_loss=0.06239, over 7198.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2989, pruned_loss=0.06408, over 1464658.94 frames.], batch size: 18, lr: 6.04e-04
2022-07-26 09:36:22,352 INFO [train.py:850] (2/4) Epoch 9, batch 2800, loss[loss=0.1911, simple_loss=0.282, pruned_loss=0.05004, over 7384.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2985, pruned_loss=0.06346, over 1464716.58 frames.], batch size: 20, lr: 6.04e-04
2022-07-26 09:37:06,892 INFO [train.py:850] (2/4) Epoch 9, batch 2850, loss[loss=0.2122, simple_loss=0.3034, pruned_loss=0.06053, over 7312.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2984, pruned_loss=0.06321, over 1464571.28 frames.], batch size: 22, lr: 6.03e-04
2022-07-26 09:37:50,923 INFO [train.py:850] (2/4) Epoch 9, batch 2900, loss[loss=0.1752, simple_loss=0.263, pruned_loss=0.04367, over 7390.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2979, pruned_loss=0.06289, over 1465683.24 frames.], batch size: 19, lr: 6.03e-04
2022-07-26 09:38:35,105 INFO [train.py:850] (2/4) Epoch 9, batch 2950, loss[loss=0.1989, simple_loss=0.2904, pruned_loss=0.05372, over 7198.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2988, pruned_loss=0.0635, over 1465581.52 frames.], batch size: 18, lr: 6.03e-04
2022-07-26 09:39:18,949 INFO [train.py:850] (2/4) Epoch 9, batch 3000, loss[loss=0.1689, simple_loss=0.2685, pruned_loss=0.03461, over 7202.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2981, pruned_loss=0.06349, over 1465801.14 frames.], batch size: 20, lr: 6.03e-04
2022-07-26 09:39:18,950 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 09:39:41,782 INFO [train.py:879] (2/4) Epoch 9, validation: loss=0.2007, simple_loss=0.2962, pruned_loss=0.05255, over 924787.00 frames.
2022-07-26 09:40:26,137 INFO [train.py:850] (2/4) Epoch 9, batch 3050, loss[loss=0.2227, simple_loss=0.3039, pruned_loss=0.07081, over 7391.00 frames.], tot_loss[loss=0.213, simple_loss=0.2989, pruned_loss=0.06353, over 1466474.69 frames.], batch size: 20, lr: 6.03e-04
2022-07-26 09:41:09,530 INFO [train.py:850] (2/4) Epoch 9, batch 3100, loss[loss=0.2294, simple_loss=0.3207, pruned_loss=0.06905, over 7207.00 frames.], tot_loss[loss=0.212, simple_loss=0.2981, pruned_loss=0.0629, over 1465280.09 frames.], batch size: 20, lr: 6.02e-04
2022-07-26 09:41:53,447 INFO [train.py:850] (2/4) Epoch 9, batch 3150, loss[loss=0.1958, simple_loss=0.2691, pruned_loss=0.06126, over 7437.00 frames.], tot_loss[loss=0.2107, simple_loss=0.2971, pruned_loss=0.06218, over 1465186.09 frames.], batch size: 18, lr: 6.02e-04
2022-07-26 09:42:36,411 INFO [train.py:850] (2/4) Epoch 9, batch 3200, loss[loss=0.2438, simple_loss=0.3162, pruned_loss=0.08574, over 7312.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2962, pruned_loss=0.06213, over 1464718.96 frames.], batch size: 18, lr: 6.02e-04
2022-07-26 09:43:20,632 INFO [train.py:850] (2/4) Epoch 9, batch 3250, loss[loss=0.2317, simple_loss=0.3088, pruned_loss=0.07733, over 7288.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2976, pruned_loss=0.06294, over 1466068.85 frames.], batch size: 19, lr: 6.02e-04
2022-07-26 09:44:03,813 INFO [train.py:850] (2/4) Epoch 9, batch 3300, loss[loss=0.2379, simple_loss=0.3277, pruned_loss=0.07407, over 7212.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2972, pruned_loss=0.06269, over 1464976.57 frames.], batch size: 25, lr: 6.02e-04
2022-07-26 09:44:47,665 INFO [train.py:850] (2/4) Epoch 9, batch 3350, loss[loss=0.1869, simple_loss=0.2787, pruned_loss=0.04752, over 7357.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2972, pruned_loss=0.06278, over 1464916.64 frames.], batch size: 23, lr: 6.01e-04
2022-07-26 09:45:32,539 INFO [train.py:850] (2/4) Epoch 9, batch 3400, loss[loss=0.2181, simple_loss=0.304, pruned_loss=0.06615, over 7482.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2976, pruned_loss=0.0631, over 1464834.28 frames.], batch size: 23, lr: 6.01e-04
2022-07-26 09:46:15,250 INFO [train.py:850] (2/4) Epoch 9, batch 3450, loss[loss=0.1821, simple_loss=0.2708, pruned_loss=0.04666, over 7485.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2964, pruned_loss=0.06268, over 1464582.27 frames.], batch size: 20, lr: 6.01e-04
2022-07-26 09:46:59,257 INFO [train.py:850] (2/4) Epoch 9, batch 3500, loss[loss=0.2843, simple_loss=0.3615, pruned_loss=0.1036, over 7441.00 frames.], tot_loss[loss=0.211, simple_loss=0.2962, pruned_loss=0.06289, over 1464658.03 frames.], batch size: 73, lr: 6.01e-04
2022-07-26 09:47:43,128 INFO [train.py:850] (2/4) Epoch 9, batch 3550, loss[loss=0.2095, simple_loss=0.3023, pruned_loss=0.05832, over 7299.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2961, pruned_loss=0.06223, over 1465385.65 frames.], batch size: 22, lr: 6.01e-04
2022-07-26 09:48:27,259 INFO [train.py:850] (2/4) Epoch 9, batch 3600, loss[loss=0.1733, simple_loss=0.2561, pruned_loss=0.04522, over 7461.00 frames.], tot_loss[loss=0.2107, simple_loss=0.2962, pruned_loss=0.06257, over 1465988.01 frames.], batch size: 17, lr: 6.00e-04
2022-07-26 09:49:11,573 INFO [train.py:850] (2/4) Epoch 9, batch 3650, loss[loss=0.1939, simple_loss=0.2872, pruned_loss=0.05033, over 7392.00 frames.], tot_loss[loss=0.212, simple_loss=0.2976, pruned_loss=0.06314, over 1465553.27 frames.], batch size: 19, lr: 6.00e-04
2022-07-26 09:49:54,626 INFO [train.py:850] (2/4) Epoch 9, batch 3700, loss[loss=0.1955, simple_loss=0.2921, pruned_loss=0.04942, over 7183.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2968, pruned_loss=0.06254, over 1465600.09 frames.], batch size: 21, lr: 6.00e-04
2022-07-26 09:50:38,421 INFO [train.py:850] (2/4) Epoch 9, batch 3750, loss[loss=0.216, simple_loss=0.2841, pruned_loss=0.0739, over 7457.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2964, pruned_loss=0.06239, over 1465741.80 frames.], batch size: 18, lr: 6.00e-04
2022-07-26 09:51:21,863 INFO [train.py:850] (2/4) Epoch 9, batch 3800, loss[loss=0.2105, simple_loss=0.3117, pruned_loss=0.05465, over 7487.00 frames.], tot_loss[loss=0.2107, simple_loss=0.297, pruned_loss=0.06219, over 1465923.78 frames.], batch size: 24, lr: 6.00e-04
2022-07-26 09:52:06,016 INFO [train.py:850] (2/4) Epoch 9, batch 3850, loss[loss=0.2029, simple_loss=0.2905, pruned_loss=0.05765, over 7407.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2975, pruned_loss=0.06257, over 1466670.55 frames.], batch size: 22, lr: 5.99e-04
2022-07-26 09:52:50,216 INFO [train.py:850] (2/4) Epoch 9, batch 3900, loss[loss=0.1996, simple_loss=0.2942, pruned_loss=0.05252, over 7469.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2975, pruned_loss=0.06251, over 1466100.83 frames.], batch size: 21, lr: 5.99e-04
2022-07-26 09:53:34,203 INFO [train.py:850] (2/4) Epoch 9, batch 3950, loss[loss=0.2217, simple_loss=0.311, pruned_loss=0.06624, over 7183.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2971, pruned_loss=0.06238, over 1466570.23 frames.], batch size: 21, lr: 5.99e-04
2022-07-26 09:54:17,354 INFO [train.py:850] (2/4) Epoch 9, batch 4000, loss[loss=0.2097, simple_loss=0.299, pruned_loss=0.06017, over 7477.00 frames.], tot_loss[loss=0.2101, simple_loss=0.296, pruned_loss=0.06209, over 1466000.58 frames.], batch size: 24, lr: 5.99e-04
2022-07-26 09:55:00,646 INFO [train.py:850] (2/4) Epoch 9, batch 4050, loss[loss=0.2685, simple_loss=0.3497, pruned_loss=0.09367, over 7297.00 frames.], tot_loss[loss=0.2115, simple_loss=0.2974, pruned_loss=0.06274, over 1464466.01 frames.], batch size: 38, lr: 5.99e-04
2022-07-26 09:55:44,574 INFO [train.py:850] (2/4) Epoch 9, batch 4100, loss[loss=0.2459, simple_loss=0.3318, pruned_loss=0.07997, over 7187.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2971, pruned_loss=0.06339, over 1463710.99 frames.], batch size: 21, lr: 5.98e-04
2022-07-26 09:56:29,297 INFO [train.py:850] (2/4) Epoch 9, batch 4150, loss[loss=0.2202, simple_loss=0.3146, pruned_loss=0.06287, over 7422.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2984, pruned_loss=0.06435, over 1463172.80 frames.], batch size: 22, lr: 5.98e-04
2022-07-26 09:57:13,214 INFO [train.py:850] (2/4) Epoch 9, batch 4200, loss[loss=0.1961, simple_loss=0.2742, pruned_loss=0.05897, over 7147.00 frames.], tot_loss[loss=0.2164, simple_loss=0.3002, pruned_loss=0.06631, over 1464174.42 frames.], batch size: 17, lr: 5.98e-04
2022-07-26 09:57:57,561 INFO [train.py:850] (2/4) Epoch 9, batch 4250, loss[loss=0.2084, simple_loss=0.2967, pruned_loss=0.06011, over 7406.00 frames.], tot_loss[loss=0.2193, simple_loss=0.3019, pruned_loss=0.06832, over 1464355.18 frames.], batch size: 22, lr: 5.98e-04
2022-07-26 09:58:40,147 INFO [train.py:850] (2/4) Epoch 9, batch 4300, loss[loss=0.2303, simple_loss=0.3061, pruned_loss=0.07725, over 7161.00 frames.], tot_loss[loss=0.2227, simple_loss=0.3035, pruned_loss=0.07093, over 1465031.51 frames.], batch size: 17, lr: 5.98e-04
2022-07-26 09:59:23,639 INFO [train.py:850] (2/4) Epoch 9, batch 4350, loss[loss=0.2188, simple_loss=0.2949, pruned_loss=0.07136, over 7490.00 frames.], tot_loss[loss=0.225, simple_loss=0.3053, pruned_loss=0.07234, over 1466343.13 frames.], batch size: 23, lr: 5.97e-04
2022-07-26 10:00:07,406 INFO [train.py:850] (2/4) Epoch 9, batch 4400, loss[loss=0.2728, simple_loss=0.3433, pruned_loss=0.1011, over 7100.00 frames.], tot_loss[loss=0.227, simple_loss=0.3062, pruned_loss=0.07391, over 1466100.59 frames.], batch size: 18, lr: 5.97e-04
2022-07-26 10:00:51,042 INFO [train.py:850] (2/4) Epoch 9, batch 4450, loss[loss=0.2354, simple_loss=0.31, pruned_loss=0.08044, over 7392.00 frames.], tot_loss[loss=0.2271, simple_loss=0.3055, pruned_loss=0.07438, over 1465242.09 frames.], batch size: 20, lr: 5.97e-04
2022-07-26 10:01:35,320 INFO [train.py:850] (2/4) Epoch 9, batch 4500, loss[loss=0.2018, simple_loss=0.2842, pruned_loss=0.05969, over 7199.00 frames.], tot_loss[loss=0.2293, simple_loss=0.3071, pruned_loss=0.07576, over 1466413.70 frames.], batch size: 19, lr: 5.97e-04
2022-07-26 10:02:18,633 INFO [train.py:850] (2/4) Epoch 9, batch 4550, loss[loss=0.2501, simple_loss=0.3194, pruned_loss=0.09041, over 7382.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3091, pruned_loss=0.07751, over 1465622.54 frames.], batch size: 21, lr: 5.97e-04
2022-07-26 10:03:03,530 INFO [train.py:850] (2/4) Epoch 9, batch 4600, loss[loss=0.1944, simple_loss=0.2726, pruned_loss=0.05806, over 7201.00 frames.], tot_loss[loss=0.2328, simple_loss=0.3095, pruned_loss=0.07801, over 1466553.63 frames.], batch size: 19, lr: 5.96e-04
2022-07-26 10:03:47,783 INFO [train.py:850] (2/4) Epoch 9, batch 4650, loss[loss=0.2202, simple_loss=0.2897, pruned_loss=0.07534, over 7446.00 frames.], tot_loss[loss=0.2336, simple_loss=0.3099, pruned_loss=0.07865, over 1466493.52 frames.], batch size: 18, lr: 5.96e-04
2022-07-26 10:04:31,186 INFO [train.py:850] (2/4) Epoch 9, batch 4700, loss[loss=0.3333, simple_loss=0.403, pruned_loss=0.1318, over 7390.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3106, pruned_loss=0.08014, over 1466385.96 frames.], batch size: 38, lr: 5.96e-04
2022-07-26 10:05:15,245 INFO [train.py:850] (2/4) Epoch 9, batch 4750, loss[loss=0.2556, simple_loss=0.3342, pruned_loss=0.08848, over 7227.00 frames.], tot_loss[loss=0.2347, simple_loss=0.3097, pruned_loss=0.07985, over 1466134.10 frames.], batch size: 25, lr: 5.96e-04
2022-07-26 10:05:59,040 INFO [train.py:850] (2/4) Epoch 9, batch 4800, loss[loss=0.218, simple_loss=0.3101, pruned_loss=0.06297, over 7335.00 frames.], tot_loss[loss=0.2345, simple_loss=0.3089, pruned_loss=0.08007, over 1465488.92 frames.], batch size: 31, lr: 5.96e-04
2022-07-26 10:06:57,718 INFO [train.py:850] (2/4) Epoch 9, batch 4850, loss[loss=0.2483, simple_loss=0.3169, pruned_loss=0.08981, over 7393.00 frames.], tot_loss[loss=0.2329, simple_loss=0.3074, pruned_loss=0.07919, over 1465975.67 frames.], batch size: 19, lr: 5.95e-04
2022-07-26 10:07:42,083 INFO [train.py:850] (2/4) Epoch 9, batch 4900, loss[loss=0.2237, simple_loss=0.2862, pruned_loss=0.08058, over 7455.00 frames.], tot_loss[loss=0.2349, simple_loss=0.3087, pruned_loss=0.08053, over 1465866.33 frames.], batch size: 17, lr: 5.95e-04
2022-07-26 10:08:26,294 INFO [train.py:850] (2/4) Epoch 9, batch 4950, loss[loss=0.2644, simple_loss=0.33, pruned_loss=0.09939, over 7457.00 frames.], tot_loss[loss=0.2357, simple_loss=0.309, pruned_loss=0.08122, over 1464991.13 frames.], batch size: 40, lr: 5.95e-04
2022-07-26 10:09:10,491 INFO [train.py:850] (2/4) Epoch 9, batch 5000, loss[loss=0.2059, simple_loss=0.2861, pruned_loss=0.06285, over 7439.00 frames.], tot_loss[loss=0.2364, simple_loss=0.3095, pruned_loss=0.0817, over 1465841.12 frames.], batch size: 18, lr: 5.95e-04
2022-07-26 10:09:53,913 INFO [train.py:850] (2/4) Epoch 9, batch 5050, loss[loss=0.1597, simple_loss=0.2448, pruned_loss=0.03731, over 7275.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3082, pruned_loss=0.08046, over 1466734.99 frames.], batch size: 16, lr: 5.95e-04
2022-07-26 10:10:38,185 INFO [train.py:850] (2/4) Epoch 9, batch 5100, loss[loss=0.2539, simple_loss=0.332, pruned_loss=0.08788, over 7473.00 frames.], tot_loss[loss=0.2343, simple_loss=0.3078, pruned_loss=0.0804, over 1466628.30 frames.], batch size: 26, lr: 5.94e-04
2022-07-26 10:11:22,372 INFO [train.py:850] (2/4) Epoch 9, batch 5150, loss[loss=0.2149, simple_loss=0.2832, pruned_loss=0.0733, over 7446.00 frames.], tot_loss[loss=0.2328, simple_loss=0.3063, pruned_loss=0.07967, over 1465956.77 frames.], batch size: 18, lr: 5.94e-04
2022-07-26 10:12:05,800 INFO [train.py:850] (2/4) Epoch 9, batch 5200, loss[loss=0.2535, simple_loss=0.3042, pruned_loss=0.1014, over 7463.00 frames.], tot_loss[loss=0.233, simple_loss=0.3067, pruned_loss=0.07965, over 1465211.82 frames.], batch size: 18, lr: 5.94e-04
2022-07-26 10:12:50,108 INFO [train.py:850] (2/4) Epoch 9, batch 5250, loss[loss=0.2364, simple_loss=0.3182, pruned_loss=0.07728, over 7482.00 frames.], tot_loss[loss=0.2329, simple_loss=0.3065, pruned_loss=0.07968, over 1464747.97 frames.], batch size: 24, lr: 5.94e-04
2022-07-26 10:13:34,505 INFO [train.py:850] (2/4) Epoch 9, batch 5300, loss[loss=0.2522, simple_loss=0.3225, pruned_loss=0.091, over 7321.00 frames.], tot_loss[loss=0.235, simple_loss=0.3081, pruned_loss=0.08094, over 1466075.79 frames.], batch size: 31, lr: 5.94e-04
2022-07-26 10:14:18,758 INFO [train.py:850] (2/4) Epoch 9, batch 5350, loss[loss=0.25, simple_loss=0.3182, pruned_loss=0.09085, over 7374.00 frames.], tot_loss[loss=0.2342, simple_loss=0.3078, pruned_loss=0.08033, over 1466039.66 frames.], batch size: 31, lr: 5.93e-04
2022-07-26 10:15:01,961 INFO [train.py:850] (2/4) Epoch 9, batch 5400, loss[loss=0.2569, simple_loss=0.3302, pruned_loss=0.09173, over 7185.00 frames.], tot_loss[loss=0.233, simple_loss=0.307, pruned_loss=0.07955, over 1465384.75 frames.], batch size: 21, lr: 5.93e-04
2022-07-26 10:15:46,293 INFO [train.py:850] (2/4) Epoch 9, batch 5450, loss[loss=0.261, simple_loss=0.3305, pruned_loss=0.09573, over 7470.00 frames.], tot_loss[loss=0.2337, simple_loss=0.3074, pruned_loss=0.07995, over 1464980.17 frames.], batch size: 24, lr: 5.93e-04
2022-07-26 10:16:30,529 INFO [train.py:850] (2/4) Epoch 9, batch 5500, loss[loss=0.2098, simple_loss=0.2972, pruned_loss=0.06123, over 7379.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3079, pruned_loss=0.07991, over 1465603.93 frames.], batch size: 21, lr: 5.93e-04
2022-07-26 10:17:14,959 INFO [train.py:850] (2/4) Epoch 9, batch 5550, loss[loss=0.1758, simple_loss=0.2605, pruned_loss=0.04554, over 7448.00 frames.], tot_loss[loss=0.2334, simple_loss=0.3077, pruned_loss=0.07951, over 1465844.06 frames.], batch size: 17, lr: 5.93e-04
2022-07-26 10:17:58,873 INFO [train.py:850] (2/4) Epoch 9, batch 5600, loss[loss=0.2198, simple_loss=0.2961, pruned_loss=0.07179, over 7420.00 frames.], tot_loss[loss=0.2331, simple_loss=0.3076, pruned_loss=0.07932, over 1466635.29 frames.], batch size: 31, lr: 5.92e-04
2022-07-26 10:18:43,281 INFO [train.py:850] (2/4) Epoch 9, batch 5650, loss[loss=0.212, simple_loss=0.2967, pruned_loss=0.06367, over 7328.00 frames.], tot_loss[loss=0.2337, simple_loss=0.3077, pruned_loss=0.07987, over 1466918.19 frames.], batch size: 23, lr: 5.92e-04
2022-07-26 10:19:27,718 INFO [train.py:850] (2/4) Epoch 9, batch 5700, loss[loss=0.2408, simple_loss=0.3211, pruned_loss=0.08024, over 7487.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3067, pruned_loss=0.07849, over 1467291.36 frames.], batch size: 23, lr: 5.92e-04
2022-07-26 10:20:11,256 INFO [train.py:850] (2/4) Epoch 9, batch 5750, loss[loss=0.2364, simple_loss=0.2976, pruned_loss=0.08761, over 7386.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3072, pruned_loss=0.07851, over 1466301.61 frames.], batch size: 19, lr: 5.92e-04
2022-07-26 10:20:54,841 INFO [train.py:850] (2/4) Epoch 9, batch 5800, loss[loss=0.2182, simple_loss=0.3018, pruned_loss=0.06725, over 7340.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3068, pruned_loss=0.07866, over 1466290.81 frames.], batch size: 23, lr: 5.92e-04
2022-07-26 10:21:39,548 INFO [train.py:850] (2/4) Epoch 9, batch 5850, loss[loss=0.2489, simple_loss=0.3271, pruned_loss=0.08533, over 7478.00 frames.], tot_loss[loss=0.2316, simple_loss=0.3065, pruned_loss=0.07837, over 1467180.62 frames.], batch size: 24, lr: 5.92e-04
2022-07-26 10:22:22,936 INFO [train.py:850] (2/4) Epoch 9, batch 5900, loss[loss=0.2118, simple_loss=0.2998, pruned_loss=0.06189, over 7294.00 frames.], tot_loss[loss=0.2321, simple_loss=0.307, pruned_loss=0.07859, over 1466845.28 frames.], batch size: 22, lr: 5.91e-04
2022-07-26 10:23:07,327 INFO [train.py:850] (2/4) Epoch 9, batch 5950, loss[loss=0.1914, simple_loss=0.2655, pruned_loss=0.05869, over 7169.00 frames.], tot_loss[loss=0.2322, simple_loss=0.307, pruned_loss=0.07872, over 1466639.39 frames.], batch size: 17, lr: 5.91e-04
2022-07-26 10:23:51,692 INFO [train.py:850] (2/4) Epoch 9, batch 6000, loss[loss=0.2769, simple_loss=0.3423, pruned_loss=0.1058, over 7466.00 frames.], tot_loss[loss=0.2309, simple_loss=0.3062, pruned_loss=0.07785, over 1466360.62 frames.], batch size: 24, lr: 5.91e-04
2022-07-26 10:23:51,694 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 10:24:15,092 INFO [train.py:879] (2/4) Epoch 9, validation: loss=0.1926, simple_loss=0.2902, pruned_loss=0.04755, over 924787.00 frames.
2022-07-26 10:24:58,670 INFO [train.py:850] (2/4) Epoch 9, batch 6050, loss[loss=0.1894, simple_loss=0.2602, pruned_loss=0.05927, over 7319.00 frames.], tot_loss[loss=0.2312, simple_loss=0.3061, pruned_loss=0.07816, over 1466447.83 frames.], batch size: 18, lr: 5.91e-04
2022-07-26 10:25:42,200 INFO [train.py:850] (2/4) Epoch 9, batch 6100, loss[loss=0.2018, simple_loss=0.2816, pruned_loss=0.06105, over 7482.00 frames.], tot_loss[loss=0.2324, simple_loss=0.3072, pruned_loss=0.07882, over 1466720.05 frames.], batch size: 21, lr: 5.91e-04
2022-07-26 10:26:27,425 INFO [train.py:850] (2/4) Epoch 9, batch 6150, loss[loss=0.2595, simple_loss=0.3348, pruned_loss=0.09208, over 7354.00 frames.], tot_loss[loss=0.2333, simple_loss=0.3077, pruned_loss=0.07947, over 1466455.36 frames.], batch size: 38, lr: 5.90e-04
2022-07-26 10:27:12,425 INFO [train.py:850] (2/4) Epoch 9, batch 6200, loss[loss=0.2176, simple_loss=0.3078, pruned_loss=0.06374, over 7421.00 frames.], tot_loss[loss=0.2317, simple_loss=0.3063, pruned_loss=0.07857, over 1466438.92 frames.], batch size: 22, lr: 5.90e-04
2022-07-26 10:27:56,577 INFO [train.py:850] (2/4) Epoch 9, batch 6250, loss[loss=0.2251, simple_loss=0.3107, pruned_loss=0.06971, over 7184.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3059, pruned_loss=0.07788, over 1466874.18 frames.], batch size: 22, lr: 5.90e-04
2022-07-26 10:28:40,244 INFO [train.py:850] (2/4) Epoch 9, batch 6300, loss[loss=0.2763, simple_loss=0.347, pruned_loss=0.1028, over 7471.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3067, pruned_loss=0.07841, over 1466476.11 frames.], batch size: 21, lr: 5.90e-04
2022-07-26 10:29:25,537 INFO [train.py:850] (2/4) Epoch 9, batch 6350, loss[loss=0.2603, simple_loss=0.3397, pruned_loss=0.09045, over 7412.00 frames.], tot_loss[loss=0.2323, simple_loss=0.3074, pruned_loss=0.07866, over 1465831.96 frames.], batch size: 22, lr: 5.90e-04
2022-07-26 10:30:09,412 INFO [train.py:850] (2/4) Epoch 9, batch 6400, loss[loss=0.2214, simple_loss=0.3005, pruned_loss=0.07121, over 7189.00 frames.], tot_loss[loss=0.2341, simple_loss=0.3083, pruned_loss=0.07996, over 1466589.35 frames.], batch size: 21, lr: 5.89e-04
2022-07-26 10:30:53,927 INFO [train.py:850] (2/4) Epoch 9, batch 6450, loss[loss=0.233, simple_loss=0.3046, pruned_loss=0.08066, over 7487.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3084, pruned_loss=0.07964, over 1466269.20 frames.], batch size: 20, lr: 5.89e-04
2022-07-26 10:31:37,929 INFO [train.py:850] (2/4) Epoch 9, batch 6500, loss[loss=0.2699, simple_loss=0.3409, pruned_loss=0.09939, over 7179.00 frames.], tot_loss[loss=0.2336, simple_loss=0.3081, pruned_loss=0.07959, over 1466609.38 frames.], batch size: 23, lr: 5.89e-04
2022-07-26 10:32:21,125 INFO [train.py:850] (2/4) Epoch 9, batch 6550, loss[loss=0.2358, simple_loss=0.3091, pruned_loss=0.08131, over 7307.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3068, pruned_loss=0.07932, over 1466888.87 frames.], batch size: 39, lr: 5.89e-04
2022-07-26 10:33:06,310 INFO [train.py:850] (2/4) Epoch 9, batch 6600, loss[loss=0.2091, simple_loss=0.272, pruned_loss=0.07311, over 7154.00 frames.], tot_loss[loss=0.2305, simple_loss=0.3052, pruned_loss=0.07793, over 1467243.18 frames.], batch size: 17, lr: 5.89e-04
2022-07-26 10:33:50,321 INFO [train.py:850] (2/4) Epoch 9, batch 6650, loss[loss=0.2308, simple_loss=0.3065, pruned_loss=0.07756, over 7369.00 frames.], tot_loss[loss=0.2292, simple_loss=0.3042, pruned_loss=0.07715, over 1466784.90 frames.], batch size: 19, lr: 5.88e-04
2022-07-26 10:34:35,255 INFO [train.py:850] (2/4) Epoch 9, batch 6700, loss[loss=0.2651, simple_loss=0.3469, pruned_loss=0.09165, over 7285.00 frames.], tot_loss[loss=0.2287, simple_loss=0.304, pruned_loss=0.07675, over 1465190.03 frames.], batch size: 21, lr: 5.88e-04
2022-07-26 10:35:19,869 INFO [train.py:850] (2/4) Epoch 9, batch 6750, loss[loss=0.2242, simple_loss=0.2854, pruned_loss=0.08153, over 7338.00 frames.], tot_loss[loss=0.2311, simple_loss=0.3058, pruned_loss=0.07826, over 1466151.65 frames.], batch size: 17, lr: 5.88e-04
2022-07-26 10:36:03,859 INFO [train.py:850] (2/4) Epoch 9, batch 6800, loss[loss=0.2713, simple_loss=0.3407, pruned_loss=0.101, over 7216.00 frames.], tot_loss[loss=0.2305, simple_loss=0.3049, pruned_loss=0.07801, over 1465481.36 frames.], batch size: 25, lr: 5.88e-04
2022-07-26 10:36:47,732 INFO [train.py:850] (2/4) Epoch 9, batch 6850, loss[loss=0.2046, simple_loss=0.2718, pruned_loss=0.06871, over 7390.00 frames.], tot_loss[loss=0.2294, simple_loss=0.3043, pruned_loss=0.07726, over 1466447.21 frames.], batch size: 19, lr: 5.88e-04
2022-07-26 10:37:31,217 INFO [train.py:850] (2/4) Epoch 9, batch 6900, loss[loss=0.2256, simple_loss=0.2995, pruned_loss=0.07588, over 7208.00 frames.], tot_loss[loss=0.231, simple_loss=0.3057, pruned_loss=0.07815, over 1466591.61 frames.], batch size: 19, lr: 5.88e-04
2022-07-26 10:38:16,097 INFO [train.py:850] (2/4) Epoch 9, batch 6950, loss[loss=0.2864, simple_loss=0.3457, pruned_loss=0.1135, over 7440.00 frames.], tot_loss[loss=0.2326, simple_loss=0.3072, pruned_loss=0.07899, over 1466224.22 frames.], batch size: 31, lr: 5.87e-04
2022-07-26 10:38:59,590 INFO [train.py:850] (2/4) Epoch 9, batch 7000, loss[loss=0.2672, simple_loss=0.3315, pruned_loss=0.1015, over 7374.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3056, pruned_loss=0.0779, over 1465464.36 frames.], batch size: 21, lr: 5.87e-04
2022-07-26 10:39:44,380 INFO [train.py:850] (2/4) Epoch 9, batch 7050, loss[loss=0.2404, simple_loss=0.3144, pruned_loss=0.08319, over 7220.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3052, pruned_loss=0.07757, over 1465474.99 frames.], batch size: 25, lr: 5.87e-04
2022-07-26 10:40:28,723 INFO [train.py:850] (2/4) Epoch 9, batch 7100, loss[loss=0.2362, simple_loss=0.3154, pruned_loss=0.0785, over 7175.00 frames.], tot_loss[loss=0.2319, simple_loss=0.3067, pruned_loss=0.07852, over 1464880.32 frames.], batch size: 21, lr: 5.87e-04
2022-07-26 10:41:14,143 INFO [train.py:850] (2/4) Epoch 9, batch 7150, loss[loss=0.166, simple_loss=0.249, pruned_loss=0.04154, over 7451.00 frames.], tot_loss[loss=0.2309, simple_loss=0.3057, pruned_loss=0.07808, over 1465778.01 frames.], batch size: 17, lr: 5.87e-04
2022-07-26 10:41:58,916 INFO [train.py:850] (2/4) Epoch 9, batch 7200, loss[loss=0.1963, simple_loss=0.2823, pruned_loss=0.05511, over 7285.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3049, pruned_loss=0.07764, over 1466599.41 frames.], batch size: 27, lr: 5.86e-04
2022-07-26 10:42:43,066 INFO [train.py:850] (2/4) Epoch 9, batch 7250, loss[loss=0.2406, simple_loss=0.3145, pruned_loss=0.08334, over 7389.00 frames.], tot_loss[loss=0.2304, simple_loss=0.3051, pruned_loss=0.0779, over 1466848.61 frames.], batch size: 20, lr: 5.86e-04
2022-07-26 10:43:27,378 INFO [train.py:850] (2/4) Epoch 9, batch 7300, loss[loss=0.2656, simple_loss=0.3274, pruned_loss=0.1019, over 7330.00 frames.], tot_loss[loss=0.2289, simple_loss=0.3042, pruned_loss=0.07678, over 1465703.43 frames.], batch size: 23, lr: 5.86e-04
2022-07-26 10:44:11,761 INFO [train.py:850] (2/4) Epoch 9, batch 7350, loss[loss=0.2192, simple_loss=0.2968, pruned_loss=0.07082, over 7424.00 frames.], tot_loss[loss=0.2283, simple_loss=0.3039, pruned_loss=0.07641, over 1465842.48 frames.], batch size: 22, lr: 5.86e-04
2022-07-26 10:44:55,665 INFO [train.py:850] (2/4) Epoch 9, batch 7400, loss[loss=0.2178, simple_loss=0.3086, pruned_loss=0.06349, over 7176.00 frames.], tot_loss[loss=0.2271, simple_loss=0.3028, pruned_loss=0.07565, over 1464920.36 frames.], batch size: 21, lr: 5.86e-04
2022-07-26 10:45:41,497 INFO [train.py:850] (2/4) Epoch 9, batch 7450, loss[loss=0.249, simple_loss=0.3001, pruned_loss=0.09897, over 7251.00 frames.], tot_loss[loss=0.227, simple_loss=0.3026, pruned_loss=0.07568, over 1464892.45 frames.], batch size: 16, lr: 5.85e-04
2022-07-26 10:46:27,233 INFO [train.py:850] (2/4) Epoch 9, batch 7500, loss[loss=0.2765, simple_loss=0.3462, pruned_loss=0.1034, over 7266.00 frames.], tot_loss[loss=0.2281, simple_loss=0.3032, pruned_loss=0.07652, over 1465454.50 frames.], batch size: 21, lr: 5.85e-04
2022-07-26 10:47:14,105 INFO [train.py:850] (2/4) Epoch 9, batch 7550, loss[loss=0.198, simple_loss=0.2622, pruned_loss=0.06691, over 7298.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3049, pruned_loss=0.07746, over 1465395.00 frames.], batch size: 16, lr: 5.85e-04
2022-07-26 10:47:58,584 INFO [train.py:850] (2/4) Epoch 9, batch 7600, loss[loss=0.244, simple_loss=0.3159, pruned_loss=0.08609, over 7185.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3056, pruned_loss=0.07801, over 1465593.06 frames.], batch size: 21, lr: 5.85e-04
2022-07-26 10:48:43,788 INFO [train.py:850] (2/4) Epoch 9, batch 7650, loss[loss=0.2939, simple_loss=0.3712, pruned_loss=0.1083, over 7176.00 frames.], tot_loss[loss=0.23, simple_loss=0.3051, pruned_loss=0.07746, over 1465132.55 frames.], batch size: 22, lr: 5.85e-04
2022-07-26 10:49:26,861 INFO [train.py:850] (2/4) Epoch 9, batch 7700, loss[loss=0.2381, simple_loss=0.307, pruned_loss=0.08457, over 7420.00 frames.], tot_loss[loss=0.2304, simple_loss=0.3053, pruned_loss=0.07776, over 1464204.28 frames.], batch size: 66, lr: 5.85e-04
2022-07-26 10:50:11,008 INFO [train.py:850] (2/4) Epoch 9, batch 7750, loss[loss=0.242, simple_loss=0.3154, pruned_loss=0.08429, over 7381.00 frames.], tot_loss[loss=0.229, simple_loss=0.3047, pruned_loss=0.07665, over 1464395.44 frames.], batch size: 21, lr: 5.84e-04
2022-07-26 10:50:53,743 INFO [train.py:850] (2/4) Epoch 9, batch 7800, loss[loss=0.2372, simple_loss=0.3059, pruned_loss=0.0842, over 7484.00 frames.], tot_loss[loss=0.2317, simple_loss=0.3064, pruned_loss=0.07845, over 1464484.23 frames.], batch size: 19, lr: 5.84e-04
2022-07-26 10:51:37,922 INFO [train.py:850] (2/4) Epoch 9, batch 7850, loss[loss=0.2854, simple_loss=0.3497, pruned_loss=0.1105, over 7267.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3069, pruned_loss=0.07867, over 1465326.00 frames.], batch size: 21, lr: 5.84e-04
2022-07-26 10:52:22,197 INFO [train.py:850] (2/4) Epoch 9, batch 7900, loss[loss=0.2236, simple_loss=0.2833, pruned_loss=0.08199, over 7306.00 frames.], tot_loss[loss=0.2323, simple_loss=0.3068, pruned_loss=0.07885, over 1466194.36 frames.], batch size: 18, lr: 5.84e-04
2022-07-26 10:53:06,663 INFO [train.py:850] (2/4) Epoch 9, batch 7950, loss[loss=0.2708, simple_loss=0.3341, pruned_loss=0.1038, over 7222.00 frames.], tot_loss[loss=0.2321, simple_loss=0.307, pruned_loss=0.07859, over 1465684.05 frames.], batch size: 24, lr: 5.84e-04
2022-07-26 10:53:53,386 INFO [train.py:850] (2/4) Epoch 9, batch 8000, loss[loss=0.2082, simple_loss=0.2764, pruned_loss=0.06998, over 7446.00 frames.], tot_loss[loss=0.2311, simple_loss=0.3066, pruned_loss=0.07781, over 1465597.33 frames.], batch size: 17, lr: 5.83e-04
2022-07-26 10:54:38,447 INFO [train.py:850] (2/4) Epoch 9, batch 8050, loss[loss=0.311, simple_loss=0.3602, pruned_loss=0.131, over 7448.00 frames.], tot_loss[loss=0.2304, simple_loss=0.3063, pruned_loss=0.0772, over 1465492.96 frames.], batch size: 67, lr: 5.83e-04
2022-07-26 10:55:23,382 INFO [train.py:850] (2/4) Epoch 9, batch 8100, loss[loss=0.1994, simple_loss=0.2741, pruned_loss=0.06232, over 7169.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3078, pruned_loss=0.07788, over 1464861.98 frames.], batch size: 17, lr: 5.83e-04
2022-07-26 10:56:07,616 INFO [train.py:850] (2/4) Epoch 9, batch 8150, loss[loss=0.2542, simple_loss=0.334, pruned_loss=0.08726, over 7345.00 frames.], tot_loss[loss=0.2313, simple_loss=0.3071, pruned_loss=0.07779, over 1465393.47 frames.], batch size: 23, lr: 5.83e-04
2022-07-26 10:56:52,043 INFO [train.py:850] (2/4) Epoch 9, batch 8200, loss[loss=0.1801, simple_loss=0.2622, pruned_loss=0.049, over 7317.00 frames.], tot_loss[loss=0.2311, simple_loss=0.307, pruned_loss=0.07766, over 1465454.84 frames.], batch size: 18, lr: 5.83e-04
2022-07-26 10:57:36,814 INFO [train.py:850] (2/4) Epoch 9, batch 8250, loss[loss=0.1844, simple_loss=0.2666, pruned_loss=0.05105, over 7308.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3064, pruned_loss=0.07692, over 1465652.31 frames.], batch size: 18, lr: 5.83e-04
2022-07-26 10:58:20,373 INFO [train.py:850] (2/4) Epoch 9, batch 8300, loss[loss=0.1847, simple_loss=0.2631, pruned_loss=0.05312, over 7284.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3059, pruned_loss=0.07729, over 1466409.25 frames.], batch size: 16, lr: 5.82e-04
2022-07-26 10:59:05,763 INFO [train.py:850] (2/4) Epoch 9, batch 8350, loss[loss=0.2195, simple_loss=0.303, pruned_loss=0.068, over 7387.00 frames.], tot_loss[loss=0.2294, simple_loss=0.3053, pruned_loss=0.07675, over 1466097.35 frames.], batch size: 21, lr: 5.82e-04
2022-07-26 10:59:48,328 INFO [train.py:850] (2/4) Epoch 9, batch 8400, loss[loss=0.2499, simple_loss=0.3218, pruned_loss=0.08896, over 7392.00 frames.], tot_loss[loss=0.2283, simple_loss=0.3044, pruned_loss=0.07615, over 1465078.77 frames.], batch size: 20, lr: 5.82e-04
2022-07-26 11:00:33,116 INFO [train.py:850] (2/4) Epoch 9, batch 8450, loss[loss=0.1961, simple_loss=0.2658, pruned_loss=0.06324, over 7314.00 frames.], tot_loss[loss=0.2276, simple_loss=0.3036, pruned_loss=0.07581, over 1464908.11 frames.], batch size: 17, lr: 5.82e-04
2022-07-26 11:01:16,747 INFO [train.py:850] (2/4) Epoch 9, batch 8500, loss[loss=0.2314, simple_loss=0.3086, pruned_loss=0.07713, over 7406.00 frames.], tot_loss[loss=0.2271, simple_loss=0.3033, pruned_loss=0.07546, over 1464946.88 frames.], batch size: 22, lr: 5.82e-04
2022-07-26 11:02:00,668 INFO [train.py:850] (2/4) Epoch 9, batch 8550, loss[loss=0.2219, simple_loss=0.2975, pruned_loss=0.07316, over 7388.00 frames.], tot_loss[loss=0.2275, simple_loss=0.304, pruned_loss=0.07556, over 1465392.54 frames.], batch size: 19, lr: 5.81e-04
2022-07-26 11:02:44,245 INFO [train.py:850] (2/4) Epoch 9, batch 8600, loss[loss=0.1949, simple_loss=0.2877, pruned_loss=0.05103, over 7173.00 frames.], tot_loss[loss=0.2249, simple_loss=0.3012, pruned_loss=0.0743, over 1465348.72 frames.], batch size: 21, lr: 5.81e-04
2022-07-26 11:03:28,650 INFO [train.py:850] (2/4) Epoch 9, batch 8650, loss[loss=0.2152, simple_loss=0.2973, pruned_loss=0.06658, over 7194.00 frames.], tot_loss[loss=0.2279, simple_loss=0.3036, pruned_loss=0.0761, over 1464630.54 frames.], batch size: 19, lr: 5.81e-04
2022-07-26 11:04:13,270 INFO [train.py:850] (2/4) Epoch 9, batch 8700, loss[loss=0.1942, simple_loss=0.2812, pruned_loss=0.0536, over 7286.00 frames.], tot_loss[loss=0.2298, simple_loss=0.3056, pruned_loss=0.07696, over 1465921.32 frames.], batch size: 19, lr: 5.81e-04
2022-07-26 11:04:58,435 INFO [train.py:850] (2/4) Epoch 9, batch 8750, loss[loss=0.1911, simple_loss=0.2761, pruned_loss=0.05306, over 7230.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3026, pruned_loss=0.07431, over 1465153.89 frames.], batch size: 24, lr: 5.81e-04
2022-07-26 11:05:42,562 INFO [train.py:850] (2/4) Epoch 9, batch 8800, loss[loss=0.2284, simple_loss=0.3103, pruned_loss=0.07326, over 7348.00 frames.], tot_loss[loss=0.2257, simple_loss=0.3025, pruned_loss=0.07443, over 1464929.16 frames.], batch size: 23, lr: 5.81e-04
2022-07-26 11:06:42,748 INFO [train.py:850] (2/4) Epoch 9, batch 8850, loss[loss=0.2518, simple_loss=0.3255, pruned_loss=0.08902, over 7440.00 frames.], tot_loss[loss=0.2261, simple_loss=0.303, pruned_loss=0.07457, over 1465149.91 frames.], batch size: 69, lr: 5.80e-04
2022-07-26 11:08:08,224 INFO [train.py:850] (2/4) Epoch 10, batch 0, loss[loss=0.2256, simple_loss=0.3108, pruned_loss=0.0702, over 7190.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3108, pruned_loss=0.0702, over 7190.00 frames.], batch size: 21, lr: 5.58e-04
2022-07-26 11:08:52,615 INFO [train.py:850] (2/4) Epoch 10, batch 50, loss[loss=0.249, simple_loss=0.3348, pruned_loss=0.08157, over 7462.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2992, pruned_loss=0.06701, over 330580.81 frames.], batch size: 31, lr: 5.58e-04
2022-07-26 11:09:37,094 INFO [train.py:850] (2/4) Epoch 10, batch 100, loss[loss=0.1922, simple_loss=0.2893, pruned_loss=0.04758, over 7204.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2948, pruned_loss=0.06301, over 582111.98 frames.], batch size: 20, lr: 5.58e-04
2022-07-26 11:10:20,989 INFO [train.py:850] (2/4) Epoch 10, batch 150, loss[loss=0.2699, simple_loss=0.3471, pruned_loss=0.09629, over 7236.00 frames.], tot_loss[loss=0.21, simple_loss=0.2948, pruned_loss=0.06256, over 778994.69 frames.], batch size: 24, lr: 5.57e-04
2022-07-26 11:11:05,385 INFO [train.py:850] (2/4) Epoch 10, batch 200, loss[loss=0.2018, simple_loss=0.2834, pruned_loss=0.06005, over 7192.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2941, pruned_loss=0.06252, over 931041.69 frames.], batch size: 18, lr: 5.57e-04
2022-07-26 11:11:50,344 INFO [train.py:850] (2/4) Epoch 10, batch 250, loss[loss=0.219, simple_loss=0.308, pruned_loss=0.06501, over 7282.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2942, pruned_loss=0.06206, over 1048980.12 frames.], batch size: 27, lr: 5.57e-04
2022-07-26 11:12:35,187 INFO [train.py:850] (2/4) Epoch 10, batch 300, loss[loss=0.2617, simple_loss=0.3387, pruned_loss=0.09233, over 7213.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2929, pruned_loss=0.06167, over 1140787.84 frames.], batch size: 25, lr: 5.57e-04
2022-07-26 11:13:19,157 INFO [train.py:850] (2/4) Epoch 10, batch 350, loss[loss=0.2488, simple_loss=0.3309, pruned_loss=0.08331, over 7297.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2951, pruned_loss=0.0625, over 1213372.51 frames.], batch size: 22, lr: 5.57e-04
2022-07-26 11:14:02,873 INFO [train.py:850] (2/4) Epoch 10, batch 400, loss[loss=0.2166, simple_loss=0.3129, pruned_loss=0.06013, over 7301.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2945, pruned_loss=0.06183, over 1269430.11 frames.], batch size: 22, lr: 5.56e-04
2022-07-26 11:14:47,112 INFO [train.py:850] (2/4) Epoch 10, batch 450, loss[loss=0.1742, simple_loss=0.2539, pruned_loss=0.04725, over 7315.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2936, pruned_loss=0.06167, over 1312461.82 frames.], batch size: 16, lr: 5.56e-04
2022-07-26 11:15:30,920 INFO [train.py:850] (2/4) Epoch 10, batch 500, loss[loss=0.2145, simple_loss=0.3027, pruned_loss=0.06315, over 7181.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2933, pruned_loss=0.06087, over 1346059.59 frames.], batch size: 22, lr: 5.56e-04
2022-07-26 11:16:14,552 INFO [train.py:850] (2/4) Epoch 10, batch 550, loss[loss=0.1956, simple_loss=0.2794, pruned_loss=0.05594, over 7396.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2914, pruned_loss=0.06023, over 1372592.14 frames.], batch size: 19, lr: 5.56e-04
2022-07-26 11:16:57,865 INFO [train.py:850] (2/4) Epoch 10, batch 600, loss[loss=0.1968, simple_loss=0.2782, pruned_loss=0.05767, over 7198.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2912, pruned_loss=0.05926, over 1393278.90 frames.], batch size: 18, lr: 5.56e-04
2022-07-26 11:17:40,739 INFO [train.py:850] (2/4) Epoch 10, batch 650, loss[loss=0.231, simple_loss=0.3097, pruned_loss=0.07612, over 7301.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2906, pruned_loss=0.05908, over 1409267.72 frames.], batch size: 19, lr: 5.56e-04
2022-07-26 11:18:24,943 INFO [train.py:850] (2/4) Epoch 10, batch 700, loss[loss=0.2169, simple_loss=0.3035, pruned_loss=0.06511, over 7474.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2898, pruned_loss=0.05859, over 1421598.80 frames.], batch size: 21, lr: 5.55e-04
2022-07-26 11:19:08,217 INFO [train.py:850] (2/4) Epoch 10, batch 750, loss[loss=0.2377, simple_loss=0.318, pruned_loss=0.07872, over 7407.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2906, pruned_loss=0.05884, over 1430676.70 frames.], batch size: 22, lr: 5.55e-04
2022-07-26 11:19:52,091 INFO [train.py:850] (2/4) Epoch 10, batch 800, loss[loss=0.2138, simple_loss=0.3077, pruned_loss=0.06, over 7491.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2907, pruned_loss=0.05882, over 1439135.21 frames.], batch size: 23, lr: 5.55e-04
2022-07-26 11:20:34,885 INFO [train.py:850] (2/4) Epoch 10, batch 850, loss[loss=0.2224, simple_loss=0.313, pruned_loss=0.06587, over 7429.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2907, pruned_loss=0.05892, over 1445489.64 frames.], batch size: 39, lr: 5.55e-04
2022-07-26 11:21:18,234 INFO [train.py:850] (2/4) Epoch 10, batch 900, loss[loss=0.274, simple_loss=0.3528, pruned_loss=0.09764, over 7426.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2921, pruned_loss=0.05977, over 1450682.18 frames.], batch size: 39, lr: 5.55e-04
2022-07-26 11:22:02,279 INFO [train.py:850] (2/4) Epoch 10, batch 950, loss[loss=0.2765, simple_loss=0.3493, pruned_loss=0.1018, over 7291.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2958, pruned_loss=0.06164, over 1454260.50 frames.], batch size: 21, lr: 5.55e-04
2022-07-26 11:22:45,891 INFO [train.py:850] (2/4) Epoch 10, batch 1000, loss[loss=0.2158, simple_loss=0.2908, pruned_loss=0.07036, over 7472.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2954, pruned_loss=0.06207, over 1456001.91 frames.], batch size: 20, lr: 5.54e-04
2022-07-26 11:23:29,367 INFO [train.py:850] (2/4) Epoch 10, batch 1050, loss[loss=0.2172, simple_loss=0.3073, pruned_loss=0.06354, over 7348.00 frames.], tot_loss[loss=0.212, simple_loss=0.2975, pruned_loss=0.06322, over 1458540.48 frames.], batch size: 23, lr: 5.54e-04
2022-07-26 11:24:14,124 INFO [train.py:850] (2/4) Epoch 10, batch 1100, loss[loss=0.2276, simple_loss=0.3069, pruned_loss=0.07422, over 7470.00 frames.], tot_loss[loss=0.213, simple_loss=0.2981, pruned_loss=0.06398, over 1459732.93 frames.], batch size: 21, lr: 5.54e-04
2022-07-26 11:24:58,733 INFO [train.py:850] (2/4) Epoch 10, batch 1150, loss[loss=0.1811, simple_loss=0.2581, pruned_loss=0.05203, over 7294.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2988, pruned_loss=0.06449, over 1461462.84 frames.], batch size: 17, lr: 5.54e-04
2022-07-26 11:25:43,606 INFO [train.py:850] (2/4) Epoch 10, batch 1200, loss[loss=0.2242, simple_loss=0.3083, pruned_loss=0.07004, over 7393.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2984, pruned_loss=0.06441, over 1463302.49 frames.], batch size: 19, lr: 5.54e-04
2022-07-26 11:26:26,756 INFO [train.py:850] (2/4) Epoch 10, batch 1250, loss[loss=0.2789, simple_loss=0.3636, pruned_loss=0.09712, over 7412.00 frames.], tot_loss[loss=0.215, simple_loss=0.2998, pruned_loss=0.06505, over 1463611.13 frames.], batch size: 31, lr: 5.54e-04
2022-07-26 11:27:10,624 INFO [train.py:850] (2/4) Epoch 10, batch 1300, loss[loss=0.2024, simple_loss=0.293, pruned_loss=0.05589, over 7201.00 frames.], tot_loss[loss=0.2139, simple_loss=0.299, pruned_loss=0.06441, over 1462638.93 frames.], batch size: 19, lr: 5.53e-04
2022-07-26 11:27:54,007 INFO [train.py:850] (2/4) Epoch 10, batch 1350, loss[loss=0.225, simple_loss=0.316, pruned_loss=0.06696, over 7228.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2995, pruned_loss=0.06438, over 1464540.73 frames.], batch size: 25, lr: 5.53e-04
2022-07-26 11:28:36,383 INFO [train.py:850] (2/4) Epoch 10, batch 1400, loss[loss=0.2633, simple_loss=0.35, pruned_loss=0.08833, over 7406.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2987, pruned_loss=0.06393, over 1465228.77 frames.], batch size: 31, lr: 5.53e-04
2022-07-26 11:29:20,870 INFO [train.py:850] (2/4) Epoch 10, batch 1450, loss[loss=0.2362, simple_loss=0.3101, pruned_loss=0.08116, over 7386.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2984, pruned_loss=0.06405, over 1465227.65 frames.], batch size: 19, lr: 5.53e-04
2022-07-26 11:30:03,835 INFO [train.py:850] (2/4) Epoch 10, batch 1500, loss[loss=0.2188, simple_loss=0.302, pruned_loss=0.06779, over 7165.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2984, pruned_loss=0.06434, over 1465692.83 frames.], batch size: 22, lr: 5.53e-04
2022-07-26 11:30:47,277 INFO [train.py:850] (2/4) Epoch 10, batch 1550, loss[loss=0.2575, simple_loss=0.3201, pruned_loss=0.09747, over 7478.00 frames.], tot_loss[loss=0.2126, simple_loss=0.298, pruned_loss=0.06363, over 1465266.05 frames.], batch size: 20, lr: 5.53e-04
2022-07-26 11:31:31,396 INFO [train.py:850] (2/4) Epoch 10, batch 1600, loss[loss=0.1977, simple_loss=0.293, pruned_loss=0.05117, over 7287.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2974, pruned_loss=0.06345, over 1465700.37 frames.], batch size: 20, lr: 5.52e-04
2022-07-26 11:32:14,851 INFO [train.py:850] (2/4) Epoch 10, batch 1650, loss[loss=0.183, simple_loss=0.2816, pruned_loss=0.04221, over 7207.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2986, pruned_loss=0.06359, over 1466068.76 frames.], batch size: 20, lr: 5.52e-04
2022-07-26 11:32:59,855 INFO [train.py:850] (2/4) Epoch 10, batch 1700, loss[loss=0.2166, simple_loss=0.3046, pruned_loss=0.06432, over 7237.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2991, pruned_loss=0.06371, over 1465319.48 frames.], batch size: 24, lr: 5.52e-04
2022-07-26 11:33:45,087 INFO [train.py:850] (2/4) Epoch 10, batch 1750, loss[loss=0.1774, simple_loss=0.2607, pruned_loss=0.04711, over 7153.00 frames.], tot_loss[loss=0.213, simple_loss=0.2992, pruned_loss=0.06344, over 1464453.00 frames.], batch size: 17, lr: 5.52e-04
2022-07-26 11:34:30,189 INFO [train.py:850] (2/4) Epoch 10, batch 1800, loss[loss=0.252, simple_loss=0.331, pruned_loss=0.08645, over 7202.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2994, pruned_loss=0.06355, over 1463687.20 frames.], batch size: 20, lr: 5.52e-04
2022-07-26 11:35:13,707 INFO [train.py:850] (2/4) Epoch 10, batch 1850, loss[loss=0.2013, simple_loss=0.2864, pruned_loss=0.0581, over 7272.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2998, pruned_loss=0.06367, over 1464561.41 frames.], batch size: 16, lr: 5.52e-04
2022-07-26 11:35:58,258 INFO [train.py:850] (2/4) Epoch 10, batch 1900, loss[loss=0.1988, simple_loss=0.2717, pruned_loss=0.06298, over 7305.00 frames.], tot_loss[loss=0.2119, simple_loss=0.298, pruned_loss=0.0629, over 1464690.05 frames.], batch size: 17, lr: 5.51e-04
2022-07-26 11:36:42,932 INFO [train.py:850] (2/4) Epoch 10, batch 1950, loss[loss=0.2215, simple_loss=0.3177, pruned_loss=0.06261, over 7375.00 frames.], tot_loss[loss=0.2118, simple_loss=0.2978, pruned_loss=0.06288, over 1464464.08 frames.], batch size: 21, lr: 5.51e-04
2022-07-26 11:37:28,898 INFO [train.py:850] (2/4) Epoch 10, batch 2000, loss[loss=0.2461, simple_loss=0.3333, pruned_loss=0.07952, over 7285.00 frames.], tot_loss[loss=0.211, simple_loss=0.2969, pruned_loss=0.06257, over 1463780.64 frames.], batch size: 21, lr: 5.51e-04
2022-07-26 11:38:15,071 INFO [train.py:850] (2/4) Epoch 10, batch 2050, loss[loss=0.1668, simple_loss=0.2546, pruned_loss=0.03955, over 7394.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2964, pruned_loss=0.06209, over 1464385.94 frames.], batch size: 19, lr: 5.51e-04
2022-07-26 11:38:58,598 INFO [train.py:850] (2/4) Epoch 10, batch 2100, loss[loss=0.2429, simple_loss=0.328, pruned_loss=0.0789, over 7489.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2959, pruned_loss=0.06156, over 1464492.62 frames.], batch size: 23, lr: 5.51e-04
2022-07-26 11:39:42,132 INFO [train.py:850] (2/4) Epoch 10, batch 2150, loss[loss=0.173, simple_loss=0.2557, pruned_loss=0.04513, over 7478.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2959, pruned_loss=0.06161, over 1464255.25 frames.], batch size: 17, lr: 5.51e-04
2022-07-26 11:40:25,230 INFO [train.py:850] (2/4) Epoch 10, batch 2200, loss[loss=0.222, simple_loss=0.3001, pruned_loss=0.07194, over 7478.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2949, pruned_loss=0.0611, over 1464688.35 frames.], batch size: 24, lr: 5.50e-04
2022-07-26 11:41:07,908 INFO [train.py:850] (2/4) Epoch 10, batch 2250, loss[loss=0.2287, simple_loss=0.3171, pruned_loss=0.07016, over 7381.00 frames.], tot_loss[loss=0.2089, simple_loss=0.2952, pruned_loss=0.06131, over 1465146.32 frames.], batch size: 21, lr: 5.50e-04
2022-07-26 11:41:53,503 INFO [train.py:850] (2/4) Epoch 10, batch 2300, loss[loss=0.2, simple_loss=0.2822, pruned_loss=0.05893, over 7379.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2961, pruned_loss=0.06231, over 1465484.56 frames.], batch size: 20, lr: 5.50e-04
2022-07-26 11:42:37,548 INFO [train.py:850] (2/4) Epoch 10, batch 2350, loss[loss=0.2042, simple_loss=0.2822, pruned_loss=0.0631, over 7258.00 frames.], tot_loss[loss=0.2115, simple_loss=0.2971, pruned_loss=0.06298, over 1465494.36 frames.], batch size: 16, lr: 5.50e-04
2022-07-26 11:43:21,440 INFO [train.py:850] (2/4) Epoch 10, batch 2400, loss[loss=0.1952, simple_loss=0.2732, pruned_loss=0.05867, over 7401.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2985, pruned_loss=0.06328, over 1464129.76 frames.], batch size: 19, lr: 5.50e-04
2022-07-26 11:44:04,780 INFO [train.py:850] (2/4) Epoch 10, batch 2450, loss[loss=0.1785, simple_loss=0.2628, pruned_loss=0.04711, over 7309.00 frames.], tot_loss[loss=0.2112, simple_loss=0.2969, pruned_loss=0.06276, over 1464110.43 frames.], batch size: 18, lr: 5.50e-04
2022-07-26 11:44:48,585 INFO [train.py:850] (2/4) Epoch 10, batch 2500, loss[loss=0.2139, simple_loss=0.2997, pruned_loss=0.06407, over 7166.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2959, pruned_loss=0.06225, over 1464157.53 frames.], batch size: 22, lr: 5.49e-04
2022-07-26 11:45:32,289 INFO [train.py:850] (2/4) Epoch 10, batch 2550, loss[loss=0.19, simple_loss=0.2756, pruned_loss=0.05216, over 7489.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2962, pruned_loss=0.06253, over 1465571.68 frames.], batch size: 19, lr: 5.49e-04
2022-07-26 11:46:16,070 INFO [train.py:850] (2/4) Epoch 10, batch 2600, loss[loss=0.2167, simple_loss=0.2967, pruned_loss=0.06834, over 7199.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2942, pruned_loss=0.06128, over 1465816.17 frames.], batch size: 18, lr: 5.49e-04
2022-07-26 11:46:59,473 INFO [train.py:850] (2/4) Epoch 10, batch 2650, loss[loss=0.1899, simple_loss=0.2751, pruned_loss=0.05228, over 7303.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2932, pruned_loss=0.06018, over 1465728.39 frames.], batch size: 19, lr: 5.49e-04
2022-07-26 11:47:43,041 INFO [train.py:850] (2/4) Epoch 10, batch 2700, loss[loss=0.156, simple_loss=0.2411, pruned_loss=0.03545, over 7310.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2941, pruned_loss=0.06061, over 1465471.54 frames.], batch size: 17, lr: 5.49e-04
2022-07-26 11:48:27,093 INFO [train.py:850] (2/4) Epoch 10, batch 2750, loss[loss=0.2856, simple_loss=0.3418, pruned_loss=0.1147, over 7493.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2945, pruned_loss=0.06068, over 1465495.79 frames.], batch size: 19, lr: 5.49e-04
2022-07-26 11:49:10,919 INFO [train.py:850] (2/4) Epoch 10, batch 2800, loss[loss=0.1828, simple_loss=0.2945, pruned_loss=0.0355, over 7301.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2943, pruned_loss=0.06041, over 1465291.63 frames.], batch size: 22, lr: 5.48e-04
2022-07-26 11:49:54,225 INFO [train.py:850] (2/4) Epoch 10, batch 2850, loss[loss=0.1847, simple_loss=0.2628, pruned_loss=0.0533, over 7233.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2942, pruned_loss=0.06076, over 1465420.92 frames.], batch size: 16, lr: 5.48e-04
2022-07-26 11:50:38,714 INFO [train.py:850] (2/4) Epoch 10, batch 2900, loss[loss=0.1965, simple_loss=0.2833, pruned_loss=0.05483, over 7304.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2937, pruned_loss=0.06027, over 1466536.04 frames.], batch size: 18, lr: 5.48e-04
2022-07-26 11:51:22,499 INFO [train.py:850] (2/4) Epoch 10, batch 2950, loss[loss=0.2148, simple_loss=0.3013, pruned_loss=0.06416, over 7361.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2934, pruned_loss=0.06012, over 1468004.30 frames.], batch size: 23, lr: 5.48e-04
2022-07-26 11:52:06,779 INFO [train.py:850] (2/4) Epoch 10, batch 3000, loss[loss=0.1999, simple_loss=0.2907, pruned_loss=0.05456, over 7281.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2941, pruned_loss=0.06088, over 1466465.96 frames.], batch size: 20, lr: 5.48e-04
2022-07-26 11:52:06,780 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 11:52:29,602 INFO [train.py:879] (2/4) Epoch 10, validation: loss=0.1986, simple_loss=0.2943, pruned_loss=0.05146, over 924787.00 frames.
2022-07-26 11:53:12,831 INFO [train.py:850] (2/4) Epoch 10, batch 3050, loss[loss=0.1984, simple_loss=0.2943, pruned_loss=0.05124, over 7202.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2932, pruned_loss=0.06012, over 1466507.51 frames.], batch size: 20, lr: 5.48e-04
2022-07-26 11:53:57,487 INFO [train.py:850] (2/4) Epoch 10, batch 3100, loss[loss=0.2312, simple_loss=0.317, pruned_loss=0.07273, over 7470.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2938, pruned_loss=0.06018, over 1466825.85 frames.], batch size: 39, lr: 5.47e-04
2022-07-26 11:54:40,368 INFO [train.py:850] (2/4) Epoch 10, batch 3150, loss[loss=0.1917, simple_loss=0.2774, pruned_loss=0.05297, over 7281.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2931, pruned_loss=0.05989, over 1466146.00 frames.], batch size: 20, lr: 5.47e-04
2022-07-26 11:55:25,054 INFO [train.py:850] (2/4) Epoch 10, batch 3200, loss[loss=0.1902, simple_loss=0.2808, pruned_loss=0.04985, over 7390.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2943, pruned_loss=0.0607, over 1465415.72 frames.], batch size: 21, lr: 5.47e-04
2022-07-26 11:56:08,300 INFO [train.py:850] (2/4) Epoch 10, batch 3250, loss[loss=0.2074, simple_loss=0.3072, pruned_loss=0.05378, over 7292.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2951, pruned_loss=0.06097, over 1465089.14 frames.], batch size: 19, lr: 5.47e-04
2022-07-26 11:56:51,995 INFO [train.py:850] (2/4) Epoch 10, batch 3300, loss[loss=0.219, simple_loss=0.3065, pruned_loss=0.06571, over 7409.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2959, pruned_loss=0.06146, over 1464135.79 frames.], batch size: 22, lr: 5.47e-04
2022-07-26 11:57:36,577 INFO [train.py:850] (2/4) Epoch 10, batch 3350, loss[loss=0.2818, simple_loss=0.3568, pruned_loss=0.1034, over 7472.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2948, pruned_loss=0.06071, over 1463855.41 frames.], batch size: 71, lr: 5.47e-04
2022-07-26 11:58:20,786 INFO [train.py:850] (2/4) Epoch 10, batch 3400, loss[loss=0.1924, simple_loss=0.2961, pruned_loss=0.04434, over 7390.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2937, pruned_loss=0.05992, over 1464718.99 frames.], batch size: 21, lr: 5.46e-04
2022-07-26 11:59:05,497 INFO [train.py:850] (2/4) Epoch 10, batch 3450, loss[loss=0.223, simple_loss=0.3032, pruned_loss=0.07137, over 7383.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2937, pruned_loss=0.05989, over 1464940.00 frames.], batch size: 20, lr: 5.46e-04
2022-07-26 11:59:48,795 INFO [train.py:850] (2/4) Epoch 10, batch 3500, loss[loss=0.197, simple_loss=0.2956, pruned_loss=0.04924, over 7375.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2941, pruned_loss=0.06026, over 1464428.59 frames.], batch size: 21, lr: 5.46e-04
2022-07-26 12:00:33,392 INFO [train.py:850] (2/4) Epoch 10, batch 3550, loss[loss=0.2167, simple_loss=0.3061, pruned_loss=0.06362, over 7331.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2951, pruned_loss=0.0602, over 1465053.27 frames.], batch size: 38, lr: 5.46e-04
2022-07-26 12:01:17,905 INFO [train.py:850] (2/4) Epoch 10, batch 3600, loss[loss=0.1673, simple_loss=0.2465, pruned_loss=0.044, over 7450.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2943, pruned_loss=0.05981, over 1465237.55 frames.], batch size: 17, lr: 5.46e-04
2022-07-26 12:02:02,610 INFO [train.py:850] (2/4) Epoch 10, batch 3650, loss[loss=0.2314, simple_loss=0.3219, pruned_loss=0.07043, over 7220.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2949, pruned_loss=0.06058, over 1464579.20 frames.], batch size: 25, lr: 5.46e-04
2022-07-26 12:02:47,042 INFO [train.py:850] (2/4) Epoch 10, batch 3700, loss[loss=0.1887, simple_loss=0.2923, pruned_loss=0.04252, over 7430.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2955, pruned_loss=0.0607, over 1465227.62 frames.], batch size: 22, lr: 5.45e-04
2022-07-26 12:03:30,445 INFO [train.py:850] (2/4) Epoch 10, batch 3750, loss[loss=0.2345, simple_loss=0.3143, pruned_loss=0.07732, over 7186.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2956, pruned_loss=0.06066, over 1464397.94 frames.], batch size: 22, lr: 5.45e-04
2022-07-26 12:04:14,884 INFO [train.py:850] (2/4) Epoch 10, batch 3800, loss[loss=0.2483, simple_loss=0.3283, pruned_loss=0.08416, over 7188.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2956, pruned_loss=0.0608, over 1463452.63 frames.], batch size: 22, lr: 5.45e-04
2022-07-26 12:04:57,786 INFO [train.py:850] (2/4) Epoch 10, batch 3850, loss[loss=0.1759, simple_loss=0.2766, pruned_loss=0.0376, over 7194.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2931, pruned_loss=0.05938, over 1463854.44 frames.], batch size: 20, lr: 5.45e-04
2022-07-26 12:05:41,458 INFO [train.py:850] (2/4) Epoch 10, batch 3900, loss[loss=0.2446, simple_loss=0.3307, pruned_loss=0.07921, over 7455.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2931, pruned_loss=0.06008, over 1464331.97 frames.], batch size: 24, lr: 5.45e-04
2022-07-26 12:06:24,789 INFO [train.py:850] (2/4) Epoch 10, batch 3950, loss[loss=0.221, simple_loss=0.3081, pruned_loss=0.06702, over 7375.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2935, pruned_loss=0.05978, over 1465082.69 frames.], batch size: 21, lr: 5.45e-04
2022-07-26 12:07:24,565 INFO [train.py:850] (2/4) Epoch 10, batch 4000, loss[loss=0.1743, simple_loss=0.2516, pruned_loss=0.04846, over 7112.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2944, pruned_loss=0.05998, over 1465949.89 frames.], batch size: 18, lr: 5.44e-04
2022-07-26 12:08:08,140 INFO [train.py:850] (2/4) Epoch 10, batch 4050, loss[loss=0.2345, simple_loss=0.3114, pruned_loss=0.07881, over 7197.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2931, pruned_loss=0.05984, over 1465012.59 frames.], batch size: 19, lr: 5.44e-04
2022-07-26 12:08:51,551 INFO [train.py:850] (2/4) Epoch 10, batch 4100, loss[loss=0.2264, simple_loss=0.3061, pruned_loss=0.07333, over 7408.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2928, pruned_loss=0.05986, over 1465216.39 frames.], batch size: 22, lr: 5.44e-04
2022-07-26 12:09:34,716 INFO [train.py:850] (2/4) Epoch 10, batch 4150, loss[loss=0.187, simple_loss=0.2751, pruned_loss=0.04948, over 7305.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2945, pruned_loss=0.06179, over 1466066.35 frames.], batch size: 19, lr: 5.44e-04
2022-07-26 12:10:19,942 INFO [train.py:850] (2/4) Epoch 10, batch 4200, loss[loss=0.1922, simple_loss=0.2663, pruned_loss=0.05907, over 7156.00 frames.], tot_loss[loss=0.2128, simple_loss=0.297, pruned_loss=0.06428, over 1466885.74 frames.], batch size: 17, lr: 5.44e-04
2022-07-26 12:11:04,778 INFO [train.py:850] (2/4) Epoch 10, batch 4250, loss[loss=0.2053, simple_loss=0.2928, pruned_loss=0.05886, over 7190.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2974, pruned_loss=0.06538, over 1465520.98 frames.], batch size: 20, lr: 5.44e-04
2022-07-26 12:11:49,817 INFO [train.py:850] (2/4) Epoch 10, batch 4300, loss[loss=0.2311, simple_loss=0.2914, pruned_loss=0.08536, over 7431.00 frames.], tot_loss[loss=0.2173, simple_loss=0.2995, pruned_loss=0.06757, over 1465188.87 frames.], batch size: 18, lr: 5.44e-04
2022-07-26 12:12:33,278 INFO [train.py:850] (2/4) Epoch 10, batch 4350, loss[loss=0.2535, simple_loss=0.3307, pruned_loss=0.08815, over 7336.00 frames.], tot_loss[loss=0.2205, simple_loss=0.3019, pruned_loss=0.06957, over 1464627.66 frames.], batch size: 23, lr: 5.43e-04
2022-07-26 12:13:16,874 INFO [train.py:850] (2/4) Epoch 10, batch 4400, loss[loss=0.2206, simple_loss=0.2853, pruned_loss=0.07793, over 7296.00 frames.], tot_loss[loss=0.2199, simple_loss=0.3007, pruned_loss=0.06953, over 1465211.61 frames.], batch size: 18, lr: 5.43e-04
2022-07-26 12:14:00,112 INFO [train.py:850] (2/4) Epoch 10, batch 4450, loss[loss=0.2326, simple_loss=0.3187, pruned_loss=0.0732, over 7382.00 frames.], tot_loss[loss=0.2216, simple_loss=0.3013, pruned_loss=0.07097, over 1464847.14 frames.], batch size: 20, lr: 5.43e-04
2022-07-26 12:14:43,418 INFO [train.py:850] (2/4) Epoch 10, batch 4500, loss[loss=0.1959, simple_loss=0.2529, pruned_loss=0.06943, over 7440.00 frames.], tot_loss[loss=0.2216, simple_loss=0.3009, pruned_loss=0.07116, over 1465174.48 frames.], batch size: 18, lr: 5.43e-04
2022-07-26 12:15:27,559 INFO [train.py:850] (2/4) Epoch 10, batch 4550, loss[loss=0.2179, simple_loss=0.3137, pruned_loss=0.06102, over 7378.00 frames.], tot_loss[loss=0.2229, simple_loss=0.3015, pruned_loss=0.07209, over 1464587.56 frames.], batch size: 20, lr: 5.43e-04
2022-07-26 12:16:11,195 INFO [train.py:850] (2/4) Epoch 10, batch 4600, loss[loss=0.2379, simple_loss=0.3096, pruned_loss=0.08311, over 7365.00 frames.], tot_loss[loss=0.2229, simple_loss=0.3013, pruned_loss=0.07225, over 1464506.51 frames.], batch size: 31, lr: 5.43e-04
2022-07-26 12:16:55,943 INFO [train.py:850] (2/4) Epoch 10, batch 4650, loss[loss=0.2329, simple_loss=0.2932, pruned_loss=0.08626, over 7473.00 frames.], tot_loss[loss=0.2236, simple_loss=0.3015, pruned_loss=0.07284, over 1464786.12 frames.], batch size: 20, lr: 5.42e-04
2022-07-26 12:17:39,970 INFO [train.py:850] (2/4) Epoch 10, batch 4700, loss[loss=0.2724, simple_loss=0.3276, pruned_loss=0.1086, over 7291.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3026, pruned_loss=0.07423, over 1466276.15 frames.], batch size: 20, lr: 5.42e-04
2022-07-26 12:18:23,244 INFO [train.py:850] (2/4) Epoch 10, batch 4750, loss[loss=0.2595, simple_loss=0.3396, pruned_loss=0.08971, over 7308.00 frames.], tot_loss[loss=0.229, simple_loss=0.3047, pruned_loss=0.07663, over 1466684.48 frames.], batch size: 22, lr: 5.42e-04
2022-07-26 12:19:08,048 INFO [train.py:850] (2/4) Epoch 10, batch 4800, loss[loss=0.2334, simple_loss=0.3031, pruned_loss=0.08188, over 7199.00 frames.], tot_loss[loss=0.2288, simple_loss=0.3044, pruned_loss=0.07655, over 1466666.44 frames.], batch size: 19, lr: 5.42e-04
2022-07-26 12:19:51,824 INFO [train.py:850] (2/4) Epoch 10, batch 4850, loss[loss=0.1973, simple_loss=0.278, pruned_loss=0.05831, over 7198.00 frames.], tot_loss[loss=0.229, simple_loss=0.3049, pruned_loss=0.07657, over 1465439.81 frames.], batch size: 18, lr: 5.42e-04
2022-07-26 12:20:38,171 INFO [train.py:850] (2/4) Epoch 10, batch 4900, loss[loss=0.2107, simple_loss=0.2779, pruned_loss=0.07171, over 7150.00 frames.], tot_loss[loss=0.2279, simple_loss=0.3041, pruned_loss=0.0758, over 1466220.21 frames.], batch size: 17, lr: 5.42e-04
2022-07-26 12:21:21,502 INFO [train.py:850] (2/4) Epoch 10, batch 4950, loss[loss=0.1921, simple_loss=0.2775, pruned_loss=0.05334, over 7323.00 frames.], tot_loss[loss=0.2266, simple_loss=0.3029, pruned_loss=0.07519, over 1464910.95 frames.], batch size: 18, lr: 5.41e-04
2022-07-26 12:22:05,617 INFO [train.py:850] (2/4) Epoch 10, batch 5000, loss[loss=0.1697, simple_loss=0.258, pruned_loss=0.04068, over 7313.00 frames.], tot_loss[loss=0.2262, simple_loss=0.3021, pruned_loss=0.07515, over 1465104.88 frames.], batch size: 18, lr: 5.41e-04
2022-07-26 12:22:49,468 INFO [train.py:850] (2/4) Epoch 10, batch 5050, loss[loss=0.2204, simple_loss=0.2889, pruned_loss=0.07595, over 7448.00 frames.], tot_loss[loss=0.2275, simple_loss=0.3031, pruned_loss=0.07599, over 1463763.75 frames.], batch size: 24, lr: 5.41e-04
2022-07-26 12:23:34,215 INFO [train.py:850] (2/4) Epoch 10, batch 5100, loss[loss=0.2372, simple_loss=0.315, pruned_loss=0.07967, over 7281.00 frames.], tot_loss[loss=0.2282, simple_loss=0.3035, pruned_loss=0.07649, over 1464512.88 frames.], batch size: 21, lr: 5.41e-04
2022-07-26 12:24:18,316 INFO [train.py:850] (2/4) Epoch 10, batch 5150, loss[loss=0.2377, simple_loss=0.3084, pruned_loss=0.08349, over 7387.00 frames.], tot_loss[loss=0.2274, simple_loss=0.303, pruned_loss=0.07593, over 1465346.47 frames.], batch size: 21, lr: 5.41e-04
2022-07-26 12:25:02,543 INFO [train.py:850] (2/4) Epoch 10, batch 5200, loss[loss=0.2257, simple_loss=0.2878, pruned_loss=0.08179, over 7287.00 frames.], tot_loss[loss=0.2269, simple_loss=0.3025, pruned_loss=0.07564, over 1464884.04 frames.], batch size: 17, lr: 5.41e-04
2022-07-26 12:25:47,632 INFO [train.py:850] (2/4) Epoch 10, batch 5250, loss[loss=0.2472, simple_loss=0.3136, pruned_loss=0.09045, over 7459.00 frames.], tot_loss[loss=0.2281, simple_loss=0.3035, pruned_loss=0.07636, over 1465004.95 frames.], batch size: 24, lr: 5.41e-04
2022-07-26 12:26:31,068 INFO [train.py:850] (2/4) Epoch 10, batch 5300, loss[loss=0.2188, simple_loss=0.2855, pruned_loss=0.07606, over 7199.00 frames.], tot_loss[loss=0.2284, simple_loss=0.3036, pruned_loss=0.07659, over 1465012.26 frames.], batch size: 18, lr: 5.40e-04
2022-07-26 12:27:13,877 INFO [train.py:850] (2/4) Epoch 10, batch 5350, loss[loss=0.1724, simple_loss=0.2545, pruned_loss=0.04513, over 7268.00 frames.], tot_loss[loss=0.2271, simple_loss=0.3028, pruned_loss=0.07569, over 1465227.09 frames.], batch size: 16, lr: 5.40e-04
2022-07-26 12:27:58,058 INFO [train.py:850] (2/4) Epoch 10, batch 5400, loss[loss=0.2863, simple_loss=0.3657, pruned_loss=0.1035, over 7176.00 frames.], tot_loss[loss=0.2276, simple_loss=0.303, pruned_loss=0.07609, over 1465754.46 frames.], batch size: 22, lr: 5.40e-04
2022-07-26 12:28:40,762 INFO [train.py:850] (2/4) Epoch 10, batch 5450, loss[loss=0.239, simple_loss=0.3025, pruned_loss=0.08773, over 7097.00 frames.], tot_loss[loss=0.2274, simple_loss=0.303, pruned_loss=0.07591, over 1465423.69 frames.], batch size: 18, lr: 5.40e-04
2022-07-26 12:29:25,770 INFO [train.py:850] (2/4) Epoch 10, batch 5500, loss[loss=0.1962, simple_loss=0.2752, pruned_loss=0.05859, over 7379.00 frames.], tot_loss[loss=0.2277, simple_loss=0.3034, pruned_loss=0.07605, over 1465432.44 frames.], batch size: 20, lr: 5.40e-04
2022-07-26 12:30:08,883 INFO [train.py:850] (2/4) Epoch 10, batch 5550, loss[loss=0.2873, simple_loss=0.3485, pruned_loss=0.113, over 7450.00 frames.], tot_loss[loss=0.23, simple_loss=0.305, pruned_loss=0.07749, over 1465030.46 frames.], batch size: 71, lr: 5.40e-04
2022-07-26 12:30:52,669 INFO [train.py:850] (2/4) Epoch 10, batch 5600, loss[loss=0.2112, simple_loss=0.3008, pruned_loss=0.0608, over 7479.00 frames.], tot_loss[loss=0.2288, simple_loss=0.3039, pruned_loss=0.07686, over 1465970.26 frames.], batch size: 24, lr: 5.39e-04
2022-07-26 12:31:36,398 INFO [train.py:850] (2/4) Epoch 10, batch 5650, loss[loss=0.2285, simple_loss=0.3024, pruned_loss=0.07733, over 7246.00 frames.], tot_loss[loss=0.2278, simple_loss=0.3027, pruned_loss=0.07638, over 1465521.93 frames.], batch size: 30, lr: 5.39e-04
2022-07-26 12:32:21,019 INFO [train.py:850] (2/4) Epoch 10, batch 5700, loss[loss=0.1881, simple_loss=0.2751, pruned_loss=0.05057, over 7189.00 frames.], tot_loss[loss=0.2304, simple_loss=0.3055, pruned_loss=0.07762, over 1465202.93 frames.], batch size: 18, lr: 5.39e-04
2022-07-26 12:33:06,177 INFO [train.py:850] (2/4) Epoch 10, batch 5750, loss[loss=0.3049, simple_loss=0.3461, pruned_loss=0.1319, over 7384.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3051, pruned_loss=0.07754, over 1465749.45 frames.], batch size: 19, lr: 5.39e-04
2022-07-26 12:33:50,830 INFO [train.py:850] (2/4) Epoch 10, batch 5800, loss[loss=0.1933, simple_loss=0.2856, pruned_loss=0.05055, over 7298.00 frames.], tot_loss[loss=0.2314, simple_loss=0.3066, pruned_loss=0.07812, over 1465692.21 frames.], batch size: 22, lr: 5.39e-04
2022-07-26 12:34:34,635 INFO [train.py:850] (2/4) Epoch 10, batch 5850, loss[loss=0.2701, simple_loss=0.3467, pruned_loss=0.09675, over 7236.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3056, pruned_loss=0.0771, over 1465088.86 frames.], batch size: 25, lr: 5.39e-04
2022-07-26 12:35:18,499 INFO [train.py:850] (2/4) Epoch 10, batch 5900, loss[loss=0.1937, simple_loss=0.2784, pruned_loss=0.05449, over 7391.00 frames.], tot_loss[loss=0.2277, simple_loss=0.3039, pruned_loss=0.07572, over 1465807.31 frames.], batch size: 20, lr: 5.38e-04
2022-07-26 12:36:02,137 INFO [train.py:850] (2/4) Epoch 10, batch 5950, loss[loss=0.2421, simple_loss=0.3174, pruned_loss=0.08346, over 7226.00 frames.], tot_loss[loss=0.2259, simple_loss=0.3022, pruned_loss=0.07478, over 1466159.07 frames.], batch size: 24, lr: 5.38e-04
2022-07-26 12:36:45,340 INFO [train.py:850] (2/4) Epoch 10, batch 6000, loss[loss=0.2085, simple_loss=0.2905, pruned_loss=0.0632, over 7403.00 frames.], tot_loss[loss=0.2254, simple_loss=0.3017, pruned_loss=0.07457, over 1466219.14 frames.], batch size: 22, lr: 5.38e-04
2022-07-26 12:36:45,341 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 12:37:08,175 INFO [train.py:879] (2/4) Epoch 10, validation: loss=0.1957, simple_loss=0.2939, pruned_loss=0.04876, over 924787.00 frames.
2022-07-26 12:37:52,486 INFO [train.py:850] (2/4) Epoch 10, batch 6050, loss[loss=0.2608, simple_loss=0.3424, pruned_loss=0.08967, over 7306.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3005, pruned_loss=0.07387, over 1466409.42 frames.], batch size: 22, lr: 5.38e-04
2022-07-26 12:38:36,811 INFO [train.py:850] (2/4) Epoch 10, batch 6100, loss[loss=0.2493, simple_loss=0.3182, pruned_loss=0.09025, over 7406.00 frames.], tot_loss[loss=0.2251, simple_loss=0.3009, pruned_loss=0.07467, over 1465972.16 frames.], batch size: 19, lr: 5.38e-04
2022-07-26 12:39:21,185 INFO [train.py:850] (2/4) Epoch 10, batch 6150, loss[loss=0.2388, simple_loss=0.3233, pruned_loss=0.07712, over 7180.00 frames.], tot_loss[loss=0.2253, simple_loss=0.3016, pruned_loss=0.07449, over 1465787.10 frames.], batch size: 22, lr: 5.38e-04
2022-07-26 12:40:04,989 INFO [train.py:850] (2/4) Epoch 10, batch 6200, loss[loss=0.2629, simple_loss=0.3239, pruned_loss=0.101, over 7401.00 frames.], tot_loss[loss=0.2263, simple_loss=0.3023, pruned_loss=0.07512, over 1464893.55 frames.], batch size: 22, lr: 5.38e-04
2022-07-26 12:40:47,863 INFO [train.py:850] (2/4) Epoch 10, batch 6250, loss[loss=0.2004, simple_loss=0.2654, pruned_loss=0.06768, over 7284.00 frames.], tot_loss[loss=0.2245, simple_loss=0.3003, pruned_loss=0.07432, over 1464604.02 frames.], batch size: 16, lr: 5.37e-04
2022-07-26 12:41:32,539 INFO [train.py:850] (2/4) Epoch 10, batch 6300, loss[loss=0.1918, simple_loss=0.267, pruned_loss=0.0583, over 7162.00 frames.], tot_loss[loss=0.2238, simple_loss=0.2999, pruned_loss=0.07381, over 1464922.68 frames.], batch size: 17, lr: 5.37e-04
2022-07-26 12:42:15,943 INFO [train.py:850] (2/4) Epoch 10, batch 6350, loss[loss=0.2731, simple_loss=0.3398, pruned_loss=0.1032, over 7341.00 frames.], tot_loss[loss=0.2246, simple_loss=0.3008, pruned_loss=0.07416, over 1464205.85 frames.], batch size: 31, lr: 5.37e-04
2022-07-26 12:43:01,254 INFO [train.py:850] (2/4) Epoch 10, batch 6400, loss[loss=0.1923, simple_loss=0.2883, pruned_loss=0.04819, over 7309.00 frames.], tot_loss[loss=0.2272, simple_loss=0.3028, pruned_loss=0.07578, over 1465168.38 frames.], batch size: 18, lr: 5.37e-04
2022-07-26 12:43:44,278 INFO [train.py:850] (2/4) Epoch 10, batch 6450, loss[loss=0.2091, simple_loss=0.2975, pruned_loss=0.06038, over 7412.00 frames.], tot_loss[loss=0.2263, simple_loss=0.3024, pruned_loss=0.07508, over 1466101.45 frames.], batch size: 22, lr: 5.37e-04
2022-07-26 12:44:28,035 INFO [train.py:850] (2/4) Epoch 10, batch 6500, loss[loss=0.1919, simple_loss=0.2813, pruned_loss=0.05126, over 7284.00 frames.], tot_loss[loss=0.225, simple_loss=0.3014, pruned_loss=0.07435, over 1466057.04 frames.], batch size: 20, lr: 5.37e-04
2022-07-26 12:45:11,528 INFO [train.py:850] (2/4) Epoch 10, batch 6550, loss[loss=0.2126, simple_loss=0.2889, pruned_loss=0.06817, over 7384.00 frames.], tot_loss[loss=0.2232, simple_loss=0.2998, pruned_loss=0.07328, over 1466322.26 frames.], batch size: 20, lr: 5.36e-04
2022-07-26 12:45:55,959 INFO [train.py:850] (2/4) Epoch 10, batch 6600, loss[loss=0.2089, simple_loss=0.2957, pruned_loss=0.06104, over 7381.00 frames.], tot_loss[loss=0.2238, simple_loss=0.3006, pruned_loss=0.0735, over 1466427.90 frames.], batch size: 21, lr: 5.36e-04
2022-07-26 12:46:40,509 INFO [train.py:850] (2/4) Epoch 10, batch 6650, loss[loss=0.2522, simple_loss=0.3251, pruned_loss=0.08964, over 7424.00 frames.], tot_loss[loss=0.224, simple_loss=0.3007, pruned_loss=0.07368, over 1466389.72 frames.], batch size: 38, lr: 5.36e-04
2022-07-26 12:47:23,956 INFO [train.py:850] (2/4) Epoch 10, batch 6700, loss[loss=0.1664, simple_loss=0.2472, pruned_loss=0.04278, over 7450.00 frames.], tot_loss[loss=0.224, simple_loss=0.3005, pruned_loss=0.0738, over 1465384.73 frames.], batch size: 17, lr: 5.36e-04
2022-07-26 12:48:08,392 INFO [train.py:850] (2/4) Epoch 10, batch 6750, loss[loss=0.2154, simple_loss=0.2892, pruned_loss=0.07086, over 7320.00 frames.], tot_loss[loss=0.2239, simple_loss=0.3002, pruned_loss=0.07375, over 1466342.00 frames.], batch size: 18, lr: 5.36e-04
2022-07-26 12:48:52,063 INFO [train.py:850] (2/4) Epoch 10, batch 6800, loss[loss=0.2619, simple_loss=0.3401, pruned_loss=0.09188, over 7320.00 frames.], tot_loss[loss=0.225, simple_loss=0.3011, pruned_loss=0.07448, over 1467428.40 frames.], batch size: 39, lr: 5.36e-04
2022-07-26 12:49:36,284 INFO [train.py:850] (2/4) Epoch 10, batch 6850, loss[loss=0.2349, simple_loss=0.3046, pruned_loss=0.08257, over 7471.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3022, pruned_loss=0.0745, over 1467601.92 frames.], batch size: 21, lr: 5.36e-04
2022-07-26 12:50:20,183 INFO [train.py:850] (2/4) Epoch 10, batch 6900, loss[loss=0.2216, simple_loss=0.3088, pruned_loss=0.06726, over 7283.00 frames.], tot_loss[loss=0.2245, simple_loss=0.3012, pruned_loss=0.07391, over 1467415.63 frames.], batch size: 21, lr: 5.35e-04
2022-07-26 12:51:03,408 INFO [train.py:850] (2/4) Epoch 10, batch 6950, loss[loss=0.2144, simple_loss=0.2934, pruned_loss=0.06774, over 7171.00 frames.], tot_loss[loss=0.2242, simple_loss=0.3006, pruned_loss=0.07387, over 1466375.32 frames.], batch size: 21, lr: 5.35e-04
2022-07-26 12:51:48,604 INFO [train.py:850] (2/4) Epoch 10, batch 7000, loss[loss=0.3262, simple_loss=0.3749, pruned_loss=0.1388, over 7197.00 frames.], tot_loss[loss=0.2245, simple_loss=0.3008, pruned_loss=0.07407, over 1466036.48 frames.], batch size: 19, lr: 5.35e-04
2022-07-26 12:52:32,111 INFO [train.py:850] (2/4) Epoch 10, batch 7050, loss[loss=0.2154, simple_loss=0.2996, pruned_loss=0.06555, over 7282.00 frames.], tot_loss[loss=0.2252, simple_loss=0.3014, pruned_loss=0.07451, over 1465915.62 frames.], batch size: 21, lr: 5.35e-04
2022-07-26 12:53:16,811 INFO [train.py:850] (2/4) Epoch 10, batch 7100, loss[loss=0.2675, simple_loss=0.34, pruned_loss=0.09751, over 7303.00 frames.], tot_loss[loss=0.2254, simple_loss=0.3019, pruned_loss=0.07441, over 1465219.36 frames.], batch size: 22, lr: 5.35e-04
2022-07-26 12:53:59,867 INFO [train.py:850] (2/4) Epoch 10, batch 7150, loss[loss=0.2133, simple_loss=0.2986, pruned_loss=0.06404, over 7396.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3011, pruned_loss=0.07353, over 1464415.10 frames.], batch size: 19, lr: 5.35e-04
2022-07-26 12:54:44,575 INFO [train.py:850] (2/4) Epoch 10, batch 7200, loss[loss=0.2347, simple_loss=0.3204, pruned_loss=0.0745, over 7170.00 frames.], tot_loss[loss=0.2245, simple_loss=0.3017, pruned_loss=0.0737, over 1465238.08 frames.], batch size: 22, lr: 5.34e-04
2022-07-26 12:55:28,322 INFO [train.py:850] (2/4) Epoch 10, batch 7250, loss[loss=0.2315, simple_loss=0.308, pruned_loss=0.07752, over 7378.00 frames.], tot_loss[loss=0.2261, simple_loss=0.3032, pruned_loss=0.07447, over 1465165.45 frames.], batch size: 21, lr: 5.34e-04
2022-07-26 12:56:12,183 INFO [train.py:850] (2/4) Epoch 10, batch 7300, loss[loss=0.1941, simple_loss=0.2866, pruned_loss=0.05084, over 7417.00 frames.], tot_loss[loss=0.2246, simple_loss=0.302, pruned_loss=0.07363, over 1464379.43 frames.], batch size: 22, lr: 5.34e-04
2022-07-26 12:56:56,384 INFO [train.py:850] (2/4) Epoch 10, batch 7350, loss[loss=0.2043, simple_loss=0.2803, pruned_loss=0.06412, over 7307.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3026, pruned_loss=0.07419, over 1465074.70 frames.], batch size: 18, lr: 5.34e-04
2022-07-26 12:57:40,186 INFO [train.py:850] (2/4) Epoch 10, batch 7400, loss[loss=0.2448, simple_loss=0.3259, pruned_loss=0.08183, over 7486.00 frames.], tot_loss[loss=0.2242, simple_loss=0.3018, pruned_loss=0.0733, over 1464853.45 frames.], batch size: 31, lr: 5.34e-04
2022-07-26 12:58:24,479 INFO [train.py:850] (2/4) Epoch 10, batch 7450, loss[loss=0.2417, simple_loss=0.3219, pruned_loss=0.0808, over 7424.00 frames.], tot_loss[loss=0.2222, simple_loss=0.3, pruned_loss=0.07224, over 1464687.14 frames.], batch size: 22, lr: 5.34e-04
2022-07-26 12:59:07,714 INFO [train.py:850] (2/4) Epoch 10, batch 7500, loss[loss=0.1649, simple_loss=0.2456, pruned_loss=0.04209, over 7433.00 frames.], tot_loss[loss=0.2224, simple_loss=0.3003, pruned_loss=0.07225, over 1465071.26 frames.], batch size: 18, lr: 5.34e-04
2022-07-26 12:59:52,370 INFO [train.py:850] (2/4) Epoch 10, batch 7550, loss[loss=0.2753, simple_loss=0.3446, pruned_loss=0.103, over 7458.00 frames.], tot_loss[loss=0.2242, simple_loss=0.3011, pruned_loss=0.07368, over 1465621.86 frames.], batch size: 24, lr: 5.33e-04
2022-07-26 13:00:35,868 INFO [train.py:850] (2/4) Epoch 10, batch 7600, loss[loss=0.1876, simple_loss=0.2851, pruned_loss=0.04504, over 7206.00 frames.], tot_loss[loss=0.2234, simple_loss=0.3003, pruned_loss=0.07331, over 1465012.64 frames.], batch size: 20, lr: 5.33e-04
2022-07-26 13:01:18,972 INFO [train.py:850] (2/4) Epoch 10, batch 7650, loss[loss=0.2357, simple_loss=0.3085, pruned_loss=0.08149, over 7461.00 frames.], tot_loss[loss=0.2228, simple_loss=0.3001, pruned_loss=0.07278, over 1465432.15 frames.], batch size: 26, lr: 5.33e-04
2022-07-26 13:02:03,385 INFO [train.py:850] (2/4) Epoch 10, batch 7700, loss[loss=0.1968, simple_loss=0.2716, pruned_loss=0.06105, over 7105.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2996, pruned_loss=0.07205, over 1465119.21 frames.], batch size: 18, lr: 5.33e-04
2022-07-26 13:02:46,700 INFO [train.py:850] (2/4) Epoch 10, batch 7750, loss[loss=0.2561, simple_loss=0.3274, pruned_loss=0.09244, over 7472.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3007, pruned_loss=0.07313, over 1465896.04 frames.], batch size: 26, lr: 5.33e-04
2022-07-26 13:03:32,084 INFO [train.py:850] (2/4) Epoch 10, batch 7800, loss[loss=0.1681, simple_loss=0.2479, pruned_loss=0.04421, over 7305.00 frames.], tot_loss[loss=0.224, simple_loss=0.3013, pruned_loss=0.07341, over 1465442.15 frames.], batch size: 16, lr: 5.33e-04
2022-07-26 13:04:15,995 INFO [train.py:850] (2/4) Epoch 10, batch 7850, loss[loss=0.2256, simple_loss=0.3137, pruned_loss=0.06873, over 7206.00 frames.], tot_loss[loss=0.2236, simple_loss=0.3013, pruned_loss=0.07295, over 1464797.51 frames.], batch size: 24, lr: 5.32e-04
2022-07-26 13:05:01,172 INFO [train.py:850] (2/4) Epoch 10, batch 7900, loss[loss=0.1638, simple_loss=0.2583, pruned_loss=0.03462, over 7381.00 frames.], tot_loss[loss=0.2227, simple_loss=0.3004, pruned_loss=0.07247, over 1464839.69 frames.], batch size: 20, lr: 5.32e-04
2022-07-26 13:05:45,525 INFO [train.py:850] (2/4) Epoch 10, batch 7950, loss[loss=0.2756, simple_loss=0.3491, pruned_loss=0.1011, over 7297.00 frames.], tot_loss[loss=0.2239, simple_loss=0.3014, pruned_loss=0.07317, over 1465257.31 frames.], batch size: 27, lr: 5.32e-04
2022-07-26 13:06:45,072 INFO [train.py:850] (2/4) Epoch 10, batch 8000, loss[loss=0.2264, simple_loss=0.3082, pruned_loss=0.07227, over 7175.00 frames.], tot_loss[loss=0.2239, simple_loss=0.3018, pruned_loss=0.07305, over 1464378.12 frames.], batch size: 22, lr: 5.32e-04
2022-07-26 13:07:29,647 INFO [train.py:850] (2/4) Epoch 10, batch 8050, loss[loss=0.2057, simple_loss=0.2834, pruned_loss=0.06393, over 7310.00 frames.], tot_loss[loss=0.2236, simple_loss=0.301, pruned_loss=0.07311, over 1464832.65 frames.], batch size: 18, lr: 5.32e-04
2022-07-26 13:08:13,315 INFO [train.py:850] (2/4) Epoch 10, batch 8100, loss[loss=0.2287, simple_loss=0.3123, pruned_loss=0.07259, over 7208.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3016, pruned_loss=0.07333, over 1464130.38 frames.], batch size: 20, lr: 5.32e-04
2022-07-26 13:08:58,299 INFO [train.py:850] (2/4) Epoch 10, batch 8150, loss[loss=0.2006, simple_loss=0.281, pruned_loss=0.06007, over 7383.00 frames.], tot_loss[loss=0.2233, simple_loss=0.301, pruned_loss=0.07282, over 1465153.49 frames.], batch size: 21, lr: 5.32e-04
2022-07-26 13:09:42,454 INFO [train.py:850] (2/4) Epoch 10, batch 8200, loss[loss=0.2413, simple_loss=0.3176, pruned_loss=0.08254, over 7309.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2996, pruned_loss=0.07179, over 1466440.99 frames.], batch size: 27, lr: 5.31e-04
2022-07-26 13:10:27,775 INFO [train.py:850] (2/4) Epoch 10, batch 8250, loss[loss=0.2168, simple_loss=0.3003, pruned_loss=0.06664, over 7471.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2997, pruned_loss=0.07171, over 1465424.60 frames.], batch size: 21, lr: 5.31e-04
2022-07-26 13:11:12,020 INFO [train.py:850] (2/4) Epoch 10, batch 8300, loss[loss=0.2216, simple_loss=0.3021, pruned_loss=0.07054, over 7480.00 frames.], tot_loss[loss=0.2211, simple_loss=0.2992, pruned_loss=0.07145, over 1465690.00 frames.], batch size: 20, lr: 5.31e-04
2022-07-26 13:11:55,452 INFO [train.py:850] (2/4) Epoch 10, batch 8350, loss[loss=0.2651, simple_loss=0.3423, pruned_loss=0.09398, over 7186.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2989, pruned_loss=0.07188, over 1465274.40 frames.], batch size: 21, lr: 5.31e-04
2022-07-26 13:12:40,130 INFO [train.py:850] (2/4) Epoch 10, batch 8400, loss[loss=0.1607, simple_loss=0.2431, pruned_loss=0.03914, over 7238.00 frames.], tot_loss[loss=0.2228, simple_loss=0.3, pruned_loss=0.07286, over 1465297.54 frames.], batch size: 16, lr: 5.31e-04
2022-07-26 13:13:23,869 INFO [train.py:850] (2/4) Epoch 10, batch 8450, loss[loss=0.1937, simple_loss=0.2848, pruned_loss=0.05134, over 7361.00 frames.], tot_loss[loss=0.2223, simple_loss=0.2998, pruned_loss=0.07244, over 1465232.41 frames.], batch size: 31, lr: 5.31e-04
2022-07-26 13:14:08,966 INFO [train.py:850] (2/4) Epoch 10, batch 8500, loss[loss=0.2223, simple_loss=0.3008, pruned_loss=0.07187, over 7291.00 frames.], tot_loss[loss=0.2234, simple_loss=0.3005, pruned_loss=0.07316, over 1465426.58 frames.], batch size: 22, lr: 5.31e-04
2022-07-26 13:14:52,150 INFO [train.py:850] (2/4) Epoch 10, batch 8550, loss[loss=0.2419, simple_loss=0.3196, pruned_loss=0.08208, over 7221.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2988, pruned_loss=0.07214, over 1466007.22 frames.], batch size: 25, lr: 5.30e-04
2022-07-26 13:15:38,316 INFO [train.py:850] (2/4) Epoch 10, batch 8600, loss[loss=0.1789, simple_loss=0.2531, pruned_loss=0.05233, over 7458.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2997, pruned_loss=0.07222, over 1466515.69 frames.], batch size: 17, lr: 5.30e-04
2022-07-26 13:16:21,670 INFO [train.py:850] (2/4) Epoch 10, batch 8650, loss[loss=0.219, simple_loss=0.2952, pruned_loss=0.07141, over 7379.00 frames.], tot_loss[loss=0.2225, simple_loss=0.3, pruned_loss=0.07248, over 1465946.60 frames.], batch size: 21, lr: 5.30e-04
2022-07-26 13:17:04,909 INFO [train.py:850] (2/4) Epoch 10, batch 8700, loss[loss=0.2282, simple_loss=0.3006, pruned_loss=0.07789, over 7195.00 frames.], tot_loss[loss=0.2224, simple_loss=0.2996, pruned_loss=0.07258, over 1464986.54 frames.], batch size: 20, lr: 5.30e-04
2022-07-26 13:17:47,316 INFO [train.py:850] (2/4) Epoch 10, batch 8750, loss[loss=0.2292, simple_loss=0.3096, pruned_loss=0.07439, over 7172.00 frames.], tot_loss[loss=0.2236, simple_loss=0.3006, pruned_loss=0.07329, over 1464807.77 frames.], batch size: 22, lr: 5.30e-04
2022-07-26 13:18:30,337 INFO [train.py:850] (2/4) Epoch 10, batch 8800, loss[loss=0.2075, simple_loss=0.2909, pruned_loss=0.06203, over 7389.00 frames.], tot_loss[loss=0.2224, simple_loss=0.2997, pruned_loss=0.0726, over 1465090.10 frames.], batch size: 19, lr: 5.30e-04
2022-07-26 13:19:13,023 INFO [train.py:850] (2/4) Epoch 10, batch 8850, loss[loss=0.2301, simple_loss=0.3124, pruned_loss=0.07394, over 7466.00 frames.], tot_loss[loss=0.2215, simple_loss=0.299, pruned_loss=0.072, over 1464937.56 frames.], batch size: 31, lr: 5.29e-04
2022-07-26 13:20:40,752 INFO [train.py:850] (2/4) Epoch 11, batch 0, loss[loss=0.2143, simple_loss=0.3008, pruned_loss=0.06395, over 7370.00 frames.], tot_loss[loss=0.2143, simple_loss=0.3008, pruned_loss=0.06395, over 7370.00 frames.], batch size: 21, lr: 5.10e-04
2022-07-26 13:21:23,278 INFO [train.py:850] (2/4) Epoch 11, batch 50, loss[loss=0.2307, simple_loss=0.3234, pruned_loss=0.06899, over 7460.00 frames.], tot_loss[loss=0.2141, simple_loss=0.3015, pruned_loss=0.06333, over 330911.35 frames.], batch size: 26, lr: 5.10e-04
2022-07-26 13:22:08,098 INFO [train.py:850] (2/4) Epoch 11, batch 100, loss[loss=0.253, simple_loss=0.3123, pruned_loss=0.09688, over 7236.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2987, pruned_loss=0.06298, over 582407.54 frames.], batch size: 16, lr: 5.10e-04
2022-07-26 13:22:50,410 INFO [train.py:850] (2/4) Epoch 11, batch 150, loss[loss=0.2028, simple_loss=0.2918, pruned_loss=0.05688, over 7189.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2973, pruned_loss=0.06305, over 776792.42 frames.], batch size: 19, lr: 5.09e-04
2022-07-26 13:23:35,827 INFO [train.py:850] (2/4) Epoch 11, batch 200, loss[loss=0.2188, simple_loss=0.3072, pruned_loss=0.06513, over 7304.00 frames.], tot_loss[loss=0.209, simple_loss=0.2955, pruned_loss=0.06127, over 929717.09 frames.], batch size: 38, lr: 5.09e-04
2022-07-26 13:24:19,655 INFO [train.py:850] (2/4) Epoch 11, batch 250, loss[loss=0.1824, simple_loss=0.2807, pruned_loss=0.04207, over 7180.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2935, pruned_loss=0.06035, over 1048454.53 frames.], batch size: 21, lr: 5.09e-04
2022-07-26 13:25:04,701 INFO [train.py:850] (2/4) Epoch 11, batch 300, loss[loss=0.1709, simple_loss=0.2593, pruned_loss=0.04126, over 7317.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2913, pruned_loss=0.05944, over 1140065.42 frames.], batch size: 17, lr: 5.09e-04
2022-07-26 13:25:49,241 INFO [train.py:850] (2/4) Epoch 11, batch 350, loss[loss=0.2054, simple_loss=0.2974, pruned_loss=0.05673, over 7305.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2909, pruned_loss=0.05916, over 1212456.14 frames.], batch size: 22, lr: 5.09e-04
2022-07-26 13:26:33,085 INFO [train.py:850] (2/4) Epoch 11, batch 400, loss[loss=0.2038, simple_loss=0.2983, pruned_loss=0.05465, over 7291.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2897, pruned_loss=0.0585, over 1267619.77 frames.], batch size: 20, lr: 5.09e-04
2022-07-26 13:27:18,925 INFO [train.py:850] (2/4) Epoch 11, batch 450, loss[loss=0.1903, simple_loss=0.2947, pruned_loss=0.04296, over 7286.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2892, pruned_loss=0.05789, over 1311167.02 frames.], batch size: 27, lr: 5.09e-04
2022-07-26 13:28:01,565 INFO [train.py:850] (2/4) Epoch 11, batch 500, loss[loss=0.1997, simple_loss=0.2899, pruned_loss=0.05477, over 7421.00 frames.], tot_loss[loss=0.201, simple_loss=0.2882, pruned_loss=0.05694, over 1345033.64 frames.], batch size: 39, lr: 5.08e-04
2022-07-26 13:28:45,355 INFO [train.py:850] (2/4) Epoch 11, batch 550, loss[loss=0.1755, simple_loss=0.2598, pruned_loss=0.04566, over 7444.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2879, pruned_loss=0.05648, over 1372059.86 frames.], batch size: 18, lr: 5.08e-04
2022-07-26 13:29:29,979 INFO [train.py:850] (2/4) Epoch 11, batch 600, loss[loss=0.195, simple_loss=0.2771, pruned_loss=0.05649, over 7373.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2875, pruned_loss=0.05644, over 1392324.86 frames.], batch size: 20, lr: 5.08e-04
2022-07-26 13:30:12,567 INFO [train.py:850] (2/4) Epoch 11, batch 650, loss[loss=0.1738, simple_loss=0.2641, pruned_loss=0.04175, over 7476.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2874, pruned_loss=0.05687, over 1408734.50 frames.], batch size: 20, lr: 5.08e-04
2022-07-26 13:30:57,736 INFO [train.py:850] (2/4) Epoch 11, batch 700, loss[loss=0.1897, simple_loss=0.2797, pruned_loss=0.04986, over 7288.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2878, pruned_loss=0.05678, over 1421504.31 frames.], batch size: 19, lr: 5.08e-04
2022-07-26 13:31:40,223 INFO [train.py:850] (2/4) Epoch 11, batch 750, loss[loss=0.2357, simple_loss=0.3269, pruned_loss=0.07223, over 7254.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2874, pruned_loss=0.05669, over 1430249.56 frames.], batch size: 24, lr: 5.08e-04
2022-07-26 13:32:24,100 INFO [train.py:850] (2/4) Epoch 11, batch 800, loss[loss=0.1703, simple_loss=0.2465, pruned_loss=0.04709, over 7308.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2877, pruned_loss=0.05692, over 1437308.75 frames.], batch size: 18, lr: 5.08e-04
2022-07-26 13:33:07,923 INFO [train.py:850] (2/4) Epoch 11, batch 850, loss[loss=0.1853, simple_loss=0.2661, pruned_loss=0.05228, over 7489.00 frames.], tot_loss[loss=0.2, simple_loss=0.2871, pruned_loss=0.05642, over 1443385.56 frames.], batch size: 20, lr: 5.07e-04
2022-07-26 13:33:51,026 INFO [train.py:850] (2/4) Epoch 11, batch 900, loss[loss=0.1934, simple_loss=0.2821, pruned_loss=0.05239, over 7472.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2879, pruned_loss=0.05682, over 1448184.08 frames.], batch size: 20, lr: 5.07e-04
2022-07-26 13:34:35,636 INFO [train.py:850] (2/4) Epoch 11, batch 950, loss[loss=0.2049, simple_loss=0.2889, pruned_loss=0.06052, over 7455.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2886, pruned_loss=0.05746, over 1452498.56 frames.], batch size: 31, lr: 5.07e-04
2022-07-26 13:35:18,956 INFO [train.py:850] (2/4) Epoch 11, batch 1000, loss[loss=0.1947, simple_loss=0.2755, pruned_loss=0.05697, over 7104.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2911, pruned_loss=0.05885, over 1454554.04 frames.], batch size: 18, lr: 5.07e-04
2022-07-26 13:36:02,266 INFO [train.py:850] (2/4) Epoch 11, batch 1050, loss[loss=0.2175, simple_loss=0.299, pruned_loss=0.06799, over 7191.00 frames.], tot_loss[loss=0.206, simple_loss=0.2927, pruned_loss=0.05966, over 1457664.62 frames.], batch size: 21, lr: 5.07e-04
2022-07-26 13:36:46,569 INFO [train.py:850] (2/4) Epoch 11, batch 1100, loss[loss=0.2313, simple_loss=0.3096, pruned_loss=0.07656, over 7487.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2937, pruned_loss=0.06054, over 1459290.84 frames.], batch size: 19, lr: 5.07e-04
2022-07-26 13:37:30,499 INFO [train.py:850] (2/4) Epoch 11, batch 1150, loss[loss=0.1933, simple_loss=0.2902, pruned_loss=0.04821, over 7409.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2932, pruned_loss=0.06012, over 1460421.04 frames.], batch size: 22, lr: 5.07e-04
2022-07-26 13:38:15,326 INFO [train.py:850] (2/4) Epoch 11, batch 1200, loss[loss=0.2298, simple_loss=0.3253, pruned_loss=0.06719, over 7200.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2935, pruned_loss=0.06064, over 1461235.83 frames.], batch size: 20, lr: 5.06e-04
2022-07-26 13:38:57,987 INFO [train.py:850] (2/4) Epoch 11, batch 1250, loss[loss=0.1701, simple_loss=0.2479, pruned_loss=0.0461, over 7259.00 frames.], tot_loss[loss=0.2092, simple_loss=0.295, pruned_loss=0.06169, over 1462776.00 frames.], batch size: 16, lr: 5.06e-04
2022-07-26 13:39:42,245 INFO [train.py:850] (2/4) Epoch 11, batch 1300, loss[loss=0.1726, simple_loss=0.2611, pruned_loss=0.04207, over 7293.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2957, pruned_loss=0.06191, over 1462100.60 frames.], batch size: 16, lr: 5.06e-04
2022-07-26 13:40:25,817 INFO [train.py:850] (2/4) Epoch 11, batch 1350, loss[loss=0.2817, simple_loss=0.3634, pruned_loss=0.1001, over 7481.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2968, pruned_loss=0.06203, over 1462448.49 frames.], batch size: 24, lr: 5.06e-04
2022-07-26 13:41:08,954 INFO [train.py:850] (2/4) Epoch 11, batch 1400, loss[loss=0.2313, simple_loss=0.3141, pruned_loss=0.07422, over 7292.00 frames.], tot_loss[loss=0.2107, simple_loss=0.2971, pruned_loss=0.06211, over 1462910.21 frames.], batch size: 22, lr: 5.06e-04
2022-07-26 13:41:53,684 INFO [train.py:850] (2/4) Epoch 11, batch 1450, loss[loss=0.2499, simple_loss=0.3358, pruned_loss=0.08196, over 7469.00 frames.], tot_loss[loss=0.2107, simple_loss=0.2974, pruned_loss=0.06202, over 1463782.17 frames.], batch size: 24, lr: 5.06e-04
2022-07-26 13:42:36,179 INFO [train.py:850] (2/4) Epoch 11, batch 1500, loss[loss=0.1805, simple_loss=0.2794, pruned_loss=0.04077, over 7264.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2962, pruned_loss=0.06102, over 1463755.95 frames.], batch size: 27, lr: 5.06e-04
2022-07-26 13:43:20,829 INFO [train.py:850] (2/4) Epoch 11, batch 1550, loss[loss=0.2516, simple_loss=0.3246, pruned_loss=0.08934, over 7185.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2971, pruned_loss=0.0617, over 1463297.77 frames.], batch size: 21, lr: 5.05e-04
2022-07-26 13:44:05,539 INFO [train.py:850] (2/4) Epoch 11, batch 1600, loss[loss=0.2297, simple_loss=0.3234, pruned_loss=0.06799, over 7345.00 frames.], tot_loss[loss=0.209, simple_loss=0.2961, pruned_loss=0.06098, over 1462943.04 frames.], batch size: 31, lr: 5.05e-04
2022-07-26 13:44:48,006 INFO [train.py:850] (2/4) Epoch 11, batch 1650, loss[loss=0.274, simple_loss=0.3396, pruned_loss=0.1042, over 7212.00 frames.], tot_loss[loss=0.209, simple_loss=0.2959, pruned_loss=0.0611, over 1463823.65 frames.], batch size: 19, lr: 5.05e-04
2022-07-26 13:45:34,513 INFO [train.py:850] (2/4) Epoch 11, batch 1700, loss[loss=0.2062, simple_loss=0.307, pruned_loss=0.05268, over 7240.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2952, pruned_loss=0.06025, over 1463044.89 frames.], batch size: 24, lr: 5.05e-04
2022-07-26 13:46:18,405 INFO [train.py:850] (2/4) Epoch 11, batch 1750, loss[loss=0.2014, simple_loss=0.2888, pruned_loss=0.05696, over 7276.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2947, pruned_loss=0.06052, over 1463493.31 frames.], batch size: 20, lr: 5.05e-04
2022-07-26 13:47:04,284 INFO [train.py:850] (2/4) Epoch 11, batch 1800, loss[loss=0.2075, simple_loss=0.3053, pruned_loss=0.05487, over 7292.00 frames.], tot_loss[loss=0.207, simple_loss=0.2938, pruned_loss=0.06015, over 1464874.04 frames.], batch size: 22, lr: 5.05e-04
2022-07-26 13:47:47,556 INFO [train.py:850] (2/4) Epoch 11, batch 1850, loss[loss=0.2142, simple_loss=0.3078, pruned_loss=0.06025, over 7414.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2942, pruned_loss=0.06052, over 1464463.43 frames.], batch size: 22, lr: 5.05e-04
2022-07-26 13:48:31,761 INFO [train.py:850] (2/4) Epoch 11, batch 1900, loss[loss=0.1721, simple_loss=0.2645, pruned_loss=0.03987, over 7396.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2933, pruned_loss=0.05948, over 1464689.63 frames.], batch size: 19, lr: 5.04e-04
2022-07-26 13:49:15,014 INFO [train.py:850] (2/4) Epoch 11, batch 1950, loss[loss=0.1704, simple_loss=0.2698, pruned_loss=0.03551, over 7467.00 frames.], tot_loss[loss=0.2059, simple_loss=0.293, pruned_loss=0.05935, over 1465872.50 frames.], batch size: 31, lr: 5.04e-04
2022-07-26 13:49:58,187 INFO [train.py:850] (2/4) Epoch 11, batch 2000, loss[loss=0.2046, simple_loss=0.2971, pruned_loss=0.05604, over 7277.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2926, pruned_loss=0.05931, over 1465523.56 frames.], batch size: 21, lr: 5.04e-04
2022-07-26 13:50:42,815 INFO [train.py:850] (2/4) Epoch 11, batch 2050, loss[loss=0.2173, simple_loss=0.3076, pruned_loss=0.06347, over 7306.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2929, pruned_loss=0.05947, over 1464556.37 frames.], batch size: 22, lr: 5.04e-04
2022-07-26 13:51:25,826 INFO [train.py:850] (2/4) Epoch 11, batch 2100, loss[loss=0.2058, simple_loss=0.3053, pruned_loss=0.05315, over 7273.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2929, pruned_loss=0.05947, over 1463273.70 frames.], batch size: 27, lr: 5.04e-04
2022-07-26 13:52:09,001 INFO [train.py:850] (2/4) Epoch 11, batch 2150, loss[loss=0.2058, simple_loss=0.2959, pruned_loss=0.05783, over 7466.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2938, pruned_loss=0.05974, over 1463861.22 frames.], batch size: 40, lr: 5.04e-04
2022-07-26 13:52:52,679 INFO [train.py:850] (2/4) Epoch 11, batch 2200, loss[loss=0.198, simple_loss=0.2917, pruned_loss=0.05218, over 7468.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2925, pruned_loss=0.05909, over 1464797.39 frames.], batch size: 21, lr: 5.04e-04
2022-07-26 13:53:35,210 INFO [train.py:850] (2/4) Epoch 11, batch 2250, loss[loss=0.1805, simple_loss=0.2778, pruned_loss=0.04157, over 7420.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2927, pruned_loss=0.05889, over 1465599.80 frames.], batch size: 39, lr: 5.03e-04
2022-07-26 13:54:21,045 INFO [train.py:850] (2/4) Epoch 11, batch 2300, loss[loss=0.2111, simple_loss=0.2998, pruned_loss=0.06122, over 7176.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2935, pruned_loss=0.05918, over 1464545.74 frames.], batch size: 22, lr: 5.03e-04
2022-07-26 13:55:03,950 INFO [train.py:850] (2/4) Epoch 11, batch 2350, loss[loss=0.188, simple_loss=0.2682, pruned_loss=0.05396, over 7171.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2922, pruned_loss=0.05882, over 1463744.69 frames.], batch size: 17, lr: 5.03e-04
2022-07-26 13:55:48,372 INFO [train.py:850] (2/4) Epoch 11, batch 2400, loss[loss=0.2344, simple_loss=0.3155, pruned_loss=0.07667, over 7384.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2924, pruned_loss=0.05914, over 1463842.77 frames.], batch size: 20, lr: 5.03e-04
2022-07-26 13:56:31,471 INFO [train.py:850] (2/4) Epoch 11, batch 2450, loss[loss=0.2183, simple_loss=0.3097, pruned_loss=0.06344, over 7252.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2916, pruned_loss=0.05847, over 1464531.07 frames.], batch size: 27, lr: 5.03e-04
2022-07-26 13:57:14,423 INFO [train.py:850] (2/4) Epoch 11, batch 2500, loss[loss=0.1864, simple_loss=0.2921, pruned_loss=0.04037, over 7298.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2919, pruned_loss=0.05853, over 1463578.85 frames.], batch size: 22, lr: 5.03e-04
2022-07-26 13:57:59,419 INFO [train.py:850] (2/4) Epoch 11, batch 2550, loss[loss=0.1954, simple_loss=0.2847, pruned_loss=0.05305, over 7371.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2921, pruned_loss=0.05846, over 1464275.97 frames.], batch size: 21, lr: 5.03e-04
2022-07-26 13:58:42,324 INFO [train.py:850] (2/4) Epoch 11, batch 2600, loss[loss=0.2032, simple_loss=0.291, pruned_loss=0.05767, over 7491.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2925, pruned_loss=0.05842, over 1464384.76 frames.], batch size: 19, lr: 5.03e-04
2022-07-26 13:59:26,299 INFO [train.py:850] (2/4) Epoch 11, batch 2650, loss[loss=0.1753, simple_loss=0.2752, pruned_loss=0.03765, over 7379.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2914, pruned_loss=0.05773, over 1464130.77 frames.], batch size: 21, lr: 5.02e-04
2022-07-26 14:00:10,157 INFO [train.py:850] (2/4) Epoch 11, batch 2700, loss[loss=0.172, simple_loss=0.2571, pruned_loss=0.0434, over 7323.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2916, pruned_loss=0.05771, over 1465668.51 frames.], batch size: 17, lr: 5.02e-04
2022-07-26 14:00:53,305 INFO [train.py:850] (2/4) Epoch 11, batch 2750, loss[loss=0.1997, simple_loss=0.2926, pruned_loss=0.05334, over 7336.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2916, pruned_loss=0.05803, over 1466514.93 frames.], batch size: 30, lr: 5.02e-04
2022-07-26 14:01:37,743 INFO [train.py:850] (2/4) Epoch 11, batch 2800, loss[loss=0.1662, simple_loss=0.2575, pruned_loss=0.03744, over 7485.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2911, pruned_loss=0.05796, over 1466167.78 frames.], batch size: 19, lr: 5.02e-04
2022-07-26 14:02:20,669 INFO [train.py:850] (2/4) Epoch 11, batch 2850, loss[loss=0.2583, simple_loss=0.3449, pruned_loss=0.08585, over 7202.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2926, pruned_loss=0.05888, over 1466162.66 frames.], batch size: 25, lr: 5.02e-04
2022-07-26 14:03:06,610 INFO [train.py:850] (2/4) Epoch 11, batch 2900, loss[loss=0.1929, simple_loss=0.3004, pruned_loss=0.04269, over 7480.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2929, pruned_loss=0.05898, over 1466443.35 frames.], batch size: 21, lr: 5.02e-04
2022-07-26 14:03:50,419 INFO [train.py:850] (2/4) Epoch 11, batch 2950, loss[loss=0.2216, simple_loss=0.3152, pruned_loss=0.06403, over 7333.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2929, pruned_loss=0.05886, over 1468045.92 frames.], batch size: 23, lr: 5.02e-04
2022-07-26 14:04:35,033 INFO [train.py:850] (2/4) Epoch 11, batch 3000, loss[loss=0.173, simple_loss=0.2753, pruned_loss=0.03533, over 7173.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2915, pruned_loss=0.05842, over 1467951.67 frames.], batch size: 21, lr: 5.01e-04
2022-07-26 14:04:35,034 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 14:04:58,091 INFO [train.py:879] (2/4) Epoch 11, validation: loss=0.1955, simple_loss=0.2909, pruned_loss=0.05008, over 924787.00 frames.
