diff --git "a/exp/log/log-train-2022-04-28-06-39-03-7" "b/exp/log/log-train-2022-04-28-06-39-03-7"
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+++ "b/exp/log/log-train-2022-04-28-06-39-03-7"
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+2022-04-28 06:39:03,130 INFO [train.py:827] (7/8) Training started
+2022-04-28 06:39:03,130 INFO [train.py:837] (7/8) Device: cuda:7
+2022-04-28 06:39:03,161 INFO [train.py:846] (7/8) {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'model_warm_step': 3000, 'env_info': {'k2-version': '1.14', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '3b83183234d0f1d8391872630551c5af7c491ed2', 'k2-git-date': 'Tue Apr 12 08:26:41 2022', 'lhotse-version': '1.1.0.dev+missing.version.file', 'torch-version': '1.10.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'deeper-conformer', 'icefall-git-sha1': 'd79f5fe-dirty', 'icefall-git-date': 'Mon Apr 25 17:26:43 2022', 'icefall-path': '/ceph-fj/fangjun/open-source-2/icefall-deeper-conformer', 'k2-path': '/ceph-fj/fangjun/open-source-2/k2-multi-2/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-fj/fangjun/open-source-2/lhotse-multi-3/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-7-0309102938-68688b4cbd-xhtcg', 'IP address': '10.48.32.137'}, 'world_size': 8, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 40, 'start_epoch': 0, 'start_batch': 0, 'exp_dir': PosixPath('pruned_transducer_stateless4/exp-L'), 'bpe_model': 'data/lang_bpe_500/bpe.model', 'initial_lr': 0.003, 'lr_batches': 5000, 'lr_epochs': 6, 'context_size': 2, 'prune_range': 5, 'lm_scale': 0.25, 'am_scale': 0.0, 'simple_loss_scale': 0.5, 'seed': 42, 'print_diagnostics': False, 'save_every_n': 8000, 'keep_last_k': 20, 'use_fp16': False, 'num_encoder_layers': 18, 'dim_feedforward': 2048, 'nhead': 8, 'encoder_dim': 512, 'decoder_dim': 512, 'joiner_dim': 512, 'full_libri': True, 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 300, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'blank_id': 0, 'vocab_size': 500}
+2022-04-28 06:39:03,161 INFO [train.py:848] (7/8) About to create model
+2022-04-28 06:39:03,697 INFO [train.py:852] (7/8) Number of model parameters: 118129516
+2022-04-28 06:39:09,973 INFO [train.py:858] (7/8) Using DDP
+2022-04-28 06:39:10,515 INFO [asr_datamodule.py:391] (7/8) About to get train-clean-100 cuts
+2022-04-28 06:39:16,508 INFO [asr_datamodule.py:398] (7/8) About to get train-clean-360 cuts
+2022-04-28 06:39:41,309 INFO [asr_datamodule.py:405] (7/8) About to get train-other-500 cuts
+2022-04-28 06:40:22,970 INFO [asr_datamodule.py:209] (7/8) Enable MUSAN
+2022-04-28 06:40:22,970 INFO [asr_datamodule.py:210] (7/8) About to get Musan cuts
+2022-04-28 06:40:24,361 INFO [asr_datamodule.py:238] (7/8) Enable SpecAugment
+2022-04-28 06:40:24,362 INFO [asr_datamodule.py:239] (7/8) Time warp factor: 80
+2022-04-28 06:40:24,362 INFO [asr_datamodule.py:251] (7/8) Num frame mask: 10
+2022-04-28 06:40:24,362 INFO [asr_datamodule.py:264] (7/8) About to create train dataset
+2022-04-28 06:40:24,362 INFO [asr_datamodule.py:292] (7/8) Using BucketingSampler.
+2022-04-28 06:40:28,770 INFO [asr_datamodule.py:308] (7/8) About to create train dataloader
+2022-04-28 06:40:28,774 INFO [asr_datamodule.py:412] (7/8) About to get dev-clean cuts
+2022-04-28 06:40:29,051 INFO [asr_datamodule.py:417] (7/8) About to get dev-other cuts
+2022-04-28 06:40:29,201 INFO [asr_datamodule.py:339] (7/8) About to create dev dataset
+2022-04-28 06:40:29,212 INFO [asr_datamodule.py:358] (7/8) About to create dev dataloader
+2022-04-28 06:40:29,213 INFO [train.py:987] (7/8) Sanity check -- see if any of the batches in epoch 0 would cause OOM.
+2022-04-28 06:40:42,725 INFO [distributed.py:874] (7/8) Reducer buckets have been rebuilt in this iteration.
+2022-04-28 06:41:17,071 INFO [train.py:763] (7/8) Epoch 0, batch 0, loss[loss=0.7297, simple_loss=1.459, pruned_loss=7.161, over 7292.00 frames.], tot_loss[loss=0.7297, simple_loss=1.459, pruned_loss=7.161, over 7292.00 frames.], batch size: 17, lr: 3.00e-03
+2022-04-28 06:42:23,572 INFO [train.py:763] (7/8) Epoch 0, batch 50, loss[loss=0.5032, simple_loss=1.006, pruned_loss=6.609, over 7156.00 frames.], tot_loss[loss=0.5719, simple_loss=1.144, pruned_loss=6.961, over 323654.95 frames.], batch size: 19, lr: 3.00e-03
+2022-04-28 06:43:30,306 INFO [train.py:763] (7/8) Epoch 0, batch 100, loss[loss=0.4251, simple_loss=0.8503, pruned_loss=6.781, over 6992.00 frames.], tot_loss[loss=0.5094, simple_loss=1.019, pruned_loss=6.874, over 565893.19 frames.], batch size: 16, lr: 3.00e-03
+2022-04-28 06:44:37,543 INFO [train.py:763] (7/8) Epoch 0, batch 150, loss[loss=0.3672, simple_loss=0.7345, pruned_loss=6.563, over 7429.00 frames.], tot_loss[loss=0.4764, simple_loss=0.9528, pruned_loss=6.861, over 758349.43 frames.], batch size: 17, lr: 3.00e-03
+2022-04-28 06:45:44,965 INFO [train.py:763] (7/8) Epoch 0, batch 200, loss[loss=0.4325, simple_loss=0.8649, pruned_loss=6.815, over 7295.00 frames.], tot_loss[loss=0.4519, simple_loss=0.9038, pruned_loss=6.831, over 907946.06 frames.], batch size: 25, lr: 3.00e-03