2022-07-26 14:05:40,705 INFO [train.py:850] (2/4) Epoch 11, batch 3050, loss[loss=0.1523, simple_loss=0.2525, pruned_loss=0.02607, over 7310.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2906, pruned_loss=0.05798, over 1467704.63 frames.], batch size: 17, lr: 5.01e-04
2022-07-26 14:06:40,240 INFO [train.py:850] (2/4) Epoch 11, batch 3100, loss[loss=0.1989, simple_loss=0.2859, pruned_loss=0.05598, over 7412.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2911, pruned_loss=0.05833, over 1467713.89 frames.], batch size: 22, lr: 5.01e-04
2022-07-26 14:07:23,914 INFO [train.py:850] (2/4) Epoch 11, batch 3150, loss[loss=0.1981, simple_loss=0.2871, pruned_loss=0.05453, over 7401.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2912, pruned_loss=0.05806, over 1467485.94 frames.], batch size: 31, lr: 5.01e-04
2022-07-26 14:08:08,196 INFO [train.py:850] (2/4) Epoch 11, batch 3200, loss[loss=0.2278, simple_loss=0.3232, pruned_loss=0.06617, over 7225.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2919, pruned_loss=0.05833, over 1465254.87 frames.], batch size: 24, lr: 5.01e-04
2022-07-26 14:08:52,520 INFO [train.py:850] (2/4) Epoch 11, batch 3250, loss[loss=0.2048, simple_loss=0.3019, pruned_loss=0.05382, over 7358.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2926, pruned_loss=0.05879, over 1466058.58 frames.], batch size: 31, lr: 5.01e-04
2022-07-26 14:09:35,147 INFO [train.py:850] (2/4) Epoch 11, batch 3300, loss[loss=0.1828, simple_loss=0.2841, pruned_loss=0.04077, over 7395.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2924, pruned_loss=0.05817, over 1466146.46 frames.], batch size: 20, lr: 5.01e-04
2022-07-26 14:10:19,605 INFO [train.py:850] (2/4) Epoch 11, batch 3350, loss[loss=0.2128, simple_loss=0.2982, pruned_loss=0.06369, over 7300.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2918, pruned_loss=0.05796, over 1466158.47 frames.], batch size: 22, lr: 5.00e-04
2022-07-26 14:11:03,064 INFO [train.py:850] (2/4) Epoch 11, batch 3400, loss[loss=0.2789, simple_loss=0.3398, pruned_loss=0.109, over 7225.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2914, pruned_loss=0.05789, over 1466219.68 frames.], batch size: 25, lr: 5.00e-04
2022-07-26 14:11:47,953 INFO [train.py:850] (2/4) Epoch 11, batch 3450, loss[loss=0.1863, simple_loss=0.2724, pruned_loss=0.05009, over 7247.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2902, pruned_loss=0.05722, over 1465167.76 frames.], batch size: 16, lr: 5.00e-04
2022-07-26 14:12:31,844 INFO [train.py:850] (2/4) Epoch 11, batch 3500, loss[loss=0.1543, simple_loss=0.2357, pruned_loss=0.03651, over 7132.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2907, pruned_loss=0.0575, over 1463738.41 frames.], batch size: 17, lr: 5.00e-04
2022-07-26 14:13:14,404 INFO [train.py:850] (2/4) Epoch 11, batch 3550, loss[loss=0.1755, simple_loss=0.2607, pruned_loss=0.0452, over 7207.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2905, pruned_loss=0.05705, over 1464164.50 frames.], batch size: 18, lr: 5.00e-04
2022-07-26 14:14:00,497 INFO [train.py:850] (2/4) Epoch 11, batch 3600, loss[loss=0.1845, simple_loss=0.2695, pruned_loss=0.04974, over 7488.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2903, pruned_loss=0.05719, over 1464759.34 frames.], batch size: 20, lr: 5.00e-04
2022-07-26 14:14:43,413 INFO [train.py:850] (2/4) Epoch 11, batch 3650, loss[loss=0.1821, simple_loss=0.277, pruned_loss=0.04361, over 7428.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2915, pruned_loss=0.05778, over 1464909.65 frames.], batch size: 38, lr: 5.00e-04
2022-07-26 14:15:28,404 INFO [train.py:850] (2/4) Epoch 11, batch 3700, loss[loss=0.1691, simple_loss=0.2582, pruned_loss=0.03999, over 7486.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2919, pruned_loss=0.05852, over 1464093.60 frames.], batch size: 20, lr: 5.00e-04
2022-07-26 14:16:12,113 INFO [train.py:850] (2/4) Epoch 11, batch 3750, loss[loss=0.1591, simple_loss=0.253, pruned_loss=0.03259, over 7158.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2917, pruned_loss=0.0584, over 1464493.48 frames.], batch size: 17, lr: 4.99e-04
2022-07-26 14:16:55,198 INFO [train.py:850] (2/4) Epoch 11, batch 3800, loss[loss=0.1701, simple_loss=0.2707, pruned_loss=0.0347, over 7099.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2919, pruned_loss=0.05863, over 1464504.77 frames.], batch size: 18, lr: 4.99e-04
2022-07-26 14:17:40,176 INFO [train.py:850] (2/4) Epoch 11, batch 3850, loss[loss=0.2051, simple_loss=0.295, pruned_loss=0.05754, over 7185.00 frames.], tot_loss[loss=0.2044, simple_loss=0.292, pruned_loss=0.05841, over 1465625.83 frames.], batch size: 22, lr: 4.99e-04
2022-07-26 14:18:22,685 INFO [train.py:850] (2/4) Epoch 11, batch 3900, loss[loss=0.1644, simple_loss=0.2478, pruned_loss=0.04054, over 7441.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2911, pruned_loss=0.05756, over 1465620.04 frames.], batch size: 18, lr: 4.99e-04
2022-07-26 14:19:07,015 INFO [train.py:850] (2/4) Epoch 11, batch 3950, loss[loss=0.1723, simple_loss=0.2574, pruned_loss=0.04356, over 7448.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2909, pruned_loss=0.05739, over 1464888.86 frames.], batch size: 18, lr: 4.99e-04
2022-07-26 14:19:50,264 INFO [train.py:850] (2/4) Epoch 11, batch 4000, loss[loss=0.1894, simple_loss=0.2959, pruned_loss=0.04142, over 7379.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2909, pruned_loss=0.05697, over 1465281.93 frames.], batch size: 21, lr: 4.99e-04
2022-07-26 14:20:33,902 INFO [train.py:850] (2/4) Epoch 11, batch 4050, loss[loss=0.1975, simple_loss=0.2916, pruned_loss=0.0517, over 7488.00 frames.], tot_loss[loss=0.2026, simple_loss=0.291, pruned_loss=0.05712, over 1465254.75 frames.], batch size: 23, lr: 4.99e-04
2022-07-26 14:21:19,639 INFO [train.py:850] (2/4) Epoch 11, batch 4100, loss[loss=0.2189, simple_loss=0.3076, pruned_loss=0.06508, over 7481.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2922, pruned_loss=0.05812, over 1465585.81 frames.], batch size: 20, lr: 4.98e-04
2022-07-26 14:22:02,235 INFO [train.py:850] (2/4) Epoch 11, batch 4150, loss[loss=0.2441, simple_loss=0.3211, pruned_loss=0.08356, over 7290.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2919, pruned_loss=0.05881, over 1465868.27 frames.], batch size: 20, lr: 4.98e-04
2022-07-26 14:22:47,987 INFO [train.py:850] (2/4) Epoch 11, batch 4200, loss[loss=0.2, simple_loss=0.2903, pruned_loss=0.05486, over 7288.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2923, pruned_loss=0.06, over 1465662.63 frames.], batch size: 20, lr: 4.98e-04
2022-07-26 14:23:32,060 INFO [train.py:850] (2/4) Epoch 11, batch 4250, loss[loss=0.1995, simple_loss=0.2775, pruned_loss=0.0608, over 7443.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2936, pruned_loss=0.06177, over 1465331.67 frames.], batch size: 18, lr: 4.98e-04
2022-07-26 14:24:15,783 INFO [train.py:850] (2/4) Epoch 11, batch 4300, loss[loss=0.1635, simple_loss=0.2376, pruned_loss=0.04473, over 7174.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2943, pruned_loss=0.06317, over 1463718.63 frames.], batch size: 17, lr: 4.98e-04
2022-07-26 14:25:00,121 INFO [train.py:850] (2/4) Epoch 11, batch 4350, loss[loss=0.2225, simple_loss=0.2864, pruned_loss=0.07929, over 7195.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2955, pruned_loss=0.06464, over 1464107.67 frames.], batch size: 18, lr: 4.98e-04
2022-07-26 14:25:45,021 INFO [train.py:850] (2/4) Epoch 11, batch 4400, loss[loss=0.2535, simple_loss=0.3244, pruned_loss=0.09124, over 7359.00 frames.], tot_loss[loss=0.2149, simple_loss=0.297, pruned_loss=0.06642, over 1464119.70 frames.], batch size: 23, lr: 4.98e-04
2022-07-26 14:26:29,669 INFO [train.py:850] (2/4) Epoch 11, batch 4450, loss[loss=0.2272, simple_loss=0.3, pruned_loss=0.07719, over 7406.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2982, pruned_loss=0.06778, over 1465478.20 frames.], batch size: 72, lr: 4.98e-04
2022-07-26 14:27:12,416 INFO [train.py:850] (2/4) Epoch 11, batch 4500, loss[loss=0.2173, simple_loss=0.2893, pruned_loss=0.07259, over 7481.00 frames.], tot_loss[loss=0.2185, simple_loss=0.2988, pruned_loss=0.06911, over 1466019.82 frames.], batch size: 20, lr: 4.97e-04
2022-07-26 14:27:56,148 INFO [train.py:850] (2/4) Epoch 11, batch 4550, loss[loss=0.2701, simple_loss=0.3426, pruned_loss=0.09878, over 7448.00 frames.], tot_loss[loss=0.2206, simple_loss=0.3001, pruned_loss=0.07055, over 1465808.24 frames.], batch size: 31, lr: 4.97e-04
2022-07-26 14:28:40,877 INFO [train.py:850] (2/4) Epoch 11, batch 4600, loss[loss=0.1879, simple_loss=0.2801, pruned_loss=0.04782, over 7290.00 frames.], tot_loss[loss=0.2203, simple_loss=0.2993, pruned_loss=0.07063, over 1465689.01 frames.], batch size: 21, lr: 4.97e-04
2022-07-26 14:29:23,601 INFO [train.py:850] (2/4) Epoch 11, batch 4650, loss[loss=0.2386, simple_loss=0.3197, pruned_loss=0.0787, over 7202.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2994, pruned_loss=0.07082, over 1464853.75 frames.], batch size: 20, lr: 4.97e-04
2022-07-26 14:30:08,187 INFO [train.py:850] (2/4) Epoch 11, batch 4700, loss[loss=0.2581, simple_loss=0.3274, pruned_loss=0.09442, over 7177.00 frames.], tot_loss[loss=0.2208, simple_loss=0.2993, pruned_loss=0.07118, over 1465661.29 frames.], batch size: 21, lr: 4.97e-04
2022-07-26 14:30:50,990 INFO [train.py:850] (2/4) Epoch 11, batch 4750, loss[loss=0.1737, simple_loss=0.257, pruned_loss=0.04515, over 7308.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2997, pruned_loss=0.07191, over 1465134.60 frames.], batch size: 18, lr: 4.97e-04
2022-07-26 14:31:35,057 INFO [train.py:850] (2/4) Epoch 11, batch 4800, loss[loss=0.2332, simple_loss=0.3097, pruned_loss=0.07832, over 7221.00 frames.], tot_loss[loss=0.2202, simple_loss=0.2978, pruned_loss=0.07125, over 1465326.67 frames.], batch size: 24, lr: 4.97e-04
2022-07-26 14:32:19,849 INFO [train.py:850] (2/4) Epoch 11, batch 4850, loss[loss=0.1937, simple_loss=0.2711, pruned_loss=0.05812, over 7323.00 frames.], tot_loss[loss=0.2196, simple_loss=0.2975, pruned_loss=0.07083, over 1464707.72 frames.], batch size: 18, lr: 4.96e-04
2022-07-26 14:33:05,150 INFO [train.py:850] (2/4) Epoch 11, batch 4900, loss[loss=0.2411, simple_loss=0.3238, pruned_loss=0.07916, over 7388.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2987, pruned_loss=0.07158, over 1465587.39 frames.], batch size: 21, lr: 4.96e-04
2022-07-26 14:33:49,735 INFO [train.py:850] (2/4) Epoch 11, batch 4950, loss[loss=0.1871, simple_loss=0.2772, pruned_loss=0.04853, over 7317.00 frames.], tot_loss[loss=0.2226, simple_loss=0.3, pruned_loss=0.07261, over 1465470.80 frames.], batch size: 18, lr: 4.96e-04
2022-07-26 14:34:34,078 INFO [train.py:850] (2/4) Epoch 11, batch 5000, loss[loss=0.196, simple_loss=0.2726, pruned_loss=0.05968, over 7443.00 frames.], tot_loss[loss=0.2222, simple_loss=0.2993, pruned_loss=0.07256, over 1465339.24 frames.], batch size: 18, lr: 4.96e-04
2022-07-26 14:35:18,816 INFO [train.py:850] (2/4) Epoch 11, batch 5050, loss[loss=0.2391, simple_loss=0.3037, pruned_loss=0.08726, over 7164.00 frames.], tot_loss[loss=0.2216, simple_loss=0.299, pruned_loss=0.07212, over 1464386.48 frames.], batch size: 17, lr: 4.96e-04
2022-07-26 14:36:02,281 INFO [train.py:850] (2/4) Epoch 11, batch 5100, loss[loss=0.186, simple_loss=0.2674, pruned_loss=0.05224, over 7316.00 frames.], tot_loss[loss=0.2233, simple_loss=0.3007, pruned_loss=0.07294, over 1464598.48 frames.], batch size: 18, lr: 4.96e-04
2022-07-26 14:36:47,411 INFO [train.py:850] (2/4) Epoch 11, batch 5150, loss[loss=0.2263, simple_loss=0.3157, pruned_loss=0.06844, over 7411.00 frames.], tot_loss[loss=0.2232, simple_loss=0.2998, pruned_loss=0.07335, over 1464740.10 frames.], batch size: 22, lr: 4.96e-04
2022-07-26 14:37:30,788 INFO [train.py:850] (2/4) Epoch 11, batch 5200, loss[loss=0.218, simple_loss=0.2958, pruned_loss=0.07006, over 7215.00 frames.], tot_loss[loss=0.2242, simple_loss=0.3001, pruned_loss=0.07411, over 1463639.04 frames.], batch size: 18, lr: 4.96e-04
2022-07-26 14:38:14,050 INFO [train.py:850] (2/4) Epoch 11, batch 5250, loss[loss=0.2627, simple_loss=0.3281, pruned_loss=0.09868, over 7471.00 frames.], tot_loss[loss=0.2237, simple_loss=0.3001, pruned_loss=0.0737, over 1463860.71 frames.], batch size: 24, lr: 4.95e-04
2022-07-26 14:38:58,504 INFO [train.py:850] (2/4) Epoch 11, batch 5300, loss[loss=0.2534, simple_loss=0.3317, pruned_loss=0.08759, over 7406.00 frames.], tot_loss[loss=0.2232, simple_loss=0.2999, pruned_loss=0.07319, over 1464435.59 frames.], batch size: 22, lr: 4.95e-04
2022-07-26 14:39:42,720 INFO [train.py:850] (2/4) Epoch 11, batch 5350, loss[loss=0.1737, simple_loss=0.2483, pruned_loss=0.04953, over 7159.00 frames.], tot_loss[loss=0.2228, simple_loss=0.2998, pruned_loss=0.07291, over 1465795.31 frames.], batch size: 17, lr: 4.95e-04
2022-07-26 14:40:28,541 INFO [train.py:850] (2/4) Epoch 11, batch 5400, loss[loss=0.1945, simple_loss=0.285, pruned_loss=0.05198, over 7209.00 frames.], tot_loss[loss=0.2238, simple_loss=0.3009, pruned_loss=0.07338, over 1465791.51 frames.], batch size: 20, lr: 4.95e-04
2022-07-26 14:41:11,261 INFO [train.py:850] (2/4) Epoch 11, batch 5450, loss[loss=0.2584, simple_loss=0.3215, pruned_loss=0.0976, over 7398.00 frames.], tot_loss[loss=0.2231, simple_loss=0.2999, pruned_loss=0.07312, over 1466153.02 frames.], batch size: 71, lr: 4.95e-04
2022-07-26 14:41:55,269 INFO [train.py:850] (2/4) Epoch 11, batch 5500, loss[loss=0.2375, simple_loss=0.3171, pruned_loss=0.0789, over 7235.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2992, pruned_loss=0.07251, over 1464772.65 frames.], batch size: 24, lr: 4.95e-04
2022-07-26 14:42:39,352 INFO [train.py:850] (2/4) Epoch 11, batch 5550, loss[loss=0.1867, simple_loss=0.2673, pruned_loss=0.05305, over 7099.00 frames.], tot_loss[loss=0.2214, simple_loss=0.2985, pruned_loss=0.0721, over 1465858.55 frames.], batch size: 18, lr: 4.95e-04
2022-07-26 14:43:23,767 INFO [train.py:850] (2/4) Epoch 11, batch 5600, loss[loss=0.2132, simple_loss=0.2854, pruned_loss=0.07053, over 7419.00 frames.], tot_loss[loss=0.2222, simple_loss=0.2988, pruned_loss=0.07273, over 1466870.98 frames.], batch size: 38, lr: 4.95e-04
2022-07-26 14:44:07,822 INFO [train.py:850] (2/4) Epoch 11, batch 5650, loss[loss=0.2541, simple_loss=0.3248, pruned_loss=0.09166, over 7360.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2986, pruned_loss=0.07279, over 1466312.06 frames.], batch size: 38, lr: 4.94e-04
2022-07-26 14:44:50,647 INFO [train.py:850] (2/4) Epoch 11, batch 5700, loss[loss=0.2108, simple_loss=0.29, pruned_loss=0.06586, over 7292.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2981, pruned_loss=0.07225, over 1467017.61 frames.], batch size: 19, lr: 4.94e-04
2022-07-26 14:45:36,784 INFO [train.py:850] (2/4) Epoch 11, batch 5750, loss[loss=0.2207, simple_loss=0.281, pruned_loss=0.0802, over 7478.00 frames.], tot_loss[loss=0.2216, simple_loss=0.2988, pruned_loss=0.07224, over 1467511.50 frames.], batch size: 19, lr: 4.94e-04
2022-07-26 14:46:22,601 INFO [train.py:850] (2/4) Epoch 11, batch 5800, loss[loss=0.1867, simple_loss=0.2725, pruned_loss=0.05049, over 7201.00 frames.], tot_loss[loss=0.2206, simple_loss=0.298, pruned_loss=0.07162, over 1466210.97 frames.], batch size: 20, lr: 4.94e-04
2022-07-26 14:47:09,104 INFO [train.py:850] (2/4) Epoch 11, batch 5850, loss[loss=0.2241, simple_loss=0.306, pruned_loss=0.07111, over 7304.00 frames.], tot_loss[loss=0.2202, simple_loss=0.2976, pruned_loss=0.07133, over 1465736.62 frames.], batch size: 21, lr: 4.94e-04
2022-07-26 14:47:52,813 INFO [train.py:850] (2/4) Epoch 11, batch 5900, loss[loss=0.2058, simple_loss=0.2753, pruned_loss=0.0682, over 7297.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2968, pruned_loss=0.07104, over 1466075.30 frames.], batch size: 19, lr: 4.94e-04
2022-07-26 14:48:37,817 INFO [train.py:850] (2/4) Epoch 11, batch 5950, loss[loss=0.3022, simple_loss=0.3591, pruned_loss=0.1226, over 7358.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2967, pruned_loss=0.07117, over 1465715.88 frames.], batch size: 64, lr: 4.94e-04
2022-07-26 14:49:22,692 INFO [train.py:850] (2/4) Epoch 11, batch 6000, loss[loss=0.2094, simple_loss=0.2803, pruned_loss=0.06925, over 7203.00 frames.], tot_loss[loss=0.2217, simple_loss=0.299, pruned_loss=0.07219, over 1465253.64 frames.], batch size: 18, lr: 4.93e-04
2022-07-26 14:49:22,693 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 14:49:45,611 INFO [train.py:879] (2/4) Epoch 11, validation: loss=0.1869, simple_loss=0.2853, pruned_loss=0.04423, over 924787.00 frames.
2022-07-26 14:50:30,245 INFO [train.py:850] (2/4) Epoch 11, batch 6050, loss[loss=0.2116, simple_loss=0.295, pruned_loss=0.06409, over 7278.00 frames.], tot_loss[loss=0.2192, simple_loss=0.2964, pruned_loss=0.07099, over 1465421.26 frames.], batch size: 20, lr: 4.93e-04
2022-07-26 14:51:14,395 INFO [train.py:850] (2/4) Epoch 11, batch 6100, loss[loss=0.2338, simple_loss=0.3127, pruned_loss=0.0775, over 7208.00 frames.], tot_loss[loss=0.2194, simple_loss=0.297, pruned_loss=0.07088, over 1465175.34 frames.], batch size: 20, lr: 4.93e-04
2022-07-26 14:52:00,332 INFO [train.py:850] (2/4) Epoch 11, batch 6150, loss[loss=0.1738, simple_loss=0.253, pruned_loss=0.0473, over 7451.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2989, pruned_loss=0.07183, over 1464246.05 frames.], batch size: 17, lr: 4.93e-04
2022-07-26 14:52:46,601 INFO [train.py:850] (2/4) Epoch 11, batch 6200, loss[loss=0.1895, simple_loss=0.2819, pruned_loss=0.04855, over 7415.00 frames.], tot_loss[loss=0.2242, simple_loss=0.301, pruned_loss=0.0737, over 1464441.12 frames.], batch size: 22, lr: 4.93e-04
2022-07-26 14:53:29,640 INFO [train.py:850] (2/4) Epoch 11, batch 6250, loss[loss=0.2229, simple_loss=0.2979, pruned_loss=0.07393, over 7457.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2992, pruned_loss=0.07194, over 1464301.86 frames.], batch size: 74, lr: 4.93e-04
2022-07-26 14:54:14,392 INFO [train.py:850] (2/4) Epoch 11, batch 6300, loss[loss=0.1629, simple_loss=0.2385, pruned_loss=0.04368, over 7468.00 frames.], tot_loss[loss=0.2229, simple_loss=0.3007, pruned_loss=0.07258, over 1464841.19 frames.], batch size: 17, lr: 4.93e-04
2022-07-26 14:54:57,619 INFO [train.py:850] (2/4) Epoch 11, batch 6350, loss[loss=0.2415, simple_loss=0.3214, pruned_loss=0.08083, over 7480.00 frames.], tot_loss[loss=0.222, simple_loss=0.3, pruned_loss=0.07196, over 1464921.21 frames.], batch size: 26, lr: 4.93e-04
2022-07-26 14:55:43,840 INFO [train.py:850] (2/4) Epoch 11, batch 6400, loss[loss=0.2731, simple_loss=0.3485, pruned_loss=0.09883, over 7397.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2998, pruned_loss=0.07217, over 1464272.71 frames.], batch size: 68, lr: 4.92e-04
2022-07-26 14:56:28,336 INFO [train.py:850] (2/4) Epoch 11, batch 6450, loss[loss=0.2468, simple_loss=0.3167, pruned_loss=0.08843, over 7282.00 frames.], tot_loss[loss=0.2224, simple_loss=0.3001, pruned_loss=0.07239, over 1464638.79 frames.], batch size: 27, lr: 4.92e-04
2022-07-26 14:57:12,949 INFO [train.py:850] (2/4) Epoch 11, batch 6500, loss[loss=0.1757, simple_loss=0.249, pruned_loss=0.05121, over 7195.00 frames.], tot_loss[loss=0.2226, simple_loss=0.3003, pruned_loss=0.07247, over 1465660.82 frames.], batch size: 18, lr: 4.92e-04
2022-07-26 14:57:56,534 INFO [train.py:850] (2/4) Epoch 11, batch 6550, loss[loss=0.2564, simple_loss=0.3277, pruned_loss=0.09253, over 7269.00 frames.], tot_loss[loss=0.2207, simple_loss=0.2984, pruned_loss=0.07153, over 1465337.26 frames.], batch size: 27, lr: 4.92e-04
2022-07-26 14:58:40,063 INFO [train.py:850] (2/4) Epoch 11, batch 6600, loss[loss=0.2244, simple_loss=0.2969, pruned_loss=0.07592, over 7207.00 frames.], tot_loss[loss=0.2202, simple_loss=0.2979, pruned_loss=0.07122, over 1465113.63 frames.], batch size: 18, lr: 4.92e-04
2022-07-26 14:59:25,008 INFO [train.py:850] (2/4) Epoch 11, batch 6650, loss[loss=0.2361, simple_loss=0.3203, pruned_loss=0.07596, over 7435.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2994, pruned_loss=0.07164, over 1465915.00 frames.], batch size: 31, lr: 4.92e-04
2022-07-26 15:00:08,792 INFO [train.py:850] (2/4) Epoch 11, batch 6700, loss[loss=0.184, simple_loss=0.2738, pruned_loss=0.04716, over 7466.00 frames.], tot_loss[loss=0.2223, simple_loss=0.3002, pruned_loss=0.07217, over 1466066.14 frames.], batch size: 24, lr: 4.92e-04
2022-07-26 15:00:53,083 INFO [train.py:850] (2/4) Epoch 11, batch 6750, loss[loss=0.2291, simple_loss=0.3011, pruned_loss=0.07855, over 7201.00 frames.], tot_loss[loss=0.2217, simple_loss=0.2996, pruned_loss=0.0719, over 1466546.95 frames.], batch size: 19, lr: 4.92e-04
2022-07-26 15:01:36,096 INFO [train.py:850] (2/4) Epoch 11, batch 6800, loss[loss=0.2034, simple_loss=0.2896, pruned_loss=0.05862, over 7274.00 frames.], tot_loss[loss=0.2208, simple_loss=0.2986, pruned_loss=0.07146, over 1466604.75 frames.], batch size: 21, lr: 4.91e-04
2022-07-26 15:02:18,962 INFO [train.py:850] (2/4) Epoch 11, batch 6850, loss[loss=0.2236, simple_loss=0.3009, pruned_loss=0.07318, over 7372.00 frames.], tot_loss[loss=0.2221, simple_loss=0.2994, pruned_loss=0.07233, over 1466281.74 frames.], batch size: 39, lr: 4.91e-04
2022-07-26 15:03:04,364 INFO [train.py:850] (2/4) Epoch 11, batch 6900, loss[loss=0.2559, simple_loss=0.3263, pruned_loss=0.09274, over 7445.00 frames.], tot_loss[loss=0.2219, simple_loss=0.2996, pruned_loss=0.07214, over 1466841.59 frames.], batch size: 66, lr: 4.91e-04
2022-07-26 15:03:48,098 INFO [train.py:850] (2/4) Epoch 11, batch 6950, loss[loss=0.2577, simple_loss=0.3341, pruned_loss=0.09063, over 7302.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2984, pruned_loss=0.07172, over 1466834.86 frames.], batch size: 22, lr: 4.91e-04
2022-07-26 15:04:32,820 INFO [train.py:850] (2/4) Epoch 11, batch 7000, loss[loss=0.1769, simple_loss=0.2605, pruned_loss=0.04666, over 7382.00 frames.], tot_loss[loss=0.2192, simple_loss=0.2965, pruned_loss=0.07089, over 1467200.40 frames.], batch size: 20, lr: 4.91e-04
2022-07-26 15:05:16,060 INFO [train.py:850] (2/4) Epoch 11, batch 7050, loss[loss=0.2188, simple_loss=0.2965, pruned_loss=0.07054, over 7285.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2973, pruned_loss=0.07078, over 1465892.73 frames.], batch size: 20, lr: 4.91e-04
2022-07-26 15:06:15,605 INFO [train.py:850] (2/4) Epoch 11, batch 7100, loss[loss=0.2054, simple_loss=0.2807, pruned_loss=0.06509, over 7263.00 frames.], tot_loss[loss=0.221, simple_loss=0.2985, pruned_loss=0.0717, over 1466541.19 frames.], batch size: 16, lr: 4.91e-04
2022-07-26 15:07:00,045 INFO [train.py:850] (2/4) Epoch 11, batch 7150, loss[loss=0.2445, simple_loss=0.3198, pruned_loss=0.08462, over 7284.00 frames.], tot_loss[loss=0.2215, simple_loss=0.2988, pruned_loss=0.07206, over 1466528.23 frames.], batch size: 21, lr: 4.91e-04
2022-07-26 15:07:45,374 INFO [train.py:850] (2/4) Epoch 11, batch 7200, loss[loss=0.1855, simple_loss=0.263, pruned_loss=0.05399, over 7293.00 frames.], tot_loss[loss=0.2214, simple_loss=0.2991, pruned_loss=0.07185, over 1466448.61 frames.], batch size: 17, lr: 4.90e-04
2022-07-26 15:08:30,409 INFO [train.py:850] (2/4) Epoch 11, batch 7250, loss[loss=0.222, simple_loss=0.3106, pruned_loss=0.06671, over 7406.00 frames.], tot_loss[loss=0.2207, simple_loss=0.2985, pruned_loss=0.07142, over 1467738.45 frames.], batch size: 22, lr: 4.90e-04
2022-07-26 15:09:13,524 INFO [train.py:850] (2/4) Epoch 11, batch 7300, loss[loss=0.1751, simple_loss=0.2551, pruned_loss=0.04753, over 7285.00 frames.], tot_loss[loss=0.2203, simple_loss=0.2983, pruned_loss=0.07111, over 1467578.70 frames.], batch size: 19, lr: 4.90e-04
2022-07-26 15:09:58,624 INFO [train.py:850] (2/4) Epoch 11, batch 7350, loss[loss=0.2222, simple_loss=0.2849, pruned_loss=0.0798, over 7144.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2976, pruned_loss=0.07061, over 1466657.85 frames.], batch size: 17, lr: 4.90e-04
2022-07-26 15:10:43,223 INFO [train.py:850] (2/4) Epoch 11, batch 7400, loss[loss=0.2796, simple_loss=0.3473, pruned_loss=0.106, over 7415.00 frames.], tot_loss[loss=0.2206, simple_loss=0.2984, pruned_loss=0.0714, over 1467580.45 frames.], batch size: 22, lr: 4.90e-04
2022-07-26 15:11:27,059 INFO [train.py:850] (2/4) Epoch 11, batch 7450, loss[loss=0.2461, simple_loss=0.311, pruned_loss=0.09055, over 7277.00 frames.], tot_loss[loss=0.2203, simple_loss=0.2985, pruned_loss=0.07102, over 1466891.49 frames.], batch size: 20, lr: 4.90e-04
2022-07-26 15:12:10,899 INFO [train.py:850] (2/4) Epoch 11, batch 7500, loss[loss=0.1809, simple_loss=0.2491, pruned_loss=0.05631, over 7295.00 frames.], tot_loss[loss=0.221, simple_loss=0.299, pruned_loss=0.07148, over 1465894.02 frames.], batch size: 17, lr: 4.90e-04
2022-07-26 15:12:53,876 INFO [train.py:850] (2/4) Epoch 11, batch 7550, loss[loss=0.1769, simple_loss=0.2487, pruned_loss=0.05257, over 7285.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2981, pruned_loss=0.07102, over 1464521.68 frames.], batch size: 16, lr: 4.90e-04
2022-07-26 15:13:39,668 INFO [train.py:850] (2/4) Epoch 11, batch 7600, loss[loss=0.2582, simple_loss=0.3331, pruned_loss=0.09162, over 7189.00 frames.], tot_loss[loss=0.2214, simple_loss=0.2991, pruned_loss=0.07183, over 1465821.45 frames.], batch size: 21, lr: 4.89e-04
2022-07-26 15:14:22,626 INFO [train.py:850] (2/4) Epoch 11, batch 7650, loss[loss=0.2148, simple_loss=0.2951, pruned_loss=0.06719, over 7097.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2978, pruned_loss=0.07064, over 1465961.60 frames.], batch size: 18, lr: 4.89e-04
2022-07-26 15:15:08,219 INFO [train.py:850] (2/4) Epoch 11, batch 7700, loss[loss=0.2169, simple_loss=0.3061, pruned_loss=0.06383, over 7286.00 frames.], tot_loss[loss=0.2197, simple_loss=0.298, pruned_loss=0.07076, over 1465792.90 frames.], batch size: 21, lr: 4.89e-04
2022-07-26 15:15:51,623 INFO [train.py:850] (2/4) Epoch 11, batch 7750, loss[loss=0.1715, simple_loss=0.2533, pruned_loss=0.04489, over 7285.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2966, pruned_loss=0.06982, over 1465106.44 frames.], batch size: 19, lr: 4.89e-04
2022-07-26 15:16:36,296 INFO [train.py:850] (2/4) Epoch 11, batch 7800, loss[loss=0.2191, simple_loss=0.2964, pruned_loss=0.0709, over 7486.00 frames.], tot_loss[loss=0.2178, simple_loss=0.2962, pruned_loss=0.06969, over 1465231.55 frames.], batch size: 20, lr: 4.89e-04
2022-07-26 15:17:20,689 INFO [train.py:850] (2/4) Epoch 11, batch 7850, loss[loss=0.1851, simple_loss=0.2677, pruned_loss=0.05126, over 7453.00 frames.], tot_loss[loss=0.2174, simple_loss=0.296, pruned_loss=0.06934, over 1466040.82 frames.], batch size: 18, lr: 4.89e-04
2022-07-26 15:18:05,727 INFO [train.py:850] (2/4) Epoch 11, batch 7900, loss[loss=0.173, simple_loss=0.249, pruned_loss=0.04847, over 7329.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2947, pruned_loss=0.0687, over 1466549.57 frames.], batch size: 17, lr: 4.89e-04
2022-07-26 15:18:50,242 INFO [train.py:850] (2/4) Epoch 11, batch 7950, loss[loss=0.2781, simple_loss=0.3452, pruned_loss=0.1056, over 7446.00 frames.], tot_loss[loss=0.217, simple_loss=0.2954, pruned_loss=0.06935, over 1466879.64 frames.], batch size: 66, lr: 4.89e-04
2022-07-26 15:19:34,109 INFO [train.py:850] (2/4) Epoch 11, batch 8000, loss[loss=0.2294, simple_loss=0.2928, pruned_loss=0.08297, over 7251.00 frames.], tot_loss[loss=0.2164, simple_loss=0.295, pruned_loss=0.06893, over 1467197.25 frames.], batch size: 16, lr: 4.88e-04
2022-07-26 15:20:18,599 INFO [train.py:850] (2/4) Epoch 11, batch 8050, loss[loss=0.2116, simple_loss=0.2839, pruned_loss=0.06963, over 7256.00 frames.], tot_loss[loss=0.2173, simple_loss=0.296, pruned_loss=0.06927, over 1467441.83 frames.], batch size: 16, lr: 4.88e-04
2022-07-26 15:21:02,122 INFO [train.py:850] (2/4) Epoch 11, batch 8100, loss[loss=0.2316, simple_loss=0.3187, pruned_loss=0.07224, over 7289.00 frames.], tot_loss[loss=0.2184, simple_loss=0.2974, pruned_loss=0.06969, over 1467972.43 frames.], batch size: 21, lr: 4.88e-04
2022-07-26 15:21:45,416 INFO [train.py:850] (2/4) Epoch 11, batch 8150, loss[loss=0.172, simple_loss=0.255, pruned_loss=0.04449, over 7281.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2978, pruned_loss=0.06973, over 1467533.36 frames.], batch size: 16, lr: 4.88e-04
2022-07-26 15:22:29,740 INFO [train.py:850] (2/4) Epoch 11, batch 8200, loss[loss=0.1994, simple_loss=0.2868, pruned_loss=0.05605, over 7370.00 frames.], tot_loss[loss=0.218, simple_loss=0.2971, pruned_loss=0.06947, over 1466963.81 frames.], batch size: 21, lr: 4.88e-04
2022-07-26 15:23:13,409 INFO [train.py:850] (2/4) Epoch 11, batch 8250, loss[loss=0.2399, simple_loss=0.3166, pruned_loss=0.08161, over 7410.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2971, pruned_loss=0.06935, over 1466879.93 frames.], batch size: 39, lr: 4.88e-04
2022-07-26 15:23:58,172 INFO [train.py:850] (2/4) Epoch 11, batch 8300, loss[loss=0.2364, simple_loss=0.3118, pruned_loss=0.08045, over 7415.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2982, pruned_loss=0.07005, over 1466653.05 frames.], batch size: 22, lr: 4.88e-04
2022-07-26 15:24:40,465 INFO [train.py:850] (2/4) Epoch 11, batch 8350, loss[loss=0.2012, simple_loss=0.2964, pruned_loss=0.05304, over 7468.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2973, pruned_loss=0.06947, over 1467422.19 frames.], batch size: 21, lr: 4.87e-04
2022-07-26 15:25:25,761 INFO [train.py:850] (2/4) Epoch 11, batch 8400, loss[loss=0.252, simple_loss=0.3384, pruned_loss=0.08283, over 7440.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2985, pruned_loss=0.07007, over 1467185.49 frames.], batch size: 39, lr: 4.87e-04
2022-07-26 15:26:09,262 INFO [train.py:850] (2/4) Epoch 11, batch 8450, loss[loss=0.1756, simple_loss=0.2474, pruned_loss=0.05191, over 7208.00 frames.], tot_loss[loss=0.218, simple_loss=0.2967, pruned_loss=0.06968, over 1466628.04 frames.], batch size: 16, lr: 4.87e-04
2022-07-26 15:26:53,115 INFO [train.py:850] (2/4) Epoch 11, batch 8500, loss[loss=0.2043, simple_loss=0.2954, pruned_loss=0.05656, over 7286.00 frames.], tot_loss[loss=0.2184, simple_loss=0.2971, pruned_loss=0.0699, over 1466278.21 frames.], batch size: 21, lr: 4.87e-04
2022-07-26 15:27:37,143 INFO [train.py:850] (2/4) Epoch 11, batch 8550, loss[loss=0.2026, simple_loss=0.2868, pruned_loss=0.05925, over 7190.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2967, pruned_loss=0.06982, over 1466139.37 frames.], batch size: 19, lr: 4.87e-04
2022-07-26 15:28:20,457 INFO [train.py:850] (2/4) Epoch 11, batch 8600, loss[loss=0.3183, simple_loss=0.3679, pruned_loss=0.1344, over 7403.00 frames.], tot_loss[loss=0.2198, simple_loss=0.2975, pruned_loss=0.07102, over 1467032.19 frames.], batch size: 22, lr: 4.87e-04
2022-07-26 15:29:05,746 INFO [train.py:850] (2/4) Epoch 11, batch 8650, loss[loss=0.2188, simple_loss=0.3003, pruned_loss=0.0687, over 7271.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2976, pruned_loss=0.07049, over 1467604.82 frames.], batch size: 27, lr: 4.87e-04
2022-07-26 15:29:48,339 INFO [train.py:850] (2/4) Epoch 11, batch 8700, loss[loss=0.2095, simple_loss=0.2884, pruned_loss=0.06527, over 7204.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2975, pruned_loss=0.07063, over 1466409.12 frames.], batch size: 19, lr: 4.87e-04
2022-07-26 15:30:30,212 INFO [train.py:850] (2/4) Epoch 11, batch 8750, loss[loss=0.2262, simple_loss=0.3104, pruned_loss=0.07102, over 7260.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2968, pruned_loss=0.06965, over 1465060.01 frames.], batch size: 30, lr: 4.87e-04
2022-07-26 15:31:13,168 INFO [train.py:850] (2/4) Epoch 11, batch 8800, loss[loss=0.2065, simple_loss=0.2988, pruned_loss=0.05711, over 7264.00 frames.], tot_loss[loss=0.2168, simple_loss=0.296, pruned_loss=0.06876, over 1465859.48 frames.], batch size: 27, lr: 4.86e-04
2022-07-26 15:31:55,484 INFO [train.py:850] (2/4) Epoch 11, batch 8850, loss[loss=0.1864, simple_loss=0.2628, pruned_loss=0.055, over 7462.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2958, pruned_loss=0.06878, over 1465778.05 frames.], batch size: 17, lr: 4.86e-04
2022-07-26 15:33:36,949 INFO [train.py:850] (2/4) Epoch 12, batch 0, loss[loss=0.1896, simple_loss=0.2698, pruned_loss=0.0547, over 7382.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2698, pruned_loss=0.0547, over 7382.00 frames.], batch size: 20, lr: 4.69e-04
2022-07-26 15:34:20,771 INFO [train.py:850] (2/4) Epoch 12, batch 50, loss[loss=0.2035, simple_loss=0.2948, pruned_loss=0.05608, over 7336.00 frames.], tot_loss[loss=0.2129, simple_loss=0.3001, pruned_loss=0.06282, over 331418.83 frames.], batch size: 23, lr: 4.69e-04
2022-07-26 15:35:05,526 INFO [train.py:850] (2/4) Epoch 12, batch 100, loss[loss=0.2308, simple_loss=0.3128, pruned_loss=0.07444, over 7394.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2953, pruned_loss=0.05987, over 582750.98 frames.], batch size: 19, lr: 4.69e-04
2022-07-26 15:35:49,307 INFO [train.py:850] (2/4) Epoch 12, batch 150, loss[loss=0.164, simple_loss=0.254, pruned_loss=0.03705, over 7442.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2943, pruned_loss=0.06014, over 778497.39 frames.], batch size: 18, lr: 4.69e-04
2022-07-26 15:36:35,270 INFO [train.py:850] (2/4) Epoch 12, batch 200, loss[loss=0.188, simple_loss=0.2773, pruned_loss=0.04934, over 7278.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2914, pruned_loss=0.05851, over 931728.68 frames.], batch size: 27, lr: 4.69e-04
2022-07-26 15:37:18,182 INFO [train.py:850] (2/4) Epoch 12, batch 250, loss[loss=0.1682, simple_loss=0.2532, pruned_loss=0.04156, over 7328.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2909, pruned_loss=0.05789, over 1049969.60 frames.], batch size: 18, lr: 4.68e-04
2022-07-26 15:38:01,343 INFO [train.py:850] (2/4) Epoch 12, batch 300, loss[loss=0.2069, simple_loss=0.2974, pruned_loss=0.05823, over 7296.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2901, pruned_loss=0.0575, over 1142271.10 frames.], batch size: 19, lr: 4.68e-04
2022-07-26 15:38:45,986 INFO [train.py:850] (2/4) Epoch 12, batch 350, loss[loss=0.1891, simple_loss=0.2877, pruned_loss=0.04526, over 7200.00 frames.], tot_loss[loss=0.2009, simple_loss=0.288, pruned_loss=0.05687, over 1214553.22 frames.], batch size: 25, lr: 4.68e-04
2022-07-26 15:39:30,069 INFO [train.py:850] (2/4) Epoch 12, batch 400, loss[loss=0.2024, simple_loss=0.297, pruned_loss=0.05384, over 7290.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2892, pruned_loss=0.05721, over 1270547.39 frames.], batch size: 19, lr: 4.68e-04
2022-07-26 15:40:13,567 INFO [train.py:850] (2/4) Epoch 12, batch 450, loss[loss=0.1759, simple_loss=0.2619, pruned_loss=0.04493, over 7387.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2889, pruned_loss=0.05695, over 1312903.42 frames.], batch size: 19, lr: 4.68e-04
2022-07-26 15:40:57,127 INFO [train.py:850] (2/4) Epoch 12, batch 500, loss[loss=0.2075, simple_loss=0.2888, pruned_loss=0.06313, over 7327.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2875, pruned_loss=0.05619, over 1346577.14 frames.], batch size: 18, lr: 4.68e-04
2022-07-26 15:41:40,570 INFO [train.py:850] (2/4) Epoch 12, batch 550, loss[loss=0.1979, simple_loss=0.2745, pruned_loss=0.06067, over 7195.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2859, pruned_loss=0.05549, over 1373598.45 frames.], batch size: 18, lr: 4.68e-04
2022-07-26 15:42:24,065 INFO [train.py:850] (2/4) Epoch 12, batch 600, loss[loss=0.1945, simple_loss=0.2813, pruned_loss=0.05379, over 7280.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2859, pruned_loss=0.05575, over 1393849.07 frames.], batch size: 21, lr: 4.68e-04
2022-07-26 15:43:08,058 INFO [train.py:850] (2/4) Epoch 12, batch 650, loss[loss=0.2088, simple_loss=0.3002, pruned_loss=0.05875, over 7287.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2869, pruned_loss=0.056, over 1410535.54 frames.], batch size: 21, lr: 4.67e-04
2022-07-26 15:43:52,157 INFO [train.py:850] (2/4) Epoch 12, batch 700, loss[loss=0.1986, simple_loss=0.2715, pruned_loss=0.06284, over 7323.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2847, pruned_loss=0.05472, over 1423083.93 frames.], batch size: 17, lr: 4.67e-04
2022-07-26 15:44:35,892 INFO [train.py:850] (2/4) Epoch 12, batch 750, loss[loss=0.2609, simple_loss=0.3341, pruned_loss=0.09389, over 7431.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2861, pruned_loss=0.05521, over 1431284.65 frames.], batch size: 68, lr: 4.67e-04
2022-07-26 15:45:18,499 INFO [train.py:850] (2/4) Epoch 12, batch 800, loss[loss=0.1764, simple_loss=0.2684, pruned_loss=0.0422, over 7293.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2853, pruned_loss=0.05512, over 1438540.39 frames.], batch size: 19, lr: 4.67e-04
2022-07-26 15:46:02,539 INFO [train.py:850] (2/4) Epoch 12, batch 850, loss[loss=0.1852, simple_loss=0.2718, pruned_loss=0.04932, over 7196.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2868, pruned_loss=0.05544, over 1444529.92 frames.], batch size: 18, lr: 4.67e-04
2022-07-26 15:46:45,925 INFO [train.py:850] (2/4) Epoch 12, batch 900, loss[loss=0.1802, simple_loss=0.2601, pruned_loss=0.05013, over 7138.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2867, pruned_loss=0.05537, over 1448412.90 frames.], batch size: 17, lr: 4.67e-04
2022-07-26 15:47:29,248 INFO [train.py:850] (2/4) Epoch 12, batch 950, loss[loss=0.1735, simple_loss=0.2602, pruned_loss=0.04345, over 7430.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2883, pruned_loss=0.05628, over 1452180.95 frames.], batch size: 18, lr: 4.67e-04
2022-07-26 15:48:13,076 INFO [train.py:850] (2/4) Epoch 12, batch 1000, loss[loss=0.221, simple_loss=0.3129, pruned_loss=0.06451, over 7345.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2893, pruned_loss=0.05712, over 1454229.67 frames.], batch size: 23, lr: 4.67e-04
2022-07-26 15:48:55,541 INFO [train.py:850] (2/4) Epoch 12, batch 1050, loss[loss=0.2004, simple_loss=0.2837, pruned_loss=0.05854, over 7437.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2894, pruned_loss=0.05689, over 1457359.53 frames.], batch size: 18, lr: 4.67e-04
2022-07-26 15:49:40,206 INFO [train.py:850] (2/4) Epoch 12, batch 1100, loss[loss=0.1886, simple_loss=0.2767, pruned_loss=0.05026, over 7486.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2916, pruned_loss=0.05796, over 1458742.15 frames.], batch size: 24, lr: 4.66e-04
2022-07-26 15:50:23,033 INFO [train.py:850] (2/4) Epoch 12, batch 1150, loss[loss=0.1812, simple_loss=0.2623, pruned_loss=0.05007, over 7315.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2919, pruned_loss=0.05809, over 1460225.07 frames.], batch size: 18, lr: 4.66e-04
2022-07-26 15:51:07,389 INFO [train.py:850] (2/4) Epoch 12, batch 1200, loss[loss=0.2238, simple_loss=0.3114, pruned_loss=0.06811, over 7300.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2928, pruned_loss=0.05866, over 1462126.18 frames.], batch size: 20, lr: 4.66e-04
2022-07-26 15:51:51,595 INFO [train.py:850] (2/4) Epoch 12, batch 1250, loss[loss=0.2, simple_loss=0.2909, pruned_loss=0.05453, over 7283.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2928, pruned_loss=0.05873, over 1461882.75 frames.], batch size: 20, lr: 4.66e-04
2022-07-26 15:52:36,092 INFO [train.py:850] (2/4) Epoch 12, batch 1300, loss[loss=0.1896, simple_loss=0.2719, pruned_loss=0.05362, over 7291.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2933, pruned_loss=0.05883, over 1463089.65 frames.], batch size: 19, lr: 4.66e-04
2022-07-26 15:53:19,573 INFO [train.py:850] (2/4) Epoch 12, batch 1350, loss[loss=0.1833, simple_loss=0.2749, pruned_loss=0.04587, over 7184.00 frames.], tot_loss[loss=0.2053, simple_loss=0.293, pruned_loss=0.05873, over 1464019.96 frames.], batch size: 21, lr: 4.66e-04
2022-07-26 15:54:03,907 INFO [train.py:850] (2/4) Epoch 12, batch 1400, loss[loss=0.1868, simple_loss=0.2623, pruned_loss=0.05561, over 7473.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2921, pruned_loss=0.0581, over 1463734.85 frames.], batch size: 17, lr: 4.66e-04
2022-07-26 15:54:47,208 INFO [train.py:850] (2/4) Epoch 12, batch 1450, loss[loss=0.1788, simple_loss=0.26, pruned_loss=0.04883, over 7305.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2919, pruned_loss=0.05777, over 1464625.31 frames.], batch size: 17, lr: 4.66e-04
2022-07-26 15:55:31,797 INFO [train.py:850] (2/4) Epoch 12, batch 1500, loss[loss=0.1728, simple_loss=0.2679, pruned_loss=0.03882, over 7211.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2929, pruned_loss=0.05802, over 1465212.84 frames.], batch size: 19, lr: 4.65e-04
2022-07-26 15:56:16,733 INFO [train.py:850] (2/4) Epoch 12, batch 1550, loss[loss=0.2124, simple_loss=0.3037, pruned_loss=0.06056, over 7483.00 frames.], tot_loss[loss=0.2057, simple_loss=0.294, pruned_loss=0.0587, over 1466407.02 frames.], batch size: 39, lr: 4.65e-04
2022-07-26 15:57:02,477 INFO [train.py:850] (2/4) Epoch 12, batch 1600, loss[loss=0.2109, simple_loss=0.2893, pruned_loss=0.06628, over 7489.00 frames.], tot_loss[loss=0.2047, simple_loss=0.293, pruned_loss=0.05822, over 1466134.26 frames.], batch size: 19, lr: 4.65e-04
2022-07-26 15:57:46,595 INFO [train.py:850] (2/4) Epoch 12, batch 1650, loss[loss=0.2261, simple_loss=0.3174, pruned_loss=0.06744, over 7479.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2935, pruned_loss=0.05843, over 1466629.04 frames.], batch size: 23, lr: 4.65e-04
2022-07-26 15:58:32,898 INFO [train.py:850] (2/4) Epoch 12, batch 1700, loss[loss=0.2298, simple_loss=0.305, pruned_loss=0.07732, over 7176.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2928, pruned_loss=0.05833, over 1466283.83 frames.], batch size: 22, lr: 4.65e-04
2022-07-26 15:59:17,111 INFO [train.py:850] (2/4) Epoch 12, batch 1750, loss[loss=0.2172, simple_loss=0.3155, pruned_loss=0.05949, over 7486.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2927, pruned_loss=0.05825, over 1466237.11 frames.], batch size: 21, lr: 4.65e-04
2022-07-26 16:00:00,988 INFO [train.py:850] (2/4) Epoch 12, batch 1800, loss[loss=0.2019, simple_loss=0.302, pruned_loss=0.05088, over 7371.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2929, pruned_loss=0.05822, over 1466405.30 frames.], batch size: 21, lr: 4.65e-04
2022-07-26 16:00:44,151 INFO [train.py:850] (2/4) Epoch 12, batch 1850, loss[loss=0.2249, simple_loss=0.3132, pruned_loss=0.0683, over 7416.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2924, pruned_loss=0.05787, over 1464963.90 frames.], batch size: 22, lr: 4.65e-04
2022-07-26 16:01:28,157 INFO [train.py:850] (2/4) Epoch 12, batch 1900, loss[loss=0.1743, simple_loss=0.2506, pruned_loss=0.04897, over 7471.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2912, pruned_loss=0.05731, over 1463675.04 frames.], batch size: 17, lr: 4.65e-04
2022-07-26 16:02:12,266 INFO [train.py:850] (2/4) Epoch 12, batch 1950, loss[loss=0.2051, simple_loss=0.3095, pruned_loss=0.05032, over 7304.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2913, pruned_loss=0.057, over 1464185.17 frames.], batch size: 22, lr: 4.64e-04
2022-07-26 16:02:55,772 INFO [train.py:850] (2/4) Epoch 12, batch 2000, loss[loss=0.2284, simple_loss=0.3179, pruned_loss=0.06941, over 7392.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2914, pruned_loss=0.05711, over 1464545.62 frames.], batch size: 39, lr: 4.64e-04
2022-07-26 16:03:39,178 INFO [train.py:850] (2/4) Epoch 12, batch 2050, loss[loss=0.235, simple_loss=0.3181, pruned_loss=0.07598, over 7464.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2905, pruned_loss=0.05689, over 1465037.62 frames.], batch size: 24, lr: 4.64e-04
2022-07-26 16:04:23,275 INFO [train.py:850] (2/4) Epoch 12, batch 2100, loss[loss=0.1862, simple_loss=0.2743, pruned_loss=0.04901, over 7390.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2905, pruned_loss=0.05747, over 1464994.05 frames.], batch size: 19, lr: 4.64e-04
2022-07-26 16:05:06,431 INFO [train.py:850] (2/4) Epoch 12, batch 2150, loss[loss=0.2044, simple_loss=0.2906, pruned_loss=0.05916, over 7382.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2913, pruned_loss=0.05787, over 1465713.14 frames.], batch size: 19, lr: 4.64e-04
2022-07-26 16:06:05,964 INFO [train.py:850] (2/4) Epoch 12, batch 2200, loss[loss=0.2057, simple_loss=0.2972, pruned_loss=0.05705, over 7185.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2918, pruned_loss=0.05794, over 1465208.86 frames.], batch size: 21, lr: 4.64e-04
2022-07-26 16:06:49,007 INFO [train.py:850] (2/4) Epoch 12, batch 2250, loss[loss=0.1735, simple_loss=0.2654, pruned_loss=0.04078, over 7482.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2923, pruned_loss=0.05804, over 1465853.77 frames.], batch size: 24, lr: 4.64e-04
2022-07-26 16:07:34,833 INFO [train.py:850] (2/4) Epoch 12, batch 2300, loss[loss=0.2067, simple_loss=0.3041, pruned_loss=0.05466, over 7439.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2927, pruned_loss=0.05815, over 1465379.92 frames.], batch size: 31, lr: 4.64e-04
2022-07-26 16:08:17,287 INFO [train.py:850] (2/4) Epoch 12, batch 2350, loss[loss=0.1838, simple_loss=0.2817, pruned_loss=0.04291, over 7177.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2916, pruned_loss=0.05735, over 1465231.25 frames.], batch size: 21, lr: 4.63e-04
2022-07-26 16:09:02,125 INFO [train.py:850] (2/4) Epoch 12, batch 2400, loss[loss=0.2281, simple_loss=0.3154, pruned_loss=0.07037, over 7481.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2908, pruned_loss=0.05735, over 1465847.76 frames.], batch size: 40, lr: 4.63e-04
2022-07-26 16:09:46,324 INFO [train.py:850] (2/4) Epoch 12, batch 2450, loss[loss=0.1821, simple_loss=0.2652, pruned_loss=0.04944, over 7312.00 frames.], tot_loss[loss=0.202, simple_loss=0.2902, pruned_loss=0.0569, over 1465602.94 frames.], batch size: 18, lr: 4.63e-04
2022-07-26 16:10:30,899 INFO [train.py:850] (2/4) Epoch 12, batch 2500, loss[loss=0.1811, simple_loss=0.2827, pruned_loss=0.03976, over 7180.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2894, pruned_loss=0.05655, over 1464508.88 frames.], batch size: 21, lr: 4.63e-04
2022-07-26 16:11:15,291 INFO [train.py:850] (2/4) Epoch 12, batch 2550, loss[loss=0.1733, simple_loss=0.2608, pruned_loss=0.04294, over 7143.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2893, pruned_loss=0.05653, over 1465773.62 frames.], batch size: 17, lr: 4.63e-04
2022-07-26 16:11:59,203 INFO [train.py:850] (2/4) Epoch 12, batch 2600, loss[loss=0.189, simple_loss=0.2702, pruned_loss=0.05388, over 7296.00 frames.], tot_loss[loss=0.2, simple_loss=0.2882, pruned_loss=0.05587, over 1465535.47 frames.], batch size: 17, lr: 4.63e-04
2022-07-26 16:12:43,036 INFO [train.py:850] (2/4) Epoch 12, batch 2650, loss[loss=0.2164, simple_loss=0.3053, pruned_loss=0.06373, over 7214.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2875, pruned_loss=0.05543, over 1465997.96 frames.], batch size: 25, lr: 4.63e-04
2022-07-26 16:13:26,638 INFO [train.py:850] (2/4) Epoch 12, batch 2700, loss[loss=0.1557, simple_loss=0.2373, pruned_loss=0.03712, over 7302.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2872, pruned_loss=0.05569, over 1465101.38 frames.], batch size: 17, lr: 4.63e-04
2022-07-26 16:14:10,204 INFO [train.py:850] (2/4) Epoch 12, batch 2750, loss[loss=0.2445, simple_loss=0.3223, pruned_loss=0.08329, over 7356.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2889, pruned_loss=0.05599, over 1464229.75 frames.], batch size: 69, lr: 4.63e-04
2022-07-26 16:14:54,627 INFO [train.py:850] (2/4) Epoch 12, batch 2800, loss[loss=0.1858, simple_loss=0.2772, pruned_loss=0.04719, over 7418.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2893, pruned_loss=0.05614, over 1465213.83 frames.], batch size: 22, lr: 4.62e-04
2022-07-26 16:15:38,594 INFO [train.py:850] (2/4) Epoch 12, batch 2850, loss[loss=0.1674, simple_loss=0.2419, pruned_loss=0.04642, over 7302.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2886, pruned_loss=0.05596, over 1465641.79 frames.], batch size: 17, lr: 4.62e-04
2022-07-26 16:16:22,837 INFO [train.py:850] (2/4) Epoch 12, batch 2900, loss[loss=0.2024, simple_loss=0.2955, pruned_loss=0.05465, over 7303.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2891, pruned_loss=0.05605, over 1465985.93 frames.], batch size: 27, lr: 4.62e-04
2022-07-26 16:17:06,711 INFO [train.py:850] (2/4) Epoch 12, batch 2950, loss[loss=0.2586, simple_loss=0.3363, pruned_loss=0.09047, over 7412.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2892, pruned_loss=0.05617, over 1465343.65 frames.], batch size: 22, lr: 4.62e-04
2022-07-26 16:17:52,679 INFO [train.py:850] (2/4) Epoch 12, batch 3000, loss[loss=0.2142, simple_loss=0.3061, pruned_loss=0.06113, over 7171.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2897, pruned_loss=0.05626, over 1465727.53 frames.], batch size: 22, lr: 4.62e-04
2022-07-26 16:17:52,680 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 16:18:16,322 INFO [train.py:879] (2/4) Epoch 12, validation: loss=0.195, simple_loss=0.2914, pruned_loss=0.04928, over 924787.00 frames.
2022-07-26 16:18:59,571 INFO [train.py:850] (2/4) Epoch 12, batch 3050, loss[loss=0.1922, simple_loss=0.2874, pruned_loss=0.04847, over 7478.00 frames.], tot_loss[loss=0.2002, simple_loss=0.289, pruned_loss=0.05574, over 1466268.56 frames.], batch size: 21, lr: 4.62e-04
2022-07-26 16:19:43,915 INFO [train.py:850] (2/4) Epoch 12, batch 3100, loss[loss=0.1957, simple_loss=0.2908, pruned_loss=0.05033, over 7309.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2874, pruned_loss=0.05515, over 1465286.71 frames.], batch size: 27, lr: 4.62e-04
2022-07-26 16:20:27,512 INFO [train.py:850] (2/4) Epoch 12, batch 3150, loss[loss=0.2341, simple_loss=0.3092, pruned_loss=0.07945, over 7351.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2876, pruned_loss=0.05589, over 1465190.81 frames.], batch size: 23, lr: 4.62e-04
2022-07-26 16:21:11,905 INFO [train.py:850] (2/4) Epoch 12, batch 3200, loss[loss=0.2213, simple_loss=0.3095, pruned_loss=0.06657, over 7183.00 frames.], tot_loss[loss=0.1991, simple_loss=0.287, pruned_loss=0.05566, over 1464688.26 frames.], batch size: 21, lr: 4.62e-04
2022-07-26 16:21:54,654 INFO [train.py:850] (2/4) Epoch 12, batch 3250, loss[loss=0.2179, simple_loss=0.278, pruned_loss=0.07889, over 7204.00 frames.], tot_loss[loss=0.2, simple_loss=0.2881, pruned_loss=0.05597, over 1464060.88 frames.], batch size: 16, lr: 4.61e-04
2022-07-26 16:22:38,086 INFO [train.py:850] (2/4) Epoch 12, batch 3300, loss[loss=0.1784, simple_loss=0.259, pruned_loss=0.04893, over 7460.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2867, pruned_loss=0.05532, over 1464340.67 frames.], batch size: 17, lr: 4.61e-04
2022-07-26 16:23:22,971 INFO [train.py:850] (2/4) Epoch 12, batch 3350, loss[loss=0.186, simple_loss=0.2669, pruned_loss=0.0526, over 7306.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2881, pruned_loss=0.05585, over 1464162.40 frames.], batch size: 17, lr: 4.61e-04
2022-07-26 16:24:07,261 INFO [train.py:850] (2/4) Epoch 12, batch 3400, loss[loss=0.1898, simple_loss=0.2716, pruned_loss=0.054, over 7455.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2879, pruned_loss=0.05565, over 1464841.78 frames.], batch size: 17, lr: 4.61e-04
2022-07-26 16:24:51,099 INFO [train.py:850] (2/4) Epoch 12, batch 3450, loss[loss=0.2041, simple_loss=0.3067, pruned_loss=0.05078, over 7369.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2873, pruned_loss=0.05513, over 1466181.43 frames.], batch size: 21, lr: 4.61e-04
2022-07-26 16:25:34,731 INFO [train.py:850] (2/4) Epoch 12, batch 3500, loss[loss=0.1767, simple_loss=0.2649, pruned_loss=0.04421, over 7477.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2868, pruned_loss=0.05477, over 1465052.03 frames.], batch size: 20, lr: 4.61e-04
2022-07-26 16:26:17,481 INFO [train.py:850] (2/4) Epoch 12, batch 3550, loss[loss=0.2142, simple_loss=0.311, pruned_loss=0.05871, over 7180.00 frames.], tot_loss[loss=0.198, simple_loss=0.2868, pruned_loss=0.05459, over 1464980.98 frames.], batch size: 21, lr: 4.61e-04
2022-07-26 16:27:01,250 INFO [train.py:850] (2/4) Epoch 12, batch 3600, loss[loss=0.1835, simple_loss=0.2666, pruned_loss=0.05019, over 7316.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2884, pruned_loss=0.05525, over 1465828.52 frames.], batch size: 18, lr: 4.61e-04
2022-07-26 16:27:44,167 INFO [train.py:850] (2/4) Epoch 12, batch 3650, loss[loss=0.2171, simple_loss=0.3071, pruned_loss=0.06357, over 7396.00 frames.], tot_loss[loss=0.199, simple_loss=0.2878, pruned_loss=0.0551, over 1465631.10 frames.], batch size: 38, lr: 4.61e-04
2022-07-26 16:28:28,276 INFO [train.py:850] (2/4) Epoch 12, batch 3700, loss[loss=0.2339, simple_loss=0.3259, pruned_loss=0.07093, over 7389.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2893, pruned_loss=0.05561, over 1465936.07 frames.], batch size: 39, lr: 4.60e-04
2022-07-26 16:29:11,687 INFO [train.py:850] (2/4) Epoch 12, batch 3750, loss[loss=0.2281, simple_loss=0.3112, pruned_loss=0.07254, over 7421.00 frames.], tot_loss[loss=0.2, simple_loss=0.2886, pruned_loss=0.05564, over 1465968.48 frames.], batch size: 69, lr: 4.60e-04
2022-07-26 16:29:55,087 INFO [train.py:850] (2/4) Epoch 12, batch 3800, loss[loss=0.1806, simple_loss=0.2708, pruned_loss=0.0452, over 7277.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2901, pruned_loss=0.05644, over 1466135.80 frames.], batch size: 16, lr: 4.60e-04
2022-07-26 16:30:38,893 INFO [train.py:850] (2/4) Epoch 12, batch 3850, loss[loss=0.197, simple_loss=0.2848, pruned_loss=0.05459, over 7102.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2895, pruned_loss=0.05613, over 1465805.71 frames.], batch size: 18, lr: 4.60e-04
2022-07-26 16:31:23,195 INFO [train.py:850] (2/4) Epoch 12, batch 3900, loss[loss=0.1843, simple_loss=0.2807, pruned_loss=0.044, over 7480.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2876, pruned_loss=0.05504, over 1466591.67 frames.], batch size: 21, lr: 4.60e-04
2022-07-26 16:32:09,083 INFO [train.py:850] (2/4) Epoch 12, batch 3950, loss[loss=0.1755, simple_loss=0.2548, pruned_loss=0.04809, over 7201.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2882, pruned_loss=0.05473, over 1466639.17 frames.], batch size: 19, lr: 4.60e-04
2022-07-26 16:32:53,311 INFO [train.py:850] (2/4) Epoch 12, batch 4000, loss[loss=0.1977, simple_loss=0.2822, pruned_loss=0.0566, over 7341.00 frames.], tot_loss[loss=0.1999, simple_loss=0.289, pruned_loss=0.05535, over 1466651.55 frames.], batch size: 23, lr: 4.60e-04
2022-07-26 16:33:36,037 INFO [train.py:850] (2/4) Epoch 12, batch 4050, loss[loss=0.1792, simple_loss=0.276, pruned_loss=0.04121, over 7187.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2887, pruned_loss=0.05508, over 1465922.84 frames.], batch size: 21, lr: 4.60e-04
2022-07-26 16:34:20,283 INFO [train.py:850] (2/4) Epoch 12, batch 4100, loss[loss=0.1956, simple_loss=0.2922, pruned_loss=0.04951, over 7453.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2887, pruned_loss=0.0558, over 1466685.22 frames.], batch size: 38, lr: 4.60e-04
2022-07-26 16:35:04,381 INFO [train.py:850] (2/4) Epoch 12, batch 4150, loss[loss=0.1863, simple_loss=0.2764, pruned_loss=0.04806, over 7301.00 frames.], tot_loss[loss=0.2021, simple_loss=0.29, pruned_loss=0.05705, over 1466883.58 frames.], batch size: 21, lr: 4.59e-04
2022-07-26 16:35:48,482 INFO [train.py:850] (2/4) Epoch 12, batch 4200, loss[loss=0.2619, simple_loss=0.3433, pruned_loss=0.0903, over 7203.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2924, pruned_loss=0.05914, over 1467192.94 frames.], batch size: 20, lr: 4.59e-04
2022-07-26 16:36:32,144 INFO [train.py:850] (2/4) Epoch 12, batch 4250, loss[loss=0.198, simple_loss=0.2665, pruned_loss=0.06472, over 7155.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2925, pruned_loss=0.05985, over 1467006.07 frames.], batch size: 17, lr: 4.59e-04
2022-07-26 16:37:15,757 INFO [train.py:850] (2/4) Epoch 12, batch 4300, loss[loss=0.271, simple_loss=0.3152, pruned_loss=0.1134, over 7321.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2926, pruned_loss=0.06058, over 1465712.97 frames.], batch size: 18, lr: 4.59e-04
2022-07-26 16:38:00,103 INFO [train.py:850] (2/4) Epoch 12, batch 4350, loss[loss=0.2681, simple_loss=0.3396, pruned_loss=0.09825, over 7280.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2952, pruned_loss=0.0629, over 1464655.23 frames.], batch size: 21, lr: 4.59e-04
2022-07-26 16:38:43,903 INFO [train.py:850] (2/4) Epoch 12, batch 4400, loss[loss=0.2332, simple_loss=0.3024, pruned_loss=0.08205, over 7370.00 frames.], tot_loss[loss=0.212, simple_loss=0.2956, pruned_loss=0.06424, over 1464765.62 frames.], batch size: 20, lr: 4.59e-04
2022-07-26 16:39:28,089 INFO [train.py:850] (2/4) Epoch 12, batch 4450, loss[loss=0.2343, simple_loss=0.3185, pruned_loss=0.07507, over 7375.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2977, pruned_loss=0.0664, over 1465908.41 frames.], batch size: 39, lr: 4.59e-04
2022-07-26 16:40:12,533 INFO [train.py:850] (2/4) Epoch 12, batch 4500, loss[loss=0.1971, simple_loss=0.295, pruned_loss=0.04965, over 7421.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2981, pruned_loss=0.0673, over 1464660.36 frames.], batch size: 22, lr: 4.59e-04
2022-07-26 16:40:56,214 INFO [train.py:850] (2/4) Epoch 12, batch 4550, loss[loss=0.2125, simple_loss=0.2998, pruned_loss=0.06262, over 7420.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2984, pruned_loss=0.06827, over 1464222.48 frames.], batch size: 22, lr: 4.58e-04
2022-07-26 16:41:40,741 INFO [train.py:850] (2/4) Epoch 12, batch 4600, loss[loss=0.1989, simple_loss=0.2915, pruned_loss=0.05314, over 7295.00 frames.], tot_loss[loss=0.2185, simple_loss=0.2988, pruned_loss=0.06914, over 1463818.39 frames.], batch size: 22, lr: 4.58e-04
2022-07-26 16:42:25,969 INFO [train.py:850] (2/4) Epoch 12, batch 4650, loss[loss=0.2292, simple_loss=0.3092, pruned_loss=0.07457, over 7182.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2981, pruned_loss=0.06884, over 1463857.09 frames.], batch size: 21, lr: 4.58e-04
2022-07-26 16:43:12,833 INFO [train.py:850] (2/4) Epoch 12, batch 4700, loss[loss=0.2525, simple_loss=0.3202, pruned_loss=0.09234, over 7289.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2992, pruned_loss=0.07007, over 1464099.85 frames.], batch size: 20, lr: 4.58e-04
2022-07-26 16:43:56,742 INFO [train.py:850] (2/4) Epoch 12, batch 4750, loss[loss=0.2301, simple_loss=0.3166, pruned_loss=0.0718, over 7235.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2988, pruned_loss=0.07016, over 1463654.45 frames.], batch size: 24, lr: 4.58e-04
2022-07-26 16:44:41,273 INFO [train.py:850] (2/4) Epoch 12, batch 4800, loss[loss=0.2571, simple_loss=0.3217, pruned_loss=0.0963, over 7294.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2978, pruned_loss=0.06968, over 1465030.15 frames.], batch size: 19, lr: 4.58e-04
2022-07-26 16:45:25,872 INFO [train.py:850] (2/4) Epoch 12, batch 4850, loss[loss=0.164, simple_loss=0.247, pruned_loss=0.04048, over 7113.00 frames.], tot_loss[loss=0.2178, simple_loss=0.2968, pruned_loss=0.06939, over 1464804.80 frames.], batch size: 18, lr: 4.58e-04
2022-07-26 16:46:10,655 INFO [train.py:850] (2/4) Epoch 12, batch 4900, loss[loss=0.242, simple_loss=0.3187, pruned_loss=0.08268, over 7177.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2985, pruned_loss=0.0703, over 1465325.68 frames.], batch size: 21, lr: 4.58e-04
2022-07-26 16:46:54,051 INFO [train.py:850] (2/4) Epoch 12, batch 4950, loss[loss=0.1874, simple_loss=0.257, pruned_loss=0.05888, over 7462.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2975, pruned_loss=0.07018, over 1466258.61 frames.], batch size: 17, lr: 4.58e-04
2022-07-26 16:47:37,664 INFO [train.py:850] (2/4) Epoch 12, batch 5000, loss[loss=0.2186, simple_loss=0.3021, pruned_loss=0.06752, over 7242.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2973, pruned_loss=0.07048, over 1465750.76 frames.], batch size: 24, lr: 4.57e-04
2022-07-26 16:48:20,926 INFO [train.py:850] (2/4) Epoch 12, batch 5050, loss[loss=0.262, simple_loss=0.3128, pruned_loss=0.1057, over 7157.00 frames.], tot_loss[loss=0.218, simple_loss=0.2962, pruned_loss=0.06994, over 1466111.47 frames.], batch size: 17, lr: 4.57e-04
2022-07-26 16:49:04,708 INFO [train.py:850] (2/4) Epoch 12, batch 5100, loss[loss=0.2067, simple_loss=0.2763, pruned_loss=0.06857, over 7450.00 frames.], tot_loss[loss=0.2189, simple_loss=0.297, pruned_loss=0.07042, over 1465401.19 frames.], batch size: 18, lr: 4.57e-04
2022-07-26 16:49:47,518 INFO [train.py:850] (2/4) Epoch 12, batch 5150, loss[loss=0.2082, simple_loss=0.292, pruned_loss=0.06219, over 7337.00 frames.], tot_loss[loss=0.2177, simple_loss=0.296, pruned_loss=0.06973, over 1464699.93 frames.], batch size: 23, lr: 4.57e-04
2022-07-26 16:50:31,772 INFO [train.py:850] (2/4) Epoch 12, batch 5200, loss[loss=0.2177, simple_loss=0.2995, pruned_loss=0.06795, over 7469.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2959, pruned_loss=0.06944, over 1465686.85 frames.], batch size: 21, lr: 4.57e-04
2022-07-26 16:51:13,726 INFO [train.py:850] (2/4) Epoch 12, batch 5250, loss[loss=0.2627, simple_loss=0.3327, pruned_loss=0.09637, over 7327.00 frames.], tot_loss[loss=0.218, simple_loss=0.2962, pruned_loss=0.06991, over 1465193.68 frames.], batch size: 23, lr: 4.57e-04
2022-07-26 16:51:57,901 INFO [train.py:850] (2/4) Epoch 12, batch 5300, loss[loss=0.178, simple_loss=0.2622, pruned_loss=0.04689, over 7195.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2968, pruned_loss=0.07036, over 1464128.83 frames.], batch size: 19, lr: 4.57e-04
2022-07-26 16:52:42,059 INFO [train.py:850] (2/4) Epoch 12, batch 5350, loss[loss=0.2473, simple_loss=0.3157, pruned_loss=0.08941, over 7423.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2958, pruned_loss=0.07001, over 1464989.88 frames.], batch size: 22, lr: 4.57e-04
2022-07-26 16:53:28,156 INFO [train.py:850] (2/4) Epoch 12, batch 5400, loss[loss=0.2181, simple_loss=0.2838, pruned_loss=0.07621, over 7443.00 frames.], tot_loss[loss=0.217, simple_loss=0.2953, pruned_loss=0.06929, over 1465459.71 frames.], batch size: 18, lr: 4.57e-04
2022-07-26 16:54:12,833 INFO [train.py:850] (2/4) Epoch 12, batch 5450, loss[loss=0.2031, simple_loss=0.2793, pruned_loss=0.06346, over 7291.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2957, pruned_loss=0.06868, over 1465597.60 frames.], batch size: 20, lr: 4.56e-04
2022-07-26 16:54:57,148 INFO [train.py:850] (2/4) Epoch 12, batch 5500, loss[loss=0.1851, simple_loss=0.2712, pruned_loss=0.0495, over 7483.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2951, pruned_loss=0.06858, over 1465523.50 frames.], batch size: 20, lr: 4.56e-04
2022-07-26 16:55:42,249 INFO [train.py:850] (2/4) Epoch 12, batch 5550, loss[loss=0.2211, simple_loss=0.3069, pruned_loss=0.06765, over 7485.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2943, pruned_loss=0.06804, over 1466180.89 frames.], batch size: 39, lr: 4.56e-04
2022-07-26 16:56:25,144 INFO [train.py:850] (2/4) Epoch 12, batch 5600, loss[loss=0.2394, simple_loss=0.3127, pruned_loss=0.08304, over 7189.00 frames.], tot_loss[loss=0.2147, simple_loss=0.294, pruned_loss=0.06773, over 1465839.89 frames.], batch size: 21, lr: 4.56e-04
2022-07-26 16:57:08,717 INFO [train.py:850] (2/4) Epoch 12, batch 5650, loss[loss=0.2779, simple_loss=0.3445, pruned_loss=0.1056, over 7199.00 frames.], tot_loss[loss=0.2145, simple_loss=0.2939, pruned_loss=0.06754, over 1466202.71 frames.], batch size: 20, lr: 4.56e-04
2022-07-26 16:57:52,852 INFO [train.py:850] (2/4) Epoch 12, batch 5700, loss[loss=0.1873, simple_loss=0.2706, pruned_loss=0.05195, over 7165.00 frames.], tot_loss[loss=0.216, simple_loss=0.2954, pruned_loss=0.06829, over 1466353.93 frames.], batch size: 17, lr: 4.56e-04
2022-07-26 16:58:37,188 INFO [train.py:850] (2/4) Epoch 12, batch 5750, loss[loss=0.2534, simple_loss=0.3152, pruned_loss=0.09584, over 7167.00 frames.], tot_loss[loss=0.2177, simple_loss=0.2964, pruned_loss=0.06951, over 1465932.73 frames.], batch size: 22, lr: 4.56e-04
2022-07-26 16:59:21,835 INFO [train.py:850] (2/4) Epoch 12, batch 5800, loss[loss=0.2101, simple_loss=0.2975, pruned_loss=0.06142, over 7233.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2972, pruned_loss=0.07027, over 1465814.25 frames.], batch size: 24, lr: 4.56e-04
2022-07-26 17:00:04,924 INFO [train.py:850] (2/4) Epoch 12, batch 5850, loss[loss=0.226, simple_loss=0.282, pruned_loss=0.08495, over 7464.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2965, pruned_loss=0.06927, over 1465344.82 frames.], batch size: 17, lr: 4.56e-04
2022-07-26 17:00:48,864 INFO [train.py:850] (2/4) Epoch 12, batch 5900, loss[loss=0.2099, simple_loss=0.296, pruned_loss=0.06194, over 7392.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2956, pruned_loss=0.06868, over 1465606.10 frames.], batch size: 31, lr: 4.56e-04
2022-07-26 17:01:32,958 INFO [train.py:850] (2/4) Epoch 12, batch 5950, loss[loss=0.2085, simple_loss=0.2818, pruned_loss=0.06756, over 7395.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2956, pruned_loss=0.06912, over 1466232.62 frames.], batch size: 19, lr: 4.55e-04
2022-07-26 17:02:17,177 INFO [train.py:850] (2/4) Epoch 12, batch 6000, loss[loss=0.2075, simple_loss=0.2903, pruned_loss=0.06233, over 7293.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2955, pruned_loss=0.06863, over 1465411.58 frames.], batch size: 27, lr: 4.55e-04
2022-07-26 17:02:17,178 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 17:02:40,193 INFO [train.py:879] (2/4) Epoch 12, validation: loss=0.1882, simple_loss=0.2865, pruned_loss=0.04492, over 924787.00 frames.
2022-07-26 17:03:22,771 INFO [train.py:850] (2/4) Epoch 12, batch 6050, loss[loss=0.2664, simple_loss=0.3477, pruned_loss=0.09255, over 7337.00 frames.], tot_loss[loss=0.216, simple_loss=0.2956, pruned_loss=0.06817, over 1465141.35 frames.], batch size: 38, lr: 4.55e-04
2022-07-26 17:04:07,289 INFO [train.py:850] (2/4) Epoch 12, batch 6100, loss[loss=0.2198, simple_loss=0.303, pruned_loss=0.06833, over 7386.00 frames.], tot_loss[loss=0.216, simple_loss=0.2954, pruned_loss=0.06833, over 1464989.22 frames.], batch size: 21, lr: 4.55e-04
2022-07-26 17:04:50,232 INFO [train.py:850] (2/4) Epoch 12, batch 6150, loss[loss=0.2296, simple_loss=0.309, pruned_loss=0.07507, over 7281.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2954, pruned_loss=0.0685, over 1464542.66 frames.], batch size: 27, lr: 4.55e-04
2022-07-26 17:05:49,745 INFO [train.py:850] (2/4) Epoch 12, batch 6200, loss[loss=0.2386, simple_loss=0.3211, pruned_loss=0.07809, over 7252.00 frames.], tot_loss[loss=0.217, simple_loss=0.2957, pruned_loss=0.06913, over 1465636.86 frames.], batch size: 27, lr: 4.55e-04
2022-07-26 17:06:33,507 INFO [train.py:850] (2/4) Epoch 12, batch 6250, loss[loss=0.2115, simple_loss=0.2793, pruned_loss=0.0718, over 7315.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2954, pruned_loss=0.06866, over 1464394.80 frames.], batch size: 17, lr: 4.55e-04
2022-07-26 17:07:17,660 INFO [train.py:850] (2/4) Epoch 12, batch 6300, loss[loss=0.1971, simple_loss=0.2802, pruned_loss=0.05694, over 7488.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2942, pruned_loss=0.06826, over 1463993.30 frames.], batch size: 28, lr: 4.55e-04
2022-07-26 17:08:02,641 INFO [train.py:850] (2/4) Epoch 12, batch 6350, loss[loss=0.183, simple_loss=0.2761, pruned_loss=0.04498, over 7413.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2938, pruned_loss=0.06747, over 1464926.60 frames.], batch size: 22, lr: 4.55e-04
2022-07-26 17:08:45,904 INFO [train.py:850] (2/4) Epoch 12, batch 6400, loss[loss=0.2455, simple_loss=0.3262, pruned_loss=0.08239, over 7284.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2944, pruned_loss=0.06786, over 1464951.57 frames.], batch size: 27, lr: 4.54e-04
2022-07-26 17:09:29,846 INFO [train.py:850] (2/4) Epoch 12, batch 6450, loss[loss=0.1624, simple_loss=0.2396, pruned_loss=0.04262, over 7440.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2949, pruned_loss=0.06794, over 1465606.27 frames.], batch size: 18, lr: 4.54e-04
2022-07-26 17:10:14,333 INFO [train.py:850] (2/4) Epoch 12, batch 6500, loss[loss=0.19, simple_loss=0.2658, pruned_loss=0.05707, over 7397.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2958, pruned_loss=0.06879, over 1465980.79 frames.], batch size: 19, lr: 4.54e-04
2022-07-26 17:10:58,154 INFO [train.py:850] (2/4) Epoch 12, batch 6550, loss[loss=0.21, simple_loss=0.2924, pruned_loss=0.06378, over 7484.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2955, pruned_loss=0.06868, over 1467635.78 frames.], batch size: 20, lr: 4.54e-04
2022-07-26 17:11:43,008 INFO [train.py:850] (2/4) Epoch 12, batch 6600, loss[loss=0.2113, simple_loss=0.2924, pruned_loss=0.06513, over 7214.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2956, pruned_loss=0.06846, over 1467786.81 frames.], batch size: 25, lr: 4.54e-04
2022-07-26 17:12:25,760 INFO [train.py:850] (2/4) Epoch 12, batch 6650, loss[loss=0.2027, simple_loss=0.2893, pruned_loss=0.05799, over 7388.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2952, pruned_loss=0.06804, over 1467365.70 frames.], batch size: 20, lr: 4.54e-04
2022-07-26 17:13:08,817 INFO [train.py:850] (2/4) Epoch 12, batch 6700, loss[loss=0.2611, simple_loss=0.3284, pruned_loss=0.09689, over 7105.00 frames.], tot_loss[loss=0.216, simple_loss=0.2954, pruned_loss=0.06826, over 1466851.67 frames.], batch size: 18, lr: 4.54e-04
2022-07-26 17:13:52,287 INFO [train.py:850] (2/4) Epoch 12, batch 6750, loss[loss=0.2127, simple_loss=0.3004, pruned_loss=0.06251, over 7225.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2962, pruned_loss=0.0687, over 1466590.92 frames.], batch size: 24, lr: 4.54e-04
2022-07-26 17:14:36,534 INFO [train.py:850] (2/4) Epoch 12, batch 6800, loss[loss=0.224, simple_loss=0.3154, pruned_loss=0.06633, over 7380.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2963, pruned_loss=0.06834, over 1466281.07 frames.], batch size: 21, lr: 4.54e-04
2022-07-26 17:15:20,718 INFO [train.py:850] (2/4) Epoch 12, batch 6850, loss[loss=0.1782, simple_loss=0.2647, pruned_loss=0.0459, over 7193.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2965, pruned_loss=0.06871, over 1466490.36 frames.], batch size: 19, lr: 4.53e-04
2022-07-26 17:16:05,614 INFO [train.py:850] (2/4) Epoch 12, batch 6900, loss[loss=0.2645, simple_loss=0.3412, pruned_loss=0.0939, over 7310.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2939, pruned_loss=0.06776, over 1467572.13 frames.], batch size: 39, lr: 4.53e-04
2022-07-26 17:16:49,937 INFO [train.py:850] (2/4) Epoch 12, batch 6950, loss[loss=0.2313, simple_loss=0.3131, pruned_loss=0.07471, over 7279.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2935, pruned_loss=0.06706, over 1467915.72 frames.], batch size: 27, lr: 4.53e-04
2022-07-26 17:17:33,277 INFO [train.py:850] (2/4) Epoch 12, batch 7000, loss[loss=0.199, simple_loss=0.2936, pruned_loss=0.05225, over 7470.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2935, pruned_loss=0.06768, over 1468344.04 frames.], batch size: 21, lr: 4.53e-04
2022-07-26 17:18:16,813 INFO [train.py:850] (2/4) Epoch 12, batch 7050, loss[loss=0.2756, simple_loss=0.3439, pruned_loss=0.1036, over 7360.00 frames.], tot_loss[loss=0.2159, simple_loss=0.295, pruned_loss=0.06843, over 1468405.17 frames.], batch size: 40, lr: 4.53e-04
2022-07-26 17:19:00,401 INFO [train.py:850] (2/4) Epoch 12, batch 7100, loss[loss=0.2048, simple_loss=0.288, pruned_loss=0.06086, over 7290.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2948, pruned_loss=0.06839, over 1465892.45 frames.], batch size: 20, lr: 4.53e-04
2022-07-26 17:19:44,622 INFO [train.py:850] (2/4) Epoch 12, batch 7150, loss[loss=0.2725, simple_loss=0.3328, pruned_loss=0.1061, over 7302.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2949, pruned_loss=0.06875, over 1465403.02 frames.], batch size: 19, lr: 4.53e-04
2022-07-26 17:20:29,139 INFO [train.py:850] (2/4) Epoch 12, batch 7200, loss[loss=0.2359, simple_loss=0.3159, pruned_loss=0.078, over 7219.00 frames.], tot_loss[loss=0.2167, simple_loss=0.2958, pruned_loss=0.06876, over 1465291.80 frames.], batch size: 24, lr: 4.53e-04
2022-07-26 17:21:12,830 INFO [train.py:850] (2/4) Epoch 12, batch 7250, loss[loss=0.1594, simple_loss=0.2374, pruned_loss=0.04063, over 7314.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2945, pruned_loss=0.06795, over 1465177.89 frames.], batch size: 18, lr: 4.53e-04
2022-07-26 17:21:57,062 INFO [train.py:850] (2/4) Epoch 12, batch 7300, loss[loss=0.2003, simple_loss=0.2819, pruned_loss=0.05932, over 7389.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2942, pruned_loss=0.0675, over 1466525.57 frames.], batch size: 19, lr: 4.52e-04
2022-07-26 17:22:40,481 INFO [train.py:850] (2/4) Epoch 12, batch 7350, loss[loss=0.1921, simple_loss=0.2811, pruned_loss=0.05152, over 7469.00 frames.], tot_loss[loss=0.2157, simple_loss=0.295, pruned_loss=0.0682, over 1466082.81 frames.], batch size: 39, lr: 4.52e-04
2022-07-26 17:23:25,369 INFO [train.py:850] (2/4) Epoch 12, batch 7400, loss[loss=0.2029, simple_loss=0.264, pruned_loss=0.07094, over 7475.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2951, pruned_loss=0.06827, over 1466394.17 frames.], batch size: 20, lr: 4.52e-04
2022-07-26 17:24:09,379 INFO [train.py:850] (2/4) Epoch 12, batch 7450, loss[loss=0.2182, simple_loss=0.299, pruned_loss=0.06869, over 7177.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2939, pruned_loss=0.0675, over 1465322.42 frames.], batch size: 21, lr: 4.52e-04
2022-07-26 17:24:54,019 INFO [train.py:850] (2/4) Epoch 12, batch 7500, loss[loss=0.2313, simple_loss=0.3063, pruned_loss=0.07817, over 7491.00 frames.], tot_loss[loss=0.2144, simple_loss=0.294, pruned_loss=0.06739, over 1465984.04 frames.], batch size: 23, lr: 4.52e-04
2022-07-26 17:25:38,068 INFO [train.py:850] (2/4) Epoch 12, batch 7550, loss[loss=0.2238, simple_loss=0.3042, pruned_loss=0.07169, over 7423.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2939, pruned_loss=0.06691, over 1465058.11 frames.], batch size: 31, lr: 4.52e-04
2022-07-26 17:26:22,784 INFO [train.py:850] (2/4) Epoch 12, batch 7600, loss[loss=0.1933, simple_loss=0.278, pruned_loss=0.05429, over 7462.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2943, pruned_loss=0.06719, over 1465744.42 frames.], batch size: 39, lr: 4.52e-04
2022-07-26 17:27:06,489 INFO [train.py:850] (2/4) Epoch 12, batch 7650, loss[loss=0.2619, simple_loss=0.3276, pruned_loss=0.09805, over 7258.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2939, pruned_loss=0.06744, over 1466142.85 frames.], batch size: 30, lr: 4.52e-04
2022-07-26 17:27:50,290 INFO [train.py:850] (2/4) Epoch 12, batch 7700, loss[loss=0.2296, simple_loss=0.2966, pruned_loss=0.08132, over 7305.00 frames.], tot_loss[loss=0.2151, simple_loss=0.295, pruned_loss=0.06755, over 1467061.42 frames.], batch size: 18, lr: 4.52e-04
2022-07-26 17:28:35,128 INFO [train.py:850] (2/4) Epoch 12, batch 7750, loss[loss=0.2177, simple_loss=0.3014, pruned_loss=0.06704, over 7290.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2962, pruned_loss=0.06825, over 1466638.15 frames.], batch size: 22, lr: 4.52e-04
2022-07-26 17:29:20,806 INFO [train.py:850] (2/4) Epoch 12, batch 7800, loss[loss=0.1743, simple_loss=0.2606, pruned_loss=0.04397, over 7428.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2961, pruned_loss=0.06821, over 1466016.79 frames.], batch size: 18, lr: 4.51e-04
2022-07-26 17:30:04,361 INFO [train.py:850] (2/4) Epoch 12, batch 7850, loss[loss=0.2121, simple_loss=0.2801, pruned_loss=0.07199, over 7317.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2956, pruned_loss=0.06779, over 1466665.27 frames.], batch size: 16, lr: 4.51e-04
2022-07-26 17:30:48,226 INFO [train.py:850] (2/4) Epoch 12, batch 7900, loss[loss=0.231, simple_loss=0.3169, pruned_loss=0.07255, over 7280.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2956, pruned_loss=0.06743, over 1465338.86 frames.], batch size: 21, lr: 4.51e-04
2022-07-26 17:31:32,162 INFO [train.py:850] (2/4) Epoch 12, batch 7950, loss[loss=0.2014, simple_loss=0.2824, pruned_loss=0.06026, over 7348.00 frames.], tot_loss[loss=0.2147, simple_loss=0.295, pruned_loss=0.06718, over 1464923.14 frames.], batch size: 23, lr: 4.51e-04
2022-07-26 17:32:17,023 INFO [train.py:850] (2/4) Epoch 12, batch 8000, loss[loss=0.2224, simple_loss=0.3099, pruned_loss=0.06742, over 7245.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2946, pruned_loss=0.06663, over 1465650.76 frames.], batch size: 27, lr: 4.51e-04
2022-07-26 17:33:01,036 INFO [train.py:850] (2/4) Epoch 12, batch 8050, loss[loss=0.1977, simple_loss=0.2805, pruned_loss=0.05744, over 7296.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2932, pruned_loss=0.06615, over 1466311.51 frames.], batch size: 19, lr: 4.51e-04
2022-07-26 17:33:45,321 INFO [train.py:850] (2/4) Epoch 12, batch 8100, loss[loss=0.2161, simple_loss=0.3079, pruned_loss=0.06212, over 7416.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2939, pruned_loss=0.06691, over 1466107.39 frames.], batch size: 22, lr: 4.51e-04
2022-07-26 17:34:28,418 INFO [train.py:850] (2/4) Epoch 12, batch 8150, loss[loss=0.236, simple_loss=0.3182, pruned_loss=0.0769, over 7369.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2946, pruned_loss=0.06715, over 1467462.40 frames.], batch size: 38, lr: 4.51e-04
2022-07-26 17:35:12,803 INFO [train.py:850] (2/4) Epoch 12, batch 8200, loss[loss=0.2069, simple_loss=0.2774, pruned_loss=0.06821, over 7466.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2952, pruned_loss=0.06777, over 1467615.27 frames.], batch size: 17, lr: 4.51e-04
2022-07-26 17:35:56,692 INFO [train.py:850] (2/4) Epoch 12, batch 8250, loss[loss=0.2307, simple_loss=0.3066, pruned_loss=0.07734, over 7279.00 frames.], tot_loss[loss=0.2145, simple_loss=0.2943, pruned_loss=0.06735, over 1467273.51 frames.], batch size: 21, lr: 4.50e-04
2022-07-26 17:36:41,791 INFO [train.py:850] (2/4) Epoch 12, batch 8300, loss[loss=0.2021, simple_loss=0.2898, pruned_loss=0.05718, over 7480.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2941, pruned_loss=0.06741, over 1466975.43 frames.], batch size: 20, lr: 4.50e-04
2022-07-26 17:37:26,889 INFO [train.py:850] (2/4) Epoch 12, batch 8350, loss[loss=0.1898, simple_loss=0.2798, pruned_loss=0.04992, over 7232.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2943, pruned_loss=0.06777, over 1466762.53 frames.], batch size: 25, lr: 4.50e-04
2022-07-26 17:38:09,759 INFO [train.py:850] (2/4) Epoch 12, batch 8400, loss[loss=0.178, simple_loss=0.2648, pruned_loss=0.0456, over 7188.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2935, pruned_loss=0.0668, over 1467011.27 frames.], batch size: 18, lr: 4.50e-04
2022-07-26 17:38:53,940 INFO [train.py:850] (2/4) Epoch 12, batch 8450, loss[loss=0.2355, simple_loss=0.3048, pruned_loss=0.08312, over 7357.00 frames.], tot_loss[loss=0.2153, simple_loss=0.295, pruned_loss=0.06783, over 1467810.27 frames.], batch size: 74, lr: 4.50e-04
2022-07-26 17:39:39,364 INFO [train.py:850] (2/4) Epoch 12, batch 8500, loss[loss=0.1773, simple_loss=0.2645, pruned_loss=0.04508, over 7485.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2945, pruned_loss=0.06708, over 1467656.65 frames.], batch size: 19, lr: 4.50e-04
2022-07-26 17:40:23,191 INFO [train.py:850] (2/4) Epoch 12, batch 8550, loss[loss=0.2162, simple_loss=0.2852, pruned_loss=0.07363, over 7273.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2942, pruned_loss=0.06678, over 1466868.04 frames.], batch size: 16, lr: 4.50e-04
2022-07-26 17:41:08,380 INFO [train.py:850] (2/4) Epoch 12, batch 8600, loss[loss=0.1868, simple_loss=0.2729, pruned_loss=0.05038, over 7377.00 frames.], tot_loss[loss=0.2129, simple_loss=0.293, pruned_loss=0.06643, over 1467733.33 frames.], batch size: 20, lr: 4.50e-04
2022-07-26 17:41:53,132 INFO [train.py:850] (2/4) Epoch 12, batch 8650, loss[loss=0.2165, simple_loss=0.2958, pruned_loss=0.06862, over 7339.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2937, pruned_loss=0.06677, over 1467539.32 frames.], batch size: 27, lr: 4.50e-04
2022-07-26 17:42:36,712 INFO [train.py:850] (2/4) Epoch 12, batch 8700, loss[loss=0.1978, simple_loss=0.2827, pruned_loss=0.0565, over 7422.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2927, pruned_loss=0.06608, over 1466198.99 frames.], batch size: 31, lr: 4.49e-04
2022-07-26 17:43:19,005 INFO [train.py:850] (2/4) Epoch 12, batch 8750, loss[loss=0.2302, simple_loss=0.3135, pruned_loss=0.0735, over 7247.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2918, pruned_loss=0.0652, over 1465640.82 frames.], batch size: 30, lr: 4.49e-04
2022-07-26 17:44:02,852 INFO [train.py:850] (2/4) Epoch 12, batch 8800, loss[loss=0.1968, simple_loss=0.271, pruned_loss=0.06134, over 7128.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2909, pruned_loss=0.06467, over 1465925.81 frames.], batch size: 17, lr: 4.49e-04
2022-07-26 17:44:45,555 INFO [train.py:850] (2/4) Epoch 12, batch 8850, loss[loss=0.204, simple_loss=0.2672, pruned_loss=0.07044, over 7155.00 frames.], tot_loss[loss=0.2112, simple_loss=0.2915, pruned_loss=0.06544, over 1464303.38 frames.], batch size: 17, lr: 4.49e-04
2022-07-26 17:46:12,065 INFO [train.py:850] (2/4) Epoch 13, batch 0, loss[loss=0.1876, simple_loss=0.277, pruned_loss=0.0491, over 7391.00 frames.], tot_loss[loss=0.1876, simple_loss=0.277, pruned_loss=0.0491, over 7391.00 frames.], batch size: 19, lr: 4.34e-04
2022-07-26 17:46:56,319 INFO [train.py:850] (2/4) Epoch 13, batch 50, loss[loss=0.1925, simple_loss=0.2816, pruned_loss=0.05169, over 7297.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2937, pruned_loss=0.05859, over 330144.61 frames.], batch size: 19, lr: 4.34e-04
2022-07-26 17:47:41,285 INFO [train.py:850] (2/4) Epoch 13, batch 100, loss[loss=0.1658, simple_loss=0.2655, pruned_loss=0.03306, over 7190.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2924, pruned_loss=0.05743, over 581974.27 frames.], batch size: 21, lr: 4.34e-04
2022-07-26 17:48:24,848 INFO [train.py:850] (2/4) Epoch 13, batch 150, loss[loss=0.1748, simple_loss=0.2688, pruned_loss=0.04036, over 7194.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2896, pruned_loss=0.05664, over 776469.05 frames.], batch size: 20, lr: 4.34e-04
2022-07-26 17:49:07,851 INFO [train.py:850] (2/4) Epoch 13, batch 200, loss[loss=0.214, simple_loss=0.2979, pruned_loss=0.06509, over 7395.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2876, pruned_loss=0.0551, over 928377.99 frames.], batch size: 19, lr: 4.34e-04
2022-07-26 17:49:51,438 INFO [train.py:850] (2/4) Epoch 13, batch 250, loss[loss=0.2264, simple_loss=0.3101, pruned_loss=0.07137, over 7302.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2882, pruned_loss=0.05619, over 1046304.85 frames.], batch size: 22, lr: 4.33e-04
2022-07-26 17:50:34,781 INFO [train.py:850] (2/4) Epoch 13, batch 300, loss[loss=0.1965, simple_loss=0.2807, pruned_loss=0.05618, over 7375.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2861, pruned_loss=0.05536, over 1139764.44 frames.], batch size: 20, lr: 4.33e-04
2022-07-26 17:51:19,587 INFO [train.py:850] (2/4) Epoch 13, batch 350, loss[loss=0.1553, simple_loss=0.2529, pruned_loss=0.02888, over 7197.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2859, pruned_loss=0.05483, over 1211522.29 frames.], batch size: 18, lr: 4.33e-04
2022-07-26 17:52:03,620 INFO [train.py:850] (2/4) Epoch 13, batch 400, loss[loss=0.2143, simple_loss=0.2978, pruned_loss=0.06534, over 7208.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2859, pruned_loss=0.05479, over 1267567.53 frames.], batch size: 19, lr: 4.33e-04
2022-07-26 17:52:47,299 INFO [train.py:850] (2/4) Epoch 13, batch 450, loss[loss=0.2538, simple_loss=0.3333, pruned_loss=0.0872, over 7468.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2849, pruned_loss=0.05433, over 1310641.04 frames.], batch size: 24, lr: 4.33e-04
2022-07-26 17:53:30,896 INFO [train.py:850] (2/4) Epoch 13, batch 500, loss[loss=0.1954, simple_loss=0.2838, pruned_loss=0.05346, over 7175.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2841, pruned_loss=0.0538, over 1345523.74 frames.], batch size: 17, lr: 4.33e-04
2022-07-26 17:54:14,506 INFO [train.py:850] (2/4) Epoch 13, batch 550, loss[loss=0.2282, simple_loss=0.3153, pruned_loss=0.07052, over 7475.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2837, pruned_loss=0.05355, over 1372204.39 frames.], batch size: 21, lr: 4.33e-04
2022-07-26 17:54:58,706 INFO [train.py:850] (2/4) Epoch 13, batch 600, loss[loss=0.1498, simple_loss=0.2394, pruned_loss=0.03013, over 7458.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2824, pruned_loss=0.05241, over 1391585.27 frames.], batch size: 17, lr: 4.33e-04
2022-07-26 17:55:42,658 INFO [train.py:850] (2/4) Epoch 13, batch 650, loss[loss=0.2065, simple_loss=0.3036, pruned_loss=0.05465, over 7319.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2834, pruned_loss=0.05282, over 1407692.51 frames.], batch size: 22, lr: 4.33e-04
2022-07-26 17:56:27,068 INFO [train.py:850] (2/4) Epoch 13, batch 700, loss[loss=0.1646, simple_loss=0.2596, pruned_loss=0.03483, over 7492.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2824, pruned_loss=0.05244, over 1420340.99 frames.], batch size: 19, lr: 4.33e-04
2022-07-26 17:57:08,998 INFO [train.py:850] (2/4) Epoch 13, batch 750, loss[loss=0.1719, simple_loss=0.2578, pruned_loss=0.04298, over 7286.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2825, pruned_loss=0.05225, over 1429607.02 frames.], batch size: 19, lr: 4.32e-04
2022-07-26 17:57:52,458 INFO [train.py:850] (2/4) Epoch 13, batch 800, loss[loss=0.1974, simple_loss=0.281, pruned_loss=0.05694, over 7203.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2834, pruned_loss=0.05283, over 1436110.88 frames.], batch size: 18, lr: 4.32e-04
2022-07-26 17:58:36,720 INFO [train.py:850] (2/4) Epoch 13, batch 850, loss[loss=0.1939, simple_loss=0.2862, pruned_loss=0.0508, over 7251.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2843, pruned_loss=0.05314, over 1442332.21 frames.], batch size: 27, lr: 4.32e-04
2022-07-26 17:59:20,086 INFO [train.py:850] (2/4) Epoch 13, batch 900, loss[loss=0.1654, simple_loss=0.2563, pruned_loss=0.03728, over 7386.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2847, pruned_loss=0.05349, over 1447104.71 frames.], batch size: 20, lr: 4.32e-04
2022-07-26 18:00:03,952 INFO [train.py:850] (2/4) Epoch 13, batch 950, loss[loss=0.2246, simple_loss=0.321, pruned_loss=0.06407, over 7295.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2858, pruned_loss=0.05416, over 1452242.28 frames.], batch size: 22, lr: 4.32e-04
2022-07-26 18:00:46,969 INFO [train.py:850] (2/4) Epoch 13, batch 1000, loss[loss=0.1787, simple_loss=0.2583, pruned_loss=0.04958, over 7448.00 frames.], tot_loss[loss=0.196, simple_loss=0.2842, pruned_loss=0.05387, over 1455758.50 frames.], batch size: 18, lr: 4.32e-04
2022-07-26 18:01:30,116 INFO [train.py:850] (2/4) Epoch 13, batch 1050, loss[loss=0.195, simple_loss=0.2817, pruned_loss=0.05415, over 7426.00 frames.], tot_loss[loss=0.1976, simple_loss=0.286, pruned_loss=0.05459, over 1458108.14 frames.], batch size: 22, lr: 4.32e-04
2022-07-26 18:02:15,172 INFO [train.py:850] (2/4) Epoch 13, batch 1100, loss[loss=0.2112, simple_loss=0.3018, pruned_loss=0.0603, over 7474.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2873, pruned_loss=0.05545, over 1460398.38 frames.], batch size: 31, lr: 4.32e-04
2022-07-26 18:02:58,524 INFO [train.py:850] (2/4) Epoch 13, batch 1150, loss[loss=0.1784, simple_loss=0.2806, pruned_loss=0.03811, over 7479.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2886, pruned_loss=0.05638, over 1462015.43 frames.], batch size: 21, lr: 4.32e-04
2022-07-26 18:03:44,080 INFO [train.py:850] (2/4) Epoch 13, batch 1200, loss[loss=0.2019, simple_loss=0.2926, pruned_loss=0.05562, over 7398.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2893, pruned_loss=0.05716, over 1462854.44 frames.], batch size: 31, lr: 4.32e-04
2022-07-26 18:04:27,759 INFO [train.py:850] (2/4) Epoch 13, batch 1250, loss[loss=0.2115, simple_loss=0.2961, pruned_loss=0.06347, over 7488.00 frames.], tot_loss[loss=0.2024, simple_loss=0.29, pruned_loss=0.05745, over 1464603.40 frames.], batch size: 24, lr: 4.31e-04
2022-07-26 18:05:26,155 INFO [train.py:850] (2/4) Epoch 13, batch 1300, loss[loss=0.2153, simple_loss=0.3089, pruned_loss=0.06086, over 7464.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2912, pruned_loss=0.05725, over 1465927.70 frames.], batch size: 24, lr: 4.31e-04
2022-07-26 18:06:10,486 INFO [train.py:850] (2/4) Epoch 13, batch 1350, loss[loss=0.2116, simple_loss=0.2956, pruned_loss=0.06377, over 7198.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2914, pruned_loss=0.05791, over 1465704.27 frames.], batch size: 20, lr: 4.31e-04
2022-07-26 18:06:54,263 INFO [train.py:850] (2/4) Epoch 13, batch 1400, loss[loss=0.2194, simple_loss=0.2974, pruned_loss=0.07069, over 7390.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2904, pruned_loss=0.05736, over 1465083.67 frames.], batch size: 20, lr: 4.31e-04
2022-07-26 18:07:38,113 INFO [train.py:850] (2/4) Epoch 13, batch 1450, loss[loss=0.1646, simple_loss=0.2609, pruned_loss=0.0342, over 7307.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2894, pruned_loss=0.05679, over 1465369.94 frames.], batch size: 19, lr: 4.31e-04
2022-07-26 18:08:21,630 INFO [train.py:850] (2/4) Epoch 13, batch 1500, loss[loss=0.2049, simple_loss=0.3034, pruned_loss=0.05321, over 7282.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2897, pruned_loss=0.05665, over 1464414.12 frames.], batch size: 27, lr: 4.31e-04
2022-07-26 18:09:06,045 INFO [train.py:850] (2/4) Epoch 13, batch 1550, loss[loss=0.247, simple_loss=0.3307, pruned_loss=0.08167, over 7190.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2918, pruned_loss=0.05741, over 1464198.69 frames.], batch size: 21, lr: 4.31e-04
2022-07-26 18:09:49,231 INFO [train.py:850] (2/4) Epoch 13, batch 1600, loss[loss=0.22, simple_loss=0.3219, pruned_loss=0.05899, over 7476.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2917, pruned_loss=0.05762, over 1465074.67 frames.], batch size: 21, lr: 4.31e-04
2022-07-26 18:10:32,655 INFO [train.py:850] (2/4) Epoch 13, batch 1650, loss[loss=0.2155, simple_loss=0.3077, pruned_loss=0.06164, over 7291.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2915, pruned_loss=0.05702, over 1464826.45 frames.], batch size: 20, lr: 4.31e-04
2022-07-26 18:11:16,948 INFO [train.py:850] (2/4) Epoch 13, batch 1700, loss[loss=0.1753, simple_loss=0.2717, pruned_loss=0.03943, over 7356.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2914, pruned_loss=0.05696, over 1465234.67 frames.], batch size: 23, lr: 4.31e-04
2022-07-26 18:12:01,139 INFO [train.py:850] (2/4) Epoch 13, batch 1750, loss[loss=0.1712, simple_loss=0.2581, pruned_loss=0.04214, over 7317.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2908, pruned_loss=0.0567, over 1466275.76 frames.], batch size: 18, lr: 4.30e-04
2022-07-26 18:12:46,961 INFO [train.py:850] (2/4) Epoch 13, batch 1800, loss[loss=0.2024, simple_loss=0.2992, pruned_loss=0.0528, over 7283.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2905, pruned_loss=0.05626, over 1465825.90 frames.], batch size: 21, lr: 4.30e-04
2022-07-26 18:13:30,337 INFO [train.py:850] (2/4) Epoch 13, batch 1850, loss[loss=0.2121, simple_loss=0.2995, pruned_loss=0.0624, over 7477.00 frames.], tot_loss[loss=0.202, simple_loss=0.2907, pruned_loss=0.05661, over 1466986.45 frames.], batch size: 20, lr: 4.30e-04
2022-07-26 18:14:14,430 INFO [train.py:850] (2/4) Epoch 13, batch 1900, loss[loss=0.2026, simple_loss=0.2932, pruned_loss=0.05599, over 7484.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2895, pruned_loss=0.05594, over 1466943.71 frames.], batch size: 20, lr: 4.30e-04
2022-07-26 18:14:58,877 INFO [train.py:850] (2/4) Epoch 13, batch 1950, loss[loss=0.229, simple_loss=0.3237, pruned_loss=0.0672, over 7462.00 frames.], tot_loss[loss=0.2007, simple_loss=0.29, pruned_loss=0.05573, over 1466862.99 frames.], batch size: 31, lr: 4.30e-04
2022-07-26 18:15:42,025 INFO [train.py:850] (2/4) Epoch 13, batch 2000, loss[loss=0.2259, simple_loss=0.3276, pruned_loss=0.06215, over 7196.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2896, pruned_loss=0.05527, over 1465602.25 frames.], batch size: 20, lr: 4.30e-04
2022-07-26 18:16:25,669 INFO [train.py:850] (2/4) Epoch 13, batch 2050, loss[loss=0.201, simple_loss=0.2883, pruned_loss=0.05687, over 7367.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2904, pruned_loss=0.05565, over 1465892.56 frames.], batch size: 67, lr: 4.30e-04
2022-07-26 18:17:08,611 INFO [train.py:850] (2/4) Epoch 13, batch 2100, loss[loss=0.2001, simple_loss=0.2941, pruned_loss=0.05307, over 7345.00 frames.], tot_loss[loss=0.2, simple_loss=0.2897, pruned_loss=0.05508, over 1466335.32 frames.], batch size: 23, lr: 4.30e-04
2022-07-26 18:17:51,128 INFO [train.py:850] (2/4) Epoch 13, batch 2150, loss[loss=0.2458, simple_loss=0.3115, pruned_loss=0.09004, over 7418.00 frames.], tot_loss[loss=0.201, simple_loss=0.2905, pruned_loss=0.05575, over 1465870.07 frames.], batch size: 71, lr: 4.30e-04
2022-07-26 18:18:36,459 INFO [train.py:850] (2/4) Epoch 13, batch 2200, loss[loss=0.1911, simple_loss=0.2885, pruned_loss=0.04688, over 7305.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2887, pruned_loss=0.05538, over 1466022.51 frames.], batch size: 39, lr: 4.30e-04
2022-07-26 18:19:20,122 INFO [train.py:850] (2/4) Epoch 13, batch 2250, loss[loss=0.1798, simple_loss=0.2757, pruned_loss=0.04192, over 7384.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2879, pruned_loss=0.05539, over 1466029.62 frames.], batch size: 20, lr: 4.30e-04
2022-07-26 18:20:04,791 INFO [train.py:850] (2/4) Epoch 13, batch 2300, loss[loss=0.197, simple_loss=0.2895, pruned_loss=0.05226, over 7480.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2882, pruned_loss=0.05537, over 1466379.71 frames.], batch size: 20, lr: 4.29e-04
2022-07-26 18:20:47,780 INFO [train.py:850] (2/4) Epoch 13, batch 2350, loss[loss=0.2022, simple_loss=0.2925, pruned_loss=0.05595, over 7389.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2889, pruned_loss=0.05572, over 1466156.03 frames.], batch size: 19, lr: 4.29e-04
2022-07-26 18:21:31,048 INFO [train.py:850] (2/4) Epoch 13, batch 2400, loss[loss=0.2116, simple_loss=0.3064, pruned_loss=0.05841, over 7191.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2895, pruned_loss=0.05559, over 1466442.80 frames.], batch size: 21, lr: 4.29e-04
2022-07-26 18:22:15,308 INFO [train.py:850] (2/4) Epoch 13, batch 2450, loss[loss=0.1923, simple_loss=0.2823, pruned_loss=0.05118, over 7198.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2893, pruned_loss=0.05569, over 1465989.98 frames.], batch size: 19, lr: 4.29e-04
2022-07-26 18:22:59,451 INFO [train.py:850] (2/4) Epoch 13, batch 2500, loss[loss=0.1987, simple_loss=0.2827, pruned_loss=0.05736, over 7308.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2898, pruned_loss=0.05578, over 1467115.01 frames.], batch size: 18, lr: 4.29e-04
2022-07-26 18:23:44,024 INFO [train.py:850] (2/4) Epoch 13, batch 2550, loss[loss=0.2169, simple_loss=0.3037, pruned_loss=0.065, over 7199.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2887, pruned_loss=0.05547, over 1465210.45 frames.], batch size: 19, lr: 4.29e-04
2022-07-26 18:24:27,984 INFO [train.py:850] (2/4) Epoch 13, batch 2600, loss[loss=0.1773, simple_loss=0.2699, pruned_loss=0.04238, over 7431.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2884, pruned_loss=0.05532, over 1465370.65 frames.], batch size: 18, lr: 4.29e-04
2022-07-26 18:25:10,252 INFO [train.py:850] (2/4) Epoch 13, batch 2650, loss[loss=0.1569, simple_loss=0.2509, pruned_loss=0.03145, over 7260.00 frames.], tot_loss[loss=0.1977, simple_loss=0.287, pruned_loss=0.05422, over 1465101.09 frames.], batch size: 16, lr: 4.29e-04
2022-07-26 18:25:56,589 INFO [train.py:850] (2/4) Epoch 13, batch 2700, loss[loss=0.1626, simple_loss=0.2482, pruned_loss=0.03852, over 7451.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2877, pruned_loss=0.05463, over 1464912.06 frames.], batch size: 17, lr: 4.29e-04
2022-07-26 18:26:38,755 INFO [train.py:850] (2/4) Epoch 13, batch 2750, loss[loss=0.1994, simple_loss=0.2753, pruned_loss=0.06179, over 7382.00 frames.], tot_loss[loss=0.198, simple_loss=0.2874, pruned_loss=0.05432, over 1464227.74 frames.], batch size: 19, lr: 4.29e-04
2022-07-26 18:27:23,319 INFO [train.py:850] (2/4) Epoch 13, batch 2800, loss[loss=0.21, simple_loss=0.3032, pruned_loss=0.05842, over 7474.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2881, pruned_loss=0.05462, over 1465246.40 frames.], batch size: 39, lr: 4.28e-04
2022-07-26 18:28:06,688 INFO [train.py:850] (2/4) Epoch 13, batch 2850, loss[loss=0.1753, simple_loss=0.2626, pruned_loss=0.04396, over 7193.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2872, pruned_loss=0.05387, over 1464463.16 frames.], batch size: 20, lr: 4.28e-04
2022-07-26 18:28:51,073 INFO [train.py:850] (2/4) Epoch 13, batch 2900, loss[loss=0.1745, simple_loss=0.2645, pruned_loss=0.04225, over 7479.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2886, pruned_loss=0.05516, over 1464961.14 frames.], batch size: 19, lr: 4.28e-04
2022-07-26 18:29:34,941 INFO [train.py:850] (2/4) Epoch 13, batch 2950, loss[loss=0.2074, simple_loss=0.3033, pruned_loss=0.05571, over 7293.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2885, pruned_loss=0.0549, over 1465272.12 frames.], batch size: 20, lr: 4.28e-04
2022-07-26 18:30:19,785 INFO [train.py:850] (2/4) Epoch 13, batch 3000, loss[loss=0.2057, simple_loss=0.2888, pruned_loss=0.06126, over 7207.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2889, pruned_loss=0.05507, over 1464577.62 frames.], batch size: 20, lr: 4.28e-04
2022-07-26 18:30:19,786 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 18:30:42,780 INFO [train.py:879] (2/4) Epoch 13, validation: loss=0.1959, simple_loss=0.2919, pruned_loss=0.04992, over 924787.00 frames.
2022-07-26 18:31:25,296 INFO [train.py:850] (2/4) Epoch 13, batch 3050, loss[loss=0.2095, simple_loss=0.3021, pruned_loss=0.05843, over 7477.00 frames.], tot_loss[loss=0.1995, simple_loss=0.289, pruned_loss=0.055, over 1465411.59 frames.], batch size: 26, lr: 4.28e-04
2022-07-26 18:32:07,942 INFO [train.py:850] (2/4) Epoch 13, batch 3100, loss[loss=0.2251, simple_loss=0.3116, pruned_loss=0.06927, over 7292.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2883, pruned_loss=0.05439, over 1465845.55 frames.], batch size: 20, lr: 4.28e-04
2022-07-26 18:32:51,837 INFO [train.py:850] (2/4) Epoch 13, batch 3150, loss[loss=0.1753, simple_loss=0.2575, pruned_loss=0.04652, over 7461.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2885, pruned_loss=0.05468, over 1465661.86 frames.], batch size: 17, lr: 4.28e-04
2022-07-26 18:33:36,132 INFO [train.py:850] (2/4) Epoch 13, batch 3200, loss[loss=0.231, simple_loss=0.3084, pruned_loss=0.07681, over 7432.00 frames.], tot_loss[loss=0.1996, simple_loss=0.289, pruned_loss=0.05508, over 1466313.31 frames.], batch size: 73, lr: 4.28e-04
2022-07-26 18:34:20,293 INFO [train.py:850] (2/4) Epoch 13, batch 3250, loss[loss=0.1593, simple_loss=0.247, pruned_loss=0.03579, over 7320.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2872, pruned_loss=0.05454, over 1465541.81 frames.], batch size: 17, lr: 4.28e-04
2022-07-26 18:35:04,124 INFO [train.py:850] (2/4) Epoch 13, batch 3300, loss[loss=0.1959, simple_loss=0.274, pruned_loss=0.0589, over 7296.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2889, pruned_loss=0.0552, over 1466295.77 frames.], batch size: 19, lr: 4.27e-04
2022-07-26 18:35:46,200 INFO [train.py:850] (2/4) Epoch 13, batch 3350, loss[loss=0.2139, simple_loss=0.2973, pruned_loss=0.06525, over 7483.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2885, pruned_loss=0.05462, over 1466871.44 frames.], batch size: 19, lr: 4.27e-04
2022-07-26 18:36:30,891 INFO [train.py:850] (2/4) Epoch 13, batch 3400, loss[loss=0.2278, simple_loss=0.3173, pruned_loss=0.06914, over 7175.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2878, pruned_loss=0.05416, over 1466315.54 frames.], batch size: 21, lr: 4.27e-04
2022-07-26 18:37:14,174 INFO [train.py:850] (2/4) Epoch 13, batch 3450, loss[loss=0.1667, simple_loss=0.2464, pruned_loss=0.04351, over 7295.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2875, pruned_loss=0.05437, over 1465704.57 frames.], batch size: 17, lr: 4.27e-04
2022-07-26 18:37:58,738 INFO [train.py:850] (2/4) Epoch 13, batch 3500, loss[loss=0.2093, simple_loss=0.298, pruned_loss=0.06032, over 7276.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2875, pruned_loss=0.05476, over 1465825.08 frames.], batch size: 21, lr: 4.27e-04
2022-07-26 18:38:41,618 INFO [train.py:850] (2/4) Epoch 13, batch 3550, loss[loss=0.2383, simple_loss=0.328, pruned_loss=0.07432, over 7386.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2886, pruned_loss=0.05516, over 1465426.87 frames.], batch size: 21, lr: 4.27e-04
2022-07-26 18:39:26,191 INFO [train.py:850] (2/4) Epoch 13, batch 3600, loss[loss=0.2057, simple_loss=0.2981, pruned_loss=0.05666, over 7463.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2883, pruned_loss=0.05515, over 1465197.98 frames.], batch size: 21, lr: 4.27e-04
2022-07-26 18:40:09,135 INFO [train.py:850] (2/4) Epoch 13, batch 3650, loss[loss=0.2052, simple_loss=0.2952, pruned_loss=0.05765, over 7107.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2867, pruned_loss=0.05425, over 1465899.59 frames.], batch size: 18, lr: 4.27e-04
2022-07-26 18:40:52,118 INFO [train.py:850] (2/4) Epoch 13, batch 3700, loss[loss=0.1907, simple_loss=0.2831, pruned_loss=0.0492, over 7190.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2854, pruned_loss=0.05366, over 1465398.17 frames.], batch size: 20, lr: 4.27e-04
2022-07-26 18:41:36,881 INFO [train.py:850] (2/4) Epoch 13, batch 3750, loss[loss=0.176, simple_loss=0.2676, pruned_loss=0.04221, over 7192.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2854, pruned_loss=0.05325, over 1464932.43 frames.], batch size: 19, lr: 4.27e-04
2022-07-26 18:42:20,608 INFO [train.py:850] (2/4) Epoch 13, batch 3800, loss[loss=0.2214, simple_loss=0.2983, pruned_loss=0.07228, over 7483.00 frames.], tot_loss[loss=0.197, simple_loss=0.2862, pruned_loss=0.05389, over 1465806.55 frames.], batch size: 26, lr: 4.26e-04
2022-07-26 18:43:05,399 INFO [train.py:850] (2/4) Epoch 13, batch 3850, loss[loss=0.1951, simple_loss=0.2854, pruned_loss=0.05237, over 7486.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2855, pruned_loss=0.05373, over 1465254.00 frames.], batch size: 31, lr: 4.26e-04
2022-07-26 18:43:48,617 INFO [train.py:850] (2/4) Epoch 13, batch 3900, loss[loss=0.1688, simple_loss=0.2582, pruned_loss=0.03974, over 7208.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2858, pruned_loss=0.05345, over 1466171.87 frames.], batch size: 18, lr: 4.26e-04
2022-07-26 18:44:31,389 INFO [train.py:850] (2/4) Epoch 13, batch 3950, loss[loss=0.2011, simple_loss=0.2965, pruned_loss=0.05281, over 7383.00 frames.], tot_loss[loss=0.195, simple_loss=0.2845, pruned_loss=0.05278, over 1466098.59 frames.], batch size: 21, lr: 4.26e-04
2022-07-26 18:45:16,011 INFO [train.py:850] (2/4) Epoch 13, batch 4000, loss[loss=0.2057, simple_loss=0.3025, pruned_loss=0.05447, over 7189.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2858, pruned_loss=0.0528, over 1466372.43 frames.], batch size: 21, lr: 4.26e-04
2022-07-26 18:45:59,496 INFO [train.py:850] (2/4) Epoch 13, batch 4050, loss[loss=0.1768, simple_loss=0.2714, pruned_loss=0.04114, over 7305.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2856, pruned_loss=0.05294, over 1466391.42 frames.], batch size: 19, lr: 4.26e-04
2022-07-26 18:46:44,278 INFO [train.py:850] (2/4) Epoch 13, batch 4100, loss[loss=0.1775, simple_loss=0.2764, pruned_loss=0.03936, over 7385.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2876, pruned_loss=0.05441, over 1466119.52 frames.], batch size: 21, lr: 4.26e-04
2022-07-26 18:47:27,263 INFO [train.py:850] (2/4) Epoch 13, batch 4150, loss[loss=0.2272, simple_loss=0.3198, pruned_loss=0.06736, over 7287.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2884, pruned_loss=0.05565, over 1466298.60 frames.], batch size: 20, lr: 4.26e-04
2022-07-26 18:48:11,298 INFO [train.py:850] (2/4) Epoch 13, batch 4200, loss[loss=0.2104, simple_loss=0.2979, pruned_loss=0.0615, over 7489.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2899, pruned_loss=0.05688, over 1467370.95 frames.], batch size: 19, lr: 4.26e-04
2022-07-26 18:48:56,647 INFO [train.py:850] (2/4) Epoch 13, batch 4250, loss[loss=0.2455, simple_loss=0.3258, pruned_loss=0.08259, over 7173.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2904, pruned_loss=0.05757, over 1467013.31 frames.], batch size: 22, lr: 4.26e-04
2022-07-26 18:49:39,730 INFO [train.py:850] (2/4) Epoch 13, batch 4300, loss[loss=0.1914, simple_loss=0.2825, pruned_loss=0.05015, over 7426.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2895, pruned_loss=0.05778, over 1466470.86 frames.], batch size: 22, lr: 4.26e-04
2022-07-26 18:50:24,833 INFO [train.py:850] (2/4) Epoch 13, batch 4350, loss[loss=0.174, simple_loss=0.267, pruned_loss=0.0405, over 7377.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2904, pruned_loss=0.05903, over 1467505.21 frames.], batch size: 21, lr: 4.25e-04
2022-07-26 18:51:08,379 INFO [train.py:850] (2/4) Epoch 13, batch 4400, loss[loss=0.2521, simple_loss=0.3352, pruned_loss=0.08451, over 7289.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2904, pruned_loss=0.05994, over 1465527.20 frames.], batch size: 20, lr: 4.25e-04
2022-07-26 18:51:52,718 INFO [train.py:850] (2/4) Epoch 13, batch 4450, loss[loss=0.2507, simple_loss=0.33, pruned_loss=0.08567, over 7474.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2927, pruned_loss=0.06291, over 1465641.63 frames.], batch size: 24, lr: 4.25e-04
2022-07-26 18:52:36,551 INFO [train.py:850] (2/4) Epoch 13, batch 4500, loss[loss=0.2488, simple_loss=0.3231, pruned_loss=0.08729, over 7390.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2926, pruned_loss=0.06401, over 1465491.35 frames.], batch size: 19, lr: 4.25e-04
2022-07-26 18:53:19,231 INFO [train.py:850] (2/4) Epoch 13, batch 4550, loss[loss=0.1562, simple_loss=0.239, pruned_loss=0.03674, over 7312.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2923, pruned_loss=0.06423, over 1464420.72 frames.], batch size: 18, lr: 4.25e-04
2022-07-26 18:54:04,624 INFO [train.py:850] (2/4) Epoch 13, batch 4600, loss[loss=0.2211, simple_loss=0.3017, pruned_loss=0.07021, over 7393.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2936, pruned_loss=0.06565, over 1463687.00 frames.], batch size: 31, lr: 4.25e-04
2022-07-26 18:54:47,986 INFO [train.py:850] (2/4) Epoch 13, batch 4650, loss[loss=0.2214, simple_loss=0.301, pruned_loss=0.07092, over 7193.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2942, pruned_loss=0.06619, over 1465424.30 frames.], batch size: 20, lr: 4.25e-04
2022-07-26 18:55:33,108 INFO [train.py:850] (2/4) Epoch 13, batch 4700, loss[loss=0.2764, simple_loss=0.3506, pruned_loss=0.1011, over 7425.00 frames.], tot_loss[loss=0.2144, simple_loss=0.295, pruned_loss=0.06691, over 1465228.29 frames.], batch size: 22, lr: 4.25e-04
2022-07-26 18:56:16,879 INFO [train.py:850] (2/4) Epoch 13, batch 4750, loss[loss=0.2777, simple_loss=0.3492, pruned_loss=0.1032, over 7221.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2945, pruned_loss=0.06681, over 1464844.32 frames.], batch size: 24, lr: 4.25e-04
2022-07-26 18:57:00,975 INFO [train.py:850] (2/4) Epoch 13, batch 4800, loss[loss=0.2335, simple_loss=0.3154, pruned_loss=0.07574, over 7281.00 frames.], tot_loss[loss=0.214, simple_loss=0.2943, pruned_loss=0.06685, over 1465054.38 frames.], batch size: 20, lr: 4.25e-04
2022-07-26 18:57:45,028 INFO [train.py:850] (2/4) Epoch 13, batch 4850, loss[loss=0.2266, simple_loss=0.2988, pruned_loss=0.07724, over 7163.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2958, pruned_loss=0.06826, over 1465721.70 frames.], batch size: 17, lr: 4.24e-04
2022-07-26 18:58:29,203 INFO [train.py:850] (2/4) Epoch 13, batch 4900, loss[loss=0.223, simple_loss=0.2986, pruned_loss=0.07373, over 7235.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2955, pruned_loss=0.06768, over 1465519.68 frames.], batch size: 24, lr: 4.24e-04
2022-07-26 18:59:14,111 INFO [train.py:850] (2/4) Epoch 13, batch 4950, loss[loss=0.1924, simple_loss=0.2716, pruned_loss=0.05657, over 7198.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2965, pruned_loss=0.06856, over 1464259.44 frames.], batch size: 19, lr: 4.24e-04
2022-07-26 18:59:57,844 INFO [train.py:850] (2/4) Epoch 13, batch 5000, loss[loss=0.2347, simple_loss=0.3128, pruned_loss=0.07828, over 7456.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2952, pruned_loss=0.06824, over 1463875.46 frames.], batch size: 40, lr: 4.24e-04
2022-07-26 19:00:42,017 INFO [train.py:850] (2/4) Epoch 13, batch 5050, loss[loss=0.2044, simple_loss=0.2966, pruned_loss=0.05613, over 7238.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2952, pruned_loss=0.06825, over 1464258.78 frames.], batch size: 24, lr: 4.24e-04
2022-07-26 19:01:25,471 INFO [train.py:850] (2/4) Epoch 13, batch 5100, loss[loss=0.2179, simple_loss=0.2977, pruned_loss=0.06904, over 7217.00 frames.], tot_loss[loss=0.217, simple_loss=0.2963, pruned_loss=0.06883, over 1464911.00 frames.], batch size: 24, lr: 4.24e-04
2022-07-26 19:02:08,184 INFO [train.py:850] (2/4) Epoch 13, batch 5150, loss[loss=0.191, simple_loss=0.2702, pruned_loss=0.05589, over 7305.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2955, pruned_loss=0.06858, over 1464717.25 frames.], batch size: 18, lr: 4.24e-04
2022-07-26 19:02:53,748 INFO [train.py:850] (2/4) Epoch 13, batch 5200, loss[loss=0.2317, simple_loss=0.3212, pruned_loss=0.07107, over 7293.00 frames.], tot_loss[loss=0.216, simple_loss=0.2952, pruned_loss=0.06835, over 1464727.11 frames.], batch size: 27, lr: 4.24e-04
2022-07-26 19:03:37,782 INFO [train.py:850] (2/4) Epoch 13, batch 5250, loss[loss=0.2609, simple_loss=0.346, pruned_loss=0.08794, over 7490.00 frames.], tot_loss[loss=0.2166, simple_loss=0.2961, pruned_loss=0.0686, over 1464854.13 frames.], batch size: 23, lr: 4.24e-04
2022-07-26 19:04:38,079 INFO [train.py:850] (2/4) Epoch 13, batch 5300, loss[loss=0.2296, simple_loss=0.3048, pruned_loss=0.07715, over 7384.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2955, pruned_loss=0.06834, over 1465591.91 frames.], batch size: 21, lr: 4.24e-04
2022-07-26 19:05:21,096 INFO [train.py:850] (2/4) Epoch 13, batch 5350, loss[loss=0.1974, simple_loss=0.295, pruned_loss=0.04991, over 7368.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2951, pruned_loss=0.06733, over 1464592.10 frames.], batch size: 21, lr: 4.24e-04
2022-07-26 19:06:05,046 INFO [train.py:850] (2/4) Epoch 13, batch 5400, loss[loss=0.2705, simple_loss=0.3441, pruned_loss=0.09848, over 7294.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2955, pruned_loss=0.06759, over 1465221.94 frames.], batch size: 27, lr: 4.23e-04
2022-07-26 19:06:48,420 INFO [train.py:850] (2/4) Epoch 13, batch 5450, loss[loss=0.2728, simple_loss=0.3357, pruned_loss=0.1049, over 7488.00 frames.], tot_loss[loss=0.214, simple_loss=0.2942, pruned_loss=0.06692, over 1465220.57 frames.], batch size: 24, lr: 4.23e-04
2022-07-26 19:07:32,296 INFO [train.py:850] (2/4) Epoch 13, batch 5500, loss[loss=0.1953, simple_loss=0.2799, pruned_loss=0.05536, over 7461.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2948, pruned_loss=0.06778, over 1464713.94 frames.], batch size: 40, lr: 4.23e-04
2022-07-26 19:08:16,875 INFO [train.py:850] (2/4) Epoch 13, batch 5550, loss[loss=0.2589, simple_loss=0.3314, pruned_loss=0.09314, over 7255.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2948, pruned_loss=0.06795, over 1464854.31 frames.], batch size: 25, lr: 4.23e-04
2022-07-26 19:09:01,228 INFO [train.py:850] (2/4) Epoch 13, batch 5600, loss[loss=0.2346, simple_loss=0.3143, pruned_loss=0.07744, over 7169.00 frames.], tot_loss[loss=0.2159, simple_loss=0.2952, pruned_loss=0.06825, over 1465327.11 frames.], batch size: 22, lr: 4.23e-04
2022-07-26 19:09:44,832 INFO [train.py:850] (2/4) Epoch 13, batch 5650, loss[loss=0.2091, simple_loss=0.2787, pruned_loss=0.06974, over 7315.00 frames.], tot_loss[loss=0.2161, simple_loss=0.2957, pruned_loss=0.06823, over 1466540.45 frames.], batch size: 18, lr: 4.23e-04
2022-07-26 19:10:28,785 INFO [train.py:850] (2/4) Epoch 13, batch 5700, loss[loss=0.2698, simple_loss=0.3437, pruned_loss=0.09797, over 7385.00 frames.], tot_loss[loss=0.215, simple_loss=0.2947, pruned_loss=0.06766, over 1466102.70 frames.], batch size: 21, lr: 4.23e-04
2022-07-26 19:11:13,405 INFO [train.py:850] (2/4) Epoch 13, batch 5750, loss[loss=0.2325, simple_loss=0.3129, pruned_loss=0.07608, over 7429.00 frames.], tot_loss[loss=0.2145, simple_loss=0.2946, pruned_loss=0.06715, over 1465591.77 frames.], batch size: 31, lr: 4.23e-04
2022-07-26 19:12:01,053 INFO [train.py:850] (2/4) Epoch 13, batch 5800, loss[loss=0.2366, simple_loss=0.316, pruned_loss=0.07861, over 7211.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2949, pruned_loss=0.06741, over 1466581.33 frames.], batch size: 25, lr: 4.23e-04
2022-07-26 19:12:43,635 INFO [train.py:850] (2/4) Epoch 13, batch 5850, loss[loss=0.244, simple_loss=0.3333, pruned_loss=0.07731, over 7488.00 frames.], tot_loss[loss=0.2145, simple_loss=0.2949, pruned_loss=0.06705, over 1466573.86 frames.], batch size: 31, lr: 4.23e-04
2022-07-26 19:13:30,177 INFO [train.py:850] (2/4) Epoch 13, batch 5900, loss[loss=0.2065, simple_loss=0.2927, pruned_loss=0.06015, over 7191.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2928, pruned_loss=0.06567, over 1465982.66 frames.], batch size: 20, lr: 4.23e-04
2022-07-26 19:14:15,305 INFO [train.py:850] (2/4) Epoch 13, batch 5950, loss[loss=0.2361, simple_loss=0.3137, pruned_loss=0.0793, over 7291.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2937, pruned_loss=0.06561, over 1465462.66 frames.], batch size: 20, lr: 4.22e-04
2022-07-26 19:15:00,342 INFO [train.py:850] (2/4) Epoch 13, batch 6000, loss[loss=0.1925, simple_loss=0.277, pruned_loss=0.054, over 7266.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2943, pruned_loss=0.066, over 1465720.68 frames.], batch size: 16, lr: 4.22e-04
2022-07-26 19:15:00,343 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 19:15:23,844 INFO [train.py:879] (2/4) Epoch 13, validation: loss=0.1859, simple_loss=0.2843, pruned_loss=0.04381, over 924787.00 frames.
2022-07-26 19:16:07,063 INFO [train.py:850] (2/4) Epoch 13, batch 6050, loss[loss=0.1924, simple_loss=0.2673, pruned_loss=0.05882, over 7284.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2951, pruned_loss=0.06678, over 1464879.16 frames.], batch size: 16, lr: 4.22e-04
2022-07-26 19:16:52,656 INFO [train.py:850] (2/4) Epoch 13, batch 6100, loss[loss=0.1994, simple_loss=0.2921, pruned_loss=0.05335, over 7297.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2958, pruned_loss=0.0673, over 1466096.85 frames.], batch size: 21, lr: 4.22e-04
2022-07-26 19:17:36,515 INFO [train.py:850] (2/4) Epoch 13, batch 6150, loss[loss=0.2448, simple_loss=0.3186, pruned_loss=0.08556, over 7470.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2945, pruned_loss=0.06695, over 1466978.40 frames.], batch size: 24, lr: 4.22e-04
2022-07-26 19:18:21,417 INFO [train.py:850] (2/4) Epoch 13, batch 6200, loss[loss=0.2385, simple_loss=0.3133, pruned_loss=0.08185, over 7459.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2935, pruned_loss=0.06638, over 1467107.62 frames.], batch size: 24, lr: 4.22e-04
2022-07-26 19:19:05,275 INFO [train.py:850] (2/4) Epoch 13, batch 6250, loss[loss=0.2149, simple_loss=0.3053, pruned_loss=0.06222, over 7390.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2936, pruned_loss=0.06629, over 1466689.91 frames.], batch size: 21, lr: 4.22e-04
2022-07-26 19:19:48,656 INFO [train.py:850] (2/4) Epoch 13, batch 6300, loss[loss=0.1935, simple_loss=0.2774, pruned_loss=0.05481, over 7416.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2943, pruned_loss=0.06657, over 1467215.36 frames.], batch size: 22, lr: 4.22e-04
2022-07-26 19:20:33,830 INFO [train.py:850] (2/4) Epoch 13, batch 6350, loss[loss=0.2295, simple_loss=0.3075, pruned_loss=0.07576, over 7477.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2934, pruned_loss=0.06664, over 1467799.37 frames.], batch size: 31, lr: 4.22e-04
2022-07-26 19:21:17,064 INFO [train.py:850] (2/4) Epoch 13, batch 6400, loss[loss=0.2151, simple_loss=0.2997, pruned_loss=0.06524, over 7379.00 frames.], tot_loss[loss=0.2143, simple_loss=0.294, pruned_loss=0.06727, over 1468186.52 frames.], batch size: 20, lr: 4.22e-04
2022-07-26 19:22:01,875 INFO [train.py:850] (2/4) Epoch 13, batch 6450, loss[loss=0.1856, simple_loss=0.2675, pruned_loss=0.05186, over 7476.00 frames.], tot_loss[loss=0.2119, simple_loss=0.292, pruned_loss=0.06595, over 1468268.02 frames.], batch size: 20, lr: 4.21e-04
2022-07-26 19:22:45,808 INFO [train.py:850] (2/4) Epoch 13, batch 6500, loss[loss=0.1539, simple_loss=0.2358, pruned_loss=0.03605, over 7163.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2908, pruned_loss=0.0652, over 1467168.85 frames.], batch size: 17, lr: 4.21e-04
2022-07-26 19:23:30,033 INFO [train.py:850] (2/4) Epoch 13, batch 6550, loss[loss=0.2062, simple_loss=0.2933, pruned_loss=0.0596, over 7173.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2906, pruned_loss=0.06488, over 1466341.89 frames.], batch size: 22, lr: 4.21e-04
2022-07-26 19:24:13,589 INFO [train.py:850] (2/4) Epoch 13, batch 6600, loss[loss=0.227, simple_loss=0.2927, pruned_loss=0.08069, over 7439.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2927, pruned_loss=0.06592, over 1465698.16 frames.], batch size: 18, lr: 4.21e-04
2022-07-26 19:24:57,270 INFO [train.py:850] (2/4) Epoch 13, batch 6650, loss[loss=0.2557, simple_loss=0.3275, pruned_loss=0.09192, over 7451.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2942, pruned_loss=0.06681, over 1465838.94 frames.], batch size: 40, lr: 4.21e-04
2022-07-26 19:25:42,898 INFO [train.py:850] (2/4) Epoch 13, batch 6700, loss[loss=0.2082, simple_loss=0.2967, pruned_loss=0.05984, over 7254.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2942, pruned_loss=0.06716, over 1465683.13 frames.], batch size: 25, lr: 4.21e-04
2022-07-26 19:26:25,628 INFO [train.py:850] (2/4) Epoch 13, batch 6750, loss[loss=0.2293, simple_loss=0.3122, pruned_loss=0.07316, over 7336.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2948, pruned_loss=0.0672, over 1466985.20 frames.], batch size: 23, lr: 4.21e-04
2022-07-26 19:27:10,797 INFO [train.py:850] (2/4) Epoch 13, batch 6800, loss[loss=0.2049, simple_loss=0.299, pruned_loss=0.0554, over 7293.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2949, pruned_loss=0.06692, over 1466767.00 frames.], batch size: 20, lr: 4.21e-04
2022-07-26 19:27:54,345 INFO [train.py:850] (2/4) Epoch 13, batch 6850, loss[loss=0.2293, simple_loss=0.3097, pruned_loss=0.07451, over 7382.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2939, pruned_loss=0.06698, over 1465964.58 frames.], batch size: 20, lr: 4.21e-04
2022-07-26 19:28:39,122 INFO [train.py:850] (2/4) Epoch 13, batch 6900, loss[loss=0.2147, simple_loss=0.3013, pruned_loss=0.06405, over 7378.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2931, pruned_loss=0.06669, over 1466573.72 frames.], batch size: 20, lr: 4.21e-04
2022-07-26 19:29:23,052 INFO [train.py:850] (2/4) Epoch 13, batch 6950, loss[loss=0.1877, simple_loss=0.2837, pruned_loss=0.04585, over 7386.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2938, pruned_loss=0.06734, over 1467084.22 frames.], batch size: 21, lr: 4.21e-04
2022-07-26 19:30:06,571 INFO [train.py:850] (2/4) Epoch 13, batch 7000, loss[loss=0.2152, simple_loss=0.3, pruned_loss=0.06522, over 7389.00 frames.], tot_loss[loss=0.2146, simple_loss=0.294, pruned_loss=0.06759, over 1467125.03 frames.], batch size: 20, lr: 4.20e-04
2022-07-26 19:30:51,942 INFO [train.py:850] (2/4) Epoch 13, batch 7050, loss[loss=0.1664, simple_loss=0.2502, pruned_loss=0.04128, over 7305.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2937, pruned_loss=0.06699, over 1465557.58 frames.], batch size: 19, lr: 4.20e-04
2022-07-26 19:31:35,567 INFO [train.py:850] (2/4) Epoch 13, batch 7100, loss[loss=0.2291, simple_loss=0.3114, pruned_loss=0.07342, over 7476.00 frames.], tot_loss[loss=0.2127, simple_loss=0.293, pruned_loss=0.06618, over 1465086.78 frames.], batch size: 21, lr: 4.20e-04
2022-07-26 19:32:21,045 INFO [train.py:850] (2/4) Epoch 13, batch 7150, loss[loss=0.2091, simple_loss=0.2759, pruned_loss=0.07112, over 7294.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2934, pruned_loss=0.06694, over 1466439.50 frames.], batch size: 17, lr: 4.20e-04
2022-07-26 19:33:05,043 INFO [train.py:850] (2/4) Epoch 13, batch 7200, loss[loss=0.1759, simple_loss=0.2549, pruned_loss=0.04845, over 7281.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2928, pruned_loss=0.06651, over 1466335.42 frames.], batch size: 17, lr: 4.20e-04
2022-07-26 19:33:49,591 INFO [train.py:850] (2/4) Epoch 13, batch 7250, loss[loss=0.2237, simple_loss=0.2997, pruned_loss=0.07385, over 7291.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2927, pruned_loss=0.06623, over 1467005.77 frames.], batch size: 27, lr: 4.20e-04
2022-07-26 19:34:34,010 INFO [train.py:850] (2/4) Epoch 13, batch 7300, loss[loss=0.1922, simple_loss=0.2732, pruned_loss=0.0556, over 7200.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2935, pruned_loss=0.06694, over 1467766.54 frames.], batch size: 18, lr: 4.20e-04
2022-07-26 19:35:18,337 INFO [train.py:850] (2/4) Epoch 13, batch 7350, loss[loss=0.1927, simple_loss=0.2703, pruned_loss=0.05758, over 7321.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2943, pruned_loss=0.06708, over 1467534.47 frames.], batch size: 18, lr: 4.20e-04
2022-07-26 19:36:04,335 INFO [train.py:850] (2/4) Epoch 13, batch 7400, loss[loss=0.2149, simple_loss=0.2876, pruned_loss=0.07113, over 7104.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2941, pruned_loss=0.06717, over 1466340.19 frames.], batch size: 18, lr: 4.20e-04
2022-07-26 19:36:46,776 INFO [train.py:850] (2/4) Epoch 13, batch 7450, loss[loss=0.1922, simple_loss=0.2723, pruned_loss=0.05608, over 7389.00 frames.], tot_loss[loss=0.214, simple_loss=0.2939, pruned_loss=0.06706, over 1465579.28 frames.], batch size: 19, lr: 4.20e-04
2022-07-26 19:37:32,408 INFO [train.py:850] (2/4) Epoch 13, batch 7500, loss[loss=0.1619, simple_loss=0.2524, pruned_loss=0.03566, over 7488.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2922, pruned_loss=0.06577, over 1466215.86 frames.], batch size: 20, lr: 4.20e-04
2022-07-26 19:38:16,338 INFO [train.py:850] (2/4) Epoch 13, batch 7550, loss[loss=0.2121, simple_loss=0.2959, pruned_loss=0.06417, over 7189.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2932, pruned_loss=0.0665, over 1467349.01 frames.], batch size: 21, lr: 4.19e-04
2022-07-26 19:39:02,330 INFO [train.py:850] (2/4) Epoch 13, batch 7600, loss[loss=0.1625, simple_loss=0.2376, pruned_loss=0.04369, over 7306.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2914, pruned_loss=0.06486, over 1466776.71 frames.], batch size: 17, lr: 4.19e-04
2022-07-26 19:39:45,130 INFO [train.py:850] (2/4) Epoch 13, batch 7650, loss[loss=0.2374, simple_loss=0.3164, pruned_loss=0.07919, over 7448.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2928, pruned_loss=0.0653, over 1465684.66 frames.], batch size: 68, lr: 4.19e-04
2022-07-26 19:40:29,355 INFO [train.py:850] (2/4) Epoch 13, batch 7700, loss[loss=0.1654, simple_loss=0.2435, pruned_loss=0.04368, over 7305.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2928, pruned_loss=0.06578, over 1465340.21 frames.], batch size: 16, lr: 4.19e-04
2022-07-26 19:41:13,523 INFO [train.py:850] (2/4) Epoch 13, batch 7750, loss[loss=0.2666, simple_loss=0.3447, pruned_loss=0.09431, over 7393.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2931, pruned_loss=0.06585, over 1465536.51 frames.], batch size: 76, lr: 4.19e-04
2022-07-26 19:41:57,436 INFO [train.py:850] (2/4) Epoch 13, batch 7800, loss[loss=0.2114, simple_loss=0.3109, pruned_loss=0.056, over 7287.00 frames.], tot_loss[loss=0.2116, simple_loss=0.2929, pruned_loss=0.06516, over 1466246.05 frames.], batch size: 21, lr: 4.19e-04
2022-07-26 19:42:41,368 INFO [train.py:850] (2/4) Epoch 13, batch 7850, loss[loss=0.1779, simple_loss=0.2593, pruned_loss=0.04818, over 7297.00 frames.], tot_loss[loss=0.2112, simple_loss=0.2924, pruned_loss=0.06505, over 1466561.52 frames.], batch size: 16, lr: 4.19e-04
2022-07-26 19:43:24,839 INFO [train.py:850] (2/4) Epoch 13, batch 7900, loss[loss=0.1795, simple_loss=0.2504, pruned_loss=0.05425, over 7314.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2911, pruned_loss=0.06426, over 1467394.03 frames.], batch size: 18, lr: 4.19e-04
2022-07-26 19:44:09,803 INFO [train.py:850] (2/4) Epoch 13, batch 7950, loss[loss=0.2399, simple_loss=0.3185, pruned_loss=0.08066, over 7471.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2916, pruned_loss=0.06435, over 1468894.56 frames.], batch size: 21, lr: 4.19e-04
2022-07-26 19:44:54,315 INFO [train.py:850] (2/4) Epoch 13, batch 8000, loss[loss=0.2535, simple_loss=0.342, pruned_loss=0.08246, over 7176.00 frames.], tot_loss[loss=0.2096, simple_loss=0.291, pruned_loss=0.06413, over 1468864.85 frames.], batch size: 22, lr: 4.19e-04
2022-07-26 19:45:39,030 INFO [train.py:850] (2/4) Epoch 13, batch 8050, loss[loss=0.2393, simple_loss=0.3139, pruned_loss=0.08233, over 7291.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2922, pruned_loss=0.06478, over 1467653.55 frames.], batch size: 19, lr: 4.19e-04
2022-07-26 19:46:23,656 INFO [train.py:850] (2/4) Epoch 13, batch 8100, loss[loss=0.1885, simple_loss=0.267, pruned_loss=0.05503, over 7207.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2914, pruned_loss=0.06474, over 1467861.86 frames.], batch size: 19, lr: 4.18e-04
2022-07-26 19:47:06,418 INFO [train.py:850] (2/4) Epoch 13, batch 8150, loss[loss=0.2502, simple_loss=0.3341, pruned_loss=0.08316, over 7258.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2925, pruned_loss=0.06518, over 1467509.75 frames.], batch size: 27, lr: 4.18e-04
2022-07-26 19:47:50,711 INFO [train.py:850] (2/4) Epoch 13, batch 8200, loss[loss=0.1683, simple_loss=0.2495, pruned_loss=0.04354, over 7435.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2918, pruned_loss=0.06457, over 1466062.76 frames.], batch size: 17, lr: 4.18e-04
2022-07-26 19:48:33,758 INFO [train.py:850] (2/4) Epoch 13, batch 8250, loss[loss=0.2306, simple_loss=0.3152, pruned_loss=0.07298, over 7330.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2914, pruned_loss=0.06455, over 1467308.20 frames.], batch size: 39, lr: 4.18e-04
2022-07-26 19:49:19,011 INFO [train.py:850] (2/4) Epoch 13, batch 8300, loss[loss=0.1939, simple_loss=0.2762, pruned_loss=0.05576, over 7197.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2905, pruned_loss=0.06405, over 1466315.70 frames.], batch size: 18, lr: 4.18e-04
2022-07-26 19:50:02,333 INFO [train.py:850] (2/4) Epoch 13, batch 8350, loss[loss=0.1986, simple_loss=0.2821, pruned_loss=0.05753, over 7293.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2904, pruned_loss=0.06392, over 1466662.85 frames.], batch size: 20, lr: 4.18e-04
2022-07-26 19:50:47,132 INFO [train.py:850] (2/4) Epoch 13, batch 8400, loss[loss=0.2266, simple_loss=0.2998, pruned_loss=0.07674, over 7169.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2905, pruned_loss=0.06411, over 1467228.75 frames.], batch size: 22, lr: 4.18e-04
2022-07-26 19:51:30,965 INFO [train.py:850] (2/4) Epoch 13, batch 8450, loss[loss=0.244, simple_loss=0.3036, pruned_loss=0.09221, over 7479.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2909, pruned_loss=0.06494, over 1467053.61 frames.], batch size: 20, lr: 4.18e-04
2022-07-26 19:52:15,302 INFO [train.py:850] (2/4) Epoch 13, batch 8500, loss[loss=0.2061, simple_loss=0.2917, pruned_loss=0.06029, over 7203.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2901, pruned_loss=0.06484, over 1467092.49 frames.], batch size: 25, lr: 4.18e-04
2022-07-26 19:53:01,097 INFO [train.py:850] (2/4) Epoch 13, batch 8550, loss[loss=0.2371, simple_loss=0.3142, pruned_loss=0.08005, over 7295.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2911, pruned_loss=0.06497, over 1465702.81 frames.], batch size: 38, lr: 4.18e-04
2022-07-26 19:53:45,091 INFO [train.py:850] (2/4) Epoch 13, batch 8600, loss[loss=0.187, simple_loss=0.2722, pruned_loss=0.05092, over 7288.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2916, pruned_loss=0.06534, over 1465390.93 frames.], batch size: 20, lr: 4.18e-04
2022-07-26 19:54:29,041 INFO [train.py:850] (2/4) Epoch 13, batch 8650, loss[loss=0.2006, simple_loss=0.2781, pruned_loss=0.06159, over 7184.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2919, pruned_loss=0.06573, over 1466782.01 frames.], batch size: 22, lr: 4.17e-04
2022-07-26 19:55:12,903 INFO [train.py:850] (2/4) Epoch 13, batch 8700, loss[loss=0.2072, simple_loss=0.3119, pruned_loss=0.05131, over 7469.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2908, pruned_loss=0.06479, over 1465352.98 frames.], batch size: 21, lr: 4.17e-04
2022-07-26 19:55:54,918 INFO [train.py:850] (2/4) Epoch 13, batch 8750, loss[loss=0.2257, simple_loss=0.3018, pruned_loss=0.07485, over 7438.00 frames.], tot_loss[loss=0.2101, simple_loss=0.291, pruned_loss=0.06458, over 1464951.01 frames.], batch size: 31, lr: 4.17e-04
2022-07-26 19:56:38,412 INFO [train.py:850] (2/4) Epoch 13, batch 8800, loss[loss=0.1857, simple_loss=0.2592, pruned_loss=0.05605, over 7292.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2924, pruned_loss=0.06514, over 1465959.87 frames.], batch size: 21, lr: 4.17e-04
2022-07-26 19:57:22,664 INFO [train.py:850] (2/4) Epoch 13, batch 8850, loss[loss=0.2328, simple_loss=0.3281, pruned_loss=0.06881, over 7368.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2924, pruned_loss=0.06511, over 1466476.36 frames.], batch size: 38, lr: 4.17e-04
2022-07-26 19:59:02,347 INFO [train.py:850] (2/4) Epoch 14, batch 0, loss[loss=0.1792, simple_loss=0.2629, pruned_loss=0.04773, over 7385.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2629, pruned_loss=0.04773, over 7385.00 frames.], batch size: 19, lr: 4.04e-04
2022-07-26 19:59:46,062 INFO [train.py:850] (2/4) Epoch 14, batch 50, loss[loss=0.2049, simple_loss=0.2934, pruned_loss=0.05824, over 7484.00 frames.], tot_loss[loss=0.202, simple_loss=0.2899, pruned_loss=0.05703, over 330318.08 frames.], batch size: 23, lr: 4.04e-04
2022-07-26 20:00:29,271 INFO [train.py:850] (2/4) Epoch 14, batch 100, loss[loss=0.1811, simple_loss=0.2792, pruned_loss=0.04149, over 7303.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2871, pruned_loss=0.05603, over 581068.06 frames.], batch size: 22, lr: 4.03e-04
2022-07-26 20:01:13,878 INFO [train.py:850] (2/4) Epoch 14, batch 150, loss[loss=0.1987, simple_loss=0.2884, pruned_loss=0.05453, over 7468.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2859, pruned_loss=0.0548, over 778560.04 frames.], batch size: 20, lr: 4.03e-04
2022-07-26 20:01:57,252 INFO [train.py:850] (2/4) Epoch 14, batch 200, loss[loss=0.1892, simple_loss=0.2642, pruned_loss=0.05709, over 7207.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2851, pruned_loss=0.05474, over 929752.05 frames.], batch size: 19, lr: 4.03e-04
2022-07-26 20:02:41,202 INFO [train.py:850] (2/4) Epoch 14, batch 250, loss[loss=0.1973, simple_loss=0.2911, pruned_loss=0.05179, over 7191.00 frames.], tot_loss[loss=0.197, simple_loss=0.285, pruned_loss=0.05443, over 1048821.22 frames.], batch size: 20, lr: 4.03e-04
2022-07-26 20:03:25,434 INFO [train.py:850] (2/4) Epoch 14, batch 300, loss[loss=0.19, simple_loss=0.2761, pruned_loss=0.05188, over 7199.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2837, pruned_loss=0.05351, over 1140816.93 frames.], batch size: 18, lr: 4.03e-04
2022-07-26 20:04:08,665 INFO [train.py:850] (2/4) Epoch 14, batch 350, loss[loss=0.2442, simple_loss=0.3298, pruned_loss=0.0793, over 7192.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2846, pruned_loss=0.05316, over 1211107.50 frames.], batch size: 18, lr: 4.03e-04
2022-07-26 20:05:08,899 INFO [train.py:850] (2/4) Epoch 14, batch 400, loss[loss=0.2081, simple_loss=0.2963, pruned_loss=0.05994, over 7451.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2854, pruned_loss=0.05364, over 1268524.27 frames.], batch size: 39, lr: 4.03e-04
2022-07-26 20:05:52,131 INFO [train.py:850] (2/4) Epoch 14, batch 450, loss[loss=0.164, simple_loss=0.2512, pruned_loss=0.03841, over 7292.00 frames.], tot_loss[loss=0.195, simple_loss=0.2843, pruned_loss=0.05281, over 1312015.62 frames.], batch size: 20, lr: 4.03e-04
2022-07-26 20:06:36,331 INFO [train.py:850] (2/4) Epoch 14, batch 500, loss[loss=0.1616, simple_loss=0.2595, pruned_loss=0.0318, over 7347.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2844, pruned_loss=0.05261, over 1348003.54 frames.], batch size: 23, lr: 4.03e-04
2022-07-26 20:07:19,339 INFO [train.py:850] (2/4) Epoch 14, batch 550, loss[loss=0.1468, simple_loss=0.2291, pruned_loss=0.03224, over 7322.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2846, pruned_loss=0.05293, over 1373829.66 frames.], batch size: 17, lr: 4.03e-04
2022-07-26 20:08:03,390 INFO [train.py:850] (2/4) Epoch 14, batch 600, loss[loss=0.1458, simple_loss=0.2411, pruned_loss=0.02523, over 7392.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2835, pruned_loss=0.05257, over 1395704.11 frames.], batch size: 19, lr: 4.03e-04
2022-07-26 20:08:47,195 INFO [train.py:850] (2/4) Epoch 14, batch 650, loss[loss=0.1967, simple_loss=0.2941, pruned_loss=0.04966, over 7411.00 frames.], tot_loss[loss=0.1939, simple_loss=0.283, pruned_loss=0.05237, over 1411897.70 frames.], batch size: 22, lr: 4.03e-04
2022-07-26 20:09:30,448 INFO [train.py:850] (2/4) Epoch 14, batch 700, loss[loss=0.1596, simple_loss=0.2493, pruned_loss=0.03495, over 7431.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2822, pruned_loss=0.05196, over 1422438.67 frames.], batch size: 18, lr: 4.02e-04
2022-07-26 20:10:13,840 INFO [train.py:850] (2/4) Epoch 14, batch 750, loss[loss=0.1992, simple_loss=0.2889, pruned_loss=0.05476, over 7472.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2807, pruned_loss=0.05106, over 1432928.98 frames.], batch size: 21, lr: 4.02e-04
2022-07-26 20:10:57,350 INFO [train.py:850] (2/4) Epoch 14, batch 800, loss[loss=0.1649, simple_loss=0.2477, pruned_loss=0.04107, over 7492.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2802, pruned_loss=0.05069, over 1440435.88 frames.], batch size: 19, lr: 4.02e-04
2022-07-26 20:11:41,071 INFO [train.py:850] (2/4) Epoch 14, batch 850, loss[loss=0.2073, simple_loss=0.3044, pruned_loss=0.05508, over 7179.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2812, pruned_loss=0.05117, over 1446178.12 frames.], batch size: 22, lr: 4.02e-04
2022-07-26 20:12:24,583 INFO [train.py:850] (2/4) Epoch 14, batch 900, loss[loss=0.2913, simple_loss=0.3699, pruned_loss=0.1063, over 7289.00 frames.], tot_loss[loss=0.1926, simple_loss=0.282, pruned_loss=0.05157, over 1450395.08 frames.], batch size: 27, lr: 4.02e-04
2022-07-26 20:13:08,243 INFO [train.py:850] (2/4) Epoch 14, batch 950, loss[loss=0.1733, simple_loss=0.2501, pruned_loss=0.04824, over 7460.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2825, pruned_loss=0.05219, over 1452173.06 frames.], batch size: 17, lr: 4.02e-04
2022-07-26 20:13:52,069 INFO [train.py:850] (2/4) Epoch 14, batch 1000, loss[loss=0.1585, simple_loss=0.2489, pruned_loss=0.03403, over 7315.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2834, pruned_loss=0.05283, over 1455091.31 frames.], batch size: 17, lr: 4.02e-04
2022-07-26 20:14:35,282 INFO [train.py:850] (2/4) Epoch 14, batch 1050, loss[loss=0.2175, simple_loss=0.3092, pruned_loss=0.06285, over 7317.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2843, pruned_loss=0.05344, over 1456955.02 frames.], batch size: 27, lr: 4.02e-04
2022-07-26 20:15:20,479 INFO [train.py:850] (2/4) Epoch 14, batch 1100, loss[loss=0.1704, simple_loss=0.252, pruned_loss=0.04443, over 7313.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2846, pruned_loss=0.05405, over 1459792.46 frames.], batch size: 18, lr: 4.02e-04
2022-07-26 20:16:04,911 INFO [train.py:850] (2/4) Epoch 14, batch 1150, loss[loss=0.2382, simple_loss=0.3268, pruned_loss=0.07482, over 7169.00 frames.], tot_loss[loss=0.198, simple_loss=0.2862, pruned_loss=0.05486, over 1460540.29 frames.], batch size: 22, lr: 4.02e-04
2022-07-26 20:16:49,452 INFO [train.py:850] (2/4) Epoch 14, batch 1200, loss[loss=0.1793, simple_loss=0.2808, pruned_loss=0.03887, over 7281.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2876, pruned_loss=0.05528, over 1462161.46 frames.], batch size: 24, lr: 4.02e-04
2022-07-26 20:17:33,571 INFO [train.py:850] (2/4) Epoch 14, batch 1250, loss[loss=0.2401, simple_loss=0.3201, pruned_loss=0.08008, over 7371.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2878, pruned_loss=0.05491, over 1463701.93 frames.], batch size: 73, lr: 4.02e-04
2022-07-26 20:18:16,697 INFO [train.py:850] (2/4) Epoch 14, batch 1300, loss[loss=0.1955, simple_loss=0.2661, pruned_loss=0.06246, over 7314.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2866, pruned_loss=0.05433, over 1464733.29 frames.], batch size: 17, lr: 4.01e-04
2022-07-26 20:19:00,228 INFO [train.py:850] (2/4) Epoch 14, batch 1350, loss[loss=0.2082, simple_loss=0.29, pruned_loss=0.06324, over 7477.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2878, pruned_loss=0.05481, over 1464764.61 frames.], batch size: 20, lr: 4.01e-04
2022-07-26 20:19:43,457 INFO [train.py:850] (2/4) Epoch 14, batch 1400, loss[loss=0.1979, simple_loss=0.2903, pruned_loss=0.05278, over 7206.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2893, pruned_loss=0.05563, over 1464172.96 frames.], batch size: 19, lr: 4.01e-04
2022-07-26 20:20:26,666 INFO [train.py:850] (2/4) Epoch 14, batch 1450, loss[loss=0.3279, simple_loss=0.3868, pruned_loss=0.1345, over 7377.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2895, pruned_loss=0.05575, over 1464739.28 frames.], batch size: 73, lr: 4.01e-04
2022-07-26 20:21:11,531 INFO [train.py:850] (2/4) Epoch 14, batch 1500, loss[loss=0.1886, simple_loss=0.2833, pruned_loss=0.04698, over 7384.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2882, pruned_loss=0.05499, over 1464069.44 frames.], batch size: 20, lr: 4.01e-04
2022-07-26 20:21:54,883 INFO [train.py:850] (2/4) Epoch 14, batch 1550, loss[loss=0.1758, simple_loss=0.2655, pruned_loss=0.04301, over 7451.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2887, pruned_loss=0.05514, over 1465155.26 frames.], batch size: 17, lr: 4.01e-04
2022-07-26 20:22:39,423 INFO [train.py:850] (2/4) Epoch 14, batch 1600, loss[loss=0.2497, simple_loss=0.3213, pruned_loss=0.08903, over 7405.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2891, pruned_loss=0.05553, over 1466265.45 frames.], batch size: 22, lr: 4.01e-04
2022-07-26 20:23:22,607 INFO [train.py:850] (2/4) Epoch 14, batch 1650, loss[loss=0.1683, simple_loss=0.2717, pruned_loss=0.03244, over 7394.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2884, pruned_loss=0.05488, over 1466754.90 frames.], batch size: 20, lr: 4.01e-04
2022-07-26 20:24:07,472 INFO [train.py:850] (2/4) Epoch 14, batch 1700, loss[loss=0.2102, simple_loss=0.3032, pruned_loss=0.05861, over 7389.00 frames.], tot_loss[loss=0.1992, simple_loss=0.289, pruned_loss=0.05469, over 1467013.29 frames.], batch size: 21, lr: 4.01e-04
2022-07-26 20:24:50,278 INFO [train.py:850] (2/4) Epoch 14, batch 1750, loss[loss=0.2188, simple_loss=0.3067, pruned_loss=0.06543, over 7346.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2888, pruned_loss=0.05467, over 1467090.82 frames.], batch size: 23, lr: 4.01e-04
2022-07-26 20:25:33,447 INFO [train.py:850] (2/4) Epoch 14, batch 1800, loss[loss=0.1794, simple_loss=0.2629, pruned_loss=0.04797, over 7193.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2877, pruned_loss=0.05396, over 1466667.72 frames.], batch size: 19, lr: 4.01e-04
2022-07-26 20:26:17,395 INFO [train.py:850] (2/4) Epoch 14, batch 1850, loss[loss=0.218, simple_loss=0.3075, pruned_loss=0.06426, over 7420.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2875, pruned_loss=0.05362, over 1465906.89 frames.], batch size: 22, lr: 4.00e-04
2022-07-26 20:27:03,539 INFO [train.py:850] (2/4) Epoch 14, batch 1900, loss[loss=0.2503, simple_loss=0.3187, pruned_loss=0.09093, over 7389.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2864, pruned_loss=0.05334, over 1465888.11 frames.], batch size: 72, lr: 4.00e-04
2022-07-26 20:27:49,063 INFO [train.py:850] (2/4) Epoch 14, batch 1950, loss[loss=0.2108, simple_loss=0.3101, pruned_loss=0.05579, over 7472.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2865, pruned_loss=0.05314, over 1466145.14 frames.], batch size: 40, lr: 4.00e-04
2022-07-26 20:28:33,797 INFO [train.py:850] (2/4) Epoch 14, batch 2000, loss[loss=0.2054, simple_loss=0.3024, pruned_loss=0.05418, over 7384.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2869, pruned_loss=0.05312, over 1466220.83 frames.], batch size: 21, lr: 4.00e-04
2022-07-26 20:29:16,533 INFO [train.py:850] (2/4) Epoch 14, batch 2050, loss[loss=0.1899, simple_loss=0.2838, pruned_loss=0.04796, over 7343.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2874, pruned_loss=0.05377, over 1466079.33 frames.], batch size: 23, lr: 4.00e-04
2022-07-26 20:30:01,324 INFO [train.py:850] (2/4) Epoch 14, batch 2100, loss[loss=0.2105, simple_loss=0.2956, pruned_loss=0.06265, over 7195.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2878, pruned_loss=0.05416, over 1466090.26 frames.], batch size: 20, lr: 4.00e-04
2022-07-26 20:30:44,418 INFO [train.py:850] (2/4) Epoch 14, batch 2150, loss[loss=0.2084, simple_loss=0.3039, pruned_loss=0.05649, over 7483.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2885, pruned_loss=0.05468, over 1465985.89 frames.], batch size: 24, lr: 4.00e-04
2022-07-26 20:31:28,720 INFO [train.py:850] (2/4) Epoch 14, batch 2200, loss[loss=0.1953, simple_loss=0.2936, pruned_loss=0.04853, over 7250.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2888, pruned_loss=0.05444, over 1465868.85 frames.], batch size: 27, lr: 4.00e-04
2022-07-26 20:32:11,830 INFO [train.py:850] (2/4) Epoch 14, batch 2250, loss[loss=0.2126, simple_loss=0.2994, pruned_loss=0.06294, over 7384.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2874, pruned_loss=0.05404, over 1465553.46 frames.], batch size: 21, lr: 4.00e-04
2022-07-26 20:32:55,884 INFO [train.py:850] (2/4) Epoch 14, batch 2300, loss[loss=0.2278, simple_loss=0.3158, pruned_loss=0.06997, over 7233.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2865, pruned_loss=0.05428, over 1465430.58 frames.], batch size: 24, lr: 4.00e-04
2022-07-26 20:33:39,122 INFO [train.py:850] (2/4) Epoch 14, batch 2350, loss[loss=0.174, simple_loss=0.2721, pruned_loss=0.038, over 7184.00 frames.], tot_loss[loss=0.197, simple_loss=0.2862, pruned_loss=0.05395, over 1464762.32 frames.], batch size: 21, lr: 4.00e-04
2022-07-26 20:34:22,264 INFO [train.py:850] (2/4) Epoch 14, batch 2400, loss[loss=0.1847, simple_loss=0.2905, pruned_loss=0.0395, over 7192.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2861, pruned_loss=0.05359, over 1463887.46 frames.], batch size: 21, lr: 4.00e-04
2022-07-26 20:35:05,760 INFO [train.py:850] (2/4) Epoch 14, batch 2450, loss[loss=0.2168, simple_loss=0.3039, pruned_loss=0.06488, over 7419.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2849, pruned_loss=0.0526, over 1464465.16 frames.], batch size: 31, lr: 3.99e-04
2022-07-26 20:35:50,055 INFO [train.py:850] (2/4) Epoch 14, batch 2500, loss[loss=0.233, simple_loss=0.3226, pruned_loss=0.07166, over 7228.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2849, pruned_loss=0.05236, over 1464790.50 frames.], batch size: 25, lr: 3.99e-04
2022-07-26 20:36:34,771 INFO [train.py:850] (2/4) Epoch 14, batch 2550, loss[loss=0.201, simple_loss=0.2803, pruned_loss=0.06088, over 7399.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2859, pruned_loss=0.05231, over 1464318.26 frames.], batch size: 19, lr: 3.99e-04
2022-07-26 20:37:19,728 INFO [train.py:850] (2/4) Epoch 14, batch 2600, loss[loss=0.1839, simple_loss=0.2681, pruned_loss=0.04983, over 7443.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2851, pruned_loss=0.05186, over 1464867.60 frames.], batch size: 18, lr: 3.99e-04
2022-07-26 20:38:03,643 INFO [train.py:850] (2/4) Epoch 14, batch 2650, loss[loss=0.2037, simple_loss=0.2932, pruned_loss=0.0571, over 7463.00 frames.], tot_loss[loss=0.1945, simple_loss=0.285, pruned_loss=0.05203, over 1465207.03 frames.], batch size: 24, lr: 3.99e-04
2022-07-26 20:38:47,210 INFO [train.py:850] (2/4) Epoch 14, batch 2700, loss[loss=0.1909, simple_loss=0.2906, pruned_loss=0.04558, over 7310.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2857, pruned_loss=0.05209, over 1465950.39 frames.], batch size: 22, lr: 3.99e-04
2022-07-26 20:39:30,093 INFO [train.py:850] (2/4) Epoch 14, batch 2750, loss[loss=0.1927, simple_loss=0.2928, pruned_loss=0.04633, over 7376.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2853, pruned_loss=0.05179, over 1465470.48 frames.], batch size: 21, lr: 3.99e-04
2022-07-26 20:40:14,906 INFO [train.py:850] (2/4) Epoch 14, batch 2800, loss[loss=0.1822, simple_loss=0.2894, pruned_loss=0.0375, over 7375.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2848, pruned_loss=0.05187, over 1465152.18 frames.], batch size: 38, lr: 3.99e-04
2022-07-26 20:40:59,008 INFO [train.py:850] (2/4) Epoch 14, batch 2850, loss[loss=0.2341, simple_loss=0.2934, pruned_loss=0.08744, over 7331.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2848, pruned_loss=0.05202, over 1465447.46 frames.], batch size: 18, lr: 3.99e-04
2022-07-26 20:41:42,366 INFO [train.py:850] (2/4) Epoch 14, batch 2900, loss[loss=0.198, simple_loss=0.2924, pruned_loss=0.05177, over 7355.00 frames.], tot_loss[loss=0.195, simple_loss=0.285, pruned_loss=0.05245, over 1465859.83 frames.], batch size: 27, lr: 3.99e-04
2022-07-26 20:42:25,834 INFO [train.py:850] (2/4) Epoch 14, batch 2950, loss[loss=0.2063, simple_loss=0.2843, pruned_loss=0.06414, over 7452.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2853, pruned_loss=0.05318, over 1465547.59 frames.], batch size: 17, lr: 3.99e-04
2022-07-26 20:43:10,419 INFO [train.py:850] (2/4) Epoch 14, batch 3000, loss[loss=0.1714, simple_loss=0.2585, pruned_loss=0.04214, over 7165.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2847, pruned_loss=0.05252, over 1465740.54 frames.], batch size: 17, lr: 3.99e-04
2022-07-26 20:43:10,421 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 20:43:35,029 INFO [train.py:879] (2/4) Epoch 14, validation: loss=0.1967, simple_loss=0.2917, pruned_loss=0.0509, over 924787.00 frames.
2022-07-26 20:44:18,676 INFO [train.py:850] (2/4) Epoch 14, batch 3050, loss[loss=0.1834, simple_loss=0.2805, pruned_loss=0.04311, over 7347.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2849, pruned_loss=0.0526, over 1465095.38 frames.], batch size: 27, lr: 3.98e-04
2022-07-26 20:45:02,387 INFO [train.py:850] (2/4) Epoch 14, batch 3100, loss[loss=0.2107, simple_loss=0.308, pruned_loss=0.05666, over 7244.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2858, pruned_loss=0.0528, over 1464427.79 frames.], batch size: 27, lr: 3.98e-04
2022-07-26 20:45:46,321 INFO [train.py:850] (2/4) Epoch 14, batch 3150, loss[loss=0.1922, simple_loss=0.2872, pruned_loss=0.04867, over 7477.00 frames.], tot_loss[loss=0.1956, simple_loss=0.286, pruned_loss=0.05264, over 1464395.44 frames.], batch size: 20, lr: 3.98e-04
2022-07-26 20:46:30,093 INFO [train.py:850] (2/4) Epoch 14, batch 3200, loss[loss=0.1867, simple_loss=0.2856, pruned_loss=0.04389, over 7377.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2853, pruned_loss=0.05212, over 1464894.64 frames.], batch size: 21, lr: 3.98e-04
2022-07-26 20:47:13,959 INFO [train.py:850] (2/4) Epoch 14, batch 3250, loss[loss=0.1994, simple_loss=0.2789, pruned_loss=0.05995, over 7376.00 frames.], tot_loss[loss=0.195, simple_loss=0.2856, pruned_loss=0.0522, over 1464394.03 frames.], batch size: 19, lr: 3.98e-04
2022-07-26 20:47:57,030 INFO [train.py:850] (2/4) Epoch 14, batch 3300, loss[loss=0.1805, simple_loss=0.2712, pruned_loss=0.04494, over 7446.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2845, pruned_loss=0.05189, over 1464176.46 frames.], batch size: 17, lr: 3.98e-04
2022-07-26 20:48:39,945 INFO [train.py:850] (2/4) Epoch 14, batch 3350, loss[loss=0.1822, simple_loss=0.2649, pruned_loss=0.04971, over 7301.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2846, pruned_loss=0.05215, over 1465118.83 frames.], batch size: 18, lr: 3.98e-04
2022-07-26 20:49:25,297 INFO [train.py:850] (2/4) Epoch 14, batch 3400, loss[loss=0.198, simple_loss=0.2941, pruned_loss=0.05093, over 7213.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2852, pruned_loss=0.05183, over 1465557.51 frames.], batch size: 24, lr: 3.98e-04
2022-07-26 20:50:09,920 INFO [train.py:850] (2/4) Epoch 14, batch 3450, loss[loss=0.17, simple_loss=0.2558, pruned_loss=0.04211, over 7103.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2843, pruned_loss=0.05112, over 1465159.53 frames.], batch size: 18, lr: 3.98e-04
2022-07-26 20:50:53,886 INFO [train.py:850] (2/4) Epoch 14, batch 3500, loss[loss=0.212, simple_loss=0.3139, pruned_loss=0.05508, over 7298.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2848, pruned_loss=0.05135, over 1466247.30 frames.], batch size: 22, lr: 3.98e-04
2022-07-26 20:51:37,184 INFO [train.py:850] (2/4) Epoch 14, batch 3550, loss[loss=0.2272, simple_loss=0.3131, pruned_loss=0.07066, over 7488.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2853, pruned_loss=0.05126, over 1466176.49 frames.], batch size: 26, lr: 3.98e-04
2022-07-26 20:52:21,263 INFO [train.py:850] (2/4) Epoch 14, batch 3600, loss[loss=0.2154, simple_loss=0.311, pruned_loss=0.05988, over 7271.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2851, pruned_loss=0.05137, over 1466626.19 frames.], batch size: 30, lr: 3.98e-04
2022-07-26 20:53:04,341 INFO [train.py:850] (2/4) Epoch 14, batch 3650, loss[loss=0.1964, simple_loss=0.282, pruned_loss=0.05537, over 7296.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2841, pruned_loss=0.05116, over 1467109.11 frames.], batch size: 19, lr: 3.97e-04
2022-07-26 20:53:47,989 INFO [train.py:850] (2/4) Epoch 14, batch 3700, loss[loss=0.2007, simple_loss=0.2882, pruned_loss=0.0566, over 7188.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2839, pruned_loss=0.0513, over 1466872.92 frames.], batch size: 21, lr: 3.97e-04
2022-07-26 20:54:32,253 INFO [train.py:850] (2/4) Epoch 14, batch 3750, loss[loss=0.1804, simple_loss=0.2748, pruned_loss=0.04305, over 7484.00 frames.], tot_loss[loss=0.193, simple_loss=0.2836, pruned_loss=0.05124, over 1467193.69 frames.], batch size: 20, lr: 3.97e-04
2022-07-26 20:55:16,563 INFO [train.py:850] (2/4) Epoch 14, batch 3800, loss[loss=0.1646, simple_loss=0.2563, pruned_loss=0.03645, over 7387.00 frames.], tot_loss[loss=0.1917, simple_loss=0.282, pruned_loss=0.05068, over 1466704.50 frames.], batch size: 21, lr: 3.97e-04
2022-07-26 20:56:00,252 INFO [train.py:850] (2/4) Epoch 14, batch 3850, loss[loss=0.1696, simple_loss=0.2567, pruned_loss=0.04129, over 7290.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2822, pruned_loss=0.05034, over 1466969.84 frames.], batch size: 18, lr: 3.97e-04
2022-07-26 20:56:43,985 INFO [train.py:850] (2/4) Epoch 14, batch 3900, loss[loss=0.2036, simple_loss=0.2958, pruned_loss=0.05565, over 7433.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2827, pruned_loss=0.05042, over 1465822.80 frames.], batch size: 31, lr: 3.97e-04
2022-07-26 20:57:27,941 INFO [train.py:850] (2/4) Epoch 14, batch 3950, loss[loss=0.2295, simple_loss=0.3133, pruned_loss=0.07287, over 7418.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2831, pruned_loss=0.05071, over 1465837.32 frames.], batch size: 22, lr: 3.97e-04
2022-07-26 20:58:12,022 INFO [train.py:850] (2/4) Epoch 14, batch 4000, loss[loss=0.1892, simple_loss=0.2887, pruned_loss=0.04479, over 7421.00 frames.], tot_loss[loss=0.1922, simple_loss=0.283, pruned_loss=0.05065, over 1464615.83 frames.], batch size: 22, lr: 3.97e-04
2022-07-26 20:58:55,562 INFO [train.py:850] (2/4) Epoch 14, batch 4050, loss[loss=0.2117, simple_loss=0.3165, pruned_loss=0.05342, over 7181.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2833, pruned_loss=0.05123, over 1463655.77 frames.], batch size: 21, lr: 3.97e-04
2022-07-26 20:59:39,564 INFO [train.py:850] (2/4) Epoch 14, batch 4100, loss[loss=0.1951, simple_loss=0.2983, pruned_loss=0.04591, over 7206.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2841, pruned_loss=0.05127, over 1463919.03 frames.], batch size: 24, lr: 3.97e-04
2022-07-26 21:00:23,842 INFO [train.py:850] (2/4) Epoch 14, batch 4150, loss[loss=0.214, simple_loss=0.3014, pruned_loss=0.06334, over 7386.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2852, pruned_loss=0.0528, over 1465530.91 frames.], batch size: 21, lr: 3.97e-04
2022-07-26 21:01:07,699 INFO [train.py:850] (2/4) Epoch 14, batch 4200, loss[loss=0.2118, simple_loss=0.2965, pruned_loss=0.06348, over 7189.00 frames.], tot_loss[loss=0.197, simple_loss=0.2861, pruned_loss=0.0539, over 1465874.93 frames.], batch size: 23, lr: 3.97e-04
2022-07-26 21:01:50,848 INFO [train.py:850] (2/4) Epoch 14, batch 4250, loss[loss=0.2415, simple_loss=0.326, pruned_loss=0.07852, over 7172.00 frames.], tot_loss[loss=0.2, simple_loss=0.288, pruned_loss=0.05596, over 1465300.64 frames.], batch size: 22, lr: 3.96e-04
2022-07-26 21:02:34,758 INFO [train.py:850] (2/4) Epoch 14, batch 4300, loss[loss=0.1605, simple_loss=0.2315, pruned_loss=0.04468, over 7296.00 frames.], tot_loss[loss=0.201, simple_loss=0.288, pruned_loss=0.05695, over 1464441.62 frames.], batch size: 17, lr: 3.96e-04
2022-07-26 21:03:19,398 INFO [train.py:850] (2/4) Epoch 14, batch 4350, loss[loss=0.1967, simple_loss=0.2715, pruned_loss=0.06099, over 7304.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2886, pruned_loss=0.0579, over 1465319.85 frames.], batch size: 19, lr: 3.96e-04
2022-07-26 21:04:18,323 INFO [train.py:850] (2/4) Epoch 14, batch 4400, loss[loss=0.1925, simple_loss=0.2644, pruned_loss=0.06023, over 7257.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2887, pruned_loss=0.05886, over 1465928.59 frames.], batch size: 16, lr: 3.96e-04
2022-07-26 21:05:02,276 INFO [train.py:850] (2/4) Epoch 14, batch 4450, loss[loss=0.198, simple_loss=0.2875, pruned_loss=0.05428, over 7310.00 frames.], tot_loss[loss=0.2067, simple_loss=0.291, pruned_loss=0.0612, over 1465403.51 frames.], batch size: 18, lr: 3.96e-04
2022-07-26 21:05:47,384 INFO [train.py:850] (2/4) Epoch 14, batch 4500, loss[loss=0.2177, simple_loss=0.3071, pruned_loss=0.06413, over 7407.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2903, pruned_loss=0.06129, over 1465219.04 frames.], batch size: 22, lr: 3.96e-04
2022-07-26 21:06:31,066 INFO [train.py:850] (2/4) Epoch 14, batch 4550, loss[loss=0.1782, simple_loss=0.2562, pruned_loss=0.05014, over 7465.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2893, pruned_loss=0.06149, over 1466362.87 frames.], batch size: 18, lr: 3.96e-04
2022-07-26 21:07:16,128 INFO [train.py:850] (2/4) Epoch 14, batch 4600, loss[loss=0.2032, simple_loss=0.285, pruned_loss=0.06069, over 7358.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2893, pruned_loss=0.06206, over 1466217.34 frames.], batch size: 31, lr: 3.96e-04
2022-07-26 21:08:01,581 INFO [train.py:850] (2/4) Epoch 14, batch 4650, loss[loss=0.322, simple_loss=0.3809, pruned_loss=0.1315, over 7226.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2906, pruned_loss=0.06319, over 1466028.41 frames.], batch size: 24, lr: 3.96e-04
2022-07-26 21:08:46,170 INFO [train.py:850] (2/4) Epoch 14, batch 4700, loss[loss=0.1856, simple_loss=0.2621, pruned_loss=0.05457, over 7283.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2911, pruned_loss=0.0635, over 1466003.18 frames.], batch size: 17, lr: 3.96e-04
2022-07-26 21:09:29,652 INFO [train.py:850] (2/4) Epoch 14, batch 4750, loss[loss=0.181, simple_loss=0.2758, pruned_loss=0.04312, over 7409.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2917, pruned_loss=0.06393, over 1466016.40 frames.], batch size: 22, lr: 3.96e-04
2022-07-26 21:10:13,888 INFO [train.py:850] (2/4) Epoch 14, batch 4800, loss[loss=0.2351, simple_loss=0.3182, pruned_loss=0.07602, over 7465.00 frames.], tot_loss[loss=0.2116, simple_loss=0.2927, pruned_loss=0.0653, over 1466240.56 frames.], batch size: 40, lr: 3.96e-04
2022-07-26 21:10:57,092 INFO [train.py:850] (2/4) Epoch 14, batch 4850, loss[loss=0.2293, simple_loss=0.2972, pruned_loss=0.08075, over 7155.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2921, pruned_loss=0.06533, over 1465106.44 frames.], batch size: 17, lr: 3.95e-04
2022-07-26 21:11:41,320 INFO [train.py:850] (2/4) Epoch 14, batch 4900, loss[loss=0.1569, simple_loss=0.2409, pruned_loss=0.03645, over 7182.00 frames.], tot_loss[loss=0.212, simple_loss=0.2924, pruned_loss=0.0658, over 1464035.36 frames.], batch size: 18, lr: 3.95e-04
2022-07-26 21:12:25,036 INFO [train.py:850] (2/4) Epoch 14, batch 4950, loss[loss=0.1834, simple_loss=0.2679, pruned_loss=0.04947, over 7161.00 frames.], tot_loss[loss=0.211, simple_loss=0.2914, pruned_loss=0.06528, over 1464738.60 frames.], batch size: 17, lr: 3.95e-04
2022-07-26 21:13:09,451 INFO [train.py:850] (2/4) Epoch 14, batch 5000, loss[loss=0.3418, simple_loss=0.3855, pruned_loss=0.1491, over 7263.00 frames.], tot_loss[loss=0.211, simple_loss=0.2915, pruned_loss=0.06524, over 1463861.16 frames.], batch size: 24, lr: 3.95e-04
2022-07-26 21:13:53,129 INFO [train.py:850] (2/4) Epoch 14, batch 5050, loss[loss=0.191, simple_loss=0.2831, pruned_loss=0.0494, over 7480.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2923, pruned_loss=0.06612, over 1464876.56 frames.], batch size: 21, lr: 3.95e-04
2022-07-26 21:14:36,465 INFO [train.py:850] (2/4) Epoch 14, batch 5100, loss[loss=0.2075, simple_loss=0.2821, pruned_loss=0.06641, over 7389.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2917, pruned_loss=0.06552, over 1465192.85 frames.], batch size: 20, lr: 3.95e-04
2022-07-26 21:15:20,094 INFO [train.py:850] (2/4) Epoch 14, batch 5150, loss[loss=0.234, simple_loss=0.3132, pruned_loss=0.07737, over 7284.00 frames.], tot_loss[loss=0.2118, simple_loss=0.2922, pruned_loss=0.0657, over 1465971.63 frames.], batch size: 20, lr: 3.95e-04
2022-07-26 21:16:04,598 INFO [train.py:850] (2/4) Epoch 14, batch 5200, loss[loss=0.2437, simple_loss=0.3301, pruned_loss=0.07872, over 7470.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2928, pruned_loss=0.06609, over 1467430.67 frames.], batch size: 21, lr: 3.95e-04
2022-07-26 21:16:48,545 INFO [train.py:850] (2/4) Epoch 14, batch 5250, loss[loss=0.2477, simple_loss=0.3278, pruned_loss=0.0838, over 7177.00 frames.], tot_loss[loss=0.2117, simple_loss=0.292, pruned_loss=0.0657, over 1466509.26 frames.], batch size: 22, lr: 3.95e-04
2022-07-26 21:17:33,101 INFO [train.py:850] (2/4) Epoch 14, batch 5300, loss[loss=0.2382, simple_loss=0.301, pruned_loss=0.08771, over 7310.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2941, pruned_loss=0.06703, over 1466045.27 frames.], batch size: 18, lr: 3.95e-04
2022-07-26 21:18:16,176 INFO [train.py:850] (2/4) Epoch 14, batch 5350, loss[loss=0.248, simple_loss=0.3121, pruned_loss=0.09197, over 7383.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2943, pruned_loss=0.067, over 1466517.69 frames.], batch size: 72, lr: 3.95e-04
2022-07-26 21:19:00,627 INFO [train.py:850] (2/4) Epoch 14, batch 5400, loss[loss=0.1532, simple_loss=0.232, pruned_loss=0.03718, over 7304.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2937, pruned_loss=0.0668, over 1465925.12 frames.], batch size: 17, lr: 3.95e-04
2022-07-26 21:19:44,036 INFO [train.py:850] (2/4) Epoch 14, batch 5450, loss[loss=0.2037, simple_loss=0.3022, pruned_loss=0.05261, over 7192.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2931, pruned_loss=0.06653, over 1465719.93 frames.], batch size: 21, lr: 3.94e-04
2022-07-26 21:20:28,865 INFO [train.py:850] (2/4) Epoch 14, batch 5500, loss[loss=0.1961, simple_loss=0.2714, pruned_loss=0.06045, over 7314.00 frames.], tot_loss[loss=0.213, simple_loss=0.2929, pruned_loss=0.06659, over 1466658.31 frames.], batch size: 18, lr: 3.94e-04
2022-07-26 21:21:12,242 INFO [train.py:850] (2/4) Epoch 14, batch 5550, loss[loss=0.2001, simple_loss=0.2725, pruned_loss=0.06383, over 7311.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2909, pruned_loss=0.06495, over 1465999.27 frames.], batch size: 17, lr: 3.94e-04
2022-07-26 21:21:55,519 INFO [train.py:850] (2/4) Epoch 14, batch 5600, loss[loss=0.2069, simple_loss=0.2921, pruned_loss=0.06083, over 7480.00 frames.], tot_loss[loss=0.21, simple_loss=0.2904, pruned_loss=0.06476, over 1465854.06 frames.], batch size: 26, lr: 3.94e-04
2022-07-26 21:22:39,245 INFO [train.py:850] (2/4) Epoch 14, batch 5650, loss[loss=0.2262, simple_loss=0.3149, pruned_loss=0.0687, over 7467.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2918, pruned_loss=0.06545, over 1465082.19 frames.], batch size: 24, lr: 3.94e-04
2022-07-26 21:23:22,486 INFO [train.py:850] (2/4) Epoch 14, batch 5700, loss[loss=0.1953, simple_loss=0.2732, pruned_loss=0.05871, over 7331.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2908, pruned_loss=0.06475, over 1464156.22 frames.], batch size: 23, lr: 3.94e-04
2022-07-26 21:24:06,250 INFO [train.py:850] (2/4) Epoch 14, batch 5750, loss[loss=0.2497, simple_loss=0.3299, pruned_loss=0.08481, over 7286.00 frames.], tot_loss[loss=0.211, simple_loss=0.2915, pruned_loss=0.0653, over 1462813.04 frames.], batch size: 30, lr: 3.94e-04
2022-07-26 21:24:50,080 INFO [train.py:850] (2/4) Epoch 14, batch 5800, loss[loss=0.2372, simple_loss=0.3188, pruned_loss=0.07779, over 7384.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2905, pruned_loss=0.06479, over 1462210.61 frames.], batch size: 21, lr: 3.94e-04
2022-07-26 21:25:33,761 INFO [train.py:850] (2/4) Epoch 14, batch 5850, loss[loss=0.2076, simple_loss=0.2917, pruned_loss=0.0617, over 7231.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2907, pruned_loss=0.06429, over 1464628.27 frames.], batch size: 24, lr: 3.94e-04
2022-07-26 21:26:17,639 INFO [train.py:850] (2/4) Epoch 14, batch 5900, loss[loss=0.2288, simple_loss=0.3056, pruned_loss=0.07601, over 7217.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2898, pruned_loss=0.06333, over 1464762.92 frames.], batch size: 25, lr: 3.94e-04
2022-07-26 21:27:00,831 INFO [train.py:850] (2/4) Epoch 14, batch 5950, loss[loss=0.2126, simple_loss=0.2799, pruned_loss=0.07263, over 7149.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2894, pruned_loss=0.06286, over 1464694.75 frames.], batch size: 17, lr: 3.94e-04
2022-07-26 21:27:43,945 INFO [train.py:850] (2/4) Epoch 14, batch 6000, loss[loss=0.2128, simple_loss=0.3052, pruned_loss=0.06016, over 7240.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2879, pruned_loss=0.06197, over 1464658.66 frames.], batch size: 24, lr: 3.94e-04
2022-07-26 21:27:43,946 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 21:28:09,313 INFO [train.py:879] (2/4) Epoch 14, validation: loss=0.1866, simple_loss=0.284, pruned_loss=0.04462, over 924787.00 frames.
2022-07-26 21:28:53,014 INFO [train.py:850] (2/4) Epoch 14, batch 6050, loss[loss=0.1805, simple_loss=0.2706, pruned_loss=0.04522, over 7443.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2889, pruned_loss=0.06245, over 1465605.81 frames.], batch size: 24, lr: 3.94e-04
2022-07-26 21:29:36,978 INFO [train.py:850] (2/4) Epoch 14, batch 6100, loss[loss=0.2081, simple_loss=0.3057, pruned_loss=0.05527, over 7284.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2882, pruned_loss=0.06196, over 1464434.26 frames.], batch size: 22, lr: 3.93e-04
2022-07-26 21:30:20,302 INFO [train.py:850] (2/4) Epoch 14, batch 6150, loss[loss=0.1556, simple_loss=0.2491, pruned_loss=0.03104, over 7196.00 frames.], tot_loss[loss=0.208, simple_loss=0.29, pruned_loss=0.06304, over 1465162.77 frames.], batch size: 18, lr: 3.93e-04
2022-07-26 21:31:05,592 INFO [train.py:850] (2/4) Epoch 14, batch 6200, loss[loss=0.2279, simple_loss=0.309, pruned_loss=0.07338, over 7283.00 frames.], tot_loss[loss=0.2081, simple_loss=0.29, pruned_loss=0.06309, over 1465511.58 frames.], batch size: 21, lr: 3.93e-04
2022-07-26 21:31:50,214 INFO [train.py:850] (2/4) Epoch 14, batch 6250, loss[loss=0.2013, simple_loss=0.29, pruned_loss=0.05632, over 7381.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2902, pruned_loss=0.06347, over 1464774.15 frames.], batch size: 21, lr: 3.93e-04
2022-07-26 21:32:34,384 INFO [train.py:850] (2/4) Epoch 14, batch 6300, loss[loss=0.2397, simple_loss=0.3119, pruned_loss=0.08369, over 7459.00 frames.], tot_loss[loss=0.209, simple_loss=0.2905, pruned_loss=0.06374, over 1465294.55 frames.], batch size: 76, lr: 3.93e-04
2022-07-26 21:33:17,390 INFO [train.py:850] (2/4) Epoch 14, batch 6350, loss[loss=0.2012, simple_loss=0.2989, pruned_loss=0.05173, over 7285.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2908, pruned_loss=0.06391, over 1465179.77 frames.], batch size: 20, lr: 3.93e-04
2022-07-26 21:34:01,949 INFO [train.py:850] (2/4) Epoch 14, batch 6400, loss[loss=0.1779, simple_loss=0.2659, pruned_loss=0.04492, over 7291.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2914, pruned_loss=0.06458, over 1466054.16 frames.], batch size: 21, lr: 3.93e-04
2022-07-26 21:34:45,309 INFO [train.py:850] (2/4) Epoch 14, batch 6450, loss[loss=0.1889, simple_loss=0.2832, pruned_loss=0.04725, over 7269.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2902, pruned_loss=0.06407, over 1465212.32 frames.], batch size: 27, lr: 3.93e-04
2022-07-26 21:35:30,426 INFO [train.py:850] (2/4) Epoch 14, batch 6500, loss[loss=0.1441, simple_loss=0.2342, pruned_loss=0.02695, over 7146.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2909, pruned_loss=0.06395, over 1465912.58 frames.], batch size: 17, lr: 3.93e-04
2022-07-26 21:36:14,411 INFO [train.py:850] (2/4) Epoch 14, batch 6550, loss[loss=0.2352, simple_loss=0.2985, pruned_loss=0.08598, over 7478.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2902, pruned_loss=0.06399, over 1466551.31 frames.], batch size: 20, lr: 3.93e-04
2022-07-26 21:36:58,096 INFO [train.py:850] (2/4) Epoch 14, batch 6600, loss[loss=0.2529, simple_loss=0.3181, pruned_loss=0.09386, over 7321.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2905, pruned_loss=0.06414, over 1466522.65 frames.], batch size: 18, lr: 3.93e-04
2022-07-26 21:37:41,507 INFO [train.py:850] (2/4) Epoch 14, batch 6650, loss[loss=0.1878, simple_loss=0.2693, pruned_loss=0.05318, over 7290.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2903, pruned_loss=0.06411, over 1466702.71 frames.], batch size: 17, lr: 3.93e-04
2022-07-26 21:38:26,073 INFO [train.py:850] (2/4) Epoch 14, batch 6700, loss[loss=0.2333, simple_loss=0.3021, pruned_loss=0.08226, over 7167.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2906, pruned_loss=0.06416, over 1466207.46 frames.], batch size: 17, lr: 3.92e-04
2022-07-26 21:39:11,071 INFO [train.py:850] (2/4) Epoch 14, batch 6750, loss[loss=0.1681, simple_loss=0.2568, pruned_loss=0.03974, over 7184.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2905, pruned_loss=0.06452, over 1466623.27 frames.], batch size: 22, lr: 3.92e-04
2022-07-26 21:39:55,260 INFO [train.py:850] (2/4) Epoch 14, batch 6800, loss[loss=0.1958, simple_loss=0.2929, pruned_loss=0.04932, over 7262.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2912, pruned_loss=0.06457, over 1465740.51 frames.], batch size: 25, lr: 3.92e-04
2022-07-26 21:40:39,035 INFO [train.py:850] (2/4) Epoch 14, batch 6850, loss[loss=0.2237, simple_loss=0.2983, pruned_loss=0.07456, over 7488.00 frames.], tot_loss[loss=0.2107, simple_loss=0.2919, pruned_loss=0.06476, over 1465578.75 frames.], batch size: 19, lr: 3.92e-04
2022-07-26 21:41:23,092 INFO [train.py:850] (2/4) Epoch 14, batch 6900, loss[loss=0.2197, simple_loss=0.2985, pruned_loss=0.07045, over 7384.00 frames.], tot_loss[loss=0.209, simple_loss=0.2898, pruned_loss=0.0641, over 1465874.45 frames.], batch size: 21, lr: 3.92e-04
2022-07-26 21:42:06,121 INFO [train.py:850] (2/4) Epoch 14, batch 6950, loss[loss=0.2415, simple_loss=0.3166, pruned_loss=0.08315, over 7373.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2886, pruned_loss=0.06322, over 1466072.82 frames.], batch size: 21, lr: 3.92e-04
2022-07-26 21:42:50,864 INFO [train.py:850] (2/4) Epoch 14, batch 7000, loss[loss=0.1854, simple_loss=0.2661, pruned_loss=0.05234, over 7433.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2881, pruned_loss=0.06301, over 1465517.93 frames.], batch size: 18, lr: 3.92e-04
2022-07-26 21:43:34,484 INFO [train.py:850] (2/4) Epoch 14, batch 7050, loss[loss=0.2084, simple_loss=0.2798, pruned_loss=0.06854, over 7207.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2877, pruned_loss=0.0626, over 1466741.68 frames.], batch size: 19, lr: 3.92e-04
2022-07-26 21:44:17,706 INFO [train.py:850] (2/4) Epoch 14, batch 7100, loss[loss=0.1923, simple_loss=0.2833, pruned_loss=0.05065, over 7463.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2879, pruned_loss=0.06257, over 1467025.26 frames.], batch size: 31, lr: 3.92e-04
2022-07-26 21:45:02,080 INFO [train.py:850] (2/4) Epoch 14, batch 7150, loss[loss=0.2539, simple_loss=0.3301, pruned_loss=0.08879, over 7472.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2872, pruned_loss=0.06217, over 1467324.38 frames.], batch size: 23, lr: 3.92e-04
2022-07-26 21:45:46,331 INFO [train.py:850] (2/4) Epoch 14, batch 7200, loss[loss=0.2517, simple_loss=0.3226, pruned_loss=0.09044, over 7437.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2887, pruned_loss=0.06282, over 1467482.87 frames.], batch size: 40, lr: 3.92e-04
2022-07-26 21:46:30,910 INFO [train.py:850] (2/4) Epoch 14, batch 7250, loss[loss=0.1805, simple_loss=0.2726, pruned_loss=0.04418, over 7301.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2882, pruned_loss=0.06299, over 1465910.53 frames.], batch size: 19, lr: 3.92e-04
2022-07-26 21:47:15,400 INFO [train.py:850] (2/4) Epoch 14, batch 7300, loss[loss=0.2252, simple_loss=0.3101, pruned_loss=0.07013, over 7447.00 frames.], tot_loss[loss=0.2067, simple_loss=0.288, pruned_loss=0.06265, over 1466476.53 frames.], batch size: 31, lr: 3.92e-04
2022-07-26 21:47:58,265 INFO [train.py:850] (2/4) Epoch 14, batch 7350, loss[loss=0.2049, simple_loss=0.2883, pruned_loss=0.0607, over 7279.00 frames.], tot_loss[loss=0.206, simple_loss=0.2877, pruned_loss=0.06215, over 1467045.87 frames.], batch size: 20, lr: 3.91e-04
2022-07-26 21:48:43,018 INFO [train.py:850] (2/4) Epoch 14, batch 7400, loss[loss=0.2093, simple_loss=0.3039, pruned_loss=0.05732, over 7474.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2891, pruned_loss=0.06255, over 1466371.02 frames.], batch size: 21, lr: 3.91e-04
2022-07-26 21:49:26,185 INFO [train.py:850] (2/4) Epoch 14, batch 7450, loss[loss=0.2069, simple_loss=0.2884, pruned_loss=0.06275, over 7103.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2888, pruned_loss=0.06229, over 1465071.65 frames.], batch size: 18, lr: 3.91e-04
2022-07-26 21:50:10,535 INFO [train.py:850] (2/4) Epoch 14, batch 7500, loss[loss=0.2138, simple_loss=0.2909, pruned_loss=0.06829, over 7197.00 frames.], tot_loss[loss=0.2081, simple_loss=0.29, pruned_loss=0.06308, over 1464914.31 frames.], batch size: 20, lr: 3.91e-04
2022-07-26 21:50:55,945 INFO [train.py:850] (2/4) Epoch 14, batch 7550, loss[loss=0.1961, simple_loss=0.2738, pruned_loss=0.05921, over 7393.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2897, pruned_loss=0.06326, over 1464748.87 frames.], batch size: 19, lr: 3.91e-04
2022-07-26 21:51:42,249 INFO [train.py:850] (2/4) Epoch 14, batch 7600, loss[loss=0.2055, simple_loss=0.2889, pruned_loss=0.06106, over 7479.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2907, pruned_loss=0.06374, over 1465967.38 frames.], batch size: 24, lr: 3.91e-04
2022-07-26 21:52:27,792 INFO [train.py:850] (2/4) Epoch 14, batch 7650, loss[loss=0.1749, simple_loss=0.2505, pruned_loss=0.04969, over 7202.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2906, pruned_loss=0.064, over 1466079.93 frames.], batch size: 18, lr: 3.91e-04
2022-07-26 21:53:12,378 INFO [train.py:850] (2/4) Epoch 14, batch 7700, loss[loss=0.2197, simple_loss=0.2994, pruned_loss=0.07, over 7484.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2907, pruned_loss=0.06402, over 1465534.33 frames.], batch size: 20, lr: 3.91e-04
2022-07-26 21:53:55,866 INFO [train.py:850] (2/4) Epoch 14, batch 7750, loss[loss=0.2731, simple_loss=0.3404, pruned_loss=0.1029, over 7458.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2899, pruned_loss=0.06364, over 1465434.33 frames.], batch size: 71, lr: 3.91e-04
2022-07-26 21:54:40,546 INFO [train.py:850] (2/4) Epoch 14, batch 7800, loss[loss=0.1913, simple_loss=0.2682, pruned_loss=0.0572, over 7394.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2896, pruned_loss=0.06352, over 1466046.61 frames.], batch size: 19, lr: 3.91e-04
2022-07-26 21:55:24,442 INFO [train.py:850] (2/4) Epoch 14, batch 7850, loss[loss=0.2004, simple_loss=0.28, pruned_loss=0.06037, over 7326.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2889, pruned_loss=0.06272, over 1465441.84 frames.], batch size: 18, lr: 3.91e-04
2022-07-26 21:56:07,975 INFO [train.py:850] (2/4) Epoch 14, batch 7900, loss[loss=0.2096, simple_loss=0.2825, pruned_loss=0.06833, over 7389.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2909, pruned_loss=0.06315, over 1466223.08 frames.], batch size: 20, lr: 3.91e-04
2022-07-26 21:56:53,142 INFO [train.py:850] (2/4) Epoch 14, batch 7950, loss[loss=0.2403, simple_loss=0.3232, pruned_loss=0.07872, over 7388.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2899, pruned_loss=0.06255, over 1467098.04 frames.], batch size: 21, lr: 3.90e-04
2022-07-26 21:57:36,890 INFO [train.py:850] (2/4) Epoch 14, batch 8000, loss[loss=0.2349, simple_loss=0.3064, pruned_loss=0.08164, over 7199.00 frames.], tot_loss[loss=0.2083, simple_loss=0.29, pruned_loss=0.06328, over 1466662.18 frames.], batch size: 20, lr: 3.90e-04
2022-07-26 21:58:21,248 INFO [train.py:850] (2/4) Epoch 14, batch 8050, loss[loss=0.2424, simple_loss=0.3192, pruned_loss=0.08285, over 7202.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2887, pruned_loss=0.06301, over 1465812.75 frames.], batch size: 20, lr: 3.90e-04
2022-07-26 21:59:06,267 INFO [train.py:850] (2/4) Epoch 14, batch 8100, loss[loss=0.1919, simple_loss=0.264, pruned_loss=0.05995, over 7104.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2879, pruned_loss=0.06289, over 1464899.76 frames.], batch size: 18, lr: 3.90e-04
2022-07-26 21:59:49,418 INFO [train.py:850] (2/4) Epoch 14, batch 8150, loss[loss=0.18, simple_loss=0.2627, pruned_loss=0.0487, over 7198.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2877, pruned_loss=0.06222, over 1465082.86 frames.], batch size: 18, lr: 3.90e-04
2022-07-26 22:00:34,037 INFO [train.py:850] (2/4) Epoch 14, batch 8200, loss[loss=0.1907, simple_loss=0.2658, pruned_loss=0.05785, over 7154.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2868, pruned_loss=0.06183, over 1465238.70 frames.], batch size: 17, lr: 3.90e-04
2022-07-26 22:01:17,420 INFO [train.py:850] (2/4) Epoch 14, batch 8250, loss[loss=0.172, simple_loss=0.2499, pruned_loss=0.04704, over 7299.00 frames.], tot_loss[loss=0.2055, simple_loss=0.287, pruned_loss=0.06197, over 1465328.33 frames.], batch size: 16, lr: 3.90e-04
2022-07-26 22:02:01,386 INFO [train.py:850] (2/4) Epoch 14, batch 8300, loss[loss=0.1963, simple_loss=0.2837, pruned_loss=0.05446, over 7296.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2882, pruned_loss=0.06271, over 1466324.07 frames.], batch size: 18, lr: 3.90e-04
2022-07-26 22:02:44,580 INFO [train.py:850] (2/4) Epoch 14, batch 8350, loss[loss=0.2346, simple_loss=0.3118, pruned_loss=0.07874, over 7298.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2895, pruned_loss=0.06341, over 1466851.42 frames.], batch size: 27, lr: 3.90e-04
2022-07-26 22:03:44,681 INFO [train.py:850] (2/4) Epoch 14, batch 8400, loss[loss=0.1656, simple_loss=0.2478, pruned_loss=0.04172, over 7311.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2884, pruned_loss=0.06199, over 1467637.95 frames.], batch size: 18, lr: 3.90e-04
2022-07-26 22:04:28,912 INFO [train.py:850] (2/4) Epoch 14, batch 8450, loss[loss=0.256, simple_loss=0.3343, pruned_loss=0.08883, over 7285.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2889, pruned_loss=0.06231, over 1466161.01 frames.], batch size: 27, lr: 3.90e-04
2022-07-26 22:05:13,152 INFO [train.py:850] (2/4) Epoch 14, batch 8500, loss[loss=0.2146, simple_loss=0.3014, pruned_loss=0.06389, over 7379.00 frames.], tot_loss[loss=0.2049, simple_loss=0.287, pruned_loss=0.06138, over 1466818.74 frames.], batch size: 21, lr: 3.90e-04
2022-07-26 22:05:56,259 INFO [train.py:850] (2/4) Epoch 14, batch 8550, loss[loss=0.2147, simple_loss=0.3079, pruned_loss=0.06074, over 7183.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2885, pruned_loss=0.062, over 1466831.40 frames.], batch size: 21, lr: 3.90e-04
2022-07-26 22:06:40,534 INFO [train.py:850] (2/4) Epoch 14, batch 8600, loss[loss=0.184, simple_loss=0.2707, pruned_loss=0.04866, over 7182.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2887, pruned_loss=0.06216, over 1465960.61 frames.], batch size: 21, lr: 3.89e-04
2022-07-26 22:07:23,818 INFO [train.py:850] (2/4) Epoch 14, batch 8650, loss[loss=0.2107, simple_loss=0.2937, pruned_loss=0.06385, over 7334.00 frames.], tot_loss[loss=0.208, simple_loss=0.2901, pruned_loss=0.06299, over 1466139.33 frames.], batch size: 23, lr: 3.89e-04
2022-07-26 22:08:06,663 INFO [train.py:850] (2/4) Epoch 14, batch 8700, loss[loss=0.2234, simple_loss=0.3177, pruned_loss=0.06451, over 7385.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2907, pruned_loss=0.06325, over 1465557.18 frames.], batch size: 21, lr: 3.89e-04
2022-07-26 22:08:51,578 INFO [train.py:850] (2/4) Epoch 14, batch 8750, loss[loss=0.2394, simple_loss=0.3125, pruned_loss=0.08318, over 7174.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2904, pruned_loss=0.06292, over 1465400.18 frames.], batch size: 22, lr: 3.89e-04
2022-07-26 22:09:36,156 INFO [train.py:850] (2/4) Epoch 14, batch 8800, loss[loss=0.2406, simple_loss=0.3136, pruned_loss=0.08374, over 7203.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2912, pruned_loss=0.06317, over 1465438.35 frames.], batch size: 20, lr: 3.89e-04
2022-07-26 22:10:18,685 INFO [train.py:850] (2/4) Epoch 14, batch 8850, loss[loss=0.2117, simple_loss=0.2917, pruned_loss=0.06588, over 7385.00 frames.], tot_loss[loss=0.2077, simple_loss=0.29, pruned_loss=0.06271, over 1463559.67 frames.], batch size: 21, lr: 3.89e-04
2022-07-26 22:11:46,792 INFO [train.py:850] (2/4) Epoch 15, batch 0, loss[loss=0.2011, simple_loss=0.2909, pruned_loss=0.05565, over 7487.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2909, pruned_loss=0.05565, over 7487.00 frames.], batch size: 19, lr: 3.77e-04
2022-07-26 22:12:30,033 INFO [train.py:850] (2/4) Epoch 15, batch 50, loss[loss=0.1649, simple_loss=0.2591, pruned_loss=0.03535, over 7215.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2897, pruned_loss=0.05374, over 330726.09 frames.], batch size: 19, lr: 3.77e-04
2022-07-26 22:13:15,336 INFO [train.py:850] (2/4) Epoch 15, batch 100, loss[loss=0.2057, simple_loss=0.3015, pruned_loss=0.05495, over 7280.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2835, pruned_loss=0.05191, over 582654.18 frames.], batch size: 21, lr: 3.77e-04
2022-07-26 22:13:57,919 INFO [train.py:850] (2/4) Epoch 15, batch 150, loss[loss=0.1628, simple_loss=0.2595, pruned_loss=0.03302, over 7295.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2829, pruned_loss=0.05168, over 778467.98 frames.], batch size: 19, lr: 3.77e-04
2022-07-26 22:14:41,645 INFO [train.py:850] (2/4) Epoch 15, batch 200, loss[loss=0.1759, simple_loss=0.2727, pruned_loss=0.03958, over 7286.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2817, pruned_loss=0.05132, over 932038.39 frames.], batch size: 21, lr: 3.77e-04
2022-07-26 22:15:25,243 INFO [train.py:850] (2/4) Epoch 15, batch 250, loss[loss=0.1847, simple_loss=0.2612, pruned_loss=0.05414, over 7452.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2828, pruned_loss=0.05129, over 1051170.66 frames.], batch size: 17, lr: 3.77e-04
2022-07-26 22:16:08,504 INFO [train.py:850] (2/4) Epoch 15, batch 300, loss[loss=0.1738, simple_loss=0.2587, pruned_loss=0.04446, over 7117.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2835, pruned_loss=0.05159, over 1142331.80 frames.], batch size: 18, lr: 3.77e-04
2022-07-26 22:16:52,962 INFO [train.py:850] (2/4) Epoch 15, batch 350, loss[loss=0.2136, simple_loss=0.2962, pruned_loss=0.0655, over 7215.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2817, pruned_loss=0.05103, over 1215306.42 frames.], batch size: 24, lr: 3.77e-04
2022-07-26 22:17:36,864 INFO [train.py:850] (2/4) Epoch 15, batch 400, loss[loss=0.2033, simple_loss=0.293, pruned_loss=0.05681, over 7203.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2802, pruned_loss=0.0504, over 1269964.97 frames.], batch size: 20, lr: 3.77e-04
2022-07-26 22:18:19,939 INFO [train.py:850] (2/4) Epoch 15, batch 450, loss[loss=0.1926, simple_loss=0.279, pruned_loss=0.0531, over 7391.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2785, pruned_loss=0.0499, over 1312985.69 frames.], batch size: 19, lr: 3.76e-04
2022-07-26 22:19:03,369 INFO [train.py:850] (2/4) Epoch 15, batch 500, loss[loss=0.1857, simple_loss=0.2906, pruned_loss=0.04036, over 7182.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2776, pruned_loss=0.04904, over 1345797.15 frames.], batch size: 21, lr: 3.76e-04
2022-07-26 22:19:47,646 INFO [train.py:850] (2/4) Epoch 15, batch 550, loss[loss=0.1897, simple_loss=0.2789, pruned_loss=0.05029, over 7471.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2772, pruned_loss=0.04865, over 1372579.11 frames.], batch size: 21, lr: 3.76e-04
2022-07-26 22:20:31,891 INFO [train.py:850] (2/4) Epoch 15, batch 600, loss[loss=0.1886, simple_loss=0.2671, pruned_loss=0.05501, over 7285.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2779, pruned_loss=0.04887, over 1392980.42 frames.], batch size: 16, lr: 3.76e-04
2022-07-26 22:21:15,061 INFO [train.py:850] (2/4) Epoch 15, batch 650, loss[loss=0.1796, simple_loss=0.2548, pruned_loss=0.05217, over 7305.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2774, pruned_loss=0.04888, over 1408625.00 frames.], batch size: 18, lr: 3.76e-04
2022-07-26 22:22:00,573 INFO [train.py:850] (2/4) Epoch 15, batch 700, loss[loss=0.2328, simple_loss=0.3149, pruned_loss=0.07535, over 7367.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2783, pruned_loss=0.04939, over 1421826.65 frames.], batch size: 21, lr: 3.76e-04
2022-07-26 22:22:45,491 INFO [train.py:850] (2/4) Epoch 15, batch 750, loss[loss=0.1815, simple_loss=0.2658, pruned_loss=0.04858, over 7376.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2789, pruned_loss=0.04987, over 1431377.52 frames.], batch size: 20, lr: 3.76e-04
2022-07-26 22:23:30,721 INFO [train.py:850] (2/4) Epoch 15, batch 800, loss[loss=0.1913, simple_loss=0.2935, pruned_loss=0.04449, over 7349.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2789, pruned_loss=0.04979, over 1439175.57 frames.], batch size: 23, lr: 3.76e-04
2022-07-26 22:24:13,940 INFO [train.py:850] (2/4) Epoch 15, batch 850, loss[loss=0.1824, simple_loss=0.2762, pruned_loss=0.04433, over 7179.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2793, pruned_loss=0.04964, over 1445947.54 frames.], batch size: 22, lr: 3.76e-04
2022-07-26 22:24:57,241 INFO [train.py:850] (2/4) Epoch 15, batch 900, loss[loss=0.23, simple_loss=0.3178, pruned_loss=0.07108, over 7281.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2802, pruned_loss=0.05039, over 1448948.07 frames.], batch size: 21, lr: 3.76e-04
2022-07-26 22:25:41,094 INFO [train.py:850] (2/4) Epoch 15, batch 950, loss[loss=0.1954, simple_loss=0.2823, pruned_loss=0.05427, over 7478.00 frames.], tot_loss[loss=0.1922, simple_loss=0.282, pruned_loss=0.05121, over 1453039.50 frames.], batch size: 21, lr: 3.76e-04
2022-07-26 22:26:24,880 INFO [train.py:850] (2/4) Epoch 15, batch 1000, loss[loss=0.1919, simple_loss=0.2743, pruned_loss=0.05476, over 7381.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2831, pruned_loss=0.05174, over 1456065.78 frames.], batch size: 20, lr: 3.76e-04
2022-07-26 22:27:09,403 INFO [train.py:850] (2/4) Epoch 15, batch 1050, loss[loss=0.1695, simple_loss=0.2511, pruned_loss=0.04398, over 7288.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2817, pruned_loss=0.0511, over 1459636.49 frames.], batch size: 16, lr: 3.76e-04
2022-07-26 22:27:52,285 INFO [train.py:850] (2/4) Epoch 15, batch 1100, loss[loss=0.2104, simple_loss=0.3096, pruned_loss=0.05564, over 7461.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2819, pruned_loss=0.05115, over 1459579.21 frames.], batch size: 39, lr: 3.76e-04
2022-07-26 22:28:34,856 INFO [train.py:850] (2/4) Epoch 15, batch 1150, loss[loss=0.1963, simple_loss=0.2948, pruned_loss=0.04886, over 7308.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2826, pruned_loss=0.05147, over 1459877.48 frames.], batch size: 31, lr: 3.75e-04
2022-07-26 22:29:19,025 INFO [train.py:850] (2/4) Epoch 15, batch 1200, loss[loss=0.1958, simple_loss=0.2881, pruned_loss=0.05177, over 7220.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2825, pruned_loss=0.05115, over 1461338.17 frames.], batch size: 24, lr: 3.75e-04
2022-07-26 22:30:01,626 INFO [train.py:850] (2/4) Epoch 15, batch 1250, loss[loss=0.1835, simple_loss=0.2749, pruned_loss=0.04605, over 7441.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2825, pruned_loss=0.05165, over 1461192.54 frames.], batch size: 18, lr: 3.75e-04
2022-07-26 22:30:45,350 INFO [train.py:850] (2/4) Epoch 15, batch 1300, loss[loss=0.1931, simple_loss=0.2959, pruned_loss=0.04518, over 7416.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2839, pruned_loss=0.05254, over 1462254.95 frames.], batch size: 31, lr: 3.75e-04
2022-07-26 22:31:28,405 INFO [train.py:850] (2/4) Epoch 15, batch 1350, loss[loss=0.2018, simple_loss=0.2997, pruned_loss=0.05197, over 7183.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2836, pruned_loss=0.0524, over 1462485.47 frames.], batch size: 22, lr: 3.75e-04
2022-07-26 22:32:12,846 INFO [train.py:850] (2/4) Epoch 15, batch 1400, loss[loss=0.2267, simple_loss=0.3106, pruned_loss=0.07134, over 7480.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2843, pruned_loss=0.0529, over 1463736.04 frames.], batch size: 26, lr: 3.75e-04
2022-07-26 22:32:57,419 INFO [train.py:850] (2/4) Epoch 15, batch 1450, loss[loss=0.165, simple_loss=0.252, pruned_loss=0.03898, over 7158.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2851, pruned_loss=0.05318, over 1464717.03 frames.], batch size: 17, lr: 3.75e-04
2022-07-26 22:33:41,932 INFO [train.py:850] (2/4) Epoch 15, batch 1500, loss[loss=0.1954, simple_loss=0.2815, pruned_loss=0.05461, over 7285.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2857, pruned_loss=0.05323, over 1465148.09 frames.], batch size: 19, lr: 3.75e-04
2022-07-26 22:34:26,900 INFO [train.py:850] (2/4) Epoch 15, batch 1550, loss[loss=0.2224, simple_loss=0.3083, pruned_loss=0.06822, over 7472.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2852, pruned_loss=0.05289, over 1464876.35 frames.], batch size: 24, lr: 3.75e-04
2022-07-26 22:35:10,927 INFO [train.py:850] (2/4) Epoch 15, batch 1600, loss[loss=0.1882, simple_loss=0.2752, pruned_loss=0.05064, over 7398.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2857, pruned_loss=0.05294, over 1465017.15 frames.], batch size: 19, lr: 3.75e-04
2022-07-26 22:35:53,539 INFO [train.py:850] (2/4) Epoch 15, batch 1650, loss[loss=0.1695, simple_loss=0.2642, pruned_loss=0.03736, over 7198.00 frames.], tot_loss[loss=0.1954, simple_loss=0.285, pruned_loss=0.05285, over 1464969.34 frames.], batch size: 19, lr: 3.75e-04
2022-07-26 22:36:38,038 INFO [train.py:850] (2/4) Epoch 15, batch 1700, loss[loss=0.1989, simple_loss=0.2923, pruned_loss=0.05274, over 7315.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2862, pruned_loss=0.05323, over 1466032.74 frames.], batch size: 27, lr: 3.75e-04
2022-07-26 22:37:21,015 INFO [train.py:850] (2/4) Epoch 15, batch 1750, loss[loss=0.1946, simple_loss=0.2908, pruned_loss=0.04922, over 7405.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2854, pruned_loss=0.05272, over 1465927.71 frames.], batch size: 38, lr: 3.75e-04
2022-07-26 22:38:05,056 INFO [train.py:850] (2/4) Epoch 15, batch 1800, loss[loss=0.1819, simple_loss=0.2716, pruned_loss=0.04608, over 7204.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2848, pruned_loss=0.05228, over 1465576.24 frames.], batch size: 20, lr: 3.74e-04
2022-07-26 22:38:48,342 INFO [train.py:850] (2/4) Epoch 15, batch 1850, loss[loss=0.2315, simple_loss=0.3059, pruned_loss=0.07856, over 7489.00 frames.], tot_loss[loss=0.1945, simple_loss=0.285, pruned_loss=0.05199, over 1465591.21 frames.], batch size: 23, lr: 3.74e-04
2022-07-26 22:39:32,709 INFO [train.py:850] (2/4) Epoch 15, batch 1900, loss[loss=0.1486, simple_loss=0.2331, pruned_loss=0.03209, over 7436.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2845, pruned_loss=0.05186, over 1466256.76 frames.], batch size: 18, lr: 3.74e-04
2022-07-26 22:40:16,223 INFO [train.py:850] (2/4) Epoch 15, batch 1950, loss[loss=0.1863, simple_loss=0.2732, pruned_loss=0.04968, over 7456.00 frames.], tot_loss[loss=0.194, simple_loss=0.2844, pruned_loss=0.05175, over 1466484.22 frames.], batch size: 17, lr: 3.74e-04
2022-07-26 22:41:00,085 INFO [train.py:850] (2/4) Epoch 15, batch 2000, loss[loss=0.1658, simple_loss=0.2627, pruned_loss=0.03442, over 7198.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2848, pruned_loss=0.05189, over 1466081.24 frames.], batch size: 19, lr: 3.74e-04
2022-07-26 22:41:43,895 INFO [train.py:850] (2/4) Epoch 15, batch 2050, loss[loss=0.2304, simple_loss=0.3109, pruned_loss=0.07493, over 7245.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2841, pruned_loss=0.05151, over 1466150.20 frames.], batch size: 30, lr: 3.74e-04
2022-07-26 22:42:27,133 INFO [train.py:850] (2/4) Epoch 15, batch 2100, loss[loss=0.1775, simple_loss=0.2569, pruned_loss=0.04899, over 7446.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2842, pruned_loss=0.05155, over 1467388.19 frames.], batch size: 17, lr: 3.74e-04
2022-07-26 22:43:10,797 INFO [train.py:850] (2/4) Epoch 15, batch 2150, loss[loss=0.1848, simple_loss=0.2757, pruned_loss=0.04691, over 7347.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2836, pruned_loss=0.05133, over 1466595.11 frames.], batch size: 23, lr: 3.74e-04
2022-07-26 22:43:54,187 INFO [train.py:850] (2/4) Epoch 15, batch 2200, loss[loss=0.2452, simple_loss=0.3372, pruned_loss=0.07663, over 7490.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2847, pruned_loss=0.05177, over 1466891.61 frames.], batch size: 26, lr: 3.74e-04
2022-07-26 22:44:37,552 INFO [train.py:850] (2/4) Epoch 15, batch 2250, loss[loss=0.2773, simple_loss=0.3592, pruned_loss=0.09764, over 7307.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2856, pruned_loss=0.05189, over 1465434.35 frames.], batch size: 22, lr: 3.74e-04
2022-07-26 22:45:22,643 INFO [train.py:850] (2/4) Epoch 15, batch 2300, loss[loss=0.1711, simple_loss=0.2558, pruned_loss=0.04316, over 7453.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2845, pruned_loss=0.05063, over 1465691.94 frames.], batch size: 18, lr: 3.74e-04
2022-07-26 22:46:06,572 INFO [train.py:850] (2/4) Epoch 15, batch 2350, loss[loss=0.2013, simple_loss=0.2872, pruned_loss=0.05768, over 7187.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2837, pruned_loss=0.05052, over 1465285.50 frames.], batch size: 21, lr: 3.74e-04
2022-07-26 22:46:50,995 INFO [train.py:850] (2/4) Epoch 15, batch 2400, loss[loss=0.1755, simple_loss=0.2624, pruned_loss=0.0443, over 7296.00 frames.], tot_loss[loss=0.193, simple_loss=0.2842, pruned_loss=0.05096, over 1465425.66 frames.], batch size: 17, lr: 3.74e-04
2022-07-26 22:47:34,467 INFO [train.py:850] (2/4) Epoch 15, batch 2450, loss[loss=0.1932, simple_loss=0.2909, pruned_loss=0.04779, over 7205.00 frames.], tot_loss[loss=0.1928, simple_loss=0.284, pruned_loss=0.05083, over 1466120.10 frames.], batch size: 25, lr: 3.74e-04
2022-07-26 22:48:17,807 INFO [train.py:850] (2/4) Epoch 15, batch 2500, loss[loss=0.1758, simple_loss=0.2637, pruned_loss=0.04393, over 7475.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2844, pruned_loss=0.05123, over 1466304.43 frames.], batch size: 20, lr: 3.73e-04
2022-07-26 22:49:01,676 INFO [train.py:850] (2/4) Epoch 15, batch 2550, loss[loss=0.1925, simple_loss=0.2901, pruned_loss=0.04744, over 7292.00 frames.], tot_loss[loss=0.193, simple_loss=0.284, pruned_loss=0.051, over 1465155.70 frames.], batch size: 20, lr: 3.73e-04
2022-07-26 22:49:45,794 INFO [train.py:850] (2/4) Epoch 15, batch 2600, loss[loss=0.1866, simple_loss=0.2803, pruned_loss=0.04644, over 7290.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2834, pruned_loss=0.0509, over 1465138.88 frames.], batch size: 22, lr: 3.73e-04
2022-07-26 22:50:28,388 INFO [train.py:850] (2/4) Epoch 15, batch 2650, loss[loss=0.2098, simple_loss=0.3028, pruned_loss=0.0584, over 7442.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2844, pruned_loss=0.05149, over 1465351.36 frames.], batch size: 24, lr: 3.73e-04
2022-07-26 22:51:12,163 INFO [train.py:850] (2/4) Epoch 15, batch 2700, loss[loss=0.2054, simple_loss=0.3038, pruned_loss=0.05346, over 7470.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2834, pruned_loss=0.05066, over 1465400.83 frames.], batch size: 21, lr: 3.73e-04
2022-07-26 22:51:55,258 INFO [train.py:850] (2/4) Epoch 15, batch 2750, loss[loss=0.2336, simple_loss=0.3178, pruned_loss=0.07463, over 7352.00 frames.], tot_loss[loss=0.192, simple_loss=0.2829, pruned_loss=0.05052, over 1465158.24 frames.], batch size: 23, lr: 3.73e-04
2022-07-26 22:52:39,903 INFO [train.py:850] (2/4) Epoch 15, batch 2800, loss[loss=0.1589, simple_loss=0.2652, pruned_loss=0.02635, over 7473.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2829, pruned_loss=0.05021, over 1464620.99 frames.], batch size: 21, lr: 3.73e-04
2022-07-26 22:53:22,623 INFO [train.py:850] (2/4) Epoch 15, batch 2850, loss[loss=0.2057, simple_loss=0.2994, pruned_loss=0.05598, over 7379.00 frames.], tot_loss[loss=0.1919, simple_loss=0.283, pruned_loss=0.05046, over 1464235.11 frames.], batch size: 21, lr: 3.73e-04
2022-07-26 22:54:06,601 INFO [train.py:850] (2/4) Epoch 15, batch 2900, loss[loss=0.2233, simple_loss=0.3162, pruned_loss=0.06522, over 7197.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2819, pruned_loss=0.05017, over 1464378.14 frames.], batch size: 22, lr: 3.73e-04
2022-07-26 22:54:51,851 INFO [train.py:850] (2/4) Epoch 15, batch 2950, loss[loss=0.1878, simple_loss=0.2858, pruned_loss=0.04494, over 7186.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2816, pruned_loss=0.05013, over 1464766.96 frames.], batch size: 21, lr: 3.73e-04
2022-07-26 22:55:37,449 INFO [train.py:850] (2/4) Epoch 15, batch 3000, loss[loss=0.1854, simple_loss=0.2897, pruned_loss=0.04056, over 7490.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2831, pruned_loss=0.0506, over 1464943.24 frames.], batch size: 23, lr: 3.73e-04
2022-07-26 22:55:37,450 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 22:56:00,290 INFO [train.py:879] (2/4) Epoch 15, validation: loss=0.1933, simple_loss=0.2893, pruned_loss=0.04862, over 924787.00 frames.