+2022-04-28 06:46:51,032 INFO [train.py:763] (7/8) Epoch 0, batch 250, loss[loss=0.4145, simple_loss=0.8289, pruned_loss=6.762, over 7337.00 frames.], tot_loss[loss=0.4357, simple_loss=0.8714, pruned_loss=6.796, over 1016113.76 frames.], batch size: 21, lr: 3.00e-03
+2022-04-28 06:47:58,774 INFO [train.py:763] (7/8) Epoch 0, batch 300, loss[loss=0.4037, simple_loss=0.8073, pruned_loss=6.779, over 7271.00 frames.], tot_loss[loss=0.4235, simple_loss=0.8471, pruned_loss=6.766, over 1108046.42 frames.], batch size: 25, lr: 3.00e-03
+2022-04-28 06:49:06,205 INFO [train.py:763] (7/8) Epoch 0, batch 350, loss[loss=0.3696, simple_loss=0.7393, pruned_loss=6.612, over 7251.00 frames.], tot_loss[loss=0.4125, simple_loss=0.825, pruned_loss=6.725, over 1178560.34 frames.], batch size: 19, lr: 3.00e-03
+2022-04-28 06:50:12,124 INFO [train.py:763] (7/8) Epoch 0, batch 400, loss[loss=0.3621, simple_loss=0.7241, pruned_loss=6.577, over 7416.00 frames.], tot_loss[loss=0.4042, simple_loss=0.8084, pruned_loss=6.704, over 1231256.83 frames.], batch size: 21, lr: 3.00e-03
+2022-04-28 06:51:17,836 INFO [train.py:763] (7/8) Epoch 0, batch 450, loss[loss=0.339, simple_loss=0.678, pruned_loss=6.616, over 7415.00 frames.], tot_loss[loss=0.3919, simple_loss=0.7839, pruned_loss=6.686, over 1267392.15 frames.], batch size: 21, lr: 2.99e-03
+2022-04-28 06:52:24,508 INFO [train.py:763] (7/8) Epoch 0, batch 500, loss[loss=0.3137, simple_loss=0.6275, pruned_loss=6.642, over 7200.00 frames.], tot_loss[loss=0.3757, simple_loss=0.7513, pruned_loss=6.674, over 1303605.71 frames.], batch size: 22, lr: 2.99e-03
+2022-04-28 06:53:30,003 INFO [train.py:763] (7/8) Epoch 0, batch 550, loss[loss=0.3317, simple_loss=0.6633, pruned_loss=6.739, over 7338.00 frames.], tot_loss[loss=0.3612, simple_loss=0.7224, pruned_loss=6.672, over 1329819.61 frames.], batch size: 22, lr: 2.99e-03
+2022-04-28 06:54:36,579 INFO [train.py:763] (7/8) Epoch 0, batch 600, loss[loss=0.2748, simple_loss=0.5496, pruned_loss=6.62, over 7107.00 frames.], tot_loss[loss=0.346, simple_loss=0.6919, pruned_loss=6.667, over 1350521.69 frames.], batch size: 21, lr: 2.99e-03
+2022-04-28 06:55:42,131 INFO [train.py:763] (7/8) Epoch 0, batch 650, loss[loss=0.22, simple_loss=0.4401, pruned_loss=6.475, over 7007.00 frames.], tot_loss[loss=0.3321, simple_loss=0.6642, pruned_loss=6.658, over 1368818.91 frames.], batch size: 16, lr: 2.99e-03
+2022-04-28 06:56:47,781 INFO [train.py:763] (7/8) Epoch 0, batch 700, loss[loss=0.2869, simple_loss=0.5738, pruned_loss=6.691, over 7214.00 frames.], tot_loss[loss=0.3173, simple_loss=0.6345, pruned_loss=6.641, over 1380636.47 frames.], batch size: 23, lr: 2.99e-03
+2022-04-28 06:57:54,492 INFO [train.py:763] (7/8) Epoch 0, batch 750, loss[loss=0.2195, simple_loss=0.4389, pruned_loss=6.325, over 7282.00 frames.], tot_loss[loss=0.304, simple_loss=0.608, pruned_loss=6.625, over 1392393.26 frames.], batch size: 17, lr: 2.98e-03
+2022-04-28 06:59:01,275 INFO [train.py:763] (7/8) Epoch 0, batch 800, loss[loss=0.2598, simple_loss=0.5196, pruned_loss=6.539, over 7117.00 frames.], tot_loss[loss=0.2938, simple_loss=0.5876, pruned_loss=6.614, over 1397508.98 frames.], batch size: 21, lr: 2.98e-03
+2022-04-28 07:00:07,440 INFO [train.py:763] (7/8) Epoch 0, batch 850, loss[loss=0.2641, simple_loss=0.5282, pruned_loss=6.627, over 7221.00 frames.], tot_loss[loss=0.2844, simple_loss=0.5688, pruned_loss=6.603, over 1402758.36 frames.], batch size: 21, lr: 2.98e-03
+2022-04-28 07:01:13,436 INFO [train.py:763] (7/8) Epoch 0, batch 900, loss[loss=0.2622, simple_loss=0.5244, pruned_loss=6.625, over 7319.00 frames.], tot_loss[loss=0.2746, simple_loss=0.5492, pruned_loss=6.587, over 1408475.78 frames.], batch size: 21, lr: 2.98e-03
+2022-04-28 07:02:19,019 INFO [train.py:763] (7/8) Epoch 0, batch 950, loss[loss=0.2045, simple_loss=0.409, pruned_loss=6.443, over 7021.00 frames.], tot_loss[loss=0.2685, simple_loss=0.537, pruned_loss=6.581, over 1405706.88 frames.], batch size: 16, lr: 2.97e-03
+2022-04-28 07:03:26,149 INFO [train.py:763] (7/8) Epoch 0, batch 1000, loss[loss=0.204, simple_loss=0.4081, pruned_loss=6.442, over 7014.00 frames.], tot_loss[loss=0.2628, simple_loss=0.5257, pruned_loss=6.577, over 1406348.85 frames.], batch size: 16, lr: 2.97e-03
+2022-04-28 07:04:32,985 INFO [train.py:763] (7/8) Epoch 0, batch 1050, loss[loss=0.2098, simple_loss=0.4197, pruned_loss=6.511, over 6999.00 frames.], tot_loss[loss=0.2585, simple_loss=0.5171, pruned_loss=6.577, over 1408441.01 frames.], batch size: 16, lr: 2.97e-03
+2022-04-28 07:05:39,537 INFO [train.py:763] (7/8) Epoch 0, batch 1100, loss[loss=0.2346, simple_loss=0.4691, pruned_loss=6.729, over 7208.00 frames.], tot_loss[loss=0.2536, simple_loss=0.5071, pruned_loss=6.579, over 1411458.53 frames.], batch size: 22, lr: 2.96e-03
+2022-04-28 07:06:46,919 INFO [train.py:763] (7/8) Epoch 0, batch 1150, loss[loss=0.2426, simple_loss=0.4852, pruned_loss=6.623, over 6817.00 frames.], tot_loss[loss=0.2477, simple_loss=0.4955, pruned_loss=6.573, over 1412922.55 frames.], batch size: 31, lr: 2.96e-03
+2022-04-28 07:07:52,791 INFO [train.py:763] (7/8) Epoch 0, batch 1200, loss[loss=0.2631, simple_loss=0.5262, pruned_loss=6.744, over 7151.00 frames.], tot_loss[loss=0.2436, simple_loss=0.4872, pruned_loss=6.577, over 1420383.35 frames.], batch size: 26, lr: 2.96e-03
+2022-04-28 07:08:58,138 INFO [train.py:763] (7/8) Epoch 0, batch 1250, loss[loss=0.2505, simple_loss=0.5009, pruned_loss=6.651, over 7384.00 frames.], tot_loss[loss=0.2398, simple_loss=0.4795, pruned_loss=6.579, over 1414223.27 frames.], batch size: 23, lr: 2.95e-03
+2022-04-28 07:10:04,051 INFO [train.py:763] (7/8) Epoch 0, batch 1300, loss[loss=0.2449, simple_loss=0.4899, pruned_loss=6.75, over 7271.00 frames.], tot_loss[loss=0.2369, simple_loss=0.4738, pruned_loss=6.586, over 1421399.42 frames.], batch size: 24, lr: 2.95e-03
+2022-04-28 07:11:09,808 INFO [train.py:763] (7/8) Epoch 0, batch 1350, loss[loss=0.2667, simple_loss=0.5334, pruned_loss=6.681, over 7154.00 frames.], tot_loss[loss=0.2329, simple_loss=0.4658, pruned_loss=6.583, over 1422770.83 frames.], batch size: 20, lr: 2.95e-03
+2022-04-28 07:12:15,121 INFO [train.py:763] (7/8) Epoch 0, batch 1400, loss[loss=0.2276, simple_loss=0.4553, pruned_loss=6.61, over 7312.00 frames.], tot_loss[loss=0.2318, simple_loss=0.4636, pruned_loss=6.594, over 1418882.25 frames.], batch size: 24, lr: 2.94e-03
+2022-04-28 07:13:21,024 INFO [train.py:763] (7/8) Epoch 0, batch 1450, loss[loss=0.1757, simple_loss=0.3514, pruned_loss=6.417, over 7128.00 frames.], tot_loss[loss=0.2286, simple_loss=0.4573, pruned_loss=6.589, over 1419888.77 frames.], batch size: 17, lr: 2.94e-03
+2022-04-28 07:14:26,719 INFO [train.py:763] (7/8) Epoch 0, batch 1500, loss[loss=0.2305, simple_loss=0.461, pruned_loss=6.695, over 7293.00 frames.], tot_loss[loss=0.2264, simple_loss=0.4529, pruned_loss=6.584, over 1422685.94 frames.], batch size: 24, lr: 2.94e-03
+2022-04-28 07:15:32,258 INFO [train.py:763] (7/8) Epoch 0, batch 1550, loss[loss=0.2177, simple_loss=0.4355, pruned_loss=6.534, over 7108.00 frames.], tot_loss[loss=0.2242, simple_loss=0.4483, pruned_loss=6.579, over 1422533.61 frames.], batch size: 21, lr: 2.93e-03
+2022-04-28 07:16:38,338 INFO [train.py:763] (7/8) Epoch 0, batch 1600, loss[loss=0.2073, simple_loss=0.4146, pruned_loss=6.527, over 7331.00 frames.], tot_loss[loss=0.2221, simple_loss=0.4443, pruned_loss=6.574, over 1420066.34 frames.], batch size: 20, lr: 2.93e-03
+2022-04-28 07:17:45,351 INFO [train.py:763] (7/8) Epoch 0, batch 1650, loss[loss=0.2133, simple_loss=0.4267, pruned_loss=6.556, over 7163.00 frames.], tot_loss[loss=0.22, simple_loss=0.4399, pruned_loss=6.572, over 1421842.80 frames.], batch size: 18, lr: 2.92e-03
+2022-04-28 07:18:52,003 INFO [train.py:763] (7/8) Epoch 0, batch 1700, loss[loss=0.2187, simple_loss=0.4374, pruned_loss=6.542, over 6529.00 frames.], tot_loss[loss=0.2181, simple_loss=0.4362, pruned_loss=6.569, over 1417238.91 frames.], batch size: 38, lr: 2.92e-03
+2022-04-28 07:19:58,708 INFO [train.py:763] (7/8) Epoch 0, batch 1750, loss[loss=0.2011, simple_loss=0.4022, pruned_loss=6.465, over 6374.00 frames.], tot_loss[loss=0.2148, simple_loss=0.4296, pruned_loss=6.564, over 1417098.62 frames.], batch size: 37, lr: 2.91e-03
+2022-04-28 07:21:06,373 INFO [train.py:763] (7/8) Epoch 0, batch 1800, loss[loss=0.2179, simple_loss=0.4359, pruned_loss=6.565, over 7030.00 frames.], tot_loss[loss=0.2127, simple_loss=0.4253, pruned_loss=6.56, over 1417496.87 frames.], batch size: 28, lr: 2.91e-03
+2022-04-28 07:22:12,431 INFO [train.py:763] (7/8) Epoch 0, batch 1850, loss[loss=0.2444, simple_loss=0.4888, pruned_loss=6.544, over 5370.00 frames.], tot_loss[loss=0.2112, simple_loss=0.4223, pruned_loss=6.56, over 1418566.32 frames.], batch size: 53, lr: 2.91e-03
+2022-04-28 07:23:18,925 INFO [train.py:763] (7/8) Epoch 0, batch 1900, loss[loss=0.1995, simple_loss=0.3991, pruned_loss=6.602, over 7260.00 frames.], tot_loss[loss=0.2102, simple_loss=0.4203, pruned_loss=6.564, over 1418525.20 frames.], batch size: 19, lr: 2.90e-03
+2022-04-28 07:24:26,538 INFO [train.py:763] (7/8) Epoch 0, batch 1950, loss[loss=0.248, simple_loss=0.496, pruned_loss=6.823, over 7321.00 frames.], tot_loss[loss=0.2088, simple_loss=0.4175, pruned_loss=6.565, over 1421328.80 frames.], batch size: 21, lr: 2.90e-03
+2022-04-28 07:25:34,078 INFO [train.py:763] (7/8) Epoch 0, batch 2000, loss[loss=0.1954, simple_loss=0.3908, pruned_loss=6.423, over 6733.00 frames.], tot_loss[loss=0.2074, simple_loss=0.4148, pruned_loss=6.565, over 1421835.13 frames.], batch size: 15, lr: 2.89e-03
+2022-04-28 07:26:39,978 INFO [train.py:763] (7/8) Epoch 0, batch 2050, loss[loss=0.2082, simple_loss=0.4165, pruned_loss=6.588, over 7213.00 frames.], tot_loss[loss=0.2061, simple_loss=0.4122, pruned_loss=6.564, over 1421009.57 frames.], batch size: 26, lr: 2.89e-03
+2022-04-28 07:27:45,828 INFO [train.py:763] (7/8) Epoch 0, batch 2100, loss[loss=0.1903, simple_loss=0.3805, pruned_loss=6.484, over 7167.00 frames.], tot_loss[loss=0.2054, simple_loss=0.4109, pruned_loss=6.568, over 1418723.33 frames.], batch size: 18, lr: 2.88e-03