2022-07-26 22:56:44,078 INFO [train.py:850] (2/4) Epoch 15, batch 3050, loss[loss=0.1648, simple_loss=0.2538, pruned_loss=0.03792, over 7197.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2818, pruned_loss=0.04997, over 1464926.59 frames.], batch size: 18, lr: 3.73e-04
2022-07-26 22:57:28,521 INFO [train.py:850] (2/4) Epoch 15, batch 3100, loss[loss=0.1545, simple_loss=0.2437, pruned_loss=0.03263, over 7283.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2818, pruned_loss=0.05066, over 1466022.20 frames.], batch size: 16, lr: 3.73e-04
2022-07-26 22:58:11,393 INFO [train.py:850] (2/4) Epoch 15, batch 3150, loss[loss=0.1542, simple_loss=0.2442, pruned_loss=0.03212, over 7190.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2824, pruned_loss=0.05064, over 1464243.86 frames.], batch size: 16, lr: 3.72e-04
2022-07-26 22:58:54,513 INFO [train.py:850] (2/4) Epoch 15, batch 3200, loss[loss=0.2033, simple_loss=0.2942, pruned_loss=0.05624, over 7469.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2829, pruned_loss=0.05104, over 1465507.79 frames.], batch size: 31, lr: 3.72e-04
2022-07-26 22:59:38,209 INFO [train.py:850] (2/4) Epoch 15, batch 3250, loss[loss=0.1663, simple_loss=0.2565, pruned_loss=0.03802, over 7486.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2829, pruned_loss=0.0511, over 1466487.40 frames.], batch size: 20, lr: 3.72e-04
2022-07-26 23:00:21,850 INFO [train.py:850] (2/4) Epoch 15, batch 3300, loss[loss=0.1817, simple_loss=0.2788, pruned_loss=0.0423, over 7238.00 frames.], tot_loss[loss=0.191, simple_loss=0.2817, pruned_loss=0.05016, over 1466305.94 frames.], batch size: 25, lr: 3.72e-04
2022-07-26 23:01:05,750 INFO [train.py:850] (2/4) Epoch 15, batch 3350, loss[loss=0.2207, simple_loss=0.3036, pruned_loss=0.06888, over 7448.00 frames.], tot_loss[loss=0.192, simple_loss=0.2829, pruned_loss=0.0505, over 1465576.91 frames.], batch size: 69, lr: 3.72e-04
2022-07-26 23:01:48,910 INFO [train.py:850] (2/4) Epoch 15, batch 3400, loss[loss=0.1871, simple_loss=0.2875, pruned_loss=0.04337, over 7332.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2832, pruned_loss=0.05074, over 1465965.08 frames.], batch size: 37, lr: 3.72e-04
2022-07-26 23:02:32,241 INFO [train.py:850] (2/4) Epoch 15, batch 3450, loss[loss=0.2117, simple_loss=0.3065, pruned_loss=0.05846, over 7209.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2833, pruned_loss=0.05071, over 1465803.80 frames.], batch size: 20, lr: 3.72e-04
2022-07-26 23:03:31,845 INFO [train.py:850] (2/4) Epoch 15, batch 3500, loss[loss=0.1724, simple_loss=0.2647, pruned_loss=0.04007, over 7193.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2823, pruned_loss=0.05044, over 1466005.72 frames.], batch size: 18, lr: 3.72e-04
2022-07-26 23:04:14,846 INFO [train.py:850] (2/4) Epoch 15, batch 3550, loss[loss=0.2354, simple_loss=0.3157, pruned_loss=0.07758, over 7469.00 frames.], tot_loss[loss=0.1915, simple_loss=0.282, pruned_loss=0.05046, over 1464461.71 frames.], batch size: 24, lr: 3.72e-04
2022-07-26 23:04:58,020 INFO [train.py:850] (2/4) Epoch 15, batch 3600, loss[loss=0.223, simple_loss=0.318, pruned_loss=0.06403, over 7202.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2806, pruned_loss=0.04977, over 1464012.92 frames.], batch size: 20, lr: 3.72e-04
2022-07-26 23:05:41,235 INFO [train.py:850] (2/4) Epoch 15, batch 3650, loss[loss=0.2189, simple_loss=0.3071, pruned_loss=0.0653, over 7443.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2819, pruned_loss=0.05048, over 1464310.96 frames.], batch size: 67, lr: 3.72e-04
2022-07-26 23:06:25,297 INFO [train.py:850] (2/4) Epoch 15, batch 3700, loss[loss=0.1586, simple_loss=0.2536, pruned_loss=0.03178, over 7318.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2812, pruned_loss=0.05046, over 1463286.84 frames.], batch size: 18, lr: 3.72e-04
2022-07-26 23:07:08,845 INFO [train.py:850] (2/4) Epoch 15, batch 3750, loss[loss=0.181, simple_loss=0.2783, pruned_loss=0.04187, over 7280.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2804, pruned_loss=0.04954, over 1462815.04 frames.], batch size: 19, lr: 3.72e-04
2022-07-26 23:07:52,304 INFO [train.py:850] (2/4) Epoch 15, batch 3800, loss[loss=0.1725, simple_loss=0.274, pruned_loss=0.03546, over 7341.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2816, pruned_loss=0.05003, over 1462702.63 frames.], batch size: 30, lr: 3.72e-04
2022-07-26 23:08:35,872 INFO [train.py:850] (2/4) Epoch 15, batch 3850, loss[loss=0.1846, simple_loss=0.2563, pruned_loss=0.05643, over 7304.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2815, pruned_loss=0.05001, over 1463675.54 frames.], batch size: 17, lr: 3.71e-04
2022-07-26 23:09:19,251 INFO [train.py:850] (2/4) Epoch 15, batch 3900, loss[loss=0.1713, simple_loss=0.2432, pruned_loss=0.04966, over 7149.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2822, pruned_loss=0.05037, over 1465330.00 frames.], batch size: 17, lr: 3.71e-04
2022-07-26 23:10:02,470 INFO [train.py:850] (2/4) Epoch 15, batch 3950, loss[loss=0.1644, simple_loss=0.2634, pruned_loss=0.03275, over 7196.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2815, pruned_loss=0.04994, over 1465233.90 frames.], batch size: 20, lr: 3.71e-04
2022-07-26 23:10:46,428 INFO [train.py:850] (2/4) Epoch 15, batch 4000, loss[loss=0.1774, simple_loss=0.2679, pruned_loss=0.04346, over 7280.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2828, pruned_loss=0.05037, over 1466093.67 frames.], batch size: 20, lr: 3.71e-04
2022-07-26 23:11:30,030 INFO [train.py:850] (2/4) Epoch 15, batch 4050, loss[loss=0.2285, simple_loss=0.3168, pruned_loss=0.07006, over 7349.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2834, pruned_loss=0.05067, over 1466623.12 frames.], batch size: 23, lr: 3.71e-04
2022-07-26 23:12:14,624 INFO [train.py:850] (2/4) Epoch 15, batch 4100, loss[loss=0.23, simple_loss=0.3198, pruned_loss=0.07004, over 7182.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2839, pruned_loss=0.05169, over 1466131.16 frames.], batch size: 21, lr: 3.71e-04
2022-07-26 23:12:58,205 INFO [train.py:850] (2/4) Epoch 15, batch 4150, loss[loss=0.2451, simple_loss=0.3235, pruned_loss=0.08333, over 7424.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2845, pruned_loss=0.0523, over 1466690.82 frames.], batch size: 22, lr: 3.71e-04
2022-07-26 23:13:44,216 INFO [train.py:850] (2/4) Epoch 15, batch 4200, loss[loss=0.1926, simple_loss=0.2845, pruned_loss=0.05035, over 7276.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2851, pruned_loss=0.05332, over 1466902.31 frames.], batch size: 27, lr: 3.71e-04
2022-07-26 23:14:27,274 INFO [train.py:850] (2/4) Epoch 15, batch 4250, loss[loss=0.2128, simple_loss=0.2936, pruned_loss=0.06594, over 7185.00 frames.], tot_loss[loss=0.197, simple_loss=0.2852, pruned_loss=0.0544, over 1466238.00 frames.], batch size: 21, lr: 3.71e-04
2022-07-26 23:15:10,769 INFO [train.py:850] (2/4) Epoch 15, batch 4300, loss[loss=0.1916, simple_loss=0.2905, pruned_loss=0.04634, over 7204.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2865, pruned_loss=0.05544, over 1466304.01 frames.], batch size: 19, lr: 3.71e-04
2022-07-26 23:15:55,054 INFO [train.py:850] (2/4) Epoch 15, batch 4350, loss[loss=0.1922, simple_loss=0.2856, pruned_loss=0.04939, over 7410.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2868, pruned_loss=0.05667, over 1465265.33 frames.], batch size: 39, lr: 3.71e-04
2022-07-26 23:16:38,508 INFO [train.py:850] (2/4) Epoch 15, batch 4400, loss[loss=0.2434, simple_loss=0.3144, pruned_loss=0.0862, over 7201.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2878, pruned_loss=0.05764, over 1465000.99 frames.], batch size: 18, lr: 3.71e-04
2022-07-26 23:17:22,774 INFO [train.py:850] (2/4) Epoch 15, batch 4450, loss[loss=0.1968, simple_loss=0.2744, pruned_loss=0.05965, over 7316.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2884, pruned_loss=0.05866, over 1465171.71 frames.], batch size: 18, lr: 3.71e-04
2022-07-26 23:18:07,498 INFO [train.py:850] (2/4) Epoch 15, batch 4500, loss[loss=0.2101, simple_loss=0.2946, pruned_loss=0.06279, over 7410.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2898, pruned_loss=0.06, over 1464762.59 frames.], batch size: 22, lr: 3.71e-04
2022-07-26 23:18:53,193 INFO [train.py:850] (2/4) Epoch 15, batch 4550, loss[loss=0.1835, simple_loss=0.265, pruned_loss=0.051, over 7398.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2888, pruned_loss=0.06022, over 1464542.17 frames.], batch size: 19, lr: 3.70e-04
2022-07-26 23:19:38,046 INFO [train.py:850] (2/4) Epoch 15, batch 4600, loss[loss=0.2189, simple_loss=0.2967, pruned_loss=0.07051, over 7401.00 frames.], tot_loss[loss=0.205, simple_loss=0.2886, pruned_loss=0.0607, over 1465296.85 frames.], batch size: 73, lr: 3.70e-04
2022-07-26 23:20:20,892 INFO [train.py:850] (2/4) Epoch 15, batch 4650, loss[loss=0.2173, simple_loss=0.2947, pruned_loss=0.06993, over 7303.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2893, pruned_loss=0.06101, over 1465141.48 frames.], batch size: 19, lr: 3.70e-04
2022-07-26 23:21:04,801 INFO [train.py:850] (2/4) Epoch 15, batch 4700, loss[loss=0.1887, simple_loss=0.2625, pruned_loss=0.05742, over 7456.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2906, pruned_loss=0.0631, over 1466065.54 frames.], batch size: 17, lr: 3.70e-04
2022-07-26 23:21:47,765 INFO [train.py:850] (2/4) Epoch 15, batch 4750, loss[loss=0.1875, simple_loss=0.263, pruned_loss=0.05603, over 7103.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2898, pruned_loss=0.06258, over 1464636.13 frames.], batch size: 18, lr: 3.70e-04
2022-07-26 23:22:31,161 INFO [train.py:850] (2/4) Epoch 15, batch 4800, loss[loss=0.2226, simple_loss=0.2963, pruned_loss=0.07452, over 7199.00 frames.], tot_loss[loss=0.208, simple_loss=0.2895, pruned_loss=0.06327, over 1464310.98 frames.], batch size: 18, lr: 3.70e-04
2022-07-26 23:23:14,013 INFO [train.py:850] (2/4) Epoch 15, batch 4850, loss[loss=0.2025, simple_loss=0.2959, pruned_loss=0.05453, over 7255.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2898, pruned_loss=0.06346, over 1465185.39 frames.], batch size: 30, lr: 3.70e-04
2022-07-26 23:23:57,239 INFO [train.py:850] (2/4) Epoch 15, batch 4900, loss[loss=0.1979, simple_loss=0.2843, pruned_loss=0.05572, over 7306.00 frames.], tot_loss[loss=0.208, simple_loss=0.2895, pruned_loss=0.06329, over 1465241.69 frames.], batch size: 22, lr: 3.70e-04
2022-07-26 23:24:41,449 INFO [train.py:850] (2/4) Epoch 15, batch 4950, loss[loss=0.1921, simple_loss=0.2802, pruned_loss=0.05202, over 7214.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2901, pruned_loss=0.06353, over 1465795.37 frames.], batch size: 25, lr: 3.70e-04
2022-07-26 23:25:25,322 INFO [train.py:850] (2/4) Epoch 15, batch 5000, loss[loss=0.1923, simple_loss=0.2691, pruned_loss=0.05774, over 7295.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2921, pruned_loss=0.06448, over 1465998.09 frames.], batch size: 19, lr: 3.70e-04
2022-07-26 23:26:10,401 INFO [train.py:850] (2/4) Epoch 15, batch 5050, loss[loss=0.2649, simple_loss=0.3425, pruned_loss=0.09365, over 7433.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2919, pruned_loss=0.06443, over 1466020.29 frames.], batch size: 74, lr: 3.70e-04
2022-07-26 23:26:54,802 INFO [train.py:850] (2/4) Epoch 15, batch 5100, loss[loss=0.1913, simple_loss=0.2574, pruned_loss=0.06261, over 7306.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2932, pruned_loss=0.06524, over 1466556.22 frames.], batch size: 16, lr: 3.70e-04
2022-07-26 23:27:38,124 INFO [train.py:850] (2/4) Epoch 15, batch 5150, loss[loss=0.1814, simple_loss=0.2546, pruned_loss=0.05405, over 7168.00 frames.], tot_loss[loss=0.2109, simple_loss=0.292, pruned_loss=0.06489, over 1465527.91 frames.], batch size: 17, lr: 3.70e-04
2022-07-26 23:28:22,222 INFO [train.py:850] (2/4) Epoch 15, batch 5200, loss[loss=0.2119, simple_loss=0.28, pruned_loss=0.07188, over 7378.00 frames.], tot_loss[loss=0.2104, simple_loss=0.291, pruned_loss=0.06489, over 1465694.39 frames.], batch size: 21, lr: 3.70e-04
2022-07-26 23:29:05,052 INFO [train.py:850] (2/4) Epoch 15, batch 5250, loss[loss=0.2302, simple_loss=0.3088, pruned_loss=0.0758, over 7387.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2914, pruned_loss=0.06468, over 1464671.96 frames.], batch size: 21, lr: 3.69e-04
2022-07-26 23:29:50,061 INFO [train.py:850] (2/4) Epoch 15, batch 5300, loss[loss=0.1956, simple_loss=0.2712, pruned_loss=0.05998, over 7427.00 frames.], tot_loss[loss=0.211, simple_loss=0.2919, pruned_loss=0.06511, over 1464778.19 frames.], batch size: 71, lr: 3.69e-04
2022-07-26 23:30:33,495 INFO [train.py:850] (2/4) Epoch 15, batch 5350, loss[loss=0.2484, simple_loss=0.3292, pruned_loss=0.08376, over 7301.00 frames.], tot_loss[loss=0.211, simple_loss=0.2919, pruned_loss=0.06511, over 1464580.55 frames.], batch size: 22, lr: 3.69e-04
2022-07-26 23:31:17,260 INFO [train.py:850] (2/4) Epoch 15, batch 5400, loss[loss=0.198, simple_loss=0.2834, pruned_loss=0.05634, over 7196.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2904, pruned_loss=0.06403, over 1464261.55 frames.], batch size: 19, lr: 3.69e-04
2022-07-26 23:32:01,059 INFO [train.py:850] (2/4) Epoch 15, batch 5450, loss[loss=0.1552, simple_loss=0.2362, pruned_loss=0.03704, over 7438.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2893, pruned_loss=0.06343, over 1465728.82 frames.], batch size: 17, lr: 3.69e-04
2022-07-26 23:32:45,241 INFO [train.py:850] (2/4) Epoch 15, batch 5500, loss[loss=0.1703, simple_loss=0.2524, pruned_loss=0.04408, over 7162.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2886, pruned_loss=0.06292, over 1465328.22 frames.], batch size: 17, lr: 3.69e-04
2022-07-26 23:33:28,293 INFO [train.py:850] (2/4) Epoch 15, batch 5550, loss[loss=0.2849, simple_loss=0.3478, pruned_loss=0.111, over 7288.00 frames.], tot_loss[loss=0.2077, simple_loss=0.289, pruned_loss=0.06321, over 1465426.85 frames.], batch size: 21, lr: 3.69e-04
2022-07-26 23:34:12,131 INFO [train.py:850] (2/4) Epoch 15, batch 5600, loss[loss=0.2816, simple_loss=0.3344, pruned_loss=0.1144, over 7362.00 frames.], tot_loss[loss=0.207, simple_loss=0.2881, pruned_loss=0.063, over 1465661.50 frames.], batch size: 75, lr: 3.69e-04
2022-07-26 23:34:56,050 INFO [train.py:850] (2/4) Epoch 15, batch 5650, loss[loss=0.2516, simple_loss=0.3201, pruned_loss=0.09161, over 7166.00 frames.], tot_loss[loss=0.208, simple_loss=0.2891, pruned_loss=0.0634, over 1465429.86 frames.], batch size: 22, lr: 3.69e-04
2022-07-26 23:35:39,886 INFO [train.py:850] (2/4) Epoch 15, batch 5700, loss[loss=0.1867, simple_loss=0.2649, pruned_loss=0.05419, over 7485.00 frames.], tot_loss[loss=0.2076, simple_loss=0.289, pruned_loss=0.06315, over 1465387.56 frames.], batch size: 20, lr: 3.69e-04
2022-07-26 23:36:23,817 INFO [train.py:850] (2/4) Epoch 15, batch 5750, loss[loss=0.2163, simple_loss=0.2814, pruned_loss=0.0756, over 7210.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2877, pruned_loss=0.06199, over 1465224.51 frames.], batch size: 19, lr: 3.69e-04
2022-07-26 23:37:07,809 INFO [train.py:850] (2/4) Epoch 15, batch 5800, loss[loss=0.2133, simple_loss=0.2842, pruned_loss=0.07121, over 7206.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2876, pruned_loss=0.06242, over 1464870.69 frames.], batch size: 18, lr: 3.69e-04
2022-07-26 23:37:52,181 INFO [train.py:850] (2/4) Epoch 15, batch 5850, loss[loss=0.2408, simple_loss=0.3153, pruned_loss=0.08319, over 7453.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2871, pruned_loss=0.06176, over 1465635.73 frames.], batch size: 70, lr: 3.69e-04
2022-07-26 23:38:36,526 INFO [train.py:850] (2/4) Epoch 15, batch 5900, loss[loss=0.1675, simple_loss=0.2424, pruned_loss=0.04628, over 7300.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2863, pruned_loss=0.0617, over 1465338.39 frames.], batch size: 17, lr: 3.69e-04
2022-07-26 23:39:19,536 INFO [train.py:850] (2/4) Epoch 15, batch 5950, loss[loss=0.2004, simple_loss=0.2793, pruned_loss=0.06072, over 7378.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2867, pruned_loss=0.06176, over 1464467.64 frames.], batch size: 21, lr: 3.68e-04
2022-07-26 23:40:03,955 INFO [train.py:850] (2/4) Epoch 15, batch 6000, loss[loss=0.2359, simple_loss=0.3043, pruned_loss=0.08377, over 7455.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2872, pruned_loss=0.06156, over 1465384.79 frames.], batch size: 71, lr: 3.68e-04
2022-07-26 23:40:03,956 INFO [train.py:870] (2/4) Computing validation loss
2022-07-26 23:40:26,740 INFO [train.py:879] (2/4) Epoch 15, validation: loss=0.186, simple_loss=0.2829, pruned_loss=0.04452, over 924787.00 frames.
2022-07-26 23:41:12,965 INFO [train.py:850] (2/4) Epoch 15, batch 6050, loss[loss=0.2271, simple_loss=0.3077, pruned_loss=0.07331, over 7173.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2876, pruned_loss=0.06178, over 1465339.33 frames.], batch size: 22, lr: 3.68e-04
2022-07-26 23:41:58,659 INFO [train.py:850] (2/4) Epoch 15, batch 6100, loss[loss=0.242, simple_loss=0.3381, pruned_loss=0.07299, over 7349.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2886, pruned_loss=0.06213, over 1464815.23 frames.], batch size: 23, lr: 3.68e-04
2022-07-26 23:42:42,508 INFO [train.py:850] (2/4) Epoch 15, batch 6150, loss[loss=0.2167, simple_loss=0.2975, pruned_loss=0.06792, over 7473.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2877, pruned_loss=0.06145, over 1465310.65 frames.], batch size: 26, lr: 3.68e-04
2022-07-26 23:43:27,038 INFO [train.py:850] (2/4) Epoch 15, batch 6200, loss[loss=0.1735, simple_loss=0.2451, pruned_loss=0.05094, over 7443.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2885, pruned_loss=0.06197, over 1464999.21 frames.], batch size: 17, lr: 3.68e-04
2022-07-26 23:44:09,201 INFO [train.py:850] (2/4) Epoch 15, batch 6250, loss[loss=0.1923, simple_loss=0.2692, pruned_loss=0.05772, over 7306.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2874, pruned_loss=0.06152, over 1465354.02 frames.], batch size: 17, lr: 3.68e-04
2022-07-26 23:44:55,248 INFO [train.py:850] (2/4) Epoch 15, batch 6300, loss[loss=0.2345, simple_loss=0.3134, pruned_loss=0.07776, over 7182.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2879, pruned_loss=0.06182, over 1464331.01 frames.], batch size: 21, lr: 3.68e-04
2022-07-26 23:45:40,704 INFO [train.py:850] (2/4) Epoch 15, batch 6350, loss[loss=0.2492, simple_loss=0.3279, pruned_loss=0.08529, over 7359.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2891, pruned_loss=0.06254, over 1464499.59 frames.], batch size: 39, lr: 3.68e-04
2022-07-26 23:46:26,823 INFO [train.py:850] (2/4) Epoch 15, batch 6400, loss[loss=0.1729, simple_loss=0.2701, pruned_loss=0.0379, over 7172.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2909, pruned_loss=0.06359, over 1465233.65 frames.], batch size: 22, lr: 3.68e-04
2022-07-26 23:47:12,333 INFO [train.py:850] (2/4) Epoch 15, batch 6450, loss[loss=0.2766, simple_loss=0.3495, pruned_loss=0.1018, over 7450.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2902, pruned_loss=0.06315, over 1464965.31 frames.], batch size: 70, lr: 3.68e-04
2022-07-26 23:47:57,680 INFO [train.py:850] (2/4) Epoch 15, batch 6500, loss[loss=0.2629, simple_loss=0.3334, pruned_loss=0.09621, over 7105.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2907, pruned_loss=0.06347, over 1465677.26 frames.], batch size: 18, lr: 3.68e-04
2022-07-26 23:48:44,516 INFO [train.py:850] (2/4) Epoch 15, batch 6550, loss[loss=0.285, simple_loss=0.3625, pruned_loss=0.1038, over 7189.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2914, pruned_loss=0.06383, over 1465989.65 frames.], batch size: 21, lr: 3.68e-04
2022-07-26 23:49:27,383 INFO [train.py:850] (2/4) Epoch 15, batch 6600, loss[loss=0.2078, simple_loss=0.2866, pruned_loss=0.06453, over 7302.00 frames.], tot_loss[loss=0.209, simple_loss=0.291, pruned_loss=0.0635, over 1465392.50 frames.], batch size: 19, lr: 3.68e-04
2022-07-26 23:50:11,445 INFO [train.py:850] (2/4) Epoch 15, batch 6650, loss[loss=0.2005, simple_loss=0.2904, pruned_loss=0.0553, over 7196.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2898, pruned_loss=0.06293, over 1465229.38 frames.], batch size: 20, lr: 3.67e-04
2022-07-26 23:50:54,383 INFO [train.py:850] (2/4) Epoch 15, batch 6700, loss[loss=0.1818, simple_loss=0.278, pruned_loss=0.04285, over 7292.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2903, pruned_loss=0.06321, over 1465070.03 frames.], batch size: 19, lr: 3.67e-04
2022-07-26 23:51:38,403 INFO [train.py:850] (2/4) Epoch 15, batch 6750, loss[loss=0.2362, simple_loss=0.3147, pruned_loss=0.07883, over 7299.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2902, pruned_loss=0.06275, over 1465058.58 frames.], batch size: 30, lr: 3.67e-04
2022-07-26 23:52:22,930 INFO [train.py:850] (2/4) Epoch 15, batch 6800, loss[loss=0.2605, simple_loss=0.333, pruned_loss=0.09396, over 7465.00 frames.], tot_loss[loss=0.2068, simple_loss=0.289, pruned_loss=0.06233, over 1466530.56 frames.], batch size: 75, lr: 3.67e-04
2022-07-26 23:53:06,405 INFO [train.py:850] (2/4) Epoch 15, batch 6850, loss[loss=0.2159, simple_loss=0.2947, pruned_loss=0.0685, over 7491.00 frames.], tot_loss[loss=0.2056, simple_loss=0.288, pruned_loss=0.06162, over 1465685.00 frames.], batch size: 26, lr: 3.67e-04
2022-07-26 23:53:50,491 INFO [train.py:850] (2/4) Epoch 15, batch 6900, loss[loss=0.231, simple_loss=0.3035, pruned_loss=0.07929, over 7439.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2882, pruned_loss=0.06179, over 1466716.09 frames.], batch size: 70, lr: 3.67e-04
2022-07-26 23:54:33,761 INFO [train.py:850] (2/4) Epoch 15, batch 6950, loss[loss=0.2128, simple_loss=0.2997, pruned_loss=0.06292, over 7238.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2879, pruned_loss=0.06162, over 1466408.08 frames.], batch size: 24, lr: 3.67e-04
2022-07-26 23:55:18,618 INFO [train.py:850] (2/4) Epoch 15, batch 7000, loss[loss=0.2074, simple_loss=0.2963, pruned_loss=0.05924, over 7484.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2873, pruned_loss=0.06124, over 1467829.85 frames.], batch size: 23, lr: 3.67e-04
2022-07-26 23:56:01,812 INFO [train.py:850] (2/4) Epoch 15, batch 7050, loss[loss=0.1688, simple_loss=0.2529, pruned_loss=0.04234, over 7104.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2873, pruned_loss=0.06084, over 1466714.47 frames.], batch size: 18, lr: 3.67e-04
2022-07-26 23:56:45,800 INFO [train.py:850] (2/4) Epoch 15, batch 7100, loss[loss=0.2375, simple_loss=0.3158, pruned_loss=0.0796, over 7382.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2863, pruned_loss=0.06039, over 1466083.24 frames.], batch size: 21, lr: 3.67e-04
2022-07-26 23:57:29,287 INFO [train.py:850] (2/4) Epoch 15, batch 7150, loss[loss=0.1848, simple_loss=0.2871, pruned_loss=0.04126, over 7478.00 frames.], tot_loss[loss=0.203, simple_loss=0.2856, pruned_loss=0.06018, over 1466721.41 frames.], batch size: 24, lr: 3.67e-04
2022-07-26 23:58:12,893 INFO [train.py:850] (2/4) Epoch 15, batch 7200, loss[loss=0.1592, simple_loss=0.2392, pruned_loss=0.03967, over 7428.00 frames.], tot_loss[loss=0.2019, simple_loss=0.284, pruned_loss=0.05986, over 1467590.30 frames.], batch size: 18, lr: 3.67e-04
2022-07-26 23:58:56,740 INFO [train.py:850] (2/4) Epoch 15, batch 7250, loss[loss=0.1909, simple_loss=0.2891, pruned_loss=0.04633, over 7209.00 frames.], tot_loss[loss=0.2031, simple_loss=0.285, pruned_loss=0.06061, over 1466948.18 frames.], batch size: 25, lr: 3.67e-04
2022-07-26 23:59:40,154 INFO [train.py:850] (2/4) Epoch 15, batch 7300, loss[loss=0.171, simple_loss=0.2518, pruned_loss=0.04513, over 7451.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2865, pruned_loss=0.06115, over 1465432.55 frames.], batch size: 18, lr: 3.67e-04
2022-07-27 00:00:24,409 INFO [train.py:850] (2/4) Epoch 15, batch 7350, loss[loss=0.1964, simple_loss=0.2864, pruned_loss=0.05314, over 7179.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2866, pruned_loss=0.06133, over 1464544.31 frames.], batch size: 22, lr: 3.67e-04
2022-07-27 00:01:08,977 INFO [train.py:850] (2/4) Epoch 15, batch 7400, loss[loss=0.2409, simple_loss=0.3225, pruned_loss=0.07962, over 7201.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2868, pruned_loss=0.06141, over 1466730.88 frames.], batch size: 20, lr: 3.66e-04
2022-07-27 00:01:52,968 INFO [train.py:850] (2/4) Epoch 15, batch 7450, loss[loss=0.1777, simple_loss=0.275, pruned_loss=0.04015, over 7227.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2867, pruned_loss=0.06092, over 1466402.27 frames.], batch size: 24, lr: 3.66e-04
2022-07-27 00:02:53,159 INFO [train.py:850] (2/4) Epoch 15, batch 7500, loss[loss=0.2071, simple_loss=0.2951, pruned_loss=0.05954, over 7168.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2866, pruned_loss=0.06129, over 1465386.90 frames.], batch size: 22, lr: 3.66e-04
2022-07-27 00:03:36,399 INFO [train.py:850] (2/4) Epoch 15, batch 7550, loss[loss=0.2177, simple_loss=0.295, pruned_loss=0.07018, over 7379.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2863, pruned_loss=0.06113, over 1466210.35 frames.], batch size: 20, lr: 3.66e-04
2022-07-27 00:04:21,184 INFO [train.py:850] (2/4) Epoch 15, batch 7600, loss[loss=0.2169, simple_loss=0.2954, pruned_loss=0.06915, over 7296.00 frames.], tot_loss[loss=0.204, simple_loss=0.286, pruned_loss=0.06096, over 1465227.49 frames.], batch size: 20, lr: 3.66e-04
2022-07-27 00:05:04,033 INFO [train.py:850] (2/4) Epoch 15, batch 7650, loss[loss=0.1946, simple_loss=0.2881, pruned_loss=0.05059, over 7476.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2865, pruned_loss=0.06132, over 1465148.52 frames.], batch size: 24, lr: 3.66e-04
2022-07-27 00:05:48,719 INFO [train.py:850] (2/4) Epoch 15, batch 7700, loss[loss=0.2414, simple_loss=0.3061, pruned_loss=0.0883, over 7157.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2873, pruned_loss=0.06163, over 1464070.73 frames.], batch size: 17, lr: 3.66e-04
2022-07-27 00:06:32,249 INFO [train.py:850] (2/4) Epoch 15, batch 7750, loss[loss=0.1862, simple_loss=0.2721, pruned_loss=0.05012, over 7202.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2867, pruned_loss=0.06108, over 1464790.41 frames.], batch size: 19, lr: 3.66e-04
2022-07-27 00:07:16,711 INFO [train.py:850] (2/4) Epoch 15, batch 7800, loss[loss=0.256, simple_loss=0.3282, pruned_loss=0.09193, over 7188.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2868, pruned_loss=0.06147, over 1464281.74 frames.], batch size: 23, lr: 3.66e-04
2022-07-27 00:08:01,804 INFO [train.py:850] (2/4) Epoch 15, batch 7850, loss[loss=0.1915, simple_loss=0.2602, pruned_loss=0.06138, over 7445.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2858, pruned_loss=0.06089, over 1464735.63 frames.], batch size: 17, lr: 3.66e-04
2022-07-27 00:08:45,307 INFO [train.py:850] (2/4) Epoch 15, batch 7900, loss[loss=0.2492, simple_loss=0.3329, pruned_loss=0.0828, over 7207.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2854, pruned_loss=0.06008, over 1464406.63 frames.], batch size: 20, lr: 3.66e-04
2022-07-27 00:09:28,201 INFO [train.py:850] (2/4) Epoch 15, batch 7950, loss[loss=0.1873, simple_loss=0.2725, pruned_loss=0.05104, over 7440.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2856, pruned_loss=0.05996, over 1464993.51 frames.], batch size: 39, lr: 3.66e-04
2022-07-27 00:10:12,156 INFO [train.py:850] (2/4) Epoch 15, batch 8000, loss[loss=0.1903, simple_loss=0.284, pruned_loss=0.04827, over 7478.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2857, pruned_loss=0.06003, over 1466572.05 frames.], batch size: 20, lr: 3.66e-04
2022-07-27 00:10:56,902 INFO [train.py:850] (2/4) Epoch 15, batch 8050, loss[loss=0.1937, simple_loss=0.2723, pruned_loss=0.05757, over 7104.00 frames.], tot_loss[loss=0.2025, simple_loss=0.286, pruned_loss=0.05956, over 1465870.25 frames.], batch size: 18, lr: 3.66e-04
2022-07-27 00:11:42,604 INFO [train.py:850] (2/4) Epoch 15, batch 8100, loss[loss=0.1999, simple_loss=0.2834, pruned_loss=0.0582, over 7487.00 frames.], tot_loss[loss=0.203, simple_loss=0.2864, pruned_loss=0.05981, over 1466236.39 frames.], batch size: 20, lr: 3.65e-04
2022-07-27 00:12:27,886 INFO [train.py:850] (2/4) Epoch 15, batch 8150, loss[loss=0.1722, simple_loss=0.2696, pruned_loss=0.03734, over 7188.00 frames.], tot_loss[loss=0.2039, simple_loss=0.287, pruned_loss=0.06042, over 1465932.25 frames.], batch size: 21, lr: 3.65e-04
2022-07-27 00:13:14,467 INFO [train.py:850] (2/4) Epoch 15, batch 8200, loss[loss=0.2193, simple_loss=0.3011, pruned_loss=0.06869, over 7226.00 frames.], tot_loss[loss=0.203, simple_loss=0.2858, pruned_loss=0.06011, over 1466067.65 frames.], batch size: 25, lr: 3.65e-04
2022-07-27 00:13:59,328 INFO [train.py:850] (2/4) Epoch 15, batch 8250, loss[loss=0.214, simple_loss=0.3028, pruned_loss=0.06259, over 7192.00 frames.], tot_loss[loss=0.203, simple_loss=0.2856, pruned_loss=0.06022, over 1465379.40 frames.], batch size: 20, lr: 3.65e-04
2022-07-27 00:14:45,003 INFO [train.py:850] (2/4) Epoch 15, batch 8300, loss[loss=0.2252, simple_loss=0.3101, pruned_loss=0.07018, over 7342.00 frames.], tot_loss[loss=0.203, simple_loss=0.286, pruned_loss=0.06004, over 1464629.21 frames.], batch size: 23, lr: 3.65e-04
2022-07-27 00:15:28,336 INFO [train.py:850] (2/4) Epoch 15, batch 8350, loss[loss=0.1942, simple_loss=0.2749, pruned_loss=0.05668, over 7391.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2868, pruned_loss=0.0605, over 1464916.00 frames.], batch size: 20, lr: 3.65e-04
2022-07-27 00:16:12,873 INFO [train.py:850] (2/4) Epoch 15, batch 8400, loss[loss=0.231, simple_loss=0.315, pruned_loss=0.0735, over 7289.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2862, pruned_loss=0.06035, over 1465320.77 frames.], batch size: 21, lr: 3.65e-04
2022-07-27 00:16:56,132 INFO [train.py:850] (2/4) Epoch 15, batch 8450, loss[loss=0.1773, simple_loss=0.2497, pruned_loss=0.0524, over 7246.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2862, pruned_loss=0.06058, over 1465060.47 frames.], batch size: 16, lr: 3.65e-04
2022-07-27 00:17:40,121 INFO [train.py:850] (2/4) Epoch 15, batch 8500, loss[loss=0.2071, simple_loss=0.292, pruned_loss=0.06113, over 7479.00 frames.], tot_loss[loss=0.205, simple_loss=0.2875, pruned_loss=0.06125, over 1466055.11 frames.], batch size: 20, lr: 3.65e-04
2022-07-27 00:18:23,987 INFO [train.py:850] (2/4) Epoch 15, batch 8550, loss[loss=0.2056, simple_loss=0.2896, pruned_loss=0.06085, over 7378.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2868, pruned_loss=0.06104, over 1465786.27 frames.], batch size: 21, lr: 3.65e-04
2022-07-27 00:19:07,957 INFO [train.py:850] (2/4) Epoch 15, batch 8600, loss[loss=0.1895, simple_loss=0.2801, pruned_loss=0.04939, over 7207.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2873, pruned_loss=0.06171, over 1466277.82 frames.], batch size: 20, lr: 3.65e-04
2022-07-27 00:19:51,503 INFO [train.py:850] (2/4) Epoch 15, batch 8650, loss[loss=0.1972, simple_loss=0.2826, pruned_loss=0.05596, over 7468.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2858, pruned_loss=0.0607, over 1466052.85 frames.], batch size: 21, lr: 3.65e-04
2022-07-27 00:20:35,017 INFO [train.py:850] (2/4) Epoch 15, batch 8700, loss[loss=0.1788, simple_loss=0.2568, pruned_loss=0.05043, over 7205.00 frames.], tot_loss[loss=0.2025, simple_loss=0.285, pruned_loss=0.06001, over 1465958.32 frames.], batch size: 18, lr: 3.65e-04
2022-07-27 00:21:17,291 INFO [train.py:850] (2/4) Epoch 15, batch 8750, loss[loss=0.2102, simple_loss=0.3126, pruned_loss=0.05388, over 7307.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2852, pruned_loss=0.06048, over 1466912.80 frames.], batch size: 22, lr: 3.65e-04
2022-07-27 00:21:59,377 INFO [train.py:850] (2/4) Epoch 15, batch 8800, loss[loss=0.1904, simple_loss=0.2747, pruned_loss=0.05303, over 7281.00 frames.], tot_loss[loss=0.203, simple_loss=0.285, pruned_loss=0.06054, over 1465546.98 frames.], batch size: 21, lr: 3.65e-04
2022-07-27 00:22:42,000 INFO [train.py:850] (2/4) Epoch 15, batch 8850, loss[loss=0.2199, simple_loss=0.3064, pruned_loss=0.06668, over 7485.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2862, pruned_loss=0.06041, over 1465473.61 frames.], batch size: 21, lr: 3.64e-04
2022-07-27 00:24:08,091 INFO [train.py:850] (2/4) Epoch 16, batch 0, loss[loss=0.1661, simple_loss=0.2591, pruned_loss=0.0366, over 7290.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2591, pruned_loss=0.0366, over 7290.00 frames.], batch size: 20, lr: 3.54e-04
2022-07-27 00:24:51,143 INFO [train.py:850] (2/4) Epoch 16, batch 50, loss[loss=0.2085, simple_loss=0.3072, pruned_loss=0.05495, over 7286.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2867, pruned_loss=0.05404, over 330753.84 frames.], batch size: 21, lr: 3.54e-04
2022-07-27 00:25:35,169 INFO [train.py:850] (2/4) Epoch 16, batch 100, loss[loss=0.1542, simple_loss=0.2431, pruned_loss=0.03262, over 7424.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2834, pruned_loss=0.0517, over 582672.17 frames.], batch size: 18, lr: 3.54e-04
2022-07-27 00:26:17,522 INFO [train.py:850] (2/4) Epoch 16, batch 150, loss[loss=0.1766, simple_loss=0.2717, pruned_loss=0.04076, over 7170.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2817, pruned_loss=0.0506, over 778040.84 frames.], batch size: 21, lr: 3.54e-04
2022-07-27 00:27:01,670 INFO [train.py:850] (2/4) Epoch 16, batch 200, loss[loss=0.2029, simple_loss=0.2953, pruned_loss=0.0553, over 7420.00 frames.], tot_loss[loss=0.191, simple_loss=0.2806, pruned_loss=0.05068, over 930238.90 frames.], batch size: 22, lr: 3.54e-04
2022-07-27 00:27:45,534 INFO [train.py:850] (2/4) Epoch 16, batch 250, loss[loss=0.1824, simple_loss=0.2832, pruned_loss=0.04082, over 7298.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2794, pruned_loss=0.04975, over 1049386.12 frames.], batch size: 27, lr: 3.53e-04
2022-07-27 00:28:29,003 INFO [train.py:850] (2/4) Epoch 16, batch 300, loss[loss=0.1702, simple_loss=0.2621, pruned_loss=0.03918, over 7307.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2815, pruned_loss=0.05078, over 1142156.02 frames.], batch size: 30, lr: 3.53e-04
2022-07-27 00:29:12,541 INFO [train.py:850] (2/4) Epoch 16, batch 350, loss[loss=0.196, simple_loss=0.2868, pruned_loss=0.05261, over 7476.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2819, pruned_loss=0.05051, over 1214902.36 frames.], batch size: 20, lr: 3.53e-04
2022-07-27 00:29:55,896 INFO [train.py:850] (2/4) Epoch 16, batch 400, loss[loss=0.2023, simple_loss=0.2956, pruned_loss=0.05451, over 7481.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2797, pruned_loss=0.04941, over 1270109.58 frames.], batch size: 20, lr: 3.53e-04
2022-07-27 00:30:39,887 INFO [train.py:850] (2/4) Epoch 16, batch 450, loss[loss=0.1749, simple_loss=0.26, pruned_loss=0.0449, over 7186.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2796, pruned_loss=0.04954, over 1312051.85 frames.], batch size: 18, lr: 3.53e-04
2022-07-27 00:31:23,156 INFO [train.py:850] (2/4) Epoch 16, batch 500, loss[loss=0.1831, simple_loss=0.2705, pruned_loss=0.04784, over 7240.00 frames.], tot_loss[loss=0.19, simple_loss=0.2803, pruned_loss=0.04987, over 1345564.67 frames.], batch size: 16, lr: 3.53e-04
2022-07-27 00:32:07,184 INFO [train.py:850] (2/4) Epoch 16, batch 550, loss[loss=0.1809, simple_loss=0.2842, pruned_loss=0.03885, over 7421.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2806, pruned_loss=0.04978, over 1372014.79 frames.], batch size: 22, lr: 3.53e-04
2022-07-27 00:32:51,748 INFO [train.py:850] (2/4) Epoch 16, batch 600, loss[loss=0.1699, simple_loss=0.2499, pruned_loss=0.04493, over 7158.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2797, pruned_loss=0.04957, over 1392376.48 frames.], batch size: 17, lr: 3.53e-04
2022-07-27 00:33:36,274 INFO [train.py:850] (2/4) Epoch 16, batch 650, loss[loss=0.2107, simple_loss=0.3002, pruned_loss=0.0606, over 7400.00 frames.], tot_loss[loss=0.1886, simple_loss=0.279, pruned_loss=0.04911, over 1409633.15 frames.], batch size: 31, lr: 3.53e-04
2022-07-27 00:34:20,253 INFO [train.py:850] (2/4) Epoch 16, batch 700, loss[loss=0.2192, simple_loss=0.3048, pruned_loss=0.06678, over 7397.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2788, pruned_loss=0.04882, over 1422194.45 frames.], batch size: 19, lr: 3.53e-04
2022-07-27 00:35:02,818 INFO [train.py:850] (2/4) Epoch 16, batch 750, loss[loss=0.1741, simple_loss=0.2734, pruned_loss=0.03736, over 7279.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2783, pruned_loss=0.04854, over 1431093.57 frames.], batch size: 21, lr: 3.53e-04
2022-07-27 00:35:45,985 INFO [train.py:850] (2/4) Epoch 16, batch 800, loss[loss=0.2092, simple_loss=0.3053, pruned_loss=0.05656, over 7356.00 frames.], tot_loss[loss=0.1882, simple_loss=0.279, pruned_loss=0.04866, over 1438502.23 frames.], batch size: 38, lr: 3.53e-04
2022-07-27 00:36:29,923 INFO [train.py:850] (2/4) Epoch 16, batch 850, loss[loss=0.1723, simple_loss=0.2645, pruned_loss=0.04007, over 7214.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2793, pruned_loss=0.04878, over 1445493.69 frames.], batch size: 25, lr: 3.53e-04
2022-07-27 00:37:13,345 INFO [train.py:850] (2/4) Epoch 16, batch 900, loss[loss=0.1726, simple_loss=0.2592, pruned_loss=0.04297, over 7167.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2794, pruned_loss=0.04894, over 1449553.81 frames.], batch size: 17, lr: 3.53e-04
2022-07-27 00:37:56,848 INFO [train.py:850] (2/4) Epoch 16, batch 950, loss[loss=0.1911, simple_loss=0.2855, pruned_loss=0.04832, over 7287.00 frames.], tot_loss[loss=0.189, simple_loss=0.2795, pruned_loss=0.04925, over 1453714.44 frames.], batch size: 21, lr: 3.53e-04
2022-07-27 00:38:40,243 INFO [train.py:850] (2/4) Epoch 16, batch 1000, loss[loss=0.2345, simple_loss=0.3125, pruned_loss=0.07823, over 7183.00 frames.], tot_loss[loss=0.191, simple_loss=0.2817, pruned_loss=0.05016, over 1455812.85 frames.], batch size: 21, lr: 3.52e-04
2022-07-27 00:39:23,382 INFO [train.py:850] (2/4) Epoch 16, batch 1050, loss[loss=0.2511, simple_loss=0.3355, pruned_loss=0.08334, over 7422.00 frames.], tot_loss[loss=0.1911, simple_loss=0.282, pruned_loss=0.05011, over 1458040.66 frames.], batch size: 22, lr: 3.52e-04
2022-07-27 00:40:07,870 INFO [train.py:850] (2/4) Epoch 16, batch 1100, loss[loss=0.1809, simple_loss=0.271, pruned_loss=0.0454, over 7314.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2826, pruned_loss=0.05035, over 1459074.96 frames.], batch size: 17, lr: 3.52e-04
2022-07-27 00:40:49,907 INFO [train.py:850] (2/4) Epoch 16, batch 1150, loss[loss=0.2009, simple_loss=0.3007, pruned_loss=0.05053, over 7203.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2818, pruned_loss=0.04995, over 1460822.38 frames.], batch size: 24, lr: 3.52e-04
2022-07-27 00:41:34,865 INFO [train.py:850] (2/4) Epoch 16, batch 1200, loss[loss=0.221, simple_loss=0.3049, pruned_loss=0.0685, over 7465.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2828, pruned_loss=0.05087, over 1462352.30 frames.], batch size: 24, lr: 3.52e-04
2022-07-27 00:42:17,892 INFO [train.py:850] (2/4) Epoch 16, batch 1250, loss[loss=0.1693, simple_loss=0.2542, pruned_loss=0.04215, over 7254.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2832, pruned_loss=0.05133, over 1462954.80 frames.], batch size: 16, lr: 3.52e-04
2022-07-27 00:43:01,025 INFO [train.py:850] (2/4) Epoch 16, batch 1300, loss[loss=0.1794, simple_loss=0.2737, pruned_loss=0.04258, over 7180.00 frames.], tot_loss[loss=0.1939, simple_loss=0.284, pruned_loss=0.05187, over 1464454.27 frames.], batch size: 18, lr: 3.52e-04
2022-07-27 00:43:45,610 INFO [train.py:850] (2/4) Epoch 16, batch 1350, loss[loss=0.1908, simple_loss=0.2853, pruned_loss=0.04815, over 7280.00 frames.], tot_loss[loss=0.195, simple_loss=0.2856, pruned_loss=0.05218, over 1465501.40 frames.], batch size: 20, lr: 3.52e-04
2022-07-27 00:44:29,589 INFO [train.py:850] (2/4) Epoch 16, batch 1400, loss[loss=0.1608, simple_loss=0.259, pruned_loss=0.03132, over 7475.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2864, pruned_loss=0.05269, over 1466845.23 frames.], batch size: 19, lr: 3.52e-04
2022-07-27 00:45:12,557 INFO [train.py:850] (2/4) Epoch 16, batch 1450, loss[loss=0.1557, simple_loss=0.2489, pruned_loss=0.03128, over 7294.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2849, pruned_loss=0.05194, over 1464944.90 frames.], batch size: 19, lr: 3.52e-04
2022-07-27 00:45:57,126 INFO [train.py:850] (2/4) Epoch 16, batch 1500, loss[loss=0.1787, simple_loss=0.2849, pruned_loss=0.03627, over 7381.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2837, pruned_loss=0.05157, over 1465796.83 frames.], batch size: 20, lr: 3.52e-04
2022-07-27 00:46:42,747 INFO [train.py:850] (2/4) Epoch 16, batch 1550, loss[loss=0.2087, simple_loss=0.2975, pruned_loss=0.05995, over 7411.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2836, pruned_loss=0.05146, over 1466572.05 frames.], batch size: 22, lr: 3.52e-04
2022-07-27 00:47:26,939 INFO [train.py:850] (2/4) Epoch 16, batch 1600, loss[loss=0.1509, simple_loss=0.2489, pruned_loss=0.02643, over 7379.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2835, pruned_loss=0.05145, over 1465777.82 frames.], batch size: 20, lr: 3.52e-04
2022-07-27 00:48:09,699 INFO [train.py:850] (2/4) Epoch 16, batch 1650, loss[loss=0.2017, simple_loss=0.2907, pruned_loss=0.05631, over 7307.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2842, pruned_loss=0.05184, over 1467022.65 frames.], batch size: 27, lr: 3.52e-04
2022-07-27 00:48:53,828 INFO [train.py:850] (2/4) Epoch 16, batch 1700, loss[loss=0.2062, simple_loss=0.2956, pruned_loss=0.05839, over 7318.00 frames.], tot_loss[loss=0.194, simple_loss=0.2844, pruned_loss=0.05186, over 1466070.21 frames.], batch size: 22, lr: 3.52e-04
2022-07-27 00:49:37,309 INFO [train.py:850] (2/4) Epoch 16, batch 1750, loss[loss=0.1941, simple_loss=0.2839, pruned_loss=0.05215, over 7301.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2838, pruned_loss=0.0519, over 1467484.55 frames.], batch size: 18, lr: 3.52e-04
2022-07-27 00:50:21,947 INFO [train.py:850] (2/4) Epoch 16, batch 1800, loss[loss=0.1923, simple_loss=0.2913, pruned_loss=0.04672, over 7217.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2832, pruned_loss=0.05112, over 1466607.91 frames.], batch size: 24, lr: 3.51e-04
2022-07-27 00:51:04,830 INFO [train.py:850] (2/4) Epoch 16, batch 1850, loss[loss=0.2098, simple_loss=0.3031, pruned_loss=0.05825, over 7177.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2839, pruned_loss=0.05141, over 1466285.36 frames.], batch size: 22, lr: 3.51e-04
2022-07-27 00:51:47,764 INFO [train.py:850] (2/4) Epoch 16, batch 1900, loss[loss=0.181, simple_loss=0.2752, pruned_loss=0.04335, over 7477.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2815, pruned_loss=0.05032, over 1465192.65 frames.], batch size: 20, lr: 3.51e-04
2022-07-27 00:52:30,795 INFO [train.py:850] (2/4) Epoch 16, batch 1950, loss[loss=0.1807, simple_loss=0.2569, pruned_loss=0.05224, over 7313.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2811, pruned_loss=0.05033, over 1462639.42 frames.], batch size: 18, lr: 3.51e-04
2022-07-27 00:53:14,224 INFO [train.py:850] (2/4) Epoch 16, batch 2000, loss[loss=0.2118, simple_loss=0.2961, pruned_loss=0.06379, over 7377.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2816, pruned_loss=0.05083, over 1463223.24 frames.], batch size: 31, lr: 3.51e-04
2022-07-27 00:53:57,893 INFO [train.py:850] (2/4) Epoch 16, batch 2050, loss[loss=0.1937, simple_loss=0.2641, pruned_loss=0.06168, over 7452.00 frames.], tot_loss[loss=0.191, simple_loss=0.2814, pruned_loss=0.05029, over 1462478.00 frames.], batch size: 18, lr: 3.51e-04
2022-07-27 00:54:41,342 INFO [train.py:850] (2/4) Epoch 16, batch 2100, loss[loss=0.255, simple_loss=0.3418, pruned_loss=0.08405, over 7472.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2816, pruned_loss=0.05005, over 1463036.30 frames.], batch size: 26, lr: 3.51e-04
2022-07-27 00:55:24,894 INFO [train.py:850] (2/4) Epoch 16, batch 2150, loss[loss=0.1806, simple_loss=0.2665, pruned_loss=0.04732, over 7198.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2808, pruned_loss=0.05002, over 1463976.43 frames.], batch size: 19, lr: 3.51e-04
2022-07-27 00:56:09,895 INFO [train.py:850] (2/4) Epoch 16, batch 2200, loss[loss=0.1873, simple_loss=0.283, pruned_loss=0.04582, over 7306.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2815, pruned_loss=0.05016, over 1464415.99 frames.], batch size: 22, lr: 3.51e-04
2022-07-27 00:56:54,333 INFO [train.py:850] (2/4) Epoch 16, batch 2250, loss[loss=0.1804, simple_loss=0.2701, pruned_loss=0.04535, over 7379.00 frames.], tot_loss[loss=0.192, simple_loss=0.2824, pruned_loss=0.0508, over 1464514.09 frames.], batch size: 19, lr: 3.51e-04
2022-07-27 00:57:40,747 INFO [train.py:850] (2/4) Epoch 16, batch 2300, loss[loss=0.1578, simple_loss=0.2418, pruned_loss=0.03688, over 7300.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2839, pruned_loss=0.05141, over 1465565.88 frames.], batch size: 17, lr: 3.51e-04
2022-07-27 00:58:24,687 INFO [train.py:850] (2/4) Epoch 16, batch 2350, loss[loss=0.2214, simple_loss=0.3123, pruned_loss=0.06529, over 7338.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2843, pruned_loss=0.05146, over 1465544.58 frames.], batch size: 23, lr: 3.51e-04
2022-07-27 00:59:09,944 INFO [train.py:850] (2/4) Epoch 16, batch 2400, loss[loss=0.1793, simple_loss=0.2803, pruned_loss=0.03921, over 7289.00 frames.], tot_loss[loss=0.193, simple_loss=0.2842, pruned_loss=0.05094, over 1465251.02 frames.], batch size: 21, lr: 3.51e-04
2022-07-27 00:59:54,468 INFO [train.py:850] (2/4) Epoch 16, batch 2450, loss[loss=0.2268, simple_loss=0.3134, pruned_loss=0.07016, over 7447.00 frames.], tot_loss[loss=0.1929, simple_loss=0.284, pruned_loss=0.05087, over 1465879.85 frames.], batch size: 68, lr: 3.51e-04
2022-07-27 01:00:38,255 INFO [train.py:850] (2/4) Epoch 16, batch 2500, loss[loss=0.2048, simple_loss=0.3066, pruned_loss=0.05152, over 7375.00 frames.], tot_loss[loss=0.1914, simple_loss=0.283, pruned_loss=0.04997, over 1465214.38 frames.], batch size: 21, lr: 3.51e-04
2022-07-27 01:01:21,738 INFO [train.py:850] (2/4) Epoch 16, batch 2550, loss[loss=0.2066, simple_loss=0.307, pruned_loss=0.05315, over 7385.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2823, pruned_loss=0.04996, over 1464464.87 frames.], batch size: 27, lr: 3.50e-04
2022-07-27 01:02:20,102 INFO [train.py:850] (2/4) Epoch 16, batch 2600, loss[loss=0.1919, simple_loss=0.2796, pruned_loss=0.05208, over 7312.00 frames.], tot_loss[loss=0.1902, simple_loss=0.281, pruned_loss=0.04971, over 1464082.42 frames.], batch size: 18, lr: 3.50e-04
2022-07-27 01:03:04,230 INFO [train.py:850] (2/4) Epoch 16, batch 2650, loss[loss=0.1792, simple_loss=0.2659, pruned_loss=0.04629, over 7307.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2815, pruned_loss=0.04993, over 1465934.92 frames.], batch size: 19, lr: 3.50e-04
2022-07-27 01:03:47,609 INFO [train.py:850] (2/4) Epoch 16, batch 2700, loss[loss=0.1629, simple_loss=0.2498, pruned_loss=0.03803, over 7434.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2813, pruned_loss=0.04961, over 1465255.04 frames.], batch size: 18, lr: 3.50e-04
2022-07-27 01:04:30,726 INFO [train.py:850] (2/4) Epoch 16, batch 2750, loss[loss=0.1846, simple_loss=0.2813, pruned_loss=0.04398, over 7483.00 frames.], tot_loss[loss=0.19, simple_loss=0.281, pruned_loss=0.04948, over 1465554.41 frames.], batch size: 21, lr: 3.50e-04
2022-07-27 01:05:14,534 INFO [train.py:850] (2/4) Epoch 16, batch 2800, loss[loss=0.1732, simple_loss=0.2641, pruned_loss=0.04116, over 7205.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2807, pruned_loss=0.04898, over 1465895.57 frames.], batch size: 19, lr: 3.50e-04
2022-07-27 01:05:57,457 INFO [train.py:850] (2/4) Epoch 16, batch 2850, loss[loss=0.2099, simple_loss=0.3066, pruned_loss=0.05664, over 7469.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2809, pruned_loss=0.04912, over 1466004.69 frames.], batch size: 21, lr: 3.50e-04
2022-07-27 01:06:43,169 INFO [train.py:850] (2/4) Epoch 16, batch 2900, loss[loss=0.1995, simple_loss=0.2994, pruned_loss=0.04978, over 7287.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2814, pruned_loss=0.04926, over 1466223.98 frames.], batch size: 27, lr: 3.50e-04
2022-07-27 01:07:26,383 INFO [train.py:850] (2/4) Epoch 16, batch 2950, loss[loss=0.1771, simple_loss=0.2713, pruned_loss=0.04141, over 7386.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2816, pruned_loss=0.04927, over 1465714.28 frames.], batch size: 21, lr: 3.50e-04
2022-07-27 01:08:10,112 INFO [train.py:850] (2/4) Epoch 16, batch 3000, loss[loss=0.2352, simple_loss=0.3363, pruned_loss=0.06704, over 7305.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2814, pruned_loss=0.04903, over 1466214.68 frames.], batch size: 27, lr: 3.50e-04
2022-07-27 01:08:10,113 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 01:08:33,036 INFO [train.py:879] (2/4) Epoch 16, validation: loss=0.1907, simple_loss=0.285, pruned_loss=0.04816, over 924787.00 frames.
2022-07-27 01:09:16,549 INFO [train.py:850] (2/4) Epoch 16, batch 3050, loss[loss=0.2014, simple_loss=0.2946, pruned_loss=0.05407, over 7340.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2823, pruned_loss=0.04964, over 1465909.85 frames.], batch size: 30, lr: 3.50e-04
2022-07-27 01:10:00,753 INFO [train.py:850] (2/4) Epoch 16, batch 3100, loss[loss=0.1707, simple_loss=0.2562, pruned_loss=0.04264, over 7301.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2831, pruned_loss=0.05009, over 1465400.80 frames.], batch size: 19, lr: 3.50e-04
2022-07-27 01:10:43,671 INFO [train.py:850] (2/4) Epoch 16, batch 3150, loss[loss=0.188, simple_loss=0.2667, pruned_loss=0.05464, over 7290.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2816, pruned_loss=0.04964, over 1465378.97 frames.], batch size: 17, lr: 3.50e-04
2022-07-27 01:11:27,416 INFO [train.py:850] (2/4) Epoch 16, batch 3200, loss[loss=0.2153, simple_loss=0.3041, pruned_loss=0.06326, over 7230.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2806, pruned_loss=0.04913, over 1465682.13 frames.], batch size: 24, lr: 3.50e-04
2022-07-27 01:12:10,750 INFO [train.py:850] (2/4) Epoch 16, batch 3250, loss[loss=0.1851, simple_loss=0.2776, pruned_loss=0.04628, over 7478.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2796, pruned_loss=0.04857, over 1465712.91 frames.], batch size: 20, lr: 3.50e-04
2022-07-27 01:12:54,575 INFO [train.py:850] (2/4) Epoch 16, batch 3300, loss[loss=0.2153, simple_loss=0.3141, pruned_loss=0.05823, over 7435.00 frames.], tot_loss[loss=0.1886, simple_loss=0.28, pruned_loss=0.0486, over 1465206.06 frames.], batch size: 31, lr: 3.50e-04
2022-07-27 01:13:38,717 INFO [train.py:850] (2/4) Epoch 16, batch 3350, loss[loss=0.1928, simple_loss=0.2914, pruned_loss=0.04713, over 7306.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2801, pruned_loss=0.04863, over 1464221.16 frames.], batch size: 22, lr: 3.49e-04
2022-07-27 01:14:22,232 INFO [train.py:850] (2/4) Epoch 16, batch 3400, loss[loss=0.2035, simple_loss=0.2898, pruned_loss=0.05859, over 7489.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2803, pruned_loss=0.04894, over 1463712.93 frames.], batch size: 26, lr: 3.49e-04
2022-07-27 01:15:04,510 INFO [train.py:850] (2/4) Epoch 16, batch 3450, loss[loss=0.2009, simple_loss=0.2885, pruned_loss=0.05668, over 7319.00 frames.], tot_loss[loss=0.19, simple_loss=0.281, pruned_loss=0.04945, over 1463563.53 frames.], batch size: 18, lr: 3.49e-04
2022-07-27 01:15:49,123 INFO [train.py:850] (2/4) Epoch 16, batch 3500, loss[loss=0.2137, simple_loss=0.3009, pruned_loss=0.06324, over 7381.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2813, pruned_loss=0.04916, over 1464947.00 frames.], batch size: 21, lr: 3.49e-04
2022-07-27 01:16:32,502 INFO [train.py:850] (2/4) Epoch 16, batch 3550, loss[loss=0.1611, simple_loss=0.2487, pruned_loss=0.03676, over 7484.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2813, pruned_loss=0.04898, over 1465171.15 frames.], batch size: 17, lr: 3.49e-04
2022-07-27 01:17:16,210 INFO [train.py:850] (2/4) Epoch 16, batch 3600, loss[loss=0.2106, simple_loss=0.3061, pruned_loss=0.0576, over 7367.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2811, pruned_loss=0.04856, over 1464456.72 frames.], batch size: 21, lr: 3.49e-04
2022-07-27 01:17:59,023 INFO [train.py:850] (2/4) Epoch 16, batch 3650, loss[loss=0.2023, simple_loss=0.3006, pruned_loss=0.05205, over 7399.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2819, pruned_loss=0.04927, over 1465055.15 frames.], batch size: 21, lr: 3.49e-04
2022-07-27 01:18:41,966 INFO [train.py:850] (2/4) Epoch 16, batch 3700, loss[loss=0.1727, simple_loss=0.2761, pruned_loss=0.03465, over 7477.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2815, pruned_loss=0.04909, over 1465029.63 frames.], batch size: 21, lr: 3.49e-04
2022-07-27 01:19:25,154 INFO [train.py:850] (2/4) Epoch 16, batch 3750, loss[loss=0.1867, simple_loss=0.2842, pruned_loss=0.04459, over 7279.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2813, pruned_loss=0.04905, over 1465325.59 frames.], batch size: 21, lr: 3.49e-04
2022-07-27 01:20:09,185 INFO [train.py:850] (2/4) Epoch 16, batch 3800, loss[loss=0.173, simple_loss=0.2625, pruned_loss=0.04179, over 7315.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2811, pruned_loss=0.04904, over 1464512.44 frames.], batch size: 18, lr: 3.49e-04
2022-07-27 01:20:52,094 INFO [train.py:850] (2/4) Epoch 16, batch 3850, loss[loss=0.1962, simple_loss=0.3016, pruned_loss=0.04539, over 7275.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2821, pruned_loss=0.04955, over 1464563.88 frames.], batch size: 21, lr: 3.49e-04
2022-07-27 01:21:35,651 INFO [train.py:850] (2/4) Epoch 16, batch 3900, loss[loss=0.1869, simple_loss=0.2861, pruned_loss=0.04391, over 7312.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2815, pruned_loss=0.04915, over 1465260.71 frames.], batch size: 18, lr: 3.49e-04
2022-07-27 01:22:18,911 INFO [train.py:850] (2/4) Epoch 16, batch 3950, loss[loss=0.154, simple_loss=0.247, pruned_loss=0.03048, over 7447.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2812, pruned_loss=0.04924, over 1464580.97 frames.], batch size: 18, lr: 3.49e-04
2022-07-27 01:23:03,255 INFO [train.py:850] (2/4) Epoch 16, batch 4000, loss[loss=0.1549, simple_loss=0.2373, pruned_loss=0.03629, over 7160.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2802, pruned_loss=0.04879, over 1464674.94 frames.], batch size: 17, lr: 3.49e-04
2022-07-27 01:23:46,700 INFO [train.py:850] (2/4) Epoch 16, batch 4050, loss[loss=0.1839, simple_loss=0.2726, pruned_loss=0.04753, over 7196.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2795, pruned_loss=0.04828, over 1464545.30 frames.], batch size: 19, lr: 3.49e-04
2022-07-27 01:24:30,871 INFO [train.py:850] (2/4) Epoch 16, batch 4100, loss[loss=0.1757, simple_loss=0.2734, pruned_loss=0.03897, over 7391.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2799, pruned_loss=0.04857, over 1464689.36 frames.], batch size: 31, lr: 3.49e-04
2022-07-27 01:25:13,582 INFO [train.py:850] (2/4) Epoch 16, batch 4150, loss[loss=0.254, simple_loss=0.3329, pruned_loss=0.08751, over 7481.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2813, pruned_loss=0.05013, over 1464368.62 frames.], batch size: 21, lr: 3.48e-04
2022-07-27 01:25:57,630 INFO [train.py:850] (2/4) Epoch 16, batch 4200, loss[loss=0.1724, simple_loss=0.2561, pruned_loss=0.0444, over 7321.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2803, pruned_loss=0.05075, over 1465459.64 frames.], batch size: 18, lr: 3.48e-04
2022-07-27 01:26:41,022 INFO [train.py:850] (2/4) Epoch 16, batch 4250, loss[loss=0.1799, simple_loss=0.2638, pruned_loss=0.04803, over 7291.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2805, pruned_loss=0.0515, over 1465106.10 frames.], batch size: 19, lr: 3.48e-04
2022-07-27 01:27:25,057 INFO [train.py:850] (2/4) Epoch 16, batch 4300, loss[loss=0.2077, simple_loss=0.278, pruned_loss=0.06871, over 7259.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2815, pruned_loss=0.05265, over 1465040.08 frames.], batch size: 16, lr: 3.48e-04
2022-07-27 01:28:08,507 INFO [train.py:850] (2/4) Epoch 16, batch 4350, loss[loss=0.1877, simple_loss=0.2748, pruned_loss=0.05026, over 7281.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2823, pruned_loss=0.05376, over 1464404.77 frames.], batch size: 21, lr: 3.48e-04
2022-07-27 01:28:53,512 INFO [train.py:850] (2/4) Epoch 16, batch 4400, loss[loss=0.1864, simple_loss=0.2706, pruned_loss=0.05116, over 7416.00 frames.], tot_loss[loss=0.1958, simple_loss=0.283, pruned_loss=0.0543, over 1465351.39 frames.], batch size: 22, lr: 3.48e-04
2022-07-27 01:29:37,005 INFO [train.py:850] (2/4) Epoch 16, batch 4450, loss[loss=0.2004, simple_loss=0.2958, pruned_loss=0.0525, over 7412.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2829, pruned_loss=0.0545, over 1464885.67 frames.], batch size: 22, lr: 3.48e-04
2022-07-27 01:30:20,696 INFO [train.py:850] (2/4) Epoch 16, batch 4500, loss[loss=0.1858, simple_loss=0.2508, pruned_loss=0.06038, over 7444.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2833, pruned_loss=0.05603, over 1465470.82 frames.], batch size: 17, lr: 3.48e-04
2022-07-27 01:31:04,197 INFO [train.py:850] (2/4) Epoch 16, batch 4550, loss[loss=0.3117, simple_loss=0.3682, pruned_loss=0.1276, over 7386.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2863, pruned_loss=0.05856, over 1465440.72 frames.], batch size: 73, lr: 3.48e-04
2022-07-27 01:31:48,051 INFO [train.py:850] (2/4) Epoch 16, batch 4600, loss[loss=0.2106, simple_loss=0.2973, pruned_loss=0.06196, over 7188.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2867, pruned_loss=0.05915, over 1465257.73 frames.], batch size: 22, lr: 3.48e-04
2022-07-27 01:32:30,531 INFO [train.py:850] (2/4) Epoch 16, batch 4650, loss[loss=0.2033, simple_loss=0.2753, pruned_loss=0.06563, over 7170.00 frames.], tot_loss[loss=0.2025, simple_loss=0.286, pruned_loss=0.05946, over 1464237.60 frames.], batch size: 17, lr: 3.48e-04
2022-07-27 01:33:14,855 INFO [train.py:850] (2/4) Epoch 16, batch 4700, loss[loss=0.236, simple_loss=0.3099, pruned_loss=0.08111, over 7213.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2865, pruned_loss=0.05989, over 1464245.87 frames.], batch size: 24, lr: 3.48e-04
2022-07-27 01:33:58,138 INFO [train.py:850] (2/4) Epoch 16, batch 4750, loss[loss=0.2478, simple_loss=0.3282, pruned_loss=0.08374, over 7383.00 frames.], tot_loss[loss=0.204, simple_loss=0.287, pruned_loss=0.06048, over 1465149.46 frames.], batch size: 69, lr: 3.48e-04
2022-07-27 01:34:42,895 INFO [train.py:850] (2/4) Epoch 16, batch 4800, loss[loss=0.2448, simple_loss=0.3117, pruned_loss=0.08891, over 7439.00 frames.], tot_loss[loss=0.2039, simple_loss=0.287, pruned_loss=0.06036, over 1464896.96 frames.], batch size: 77, lr: 3.48e-04
2022-07-27 01:35:26,262 INFO [train.py:850] (2/4) Epoch 16, batch 4850, loss[loss=0.2114, simple_loss=0.3017, pruned_loss=0.06057, over 7404.00 frames.], tot_loss[loss=0.204, simple_loss=0.2873, pruned_loss=0.06037, over 1464910.40 frames.], batch size: 31, lr: 3.48e-04
2022-07-27 01:36:10,363 INFO [train.py:850] (2/4) Epoch 16, batch 4900, loss[loss=0.2201, simple_loss=0.3048, pruned_loss=0.06765, over 7429.00 frames.], tot_loss[loss=0.206, simple_loss=0.2891, pruned_loss=0.06145, over 1465539.05 frames.], batch size: 39, lr: 3.47e-04
2022-07-27 01:36:53,983 INFO [train.py:850] (2/4) Epoch 16, batch 4950, loss[loss=0.1761, simple_loss=0.2634, pruned_loss=0.04443, over 7197.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2887, pruned_loss=0.06103, over 1465717.27 frames.], batch size: 20, lr: 3.47e-04
2022-07-27 01:37:37,078 INFO [train.py:850] (2/4) Epoch 16, batch 5000, loss[loss=0.1729, simple_loss=0.2601, pruned_loss=0.04286, over 7478.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2894, pruned_loss=0.06183, over 1466480.34 frames.], batch size: 20, lr: 3.47e-04
2022-07-27 01:38:20,628 INFO [train.py:850] (2/4) Epoch 16, batch 5050, loss[loss=0.2361, simple_loss=0.3192, pruned_loss=0.07653, over 7378.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2898, pruned_loss=0.06249, over 1466609.94 frames.], batch size: 21, lr: 3.47e-04
2022-07-27 01:39:03,279 INFO [train.py:850] (2/4) Epoch 16, batch 5100, loss[loss=0.1577, simple_loss=0.2542, pruned_loss=0.03057, over 7488.00 frames.], tot_loss[loss=0.207, simple_loss=0.2893, pruned_loss=0.0624, over 1464546.70 frames.], batch size: 20, lr: 3.47e-04
2022-07-27 01:39:47,229 INFO [train.py:850] (2/4) Epoch 16, batch 5150, loss[loss=0.202, simple_loss=0.2912, pruned_loss=0.0564, over 7350.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2886, pruned_loss=0.06197, over 1464901.28 frames.], batch size: 27, lr: 3.47e-04
2022-07-27 01:40:31,148 INFO [train.py:850] (2/4) Epoch 16, batch 5200, loss[loss=0.2306, simple_loss=0.295, pruned_loss=0.08313, over 7427.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2888, pruned_loss=0.06202, over 1464467.34 frames.], batch size: 73, lr: 3.47e-04
2022-07-27 01:41:14,369 INFO [train.py:850] (2/4) Epoch 16, batch 5250, loss[loss=0.1852, simple_loss=0.265, pruned_loss=0.05274, over 7217.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2896, pruned_loss=0.06255, over 1464750.62 frames.], batch size: 16, lr: 3.47e-04
2022-07-27 01:41:58,150 INFO [train.py:850] (2/4) Epoch 16, batch 5300, loss[loss=0.1951, simple_loss=0.2746, pruned_loss=0.05777, over 7291.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2903, pruned_loss=0.06301, over 1464148.86 frames.], batch size: 20, lr: 3.47e-04
2022-07-27 01:42:41,848 INFO [train.py:850] (2/4) Epoch 16, batch 5350, loss[loss=0.2017, simple_loss=0.2707, pruned_loss=0.06633, over 7314.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2885, pruned_loss=0.06195, over 1464838.29 frames.], batch size: 18, lr: 3.47e-04
2022-07-27 01:43:26,403 INFO [train.py:850] (2/4) Epoch 16, batch 5400, loss[loss=0.1535, simple_loss=0.2421, pruned_loss=0.03249, over 7311.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2885, pruned_loss=0.06193, over 1463452.38 frames.], batch size: 17, lr: 3.47e-04
2022-07-27 01:44:09,821 INFO [train.py:850] (2/4) Epoch 16, batch 5450, loss[loss=0.2462, simple_loss=0.3273, pruned_loss=0.08249, over 7306.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2865, pruned_loss=0.06106, over 1463123.31 frames.], batch size: 38, lr: 3.47e-04
2022-07-27 01:44:53,407 INFO [train.py:850] (2/4) Epoch 16, batch 5500, loss[loss=0.1961, simple_loss=0.2841, pruned_loss=0.05406, over 7288.00 frames.], tot_loss[loss=0.204, simple_loss=0.2864, pruned_loss=0.06081, over 1463463.22 frames.], batch size: 21, lr: 3.47e-04
2022-07-27 01:45:38,354 INFO [train.py:850] (2/4) Epoch 16, batch 5550, loss[loss=0.1972, simple_loss=0.2879, pruned_loss=0.05324, over 7413.00 frames.], tot_loss[loss=0.203, simple_loss=0.2851, pruned_loss=0.06044, over 1464331.04 frames.], batch size: 22, lr: 3.47e-04
2022-07-27 01:46:22,106 INFO [train.py:850] (2/4) Epoch 16, batch 5600, loss[loss=0.1737, simple_loss=0.2576, pruned_loss=0.04491, over 7201.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2847, pruned_loss=0.06054, over 1464943.59 frames.], batch size: 18, lr: 3.47e-04
2022-07-27 01:47:06,871 INFO [train.py:850] (2/4) Epoch 16, batch 5650, loss[loss=0.203, simple_loss=0.2845, pruned_loss=0.06079, over 7485.00 frames.], tot_loss[loss=0.204, simple_loss=0.2859, pruned_loss=0.06102, over 1465162.31 frames.], batch size: 19, lr: 3.47e-04
2022-07-27 01:47:51,508 INFO [train.py:850] (2/4) Epoch 16, batch 5700, loss[loss=0.2146, simple_loss=0.2857, pruned_loss=0.07181, over 7451.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2874, pruned_loss=0.06146, over 1466506.82 frames.], batch size: 18, lr: 3.47e-04
2022-07-27 01:48:35,111 INFO [train.py:850] (2/4) Epoch 16, batch 5750, loss[loss=0.2057, simple_loss=0.2828, pruned_loss=0.06427, over 7196.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2887, pruned_loss=0.06251, over 1467248.26 frames.], batch size: 19, lr: 3.46e-04
2022-07-27 01:49:18,752 INFO [train.py:850] (2/4) Epoch 16, batch 5800, loss[loss=0.1741, simple_loss=0.2517, pruned_loss=0.04819, over 7192.00 frames.], tot_loss[loss=0.2064, simple_loss=0.2884, pruned_loss=0.06222, over 1466943.65 frames.], batch size: 18, lr: 3.46e-04
2022-07-27 01:50:02,093 INFO [train.py:850] (2/4) Epoch 16, batch 5850, loss[loss=0.1951, simple_loss=0.2861, pruned_loss=0.05207, over 7415.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2883, pruned_loss=0.06194, over 1466557.58 frames.], batch size: 22, lr: 3.46e-04
2022-07-27 01:50:46,895 INFO [train.py:850] (2/4) Epoch 16, batch 5900, loss[loss=0.1727, simple_loss=0.2529, pruned_loss=0.04628, over 7459.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2878, pruned_loss=0.06168, over 1466787.94 frames.], batch size: 17, lr: 3.46e-04
2022-07-27 01:51:29,594 INFO [train.py:850] (2/4) Epoch 16, batch 5950, loss[loss=0.195, simple_loss=0.2915, pruned_loss=0.04927, over 7204.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2871, pruned_loss=0.06157, over 1466701.06 frames.], batch size: 20, lr: 3.46e-04
2022-07-27 01:52:14,404 INFO [train.py:850] (2/4) Epoch 16, batch 6000, loss[loss=0.1492, simple_loss=0.2331, pruned_loss=0.03263, over 7320.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2876, pruned_loss=0.06188, over 1466823.38 frames.], batch size: 18, lr: 3.46e-04
2022-07-27 01:52:14,405 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 01:52:37,116 INFO [train.py:879] (2/4) Epoch 16, validation: loss=0.1842, simple_loss=0.2809, pruned_loss=0.04372, over 924787.00 frames.
2022-07-27 01:53:21,111 INFO [train.py:850] (2/4) Epoch 16, batch 6050, loss[loss=0.1851, simple_loss=0.2747, pruned_loss=0.04773, over 7375.00 frames.], tot_loss[loss=0.206, simple_loss=0.288, pruned_loss=0.06201, over 1466794.32 frames.], batch size: 39, lr: 3.46e-04
2022-07-27 01:54:06,378 INFO [train.py:850] (2/4) Epoch 16, batch 6100, loss[loss=0.2734, simple_loss=0.3433, pruned_loss=0.1017, over 7207.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2874, pruned_loss=0.06178, over 1465444.24 frames.], batch size: 25, lr: 3.46e-04
2022-07-27 01:54:49,946 INFO [train.py:850] (2/4) Epoch 16, batch 6150, loss[loss=0.2091, simple_loss=0.283, pruned_loss=0.06764, over 7199.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2871, pruned_loss=0.06183, over 1465237.40 frames.], batch size: 20, lr: 3.46e-04
2022-07-27 01:55:35,303 INFO [train.py:850] (2/4) Epoch 16, batch 6200, loss[loss=0.2445, simple_loss=0.3178, pruned_loss=0.08559, over 7269.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2872, pruned_loss=0.06163, over 1465708.32 frames.], batch size: 21, lr: 3.46e-04
2022-07-27 01:56:18,106 INFO [train.py:850] (2/4) Epoch 16, batch 6250, loss[loss=0.163, simple_loss=0.2458, pruned_loss=0.04006, over 7479.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2882, pruned_loss=0.06216, over 1465981.95 frames.], batch size: 20, lr: 3.46e-04
2022-07-27 01:57:01,565 INFO [train.py:850] (2/4) Epoch 16, batch 6300, loss[loss=0.2588, simple_loss=0.3368, pruned_loss=0.0904, over 7413.00 frames.], tot_loss[loss=0.207, simple_loss=0.2892, pruned_loss=0.06238, over 1465282.58 frames.], batch size: 39, lr: 3.46e-04
2022-07-27 01:57:45,490 INFO [train.py:850] (2/4) Epoch 16, batch 6350, loss[loss=0.2265, simple_loss=0.3077, pruned_loss=0.07268, over 7174.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2874, pruned_loss=0.06116, over 1465865.71 frames.], batch size: 22, lr: 3.46e-04
2022-07-27 01:58:28,728 INFO [train.py:850] (2/4) Epoch 16, batch 6400, loss[loss=0.187, simple_loss=0.2788, pruned_loss=0.04763, over 7389.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2866, pruned_loss=0.06054, over 1465912.70 frames.], batch size: 31, lr: 3.46e-04
2022-07-27 01:59:12,571 INFO [train.py:850] (2/4) Epoch 16, batch 6450, loss[loss=0.1936, simple_loss=0.2842, pruned_loss=0.05154, over 7475.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2864, pruned_loss=0.06047, over 1465999.21 frames.], batch size: 23, lr: 3.46e-04
2022-07-27 01:59:56,119 INFO [train.py:850] (2/4) Epoch 16, batch 6500, loss[loss=0.2085, simple_loss=0.2996, pruned_loss=0.05875, over 7285.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2869, pruned_loss=0.06046, over 1465407.98 frames.], batch size: 22, lr: 3.46e-04
2022-07-27 02:00:40,895 INFO [train.py:850] (2/4) Epoch 16, batch 6550, loss[loss=0.2162, simple_loss=0.3005, pruned_loss=0.06595, over 7301.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2865, pruned_loss=0.06002, over 1467165.78 frames.], batch size: 22, lr: 3.45e-04
2022-07-27 02:01:39,589 INFO [train.py:850] (2/4) Epoch 16, batch 6600, loss[loss=0.1647, simple_loss=0.2591, pruned_loss=0.03516, over 7478.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2859, pruned_loss=0.05978, over 1466377.41 frames.], batch size: 20, lr: 3.45e-04
2022-07-27 02:02:24,338 INFO [train.py:850] (2/4) Epoch 16, batch 6650, loss[loss=0.1775, simple_loss=0.2557, pruned_loss=0.04969, over 7458.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2859, pruned_loss=0.06018, over 1466631.62 frames.], batch size: 17, lr: 3.45e-04
2022-07-27 02:03:08,752 INFO [train.py:850] (2/4) Epoch 16, batch 6700, loss[loss=0.2506, simple_loss=0.3249, pruned_loss=0.08818, over 7470.00 frames.], tot_loss[loss=0.2032, simple_loss=0.286, pruned_loss=0.06024, over 1467100.15 frames.], batch size: 79, lr: 3.45e-04
2022-07-27 02:03:51,938 INFO [train.py:850] (2/4) Epoch 16, batch 6750, loss[loss=0.2296, simple_loss=0.2983, pruned_loss=0.0805, over 7398.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2869, pruned_loss=0.06104, over 1467117.99 frames.], batch size: 19, lr: 3.45e-04
2022-07-27 02:04:36,145 INFO [train.py:850] (2/4) Epoch 16, batch 6800, loss[loss=0.199, simple_loss=0.2855, pruned_loss=0.05625, over 7233.00 frames.], tot_loss[loss=0.204, simple_loss=0.2865, pruned_loss=0.06076, over 1465583.11 frames.], batch size: 25, lr: 3.45e-04
2022-07-27 02:05:19,479 INFO [train.py:850] (2/4) Epoch 16, batch 6850, loss[loss=0.1915, simple_loss=0.2764, pruned_loss=0.05333, over 7395.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2852, pruned_loss=0.06048, over 1465024.87 frames.], batch size: 19, lr: 3.45e-04
2022-07-27 02:06:04,766 INFO [train.py:850] (2/4) Epoch 16, batch 6900, loss[loss=0.2091, simple_loss=0.2863, pruned_loss=0.066, over 7486.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2849, pruned_loss=0.06024, over 1464394.97 frames.], batch size: 19, lr: 3.45e-04
2022-07-27 02:06:48,215 INFO [train.py:850] (2/4) Epoch 16, batch 6950, loss[loss=0.1905, simple_loss=0.2719, pruned_loss=0.05457, over 7466.00 frames.], tot_loss[loss=0.2017, simple_loss=0.284, pruned_loss=0.05968, over 1465166.96 frames.], batch size: 24, lr: 3.45e-04
2022-07-27 02:07:32,809 INFO [train.py:850] (2/4) Epoch 16, batch 7000, loss[loss=0.2441, simple_loss=0.3204, pruned_loss=0.08389, over 7173.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2848, pruned_loss=0.06037, over 1464874.61 frames.], batch size: 22, lr: 3.45e-04
2022-07-27 02:08:17,712 INFO [train.py:850] (2/4) Epoch 16, batch 7050, loss[loss=0.1944, simple_loss=0.2875, pruned_loss=0.05071, over 7378.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2856, pruned_loss=0.06065, over 1466673.61 frames.], batch size: 20, lr: 3.45e-04
2022-07-27 02:09:00,963 INFO [train.py:850] (2/4) Epoch 16, batch 7100, loss[loss=0.2317, simple_loss=0.3101, pruned_loss=0.07664, over 7462.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2879, pruned_loss=0.06168, over 1466991.91 frames.], batch size: 24, lr: 3.45e-04
2022-07-27 02:09:45,813 INFO [train.py:850] (2/4) Epoch 16, batch 7150, loss[loss=0.2672, simple_loss=0.3426, pruned_loss=0.09589, over 7372.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2882, pruned_loss=0.0622, over 1467094.99 frames.], batch size: 73, lr: 3.45e-04
2022-07-27 02:10:29,386 INFO [train.py:850] (2/4) Epoch 16, batch 7200, loss[loss=0.2429, simple_loss=0.306, pruned_loss=0.08988, over 7298.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2884, pruned_loss=0.06241, over 1467200.42 frames.], batch size: 22, lr: 3.45e-04
2022-07-27 02:11:14,253 INFO [train.py:850] (2/4) Epoch 16, batch 7250, loss[loss=0.1788, simple_loss=0.2558, pruned_loss=0.05097, over 7140.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2891, pruned_loss=0.06333, over 1466387.61 frames.], batch size: 17, lr: 3.45e-04
2022-07-27 02:11:57,610 INFO [train.py:850] (2/4) Epoch 16, batch 7300, loss[loss=0.2034, simple_loss=0.2944, pruned_loss=0.05616, over 7180.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2875, pruned_loss=0.06192, over 1466940.12 frames.], batch size: 21, lr: 3.45e-04
2022-07-27 02:12:40,606 INFO [train.py:850] (2/4) Epoch 16, batch 7350, loss[loss=0.2207, simple_loss=0.301, pruned_loss=0.07022, over 7279.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2859, pruned_loss=0.06089, over 1465926.89 frames.], batch size: 20, lr: 3.44e-04
2022-07-27 02:13:24,372 INFO [train.py:850] (2/4) Epoch 16, batch 7400, loss[loss=0.2192, simple_loss=0.3103, pruned_loss=0.064, over 7316.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2851, pruned_loss=0.05971, over 1465286.28 frames.], batch size: 22, lr: 3.44e-04
2022-07-27 02:14:07,449 INFO [train.py:850] (2/4) Epoch 16, batch 7450, loss[loss=0.1998, simple_loss=0.2833, pruned_loss=0.05815, over 7100.00 frames.], tot_loss[loss=0.202, simple_loss=0.2846, pruned_loss=0.05974, over 1464962.27 frames.], batch size: 18, lr: 3.44e-04
2022-07-27 02:14:51,355 INFO [train.py:850] (2/4) Epoch 16, batch 7500, loss[loss=0.1924, simple_loss=0.2766, pruned_loss=0.05409, over 7258.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2859, pruned_loss=0.06022, over 1465853.45 frames.], batch size: 25, lr: 3.44e-04
2022-07-27 02:15:34,392 INFO [train.py:850] (2/4) Epoch 16, batch 7550, loss[loss=0.2105, simple_loss=0.2933, pruned_loss=0.06389, over 7479.00 frames.], tot_loss[loss=0.2053, simple_loss=0.288, pruned_loss=0.06126, over 1466333.56 frames.], batch size: 21, lr: 3.44e-04
2022-07-27 02:16:18,662 INFO [train.py:850] (2/4) Epoch 16, batch 7600, loss[loss=0.1532, simple_loss=0.2441, pruned_loss=0.03117, over 7196.00 frames.], tot_loss[loss=0.206, simple_loss=0.2884, pruned_loss=0.06181, over 1466658.71 frames.], batch size: 20, lr: 3.44e-04
2022-07-27 02:17:01,999 INFO [train.py:850] (2/4) Epoch 16, batch 7650, loss[loss=0.1931, simple_loss=0.2719, pruned_loss=0.05716, over 7388.00 frames.], tot_loss[loss=0.2053, simple_loss=0.288, pruned_loss=0.06127, over 1467075.19 frames.], batch size: 19, lr: 3.44e-04
2022-07-27 02:17:46,662 INFO [train.py:850] (2/4) Epoch 16, batch 7700, loss[loss=0.2153, simple_loss=0.2943, pruned_loss=0.06813, over 7290.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2882, pruned_loss=0.06067, over 1467738.13 frames.], batch size: 20, lr: 3.44e-04
2022-07-27 02:18:30,620 INFO [train.py:850] (2/4) Epoch 16, batch 7750, loss[loss=0.206, simple_loss=0.2902, pruned_loss=0.06089, over 7183.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2876, pruned_loss=0.06031, over 1467997.33 frames.], batch size: 21, lr: 3.44e-04
2022-07-27 02:19:14,641 INFO [train.py:850] (2/4) Epoch 16, batch 7800, loss[loss=0.1706, simple_loss=0.2592, pruned_loss=0.04103, over 7480.00 frames.], tot_loss[loss=0.203, simple_loss=0.2865, pruned_loss=0.05981, over 1466890.78 frames.], batch size: 20, lr: 3.44e-04
2022-07-27 02:19:59,169 INFO [train.py:850] (2/4) Epoch 16, batch 7850, loss[loss=0.2532, simple_loss=0.3326, pruned_loss=0.08689, over 7389.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2859, pruned_loss=0.05969, over 1466765.73 frames.], batch size: 21, lr: 3.44e-04
2022-07-27 02:20:42,529 INFO [train.py:850] (2/4) Epoch 16, batch 7900, loss[loss=0.262, simple_loss=0.3361, pruned_loss=0.09399, over 7461.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2857, pruned_loss=0.05973, over 1467046.62 frames.], batch size: 69, lr: 3.44e-04
2022-07-27 02:21:27,036 INFO [train.py:850] (2/4) Epoch 16, batch 7950, loss[loss=0.1782, simple_loss=0.2665, pruned_loss=0.0449, over 7481.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2854, pruned_loss=0.05976, over 1467249.74 frames.], batch size: 24, lr: 3.44e-04
2022-07-27 02:22:10,605 INFO [train.py:850] (2/4) Epoch 16, batch 8000, loss[loss=0.1849, simple_loss=0.2636, pruned_loss=0.05315, over 7395.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2856, pruned_loss=0.05998, over 1466725.14 frames.], batch size: 19, lr: 3.44e-04
2022-07-27 02:22:54,603 INFO [train.py:850] (2/4) Epoch 16, batch 8050, loss[loss=0.2392, simple_loss=0.3238, pruned_loss=0.07727, over 7470.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2857, pruned_loss=0.05979, over 1468402.40 frames.], batch size: 24, lr: 3.44e-04
2022-07-27 02:23:38,919 INFO [train.py:850] (2/4) Epoch 16, batch 8100, loss[loss=0.2216, simple_loss=0.2964, pruned_loss=0.07335, over 7200.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2855, pruned_loss=0.06013, over 1467363.92 frames.], batch size: 19, lr: 3.44e-04
2022-07-27 02:24:21,793 INFO [train.py:850] (2/4) Epoch 16, batch 8150, loss[loss=0.2293, simple_loss=0.313, pruned_loss=0.07283, over 7291.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2862, pruned_loss=0.06006, over 1465405.40 frames.], batch size: 22, lr: 3.43e-04
2022-07-27 02:25:06,798 INFO [train.py:850] (2/4) Epoch 16, batch 8200, loss[loss=0.1618, simple_loss=0.2413, pruned_loss=0.04121, over 7167.00 frames.], tot_loss[loss=0.2028, simple_loss=0.286, pruned_loss=0.05985, over 1465424.67 frames.], batch size: 17, lr: 3.43e-04
2022-07-27 02:25:49,468 INFO [train.py:850] (2/4) Epoch 16, batch 8250, loss[loss=0.1906, simple_loss=0.2818, pruned_loss=0.04965, over 7296.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2857, pruned_loss=0.05965, over 1465016.37 frames.], batch size: 19, lr: 3.43e-04
2022-07-27 02:26:34,313 INFO [train.py:850] (2/4) Epoch 16, batch 8300, loss[loss=0.2055, simple_loss=0.2937, pruned_loss=0.0587, over 7220.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2841, pruned_loss=0.05883, over 1465188.69 frames.], batch size: 25, lr: 3.43e-04
2022-07-27 02:27:17,644 INFO [train.py:850] (2/4) Epoch 16, batch 8350, loss[loss=0.1777, simple_loss=0.2744, pruned_loss=0.04048, over 7463.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2835, pruned_loss=0.05837, over 1464223.71 frames.], batch size: 24, lr: 3.43e-04
2022-07-27 02:28:01,974 INFO [train.py:850] (2/4) Epoch 16, batch 8400, loss[loss=0.1868, simple_loss=0.2731, pruned_loss=0.05023, over 7475.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2841, pruned_loss=0.05857, over 1464497.84 frames.], batch size: 20, lr: 3.43e-04
2022-07-27 02:28:45,131 INFO [train.py:850] (2/4) Epoch 16, batch 8450, loss[loss=0.161, simple_loss=0.2501, pruned_loss=0.03592, over 7194.00 frames.], tot_loss[loss=0.2005, simple_loss=0.284, pruned_loss=0.05853, over 1464779.52 frames.], batch size: 18, lr: 3.43e-04
2022-07-27 02:29:28,290 INFO [train.py:850] (2/4) Epoch 16, batch 8500, loss[loss=0.2123, simple_loss=0.2995, pruned_loss=0.06253, over 7471.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2843, pruned_loss=0.05875, over 1466331.52 frames.], batch size: 24, lr: 3.43e-04
2022-07-27 02:30:12,893 INFO [train.py:850] (2/4) Epoch 16, batch 8550, loss[loss=0.1784, simple_loss=0.2645, pruned_loss=0.04614, over 7304.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2848, pruned_loss=0.05932, over 1465820.01 frames.], batch size: 18, lr: 3.43e-04
2022-07-27 02:30:56,633 INFO [train.py:850] (2/4) Epoch 16, batch 8600, loss[loss=0.2568, simple_loss=0.3309, pruned_loss=0.09136, over 7451.00 frames.], tot_loss[loss=0.2028, simple_loss=0.286, pruned_loss=0.05986, over 1466014.73 frames.], batch size: 75, lr: 3.43e-04
2022-07-27 02:31:40,365 INFO [train.py:850] (2/4) Epoch 16, batch 8650, loss[loss=0.2602, simple_loss=0.3279, pruned_loss=0.09625, over 7326.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2866, pruned_loss=0.06082, over 1466560.74 frames.], batch size: 72, lr: 3.43e-04
2022-07-27 02:32:24,734 INFO [train.py:850] (2/4) Epoch 16, batch 8700, loss[loss=0.194, simple_loss=0.2815, pruned_loss=0.0533, over 7282.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2861, pruned_loss=0.06072, over 1466561.90 frames.], batch size: 20, lr: 3.43e-04
2022-07-27 02:33:06,965 INFO [train.py:850] (2/4) Epoch 16, batch 8750, loss[loss=0.198, simple_loss=0.2668, pruned_loss=0.06462, over 7456.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2865, pruned_loss=0.06055, over 1466787.23 frames.], batch size: 18, lr: 3.43e-04
2022-07-27 02:33:49,363 INFO [train.py:850] (2/4) Epoch 16, batch 8800, loss[loss=0.2359, simple_loss=0.3238, pruned_loss=0.07401, over 7165.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2851, pruned_loss=0.05974, over 1465978.44 frames.], batch size: 22, lr: 3.43e-04
2022-07-27 02:34:31,497 INFO [train.py:850] (2/4) Epoch 16, batch 8850, loss[loss=0.1925, simple_loss=0.2675, pruned_loss=0.05873, over 7306.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2844, pruned_loss=0.05942, over 1465616.76 frames.], batch size: 17, lr: 3.43e-04
2022-07-27 02:36:12,131 INFO [train.py:850] (2/4) Epoch 17, batch 0, loss[loss=0.2202, simple_loss=0.2938, pruned_loss=0.07328, over 7207.00 frames.], tot_loss[loss=0.2202, simple_loss=0.2938, pruned_loss=0.07328, over 7207.00 frames.], batch size: 20, lr: 3.33e-04
2022-07-27 02:36:56,324 INFO [train.py:850] (2/4) Epoch 17, batch 50, loss[loss=0.173, simple_loss=0.2719, pruned_loss=0.0371, over 7351.00 frames.], tot_loss[loss=0.194, simple_loss=0.2847, pruned_loss=0.05165, over 330379.22 frames.], batch size: 23, lr: 3.33e-04
2022-07-27 02:37:40,078 INFO [train.py:850] (2/4) Epoch 17, batch 100, loss[loss=0.1884, simple_loss=0.2777, pruned_loss=0.04957, over 7454.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2845, pruned_loss=0.05162, over 582330.12 frames.], batch size: 24, lr: 3.33e-04
2022-07-27 02:38:24,958 INFO [train.py:850] (2/4) Epoch 17, batch 150, loss[loss=0.195, simple_loss=0.292, pruned_loss=0.04896, over 7378.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2835, pruned_loss=0.05138, over 777632.68 frames.], batch size: 21, lr: 3.33e-04
2022-07-27 02:39:08,697 INFO [train.py:850] (2/4) Epoch 17, batch 200, loss[loss=0.1668, simple_loss=0.2556, pruned_loss=0.03897, over 7196.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2833, pruned_loss=0.05084, over 929663.01 frames.], batch size: 19, lr: 3.33e-04
2022-07-27 02:39:51,078 INFO [train.py:850] (2/4) Epoch 17, batch 250, loss[loss=0.1904, simple_loss=0.2806, pruned_loss=0.05016, over 7477.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2825, pruned_loss=0.0502, over 1049123.94 frames.], batch size: 40, lr: 3.33e-04
2022-07-27 02:40:34,806 INFO [train.py:850] (2/4) Epoch 17, batch 300, loss[loss=0.1371, simple_loss=0.2267, pruned_loss=0.02372, over 7296.00 frames.], tot_loss[loss=0.1906, simple_loss=0.282, pruned_loss=0.04964, over 1142014.18 frames.], batch size: 17, lr: 3.33e-04
2022-07-27 02:41:18,608 INFO [train.py:850] (2/4) Epoch 17, batch 350, loss[loss=0.1789, simple_loss=0.2748, pruned_loss=0.04153, over 7451.00 frames.], tot_loss[loss=0.19, simple_loss=0.2814, pruned_loss=0.04935, over 1214802.92 frames.], batch size: 31, lr: 3.33e-04
2022-07-27 02:42:02,897 INFO [train.py:850] (2/4) Epoch 17, batch 400, loss[loss=0.1878, simple_loss=0.2801, pruned_loss=0.0477, over 7284.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2801, pruned_loss=0.04888, over 1271573.62 frames.], batch size: 21, lr: 3.33e-04
2022-07-27 02:42:46,007 INFO [train.py:850] (2/4) Epoch 17, batch 450, loss[loss=0.1731, simple_loss=0.2625, pruned_loss=0.04181, over 7388.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2791, pruned_loss=0.04866, over 1314688.84 frames.], batch size: 19, lr: 3.33e-04
2022-07-27 02:43:29,332 INFO [train.py:850] (2/4) Epoch 17, batch 500, loss[loss=0.1997, simple_loss=0.2839, pruned_loss=0.05772, over 7101.00 frames.], tot_loss[loss=0.188, simple_loss=0.2789, pruned_loss=0.04854, over 1347688.58 frames.], batch size: 18, lr: 3.33e-04
2022-07-27 02:44:12,742 INFO [train.py:850] (2/4) Epoch 17, batch 550, loss[loss=0.1853, simple_loss=0.2673, pruned_loss=0.05165, over 7197.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2788, pruned_loss=0.04803, over 1373308.44 frames.], batch size: 19, lr: 3.32e-04
2022-07-27 02:44:56,175 INFO [train.py:850] (2/4) Epoch 17, batch 600, loss[loss=0.1668, simple_loss=0.2675, pruned_loss=0.0331, over 7466.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2774, pruned_loss=0.04723, over 1394225.27 frames.], batch size: 40, lr: 3.32e-04
2022-07-27 02:45:40,384 INFO [train.py:850] (2/4) Epoch 17, batch 650, loss[loss=0.1761, simple_loss=0.2612, pruned_loss=0.04551, over 7315.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2768, pruned_loss=0.04729, over 1410356.03 frames.], batch size: 18, lr: 3.32e-04
2022-07-27 02:46:23,589 INFO [train.py:850] (2/4) Epoch 17, batch 700, loss[loss=0.2094, simple_loss=0.3066, pruned_loss=0.0561, over 7466.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2777, pruned_loss=0.04737, over 1421314.43 frames.], batch size: 21, lr: 3.32e-04
2022-07-27 02:47:06,320 INFO [train.py:850] (2/4) Epoch 17, batch 750, loss[loss=0.188, simple_loss=0.284, pruned_loss=0.04603, over 7293.00 frames.], tot_loss[loss=0.186, simple_loss=0.278, pruned_loss=0.04706, over 1430959.29 frames.], batch size: 22, lr: 3.32e-04
2022-07-27 02:47:50,511 INFO [train.py:850] (2/4) Epoch 17, batch 800, loss[loss=0.2015, simple_loss=0.2881, pruned_loss=0.05743, over 7378.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2779, pruned_loss=0.04719, over 1438284.04 frames.], batch size: 20, lr: 3.32e-04
2022-07-27 02:48:33,297 INFO [train.py:850] (2/4) Epoch 17, batch 850, loss[loss=0.2471, simple_loss=0.3284, pruned_loss=0.0829, over 7428.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2771, pruned_loss=0.04704, over 1444052.38 frames.], batch size: 72, lr: 3.32e-04
2022-07-27 02:49:18,125 INFO [train.py:850] (2/4) Epoch 17, batch 900, loss[loss=0.1692, simple_loss=0.248, pruned_loss=0.04518, over 7454.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2787, pruned_loss=0.04837, over 1448773.58 frames.], batch size: 17, lr: 3.32e-04
2022-07-27 02:50:01,706 INFO [train.py:850] (2/4) Epoch 17, batch 950, loss[loss=0.2086, simple_loss=0.2985, pruned_loss=0.05942, over 7285.00 frames.], tot_loss[loss=0.1893, simple_loss=0.28, pruned_loss=0.04933, over 1453766.27 frames.], batch size: 20, lr: 3.32e-04
2022-07-27 02:50:45,108 INFO [train.py:850] (2/4) Epoch 17, batch 1000, loss[loss=0.1732, simple_loss=0.2609, pruned_loss=0.04276, over 7440.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2809, pruned_loss=0.04972, over 1456217.20 frames.], batch size: 18, lr: 3.32e-04
2022-07-27 02:51:28,716 INFO [train.py:850] (2/4) Epoch 17, batch 1050, loss[loss=0.1861, simple_loss=0.2716, pruned_loss=0.05031, over 7207.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2809, pruned_loss=0.04989, over 1459464.98 frames.], batch size: 19, lr: 3.32e-04
2022-07-27 02:52:12,265 INFO [train.py:850] (2/4) Epoch 17, batch 1100, loss[loss=0.1774, simple_loss=0.2667, pruned_loss=0.04406, over 7443.00 frames.], tot_loss[loss=0.189, simple_loss=0.28, pruned_loss=0.04906, over 1461067.91 frames.], batch size: 18, lr: 3.32e-04
2022-07-27 02:52:56,808 INFO [train.py:850] (2/4) Epoch 17, batch 1150, loss[loss=0.1713, simple_loss=0.2656, pruned_loss=0.0385, over 7191.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2822, pruned_loss=0.0497, over 1462255.34 frames.], batch size: 18, lr: 3.32e-04
2022-07-27 02:53:39,838 INFO [train.py:850] (2/4) Epoch 17, batch 1200, loss[loss=0.1871, simple_loss=0.2683, pruned_loss=0.05298, over 7306.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2819, pruned_loss=0.04968, over 1463108.99 frames.], batch size: 17, lr: 3.32e-04
2022-07-27 02:54:22,979 INFO [train.py:850] (2/4) Epoch 17, batch 1250, loss[loss=0.2242, simple_loss=0.3135, pruned_loss=0.06746, over 7211.00 frames.], tot_loss[loss=0.191, simple_loss=0.282, pruned_loss=0.05001, over 1462401.85 frames.], batch size: 25, lr: 3.32e-04
2022-07-27 02:55:06,467 INFO [train.py:850] (2/4) Epoch 17, batch 1300, loss[loss=0.1834, simple_loss=0.2664, pruned_loss=0.0502, over 7398.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2819, pruned_loss=0.05016, over 1462771.94 frames.], batch size: 19, lr: 3.32e-04
2022-07-27 02:55:49,360 INFO [train.py:850] (2/4) Epoch 17, batch 1350, loss[loss=0.1976, simple_loss=0.2893, pruned_loss=0.05294, over 7338.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2804, pruned_loss=0.04946, over 1462634.47 frames.], batch size: 23, lr: 3.32e-04
2022-07-27 02:56:33,658 INFO [train.py:850] (2/4) Epoch 17, batch 1400, loss[loss=0.2085, simple_loss=0.3099, pruned_loss=0.05358, over 7480.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2814, pruned_loss=0.04971, over 1463180.60 frames.], batch size: 24, lr: 3.31e-04
2022-07-27 02:57:16,632 INFO [train.py:850] (2/4) Epoch 17, batch 1450, loss[loss=0.1739, simple_loss=0.2735, pruned_loss=0.03716, over 7283.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2816, pruned_loss=0.05038, over 1463198.83 frames.], batch size: 20, lr: 3.31e-04
2022-07-27 02:58:00,623 INFO [train.py:850] (2/4) Epoch 17, batch 1500, loss[loss=0.1871, simple_loss=0.2861, pruned_loss=0.04409, over 7282.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2832, pruned_loss=0.0508, over 1462427.01 frames.], batch size: 20, lr: 3.31e-04
2022-07-27 02:58:43,531 INFO [train.py:850] (2/4) Epoch 17, batch 1550, loss[loss=0.1953, simple_loss=0.2885, pruned_loss=0.05101, over 7289.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2825, pruned_loss=0.05036, over 1464286.47 frames.], batch size: 21, lr: 3.31e-04
2022-07-27 02:59:27,738 INFO [train.py:850] (2/4) Epoch 17, batch 1600, loss[loss=0.2019, simple_loss=0.3024, pruned_loss=0.05073, over 7381.00 frames.], tot_loss[loss=0.1918, simple_loss=0.283, pruned_loss=0.05031, over 1463731.01 frames.], batch size: 21, lr: 3.31e-04
2022-07-27 03:00:13,207 INFO [train.py:850] (2/4) Epoch 17, batch 1650, loss[loss=0.1912, simple_loss=0.279, pruned_loss=0.05171, over 7442.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2825, pruned_loss=0.05038, over 1464611.57 frames.], batch size: 17, lr: 3.31e-04
2022-07-27 03:01:11,865 INFO [train.py:850] (2/4) Epoch 17, batch 1700, loss[loss=0.1761, simple_loss=0.2628, pruned_loss=0.04469, over 7457.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2818, pruned_loss=0.04971, over 1465897.17 frames.], batch size: 17, lr: 3.31e-04
2022-07-27 03:01:55,517 INFO [train.py:850] (2/4) Epoch 17, batch 1750, loss[loss=0.1999, simple_loss=0.2879, pruned_loss=0.05598, over 7431.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2818, pruned_loss=0.04987, over 1466841.59 frames.], batch size: 31, lr: 3.31e-04
2022-07-27 03:02:38,849 INFO [train.py:850] (2/4) Epoch 17, batch 1800, loss[loss=0.1522, simple_loss=0.2428, pruned_loss=0.03076, over 7457.00 frames.], tot_loss[loss=0.191, simple_loss=0.2821, pruned_loss=0.04993, over 1467667.94 frames.], batch size: 18, lr: 3.31e-04
2022-07-27 03:03:21,559 INFO [train.py:850] (2/4) Epoch 17, batch 1850, loss[loss=0.1943, simple_loss=0.2892, pruned_loss=0.04971, over 7374.00 frames.], tot_loss[loss=0.191, simple_loss=0.2818, pruned_loss=0.05009, over 1466602.74 frames.], batch size: 21, lr: 3.31e-04
2022-07-27 03:04:06,326 INFO [train.py:850] (2/4) Epoch 17, batch 1900, loss[loss=0.1461, simple_loss=0.2326, pruned_loss=0.02981, over 7437.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2827, pruned_loss=0.05024, over 1466357.67 frames.], batch size: 18, lr: 3.31e-04
2022-07-27 03:04:49,282 INFO [train.py:850] (2/4) Epoch 17, batch 1950, loss[loss=0.1659, simple_loss=0.2653, pruned_loss=0.03321, over 7381.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2828, pruned_loss=0.05037, over 1465811.06 frames.], batch size: 21, lr: 3.31e-04
2022-07-27 03:05:34,162 INFO [train.py:850] (2/4) Epoch 17, batch 2000, loss[loss=0.2127, simple_loss=0.3023, pruned_loss=0.06154, over 7392.00 frames.], tot_loss[loss=0.1908, simple_loss=0.282, pruned_loss=0.04975, over 1465416.06 frames.], batch size: 19, lr: 3.31e-04
2022-07-27 03:06:17,313 INFO [train.py:850] (2/4) Epoch 17, batch 2050, loss[loss=0.1686, simple_loss=0.2715, pruned_loss=0.03289, over 7383.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2819, pruned_loss=0.04972, over 1465601.09 frames.], batch size: 21, lr: 3.31e-04
2022-07-27 03:07:00,981 INFO [train.py:850] (2/4) Epoch 17, batch 2100, loss[loss=0.2241, simple_loss=0.3129, pruned_loss=0.06765, over 7325.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2822, pruned_loss=0.04953, over 1464445.05 frames.], batch size: 39, lr: 3.31e-04
2022-07-27 03:07:44,206 INFO [train.py:850] (2/4) Epoch 17, batch 2150, loss[loss=0.2012, simple_loss=0.2964, pruned_loss=0.05305, over 7284.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2819, pruned_loss=0.04963, over 1465147.38 frames.], batch size: 21, lr: 3.31e-04
2022-07-27 03:08:28,480 INFO [train.py:850] (2/4) Epoch 17, batch 2200, loss[loss=0.1811, simple_loss=0.2818, pruned_loss=0.04015, over 7472.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2823, pruned_loss=0.04959, over 1464823.98 frames.], batch size: 21, lr: 3.31e-04
2022-07-27 03:09:13,557 INFO [train.py:850] (2/4) Epoch 17, batch 2250, loss[loss=0.2232, simple_loss=0.3172, pruned_loss=0.06462, over 7287.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2832, pruned_loss=0.05014, over 1465069.94 frames.], batch size: 21, lr: 3.31e-04
2022-07-27 03:09:56,690 INFO [train.py:850] (2/4) Epoch 17, batch 2300, loss[loss=0.1874, simple_loss=0.2852, pruned_loss=0.04485, over 7403.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2821, pruned_loss=0.04952, over 1465384.02 frames.], batch size: 31, lr: 3.30e-04
2022-07-27 03:10:40,841 INFO [train.py:850] (2/4) Epoch 17, batch 2350, loss[loss=0.1974, simple_loss=0.2958, pruned_loss=0.04951, over 7422.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2824, pruned_loss=0.04933, over 1465532.87 frames.], batch size: 22, lr: 3.30e-04
2022-07-27 03:11:24,401 INFO [train.py:850] (2/4) Epoch 17, batch 2400, loss[loss=0.1865, simple_loss=0.2859, pruned_loss=0.04359, over 7476.00 frames.], tot_loss[loss=0.1902, simple_loss=0.282, pruned_loss=0.04926, over 1465478.83 frames.], batch size: 21, lr: 3.30e-04
2022-07-27 03:12:08,091 INFO [train.py:850] (2/4) Epoch 17, batch 2450, loss[loss=0.1775, simple_loss=0.268, pruned_loss=0.04354, over 7390.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2837, pruned_loss=0.05035, over 1465709.90 frames.], batch size: 19, lr: 3.30e-04
2022-07-27 03:12:51,681 INFO [train.py:850] (2/4) Epoch 17, batch 2500, loss[loss=0.1463, simple_loss=0.2389, pruned_loss=0.02681, over 7106.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2832, pruned_loss=0.04992, over 1464907.25 frames.], batch size: 18, lr: 3.30e-04
2022-07-27 03:13:34,534 INFO [train.py:850] (2/4) Epoch 17, batch 2550, loss[loss=0.2061, simple_loss=0.3025, pruned_loss=0.05492, over 7416.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2828, pruned_loss=0.04976, over 1464467.23 frames.], batch size: 22, lr: 3.30e-04
2022-07-27 03:14:18,871 INFO [train.py:850] (2/4) Epoch 17, batch 2600, loss[loss=0.1735, simple_loss=0.2568, pruned_loss=0.04511, over 7296.00 frames.], tot_loss[loss=0.1905, simple_loss=0.282, pruned_loss=0.04948, over 1464745.69 frames.], batch size: 17, lr: 3.30e-04
2022-07-27 03:15:01,530 INFO [train.py:850] (2/4) Epoch 17, batch 2650, loss[loss=0.1897, simple_loss=0.2739, pruned_loss=0.05278, over 7469.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2832, pruned_loss=0.05005, over 1464515.63 frames.], batch size: 24, lr: 3.30e-04
2022-07-27 03:15:45,833 INFO [train.py:850] (2/4) Epoch 17, batch 2700, loss[loss=0.1829, simple_loss=0.2735, pruned_loss=0.04611, over 7201.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2827, pruned_loss=0.04979, over 1463875.43 frames.], batch size: 18, lr: 3.30e-04
2022-07-27 03:16:29,077 INFO [train.py:850] (2/4) Epoch 17, batch 2750, loss[loss=0.1835, simple_loss=0.2772, pruned_loss=0.04491, over 7187.00 frames.], tot_loss[loss=0.191, simple_loss=0.2826, pruned_loss=0.04972, over 1464485.57 frames.], batch size: 21, lr: 3.30e-04
2022-07-27 03:17:12,751 INFO [train.py:850] (2/4) Epoch 17, batch 2800, loss[loss=0.15, simple_loss=0.2403, pruned_loss=0.02981, over 7202.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2809, pruned_loss=0.04897, over 1464681.82 frames.], batch size: 19, lr: 3.30e-04
2022-07-27 03:17:56,315 INFO [train.py:850] (2/4) Epoch 17, batch 2850, loss[loss=0.1788, simple_loss=0.2686, pruned_loss=0.0445, over 7301.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2816, pruned_loss=0.04904, over 1464663.91 frames.], batch size: 19, lr: 3.30e-04
2022-07-27 03:18:39,513 INFO [train.py:850] (2/4) Epoch 17, batch 2900, loss[loss=0.2153, simple_loss=0.3097, pruned_loss=0.06047, over 7380.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2809, pruned_loss=0.04874, over 1465031.06 frames.], batch size: 21, lr: 3.30e-04
2022-07-27 03:19:23,643 INFO [train.py:850] (2/4) Epoch 17, batch 2950, loss[loss=0.2282, simple_loss=0.3198, pruned_loss=0.06834, over 7401.00 frames.], tot_loss[loss=0.19, simple_loss=0.282, pruned_loss=0.04902, over 1465353.55 frames.], batch size: 73, lr: 3.30e-04
2022-07-27 03:20:07,888 INFO [train.py:850] (2/4) Epoch 17, batch 3000, loss[loss=0.1836, simple_loss=0.2907, pruned_loss=0.03827, over 7300.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2831, pruned_loss=0.04973, over 1465918.64 frames.], batch size: 22, lr: 3.30e-04
2022-07-27 03:20:07,889 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 03:20:31,015 INFO [train.py:879] (2/4) Epoch 17, validation: loss=0.1923, simple_loss=0.2868, pruned_loss=0.04889, over 924787.00 frames.
2022-07-27 03:21:15,184 INFO [train.py:850] (2/4) Epoch 17, batch 3050, loss[loss=0.2073, simple_loss=0.2924, pruned_loss=0.06111, over 7296.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2814, pruned_loss=0.04909, over 1465875.20 frames.], batch size: 27, lr: 3.30e-04
2022-07-27 03:21:58,080 INFO [train.py:850] (2/4) Epoch 17, batch 3100, loss[loss=0.2014, simple_loss=0.2942, pruned_loss=0.05431, over 7292.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2811, pruned_loss=0.04895, over 1466149.19 frames.], batch size: 27, lr: 3.30e-04
2022-07-27 03:22:41,779 INFO [train.py:850] (2/4) Epoch 17, batch 3150, loss[loss=0.2219, simple_loss=0.3175, pruned_loss=0.0631, over 7477.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2799, pruned_loss=0.04824, over 1465956.80 frames.], batch size: 24, lr: 3.29e-04
2022-07-27 03:23:25,748 INFO [train.py:850] (2/4) Epoch 17, batch 3200, loss[loss=0.1892, simple_loss=0.2897, pruned_loss=0.04433, over 7429.00 frames.], tot_loss[loss=0.188, simple_loss=0.2799, pruned_loss=0.04802, over 1465723.12 frames.], batch size: 39, lr: 3.29e-04
2022-07-27 03:24:08,499 INFO [train.py:850] (2/4) Epoch 17, batch 3250, loss[loss=0.168, simple_loss=0.254, pruned_loss=0.04102, over 7306.00 frames.], tot_loss[loss=0.1891, simple_loss=0.281, pruned_loss=0.04863, over 1465581.22 frames.], batch size: 18, lr: 3.29e-04
2022-07-27 03:24:52,904 INFO [train.py:850] (2/4) Epoch 17, batch 3300, loss[loss=0.178, simple_loss=0.2773, pruned_loss=0.03938, over 7374.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2803, pruned_loss=0.04817, over 1465473.21 frames.], batch size: 21, lr: 3.29e-04
2022-07-27 03:25:35,364 INFO [train.py:850] (2/4) Epoch 17, batch 3350, loss[loss=0.1627, simple_loss=0.2505, pruned_loss=0.03747, over 7464.00 frames.], tot_loss[loss=0.189, simple_loss=0.2808, pruned_loss=0.04854, over 1465097.29 frames.], batch size: 17, lr: 3.29e-04
2022-07-27 03:26:19,725 INFO [train.py:850] (2/4) Epoch 17, batch 3400, loss[loss=0.1884, simple_loss=0.2845, pruned_loss=0.04615, over 7201.00 frames.], tot_loss[loss=0.189, simple_loss=0.2811, pruned_loss=0.0484, over 1465266.52 frames.], batch size: 18, lr: 3.29e-04
2022-07-27 03:27:02,764 INFO [train.py:850] (2/4) Epoch 17, batch 3450, loss[loss=0.1475, simple_loss=0.2349, pruned_loss=0.03009, over 7102.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2819, pruned_loss=0.04857, over 1465940.25 frames.], batch size: 18, lr: 3.29e-04
2022-07-27 03:27:46,432 INFO [train.py:850] (2/4) Epoch 17, batch 3500, loss[loss=0.2157, simple_loss=0.3131, pruned_loss=0.05911, over 7482.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2821, pruned_loss=0.0488, over 1465196.39 frames.], batch size: 26, lr: 3.29e-04
2022-07-27 03:28:30,461 INFO [train.py:850] (2/4) Epoch 17, batch 3550, loss[loss=0.1761, simple_loss=0.2728, pruned_loss=0.0397, over 7287.00 frames.], tot_loss[loss=0.19, simple_loss=0.2821, pruned_loss=0.0489, over 1464731.59 frames.], batch size: 20, lr: 3.29e-04
2022-07-27 03:29:13,673 INFO [train.py:850] (2/4) Epoch 17, batch 3600, loss[loss=0.1357, simple_loss=0.2234, pruned_loss=0.02405, over 7462.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2813, pruned_loss=0.04856, over 1464118.28 frames.], batch size: 17, lr: 3.29e-04
2022-07-27 03:29:59,094 INFO [train.py:850] (2/4) Epoch 17, batch 3650, loss[loss=0.1693, simple_loss=0.2711, pruned_loss=0.0338, over 7377.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2808, pruned_loss=0.04833, over 1463686.44 frames.], batch size: 21, lr: 3.29e-04
2022-07-27 03:30:41,905 INFO [train.py:850] (2/4) Epoch 17, batch 3700, loss[loss=0.1896, simple_loss=0.2747, pruned_loss=0.0522, over 7168.00 frames.], tot_loss[loss=0.188, simple_loss=0.2797, pruned_loss=0.04815, over 1463831.01 frames.], batch size: 22, lr: 3.29e-04
2022-07-27 03:31:25,295 INFO [train.py:850] (2/4) Epoch 17, batch 3750, loss[loss=0.1926, simple_loss=0.3011, pruned_loss=0.042, over 7290.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2804, pruned_loss=0.04823, over 1464614.65 frames.], batch size: 21, lr: 3.29e-04
2022-07-27 03:32:09,242 INFO [train.py:850] (2/4) Epoch 17, batch 3800, loss[loss=0.1475, simple_loss=0.2442, pruned_loss=0.02541, over 7322.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2808, pruned_loss=0.04867, over 1463968.52 frames.], batch size: 18, lr: 3.29e-04
2022-07-27 03:32:51,621 INFO [train.py:850] (2/4) Epoch 17, batch 3850, loss[loss=0.1428, simple_loss=0.2391, pruned_loss=0.02326, over 7490.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2795, pruned_loss=0.0476, over 1464943.02 frames.], batch size: 19, lr: 3.29e-04
2022-07-27 03:33:36,377 INFO [train.py:850] (2/4) Epoch 17, batch 3900, loss[loss=0.186, simple_loss=0.2634, pruned_loss=0.05427, over 7172.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2792, pruned_loss=0.04767, over 1465097.59 frames.], batch size: 17, lr: 3.29e-04
2022-07-27 03:34:18,644 INFO [train.py:850] (2/4) Epoch 17, batch 3950, loss[loss=0.1763, simple_loss=0.2711, pruned_loss=0.04068, over 7279.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2801, pruned_loss=0.04806, over 1466163.39 frames.], batch size: 21, lr: 3.29e-04
2022-07-27 03:35:03,016 INFO [train.py:850] (2/4) Epoch 17, batch 4000, loss[loss=0.2047, simple_loss=0.2935, pruned_loss=0.0579, over 7485.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2799, pruned_loss=0.04811, over 1466520.92 frames.], batch size: 26, lr: 3.29e-04
2022-07-27 03:35:46,182 INFO [train.py:850] (2/4) Epoch 17, batch 4050, loss[loss=0.19, simple_loss=0.2871, pruned_loss=0.04644, over 7189.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2797, pruned_loss=0.04782, over 1466494.24 frames.], batch size: 21, lr: 3.28e-04
2022-07-27 03:36:29,025 INFO [train.py:850] (2/4) Epoch 17, batch 4100, loss[loss=0.2151, simple_loss=0.3057, pruned_loss=0.06221, over 7222.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2799, pruned_loss=0.04838, over 1465167.52 frames.], batch size: 24, lr: 3.28e-04
2022-07-27 03:37:14,130 INFO [train.py:850] (2/4) Epoch 17, batch 4150, loss[loss=0.1604, simple_loss=0.2581, pruned_loss=0.03132, over 7387.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2804, pruned_loss=0.04952, over 1464573.90 frames.], batch size: 21, lr: 3.28e-04
2022-07-27 03:37:58,247 INFO [train.py:850] (2/4) Epoch 17, batch 4200, loss[loss=0.2419, simple_loss=0.3284, pruned_loss=0.07766, over 7404.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2815, pruned_loss=0.05118, over 1465370.72 frames.], batch size: 73, lr: 3.28e-04
2022-07-27 03:38:42,572 INFO [train.py:850] (2/4) Epoch 17, batch 4250, loss[loss=0.1872, simple_loss=0.2658, pruned_loss=0.05429, over 7389.00 frames.], tot_loss[loss=0.1918, simple_loss=0.281, pruned_loss=0.05132, over 1465606.03 frames.], batch size: 19, lr: 3.28e-04
2022-07-27 03:39:26,697 INFO [train.py:850] (2/4) Epoch 17, batch 4300, loss[loss=0.2015, simple_loss=0.2866, pruned_loss=0.05819, over 7284.00 frames.], tot_loss[loss=0.194, simple_loss=0.2824, pruned_loss=0.05279, over 1465050.42 frames.], batch size: 19, lr: 3.28e-04
2022-07-27 03:40:09,548 INFO [train.py:850] (2/4) Epoch 17, batch 4350, loss[loss=0.2142, simple_loss=0.3006, pruned_loss=0.06384, over 7354.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2836, pruned_loss=0.05401, over 1466079.85 frames.], batch size: 76, lr: 3.28e-04
2022-07-27 03:40:53,969 INFO [train.py:850] (2/4) Epoch 17, batch 4400, loss[loss=0.1911, simple_loss=0.2857, pruned_loss=0.0482, over 7288.00 frames.], tot_loss[loss=0.197, simple_loss=0.2844, pruned_loss=0.0548, over 1466542.03 frames.], batch size: 20, lr: 3.28e-04
2022-07-27 03:41:37,232 INFO [train.py:850] (2/4) Epoch 17, batch 4450, loss[loss=0.2428, simple_loss=0.3201, pruned_loss=0.0828, over 7459.00 frames.], tot_loss[loss=0.199, simple_loss=0.2859, pruned_loss=0.05603, over 1466551.65 frames.], batch size: 21, lr: 3.28e-04
2022-07-27 03:42:24,566 INFO [train.py:850] (2/4) Epoch 17, batch 4500, loss[loss=0.2058, simple_loss=0.2798, pruned_loss=0.06584, over 7204.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2855, pruned_loss=0.05656, over 1465914.08 frames.], batch size: 19, lr: 3.28e-04
2022-07-27 03:43:08,094 INFO [train.py:850] (2/4) Epoch 17, batch 4550, loss[loss=0.207, simple_loss=0.2995, pruned_loss=0.05722, over 7473.00 frames.], tot_loss[loss=0.2003, simple_loss=0.286, pruned_loss=0.05731, over 1465287.31 frames.], batch size: 21, lr: 3.28e-04
2022-07-27 03:43:51,611 INFO [train.py:850] (2/4) Epoch 17, batch 4600, loss[loss=0.1781, simple_loss=0.271, pruned_loss=0.04257, over 7289.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2867, pruned_loss=0.05806, over 1465682.77 frames.], batch size: 20, lr: 3.28e-04
2022-07-27 03:44:36,002 INFO [train.py:850] (2/4) Epoch 17, batch 4650, loss[loss=0.1852, simple_loss=0.2741, pruned_loss=0.04811, over 7489.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2856, pruned_loss=0.05778, over 1466175.81 frames.], batch size: 20, lr: 3.28e-04
2022-07-27 03:45:18,848 INFO [train.py:850] (2/4) Epoch 17, batch 4700, loss[loss=0.2069, simple_loss=0.2855, pruned_loss=0.06416, over 7293.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2863, pruned_loss=0.05876, over 1465268.16 frames.], batch size: 21, lr: 3.28e-04
2022-07-27 03:46:03,319 INFO [train.py:850] (2/4) Epoch 17, batch 4750, loss[loss=0.2037, simple_loss=0.2855, pruned_loss=0.06094, over 7215.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2865, pruned_loss=0.05909, over 1465723.20 frames.], batch size: 24, lr: 3.28e-04
2022-07-27 03:46:46,034 INFO [train.py:850] (2/4) Epoch 17, batch 4800, loss[loss=0.2029, simple_loss=0.2838, pruned_loss=0.06104, over 7481.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2873, pruned_loss=0.05946, over 1465604.52 frames.], batch size: 21, lr: 3.28e-04
2022-07-27 03:47:28,513 INFO [train.py:850] (2/4) Epoch 17, batch 4850, loss[loss=0.2002, simple_loss=0.288, pruned_loss=0.05622, over 7199.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2879, pruned_loss=0.05989, over 1465450.82 frames.], batch size: 20, lr: 3.28e-04
2022-07-27 03:48:13,164 INFO [train.py:850] (2/4) Epoch 17, batch 4900, loss[loss=0.1686, simple_loss=0.263, pruned_loss=0.03707, over 7198.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2875, pruned_loss=0.05987, over 1465844.67 frames.], batch size: 18, lr: 3.28e-04
2022-07-27 03:48:55,378 INFO [train.py:850] (2/4) Epoch 17, batch 4950, loss[loss=0.2663, simple_loss=0.3375, pruned_loss=0.09753, over 7449.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2876, pruned_loss=0.06013, over 1467444.69 frames.], batch size: 39, lr: 3.27e-04
2022-07-27 03:49:40,269 INFO [train.py:850] (2/4) Epoch 17, batch 5000, loss[loss=0.1812, simple_loss=0.2729, pruned_loss=0.04472, over 7289.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2874, pruned_loss=0.06037, over 1466112.48 frames.], batch size: 20, lr: 3.27e-04
2022-07-27 03:50:22,908 INFO [train.py:850] (2/4) Epoch 17, batch 5050, loss[loss=0.2402, simple_loss=0.3199, pruned_loss=0.08024, over 7346.00 frames.], tot_loss[loss=0.2039, simple_loss=0.287, pruned_loss=0.06037, over 1466246.29 frames.], batch size: 23, lr: 3.27e-04
2022-07-27 03:51:08,474 INFO [train.py:850] (2/4) Epoch 17, batch 5100, loss[loss=0.203, simple_loss=0.2733, pruned_loss=0.06637, over 7190.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2866, pruned_loss=0.06014, over 1464573.25 frames.], batch size: 16, lr: 3.27e-04
2022-07-27 03:51:50,876 INFO [train.py:850] (2/4) Epoch 17, batch 5150, loss[loss=0.1725, simple_loss=0.257, pruned_loss=0.04401, over 7297.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2869, pruned_loss=0.06015, over 1465326.50 frames.], batch size: 18, lr: 3.27e-04
2022-07-27 03:52:33,600 INFO [train.py:850] (2/4) Epoch 17, batch 5200, loss[loss=0.2087, simple_loss=0.2811, pruned_loss=0.06813, over 7300.00 frames.], tot_loss[loss=0.204, simple_loss=0.2872, pruned_loss=0.06043, over 1465209.85 frames.], batch size: 27, lr: 3.27e-04
2022-07-27 03:53:18,048 INFO [train.py:850] (2/4) Epoch 17, batch 5250, loss[loss=0.1996, simple_loss=0.2899, pruned_loss=0.05466, over 7281.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2871, pruned_loss=0.06013, over 1465079.05 frames.], batch size: 21, lr: 3.27e-04
2022-07-27 03:54:01,610 INFO [train.py:850] (2/4) Epoch 17, batch 5300, loss[loss=0.2238, simple_loss=0.2974, pruned_loss=0.07509, over 7455.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2871, pruned_loss=0.06082, over 1464545.55 frames.], batch size: 73, lr: 3.27e-04
2022-07-27 03:54:45,847 INFO [train.py:850] (2/4) Epoch 17, batch 5350, loss[loss=0.1949, simple_loss=0.2879, pruned_loss=0.05095, over 7249.00 frames.], tot_loss[loss=0.2042, simple_loss=0.287, pruned_loss=0.06072, over 1464541.21 frames.], batch size: 27, lr: 3.27e-04
2022-07-27 03:55:29,344 INFO [train.py:850] (2/4) Epoch 17, batch 5400, loss[loss=0.2037, simple_loss=0.289, pruned_loss=0.05922, over 7199.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2868, pruned_loss=0.06015, over 1465492.16 frames.], batch size: 18, lr: 3.27e-04
2022-07-27 03:56:11,503 INFO [train.py:850] (2/4) Epoch 17, batch 5450, loss[loss=0.1937, simple_loss=0.2762, pruned_loss=0.05559, over 7408.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2864, pruned_loss=0.06008, over 1464591.86 frames.], batch size: 22, lr: 3.27e-04
2022-07-27 03:56:56,394 INFO [train.py:850] (2/4) Epoch 17, batch 5500, loss[loss=0.2166, simple_loss=0.2818, pruned_loss=0.07565, over 7461.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2869, pruned_loss=0.06012, over 1463774.12 frames.], batch size: 17, lr: 3.27e-04
2022-07-27 03:57:39,475 INFO [train.py:850] (2/4) Epoch 17, batch 5550, loss[loss=0.2146, simple_loss=0.309, pruned_loss=0.06013, over 7285.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2873, pruned_loss=0.06053, over 1464335.60 frames.], batch size: 21, lr: 3.27e-04
2022-07-27 03:58:24,754 INFO [train.py:850] (2/4) Epoch 17, batch 5600, loss[loss=0.2216, simple_loss=0.2996, pruned_loss=0.07178, over 7282.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2874, pruned_loss=0.06081, over 1464878.01 frames.], batch size: 20, lr: 3.27e-04
2022-07-27 03:59:07,653 INFO [train.py:850] (2/4) Epoch 17, batch 5650, loss[loss=0.2016, simple_loss=0.2855, pruned_loss=0.0588, over 7167.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2864, pruned_loss=0.06037, over 1463685.26 frames.], batch size: 22, lr: 3.27e-04
2022-07-27 04:00:07,205 INFO [train.py:850] (2/4) Epoch 17, batch 5700, loss[loss=0.1995, simple_loss=0.2883, pruned_loss=0.05535, over 7456.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2861, pruned_loss=0.05967, over 1463263.71 frames.], batch size: 39, lr: 3.27e-04
2022-07-27 04:00:49,441 INFO [train.py:850] (2/4) Epoch 17, batch 5750, loss[loss=0.2546, simple_loss=0.3173, pruned_loss=0.09596, over 7357.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2848, pruned_loss=0.05914, over 1463196.97 frames.], batch size: 73, lr: 3.27e-04
2022-07-27 04:01:32,940 INFO [train.py:850] (2/4) Epoch 17, batch 5800, loss[loss=0.2193, simple_loss=0.3091, pruned_loss=0.06469, over 7296.00 frames.], tot_loss[loss=0.2009, simple_loss=0.284, pruned_loss=0.05891, over 1463410.69 frames.], batch size: 21, lr: 3.27e-04
2022-07-27 04:02:16,730 INFO [train.py:850] (2/4) Epoch 17, batch 5850, loss[loss=0.2086, simple_loss=0.2813, pruned_loss=0.06798, over 7473.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2837, pruned_loss=0.05861, over 1464284.24 frames.], batch size: 20, lr: 3.26e-04
2022-07-27 04:02:59,664 INFO [train.py:850] (2/4) Epoch 17, batch 5900, loss[loss=0.1885, simple_loss=0.2757, pruned_loss=0.05068, over 7212.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2829, pruned_loss=0.05788, over 1464777.87 frames.], batch size: 20, lr: 3.26e-04
2022-07-27 04:03:45,124 INFO [train.py:850] (2/4) Epoch 17, batch 5950, loss[loss=0.2114, simple_loss=0.3003, pruned_loss=0.06127, over 7414.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2841, pruned_loss=0.0584, over 1465775.37 frames.], batch size: 22, lr: 3.26e-04
2022-07-27 04:04:29,095 INFO [train.py:850] (2/4) Epoch 17, batch 6000, loss[loss=0.2117, simple_loss=0.3046, pruned_loss=0.05943, over 7298.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2838, pruned_loss=0.05794, over 1466129.89 frames.], batch size: 22, lr: 3.26e-04
2022-07-27 04:04:29,096 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 04:04:51,814 INFO [train.py:879] (2/4) Epoch 17, validation: loss=0.187, simple_loss=0.2839, pruned_loss=0.04502, over 924787.00 frames.