+2022-04-28 07:28:51,558 INFO [train.py:763] (7/8) Epoch 0, batch 2150, loss[loss=0.2023, simple_loss=0.4046, pruned_loss=6.587, over 7340.00 frames.], tot_loss[loss=0.2048, simple_loss=0.4096, pruned_loss=6.573, over 1422125.29 frames.], batch size: 22, lr: 2.88e-03
+2022-04-28 07:29:57,485 INFO [train.py:763] (7/8) Epoch 0, batch 2200, loss[loss=0.2195, simple_loss=0.4389, pruned_loss=6.632, over 7295.00 frames.], tot_loss[loss=0.2036, simple_loss=0.4072, pruned_loss=6.58, over 1421458.99 frames.], batch size: 25, lr: 2.87e-03
+2022-04-28 07:31:03,297 INFO [train.py:763] (7/8) Epoch 0, batch 2250, loss[loss=0.2224, simple_loss=0.4448, pruned_loss=6.722, over 7220.00 frames.], tot_loss[loss=0.2022, simple_loss=0.4044, pruned_loss=6.58, over 1421141.95 frames.], batch size: 21, lr: 2.86e-03
+2022-04-28 07:32:08,997 INFO [train.py:763] (7/8) Epoch 0, batch 2300, loss[loss=0.1919, simple_loss=0.3838, pruned_loss=6.575, over 7261.00 frames.], tot_loss[loss=0.2019, simple_loss=0.4039, pruned_loss=6.581, over 1416548.90 frames.], batch size: 19, lr: 2.86e-03
+2022-04-28 07:33:14,415 INFO [train.py:763] (7/8) Epoch 0, batch 2350, loss[loss=0.2291, simple_loss=0.4582, pruned_loss=6.552, over 5169.00 frames.], tot_loss[loss=0.2016, simple_loss=0.4032, pruned_loss=6.585, over 1415764.62 frames.], batch size: 52, lr: 2.85e-03
+2022-04-28 07:34:20,295 INFO [train.py:763] (7/8) Epoch 0, batch 2400, loss[loss=0.2105, simple_loss=0.421, pruned_loss=6.672, over 7434.00 frames.], tot_loss[loss=0.2012, simple_loss=0.4024, pruned_loss=6.583, over 1411490.93 frames.], batch size: 20, lr: 2.85e-03
+2022-04-28 07:35:25,724 INFO [train.py:763] (7/8) Epoch 0, batch 2450, loss[loss=0.2388, simple_loss=0.4775, pruned_loss=6.67, over 5039.00 frames.], tot_loss[loss=0.2003, simple_loss=0.4006, pruned_loss=6.585, over 1411544.76 frames.], batch size: 52, lr: 2.84e-03
+2022-04-28 07:36:32,860 INFO [train.py:763] (7/8) Epoch 0, batch 2500, loss[loss=0.1818, simple_loss=0.3636, pruned_loss=6.631, over 7338.00 frames.], tot_loss[loss=0.1993, simple_loss=0.3987, pruned_loss=6.587, over 1417263.10 frames.], batch size: 20, lr: 2.84e-03
+2022-04-28 07:37:40,464 INFO [train.py:763] (7/8) Epoch 0, batch 2550, loss[loss=0.1814, simple_loss=0.3628, pruned_loss=6.46, over 7406.00 frames.], tot_loss[loss=0.1997, simple_loss=0.3995, pruned_loss=6.595, over 1417722.95 frames.], batch size: 18, lr: 2.83e-03
+2022-04-28 07:38:46,553 INFO [train.py:763] (7/8) Epoch 0, batch 2600, loss[loss=0.2125, simple_loss=0.4251, pruned_loss=6.811, over 7224.00 frames.], tot_loss[loss=0.1987, simple_loss=0.3975, pruned_loss=6.6, over 1419797.69 frames.], batch size: 20, lr: 2.83e-03
+2022-04-28 07:39:52,347 INFO [train.py:763] (7/8) Epoch 0, batch 2650, loss[loss=0.1923, simple_loss=0.3845, pruned_loss=6.442, over 7236.00 frames.], tot_loss[loss=0.1975, simple_loss=0.3951, pruned_loss=6.597, over 1421737.22 frames.], batch size: 20, lr: 2.82e-03
+2022-04-28 07:40:58,211 INFO [train.py:763] (7/8) Epoch 0, batch 2700, loss[loss=0.2048, simple_loss=0.4096, pruned_loss=6.643, over 7147.00 frames.], tot_loss[loss=0.1964, simple_loss=0.3929, pruned_loss=6.593, over 1421672.96 frames.], batch size: 20, lr: 2.81e-03
+2022-04-28 07:42:03,324 INFO [train.py:763] (7/8) Epoch 0, batch 2750, loss[loss=0.1855, simple_loss=0.3711, pruned_loss=6.503, over 7330.00 frames.], tot_loss[loss=0.1963, simple_loss=0.3927, pruned_loss=6.597, over 1422537.92 frames.], batch size: 20, lr: 2.81e-03
+2022-04-28 07:43:09,955 INFO [train.py:763] (7/8) Epoch 0, batch 2800, loss[loss=0.2027, simple_loss=0.4054, pruned_loss=6.662, over 7139.00 frames.], tot_loss[loss=0.1959, simple_loss=0.3917, pruned_loss=6.598, over 1421071.69 frames.], batch size: 20, lr: 2.80e-03
+2022-04-28 07:44:16,834 INFO [train.py:763] (7/8) Epoch 0, batch 2850, loss[loss=0.1823, simple_loss=0.3645, pruned_loss=6.566, over 7363.00 frames.], tot_loss[loss=0.1947, simple_loss=0.3893, pruned_loss=6.596, over 1424328.61 frames.], batch size: 19, lr: 2.80e-03
+2022-04-28 07:45:22,344 INFO [train.py:763] (7/8) Epoch 0, batch 2900, loss[loss=0.1739, simple_loss=0.3479, pruned_loss=6.604, over 7323.00 frames.], tot_loss[loss=0.1953, simple_loss=0.3906, pruned_loss=6.603, over 1419939.87 frames.], batch size: 20, lr: 2.79e-03
+2022-04-28 07:46:27,661 INFO [train.py:763] (7/8) Epoch 0, batch 2950, loss[loss=0.2168, simple_loss=0.4336, pruned_loss=6.65, over 7216.00 frames.], tot_loss[loss=0.195, simple_loss=0.39, pruned_loss=6.605, over 1414920.49 frames.], batch size: 26, lr: 2.78e-03
+2022-04-28 07:47:32,897 INFO [train.py:763] (7/8) Epoch 0, batch 3000, loss[loss=0.3433, simple_loss=0.4013, pruned_loss=1.426, over 7289.00 frames.], tot_loss[loss=0.2273, simple_loss=0.3885, pruned_loss=6.582, over 1419275.99 frames.], batch size: 17, lr: 2.78e-03
+2022-04-28 07:47:32,897 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-28 07:47:50,998 INFO [train.py:792] (7/8) Epoch 0, validation: loss=2.072, simple_loss=0.4419, pruned_loss=1.851, over 698248.00 frames.
+2022-04-28 07:48:57,682 INFO [train.py:763] (7/8) Epoch 0, batch 3050, loss[loss=0.3143, simple_loss=0.4339, pruned_loss=0.9735, over 6376.00 frames.], tot_loss[loss=0.252, simple_loss=0.3981, pruned_loss=5.396, over 1418876.73 frames.], batch size: 37, lr: 2.77e-03
+2022-04-28 07:50:04,092 INFO [train.py:763] (7/8) Epoch 0, batch 3100, loss[loss=0.2463, simple_loss=0.3861, pruned_loss=0.5321, over 7408.00 frames.], tot_loss[loss=0.2528, simple_loss=0.3931, pruned_loss=4.335, over 1424548.72 frames.], batch size: 21, lr: 2.77e-03
+2022-04-28 07:51:10,062 INFO [train.py:763] (7/8) Epoch 0, batch 3150, loss[loss=0.2182, simple_loss=0.3715, pruned_loss=0.3251, over 7419.00 frames.], tot_loss[loss=0.2479, simple_loss=0.3899, pruned_loss=3.463, over 1426117.12 frames.], batch size: 21, lr: 2.76e-03
+2022-04-28 07:52:16,867 INFO [train.py:763] (7/8) Epoch 0, batch 3200, loss[loss=0.2283, simple_loss=0.4026, pruned_loss=0.2697, over 7302.00 frames.], tot_loss[loss=0.2432, simple_loss=0.3896, pruned_loss=2.772, over 1422439.89 frames.], batch size: 24, lr: 2.75e-03
+2022-04-28 07:53:24,370 INFO [train.py:763] (7/8) Epoch 0, batch 3250, loss[loss=0.2342, simple_loss=0.4149, pruned_loss=0.2681, over 7136.00 frames.], tot_loss[loss=0.2378, simple_loss=0.3886, pruned_loss=2.216, over 1422366.58 frames.], batch size: 20, lr: 2.75e-03
+2022-04-28 07:54:30,954 INFO [train.py:763] (7/8) Epoch 0, batch 3300, loss[loss=0.2295, simple_loss=0.4068, pruned_loss=0.2611, over 7373.00 frames.], tot_loss[loss=0.2332, simple_loss=0.3878, pruned_loss=1.784, over 1417928.87 frames.], batch size: 23, lr: 2.74e-03
+2022-04-28 07:55:37,626 INFO [train.py:763] (7/8) Epoch 0, batch 3350, loss[loss=0.2287, simple_loss=0.4045, pruned_loss=0.2649, over 7298.00 frames.], tot_loss[loss=0.2293, simple_loss=0.3873, pruned_loss=1.435, over 1422523.82 frames.], batch size: 24, lr: 2.73e-03
+2022-04-28 07:56:43,243 INFO [train.py:763] (7/8) Epoch 0, batch 3400, loss[loss=0.1804, simple_loss=0.3279, pruned_loss=0.1645, over 7255.00 frames.], tot_loss[loss=0.2264, simple_loss=0.3877, pruned_loss=1.165, over 1423700.37 frames.], batch size: 19, lr: 2.73e-03
+2022-04-28 07:57:49,079 INFO [train.py:763] (7/8) Epoch 0, batch 3450, loss[loss=0.2144, simple_loss=0.39, pruned_loss=0.1936, over 7290.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3871, pruned_loss=0.9529, over 1424174.57 frames.], batch size: 25, lr: 2.72e-03
+2022-04-28 07:58:54,335 INFO [train.py:763] (7/8) Epoch 0, batch 3500, loss[loss=0.2162, simple_loss=0.3927, pruned_loss=0.1984, over 7160.00 frames.], tot_loss[loss=0.2205, simple_loss=0.3856, pruned_loss=0.7859, over 1421740.46 frames.], batch size: 26, lr: 2.72e-03
+2022-04-28 08:00:00,016 INFO [train.py:763] (7/8) Epoch 0, batch 3550, loss[loss=0.212, simple_loss=0.3881, pruned_loss=0.1795, over 7237.00 frames.], tot_loss[loss=0.217, simple_loss=0.3825, pruned_loss=0.6527, over 1423005.20 frames.], batch size: 21, lr: 2.71e-03
+2022-04-28 08:01:06,101 INFO [train.py:763] (7/8) Epoch 0, batch 3600, loss[loss=0.1919, simple_loss=0.35, pruned_loss=0.1689, over 6992.00 frames.], tot_loss[loss=0.2145, simple_loss=0.3807, pruned_loss=0.55, over 1421569.82 frames.], batch size: 16, lr: 2.70e-03
+2022-04-28 08:02:21,113 INFO [train.py:763] (7/8) Epoch 0, batch 3650, loss[loss=0.2042, simple_loss=0.3716, pruned_loss=0.1836, over 7228.00 frames.], tot_loss[loss=0.2109, simple_loss=0.3768, pruned_loss=0.4653, over 1421649.83 frames.], batch size: 21, lr: 2.70e-03
+2022-04-28 08:04:03,473 INFO [train.py:763] (7/8) Epoch 0, batch 3700, loss[loss=0.2258, simple_loss=0.4114, pruned_loss=0.2007, over 6793.00 frames.], tot_loss[loss=0.2089, simple_loss=0.3751, pruned_loss=0.4001, over 1427061.89 frames.], batch size: 31, lr: 2.69e-03
+2022-04-28 08:05:34,895 INFO [train.py:763] (7/8) Epoch 0, batch 3750, loss[loss=0.2145, simple_loss=0.3865, pruned_loss=0.2123, over 7271.00 frames.], tot_loss[loss=0.2077, simple_loss=0.3742, pruned_loss=0.3518, over 1419788.77 frames.], batch size: 18, lr: 2.68e-03
+2022-04-28 08:06:40,603 INFO [train.py:763] (7/8) Epoch 0, batch 3800, loss[loss=0.1938, simple_loss=0.356, pruned_loss=0.1583, over 7130.00 frames.], tot_loss[loss=0.206, simple_loss=0.3726, pruned_loss=0.3104, over 1419538.54 frames.], batch size: 17, lr: 2.68e-03
+2022-04-28 08:07:46,197 INFO [train.py:763] (7/8) Epoch 0, batch 3850, loss[loss=0.1888, simple_loss=0.3472, pruned_loss=0.1517, over 7145.00 frames.], tot_loss[loss=0.2045, simple_loss=0.3711, pruned_loss=0.2778, over 1424953.07 frames.], batch size: 17, lr: 2.67e-03
+2022-04-28 08:08:52,453 INFO [train.py:763] (7/8) Epoch 0, batch 3900, loss[loss=0.1932, simple_loss=0.3524, pruned_loss=0.1699, over 6763.00 frames.], tot_loss[loss=0.2041, simple_loss=0.3712, pruned_loss=0.2539, over 1420102.65 frames.], batch size: 15, lr: 2.66e-03