2022-07-27 04:05:36,234 INFO [train.py:850] (2/4) Epoch 17, batch 6050, loss[loss=0.1766, simple_loss=0.2616, pruned_loss=0.04579, over 7207.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2836, pruned_loss=0.05808, over 1465904.29 frames.], batch size: 19, lr: 3.26e-04
2022-07-27 04:06:20,373 INFO [train.py:850] (2/4) Epoch 17, batch 6100, loss[loss=0.1735, simple_loss=0.2582, pruned_loss=0.04442, over 7291.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2828, pruned_loss=0.05764, over 1465483.72 frames.], batch size: 20, lr: 3.26e-04
2022-07-27 04:07:05,952 INFO [train.py:850] (2/4) Epoch 17, batch 6150, loss[loss=0.2582, simple_loss=0.3345, pruned_loss=0.0909, over 7187.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2836, pruned_loss=0.05835, over 1465066.87 frames.], batch size: 21, lr: 3.26e-04
2022-07-27 04:07:51,067 INFO [train.py:850] (2/4) Epoch 17, batch 6200, loss[loss=0.2307, simple_loss=0.3118, pruned_loss=0.07476, over 7181.00 frames.], tot_loss[loss=0.2, simple_loss=0.2834, pruned_loss=0.05829, over 1464662.24 frames.], batch size: 23, lr: 3.26e-04
2022-07-27 04:08:37,859 INFO [train.py:850] (2/4) Epoch 17, batch 6250, loss[loss=0.2243, simple_loss=0.3053, pruned_loss=0.07161, over 7299.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2833, pruned_loss=0.05799, over 1465734.51 frames.], batch size: 19, lr: 3.26e-04
2022-07-27 04:09:22,853 INFO [train.py:850] (2/4) Epoch 17, batch 6300, loss[loss=0.1765, simple_loss=0.259, pruned_loss=0.047, over 7151.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2838, pruned_loss=0.05827, over 1466033.77 frames.], batch size: 17, lr: 3.26e-04
2022-07-27 04:10:08,352 INFO [train.py:850] (2/4) Epoch 17, batch 6350, loss[loss=0.2096, simple_loss=0.2806, pruned_loss=0.06929, over 7308.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2848, pruned_loss=0.0591, over 1466521.09 frames.], batch size: 17, lr: 3.26e-04
2022-07-27 04:10:55,371 INFO [train.py:850] (2/4) Epoch 17, batch 6400, loss[loss=0.2513, simple_loss=0.3286, pruned_loss=0.08694, over 7475.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2837, pruned_loss=0.05834, over 1465556.35 frames.], batch size: 26, lr: 3.26e-04
2022-07-27 04:11:39,024 INFO [train.py:850] (2/4) Epoch 17, batch 6450, loss[loss=0.1888, simple_loss=0.2663, pruned_loss=0.05572, over 7203.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2838, pruned_loss=0.05853, over 1465232.94 frames.], batch size: 18, lr: 3.26e-04
2022-07-27 04:12:24,071 INFO [train.py:850] (2/4) Epoch 17, batch 6500, loss[loss=0.1779, simple_loss=0.2718, pruned_loss=0.04198, over 7491.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2832, pruned_loss=0.05802, over 1464709.48 frames.], batch size: 23, lr: 3.26e-04
2022-07-27 04:13:07,719 INFO [train.py:850] (2/4) Epoch 17, batch 6550, loss[loss=0.1826, simple_loss=0.2863, pruned_loss=0.03946, over 7386.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2838, pruned_loss=0.05828, over 1465438.21 frames.], batch size: 21, lr: 3.26e-04
2022-07-27 04:13:52,550 INFO [train.py:850] (2/4) Epoch 17, batch 6600, loss[loss=0.2182, simple_loss=0.3132, pruned_loss=0.06162, over 7335.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2848, pruned_loss=0.05877, over 1464176.54 frames.], batch size: 23, lr: 3.26e-04
2022-07-27 04:14:35,877 INFO [train.py:850] (2/4) Epoch 17, batch 6650, loss[loss=0.1726, simple_loss=0.2658, pruned_loss=0.03976, over 7216.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2844, pruned_loss=0.0587, over 1465650.96 frames.], batch size: 24, lr: 3.26e-04
2022-07-27 04:15:19,166 INFO [train.py:850] (2/4) Epoch 17, batch 6700, loss[loss=0.2555, simple_loss=0.3285, pruned_loss=0.09126, over 7345.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2854, pruned_loss=0.05972, over 1466817.43 frames.], batch size: 38, lr: 3.26e-04
2022-07-27 04:16:04,100 INFO [train.py:850] (2/4) Epoch 17, batch 6750, loss[loss=0.2257, simple_loss=0.2958, pruned_loss=0.07783, over 7197.00 frames.], tot_loss[loss=0.2003, simple_loss=0.284, pruned_loss=0.05829, over 1467033.84 frames.], batch size: 18, lr: 3.25e-04
2022-07-27 04:16:47,744 INFO [train.py:850] (2/4) Epoch 17, batch 6800, loss[loss=0.1724, simple_loss=0.2412, pruned_loss=0.05183, over 7294.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2829, pruned_loss=0.05809, over 1466927.56 frames.], batch size: 17, lr: 3.25e-04
2022-07-27 04:17:32,283 INFO [train.py:850] (2/4) Epoch 17, batch 6850, loss[loss=0.2131, simple_loss=0.3059, pruned_loss=0.0601, over 7405.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2819, pruned_loss=0.05769, over 1467137.10 frames.], batch size: 31, lr: 3.25e-04
2022-07-27 04:18:15,466 INFO [train.py:850] (2/4) Epoch 17, batch 6900, loss[loss=0.2141, simple_loss=0.305, pruned_loss=0.06163, over 7470.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2837, pruned_loss=0.05863, over 1467469.67 frames.], batch size: 21, lr: 3.25e-04
2022-07-27 04:18:59,255 INFO [train.py:850] (2/4) Epoch 17, batch 6950, loss[loss=0.1911, simple_loss=0.2803, pruned_loss=0.05092, over 7097.00 frames.], tot_loss[loss=0.2007, simple_loss=0.284, pruned_loss=0.05875, over 1466815.10 frames.], batch size: 18, lr: 3.25e-04
2022-07-27 04:19:42,920 INFO [train.py:850] (2/4) Epoch 17, batch 7000, loss[loss=0.1887, simple_loss=0.2734, pruned_loss=0.05201, over 7309.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2843, pruned_loss=0.05895, over 1467720.98 frames.], batch size: 17, lr: 3.25e-04
2022-07-27 04:20:26,866 INFO [train.py:850] (2/4) Epoch 17, batch 7050, loss[loss=0.1772, simple_loss=0.251, pruned_loss=0.05173, over 7396.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2846, pruned_loss=0.05913, over 1467315.58 frames.], batch size: 19, lr: 3.25e-04
2022-07-27 04:21:11,186 INFO [train.py:850] (2/4) Epoch 17, batch 7100, loss[loss=0.197, simple_loss=0.281, pruned_loss=0.05651, over 7293.00 frames.], tot_loss[loss=0.2002, simple_loss=0.283, pruned_loss=0.05869, over 1466802.53 frames.], batch size: 19, lr: 3.25e-04
2022-07-27 04:21:55,471 INFO [train.py:850] (2/4) Epoch 17, batch 7150, loss[loss=0.2162, simple_loss=0.3068, pruned_loss=0.06278, over 7486.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2821, pruned_loss=0.0582, over 1466816.88 frames.], batch size: 39, lr: 3.25e-04
2022-07-27 04:22:41,131 INFO [train.py:850] (2/4) Epoch 17, batch 7200, loss[loss=0.2134, simple_loss=0.2974, pruned_loss=0.06468, over 7452.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2809, pruned_loss=0.05743, over 1466003.91 frames.], batch size: 24, lr: 3.25e-04
2022-07-27 04:23:24,048 INFO [train.py:850] (2/4) Epoch 17, batch 7250, loss[loss=0.2064, simple_loss=0.3011, pruned_loss=0.05588, over 7208.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2822, pruned_loss=0.05816, over 1465782.33 frames.], batch size: 20, lr: 3.25e-04
2022-07-27 04:24:08,899 INFO [train.py:850] (2/4) Epoch 17, batch 7300, loss[loss=0.1805, simple_loss=0.2643, pruned_loss=0.04832, over 7486.00 frames.], tot_loss[loss=0.2009, simple_loss=0.284, pruned_loss=0.05887, over 1465838.72 frames.], batch size: 31, lr: 3.25e-04
2022-07-27 04:24:51,821 INFO [train.py:850] (2/4) Epoch 17, batch 7350, loss[loss=0.2093, simple_loss=0.3071, pruned_loss=0.05569, over 7442.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2842, pruned_loss=0.05862, over 1465674.52 frames.], batch size: 24, lr: 3.25e-04
2022-07-27 04:25:35,816 INFO [train.py:850] (2/4) Epoch 17, batch 7400, loss[loss=0.2538, simple_loss=0.328, pruned_loss=0.08984, over 7406.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2831, pruned_loss=0.05793, over 1465839.72 frames.], batch size: 69, lr: 3.25e-04
2022-07-27 04:26:20,562 INFO [train.py:850] (2/4) Epoch 17, batch 7450, loss[loss=0.2119, simple_loss=0.3032, pruned_loss=0.06028, over 7286.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2843, pruned_loss=0.05895, over 1465888.04 frames.], batch size: 30, lr: 3.25e-04
2022-07-27 04:27:03,216 INFO [train.py:850] (2/4) Epoch 17, batch 7500, loss[loss=0.1979, simple_loss=0.2908, pruned_loss=0.05251, over 7294.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2853, pruned_loss=0.05946, over 1465025.65 frames.], batch size: 22, lr: 3.25e-04
2022-07-27 04:27:48,261 INFO [train.py:850] (2/4) Epoch 17, batch 7550, loss[loss=0.1619, simple_loss=0.2601, pruned_loss=0.03186, over 7283.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2837, pruned_loss=0.05847, over 1464480.59 frames.], batch size: 20, lr: 3.25e-04
2022-07-27 04:28:31,495 INFO [train.py:850] (2/4) Epoch 17, batch 7600, loss[loss=0.1873, simple_loss=0.2747, pruned_loss=0.04996, over 7289.00 frames.], tot_loss[loss=0.1992, simple_loss=0.283, pruned_loss=0.05766, over 1464810.02 frames.], batch size: 19, lr: 3.25e-04
2022-07-27 04:29:16,333 INFO [train.py:850] (2/4) Epoch 17, batch 7650, loss[loss=0.2428, simple_loss=0.2936, pruned_loss=0.09599, over 7302.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2826, pruned_loss=0.05738, over 1465307.73 frames.], batch size: 17, lr: 3.25e-04
2022-07-27 04:29:59,783 INFO [train.py:850] (2/4) Epoch 17, batch 7700, loss[loss=0.2334, simple_loss=0.3229, pruned_loss=0.07195, over 7252.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2843, pruned_loss=0.05837, over 1464982.77 frames.], batch size: 30, lr: 3.24e-04
2022-07-27 04:30:44,171 INFO [train.py:850] (2/4) Epoch 17, batch 7750, loss[loss=0.2439, simple_loss=0.3259, pruned_loss=0.08097, over 7316.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2851, pruned_loss=0.05882, over 1464504.34 frames.], batch size: 38, lr: 3.24e-04
2022-07-27 04:31:30,577 INFO [train.py:850] (2/4) Epoch 17, batch 7800, loss[loss=0.2076, simple_loss=0.2933, pruned_loss=0.06095, over 7377.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2849, pruned_loss=0.05872, over 1464813.77 frames.], batch size: 21, lr: 3.24e-04
2022-07-27 04:32:14,535 INFO [train.py:850] (2/4) Epoch 17, batch 7850, loss[loss=0.1603, simple_loss=0.2589, pruned_loss=0.03091, over 7309.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2847, pruned_loss=0.05861, over 1464822.01 frames.], batch size: 22, lr: 3.24e-04
2022-07-27 04:33:00,928 INFO [train.py:850] (2/4) Epoch 17, batch 7900, loss[loss=0.1697, simple_loss=0.2581, pruned_loss=0.04067, over 7444.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2841, pruned_loss=0.05859, over 1465094.45 frames.], batch size: 18, lr: 3.24e-04
2022-07-27 04:33:45,109 INFO [train.py:850] (2/4) Epoch 17, batch 7950, loss[loss=0.1978, simple_loss=0.291, pruned_loss=0.05229, over 7436.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2849, pruned_loss=0.05871, over 1464829.78 frames.], batch size: 40, lr: 3.24e-04
2022-07-27 04:34:31,178 INFO [train.py:850] (2/4) Epoch 17, batch 8000, loss[loss=0.194, simple_loss=0.2905, pruned_loss=0.04876, over 7328.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2838, pruned_loss=0.05803, over 1464487.41 frames.], batch size: 23, lr: 3.24e-04
2022-07-27 04:35:14,108 INFO [train.py:850] (2/4) Epoch 17, batch 8050, loss[loss=0.18, simple_loss=0.2547, pruned_loss=0.05267, over 7446.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2836, pruned_loss=0.0574, over 1464758.49 frames.], batch size: 18, lr: 3.24e-04
2022-07-27 04:35:57,430 INFO [train.py:850] (2/4) Epoch 17, batch 8100, loss[loss=0.2196, simple_loss=0.2976, pruned_loss=0.07084, over 7216.00 frames.], tot_loss[loss=0.1999, simple_loss=0.284, pruned_loss=0.05791, over 1463519.70 frames.], batch size: 25, lr: 3.24e-04
2022-07-27 04:36:41,867 INFO [train.py:850] (2/4) Epoch 17, batch 8150, loss[loss=0.257, simple_loss=0.3306, pruned_loss=0.0917, over 7386.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2849, pruned_loss=0.05834, over 1464804.50 frames.], batch size: 78, lr: 3.24e-04
2022-07-27 04:37:25,349 INFO [train.py:850] (2/4) Epoch 17, batch 8200, loss[loss=0.1873, simple_loss=0.2775, pruned_loss=0.0486, over 7306.00 frames.], tot_loss[loss=0.2, simple_loss=0.2841, pruned_loss=0.05793, over 1466274.25 frames.], batch size: 22, lr: 3.24e-04
2022-07-27 04:38:10,043 INFO [train.py:850] (2/4) Epoch 17, batch 8250, loss[loss=0.2153, simple_loss=0.2978, pruned_loss=0.06644, over 7469.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2839, pruned_loss=0.05819, over 1465786.62 frames.], batch size: 21, lr: 3.24e-04
2022-07-27 04:38:53,534 INFO [train.py:850] (2/4) Epoch 17, batch 8300, loss[loss=0.1897, simple_loss=0.2592, pruned_loss=0.06009, over 7314.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2842, pruned_loss=0.05833, over 1464914.47 frames.], batch size: 17, lr: 3.24e-04
2022-07-27 04:39:38,139 INFO [train.py:850] (2/4) Epoch 17, batch 8350, loss[loss=0.2125, simple_loss=0.2857, pruned_loss=0.06962, over 7492.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2833, pruned_loss=0.0578, over 1464971.05 frames.], batch size: 19, lr: 3.24e-04
2022-07-27 04:40:20,939 INFO [train.py:850] (2/4) Epoch 17, batch 8400, loss[loss=0.1647, simple_loss=0.2465, pruned_loss=0.04141, over 7194.00 frames.], tot_loss[loss=0.198, simple_loss=0.2821, pruned_loss=0.05697, over 1464357.93 frames.], batch size: 18, lr: 3.24e-04
2022-07-27 04:41:04,002 INFO [train.py:850] (2/4) Epoch 17, batch 8450, loss[loss=0.1933, simple_loss=0.2621, pruned_loss=0.06221, over 7440.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2822, pruned_loss=0.057, over 1465138.50 frames.], batch size: 18, lr: 3.24e-04
2022-07-27 04:41:49,181 INFO [train.py:850] (2/4) Epoch 17, batch 8500, loss[loss=0.1918, simple_loss=0.2866, pruned_loss=0.04844, over 7330.00 frames.], tot_loss[loss=0.1991, simple_loss=0.283, pruned_loss=0.05764, over 1466008.13 frames.], batch size: 23, lr: 3.24e-04
2022-07-27 04:42:32,190 INFO [train.py:850] (2/4) Epoch 17, batch 8550, loss[loss=0.1851, simple_loss=0.274, pruned_loss=0.04811, over 7299.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2837, pruned_loss=0.05764, over 1466630.88 frames.], batch size: 18, lr: 3.24e-04
2022-07-27 04:43:16,765 INFO [train.py:850] (2/4) Epoch 17, batch 8600, loss[loss=0.1471, simple_loss=0.2245, pruned_loss=0.03489, over 7299.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2821, pruned_loss=0.05662, over 1467003.71 frames.], batch size: 17, lr: 3.23e-04
2022-07-27 04:43:59,068 INFO [train.py:850] (2/4) Epoch 17, batch 8650, loss[loss=0.2627, simple_loss=0.3262, pruned_loss=0.09958, over 7474.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2815, pruned_loss=0.05657, over 1467597.53 frames.], batch size: 72, lr: 3.23e-04
2022-07-27 04:44:41,425 INFO [train.py:850] (2/4) Epoch 17, batch 8700, loss[loss=0.18, simple_loss=0.2753, pruned_loss=0.04238, over 7377.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2812, pruned_loss=0.05674, over 1468106.93 frames.], batch size: 21, lr: 3.23e-04
2022-07-27 04:45:23,593 INFO [train.py:850] (2/4) Epoch 17, batch 8750, loss[loss=0.2013, simple_loss=0.2881, pruned_loss=0.05724, over 7242.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2814, pruned_loss=0.05668, over 1469013.43 frames.], batch size: 24, lr: 3.23e-04
2022-07-27 04:46:07,144 INFO [train.py:850] (2/4) Epoch 17, batch 8800, loss[loss=0.1886, simple_loss=0.2821, pruned_loss=0.04754, over 7317.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2826, pruned_loss=0.05723, over 1468515.46 frames.], batch size: 27, lr: 3.23e-04
2022-07-27 04:46:50,735 INFO [train.py:850] (2/4) Epoch 17, batch 8850, loss[loss=0.1889, simple_loss=0.2905, pruned_loss=0.04368, over 7473.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2831, pruned_loss=0.05752, over 1467031.46 frames.], batch size: 24, lr: 3.23e-04
2022-07-27 04:48:16,715 INFO [train.py:850] (2/4) Epoch 18, batch 0, loss[loss=0.154, simple_loss=0.2422, pruned_loss=0.03286, over 7437.00 frames.], tot_loss[loss=0.154, simple_loss=0.2422, pruned_loss=0.03286, over 7437.00 frames.], batch size: 18, lr: 3.15e-04
2022-07-27 04:48:59,895 INFO [train.py:850] (2/4) Epoch 18, batch 50, loss[loss=0.1668, simple_loss=0.2644, pruned_loss=0.03463, over 7202.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2808, pruned_loss=0.04912, over 329684.74 frames.], batch size: 20, lr: 3.15e-04
2022-07-27 04:49:44,479 INFO [train.py:850] (2/4) Epoch 18, batch 100, loss[loss=0.1792, simple_loss=0.2722, pruned_loss=0.04316, over 7204.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2815, pruned_loss=0.04914, over 582764.61 frames.], batch size: 20, lr: 3.15e-04
2022-07-27 04:50:27,242 INFO [train.py:850] (2/4) Epoch 18, batch 150, loss[loss=0.1413, simple_loss=0.2306, pruned_loss=0.02603, over 7458.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2802, pruned_loss=0.04841, over 778148.90 frames.], batch size: 17, lr: 3.14e-04
2022-07-27 04:51:11,155 INFO [train.py:850] (2/4) Epoch 18, batch 200, loss[loss=0.1916, simple_loss=0.2798, pruned_loss=0.05169, over 7371.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2802, pruned_loss=0.04899, over 930229.98 frames.], batch size: 38, lr: 3.14e-04
2022-07-27 04:51:54,883 INFO [train.py:850] (2/4) Epoch 18, batch 250, loss[loss=0.2099, simple_loss=0.2909, pruned_loss=0.06441, over 7485.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2805, pruned_loss=0.04947, over 1049340.00 frames.], batch size: 20, lr: 3.14e-04
2022-07-27 04:52:37,234 INFO [train.py:850] (2/4) Epoch 18, batch 300, loss[loss=0.1641, simple_loss=0.2508, pruned_loss=0.03865, over 7262.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2778, pruned_loss=0.04843, over 1141137.74 frames.], batch size: 16, lr: 3.14e-04
2022-07-27 04:53:22,190 INFO [train.py:850] (2/4) Epoch 18, batch 350, loss[loss=0.1573, simple_loss=0.2495, pruned_loss=0.03256, over 7290.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2781, pruned_loss=0.04847, over 1212934.74 frames.], batch size: 19, lr: 3.14e-04
2022-07-27 04:54:04,685 INFO [train.py:850] (2/4) Epoch 18, batch 400, loss[loss=0.1983, simple_loss=0.282, pruned_loss=0.05735, over 7204.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2774, pruned_loss=0.04785, over 1268556.71 frames.], batch size: 20, lr: 3.14e-04
2022-07-27 04:54:48,945 INFO [train.py:850] (2/4) Epoch 18, batch 450, loss[loss=0.2213, simple_loss=0.3035, pruned_loss=0.06953, over 7418.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2763, pruned_loss=0.04738, over 1311927.01 frames.], batch size: 69, lr: 3.14e-04
2022-07-27 04:55:32,273 INFO [train.py:850] (2/4) Epoch 18, batch 500, loss[loss=0.1795, simple_loss=0.2586, pruned_loss=0.05016, over 7268.00 frames.], tot_loss[loss=0.1853, simple_loss=0.276, pruned_loss=0.0473, over 1345753.24 frames.], batch size: 16, lr: 3.14e-04
2022-07-27 04:56:16,040 INFO [train.py:850] (2/4) Epoch 18, batch 550, loss[loss=0.1832, simple_loss=0.2889, pruned_loss=0.0387, over 7226.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2763, pruned_loss=0.04745, over 1371903.00 frames.], batch size: 25, lr: 3.14e-04
2022-07-27 04:57:00,071 INFO [train.py:850] (2/4) Epoch 18, batch 600, loss[loss=0.1998, simple_loss=0.3017, pruned_loss=0.0489, over 7411.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2775, pruned_loss=0.04795, over 1393007.52 frames.], batch size: 22, lr: 3.14e-04
2022-07-27 04:57:43,564 INFO [train.py:850] (2/4) Epoch 18, batch 650, loss[loss=0.1596, simple_loss=0.2371, pruned_loss=0.04104, over 7299.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2762, pruned_loss=0.04704, over 1409357.27 frames.], batch size: 17, lr: 3.14e-04
2022-07-27 04:58:28,304 INFO [train.py:850] (2/4) Epoch 18, batch 700, loss[loss=0.183, simple_loss=0.2846, pruned_loss=0.04071, over 7284.00 frames.], tot_loss[loss=0.185, simple_loss=0.276, pruned_loss=0.04699, over 1422303.01 frames.], batch size: 21, lr: 3.14e-04
2022-07-27 04:59:11,067 INFO [train.py:850] (2/4) Epoch 18, batch 750, loss[loss=0.1729, simple_loss=0.2626, pruned_loss=0.04162, over 7478.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2756, pruned_loss=0.0465, over 1432620.38 frames.], batch size: 19, lr: 3.14e-04
2022-07-27 04:59:55,069 INFO [train.py:850] (2/4) Epoch 18, batch 800, loss[loss=0.1965, simple_loss=0.2848, pruned_loss=0.05411, over 7205.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2767, pruned_loss=0.04687, over 1440377.61 frames.], batch size: 20, lr: 3.14e-04
2022-07-27 05:00:55,732 INFO [train.py:850] (2/4) Epoch 18, batch 850, loss[loss=0.1727, simple_loss=0.264, pruned_loss=0.04064, over 7110.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2763, pruned_loss=0.04659, over 1445661.15 frames.], batch size: 18, lr: 3.14e-04
2022-07-27 05:01:40,169 INFO [train.py:850] (2/4) Epoch 18, batch 900, loss[loss=0.1796, simple_loss=0.2729, pruned_loss=0.04312, over 7302.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2767, pruned_loss=0.04693, over 1450450.48 frames.], batch size: 19, lr: 3.14e-04
2022-07-27 05:02:26,774 INFO [train.py:850] (2/4) Epoch 18, batch 950, loss[loss=0.2229, simple_loss=0.3108, pruned_loss=0.06753, over 7396.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2786, pruned_loss=0.04781, over 1452706.95 frames.], batch size: 38, lr: 3.14e-04
2022-07-27 05:03:11,332 INFO [train.py:850] (2/4) Epoch 18, batch 1000, loss[loss=0.1776, simple_loss=0.2783, pruned_loss=0.03845, over 7209.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2788, pruned_loss=0.04835, over 1454772.26 frames.], batch size: 20, lr: 3.14e-04
2022-07-27 05:03:58,263 INFO [train.py:850] (2/4) Epoch 18, batch 1050, loss[loss=0.1638, simple_loss=0.2481, pruned_loss=0.03976, over 7314.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2806, pruned_loss=0.04957, over 1457884.74 frames.], batch size: 17, lr: 3.14e-04
2022-07-27 05:04:41,532 INFO [train.py:850] (2/4) Epoch 18, batch 1100, loss[loss=0.2165, simple_loss=0.3019, pruned_loss=0.06553, over 7293.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2799, pruned_loss=0.04911, over 1459204.02 frames.], batch size: 19, lr: 3.14e-04
2022-07-27 05:05:24,924 INFO [train.py:850] (2/4) Epoch 18, batch 1150, loss[loss=0.1775, simple_loss=0.2656, pruned_loss=0.04467, over 7463.00 frames.], tot_loss[loss=0.1896, simple_loss=0.28, pruned_loss=0.04958, over 1460462.29 frames.], batch size: 18, lr: 3.13e-04
2022-07-27 05:06:09,263 INFO [train.py:850] (2/4) Epoch 18, batch 1200, loss[loss=0.2102, simple_loss=0.3028, pruned_loss=0.05879, over 7315.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2813, pruned_loss=0.05021, over 1461541.04 frames.], batch size: 22, lr: 3.13e-04
2022-07-27 05:06:53,502 INFO [train.py:850] (2/4) Epoch 18, batch 1250, loss[loss=0.2165, simple_loss=0.3103, pruned_loss=0.06129, over 7344.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2821, pruned_loss=0.05015, over 1463141.95 frames.], batch size: 23, lr: 3.13e-04
2022-07-27 05:07:36,865 INFO [train.py:850] (2/4) Epoch 18, batch 1300, loss[loss=0.1804, simple_loss=0.284, pruned_loss=0.03839, over 7231.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2807, pruned_loss=0.04909, over 1462396.18 frames.], batch size: 25, lr: 3.13e-04
2022-07-27 05:08:19,556 INFO [train.py:850] (2/4) Epoch 18, batch 1350, loss[loss=0.1965, simple_loss=0.2962, pruned_loss=0.04838, over 7469.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2806, pruned_loss=0.04835, over 1462672.94 frames.], batch size: 24, lr: 3.13e-04
2022-07-27 05:09:01,704 INFO [train.py:850] (2/4) Epoch 18, batch 1400, loss[loss=0.168, simple_loss=0.2498, pruned_loss=0.04316, over 7159.00 frames.], tot_loss[loss=0.188, simple_loss=0.2805, pruned_loss=0.04781, over 1463807.38 frames.], batch size: 17, lr: 3.13e-04
2022-07-27 05:09:46,498 INFO [train.py:850] (2/4) Epoch 18, batch 1450, loss[loss=0.1821, simple_loss=0.2693, pruned_loss=0.04742, over 7153.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2818, pruned_loss=0.04866, over 1465140.28 frames.], batch size: 17, lr: 3.13e-04
2022-07-27 05:10:28,229 INFO [train.py:850] (2/4) Epoch 18, batch 1500, loss[loss=0.1806, simple_loss=0.279, pruned_loss=0.04107, over 7258.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2811, pruned_loss=0.0484, over 1464971.05 frames.], batch size: 30, lr: 3.13e-04
2022-07-27 05:11:12,482 INFO [train.py:850] (2/4) Epoch 18, batch 1550, loss[loss=0.1818, simple_loss=0.2787, pruned_loss=0.04247, over 7485.00 frames.], tot_loss[loss=0.1908, simple_loss=0.283, pruned_loss=0.0493, over 1464184.67 frames.], batch size: 20, lr: 3.13e-04
2022-07-27 05:11:55,181 INFO [train.py:850] (2/4) Epoch 18, batch 1600, loss[loss=0.1897, simple_loss=0.2795, pruned_loss=0.04993, over 7155.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2818, pruned_loss=0.04895, over 1462859.41 frames.], batch size: 17, lr: 3.13e-04
2022-07-27 05:12:38,680 INFO [train.py:850] (2/4) Epoch 18, batch 1650, loss[loss=0.2464, simple_loss=0.3266, pruned_loss=0.0831, over 7468.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2818, pruned_loss=0.04875, over 1463512.36 frames.], batch size: 72, lr: 3.13e-04
2022-07-27 05:13:24,060 INFO [train.py:850] (2/4) Epoch 18, batch 1700, loss[loss=0.1554, simple_loss=0.2348, pruned_loss=0.03803, over 7305.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2816, pruned_loss=0.04887, over 1464475.12 frames.], batch size: 17, lr: 3.13e-04
2022-07-27 05:14:07,827 INFO [train.py:850] (2/4) Epoch 18, batch 1750, loss[loss=0.1935, simple_loss=0.2924, pruned_loss=0.0473, over 7452.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2806, pruned_loss=0.04848, over 1464504.61 frames.], batch size: 39, lr: 3.13e-04
2022-07-27 05:14:52,847 INFO [train.py:850] (2/4) Epoch 18, batch 1800, loss[loss=0.1637, simple_loss=0.2598, pruned_loss=0.03384, over 7292.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2796, pruned_loss=0.04795, over 1465341.23 frames.], batch size: 19, lr: 3.13e-04
2022-07-27 05:15:36,337 INFO [train.py:850] (2/4) Epoch 18, batch 1850, loss[loss=0.2269, simple_loss=0.3063, pruned_loss=0.07378, over 7290.00 frames.], tot_loss[loss=0.188, simple_loss=0.28, pruned_loss=0.04802, over 1464495.05 frames.], batch size: 20, lr: 3.13e-04
2022-07-27 05:16:19,088 INFO [train.py:850] (2/4) Epoch 18, batch 1900, loss[loss=0.2211, simple_loss=0.3181, pruned_loss=0.06208, over 7321.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2797, pruned_loss=0.04812, over 1465491.50 frames.], batch size: 27, lr: 3.13e-04
2022-07-27 05:17:03,440 INFO [train.py:850] (2/4) Epoch 18, batch 1950, loss[loss=0.206, simple_loss=0.3015, pruned_loss=0.05523, over 7188.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2777, pruned_loss=0.04702, over 1464865.19 frames.], batch size: 22, lr: 3.13e-04
2022-07-27 05:17:46,169 INFO [train.py:850] (2/4) Epoch 18, batch 2000, loss[loss=0.2073, simple_loss=0.305, pruned_loss=0.05483, over 7453.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2779, pruned_loss=0.04739, over 1464577.21 frames.], batch size: 24, lr: 3.13e-04
2022-07-27 05:18:30,234 INFO [train.py:850] (2/4) Epoch 18, batch 2050, loss[loss=0.1755, simple_loss=0.2762, pruned_loss=0.03745, over 7411.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2783, pruned_loss=0.04765, over 1464447.47 frames.], batch size: 22, lr: 3.13e-04
2022-07-27 05:19:12,906 INFO [train.py:850] (2/4) Epoch 18, batch 2100, loss[loss=0.2259, simple_loss=0.3127, pruned_loss=0.06954, over 7203.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2777, pruned_loss=0.04708, over 1464527.07 frames.], batch size: 24, lr: 3.12e-04
2022-07-27 05:19:56,554 INFO [train.py:850] (2/4) Epoch 18, batch 2150, loss[loss=0.1912, simple_loss=0.2856, pruned_loss=0.04842, over 7424.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2781, pruned_loss=0.04728, over 1464403.78 frames.], batch size: 22, lr: 3.12e-04
2022-07-27 05:20:40,099 INFO [train.py:850] (2/4) Epoch 18, batch 2200, loss[loss=0.1746, simple_loss=0.2762, pruned_loss=0.03654, over 7486.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2787, pruned_loss=0.0473, over 1464222.43 frames.], batch size: 24, lr: 3.12e-04
2022-07-27 05:21:23,192 INFO [train.py:850] (2/4) Epoch 18, batch 2250, loss[loss=0.1626, simple_loss=0.2669, pruned_loss=0.02912, over 7413.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2796, pruned_loss=0.04775, over 1465522.10 frames.], batch size: 22, lr: 3.12e-04
2022-07-27 05:22:07,601 INFO [train.py:850] (2/4) Epoch 18, batch 2300, loss[loss=0.1981, simple_loss=0.2837, pruned_loss=0.0562, over 7474.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2797, pruned_loss=0.0478, over 1465546.18 frames.], batch size: 20, lr: 3.12e-04
2022-07-27 05:22:51,309 INFO [train.py:850] (2/4) Epoch 18, batch 2350, loss[loss=0.2116, simple_loss=0.3002, pruned_loss=0.06146, over 7464.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2794, pruned_loss=0.04795, over 1465280.47 frames.], batch size: 24, lr: 3.12e-04
2022-07-27 05:23:35,296 INFO [train.py:850] (2/4) Epoch 18, batch 2400, loss[loss=0.2066, simple_loss=0.281, pruned_loss=0.06615, over 7483.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2801, pruned_loss=0.0479, over 1465270.15 frames.], batch size: 20, lr: 3.12e-04
2022-07-27 05:24:18,519 INFO [train.py:850] (2/4) Epoch 18, batch 2450, loss[loss=0.1747, simple_loss=0.2625, pruned_loss=0.04338, over 7164.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2797, pruned_loss=0.04772, over 1465586.65 frames.], batch size: 17, lr: 3.12e-04
2022-07-27 05:25:01,262 INFO [train.py:850] (2/4) Epoch 18, batch 2500, loss[loss=0.2104, simple_loss=0.3122, pruned_loss=0.05427, over 7340.00 frames.], tot_loss[loss=0.187, simple_loss=0.2792, pruned_loss=0.04737, over 1465874.81 frames.], batch size: 23, lr: 3.12e-04
2022-07-27 05:25:45,530 INFO [train.py:850] (2/4) Epoch 18, batch 2550, loss[loss=0.1596, simple_loss=0.2713, pruned_loss=0.02393, over 7189.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2798, pruned_loss=0.04743, over 1464170.45 frames.], batch size: 21, lr: 3.12e-04
2022-07-27 05:26:28,563 INFO [train.py:850] (2/4) Epoch 18, batch 2600, loss[loss=0.1881, simple_loss=0.2866, pruned_loss=0.04483, over 7360.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2796, pruned_loss=0.04802, over 1464801.04 frames.], batch size: 23, lr: 3.12e-04
2022-07-27 05:27:14,490 INFO [train.py:850] (2/4) Epoch 18, batch 2650, loss[loss=0.1854, simple_loss=0.2713, pruned_loss=0.0497, over 7285.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2804, pruned_loss=0.0484, over 1463997.37 frames.], batch size: 19, lr: 3.12e-04
2022-07-27 05:27:57,857 INFO [train.py:850] (2/4) Epoch 18, batch 2700, loss[loss=0.1652, simple_loss=0.2567, pruned_loss=0.03689, over 7397.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2814, pruned_loss=0.04879, over 1466204.02 frames.], batch size: 19, lr: 3.12e-04
2022-07-27 05:28:42,181 INFO [train.py:850] (2/4) Epoch 18, batch 2750, loss[loss=0.1688, simple_loss=0.2681, pruned_loss=0.03468, over 7399.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2802, pruned_loss=0.04835, over 1465573.20 frames.], batch size: 31, lr: 3.12e-04
2022-07-27 05:29:25,293 INFO [train.py:850] (2/4) Epoch 18, batch 2800, loss[loss=0.2137, simple_loss=0.3018, pruned_loss=0.06278, over 7222.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2802, pruned_loss=0.04816, over 1465871.09 frames.], batch size: 24, lr: 3.12e-04
2022-07-27 05:30:07,996 INFO [train.py:850] (2/4) Epoch 18, batch 2850, loss[loss=0.2019, simple_loss=0.2926, pruned_loss=0.05563, over 7276.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2811, pruned_loss=0.04801, over 1465475.62 frames.], batch size: 27, lr: 3.12e-04
2022-07-27 05:30:52,030 INFO [train.py:850] (2/4) Epoch 18, batch 2900, loss[loss=0.1793, simple_loss=0.2603, pruned_loss=0.04911, over 7214.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2817, pruned_loss=0.04844, over 1465783.26 frames.], batch size: 16, lr: 3.12e-04
2022-07-27 05:31:35,257 INFO [train.py:850] (2/4) Epoch 18, batch 2950, loss[loss=0.1904, simple_loss=0.2885, pruned_loss=0.04613, over 7363.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2812, pruned_loss=0.04807, over 1464524.48 frames.], batch size: 38, lr: 3.12e-04
2022-07-27 05:32:19,179 INFO [train.py:850] (2/4) Epoch 18, batch 3000, loss[loss=0.1982, simple_loss=0.2902, pruned_loss=0.05314, over 7200.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2799, pruned_loss=0.0473, over 1465433.70 frames.], batch size: 20, lr: 3.12e-04
2022-07-27 05:32:19,180 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 05:32:41,997 INFO [train.py:879] (2/4) Epoch 18, validation: loss=0.1918, simple_loss=0.285, pruned_loss=0.04925, over 924787.00 frames.
2022-07-27 05:33:25,092 INFO [train.py:850] (2/4) Epoch 18, batch 3050, loss[loss=0.2177, simple_loss=0.3032, pruned_loss=0.06609, over 7377.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2802, pruned_loss=0.04749, over 1465803.23 frames.], batch size: 20, lr: 3.12e-04
2022-07-27 05:34:09,682 INFO [train.py:850] (2/4) Epoch 18, batch 3100, loss[loss=0.1748, simple_loss=0.2626, pruned_loss=0.04354, over 7206.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2792, pruned_loss=0.04767, over 1466071.52 frames.], batch size: 19, lr: 3.11e-04
2022-07-27 05:34:52,704 INFO [train.py:850] (2/4) Epoch 18, batch 3150, loss[loss=0.1993, simple_loss=0.2858, pruned_loss=0.0564, over 7187.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2795, pruned_loss=0.04762, over 1466252.38 frames.], batch size: 21, lr: 3.11e-04
2022-07-27 05:35:37,688 INFO [train.py:850] (2/4) Epoch 18, batch 3200, loss[loss=0.1611, simple_loss=0.2388, pruned_loss=0.04173, over 7298.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2791, pruned_loss=0.0473, over 1466068.04 frames.], batch size: 17, lr: 3.11e-04
2022-07-27 05:36:21,968 INFO [train.py:850] (2/4) Epoch 18, batch 3250, loss[loss=0.1886, simple_loss=0.2901, pruned_loss=0.04352, over 7280.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2802, pruned_loss=0.04808, over 1466042.64 frames.], batch size: 21, lr: 3.11e-04
2022-07-27 05:37:03,948 INFO [train.py:850] (2/4) Epoch 18, batch 3300, loss[loss=0.1776, simple_loss=0.2708, pruned_loss=0.04221, over 7358.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2806, pruned_loss=0.04814, over 1466286.74 frames.], batch size: 23, lr: 3.11e-04
2022-07-27 05:37:48,669 INFO [train.py:850] (2/4) Epoch 18, batch 3350, loss[loss=0.1779, simple_loss=0.2666, pruned_loss=0.04461, over 7305.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2795, pruned_loss=0.04781, over 1465707.18 frames.], batch size: 17, lr: 3.11e-04
2022-07-27 05:38:31,515 INFO [train.py:850] (2/4) Epoch 18, batch 3400, loss[loss=0.1884, simple_loss=0.284, pruned_loss=0.04639, over 7339.00 frames.], tot_loss[loss=0.187, simple_loss=0.2791, pruned_loss=0.04747, over 1465993.28 frames.], batch size: 39, lr: 3.11e-04
2022-07-27 05:39:15,950 INFO [train.py:850] (2/4) Epoch 18, batch 3450, loss[loss=0.2492, simple_loss=0.3355, pruned_loss=0.08139, over 7475.00 frames.], tot_loss[loss=0.1871, simple_loss=0.279, pruned_loss=0.04764, over 1465066.00 frames.], batch size: 74, lr: 3.11e-04
2022-07-27 05:39:58,941 INFO [train.py:850] (2/4) Epoch 18, batch 3500, loss[loss=0.2247, simple_loss=0.3221, pruned_loss=0.06365, over 7179.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2797, pruned_loss=0.04769, over 1465070.14 frames.], batch size: 21, lr: 3.11e-04
2022-07-27 05:40:43,336 INFO [train.py:850] (2/4) Epoch 18, batch 3550, loss[loss=0.2151, simple_loss=0.3048, pruned_loss=0.0627, over 7226.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2806, pruned_loss=0.04842, over 1464609.69 frames.], batch size: 24, lr: 3.11e-04
2022-07-27 05:41:26,175 INFO [train.py:850] (2/4) Epoch 18, batch 3600, loss[loss=0.1836, simple_loss=0.26, pruned_loss=0.05357, over 7249.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2793, pruned_loss=0.04797, over 1464218.91 frames.], batch size: 16, lr: 3.11e-04
2022-07-27 05:42:09,375 INFO [train.py:850] (2/4) Epoch 18, batch 3650, loss[loss=0.171, simple_loss=0.271, pruned_loss=0.03555, over 7353.00 frames.], tot_loss[loss=0.187, simple_loss=0.2792, pruned_loss=0.0474, over 1463444.04 frames.], batch size: 38, lr: 3.11e-04
2022-07-27 05:42:53,117 INFO [train.py:850] (2/4) Epoch 18, batch 3700, loss[loss=0.1637, simple_loss=0.265, pruned_loss=0.03115, over 7417.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2799, pruned_loss=0.04769, over 1464203.63 frames.], batch size: 22, lr: 3.11e-04
2022-07-27 05:43:37,067 INFO [train.py:850] (2/4) Epoch 18, batch 3750, loss[loss=0.1931, simple_loss=0.2923, pruned_loss=0.04697, over 7269.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2808, pruned_loss=0.04808, over 1464627.89 frames.], batch size: 27, lr: 3.11e-04
2022-07-27 05:44:20,089 INFO [train.py:850] (2/4) Epoch 18, batch 3800, loss[loss=0.1886, simple_loss=0.2819, pruned_loss=0.04761, over 7298.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2808, pruned_loss=0.04822, over 1465061.26 frames.], batch size: 20, lr: 3.11e-04
2022-07-27 05:45:04,147 INFO [train.py:850] (2/4) Epoch 18, batch 3850, loss[loss=0.2604, simple_loss=0.3496, pruned_loss=0.08555, over 7424.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2806, pruned_loss=0.04794, over 1465668.76 frames.], batch size: 72, lr: 3.11e-04
2022-07-27 05:45:47,279 INFO [train.py:850] (2/4) Epoch 18, batch 3900, loss[loss=0.1913, simple_loss=0.2834, pruned_loss=0.04958, over 7296.00 frames.], tot_loss[loss=0.1877, simple_loss=0.28, pruned_loss=0.04769, over 1465317.83 frames.], batch size: 19, lr: 3.11e-04
2022-07-27 05:46:31,303 INFO [train.py:850] (2/4) Epoch 18, batch 3950, loss[loss=0.2099, simple_loss=0.3076, pruned_loss=0.05605, over 7288.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2792, pruned_loss=0.0477, over 1465407.39 frames.], batch size: 20, lr: 3.11e-04
2022-07-27 05:47:15,131 INFO [train.py:850] (2/4) Epoch 18, batch 4000, loss[loss=0.1854, simple_loss=0.2856, pruned_loss=0.04263, over 7185.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2808, pruned_loss=0.0482, over 1465880.91 frames.], batch size: 21, lr: 3.11e-04
2022-07-27 05:48:00,458 INFO [train.py:850] (2/4) Epoch 18, batch 4050, loss[loss=0.2483, simple_loss=0.3295, pruned_loss=0.08358, over 7268.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2814, pruned_loss=0.04896, over 1466563.13 frames.], batch size: 27, lr: 3.11e-04
2022-07-27 05:48:45,219 INFO [train.py:850] (2/4) Epoch 18, batch 4100, loss[loss=0.1948, simple_loss=0.2846, pruned_loss=0.0525, over 7177.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2819, pruned_loss=0.04975, over 1467283.59 frames.], batch size: 21, lr: 3.10e-04
2022-07-27 05:49:27,800 INFO [train.py:850] (2/4) Epoch 18, batch 4150, loss[loss=0.1556, simple_loss=0.2484, pruned_loss=0.03134, over 7101.00 frames.], tot_loss[loss=0.191, simple_loss=0.2819, pruned_loss=0.05009, over 1466124.62 frames.], batch size: 18, lr: 3.10e-04
2022-07-27 05:50:12,319 INFO [train.py:850] (2/4) Epoch 18, batch 4200, loss[loss=0.2182, simple_loss=0.3125, pruned_loss=0.062, over 7293.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2827, pruned_loss=0.05119, over 1467540.84 frames.], batch size: 22, lr: 3.10e-04
2022-07-27 05:50:55,552 INFO [train.py:850] (2/4) Epoch 18, batch 4250, loss[loss=0.167, simple_loss=0.2403, pruned_loss=0.04689, over 7317.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2822, pruned_loss=0.0513, over 1466012.09 frames.], batch size: 18, lr: 3.10e-04
2022-07-27 05:51:39,668 INFO [train.py:850] (2/4) Epoch 18, batch 4300, loss[loss=0.1838, simple_loss=0.2716, pruned_loss=0.04802, over 7198.00 frames.], tot_loss[loss=0.1941, simple_loss=0.283, pruned_loss=0.05261, over 1465846.05 frames.], batch size: 19, lr: 3.10e-04
2022-07-27 05:52:23,477 INFO [train.py:850] (2/4) Epoch 18, batch 4350, loss[loss=0.2032, simple_loss=0.2979, pruned_loss=0.05423, over 7390.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2827, pruned_loss=0.05288, over 1464837.39 frames.], batch size: 21, lr: 3.10e-04
2022-07-27 05:53:06,164 INFO [train.py:850] (2/4) Epoch 18, batch 4400, loss[loss=0.1903, simple_loss=0.2618, pruned_loss=0.05941, over 7450.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2829, pruned_loss=0.05401, over 1465551.60 frames.], batch size: 18, lr: 3.10e-04
2022-07-27 05:53:50,521 INFO [train.py:850] (2/4) Epoch 18, batch 4450, loss[loss=0.2383, simple_loss=0.3099, pruned_loss=0.08337, over 7492.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2833, pruned_loss=0.05482, over 1465073.82 frames.], batch size: 23, lr: 3.10e-04
2022-07-27 05:54:33,345 INFO [train.py:850] (2/4) Epoch 18, batch 4500, loss[loss=0.2131, simple_loss=0.3124, pruned_loss=0.05689, over 7279.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2836, pruned_loss=0.05481, over 1466028.57 frames.], batch size: 30, lr: 3.10e-04
2022-07-27 05:55:18,048 INFO [train.py:850] (2/4) Epoch 18, batch 4550, loss[loss=0.1786, simple_loss=0.2748, pruned_loss=0.04118, over 7287.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2851, pruned_loss=0.05606, over 1466580.68 frames.], batch size: 21, lr: 3.10e-04
2022-07-27 05:56:01,358 INFO [train.py:850] (2/4) Epoch 18, batch 4600, loss[loss=0.2402, simple_loss=0.314, pruned_loss=0.08313, over 7336.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2857, pruned_loss=0.05687, over 1466861.93 frames.], batch size: 23, lr: 3.10e-04
2022-07-27 05:56:45,219 INFO [train.py:850] (2/4) Epoch 18, batch 4650, loss[loss=0.2207, simple_loss=0.2861, pruned_loss=0.07769, over 7295.00 frames.], tot_loss[loss=0.201, simple_loss=0.2864, pruned_loss=0.05782, over 1467613.11 frames.], batch size: 19, lr: 3.10e-04
2022-07-27 05:57:28,228 INFO [train.py:850] (2/4) Epoch 18, batch 4700, loss[loss=0.1984, simple_loss=0.2871, pruned_loss=0.05481, over 7211.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2864, pruned_loss=0.05844, over 1467347.62 frames.], batch size: 24, lr: 3.10e-04
2022-07-27 05:58:12,169 INFO [train.py:850] (2/4) Epoch 18, batch 4750, loss[loss=0.1945, simple_loss=0.2857, pruned_loss=0.05167, over 7285.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2851, pruned_loss=0.05784, over 1467672.89 frames.], batch size: 20, lr: 3.10e-04
2022-07-27 05:58:56,059 INFO [train.py:850] (2/4) Epoch 18, batch 4800, loss[loss=0.2017, simple_loss=0.2861, pruned_loss=0.05862, over 7310.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2851, pruned_loss=0.05782, over 1467132.84 frames.], batch size: 39, lr: 3.10e-04
2022-07-27 05:59:55,336 INFO [train.py:850] (2/4) Epoch 18, batch 4850, loss[loss=0.1687, simple_loss=0.2704, pruned_loss=0.03354, over 7224.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2832, pruned_loss=0.0571, over 1466421.73 frames.], batch size: 24, lr: 3.10e-04
2022-07-27 06:00:39,424 INFO [train.py:850] (2/4) Epoch 18, batch 4900, loss[loss=0.1995, simple_loss=0.2916, pruned_loss=0.05372, over 7401.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2828, pruned_loss=0.05732, over 1466273.18 frames.], batch size: 22, lr: 3.10e-04
2022-07-27 06:01:22,877 INFO [train.py:850] (2/4) Epoch 18, batch 4950, loss[loss=0.2158, simple_loss=0.3038, pruned_loss=0.06385, over 7376.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2839, pruned_loss=0.05825, over 1465988.17 frames.], batch size: 20, lr: 3.10e-04
2022-07-27 06:02:05,570 INFO [train.py:850] (2/4) Epoch 18, batch 5000, loss[loss=0.1514, simple_loss=0.238, pruned_loss=0.03236, over 7311.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2852, pruned_loss=0.05874, over 1466775.38 frames.], batch size: 18, lr: 3.10e-04
2022-07-27 06:02:49,955 INFO [train.py:850] (2/4) Epoch 18, batch 5050, loss[loss=0.1912, simple_loss=0.2791, pruned_loss=0.0516, over 7283.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2855, pruned_loss=0.0588, over 1466132.34 frames.], batch size: 21, lr: 3.10e-04
2022-07-27 06:03:33,366 INFO [train.py:850] (2/4) Epoch 18, batch 5100, loss[loss=0.2347, simple_loss=0.3171, pruned_loss=0.07615, over 7468.00 frames.], tot_loss[loss=0.202, simple_loss=0.2856, pruned_loss=0.05914, over 1466143.06 frames.], batch size: 26, lr: 3.09e-04
2022-07-27 06:04:17,925 INFO [train.py:850] (2/4) Epoch 18, batch 5150, loss[loss=0.1692, simple_loss=0.2472, pruned_loss=0.04558, over 7435.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2851, pruned_loss=0.05909, over 1467074.09 frames.], batch size: 17, lr: 3.09e-04
2022-07-27 06:05:00,896 INFO [train.py:850] (2/4) Epoch 18, batch 5200, loss[loss=0.17, simple_loss=0.2478, pruned_loss=0.04614, over 7159.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2838, pruned_loss=0.05861, over 1466460.83 frames.], batch size: 17, lr: 3.09e-04
2022-07-27 06:05:45,917 INFO [train.py:850] (2/4) Epoch 18, batch 5250, loss[loss=0.1791, simple_loss=0.2566, pruned_loss=0.0508, over 7456.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2835, pruned_loss=0.0584, over 1465946.17 frames.], batch size: 18, lr: 3.09e-04
2022-07-27 06:06:28,358 INFO [train.py:850] (2/4) Epoch 18, batch 5300, loss[loss=0.1796, simple_loss=0.2655, pruned_loss=0.04686, over 7444.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2838, pruned_loss=0.05876, over 1465426.29 frames.], batch size: 18, lr: 3.09e-04
2022-07-27 06:07:12,009 INFO [train.py:850] (2/4) Epoch 18, batch 5350, loss[loss=0.1977, simple_loss=0.2852, pruned_loss=0.05512, over 7378.00 frames.], tot_loss[loss=0.2007, simple_loss=0.284, pruned_loss=0.05875, over 1465526.86 frames.], batch size: 19, lr: 3.09e-04
2022-07-27 06:07:55,964 INFO [train.py:850] (2/4) Epoch 18, batch 5400, loss[loss=0.1679, simple_loss=0.2495, pruned_loss=0.04321, over 7483.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2836, pruned_loss=0.05844, over 1465440.82 frames.], batch size: 19, lr: 3.09e-04
2022-07-27 06:08:39,906 INFO [train.py:850] (2/4) Epoch 18, batch 5450, loss[loss=0.2293, simple_loss=0.3013, pruned_loss=0.07868, over 7210.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2833, pruned_loss=0.05789, over 1465173.92 frames.], batch size: 25, lr: 3.09e-04
2022-07-27 06:09:24,248 INFO [train.py:850] (2/4) Epoch 18, batch 5500, loss[loss=0.2267, simple_loss=0.3111, pruned_loss=0.07113, over 7319.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2833, pruned_loss=0.05783, over 1464671.11 frames.], batch size: 37, lr: 3.09e-04
2022-07-27 06:10:07,819 INFO [train.py:850] (2/4) Epoch 18, batch 5550, loss[loss=0.2041, simple_loss=0.2927, pruned_loss=0.05771, over 7387.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2831, pruned_loss=0.05809, over 1465569.57 frames.], batch size: 20, lr: 3.09e-04
2022-07-27 06:10:51,902 INFO [train.py:850] (2/4) Epoch 18, batch 5600, loss[loss=0.1781, simple_loss=0.255, pruned_loss=0.05062, over 7197.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2827, pruned_loss=0.05819, over 1464712.51 frames.], batch size: 18, lr: 3.09e-04
2022-07-27 06:11:36,852 INFO [train.py:850] (2/4) Epoch 18, batch 5650, loss[loss=0.2255, simple_loss=0.3073, pruned_loss=0.07192, over 7294.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2833, pruned_loss=0.05845, over 1465624.27 frames.], batch size: 21, lr: 3.09e-04
2022-07-27 06:12:20,929 INFO [train.py:850] (2/4) Epoch 18, batch 5700, loss[loss=0.2028, simple_loss=0.2976, pruned_loss=0.05401, over 7218.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2843, pruned_loss=0.05848, over 1466586.68 frames.], batch size: 25, lr: 3.09e-04
2022-07-27 06:13:06,226 INFO [train.py:850] (2/4) Epoch 18, batch 5750, loss[loss=0.1962, simple_loss=0.2846, pruned_loss=0.05392, over 7479.00 frames.], tot_loss[loss=0.2014, simple_loss=0.285, pruned_loss=0.05888, over 1465539.28 frames.], batch size: 24, lr: 3.09e-04
2022-07-27 06:13:50,770 INFO [train.py:850] (2/4) Epoch 18, batch 5800, loss[loss=0.2184, simple_loss=0.3004, pruned_loss=0.06822, over 7195.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2844, pruned_loss=0.05863, over 1464614.70 frames.], batch size: 20, lr: 3.09e-04
2022-07-27 06:14:36,476 INFO [train.py:850] (2/4) Epoch 18, batch 5850, loss[loss=0.1886, simple_loss=0.2793, pruned_loss=0.04896, over 7295.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2831, pruned_loss=0.05786, over 1465095.26 frames.], batch size: 22, lr: 3.09e-04
2022-07-27 06:15:21,552 INFO [train.py:850] (2/4) Epoch 18, batch 5900, loss[loss=0.2176, simple_loss=0.3038, pruned_loss=0.06569, over 7486.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2828, pruned_loss=0.05756, over 1466036.54 frames.], batch size: 20, lr: 3.09e-04
2022-07-27 06:16:06,340 INFO [train.py:850] (2/4) Epoch 18, batch 5950, loss[loss=0.1934, simple_loss=0.2952, pruned_loss=0.04574, over 7477.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2824, pruned_loss=0.05723, over 1466040.23 frames.], batch size: 24, lr: 3.09e-04
2022-07-27 06:16:50,846 INFO [train.py:850] (2/4) Epoch 18, batch 6000, loss[loss=0.1605, simple_loss=0.2378, pruned_loss=0.04162, over 7318.00 frames.], tot_loss[loss=0.197, simple_loss=0.2812, pruned_loss=0.05642, over 1466424.31 frames.], batch size: 17, lr: 3.09e-04
2022-07-27 06:16:50,846 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 06:17:13,575 INFO [train.py:879] (2/4) Epoch 18, validation: loss=0.184, simple_loss=0.2801, pruned_loss=0.04396, over 924787.00 frames.
2022-07-27 06:17:58,894 INFO [train.py:850] (2/4) Epoch 18, batch 6050, loss[loss=0.206, simple_loss=0.3069, pruned_loss=0.05255, over 7239.00 frames.], tot_loss[loss=0.1978, simple_loss=0.282, pruned_loss=0.05679, over 1466579.49 frames.], batch size: 25, lr: 3.09e-04
2022-07-27 06:18:42,875 INFO [train.py:850] (2/4) Epoch 18, batch 6100, loss[loss=0.2551, simple_loss=0.3294, pruned_loss=0.09045, over 7468.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2837, pruned_loss=0.05793, over 1466021.43 frames.], batch size: 21, lr: 3.08e-04
2022-07-27 06:19:26,842 INFO [train.py:850] (2/4) Epoch 18, batch 6150, loss[loss=0.1943, simple_loss=0.2822, pruned_loss=0.05316, over 7292.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2841, pruned_loss=0.05789, over 1465196.82 frames.], batch size: 21, lr: 3.08e-04
2022-07-27 06:20:10,804 INFO [train.py:850] (2/4) Epoch 18, batch 6200, loss[loss=0.2036, simple_loss=0.2716, pruned_loss=0.06781, over 7190.00 frames.], tot_loss[loss=0.1996, simple_loss=0.284, pruned_loss=0.05761, over 1464773.71 frames.], batch size: 18, lr: 3.08e-04
2022-07-27 06:20:53,862 INFO [train.py:850] (2/4) Epoch 18, batch 6250, loss[loss=0.1884, simple_loss=0.2721, pruned_loss=0.05235, over 7480.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2854, pruned_loss=0.05838, over 1466253.83 frames.], batch size: 26, lr: 3.08e-04
2022-07-27 06:21:37,352 INFO [train.py:850] (2/4) Epoch 18, batch 6300, loss[loss=0.183, simple_loss=0.2715, pruned_loss=0.04727, over 7340.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2847, pruned_loss=0.05806, over 1466828.31 frames.], batch size: 23, lr: 3.08e-04
2022-07-27 06:22:20,655 INFO [train.py:850] (2/4) Epoch 18, batch 6350, loss[loss=0.1476, simple_loss=0.2322, pruned_loss=0.03146, over 7333.00 frames.], tot_loss[loss=0.2, simple_loss=0.2844, pruned_loss=0.05779, over 1465976.89 frames.], batch size: 18, lr: 3.08e-04
2022-07-27 06:23:05,274 INFO [train.py:850] (2/4) Epoch 18, batch 6400, loss[loss=0.1754, simple_loss=0.267, pruned_loss=0.04191, over 7467.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2838, pruned_loss=0.05741, over 1465044.96 frames.], batch size: 21, lr: 3.08e-04
2022-07-27 06:23:50,007 INFO [train.py:850] (2/4) Epoch 18, batch 6450, loss[loss=0.2055, simple_loss=0.289, pruned_loss=0.06094, over 7398.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2839, pruned_loss=0.05719, over 1465529.78 frames.], batch size: 39, lr: 3.08e-04
2022-07-27 06:24:34,304 INFO [train.py:850] (2/4) Epoch 18, batch 6500, loss[loss=0.1963, simple_loss=0.2776, pruned_loss=0.05757, over 7198.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2828, pruned_loss=0.05686, over 1464832.85 frames.], batch size: 20, lr: 3.08e-04
2022-07-27 06:25:18,133 INFO [train.py:850] (2/4) Epoch 18, batch 6550, loss[loss=0.2173, simple_loss=0.3079, pruned_loss=0.06331, over 7373.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2827, pruned_loss=0.05709, over 1464785.59 frames.], batch size: 31, lr: 3.08e-04
2022-07-27 06:26:01,367 INFO [train.py:850] (2/4) Epoch 18, batch 6600, loss[loss=0.1859, simple_loss=0.2673, pruned_loss=0.0522, over 7300.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2836, pruned_loss=0.05755, over 1465111.48 frames.], batch size: 22, lr: 3.08e-04
2022-07-27 06:26:45,527 INFO [train.py:850] (2/4) Epoch 18, batch 6650, loss[loss=0.1941, simple_loss=0.2849, pruned_loss=0.05169, over 7375.00 frames.], tot_loss[loss=0.1989, simple_loss=0.283, pruned_loss=0.05741, over 1466040.29 frames.], batch size: 21, lr: 3.08e-04
2022-07-27 06:27:28,393 INFO [train.py:850] (2/4) Epoch 18, batch 6700, loss[loss=0.228, simple_loss=0.3042, pruned_loss=0.07592, over 7163.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2831, pruned_loss=0.05761, over 1465950.13 frames.], batch size: 21, lr: 3.08e-04
2022-07-27 06:28:13,151 INFO [train.py:850] (2/4) Epoch 18, batch 6750, loss[loss=0.1893, simple_loss=0.2619, pruned_loss=0.05831, over 7445.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2825, pruned_loss=0.05716, over 1466735.62 frames.], batch size: 18, lr: 3.08e-04
2022-07-27 06:28:56,452 INFO [train.py:850] (2/4) Epoch 18, batch 6800, loss[loss=0.2461, simple_loss=0.3126, pruned_loss=0.08982, over 7473.00 frames.], tot_loss[loss=0.198, simple_loss=0.2823, pruned_loss=0.0568, over 1466579.12 frames.], batch size: 70, lr: 3.08e-04
2022-07-27 06:29:39,810 INFO [train.py:850] (2/4) Epoch 18, batch 6850, loss[loss=0.1583, simple_loss=0.2453, pruned_loss=0.03565, over 7296.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2816, pruned_loss=0.0563, over 1467130.99 frames.], batch size: 19, lr: 3.08e-04
2022-07-27 06:30:23,877 INFO [train.py:850] (2/4) Epoch 18, batch 6900, loss[loss=0.1752, simple_loss=0.2719, pruned_loss=0.03929, over 7183.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2838, pruned_loss=0.05745, over 1466862.06 frames.], batch size: 23, lr: 3.08e-04
2022-07-27 06:31:07,576 INFO [train.py:850] (2/4) Epoch 18, batch 6950, loss[loss=0.1852, simple_loss=0.2658, pruned_loss=0.0523, over 7388.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2833, pruned_loss=0.05742, over 1466149.75 frames.], batch size: 20, lr: 3.08e-04
2022-07-27 06:31:52,116 INFO [train.py:850] (2/4) Epoch 18, batch 7000, loss[loss=0.2441, simple_loss=0.3115, pruned_loss=0.08838, over 7436.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2828, pruned_loss=0.0571, over 1466824.28 frames.], batch size: 70, lr: 3.08e-04
2022-07-27 06:32:35,590 INFO [train.py:850] (2/4) Epoch 18, batch 7050, loss[loss=0.1793, simple_loss=0.2549, pruned_loss=0.0519, over 7312.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2826, pruned_loss=0.05752, over 1467056.69 frames.], batch size: 17, lr: 3.08e-04
2022-07-27 06:33:19,403 INFO [train.py:850] (2/4) Epoch 18, batch 7100, loss[loss=0.2009, simple_loss=0.2927, pruned_loss=0.05455, over 7183.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2826, pruned_loss=0.05763, over 1467753.42 frames.], batch size: 21, lr: 3.08e-04
2022-07-27 06:34:03,493 INFO [train.py:850] (2/4) Epoch 18, batch 7150, loss[loss=0.1977, simple_loss=0.2908, pruned_loss=0.05231, over 7301.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2834, pruned_loss=0.05784, over 1467970.36 frames.], batch size: 27, lr: 3.07e-04
2022-07-27 06:34:46,835 INFO [train.py:850] (2/4) Epoch 18, batch 7200, loss[loss=0.1956, simple_loss=0.2652, pruned_loss=0.06297, over 7492.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2838, pruned_loss=0.05786, over 1469016.29 frames.], batch size: 19, lr: 3.07e-04
2022-07-27 06:35:32,126 INFO [train.py:850] (2/4) Epoch 18, batch 7250, loss[loss=0.1795, simple_loss=0.268, pruned_loss=0.0455, over 7283.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2822, pruned_loss=0.05703, over 1468765.26 frames.], batch size: 20, lr: 3.07e-04
2022-07-27 06:36:15,204 INFO [train.py:850] (2/4) Epoch 18, batch 7300, loss[loss=0.2327, simple_loss=0.3094, pruned_loss=0.07806, over 7472.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2824, pruned_loss=0.05755, over 1468612.13 frames.], batch size: 26, lr: 3.07e-04
2022-07-27 06:37:00,232 INFO [train.py:850] (2/4) Epoch 18, batch 7350, loss[loss=0.1794, simple_loss=0.2757, pruned_loss=0.04157, over 7386.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2826, pruned_loss=0.0572, over 1467587.65 frames.], batch size: 20, lr: 3.07e-04
2022-07-27 06:37:43,053 INFO [train.py:850] (2/4) Epoch 18, batch 7400, loss[loss=0.241, simple_loss=0.311, pruned_loss=0.08553, over 7292.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2825, pruned_loss=0.05724, over 1466751.61 frames.], batch size: 21, lr: 3.07e-04
2022-07-27 06:38:27,465 INFO [train.py:850] (2/4) Epoch 18, batch 7450, loss[loss=0.2283, simple_loss=0.3132, pruned_loss=0.07164, over 7487.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2837, pruned_loss=0.0573, over 1466889.26 frames.], batch size: 24, lr: 3.07e-04
2022-07-27 06:39:11,298 INFO [train.py:850] (2/4) Epoch 18, batch 7500, loss[loss=0.219, simple_loss=0.2975, pruned_loss=0.07021, over 7383.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2835, pruned_loss=0.05694, over 1466670.83 frames.], batch size: 20, lr: 3.07e-04
2022-07-27 06:39:54,417 INFO [train.py:850] (2/4) Epoch 18, batch 7550, loss[loss=0.2414, simple_loss=0.3185, pruned_loss=0.08211, over 7203.00 frames.], tot_loss[loss=0.1983, simple_loss=0.283, pruned_loss=0.05682, over 1467499.20 frames.], batch size: 23, lr: 3.07e-04
2022-07-27 06:40:38,871 INFO [train.py:850] (2/4) Epoch 18, batch 7600, loss[loss=0.2494, simple_loss=0.3279, pruned_loss=0.08541, over 7438.00 frames.], tot_loss[loss=0.1983, simple_loss=0.283, pruned_loss=0.05681, over 1467517.35 frames.], batch size: 39, lr: 3.07e-04
2022-07-27 06:41:24,069 INFO [train.py:850] (2/4) Epoch 18, batch 7650, loss[loss=0.2319, simple_loss=0.3189, pruned_loss=0.07243, over 7231.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2829, pruned_loss=0.05695, over 1468212.19 frames.], batch size: 24, lr: 3.07e-04
2022-07-27 06:42:09,751 INFO [train.py:850] (2/4) Epoch 18, batch 7700, loss[loss=0.2005, simple_loss=0.2725, pruned_loss=0.06422, over 7451.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2824, pruned_loss=0.05672, over 1467573.53 frames.], batch size: 18, lr: 3.07e-04
2022-07-27 06:42:53,800 INFO [train.py:850] (2/4) Epoch 18, batch 7750, loss[loss=0.167, simple_loss=0.2572, pruned_loss=0.03836, over 7385.00 frames.], tot_loss[loss=0.199, simple_loss=0.2833, pruned_loss=0.05733, over 1468015.00 frames.], batch size: 20, lr: 3.07e-04
2022-07-27 06:43:39,345 INFO [train.py:850] (2/4) Epoch 18, batch 7800, loss[loss=0.1653, simple_loss=0.2457, pruned_loss=0.04243, over 7299.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2824, pruned_loss=0.05708, over 1466714.22 frames.], batch size: 17, lr: 3.07e-04
2022-07-27 06:44:23,309 INFO [train.py:850] (2/4) Epoch 18, batch 7850, loss[loss=0.189, simple_loss=0.2849, pruned_loss=0.0465, over 7286.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2819, pruned_loss=0.05665, over 1466653.47 frames.], batch size: 21, lr: 3.07e-04
2022-07-27 06:45:06,416 INFO [train.py:850] (2/4) Epoch 18, batch 7900, loss[loss=0.2091, simple_loss=0.2798, pruned_loss=0.06916, over 7199.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2823, pruned_loss=0.05651, over 1466025.40 frames.], batch size: 19, lr: 3.07e-04
2022-07-27 06:45:50,675 INFO [train.py:850] (2/4) Epoch 18, batch 7950, loss[loss=0.2225, simple_loss=0.3018, pruned_loss=0.07158, over 7198.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2832, pruned_loss=0.05694, over 1466046.54 frames.], batch size: 20, lr: 3.07e-04
2022-07-27 06:46:33,809 INFO [train.py:850] (2/4) Epoch 18, batch 8000, loss[loss=0.194, simple_loss=0.2885, pruned_loss=0.04972, over 7286.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2818, pruned_loss=0.05641, over 1465710.97 frames.], batch size: 21, lr: 3.07e-04
2022-07-27 06:47:18,688 INFO [train.py:850] (2/4) Epoch 18, batch 8050, loss[loss=0.2222, simple_loss=0.3036, pruned_loss=0.07045, over 7183.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2823, pruned_loss=0.05642, over 1464521.11 frames.], batch size: 23, lr: 3.07e-04
2022-07-27 06:48:02,150 INFO [train.py:850] (2/4) Epoch 18, batch 8100, loss[loss=0.2239, simple_loss=0.301, pruned_loss=0.07342, over 7472.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2819, pruned_loss=0.05616, over 1464368.05 frames.], batch size: 20, lr: 3.07e-04
2022-07-27 06:48:47,149 INFO [train.py:850] (2/4) Epoch 18, batch 8150, loss[loss=0.2409, simple_loss=0.3229, pruned_loss=0.07943, over 7452.00 frames.], tot_loss[loss=0.1976, simple_loss=0.283, pruned_loss=0.05612, over 1465928.73 frames.], batch size: 70, lr: 3.07e-04
2022-07-27 06:49:30,239 INFO [train.py:850] (2/4) Epoch 18, batch 8200, loss[loss=0.2234, simple_loss=0.3085, pruned_loss=0.06911, over 7168.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2819, pruned_loss=0.0557, over 1465471.11 frames.], batch size: 22, lr: 3.06e-04
2022-07-27 06:50:14,291 INFO [train.py:850] (2/4) Epoch 18, batch 8250, loss[loss=0.1794, simple_loss=0.2595, pruned_loss=0.04962, over 7283.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2821, pruned_loss=0.05598, over 1465158.67 frames.], batch size: 16, lr: 3.06e-04
2022-07-27 06:50:58,568 INFO [train.py:850] (2/4) Epoch 18, batch 8300, loss[loss=0.2332, simple_loss=0.3158, pruned_loss=0.07526, over 7436.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2831, pruned_loss=0.0565, over 1465607.72 frames.], batch size: 71, lr: 3.06e-04
2022-07-27 06:51:42,120 INFO [train.py:850] (2/4) Epoch 18, batch 8350, loss[loss=0.1828, simple_loss=0.2685, pruned_loss=0.04855, over 7298.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2825, pruned_loss=0.05583, over 1464661.78 frames.], batch size: 19, lr: 3.06e-04
2022-07-27 06:52:26,502 INFO [train.py:850] (2/4) Epoch 18, batch 8400, loss[loss=0.1919, simple_loss=0.2821, pruned_loss=0.0509, over 7297.00 frames.], tot_loss[loss=0.198, simple_loss=0.2831, pruned_loss=0.05649, over 1465369.02 frames.], batch size: 20, lr: 3.06e-04
2022-07-27 06:53:10,438 INFO [train.py:850] (2/4) Epoch 18, batch 8450, loss[loss=0.1722, simple_loss=0.2573, pruned_loss=0.04352, over 7484.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2827, pruned_loss=0.05644, over 1464730.11 frames.], batch size: 20, lr: 3.06e-04
2022-07-27 06:53:55,466 INFO [train.py:850] (2/4) Epoch 18, batch 8500, loss[loss=0.2049, simple_loss=0.2684, pruned_loss=0.07076, over 7176.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2829, pruned_loss=0.05683, over 1464035.21 frames.], batch size: 17, lr: 3.06e-04
2022-07-27 06:54:39,143 INFO [train.py:850] (2/4) Epoch 18, batch 8550, loss[loss=0.1946, simple_loss=0.2874, pruned_loss=0.05088, over 7181.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2838, pruned_loss=0.05733, over 1463885.95 frames.], batch size: 21, lr: 3.06e-04
2022-07-27 06:55:22,554 INFO [train.py:850] (2/4) Epoch 18, batch 8600, loss[loss=0.1893, simple_loss=0.2757, pruned_loss=0.05149, over 7381.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2823, pruned_loss=0.05623, over 1464726.53 frames.], batch size: 20, lr: 3.06e-04
2022-07-27 06:56:06,277 INFO [train.py:850] (2/4) Epoch 18, batch 8650, loss[loss=0.1675, simple_loss=0.257, pruned_loss=0.039, over 7427.00 frames.], tot_loss[loss=0.197, simple_loss=0.2817, pruned_loss=0.05616, over 1465499.79 frames.], batch size: 18, lr: 3.06e-04
2022-07-27 06:56:48,411 INFO [train.py:850] (2/4) Epoch 18, batch 8700, loss[loss=0.1749, simple_loss=0.2615, pruned_loss=0.04413, over 7292.00 frames.], tot_loss[loss=0.197, simple_loss=0.2813, pruned_loss=0.05633, over 1465995.46 frames.], batch size: 19, lr: 3.06e-04
2022-07-27 06:57:31,813 INFO [train.py:850] (2/4) Epoch 18, batch 8750, loss[loss=0.1412, simple_loss=0.2217, pruned_loss=0.03032, over 7431.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2814, pruned_loss=0.056, over 1465393.94 frames.], batch size: 17, lr: 3.06e-04
2022-07-27 06:58:13,810 INFO [train.py:850] (2/4) Epoch 18, batch 8800, loss[loss=0.2154, simple_loss=0.3055, pruned_loss=0.06269, over 7176.00 frames.], tot_loss[loss=0.1973, simple_loss=0.282, pruned_loss=0.05628, over 1465368.66 frames.], batch size: 21, lr: 3.06e-04
2022-07-27 06:59:11,780 INFO [train.py:850] (2/4) Epoch 18, batch 8850, loss[loss=0.2003, simple_loss=0.2879, pruned_loss=0.05639, over 7471.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2824, pruned_loss=0.05669, over 1465939.08 frames.], batch size: 21, lr: 3.06e-04
2022-07-27 07:00:52,213 INFO [train.py:850] (2/4) Epoch 19, batch 0, loss[loss=0.1745, simple_loss=0.2644, pruned_loss=0.04227, over 7393.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2644, pruned_loss=0.04227, over 7393.00 frames.], batch size: 19, lr: 2.98e-04
2022-07-27 07:01:35,493 INFO [train.py:850] (2/4) Epoch 19, batch 50, loss[loss=0.2045, simple_loss=0.2849, pruned_loss=0.06206, over 7165.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2758, pruned_loss=0.04798, over 331008.27 frames.], batch size: 17, lr: 2.98e-04
2022-07-27 07:02:19,329 INFO [train.py:850] (2/4) Epoch 19, batch 100, loss[loss=0.2115, simple_loss=0.3117, pruned_loss=0.0557, over 7213.00 frames.], tot_loss[loss=0.1868, simple_loss=0.278, pruned_loss=0.04776, over 583599.83 frames.], batch size: 19, lr: 2.98e-04
2022-07-27 07:03:02,181 INFO [train.py:850] (2/4) Epoch 19, batch 150, loss[loss=0.1534, simple_loss=0.2342, pruned_loss=0.03631, over 7442.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2784, pruned_loss=0.04802, over 779686.81 frames.], batch size: 17, lr: 2.98e-04
2022-07-27 07:03:46,243 INFO [train.py:850] (2/4) Epoch 19, batch 200, loss[loss=0.223, simple_loss=0.3085, pruned_loss=0.0688, over 7483.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2795, pruned_loss=0.0487, over 932196.92 frames.], batch size: 23, lr: 2.98e-04
2022-07-27 07:04:28,581 INFO [train.py:850] (2/4) Epoch 19, batch 250, loss[loss=0.2009, simple_loss=0.28, pruned_loss=0.0609, over 7304.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2793, pruned_loss=0.04799, over 1050286.13 frames.], batch size: 17, lr: 2.98e-04
2022-07-27 07:05:11,776 INFO [train.py:850] (2/4) Epoch 19, batch 300, loss[loss=0.2067, simple_loss=0.2967, pruned_loss=0.05837, over 7470.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2783, pruned_loss=0.04753, over 1143097.86 frames.], batch size: 31, lr: 2.98e-04
2022-07-27 07:05:56,747 INFO [train.py:850] (2/4) Epoch 19, batch 350, loss[loss=0.214, simple_loss=0.31, pruned_loss=0.05896, over 7482.00 frames.], tot_loss[loss=0.1854, simple_loss=0.277, pruned_loss=0.04687, over 1215377.62 frames.], batch size: 24, lr: 2.98e-04
2022-07-27 07:06:39,074 INFO [train.py:850] (2/4) Epoch 19, batch 400, loss[loss=0.1572, simple_loss=0.2418, pruned_loss=0.03631, over 7453.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2754, pruned_loss=0.04623, over 1271369.60 frames.], batch size: 17, lr: 2.98e-04
2022-07-27 07:07:23,958 INFO [train.py:850] (2/4) Epoch 19, batch 450, loss[loss=0.1824, simple_loss=0.2556, pruned_loss=0.05463, over 7477.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2752, pruned_loss=0.04585, over 1314350.52 frames.], batch size: 19, lr: 2.98e-04
2022-07-27 07:08:06,757 INFO [train.py:850] (2/4) Epoch 19, batch 500, loss[loss=0.2132, simple_loss=0.2982, pruned_loss=0.06412, over 7374.00 frames.], tot_loss[loss=0.183, simple_loss=0.2743, pruned_loss=0.04582, over 1347601.71 frames.], batch size: 68, lr: 2.98e-04
2022-07-27 07:08:51,614 INFO [train.py:850] (2/4) Epoch 19, batch 550, loss[loss=0.1936, simple_loss=0.284, pruned_loss=0.05157, over 7449.00 frames.], tot_loss[loss=0.1826, simple_loss=0.274, pruned_loss=0.04564, over 1372755.48 frames.], batch size: 39, lr: 2.98e-04
2022-07-27 07:09:36,065 INFO [train.py:850] (2/4) Epoch 19, batch 600, loss[loss=0.206, simple_loss=0.2924, pruned_loss=0.05985, over 7421.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2743, pruned_loss=0.04567, over 1392518.78 frames.], batch size: 22, lr: 2.98e-04
2022-07-27 07:10:18,722 INFO [train.py:850] (2/4) Epoch 19, batch 650, loss[loss=0.1738, simple_loss=0.2781, pruned_loss=0.03481, over 7472.00 frames.], tot_loss[loss=0.182, simple_loss=0.2736, pruned_loss=0.04524, over 1408514.49 frames.], batch size: 24, lr: 2.97e-04
2022-07-27 07:11:02,641 INFO [train.py:850] (2/4) Epoch 19, batch 700, loss[loss=0.2055, simple_loss=0.296, pruned_loss=0.05752, over 7456.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2738, pruned_loss=0.04543, over 1421211.59 frames.], batch size: 31, lr: 2.97e-04
2022-07-27 07:11:45,130 INFO [train.py:850] (2/4) Epoch 19, batch 750, loss[loss=0.158, simple_loss=0.2545, pruned_loss=0.03075, over 7199.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2739, pruned_loss=0.04522, over 1430662.15 frames.], batch size: 18, lr: 2.97e-04
2022-07-27 07:12:28,664 INFO [train.py:850] (2/4) Epoch 19, batch 800, loss[loss=0.1676, simple_loss=0.2506, pruned_loss=0.04233, over 7461.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2742, pruned_loss=0.04561, over 1439139.05 frames.], batch size: 17, lr: 2.97e-04
2022-07-27 07:13:12,959 INFO [train.py:850] (2/4) Epoch 19, batch 850, loss[loss=0.2003, simple_loss=0.2966, pruned_loss=0.05201, over 7473.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2746, pruned_loss=0.04587, over 1445229.59 frames.], batch size: 21, lr: 2.97e-04
2022-07-27 07:13:55,903 INFO [train.py:850] (2/4) Epoch 19, batch 900, loss[loss=0.1639, simple_loss=0.2594, pruned_loss=0.03415, over 7180.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2752, pruned_loss=0.04624, over 1450134.27 frames.], batch size: 17, lr: 2.97e-04
2022-07-27 07:14:42,478 INFO [train.py:850] (2/4) Epoch 19, batch 950, loss[loss=0.2022, simple_loss=0.2998, pruned_loss=0.05234, over 7245.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2767, pruned_loss=0.047, over 1453613.56 frames.], batch size: 27, lr: 2.97e-04
2022-07-27 07:15:24,944 INFO [train.py:850] (2/4) Epoch 19, batch 1000, loss[loss=0.1867, simple_loss=0.2844, pruned_loss=0.04457, over 7191.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2778, pruned_loss=0.04743, over 1456418.41 frames.], batch size: 19, lr: 2.97e-04
2022-07-27 07:16:09,221 INFO [train.py:850] (2/4) Epoch 19, batch 1050, loss[loss=0.1738, simple_loss=0.2742, pruned_loss=0.03668, over 7292.00 frames.], tot_loss[loss=0.1872, simple_loss=0.279, pruned_loss=0.04777, over 1459159.46 frames.], batch size: 30, lr: 2.97e-04
2022-07-27 07:16:52,281 INFO [train.py:850] (2/4) Epoch 19, batch 1100, loss[loss=0.2056, simple_loss=0.3086, pruned_loss=0.05128, over 7174.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2792, pruned_loss=0.04802, over 1459996.20 frames.], batch size: 22, lr: 2.97e-04
2022-07-27 07:17:36,005 INFO [train.py:850] (2/4) Epoch 19, batch 1150, loss[loss=0.206, simple_loss=0.3038, pruned_loss=0.05409, over 7185.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2784, pruned_loss=0.04765, over 1460690.45 frames.], batch size: 22, lr: 2.97e-04
2022-07-27 07:18:19,968 INFO [train.py:850] (2/4) Epoch 19, batch 1200, loss[loss=0.1892, simple_loss=0.2925, pruned_loss=0.04294, over 7306.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2784, pruned_loss=0.04729, over 1462489.09 frames.], batch size: 27, lr: 2.97e-04
2022-07-27 07:19:03,009 INFO [train.py:850] (2/4) Epoch 19, batch 1250, loss[loss=0.1858, simple_loss=0.2713, pruned_loss=0.05016, over 7295.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2782, pruned_loss=0.04663, over 1463787.00 frames.], batch size: 19, lr: 2.97e-04
2022-07-27 07:19:47,281 INFO [train.py:850] (2/4) Epoch 19, batch 1300, loss[loss=0.1901, simple_loss=0.2893, pruned_loss=0.04541, over 7219.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2781, pruned_loss=0.04671, over 1463428.82 frames.], batch size: 25, lr: 2.97e-04
2022-07-27 07:20:30,985 INFO [train.py:850] (2/4) Epoch 19, batch 1350, loss[loss=0.1562, simple_loss=0.2435, pruned_loss=0.03446, over 7164.00 frames.], tot_loss[loss=0.185, simple_loss=0.2775, pruned_loss=0.0462, over 1463087.59 frames.], batch size: 17, lr: 2.97e-04
2022-07-27 07:21:13,416 INFO [train.py:850] (2/4) Epoch 19, batch 1400, loss[loss=0.1507, simple_loss=0.2378, pruned_loss=0.03184, over 7101.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2792, pruned_loss=0.04759, over 1463286.62 frames.], batch size: 18, lr: 2.97e-04
2022-07-27 07:21:57,725 INFO [train.py:850] (2/4) Epoch 19, batch 1450, loss[loss=0.2045, simple_loss=0.3061, pruned_loss=0.05144, over 7189.00 frames.], tot_loss[loss=0.187, simple_loss=0.2788, pruned_loss=0.04762, over 1464065.88 frames.], batch size: 21, lr: 2.97e-04
2022-07-27 07:22:39,898 INFO [train.py:850] (2/4) Epoch 19, batch 1500, loss[loss=0.2008, simple_loss=0.289, pruned_loss=0.05631, over 7235.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2796, pruned_loss=0.0478, over 1464261.11 frames.], batch size: 24, lr: 2.97e-04
2022-07-27 07:23:24,714 INFO [train.py:850] (2/4) Epoch 19, batch 1550, loss[loss=0.1956, simple_loss=0.2759, pruned_loss=0.05768, over 7293.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2797, pruned_loss=0.04785, over 1464609.95 frames.], batch size: 20, lr: 2.97e-04
2022-07-27 07:24:07,406 INFO [train.py:850] (2/4) Epoch 19, batch 1600, loss[loss=0.1886, simple_loss=0.2886, pruned_loss=0.04429, over 7202.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2805, pruned_loss=0.04807, over 1464510.54 frames.], batch size: 20, lr: 2.97e-04
2022-07-27 07:24:50,660 INFO [train.py:850] (2/4) Epoch 19, batch 1650, loss[loss=0.1549, simple_loss=0.2392, pruned_loss=0.03534, over 7445.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2812, pruned_loss=0.04828, over 1465324.43 frames.], batch size: 18, lr: 2.97e-04
2022-07-27 07:25:34,727 INFO [train.py:850] (2/4) Epoch 19, batch 1700, loss[loss=0.2614, simple_loss=0.3423, pruned_loss=0.09026, over 7458.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2815, pruned_loss=0.04836, over 1466113.78 frames.], batch size: 70, lr: 2.97e-04
2022-07-27 07:26:19,904 INFO [train.py:850] (2/4) Epoch 19, batch 1750, loss[loss=0.1647, simple_loss=0.2541, pruned_loss=0.03769, over 7202.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2811, pruned_loss=0.0483, over 1465990.63 frames.], batch size: 18, lr: 2.96e-04
2022-07-27 07:27:05,597 INFO [train.py:850] (2/4) Epoch 19, batch 1800, loss[loss=0.2081, simple_loss=0.2975, pruned_loss=0.05937, over 7496.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2804, pruned_loss=0.04834, over 1465451.25 frames.], batch size: 19, lr: 2.96e-04
2022-07-27 07:27:48,507 INFO [train.py:850] (2/4) Epoch 19, batch 1850, loss[loss=0.1701, simple_loss=0.2577, pruned_loss=0.04119, over 7195.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2793, pruned_loss=0.04777, over 1464634.90 frames.], batch size: 18, lr: 2.96e-04
2022-07-27 07:28:31,999 INFO [train.py:850] (2/4) Epoch 19, batch 1900, loss[loss=0.2082, simple_loss=0.3081, pruned_loss=0.0541, over 7268.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2793, pruned_loss=0.04787, over 1465358.08 frames.], batch size: 30, lr: 2.96e-04
2022-07-27 07:29:15,885 INFO [train.py:850] (2/4) Epoch 19, batch 1950, loss[loss=0.1863, simple_loss=0.2889, pruned_loss=0.04185, over 7202.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2796, pruned_loss=0.04767, over 1465173.97 frames.], batch size: 24, lr: 2.96e-04
2022-07-27 07:29:57,748 INFO [train.py:850] (2/4) Epoch 19, batch 2000, loss[loss=0.278, simple_loss=0.3616, pruned_loss=0.09717, over 7310.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2796, pruned_loss=0.04773, over 1464490.61 frames.], batch size: 22, lr: 2.96e-04
2022-07-27 07:30:42,558 INFO [train.py:850] (2/4) Epoch 19, batch 2050, loss[loss=0.1722, simple_loss=0.253, pruned_loss=0.04571, over 7445.00 frames.], tot_loss[loss=0.1873, simple_loss=0.279, pruned_loss=0.04778, over 1464483.61 frames.], batch size: 18, lr: 2.96e-04
2022-07-27 07:31:25,444 INFO [train.py:850] (2/4) Epoch 19, batch 2100, loss[loss=0.1997, simple_loss=0.3038, pruned_loss=0.04783, over 7190.00 frames.], tot_loss[loss=0.1882, simple_loss=0.28, pruned_loss=0.0482, over 1464451.36 frames.], batch size: 21, lr: 2.96e-04
2022-07-27 07:32:09,418 INFO [train.py:850] (2/4) Epoch 19, batch 2150, loss[loss=0.2158, simple_loss=0.3204, pruned_loss=0.05556, over 7415.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2795, pruned_loss=0.04773, over 1465090.24 frames.], batch size: 39, lr: 2.96e-04
2022-07-27 07:32:52,869 INFO [train.py:850] (2/4) Epoch 19, batch 2200, loss[loss=0.2266, simple_loss=0.3322, pruned_loss=0.06056, over 7238.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2789, pruned_loss=0.04759, over 1464556.24 frames.], batch size: 30, lr: 2.96e-04
2022-07-27 07:33:36,117 INFO [train.py:850] (2/4) Epoch 19, batch 2250, loss[loss=0.2472, simple_loss=0.329, pruned_loss=0.0827, over 7349.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2792, pruned_loss=0.04763, over 1464503.13 frames.], batch size: 23, lr: 2.96e-04
2022-07-27 07:34:21,789 INFO [train.py:850] (2/4) Epoch 19, batch 2300, loss[loss=0.1686, simple_loss=0.2742, pruned_loss=0.0315, over 7181.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2778, pruned_loss=0.04691, over 1464354.00 frames.], batch size: 21, lr: 2.96e-04
2022-07-27 07:35:07,279 INFO [train.py:850] (2/4) Epoch 19, batch 2350, loss[loss=0.1816, simple_loss=0.2733, pruned_loss=0.04501, over 7200.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2767, pruned_loss=0.04681, over 1464878.75 frames.], batch size: 20, lr: 2.96e-04
2022-07-27 07:35:53,266 INFO [train.py:850] (2/4) Epoch 19, batch 2400, loss[loss=0.1911, simple_loss=0.2841, pruned_loss=0.04906, over 7297.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2765, pruned_loss=0.0466, over 1465198.47 frames.], batch size: 20, lr: 2.96e-04
2022-07-27 07:36:38,737 INFO [train.py:850] (2/4) Epoch 19, batch 2450, loss[loss=0.2502, simple_loss=0.3368, pruned_loss=0.08177, over 7248.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2764, pruned_loss=0.04702, over 1464977.24 frames.], batch size: 30, lr: 2.96e-04
2022-07-27 07:37:23,228 INFO [train.py:850] (2/4) Epoch 19, batch 2500, loss[loss=0.2134, simple_loss=0.3121, pruned_loss=0.05734, over 7296.00 frames.], tot_loss[loss=0.186, simple_loss=0.2778, pruned_loss=0.04715, over 1464562.74 frames.], batch size: 21, lr: 2.96e-04
2022-07-27 07:38:07,347 INFO [train.py:850] (2/4) Epoch 19, batch 2550, loss[loss=0.1924, simple_loss=0.2998, pruned_loss=0.04245, over 7308.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2778, pruned_loss=0.04687, over 1463505.48 frames.], batch size: 27, lr: 2.96e-04
2022-07-27 07:38:50,250 INFO [train.py:850] (2/4) Epoch 19, batch 2600, loss[loss=0.163, simple_loss=0.2562, pruned_loss=0.03487, over 7392.00 frames.], tot_loss[loss=0.1859, simple_loss=0.278, pruned_loss=0.04693, over 1463042.58 frames.], batch size: 19, lr: 2.96e-04
2022-07-27 07:39:36,298 INFO [train.py:850] (2/4) Epoch 19, batch 2650, loss[loss=0.1889, simple_loss=0.2808, pruned_loss=0.04845, over 7411.00 frames.], tot_loss[loss=0.185, simple_loss=0.2769, pruned_loss=0.04652, over 1463473.96 frames.], batch size: 22, lr: 2.96e-04
2022-07-27 07:40:21,153 INFO [train.py:850] (2/4) Epoch 19, batch 2700, loss[loss=0.1667, simple_loss=0.2643, pruned_loss=0.0346, over 7295.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2776, pruned_loss=0.04641, over 1463383.61 frames.], batch size: 19, lr: 2.96e-04
2022-07-27 07:41:06,218 INFO [train.py:850] (2/4) Epoch 19, batch 2750, loss[loss=0.1505, simple_loss=0.2339, pruned_loss=0.03359, over 7433.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2787, pruned_loss=0.04657, over 1464819.68 frames.], batch size: 17, lr: 2.96e-04
2022-07-27 07:41:51,021 INFO [train.py:850] (2/4) Epoch 19, batch 2800, loss[loss=0.2247, simple_loss=0.3132, pruned_loss=0.0681, over 7409.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2788, pruned_loss=0.04708, over 1466065.45 frames.], batch size: 70, lr: 2.95e-04
2022-07-27 07:42:33,997 INFO [train.py:850] (2/4) Epoch 19, batch 2850, loss[loss=0.1728, simple_loss=0.2704, pruned_loss=0.03756, over 7386.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2802, pruned_loss=0.04755, over 1465937.66 frames.], batch size: 31, lr: 2.95e-04
2022-07-27 07:43:19,039 INFO [train.py:850] (2/4) Epoch 19, batch 2900, loss[loss=0.2142, simple_loss=0.3059, pruned_loss=0.06129, over 7421.00 frames.], tot_loss[loss=0.187, simple_loss=0.2795, pruned_loss=0.0472, over 1464595.17 frames.], batch size: 67, lr: 2.95e-04
2022-07-27 07:44:01,850 INFO [train.py:850] (2/4) Epoch 19, batch 2950, loss[loss=0.2137, simple_loss=0.311, pruned_loss=0.05821, over 7423.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2789, pruned_loss=0.04675, over 1464248.98 frames.], batch size: 39, lr: 2.95e-04
2022-07-27 07:44:44,628 INFO [train.py:850] (2/4) Epoch 19, batch 3000, loss[loss=0.2167, simple_loss=0.2925, pruned_loss=0.0704, over 7490.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2785, pruned_loss=0.04732, over 1465575.58 frames.], batch size: 19, lr: 2.95e-04
2022-07-27 07:44:44,629 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 07:45:07,345 INFO [train.py:879] (2/4) Epoch 19, validation: loss=0.192, simple_loss=0.2855, pruned_loss=0.04922, over 924787.00 frames.
2022-07-27 07:45:50,472 INFO [train.py:850] (2/4) Epoch 19, batch 3050, loss[loss=0.1879, simple_loss=0.2836, pruned_loss=0.04612, over 7236.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2792, pruned_loss=0.04768, over 1465123.00 frames.], batch size: 27, lr: 2.95e-04
2022-07-27 07:46:34,243 INFO [train.py:850] (2/4) Epoch 19, batch 3100, loss[loss=0.1684, simple_loss=0.2594, pruned_loss=0.03871, over 7201.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2793, pruned_loss=0.04781, over 1465043.23 frames.], batch size: 19, lr: 2.95e-04
2022-07-27 07:47:18,039 INFO [train.py:850] (2/4) Epoch 19, batch 3150, loss[loss=0.1716, simple_loss=0.2685, pruned_loss=0.03734, over 7101.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2787, pruned_loss=0.04721, over 1464374.12 frames.], batch size: 18, lr: 2.95e-04
2022-07-27 07:48:02,054 INFO [train.py:850] (2/4) Epoch 19, batch 3200, loss[loss=0.1539, simple_loss=0.238, pruned_loss=0.03489, over 7276.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2782, pruned_loss=0.04748, over 1465619.78 frames.], batch size: 16, lr: 2.95e-04
2022-07-27 07:48:48,100 INFO [train.py:850] (2/4) Epoch 19, batch 3250, loss[loss=0.212, simple_loss=0.301, pruned_loss=0.06152, over 7491.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2793, pruned_loss=0.04816, over 1465783.55 frames.], batch size: 20, lr: 2.95e-04
2022-07-27 07:49:31,122 INFO [train.py:850] (2/4) Epoch 19, batch 3300, loss[loss=0.178, simple_loss=0.2639, pruned_loss=0.04606, over 7312.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2792, pruned_loss=0.0477, over 1465933.80 frames.], batch size: 18, lr: 2.95e-04
2022-07-27 07:50:16,765 INFO [train.py:850] (2/4) Epoch 19, batch 3350, loss[loss=0.1667, simple_loss=0.2709, pruned_loss=0.03125, over 7275.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2785, pruned_loss=0.04695, over 1465630.67 frames.], batch size: 27, lr: 2.95e-04
2022-07-27 07:50:59,114 INFO [train.py:850] (2/4) Epoch 19, batch 3400, loss[loss=0.2054, simple_loss=0.2961, pruned_loss=0.05736, over 7489.00 frames.], tot_loss[loss=0.1866, simple_loss=0.279, pruned_loss=0.04712, over 1465687.46 frames.], batch size: 23, lr: 2.95e-04
2022-07-27 07:51:43,060 INFO [train.py:850] (2/4) Epoch 19, batch 3450, loss[loss=0.195, simple_loss=0.2807, pruned_loss=0.05466, over 7379.00 frames.], tot_loss[loss=0.185, simple_loss=0.2773, pruned_loss=0.04642, over 1466283.29 frames.], batch size: 21, lr: 2.95e-04
2022-07-27 07:52:25,925 INFO [train.py:850] (2/4) Epoch 19, batch 3500, loss[loss=0.1903, simple_loss=0.2846, pruned_loss=0.04805, over 7219.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2771, pruned_loss=0.04637, over 1467256.75 frames.], batch size: 24, lr: 2.95e-04
2022-07-27 07:53:09,293 INFO [train.py:850] (2/4) Epoch 19, batch 3550, loss[loss=0.1762, simple_loss=0.2528, pruned_loss=0.04976, over 7325.00 frames.], tot_loss[loss=0.1846, simple_loss=0.277, pruned_loss=0.04608, over 1466937.98 frames.], batch size: 17, lr: 2.95e-04
2022-07-27 07:53:53,436 INFO [train.py:850] (2/4) Epoch 19, batch 3600, loss[loss=0.1844, simple_loss=0.2776, pruned_loss=0.04559, over 7200.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2778, pruned_loss=0.04645, over 1465875.94 frames.], batch size: 18, lr: 2.95e-04
2022-07-27 07:54:36,333 INFO [train.py:850] (2/4) Epoch 19, batch 3650, loss[loss=0.1801, simple_loss=0.2867, pruned_loss=0.03679, over 7178.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2776, pruned_loss=0.04636, over 1463980.47 frames.], batch size: 21, lr: 2.95e-04
2022-07-27 07:55:19,280 INFO [train.py:850] (2/4) Epoch 19, batch 3700, loss[loss=0.1658, simple_loss=0.2642, pruned_loss=0.03373, over 7280.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2768, pruned_loss=0.04553, over 1465282.72 frames.], batch size: 19, lr: 2.95e-04
2022-07-27 07:56:03,236 INFO [train.py:850] (2/4) Epoch 19, batch 3750, loss[loss=0.1622, simple_loss=0.2687, pruned_loss=0.02781, over 7285.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2773, pruned_loss=0.04597, over 1466336.14 frames.], batch size: 37, lr: 2.95e-04
2022-07-27 07:56:46,501 INFO [train.py:850] (2/4) Epoch 19, batch 3800, loss[loss=0.1731, simple_loss=0.2775, pruned_loss=0.0344, over 7424.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2779, pruned_loss=0.04592, over 1464978.93 frames.], batch size: 22, lr: 2.95e-04
2022-07-27 07:57:30,935 INFO [train.py:850] (2/4) Epoch 19, batch 3850, loss[loss=0.2086, simple_loss=0.3041, pruned_loss=0.05653, over 7298.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2768, pruned_loss=0.04549, over 1465703.35 frames.], batch size: 22, lr: 2.95e-04
2022-07-27 07:58:13,124 INFO [train.py:850] (2/4) Epoch 19, batch 3900, loss[loss=0.2197, simple_loss=0.3141, pruned_loss=0.06264, over 7175.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2775, pruned_loss=0.04557, over 1465395.46 frames.], batch size: 21, lr: 2.95e-04
2022-07-27 07:59:12,880 INFO [train.py:850] (2/4) Epoch 19, batch 3950, loss[loss=0.1836, simple_loss=0.2931, pruned_loss=0.03703, over 7286.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2786, pruned_loss=0.0459, over 1465233.08 frames.], batch size: 22, lr: 2.94e-04
2022-07-27 07:59:57,431 INFO [train.py:850] (2/4) Epoch 19, batch 4000, loss[loss=0.2356, simple_loss=0.3264, pruned_loss=0.07237, over 7374.00 frames.], tot_loss[loss=0.185, simple_loss=0.2779, pruned_loss=0.04603, over 1465982.45 frames.], batch size: 20, lr: 2.94e-04
2022-07-27 08:00:42,036 INFO [train.py:850] (2/4) Epoch 19, batch 4050, loss[loss=0.1951, simple_loss=0.2931, pruned_loss=0.04858, over 7293.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2792, pruned_loss=0.0469, over 1466224.49 frames.], batch size: 20, lr: 2.94e-04
2022-07-27 08:01:25,883 INFO [train.py:850] (2/4) Epoch 19, batch 4100, loss[loss=0.184, simple_loss=0.287, pruned_loss=0.04045, over 7199.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2779, pruned_loss=0.04634, over 1465764.80 frames.], batch size: 20, lr: 2.94e-04
2022-07-27 08:02:09,099 INFO [train.py:850] (2/4) Epoch 19, batch 4150, loss[loss=0.2093, simple_loss=0.297, pruned_loss=0.06077, over 7200.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2786, pruned_loss=0.04738, over 1465241.23 frames.], batch size: 19, lr: 2.94e-04
2022-07-27 08:02:52,182 INFO [train.py:850] (2/4) Epoch 19, batch 4200, loss[loss=0.166, simple_loss=0.2599, pruned_loss=0.03604, over 7334.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2779, pruned_loss=0.04783, over 1464158.73 frames.], batch size: 23, lr: 2.94e-04
2022-07-27 08:03:35,918 INFO [train.py:850] (2/4) Epoch 19, batch 4250, loss[loss=0.2009, simple_loss=0.2849, pruned_loss=0.05849, over 7402.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2795, pruned_loss=0.04936, over 1464635.22 frames.], batch size: 70, lr: 2.94e-04
2022-07-27 08:04:19,631 INFO [train.py:850] (2/4) Epoch 19, batch 4300, loss[loss=0.2443, simple_loss=0.3203, pruned_loss=0.08417, over 7467.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2806, pruned_loss=0.05097, over 1463860.86 frames.], batch size: 70, lr: 2.94e-04
2022-07-27 08:05:03,808 INFO [train.py:850] (2/4) Epoch 19, batch 4350, loss[loss=0.1938, simple_loss=0.2758, pruned_loss=0.05591, over 7196.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2818, pruned_loss=0.05265, over 1464699.68 frames.], batch size: 18, lr: 2.94e-04
2022-07-27 08:05:47,032 INFO [train.py:850] (2/4) Epoch 19, batch 4400, loss[loss=0.2534, simple_loss=0.3293, pruned_loss=0.08872, over 7461.00 frames.], tot_loss[loss=0.196, simple_loss=0.2836, pruned_loss=0.05419, over 1466576.81 frames.], batch size: 74, lr: 2.94e-04
2022-07-27 08:06:32,175 INFO [train.py:850] (2/4) Epoch 19, batch 4450, loss[loss=0.1887, simple_loss=0.2568, pruned_loss=0.06034, over 7440.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2835, pruned_loss=0.05538, over 1467562.53 frames.], batch size: 17, lr: 2.94e-04
2022-07-27 08:07:14,581 INFO [train.py:850] (2/4) Epoch 19, batch 4500, loss[loss=0.2086, simple_loss=0.2802, pruned_loss=0.06849, over 7189.00 frames.], tot_loss[loss=0.1968, simple_loss=0.283, pruned_loss=0.0553, over 1467194.43 frames.], batch size: 19, lr: 2.94e-04
2022-07-27 08:07:58,813 INFO [train.py:850] (2/4) Epoch 19, batch 4550, loss[loss=0.2403, simple_loss=0.3217, pruned_loss=0.07945, over 7331.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2826, pruned_loss=0.05519, over 1467784.74 frames.], batch size: 27, lr: 2.94e-04
2022-07-27 08:08:41,755 INFO [train.py:850] (2/4) Epoch 19, batch 4600, loss[loss=0.2184, simple_loss=0.3055, pruned_loss=0.06566, over 7296.00 frames.], tot_loss[loss=0.196, simple_loss=0.2823, pruned_loss=0.05488, over 1467695.61 frames.], batch size: 38, lr: 2.94e-04
2022-07-27 08:09:25,599 INFO [train.py:850] (2/4) Epoch 19, batch 4650, loss[loss=0.2111, simple_loss=0.2843, pruned_loss=0.06899, over 7290.00 frames.], tot_loss[loss=0.196, simple_loss=0.2821, pruned_loss=0.05499, over 1467490.42 frames.], batch size: 20, lr: 2.94e-04
2022-07-27 08:10:09,936 INFO [train.py:850] (2/4) Epoch 19, batch 4700, loss[loss=0.194, simple_loss=0.2813, pruned_loss=0.05334, over 7432.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2815, pruned_loss=0.05493, over 1467485.88 frames.], batch size: 71, lr: 2.94e-04
2022-07-27 08:10:52,974 INFO [train.py:850] (2/4) Epoch 19, batch 4750, loss[loss=0.2305, simple_loss=0.3043, pruned_loss=0.07837, over 7381.00 frames.], tot_loss[loss=0.196, simple_loss=0.2821, pruned_loss=0.05497, over 1467123.05 frames.], batch size: 20, lr: 2.94e-04
2022-07-27 08:11:36,811 INFO [train.py:850] (2/4) Epoch 19, batch 4800, loss[loss=0.1858, simple_loss=0.2879, pruned_loss=0.04181, over 7207.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2823, pruned_loss=0.05504, over 1467150.64 frames.], batch size: 20, lr: 2.94e-04
2022-07-27 08:12:19,482 INFO [train.py:850] (2/4) Epoch 19, batch 4850, loss[loss=0.1688, simple_loss=0.2572, pruned_loss=0.04015, over 7299.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2836, pruned_loss=0.05559, over 1467249.06 frames.], batch size: 17, lr: 2.94e-04
2022-07-27 08:13:02,405 INFO [train.py:850] (2/4) Epoch 19, batch 4900, loss[loss=0.2046, simple_loss=0.2979, pruned_loss=0.05562, over 7401.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2839, pruned_loss=0.05627, over 1465916.91 frames.], batch size: 22, lr: 2.94e-04
2022-07-27 08:13:46,356 INFO [train.py:850] (2/4) Epoch 19, batch 4950, loss[loss=0.2001, simple_loss=0.2962, pruned_loss=0.05196, over 7372.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2848, pruned_loss=0.05733, over 1466393.95 frames.], batch size: 31, lr: 2.94e-04
2022-07-27 08:14:29,791 INFO [train.py:850] (2/4) Epoch 19, batch 5000, loss[loss=0.2197, simple_loss=0.301, pruned_loss=0.06922, over 7444.00 frames.], tot_loss[loss=0.199, simple_loss=0.2843, pruned_loss=0.05681, over 1466329.09 frames.], batch size: 70, lr: 2.94e-04
2022-07-27 08:15:15,248 INFO [train.py:850] (2/4) Epoch 19, batch 5050, loss[loss=0.1749, simple_loss=0.2595, pruned_loss=0.04512, over 7440.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2832, pruned_loss=0.05649, over 1466713.86 frames.], batch size: 18, lr: 2.93e-04
2022-07-27 08:15:57,668 INFO [train.py:850] (2/4) Epoch 19, batch 5100, loss[loss=0.1651, simple_loss=0.2655, pruned_loss=0.03229, over 7297.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2837, pruned_loss=0.0566, over 1466745.20 frames.], batch size: 22, lr: 2.93e-04
2022-07-27 08:16:41,573 INFO [train.py:850] (2/4) Epoch 19, batch 5150, loss[loss=0.1753, simple_loss=0.2599, pruned_loss=0.04537, over 7173.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2834, pruned_loss=0.05699, over 1466473.45 frames.], batch size: 17, lr: 2.93e-04
2022-07-27 08:17:25,115 INFO [train.py:850] (2/4) Epoch 19, batch 5200, loss[loss=0.1775, simple_loss=0.2742, pruned_loss=0.04041, over 7412.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2842, pruned_loss=0.05741, over 1467680.60 frames.], batch size: 22, lr: 2.93e-04
2022-07-27 08:18:09,203 INFO [train.py:850] (2/4) Epoch 19, batch 5250, loss[loss=0.1964, simple_loss=0.2905, pruned_loss=0.05113, over 7194.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2835, pruned_loss=0.05674, over 1466245.04 frames.], batch size: 19, lr: 2.93e-04
2022-07-27 08:18:52,848 INFO [train.py:850] (2/4) Epoch 19, batch 5300, loss[loss=0.2005, simple_loss=0.2891, pruned_loss=0.05595, over 7491.00 frames.], tot_loss[loss=0.198, simple_loss=0.2829, pruned_loss=0.05657, over 1466864.20 frames.], batch size: 23, lr: 2.93e-04
2022-07-27 08:19:36,213 INFO [train.py:850] (2/4) Epoch 19, batch 5350, loss[loss=0.1848, simple_loss=0.2827, pruned_loss=0.04342, over 7294.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2826, pruned_loss=0.0563, over 1466564.46 frames.], batch size: 19, lr: 2.93e-04
2022-07-27 08:20:19,562 INFO [train.py:850] (2/4) Epoch 19, batch 5400, loss[loss=0.2555, simple_loss=0.3237, pruned_loss=0.09364, over 7429.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2813, pruned_loss=0.05643, over 1465317.93 frames.], batch size: 22, lr: 2.93e-04
2022-07-27 08:21:02,827 INFO [train.py:850] (2/4) Epoch 19, batch 5450, loss[loss=0.1929, simple_loss=0.2776, pruned_loss=0.05406, over 7371.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2806, pruned_loss=0.0565, over 1465380.12 frames.], batch size: 20, lr: 2.93e-04
2022-07-27 08:21:46,652 INFO [train.py:850] (2/4) Epoch 19, batch 5500, loss[loss=0.2372, simple_loss=0.3179, pruned_loss=0.07824, over 7308.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2808, pruned_loss=0.05637, over 1466020.63 frames.], batch size: 38, lr: 2.93e-04
2022-07-27 08:22:31,211 INFO [train.py:850] (2/4) Epoch 19, batch 5550, loss[loss=0.1664, simple_loss=0.2604, pruned_loss=0.03622, over 7473.00 frames.], tot_loss[loss=0.198, simple_loss=0.2823, pruned_loss=0.05684, over 1465165.37 frames.], batch size: 21, lr: 2.93e-04
2022-07-27 08:23:14,808 INFO [train.py:850] (2/4) Epoch 19, batch 5600, loss[loss=0.2213, simple_loss=0.3058, pruned_loss=0.06842, over 7231.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2821, pruned_loss=0.05647, over 1464808.94 frames.], batch size: 25, lr: 2.93e-04
2022-07-27 08:23:59,623 INFO [train.py:850] (2/4) Epoch 19, batch 5650, loss[loss=0.2126, simple_loss=0.3039, pruned_loss=0.06064, over 7422.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2804, pruned_loss=0.05574, over 1464347.56 frames.], batch size: 22, lr: 2.93e-04
2022-07-27 08:24:43,225 INFO [train.py:850] (2/4) Epoch 19, batch 5700, loss[loss=0.174, simple_loss=0.2479, pruned_loss=0.05005, over 7452.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2806, pruned_loss=0.05546, over 1464829.56 frames.], batch size: 18, lr: 2.93e-04
2022-07-27 08:25:26,919 INFO [train.py:850] (2/4) Epoch 19, batch 5750, loss[loss=0.2424, simple_loss=0.3241, pruned_loss=0.08042, over 7340.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2812, pruned_loss=0.05562, over 1465640.13 frames.], batch size: 27, lr: 2.93e-04
2022-07-27 08:26:10,081 INFO [train.py:850] (2/4) Epoch 19, batch 5800, loss[loss=0.2155, simple_loss=0.307, pruned_loss=0.06201, over 7336.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2821, pruned_loss=0.05658, over 1466291.87 frames.], batch size: 38, lr: 2.93e-04
2022-07-27 08:26:54,569 INFO [train.py:850] (2/4) Epoch 19, batch 5850, loss[loss=0.1796, simple_loss=0.2832, pruned_loss=0.03799, over 7419.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2821, pruned_loss=0.05637, over 1465647.79 frames.], batch size: 22, lr: 2.93e-04
2022-07-27 08:27:38,420 INFO [train.py:850] (2/4) Epoch 19, batch 5900, loss[loss=0.1885, simple_loss=0.2882, pruned_loss=0.04443, over 7388.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2825, pruned_loss=0.05661, over 1466054.81 frames.], batch size: 21, lr: 2.93e-04
2022-07-27 08:28:22,279 INFO [train.py:850] (2/4) Epoch 19, batch 5950, loss[loss=0.2182, simple_loss=0.3056, pruned_loss=0.06542, over 7207.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2818, pruned_loss=0.05627, over 1465875.64 frames.], batch size: 24, lr: 2.93e-04
2022-07-27 08:29:06,830 INFO [train.py:850] (2/4) Epoch 19, batch 6000, loss[loss=0.2329, simple_loss=0.3093, pruned_loss=0.07823, over 7174.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2829, pruned_loss=0.05666, over 1465809.83 frames.], batch size: 22, lr: 2.93e-04
2022-07-27 08:29:06,831 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 08:29:29,636 INFO [train.py:879] (2/4) Epoch 19, validation: loss=0.1875, simple_loss=0.2836, pruned_loss=0.04571, over 924787.00 frames.