+2022-04-28 08:09:58,865 INFO [train.py:763] (7/8) Epoch 0, batch 3950, loss[loss=0.171, simple_loss=0.3189, pruned_loss=0.115, over 7283.00 frames.], tot_loss[loss=0.2032, simple_loss=0.3703, pruned_loss=0.2339, over 1417883.50 frames.], batch size: 16, lr: 2.66e-03
+2022-04-28 08:11:04,212 INFO [train.py:763] (7/8) Epoch 0, batch 4000, loss[loss=0.2228, simple_loss=0.4092, pruned_loss=0.1821, over 7322.00 frames.], tot_loss[loss=0.2027, simple_loss=0.3702, pruned_loss=0.2175, over 1420144.81 frames.], batch size: 21, lr: 2.65e-03
+2022-04-28 08:12:09,516 INFO [train.py:763] (7/8) Epoch 0, batch 4050, loss[loss=0.2191, simple_loss=0.4025, pruned_loss=0.1786, over 7088.00 frames.], tot_loss[loss=0.2019, simple_loss=0.3693, pruned_loss=0.2048, over 1420986.61 frames.], batch size: 28, lr: 2.64e-03
+2022-04-28 08:13:15,888 INFO [train.py:763] (7/8) Epoch 0, batch 4100, loss[loss=0.1911, simple_loss=0.3545, pruned_loss=0.1391, over 7255.00 frames.], tot_loss[loss=0.2002, simple_loss=0.3669, pruned_loss=0.1928, over 1420838.97 frames.], batch size: 19, lr: 2.64e-03
+2022-04-28 08:14:22,427 INFO [train.py:763] (7/8) Epoch 0, batch 4150, loss[loss=0.182, simple_loss=0.3377, pruned_loss=0.1318, over 7067.00 frames.], tot_loss[loss=0.1998, simple_loss=0.3666, pruned_loss=0.1846, over 1425556.72 frames.], batch size: 18, lr: 2.63e-03
+2022-04-28 08:15:27,436 INFO [train.py:763] (7/8) Epoch 0, batch 4200, loss[loss=0.1987, simple_loss=0.3668, pruned_loss=0.153, over 7202.00 frames.], tot_loss[loss=0.2002, simple_loss=0.3677, pruned_loss=0.179, over 1424509.60 frames.], batch size: 22, lr: 2.63e-03
+2022-04-28 08:16:32,491 INFO [train.py:763] (7/8) Epoch 0, batch 4250, loss[loss=0.1931, simple_loss=0.3593, pruned_loss=0.1343, over 7432.00 frames.], tot_loss[loss=0.2008, simple_loss=0.3689, pruned_loss=0.1755, over 1423321.73 frames.], batch size: 20, lr: 2.62e-03
+2022-04-28 08:17:38,314 INFO [train.py:763] (7/8) Epoch 0, batch 4300, loss[loss=0.2189, simple_loss=0.4001, pruned_loss=0.1884, over 7065.00 frames.], tot_loss[loss=0.2009, simple_loss=0.3693, pruned_loss=0.1716, over 1422956.20 frames.], batch size: 28, lr: 2.61e-03
+2022-04-28 08:18:43,778 INFO [train.py:763] (7/8) Epoch 0, batch 4350, loss[loss=0.2035, simple_loss=0.3798, pruned_loss=0.1364, over 7435.00 frames.], tot_loss[loss=0.2004, simple_loss=0.3688, pruned_loss=0.1674, over 1426595.26 frames.], batch size: 20, lr: 2.61e-03
+2022-04-28 08:19:48,923 INFO [train.py:763] (7/8) Epoch 0, batch 4400, loss[loss=0.1752, simple_loss=0.3268, pruned_loss=0.1179, over 7267.00 frames.], tot_loss[loss=0.2006, simple_loss=0.3693, pruned_loss=0.1648, over 1424121.99 frames.], batch size: 18, lr: 2.60e-03
+2022-04-28 08:20:54,089 INFO [train.py:763] (7/8) Epoch 0, batch 4450, loss[loss=0.1828, simple_loss=0.3401, pruned_loss=0.1271, over 7435.00 frames.], tot_loss[loss=0.2014, simple_loss=0.3709, pruned_loss=0.1636, over 1423293.09 frames.], batch size: 20, lr: 2.59e-03
+2022-04-28 08:21:59,580 INFO [train.py:763] (7/8) Epoch 0, batch 4500, loss[loss=0.2102, simple_loss=0.3865, pruned_loss=0.17, over 6391.00 frames.], tot_loss[loss=0.2014, simple_loss=0.3711, pruned_loss=0.1617, over 1413621.79 frames.], batch size: 38, lr: 2.59e-03
+2022-04-28 08:23:05,623 INFO [train.py:763] (7/8) Epoch 0, batch 4550, loss[loss=0.2286, simple_loss=0.4152, pruned_loss=0.2099, over 4748.00 frames.], tot_loss[loss=0.2018, simple_loss=0.3719, pruned_loss=0.1614, over 1393311.82 frames.], batch size: 52, lr: 2.58e-03
+2022-04-28 08:24:44,871 INFO [train.py:763] (7/8) Epoch 1, batch 0, loss[loss=0.2188, simple_loss=0.4005, pruned_loss=0.1854, over 7149.00 frames.], tot_loss[loss=0.2188, simple_loss=0.4005, pruned_loss=0.1854, over 7149.00 frames.], batch size: 26, lr: 2.56e-03
+2022-04-28 08:25:50,525 INFO [train.py:763] (7/8) Epoch 1, batch 50, loss[loss=0.1961, simple_loss=0.3638, pruned_loss=0.1423, over 7224.00 frames.], tot_loss[loss=0.1983, simple_loss=0.3657, pruned_loss=0.1545, over 311646.10 frames.], batch size: 20, lr: 2.55e-03
+2022-04-28 08:26:56,244 INFO [train.py:763] (7/8) Epoch 1, batch 100, loss[loss=0.1604, simple_loss=0.3023, pruned_loss=0.09254, over 7430.00 frames.], tot_loss[loss=0.195, simple_loss=0.3606, pruned_loss=0.1469, over 560206.11 frames.], batch size: 20, lr: 2.54e-03
+2022-04-28 08:28:01,403 INFO [train.py:763] (7/8) Epoch 1, batch 150, loss[loss=0.1897, simple_loss=0.3535, pruned_loss=0.1294, over 7339.00 frames.], tot_loss[loss=0.1958, simple_loss=0.3622, pruned_loss=0.1467, over 751525.52 frames.], batch size: 20, lr: 2.54e-03
+2022-04-28 08:29:06,951 INFO [train.py:763] (7/8) Epoch 1, batch 200, loss[loss=0.1669, simple_loss=0.3136, pruned_loss=0.1016, over 7162.00 frames.], tot_loss[loss=0.1956, simple_loss=0.362, pruned_loss=0.1464, over 900713.92 frames.], batch size: 19, lr: 2.53e-03
+2022-04-28 08:30:12,412 INFO [train.py:763] (7/8) Epoch 1, batch 250, loss[loss=0.2, simple_loss=0.3712, pruned_loss=0.1443, over 7377.00 frames.], tot_loss[loss=0.1952, simple_loss=0.3613, pruned_loss=0.1453, over 1015852.27 frames.], batch size: 23, lr: 2.53e-03