2022-07-27 08:30:13,909 INFO [train.py:850] (2/4) Epoch 19, batch 6050, loss[loss=0.173, simple_loss=0.2473, pruned_loss=0.04933, over 7441.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2828, pruned_loss=0.05685, over 1466351.24 frames.], batch size: 18, lr: 2.93e-04
2022-07-27 08:30:57,388 INFO [train.py:850] (2/4) Epoch 19, batch 6100, loss[loss=0.2177, simple_loss=0.28, pruned_loss=0.07772, over 7311.00 frames.], tot_loss[loss=0.1969, simple_loss=0.281, pruned_loss=0.05639, over 1465405.81 frames.], batch size: 17, lr: 2.93e-04
2022-07-27 08:31:43,218 INFO [train.py:850] (2/4) Epoch 19, batch 6150, loss[loss=0.1537, simple_loss=0.2432, pruned_loss=0.03212, over 7391.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2807, pruned_loss=0.056, over 1465489.60 frames.], batch size: 19, lr: 2.93e-04
2022-07-27 08:32:29,272 INFO [train.py:850] (2/4) Epoch 19, batch 6200, loss[loss=0.1777, simple_loss=0.2671, pruned_loss=0.04414, over 7195.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2803, pruned_loss=0.05556, over 1465808.63 frames.], batch size: 23, lr: 2.92e-04
2022-07-27 08:33:14,254 INFO [train.py:850] (2/4) Epoch 19, batch 6250, loss[loss=0.2331, simple_loss=0.3112, pruned_loss=0.07752, over 7451.00 frames.], tot_loss[loss=0.198, simple_loss=0.2826, pruned_loss=0.05668, over 1464902.63 frames.], batch size: 26, lr: 2.92e-04
2022-07-27 08:33:58,305 INFO [train.py:850] (2/4) Epoch 19, batch 6300, loss[loss=0.2031, simple_loss=0.2853, pruned_loss=0.06046, over 7457.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2815, pruned_loss=0.05615, over 1465016.48 frames.], batch size: 31, lr: 2.92e-04
2022-07-27 08:34:42,692 INFO [train.py:850] (2/4) Epoch 19, batch 6350, loss[loss=0.2296, simple_loss=0.3138, pruned_loss=0.07268, over 7342.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2813, pruned_loss=0.05593, over 1465118.28 frames.], batch size: 23, lr: 2.92e-04
2022-07-27 08:35:26,129 INFO [train.py:850] (2/4) Epoch 19, batch 6400, loss[loss=0.1767, simple_loss=0.2534, pruned_loss=0.05001, over 7156.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2812, pruned_loss=0.05599, over 1466002.85 frames.], batch size: 17, lr: 2.92e-04
2022-07-27 08:36:09,980 INFO [train.py:850] (2/4) Epoch 19, batch 6450, loss[loss=0.1702, simple_loss=0.2585, pruned_loss=0.041, over 7304.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2813, pruned_loss=0.05572, over 1465549.70 frames.], batch size: 18, lr: 2.92e-04
2022-07-27 08:36:52,737 INFO [train.py:850] (2/4) Epoch 19, batch 6500, loss[loss=0.2666, simple_loss=0.3437, pruned_loss=0.09473, over 7443.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2816, pruned_loss=0.05576, over 1464697.99 frames.], batch size: 75, lr: 2.92e-04
2022-07-27 08:37:37,180 INFO [train.py:850] (2/4) Epoch 19, batch 6550, loss[loss=0.197, simple_loss=0.2787, pruned_loss=0.05763, over 7468.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2814, pruned_loss=0.05519, over 1465417.11 frames.], batch size: 21, lr: 2.92e-04
2022-07-27 08:38:21,292 INFO [train.py:850] (2/4) Epoch 19, batch 6600, loss[loss=0.182, simple_loss=0.2705, pruned_loss=0.04678, over 7206.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2803, pruned_loss=0.05452, over 1466567.10 frames.], batch size: 19, lr: 2.92e-04
2022-07-27 08:39:05,865 INFO [train.py:850] (2/4) Epoch 19, batch 6650, loss[loss=0.2136, simple_loss=0.2945, pruned_loss=0.06632, over 7489.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2808, pruned_loss=0.05512, over 1466329.85 frames.], batch size: 23, lr: 2.92e-04
2022-07-27 08:39:48,984 INFO [train.py:850] (2/4) Epoch 19, batch 6700, loss[loss=0.1791, simple_loss=0.2612, pruned_loss=0.04848, over 7300.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2799, pruned_loss=0.05511, over 1465833.91 frames.], batch size: 17, lr: 2.92e-04
2022-07-27 08:40:33,859 INFO [train.py:850] (2/4) Epoch 19, batch 6750, loss[loss=0.2015, simple_loss=0.2719, pruned_loss=0.06559, over 7306.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2794, pruned_loss=0.05468, over 1466368.19 frames.], batch size: 17, lr: 2.92e-04
2022-07-27 08:41:17,688 INFO [train.py:850] (2/4) Epoch 19, batch 6800, loss[loss=0.1889, simple_loss=0.2796, pruned_loss=0.04912, over 7407.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2792, pruned_loss=0.05469, over 1466755.71 frames.], batch size: 22, lr: 2.92e-04
2022-07-27 08:42:00,868 INFO [train.py:850] (2/4) Epoch 19, batch 6850, loss[loss=0.1821, simple_loss=0.2673, pruned_loss=0.04847, over 7277.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2799, pruned_loss=0.05457, over 1466406.44 frames.], batch size: 21, lr: 2.92e-04
2022-07-27 08:42:45,722 INFO [train.py:850] (2/4) Epoch 19, batch 6900, loss[loss=0.2298, simple_loss=0.3021, pruned_loss=0.07871, over 7238.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2803, pruned_loss=0.05506, over 1466346.86 frames.], batch size: 30, lr: 2.92e-04
2022-07-27 08:43:29,301 INFO [train.py:850] (2/4) Epoch 19, batch 6950, loss[loss=0.2255, simple_loss=0.3028, pruned_loss=0.07411, over 7192.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2796, pruned_loss=0.05491, over 1466468.83 frames.], batch size: 21, lr: 2.92e-04
2022-07-27 08:44:12,642 INFO [train.py:850] (2/4) Epoch 19, batch 7000, loss[loss=0.1697, simple_loss=0.2643, pruned_loss=0.03761, over 7475.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2788, pruned_loss=0.05484, over 1466174.54 frames.], batch size: 21, lr: 2.92e-04
2022-07-27 08:44:56,300 INFO [train.py:850] (2/4) Epoch 19, batch 7050, loss[loss=0.1586, simple_loss=0.2459, pruned_loss=0.03568, over 7246.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2781, pruned_loss=0.05478, over 1465778.06 frames.], batch size: 16, lr: 2.92e-04
2022-07-27 08:45:39,940 INFO [train.py:850] (2/4) Epoch 19, batch 7100, loss[loss=0.1609, simple_loss=0.2488, pruned_loss=0.03645, over 7294.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2799, pruned_loss=0.05531, over 1465534.00 frames.], batch size: 19, lr: 2.92e-04
2022-07-27 08:46:23,700 INFO [train.py:850] (2/4) Epoch 19, batch 7150, loss[loss=0.1402, simple_loss=0.2292, pruned_loss=0.02558, over 7452.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2798, pruned_loss=0.05567, over 1465306.58 frames.], batch size: 18, lr: 2.92e-04
2022-07-27 08:47:07,292 INFO [train.py:850] (2/4) Epoch 19, batch 7200, loss[loss=0.1661, simple_loss=0.273, pruned_loss=0.02964, over 7210.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2789, pruned_loss=0.05421, over 1465566.97 frames.], batch size: 20, lr: 2.92e-04
2022-07-27 08:47:51,801 INFO [train.py:850] (2/4) Epoch 19, batch 7250, loss[loss=0.1618, simple_loss=0.2507, pruned_loss=0.0365, over 7102.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2791, pruned_loss=0.05405, over 1464610.14 frames.], batch size: 18, lr: 2.92e-04
2022-07-27 08:48:34,863 INFO [train.py:850] (2/4) Epoch 19, batch 7300, loss[loss=0.1611, simple_loss=0.2538, pruned_loss=0.03425, over 7305.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2789, pruned_loss=0.05387, over 1464836.31 frames.], batch size: 18, lr: 2.92e-04
2022-07-27 08:49:18,725 INFO [train.py:850] (2/4) Epoch 19, batch 7350, loss[loss=0.2352, simple_loss=0.3118, pruned_loss=0.07934, over 7281.00 frames.], tot_loss[loss=0.1933, simple_loss=0.279, pruned_loss=0.0538, over 1465396.57 frames.], batch size: 21, lr: 2.91e-04
2022-07-27 08:50:02,941 INFO [train.py:850] (2/4) Epoch 19, batch 7400, loss[loss=0.2098, simple_loss=0.3024, pruned_loss=0.05858, over 7357.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2794, pruned_loss=0.05408, over 1466115.49 frames.], batch size: 38, lr: 2.91e-04
2022-07-27 08:50:48,987 INFO [train.py:850] (2/4) Epoch 19, batch 7450, loss[loss=0.2066, simple_loss=0.2872, pruned_loss=0.06297, over 7454.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2806, pruned_loss=0.05446, over 1466509.85 frames.], batch size: 31, lr: 2.91e-04
2022-07-27 08:51:33,887 INFO [train.py:850] (2/4) Epoch 19, batch 7500, loss[loss=0.1815, simple_loss=0.2687, pruned_loss=0.0472, over 7296.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2816, pruned_loss=0.05513, over 1465869.64 frames.], batch size: 19, lr: 2.91e-04
2022-07-27 08:52:19,633 INFO [train.py:850] (2/4) Epoch 19, batch 7550, loss[loss=0.2519, simple_loss=0.3306, pruned_loss=0.08663, over 7369.00 frames.], tot_loss[loss=0.197, simple_loss=0.2826, pruned_loss=0.05575, over 1466832.62 frames.], batch size: 72, lr: 2.91e-04
2022-07-27 08:53:05,835 INFO [train.py:850] (2/4) Epoch 19, batch 7600, loss[loss=0.1802, simple_loss=0.258, pruned_loss=0.0512, over 7301.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2816, pruned_loss=0.05538, over 1465217.03 frames.], batch size: 17, lr: 2.91e-04
2022-07-27 08:53:51,892 INFO [train.py:850] (2/4) Epoch 19, batch 7650, loss[loss=0.2731, simple_loss=0.3423, pruned_loss=0.1019, over 7471.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2808, pruned_loss=0.05518, over 1465412.12 frames.], batch size: 71, lr: 2.91e-04
2022-07-27 08:54:35,053 INFO [train.py:850] (2/4) Epoch 19, batch 7700, loss[loss=0.187, simple_loss=0.2776, pruned_loss=0.04821, over 7483.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2804, pruned_loss=0.0545, over 1464948.38 frames.], batch size: 21, lr: 2.91e-04
2022-07-27 08:55:19,411 INFO [train.py:850] (2/4) Epoch 19, batch 7750, loss[loss=0.2685, simple_loss=0.3342, pruned_loss=0.1014, over 7413.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2799, pruned_loss=0.0544, over 1466014.09 frames.], batch size: 71, lr: 2.91e-04
2022-07-27 08:56:02,991 INFO [train.py:850] (2/4) Epoch 19, batch 7800, loss[loss=0.242, simple_loss=0.3198, pruned_loss=0.08213, over 7484.00 frames.], tot_loss[loss=0.197, simple_loss=0.2822, pruned_loss=0.05586, over 1465577.81 frames.], batch size: 23, lr: 2.91e-04
2022-07-27 08:56:47,742 INFO [train.py:850] (2/4) Epoch 19, batch 7850, loss[loss=0.1988, simple_loss=0.2847, pruned_loss=0.05647, over 7466.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2799, pruned_loss=0.05439, over 1466440.35 frames.], batch size: 24, lr: 2.91e-04
2022-07-27 08:57:30,738 INFO [train.py:850] (2/4) Epoch 19, batch 7900, loss[loss=0.1927, simple_loss=0.2664, pruned_loss=0.05951, over 7202.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2808, pruned_loss=0.05494, over 1466409.34 frames.], batch size: 18, lr: 2.91e-04
2022-07-27 08:58:30,494 INFO [train.py:850] (2/4) Epoch 19, batch 7950, loss[loss=0.1675, simple_loss=0.2544, pruned_loss=0.04028, over 7194.00 frames.], tot_loss[loss=0.196, simple_loss=0.2813, pruned_loss=0.05535, over 1466586.65 frames.], batch size: 18, lr: 2.91e-04
2022-07-27 08:59:13,933 INFO [train.py:850] (2/4) Epoch 19, batch 8000, loss[loss=0.1891, simple_loss=0.2736, pruned_loss=0.05229, over 7393.00 frames.], tot_loss[loss=0.1955, simple_loss=0.281, pruned_loss=0.05498, over 1466808.48 frames.], batch size: 20, lr: 2.91e-04
2022-07-27 08:59:57,734 INFO [train.py:850] (2/4) Epoch 19, batch 8050, loss[loss=0.2389, simple_loss=0.3177, pruned_loss=0.08, over 7411.00 frames.], tot_loss[loss=0.1942, simple_loss=0.28, pruned_loss=0.05418, over 1466544.76 frames.], batch size: 22, lr: 2.91e-04
2022-07-27 09:00:41,769 INFO [train.py:850] (2/4) Epoch 19, batch 8100, loss[loss=0.1838, simple_loss=0.2721, pruned_loss=0.04771, over 7285.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2811, pruned_loss=0.0547, over 1466067.27 frames.], batch size: 21, lr: 2.91e-04
2022-07-27 09:01:25,959 INFO [train.py:850] (2/4) Epoch 19, batch 8150, loss[loss=0.1771, simple_loss=0.2644, pruned_loss=0.04487, over 7476.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2803, pruned_loss=0.05469, over 1466170.72 frames.], batch size: 19, lr: 2.91e-04
2022-07-27 09:02:09,473 INFO [train.py:850] (2/4) Epoch 19, batch 8200, loss[loss=0.2633, simple_loss=0.3295, pruned_loss=0.09858, over 7335.00 frames.], tot_loss[loss=0.195, simple_loss=0.2806, pruned_loss=0.05473, over 1465559.25 frames.], batch size: 76, lr: 2.91e-04
2022-07-27 09:02:53,341 INFO [train.py:850] (2/4) Epoch 19, batch 8250, loss[loss=0.1765, simple_loss=0.2584, pruned_loss=0.04726, over 7385.00 frames.], tot_loss[loss=0.195, simple_loss=0.2803, pruned_loss=0.05479, over 1465913.96 frames.], batch size: 20, lr: 2.91e-04
2022-07-27 09:03:37,682 INFO [train.py:850] (2/4) Epoch 19, batch 8300, loss[loss=0.1826, simple_loss=0.2765, pruned_loss=0.04433, over 7373.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2799, pruned_loss=0.05488, over 1466454.58 frames.], batch size: 20, lr: 2.91e-04
2022-07-27 09:04:21,337 INFO [train.py:850] (2/4) Epoch 19, batch 8350, loss[loss=0.1661, simple_loss=0.2484, pruned_loss=0.04192, over 7315.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2815, pruned_loss=0.05584, over 1467461.29 frames.], batch size: 17, lr: 2.91e-04
2022-07-27 09:05:05,079 INFO [train.py:850] (2/4) Epoch 19, batch 8400, loss[loss=0.2461, simple_loss=0.3204, pruned_loss=0.08592, over 7394.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2814, pruned_loss=0.0556, over 1467071.54 frames.], batch size: 70, lr: 2.91e-04
2022-07-27 09:05:49,308 INFO [train.py:850] (2/4) Epoch 19, batch 8450, loss[loss=0.2264, simple_loss=0.3031, pruned_loss=0.07485, over 7352.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2802, pruned_loss=0.05511, over 1466596.56 frames.], batch size: 72, lr: 2.91e-04
2022-07-27 09:06:33,064 INFO [train.py:850] (2/4) Epoch 19, batch 8500, loss[loss=0.1727, simple_loss=0.2733, pruned_loss=0.03604, over 7381.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2809, pruned_loss=0.05476, over 1466210.74 frames.], batch size: 21, lr: 2.90e-04
2022-07-27 09:07:17,135 INFO [train.py:850] (2/4) Epoch 19, batch 8550, loss[loss=0.2044, simple_loss=0.3011, pruned_loss=0.05382, over 7263.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2802, pruned_loss=0.05496, over 1466247.54 frames.], batch size: 30, lr: 2.90e-04
2022-07-27 09:08:00,208 INFO [train.py:850] (2/4) Epoch 19, batch 8600, loss[loss=0.1784, simple_loss=0.2529, pruned_loss=0.0519, over 7154.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2795, pruned_loss=0.05488, over 1466352.79 frames.], batch size: 17, lr: 2.90e-04
2022-07-27 09:08:45,195 INFO [train.py:850] (2/4) Epoch 19, batch 8650, loss[loss=0.2579, simple_loss=0.3351, pruned_loss=0.09032, over 7311.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2788, pruned_loss=0.05477, over 1466347.00 frames.], batch size: 27, lr: 2.90e-04
2022-07-27 09:09:27,833 INFO [train.py:850] (2/4) Epoch 19, batch 8700, loss[loss=0.224, simple_loss=0.3112, pruned_loss=0.06843, over 7410.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2801, pruned_loss=0.05559, over 1465839.08 frames.], batch size: 22, lr: 2.90e-04
2022-07-27 09:10:10,976 INFO [train.py:850] (2/4) Epoch 19, batch 8750, loss[loss=0.2593, simple_loss=0.3277, pruned_loss=0.09543, over 7474.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2797, pruned_loss=0.05504, over 1465793.14 frames.], batch size: 21, lr: 2.90e-04
2022-07-27 09:10:53,218 INFO [train.py:850] (2/4) Epoch 19, batch 8800, loss[loss=0.1946, simple_loss=0.2641, pruned_loss=0.06256, over 7298.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2788, pruned_loss=0.05447, over 1465226.29 frames.], batch size: 17, lr: 2.90e-04
2022-07-27 09:11:36,657 INFO [train.py:850] (2/4) Epoch 19, batch 8850, loss[loss=0.1992, simple_loss=0.2945, pruned_loss=0.05191, over 7277.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2792, pruned_loss=0.05475, over 1465419.35 frames.], batch size: 21, lr: 2.90e-04
2022-07-27 09:13:02,210 INFO [train.py:850] (2/4) Epoch 20, batch 0, loss[loss=0.1824, simple_loss=0.2664, pruned_loss=0.04915, over 7425.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2664, pruned_loss=0.04915, over 7425.00 frames.], batch size: 18, lr: 2.83e-04
2022-07-27 09:13:46,529 INFO [train.py:850] (2/4) Epoch 20, batch 50, loss[loss=0.2098, simple_loss=0.2989, pruned_loss=0.06032, over 7297.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2845, pruned_loss=0.05066, over 330104.07 frames.], batch size: 20, lr: 2.83e-04
2022-07-27 09:14:29,690 INFO [train.py:850] (2/4) Epoch 20, batch 100, loss[loss=0.1925, simple_loss=0.27, pruned_loss=0.0575, over 7198.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2805, pruned_loss=0.04868, over 581464.96 frames.], batch size: 18, lr: 2.83e-04
2022-07-27 09:15:14,006 INFO [train.py:850] (2/4) Epoch 20, batch 150, loss[loss=0.2053, simple_loss=0.2904, pruned_loss=0.06011, over 7395.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2769, pruned_loss=0.04701, over 777892.70 frames.], batch size: 19, lr: 2.83e-04
2022-07-27 09:15:57,581 INFO [train.py:850] (2/4) Epoch 20, batch 200, loss[loss=0.1772, simple_loss=0.2819, pruned_loss=0.03626, over 7372.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2783, pruned_loss=0.04757, over 930025.98 frames.], batch size: 21, lr: 2.83e-04
2022-07-27 09:16:40,837 INFO [train.py:850] (2/4) Epoch 20, batch 250, loss[loss=0.1893, simple_loss=0.2742, pruned_loss=0.05219, over 7187.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2759, pruned_loss=0.04634, over 1048538.79 frames.], batch size: 21, lr: 2.83e-04
2022-07-27 09:17:26,199 INFO [train.py:850] (2/4) Epoch 20, batch 300, loss[loss=0.1813, simple_loss=0.2724, pruned_loss=0.04508, over 7470.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2757, pruned_loss=0.0462, over 1140666.62 frames.], batch size: 17, lr: 2.83e-04
2022-07-27 09:18:11,154 INFO [train.py:850] (2/4) Epoch 20, batch 350, loss[loss=0.1912, simple_loss=0.2805, pruned_loss=0.05091, over 7472.00 frames.], tot_loss[loss=0.1844, simple_loss=0.276, pruned_loss=0.04643, over 1212031.73 frames.], batch size: 21, lr: 2.83e-04
2022-07-27 09:18:54,934 INFO [train.py:850] (2/4) Epoch 20, batch 400, loss[loss=0.1962, simple_loss=0.2716, pruned_loss=0.06043, over 7439.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2756, pruned_loss=0.04615, over 1268894.14 frames.], batch size: 18, lr: 2.83e-04
2022-07-27 09:19:39,029 INFO [train.py:850] (2/4) Epoch 20, batch 450, loss[loss=0.2155, simple_loss=0.3021, pruned_loss=0.06441, over 7454.00 frames.], tot_loss[loss=0.1834, simple_loss=0.275, pruned_loss=0.04587, over 1312192.79 frames.], batch size: 24, lr: 2.83e-04
2022-07-27 09:20:21,249 INFO [train.py:850] (2/4) Epoch 20, batch 500, loss[loss=0.1895, simple_loss=0.2942, pruned_loss=0.04243, over 7394.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2743, pruned_loss=0.04551, over 1346900.97 frames.], batch size: 21, lr: 2.83e-04
2022-07-27 09:21:06,010 INFO [train.py:850] (2/4) Epoch 20, batch 550, loss[loss=0.212, simple_loss=0.297, pruned_loss=0.06352, over 7213.00 frames.], tot_loss[loss=0.183, simple_loss=0.2744, pruned_loss=0.04583, over 1374276.17 frames.], batch size: 24, lr: 2.83e-04
2022-07-27 09:21:48,997 INFO [train.py:850] (2/4) Epoch 20, batch 600, loss[loss=0.1899, simple_loss=0.2966, pruned_loss=0.04159, over 7374.00 frames.], tot_loss[loss=0.181, simple_loss=0.2723, pruned_loss=0.04482, over 1394208.63 frames.], batch size: 21, lr: 2.83e-04
2022-07-27 09:22:32,819 INFO [train.py:850] (2/4) Epoch 20, batch 650, loss[loss=0.161, simple_loss=0.2657, pruned_loss=0.0281, over 7299.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2735, pruned_loss=0.04556, over 1409871.79 frames.], batch size: 22, lr: 2.83e-04
2022-07-27 09:23:17,943 INFO [train.py:850] (2/4) Epoch 20, batch 700, loss[loss=0.1854, simple_loss=0.2913, pruned_loss=0.0398, over 7277.00 frames.], tot_loss[loss=0.1824, simple_loss=0.274, pruned_loss=0.04546, over 1422238.29 frames.], batch size: 21, lr: 2.83e-04
2022-07-27 09:24:02,689 INFO [train.py:850] (2/4) Epoch 20, batch 750, loss[loss=0.1786, simple_loss=0.2724, pruned_loss=0.04241, over 7476.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2731, pruned_loss=0.0446, over 1431818.70 frames.], batch size: 20, lr: 2.82e-04
2022-07-27 09:24:45,422 INFO [train.py:850] (2/4) Epoch 20, batch 800, loss[loss=0.1818, simple_loss=0.2766, pruned_loss=0.04352, over 7378.00 frames.], tot_loss[loss=0.181, simple_loss=0.2732, pruned_loss=0.04438, over 1438663.53 frames.], batch size: 21, lr: 2.82e-04
2022-07-27 09:25:30,107 INFO [train.py:850] (2/4) Epoch 20, batch 850, loss[loss=0.1838, simple_loss=0.2729, pruned_loss=0.04739, over 7167.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2734, pruned_loss=0.04448, over 1444586.13 frames.], batch size: 22, lr: 2.82e-04
2022-07-27 09:26:13,869 INFO [train.py:850] (2/4) Epoch 20, batch 900, loss[loss=0.2292, simple_loss=0.2988, pruned_loss=0.07979, over 7488.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2755, pruned_loss=0.04587, over 1448449.15 frames.], batch size: 19, lr: 2.82e-04
2022-07-27 09:26:58,173 INFO [train.py:850] (2/4) Epoch 20, batch 950, loss[loss=0.1534, simple_loss=0.2425, pruned_loss=0.03218, over 7311.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2751, pruned_loss=0.04555, over 1452552.88 frames.], batch size: 18, lr: 2.82e-04
2022-07-27 09:27:42,473 INFO [train.py:850] (2/4) Epoch 20, batch 1000, loss[loss=0.2027, simple_loss=0.2852, pruned_loss=0.06014, over 7472.00 frames.], tot_loss[loss=0.184, simple_loss=0.2761, pruned_loss=0.04602, over 1455000.33 frames.], batch size: 17, lr: 2.82e-04
2022-07-27 09:28:26,493 INFO [train.py:850] (2/4) Epoch 20, batch 1050, loss[loss=0.1706, simple_loss=0.2678, pruned_loss=0.03665, over 7474.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2781, pruned_loss=0.04709, over 1457082.73 frames.], batch size: 21, lr: 2.82e-04
2022-07-27 09:29:09,340 INFO [train.py:850] (2/4) Epoch 20, batch 1100, loss[loss=0.1746, simple_loss=0.2791, pruned_loss=0.03509, over 7282.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2771, pruned_loss=0.04667, over 1458772.06 frames.], batch size: 21, lr: 2.82e-04
2022-07-27 09:29:53,340 INFO [train.py:850] (2/4) Epoch 20, batch 1150, loss[loss=0.1713, simple_loss=0.2665, pruned_loss=0.03806, over 7387.00 frames.], tot_loss[loss=0.186, simple_loss=0.2779, pruned_loss=0.04705, over 1460243.10 frames.], batch size: 20, lr: 2.82e-04
2022-07-27 09:30:36,843 INFO [train.py:850] (2/4) Epoch 20, batch 1200, loss[loss=0.1834, simple_loss=0.2748, pruned_loss=0.04597, over 7475.00 frames.], tot_loss[loss=0.1863, simple_loss=0.278, pruned_loss=0.04727, over 1460775.92 frames.], batch size: 21, lr: 2.82e-04
2022-07-27 09:31:20,855 INFO [train.py:850] (2/4) Epoch 20, batch 1250, loss[loss=0.1821, simple_loss=0.2787, pruned_loss=0.04278, over 7419.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2782, pruned_loss=0.04698, over 1462643.05 frames.], batch size: 22, lr: 2.82e-04
2022-07-27 09:32:06,914 INFO [train.py:850] (2/4) Epoch 20, batch 1300, loss[loss=0.1946, simple_loss=0.2908, pruned_loss=0.04921, over 7188.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2789, pruned_loss=0.04707, over 1463541.44 frames.], batch size: 21, lr: 2.82e-04
2022-07-27 09:32:51,754 INFO [train.py:850] (2/4) Epoch 20, batch 1350, loss[loss=0.1442, simple_loss=0.2254, pruned_loss=0.03147, over 7149.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2798, pruned_loss=0.04727, over 1464423.59 frames.], batch size: 17, lr: 2.82e-04
2022-07-27 09:33:36,368 INFO [train.py:850] (2/4) Epoch 20, batch 1400, loss[loss=0.1798, simple_loss=0.2833, pruned_loss=0.03819, over 7460.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2802, pruned_loss=0.04777, over 1463516.27 frames.], batch size: 31, lr: 2.82e-04
2022-07-27 09:34:20,521 INFO [train.py:850] (2/4) Epoch 20, batch 1450, loss[loss=0.1902, simple_loss=0.2707, pruned_loss=0.0549, over 7148.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2797, pruned_loss=0.04788, over 1464540.36 frames.], batch size: 17, lr: 2.82e-04
2022-07-27 09:35:04,147 INFO [train.py:850] (2/4) Epoch 20, batch 1500, loss[loss=0.1594, simple_loss=0.2364, pruned_loss=0.04116, over 7444.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2788, pruned_loss=0.0475, over 1464764.85 frames.], batch size: 17, lr: 2.82e-04
2022-07-27 09:35:48,387 INFO [train.py:850] (2/4) Epoch 20, batch 1550, loss[loss=0.1636, simple_loss=0.2508, pruned_loss=0.03823, over 7308.00 frames.], tot_loss[loss=0.186, simple_loss=0.278, pruned_loss=0.04696, over 1464637.42 frames.], batch size: 18, lr: 2.82e-04
2022-07-27 09:36:31,236 INFO [train.py:850] (2/4) Epoch 20, batch 1600, loss[loss=0.2154, simple_loss=0.3154, pruned_loss=0.05768, over 7482.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2784, pruned_loss=0.04649, over 1464235.95 frames.], batch size: 24, lr: 2.82e-04
2022-07-27 09:37:14,954 INFO [train.py:850] (2/4) Epoch 20, batch 1650, loss[loss=0.1569, simple_loss=0.2404, pruned_loss=0.03671, over 7257.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2784, pruned_loss=0.04689, over 1464435.09 frames.], batch size: 16, lr: 2.82e-04
2022-07-27 09:37:58,279 INFO [train.py:850] (2/4) Epoch 20, batch 1700, loss[loss=0.1598, simple_loss=0.242, pruned_loss=0.03876, over 7463.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2768, pruned_loss=0.04641, over 1463957.56 frames.], batch size: 17, lr: 2.82e-04
2022-07-27 09:38:42,427 INFO [train.py:850] (2/4) Epoch 20, batch 1750, loss[loss=0.2112, simple_loss=0.2996, pruned_loss=0.06143, over 7267.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2773, pruned_loss=0.04665, over 1464724.22 frames.], batch size: 30, lr: 2.82e-04
2022-07-27 09:39:25,483 INFO [train.py:850] (2/4) Epoch 20, batch 1800, loss[loss=0.171, simple_loss=0.2561, pruned_loss=0.04299, over 7317.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2768, pruned_loss=0.0462, over 1464524.48 frames.], batch size: 18, lr: 2.82e-04
2022-07-27 09:40:09,257 INFO [train.py:850] (2/4) Epoch 20, batch 1850, loss[loss=0.1989, simple_loss=0.2954, pruned_loss=0.05123, over 7338.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2771, pruned_loss=0.04627, over 1464250.61 frames.], batch size: 38, lr: 2.82e-04
2022-07-27 09:40:51,956 INFO [train.py:850] (2/4) Epoch 20, batch 1900, loss[loss=0.1764, simple_loss=0.2576, pruned_loss=0.04758, over 7288.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2761, pruned_loss=0.04552, over 1464477.45 frames.], batch size: 17, lr: 2.82e-04
2022-07-27 09:41:35,654 INFO [train.py:850] (2/4) Epoch 20, batch 1950, loss[loss=0.2247, simple_loss=0.3054, pruned_loss=0.07202, over 7285.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2763, pruned_loss=0.04562, over 1464684.24 frames.], batch size: 19, lr: 2.81e-04
2022-07-27 09:42:18,912 INFO [train.py:850] (2/4) Epoch 20, batch 2000, loss[loss=0.194, simple_loss=0.2952, pruned_loss=0.04645, over 7238.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2769, pruned_loss=0.04621, over 1464384.71 frames.], batch size: 24, lr: 2.81e-04
2022-07-27 09:43:03,298 INFO [train.py:850] (2/4) Epoch 20, batch 2050, loss[loss=0.1587, simple_loss=0.2507, pruned_loss=0.03332, over 7311.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2767, pruned_loss=0.04585, over 1464466.19 frames.], batch size: 18, lr: 2.81e-04
2022-07-27 09:43:45,821 INFO [train.py:850] (2/4) Epoch 20, batch 2100, loss[loss=0.196, simple_loss=0.2971, pruned_loss=0.04744, over 7179.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2768, pruned_loss=0.04582, over 1464175.12 frames.], batch size: 21, lr: 2.81e-04
2022-07-27 09:44:29,798 INFO [train.py:850] (2/4) Epoch 20, batch 2150, loss[loss=0.2076, simple_loss=0.304, pruned_loss=0.05557, over 7422.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2769, pruned_loss=0.04613, over 1464149.52 frames.], batch size: 22, lr: 2.81e-04
2022-07-27 09:45:13,183 INFO [train.py:850] (2/4) Epoch 20, batch 2200, loss[loss=0.1642, simple_loss=0.2592, pruned_loss=0.03458, over 7254.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2765, pruned_loss=0.04554, over 1463181.74 frames.], batch size: 16, lr: 2.81e-04
2022-07-27 09:45:57,651 INFO [train.py:850] (2/4) Epoch 20, batch 2250, loss[loss=0.1982, simple_loss=0.2923, pruned_loss=0.05209, over 7401.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2756, pruned_loss=0.0453, over 1462719.08 frames.], batch size: 31, lr: 2.81e-04
2022-07-27 09:46:40,545 INFO [train.py:850] (2/4) Epoch 20, batch 2300, loss[loss=0.1855, simple_loss=0.2778, pruned_loss=0.04659, over 7205.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2756, pruned_loss=0.04547, over 1462451.68 frames.], batch size: 20, lr: 2.81e-04
2022-07-27 09:47:24,914 INFO [train.py:850] (2/4) Epoch 20, batch 2350, loss[loss=0.1869, simple_loss=0.2786, pruned_loss=0.04759, over 7292.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2759, pruned_loss=0.04561, over 1462819.09 frames.], batch size: 20, lr: 2.81e-04
2022-07-27 09:48:08,240 INFO [train.py:850] (2/4) Epoch 20, batch 2400, loss[loss=0.1748, simple_loss=0.2551, pruned_loss=0.04724, over 7141.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2751, pruned_loss=0.04527, over 1463810.37 frames.], batch size: 17, lr: 2.81e-04
2022-07-27 09:48:52,197 INFO [train.py:850] (2/4) Epoch 20, batch 2450, loss[loss=0.1731, simple_loss=0.2633, pruned_loss=0.04138, over 7392.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2759, pruned_loss=0.04532, over 1463626.36 frames.], batch size: 19, lr: 2.81e-04
2022-07-27 09:49:35,026 INFO [train.py:850] (2/4) Epoch 20, batch 2500, loss[loss=0.1612, simple_loss=0.2507, pruned_loss=0.03591, over 7314.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2757, pruned_loss=0.04476, over 1462577.79 frames.], batch size: 18, lr: 2.81e-04
2022-07-27 09:50:19,153 INFO [train.py:850] (2/4) Epoch 20, batch 2550, loss[loss=0.1927, simple_loss=0.3018, pruned_loss=0.0418, over 7466.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2761, pruned_loss=0.04511, over 1463537.02 frames.], batch size: 24, lr: 2.81e-04
2022-07-27 09:51:02,900 INFO [train.py:850] (2/4) Epoch 20, batch 2600, loss[loss=0.2068, simple_loss=0.2923, pruned_loss=0.0607, over 7333.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2767, pruned_loss=0.04522, over 1464737.40 frames.], batch size: 23, lr: 2.81e-04
2022-07-27 09:51:46,975 INFO [train.py:850] (2/4) Epoch 20, batch 2650, loss[loss=0.1564, simple_loss=0.2413, pruned_loss=0.03573, over 7308.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2766, pruned_loss=0.0453, over 1465409.70 frames.], batch size: 18, lr: 2.81e-04
2022-07-27 09:52:30,694 INFO [train.py:850] (2/4) Epoch 20, batch 2700, loss[loss=0.1324, simple_loss=0.225, pruned_loss=0.01986, over 7306.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2763, pruned_loss=0.04512, over 1465963.78 frames.], batch size: 18, lr: 2.81e-04
2022-07-27 09:53:14,069 INFO [train.py:850] (2/4) Epoch 20, batch 2750, loss[loss=0.1584, simple_loss=0.2455, pruned_loss=0.0356, over 7316.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2771, pruned_loss=0.04518, over 1465320.54 frames.], batch size: 18, lr: 2.81e-04
2022-07-27 09:53:57,522 INFO [train.py:850] (2/4) Epoch 20, batch 2800, loss[loss=0.1631, simple_loss=0.2555, pruned_loss=0.0354, over 7378.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2759, pruned_loss=0.04468, over 1465726.03 frames.], batch size: 21, lr: 2.81e-04
2022-07-27 09:54:40,985 INFO [train.py:850] (2/4) Epoch 20, batch 2850, loss[loss=0.1793, simple_loss=0.2717, pruned_loss=0.0434, over 7448.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2761, pruned_loss=0.04455, over 1466208.28 frames.], batch size: 18, lr: 2.81e-04
2022-07-27 09:55:24,414 INFO [train.py:850] (2/4) Epoch 20, batch 2900, loss[loss=0.1385, simple_loss=0.2305, pruned_loss=0.02325, over 7397.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2766, pruned_loss=0.04493, over 1465403.87 frames.], batch size: 19, lr: 2.81e-04
2022-07-27 09:56:07,652 INFO [train.py:850] (2/4) Epoch 20, batch 2950, loss[loss=0.1696, simple_loss=0.2625, pruned_loss=0.03833, over 7142.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2749, pruned_loss=0.0443, over 1464453.98 frames.], batch size: 17, lr: 2.81e-04
2022-07-27 09:56:50,916 INFO [train.py:850] (2/4) Epoch 20, batch 3000, loss[loss=0.1928, simple_loss=0.2935, pruned_loss=0.04602, over 7205.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2755, pruned_loss=0.0449, over 1465614.74 frames.], batch size: 20, lr: 2.81e-04
2022-07-27 09:56:50,917 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 09:57:13,824 INFO [train.py:879] (2/4) Epoch 20, validation: loss=0.1894, simple_loss=0.2827, pruned_loss=0.0481, over 924787.00 frames.
2022-07-27 09:58:13,073 INFO [train.py:850] (2/4) Epoch 20, batch 3050, loss[loss=0.1703, simple_loss=0.2545, pruned_loss=0.04304, over 7166.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2771, pruned_loss=0.04614, over 1466222.33 frames.], batch size: 17, lr: 2.81e-04
2022-07-27 09:58:57,885 INFO [train.py:850] (2/4) Epoch 20, batch 3100, loss[loss=0.2102, simple_loss=0.302, pruned_loss=0.05914, over 7304.00 frames.], tot_loss[loss=0.185, simple_loss=0.2773, pruned_loss=0.04633, over 1466389.18 frames.], batch size: 22, lr: 2.81e-04
2022-07-27 09:59:41,565 INFO [train.py:850] (2/4) Epoch 20, batch 3150, loss[loss=0.1499, simple_loss=0.2389, pruned_loss=0.03043, over 7317.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2779, pruned_loss=0.04609, over 1465642.29 frames.], batch size: 17, lr: 2.81e-04
2022-07-27 10:00:23,691 INFO [train.py:850] (2/4) Epoch 20, batch 3200, loss[loss=0.1887, simple_loss=0.2902, pruned_loss=0.04365, over 7406.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2767, pruned_loss=0.04534, over 1466143.55 frames.], batch size: 31, lr: 2.80e-04
2022-07-27 10:01:07,803 INFO [train.py:850] (2/4) Epoch 20, batch 3250, loss[loss=0.1798, simple_loss=0.2728, pruned_loss=0.04339, over 7196.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2764, pruned_loss=0.04537, over 1465531.69 frames.], batch size: 20, lr: 2.80e-04
2022-07-27 10:01:50,784 INFO [train.py:850] (2/4) Epoch 20, batch 3300, loss[loss=0.222, simple_loss=0.3094, pruned_loss=0.06733, over 7474.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2759, pruned_loss=0.04465, over 1465207.70 frames.], batch size: 24, lr: 2.80e-04
2022-07-27 10:02:34,749 INFO [train.py:850] (2/4) Epoch 20, batch 3350, loss[loss=0.1722, simple_loss=0.2624, pruned_loss=0.04097, over 7295.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2774, pruned_loss=0.04553, over 1465851.46 frames.], batch size: 19, lr: 2.80e-04
2022-07-27 10:03:20,168 INFO [train.py:850] (2/4) Epoch 20, batch 3400, loss[loss=0.1809, simple_loss=0.2827, pruned_loss=0.0396, over 7301.00 frames.], tot_loss[loss=0.1837, simple_loss=0.277, pruned_loss=0.04519, over 1465039.79 frames.], batch size: 22, lr: 2.80e-04
2022-07-27 10:04:04,713 INFO [train.py:850] (2/4) Epoch 20, batch 3450, loss[loss=0.1845, simple_loss=0.2837, pruned_loss=0.04262, over 7206.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2782, pruned_loss=0.04542, over 1465592.25 frames.], batch size: 20, lr: 2.80e-04
2022-07-27 10:04:49,173 INFO [train.py:850] (2/4) Epoch 20, batch 3500, loss[loss=0.2127, simple_loss=0.3088, pruned_loss=0.05831, over 7183.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2781, pruned_loss=0.0453, over 1464998.20 frames.], batch size: 23, lr: 2.80e-04
2022-07-27 10:05:32,298 INFO [train.py:850] (2/4) Epoch 20, batch 3550, loss[loss=0.1821, simple_loss=0.2827, pruned_loss=0.04075, over 7427.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2777, pruned_loss=0.04489, over 1465571.04 frames.], batch size: 39, lr: 2.80e-04
2022-07-27 10:06:15,695 INFO [train.py:850] (2/4) Epoch 20, batch 3600, loss[loss=0.23, simple_loss=0.3269, pruned_loss=0.06655, over 7421.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2784, pruned_loss=0.04539, over 1466651.18 frames.], batch size: 22, lr: 2.80e-04
2022-07-27 10:06:59,870 INFO [train.py:850] (2/4) Epoch 20, batch 3650, loss[loss=0.1885, simple_loss=0.2753, pruned_loss=0.05086, over 7198.00 frames.], tot_loss[loss=0.184, simple_loss=0.2773, pruned_loss=0.04537, over 1466447.34 frames.], batch size: 21, lr: 2.80e-04
2022-07-27 10:07:42,768 INFO [train.py:850] (2/4) Epoch 20, batch 3700, loss[loss=0.1843, simple_loss=0.2837, pruned_loss=0.0424, over 7381.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2763, pruned_loss=0.0452, over 1466503.86 frames.], batch size: 21, lr: 2.80e-04
2022-07-27 10:08:26,469 INFO [train.py:850] (2/4) Epoch 20, batch 3750, loss[loss=0.2141, simple_loss=0.3058, pruned_loss=0.06114, over 7323.00 frames.], tot_loss[loss=0.184, simple_loss=0.2766, pruned_loss=0.04569, over 1466423.51 frames.], batch size: 38, lr: 2.80e-04
2022-07-27 10:09:09,488 INFO [train.py:850] (2/4) Epoch 20, batch 3800, loss[loss=0.2457, simple_loss=0.3278, pruned_loss=0.08182, over 7414.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2763, pruned_loss=0.04555, over 1467869.44 frames.], batch size: 22, lr: 2.80e-04
2022-07-27 10:09:52,693 INFO [train.py:850] (2/4) Epoch 20, batch 3850, loss[loss=0.2172, simple_loss=0.3092, pruned_loss=0.0626, over 7217.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2769, pruned_loss=0.04578, over 1466983.67 frames.], batch size: 25, lr: 2.80e-04
2022-07-27 10:10:35,724 INFO [train.py:850] (2/4) Epoch 20, batch 3900, loss[loss=0.1765, simple_loss=0.2641, pruned_loss=0.04446, over 7447.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2768, pruned_loss=0.04583, over 1466722.79 frames.], batch size: 17, lr: 2.80e-04
2022-07-27 10:11:19,631 INFO [train.py:850] (2/4) Epoch 20, batch 3950, loss[loss=0.1696, simple_loss=0.2777, pruned_loss=0.03081, over 7290.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2768, pruned_loss=0.04553, over 1467811.14 frames.], batch size: 22, lr: 2.80e-04
2022-07-27 10:12:03,283 INFO [train.py:850] (2/4) Epoch 20, batch 4000, loss[loss=0.1692, simple_loss=0.2569, pruned_loss=0.04072, over 7388.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2772, pruned_loss=0.04603, over 1467503.33 frames.], batch size: 19, lr: 2.80e-04
2022-07-27 10:12:46,788 INFO [train.py:850] (2/4) Epoch 20, batch 4050, loss[loss=0.1732, simple_loss=0.2674, pruned_loss=0.03948, over 7208.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2768, pruned_loss=0.0457, over 1467960.65 frames.], batch size: 20, lr: 2.80e-04
2022-07-27 10:13:30,860 INFO [train.py:850] (2/4) Epoch 20, batch 4100, loss[loss=0.1698, simple_loss=0.2604, pruned_loss=0.0396, over 7397.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2767, pruned_loss=0.04579, over 1466980.34 frames.], batch size: 19, lr: 2.80e-04
2022-07-27 10:14:15,066 INFO [train.py:850] (2/4) Epoch 20, batch 4150, loss[loss=0.1703, simple_loss=0.2575, pruned_loss=0.04149, over 7299.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2771, pruned_loss=0.04617, over 1466872.90 frames.], batch size: 17, lr: 2.80e-04
2022-07-27 10:14:58,121 INFO [train.py:850] (2/4) Epoch 20, batch 4200, loss[loss=0.1805, simple_loss=0.2639, pruned_loss=0.04851, over 7439.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2782, pruned_loss=0.04754, over 1467277.24 frames.], batch size: 18, lr: 2.80e-04
2022-07-27 10:15:42,024 INFO [train.py:850] (2/4) Epoch 20, batch 4250, loss[loss=0.229, simple_loss=0.3164, pruned_loss=0.07079, over 7295.00 frames.], tot_loss[loss=0.188, simple_loss=0.2789, pruned_loss=0.04851, over 1466453.37 frames.], batch size: 20, lr: 2.80e-04
2022-07-27 10:16:24,836 INFO [train.py:850] (2/4) Epoch 20, batch 4300, loss[loss=0.1622, simple_loss=0.2458, pruned_loss=0.0393, over 7166.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2795, pruned_loss=0.04938, over 1466272.58 frames.], batch size: 17, lr: 2.80e-04
2022-07-27 10:17:09,189 INFO [train.py:850] (2/4) Epoch 20, batch 4350, loss[loss=0.1713, simple_loss=0.2577, pruned_loss=0.04243, over 7386.00 frames.], tot_loss[loss=0.19, simple_loss=0.2798, pruned_loss=0.05012, over 1465987.22 frames.], batch size: 19, lr: 2.80e-04
2022-07-27 10:17:53,091 INFO [train.py:850] (2/4) Epoch 20, batch 4400, loss[loss=0.1829, simple_loss=0.2727, pruned_loss=0.04657, over 7291.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2799, pruned_loss=0.05067, over 1466904.77 frames.], batch size: 21, lr: 2.80e-04
2022-07-27 10:18:36,767 INFO [train.py:850] (2/4) Epoch 20, batch 4450, loss[loss=0.2619, simple_loss=0.3323, pruned_loss=0.09574, over 7298.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2812, pruned_loss=0.05181, over 1466117.99 frames.], batch size: 27, lr: 2.79e-04
2022-07-27 10:19:20,570 INFO [train.py:850] (2/4) Epoch 20, batch 4500, loss[loss=0.232, simple_loss=0.3248, pruned_loss=0.06961, over 7179.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2812, pruned_loss=0.05256, over 1466412.25 frames.], batch size: 22, lr: 2.79e-04
2022-07-27 10:20:03,249 INFO [train.py:850] (2/4) Epoch 20, batch 4550, loss[loss=0.1931, simple_loss=0.2884, pruned_loss=0.04886, over 7288.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2808, pruned_loss=0.05274, over 1465791.05 frames.], batch size: 21, lr: 2.79e-04
2022-07-27 10:20:47,276 INFO [train.py:850] (2/4) Epoch 20, batch 4600, loss[loss=0.1667, simple_loss=0.2571, pruned_loss=0.03816, over 7393.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2808, pruned_loss=0.05315, over 1465734.33 frames.], batch size: 19, lr: 2.79e-04
2022-07-27 10:21:31,097 INFO [train.py:850] (2/4) Epoch 20, batch 4650, loss[loss=0.1749, simple_loss=0.2592, pruned_loss=0.04529, over 7319.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2808, pruned_loss=0.05367, over 1466142.35 frames.], batch size: 17, lr: 2.79e-04
2022-07-27 10:22:15,075 INFO [train.py:850] (2/4) Epoch 20, batch 4700, loss[loss=0.2116, simple_loss=0.2897, pruned_loss=0.06675, over 7480.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2812, pruned_loss=0.05388, over 1466587.47 frames.], batch size: 20, lr: 2.79e-04
2022-07-27 10:22:59,178 INFO [train.py:850] (2/4) Epoch 20, batch 4750, loss[loss=0.1787, simple_loss=0.2606, pruned_loss=0.04841, over 7444.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2815, pruned_loss=0.05393, over 1466068.21 frames.], batch size: 18, lr: 2.79e-04
2022-07-27 10:23:42,930 INFO [train.py:850] (2/4) Epoch 20, batch 4800, loss[loss=0.1822, simple_loss=0.2625, pruned_loss=0.05091, over 7478.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2812, pruned_loss=0.05473, over 1465157.88 frames.], batch size: 17, lr: 2.79e-04
2022-07-27 10:24:26,620 INFO [train.py:850] (2/4) Epoch 20, batch 4850, loss[loss=0.1878, simple_loss=0.2848, pruned_loss=0.04542, over 7181.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2805, pruned_loss=0.05517, over 1465689.85 frames.], batch size: 22, lr: 2.79e-04
2022-07-27 10:25:09,915 INFO [train.py:850] (2/4) Epoch 20, batch 4900, loss[loss=0.1819, simple_loss=0.2784, pruned_loss=0.04274, over 7318.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2813, pruned_loss=0.05588, over 1466953.53 frames.], batch size: 27, lr: 2.79e-04
2022-07-27 10:25:53,940 INFO [train.py:850] (2/4) Epoch 20, batch 4950, loss[loss=0.1682, simple_loss=0.2564, pruned_loss=0.04, over 7195.00 frames.], tot_loss[loss=0.1964, simple_loss=0.281, pruned_loss=0.05595, over 1465922.99 frames.], batch size: 18, lr: 2.79e-04
2022-07-27 10:26:37,680 INFO [train.py:850] (2/4) Epoch 20, batch 5000, loss[loss=0.1875, simple_loss=0.271, pruned_loss=0.05204, over 7294.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2813, pruned_loss=0.05596, over 1465773.13 frames.], batch size: 19, lr: 2.79e-04
2022-07-27 10:27:22,210 INFO [train.py:850] (2/4) Epoch 20, batch 5050, loss[loss=0.2138, simple_loss=0.3043, pruned_loss=0.06167, over 7277.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2812, pruned_loss=0.05584, over 1466050.84 frames.], batch size: 27, lr: 2.79e-04
2022-07-27 10:28:06,402 INFO [train.py:850] (2/4) Epoch 20, batch 5100, loss[loss=0.2157, simple_loss=0.3012, pruned_loss=0.06505, over 7235.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2799, pruned_loss=0.05463, over 1464569.94 frames.], batch size: 24, lr: 2.79e-04
2022-07-27 10:28:51,578 INFO [train.py:850] (2/4) Epoch 20, batch 5150, loss[loss=0.1737, simple_loss=0.2597, pruned_loss=0.0438, over 7290.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2815, pruned_loss=0.05591, over 1465582.73 frames.], batch size: 19, lr: 2.79e-04
2022-07-27 10:29:35,067 INFO [train.py:850] (2/4) Epoch 20, batch 5200, loss[loss=0.2577, simple_loss=0.3333, pruned_loss=0.09107, over 7172.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2816, pruned_loss=0.05556, over 1466569.58 frames.], batch size: 22, lr: 2.79e-04
2022-07-27 10:30:18,273 INFO [train.py:850] (2/4) Epoch 20, batch 5250, loss[loss=0.1784, simple_loss=0.2693, pruned_loss=0.04381, over 7204.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2825, pruned_loss=0.05624, over 1466348.18 frames.], batch size: 19, lr: 2.79e-04
2022-07-27 10:31:02,681 INFO [train.py:850] (2/4) Epoch 20, batch 5300, loss[loss=0.2282, simple_loss=0.3059, pruned_loss=0.07524, over 7478.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2822, pruned_loss=0.05638, over 1466535.35 frames.], batch size: 20, lr: 2.79e-04
2022-07-27 10:31:46,303 INFO [train.py:850] (2/4) Epoch 20, batch 5350, loss[loss=0.2214, simple_loss=0.3, pruned_loss=0.07135, over 7378.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2826, pruned_loss=0.05707, over 1466595.28 frames.], batch size: 76, lr: 2.79e-04
2022-07-27 10:32:29,682 INFO [train.py:850] (2/4) Epoch 20, batch 5400, loss[loss=0.2627, simple_loss=0.3326, pruned_loss=0.09636, over 7374.00 frames.], tot_loss[loss=0.1975, simple_loss=0.282, pruned_loss=0.05645, over 1465241.94 frames.], batch size: 70, lr: 2.79e-04
2022-07-27 10:33:14,096 INFO [train.py:850] (2/4) Epoch 20, batch 5450, loss[loss=0.1854, simple_loss=0.2705, pruned_loss=0.05017, over 7283.00 frames.], tot_loss[loss=0.1962, simple_loss=0.281, pruned_loss=0.05571, over 1465884.90 frames.], batch size: 20, lr: 2.79e-04
2022-07-27 10:33:57,117 INFO [train.py:850] (2/4) Epoch 20, batch 5500, loss[loss=0.2076, simple_loss=0.2931, pruned_loss=0.06101, over 7188.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2813, pruned_loss=0.05598, over 1466117.83 frames.], batch size: 22, lr: 2.79e-04
2022-07-27 10:34:40,751 INFO [train.py:850] (2/4) Epoch 20, batch 5550, loss[loss=0.1876, simple_loss=0.2818, pruned_loss=0.0467, over 7466.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2815, pruned_loss=0.05646, over 1465261.84 frames.], batch size: 21, lr: 2.79e-04
2022-07-27 10:35:24,166 INFO [train.py:850] (2/4) Epoch 20, batch 5600, loss[loss=0.1533, simple_loss=0.2444, pruned_loss=0.03114, over 7209.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2812, pruned_loss=0.05619, over 1464952.53 frames.], batch size: 19, lr: 2.79e-04
2022-07-27 10:36:08,439 INFO [train.py:850] (2/4) Epoch 20, batch 5650, loss[loss=0.1983, simple_loss=0.2785, pruned_loss=0.05905, over 7203.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2809, pruned_loss=0.05588, over 1464590.38 frames.], batch size: 24, lr: 2.79e-04
2022-07-27 10:36:52,254 INFO [train.py:850] (2/4) Epoch 20, batch 5700, loss[loss=0.2097, simple_loss=0.2962, pruned_loss=0.06158, over 7291.00 frames.], tot_loss[loss=0.1966, simple_loss=0.281, pruned_loss=0.05604, over 1464213.52 frames.], batch size: 22, lr: 2.78e-04
2022-07-27 10:37:36,142 INFO [train.py:850] (2/4) Epoch 20, batch 5750, loss[loss=0.1906, simple_loss=0.2783, pruned_loss=0.05152, over 7169.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2808, pruned_loss=0.05568, over 1463367.96 frames.], batch size: 21, lr: 2.78e-04
2022-07-27 10:38:20,813 INFO [train.py:850] (2/4) Epoch 20, batch 5800, loss[loss=0.2101, simple_loss=0.2935, pruned_loss=0.06331, over 7377.00 frames.], tot_loss[loss=0.1985, simple_loss=0.283, pruned_loss=0.05698, over 1465072.87 frames.], batch size: 21, lr: 2.78e-04
2022-07-27 10:39:04,397 INFO [train.py:850] (2/4) Epoch 20, batch 5850, loss[loss=0.1639, simple_loss=0.2607, pruned_loss=0.03358, over 7301.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2824, pruned_loss=0.05658, over 1466167.41 frames.], batch size: 22, lr: 2.78e-04
2022-07-27 10:39:48,555 INFO [train.py:850] (2/4) Epoch 20, batch 5900, loss[loss=0.1837, simple_loss=0.2697, pruned_loss=0.04886, over 7223.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2818, pruned_loss=0.05561, over 1467082.14 frames.], batch size: 24, lr: 2.78e-04
2022-07-27 10:40:32,214 INFO [train.py:850] (2/4) Epoch 20, batch 5950, loss[loss=0.1863, simple_loss=0.2821, pruned_loss=0.04521, over 7190.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2818, pruned_loss=0.05544, over 1467185.71 frames.], batch size: 21, lr: 2.78e-04
2022-07-27 10:41:16,223 INFO [train.py:850] (2/4) Epoch 20, batch 6000, loss[loss=0.1781, simple_loss=0.2626, pruned_loss=0.04678, over 7200.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2812, pruned_loss=0.05533, over 1466099.68 frames.], batch size: 19, lr: 2.78e-04
2022-07-27 10:41:16,224 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 10:41:39,017 INFO [train.py:879] (2/4) Epoch 20, validation: loss=0.1865, simple_loss=0.2821, pruned_loss=0.04541, over 924787.00 frames.
2022-07-27 10:42:22,989 INFO [train.py:850] (2/4) Epoch 20, batch 6050, loss[loss=0.2265, simple_loss=0.3179, pruned_loss=0.06749, over 7236.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2817, pruned_loss=0.05569, over 1466750.30 frames.], batch size: 25, lr: 2.78e-04
2022-07-27 10:43:06,510 INFO [train.py:850] (2/4) Epoch 20, batch 6100, loss[loss=0.1676, simple_loss=0.2474, pruned_loss=0.04393, over 7308.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2805, pruned_loss=0.0554, over 1465236.30 frames.], batch size: 17, lr: 2.78e-04
2022-07-27 10:43:50,437 INFO [train.py:850] (2/4) Epoch 20, batch 6150, loss[loss=0.1707, simple_loss=0.2684, pruned_loss=0.03652, over 7415.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2791, pruned_loss=0.05431, over 1465190.75 frames.], batch size: 22, lr: 2.78e-04
2022-07-27 10:44:34,632 INFO [train.py:850] (2/4) Epoch 20, batch 6200, loss[loss=0.1587, simple_loss=0.2531, pruned_loss=0.03221, over 7362.00 frames.], tot_loss[loss=0.1938, simple_loss=0.279, pruned_loss=0.05434, over 1465646.82 frames.], batch size: 21, lr: 2.78e-04
2022-07-27 10:45:18,567 INFO [train.py:850] (2/4) Epoch 20, batch 6250, loss[loss=0.1566, simple_loss=0.2506, pruned_loss=0.03126, over 7392.00 frames.], tot_loss[loss=0.194, simple_loss=0.2788, pruned_loss=0.05463, over 1466477.33 frames.], batch size: 20, lr: 2.78e-04
2022-07-27 10:46:01,659 INFO [train.py:850] (2/4) Epoch 20, batch 6300, loss[loss=0.2081, simple_loss=0.2906, pruned_loss=0.0628, over 7300.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2785, pruned_loss=0.05426, over 1466455.37 frames.], batch size: 19, lr: 2.78e-04
2022-07-27 10:46:46,988 INFO [train.py:850] (2/4) Epoch 20, batch 6350, loss[loss=0.2561, simple_loss=0.3241, pruned_loss=0.09405, over 7476.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2797, pruned_loss=0.05499, over 1467391.78 frames.], batch size: 73, lr: 2.78e-04
2022-07-27 10:47:29,987 INFO [train.py:850] (2/4) Epoch 20, batch 6400, loss[loss=0.2374, simple_loss=0.3257, pruned_loss=0.07452, over 7478.00 frames.], tot_loss[loss=0.195, simple_loss=0.2796, pruned_loss=0.05522, over 1466980.59 frames.], batch size: 39, lr: 2.78e-04
2022-07-27 10:48:13,812 INFO [train.py:850] (2/4) Epoch 20, batch 6450, loss[loss=0.2217, simple_loss=0.3054, pruned_loss=0.06901, over 7287.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2802, pruned_loss=0.05513, over 1467143.16 frames.], batch size: 21, lr: 2.78e-04
2022-07-27 10:48:57,137 INFO [train.py:850] (2/4) Epoch 20, batch 6500, loss[loss=0.1676, simple_loss=0.2469, pruned_loss=0.04412, over 7443.00 frames.], tot_loss[loss=0.195, simple_loss=0.2795, pruned_loss=0.05528, over 1467230.26 frames.], batch size: 18, lr: 2.78e-04
2022-07-27 10:49:40,419 INFO [train.py:850] (2/4) Epoch 20, batch 6550, loss[loss=0.1842, simple_loss=0.2832, pruned_loss=0.04259, over 7377.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2781, pruned_loss=0.05415, over 1467524.71 frames.], batch size: 21, lr: 2.78e-04
2022-07-27 10:50:25,312 INFO [train.py:850] (2/4) Epoch 20, batch 6600, loss[loss=0.1826, simple_loss=0.269, pruned_loss=0.04813, over 7205.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2775, pruned_loss=0.05363, over 1467234.98 frames.], batch size: 18, lr: 2.78e-04
2022-07-27 10:51:09,403 INFO [train.py:850] (2/4) Epoch 20, batch 6650, loss[loss=0.2087, simple_loss=0.3057, pruned_loss=0.05588, over 7279.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2789, pruned_loss=0.05401, over 1467024.37 frames.], batch size: 21, lr: 2.78e-04
2022-07-27 10:51:53,745 INFO [train.py:850] (2/4) Epoch 20, batch 6700, loss[loss=0.2006, simple_loss=0.2854, pruned_loss=0.05796, over 7472.00 frames.], tot_loss[loss=0.1927, simple_loss=0.278, pruned_loss=0.05369, over 1466930.03 frames.], batch size: 21, lr: 2.78e-04
2022-07-27 10:52:37,247 INFO [train.py:850] (2/4) Epoch 20, batch 6750, loss[loss=0.2104, simple_loss=0.2907, pruned_loss=0.06507, over 7470.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2785, pruned_loss=0.0537, over 1466857.43 frames.], batch size: 31, lr: 2.78e-04
2022-07-27 10:53:21,196 INFO [train.py:850] (2/4) Epoch 20, batch 6800, loss[loss=0.1849, simple_loss=0.2839, pruned_loss=0.04291, over 7198.00 frames.], tot_loss[loss=0.1924, simple_loss=0.278, pruned_loss=0.05344, over 1466905.19 frames.], batch size: 20, lr: 2.78e-04
2022-07-27 10:54:04,707 INFO [train.py:850] (2/4) Epoch 20, batch 6850, loss[loss=0.1578, simple_loss=0.257, pruned_loss=0.02929, over 7315.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2777, pruned_loss=0.05324, over 1466337.23 frames.], batch size: 22, lr: 2.78e-04
2022-07-27 10:54:48,367 INFO [train.py:850] (2/4) Epoch 20, batch 6900, loss[loss=0.1888, simple_loss=0.274, pruned_loss=0.0518, over 7171.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2786, pruned_loss=0.05416, over 1466389.10 frames.], batch size: 22, lr: 2.78e-04
2022-07-27 10:55:32,396 INFO [train.py:850] (2/4) Epoch 20, batch 6950, loss[loss=0.2146, simple_loss=0.3031, pruned_loss=0.06306, over 7238.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2798, pruned_loss=0.05439, over 1466268.43 frames.], batch size: 24, lr: 2.77e-04
2022-07-27 10:56:15,295 INFO [train.py:850] (2/4) Epoch 20, batch 7000, loss[loss=0.1834, simple_loss=0.2686, pruned_loss=0.04911, over 7299.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2808, pruned_loss=0.05483, over 1466378.89 frames.], batch size: 19, lr: 2.77e-04
2022-07-27 10:57:14,840 INFO [train.py:850] (2/4) Epoch 20, batch 7050, loss[loss=0.2322, simple_loss=0.3142, pruned_loss=0.07514, over 7489.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2808, pruned_loss=0.05465, over 1466113.55 frames.], batch size: 19, lr: 2.77e-04
2022-07-27 10:57:58,971 INFO [train.py:850] (2/4) Epoch 20, batch 7100, loss[loss=0.189, simple_loss=0.2705, pruned_loss=0.05377, over 7493.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2797, pruned_loss=0.05431, over 1466933.37 frames.], batch size: 19, lr: 2.77e-04
2022-07-27 10:58:43,276 INFO [train.py:850] (2/4) Epoch 20, batch 7150, loss[loss=0.1695, simple_loss=0.2601, pruned_loss=0.03939, over 7295.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2793, pruned_loss=0.0541, over 1466257.69 frames.], batch size: 20, lr: 2.77e-04
2022-07-27 10:59:26,487 INFO [train.py:850] (2/4) Epoch 20, batch 7200, loss[loss=0.1596, simple_loss=0.2423, pruned_loss=0.0385, over 7201.00 frames.], tot_loss[loss=0.1928, simple_loss=0.278, pruned_loss=0.05373, over 1466270.37 frames.], batch size: 18, lr: 2.77e-04
2022-07-27 11:00:10,757 INFO [train.py:850] (2/4) Epoch 20, batch 7250, loss[loss=0.2007, simple_loss=0.2633, pruned_loss=0.06909, over 7285.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2784, pruned_loss=0.05403, over 1465444.69 frames.], batch size: 16, lr: 2.77e-04
2022-07-27 11:00:54,406 INFO [train.py:850] (2/4) Epoch 20, batch 7300, loss[loss=0.2668, simple_loss=0.3392, pruned_loss=0.09723, over 7380.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2777, pruned_loss=0.05403, over 1465426.47 frames.], batch size: 31, lr: 2.77e-04
2022-07-27 11:01:37,628 INFO [train.py:850] (2/4) Epoch 20, batch 7350, loss[loss=0.2028, simple_loss=0.2918, pruned_loss=0.05693, over 7278.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2774, pruned_loss=0.05351, over 1464899.62 frames.], batch size: 21, lr: 2.77e-04
2022-07-27 11:02:22,314 INFO [train.py:850] (2/4) Epoch 20, batch 7400, loss[loss=0.1567, simple_loss=0.2319, pruned_loss=0.04076, over 7441.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2782, pruned_loss=0.05363, over 1466513.36 frames.], batch size: 17, lr: 2.77e-04
2022-07-27 11:03:06,040 INFO [train.py:850] (2/4) Epoch 20, batch 7450, loss[loss=0.1771, simple_loss=0.2636, pruned_loss=0.0453, over 7362.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2786, pruned_loss=0.0538, over 1465526.57 frames.], batch size: 39, lr: 2.77e-04
2022-07-27 11:03:49,916 INFO [train.py:850] (2/4) Epoch 20, batch 7500, loss[loss=0.1841, simple_loss=0.2611, pruned_loss=0.05352, over 7307.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2781, pruned_loss=0.05361, over 1465861.44 frames.], batch size: 18, lr: 2.77e-04
2022-07-27 11:04:33,849 INFO [train.py:850] (2/4) Epoch 20, batch 7550, loss[loss=0.1719, simple_loss=0.269, pruned_loss=0.03747, over 7223.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2776, pruned_loss=0.05314, over 1465761.27 frames.], batch size: 25, lr: 2.77e-04
2022-07-27 11:05:19,501 INFO [train.py:850] (2/4) Epoch 20, batch 7600, loss[loss=0.2464, simple_loss=0.3224, pruned_loss=0.08519, over 7411.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2777, pruned_loss=0.05327, over 1465820.42 frames.], batch size: 31, lr: 2.77e-04
2022-07-27 11:06:03,332 INFO [train.py:850] (2/4) Epoch 20, batch 7650, loss[loss=0.1556, simple_loss=0.2354, pruned_loss=0.03787, over 7317.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2771, pruned_loss=0.05294, over 1465676.95 frames.], batch size: 17, lr: 2.77e-04
2022-07-27 11:06:46,794 INFO [train.py:850] (2/4) Epoch 20, batch 7700, loss[loss=0.1915, simple_loss=0.2648, pruned_loss=0.05912, over 7318.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2764, pruned_loss=0.0524, over 1465944.04 frames.], batch size: 18, lr: 2.77e-04
2022-07-27 11:07:31,677 INFO [train.py:850] (2/4) Epoch 20, batch 7750, loss[loss=0.2121, simple_loss=0.2999, pruned_loss=0.06212, over 7469.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2777, pruned_loss=0.05307, over 1467356.84 frames.], batch size: 73, lr: 2.77e-04
2022-07-27 11:08:14,176 INFO [train.py:850] (2/4) Epoch 20, batch 7800, loss[loss=0.1634, simple_loss=0.2491, pruned_loss=0.03883, over 7490.00 frames.], tot_loss[loss=0.193, simple_loss=0.2785, pruned_loss=0.0537, over 1466889.39 frames.], batch size: 19, lr: 2.77e-04
2022-07-27 11:08:59,464 INFO [train.py:850] (2/4) Epoch 20, batch 7850, loss[loss=0.1755, simple_loss=0.2783, pruned_loss=0.03637, over 7478.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2775, pruned_loss=0.05275, over 1467800.00 frames.], batch size: 21, lr: 2.77e-04
2022-07-27 11:09:42,934 INFO [train.py:850] (2/4) Epoch 20, batch 7900, loss[loss=0.1908, simple_loss=0.271, pruned_loss=0.05532, over 7308.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2773, pruned_loss=0.05248, over 1466501.67 frames.], batch size: 17, lr: 2.77e-04
2022-07-27 11:10:27,632 INFO [train.py:850] (2/4) Epoch 20, batch 7950, loss[loss=0.1712, simple_loss=0.2656, pruned_loss=0.03838, over 7471.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2781, pruned_loss=0.05311, over 1467332.61 frames.], batch size: 24, lr: 2.77e-04
2022-07-27 11:11:11,466 INFO [train.py:850] (2/4) Epoch 20, batch 8000, loss[loss=0.238, simple_loss=0.3199, pruned_loss=0.07811, over 7470.00 frames.], tot_loss[loss=0.192, simple_loss=0.2782, pruned_loss=0.05295, over 1467258.77 frames.], batch size: 71, lr: 2.77e-04
2022-07-27 11:11:55,181 INFO [train.py:850] (2/4) Epoch 20, batch 8050, loss[loss=0.1715, simple_loss=0.249, pruned_loss=0.04702, over 7399.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2792, pruned_loss=0.05372, over 1466559.04 frames.], batch size: 19, lr: 2.77e-04
2022-07-27 11:12:40,052 INFO [train.py:850] (2/4) Epoch 20, batch 8100, loss[loss=0.1498, simple_loss=0.2321, pruned_loss=0.03376, over 7487.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2786, pruned_loss=0.05344, over 1467056.43 frames.], batch size: 19, lr: 2.77e-04
2022-07-27 11:13:24,781 INFO [train.py:850] (2/4) Epoch 20, batch 8150, loss[loss=0.1879, simple_loss=0.2784, pruned_loss=0.04872, over 7470.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2786, pruned_loss=0.05288, over 1467130.77 frames.], batch size: 31, lr: 2.77e-04
2022-07-27 11:14:11,241 INFO [train.py:850] (2/4) Epoch 20, batch 8200, loss[loss=0.206, simple_loss=0.2908, pruned_loss=0.06062, over 7458.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2798, pruned_loss=0.05337, over 1467812.55 frames.], batch size: 73, lr: 2.76e-04
2022-07-27 11:14:54,354 INFO [train.py:850] (2/4) Epoch 20, batch 8250, loss[loss=0.1581, simple_loss=0.2573, pruned_loss=0.02946, over 7477.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2798, pruned_loss=0.0536, over 1467618.62 frames.], batch size: 20, lr: 2.76e-04
2022-07-27 11:15:38,309 INFO [train.py:850] (2/4) Epoch 20, batch 8300, loss[loss=0.1981, simple_loss=0.2833, pruned_loss=0.05649, over 7298.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2789, pruned_loss=0.05345, over 1466737.44 frames.], batch size: 17, lr: 2.76e-04
2022-07-27 11:16:23,176 INFO [train.py:850] (2/4) Epoch 20, batch 8350, loss[loss=0.197, simple_loss=0.29, pruned_loss=0.05198, over 7274.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2783, pruned_loss=0.05339, over 1467387.78 frames.], batch size: 21, lr: 2.76e-04
2022-07-27 11:17:07,080 INFO [train.py:850] (2/4) Epoch 20, batch 8400, loss[loss=0.1585, simple_loss=0.2443, pruned_loss=0.03635, over 7382.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2784, pruned_loss=0.0537, over 1468164.07 frames.], batch size: 20, lr: 2.76e-04
2022-07-27 11:17:52,619 INFO [train.py:850] (2/4) Epoch 20, batch 8450, loss[loss=0.1867, simple_loss=0.274, pruned_loss=0.04971, over 7320.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2784, pruned_loss=0.05363, over 1468940.29 frames.], batch size: 18, lr: 2.76e-04
2022-07-27 11:18:36,613 INFO [train.py:850] (2/4) Epoch 20, batch 8500, loss[loss=0.1806, simple_loss=0.2826, pruned_loss=0.03926, over 7469.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2792, pruned_loss=0.05448, over 1467854.65 frames.], batch size: 24, lr: 2.76e-04
2022-07-27 11:19:22,193 INFO [train.py:850] (2/4) Epoch 20, batch 8550, loss[loss=0.1899, simple_loss=0.2912, pruned_loss=0.0443, over 7306.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2785, pruned_loss=0.05386, over 1467334.07 frames.], batch size: 22, lr: 2.76e-04
2022-07-27 11:20:04,669 INFO [train.py:850] (2/4) Epoch 20, batch 8600, loss[loss=0.1693, simple_loss=0.2581, pruned_loss=0.04022, over 7282.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2792, pruned_loss=0.0541, over 1467038.44 frames.], batch size: 20, lr: 2.76e-04
2022-07-27 11:20:48,603 INFO [train.py:850] (2/4) Epoch 20, batch 8650, loss[loss=0.1889, simple_loss=0.2704, pruned_loss=0.05371, over 7427.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2788, pruned_loss=0.05395, over 1467005.60 frames.], batch size: 31, lr: 2.76e-04
2022-07-27 11:21:31,928 INFO [train.py:850] (2/4) Epoch 20, batch 8700, loss[loss=0.1824, simple_loss=0.2523, pruned_loss=0.0563, over 7236.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2786, pruned_loss=0.05391, over 1466961.51 frames.], batch size: 16, lr: 2.76e-04
2022-07-27 11:22:15,781 INFO [train.py:850] (2/4) Epoch 20, batch 8750, loss[loss=0.1853, simple_loss=0.2711, pruned_loss=0.04973, over 7384.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2784, pruned_loss=0.05352, over 1467265.42 frames.], batch size: 19, lr: 2.76e-04
2022-07-27 11:22:58,963 INFO [train.py:850] (2/4) Epoch 20, batch 8800, loss[loss=0.1855, simple_loss=0.2753, pruned_loss=0.0479, over 7384.00 frames.], tot_loss[loss=0.1934, simple_loss=0.279, pruned_loss=0.05384, over 1466597.02 frames.], batch size: 20, lr: 2.76e-04
2022-07-27 11:23:42,875 INFO [train.py:850] (2/4) Epoch 20, batch 8850, loss[loss=0.1987, simple_loss=0.2824, pruned_loss=0.05743, over 7438.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2768, pruned_loss=0.05306, over 1465808.94 frames.], batch size: 72, lr: 2.76e-04
2022-07-27 11:25:07,009 INFO [train.py:850] (2/4) Epoch 21, batch 0, loss[loss=0.1744, simple_loss=0.2674, pruned_loss=0.04067, over 7394.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2674, pruned_loss=0.04067, over 7394.00 frames.], batch size: 19, lr: 2.70e-04
2022-07-27 11:25:53,789 INFO [train.py:850] (2/4) Epoch 21, batch 50, loss[loss=0.167, simple_loss=0.2768, pruned_loss=0.02862, over 7200.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2793, pruned_loss=0.04886, over 330388.96 frames.], batch size: 20, lr: 2.70e-04
2022-07-27 11:26:36,672 INFO [train.py:850] (2/4) Epoch 21, batch 100, loss[loss=0.1692, simple_loss=0.2618, pruned_loss=0.03833, over 7160.00 frames.], tot_loss[loss=0.187, simple_loss=0.2786, pruned_loss=0.0477, over 581004.38 frames.], batch size: 17, lr: 2.69e-04
2022-07-27 11:27:19,682 INFO [train.py:850] (2/4) Epoch 21, batch 150, loss[loss=0.1985, simple_loss=0.3006, pruned_loss=0.0482, over 7277.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2765, pruned_loss=0.04639, over 777374.95 frames.], batch size: 21, lr: 2.69e-04
2022-07-27 11:28:05,330 INFO [train.py:850] (2/4) Epoch 21, batch 200, loss[loss=0.1906, simple_loss=0.2718, pruned_loss=0.05467, over 7394.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2748, pruned_loss=0.04582, over 930053.65 frames.], batch size: 19, lr: 2.69e-04
2022-07-27 11:28:48,819 INFO [train.py:850] (2/4) Epoch 21, batch 250, loss[loss=0.1699, simple_loss=0.2598, pruned_loss=0.04001, over 7491.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2754, pruned_loss=0.04621, over 1049108.16 frames.], batch size: 23, lr: 2.69e-04
2022-07-27 11:29:32,610 INFO [train.py:850] (2/4) Epoch 21, batch 300, loss[loss=0.1506, simple_loss=0.2411, pruned_loss=0.03007, over 7452.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2748, pruned_loss=0.0459, over 1141545.43 frames.], batch size: 17, lr: 2.69e-04
2022-07-27 11:30:15,299 INFO [train.py:850] (2/4) Epoch 21, batch 350, loss[loss=0.1535, simple_loss=0.2338, pruned_loss=0.03661, over 7268.00 frames.], tot_loss[loss=0.182, simple_loss=0.2734, pruned_loss=0.04529, over 1212942.26 frames.], batch size: 16, lr: 2.69e-04
2022-07-27 11:30:59,181 INFO [train.py:850] (2/4) Epoch 21, batch 400, loss[loss=0.1744, simple_loss=0.27, pruned_loss=0.03939, over 7316.00 frames.], tot_loss[loss=0.1817, simple_loss=0.273, pruned_loss=0.04522, over 1268473.74 frames.], batch size: 22, lr: 2.69e-04
2022-07-27 11:31:42,891 INFO [train.py:850] (2/4) Epoch 21, batch 450, loss[loss=0.1834, simple_loss=0.2788, pruned_loss=0.04404, over 7482.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2733, pruned_loss=0.0449, over 1312351.34 frames.], batch size: 26, lr: 2.69e-04
2022-07-27 11:32:26,093 INFO [train.py:850] (2/4) Epoch 21, batch 500, loss[loss=0.1831, simple_loss=0.2865, pruned_loss=0.03979, over 7465.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2735, pruned_loss=0.04474, over 1345168.16 frames.], batch size: 24, lr: 2.69e-04
2022-07-27 11:33:10,506 INFO [train.py:850] (2/4) Epoch 21, batch 550, loss[loss=0.1869, simple_loss=0.2759, pruned_loss=0.04899, over 7409.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2719, pruned_loss=0.04378, over 1373362.91 frames.], batch size: 38, lr: 2.69e-04
2022-07-27 11:33:53,648 INFO [train.py:850] (2/4) Epoch 21, batch 600, loss[loss=0.17, simple_loss=0.2565, pruned_loss=0.04175, over 7193.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2714, pruned_loss=0.04348, over 1393043.34 frames.], batch size: 19, lr: 2.69e-04
2022-07-27 11:34:38,280 INFO [train.py:850] (2/4) Epoch 21, batch 650, loss[loss=0.1768, simple_loss=0.26, pruned_loss=0.0468, over 7390.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2712, pruned_loss=0.04326, over 1411202.03 frames.], batch size: 19, lr: 2.69e-04
2022-07-27 11:35:21,194 INFO [train.py:850] (2/4) Epoch 21, batch 700, loss[loss=0.1818, simple_loss=0.2782, pruned_loss=0.04275, over 7385.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2701, pruned_loss=0.04306, over 1424886.62 frames.], batch size: 21, lr: 2.69e-04
2022-07-27 11:36:04,798 INFO [train.py:850] (2/4) Epoch 21, batch 750, loss[loss=0.1747, simple_loss=0.258, pruned_loss=0.0457, over 7448.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2693, pruned_loss=0.04257, over 1433169.47 frames.], batch size: 17, lr: 2.69e-04
2022-07-27 11:36:48,709 INFO [train.py:850] (2/4) Epoch 21, batch 800, loss[loss=0.2181, simple_loss=0.3128, pruned_loss=0.0617, over 7302.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2716, pruned_loss=0.04393, over 1441572.06 frames.], batch size: 22, lr: 2.69e-04
2022-07-27 11:37:32,130 INFO [train.py:850] (2/4) Epoch 21, batch 850, loss[loss=0.2586, simple_loss=0.3531, pruned_loss=0.08207, over 7328.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2735, pruned_loss=0.04443, over 1447782.32 frames.], batch size: 23, lr: 2.69e-04
2022-07-27 11:38:15,962 INFO [train.py:850] (2/4) Epoch 21, batch 900, loss[loss=0.1535, simple_loss=0.2446, pruned_loss=0.03122, over 7298.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2733, pruned_loss=0.04408, over 1451306.38 frames.], batch size: 19, lr: 2.69e-04
2022-07-27 11:38:58,605 INFO [train.py:850] (2/4) Epoch 21, batch 950, loss[loss=0.1782, simple_loss=0.2773, pruned_loss=0.03955, over 7493.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2739, pruned_loss=0.04411, over 1454381.24 frames.], batch size: 20, lr: 2.69e-04
2022-07-27 11:39:42,681 INFO [train.py:850] (2/4) Epoch 21, batch 1000, loss[loss=0.2038, simple_loss=0.3091, pruned_loss=0.04924, over 7298.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2744, pruned_loss=0.04427, over 1457119.33 frames.], batch size: 22, lr: 2.69e-04
2022-07-27 11:40:26,658 INFO [train.py:850] (2/4) Epoch 21, batch 1050, loss[loss=0.1682, simple_loss=0.2661, pruned_loss=0.03509, over 7383.00 frames.], tot_loss[loss=0.182, simple_loss=0.2752, pruned_loss=0.04446, over 1460382.38 frames.], batch size: 21, lr: 2.69e-04
2022-07-27 11:41:09,240 INFO [train.py:850] (2/4) Epoch 21, batch 1100, loss[loss=0.198, simple_loss=0.2876, pruned_loss=0.0542, over 7476.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2748, pruned_loss=0.04432, over 1461074.63 frames.], batch size: 21, lr: 2.69e-04
2022-07-27 11:41:53,306 INFO [train.py:850] (2/4) Epoch 21, batch 1150, loss[loss=0.1522, simple_loss=0.2393, pruned_loss=0.03257, over 7245.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2761, pruned_loss=0.04505, over 1461611.95 frames.], batch size: 16, lr: 2.69e-04
2022-07-27 11:42:36,825 INFO [train.py:850] (2/4) Epoch 21, batch 1200, loss[loss=0.1962, simple_loss=0.2997, pruned_loss=0.04633, over 7485.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2766, pruned_loss=0.04521, over 1463058.28 frames.], batch size: 26, lr: 2.69e-04
2022-07-27 11:43:19,928 INFO [train.py:850] (2/4) Epoch 21, batch 1250, loss[loss=0.1614, simple_loss=0.2602, pruned_loss=0.03132, over 7208.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2775, pruned_loss=0.0458, over 1463233.04 frames.], batch size: 19, lr: 2.69e-04
2022-07-27 11:44:03,403 INFO [train.py:850] (2/4) Epoch 21, batch 1300, loss[loss=0.2195, simple_loss=0.2967, pruned_loss=0.0711, over 7470.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2779, pruned_loss=0.0464, over 1464413.11 frames.], batch size: 24, lr: 2.69e-04
2022-07-27 11:44:47,534 INFO [train.py:850] (2/4) Epoch 21, batch 1350, loss[loss=0.1785, simple_loss=0.2765, pruned_loss=0.0403, over 7446.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2782, pruned_loss=0.04619, over 1463650.27 frames.], batch size: 39, lr: 2.69e-04
2022-07-27 11:45:30,864 INFO [train.py:850] (2/4) Epoch 21, batch 1400, loss[loss=0.2182, simple_loss=0.3205, pruned_loss=0.05795, over 7447.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2781, pruned_loss=0.04633, over 1464604.44 frames.], batch size: 26, lr: 2.69e-04
2022-07-27 11:46:14,533 INFO [train.py:850] (2/4) Epoch 21, batch 1450, loss[loss=0.1601, simple_loss=0.2683, pruned_loss=0.02595, over 7419.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2781, pruned_loss=0.04652, over 1464824.06 frames.], batch size: 39, lr: 2.68e-04
2022-07-27 11:46:57,678 INFO [train.py:850] (2/4) Epoch 21, batch 1500, loss[loss=0.1647, simple_loss=0.2661, pruned_loss=0.03167, over 7380.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2777, pruned_loss=0.0465, over 1465395.86 frames.], batch size: 21, lr: 2.68e-04
2022-07-27 11:47:41,385 INFO [train.py:850] (2/4) Epoch 21, batch 1550, loss[loss=0.1744, simple_loss=0.2768, pruned_loss=0.03599, over 7393.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2769, pruned_loss=0.046, over 1465683.99 frames.], batch size: 19, lr: 2.68e-04
2022-07-27 11:48:25,181 INFO [train.py:850] (2/4) Epoch 21, batch 1600, loss[loss=0.1662, simple_loss=0.2613, pruned_loss=0.03562, over 7406.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2775, pruned_loss=0.04597, over 1465262.22 frames.], batch size: 22, lr: 2.68e-04
2022-07-27 11:49:08,940 INFO [train.py:850] (2/4) Epoch 21, batch 1650, loss[loss=0.1528, simple_loss=0.2417, pruned_loss=0.03195, over 7456.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2771, pruned_loss=0.04568, over 1466516.04 frames.], batch size: 17, lr: 2.68e-04
2022-07-27 11:49:51,431 INFO [train.py:850] (2/4) Epoch 21, batch 1700, loss[loss=0.2531, simple_loss=0.3377, pruned_loss=0.0843, over 7407.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2767, pruned_loss=0.04513, over 1465446.95 frames.], batch size: 69, lr: 2.68e-04
2022-07-27 11:50:35,139 INFO [train.py:850] (2/4) Epoch 21, batch 1750, loss[loss=0.2081, simple_loss=0.2983, pruned_loss=0.05891, over 7286.00 frames.], tot_loss[loss=0.1826, simple_loss=0.276, pruned_loss=0.04463, over 1465616.10 frames.], batch size: 19, lr: 2.68e-04
2022-07-27 11:51:18,491 INFO [train.py:850] (2/4) Epoch 21, batch 1800, loss[loss=0.2072, simple_loss=0.3071, pruned_loss=0.05367, over 7478.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2783, pruned_loss=0.04543, over 1465029.64 frames.], batch size: 21, lr: 2.68e-04
2022-07-27 11:52:03,698 INFO [train.py:850] (2/4) Epoch 21, batch 1850, loss[loss=0.18, simple_loss=0.2697, pruned_loss=0.04517, over 7479.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2769, pruned_loss=0.04516, over 1465283.33 frames.], batch size: 20, lr: 2.68e-04
2022-07-27 11:52:48,329 INFO [train.py:850] (2/4) Epoch 21, batch 1900, loss[loss=0.1968, simple_loss=0.2834, pruned_loss=0.05512, over 7483.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2761, pruned_loss=0.04503, over 1464454.66 frames.], batch size: 20, lr: 2.68e-04
2022-07-27 11:53:31,385 INFO [train.py:850] (2/4) Epoch 21, batch 1950, loss[loss=0.1626, simple_loss=0.2489, pruned_loss=0.03816, over 7308.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2756, pruned_loss=0.04493, over 1463870.50 frames.], batch size: 18, lr: 2.68e-04
2022-07-27 11:54:14,476 INFO [train.py:850] (2/4) Epoch 21, batch 2000, loss[loss=0.1818, simple_loss=0.2885, pruned_loss=0.03751, over 7477.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2759, pruned_loss=0.04468, over 1463837.47 frames.], batch size: 24, lr: 2.68e-04
2022-07-27 11:54:58,187 INFO [train.py:850] (2/4) Epoch 21, batch 2050, loss[loss=0.1667, simple_loss=0.2686, pruned_loss=0.03242, over 7469.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2768, pruned_loss=0.0452, over 1464555.82 frames.], batch size: 21, lr: 2.68e-04
2022-07-27 11:55:41,968 INFO [train.py:850] (2/4) Epoch 21, batch 2100, loss[loss=0.1755, simple_loss=0.271, pruned_loss=0.03995, over 7417.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2772, pruned_loss=0.04525, over 1465553.62 frames.], batch size: 22, lr: 2.68e-04
2022-07-27 11:56:41,561 INFO [train.py:850] (2/4) Epoch 21, batch 2150, loss[loss=0.1749, simple_loss=0.2706, pruned_loss=0.03955, over 7485.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2761, pruned_loss=0.04467, over 1465683.89 frames.], batch size: 20, lr: 2.68e-04
2022-07-27 11:57:24,927 INFO [train.py:850] (2/4) Epoch 21, batch 2200, loss[loss=0.1693, simple_loss=0.2628, pruned_loss=0.03789, over 7168.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2746, pruned_loss=0.04417, over 1465486.33 frames.], batch size: 22, lr: 2.68e-04
2022-07-27 11:58:09,966 INFO [train.py:850] (2/4) Epoch 21, batch 2250, loss[loss=0.1915, simple_loss=0.2916, pruned_loss=0.04567, over 7303.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2745, pruned_loss=0.04439, over 1466167.84 frames.], batch size: 27, lr: 2.68e-04
2022-07-27 11:58:52,234 INFO [train.py:850] (2/4) Epoch 21, batch 2300, loss[loss=0.1666, simple_loss=0.2504, pruned_loss=0.04142, over 7179.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2753, pruned_loss=0.04503, over 1465466.21 frames.], batch size: 17, lr: 2.68e-04
2022-07-27 11:59:35,740 INFO [train.py:850] (2/4) Epoch 21, batch 2350, loss[loss=0.1979, simple_loss=0.2903, pruned_loss=0.05277, over 7173.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2764, pruned_loss=0.0454, over 1466248.81 frames.], batch size: 22, lr: 2.68e-04
2022-07-27 12:00:19,704 INFO [train.py:850] (2/4) Epoch 21, batch 2400, loss[loss=0.2098, simple_loss=0.3044, pruned_loss=0.05759, over 7464.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2768, pruned_loss=0.04551, over 1466459.84 frames.], batch size: 21, lr: 2.68e-04
2022-07-27 12:01:04,347 INFO [train.py:850] (2/4) Epoch 21, batch 2450, loss[loss=0.1762, simple_loss=0.2782, pruned_loss=0.03709, over 7201.00 frames.], tot_loss[loss=0.1838, simple_loss=0.277, pruned_loss=0.04534, over 1467713.65 frames.], batch size: 19, lr: 2.68e-04
2022-07-27 12:01:48,140 INFO [train.py:850] (2/4) Epoch 21, batch 2500, loss[loss=0.1594, simple_loss=0.2501, pruned_loss=0.03441, over 7484.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2752, pruned_loss=0.04485, over 1467043.22 frames.], batch size: 19, lr: 2.68e-04
2022-07-27 12:02:31,768 INFO [train.py:850] (2/4) Epoch 21, batch 2550, loss[loss=0.1844, simple_loss=0.2723, pruned_loss=0.04824, over 7122.00 frames.], tot_loss[loss=0.1829, simple_loss=0.276, pruned_loss=0.04489, over 1467114.70 frames.], batch size: 19, lr: 2.68e-04
2022-07-27 12:03:14,435 INFO [train.py:850] (2/4) Epoch 21, batch 2600, loss[loss=0.197, simple_loss=0.2956, pruned_loss=0.04918, over 7258.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2777, pruned_loss=0.04526, over 1467047.88 frames.], batch size: 27, lr: 2.68e-04
2022-07-27 12:03:58,814 INFO [train.py:850] (2/4) Epoch 21, batch 2650, loss[loss=0.1948, simple_loss=0.2952, pruned_loss=0.04716, over 7371.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2776, pruned_loss=0.04525, over 1466250.04 frames.], batch size: 21, lr: 2.68e-04
2022-07-27 12:04:42,654 INFO [train.py:850] (2/4) Epoch 21, batch 2700, loss[loss=0.1894, simple_loss=0.2777, pruned_loss=0.05061, over 7483.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2768, pruned_loss=0.04515, over 1466705.34 frames.], batch size: 20, lr: 2.68e-04
2022-07-27 12:05:27,094 INFO [train.py:850] (2/4) Epoch 21, batch 2750, loss[loss=0.1462, simple_loss=0.2449, pruned_loss=0.02374, over 7479.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2765, pruned_loss=0.04466, over 1465443.74 frames.], batch size: 20, lr: 2.68e-04
2022-07-27 12:06:10,397 INFO [train.py:850] (2/4) Epoch 21, batch 2800, loss[loss=0.1999, simple_loss=0.3016, pruned_loss=0.04913, over 7347.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2757, pruned_loss=0.04435, over 1466677.53 frames.], batch size: 39, lr: 2.67e-04
2022-07-27 12:06:54,435 INFO [train.py:850] (2/4) Epoch 21, batch 2850, loss[loss=0.1846, simple_loss=0.2812, pruned_loss=0.04397, over 7472.00 frames.], tot_loss[loss=0.1816, simple_loss=0.275, pruned_loss=0.0441, over 1466280.27 frames.], batch size: 21, lr: 2.67e-04
2022-07-27 12:07:37,405 INFO [train.py:850] (2/4) Epoch 21, batch 2900, loss[loss=0.2107, simple_loss=0.2978, pruned_loss=0.06186, over 7423.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2749, pruned_loss=0.04409, over 1464741.62 frames.], batch size: 69, lr: 2.67e-04
2022-07-27 12:08:21,553 INFO [train.py:850] (2/4) Epoch 21, batch 2950, loss[loss=0.1913, simple_loss=0.2847, pruned_loss=0.04901, over 7417.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2762, pruned_loss=0.04425, over 1463937.22 frames.], batch size: 40, lr: 2.67e-04
2022-07-27 12:09:05,334 INFO [train.py:850] (2/4) Epoch 21, batch 3000, loss[loss=0.1563, simple_loss=0.2364, pruned_loss=0.03815, over 7175.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2746, pruned_loss=0.04416, over 1464612.26 frames.], batch size: 17, lr: 2.67e-04
2022-07-27 12:09:05,334 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 12:09:28,139 INFO [train.py:879] (2/4) Epoch 21, validation: loss=0.1889, simple_loss=0.2816, pruned_loss=0.04817, over 924787.00 frames.
2022-07-27 12:10:11,856 INFO [train.py:850] (2/4) Epoch 21, batch 3050, loss[loss=0.1856, simple_loss=0.2829, pruned_loss=0.04419, over 7380.00 frames.], tot_loss[loss=0.1822, simple_loss=0.275, pruned_loss=0.04471, over 1464937.68 frames.], batch size: 20, lr: 2.67e-04
2022-07-27 12:10:55,187 INFO [train.py:850] (2/4) Epoch 21, batch 3100, loss[loss=0.1483, simple_loss=0.2452, pruned_loss=0.02569, over 7294.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2742, pruned_loss=0.04452, over 1464632.28 frames.], batch size: 19, lr: 2.67e-04
2022-07-27 12:11:38,721 INFO [train.py:850] (2/4) Epoch 21, batch 3150, loss[loss=0.1979, simple_loss=0.292, pruned_loss=0.05194, over 7225.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2756, pruned_loss=0.04496, over 1464659.73 frames.], batch size: 25, lr: 2.67e-04
2022-07-27 12:12:21,762 INFO [train.py:850] (2/4) Epoch 21, batch 3200, loss[loss=0.1837, simple_loss=0.2854, pruned_loss=0.04101, over 7177.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2752, pruned_loss=0.04477, over 1465678.02 frames.], batch size: 22, lr: 2.67e-04
2022-07-27 12:13:04,829 INFO [train.py:850] (2/4) Epoch 21, batch 3250, loss[loss=0.2124, simple_loss=0.3081, pruned_loss=0.05841, over 7367.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2759, pruned_loss=0.04481, over 1466157.87 frames.], batch size: 38, lr: 2.67e-04
2022-07-27 12:13:48,072 INFO [train.py:850] (2/4) Epoch 21, batch 3300, loss[loss=0.1765, simple_loss=0.2717, pruned_loss=0.04064, over 7248.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2759, pruned_loss=0.04485, over 1465272.49 frames.], batch size: 27, lr: 2.67e-04
2022-07-27 12:14:31,860 INFO [train.py:850] (2/4) Epoch 21, batch 3350, loss[loss=0.1993, simple_loss=0.299, pruned_loss=0.04981, over 7476.00 frames.], tot_loss[loss=0.1818, simple_loss=0.275, pruned_loss=0.04429, over 1465830.41 frames.], batch size: 21, lr: 2.67e-04
2022-07-27 12:15:15,402 INFO [train.py:850] (2/4) Epoch 21, batch 3400, loss[loss=0.1738, simple_loss=0.2646, pruned_loss=0.0415, over 7203.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2742, pruned_loss=0.04363, over 1465347.76 frames.], batch size: 24, lr: 2.67e-04
2022-07-27 12:15:59,359 INFO [train.py:850] (2/4) Epoch 21, batch 3450, loss[loss=0.2158, simple_loss=0.3093, pruned_loss=0.06118, over 7473.00 frames.], tot_loss[loss=0.1818, simple_loss=0.275, pruned_loss=0.04431, over 1465465.11 frames.], batch size: 24, lr: 2.67e-04
2022-07-27 12:16:42,509 INFO [train.py:850] (2/4) Epoch 21, batch 3500, loss[loss=0.1831, simple_loss=0.275, pruned_loss=0.04564, over 7283.00 frames.], tot_loss[loss=0.181, simple_loss=0.2734, pruned_loss=0.04428, over 1465365.55 frames.], batch size: 19, lr: 2.67e-04
2022-07-27 12:17:26,932 INFO [train.py:850] (2/4) Epoch 21, batch 3550, loss[loss=0.2047, simple_loss=0.3092, pruned_loss=0.05014, over 7483.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2742, pruned_loss=0.0442, over 1465639.31 frames.], batch size: 21, lr: 2.67e-04
2022-07-27 12:18:10,043 INFO [train.py:850] (2/4) Epoch 21, batch 3600, loss[loss=0.1586, simple_loss=0.2442, pruned_loss=0.03648, over 7467.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2732, pruned_loss=0.04371, over 1465808.93 frames.], batch size: 17, lr: 2.67e-04
2022-07-27 12:18:53,739 INFO [train.py:850] (2/4) Epoch 21, batch 3650, loss[loss=0.2545, simple_loss=0.34, pruned_loss=0.08446, over 7473.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2737, pruned_loss=0.04386, over 1465950.30 frames.], batch size: 74, lr: 2.67e-04
2022-07-27 12:19:37,531 INFO [train.py:850] (2/4) Epoch 21, batch 3700, loss[loss=0.181, simple_loss=0.2632, pruned_loss=0.04934, over 7325.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2738, pruned_loss=0.0442, over 1465236.48 frames.], batch size: 18, lr: 2.67e-04
2022-07-27 12:20:21,158 INFO [train.py:850] (2/4) Epoch 21, batch 3750, loss[loss=0.2265, simple_loss=0.2911, pruned_loss=0.08096, over 7299.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2747, pruned_loss=0.04503, over 1465463.73 frames.], batch size: 17, lr: 2.67e-04
2022-07-27 12:21:04,469 INFO [train.py:850] (2/4) Epoch 21, batch 3800, loss[loss=0.1545, simple_loss=0.2454, pruned_loss=0.03177, over 7206.00 frames.], tot_loss[loss=0.182, simple_loss=0.2746, pruned_loss=0.04471, over 1464181.72 frames.], batch size: 18, lr: 2.67e-04
2022-07-27 12:21:47,982 INFO [train.py:850] (2/4) Epoch 21, batch 3850, loss[loss=0.1796, simple_loss=0.2842, pruned_loss=0.03751, over 7472.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2736, pruned_loss=0.04403, over 1464451.76 frames.], batch size: 31, lr: 2.67e-04
2022-07-27 12:22:32,500 INFO [train.py:850] (2/4) Epoch 21, batch 3900, loss[loss=0.219, simple_loss=0.3102, pruned_loss=0.06389, over 7488.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2737, pruned_loss=0.04384, over 1466072.35 frames.], batch size: 20, lr: 2.67e-04
2022-07-27 12:23:16,280 INFO [train.py:850] (2/4) Epoch 21, batch 3950, loss[loss=0.1904, simple_loss=0.2924, pruned_loss=0.04417, over 7296.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2748, pruned_loss=0.04434, over 1465114.45 frames.], batch size: 22, lr: 2.67e-04
2022-07-27 12:24:00,062 INFO [train.py:850] (2/4) Epoch 21, batch 4000, loss[loss=0.184, simple_loss=0.2757, pruned_loss=0.04611, over 7287.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2743, pruned_loss=0.04393, over 1464932.81 frames.], batch size: 22, lr: 2.67e-04
2022-07-27 12:24:44,218 INFO [train.py:850] (2/4) Epoch 21, batch 4050, loss[loss=0.2238, simple_loss=0.3105, pruned_loss=0.06857, over 7172.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2747, pruned_loss=0.04401, over 1463407.41 frames.], batch size: 22, lr: 2.67e-04
2022-07-27 12:25:30,067 INFO [train.py:850] (2/4) Epoch 21, batch 4100, loss[loss=0.2527, simple_loss=0.327, pruned_loss=0.08924, over 7342.00 frames.], tot_loss[loss=0.183, simple_loss=0.2759, pruned_loss=0.04505, over 1464130.22 frames.], batch size: 70, lr: 2.67e-04
2022-07-27 12:26:14,554 INFO [train.py:850] (2/4) Epoch 21, batch 4150, loss[loss=0.2145, simple_loss=0.2966, pruned_loss=0.06621, over 7294.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2768, pruned_loss=0.04634, over 1463912.47 frames.], batch size: 27, lr: 2.66e-04
2022-07-27 12:26:57,558 INFO [train.py:850] (2/4) Epoch 21, batch 4200, loss[loss=0.2026, simple_loss=0.2947, pruned_loss=0.05527, over 7490.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2763, pruned_loss=0.04667, over 1464261.61 frames.], batch size: 20, lr: 2.66e-04
2022-07-27 12:27:41,218 INFO [train.py:850] (2/4) Epoch 21, batch 4250, loss[loss=0.2268, simple_loss=0.3094, pruned_loss=0.07213, over 7279.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2772, pruned_loss=0.04794, over 1463917.69 frames.], batch size: 21, lr: 2.66e-04
2022-07-27 12:28:25,141 INFO [train.py:850] (2/4) Epoch 21, batch 4300, loss[loss=0.1931, simple_loss=0.2735, pruned_loss=0.05633, over 7288.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2782, pruned_loss=0.04907, over 1465149.94 frames.], batch size: 19, lr: 2.66e-04
2022-07-27 12:29:08,989 INFO [train.py:850] (2/4) Epoch 21, batch 4350, loss[loss=0.1919, simple_loss=0.2713, pruned_loss=0.05627, over 7285.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2781, pruned_loss=0.04973, over 1465902.97 frames.], batch size: 20, lr: 2.66e-04
2022-07-27 12:29:52,547 INFO [train.py:850] (2/4) Epoch 21, batch 4400, loss[loss=0.1775, simple_loss=0.2658, pruned_loss=0.04456, over 7437.00 frames.], tot_loss[loss=0.1901, simple_loss=0.279, pruned_loss=0.05061, over 1464780.44 frames.], batch size: 18, lr: 2.66e-04
2022-07-27 12:30:35,419 INFO [train.py:850] (2/4) Epoch 21, batch 4450, loss[loss=0.1631, simple_loss=0.2412, pruned_loss=0.04245, over 7469.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2799, pruned_loss=0.05127, over 1465101.09 frames.], batch size: 17, lr: 2.66e-04
2022-07-27 12:31:18,955 INFO [train.py:850] (2/4) Epoch 21, batch 4500, loss[loss=0.2276, simple_loss=0.3189, pruned_loss=0.06811, over 7286.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2788, pruned_loss=0.05095, over 1465016.95 frames.], batch size: 27, lr: 2.66e-04
2022-07-27 12:32:02,674 INFO [train.py:850] (2/4) Epoch 21, batch 4550, loss[loss=0.1774, simple_loss=0.2757, pruned_loss=0.03956, over 7169.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2789, pruned_loss=0.05118, over 1465909.98 frames.], batch size: 22, lr: 2.66e-04
2022-07-27 12:32:46,041 INFO [train.py:850] (2/4) Epoch 21, batch 4600, loss[loss=0.2049, simple_loss=0.2937, pruned_loss=0.05809, over 7430.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2813, pruned_loss=0.05358, over 1465882.69 frames.], batch size: 68, lr: 2.66e-04
2022-07-27 12:33:29,784 INFO [train.py:850] (2/4) Epoch 21, batch 4650, loss[loss=0.2164, simple_loss=0.3143, pruned_loss=0.05927, over 7179.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2823, pruned_loss=0.05434, over 1467082.44 frames.], batch size: 21, lr: 2.66e-04
2022-07-27 12:34:13,669 INFO [train.py:850] (2/4) Epoch 21, batch 4700, loss[loss=0.1454, simple_loss=0.2362, pruned_loss=0.02734, over 7169.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2811, pruned_loss=0.05385, over 1465900.26 frames.], batch size: 17, lr: 2.66e-04
2022-07-27 12:34:58,570 INFO [train.py:850] (2/4) Epoch 21, batch 4750, loss[loss=0.2192, simple_loss=0.3017, pruned_loss=0.06832, over 7239.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2818, pruned_loss=0.05443, over 1466510.44 frames.], batch size: 24, lr: 2.66e-04
2022-07-27 12:35:41,256 INFO [train.py:850] (2/4) Epoch 21, batch 4800, loss[loss=0.1736, simple_loss=0.254, pruned_loss=0.04656, over 7258.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2804, pruned_loss=0.05432, over 1467428.80 frames.], batch size: 16, lr: 2.66e-04
2022-07-27 12:36:25,031 INFO [train.py:850] (2/4) Epoch 21, batch 4850, loss[loss=0.1637, simple_loss=0.2418, pruned_loss=0.04281, over 7330.00 frames.], tot_loss[loss=0.194, simple_loss=0.28, pruned_loss=0.05403, over 1466375.23 frames.], batch size: 18, lr: 2.66e-04
2022-07-27 12:37:08,042 INFO [train.py:850] (2/4) Epoch 21, batch 4900, loss[loss=0.182, simple_loss=0.2569, pruned_loss=0.05357, over 7145.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2804, pruned_loss=0.05467, over 1466851.06 frames.], batch size: 17, lr: 2.66e-04
2022-07-27 12:37:52,615 INFO [train.py:850] (2/4) Epoch 21, batch 4950, loss[loss=0.2583, simple_loss=0.3397, pruned_loss=0.08844, over 7361.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2804, pruned_loss=0.05448, over 1465536.05 frames.], batch size: 23, lr: 2.66e-04
2022-07-27 12:38:36,358 INFO [train.py:850] (2/4) Epoch 21, batch 5000, loss[loss=0.182, simple_loss=0.2576, pruned_loss=0.05316, over 7316.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2808, pruned_loss=0.05483, over 1464183.97 frames.], batch size: 17, lr: 2.66e-04
2022-07-27 12:39:20,015 INFO [train.py:850] (2/4) Epoch 21, batch 5050, loss[loss=0.2199, simple_loss=0.2811, pruned_loss=0.07935, over 7150.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2815, pruned_loss=0.05543, over 1463073.09 frames.], batch size: 17, lr: 2.66e-04
2022-07-27 12:40:02,817 INFO [train.py:850] (2/4) Epoch 21, batch 5100, loss[loss=0.2649, simple_loss=0.3315, pruned_loss=0.09919, over 7380.00 frames.], tot_loss[loss=0.1967, simple_loss=0.282, pruned_loss=0.05567, over 1464304.19 frames.], batch size: 21, lr: 2.66e-04
2022-07-27 12:40:47,007 INFO [train.py:850] (2/4) Epoch 21, batch 5150, loss[loss=0.1882, simple_loss=0.2795, pruned_loss=0.04844, over 7172.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2802, pruned_loss=0.05458, over 1464687.64 frames.], batch size: 22, lr: 2.66e-04
2022-07-27 12:41:30,486 INFO [train.py:850] (2/4) Epoch 21, batch 5200, loss[loss=0.2416, simple_loss=0.3236, pruned_loss=0.07983, over 7470.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2804, pruned_loss=0.05464, over 1465147.55 frames.], batch size: 31, lr: 2.66e-04
2022-07-27 12:42:13,900 INFO [train.py:850] (2/4) Epoch 21, batch 5250, loss[loss=0.2301, simple_loss=0.3252, pruned_loss=0.06747, over 7281.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2799, pruned_loss=0.0545, over 1465616.99 frames.], batch size: 21, lr: 2.66e-04
2022-07-27 12:42:57,691 INFO [train.py:850] (2/4) Epoch 21, batch 5300, loss[loss=0.1695, simple_loss=0.268, pruned_loss=0.0355, over 7187.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2798, pruned_loss=0.05445, over 1465766.42 frames.], batch size: 21, lr: 2.66e-04
2022-07-27 12:43:41,479 INFO [train.py:850] (2/4) Epoch 21, batch 5350, loss[loss=0.2616, simple_loss=0.3441, pruned_loss=0.08953, over 7376.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2808, pruned_loss=0.05514, over 1465475.87 frames.], batch size: 21, lr: 2.66e-04
2022-07-27 12:44:24,652 INFO [train.py:850] (2/4) Epoch 21, batch 5400, loss[loss=0.2028, simple_loss=0.2956, pruned_loss=0.05497, over 7282.00 frames.], tot_loss[loss=0.1951, simple_loss=0.2805, pruned_loss=0.0548, over 1465371.25 frames.], batch size: 27, lr: 2.66e-04
2022-07-27 12:45:08,916 INFO [train.py:850] (2/4) Epoch 21, batch 5450, loss[loss=0.1917, simple_loss=0.2799, pruned_loss=0.05175, over 7414.00 frames.], tot_loss[loss=0.195, simple_loss=0.2807, pruned_loss=0.0547, over 1465112.36 frames.], batch size: 22, lr: 2.66e-04
2022-07-27 12:45:51,968 INFO [train.py:850] (2/4) Epoch 21, batch 5500, loss[loss=0.2419, simple_loss=0.3217, pruned_loss=0.08104, over 7388.00 frames.], tot_loss[loss=0.195, simple_loss=0.2808, pruned_loss=0.05461, over 1465204.34 frames.], batch size: 71, lr: 2.65e-04
2022-07-27 12:46:35,509 INFO [train.py:850] (2/4) Epoch 21, batch 5550, loss[loss=0.1846, simple_loss=0.2727, pruned_loss=0.04826, over 7344.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2805, pruned_loss=0.05505, over 1464232.80 frames.], batch size: 73, lr: 2.65e-04
2022-07-27 12:47:19,000 INFO [train.py:850] (2/4) Epoch 21, batch 5600, loss[loss=0.2124, simple_loss=0.2989, pruned_loss=0.06293, over 7335.00 frames.], tot_loss[loss=0.194, simple_loss=0.2793, pruned_loss=0.05436, over 1465472.54 frames.], batch size: 27, lr: 2.65e-04
2022-07-27 12:48:02,504 INFO [train.py:850] (2/4) Epoch 21, batch 5650, loss[loss=0.2009, simple_loss=0.2964, pruned_loss=0.0527, over 7302.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2778, pruned_loss=0.05338, over 1465597.51 frames.], batch size: 21, lr: 2.65e-04
2022-07-27 12:48:45,664 INFO [train.py:850] (2/4) Epoch 21, batch 5700, loss[loss=0.2536, simple_loss=0.3272, pruned_loss=0.09006, over 7440.00 frames.], tot_loss[loss=0.1925, simple_loss=0.278, pruned_loss=0.0535, over 1466152.44 frames.], batch size: 72, lr: 2.65e-04
2022-07-27 12:49:29,958 INFO [train.py:850] (2/4) Epoch 21, batch 5750, loss[loss=0.2207, simple_loss=0.3059, pruned_loss=0.06779, over 7348.00 frames.], tot_loss[loss=0.1939, simple_loss=0.279, pruned_loss=0.05444, over 1466139.84 frames.], batch size: 23, lr: 2.65e-04
2022-07-27 12:50:13,664 INFO [train.py:850] (2/4) Epoch 21, batch 5800, loss[loss=0.1995, simple_loss=0.3021, pruned_loss=0.04843, over 7457.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2802, pruned_loss=0.05523, over 1466116.78 frames.], batch size: 39, lr: 2.65e-04
2022-07-27 12:50:58,152 INFO [train.py:850] (2/4) Epoch 21, batch 5850, loss[loss=0.173, simple_loss=0.2539, pruned_loss=0.04608, over 7279.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2795, pruned_loss=0.05448, over 1466636.50 frames.], batch size: 16, lr: 2.65e-04
2022-07-27 12:51:42,695 INFO [train.py:850] (2/4) Epoch 21, batch 5900, loss[loss=0.1617, simple_loss=0.2479, pruned_loss=0.03769, over 7389.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2777, pruned_loss=0.05335, over 1466561.71 frames.], batch size: 20, lr: 2.65e-04
2022-07-27 12:52:28,022 INFO [train.py:850] (2/4) Epoch 21, batch 5950, loss[loss=0.1999, simple_loss=0.2849, pruned_loss=0.05747, over 7221.00 frames.], tot_loss[loss=0.192, simple_loss=0.2775, pruned_loss=0.05324, over 1466433.49 frames.], batch size: 25, lr: 2.65e-04
2022-07-27 12:53:11,687 INFO [train.py:850] (2/4) Epoch 21, batch 6000, loss[loss=0.2149, simple_loss=0.3125, pruned_loss=0.05864, over 7292.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2772, pruned_loss=0.0529, over 1465512.86 frames.], batch size: 21, lr: 2.65e-04
2022-07-27 12:53:11,689 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 12:53:34,804 INFO [train.py:879] (2/4) Epoch 21, validation: loss=0.185, simple_loss=0.2803, pruned_loss=0.04487, over 924787.00 frames.
2022-07-27 12:54:18,832 INFO [train.py:850] (2/4) Epoch 21, batch 6050, loss[loss=0.1996, simple_loss=0.2818, pruned_loss=0.05867, over 7171.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2786, pruned_loss=0.05398, over 1465890.28 frames.], batch size: 22, lr: 2.65e-04
2022-07-27 12:55:02,627 INFO [train.py:850] (2/4) Epoch 21, batch 6100, loss[loss=0.2133, simple_loss=0.3061, pruned_loss=0.06022, over 7417.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2786, pruned_loss=0.05345, over 1466327.30 frames.], batch size: 22, lr: 2.65e-04
2022-07-27 12:56:01,972 INFO [train.py:850] (2/4) Epoch 21, batch 6150, loss[loss=0.1962, simple_loss=0.2856, pruned_loss=0.05341, over 7186.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2789, pruned_loss=0.0535, over 1466915.87 frames.], batch size: 21, lr: 2.65e-04
2022-07-27 12:56:45,316 INFO [train.py:850] (2/4) Epoch 21, batch 6200, loss[loss=0.218, simple_loss=0.2997, pruned_loss=0.06811, over 7382.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2793, pruned_loss=0.05379, over 1466994.02 frames.], batch size: 21, lr: 2.65e-04
2022-07-27 12:57:30,350 INFO [train.py:850] (2/4) Epoch 21, batch 6250, loss[loss=0.2164, simple_loss=0.3038, pruned_loss=0.06447, over 7205.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2799, pruned_loss=0.05397, over 1467678.59 frames.], batch size: 25, lr: 2.65e-04
2022-07-27 12:58:13,093 INFO [train.py:850] (2/4) Epoch 21, batch 6300, loss[loss=0.2219, simple_loss=0.3012, pruned_loss=0.07126, over 7473.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2792, pruned_loss=0.05373, over 1467269.76 frames.], batch size: 21, lr: 2.65e-04
2022-07-27 12:58:57,729 INFO [train.py:850] (2/4) Epoch 21, batch 6350, loss[loss=0.1804, simple_loss=0.2661, pruned_loss=0.04731, over 7481.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2796, pruned_loss=0.0539, over 1466712.61 frames.], batch size: 23, lr: 2.65e-04
2022-07-27 12:59:41,261 INFO [train.py:850] (2/4) Epoch 21, batch 6400, loss[loss=0.1919, simple_loss=0.2777, pruned_loss=0.05307, over 7299.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2787, pruned_loss=0.05332, over 1466827.73 frames.], batch size: 20, lr: 2.65e-04
2022-07-27 13:00:24,496 INFO [train.py:850] (2/4) Epoch 21, batch 6450, loss[loss=0.191, simple_loss=0.2662, pruned_loss=0.05796, over 7209.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2786, pruned_loss=0.05336, over 1466267.08 frames.], batch size: 20, lr: 2.65e-04
2022-07-27 13:01:07,461 INFO [train.py:850] (2/4) Epoch 21, batch 6500, loss[loss=0.1604, simple_loss=0.2449, pruned_loss=0.03791, over 7432.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2774, pruned_loss=0.05281, over 1467569.83 frames.], batch size: 18, lr: 2.65e-04
2022-07-27 13:01:50,817 INFO [train.py:850] (2/4) Epoch 21, batch 6550, loss[loss=0.2105, simple_loss=0.3013, pruned_loss=0.0598, over 7491.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2785, pruned_loss=0.05393, over 1467776.84 frames.], batch size: 23, lr: 2.65e-04
2022-07-27 13:02:34,840 INFO [train.py:850] (2/4) Epoch 21, batch 6600, loss[loss=0.1497, simple_loss=0.2305, pruned_loss=0.03448, over 7321.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2793, pruned_loss=0.05392, over 1467501.65 frames.], batch size: 16, lr: 2.65e-04
2022-07-27 13:03:18,114 INFO [train.py:850] (2/4) Epoch 21, batch 6650, loss[loss=0.1903, simple_loss=0.2591, pruned_loss=0.06069, over 7157.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2785, pruned_loss=0.05332, over 1467766.94 frames.], batch size: 17, lr: 2.65e-04
2022-07-27 13:04:01,922 INFO [train.py:850] (2/4) Epoch 21, batch 6700, loss[loss=0.1903, simple_loss=0.2694, pruned_loss=0.05559, over 7394.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2781, pruned_loss=0.05307, over 1467452.18 frames.], batch size: 19, lr: 2.65e-04
2022-07-27 13:04:46,202 INFO [train.py:850] (2/4) Epoch 21, batch 6750, loss[loss=0.1768, simple_loss=0.2503, pruned_loss=0.05167, over 7147.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2781, pruned_loss=0.05377, over 1466834.50 frames.], batch size: 17, lr: 2.65e-04
2022-07-27 13:05:29,859 INFO [train.py:850] (2/4) Epoch 21, batch 6800, loss[loss=0.2056, simple_loss=0.2983, pruned_loss=0.05639, over 7419.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2788, pruned_loss=0.05394, over 1467898.78 frames.], batch size: 31, lr: 2.65e-04
2022-07-27 13:06:13,151 INFO [train.py:850] (2/4) Epoch 21, batch 6850, loss[loss=0.1873, simple_loss=0.2856, pruned_loss=0.04453, over 7489.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2791, pruned_loss=0.05397, over 1466938.70 frames.], batch size: 23, lr: 2.65e-04
2022-07-27 13:06:57,028 INFO [train.py:850] (2/4) Epoch 21, batch 6900, loss[loss=0.1664, simple_loss=0.2609, pruned_loss=0.03598, over 7282.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2789, pruned_loss=0.05406, over 1466201.87 frames.], batch size: 21, lr: 2.64e-04
2022-07-27 13:07:40,702 INFO [train.py:850] (2/4) Epoch 21, batch 6950, loss[loss=0.1793, simple_loss=0.2655, pruned_loss=0.04659, over 7476.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2785, pruned_loss=0.0535, over 1466545.14 frames.], batch size: 21, lr: 2.64e-04
2022-07-27 13:08:25,287 INFO [train.py:850] (2/4) Epoch 21, batch 7000, loss[loss=0.188, simple_loss=0.2624, pruned_loss=0.05685, over 7309.00 frames.], tot_loss[loss=0.1936, simple_loss=0.279, pruned_loss=0.05406, over 1466254.58 frames.], batch size: 17, lr: 2.64e-04
2022-07-27 13:09:10,429 INFO [train.py:850] (2/4) Epoch 21, batch 7050, loss[loss=0.3061, simple_loss=0.3636, pruned_loss=0.1243, over 7429.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2784, pruned_loss=0.0541, over 1466883.85 frames.], batch size: 70, lr: 2.64e-04
2022-07-27 13:09:54,366 INFO [train.py:850] (2/4) Epoch 21, batch 7100, loss[loss=0.1882, simple_loss=0.272, pruned_loss=0.05215, over 7312.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2774, pruned_loss=0.05317, over 1467327.66 frames.], batch size: 31, lr: 2.64e-04
2022-07-27 13:10:38,018 INFO [train.py:850] (2/4) Epoch 21, batch 7150, loss[loss=0.1801, simple_loss=0.2761, pruned_loss=0.04198, over 7410.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2772, pruned_loss=0.05263, over 1466474.98 frames.], batch size: 22, lr: 2.64e-04
2022-07-27 13:11:21,389 INFO [train.py:850] (2/4) Epoch 21, batch 7200, loss[loss=0.1776, simple_loss=0.2755, pruned_loss=0.0398, over 7302.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2768, pruned_loss=0.05237, over 1465957.35 frames.], batch size: 27, lr: 2.64e-04
2022-07-27 13:12:05,332 INFO [train.py:850] (2/4) Epoch 21, batch 7250, loss[loss=0.2096, simple_loss=0.2859, pruned_loss=0.0666, over 7483.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2786, pruned_loss=0.05312, over 1465564.48 frames.], batch size: 20, lr: 2.64e-04
2022-07-27 13:12:49,917 INFO [train.py:850] (2/4) Epoch 21, batch 7300, loss[loss=0.2032, simple_loss=0.2909, pruned_loss=0.05771, over 7472.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2783, pruned_loss=0.05234, over 1465566.18 frames.], batch size: 40, lr: 2.64e-04
2022-07-27 13:13:33,007 INFO [train.py:850] (2/4) Epoch 21, batch 7350, loss[loss=0.1618, simple_loss=0.2455, pruned_loss=0.03905, over 7260.00 frames.], tot_loss[loss=0.1913, simple_loss=0.278, pruned_loss=0.05229, over 1464687.61 frames.], batch size: 16, lr: 2.64e-04
2022-07-27 13:14:16,775 INFO [train.py:850] (2/4) Epoch 21, batch 7400, loss[loss=0.1989, simple_loss=0.2871, pruned_loss=0.05532, over 7254.00 frames.], tot_loss[loss=0.1913, simple_loss=0.278, pruned_loss=0.05224, over 1465670.72 frames.], batch size: 27, lr: 2.64e-04
2022-07-27 13:15:00,924 INFO [train.py:850] (2/4) Epoch 21, batch 7450, loss[loss=0.212, simple_loss=0.2773, pruned_loss=0.07339, over 7317.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2784, pruned_loss=0.05315, over 1465602.52 frames.], batch size: 16, lr: 2.64e-04
2022-07-27 13:15:45,156 INFO [train.py:850] (2/4) Epoch 21, batch 7500, loss[loss=0.1858, simple_loss=0.284, pruned_loss=0.04377, over 7181.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2796, pruned_loss=0.05411, over 1466162.47 frames.], batch size: 21, lr: 2.64e-04
2022-07-27 13:16:29,423 INFO [train.py:850] (2/4) Epoch 21, batch 7550, loss[loss=0.2081, simple_loss=0.2957, pruned_loss=0.0602, over 7486.00 frames.], tot_loss[loss=0.1935, simple_loss=0.279, pruned_loss=0.05399, over 1464757.05 frames.], batch size: 24, lr: 2.64e-04
2022-07-27 13:17:12,829 INFO [train.py:850] (2/4) Epoch 21, batch 7600, loss[loss=0.1674, simple_loss=0.2552, pruned_loss=0.03979, over 7400.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2786, pruned_loss=0.05399, over 1465563.85 frames.], batch size: 19, lr: 2.64e-04
2022-07-27 13:17:58,309 INFO [train.py:850] (2/4) Epoch 21, batch 7650, loss[loss=0.1979, simple_loss=0.2766, pruned_loss=0.05955, over 7422.00 frames.], tot_loss[loss=0.193, simple_loss=0.2784, pruned_loss=0.05385, over 1464979.65 frames.], batch size: 31, lr: 2.64e-04
2022-07-27 13:18:42,662 INFO [train.py:850] (2/4) Epoch 21, batch 7700, loss[loss=0.2391, simple_loss=0.3183, pruned_loss=0.07989, over 7178.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2777, pruned_loss=0.05359, over 1464610.57 frames.], batch size: 22, lr: 2.64e-04
2022-07-27 13:19:28,112 INFO [train.py:850] (2/4) Epoch 21, batch 7750, loss[loss=0.1665, simple_loss=0.2527, pruned_loss=0.04016, over 7312.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2774, pruned_loss=0.053, over 1464899.11 frames.], batch size: 18, lr: 2.64e-04
2022-07-27 13:20:12,343 INFO [train.py:850] (2/4) Epoch 21, batch 7800, loss[loss=0.1578, simple_loss=0.2341, pruned_loss=0.04072, over 7310.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2765, pruned_loss=0.05245, over 1464857.68 frames.], batch size: 17, lr: 2.64e-04
2022-07-27 13:20:58,896 INFO [train.py:850] (2/4) Epoch 21, batch 7850, loss[loss=0.2116, simple_loss=0.2914, pruned_loss=0.06594, over 7472.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2796, pruned_loss=0.05399, over 1465944.82 frames.], batch size: 20, lr: 2.64e-04
2022-07-27 13:21:42,895 INFO [train.py:850] (2/4) Epoch 21, batch 7900, loss[loss=0.1947, simple_loss=0.2789, pruned_loss=0.05526, over 7309.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2798, pruned_loss=0.0547, over 1466097.92 frames.], batch size: 18, lr: 2.64e-04
2022-07-27 13:22:26,114 INFO [train.py:850] (2/4) Epoch 21, batch 7950, loss[loss=0.2079, simple_loss=0.2975, pruned_loss=0.05914, over 7208.00 frames.], tot_loss[loss=0.195, simple_loss=0.2804, pruned_loss=0.05476, over 1465897.84 frames.], batch size: 25, lr: 2.64e-04
2022-07-27 13:23:10,015 INFO [train.py:850] (2/4) Epoch 21, batch 8000, loss[loss=0.2031, simple_loss=0.2847, pruned_loss=0.06076, over 7195.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2788, pruned_loss=0.05407, over 1466262.44 frames.], batch size: 18, lr: 2.64e-04
2022-07-27 13:23:53,887 INFO [train.py:850] (2/4) Epoch 21, batch 8050, loss[loss=0.1946, simple_loss=0.284, pruned_loss=0.05258, over 7379.00 frames.], tot_loss[loss=0.194, simple_loss=0.2797, pruned_loss=0.0542, over 1466398.72 frames.], batch size: 21, lr: 2.64e-04
2022-07-27 13:24:38,411 INFO [train.py:850] (2/4) Epoch 21, batch 8100, loss[loss=0.1746, simple_loss=0.2683, pruned_loss=0.04048, over 7292.00 frames.], tot_loss[loss=0.193, simple_loss=0.2791, pruned_loss=0.05348, over 1466744.05 frames.], batch size: 21, lr: 2.64e-04
2022-07-27 13:25:22,117 INFO [train.py:850] (2/4) Epoch 21, batch 8150, loss[loss=0.1595, simple_loss=0.2391, pruned_loss=0.03991, over 7329.00 frames.], tot_loss[loss=0.192, simple_loss=0.2784, pruned_loss=0.05281, over 1466567.94 frames.], batch size: 18, lr: 2.64e-04
2022-07-27 13:26:05,725 INFO [train.py:850] (2/4) Epoch 21, batch 8200, loss[loss=0.2143, simple_loss=0.2986, pruned_loss=0.06502, over 7484.00 frames.], tot_loss[loss=0.1928, simple_loss=0.279, pruned_loss=0.05335, over 1465665.12 frames.], batch size: 19, lr: 2.64e-04
2022-07-27 13:26:50,474 INFO [train.py:850] (2/4) Epoch 21, batch 8250, loss[loss=0.1677, simple_loss=0.2471, pruned_loss=0.04414, over 7301.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2792, pruned_loss=0.05324, over 1465626.88 frames.], batch size: 19, lr: 2.64e-04
2022-07-27 13:27:33,784 INFO [train.py:850] (2/4) Epoch 21, batch 8300, loss[loss=0.2067, simple_loss=0.2891, pruned_loss=0.06219, over 7472.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2789, pruned_loss=0.05309, over 1466511.56 frames.], batch size: 24, lr: 2.63e-04
2022-07-27 13:28:18,967 INFO [train.py:850] (2/4) Epoch 21, batch 8350, loss[loss=0.1631, simple_loss=0.2537, pruned_loss=0.03621, over 7484.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2787, pruned_loss=0.05331, over 1467167.14 frames.], batch size: 19, lr: 2.63e-04
2022-07-27 13:29:01,651 INFO [train.py:850] (2/4) Epoch 21, batch 8400, loss[loss=0.1775, simple_loss=0.2747, pruned_loss=0.04012, over 7428.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2779, pruned_loss=0.05281, over 1466364.09 frames.], batch size: 22, lr: 2.63e-04
2022-07-27 13:29:46,564 INFO [train.py:850] (2/4) Epoch 21, batch 8450, loss[loss=0.2069, simple_loss=0.2904, pruned_loss=0.06165, over 7303.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2793, pruned_loss=0.05341, over 1465873.97 frames.], batch size: 27, lr: 2.63e-04
2022-07-27 13:30:30,887 INFO [train.py:850] (2/4) Epoch 21, batch 8500, loss[loss=0.1698, simple_loss=0.2619, pruned_loss=0.03883, over 7284.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2789, pruned_loss=0.05331, over 1465238.18 frames.], batch size: 21, lr: 2.63e-04
2022-07-27 13:31:14,230 INFO [train.py:850] (2/4) Epoch 21, batch 8550, loss[loss=0.2043, simple_loss=0.2974, pruned_loss=0.05563, over 7169.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2794, pruned_loss=0.05355, over 1464757.52 frames.], batch size: 22, lr: 2.63e-04
2022-07-27 13:31:58,091 INFO [train.py:850] (2/4) Epoch 21, batch 8600, loss[loss=0.2083, simple_loss=0.2788, pruned_loss=0.06893, over 7454.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2779, pruned_loss=0.05315, over 1465220.94 frames.], batch size: 17, lr: 2.63e-04
2022-07-27 13:32:42,145 INFO [train.py:850] (2/4) Epoch 21, batch 8650, loss[loss=0.1422, simple_loss=0.2324, pruned_loss=0.02604, over 7377.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2773, pruned_loss=0.05251, over 1465971.84 frames.], batch size: 20, lr: 2.63e-04
2022-07-27 13:33:24,037 INFO [train.py:850] (2/4) Epoch 21, batch 8700, loss[loss=0.1659, simple_loss=0.2339, pruned_loss=0.04898, over 7462.00 frames.], tot_loss[loss=0.1909, simple_loss=0.277, pruned_loss=0.05239, over 1465572.60 frames.], batch size: 17, lr: 2.63e-04
2022-07-27 13:34:08,184 INFO [train.py:850] (2/4) Epoch 21, batch 8750, loss[loss=0.1939, simple_loss=0.2847, pruned_loss=0.05154, over 7412.00 frames.], tot_loss[loss=0.1902, simple_loss=0.276, pruned_loss=0.05217, over 1465128.78 frames.], batch size: 22, lr: 2.63e-04
2022-07-27 13:34:50,688 INFO [train.py:850] (2/4) Epoch 21, batch 8800, loss[loss=0.1996, simple_loss=0.2895, pruned_loss=0.05481, over 7359.00 frames.], tot_loss[loss=0.191, simple_loss=0.277, pruned_loss=0.05253, over 1465502.33 frames.], batch size: 68, lr: 2.63e-04
2022-07-27 13:35:34,323 INFO [train.py:850] (2/4) Epoch 21, batch 8850, loss[loss=0.1736, simple_loss=0.2615, pruned_loss=0.04286, over 7150.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2772, pruned_loss=0.05285, over 1464882.18 frames.], batch size: 17, lr: 2.63e-04
2022-07-27 13:36:45,965 INFO [train.py:850] (2/4) Epoch 22, batch 0, loss[loss=0.1681, simple_loss=0.2564, pruned_loss=0.03987, over 7104.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2564, pruned_loss=0.03987, over 7104.00 frames.], batch size: 18, lr: 2.57e-04
2022-07-27 13:37:30,115 INFO [train.py:850] (2/4) Epoch 22, batch 50, loss[loss=0.1677, simple_loss=0.2505, pruned_loss=0.04247, over 7466.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2738, pruned_loss=0.04555, over 330605.31 frames.], batch size: 17, lr: 2.57e-04
2022-07-27 13:38:13,867 INFO [train.py:850] (2/4) Epoch 22, batch 100, loss[loss=0.1789, simple_loss=0.2774, pruned_loss=0.04026, over 7383.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2736, pruned_loss=0.04584, over 582844.19 frames.], batch size: 21, lr: 2.57e-04
2022-07-27 13:38:56,856 INFO [train.py:850] (2/4) Epoch 22, batch 150, loss[loss=0.1618, simple_loss=0.2567, pruned_loss=0.03342, over 7492.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2743, pruned_loss=0.04518, over 779177.27 frames.], batch size: 23, lr: 2.57e-04
2022-07-27 13:39:40,674 INFO [train.py:850] (2/4) Epoch 22, batch 200, loss[loss=0.1746, simple_loss=0.2757, pruned_loss=0.03672, over 7219.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2728, pruned_loss=0.04421, over 930366.00 frames.], batch size: 25, lr: 2.57e-04
2022-07-27 13:40:24,351 INFO [train.py:850] (2/4) Epoch 22, batch 250, loss[loss=0.1817, simple_loss=0.2792, pruned_loss=0.0421, over 7310.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2746, pruned_loss=0.04507, over 1047760.73 frames.], batch size: 27, lr: 2.57e-04
2022-07-27 13:41:07,828 INFO [train.py:850] (2/4) Epoch 22, batch 300, loss[loss=0.1524, simple_loss=0.2509, pruned_loss=0.02698, over 7440.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2731, pruned_loss=0.04406, over 1140468.41 frames.], batch size: 17, lr: 2.57e-04
2022-07-27 13:41:51,818 INFO [train.py:850] (2/4) Epoch 22, batch 350, loss[loss=0.1513, simple_loss=0.2332, pruned_loss=0.03474, over 7229.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2721, pruned_loss=0.04419, over 1212339.59 frames.], batch size: 16, lr: 2.57e-04
2022-07-27 13:42:34,010 INFO [train.py:850] (2/4) Epoch 22, batch 400, loss[loss=0.1724, simple_loss=0.2591, pruned_loss=0.04281, over 7489.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2709, pruned_loss=0.04305, over 1268611.73 frames.], batch size: 19, lr: 2.57e-04
2022-07-27 13:43:18,193 INFO [train.py:850] (2/4) Epoch 22, batch 450, loss[loss=0.161, simple_loss=0.2628, pruned_loss=0.02959, over 7195.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2702, pruned_loss=0.04286, over 1312984.91 frames.], batch size: 20, lr: 2.57e-04
2022-07-27 13:44:01,147 INFO [train.py:850] (2/4) Epoch 22, batch 500, loss[loss=0.2307, simple_loss=0.3282, pruned_loss=0.06659, over 7414.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2705, pruned_loss=0.04299, over 1347176.95 frames.], batch size: 22, lr: 2.57e-04
2022-07-27 13:44:44,672 INFO [train.py:850] (2/4) Epoch 22, batch 550, loss[loss=0.2012, simple_loss=0.2961, pruned_loss=0.05318, over 7381.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2706, pruned_loss=0.04256, over 1373176.13 frames.], batch size: 73, lr: 2.57e-04
2022-07-27 13:45:27,983 INFO [train.py:850] (2/4) Epoch 22, batch 600, loss[loss=0.1523, simple_loss=0.2422, pruned_loss=0.03124, over 7478.00 frames.], tot_loss[loss=0.178, simple_loss=0.2709, pruned_loss=0.04253, over 1394668.54 frames.], batch size: 20, lr: 2.57e-04
2022-07-27 13:46:11,298 INFO [train.py:850] (2/4) Epoch 22, batch 650, loss[loss=0.1539, simple_loss=0.2452, pruned_loss=0.03128, over 7198.00 frames.], tot_loss[loss=0.1775, simple_loss=0.27, pruned_loss=0.04254, over 1410186.91 frames.], batch size: 19, lr: 2.57e-04
2022-07-27 13:46:56,127 INFO [train.py:850] (2/4) Epoch 22, batch 700, loss[loss=0.1648, simple_loss=0.2624, pruned_loss=0.03359, over 7380.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2692, pruned_loss=0.04245, over 1421867.96 frames.], batch size: 20, lr: 2.57e-04
2022-07-27 13:47:38,681 INFO [train.py:850] (2/4) Epoch 22, batch 750, loss[loss=0.1989, simple_loss=0.2948, pruned_loss=0.05151, over 7467.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2698, pruned_loss=0.04265, over 1432454.12 frames.], batch size: 21, lr: 2.57e-04
2022-07-27 13:48:23,091 INFO [train.py:850] (2/4) Epoch 22, batch 800, loss[loss=0.1503, simple_loss=0.2518, pruned_loss=0.02439, over 7291.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2688, pruned_loss=0.042, over 1439665.35 frames.], batch size: 19, lr: 2.57e-04
2022-07-27 13:49:06,131 INFO [train.py:850] (2/4) Epoch 22, batch 850, loss[loss=0.1695, simple_loss=0.2462, pruned_loss=0.0464, over 7388.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2699, pruned_loss=0.04237, over 1445356.12 frames.], batch size: 19, lr: 2.57e-04
2022-07-27 13:49:48,410 INFO [train.py:850] (2/4) Epoch 22, batch 900, loss[loss=0.1773, simple_loss=0.2612, pruned_loss=0.04673, over 7456.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2699, pruned_loss=0.04241, over 1449595.13 frames.], batch size: 18, lr: 2.57e-04
2022-07-27 13:50:33,182 INFO [train.py:850] (2/4) Epoch 22, batch 950, loss[loss=0.1954, simple_loss=0.2943, pruned_loss=0.04831, over 7347.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2706, pruned_loss=0.04286, over 1452578.82 frames.], batch size: 23, lr: 2.57e-04
2022-07-27 13:51:15,450 INFO [train.py:850] (2/4) Epoch 22, batch 1000, loss[loss=0.181, simple_loss=0.2692, pruned_loss=0.04641, over 7295.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2732, pruned_loss=0.04414, over 1456133.03 frames.], batch size: 20, lr: 2.57e-04
2022-07-27 13:52:00,072 INFO [train.py:850] (2/4) Epoch 22, batch 1050, loss[loss=0.2397, simple_loss=0.3275, pruned_loss=0.07597, over 7425.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2739, pruned_loss=0.04426, over 1458219.92 frames.], batch size: 72, lr: 2.57e-04
2022-07-27 13:52:43,244 INFO [train.py:850] (2/4) Epoch 22, batch 1100, loss[loss=0.1737, simple_loss=0.2768, pruned_loss=0.03533, over 7418.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2731, pruned_loss=0.04381, over 1460131.77 frames.], batch size: 22, lr: 2.56e-04
2022-07-27 13:53:26,731 INFO [train.py:850] (2/4) Epoch 22, batch 1150, loss[loss=0.1476, simple_loss=0.2501, pruned_loss=0.02252, over 7305.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2734, pruned_loss=0.04389, over 1461270.63 frames.], batch size: 22, lr: 2.56e-04
2022-07-27 13:54:10,992 INFO [train.py:850] (2/4) Epoch 22, batch 1200, loss[loss=0.1499, simple_loss=0.2416, pruned_loss=0.02913, over 7327.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2737, pruned_loss=0.04393, over 1461644.53 frames.], batch size: 18, lr: 2.56e-04
2022-07-27 13:55:09,862 INFO [train.py:850] (2/4) Epoch 22, batch 1250, loss[loss=0.197, simple_loss=0.2935, pruned_loss=0.05024, over 7312.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2749, pruned_loss=0.04429, over 1463441.03 frames.], batch size: 39, lr: 2.56e-04
2022-07-27 13:55:54,440 INFO [train.py:850] (2/4) Epoch 22, batch 1300, loss[loss=0.2378, simple_loss=0.3171, pruned_loss=0.07929, over 7492.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2759, pruned_loss=0.04483, over 1463619.56 frames.], batch size: 19, lr: 2.56e-04
2022-07-27 13:56:37,123 INFO [train.py:850] (2/4) Epoch 22, batch 1350, loss[loss=0.1789, simple_loss=0.2822, pruned_loss=0.0378, over 7476.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2765, pruned_loss=0.04518, over 1463753.50 frames.], batch size: 39, lr: 2.56e-04
2022-07-27 13:57:19,678 INFO [train.py:850] (2/4) Epoch 22, batch 1400, loss[loss=0.1778, simple_loss=0.2563, pruned_loss=0.04966, over 7340.00 frames.], tot_loss[loss=0.1839, simple_loss=0.277, pruned_loss=0.0454, over 1463826.80 frames.], batch size: 16, lr: 2.56e-04
2022-07-27 13:58:03,743 INFO [train.py:850] (2/4) Epoch 22, batch 1450, loss[loss=0.1982, simple_loss=0.2949, pruned_loss=0.05076, over 7400.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2772, pruned_loss=0.04555, over 1464686.82 frames.], batch size: 39, lr: 2.56e-04
2022-07-27 13:58:46,600 INFO [train.py:850] (2/4) Epoch 22, batch 1500, loss[loss=0.1515, simple_loss=0.2315, pruned_loss=0.03579, over 7436.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2782, pruned_loss=0.04582, over 1465421.98 frames.], batch size: 18, lr: 2.56e-04
2022-07-27 13:59:30,814 INFO [train.py:850] (2/4) Epoch 22, batch 1550, loss[loss=0.1532, simple_loss=0.2348, pruned_loss=0.0358, over 7172.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2784, pruned_loss=0.04572, over 1464761.85 frames.], batch size: 17, lr: 2.56e-04
2022-07-27 14:00:14,608 INFO [train.py:850] (2/4) Epoch 22, batch 1600, loss[loss=0.1996, simple_loss=0.2839, pruned_loss=0.05768, over 7384.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2771, pruned_loss=0.04505, over 1464846.49 frames.], batch size: 20, lr: 2.56e-04
2022-07-27 14:00:59,678 INFO [train.py:850] (2/4) Epoch 22, batch 1650, loss[loss=0.2179, simple_loss=0.3086, pruned_loss=0.06362, over 7164.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2774, pruned_loss=0.04523, over 1465257.04 frames.], batch size: 22, lr: 2.56e-04
2022-07-27 14:01:43,879 INFO [train.py:850] (2/4) Epoch 22, batch 1700, loss[loss=0.1931, simple_loss=0.2854, pruned_loss=0.05043, over 7460.00 frames.], tot_loss[loss=0.183, simple_loss=0.2767, pruned_loss=0.04464, over 1464486.59 frames.], batch size: 24, lr: 2.56e-04
2022-07-27 14:02:27,746 INFO [train.py:850] (2/4) Epoch 22, batch 1750, loss[loss=0.1612, simple_loss=0.264, pruned_loss=0.02914, over 7481.00 frames.], tot_loss[loss=0.183, simple_loss=0.277, pruned_loss=0.04455, over 1464358.90 frames.], batch size: 21, lr: 2.56e-04
2022-07-27 14:03:11,459 INFO [train.py:850] (2/4) Epoch 22, batch 1800, loss[loss=0.1798, simple_loss=0.275, pruned_loss=0.04234, over 7466.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2777, pruned_loss=0.04475, over 1465507.71 frames.], batch size: 24, lr: 2.56e-04
2022-07-27 14:03:54,714 INFO [train.py:850] (2/4) Epoch 22, batch 1850, loss[loss=0.1928, simple_loss=0.2874, pruned_loss=0.04909, over 7238.00 frames.], tot_loss[loss=0.183, simple_loss=0.277, pruned_loss=0.04448, over 1463838.04 frames.], batch size: 27, lr: 2.56e-04
2022-07-27 14:04:40,649 INFO [train.py:850] (2/4) Epoch 22, batch 1900, loss[loss=0.1993, simple_loss=0.2819, pruned_loss=0.05834, over 7485.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2759, pruned_loss=0.04452, over 1463731.70 frames.], batch size: 23, lr: 2.56e-04
2022-07-27 14:05:25,041 INFO [train.py:850] (2/4) Epoch 22, batch 1950, loss[loss=0.1755, simple_loss=0.2754, pruned_loss=0.03773, over 7473.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2769, pruned_loss=0.04494, over 1464349.21 frames.], batch size: 21, lr: 2.56e-04
2022-07-27 14:06:07,964 INFO [train.py:850] (2/4) Epoch 22, batch 2000, loss[loss=0.1863, simple_loss=0.2863, pruned_loss=0.04313, over 7290.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2771, pruned_loss=0.0449, over 1464526.17 frames.], batch size: 20, lr: 2.56e-04
2022-07-27 14:06:52,197 INFO [train.py:850] (2/4) Epoch 22, batch 2050, loss[loss=0.192, simple_loss=0.2771, pruned_loss=0.05346, over 7370.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2776, pruned_loss=0.04499, over 1464666.87 frames.], batch size: 21, lr: 2.56e-04
2022-07-27 14:07:34,674 INFO [train.py:850] (2/4) Epoch 22, batch 2100, loss[loss=0.1642, simple_loss=0.2654, pruned_loss=0.03153, over 7287.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2769, pruned_loss=0.04473, over 1465384.50 frames.], batch size: 20, lr: 2.56e-04
2022-07-27 14:08:19,742 INFO [train.py:850] (2/4) Epoch 22, batch 2150, loss[loss=0.1975, simple_loss=0.2892, pruned_loss=0.05287, over 7234.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2768, pruned_loss=0.04479, over 1465355.13 frames.], batch size: 27, lr: 2.56e-04
2022-07-27 14:09:01,693 INFO [train.py:850] (2/4) Epoch 22, batch 2200, loss[loss=0.1648, simple_loss=0.262, pruned_loss=0.0338, over 7485.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2766, pruned_loss=0.04439, over 1465171.96 frames.], batch size: 19, lr: 2.56e-04
2022-07-27 14:09:45,377 INFO [train.py:850] (2/4) Epoch 22, batch 2250, loss[loss=0.2047, simple_loss=0.2968, pruned_loss=0.05632, over 7338.00 frames.], tot_loss[loss=0.1825, simple_loss=0.276, pruned_loss=0.04448, over 1465170.42 frames.], batch size: 23, lr: 2.56e-04
2022-07-27 14:10:28,572 INFO [train.py:850] (2/4) Epoch 22, batch 2300, loss[loss=0.1889, simple_loss=0.2888, pruned_loss=0.04448, over 7400.00 frames.], tot_loss[loss=0.183, simple_loss=0.2765, pruned_loss=0.04478, over 1465701.20 frames.], batch size: 39, lr: 2.56e-04
2022-07-27 14:11:11,342 INFO [train.py:850] (2/4) Epoch 22, batch 2350, loss[loss=0.2064, simple_loss=0.3058, pruned_loss=0.05354, over 7254.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2759, pruned_loss=0.04436, over 1464997.85 frames.], batch size: 27, lr: 2.56e-04
2022-07-27 14:11:55,903 INFO [train.py:850] (2/4) Epoch 22, batch 2400, loss[loss=0.1859, simple_loss=0.2736, pruned_loss=0.04913, over 7148.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2748, pruned_loss=0.04402, over 1463705.13 frames.], batch size: 17, lr: 2.56e-04
2022-07-27 14:12:38,405 INFO [train.py:850] (2/4) Epoch 22, batch 2450, loss[loss=0.1886, simple_loss=0.2865, pruned_loss=0.04534, over 7418.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2736, pruned_loss=0.04352, over 1464267.27 frames.], batch size: 22, lr: 2.56e-04
2022-07-27 14:13:22,906 INFO [train.py:850] (2/4) Epoch 22, batch 2500, loss[loss=0.1618, simple_loss=0.249, pruned_loss=0.03724, over 7199.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2749, pruned_loss=0.04411, over 1464395.01 frames.], batch size: 18, lr: 2.56e-04
2022-07-27 14:14:06,425 INFO [train.py:850] (2/4) Epoch 22, batch 2550, loss[loss=0.1693, simple_loss=0.2498, pruned_loss=0.04446, over 7449.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2753, pruned_loss=0.04398, over 1465400.33 frames.], batch size: 18, lr: 2.55e-04
2022-07-27 14:14:49,300 INFO [train.py:850] (2/4) Epoch 22, batch 2600, loss[loss=0.1682, simple_loss=0.2537, pruned_loss=0.04136, over 7218.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2749, pruned_loss=0.04389, over 1465711.72 frames.], batch size: 16, lr: 2.55e-04
2022-07-27 14:15:33,360 INFO [train.py:850] (2/4) Epoch 22, batch 2650, loss[loss=0.2119, simple_loss=0.3037, pruned_loss=0.06011, over 7395.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2746, pruned_loss=0.04397, over 1465268.23 frames.], batch size: 20, lr: 2.55e-04
2022-07-27 14:16:16,023 INFO [train.py:850] (2/4) Epoch 22, batch 2700, loss[loss=0.176, simple_loss=0.2669, pruned_loss=0.04255, over 7479.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2748, pruned_loss=0.04423, over 1464517.51 frames.], batch size: 20, lr: 2.55e-04
2022-07-27 14:16:59,915 INFO [train.py:850] (2/4) Epoch 22, batch 2750, loss[loss=0.1707, simple_loss=0.2592, pruned_loss=0.04105, over 7102.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2739, pruned_loss=0.04349, over 1465078.30 frames.], batch size: 18, lr: 2.55e-04
2022-07-27 14:17:42,543 INFO [train.py:850] (2/4) Epoch 22, batch 2800, loss[loss=0.1873, simple_loss=0.2809, pruned_loss=0.04686, over 7179.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2745, pruned_loss=0.04381, over 1466663.06 frames.], batch size: 21, lr: 2.55e-04
2022-07-27 14:18:26,369 INFO [train.py:850] (2/4) Epoch 22, batch 2850, loss[loss=0.1619, simple_loss=0.2394, pruned_loss=0.04214, over 7305.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2747, pruned_loss=0.04359, over 1466006.70 frames.], batch size: 16, lr: 2.55e-04
2022-07-27 14:19:10,050 INFO [train.py:850] (2/4) Epoch 22, batch 2900, loss[loss=0.1807, simple_loss=0.2818, pruned_loss=0.03976, over 7378.00 frames.], tot_loss[loss=0.1803, simple_loss=0.274, pruned_loss=0.04329, over 1465098.27 frames.], batch size: 21, lr: 2.55e-04
2022-07-27 14:19:52,545 INFO [train.py:850] (2/4) Epoch 22, batch 2950, loss[loss=0.161, simple_loss=0.2401, pruned_loss=0.04099, over 7141.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2746, pruned_loss=0.04379, over 1464572.92 frames.], batch size: 17, lr: 2.55e-04
2022-07-27 14:20:37,506 INFO [train.py:850] (2/4) Epoch 22, batch 3000, loss[loss=0.2014, simple_loss=0.2896, pruned_loss=0.05663, over 7342.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2739, pruned_loss=0.04338, over 1465735.51 frames.], batch size: 23, lr: 2.55e-04
2022-07-27 14:20:37,507 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 14:21:00,422 INFO [train.py:879] (2/4) Epoch 22, validation: loss=0.197, simple_loss=0.2885, pruned_loss=0.0527, over 924787.00 frames.
2022-07-27 14:21:43,828 INFO [train.py:850] (2/4) Epoch 22, batch 3050, loss[loss=0.154, simple_loss=0.245, pruned_loss=0.03149, over 7383.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2741, pruned_loss=0.04304, over 1465670.06 frames.], batch size: 19, lr: 2.55e-04
2022-07-27 14:22:28,844 INFO [train.py:850] (2/4) Epoch 22, batch 3100, loss[loss=0.1907, simple_loss=0.2798, pruned_loss=0.0508, over 7285.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2732, pruned_loss=0.04299, over 1466539.32 frames.], batch size: 19, lr: 2.55e-04
2022-07-27 14:23:11,851 INFO [train.py:850] (2/4) Epoch 22, batch 3150, loss[loss=0.1769, simple_loss=0.2712, pruned_loss=0.04132, over 7391.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2729, pruned_loss=0.04284, over 1466306.24 frames.], batch size: 19, lr: 2.55e-04
2022-07-27 14:23:55,683 INFO [train.py:850] (2/4) Epoch 22, batch 3200, loss[loss=0.2157, simple_loss=0.3101, pruned_loss=0.06069, over 7223.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2741, pruned_loss=0.04363, over 1466053.92 frames.], batch size: 24, lr: 2.55e-04
2022-07-27 14:24:38,399 INFO [train.py:850] (2/4) Epoch 22, batch 3250, loss[loss=0.1479, simple_loss=0.2542, pruned_loss=0.02084, over 7467.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2743, pruned_loss=0.04367, over 1465397.32 frames.], batch size: 21, lr: 2.55e-04
2022-07-27 14:25:20,803 INFO [train.py:850] (2/4) Epoch 22, batch 3300, loss[loss=0.1831, simple_loss=0.2804, pruned_loss=0.04291, over 7478.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2736, pruned_loss=0.04338, over 1464921.90 frames.], batch size: 21, lr: 2.55e-04
2022-07-27 14:26:05,023 INFO [train.py:850] (2/4) Epoch 22, batch 3350, loss[loss=0.1734, simple_loss=0.2659, pruned_loss=0.04044, over 7299.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2723, pruned_loss=0.04262, over 1464385.64 frames.], batch size: 19, lr: 2.55e-04
2022-07-27 14:26:47,658 INFO [train.py:850] (2/4) Epoch 22, batch 3400, loss[loss=0.1673, simple_loss=0.2748, pruned_loss=0.02987, over 7474.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2728, pruned_loss=0.0428, over 1464207.39 frames.], batch size: 21, lr: 2.55e-04
2022-07-27 14:27:32,252 INFO [train.py:850] (2/4) Epoch 22, batch 3450, loss[loss=0.1701, simple_loss=0.252, pruned_loss=0.04407, over 7292.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2719, pruned_loss=0.04273, over 1465431.95 frames.], batch size: 17, lr: 2.55e-04
2022-07-27 14:28:15,703 INFO [train.py:850] (2/4) Epoch 22, batch 3500, loss[loss=0.203, simple_loss=0.3014, pruned_loss=0.05228, over 7180.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2731, pruned_loss=0.04314, over 1465207.07 frames.], batch size: 21, lr: 2.55e-04
2022-07-27 14:28:59,866 INFO [train.py:850] (2/4) Epoch 22, batch 3550, loss[loss=0.1854, simple_loss=0.2751, pruned_loss=0.04782, over 7445.00 frames.], tot_loss[loss=0.1795, simple_loss=0.273, pruned_loss=0.04296, over 1464918.63 frames.], batch size: 18, lr: 2.55e-04
2022-07-27 14:29:45,009 INFO [train.py:850] (2/4) Epoch 22, batch 3600, loss[loss=0.1942, simple_loss=0.296, pruned_loss=0.04618, over 7205.00 frames.], tot_loss[loss=0.18, simple_loss=0.2738, pruned_loss=0.04316, over 1465028.17 frames.], batch size: 20, lr: 2.55e-04
2022-07-27 14:30:27,820 INFO [train.py:850] (2/4) Epoch 22, batch 3650, loss[loss=0.1752, simple_loss=0.2844, pruned_loss=0.03305, over 7283.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2733, pruned_loss=0.0428, over 1464016.11 frames.], batch size: 21, lr: 2.55e-04
2022-07-27 14:31:12,306 INFO [train.py:850] (2/4) Epoch 22, batch 3700, loss[loss=0.214, simple_loss=0.3012, pruned_loss=0.06338, over 7183.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2733, pruned_loss=0.04279, over 1465112.95 frames.], batch size: 21, lr: 2.55e-04
2022-07-27 14:31:55,732 INFO [train.py:850] (2/4) Epoch 22, batch 3750, loss[loss=0.1627, simple_loss=0.2493, pruned_loss=0.03808, over 7304.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2736, pruned_loss=0.04337, over 1465406.42 frames.], batch size: 17, lr: 2.55e-04
2022-07-27 14:32:38,885 INFO [train.py:850] (2/4) Epoch 22, batch 3800, loss[loss=0.2107, simple_loss=0.2938, pruned_loss=0.06377, over 7285.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2731, pruned_loss=0.04307, over 1465889.80 frames.], batch size: 21, lr: 2.55e-04
2022-07-27 14:33:22,519 INFO [train.py:850] (2/4) Epoch 22, batch 3850, loss[loss=0.1533, simple_loss=0.2396, pruned_loss=0.03345, over 7456.00 frames.], tot_loss[loss=0.18, simple_loss=0.2734, pruned_loss=0.04328, over 1465654.64 frames.], batch size: 17, lr: 2.55e-04
2022-07-27 14:34:04,876 INFO [train.py:850] (2/4) Epoch 22, batch 3900, loss[loss=0.1821, simple_loss=0.2654, pruned_loss=0.04947, over 7314.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2748, pruned_loss=0.0442, over 1465552.09 frames.], batch size: 18, lr: 2.55e-04
2022-07-27 14:34:49,746 INFO [train.py:850] (2/4) Epoch 22, batch 3950, loss[loss=0.2272, simple_loss=0.3134, pruned_loss=0.07052, over 7283.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2749, pruned_loss=0.04403, over 1464733.65 frames.], batch size: 22, lr: 2.55e-04
2022-07-27 14:35:32,055 INFO [train.py:850] (2/4) Epoch 22, batch 4000, loss[loss=0.1858, simple_loss=0.2878, pruned_loss=0.04196, over 7180.00 frames.], tot_loss[loss=0.1816, simple_loss=0.275, pruned_loss=0.04407, over 1464860.71 frames.], batch size: 21, lr: 2.55e-04
2022-07-27 14:36:17,026 INFO [train.py:850] (2/4) Epoch 22, batch 4050, loss[loss=0.1804, simple_loss=0.2789, pruned_loss=0.0409, over 7295.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2745, pruned_loss=0.04444, over 1465278.74 frames.], batch size: 19, lr: 2.54e-04
2022-07-27 14:36:59,878 INFO [train.py:850] (2/4) Epoch 22, batch 4100, loss[loss=0.223, simple_loss=0.2968, pruned_loss=0.07458, over 7105.00 frames.], tot_loss[loss=0.1819, simple_loss=0.274, pruned_loss=0.04486, over 1465533.82 frames.], batch size: 18, lr: 2.54e-04
2022-07-27 14:37:43,221 INFO [train.py:850] (2/4) Epoch 22, batch 4150, loss[loss=0.2069, simple_loss=0.3012, pruned_loss=0.05636, over 7451.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2756, pruned_loss=0.04606, over 1464905.06 frames.], batch size: 24, lr: 2.54e-04
2022-07-27 14:38:27,467 INFO [train.py:850] (2/4) Epoch 22, batch 4200, loss[loss=0.1352, simple_loss=0.2222, pruned_loss=0.02408, over 7149.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2756, pruned_loss=0.04668, over 1465046.25 frames.], batch size: 17, lr: 2.54e-04
2022-07-27 14:39:10,520 INFO [train.py:850] (2/4) Epoch 22, batch 4250, loss[loss=0.2092, simple_loss=0.2934, pruned_loss=0.06248, over 7288.00 frames.], tot_loss[loss=0.186, simple_loss=0.2764, pruned_loss=0.04776, over 1464205.43 frames.], batch size: 20, lr: 2.54e-04
2022-07-27 14:39:55,085 INFO [train.py:850] (2/4) Epoch 22, batch 4300, loss[loss=0.1682, simple_loss=0.2478, pruned_loss=0.04425, over 7305.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2779, pruned_loss=0.04888, over 1464673.68 frames.], batch size: 17, lr: 2.54e-04
2022-07-27 14:40:38,211 INFO [train.py:850] (2/4) Epoch 22, batch 4350, loss[loss=0.2242, simple_loss=0.3062, pruned_loss=0.07112, over 7337.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2787, pruned_loss=0.0502, over 1464926.41 frames.], batch size: 23, lr: 2.54e-04
2022-07-27 14:41:21,008 INFO [train.py:850] (2/4) Epoch 22, batch 4400, loss[loss=0.1993, simple_loss=0.2906, pruned_loss=0.05396, over 7479.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2786, pruned_loss=0.05041, over 1464556.17 frames.], batch size: 31, lr: 2.54e-04
2022-07-27 14:42:05,598 INFO [train.py:850] (2/4) Epoch 22, batch 4450, loss[loss=0.1922, simple_loss=0.2814, pruned_loss=0.0515, over 7238.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2787, pruned_loss=0.05101, over 1464630.13 frames.], batch size: 24, lr: 2.54e-04
2022-07-27 14:42:48,047 INFO [train.py:850] (2/4) Epoch 22, batch 4500, loss[loss=0.211, simple_loss=0.2841, pruned_loss=0.06895, over 7295.00 frames.], tot_loss[loss=0.1902, simple_loss=0.278, pruned_loss=0.05113, over 1464924.63 frames.], batch size: 19, lr: 2.54e-04
2022-07-27 14:43:32,197 INFO [train.py:850] (2/4) Epoch 22, batch 4550, loss[loss=0.2037, simple_loss=0.2937, pruned_loss=0.05684, over 7186.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2787, pruned_loss=0.05145, over 1464651.80 frames.], batch size: 21, lr: 2.54e-04
2022-07-27 14:44:14,603 INFO [train.py:850] (2/4) Epoch 22, batch 4600, loss[loss=0.1971, simple_loss=0.2802, pruned_loss=0.05701, over 7381.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2777, pruned_loss=0.0513, over 1465308.72 frames.], batch size: 72, lr: 2.54e-04
2022-07-27 14:44:58,111 INFO [train.py:850] (2/4) Epoch 22, batch 4650, loss[loss=0.1957, simple_loss=0.2634, pruned_loss=0.06405, over 7319.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2787, pruned_loss=0.05237, over 1464942.63 frames.], batch size: 16, lr: 2.54e-04
2022-07-27 14:45:41,642 INFO [train.py:850] (2/4) Epoch 22, batch 4700, loss[loss=0.1802, simple_loss=0.2636, pruned_loss=0.04836, over 7104.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2807, pruned_loss=0.05348, over 1464329.82 frames.], batch size: 18, lr: 2.54e-04
2022-07-27 14:46:24,479 INFO [train.py:850] (2/4) Epoch 22, batch 4750, loss[loss=0.149, simple_loss=0.241, pruned_loss=0.02851, over 7492.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2802, pruned_loss=0.05341, over 1463707.09 frames.], batch size: 19, lr: 2.54e-04
2022-07-27 14:47:09,174 INFO [train.py:850] (2/4) Epoch 22, batch 4800, loss[loss=0.1754, simple_loss=0.2748, pruned_loss=0.03802, over 7309.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2811, pruned_loss=0.0541, over 1465132.36 frames.], batch size: 22, lr: 2.54e-04
2022-07-27 14:47:52,305 INFO [train.py:850] (2/4) Epoch 22, batch 4850, loss[loss=0.1906, simple_loss=0.2844, pruned_loss=0.04844, over 7468.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2804, pruned_loss=0.05387, over 1466140.45 frames.], batch size: 21, lr: 2.54e-04
2022-07-27 14:48:37,074 INFO [train.py:850] (2/4) Epoch 22, batch 4900, loss[loss=0.1844, simple_loss=0.2688, pruned_loss=0.05004, over 7475.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2802, pruned_loss=0.05385, over 1467195.21 frames.], batch size: 20, lr: 2.54e-04
2022-07-27 14:49:20,559 INFO [train.py:850] (2/4) Epoch 22, batch 4950, loss[loss=0.2305, simple_loss=0.3008, pruned_loss=0.08011, over 7157.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2786, pruned_loss=0.05312, over 1465865.98 frames.], batch size: 17, lr: 2.54e-04
2022-07-27 14:50:02,462 INFO [train.py:850] (2/4) Epoch 22, batch 5000, loss[loss=0.1583, simple_loss=0.2398, pruned_loss=0.03846, over 7485.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2781, pruned_loss=0.05278, over 1464801.75 frames.], batch size: 19, lr: 2.54e-04
2022-07-27 14:50:47,602 INFO [train.py:850] (2/4) Epoch 22, batch 5050, loss[loss=0.1904, simple_loss=0.2808, pruned_loss=0.05002, over 7197.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2785, pruned_loss=0.05269, over 1465128.41 frames.], batch size: 20, lr: 2.54e-04
2022-07-27 14:51:29,712 INFO [train.py:850] (2/4) Epoch 22, batch 5100, loss[loss=0.1882, simple_loss=0.2755, pruned_loss=0.05043, over 7214.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2786, pruned_loss=0.05276, over 1464971.67 frames.], batch size: 20, lr: 2.54e-04
2022-07-27 14:52:14,966 INFO [train.py:850] (2/4) Epoch 22, batch 5150, loss[loss=0.1915, simple_loss=0.2737, pruned_loss=0.05468, over 7281.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2797, pruned_loss=0.05351, over 1465520.81 frames.], batch size: 19, lr: 2.54e-04
2022-07-27 14:52:57,600 INFO [train.py:850] (2/4) Epoch 22, batch 5200, loss[loss=0.1813, simple_loss=0.2819, pruned_loss=0.04039, over 7187.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2788, pruned_loss=0.05322, over 1465838.74 frames.], batch size: 21, lr: 2.54e-04
2022-07-27 14:53:57,533 INFO [train.py:850] (2/4) Epoch 22, batch 5250, loss[loss=0.2253, simple_loss=0.3106, pruned_loss=0.07004, over 7404.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2786, pruned_loss=0.05302, over 1465945.49 frames.], batch size: 31, lr: 2.54e-04
2022-07-27 14:54:40,550 INFO [train.py:850] (2/4) Epoch 22, batch 5300, loss[loss=0.1785, simple_loss=0.2769, pruned_loss=0.04007, over 7227.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2777, pruned_loss=0.05244, over 1466025.72 frames.], batch size: 24, lr: 2.54e-04
2022-07-27 14:55:23,715 INFO [train.py:850] (2/4) Epoch 22, batch 5350, loss[loss=0.2173, simple_loss=0.2997, pruned_loss=0.0675, over 7307.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2787, pruned_loss=0.05275, over 1466625.27 frames.], batch size: 27, lr: 2.54e-04
2022-07-27 14:56:08,342 INFO [train.py:850] (2/4) Epoch 22, batch 5400, loss[loss=0.2154, simple_loss=0.2997, pruned_loss=0.06554, over 7351.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2797, pruned_loss=0.05367, over 1466382.02 frames.], batch size: 23, lr: 2.54e-04
2022-07-27 14:56:51,774 INFO [train.py:850] (2/4) Epoch 22, batch 5450, loss[loss=0.1804, simple_loss=0.2536, pruned_loss=0.05354, over 7433.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2799, pruned_loss=0.05385, over 1467012.76 frames.], batch size: 18, lr: 2.54e-04
2022-07-27 14:57:36,855 INFO [train.py:850] (2/4) Epoch 22, batch 5500, loss[loss=0.1929, simple_loss=0.2618, pruned_loss=0.06197, over 7304.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2805, pruned_loss=0.05441, over 1467206.36 frames.], batch size: 17, lr: 2.54e-04
2022-07-27 14:58:21,081 INFO [train.py:850] (2/4) Epoch 22, batch 5550, loss[loss=0.1817, simple_loss=0.2678, pruned_loss=0.0478, over 7179.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2805, pruned_loss=0.0546, over 1466887.54 frames.], batch size: 21, lr: 2.53e-04
2022-07-27 14:59:05,694 INFO [train.py:850] (2/4) Epoch 22, batch 5600, loss[loss=0.1441, simple_loss=0.226, pruned_loss=0.03105, over 7460.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2812, pruned_loss=0.0552, over 1466616.07 frames.], batch size: 17, lr: 2.53e-04
2022-07-27 14:59:49,998 INFO [train.py:850] (2/4) Epoch 22, batch 5650, loss[loss=0.2159, simple_loss=0.3, pruned_loss=0.06584, over 7337.00 frames.], tot_loss[loss=0.195, simple_loss=0.2808, pruned_loss=0.0546, over 1466841.58 frames.], batch size: 23, lr: 2.53e-04
2022-07-27 15:00:32,408 INFO [train.py:850] (2/4) Epoch 22, batch 5700, loss[loss=0.1636, simple_loss=0.2611, pruned_loss=0.0331, over 7178.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2811, pruned_loss=0.05507, over 1467299.79 frames.], batch size: 21, lr: 2.53e-04
2022-07-27 15:01:18,005 INFO [train.py:850] (2/4) Epoch 22, batch 5750, loss[loss=0.1694, simple_loss=0.2586, pruned_loss=0.04007, over 7292.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2816, pruned_loss=0.05529, over 1466830.99 frames.], batch size: 19, lr: 2.53e-04
2022-07-27 15:02:00,940 INFO [train.py:850] (2/4) Epoch 22, batch 5800, loss[loss=0.1528, simple_loss=0.2387, pruned_loss=0.03347, over 7447.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2818, pruned_loss=0.05537, over 1466601.99 frames.], batch size: 18, lr: 2.53e-04
2022-07-27 15:02:45,795 INFO [train.py:850] (2/4) Epoch 22, batch 5850, loss[loss=0.1476, simple_loss=0.2347, pruned_loss=0.03024, over 7151.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2802, pruned_loss=0.05435, over 1466317.83 frames.], batch size: 17, lr: 2.53e-04
2022-07-27 15:03:28,185 INFO [train.py:850] (2/4) Epoch 22, batch 5900, loss[loss=0.177, simple_loss=0.272, pruned_loss=0.04106, over 7404.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2794, pruned_loss=0.054, over 1465977.60 frames.], batch size: 31, lr: 2.53e-04
2022-07-27 15:04:11,742 INFO [train.py:850] (2/4) Epoch 22, batch 5950, loss[loss=0.1854, simple_loss=0.2644, pruned_loss=0.05317, over 7100.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2793, pruned_loss=0.05409, over 1465965.93 frames.], batch size: 18, lr: 2.53e-04
2022-07-27 15:04:55,968 INFO [train.py:850] (2/4) Epoch 22, batch 6000, loss[loss=0.1721, simple_loss=0.2705, pruned_loss=0.03687, over 7414.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2794, pruned_loss=0.05397, over 1466231.03 frames.], batch size: 22, lr: 2.53e-04
2022-07-27 15:04:55,969 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 15:05:19,399 INFO [train.py:879] (2/4) Epoch 22, validation: loss=0.1885, simple_loss=0.2823, pruned_loss=0.04732, over 924787.00 frames.
2022-07-27 15:06:04,327 INFO [train.py:850] (2/4) Epoch 22, batch 6050, loss[loss=0.156, simple_loss=0.2409, pruned_loss=0.03551, over 7259.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2796, pruned_loss=0.05357, over 1465888.21 frames.], batch size: 16, lr: 2.53e-04
2022-07-27 15:06:48,278 INFO [train.py:850] (2/4) Epoch 22, batch 6100, loss[loss=0.1817, simple_loss=0.2622, pruned_loss=0.05061, over 7436.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2794, pruned_loss=0.05342, over 1465439.81 frames.], batch size: 18, lr: 2.53e-04
2022-07-27 15:07:32,858 INFO [train.py:850] (2/4) Epoch 22, batch 6150, loss[loss=0.1673, simple_loss=0.2489, pruned_loss=0.04288, over 7479.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2805, pruned_loss=0.05414, over 1465210.58 frames.], batch size: 20, lr: 2.53e-04
2022-07-27 15:08:18,850 INFO [train.py:850] (2/4) Epoch 22, batch 6200, loss[loss=0.2288, simple_loss=0.3135, pruned_loss=0.07203, over 7475.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2801, pruned_loss=0.05383, over 1463968.52 frames.], batch size: 26, lr: 2.53e-04
2022-07-27 15:09:01,946 INFO [train.py:850] (2/4) Epoch 22, batch 6250, loss[loss=0.188, simple_loss=0.2721, pruned_loss=0.05197, over 7290.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2805, pruned_loss=0.05455, over 1464257.10 frames.], batch size: 19, lr: 2.53e-04
2022-07-27 15:09:46,731 INFO [train.py:850] (2/4) Epoch 22, batch 6300, loss[loss=0.1368, simple_loss=0.2213, pruned_loss=0.02612, over 7452.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2798, pruned_loss=0.05457, over 1465657.99 frames.], batch size: 17, lr: 2.53e-04
2022-07-27 15:10:29,495 INFO [train.py:850] (2/4) Epoch 22, batch 6350, loss[loss=0.2063, simple_loss=0.2959, pruned_loss=0.05838, over 7285.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2796, pruned_loss=0.05434, over 1465292.38 frames.], batch size: 21, lr: 2.53e-04
2022-07-27 15:11:12,962 INFO [train.py:850] (2/4) Epoch 22, batch 6400, loss[loss=0.1723, simple_loss=0.2612, pruned_loss=0.04168, over 7287.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2789, pruned_loss=0.05363, over 1465215.88 frames.], batch size: 19, lr: 2.53e-04
2022-07-27 15:11:58,001 INFO [train.py:850] (2/4) Epoch 22, batch 6450, loss[loss=0.1456, simple_loss=0.2307, pruned_loss=0.03025, over 7326.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2774, pruned_loss=0.05292, over 1464389.13 frames.], batch size: 17, lr: 2.53e-04
2022-07-27 15:12:40,976 INFO [train.py:850] (2/4) Epoch 22, batch 6500, loss[loss=0.1584, simple_loss=0.2649, pruned_loss=0.0259, over 7295.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2764, pruned_loss=0.05201, over 1465888.20 frames.], batch size: 21, lr: 2.53e-04
2022-07-27 15:13:26,000 INFO [train.py:850] (2/4) Epoch 22, batch 6550, loss[loss=0.2178, simple_loss=0.2933, pruned_loss=0.07109, over 7361.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2765, pruned_loss=0.05208, over 1465118.34 frames.], batch size: 23, lr: 2.53e-04
2022-07-27 15:14:09,124 INFO [train.py:850] (2/4) Epoch 22, batch 6600, loss[loss=0.1611, simple_loss=0.2463, pruned_loss=0.03793, over 7491.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2758, pruned_loss=0.05139, over 1465157.18 frames.], batch size: 19, lr: 2.53e-04
2022-07-27 15:14:54,255 INFO [train.py:850] (2/4) Epoch 22, batch 6650, loss[loss=0.2118, simple_loss=0.3048, pruned_loss=0.05944, over 7305.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2769, pruned_loss=0.0524, over 1465402.09 frames.], batch size: 22, lr: 2.53e-04
2022-07-27 15:15:36,979 INFO [train.py:850] (2/4) Epoch 22, batch 6700, loss[loss=0.2082, simple_loss=0.2896, pruned_loss=0.06343, over 7452.00 frames.], tot_loss[loss=0.191, simple_loss=0.2766, pruned_loss=0.05266, over 1465633.28 frames.], batch size: 24, lr: 2.53e-04
2022-07-27 15:16:20,090 INFO [train.py:850] (2/4) Epoch 22, batch 6750, loss[loss=0.1738, simple_loss=0.2604, pruned_loss=0.04365, over 7481.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2759, pruned_loss=0.05224, over 1465348.56 frames.], batch size: 20, lr: 2.53e-04
2022-07-27 15:17:05,006 INFO [train.py:850] (2/4) Epoch 22, batch 6800, loss[loss=0.1799, simple_loss=0.2651, pruned_loss=0.04735, over 7236.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2768, pruned_loss=0.05183, over 1466182.10 frames.], batch size: 24, lr: 2.53e-04
2022-07-27 15:17:48,538 INFO [train.py:850] (2/4) Epoch 22, batch 6850, loss[loss=0.2628, simple_loss=0.3283, pruned_loss=0.09865, over 7309.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2773, pruned_loss=0.05254, over 1465744.71 frames.], batch size: 22, lr: 2.53e-04
2022-07-27 15:18:32,689 INFO [train.py:850] (2/4) Epoch 22, batch 6900, loss[loss=0.1439, simple_loss=0.2301, pruned_loss=0.02885, over 7267.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2762, pruned_loss=0.05171, over 1465928.41 frames.], batch size: 16, lr: 2.53e-04
2022-07-27 15:19:15,716 INFO [train.py:850] (2/4) Epoch 22, batch 6950, loss[loss=0.1622, simple_loss=0.2552, pruned_loss=0.03467, over 7487.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2763, pruned_loss=0.05202, over 1465814.48 frames.], batch size: 20, lr: 2.53e-04
2022-07-27 15:19:59,557 INFO [train.py:850] (2/4) Epoch 22, batch 7000, loss[loss=0.2285, simple_loss=0.3093, pruned_loss=0.07386, over 7230.00 frames.], tot_loss[loss=0.189, simple_loss=0.2754, pruned_loss=0.05135, over 1465083.21 frames.], batch size: 24, lr: 2.53e-04
2022-07-27 15:20:43,216 INFO [train.py:850] (2/4) Epoch 22, batch 7050, loss[loss=0.2203, simple_loss=0.2923, pruned_loss=0.07412, over 7443.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2757, pruned_loss=0.05132, over 1465022.89 frames.], batch size: 72, lr: 2.53e-04
2022-07-27 15:21:27,186 INFO [train.py:850] (2/4) Epoch 22, batch 7100, loss[loss=0.1417, simple_loss=0.2226, pruned_loss=0.0304, over 7324.00 frames.], tot_loss[loss=0.19, simple_loss=0.2762, pruned_loss=0.0519, over 1466218.28 frames.], batch size: 17, lr: 2.52e-04
2022-07-27 15:22:11,934 INFO [train.py:850] (2/4) Epoch 22, batch 7150, loss[loss=0.1905, simple_loss=0.2844, pruned_loss=0.04829, over 7311.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2767, pruned_loss=0.05229, over 1466928.13 frames.], batch size: 22, lr: 2.52e-04
2022-07-27 15:22:54,748 INFO [train.py:850] (2/4) Epoch 22, batch 7200, loss[loss=0.2116, simple_loss=0.3083, pruned_loss=0.05748, over 7474.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2771, pruned_loss=0.05215, over 1467124.66 frames.], batch size: 21, lr: 2.52e-04
2022-07-27 15:23:39,964 INFO [train.py:850] (2/4) Epoch 22, batch 7250, loss[loss=0.1535, simple_loss=0.229, pruned_loss=0.03899, over 7312.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2762, pruned_loss=0.05161, over 1466820.62 frames.], batch size: 17, lr: 2.52e-04
2022-07-27 15:24:22,543 INFO [train.py:850] (2/4) Epoch 22, batch 7300, loss[loss=0.2359, simple_loss=0.3191, pruned_loss=0.07636, over 7473.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2765, pruned_loss=0.05204, over 1467312.18 frames.], batch size: 21, lr: 2.52e-04
2022-07-27 15:25:07,424 INFO [train.py:850] (2/4) Epoch 22, batch 7350, loss[loss=0.1909, simple_loss=0.2877, pruned_loss=0.04704, over 7207.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2763, pruned_loss=0.05158, over 1465890.24 frames.], batch size: 20, lr: 2.52e-04
2022-07-27 15:25:51,063 INFO [train.py:850] (2/4) Epoch 22, batch 7400, loss[loss=0.1322, simple_loss=0.2185, pruned_loss=0.02294, over 7308.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2752, pruned_loss=0.05121, over 1465648.19 frames.], batch size: 16, lr: 2.52e-04
2022-07-27 15:26:35,056 INFO [train.py:850] (2/4) Epoch 22, batch 7450, loss[loss=0.1814, simple_loss=0.2773, pruned_loss=0.04277, over 7476.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2756, pruned_loss=0.05108, over 1466908.87 frames.], batch size: 21, lr: 2.52e-04
2022-07-27 15:27:18,690 INFO [train.py:850] (2/4) Epoch 22, batch 7500, loss[loss=0.1575, simple_loss=0.2561, pruned_loss=0.02939, over 7380.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2756, pruned_loss=0.05135, over 1467357.12 frames.], batch size: 20, lr: 2.52e-04
2022-07-27 15:28:02,841 INFO [train.py:850] (2/4) Epoch 22, batch 7550, loss[loss=0.1832, simple_loss=0.2564, pruned_loss=0.05501, over 7299.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2762, pruned_loss=0.0522, over 1466852.69 frames.], batch size: 18, lr: 2.52e-04
2022-07-27 15:28:46,844 INFO [train.py:850] (2/4) Epoch 22, batch 7600, loss[loss=0.1767, simple_loss=0.2562, pruned_loss=0.04862, over 7478.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2776, pruned_loss=0.0529, over 1467001.83 frames.], batch size: 20, lr: 2.52e-04
2022-07-27 15:29:30,159 INFO [train.py:850] (2/4) Epoch 22, batch 7650, loss[loss=0.1715, simple_loss=0.2683, pruned_loss=0.03732, over 7474.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2773, pruned_loss=0.05253, over 1466123.11 frames.], batch size: 21, lr: 2.52e-04
2022-07-27 15:30:15,599 INFO [train.py:850] (2/4) Epoch 22, batch 7700, loss[loss=0.2008, simple_loss=0.2907, pruned_loss=0.05548, over 7361.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2765, pruned_loss=0.05234, over 1466485.15 frames.], batch size: 39, lr: 2.52e-04
2022-07-27 15:30:59,695 INFO [train.py:850] (2/4) Epoch 22, batch 7750, loss[loss=0.2467, simple_loss=0.3173, pruned_loss=0.08802, over 7326.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2773, pruned_loss=0.05254, over 1465213.71 frames.], batch size: 69, lr: 2.52e-04
2022-07-27 15:31:42,738 INFO [train.py:850] (2/4) Epoch 22, batch 7800, loss[loss=0.2168, simple_loss=0.3086, pruned_loss=0.06251, over 7465.00 frames.], tot_loss[loss=0.1909, simple_loss=0.277, pruned_loss=0.05242, over 1465520.05 frames.], batch size: 24, lr: 2.52e-04
2022-07-27 15:32:27,445 INFO [train.py:850] (2/4) Epoch 22, batch 7850, loss[loss=0.1967, simple_loss=0.2611, pruned_loss=0.06619, over 7445.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2766, pruned_loss=0.05235, over 1465121.38 frames.], batch size: 17, lr: 2.52e-04
2022-07-27 15:33:11,388 INFO [train.py:850] (2/4) Epoch 22, batch 7900, loss[loss=0.2017, simple_loss=0.2808, pruned_loss=0.06128, over 7346.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2764, pruned_loss=0.0524, over 1465087.05 frames.], batch size: 23, lr: 2.52e-04
2022-07-27 15:33:55,564 INFO [train.py:850] (2/4) Epoch 22, batch 7950, loss[loss=0.1994, simple_loss=0.2838, pruned_loss=0.0575, over 7288.00 frames.], tot_loss[loss=0.1898, simple_loss=0.276, pruned_loss=0.05181, over 1464895.38 frames.], batch size: 20, lr: 2.52e-04
2022-07-27 15:34:37,941 INFO [train.py:850] (2/4) Epoch 22, batch 8000, loss[loss=0.1754, simple_loss=0.257, pruned_loss=0.04691, over 7197.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2764, pruned_loss=0.05165, over 1465473.92 frames.], batch size: 19, lr: 2.52e-04
2022-07-27 15:35:24,169 INFO [train.py:850] (2/4) Epoch 22, batch 8050, loss[loss=0.2299, simple_loss=0.3043, pruned_loss=0.07773, over 7447.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2774, pruned_loss=0.05217, over 1466758.15 frames.], batch size: 74, lr: 2.52e-04
2022-07-27 15:36:06,934 INFO [train.py:850] (2/4) Epoch 22, batch 8100, loss[loss=0.1584, simple_loss=0.2464, pruned_loss=0.03519, over 7294.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2763, pruned_loss=0.05153, over 1465540.35 frames.], batch size: 19, lr: 2.52e-04
2022-07-27 15:36:50,908 INFO [train.py:850] (2/4) Epoch 22, batch 8150, loss[loss=0.2008, simple_loss=0.2854, pruned_loss=0.0581, over 7439.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2755, pruned_loss=0.05111, over 1465168.31 frames.], batch size: 67, lr: 2.52e-04
2022-07-27 15:37:35,645 INFO [train.py:850] (2/4) Epoch 22, batch 8200, loss[loss=0.218, simple_loss=0.2936, pruned_loss=0.07126, over 7309.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2765, pruned_loss=0.05159, over 1465790.85 frames.], batch size: 17, lr: 2.52e-04
2022-07-27 15:38:19,058 INFO [train.py:850] (2/4) Epoch 22, batch 8250, loss[loss=0.1602, simple_loss=0.2492, pruned_loss=0.03561, over 7298.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2763, pruned_loss=0.05116, over 1465944.91 frames.], batch size: 19, lr: 2.52e-04
2022-07-27 15:39:03,093 INFO [train.py:850] (2/4) Epoch 22, batch 8300, loss[loss=0.1796, simple_loss=0.2664, pruned_loss=0.04637, over 7488.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2766, pruned_loss=0.0513, over 1466864.08 frames.], batch size: 19, lr: 2.52e-04
2022-07-27 15:39:46,666 INFO [train.py:850] (2/4) Epoch 22, batch 8350, loss[loss=0.1941, simple_loss=0.291, pruned_loss=0.04866, over 7490.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2777, pruned_loss=0.05234, over 1466876.76 frames.], batch size: 23, lr: 2.52e-04
2022-07-27 15:40:31,333 INFO [train.py:850] (2/4) Epoch 22, batch 8400, loss[loss=0.1857, simple_loss=0.2561, pruned_loss=0.05763, over 7436.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2793, pruned_loss=0.05274, over 1466853.52 frames.], batch size: 18, lr: 2.52e-04
2022-07-27 15:41:14,913 INFO [train.py:850] (2/4) Epoch 22, batch 8450, loss[loss=0.1724, simple_loss=0.2619, pruned_loss=0.04144, over 7203.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2791, pruned_loss=0.05296, over 1467486.50 frames.], batch size: 20, lr: 2.52e-04
2022-07-27 15:41:59,065 INFO [train.py:850] (2/4) Epoch 22, batch 8500, loss[loss=0.1828, simple_loss=0.2786, pruned_loss=0.04348, over 7487.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2779, pruned_loss=0.05265, over 1467415.64 frames.], batch size: 26, lr: 2.52e-04
2022-07-27 15:42:44,330 INFO [train.py:850] (2/4) Epoch 22, batch 8550, loss[loss=0.2041, simple_loss=0.2802, pruned_loss=0.06397, over 7303.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2776, pruned_loss=0.05239, over 1467462.82 frames.], batch size: 20, lr: 2.52e-04
2022-07-27 15:43:29,185 INFO [train.py:850] (2/4) Epoch 22, batch 8600, loss[loss=0.2058, simple_loss=0.2887, pruned_loss=0.06145, over 7181.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2778, pruned_loss=0.05239, over 1466895.39 frames.], batch size: 21, lr: 2.52e-04
2022-07-27 15:44:14,577 INFO [train.py:850] (2/4) Epoch 22, batch 8650, loss[loss=0.1739, simple_loss=0.2582, pruned_loss=0.0448, over 7101.00 frames.], tot_loss[loss=0.189, simple_loss=0.2759, pruned_loss=0.0511, over 1465247.90 frames.], batch size: 18, lr: 2.51e-04
2022-07-27 15:44:58,188 INFO [train.py:850] (2/4) Epoch 22, batch 8700, loss[loss=0.2147, simple_loss=0.3024, pruned_loss=0.0635, over 7226.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2757, pruned_loss=0.05102, over 1465192.28 frames.], batch size: 25, lr: 2.51e-04
2022-07-27 15:45:42,205 INFO [train.py:850] (2/4) Epoch 22, batch 8750, loss[loss=0.1742, simple_loss=0.2755, pruned_loss=0.03649, over 7378.00 frames.], tot_loss[loss=0.1878, simple_loss=0.275, pruned_loss=0.05036, over 1464671.19 frames.], batch size: 21, lr: 2.51e-04
2022-07-27 15:46:25,733 INFO [train.py:850] (2/4) Epoch 22, batch 8800, loss[loss=0.1693, simple_loss=0.2568, pruned_loss=0.04091, over 7194.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2754, pruned_loss=0.05071, over 1465215.89 frames.], batch size: 19, lr: 2.51e-04
2022-07-27 15:47:08,862 INFO [train.py:850] (2/4) Epoch 22, batch 8850, loss[loss=0.2072, simple_loss=0.2943, pruned_loss=0.06008, over 7279.00 frames.], tot_loss[loss=0.188, simple_loss=0.2747, pruned_loss=0.05065, over 1465524.38 frames.], batch size: 21, lr: 2.51e-04
2022-07-27 15:48:20,884 INFO [train.py:850] (2/4) Epoch 23, batch 0, loss[loss=0.1932, simple_loss=0.2923, pruned_loss=0.04703, over 7331.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2923, pruned_loss=0.04703, over 7331.00 frames.], batch size: 23, lr: 2.46e-04
2022-07-27 15:49:05,146 INFO [train.py:850] (2/4) Epoch 23, batch 50, loss[loss=0.1903, simple_loss=0.2936, pruned_loss=0.04346, over 7474.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2787, pruned_loss=0.04675, over 330830.58 frames.], batch size: 24, lr: 2.46e-04
2022-07-27 15:49:50,858 INFO [train.py:850] (2/4) Epoch 23, batch 100, loss[loss=0.1589, simple_loss=0.2561, pruned_loss=0.03082, over 7282.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2762, pruned_loss=0.04508, over 582221.00 frames.], batch size: 20, lr: 2.46e-04
2022-07-27 15:50:35,695 INFO [train.py:850] (2/4) Epoch 23, batch 150, loss[loss=0.1524, simple_loss=0.2463, pruned_loss=0.02921, over 7306.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2756, pruned_loss=0.04477, over 779159.85 frames.], batch size: 19, lr: 2.46e-04
2022-07-27 15:51:20,894 INFO [train.py:850] (2/4) Epoch 23, batch 200, loss[loss=0.2539, simple_loss=0.3304, pruned_loss=0.08873, over 7344.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2756, pruned_loss=0.0453, over 931032.16 frames.], batch size: 23, lr: 2.46e-04
2022-07-27 15:52:05,143 INFO [train.py:850] (2/4) Epoch 23, batch 250, loss[loss=0.1609, simple_loss=0.2478, pruned_loss=0.03706, over 7260.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2749, pruned_loss=0.04507, over 1050077.42 frames.], batch size: 16, lr: 2.46e-04
2022-07-27 15:52:47,154 INFO [train.py:850] (2/4) Epoch 23, batch 300, loss[loss=0.1925, simple_loss=0.288, pruned_loss=0.04852, over 7474.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2736, pruned_loss=0.04428, over 1142208.85 frames.], batch size: 21, lr: 2.46e-04
2022-07-27 15:53:46,869 INFO [train.py:850] (2/4) Epoch 23, batch 350, loss[loss=0.1795, simple_loss=0.2789, pruned_loss=0.04006, over 7342.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2723, pruned_loss=0.04375, over 1212397.35 frames.], batch size: 27, lr: 2.46e-04
2022-07-27 15:54:31,196 INFO [train.py:850] (2/4) Epoch 23, batch 400, loss[loss=0.176, simple_loss=0.2829, pruned_loss=0.0346, over 7478.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2726, pruned_loss=0.04377, over 1270324.70 frames.], batch size: 21, lr: 2.46e-04
2022-07-27 15:55:14,824 INFO [train.py:850] (2/4) Epoch 23, batch 450, loss[loss=0.1691, simple_loss=0.2756, pruned_loss=0.03127, over 7400.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2721, pruned_loss=0.0435, over 1315047.49 frames.], batch size: 31, lr: 2.46e-04
2022-07-27 15:55:59,265 INFO [train.py:850] (2/4) Epoch 23, batch 500, loss[loss=0.1601, simple_loss=0.2376, pruned_loss=0.04132, over 7304.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2717, pruned_loss=0.0431, over 1348790.71 frames.], batch size: 18, lr: 2.46e-04
2022-07-27 15:56:42,940 INFO [train.py:850] (2/4) Epoch 23, batch 550, loss[loss=0.1762, simple_loss=0.2599, pruned_loss=0.04628, over 7098.00 frames.], tot_loss[loss=0.179, simple_loss=0.2715, pruned_loss=0.0433, over 1374797.88 frames.], batch size: 18, lr: 2.46e-04
2022-07-27 15:57:26,834 INFO [train.py:850] (2/4) Epoch 23, batch 600, loss[loss=0.1646, simple_loss=0.2675, pruned_loss=0.03088, over 7441.00 frames.], tot_loss[loss=0.178, simple_loss=0.2707, pruned_loss=0.04267, over 1395760.18 frames.], batch size: 38, lr: 2.46e-04
2022-07-27 15:58:09,926 INFO [train.py:850] (2/4) Epoch 23, batch 650, loss[loss=0.1785, simple_loss=0.2619, pruned_loss=0.04755, over 7438.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2703, pruned_loss=0.04304, over 1411143.81 frames.], batch size: 18, lr: 2.46e-04
2022-07-27 15:58:54,270 INFO [train.py:850] (2/4) Epoch 23, batch 700, loss[loss=0.1526, simple_loss=0.247, pruned_loss=0.02909, over 7375.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2688, pruned_loss=0.04217, over 1422453.51 frames.], batch size: 21, lr: 2.46e-04
2022-07-27 15:59:37,999 INFO [train.py:850] (2/4) Epoch 23, batch 750, loss[loss=0.1623, simple_loss=0.2544, pruned_loss=0.03507, over 7291.00 frames.], tot_loss[loss=0.1767, simple_loss=0.269, pruned_loss=0.04219, over 1432171.75 frames.], batch size: 19, lr: 2.45e-04
2022-07-27 16:00:20,304 INFO [train.py:850] (2/4) Epoch 23, batch 800, loss[loss=0.1941, simple_loss=0.2968, pruned_loss=0.04574, over 7344.00 frames.], tot_loss[loss=0.178, simple_loss=0.2702, pruned_loss=0.04283, over 1439232.33 frames.], batch size: 23, lr: 2.45e-04
2022-07-27 16:01:04,139 INFO [train.py:850] (2/4) Epoch 23, batch 850, loss[loss=0.1911, simple_loss=0.295, pruned_loss=0.04356, over 7300.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2704, pruned_loss=0.04247, over 1444401.43 frames.], batch size: 22, lr: 2.45e-04
2022-07-27 16:01:46,738 INFO [train.py:850] (2/4) Epoch 23, batch 900, loss[loss=0.2011, simple_loss=0.2952, pruned_loss=0.05357, over 7295.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2713, pruned_loss=0.04258, over 1448298.27 frames.], batch size: 20, lr: 2.45e-04
2022-07-27 16:02:30,199 INFO [train.py:850] (2/4) Epoch 23, batch 950, loss[loss=0.1839, simple_loss=0.2727, pruned_loss=0.04756, over 7153.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2724, pruned_loss=0.04307, over 1451687.42 frames.], batch size: 17, lr: 2.45e-04
2022-07-27 16:03:13,652 INFO [train.py:850] (2/4) Epoch 23, batch 1000, loss[loss=0.2313, simple_loss=0.3289, pruned_loss=0.06686, over 7286.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2731, pruned_loss=0.04321, over 1454316.08 frames.], batch size: 22, lr: 2.45e-04
2022-07-27 16:03:56,620 INFO [train.py:850] (2/4) Epoch 23, batch 1050, loss[loss=0.1853, simple_loss=0.2924, pruned_loss=0.03913, over 7454.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2736, pruned_loss=0.04326, over 1456273.64 frames.], batch size: 39, lr: 2.45e-04
2022-07-27 16:04:39,753 INFO [train.py:850] (2/4) Epoch 23, batch 1100, loss[loss=0.1678, simple_loss=0.2594, pruned_loss=0.03806, over 7470.00 frames.], tot_loss[loss=0.1796, simple_loss=0.273, pruned_loss=0.04313, over 1456592.96 frames.], batch size: 24, lr: 2.45e-04
2022-07-27 16:05:23,192 INFO [train.py:850] (2/4) Epoch 23, batch 1150, loss[loss=0.1545, simple_loss=0.2363, pruned_loss=0.03642, over 7168.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2744, pruned_loss=0.04401, over 1457677.71 frames.], batch size: 17, lr: 2.45e-04
2022-07-27 16:06:07,487 INFO [train.py:850] (2/4) Epoch 23, batch 1200, loss[loss=0.1824, simple_loss=0.2826, pruned_loss=0.0411, over 7316.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2746, pruned_loss=0.0445, over 1458909.63 frames.], batch size: 27, lr: 2.45e-04
2022-07-27 16:06:50,728 INFO [train.py:850] (2/4) Epoch 23, batch 1250, loss[loss=0.1729, simple_loss=0.2669, pruned_loss=0.03949, over 7290.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2743, pruned_loss=0.04466, over 1459517.15 frames.], batch size: 20, lr: 2.45e-04
2022-07-27 16:07:33,038 INFO [train.py:850] (2/4) Epoch 23, batch 1300, loss[loss=0.1453, simple_loss=0.2459, pruned_loss=0.02236, over 7374.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2736, pruned_loss=0.04438, over 1460635.94 frames.], batch size: 20, lr: 2.45e-04
2022-07-27 16:08:17,081 INFO [train.py:850] (2/4) Epoch 23, batch 1350, loss[loss=0.1891, simple_loss=0.2879, pruned_loss=0.04509, over 7219.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2741, pruned_loss=0.04419, over 1462248.62 frames.], batch size: 24, lr: 2.45e-04
2022-07-27 16:09:00,083 INFO [train.py:850] (2/4) Epoch 23, batch 1400, loss[loss=0.1962, simple_loss=0.2757, pruned_loss=0.05834, over 7465.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2734, pruned_loss=0.0439, over 1463349.86 frames.], batch size: 20, lr: 2.45e-04
2022-07-27 16:09:44,307 INFO [train.py:850] (2/4) Epoch 23, batch 1450, loss[loss=0.2376, simple_loss=0.3305, pruned_loss=0.07239, over 7229.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2738, pruned_loss=0.04398, over 1463993.34 frames.], batch size: 24, lr: 2.45e-04
2022-07-27 16:10:27,015 INFO [train.py:850] (2/4) Epoch 23, batch 1500, loss[loss=0.1793, simple_loss=0.2706, pruned_loss=0.04398, over 7493.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2746, pruned_loss=0.04452, over 1465147.83 frames.], batch size: 19, lr: 2.45e-04
2022-07-27 16:11:10,473 INFO [train.py:850] (2/4) Epoch 23, batch 1550, loss[loss=0.1887, simple_loss=0.29, pruned_loss=0.0437, over 7166.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2749, pruned_loss=0.04463, over 1464524.32 frames.], batch size: 22, lr: 2.45e-04
2022-07-27 16:11:54,611 INFO [train.py:850] (2/4) Epoch 23, batch 1600, loss[loss=0.155, simple_loss=0.2523, pruned_loss=0.02885, over 7295.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2755, pruned_loss=0.0449, over 1464699.38 frames.], batch size: 19, lr: 2.45e-04
2022-07-27 16:12:37,076 INFO [train.py:850] (2/4) Epoch 23, batch 1650, loss[loss=0.1654, simple_loss=0.257, pruned_loss=0.03685, over 7282.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2757, pruned_loss=0.04504, over 1462953.28 frames.], batch size: 21, lr: 2.45e-04
2022-07-27 16:13:20,916 INFO [train.py:850] (2/4) Epoch 23, batch 1700, loss[loss=0.1692, simple_loss=0.2597, pruned_loss=0.0393, over 7313.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2758, pruned_loss=0.04496, over 1463904.91 frames.], batch size: 18, lr: 2.45e-04
2022-07-27 16:14:04,976 INFO [train.py:850] (2/4) Epoch 23, batch 1750, loss[loss=0.1851, simple_loss=0.2868, pruned_loss=0.04172, over 7392.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2768, pruned_loss=0.04551, over 1465060.54 frames.], batch size: 19, lr: 2.45e-04
2022-07-27 16:14:48,015 INFO [train.py:850] (2/4) Epoch 23, batch 1800, loss[loss=0.1782, simple_loss=0.2611, pruned_loss=0.04762, over 7455.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2767, pruned_loss=0.0453, over 1466614.58 frames.], batch size: 17, lr: 2.45e-04
2022-07-27 16:15:32,418 INFO [train.py:850] (2/4) Epoch 23, batch 1850, loss[loss=0.1863, simple_loss=0.2875, pruned_loss=0.04258, over 7396.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2764, pruned_loss=0.04495, over 1467067.48 frames.], batch size: 20, lr: 2.45e-04
2022-07-27 16:16:15,613 INFO [train.py:850] (2/4) Epoch 23, batch 1900, loss[loss=0.2275, simple_loss=0.3265, pruned_loss=0.06429, over 7380.00 frames.], tot_loss[loss=0.184, simple_loss=0.2769, pruned_loss=0.04553, over 1466164.64 frames.], batch size: 21, lr: 2.45e-04
2022-07-27 16:17:00,368 INFO [train.py:850] (2/4) Epoch 23, batch 1950, loss[loss=0.1942, simple_loss=0.2971, pruned_loss=0.04563, over 7226.00 frames.], tot_loss[loss=0.184, simple_loss=0.2776, pruned_loss=0.0452, over 1465977.28 frames.], batch size: 24, lr: 2.45e-04
2022-07-27 16:17:44,963 INFO [train.py:850] (2/4) Epoch 23, batch 2000, loss[loss=0.1642, simple_loss=0.2503, pruned_loss=0.03907, over 7318.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2772, pruned_loss=0.04524, over 1466712.33 frames.], batch size: 18, lr: 2.45e-04
2022-07-27 16:18:30,263 INFO [train.py:850] (2/4) Epoch 23, batch 2050, loss[loss=0.1721, simple_loss=0.265, pruned_loss=0.03957, over 7275.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2765, pruned_loss=0.04499, over 1468420.20 frames.], batch size: 19, lr: 2.45e-04
2022-07-27 16:19:14,870 INFO [train.py:850] (2/4) Epoch 23, batch 2100, loss[loss=0.1741, simple_loss=0.2708, pruned_loss=0.03873, over 7283.00 frames.], tot_loss[loss=0.184, simple_loss=0.2774, pruned_loss=0.04532, over 1467257.50 frames.], batch size: 21, lr: 2.45e-04
2022-07-27 16:19:58,995 INFO [train.py:850] (2/4) Epoch 23, batch 2150, loss[loss=0.1754, simple_loss=0.2776, pruned_loss=0.03665, over 7474.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2772, pruned_loss=0.0451, over 1467419.68 frames.], batch size: 21, lr: 2.45e-04
2022-07-27 16:20:42,572 INFO [train.py:850] (2/4) Epoch 23, batch 2200, loss[loss=0.1682, simple_loss=0.273, pruned_loss=0.03166, over 7287.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2776, pruned_loss=0.04558, over 1466608.37 frames.], batch size: 20, lr: 2.45e-04
2022-07-27 16:21:27,091 INFO [train.py:850] (2/4) Epoch 23, batch 2250, loss[loss=0.1759, simple_loss=0.2736, pruned_loss=0.03909, over 7305.00 frames.], tot_loss[loss=0.183, simple_loss=0.2766, pruned_loss=0.04474, over 1466205.46 frames.], batch size: 19, lr: 2.45e-04
2022-07-27 16:22:10,403 INFO [train.py:850] (2/4) Epoch 23, batch 2300, loss[loss=0.1785, simple_loss=0.2712, pruned_loss=0.04289, over 7251.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2753, pruned_loss=0.04412, over 1466066.93 frames.], batch size: 30, lr: 2.45e-04
2022-07-27 16:22:53,506 INFO [train.py:850] (2/4) Epoch 23, batch 2350, loss[loss=0.2394, simple_loss=0.331, pruned_loss=0.07395, over 7238.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2755, pruned_loss=0.04452, over 1466680.50 frames.], batch size: 25, lr: 2.44e-04
2022-07-27 16:23:36,106 INFO [train.py:850] (2/4) Epoch 23, batch 2400, loss[loss=0.1981, simple_loss=0.29, pruned_loss=0.0531, over 7487.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2749, pruned_loss=0.04438, over 1465031.61 frames.], batch size: 31, lr: 2.44e-04
2022-07-27 16:24:20,209 INFO [train.py:850] (2/4) Epoch 23, batch 2450, loss[loss=0.147, simple_loss=0.2411, pruned_loss=0.02651, over 7457.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2756, pruned_loss=0.04445, over 1466720.55 frames.], batch size: 17, lr: 2.44e-04
2022-07-27 16:25:02,914 INFO [train.py:850] (2/4) Epoch 23, batch 2500, loss[loss=0.1731, simple_loss=0.2544, pruned_loss=0.04591, over 7445.00 frames.], tot_loss[loss=0.182, simple_loss=0.2754, pruned_loss=0.04435, over 1466402.38 frames.], batch size: 17, lr: 2.44e-04
2022-07-27 16:25:47,005 INFO [train.py:850] (2/4) Epoch 23, batch 2550, loss[loss=0.1746, simple_loss=0.2617, pruned_loss=0.04379, over 7405.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2749, pruned_loss=0.04414, over 1466347.33 frames.], batch size: 19, lr: 2.44e-04
2022-07-27 16:26:30,340 INFO [train.py:850] (2/4) Epoch 23, batch 2600, loss[loss=0.2126, simple_loss=0.3132, pruned_loss=0.05607, over 7294.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2759, pruned_loss=0.0446, over 1465896.09 frames.], batch size: 20, lr: 2.44e-04
2022-07-27 16:27:14,275 INFO [train.py:850] (2/4) Epoch 23, batch 2650, loss[loss=0.1532, simple_loss=0.2444, pruned_loss=0.03101, over 7384.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2755, pruned_loss=0.04464, over 1464971.45 frames.], batch size: 19, lr: 2.44e-04
2022-07-27 16:27:58,083 INFO [train.py:850] (2/4) Epoch 23, batch 2700, loss[loss=0.1983, simple_loss=0.2949, pruned_loss=0.05081, over 7170.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2743, pruned_loss=0.04423, over 1463745.63 frames.], batch size: 22, lr: 2.44e-04
2022-07-27 16:28:41,621 INFO [train.py:850] (2/4) Epoch 23, batch 2750, loss[loss=0.1589, simple_loss=0.243, pruned_loss=0.03742, over 7318.00 frames.], tot_loss[loss=0.18, simple_loss=0.273, pruned_loss=0.0435, over 1464120.90 frames.], batch size: 17, lr: 2.44e-04
2022-07-27 16:29:25,019 INFO [train.py:850] (2/4) Epoch 23, batch 2800, loss[loss=0.2141, simple_loss=0.3041, pruned_loss=0.06205, over 7371.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2734, pruned_loss=0.04381, over 1464359.15 frames.], batch size: 71, lr: 2.44e-04
2022-07-27 16:30:08,388 INFO [train.py:850] (2/4) Epoch 23, batch 2850, loss[loss=0.1883, simple_loss=0.282, pruned_loss=0.04734, over 7215.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2723, pruned_loss=0.04301, over 1463841.87 frames.], batch size: 25, lr: 2.44e-04
2022-07-27 16:30:51,444 INFO [train.py:850] (2/4) Epoch 23, batch 2900, loss[loss=0.1397, simple_loss=0.2353, pruned_loss=0.02202, over 7479.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2733, pruned_loss=0.04316, over 1463976.75 frames.], batch size: 20, lr: 2.44e-04
2022-07-27 16:31:35,025 INFO [train.py:850] (2/4) Epoch 23, batch 2950, loss[loss=0.1616, simple_loss=0.24, pruned_loss=0.04158, over 7292.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2729, pruned_loss=0.04365, over 1463849.32 frames.], batch size: 17, lr: 2.44e-04
2022-07-27 16:32:18,702 INFO [train.py:850] (2/4) Epoch 23, batch 3000, loss[loss=0.1831, simple_loss=0.2775, pruned_loss=0.04435, over 7471.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2729, pruned_loss=0.04374, over 1464222.84 frames.], batch size: 21, lr: 2.44e-04
2022-07-27 16:32:18,703 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 16:32:41,505 INFO [train.py:879] (2/4) Epoch 23, validation: loss=0.1905, simple_loss=0.2831, pruned_loss=0.04899, over 924787.00 frames.
2022-07-27 16:33:27,456 INFO [train.py:850] (2/4) Epoch 23, batch 3050, loss[loss=0.2053, simple_loss=0.294, pruned_loss=0.05825, over 7409.00 frames.], tot_loss[loss=0.18, simple_loss=0.273, pruned_loss=0.04345, over 1464684.86 frames.], batch size: 70, lr: 2.44e-04
2022-07-27 16:34:10,711 INFO [train.py:850] (2/4) Epoch 23, batch 3100, loss[loss=0.1642, simple_loss=0.2563, pruned_loss=0.03604, over 7289.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2747, pruned_loss=0.04421, over 1463824.14 frames.], batch size: 20, lr: 2.44e-04
2022-07-27 16:34:55,203 INFO [train.py:850] (2/4) Epoch 23, batch 3150, loss[loss=0.1594, simple_loss=0.2523, pruned_loss=0.03321, over 7189.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2736, pruned_loss=0.04366, over 1464317.29 frames.], batch size: 19, lr: 2.44e-04
2022-07-27 16:35:38,273 INFO [train.py:850] (2/4) Epoch 23, batch 3200, loss[loss=0.2144, simple_loss=0.2964, pruned_loss=0.06624, over 7253.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2735, pruned_loss=0.04354, over 1464459.02 frames.], batch size: 25, lr: 2.44e-04
2022-07-27 16:36:22,196 INFO [train.py:850] (2/4) Epoch 23, batch 3250, loss[loss=0.1916, simple_loss=0.285, pruned_loss=0.0491, over 7389.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2737, pruned_loss=0.04389, over 1464577.85 frames.], batch size: 21, lr: 2.44e-04
2022-07-27 16:37:05,081 INFO [train.py:850] (2/4) Epoch 23, batch 3300, loss[loss=0.1468, simple_loss=0.2372, pruned_loss=0.02824, over 7462.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2732, pruned_loss=0.04316, over 1464412.00 frames.], batch size: 17, lr: 2.44e-04
2022-07-27 16:37:49,569 INFO [train.py:850] (2/4) Epoch 23, batch 3350, loss[loss=0.1996, simple_loss=0.2976, pruned_loss=0.05081, over 7480.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2734, pruned_loss=0.04313, over 1465335.31 frames.], batch size: 39, lr: 2.44e-04
2022-07-27 16:38:35,452 INFO [train.py:850] (2/4) Epoch 23, batch 3400, loss[loss=0.1706, simple_loss=0.2728, pruned_loss=0.03418, over 7287.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2736, pruned_loss=0.04304, over 1465528.50 frames.], batch size: 21, lr: 2.44e-04
2022-07-27 16:39:20,930 INFO [train.py:850] (2/4) Epoch 23, batch 3450, loss[loss=0.1953, simple_loss=0.3012, pruned_loss=0.0447, over 7371.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2732, pruned_loss=0.04299, over 1465579.04 frames.], batch size: 21, lr: 2.44e-04
2022-07-27 16:40:06,537 INFO [train.py:850] (2/4) Epoch 23, batch 3500, loss[loss=0.1558, simple_loss=0.2424, pruned_loss=0.03462, over 7452.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2731, pruned_loss=0.04268, over 1465791.87 frames.], batch size: 18, lr: 2.44e-04
2022-07-27 16:40:51,719 INFO [train.py:850] (2/4) Epoch 23, batch 3550, loss[loss=0.1915, simple_loss=0.2878, pruned_loss=0.04764, over 7386.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2736, pruned_loss=0.0428, over 1465553.95 frames.], batch size: 21, lr: 2.44e-04
2022-07-27 16:41:35,453 INFO [train.py:850] (2/4) Epoch 23, batch 3600, loss[loss=0.1706, simple_loss=0.2597, pruned_loss=0.04071, over 7293.00 frames.], tot_loss[loss=0.18, simple_loss=0.2738, pruned_loss=0.04311, over 1464184.57 frames.], batch size: 20, lr: 2.44e-04
2022-07-27 16:42:19,440 INFO [train.py:850] (2/4) Epoch 23, batch 3650, loss[loss=0.1972, simple_loss=0.293, pruned_loss=0.05074, over 7445.00 frames.], tot_loss[loss=0.181, simple_loss=0.2747, pruned_loss=0.04364, over 1464569.05 frames.], batch size: 39, lr: 2.44e-04
2022-07-27 16:43:02,608 INFO [train.py:850] (2/4) Epoch 23, batch 3700, loss[loss=0.1635, simple_loss=0.2575, pruned_loss=0.03473, over 7290.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2755, pruned_loss=0.04365, over 1465881.15 frames.], batch size: 18, lr: 2.44e-04
2022-07-27 16:43:46,160 INFO [train.py:850] (2/4) Epoch 23, batch 3750, loss[loss=0.1967, simple_loss=0.2817, pruned_loss=0.05585, over 7303.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2744, pruned_loss=0.04341, over 1466035.97 frames.], batch size: 18, lr: 2.44e-04
2022-07-27 16:44:30,004 INFO [train.py:850] (2/4) Epoch 23, batch 3800, loss[loss=0.1825, simple_loss=0.2699, pruned_loss=0.04756, over 7101.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2744, pruned_loss=0.04347, over 1466217.94 frames.], batch size: 18, lr: 2.44e-04
2022-07-27 16:45:13,136 INFO [train.py:850] (2/4) Epoch 23, batch 3850, loss[loss=0.172, simple_loss=0.2512, pruned_loss=0.04642, over 7320.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2743, pruned_loss=0.04373, over 1465333.51 frames.], batch size: 18, lr: 2.44e-04
2022-07-27 16:45:56,500 INFO [train.py:850] (2/4) Epoch 23, batch 3900, loss[loss=0.1376, simple_loss=0.2269, pruned_loss=0.02414, over 7460.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2751, pruned_loss=0.04427, over 1465012.24 frames.], batch size: 18, lr: 2.44e-04
2022-07-27 16:46:39,763 INFO [train.py:850] (2/4) Epoch 23, batch 3950, loss[loss=0.1894, simple_loss=0.2889, pruned_loss=0.04498, over 7437.00 frames.], tot_loss[loss=0.1803, simple_loss=0.274, pruned_loss=0.04328, over 1465055.29 frames.], batch size: 31, lr: 2.44e-04
2022-07-27 16:47:23,063 INFO [train.py:850] (2/4) Epoch 23, batch 4000, loss[loss=0.1622, simple_loss=0.2449, pruned_loss=0.03975, over 7263.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2742, pruned_loss=0.04325, over 1465920.16 frames.], batch size: 16, lr: 2.43e-04
2022-07-27 16:48:07,014 INFO [train.py:850] (2/4) Epoch 23, batch 4050, loss[loss=0.1661, simple_loss=0.2695, pruned_loss=0.0314, over 7287.00 frames.], tot_loss[loss=0.1794, simple_loss=0.273, pruned_loss=0.04288, over 1465641.29 frames.], batch size: 21, lr: 2.43e-04
2022-07-27 16:48:50,255 INFO [train.py:850] (2/4) Epoch 23, batch 4100, loss[loss=0.1969, simple_loss=0.2807, pruned_loss=0.05654, over 7149.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2728, pruned_loss=0.04328, over 1465313.55 frames.], batch size: 17, lr: 2.43e-04
2022-07-27 16:49:34,444 INFO [train.py:850] (2/4) Epoch 23, batch 4150, loss[loss=0.1903, simple_loss=0.2759, pruned_loss=0.05229, over 7098.00 frames.], tot_loss[loss=0.1823, simple_loss=0.275, pruned_loss=0.04483, over 1465303.64 frames.], batch size: 18, lr: 2.43e-04
2022-07-27 16:50:17,246 INFO [train.py:850] (2/4) Epoch 23, batch 4200, loss[loss=0.1927, simple_loss=0.2817, pruned_loss=0.05184, over 7477.00 frames.], tot_loss[loss=0.1833, simple_loss=0.275, pruned_loss=0.04578, over 1465363.79 frames.], batch size: 21, lr: 2.43e-04
2022-07-27 16:51:02,126 INFO [train.py:850] (2/4) Epoch 23, batch 4250, loss[loss=0.1749, simple_loss=0.2708, pruned_loss=0.03953, over 7306.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2758, pruned_loss=0.04645, over 1466402.06 frames.], batch size: 20, lr: 2.43e-04
2022-07-27 16:51:45,300 INFO [train.py:850] (2/4) Epoch 23, batch 4300, loss[loss=0.1492, simple_loss=0.2431, pruned_loss=0.02766, over 7477.00 frames.], tot_loss[loss=0.1851, simple_loss=0.276, pruned_loss=0.04706, over 1467680.82 frames.], batch size: 20, lr: 2.43e-04
2022-07-27 16:52:44,398 INFO [train.py:850] (2/4) Epoch 23, batch 4350, loss[loss=0.1867, simple_loss=0.2705, pruned_loss=0.05146, over 7391.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2752, pruned_loss=0.04681, over 1467243.31 frames.], batch size: 19, lr: 2.43e-04
2022-07-27 16:53:27,330 INFO [train.py:850] (2/4) Epoch 23, batch 4400, loss[loss=0.1623, simple_loss=0.2479, pruned_loss=0.03834, over 7200.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2762, pruned_loss=0.04772, over 1466324.17 frames.], batch size: 18, lr: 2.43e-04
2022-07-27 16:54:13,399 INFO [train.py:850] (2/4) Epoch 23, batch 4450, loss[loss=0.1692, simple_loss=0.2493, pruned_loss=0.04456, over 7303.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2771, pruned_loss=0.0492, over 1465796.05 frames.], batch size: 19, lr: 2.43e-04
2022-07-27 16:54:58,331 INFO [train.py:850] (2/4) Epoch 23, batch 4500, loss[loss=0.1615, simple_loss=0.2404, pruned_loss=0.04133, over 7197.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2763, pruned_loss=0.04933, over 1464990.33 frames.], batch size: 18, lr: 2.43e-04
2022-07-27 16:55:42,467 INFO [train.py:850] (2/4) Epoch 23, batch 4550, loss[loss=0.164, simple_loss=0.2517, pruned_loss=0.03812, over 7373.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2772, pruned_loss=0.05002, over 1465996.04 frames.], batch size: 20, lr: 2.43e-04
2022-07-27 16:56:27,161 INFO [train.py:850] (2/4) Epoch 23, batch 4600, loss[loss=0.1941, simple_loss=0.282, pruned_loss=0.05309, over 7181.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2776, pruned_loss=0.05038, over 1465607.55 frames.], batch size: 21, lr: 2.43e-04
2022-07-27 16:57:12,079 INFO [train.py:850] (2/4) Epoch 23, batch 4650, loss[loss=0.1751, simple_loss=0.2556, pruned_loss=0.04735, over 7174.00 frames.], tot_loss[loss=0.1899, simple_loss=0.278, pruned_loss=0.05092, over 1466075.08 frames.], batch size: 17, lr: 2.43e-04
2022-07-27 16:57:55,725 INFO [train.py:850] (2/4) Epoch 23, batch 4700, loss[loss=0.2313, simple_loss=0.3233, pruned_loss=0.06966, over 7237.00 frames.], tot_loss[loss=0.1923, simple_loss=0.28, pruned_loss=0.05229, over 1465857.87 frames.], batch size: 25, lr: 2.43e-04
2022-07-27 16:58:38,571 INFO [train.py:850] (2/4) Epoch 23, batch 4750, loss[loss=0.1828, simple_loss=0.2825, pruned_loss=0.04156, over 7196.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2786, pruned_loss=0.05187, over 1465989.94 frames.], batch size: 21, lr: 2.43e-04
2022-07-27 16:59:21,119 INFO [train.py:850] (2/4) Epoch 23, batch 4800, loss[loss=0.2474, simple_loss=0.3165, pruned_loss=0.08921, over 7478.00 frames.], tot_loss[loss=0.1913, simple_loss=0.278, pruned_loss=0.05229, over 1465944.28 frames.], batch size: 23, lr: 2.43e-04
2022-07-27 17:00:06,571 INFO [train.py:850] (2/4) Epoch 23, batch 4850, loss[loss=0.2016, simple_loss=0.2881, pruned_loss=0.05748, over 7298.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2784, pruned_loss=0.05266, over 1465643.64 frames.], batch size: 20, lr: 2.43e-04
2022-07-27 17:00:49,396 INFO [train.py:850] (2/4) Epoch 23, batch 4900, loss[loss=0.165, simple_loss=0.2519, pruned_loss=0.0391, over 7292.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2773, pruned_loss=0.05221, over 1466176.52 frames.], batch size: 20, lr: 2.43e-04
2022-07-27 17:01:33,669 INFO [train.py:850] (2/4) Epoch 23, batch 4950, loss[loss=0.1705, simple_loss=0.2595, pruned_loss=0.04068, over 7491.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2762, pruned_loss=0.05218, over 1466162.10 frames.], batch size: 19, lr: 2.43e-04
2022-07-27 17:02:17,336 INFO [train.py:850] (2/4) Epoch 23, batch 5000, loss[loss=0.1842, simple_loss=0.276, pruned_loss=0.0462, over 7488.00 frames.], tot_loss[loss=0.191, simple_loss=0.277, pruned_loss=0.05255, over 1466060.15 frames.], batch size: 26, lr: 2.43e-04
2022-07-27 17:03:00,594 INFO [train.py:850] (2/4) Epoch 23, batch 5050, loss[loss=0.1835, simple_loss=0.274, pruned_loss=0.04654, over 7177.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2772, pruned_loss=0.05288, over 1465379.85 frames.], batch size: 21, lr: 2.43e-04
2022-07-27 17:03:43,965 INFO [train.py:850] (2/4) Epoch 23, batch 5100, loss[loss=0.1763, simple_loss=0.2578, pruned_loss=0.04735, over 7103.00 frames.], tot_loss[loss=0.192, simple_loss=0.2773, pruned_loss=0.0533, over 1465125.47 frames.], batch size: 18, lr: 2.43e-04
2022-07-27 17:04:27,472 INFO [train.py:850] (2/4) Epoch 23, batch 5150, loss[loss=0.2071, simple_loss=0.2875, pruned_loss=0.06339, over 7161.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2767, pruned_loss=0.05316, over 1465750.39 frames.], batch size: 17, lr: 2.43e-04
2022-07-27 17:05:11,395 INFO [train.py:850] (2/4) Epoch 23, batch 5200, loss[loss=0.2319, simple_loss=0.316, pruned_loss=0.07385, over 7249.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2776, pruned_loss=0.05306, over 1466280.27 frames.], batch size: 27, lr: 2.43e-04
2022-07-27 17:05:55,176 INFO [train.py:850] (2/4) Epoch 23, batch 5250, loss[loss=0.2388, simple_loss=0.3029, pruned_loss=0.08731, over 7384.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2769, pruned_loss=0.05288, over 1466864.08 frames.], batch size: 20, lr: 2.43e-04
2022-07-27 17:06:38,193 INFO [train.py:850] (2/4) Epoch 23, batch 5300, loss[loss=0.1867, simple_loss=0.2636, pruned_loss=0.05488, over 7100.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2785, pruned_loss=0.05355, over 1466307.65 frames.], batch size: 18, lr: 2.43e-04
2022-07-27 17:07:22,164 INFO [train.py:850] (2/4) Epoch 23, batch 5350, loss[loss=0.1549, simple_loss=0.246, pruned_loss=0.03187, over 7493.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2774, pruned_loss=0.05311, over 1466359.84 frames.], batch size: 19, lr: 2.43e-04
2022-07-27 17:08:06,741 INFO [train.py:850] (2/4) Epoch 23, batch 5400, loss[loss=0.1612, simple_loss=0.2428, pruned_loss=0.03981, over 7266.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2772, pruned_loss=0.05288, over 1466087.06 frames.], batch size: 16, lr: 2.43e-04
2022-07-27 17:08:51,406 INFO [train.py:850] (2/4) Epoch 23, batch 5450, loss[loss=0.1791, simple_loss=0.2758, pruned_loss=0.04123, over 7415.00 frames.], tot_loss[loss=0.192, simple_loss=0.2777, pruned_loss=0.05315, over 1466031.09 frames.], batch size: 22, lr: 2.43e-04
2022-07-27 17:09:35,319 INFO [train.py:850] (2/4) Epoch 23, batch 5500, loss[loss=0.193, simple_loss=0.2767, pruned_loss=0.05462, over 7332.00 frames.], tot_loss[loss=0.192, simple_loss=0.2777, pruned_loss=0.05318, over 1465668.02 frames.], batch size: 23, lr: 2.43e-04
2022-07-27 17:10:19,387 INFO [train.py:850] (2/4) Epoch 23, batch 5550, loss[loss=0.1662, simple_loss=0.2532, pruned_loss=0.03962, over 7462.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2775, pruned_loss=0.05258, over 1465642.31 frames.], batch size: 17, lr: 2.43e-04
2022-07-27 17:11:02,566 INFO [train.py:850] (2/4) Epoch 23, batch 5600, loss[loss=0.2403, simple_loss=0.3215, pruned_loss=0.0796, over 7187.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2777, pruned_loss=0.05273, over 1466142.76 frames.], batch size: 21, lr: 2.43e-04
2022-07-27 17:11:46,994 INFO [train.py:850] (2/4) Epoch 23, batch 5650, loss[loss=0.1707, simple_loss=0.2405, pruned_loss=0.05049, over 7328.00 frames.], tot_loss[loss=0.191, simple_loss=0.2774, pruned_loss=0.0523, over 1466205.96 frames.], batch size: 17, lr: 2.42e-04
2022-07-27 17:12:29,603 INFO [train.py:850] (2/4) Epoch 23, batch 5700, loss[loss=0.2275, simple_loss=0.3023, pruned_loss=0.07635, over 7485.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2773, pruned_loss=0.05211, over 1466675.17 frames.], batch size: 23, lr: 2.42e-04
2022-07-27 17:13:13,569 INFO [train.py:850] (2/4) Epoch 23, batch 5750, loss[loss=0.1793, simple_loss=0.2549, pruned_loss=0.05188, over 7460.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2773, pruned_loss=0.05182, over 1466450.03 frames.], batch size: 17, lr: 2.42e-04
2022-07-27 17:13:58,188 INFO [train.py:850] (2/4) Epoch 23, batch 5800, loss[loss=0.1841, simple_loss=0.2793, pruned_loss=0.04444, over 7209.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2771, pruned_loss=0.05172, over 1466567.78 frames.], batch size: 18, lr: 2.42e-04
2022-07-27 17:14:41,752 INFO [train.py:850] (2/4) Epoch 23, batch 5850, loss[loss=0.1815, simple_loss=0.2762, pruned_loss=0.04345, over 7178.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2772, pruned_loss=0.05191, over 1466428.57 frames.], batch size: 21, lr: 2.42e-04
2022-07-27 17:15:25,120 INFO [train.py:850] (2/4) Epoch 23, batch 5900, loss[loss=0.1899, simple_loss=0.2848, pruned_loss=0.04747, over 7292.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2769, pruned_loss=0.05148, over 1466784.95 frames.], batch size: 22, lr: 2.42e-04
2022-07-27 17:16:08,975 INFO [train.py:850] (2/4) Epoch 23, batch 5950, loss[loss=0.1597, simple_loss=0.2531, pruned_loss=0.03315, over 7279.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2766, pruned_loss=0.05129, over 1466362.90 frames.], batch size: 30, lr: 2.42e-04
2022-07-27 17:16:53,724 INFO [train.py:850] (2/4) Epoch 23, batch 6000, loss[loss=0.1963, simple_loss=0.2973, pruned_loss=0.0476, over 7378.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2788, pruned_loss=0.05226, over 1466620.66 frames.], batch size: 21, lr: 2.42e-04
2022-07-27 17:16:53,725 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 17:17:16,414 INFO [train.py:879] (2/4) Epoch 23, validation: loss=0.1878, simple_loss=0.2808, pruned_loss=0.04737, over 924787.00 frames.