+2022-04-28 08:31:17,608 INFO [train.py:763] (7/8) Epoch 1, batch 300, loss[loss=0.1799, simple_loss=0.3324, pruned_loss=0.1372, over 7257.00 frames.], tot_loss[loss=0.1949, simple_loss=0.3611, pruned_loss=0.1439, over 1104215.67 frames.], batch size: 19, lr: 2.52e-03
+2022-04-28 08:32:23,186 INFO [train.py:763] (7/8) Epoch 1, batch 350, loss[loss=0.1801, simple_loss=0.3366, pruned_loss=0.1179, over 7211.00 frames.], tot_loss[loss=0.1948, simple_loss=0.3607, pruned_loss=0.1441, over 1172607.84 frames.], batch size: 21, lr: 2.51e-03
+2022-04-28 08:33:29,302 INFO [train.py:763] (7/8) Epoch 1, batch 400, loss[loss=0.2247, simple_loss=0.414, pruned_loss=0.1771, over 7134.00 frames.], tot_loss[loss=0.1956, simple_loss=0.3621, pruned_loss=0.1452, over 1229396.74 frames.], batch size: 20, lr: 2.51e-03
+2022-04-28 08:34:36,196 INFO [train.py:763] (7/8) Epoch 1, batch 450, loss[loss=0.1896, simple_loss=0.3529, pruned_loss=0.132, over 7153.00 frames.], tot_loss[loss=0.1959, simple_loss=0.3628, pruned_loss=0.1449, over 1274708.61 frames.], batch size: 19, lr: 2.50e-03
+2022-04-28 08:35:42,360 INFO [train.py:763] (7/8) Epoch 1, batch 500, loss[loss=0.184, simple_loss=0.3425, pruned_loss=0.1273, over 7165.00 frames.], tot_loss[loss=0.1953, simple_loss=0.3617, pruned_loss=0.1441, over 1306705.50 frames.], batch size: 18, lr: 2.49e-03
+2022-04-28 08:36:48,851 INFO [train.py:763] (7/8) Epoch 1, batch 550, loss[loss=0.2062, simple_loss=0.377, pruned_loss=0.1765, over 7349.00 frames.], tot_loss[loss=0.1944, simple_loss=0.3602, pruned_loss=0.1427, over 1332244.17 frames.], batch size: 19, lr: 2.49e-03
+2022-04-28 08:37:55,700 INFO [train.py:763] (7/8) Epoch 1, batch 600, loss[loss=0.181, simple_loss=0.3383, pruned_loss=0.1185, over 7383.00 frames.], tot_loss[loss=0.1951, simple_loss=0.3615, pruned_loss=0.1436, over 1354373.08 frames.], batch size: 23, lr: 2.48e-03
+2022-04-28 08:39:01,291 INFO [train.py:763] (7/8) Epoch 1, batch 650, loss[loss=0.1756, simple_loss=0.3282, pruned_loss=0.115, over 7264.00 frames.], tot_loss[loss=0.1943, simple_loss=0.3601, pruned_loss=0.1418, over 1368304.23 frames.], batch size: 18, lr: 2.48e-03
+2022-04-28 08:40:06,987 INFO [train.py:763] (7/8) Epoch 1, batch 700, loss[loss=0.2191, simple_loss=0.398, pruned_loss=0.2009, over 4687.00 frames.], tot_loss[loss=0.193, simple_loss=0.3581, pruned_loss=0.14, over 1380317.03 frames.], batch size: 52, lr: 2.47e-03
+2022-04-28 08:41:12,408 INFO [train.py:763] (7/8) Epoch 1, batch 750, loss[loss=0.1923, simple_loss=0.3551, pruned_loss=0.148, over 7249.00 frames.], tot_loss[loss=0.1928, simple_loss=0.3578, pruned_loss=0.1392, over 1391466.70 frames.], batch size: 19, lr: 2.46e-03
+2022-04-28 08:42:18,209 INFO [train.py:763] (7/8) Epoch 1, batch 800, loss[loss=0.182, simple_loss=0.3391, pruned_loss=0.1241, over 7064.00 frames.], tot_loss[loss=0.1929, simple_loss=0.3579, pruned_loss=0.1393, over 1401053.14 frames.], batch size: 18, lr: 2.46e-03
+2022-04-28 08:43:24,121 INFO [train.py:763] (7/8) Epoch 1, batch 850, loss[loss=0.1722, simple_loss=0.3235, pruned_loss=0.1041, over 7330.00 frames.], tot_loss[loss=0.1921, simple_loss=0.3566, pruned_loss=0.1379, over 1409137.86 frames.], batch size: 20, lr: 2.45e-03
+2022-04-28 08:44:29,831 INFO [train.py:763] (7/8) Epoch 1, batch 900, loss[loss=0.1723, simple_loss=0.3242, pruned_loss=0.1016, over 7425.00 frames.], tot_loss[loss=0.1918, simple_loss=0.3562, pruned_loss=0.1372, over 1413798.57 frames.], batch size: 20, lr: 2.45e-03
+2022-04-28 08:45:35,256 INFO [train.py:763] (7/8) Epoch 1, batch 950, loss[loss=0.1996, simple_loss=0.3701, pruned_loss=0.1453, over 7250.00 frames.], tot_loss[loss=0.1922, simple_loss=0.3569, pruned_loss=0.1376, over 1414907.21 frames.], batch size: 19, lr: 2.44e-03
+2022-04-28 08:46:40,824 INFO [train.py:763] (7/8) Epoch 1, batch 1000, loss[loss=0.1822, simple_loss=0.3399, pruned_loss=0.1223, over 6687.00 frames.], tot_loss[loss=0.1913, simple_loss=0.3555, pruned_loss=0.136, over 1416461.46 frames.], batch size: 31, lr: 2.43e-03
+2022-04-28 08:47:46,485 INFO [train.py:763] (7/8) Epoch 1, batch 1050, loss[loss=0.1975, simple_loss=0.3676, pruned_loss=0.1369, over 7424.00 frames.], tot_loss[loss=0.1913, simple_loss=0.3554, pruned_loss=0.1358, over 1418497.38 frames.], batch size: 20, lr: 2.43e-03
+2022-04-28 08:48:51,700 INFO [train.py:763] (7/8) Epoch 1, batch 1100, loss[loss=0.1914, simple_loss=0.3538, pruned_loss=0.1452, over 7176.00 frames.], tot_loss[loss=0.1916, simple_loss=0.356, pruned_loss=0.1358, over 1420144.17 frames.], batch size: 18, lr: 2.42e-03
+2022-04-28 08:49:57,312 INFO [train.py:763] (7/8) Epoch 1, batch 1150, loss[loss=0.1828, simple_loss=0.3423, pruned_loss=0.1167, over 7239.00 frames.], tot_loss[loss=0.1903, simple_loss=0.3539, pruned_loss=0.1338, over 1424211.15 frames.], batch size: 20, lr: 2.41e-03