2022-07-27 17:18:00,169 INFO [train.py:850] (2/4) Epoch 23, batch 6050, loss[loss=0.206, simple_loss=0.2869, pruned_loss=0.06254, over 7374.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2781, pruned_loss=0.05226, over 1465819.05 frames.], batch size: 73, lr: 2.42e-04
2022-07-27 17:18:43,199 INFO [train.py:850] (2/4) Epoch 23, batch 6100, loss[loss=0.2088, simple_loss=0.3055, pruned_loss=0.05607, over 7248.00 frames.], tot_loss[loss=0.192, simple_loss=0.2784, pruned_loss=0.05286, over 1466496.42 frames.], batch size: 25, lr: 2.42e-04
2022-07-27 17:19:26,480 INFO [train.py:850] (2/4) Epoch 23, batch 6150, loss[loss=0.1478, simple_loss=0.2328, pruned_loss=0.03142, over 7445.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2783, pruned_loss=0.053, over 1467204.58 frames.], batch size: 17, lr: 2.42e-04
2022-07-27 17:20:10,476 INFO [train.py:850] (2/4) Epoch 23, batch 6200, loss[loss=0.1889, simple_loss=0.2839, pruned_loss=0.0469, over 7234.00 frames.], tot_loss[loss=0.1923, simple_loss=0.279, pruned_loss=0.05285, over 1467520.90 frames.], batch size: 25, lr: 2.42e-04
2022-07-27 17:20:54,797 INFO [train.py:850] (2/4) Epoch 23, batch 6250, loss[loss=0.166, simple_loss=0.2463, pruned_loss=0.04283, over 7434.00 frames.], tot_loss[loss=0.192, simple_loss=0.2788, pruned_loss=0.05261, over 1467784.83 frames.], batch size: 18, lr: 2.42e-04
2022-07-27 17:21:39,019 INFO [train.py:850] (2/4) Epoch 23, batch 6300, loss[loss=0.2321, simple_loss=0.3192, pruned_loss=0.07254, over 7293.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2784, pruned_loss=0.05255, over 1467794.07 frames.], batch size: 21, lr: 2.42e-04
2022-07-27 17:22:22,698 INFO [train.py:850] (2/4) Epoch 23, batch 6350, loss[loss=0.2326, simple_loss=0.3134, pruned_loss=0.0759, over 7430.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2789, pruned_loss=0.05295, over 1467165.43 frames.], batch size: 69, lr: 2.42e-04
2022-07-27 17:23:06,045 INFO [train.py:850] (2/4) Epoch 23, batch 6400, loss[loss=0.1914, simple_loss=0.2684, pruned_loss=0.05719, over 7265.00 frames.], tot_loss[loss=0.192, simple_loss=0.2786, pruned_loss=0.05271, over 1467127.69 frames.], batch size: 16, lr: 2.42e-04
2022-07-27 17:23:50,456 INFO [train.py:850] (2/4) Epoch 23, batch 6450, loss[loss=0.1731, simple_loss=0.2633, pruned_loss=0.04143, over 7189.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2782, pruned_loss=0.05263, over 1465563.59 frames.], batch size: 19, lr: 2.42e-04
2022-07-27 17:24:33,625 INFO [train.py:850] (2/4) Epoch 23, batch 6500, loss[loss=0.187, simple_loss=0.2601, pruned_loss=0.05696, over 7318.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2767, pruned_loss=0.05183, over 1466006.84 frames.], batch size: 17, lr: 2.42e-04
2022-07-27 17:25:17,247 INFO [train.py:850] (2/4) Epoch 23, batch 6550, loss[loss=0.1673, simple_loss=0.2531, pruned_loss=0.04079, over 7299.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2765, pruned_loss=0.05197, over 1465775.63 frames.], batch size: 17, lr: 2.42e-04
2022-07-27 17:26:00,868 INFO [train.py:850] (2/4) Epoch 23, batch 6600, loss[loss=0.2139, simple_loss=0.307, pruned_loss=0.06043, over 7335.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2762, pruned_loss=0.05152, over 1466464.29 frames.], batch size: 27, lr: 2.42e-04
2022-07-27 17:26:44,884 INFO [train.py:850] (2/4) Epoch 23, batch 6650, loss[loss=0.2074, simple_loss=0.3002, pruned_loss=0.05729, over 7371.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2759, pruned_loss=0.05141, over 1466966.89 frames.], batch size: 21, lr: 2.42e-04
2022-07-27 17:27:28,089 INFO [train.py:850] (2/4) Epoch 23, batch 6700, loss[loss=0.2024, simple_loss=0.2955, pruned_loss=0.05467, over 7198.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2783, pruned_loss=0.05292, over 1466949.32 frames.], batch size: 25, lr: 2.42e-04
2022-07-27 17:28:12,509 INFO [train.py:850] (2/4) Epoch 23, batch 6750, loss[loss=0.1991, simple_loss=0.28, pruned_loss=0.05916, over 7192.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2792, pruned_loss=0.05348, over 1467103.35 frames.], batch size: 18, lr: 2.42e-04
2022-07-27 17:28:56,689 INFO [train.py:850] (2/4) Epoch 23, batch 6800, loss[loss=0.1971, simple_loss=0.2882, pruned_loss=0.05299, over 7374.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2785, pruned_loss=0.05291, over 1466460.72 frames.], batch size: 21, lr: 2.42e-04
2022-07-27 17:29:41,064 INFO [train.py:850] (2/4) Epoch 23, batch 6850, loss[loss=0.1901, simple_loss=0.2876, pruned_loss=0.0463, over 7195.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2775, pruned_loss=0.05262, over 1466502.00 frames.], batch size: 22, lr: 2.42e-04
2022-07-27 17:30:25,902 INFO [train.py:850] (2/4) Epoch 23, batch 6900, loss[loss=0.1963, simple_loss=0.2791, pruned_loss=0.05678, over 7429.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2775, pruned_loss=0.05235, over 1466223.13 frames.], batch size: 39, lr: 2.42e-04
2022-07-27 17:31:11,860 INFO [train.py:850] (2/4) Epoch 23, batch 6950, loss[loss=0.2155, simple_loss=0.2959, pruned_loss=0.06755, over 7218.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2777, pruned_loss=0.05278, over 1465610.40 frames.], batch size: 25, lr: 2.42e-04
2022-07-27 17:31:58,362 INFO [train.py:850] (2/4) Epoch 23, batch 7000, loss[loss=0.1764, simple_loss=0.2677, pruned_loss=0.0425, over 7489.00 frames.], tot_loss[loss=0.1906, simple_loss=0.277, pruned_loss=0.05216, over 1466823.79 frames.], batch size: 19, lr: 2.42e-04
2022-07-27 17:32:43,237 INFO [train.py:850] (2/4) Epoch 23, batch 7050, loss[loss=0.2258, simple_loss=0.3102, pruned_loss=0.07073, over 7177.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2762, pruned_loss=0.05153, over 1466469.03 frames.], batch size: 21, lr: 2.42e-04
2022-07-27 17:33:27,636 INFO [train.py:850] (2/4) Epoch 23, batch 7100, loss[loss=0.1872, simple_loss=0.2687, pruned_loss=0.05283, over 7453.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2752, pruned_loss=0.05116, over 1466435.53 frames.], batch size: 24, lr: 2.42e-04
2022-07-27 17:34:12,257 INFO [train.py:850] (2/4) Epoch 23, batch 7150, loss[loss=0.1895, simple_loss=0.2716, pruned_loss=0.05369, over 7377.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2759, pruned_loss=0.05227, over 1466483.41 frames.], batch size: 20, lr: 2.42e-04
2022-07-27 17:34:55,305 INFO [train.py:850] (2/4) Epoch 23, batch 7200, loss[loss=0.1798, simple_loss=0.273, pruned_loss=0.04334, over 7377.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2756, pruned_loss=0.05206, over 1466375.12 frames.], batch size: 21, lr: 2.42e-04
2022-07-27 17:35:39,911 INFO [train.py:850] (2/4) Epoch 23, batch 7250, loss[loss=0.225, simple_loss=0.314, pruned_loss=0.06801, over 7392.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2756, pruned_loss=0.05167, over 1466134.96 frames.], batch size: 39, lr: 2.42e-04
2022-07-27 17:36:24,409 INFO [train.py:850] (2/4) Epoch 23, batch 7300, loss[loss=0.1995, simple_loss=0.2823, pruned_loss=0.05838, over 7209.00 frames.], tot_loss[loss=0.1908, simple_loss=0.277, pruned_loss=0.05228, over 1465623.53 frames.], batch size: 19, lr: 2.41e-04
2022-07-27 17:37:09,118 INFO [train.py:850] (2/4) Epoch 23, batch 7350, loss[loss=0.1931, simple_loss=0.28, pruned_loss=0.05308, over 7199.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2764, pruned_loss=0.05214, over 1466255.42 frames.], batch size: 20, lr: 2.41e-04
2022-07-27 17:37:53,297 INFO [train.py:850] (2/4) Epoch 23, batch 7400, loss[loss=0.1924, simple_loss=0.2836, pruned_loss=0.05064, over 7469.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2764, pruned_loss=0.05214, over 1466787.13 frames.], batch size: 39, lr: 2.41e-04
2022-07-27 17:38:36,928 INFO [train.py:850] (2/4) Epoch 23, batch 7450, loss[loss=0.2006, simple_loss=0.2928, pruned_loss=0.05417, over 7188.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2759, pruned_loss=0.05139, over 1466982.76 frames.], batch size: 21, lr: 2.41e-04
2022-07-27 17:39:21,061 INFO [train.py:850] (2/4) Epoch 23, batch 7500, loss[loss=0.1544, simple_loss=0.2488, pruned_loss=0.02996, over 7197.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2756, pruned_loss=0.05144, over 1467594.20 frames.], batch size: 19, lr: 2.41e-04
2022-07-27 17:40:04,900 INFO [train.py:850] (2/4) Epoch 23, batch 7550, loss[loss=0.1527, simple_loss=0.2408, pruned_loss=0.0323, over 7446.00 frames.], tot_loss[loss=0.189, simple_loss=0.2755, pruned_loss=0.05132, over 1468373.12 frames.], batch size: 18, lr: 2.41e-04
2022-07-27 17:40:49,328 INFO [train.py:850] (2/4) Epoch 23, batch 7600, loss[loss=0.1656, simple_loss=0.2578, pruned_loss=0.0367, over 7297.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2757, pruned_loss=0.05168, over 1467808.77 frames.], batch size: 19, lr: 2.41e-04
2022-07-27 17:41:34,190 INFO [train.py:850] (2/4) Epoch 23, batch 7650, loss[loss=0.1638, simple_loss=0.2575, pruned_loss=0.03503, over 7474.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2764, pruned_loss=0.0522, over 1467847.09 frames.], batch size: 31, lr: 2.41e-04
2022-07-27 17:42:18,954 INFO [train.py:850] (2/4) Epoch 23, batch 7700, loss[loss=0.1735, simple_loss=0.2488, pruned_loss=0.0491, over 7292.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2771, pruned_loss=0.05256, over 1467500.43 frames.], batch size: 17, lr: 2.41e-04
2022-07-27 17:43:02,378 INFO [train.py:850] (2/4) Epoch 23, batch 7750, loss[loss=0.2025, simple_loss=0.2794, pruned_loss=0.06284, over 7200.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2775, pruned_loss=0.05283, over 1467132.75 frames.], batch size: 19, lr: 2.41e-04
2022-07-27 17:43:45,396 INFO [train.py:850] (2/4) Epoch 23, batch 7800, loss[loss=0.1778, simple_loss=0.2679, pruned_loss=0.0439, over 7203.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2769, pruned_loss=0.05276, over 1467661.48 frames.], batch size: 19, lr: 2.41e-04
2022-07-27 17:44:29,890 INFO [train.py:850] (2/4) Epoch 23, batch 7850, loss[loss=0.1781, simple_loss=0.2664, pruned_loss=0.04486, over 7294.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2767, pruned_loss=0.05246, over 1465994.98 frames.], batch size: 20, lr: 2.41e-04
2022-07-27 17:45:14,252 INFO [train.py:850] (2/4) Epoch 23, batch 7900, loss[loss=0.2057, simple_loss=0.2831, pruned_loss=0.06414, over 7407.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2768, pruned_loss=0.0517, over 1466059.57 frames.], batch size: 22, lr: 2.41e-04
2022-07-27 17:46:00,054 INFO [train.py:850] (2/4) Epoch 23, batch 7950, loss[loss=0.2098, simple_loss=0.2992, pruned_loss=0.0602, over 7388.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2755, pruned_loss=0.05085, over 1464739.24 frames.], batch size: 39, lr: 2.41e-04
2022-07-27 17:46:45,112 INFO [train.py:850] (2/4) Epoch 23, batch 8000, loss[loss=0.139, simple_loss=0.2213, pruned_loss=0.0284, over 7386.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2766, pruned_loss=0.05134, over 1464283.08 frames.], batch size: 19, lr: 2.41e-04
2022-07-27 17:47:29,662 INFO [train.py:850] (2/4) Epoch 23, batch 8050, loss[loss=0.177, simple_loss=0.2648, pruned_loss=0.04461, over 7340.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2762, pruned_loss=0.05082, over 1464302.71 frames.], batch size: 27, lr: 2.41e-04
2022-07-27 17:48:12,796 INFO [train.py:850] (2/4) Epoch 23, batch 8100, loss[loss=0.1518, simple_loss=0.2452, pruned_loss=0.02917, over 7205.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2761, pruned_loss=0.05061, over 1464795.59 frames.], batch size: 18, lr: 2.41e-04
2022-07-27 17:48:56,002 INFO [train.py:850] (2/4) Epoch 23, batch 8150, loss[loss=0.187, simple_loss=0.2812, pruned_loss=0.04639, over 7304.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2761, pruned_loss=0.05054, over 1465904.03 frames.], batch size: 27, lr: 2.41e-04
2022-07-27 17:49:40,524 INFO [train.py:850] (2/4) Epoch 23, batch 8200, loss[loss=0.1921, simple_loss=0.2829, pruned_loss=0.05067, over 7475.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2765, pruned_loss=0.05087, over 1466039.78 frames.], batch size: 21, lr: 2.41e-04
2022-07-27 17:50:25,301 INFO [train.py:850] (2/4) Epoch 23, batch 8250, loss[loss=0.1845, simple_loss=0.2798, pruned_loss=0.04455, over 7479.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2771, pruned_loss=0.05139, over 1466575.07 frames.], batch size: 20, lr: 2.41e-04
2022-07-27 17:51:09,447 INFO [train.py:850] (2/4) Epoch 23, batch 8300, loss[loss=0.2413, simple_loss=0.3124, pruned_loss=0.08503, over 7361.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2763, pruned_loss=0.05103, over 1465063.17 frames.], batch size: 38, lr: 2.41e-04
2022-07-27 17:52:09,158 INFO [train.py:850] (2/4) Epoch 23, batch 8350, loss[loss=0.1893, simple_loss=0.2838, pruned_loss=0.04744, over 7417.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2766, pruned_loss=0.05079, over 1465084.18 frames.], batch size: 22, lr: 2.41e-04
2022-07-27 17:52:54,475 INFO [train.py:850] (2/4) Epoch 23, batch 8400, loss[loss=0.1843, simple_loss=0.2561, pruned_loss=0.05621, over 7485.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2768, pruned_loss=0.05112, over 1466051.36 frames.], batch size: 20, lr: 2.41e-04
2022-07-27 17:53:38,972 INFO [train.py:850] (2/4) Epoch 23, batch 8450, loss[loss=0.1612, simple_loss=0.254, pruned_loss=0.03418, over 7379.00 frames.], tot_loss[loss=0.188, simple_loss=0.275, pruned_loss=0.05046, over 1466094.00 frames.], batch size: 20, lr: 2.41e-04
2022-07-27 17:54:23,546 INFO [train.py:850] (2/4) Epoch 23, batch 8500, loss[loss=0.1568, simple_loss=0.2504, pruned_loss=0.03159, over 7201.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2759, pruned_loss=0.05043, over 1466467.16 frames.], batch size: 20, lr: 2.41e-04
2022-07-27 17:55:08,288 INFO [train.py:850] (2/4) Epoch 23, batch 8550, loss[loss=0.2115, simple_loss=0.3003, pruned_loss=0.06138, over 7175.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2768, pruned_loss=0.05142, over 1465482.19 frames.], batch size: 21, lr: 2.41e-04
2022-07-27 17:55:51,770 INFO [train.py:850] (2/4) Epoch 23, batch 8600, loss[loss=0.1962, simple_loss=0.2863, pruned_loss=0.05302, over 7449.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2771, pruned_loss=0.0519, over 1465179.64 frames.], batch size: 40, lr: 2.41e-04
2022-07-27 17:56:36,196 INFO [train.py:850] (2/4) Epoch 23, batch 8650, loss[loss=0.1771, simple_loss=0.2488, pruned_loss=0.05269, over 7461.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2781, pruned_loss=0.05282, over 1465124.94 frames.], batch size: 17, lr: 2.41e-04
2022-07-27 17:57:19,051 INFO [train.py:850] (2/4) Epoch 23, batch 8700, loss[loss=0.2385, simple_loss=0.3178, pruned_loss=0.07957, over 7410.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2761, pruned_loss=0.05178, over 1464298.82 frames.], batch size: 22, lr: 2.41e-04
2022-07-27 17:58:02,456 INFO [train.py:850] (2/4) Epoch 23, batch 8750, loss[loss=0.2056, simple_loss=0.2918, pruned_loss=0.05967, over 7173.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2747, pruned_loss=0.05097, over 1463219.65 frames.], batch size: 22, lr: 2.41e-04
2022-07-27 17:58:44,433 INFO [train.py:850] (2/4) Epoch 23, batch 8800, loss[loss=0.1761, simple_loss=0.254, pruned_loss=0.04915, over 7139.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2754, pruned_loss=0.05117, over 1463709.27 frames.], batch size: 17, lr: 2.41e-04
2022-07-27 17:59:27,085 INFO [train.py:850] (2/4) Epoch 23, batch 8850, loss[loss=0.2049, simple_loss=0.2955, pruned_loss=0.05715, over 7463.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2762, pruned_loss=0.05168, over 1464127.46 frames.], batch size: 24, lr: 2.41e-04
2022-07-27 18:00:52,474 INFO [train.py:850] (2/4) Epoch 24, batch 0, loss[loss=0.1854, simple_loss=0.2796, pruned_loss=0.04558, over 7313.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2796, pruned_loss=0.04558, over 7313.00 frames.], batch size: 22, lr: 2.36e-04
2022-07-27 18:01:38,260 INFO [train.py:850] (2/4) Epoch 24, batch 50, loss[loss=0.145, simple_loss=0.2308, pruned_loss=0.02964, over 7307.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2665, pruned_loss=0.04208, over 330857.60 frames.], batch size: 17, lr: 2.36e-04
2022-07-27 18:02:22,276 INFO [train.py:850] (2/4) Epoch 24, batch 100, loss[loss=0.1695, simple_loss=0.2724, pruned_loss=0.03333, over 7407.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2719, pruned_loss=0.04312, over 582513.77 frames.], batch size: 22, lr: 2.36e-04
2022-07-27 18:03:06,187 INFO [train.py:850] (2/4) Epoch 24, batch 150, loss[loss=0.1507, simple_loss=0.2457, pruned_loss=0.02784, over 7394.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2709, pruned_loss=0.04245, over 778352.11 frames.], batch size: 19, lr: 2.36e-04
2022-07-27 18:03:50,020 INFO [train.py:850] (2/4) Epoch 24, batch 200, loss[loss=0.1647, simple_loss=0.2517, pruned_loss=0.03887, over 7491.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2701, pruned_loss=0.04275, over 932555.66 frames.], batch size: 19, lr: 2.35e-04
2022-07-27 18:04:33,669 INFO [train.py:850] (2/4) Epoch 24, batch 250, loss[loss=0.1873, simple_loss=0.2894, pruned_loss=0.04262, over 7477.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2688, pruned_loss=0.04216, over 1051279.25 frames.], batch size: 24, lr: 2.35e-04
2022-07-27 18:05:17,873 INFO [train.py:850] (2/4) Epoch 24, batch 300, loss[loss=0.1573, simple_loss=0.2554, pruned_loss=0.02965, over 7296.00 frames.], tot_loss[loss=0.176, simple_loss=0.2684, pruned_loss=0.04179, over 1143766.27 frames.], batch size: 20, lr: 2.35e-04
2022-07-27 18:06:02,030 INFO [train.py:850] (2/4) Epoch 24, batch 350, loss[loss=0.1697, simple_loss=0.2737, pruned_loss=0.03289, over 7389.00 frames.], tot_loss[loss=0.177, simple_loss=0.2692, pruned_loss=0.04244, over 1215102.75 frames.], batch size: 21, lr: 2.35e-04
2022-07-27 18:06:45,712 INFO [train.py:850] (2/4) Epoch 24, batch 400, loss[loss=0.1536, simple_loss=0.2618, pruned_loss=0.02269, over 7490.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2695, pruned_loss=0.04217, over 1271169.03 frames.], batch size: 21, lr: 2.35e-04
2022-07-27 18:07:30,011 INFO [train.py:850] (2/4) Epoch 24, batch 450, loss[loss=0.1683, simple_loss=0.2612, pruned_loss=0.03772, over 7203.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2699, pruned_loss=0.04222, over 1314669.06 frames.], batch size: 19, lr: 2.35e-04
2022-07-27 18:08:14,136 INFO [train.py:850] (2/4) Epoch 24, batch 500, loss[loss=0.187, simple_loss=0.2763, pruned_loss=0.04884, over 7280.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2688, pruned_loss=0.04139, over 1347720.60 frames.], batch size: 21, lr: 2.35e-04
2022-07-27 18:08:58,606 INFO [train.py:850] (2/4) Epoch 24, batch 550, loss[loss=0.1899, simple_loss=0.2757, pruned_loss=0.05202, over 7349.00 frames.], tot_loss[loss=0.1762, simple_loss=0.269, pruned_loss=0.0417, over 1373652.23 frames.], batch size: 73, lr: 2.35e-04
2022-07-27 18:09:41,946 INFO [train.py:850] (2/4) Epoch 24, batch 600, loss[loss=0.1545, simple_loss=0.2398, pruned_loss=0.03455, over 7435.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2679, pruned_loss=0.04133, over 1394630.13 frames.], batch size: 18, lr: 2.35e-04
2022-07-27 18:10:26,199 INFO [train.py:850] (2/4) Epoch 24, batch 650, loss[loss=0.1606, simple_loss=0.2691, pruned_loss=0.02606, over 7377.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2686, pruned_loss=0.04151, over 1409693.34 frames.], batch size: 21, lr: 2.35e-04
2022-07-27 18:11:08,805 INFO [train.py:850] (2/4) Epoch 24, batch 700, loss[loss=0.1715, simple_loss=0.276, pruned_loss=0.0335, over 7475.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2681, pruned_loss=0.04115, over 1421992.07 frames.], batch size: 21, lr: 2.35e-04
2022-07-27 18:11:53,096 INFO [train.py:850] (2/4) Epoch 24, batch 750, loss[loss=0.1577, simple_loss=0.2619, pruned_loss=0.02669, over 7182.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2683, pruned_loss=0.04076, over 1431458.97 frames.], batch size: 21, lr: 2.35e-04
2022-07-27 18:12:36,064 INFO [train.py:850] (2/4) Epoch 24, batch 800, loss[loss=0.1684, simple_loss=0.2702, pruned_loss=0.03327, over 7335.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2677, pruned_loss=0.04074, over 1438375.34 frames.], batch size: 27, lr: 2.35e-04
2022-07-27 18:13:19,913 INFO [train.py:850] (2/4) Epoch 24, batch 850, loss[loss=0.1953, simple_loss=0.2952, pruned_loss=0.0477, over 7329.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2686, pruned_loss=0.04138, over 1444410.27 frames.], batch size: 39, lr: 2.35e-04
2022-07-27 18:14:03,319 INFO [train.py:850] (2/4) Epoch 24, batch 900, loss[loss=0.1782, simple_loss=0.2794, pruned_loss=0.03848, over 7326.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2694, pruned_loss=0.04175, over 1448244.28 frames.], batch size: 27, lr: 2.35e-04
2022-07-27 18:14:46,746 INFO [train.py:850] (2/4) Epoch 24, batch 950, loss[loss=0.1927, simple_loss=0.2898, pruned_loss=0.04778, over 7414.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2709, pruned_loss=0.04217, over 1452114.41 frames.], batch size: 38, lr: 2.35e-04
2022-07-27 18:15:30,163 INFO [train.py:850] (2/4) Epoch 24, batch 1000, loss[loss=0.2096, simple_loss=0.296, pruned_loss=0.06153, over 7469.00 frames.], tot_loss[loss=0.1784, simple_loss=0.272, pruned_loss=0.04238, over 1454746.87 frames.], batch size: 21, lr: 2.35e-04
2022-07-27 18:16:13,796 INFO [train.py:850] (2/4) Epoch 24, batch 1050, loss[loss=0.2024, simple_loss=0.3005, pruned_loss=0.05212, over 7442.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2722, pruned_loss=0.04269, over 1455577.51 frames.], batch size: 24, lr: 2.35e-04
2022-07-27 18:16:57,602 INFO [train.py:850] (2/4) Epoch 24, batch 1100, loss[loss=0.1865, simple_loss=0.2721, pruned_loss=0.05048, over 7385.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2728, pruned_loss=0.04277, over 1457035.97 frames.], batch size: 20, lr: 2.35e-04
2022-07-27 18:17:40,785 INFO [train.py:850] (2/4) Epoch 24, batch 1150, loss[loss=0.1799, simple_loss=0.2639, pruned_loss=0.04793, over 7436.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2736, pruned_loss=0.04327, over 1459137.11 frames.], batch size: 18, lr: 2.35e-04
2022-07-27 18:18:23,810 INFO [train.py:850] (2/4) Epoch 24, batch 1200, loss[loss=0.1575, simple_loss=0.2595, pruned_loss=0.02776, over 7258.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2738, pruned_loss=0.04327, over 1460253.65 frames.], batch size: 27, lr: 2.35e-04
2022-07-27 18:19:07,812 INFO [train.py:850] (2/4) Epoch 24, batch 1250, loss[loss=0.1716, simple_loss=0.2738, pruned_loss=0.03465, over 7205.00 frames.], tot_loss[loss=0.1804, simple_loss=0.274, pruned_loss=0.04343, over 1460039.31 frames.], batch size: 19, lr: 2.35e-04
2022-07-27 18:19:50,813 INFO [train.py:850] (2/4) Epoch 24, batch 1300, loss[loss=0.1647, simple_loss=0.2606, pruned_loss=0.03442, over 7315.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2737, pruned_loss=0.0434, over 1461892.49 frames.], batch size: 18, lr: 2.35e-04
2022-07-27 18:20:34,227 INFO [train.py:850] (2/4) Epoch 24, batch 1350, loss[loss=0.2311, simple_loss=0.3055, pruned_loss=0.07837, over 7296.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2738, pruned_loss=0.04351, over 1463236.98 frames.], batch size: 22, lr: 2.35e-04
2022-07-27 18:21:17,742 INFO [train.py:850] (2/4) Epoch 24, batch 1400, loss[loss=0.2076, simple_loss=0.3038, pruned_loss=0.05566, over 7347.00 frames.], tot_loss[loss=0.1808, simple_loss=0.274, pruned_loss=0.04383, over 1462918.55 frames.], batch size: 23, lr: 2.35e-04
2022-07-27 18:22:01,606 INFO [train.py:850] (2/4) Epoch 24, batch 1450, loss[loss=0.243, simple_loss=0.3336, pruned_loss=0.07623, over 7369.00 frames.], tot_loss[loss=0.1807, simple_loss=0.274, pruned_loss=0.04373, over 1464018.40 frames.], batch size: 71, lr: 2.35e-04
2022-07-27 18:22:44,821 INFO [train.py:850] (2/4) Epoch 24, batch 1500, loss[loss=0.2104, simple_loss=0.2955, pruned_loss=0.06266, over 7293.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2747, pruned_loss=0.0443, over 1463947.62 frames.], batch size: 19, lr: 2.35e-04
2022-07-27 18:23:28,640 INFO [train.py:850] (2/4) Epoch 24, batch 1550, loss[loss=0.2045, simple_loss=0.297, pruned_loss=0.056, over 7465.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2757, pruned_loss=0.04428, over 1463962.04 frames.], batch size: 71, lr: 2.35e-04
2022-07-27 18:24:12,150 INFO [train.py:850] (2/4) Epoch 24, batch 1600, loss[loss=0.1718, simple_loss=0.2652, pruned_loss=0.03917, over 7467.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2746, pruned_loss=0.04387, over 1464055.00 frames.], batch size: 21, lr: 2.35e-04
2022-07-27 18:24:55,809 INFO [train.py:850] (2/4) Epoch 24, batch 1650, loss[loss=0.1814, simple_loss=0.2676, pruned_loss=0.0476, over 7295.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2756, pruned_loss=0.04447, over 1463085.34 frames.], batch size: 19, lr: 2.35e-04
2022-07-27 18:25:38,362 INFO [train.py:850] (2/4) Epoch 24, batch 1700, loss[loss=0.1998, simple_loss=0.2883, pruned_loss=0.05567, over 7216.00 frames.], tot_loss[loss=0.1817, simple_loss=0.275, pruned_loss=0.04422, over 1463356.37 frames.], batch size: 24, lr: 2.35e-04
2022-07-27 18:26:21,246 INFO [train.py:850] (2/4) Epoch 24, batch 1750, loss[loss=0.1536, simple_loss=0.2398, pruned_loss=0.03374, over 7105.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2749, pruned_loss=0.04426, over 1465136.15 frames.], batch size: 18, lr: 2.35e-04
2022-07-27 18:27:04,348 INFO [train.py:850] (2/4) Epoch 24, batch 1800, loss[loss=0.1577, simple_loss=0.2571, pruned_loss=0.02914, over 7387.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2744, pruned_loss=0.04366, over 1464463.82 frames.], batch size: 20, lr: 2.35e-04
2022-07-27 18:27:47,833 INFO [train.py:850] (2/4) Epoch 24, batch 1850, loss[loss=0.1609, simple_loss=0.2626, pruned_loss=0.0296, over 7235.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2745, pruned_loss=0.04342, over 1465084.00 frames.], batch size: 24, lr: 2.35e-04
2022-07-27 18:28:31,191 INFO [train.py:850] (2/4) Epoch 24, batch 1900, loss[loss=0.1669, simple_loss=0.2662, pruned_loss=0.03378, over 7228.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2756, pruned_loss=0.04406, over 1464454.27 frames.], batch size: 25, lr: 2.35e-04
2022-07-27 18:29:14,437 INFO [train.py:850] (2/4) Epoch 24, batch 1950, loss[loss=0.1716, simple_loss=0.2561, pruned_loss=0.04351, over 7153.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2743, pruned_loss=0.04329, over 1464428.42 frames.], batch size: 17, lr: 2.34e-04
2022-07-27 18:29:58,670 INFO [train.py:850] (2/4) Epoch 24, batch 2000, loss[loss=0.1988, simple_loss=0.292, pruned_loss=0.05276, over 7172.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2737, pruned_loss=0.04335, over 1464120.75 frames.], batch size: 21, lr: 2.34e-04
2022-07-27 18:30:42,025 INFO [train.py:850] (2/4) Epoch 24, batch 2050, loss[loss=0.2449, simple_loss=0.3316, pruned_loss=0.07911, over 7378.00 frames.], tot_loss[loss=0.181, simple_loss=0.2748, pruned_loss=0.0436, over 1465105.07 frames.], batch size: 66, lr: 2.34e-04
2022-07-27 18:31:25,188 INFO [train.py:850] (2/4) Epoch 24, batch 2100, loss[loss=0.2266, simple_loss=0.314, pruned_loss=0.06955, over 7212.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2749, pruned_loss=0.04316, over 1464480.76 frames.], batch size: 25, lr: 2.34e-04
2022-07-27 18:32:09,207 INFO [train.py:850] (2/4) Epoch 24, batch 2150, loss[loss=0.1725, simple_loss=0.2661, pruned_loss=0.03943, over 7443.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2756, pruned_loss=0.04374, over 1464404.93 frames.], batch size: 18, lr: 2.34e-04
2022-07-27 18:32:52,191 INFO [train.py:850] (2/4) Epoch 24, batch 2200, loss[loss=0.1889, simple_loss=0.2874, pruned_loss=0.04516, over 7330.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2752, pruned_loss=0.04353, over 1465250.84 frames.], batch size: 38, lr: 2.34e-04
2022-07-27 18:33:36,107 INFO [train.py:850] (2/4) Epoch 24, batch 2250, loss[loss=0.1824, simple_loss=0.2701, pruned_loss=0.04738, over 7397.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2743, pruned_loss=0.0433, over 1465320.58 frames.], batch size: 19, lr: 2.34e-04
2022-07-27 18:34:19,252 INFO [train.py:850] (2/4) Epoch 24, batch 2300, loss[loss=0.1994, simple_loss=0.3006, pruned_loss=0.04911, over 7351.00 frames.], tot_loss[loss=0.1802, simple_loss=0.274, pruned_loss=0.04317, over 1464606.17 frames.], batch size: 23, lr: 2.34e-04
2022-07-27 18:35:03,507 INFO [train.py:850] (2/4) Epoch 24, batch 2350, loss[loss=0.1665, simple_loss=0.2554, pruned_loss=0.03883, over 7180.00 frames.], tot_loss[loss=0.18, simple_loss=0.2738, pruned_loss=0.04311, over 1465719.31 frames.], batch size: 18, lr: 2.34e-04
2022-07-27 18:35:46,717 INFO [train.py:850] (2/4) Epoch 24, batch 2400, loss[loss=0.1703, simple_loss=0.2687, pruned_loss=0.03596, over 7352.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2738, pruned_loss=0.04323, over 1465983.44 frames.], batch size: 23, lr: 2.34e-04
2022-07-27 18:36:30,079 INFO [train.py:850] (2/4) Epoch 24, batch 2450, loss[loss=0.2169, simple_loss=0.3055, pruned_loss=0.06418, over 7376.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2745, pruned_loss=0.04362, over 1466300.41 frames.], batch size: 75, lr: 2.34e-04
2022-07-27 18:37:14,105 INFO [train.py:850] (2/4) Epoch 24, batch 2500, loss[loss=0.1828, simple_loss=0.2816, pruned_loss=0.04199, over 7477.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2746, pruned_loss=0.04325, over 1466192.78 frames.], batch size: 21, lr: 2.34e-04
2022-07-27 18:37:58,285 INFO [train.py:850] (2/4) Epoch 24, batch 2550, loss[loss=0.1857, simple_loss=0.2762, pruned_loss=0.04761, over 7470.00 frames.], tot_loss[loss=0.1814, simple_loss=0.275, pruned_loss=0.04388, over 1466804.96 frames.], batch size: 39, lr: 2.34e-04
2022-07-27 18:38:42,716 INFO [train.py:850] (2/4) Epoch 24, batch 2600, loss[loss=0.1784, simple_loss=0.2709, pruned_loss=0.04299, over 7433.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2743, pruned_loss=0.0438, over 1466305.31 frames.], batch size: 66, lr: 2.34e-04
2022-07-27 18:39:26,490 INFO [train.py:850] (2/4) Epoch 24, batch 2650, loss[loss=0.2499, simple_loss=0.2994, pruned_loss=0.1002, over 7200.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2729, pruned_loss=0.04292, over 1466089.33 frames.], batch size: 19, lr: 2.34e-04
2022-07-27 18:40:10,017 INFO [train.py:850] (2/4) Epoch 24, batch 2700, loss[loss=0.1568, simple_loss=0.2567, pruned_loss=0.02843, over 7199.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2725, pruned_loss=0.04261, over 1464718.24 frames.], batch size: 20, lr: 2.34e-04
2022-07-27 18:40:53,543 INFO [train.py:850] (2/4) Epoch 24, batch 2750, loss[loss=0.2274, simple_loss=0.3176, pruned_loss=0.06857, over 7384.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2732, pruned_loss=0.04291, over 1464391.31 frames.], batch size: 72, lr: 2.34e-04
2022-07-27 18:41:37,031 INFO [train.py:850] (2/4) Epoch 24, batch 2800, loss[loss=0.2362, simple_loss=0.3238, pruned_loss=0.07428, over 7471.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2729, pruned_loss=0.04265, over 1465071.47 frames.], batch size: 65, lr: 2.34e-04
2022-07-27 18:42:20,256 INFO [train.py:850] (2/4) Epoch 24, batch 2850, loss[loss=0.1636, simple_loss=0.2491, pruned_loss=0.03905, over 7134.00 frames.], tot_loss[loss=0.1794, simple_loss=0.273, pruned_loss=0.04292, over 1465075.62 frames.], batch size: 17, lr: 2.34e-04
2022-07-27 18:43:03,431 INFO [train.py:850] (2/4) Epoch 24, batch 2900, loss[loss=0.1636, simple_loss=0.2677, pruned_loss=0.02969, over 7421.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2735, pruned_loss=0.04306, over 1465318.61 frames.], batch size: 22, lr: 2.34e-04
2022-07-27 18:43:47,151 INFO [train.py:850] (2/4) Epoch 24, batch 2950, loss[loss=0.1817, simple_loss=0.2862, pruned_loss=0.03864, over 7288.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2734, pruned_loss=0.04266, over 1464715.42 frames.], batch size: 20, lr: 2.34e-04
2022-07-27 18:44:31,146 INFO [train.py:850] (2/4) Epoch 24, batch 3000, loss[loss=0.1401, simple_loss=0.226, pruned_loss=0.02711, over 7293.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2728, pruned_loss=0.04264, over 1465225.09 frames.], batch size: 16, lr: 2.34e-04
2022-07-27 18:44:31,147 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 18:44:53,919 INFO [train.py:879] (2/4) Epoch 24, validation: loss=0.1919, simple_loss=0.2844, pruned_loss=0.04969, over 924787.00 frames.
2022-07-27 18:45:38,273 INFO [train.py:850] (2/4) Epoch 24, batch 3050, loss[loss=0.1963, simple_loss=0.3008, pruned_loss=0.04592, over 7276.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2736, pruned_loss=0.04301, over 1465933.90 frames.], batch size: 21, lr: 2.34e-04
2022-07-27 18:46:22,204 INFO [train.py:850] (2/4) Epoch 24, batch 3100, loss[loss=0.1619, simple_loss=0.2596, pruned_loss=0.0321, over 7286.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2737, pruned_loss=0.04292, over 1466293.54 frames.], batch size: 21, lr: 2.34e-04
2022-07-27 18:47:06,905 INFO [train.py:850] (2/4) Epoch 24, batch 3150, loss[loss=0.1974, simple_loss=0.2959, pruned_loss=0.04945, over 7474.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2746, pruned_loss=0.04337, over 1466246.53 frames.], batch size: 24, lr: 2.34e-04
2022-07-27 18:47:50,337 INFO [train.py:850] (2/4) Epoch 24, batch 3200, loss[loss=0.1771, simple_loss=0.2722, pruned_loss=0.04105, over 7471.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2739, pruned_loss=0.0432, over 1466462.96 frames.], batch size: 21, lr: 2.34e-04
2022-07-27 18:48:33,940 INFO [train.py:850] (2/4) Epoch 24, batch 3250, loss[loss=0.1905, simple_loss=0.2825, pruned_loss=0.04928, over 7419.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2738, pruned_loss=0.04339, over 1467109.35 frames.], batch size: 22, lr: 2.34e-04
2022-07-27 18:49:17,275 INFO [train.py:850] (2/4) Epoch 24, batch 3300, loss[loss=0.2392, simple_loss=0.3299, pruned_loss=0.07425, over 7437.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2746, pruned_loss=0.04364, over 1466952.15 frames.], batch size: 70, lr: 2.34e-04
2022-07-27 18:50:02,757 INFO [train.py:850] (2/4) Epoch 24, batch 3350, loss[loss=0.1737, simple_loss=0.2667, pruned_loss=0.04033, over 7492.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2745, pruned_loss=0.04363, over 1468351.04 frames.], batch size: 19, lr: 2.34e-04
2022-07-27 18:50:46,118 INFO [train.py:850] (2/4) Epoch 24, batch 3400, loss[loss=0.2207, simple_loss=0.3193, pruned_loss=0.06104, over 7314.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2744, pruned_loss=0.0434, over 1467935.81 frames.], batch size: 27, lr: 2.34e-04
2022-07-27 18:51:45,371 INFO [train.py:850] (2/4) Epoch 24, batch 3450, loss[loss=0.1837, simple_loss=0.2784, pruned_loss=0.04448, over 7386.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2719, pruned_loss=0.04229, over 1467392.39 frames.], batch size: 20, lr: 2.34e-04
2022-07-27 18:52:28,676 INFO [train.py:850] (2/4) Epoch 24, batch 3500, loss[loss=0.2264, simple_loss=0.3288, pruned_loss=0.06203, over 7442.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2725, pruned_loss=0.04231, over 1467687.81 frames.], batch size: 70, lr: 2.34e-04
2022-07-27 18:53:12,211 INFO [train.py:850] (2/4) Epoch 24, batch 3550, loss[loss=0.18, simple_loss=0.2651, pruned_loss=0.04746, over 7310.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2722, pruned_loss=0.04228, over 1467320.77 frames.], batch size: 17, lr: 2.34e-04
2022-07-27 18:53:55,391 INFO [train.py:850] (2/4) Epoch 24, batch 3600, loss[loss=0.1582, simple_loss=0.2549, pruned_loss=0.03078, over 7208.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2733, pruned_loss=0.04296, over 1467072.71 frames.], batch size: 19, lr: 2.34e-04
2022-07-27 18:54:38,631 INFO [train.py:850] (2/4) Epoch 24, batch 3650, loss[loss=0.1799, simple_loss=0.2769, pruned_loss=0.04147, over 7291.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2735, pruned_loss=0.04286, over 1465710.46 frames.], batch size: 21, lr: 2.34e-04
2022-07-27 18:55:21,951 INFO [train.py:850] (2/4) Epoch 24, batch 3700, loss[loss=0.1815, simple_loss=0.2884, pruned_loss=0.03728, over 7472.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2737, pruned_loss=0.04303, over 1465583.82 frames.], batch size: 39, lr: 2.34e-04
2022-07-27 18:56:06,502 INFO [train.py:850] (2/4) Epoch 24, batch 3750, loss[loss=0.1793, simple_loss=0.2742, pruned_loss=0.04219, over 7455.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2734, pruned_loss=0.0431, over 1466366.04 frames.], batch size: 31, lr: 2.33e-04
2022-07-27 18:56:50,012 INFO [train.py:850] (2/4) Epoch 24, batch 3800, loss[loss=0.182, simple_loss=0.2786, pruned_loss=0.04267, over 7302.00 frames.], tot_loss[loss=0.179, simple_loss=0.2729, pruned_loss=0.04253, over 1466172.24 frames.], batch size: 27, lr: 2.33e-04
2022-07-27 18:57:33,644 INFO [train.py:850] (2/4) Epoch 24, batch 3850, loss[loss=0.1822, simple_loss=0.2795, pruned_loss=0.04246, over 7374.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2736, pruned_loss=0.043, over 1466626.18 frames.], batch size: 71, lr: 2.33e-04
2022-07-27 18:58:16,755 INFO [train.py:850] (2/4) Epoch 24, batch 3900, loss[loss=0.2208, simple_loss=0.3093, pruned_loss=0.06614, over 7484.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2729, pruned_loss=0.04269, over 1465369.32 frames.], batch size: 24, lr: 2.33e-04
2022-07-27 18:59:01,268 INFO [train.py:850] (2/4) Epoch 24, batch 3950, loss[loss=0.1812, simple_loss=0.2864, pruned_loss=0.03802, over 7415.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2727, pruned_loss=0.04302, over 1465238.45 frames.], batch size: 22, lr: 2.33e-04
2022-07-27 18:59:45,075 INFO [train.py:850] (2/4) Epoch 24, batch 4000, loss[loss=0.1565, simple_loss=0.2406, pruned_loss=0.03621, over 7263.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2725, pruned_loss=0.04252, over 1464972.30 frames.], batch size: 16, lr: 2.33e-04
2022-07-27 19:00:28,956 INFO [train.py:850] (2/4) Epoch 24, batch 4050, loss[loss=0.1946, simple_loss=0.2975, pruned_loss=0.0458, over 7383.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2726, pruned_loss=0.04257, over 1465721.01 frames.], batch size: 31, lr: 2.33e-04
2022-07-27 19:01:12,030 INFO [train.py:850] (2/4) Epoch 24, batch 4100, loss[loss=0.1518, simple_loss=0.2409, pruned_loss=0.03131, over 7325.00 frames.], tot_loss[loss=0.1792, simple_loss=0.272, pruned_loss=0.04321, over 1465374.16 frames.], batch size: 18, lr: 2.33e-04
2022-07-27 19:01:56,146 INFO [train.py:850] (2/4) Epoch 24, batch 4150, loss[loss=0.176, simple_loss=0.2687, pruned_loss=0.04165, over 7397.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2725, pruned_loss=0.04387, over 1465316.84 frames.], batch size: 19, lr: 2.33e-04
2022-07-27 19:02:39,370 INFO [train.py:850] (2/4) Epoch 24, batch 4200, loss[loss=0.2193, simple_loss=0.3075, pruned_loss=0.06557, over 7484.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2735, pruned_loss=0.0451, over 1465730.66 frames.], batch size: 26, lr: 2.33e-04
2022-07-27 19:03:23,572 INFO [train.py:850] (2/4) Epoch 24, batch 4250, loss[loss=0.2182, simple_loss=0.2929, pruned_loss=0.07179, over 7405.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2741, pruned_loss=0.04611, over 1464548.23 frames.], batch size: 69, lr: 2.33e-04
2022-07-27 19:04:06,865 INFO [train.py:850] (2/4) Epoch 24, batch 4300, loss[loss=0.2175, simple_loss=0.3075, pruned_loss=0.06372, over 7235.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2743, pruned_loss=0.04645, over 1464591.97 frames.], batch size: 24, lr: 2.33e-04
2022-07-27 19:04:49,878 INFO [train.py:850] (2/4) Epoch 24, batch 4350, loss[loss=0.1704, simple_loss=0.2517, pruned_loss=0.04453, over 7309.00 frames.], tot_loss[loss=0.184, simple_loss=0.2741, pruned_loss=0.04698, over 1465067.77 frames.], batch size: 17, lr: 2.33e-04
2022-07-27 19:05:33,723 INFO [train.py:850] (2/4) Epoch 24, batch 4400, loss[loss=0.1885, simple_loss=0.2929, pruned_loss=0.04204, over 7487.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2743, pruned_loss=0.04727, over 1465968.67 frames.], batch size: 23, lr: 2.33e-04
2022-07-27 19:06:16,942 INFO [train.py:850] (2/4) Epoch 24, batch 4450, loss[loss=0.1677, simple_loss=0.2735, pruned_loss=0.03098, over 7206.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2739, pruned_loss=0.04713, over 1466501.40 frames.], batch size: 20, lr: 2.33e-04
2022-07-27 19:07:00,353 INFO [train.py:850] (2/4) Epoch 24, batch 4500, loss[loss=0.2154, simple_loss=0.3016, pruned_loss=0.06465, over 7482.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2756, pruned_loss=0.04873, over 1467121.29 frames.], batch size: 24, lr: 2.33e-04
2022-07-27 19:07:44,352 INFO [train.py:850] (2/4) Epoch 24, batch 4550, loss[loss=0.1581, simple_loss=0.2514, pruned_loss=0.0324, over 7166.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2779, pruned_loss=0.04973, over 1465421.56 frames.], batch size: 17, lr: 2.33e-04
2022-07-27 19:08:27,994 INFO [train.py:850] (2/4) Epoch 24, batch 4600, loss[loss=0.2196, simple_loss=0.3081, pruned_loss=0.06551, over 7481.00 frames.], tot_loss[loss=0.1894, simple_loss=0.278, pruned_loss=0.05037, over 1465208.59 frames.], batch size: 23, lr: 2.33e-04
2022-07-27 19:09:11,935 INFO [train.py:850] (2/4) Epoch 24, batch 4650, loss[loss=0.1777, simple_loss=0.2709, pruned_loss=0.04226, over 7184.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2772, pruned_loss=0.05044, over 1464867.79 frames.], batch size: 23, lr: 2.33e-04
2022-07-27 19:09:54,953 INFO [train.py:850] (2/4) Epoch 24, batch 4700, loss[loss=0.1771, simple_loss=0.2671, pruned_loss=0.04351, over 7460.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2762, pruned_loss=0.05043, over 1464229.53 frames.], batch size: 21, lr: 2.33e-04
2022-07-27 19:10:38,297 INFO [train.py:850] (2/4) Epoch 24, batch 4750, loss[loss=0.2153, simple_loss=0.3104, pruned_loss=0.06008, over 7365.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2763, pruned_loss=0.05103, over 1465171.71 frames.], batch size: 23, lr: 2.33e-04
2022-07-27 19:11:22,385 INFO [train.py:850] (2/4) Epoch 24, batch 4800, loss[loss=0.1578, simple_loss=0.2464, pruned_loss=0.03459, over 7439.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2766, pruned_loss=0.05085, over 1465955.71 frames.], batch size: 18, lr: 2.33e-04
2022-07-27 19:12:06,700 INFO [train.py:850] (2/4) Epoch 24, batch 4850, loss[loss=0.2024, simple_loss=0.2928, pruned_loss=0.05607, over 7482.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2767, pruned_loss=0.05112, over 1465645.78 frames.], batch size: 31, lr: 2.33e-04
2022-07-27 19:12:50,137 INFO [train.py:850] (2/4) Epoch 24, batch 4900, loss[loss=0.2153, simple_loss=0.3097, pruned_loss=0.0604, over 7475.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2774, pruned_loss=0.052, over 1466730.33 frames.], batch size: 21, lr: 2.33e-04
2022-07-27 19:13:35,481 INFO [train.py:850] (2/4) Epoch 24, batch 4950, loss[loss=0.2356, simple_loss=0.311, pruned_loss=0.08003, over 7169.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2772, pruned_loss=0.05193, over 1466677.54 frames.], batch size: 22, lr: 2.33e-04
2022-07-27 19:14:20,360 INFO [train.py:850] (2/4) Epoch 24, batch 5000, loss[loss=0.1612, simple_loss=0.254, pruned_loss=0.03419, over 7433.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2774, pruned_loss=0.05163, over 1465482.88 frames.], batch size: 18, lr: 2.33e-04
2022-07-27 19:15:04,535 INFO [train.py:850] (2/4) Epoch 24, batch 5050, loss[loss=0.162, simple_loss=0.2538, pruned_loss=0.03511, over 7389.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2766, pruned_loss=0.05129, over 1466233.43 frames.], batch size: 20, lr: 2.33e-04
2022-07-27 19:15:47,868 INFO [train.py:850] (2/4) Epoch 24, batch 5100, loss[loss=0.1981, simple_loss=0.2911, pruned_loss=0.0525, over 7416.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2765, pruned_loss=0.05147, over 1465821.01 frames.], batch size: 31, lr: 2.33e-04
2022-07-27 19:16:31,568 INFO [train.py:850] (2/4) Epoch 24, batch 5150, loss[loss=0.2276, simple_loss=0.3073, pruned_loss=0.07394, over 7183.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2769, pruned_loss=0.05164, over 1464753.00 frames.], batch size: 22, lr: 2.33e-04
2022-07-27 19:17:15,593 INFO [train.py:850] (2/4) Epoch 24, batch 5200, loss[loss=0.1997, simple_loss=0.261, pruned_loss=0.06918, over 7464.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2775, pruned_loss=0.05244, over 1464807.53 frames.], batch size: 17, lr: 2.33e-04
2022-07-27 19:17:59,343 INFO [train.py:850] (2/4) Epoch 24, batch 5250, loss[loss=0.2095, simple_loss=0.2978, pruned_loss=0.0606, over 7289.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2772, pruned_loss=0.05194, over 1465049.68 frames.], batch size: 27, lr: 2.33e-04
2022-07-27 19:18:42,138 INFO [train.py:850] (2/4) Epoch 24, batch 5300, loss[loss=0.1837, simple_loss=0.2811, pruned_loss=0.04313, over 7275.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2777, pruned_loss=0.05262, over 1464993.56 frames.], batch size: 30, lr: 2.33e-04
2022-07-27 19:19:26,390 INFO [train.py:850] (2/4) Epoch 24, batch 5350, loss[loss=0.1828, simple_loss=0.2684, pruned_loss=0.0486, over 7410.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2761, pruned_loss=0.05182, over 1465454.00 frames.], batch size: 22, lr: 2.33e-04
2022-07-27 19:20:10,017 INFO [train.py:850] (2/4) Epoch 24, batch 5400, loss[loss=0.1715, simple_loss=0.2543, pruned_loss=0.04442, over 7457.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2747, pruned_loss=0.05075, over 1464655.76 frames.], batch size: 18, lr: 2.33e-04
2022-07-27 19:20:54,654 INFO [train.py:850] (2/4) Epoch 24, batch 5450, loss[loss=0.2362, simple_loss=0.317, pruned_loss=0.07768, over 7352.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2754, pruned_loss=0.0514, over 1465220.14 frames.], batch size: 23, lr: 2.33e-04
2022-07-27 19:21:38,570 INFO [train.py:850] (2/4) Epoch 24, batch 5500, loss[loss=0.1967, simple_loss=0.2942, pruned_loss=0.04959, over 7222.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2769, pruned_loss=0.0517, over 1466078.11 frames.], batch size: 24, lr: 2.33e-04
2022-07-27 19:22:23,003 INFO [train.py:850] (2/4) Epoch 24, batch 5550, loss[loss=0.1948, simple_loss=0.2718, pruned_loss=0.05893, over 7156.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2776, pruned_loss=0.05248, over 1465982.31 frames.], batch size: 17, lr: 2.32e-04
2022-07-27 19:23:07,265 INFO [train.py:850] (2/4) Epoch 24, batch 5600, loss[loss=0.1522, simple_loss=0.2411, pruned_loss=0.0317, over 7469.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2778, pruned_loss=0.05269, over 1465397.21 frames.], batch size: 17, lr: 2.32e-04
2022-07-27 19:23:49,965 INFO [train.py:850] (2/4) Epoch 24, batch 5650, loss[loss=0.2125, simple_loss=0.3041, pruned_loss=0.06045, over 7313.00 frames.], tot_loss[loss=0.191, simple_loss=0.2775, pruned_loss=0.05229, over 1465515.99 frames.], batch size: 22, lr: 2.32e-04
2022-07-27 19:24:33,065 INFO [train.py:850] (2/4) Epoch 24, batch 5700, loss[loss=0.1977, simple_loss=0.2969, pruned_loss=0.04922, over 7479.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2769, pruned_loss=0.05127, over 1465247.99 frames.], batch size: 21, lr: 2.32e-04
2022-07-27 19:25:17,739 INFO [train.py:850] (2/4) Epoch 24, batch 5750, loss[loss=0.1849, simple_loss=0.2756, pruned_loss=0.04713, over 7387.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2757, pruned_loss=0.05061, over 1465434.01 frames.], batch size: 21, lr: 2.32e-04
2022-07-27 19:26:00,900 INFO [train.py:850] (2/4) Epoch 24, batch 5800, loss[loss=0.1686, simple_loss=0.2499, pruned_loss=0.04365, over 7298.00 frames.], tot_loss[loss=0.188, simple_loss=0.275, pruned_loss=0.05052, over 1465098.22 frames.], batch size: 17, lr: 2.32e-04
2022-07-27 19:26:45,424 INFO [train.py:850] (2/4) Epoch 24, batch 5850, loss[loss=0.1961, simple_loss=0.2783, pruned_loss=0.05696, over 7181.00 frames.], tot_loss[loss=0.188, simple_loss=0.2753, pruned_loss=0.05033, over 1465398.24 frames.], batch size: 21, lr: 2.32e-04
2022-07-27 19:27:28,292 INFO [train.py:850] (2/4) Epoch 24, batch 5900, loss[loss=0.1574, simple_loss=0.2434, pruned_loss=0.03573, over 7206.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2746, pruned_loss=0.05026, over 1465739.33 frames.], batch size: 18, lr: 2.32e-04
2022-07-27 19:28:12,257 INFO [train.py:850] (2/4) Epoch 24, batch 5950, loss[loss=0.1871, simple_loss=0.2791, pruned_loss=0.04758, over 7416.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2753, pruned_loss=0.0506, over 1466501.80 frames.], batch size: 22, lr: 2.32e-04
2022-07-27 19:28:55,627 INFO [train.py:850] (2/4) Epoch 24, batch 6000, loss[loss=0.1671, simple_loss=0.2548, pruned_loss=0.03975, over 7205.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2733, pruned_loss=0.04976, over 1466973.25 frames.], batch size: 20, lr: 2.32e-04
2022-07-27 19:28:55,628 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 19:29:18,481 INFO [train.py:879] (2/4) Epoch 24, validation: loss=0.1891, simple_loss=0.2826, pruned_loss=0.04783, over 924787.00 frames.
2022-07-27 19:30:02,605 INFO [train.py:850] (2/4) Epoch 24, batch 6050, loss[loss=0.1707, simple_loss=0.2512, pruned_loss=0.04514, over 7197.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2734, pruned_loss=0.04985, over 1466545.33 frames.], batch size: 18, lr: 2.32e-04
2022-07-27 19:30:46,003 INFO [train.py:850] (2/4) Epoch 24, batch 6100, loss[loss=0.1918, simple_loss=0.2864, pruned_loss=0.04856, over 7396.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2751, pruned_loss=0.05103, over 1466834.86 frames.], batch size: 20, lr: 2.32e-04
2022-07-27 19:31:30,098 INFO [train.py:850] (2/4) Epoch 24, batch 6150, loss[loss=0.1627, simple_loss=0.2593, pruned_loss=0.0331, over 7285.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2755, pruned_loss=0.05149, over 1466845.45 frames.], batch size: 20, lr: 2.32e-04
2022-07-27 19:32:13,378 INFO [train.py:850] (2/4) Epoch 24, batch 6200, loss[loss=0.171, simple_loss=0.2607, pruned_loss=0.04064, over 7292.00 frames.], tot_loss[loss=0.1895, simple_loss=0.276, pruned_loss=0.0515, over 1466581.20 frames.], batch size: 20, lr: 2.32e-04
2022-07-27 19:32:57,056 INFO [train.py:850] (2/4) Epoch 24, batch 6250, loss[loss=0.2101, simple_loss=0.2996, pruned_loss=0.0603, over 7309.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2751, pruned_loss=0.05097, over 1465390.31 frames.], batch size: 22, lr: 2.32e-04
2022-07-27 19:33:40,413 INFO [train.py:850] (2/4) Epoch 24, batch 6300, loss[loss=0.1803, simple_loss=0.2796, pruned_loss=0.04048, over 7377.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2745, pruned_loss=0.05122, over 1465637.06 frames.], batch size: 21, lr: 2.32e-04
2022-07-27 19:34:23,831 INFO [train.py:850] (2/4) Epoch 24, batch 6350, loss[loss=0.1723, simple_loss=0.276, pruned_loss=0.03431, over 7186.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2743, pruned_loss=0.051, over 1465725.90 frames.], batch size: 21, lr: 2.32e-04
2022-07-27 19:35:07,324 INFO [train.py:850] (2/4) Epoch 24, batch 6400, loss[loss=0.2077, simple_loss=0.2904, pruned_loss=0.06253, over 7187.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2743, pruned_loss=0.05113, over 1465605.23 frames.], batch size: 21, lr: 2.32e-04
2022-07-27 19:35:52,098 INFO [train.py:850] (2/4) Epoch 24, batch 6450, loss[loss=0.1978, simple_loss=0.2731, pruned_loss=0.06125, over 7476.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2739, pruned_loss=0.05064, over 1464984.12 frames.], batch size: 20, lr: 2.32e-04
2022-07-27 19:36:35,544 INFO [train.py:850] (2/4) Epoch 24, batch 6500, loss[loss=0.1942, simple_loss=0.264, pruned_loss=0.06225, over 7494.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2742, pruned_loss=0.05109, over 1464933.72 frames.], batch size: 19, lr: 2.32e-04
2022-07-27 19:37:19,558 INFO [train.py:850] (2/4) Epoch 24, batch 6550, loss[loss=0.1964, simple_loss=0.2852, pruned_loss=0.05378, over 7301.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2737, pruned_loss=0.05064, over 1464839.39 frames.], batch size: 27, lr: 2.32e-04
2022-07-27 19:38:04,059 INFO [train.py:850] (2/4) Epoch 24, batch 6600, loss[loss=0.1661, simple_loss=0.2432, pruned_loss=0.04453, over 7435.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2739, pruned_loss=0.05068, over 1465340.80 frames.], batch size: 17, lr: 2.32e-04
2022-07-27 19:38:48,157 INFO [train.py:850] (2/4) Epoch 24, batch 6650, loss[loss=0.1863, simple_loss=0.2864, pruned_loss=0.04316, over 7485.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2747, pruned_loss=0.05102, over 1464896.03 frames.], batch size: 20, lr: 2.32e-04
2022-07-27 19:39:32,468 INFO [train.py:850] (2/4) Epoch 24, batch 6700, loss[loss=0.2403, simple_loss=0.3197, pruned_loss=0.08047, over 7486.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2743, pruned_loss=0.05044, over 1466097.63 frames.], batch size: 26, lr: 2.32e-04
2022-07-27 19:40:15,993 INFO [train.py:850] (2/4) Epoch 24, batch 6750, loss[loss=0.2041, simple_loss=0.2877, pruned_loss=0.06026, over 7244.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2744, pruned_loss=0.05024, over 1464848.67 frames.], batch size: 24, lr: 2.32e-04
2022-07-27 19:41:00,099 INFO [train.py:850] (2/4) Epoch 24, batch 6800, loss[loss=0.156, simple_loss=0.2424, pruned_loss=0.03479, over 7261.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2742, pruned_loss=0.0502, over 1465595.32 frames.], batch size: 16, lr: 2.32e-04
2022-07-27 19:41:44,209 INFO [train.py:850] (2/4) Epoch 24, batch 6850, loss[loss=0.1776, simple_loss=0.2694, pruned_loss=0.0429, over 7312.00 frames.], tot_loss[loss=0.1872, simple_loss=0.274, pruned_loss=0.05026, over 1465426.63 frames.], batch size: 19, lr: 2.32e-04
2022-07-27 19:42:27,631 INFO [train.py:850] (2/4) Epoch 24, batch 6900, loss[loss=0.2008, simple_loss=0.29, pruned_loss=0.05587, over 7197.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2735, pruned_loss=0.04967, over 1465579.47 frames.], batch size: 20, lr: 2.32e-04
2022-07-27 19:43:11,439 INFO [train.py:850] (2/4) Epoch 24, batch 6950, loss[loss=0.1564, simple_loss=0.264, pruned_loss=0.02444, over 7252.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2741, pruned_loss=0.0501, over 1466045.10 frames.], batch size: 24, lr: 2.32e-04
2022-07-27 19:43:55,349 INFO [train.py:850] (2/4) Epoch 24, batch 7000, loss[loss=0.186, simple_loss=0.259, pruned_loss=0.05644, over 7452.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2746, pruned_loss=0.05001, over 1466579.35 frames.], batch size: 17, lr: 2.32e-04
2022-07-27 19:44:39,291 INFO [train.py:850] (2/4) Epoch 24, batch 7050, loss[loss=0.2037, simple_loss=0.2894, pruned_loss=0.05895, over 7284.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2738, pruned_loss=0.04983, over 1465877.11 frames.], batch size: 21, lr: 2.32e-04
2022-07-27 19:45:22,945 INFO [train.py:850] (2/4) Epoch 24, batch 7100, loss[loss=0.1954, simple_loss=0.2854, pruned_loss=0.0527, over 7312.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2743, pruned_loss=0.05008, over 1465340.42 frames.], batch size: 31, lr: 2.32e-04
2022-07-27 19:46:07,042 INFO [train.py:850] (2/4) Epoch 24, batch 7150, loss[loss=0.215, simple_loss=0.3045, pruned_loss=0.0628, over 7285.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2744, pruned_loss=0.04987, over 1465745.92 frames.], batch size: 27, lr: 2.32e-04
2022-07-27 19:46:50,080 INFO [train.py:850] (2/4) Epoch 24, batch 7200, loss[loss=0.2029, simple_loss=0.2917, pruned_loss=0.057, over 7276.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2744, pruned_loss=0.04957, over 1465271.78 frames.], batch size: 27, lr: 2.32e-04
2022-07-27 19:47:34,133 INFO [train.py:850] (2/4) Epoch 24, batch 7250, loss[loss=0.1958, simple_loss=0.2723, pruned_loss=0.05963, over 7287.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2746, pruned_loss=0.04958, over 1464211.84 frames.], batch size: 19, lr: 2.32e-04
2022-07-27 19:48:16,715 INFO [train.py:850] (2/4) Epoch 24, batch 7300, loss[loss=0.1973, simple_loss=0.2843, pruned_loss=0.05511, over 7473.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2738, pruned_loss=0.04963, over 1464527.02 frames.], batch size: 21, lr: 2.32e-04
2022-07-27 19:49:01,002 INFO [train.py:850] (2/4) Epoch 24, batch 7350, loss[loss=0.1745, simple_loss=0.2668, pruned_loss=0.04103, over 7286.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2743, pruned_loss=0.05026, over 1466476.22 frames.], batch size: 19, lr: 2.31e-04
2022-07-27 19:49:45,018 INFO [train.py:850] (2/4) Epoch 24, batch 7400, loss[loss=0.194, simple_loss=0.2912, pruned_loss=0.04836, over 7390.00 frames.], tot_loss[loss=0.189, simple_loss=0.2758, pruned_loss=0.05112, over 1466312.65 frames.], batch size: 31, lr: 2.31e-04
2022-07-27 19:50:45,046 INFO [train.py:850] (2/4) Epoch 24, batch 7450, loss[loss=0.213, simple_loss=0.2905, pruned_loss=0.06776, over 7374.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2763, pruned_loss=0.05096, over 1466622.00 frames.], batch size: 21, lr: 2.31e-04
2022-07-27 19:51:29,580 INFO [train.py:850] (2/4) Epoch 24, batch 7500, loss[loss=0.2128, simple_loss=0.2963, pruned_loss=0.06466, over 7320.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2756, pruned_loss=0.05068, over 1465710.67 frames.], batch size: 27, lr: 2.31e-04
2022-07-27 19:52:13,984 INFO [train.py:850] (2/4) Epoch 24, batch 7550, loss[loss=0.1554, simple_loss=0.2452, pruned_loss=0.0328, over 7469.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2755, pruned_loss=0.05113, over 1465599.95 frames.], batch size: 20, lr: 2.31e-04
2022-07-27 19:52:58,612 INFO [train.py:850] (2/4) Epoch 24, batch 7600, loss[loss=0.1719, simple_loss=0.2603, pruned_loss=0.0418, over 7424.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2749, pruned_loss=0.05104, over 1466377.09 frames.], batch size: 31, lr: 2.31e-04
2022-07-27 19:53:43,314 INFO [train.py:850] (2/4) Epoch 24, batch 7650, loss[loss=0.1968, simple_loss=0.2813, pruned_loss=0.05612, over 7282.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2745, pruned_loss=0.05088, over 1464761.31 frames.], batch size: 21, lr: 2.31e-04
2022-07-27 19:54:28,275 INFO [train.py:850] (2/4) Epoch 24, batch 7700, loss[loss=0.2176, simple_loss=0.294, pruned_loss=0.07059, over 7299.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2743, pruned_loss=0.0503, over 1464923.62 frames.], batch size: 19, lr: 2.31e-04
2022-07-27 19:55:12,247 INFO [train.py:850] (2/4) Epoch 24, batch 7750, loss[loss=0.1597, simple_loss=0.2409, pruned_loss=0.03921, over 7310.00 frames.], tot_loss[loss=0.1881, simple_loss=0.275, pruned_loss=0.05055, over 1465034.54 frames.], batch size: 18, lr: 2.31e-04
2022-07-27 19:55:56,355 INFO [train.py:850] (2/4) Epoch 24, batch 7800, loss[loss=0.2001, simple_loss=0.2945, pruned_loss=0.05284, over 7343.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2755, pruned_loss=0.05071, over 1465283.14 frames.], batch size: 23, lr: 2.31e-04
2022-07-27 19:56:42,321 INFO [train.py:850] (2/4) Epoch 24, batch 7850, loss[loss=0.1727, simple_loss=0.2645, pruned_loss=0.04049, over 7481.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2745, pruned_loss=0.0495, over 1465346.07 frames.], batch size: 20, lr: 2.31e-04
2022-07-27 19:57:27,362 INFO [train.py:850] (2/4) Epoch 24, batch 7900, loss[loss=0.1741, simple_loss=0.2514, pruned_loss=0.04834, over 7312.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2754, pruned_loss=0.04998, over 1464973.09 frames.], batch size: 17, lr: 2.31e-04
2022-07-27 19:58:14,292 INFO [train.py:850] (2/4) Epoch 24, batch 7950, loss[loss=0.2076, simple_loss=0.2922, pruned_loss=0.06151, over 7210.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2756, pruned_loss=0.0498, over 1465682.84 frames.], batch size: 25, lr: 2.31e-04
2022-07-27 19:58:57,067 INFO [train.py:850] (2/4) Epoch 24, batch 8000, loss[loss=0.1904, simple_loss=0.2769, pruned_loss=0.05201, over 7377.00 frames.], tot_loss[loss=0.1875, simple_loss=0.275, pruned_loss=0.05003, over 1466022.77 frames.], batch size: 21, lr: 2.31e-04
2022-07-27 19:59:41,907 INFO [train.py:850] (2/4) Epoch 24, batch 8050, loss[loss=0.1798, simple_loss=0.2677, pruned_loss=0.04592, over 7384.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2749, pruned_loss=0.04988, over 1465368.18 frames.], batch size: 20, lr: 2.31e-04
2022-07-27 20:00:25,331 INFO [train.py:850] (2/4) Epoch 24, batch 8100, loss[loss=0.1972, simple_loss=0.2876, pruned_loss=0.05343, over 7187.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2753, pruned_loss=0.05025, over 1466231.71 frames.], batch size: 21, lr: 2.31e-04
2022-07-27 20:01:09,938 INFO [train.py:850] (2/4) Epoch 24, batch 8150, loss[loss=0.16, simple_loss=0.2546, pruned_loss=0.03275, over 7289.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2754, pruned_loss=0.05061, over 1465968.72 frames.], batch size: 20, lr: 2.31e-04
2022-07-27 20:01:53,579 INFO [train.py:850] (2/4) Epoch 24, batch 8200, loss[loss=0.2195, simple_loss=0.3013, pruned_loss=0.06885, over 7462.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2759, pruned_loss=0.05046, over 1465374.36 frames.], batch size: 24, lr: 2.31e-04
2022-07-27 20:02:37,919 INFO [train.py:850] (2/4) Epoch 24, batch 8250, loss[loss=0.1574, simple_loss=0.2347, pruned_loss=0.04008, over 7309.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2752, pruned_loss=0.05028, over 1465377.97 frames.], batch size: 17, lr: 2.31e-04
2022-07-27 20:03:23,033 INFO [train.py:850] (2/4) Epoch 24, batch 8300, loss[loss=0.1697, simple_loss=0.2664, pruned_loss=0.03646, over 7197.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2739, pruned_loss=0.04947, over 1465494.34 frames.], batch size: 20, lr: 2.31e-04
2022-07-27 20:04:07,762 INFO [train.py:850] (2/4) Epoch 24, batch 8350, loss[loss=0.2104, simple_loss=0.3018, pruned_loss=0.05952, over 7250.00 frames.], tot_loss[loss=0.1864, simple_loss=0.274, pruned_loss=0.04936, over 1465209.31 frames.], batch size: 25, lr: 2.31e-04
2022-07-27 20:04:54,049 INFO [train.py:850] (2/4) Epoch 24, batch 8400, loss[loss=0.1572, simple_loss=0.2313, pruned_loss=0.04152, over 7455.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2736, pruned_loss=0.04936, over 1466745.68 frames.], batch size: 17, lr: 2.31e-04
2022-07-27 20:05:40,139 INFO [train.py:850] (2/4) Epoch 24, batch 8450, loss[loss=0.1903, simple_loss=0.293, pruned_loss=0.04379, over 7376.00 frames.], tot_loss[loss=0.1865, simple_loss=0.274, pruned_loss=0.04952, over 1465141.19 frames.], batch size: 21, lr: 2.31e-04
2022-07-27 20:06:26,096 INFO [train.py:850] (2/4) Epoch 24, batch 8500, loss[loss=0.1881, simple_loss=0.2702, pruned_loss=0.053, over 7483.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2717, pruned_loss=0.04858, over 1465229.15 frames.], batch size: 20, lr: 2.31e-04
2022-07-27 20:07:11,519 INFO [train.py:850] (2/4) Epoch 24, batch 8550, loss[loss=0.1511, simple_loss=0.2321, pruned_loss=0.03511, over 7322.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2724, pruned_loss=0.04857, over 1464987.66 frames.], batch size: 18, lr: 2.31e-04
2022-07-27 20:07:55,095 INFO [train.py:850] (2/4) Epoch 24, batch 8600, loss[loss=0.1674, simple_loss=0.2672, pruned_loss=0.03375, over 7258.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2746, pruned_loss=0.04977, over 1465599.93 frames.], batch size: 24, lr: 2.31e-04
2022-07-27 20:08:39,133 INFO [train.py:850] (2/4) Epoch 24, batch 8650, loss[loss=0.1587, simple_loss=0.2527, pruned_loss=0.03239, over 7319.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2756, pruned_loss=0.05003, over 1466175.55 frames.], batch size: 27, lr: 2.31e-04
2022-07-27 20:09:21,311 INFO [train.py:850] (2/4) Epoch 24, batch 8700, loss[loss=0.2309, simple_loss=0.296, pruned_loss=0.08295, over 7476.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2756, pruned_loss=0.05013, over 1466075.25 frames.], batch size: 20, lr: 2.31e-04
2022-07-27 20:10:03,960 INFO [train.py:850] (2/4) Epoch 24, batch 8750, loss[loss=0.2181, simple_loss=0.2961, pruned_loss=0.07006, over 7303.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2749, pruned_loss=0.04986, over 1465864.79 frames.], batch size: 22, lr: 2.31e-04
2022-07-27 20:10:46,290 INFO [train.py:850] (2/4) Epoch 24, batch 8800, loss[loss=0.1774, simple_loss=0.272, pruned_loss=0.04143, over 7377.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2753, pruned_loss=0.05005, over 1465518.67 frames.], batch size: 20, lr: 2.31e-04
2022-07-27 20:11:28,633 INFO [train.py:850] (2/4) Epoch 24, batch 8850, loss[loss=0.1883, simple_loss=0.2689, pruned_loss=0.05383, over 7195.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2749, pruned_loss=0.04975, over 1465889.39 frames.], batch size: 18, lr: 2.31e-04
2022-07-27 20:12:55,466 INFO [train.py:850] (2/4) Epoch 25, batch 0, loss[loss=0.1757, simple_loss=0.2645, pruned_loss=0.04348, over 7172.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2645, pruned_loss=0.04348, over 7172.00 frames.], batch size: 17, lr: 2.26e-04
2022-07-27 20:13:40,681 INFO [train.py:850] (2/4) Epoch 25, batch 50, loss[loss=0.223, simple_loss=0.315, pruned_loss=0.0655, over 7195.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2756, pruned_loss=0.04531, over 330357.86 frames.], batch size: 20, lr: 2.26e-04
2022-07-27 20:14:25,487 INFO [train.py:850] (2/4) Epoch 25, batch 100, loss[loss=0.1696, simple_loss=0.267, pruned_loss=0.0361, over 7191.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2723, pruned_loss=0.04417, over 581133.93 frames.], batch size: 23, lr: 2.26e-04
2022-07-27 20:15:11,335 INFO [train.py:850] (2/4) Epoch 25, batch 150, loss[loss=0.1684, simple_loss=0.2712, pruned_loss=0.03277, over 7213.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2724, pruned_loss=0.04421, over 776433.92 frames.], batch size: 24, lr: 2.26e-04
2022-07-27 20:15:55,585 INFO [train.py:850] (2/4) Epoch 25, batch 200, loss[loss=0.1653, simple_loss=0.2556, pruned_loss=0.03751, over 7463.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2732, pruned_loss=0.04453, over 929577.52 frames.], batch size: 26, lr: 2.26e-04
2022-07-27 20:16:39,590 INFO [train.py:850] (2/4) Epoch 25, batch 250, loss[loss=0.2031, simple_loss=0.2933, pruned_loss=0.05648, over 7253.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2736, pruned_loss=0.04441, over 1047848.49 frames.], batch size: 24, lr: 2.26e-04
2022-07-27 20:17:23,402 INFO [train.py:850] (2/4) Epoch 25, batch 300, loss[loss=0.1719, simple_loss=0.2723, pruned_loss=0.03577, over 7484.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2733, pruned_loss=0.04419, over 1139773.37 frames.], batch size: 20, lr: 2.26e-04
2022-07-27 20:18:07,808 INFO [train.py:850] (2/4) Epoch 25, batch 350, loss[loss=0.1533, simple_loss=0.2369, pruned_loss=0.03486, over 7277.00 frames.], tot_loss[loss=0.1802, simple_loss=0.273, pruned_loss=0.04373, over 1211269.68 frames.], batch size: 16, lr: 2.26e-04
2022-07-27 20:18:53,381 INFO [train.py:850] (2/4) Epoch 25, batch 400, loss[loss=0.1675, simple_loss=0.2584, pruned_loss=0.0383, over 7377.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2722, pruned_loss=0.04307, over 1267104.22 frames.], batch size: 20, lr: 2.26e-04
2022-07-27 20:19:38,727 INFO [train.py:850] (2/4) Epoch 25, batch 450, loss[loss=0.1747, simple_loss=0.2706, pruned_loss=0.03943, over 7478.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2724, pruned_loss=0.04249, over 1310598.78 frames.], batch size: 24, lr: 2.26e-04
2022-07-27 20:20:24,483 INFO [train.py:850] (2/4) Epoch 25, batch 500, loss[loss=0.1495, simple_loss=0.2384, pruned_loss=0.03027, over 7287.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2714, pruned_loss=0.04186, over 1344165.57 frames.], batch size: 16, lr: 2.26e-04
2022-07-27 20:21:09,021 INFO [train.py:850] (2/4) Epoch 25, batch 550, loss[loss=0.1513, simple_loss=0.2513, pruned_loss=0.02567, over 7476.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2708, pruned_loss=0.04184, over 1370915.52 frames.], batch size: 21, lr: 2.26e-04
2022-07-27 20:21:52,217 INFO [train.py:850] (2/4) Epoch 25, batch 600, loss[loss=0.2148, simple_loss=0.2952, pruned_loss=0.0672, over 7216.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2712, pruned_loss=0.04208, over 1391540.56 frames.], batch size: 24, lr: 2.26e-04
2022-07-27 20:22:36,585 INFO [train.py:850] (2/4) Epoch 25, batch 650, loss[loss=0.178, simple_loss=0.2708, pruned_loss=0.0426, over 7473.00 frames.], tot_loss[loss=0.178, simple_loss=0.2717, pruned_loss=0.04219, over 1408796.27 frames.], batch size: 21, lr: 2.26e-04
2022-07-27 20:23:20,533 INFO [train.py:850] (2/4) Epoch 25, batch 700, loss[loss=0.1597, simple_loss=0.2542, pruned_loss=0.03263, over 7208.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2704, pruned_loss=0.04157, over 1420664.92 frames.], batch size: 20, lr: 2.26e-04
2022-07-27 20:24:03,907 INFO [train.py:850] (2/4) Epoch 25, batch 750, loss[loss=0.1927, simple_loss=0.295, pruned_loss=0.0452, over 7468.00 frames.], tot_loss[loss=0.1765, simple_loss=0.27, pruned_loss=0.04149, over 1430844.51 frames.], batch size: 21, lr: 2.26e-04
2022-07-27 20:24:47,322 INFO [train.py:850] (2/4) Epoch 25, batch 800, loss[loss=0.1862, simple_loss=0.2871, pruned_loss=0.04264, over 7174.00 frames.], tot_loss[loss=0.1755, simple_loss=0.269, pruned_loss=0.04096, over 1438613.18 frames.], batch size: 22, lr: 2.26e-04
2022-07-27 20:25:31,182 INFO [train.py:850] (2/4) Epoch 25, batch 850, loss[loss=0.1656, simple_loss=0.2664, pruned_loss=0.0324, over 7303.00 frames.], tot_loss[loss=0.1745, simple_loss=0.268, pruned_loss=0.0405, over 1443530.87 frames.], batch size: 22, lr: 2.26e-04
2022-07-27 20:26:14,817 INFO [train.py:850] (2/4) Epoch 25, batch 900, loss[loss=0.1703, simple_loss=0.2618, pruned_loss=0.03943, over 7390.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2674, pruned_loss=0.03993, over 1448233.76 frames.], batch size: 20, lr: 2.26e-04
2022-07-27 20:26:58,635 INFO [train.py:850] (2/4) Epoch 25, batch 950, loss[loss=0.1906, simple_loss=0.2984, pruned_loss=0.04136, over 7379.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2692, pruned_loss=0.04076, over 1451947.83 frames.], batch size: 20, lr: 2.26e-04
2022-07-27 20:27:42,621 INFO [train.py:850] (2/4) Epoch 25, batch 1000, loss[loss=0.1808, simple_loss=0.2818, pruned_loss=0.03986, over 7180.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2696, pruned_loss=0.04084, over 1453690.13 frames.], batch size: 21, lr: 2.26e-04
2022-07-27 20:28:26,038 INFO [train.py:850] (2/4) Epoch 25, batch 1050, loss[loss=0.1608, simple_loss=0.2476, pruned_loss=0.03702, over 7220.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2705, pruned_loss=0.04131, over 1456151.12 frames.], batch size: 16, lr: 2.26e-04
2022-07-27 20:29:09,212 INFO [train.py:850] (2/4) Epoch 25, batch 1100, loss[loss=0.1786, simple_loss=0.2699, pruned_loss=0.04361, over 7390.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2702, pruned_loss=0.04165, over 1459674.79 frames.], batch size: 19, lr: 2.26e-04
2022-07-27 20:29:52,990 INFO [train.py:850] (2/4) Epoch 25, batch 1150, loss[loss=0.1759, simple_loss=0.2753, pruned_loss=0.03829, over 7329.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2711, pruned_loss=0.04211, over 1461865.75 frames.], batch size: 23, lr: 2.25e-04
2022-07-27 20:30:36,153 INFO [train.py:850] (2/4) Epoch 25, batch 1200, loss[loss=0.1453, simple_loss=0.2291, pruned_loss=0.03071, over 7260.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2712, pruned_loss=0.0423, over 1462707.66 frames.], batch size: 16, lr: 2.25e-04
2022-07-27 20:31:20,878 INFO [train.py:850] (2/4) Epoch 25, batch 1250, loss[loss=0.1664, simple_loss=0.2535, pruned_loss=0.03967, over 7395.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2725, pruned_loss=0.04288, over 1462086.96 frames.], batch size: 19, lr: 2.25e-04
2022-07-27 20:32:04,049 INFO [train.py:850] (2/4) Epoch 25, batch 1300, loss[loss=0.1972, simple_loss=0.2855, pruned_loss=0.05444, over 7380.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2718, pruned_loss=0.04255, over 1462423.22 frames.], batch size: 20, lr: 2.25e-04
2022-07-27 20:32:48,136 INFO [train.py:850] (2/4) Epoch 25, batch 1350, loss[loss=0.1759, simple_loss=0.2677, pruned_loss=0.04205, over 7221.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2711, pruned_loss=0.0422, over 1462909.80 frames.], batch size: 25, lr: 2.25e-04
2022-07-27 20:33:30,787 INFO [train.py:850] (2/4) Epoch 25, batch 1400, loss[loss=0.2002, simple_loss=0.2877, pruned_loss=0.05629, over 7301.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2723, pruned_loss=0.04321, over 1463244.24 frames.], batch size: 19, lr: 2.25e-04
2022-07-27 20:34:13,790 INFO [train.py:850] (2/4) Epoch 25, batch 1450, loss[loss=0.1591, simple_loss=0.2496, pruned_loss=0.03424, over 7318.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2725, pruned_loss=0.04332, over 1464457.69 frames.], batch size: 18, lr: 2.25e-04
2022-07-27 20:34:57,834 INFO [train.py:850] (2/4) Epoch 25, batch 1500, loss[loss=0.1772, simple_loss=0.2757, pruned_loss=0.03935, over 7202.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2738, pruned_loss=0.04366, over 1464722.12 frames.], batch size: 20, lr: 2.25e-04
2022-07-27 20:35:41,270 INFO [train.py:850] (2/4) Epoch 25, batch 1550, loss[loss=0.2149, simple_loss=0.3147, pruned_loss=0.0576, over 7483.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2746, pruned_loss=0.0436, over 1464786.24 frames.], batch size: 21, lr: 2.25e-04
2022-07-27 20:36:25,250 INFO [train.py:850] (2/4) Epoch 25, batch 1600, loss[loss=0.1726, simple_loss=0.2764, pruned_loss=0.0344, over 7494.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2741, pruned_loss=0.04335, over 1465139.33 frames.], batch size: 24, lr: 2.25e-04
2022-07-27 20:37:08,763 INFO [train.py:850] (2/4) Epoch 25, batch 1650, loss[loss=0.1634, simple_loss=0.2611, pruned_loss=0.03282, over 7297.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2735, pruned_loss=0.04314, over 1464182.18 frames.], batch size: 22, lr: 2.25e-04
2022-07-27 20:37:51,414 INFO [train.py:850] (2/4) Epoch 25, batch 1700, loss[loss=0.1807, simple_loss=0.2732, pruned_loss=0.04407, over 7345.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2725, pruned_loss=0.04242, over 1463935.08 frames.], batch size: 23, lr: 2.25e-04
2022-07-27 20:38:35,405 INFO [train.py:850] (2/4) Epoch 25, batch 1750, loss[loss=0.1598, simple_loss=0.2627, pruned_loss=0.02839, over 7478.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2739, pruned_loss=0.04272, over 1464447.26 frames.], batch size: 21, lr: 2.25e-04
2022-07-27 20:39:18,129 INFO [train.py:850] (2/4) Epoch 25, batch 1800, loss[loss=0.1765, simple_loss=0.2615, pruned_loss=0.04569, over 7289.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2731, pruned_loss=0.04239, over 1465592.17 frames.], batch size: 20, lr: 2.25e-04
2022-07-27 20:40:01,949 INFO [train.py:850] (2/4) Epoch 25, batch 1850, loss[loss=0.2075, simple_loss=0.291, pruned_loss=0.06201, over 7195.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2718, pruned_loss=0.04179, over 1466235.84 frames.], batch size: 19, lr: 2.25e-04
2022-07-27 20:40:46,624 INFO [train.py:850] (2/4) Epoch 25, batch 1900, loss[loss=0.163, simple_loss=0.2519, pruned_loss=0.03701, over 7163.00 frames.], tot_loss[loss=0.1779, simple_loss=0.272, pruned_loss=0.04187, over 1464708.76 frames.], batch size: 17, lr: 2.25e-04
2022-07-27 20:41:30,538 INFO [train.py:850] (2/4) Epoch 25, batch 1950, loss[loss=0.1667, simple_loss=0.2727, pruned_loss=0.03034, over 7184.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2736, pruned_loss=0.04277, over 1464398.53 frames.], batch size: 21, lr: 2.25e-04
2022-07-27 20:42:14,270 INFO [train.py:850] (2/4) Epoch 25, batch 2000, loss[loss=0.1906, simple_loss=0.2933, pruned_loss=0.04388, over 7260.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2742, pruned_loss=0.04305, over 1464267.12 frames.], batch size: 27, lr: 2.25e-04
2022-07-27 20:42:58,355 INFO [train.py:850] (2/4) Epoch 25, batch 2050, loss[loss=0.1499, simple_loss=0.2363, pruned_loss=0.03176, over 7143.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2735, pruned_loss=0.04308, over 1465431.51 frames.], batch size: 17, lr: 2.25e-04
2022-07-27 20:43:42,826 INFO [train.py:850] (2/4) Epoch 25, batch 2100, loss[loss=0.1873, simple_loss=0.2644, pruned_loss=0.05511, over 7445.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2732, pruned_loss=0.04265, over 1466191.45 frames.], batch size: 17, lr: 2.25e-04
2022-07-27 20:44:28,220 INFO [train.py:850] (2/4) Epoch 25, batch 2150, loss[loss=0.1614, simple_loss=0.251, pruned_loss=0.03586, over 7201.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2715, pruned_loss=0.04209, over 1465821.51 frames.], batch size: 19, lr: 2.25e-04
2022-07-27 20:45:11,465 INFO [train.py:850] (2/4) Epoch 25, batch 2200, loss[loss=0.1963, simple_loss=0.3049, pruned_loss=0.04389, over 7473.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2714, pruned_loss=0.0417, over 1464785.06 frames.], batch size: 21, lr: 2.25e-04
2022-07-27 20:45:55,451 INFO [train.py:850] (2/4) Epoch 25, batch 2250, loss[loss=0.2197, simple_loss=0.3106, pruned_loss=0.06439, over 7304.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2719, pruned_loss=0.04212, over 1464736.94 frames.], batch size: 31, lr: 2.25e-04
2022-07-27 20:46:37,880 INFO [train.py:850] (2/4) Epoch 25, batch 2300, loss[loss=0.2113, simple_loss=0.296, pruned_loss=0.06332, over 7389.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2722, pruned_loss=0.04253, over 1464699.81 frames.], batch size: 21, lr: 2.25e-04
2022-07-27 20:47:23,420 INFO [train.py:850] (2/4) Epoch 25, batch 2350, loss[loss=0.1777, simple_loss=0.2834, pruned_loss=0.03601, over 7484.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2725, pruned_loss=0.04219, over 1465542.71 frames.], batch size: 21, lr: 2.25e-04
2022-07-27 20:48:06,633 INFO [train.py:850] (2/4) Epoch 25, batch 2400, loss[loss=0.1937, simple_loss=0.3016, pruned_loss=0.04287, over 7177.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2723, pruned_loss=0.04227, over 1465759.10 frames.], batch size: 22, lr: 2.25e-04
2022-07-27 20:48:51,157 INFO [train.py:850] (2/4) Epoch 25, batch 2450, loss[loss=0.1334, simple_loss=0.2168, pruned_loss=0.02499, over 7313.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2721, pruned_loss=0.04199, over 1466720.90 frames.], batch size: 17, lr: 2.25e-04
2022-07-27 20:49:34,073 INFO [train.py:850] (2/4) Epoch 25, batch 2500, loss[loss=0.1762, simple_loss=0.2703, pruned_loss=0.04101, over 7396.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2714, pruned_loss=0.04182, over 1465597.71 frames.], batch size: 19, lr: 2.25e-04
2022-07-27 20:50:33,405 INFO [train.py:850] (2/4) Epoch 25, batch 2550, loss[loss=0.1644, simple_loss=0.2616, pruned_loss=0.03357, over 7195.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2723, pruned_loss=0.04197, over 1464679.28 frames.], batch size: 18, lr: 2.25e-04
2022-07-27 20:51:16,883 INFO [train.py:850] (2/4) Epoch 25, batch 2600, loss[loss=0.1793, simple_loss=0.2737, pruned_loss=0.04241, over 7290.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2721, pruned_loss=0.0418, over 1464961.27 frames.], batch size: 20, lr: 2.25e-04
2022-07-27 20:52:00,394 INFO [train.py:850] (2/4) Epoch 25, batch 2650, loss[loss=0.1625, simple_loss=0.2688, pruned_loss=0.02813, over 7414.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2724, pruned_loss=0.04173, over 1463921.73 frames.], batch size: 22, lr: 2.25e-04
2022-07-27 20:52:44,520 INFO [train.py:850] (2/4) Epoch 25, batch 2700, loss[loss=0.1587, simple_loss=0.2536, pruned_loss=0.03193, over 7477.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2732, pruned_loss=0.0427, over 1465420.66 frames.], batch size: 20, lr: 2.25e-04
2022-07-27 20:53:28,522 INFO [train.py:850] (2/4) Epoch 25, batch 2750, loss[loss=0.1548, simple_loss=0.2401, pruned_loss=0.03474, over 7295.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2726, pruned_loss=0.04229, over 1464809.17 frames.], batch size: 17, lr: 2.25e-04
2022-07-27 20:54:11,832 INFO [train.py:850] (2/4) Epoch 25, batch 2800, loss[loss=0.1556, simple_loss=0.2468, pruned_loss=0.03226, over 7386.00 frames.], tot_loss[loss=0.1791, simple_loss=0.273, pruned_loss=0.04262, over 1465034.98 frames.], batch size: 19, lr: 2.25e-04
2022-07-27 20:54:55,581 INFO [train.py:850] (2/4) Epoch 25, batch 2850, loss[loss=0.1883, simple_loss=0.2923, pruned_loss=0.04215, over 7172.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2742, pruned_loss=0.0432, over 1464582.38 frames.], batch size: 22, lr: 2.25e-04
2022-07-27 20:55:38,682 INFO [train.py:850] (2/4) Epoch 25, batch 2900, loss[loss=0.1855, simple_loss=0.2759, pruned_loss=0.04755, over 7389.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2739, pruned_loss=0.04373, over 1466375.45 frames.], batch size: 19, lr: 2.25e-04
2022-07-27 20:56:23,235 INFO [train.py:850] (2/4) Epoch 25, batch 2950, loss[loss=0.2133, simple_loss=0.304, pruned_loss=0.06133, over 7203.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2738, pruned_loss=0.0436, over 1465708.50 frames.], batch size: 19, lr: 2.25e-04
2022-07-27 20:57:05,791 INFO [train.py:850] (2/4) Epoch 25, batch 3000, loss[loss=0.1787, simple_loss=0.2748, pruned_loss=0.04131, over 7212.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2737, pruned_loss=0.04341, over 1466076.74 frames.], batch size: 20, lr: 2.25e-04
2022-07-27 20:57:05,792 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 20:57:28,519 INFO [train.py:879] (2/4) Epoch 25, validation: loss=0.1921, simple_loss=0.2834, pruned_loss=0.05043, over 924787.00 frames.
2022-07-27 20:58:12,833 INFO [train.py:850] (2/4) Epoch 25, batch 3050, loss[loss=0.1528, simple_loss=0.2488, pruned_loss=0.02838, over 7200.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2726, pruned_loss=0.04262, over 1466209.44 frames.], batch size: 20, lr: 2.24e-04
2022-07-27 20:58:55,509 INFO [train.py:850] (2/4) Epoch 25, batch 3100, loss[loss=0.1964, simple_loss=0.2952, pruned_loss=0.04881, over 7428.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2726, pruned_loss=0.04229, over 1465865.61 frames.], batch size: 31, lr: 2.24e-04
2022-07-27 20:59:39,735 INFO [train.py:850] (2/4) Epoch 25, batch 3150, loss[loss=0.1493, simple_loss=0.2516, pruned_loss=0.02352, over 7470.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2724, pruned_loss=0.04227, over 1464851.93 frames.], batch size: 21, lr: 2.24e-04
2022-07-27 21:00:23,660 INFO [train.py:850] (2/4) Epoch 25, batch 3200, loss[loss=0.1999, simple_loss=0.288, pruned_loss=0.05591, over 7491.00 frames.], tot_loss[loss=0.178, simple_loss=0.2719, pruned_loss=0.04204, over 1465317.34 frames.], batch size: 19, lr: 2.24e-04
2022-07-27 21:01:08,858 INFO [train.py:850] (2/4) Epoch 25, batch 3250, loss[loss=0.2058, simple_loss=0.3023, pruned_loss=0.05459, over 7194.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2723, pruned_loss=0.042, over 1465321.67 frames.], batch size: 25, lr: 2.24e-04
2022-07-27 21:01:52,063 INFO [train.py:850] (2/4) Epoch 25, batch 3300, loss[loss=0.1638, simple_loss=0.2692, pruned_loss=0.02917, over 7197.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2727, pruned_loss=0.04215, over 1465653.63 frames.], batch size: 19, lr: 2.24e-04
2022-07-27 21:02:34,679 INFO [train.py:850] (2/4) Epoch 25, batch 3350, loss[loss=0.1819, simple_loss=0.2816, pruned_loss=0.0411, over 7416.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2729, pruned_loss=0.04198, over 1465904.13 frames.], batch size: 31, lr: 2.24e-04
2022-07-27 21:03:18,201 INFO [train.py:850] (2/4) Epoch 25, batch 3400, loss[loss=0.1565, simple_loss=0.2605, pruned_loss=0.02624, over 7469.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2724, pruned_loss=0.04188, over 1464555.09 frames.], batch size: 21, lr: 2.24e-04
2022-07-27 21:04:01,925 INFO [train.py:850] (2/4) Epoch 25, batch 3450, loss[loss=0.1921, simple_loss=0.2922, pruned_loss=0.04598, over 7328.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2719, pruned_loss=0.04171, over 1463589.99 frames.], batch size: 27, lr: 2.24e-04
2022-07-27 21:04:46,173 INFO [train.py:850] (2/4) Epoch 25, batch 3500, loss[loss=0.1567, simple_loss=0.2466, pruned_loss=0.03345, over 7427.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2726, pruned_loss=0.04246, over 1464097.47 frames.], batch size: 18, lr: 2.24e-04
2022-07-27 21:05:28,917 INFO [train.py:850] (2/4) Epoch 25, batch 3550, loss[loss=0.1946, simple_loss=0.2971, pruned_loss=0.04602, over 7482.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2724, pruned_loss=0.04247, over 1464111.25 frames.], batch size: 21, lr: 2.24e-04
2022-07-27 21:06:10,883 INFO [train.py:850] (2/4) Epoch 25, batch 3600, loss[loss=0.1655, simple_loss=0.2483, pruned_loss=0.04137, over 7213.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2719, pruned_loss=0.04241, over 1463593.27 frames.], batch size: 16, lr: 2.24e-04
2022-07-27 21:06:56,065 INFO [train.py:850] (2/4) Epoch 25, batch 3650, loss[loss=0.1682, simple_loss=0.2662, pruned_loss=0.03513, over 7386.00 frames.], tot_loss[loss=0.178, simple_loss=0.2717, pruned_loss=0.04216, over 1463583.73 frames.], batch size: 20, lr: 2.24e-04
2022-07-27 21:07:39,365 INFO [train.py:850] (2/4) Epoch 25, batch 3700, loss[loss=0.1656, simple_loss=0.2594, pruned_loss=0.03588, over 7203.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2726, pruned_loss=0.04209, over 1463455.90 frames.], batch size: 20, lr: 2.24e-04
2022-07-27 21:08:24,013 INFO [train.py:850] (2/4) Epoch 25, batch 3750, loss[loss=0.1677, simple_loss=0.2751, pruned_loss=0.03018, over 7228.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2721, pruned_loss=0.0418, over 1463969.31 frames.], batch size: 27, lr: 2.24e-04
2022-07-27 21:09:06,956 INFO [train.py:850] (2/4) Epoch 25, batch 3800, loss[loss=0.1596, simple_loss=0.2338, pruned_loss=0.04275, over 7243.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2696, pruned_loss=0.04125, over 1464185.58 frames.], batch size: 16, lr: 2.24e-04
2022-07-27 21:09:50,215 INFO [train.py:850] (2/4) Epoch 25, batch 3850, loss[loss=0.1872, simple_loss=0.2814, pruned_loss=0.04651, over 7199.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2709, pruned_loss=0.04192, over 1463379.44 frames.], batch size: 19, lr: 2.24e-04
2022-07-27 21:10:33,987 INFO [train.py:850] (2/4) Epoch 25, batch 3900, loss[loss=0.1615, simple_loss=0.2706, pruned_loss=0.02617, over 7300.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2699, pruned_loss=0.04112, over 1463381.75 frames.], batch size: 22, lr: 2.24e-04
2022-07-27 21:11:17,466 INFO [train.py:850] (2/4) Epoch 25, batch 3950, loss[loss=0.1664, simple_loss=0.2683, pruned_loss=0.03229, over 7315.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2696, pruned_loss=0.04087, over 1463146.56 frames.], batch size: 22, lr: 2.24e-04
2022-07-27 21:12:01,721 INFO [train.py:850] (2/4) Epoch 25, batch 4000, loss[loss=0.1754, simple_loss=0.2642, pruned_loss=0.04337, over 7295.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2704, pruned_loss=0.04165, over 1463223.02 frames.], batch size: 20, lr: 2.24e-04
2022-07-27 21:12:45,284 INFO [train.py:850] (2/4) Epoch 25, batch 4050, loss[loss=0.178, simple_loss=0.2764, pruned_loss=0.03982, over 7344.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2708, pruned_loss=0.04138, over 1463071.75 frames.], batch size: 23, lr: 2.24e-04
2022-07-27 21:13:29,994 INFO [train.py:850] (2/4) Epoch 25, batch 4100, loss[loss=0.1748, simple_loss=0.2713, pruned_loss=0.03912, over 7295.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2715, pruned_loss=0.0416, over 1462768.86 frames.], batch size: 19, lr: 2.24e-04
2022-07-27 21:14:14,966 INFO [train.py:850] (2/4) Epoch 25, batch 4150, loss[loss=0.1589, simple_loss=0.2408, pruned_loss=0.03846, over 7311.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2718, pruned_loss=0.0425, over 1464920.45 frames.], batch size: 17, lr: 2.24e-04
2022-07-27 21:14:58,003 INFO [train.py:850] (2/4) Epoch 25, batch 4200, loss[loss=0.189, simple_loss=0.2874, pruned_loss=0.04531, over 7176.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2741, pruned_loss=0.04448, over 1465544.63 frames.], batch size: 22, lr: 2.24e-04
2022-07-27 21:15:42,280 INFO [train.py:850] (2/4) Epoch 25, batch 4250, loss[loss=0.1892, simple_loss=0.2686, pruned_loss=0.05489, over 7301.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2732, pruned_loss=0.04455, over 1465915.96 frames.], batch size: 17, lr: 2.24e-04
2022-07-27 21:16:25,452 INFO [train.py:850] (2/4) Epoch 25, batch 4300, loss[loss=0.1504, simple_loss=0.2443, pruned_loss=0.02831, over 7197.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2736, pruned_loss=0.045, over 1465558.54 frames.], batch size: 19, lr: 2.24e-04
2022-07-27 21:17:10,472 INFO [train.py:850] (2/4) Epoch 25, batch 4350, loss[loss=0.1875, simple_loss=0.2729, pruned_loss=0.05107, over 7388.00 frames.], tot_loss[loss=0.183, simple_loss=0.2745, pruned_loss=0.0458, over 1465584.65 frames.], batch size: 20, lr: 2.24e-04
2022-07-27 21:17:53,226 INFO [train.py:850] (2/4) Epoch 25, batch 4400, loss[loss=0.1642, simple_loss=0.268, pruned_loss=0.03023, over 7406.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2752, pruned_loss=0.04673, over 1465252.69 frames.], batch size: 39, lr: 2.24e-04
2022-07-27 21:18:39,068 INFO [train.py:850] (2/4) Epoch 25, batch 4450, loss[loss=0.1578, simple_loss=0.2342, pruned_loss=0.04066, over 7467.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2757, pruned_loss=0.04763, over 1464772.95 frames.], batch size: 17, lr: 2.24e-04
2022-07-27 21:19:24,554 INFO [train.py:850] (2/4) Epoch 25, batch 4500, loss[loss=0.1914, simple_loss=0.2844, pruned_loss=0.04925, over 7168.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2756, pruned_loss=0.04829, over 1464594.89 frames.], batch size: 22, lr: 2.24e-04
2022-07-27 21:20:09,915 INFO [train.py:850] (2/4) Epoch 25, batch 4550, loss[loss=0.1821, simple_loss=0.2788, pruned_loss=0.04267, over 7298.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2759, pruned_loss=0.04931, over 1464849.48 frames.], batch size: 22, lr: 2.24e-04
2022-07-27 21:20:54,327 INFO [train.py:850] (2/4) Epoch 25, batch 4600, loss[loss=0.1779, simple_loss=0.262, pruned_loss=0.04689, over 7435.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2762, pruned_loss=0.04963, over 1465944.59 frames.], batch size: 18, lr: 2.24e-04
2022-07-27 21:21:37,634 INFO [train.py:850] (2/4) Epoch 25, batch 4650, loss[loss=0.1684, simple_loss=0.2573, pruned_loss=0.03974, over 7376.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2765, pruned_loss=0.05005, over 1465249.51 frames.], batch size: 19, lr: 2.24e-04
2022-07-27 21:22:20,915 INFO [train.py:850] (2/4) Epoch 25, batch 4700, loss[loss=0.1813, simple_loss=0.2541, pruned_loss=0.05431, over 7317.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2764, pruned_loss=0.05011, over 1465854.44 frames.], batch size: 17, lr: 2.24e-04
2022-07-27 21:23:05,500 INFO [train.py:850] (2/4) Epoch 25, batch 4750, loss[loss=0.1894, simple_loss=0.2822, pruned_loss=0.04826, over 7491.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2762, pruned_loss=0.05055, over 1465793.34 frames.], batch size: 25, lr: 2.24e-04
2022-07-27 21:23:49,408 INFO [train.py:850] (2/4) Epoch 25, batch 4800, loss[loss=0.1511, simple_loss=0.2448, pruned_loss=0.02871, over 7294.00 frames.], tot_loss[loss=0.1866, simple_loss=0.274, pruned_loss=0.04957, over 1464552.30 frames.], batch size: 20, lr: 2.24e-04
2022-07-27 21:24:35,300 INFO [train.py:850] (2/4) Epoch 25, batch 4850, loss[loss=0.1638, simple_loss=0.2572, pruned_loss=0.03518, over 7194.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2749, pruned_loss=0.04997, over 1465161.09 frames.], batch size: 20, lr: 2.24e-04
2022-07-27 21:25:19,066 INFO [train.py:850] (2/4) Epoch 25, batch 4900, loss[loss=0.1896, simple_loss=0.2705, pruned_loss=0.05434, over 7183.00 frames.], tot_loss[loss=0.188, simple_loss=0.2752, pruned_loss=0.05037, over 1465890.37 frames.], batch size: 21, lr: 2.24e-04
2022-07-27 21:26:03,790 INFO [train.py:850] (2/4) Epoch 25, batch 4950, loss[loss=0.2241, simple_loss=0.3067, pruned_loss=0.07074, over 7413.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2763, pruned_loss=0.05096, over 1466144.70 frames.], batch size: 39, lr: 2.24e-04
2022-07-27 21:26:46,785 INFO [train.py:850] (2/4) Epoch 25, batch 5000, loss[loss=0.1923, simple_loss=0.2889, pruned_loss=0.04783, over 7309.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2753, pruned_loss=0.05054, over 1465158.88 frames.], batch size: 22, lr: 2.23e-04
2022-07-27 21:27:30,452 INFO [train.py:850] (2/4) Epoch 25, batch 5050, loss[loss=0.2536, simple_loss=0.3305, pruned_loss=0.08833, over 7294.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2764, pruned_loss=0.0511, over 1465240.07 frames.], batch size: 20, lr: 2.23e-04
2022-07-27 21:28:14,684 INFO [train.py:850] (2/4) Epoch 25, batch 5100, loss[loss=0.1537, simple_loss=0.2418, pruned_loss=0.03274, over 7392.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2766, pruned_loss=0.05141, over 1465607.79 frames.], batch size: 19, lr: 2.23e-04
2022-07-27 21:28:57,469 INFO [train.py:850] (2/4) Epoch 25, batch 5150, loss[loss=0.2028, simple_loss=0.3028, pruned_loss=0.05136, over 7169.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2767, pruned_loss=0.05172, over 1466557.18 frames.], batch size: 21, lr: 2.23e-04
2022-07-27 21:29:41,996 INFO [train.py:850] (2/4) Epoch 25, batch 5200, loss[loss=0.1845, simple_loss=0.2604, pruned_loss=0.05431, over 7304.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2754, pruned_loss=0.05096, over 1465595.31 frames.], batch size: 17, lr: 2.23e-04
2022-07-27 21:30:26,002 INFO [train.py:850] (2/4) Epoch 25, batch 5250, loss[loss=0.171, simple_loss=0.2531, pruned_loss=0.04439, over 7396.00 frames.], tot_loss[loss=0.189, simple_loss=0.2755, pruned_loss=0.0512, over 1466558.66 frames.], batch size: 19, lr: 2.23e-04
2022-07-27 21:31:09,466 INFO [train.py:850] (2/4) Epoch 25, batch 5300, loss[loss=0.1942, simple_loss=0.2887, pruned_loss=0.04987, over 7174.00 frames.], tot_loss[loss=0.1881, simple_loss=0.275, pruned_loss=0.0506, over 1465838.74 frames.], batch size: 22, lr: 2.23e-04
2022-07-27 21:31:53,623 INFO [train.py:850] (2/4) Epoch 25, batch 5350, loss[loss=0.2162, simple_loss=0.2845, pruned_loss=0.07391, over 7185.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2753, pruned_loss=0.05076, over 1464202.70 frames.], batch size: 18, lr: 2.23e-04
2022-07-27 21:32:36,977 INFO [train.py:850] (2/4) Epoch 25, batch 5400, loss[loss=0.168, simple_loss=0.2528, pruned_loss=0.04165, over 7307.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2758, pruned_loss=0.05089, over 1464475.36 frames.], batch size: 18, lr: 2.23e-04
2022-07-27 21:33:21,254 INFO [train.py:850] (2/4) Epoch 25, batch 5450, loss[loss=0.2057, simple_loss=0.2974, pruned_loss=0.05705, over 7301.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2755, pruned_loss=0.05093, over 1464784.48 frames.], batch size: 19, lr: 2.23e-04
2022-07-27 21:34:04,034 INFO [train.py:850] (2/4) Epoch 25, batch 5500, loss[loss=0.1645, simple_loss=0.2559, pruned_loss=0.0365, over 7294.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2756, pruned_loss=0.05071, over 1464713.86 frames.], batch size: 21, lr: 2.23e-04
2022-07-27 21:34:48,540 INFO [train.py:850] (2/4) Epoch 25, batch 5550, loss[loss=0.1714, simple_loss=0.2618, pruned_loss=0.04044, over 7225.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2756, pruned_loss=0.05036, over 1464066.64 frames.], batch size: 24, lr: 2.23e-04
2022-07-27 21:35:31,241 INFO [train.py:850] (2/4) Epoch 25, batch 5600, loss[loss=0.1681, simple_loss=0.2609, pruned_loss=0.03769, over 7197.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2754, pruned_loss=0.05008, over 1465165.99 frames.], batch size: 19, lr: 2.23e-04
2022-07-27 21:36:14,974 INFO [train.py:850] (2/4) Epoch 25, batch 5650, loss[loss=0.1866, simple_loss=0.2746, pruned_loss=0.04931, over 7346.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2762, pruned_loss=0.05078, over 1465996.24 frames.], batch size: 23, lr: 2.23e-04
2022-07-27 21:36:58,922 INFO [train.py:850] (2/4) Epoch 25, batch 5700, loss[loss=0.1615, simple_loss=0.2383, pruned_loss=0.04239, over 7166.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2766, pruned_loss=0.05091, over 1464616.48 frames.], batch size: 17, lr: 2.23e-04
2022-07-27 21:37:41,974 INFO [train.py:850] (2/4) Epoch 25, batch 5750, loss[loss=0.2395, simple_loss=0.3142, pruned_loss=0.08242, over 7446.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2764, pruned_loss=0.05094, over 1466041.17 frames.], batch size: 71, lr: 2.23e-04
2022-07-27 21:38:26,246 INFO [train.py:850] (2/4) Epoch 25, batch 5800, loss[loss=0.1832, simple_loss=0.2643, pruned_loss=0.05105, over 7111.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2748, pruned_loss=0.05014, over 1466086.57 frames.], batch size: 18, lr: 2.23e-04
2022-07-27 21:39:10,691 INFO [train.py:850] (2/4) Epoch 25, batch 5850, loss[loss=0.1532, simple_loss=0.2304, pruned_loss=0.03795, over 7215.00 frames.], tot_loss[loss=0.187, simple_loss=0.2741, pruned_loss=0.04992, over 1465764.43 frames.], batch size: 16, lr: 2.23e-04
2022-07-27 21:39:53,737 INFO [train.py:850] (2/4) Epoch 25, batch 5900, loss[loss=0.2579, simple_loss=0.3375, pruned_loss=0.08912, over 7291.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2751, pruned_loss=0.05034, over 1465838.27 frames.], batch size: 22, lr: 2.23e-04
2022-07-27 21:40:37,425 INFO [train.py:850] (2/4) Epoch 25, batch 5950, loss[loss=0.1693, simple_loss=0.271, pruned_loss=0.03379, over 7420.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2752, pruned_loss=0.04996, over 1465973.64 frames.], batch size: 22, lr: 2.23e-04
2022-07-27 21:41:20,699 INFO [train.py:850] (2/4) Epoch 25, batch 6000, loss[loss=0.2071, simple_loss=0.3001, pruned_loss=0.05704, over 7221.00 frames.], tot_loss[loss=0.1894, simple_loss=0.277, pruned_loss=0.05086, over 1465882.34 frames.], batch size: 25, lr: 2.23e-04
2022-07-27 21:41:20,700 INFO [train.py:870] (2/4) Computing validation loss
2022-07-27 21:41:43,399 INFO [train.py:879] (2/4) Epoch 25, validation: loss=0.1855, simple_loss=0.2788, pruned_loss=0.0461, over 924787.00 frames.
2022-07-27 21:42:27,180 INFO [train.py:850] (2/4) Epoch 25, batch 6050, loss[loss=0.1897, simple_loss=0.2627, pruned_loss=0.05834, over 7257.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2769, pruned_loss=0.05076, over 1465279.68 frames.], batch size: 16, lr: 2.23e-04
2022-07-27 21:43:13,431 INFO [train.py:850] (2/4) Epoch 25, batch 6100, loss[loss=0.1842, simple_loss=0.2791, pruned_loss=0.04461, over 7346.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2773, pruned_loss=0.05124, over 1465310.69 frames.], batch size: 27, lr: 2.23e-04
2022-07-27 21:43:59,809 INFO [train.py:850] (2/4) Epoch 25, batch 6150, loss[loss=0.1632, simple_loss=0.2494, pruned_loss=0.03846, over 7454.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2769, pruned_loss=0.05118, over 1465165.05 frames.], batch size: 18, lr: 2.23e-04
2022-07-27 21:44:44,584 INFO [train.py:850] (2/4) Epoch 25, batch 6200, loss[loss=0.1566, simple_loss=0.2341, pruned_loss=0.03951, over 7158.00 frames.], tot_loss[loss=0.1883, simple_loss=0.276, pruned_loss=0.05033, over 1464213.35 frames.], batch size: 17, lr: 2.23e-04
2022-07-27 21:45:29,319 INFO [train.py:850] (2/4) Epoch 25, batch 6250, loss[loss=0.2046, simple_loss=0.2869, pruned_loss=0.06115, over 7173.00 frames.], tot_loss[loss=0.1898, simple_loss=0.277, pruned_loss=0.05132, over 1465383.99 frames.], batch size: 22, lr: 2.23e-04
2022-07-27 21:46:11,874 INFO [train.py:850] (2/4) Epoch 25, batch 6300, loss[loss=0.2075, simple_loss=0.2949, pruned_loss=0.06, over 7285.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2759, pruned_loss=0.05096, over 1464109.90 frames.], batch size: 20, lr: 2.23e-04
2022-07-27 21:46:56,420 INFO [train.py:850] (2/4) Epoch 25, batch 6350, loss[loss=0.2194, simple_loss=0.309, pruned_loss=0.06494, over 7338.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2754, pruned_loss=0.05071, over 1463874.71 frames.], batch size: 23, lr: 2.23e-04
2022-07-27 21:47:39,819 INFO [train.py:850] (2/4) Epoch 25, batch 6400, loss[loss=0.1532, simple_loss=0.2345, pruned_loss=0.03599, over 7302.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2748, pruned_loss=0.05038, over 1463559.38 frames.], batch size: 18, lr: 2.23e-04
2022-07-27 21:48:24,908 INFO [train.py:850] (2/4) Epoch 25, batch 6450, loss[loss=0.1783, simple_loss=0.2642, pruned_loss=0.04619, over 7388.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2749, pruned_loss=0.05042, over 1463577.70 frames.], batch size: 19, lr: 2.23e-04
2022-07-27 21:49:09,219 INFO [train.py:850] (2/4) Epoch 25, batch 6500, loss[loss=0.1528, simple_loss=0.2314, pruned_loss=0.03703, over 7312.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2745, pruned_loss=0.05016, over 1464405.86 frames.], batch size: 17, lr: 2.23e-04
2022-07-27 21:50:07,472 INFO [train.py:850] (2/4) Epoch 25, batch 6550, loss[loss=0.171, simple_loss=0.2714, pruned_loss=0.03535, over 7206.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2751, pruned_loss=0.05013, over 1464170.11 frames.], batch size: 24, lr: 2.23e-04
2022-07-27 21:50:51,343 INFO [train.py:850] (2/4) Epoch 25, batch 6600, loss[loss=0.189, simple_loss=0.2776, pruned_loss=0.05016, over 7188.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2745, pruned_loss=0.04964, over 1464596.39 frames.], batch size: 18, lr: 2.23e-04
2022-07-27 21:51:34,942 INFO [train.py:850] (2/4) Epoch 25, batch 6650, loss[loss=0.2341, simple_loss=0.3065, pruned_loss=0.08087, over 7458.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2743, pruned_loss=0.04938, over 1464088.98 frames.], batch size: 26, lr: 2.23e-04
2022-07-27 21:52:18,808 INFO [train.py:850] (2/4) Epoch 25, batch 6700, loss[loss=0.1582, simple_loss=0.2378, pruned_loss=0.03932, over 7436.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2737, pruned_loss=0.04933, over 1465102.59 frames.], batch size: 18, lr: 2.23e-04
2022-07-27 21:53:02,406 INFO [train.py:850] (2/4) Epoch 25, batch 6750, loss[loss=0.1693, simple_loss=0.2721, pruned_loss=0.03321, over 7169.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2743, pruned_loss=0.04923, over 1465678.83 frames.], batch size: 22, lr: 2.23e-04
2022-07-27 21:53:45,109 INFO [train.py:850] (2/4) Epoch 25, batch 6800, loss[loss=0.1445, simple_loss=0.2416, pruned_loss=0.02375, over 7475.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2752, pruned_loss=0.05002, over 1465092.03 frames.], batch size: 21, lr: 2.23e-04
2022-07-27 21:54:29,307 INFO [train.py:850] (2/4) Epoch 25, batch 6850, loss[loss=0.1709, simple_loss=0.2672, pruned_loss=0.03731, over 7397.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2751, pruned_loss=0.04967, over 1466339.36 frames.], batch size: 39, lr: 2.23e-04
2022-07-27 21:55:12,438 INFO [train.py:850] (2/4) Epoch 25, batch 6900, loss[loss=0.1832, simple_loss=0.2718, pruned_loss=0.04731, over 7478.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2744, pruned_loss=0.04935, over 1466455.93 frames.], batch size: 24, lr: 2.23e-04
2022-07-27 21:55:57,522 INFO [train.py:850] (2/4) Epoch 25, batch 6950, loss[loss=0.2293, simple_loss=0.3023, pruned_loss=0.07813, over 7387.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2758, pruned_loss=0.05015, over 1466507.82 frames.], batch size: 20, lr: 2.22e-04
2022-07-27 21:56:41,213 INFO [train.py:850] (2/4) Epoch 25, batch 7000, loss[loss=0.2044, simple_loss=0.2949, pruned_loss=0.05694, over 7283.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2753, pruned_loss=0.04974, over 1465502.18 frames.], batch size: 27, lr: 2.22e-04
2022-07-27 21:57:24,737 INFO [train.py:850] (2/4) Epoch 25, batch 7050, loss[loss=0.1938, simple_loss=0.2917, pruned_loss=0.0479, over 7331.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2764, pruned_loss=0.05015, over 1466096.89 frames.], batch size: 23, lr: 2.22e-04
2022-07-27 21:58:08,629 INFO [train.py:850] (2/4) Epoch 25, batch 7100, loss[loss=0.2059, simple_loss=0.2976, pruned_loss=0.05713, over 7178.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2756, pruned_loss=0.05007, over 1465786.24 frames.], batch size: 22, lr: 2.22e-04
2022-07-27 21:58:52,539 INFO [train.py:850] (2/4) Epoch 25, batch 7150, loss[loss=0.161, simple_loss=0.244, pruned_loss=0.03905, over 7235.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2755, pruned_loss=0.04988, over 1465567.60 frames.], batch size: 16, lr: 2.22e-04
2022-07-27 21:59:36,170 INFO [train.py:850] (2/4) Epoch 25, batch 7200, loss[loss=0.1723, simple_loss=0.2584, pruned_loss=0.0431, over 7265.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2766, pruned_loss=0.05023, over 1466182.95 frames.], batch size: 16, lr: 2.22e-04
2022-07-27 22:00:21,259 INFO [train.py:850] (2/4) Epoch 25, batch 7250, loss[loss=0.1849, simple_loss=0.277, pruned_loss=0.0464, over 7288.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2767, pruned_loss=0.05058, over 1466786.62 frames.], batch size: 27, lr: 2.22e-04
2022-07-27 22:01:05,425 INFO [train.py:850] (2/4) Epoch 25, batch 7300, loss[loss=0.1632, simple_loss=0.2556, pruned_loss=0.03541, over 7438.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2755, pruned_loss=0.05035, over 1466166.68 frames.], batch size: 18, lr: 2.22e-04
2022-07-27 22:01:48,363 INFO [train.py:850] (2/4) Epoch 25, batch 7350, loss[loss=0.1931, simple_loss=0.2908, pruned_loss=0.04768, over 7347.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2762, pruned_loss=0.05067, over 1466347.05 frames.], batch size: 39, lr: 2.22e-04
2022-07-27 22:02:33,803 INFO [train.py:850] (2/4) Epoch 25, batch 7400, loss[loss=0.2112, simple_loss=0.3022, pruned_loss=0.06015, over 7286.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2772, pruned_loss=0.05165, over 1466034.29 frames.], batch size: 21, lr: 2.22e-04
2022-07-27 22:03:19,710 INFO [train.py:850] (2/4) Epoch 25, batch 7450, loss[loss=0.2247, simple_loss=0.3098, pruned_loss=0.06975, over 7284.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2775, pruned_loss=0.05153, over 1465452.67 frames.], batch size: 21, lr: 2.22e-04
2022-07-27 22:04:03,704 INFO [train.py:850] (2/4) Epoch 25, batch 7500, loss[loss=0.1993, simple_loss=0.291, pruned_loss=0.05378, over 7413.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2764, pruned_loss=0.05064, over 1465335.74 frames.], batch size: 22, lr: 2.22e-04
2022-07-27 22:04:47,588 INFO [train.py:850] (2/4) Epoch 25, batch 7550, loss[loss=0.2234, simple_loss=0.2973, pruned_loss=0.07468, over 7340.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2778, pruned_loss=0.05136, over 1466100.18 frames.], batch size: 72, lr: 2.22e-04
2022-07-27 22:05:30,729 INFO [train.py:850] (2/4) Epoch 25, batch 7600, loss[loss=0.1692, simple_loss=0.2637, pruned_loss=0.03733, over 7189.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2766, pruned_loss=0.05082, over 1465912.63 frames.], batch size: 21, lr: 2.22e-04
2022-07-27 22:06:15,456 INFO [train.py:850] (2/4) Epoch 25, batch 7650, loss[loss=0.2476, simple_loss=0.3272, pruned_loss=0.08399, over 7270.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2767, pruned_loss=0.05055, over 1465589.75 frames.], batch size: 27, lr: 2.22e-04
2022-07-27 22:06:58,561 INFO [train.py:850] (2/4) Epoch 25, batch 7700, loss[loss=0.2072, simple_loss=0.2912, pruned_loss=0.06164, over 7203.00 frames.], tot_loss[loss=0.188, simple_loss=0.2757, pruned_loss=0.0502, over 1465616.06 frames.], batch size: 19, lr: 2.22e-04
2022-07-27 22:07:43,239 INFO [train.py:850] (2/4) Epoch 25, batch 7750, loss[loss=0.184, simple_loss=0.2767, pruned_loss=0.04567, over 7210.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2753, pruned_loss=0.05012, over 1466629.02 frames.], batch size: 24, lr: 2.22e-04
2022-07-27 22:08:29,219 INFO [train.py:850] (2/4) Epoch 25, batch 7800, loss[loss=0.2021, simple_loss=0.292, pruned_loss=0.05614, over 7389.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2759, pruned_loss=0.05027, over 1466995.51 frames.], batch size: 20, lr: 2.22e-04
2022-07-27 22:09:14,757 INFO [train.py:850] (2/4) Epoch 25, batch 7850, loss[loss=0.1683, simple_loss=0.2572, pruned_loss=0.03968, over 7309.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2767, pruned_loss=0.05056, over 1466332.51 frames.], batch size: 17, lr: 2.22e-04
2022-07-27 22:10:01,012 INFO [train.py:850] (2/4) Epoch 25, batch 7900, loss[loss=0.1976, simple_loss=0.284, pruned_loss=0.05554, over 7380.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2764, pruned_loss=0.05066, over 1466773.47 frames.], batch size: 21, lr: 2.22e-04
2022-07-27 22:10:45,462 INFO [train.py:850] (2/4) Epoch 25, batch 7950, loss[loss=0.2028, simple_loss=0.2904, pruned_loss=0.05763, over 7335.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2758, pruned_loss=0.05003, over 1466588.29 frames.], batch size: 27, lr: 2.22e-04
2022-07-27 22:11:30,280 INFO [train.py:850] (2/4) Epoch 25, batch 8000, loss[loss=0.1577, simple_loss=0.2428, pruned_loss=0.03633, over 7301.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2752, pruned_loss=0.04974, over 1465409.35 frames.], batch size: 19, lr: 2.22e-04
2022-07-27 22:12:14,867 INFO [train.py:850] (2/4) Epoch 25, batch 8050, loss[loss=0.1807, simple_loss=0.2688, pruned_loss=0.04633, over 7167.00 frames.], tot_loss[loss=0.188, simple_loss=0.2756, pruned_loss=0.05018, over 1466134.74 frames.], batch size: 22, lr: 2.22e-04
2022-07-27 22:12:58,826 INFO [train.py:850] (2/4) Epoch 25, batch 8100, loss[loss=0.1799, simple_loss=0.2736, pruned_loss=0.04311, over 7180.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2748, pruned_loss=0.04934, over 1465938.99 frames.], batch size: 21, lr: 2.22e-04
2022-07-27 22:13:43,499 INFO [train.py:850] (2/4) Epoch 25, batch 8150, loss[loss=0.1953, simple_loss=0.2904, pruned_loss=0.05009, over 7346.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2744, pruned_loss=0.04897, over 1465690.81 frames.], batch size: 23, lr: 2.22e-04
2022-07-27 22:14:26,974 INFO [train.py:850] (2/4) Epoch 25, batch 8200, loss[loss=0.19, simple_loss=0.2816, pruned_loss=0.04923, over 7327.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2762, pruned_loss=0.05012, over 1466310.92 frames.], batch size: 27, lr: 2.22e-04
2022-07-27 22:15:11,835 INFO [train.py:850] (2/4) Epoch 25, batch 8250, loss[loss=0.202, simple_loss=0.2864, pruned_loss=0.05874, over 7253.00 frames.], tot_loss[loss=0.1882, simple_loss=0.276, pruned_loss=0.05019, over 1465949.17 frames.], batch size: 25, lr: 2.22e-04
2022-07-27 22:15:55,136 INFO [train.py:850] (2/4) Epoch 25, batch 8300, loss[loss=0.1877, simple_loss=0.2835, pruned_loss=0.04596, over 7298.00 frames.], tot_loss[loss=0.188, simple_loss=0.2758, pruned_loss=0.05016, over 1465624.70 frames.], batch size: 22, lr: 2.22e-04
2022-07-27 22:16:39,111 INFO [train.py:850] (2/4) Epoch 25, batch 8350, loss[loss=0.1894, simple_loss=0.2881, pruned_loss=0.04535, over 7422.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2756, pruned_loss=0.05034, over 1465568.12 frames.], batch size: 22, lr: 2.22e-04
2022-07-27 22:17:23,388 INFO [train.py:850] (2/4) Epoch 25, batch 8400, loss[loss=0.1541, simple_loss=0.2436, pruned_loss=0.03233, over 7384.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2742, pruned_loss=0.04966, over 1466266.62 frames.], batch size: 20, lr: 2.22e-04
2022-07-27 22:18:07,387 INFO [train.py:850] (2/4) Epoch 25, batch 8450, loss[loss=0.2186, simple_loss=0.3089, pruned_loss=0.06412, over 7446.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2734, pruned_loss=0.04925, over 1465781.79 frames.], batch size: 73, lr: 2.22e-04
2022-07-27 22:18:51,166 INFO [train.py:850] (2/4) Epoch 25, batch 8500, loss[loss=0.2557, simple_loss=0.3336, pruned_loss=0.08888, over 7277.00 frames.], tot_loss[loss=0.1865, simple_loss=0.274, pruned_loss=0.04949, over 1466511.62 frames.], batch size: 27, lr: 2.22e-04
2022-07-27 22:19:35,784 INFO [train.py:850] (2/4) Epoch 25, batch 8550, loss[loss=0.1839, simple_loss=0.2771, pruned_loss=0.04538, over 7364.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2744, pruned_loss=0.05006, over 1466575.19 frames.], batch size: 39, lr: 2.22e-04
2022-07-27 22:20:21,035 INFO [train.py:850] (2/4) Epoch 25, batch 8600, loss[loss=0.1633, simple_loss=0.2577, pruned_loss=0.03447, over 7424.00 frames.], tot_loss[loss=0.1881, simple_loss=0.275, pruned_loss=0.05057, over 1467693.37 frames.], batch size: 18, lr: 2.22e-04
2022-07-27 22:21:05,036 INFO [train.py:850] (2/4) Epoch 25, batch 8650, loss[loss=0.1839, simple_loss=0.2638, pruned_loss=0.05198, over 7294.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2735, pruned_loss=0.04973, over 1466917.21 frames.], batch size: 17, lr: 2.22e-04
2022-07-27 22:21:48,822 INFO [train.py:850] (2/4) Epoch 25, batch 8700, loss[loss=0.1494, simple_loss=0.2483, pruned_loss=0.02527, over 7388.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2736, pruned_loss=0.04959, over 1466720.79 frames.], batch size: 20, lr: 2.22e-04
2022-07-27 22:22:32,481 INFO [train.py:850] (2/4) Epoch 25, batch 8750, loss[loss=0.1991, simple_loss=0.2723, pruned_loss=0.06298, over 7451.00 frames.], tot_loss[loss=0.188, simple_loss=0.2751, pruned_loss=0.05043, over 1466842.83 frames.], batch size: 18, lr: 2.22e-04
2022-07-27 22:23:15,159 INFO [train.py:850] (2/4) Epoch 25, batch 8800, loss[loss=0.2188, simple_loss=0.302, pruned_loss=0.06778, over 7248.00 frames.], tot_loss[loss=0.187, simple_loss=0.2745, pruned_loss=0.04976, over 1467197.19 frames.], batch size: 27, lr: 2.22e-04
2022-07-27 22:23:58,384 INFO [train.py:850] (2/4) Epoch 25, batch 8850, loss[loss=0.2286, simple_loss=0.3164, pruned_loss=0.07041, over 7453.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2753, pruned_loss=0.05003, over 1467426.88 frames.], batch size: 40, lr: 2.22e-04
2022-07-27 22:24:41,018 INFO [train.py:1074] (2/4) Done!