+2022-04-28 08:51:02,497 INFO [train.py:763] (7/8) Epoch 1, batch 1200, loss[loss=0.1876, simple_loss=0.3509, pruned_loss=0.1213, over 7034.00 frames.], tot_loss[loss=0.1893, simple_loss=0.352, pruned_loss=0.1323, over 1423828.89 frames.], batch size: 28, lr: 2.41e-03
+2022-04-28 08:52:07,813 INFO [train.py:763] (7/8) Epoch 1, batch 1250, loss[loss=0.1769, simple_loss=0.3288, pruned_loss=0.1249, over 7289.00 frames.], tot_loss[loss=0.1897, simple_loss=0.3528, pruned_loss=0.1329, over 1424488.74 frames.], batch size: 18, lr: 2.40e-03
+2022-04-28 08:53:12,966 INFO [train.py:763] (7/8) Epoch 1, batch 1300, loss[loss=0.1883, simple_loss=0.3513, pruned_loss=0.1265, over 7226.00 frames.], tot_loss[loss=0.1904, simple_loss=0.354, pruned_loss=0.1339, over 1417624.03 frames.], batch size: 21, lr: 2.40e-03
+2022-04-28 08:54:18,362 INFO [train.py:763] (7/8) Epoch 1, batch 1350, loss[loss=0.1854, simple_loss=0.3423, pruned_loss=0.1426, over 7275.00 frames.], tot_loss[loss=0.1899, simple_loss=0.3532, pruned_loss=0.1333, over 1421165.45 frames.], batch size: 17, lr: 2.39e-03
+2022-04-28 08:55:23,460 INFO [train.py:763] (7/8) Epoch 1, batch 1400, loss[loss=0.2204, simple_loss=0.4055, pruned_loss=0.1768, over 7221.00 frames.], tot_loss[loss=0.1903, simple_loss=0.3539, pruned_loss=0.1338, over 1419575.68 frames.], batch size: 21, lr: 2.39e-03
+2022-04-28 08:56:28,956 INFO [train.py:763] (7/8) Epoch 1, batch 1450, loss[loss=0.2998, simple_loss=0.3553, pruned_loss=0.1221, over 7135.00 frames.], tot_loss[loss=0.215, simple_loss=0.3551, pruned_loss=0.1357, over 1423473.60 frames.], batch size: 26, lr: 2.38e-03
+2022-04-28 08:57:34,419 INFO [train.py:763] (7/8) Epoch 1, batch 1500, loss[loss=0.3136, simple_loss=0.3521, pruned_loss=0.1375, over 6524.00 frames.], tot_loss[loss=0.2379, simple_loss=0.3562, pruned_loss=0.1361, over 1424506.53 frames.], batch size: 38, lr: 2.37e-03
+2022-04-28 08:58:40,155 INFO [train.py:763] (7/8) Epoch 1, batch 1550, loss[loss=0.3066, simple_loss=0.3503, pruned_loss=0.1314, over 7424.00 frames.], tot_loss[loss=0.2565, simple_loss=0.3582, pruned_loss=0.1367, over 1426912.81 frames.], batch size: 20, lr: 2.37e-03
+2022-04-28 08:59:47,375 INFO [train.py:763] (7/8) Epoch 1, batch 1600, loss[loss=0.3229, simple_loss=0.3614, pruned_loss=0.1422, over 7166.00 frames.], tot_loss[loss=0.2668, simple_loss=0.3569, pruned_loss=0.1345, over 1426069.46 frames.], batch size: 18, lr: 2.36e-03
+2022-04-28 09:00:52,900 INFO [train.py:763] (7/8) Epoch 1, batch 1650, loss[loss=0.2949, simple_loss=0.3392, pruned_loss=0.1253, over 7443.00 frames.], tot_loss[loss=0.2747, simple_loss=0.3559, pruned_loss=0.1327, over 1425747.92 frames.], batch size: 20, lr: 2.36e-03
+2022-04-28 09:01:59,221 INFO [train.py:763] (7/8) Epoch 1, batch 1700, loss[loss=0.3282, simple_loss=0.3851, pruned_loss=0.1356, over 7404.00 frames.], tot_loss[loss=0.2828, simple_loss=0.3567, pruned_loss=0.1325, over 1423363.70 frames.], batch size: 21, lr: 2.35e-03
+2022-04-28 09:03:06,120 INFO [train.py:763] (7/8) Epoch 1, batch 1750, loss[loss=0.2814, simple_loss=0.3234, pruned_loss=0.1197, over 7280.00 frames.], tot_loss[loss=0.2903, simple_loss=0.3583, pruned_loss=0.133, over 1422721.00 frames.], batch size: 18, lr: 2.34e-03
+2022-04-28 09:04:13,401 INFO [train.py:763] (7/8) Epoch 1, batch 1800, loss[loss=0.2846, simple_loss=0.3318, pruned_loss=0.1187, over 7348.00 frames.], tot_loss[loss=0.2943, simple_loss=0.3579, pruned_loss=0.1323, over 1424758.71 frames.], batch size: 19, lr: 2.34e-03
+2022-04-28 09:05:20,650 INFO [train.py:763] (7/8) Epoch 1, batch 1850, loss[loss=0.2849, simple_loss=0.3425, pruned_loss=0.1136, over 7329.00 frames.], tot_loss[loss=0.2937, simple_loss=0.3556, pruned_loss=0.1291, over 1424731.24 frames.], batch size: 20, lr: 2.33e-03
+2022-04-28 09:06:26,272 INFO [train.py:763] (7/8) Epoch 1, batch 1900, loss[loss=0.258, simple_loss=0.3061, pruned_loss=0.1049, over 6996.00 frames.], tot_loss[loss=0.297, simple_loss=0.3567, pruned_loss=0.1289, over 1428342.20 frames.], batch size: 16, lr: 2.33e-03
+2022-04-28 09:07:32,765 INFO [train.py:763] (7/8) Epoch 1, batch 1950, loss[loss=0.2872, simple_loss=0.343, pruned_loss=0.1157, over 7277.00 frames.], tot_loss[loss=0.2981, simple_loss=0.3565, pruned_loss=0.1278, over 1428725.53 frames.], batch size: 18, lr: 2.32e-03
+2022-04-28 09:08:38,168 INFO [train.py:763] (7/8) Epoch 1, batch 2000, loss[loss=0.3517, simple_loss=0.4018, pruned_loss=0.1508, over 7111.00 frames.], tot_loss[loss=0.3017, simple_loss=0.3586, pruned_loss=0.1286, over 1422957.06 frames.], batch size: 21, lr: 2.32e-03
+2022-04-28 09:09:44,451 INFO [train.py:763] (7/8) Epoch 1, batch 2050, loss[loss=0.3412, simple_loss=0.3972, pruned_loss=0.1426, over 7063.00 frames.], tot_loss[loss=0.3008, simple_loss=0.3572, pruned_loss=0.127, over 1424238.31 frames.], batch size: 28, lr: 2.31e-03
+2022-04-28 09:10:49,776 INFO [train.py:763] (7/8) Epoch 1, batch 2100, loss[loss=0.2415, simple_loss=0.2984, pruned_loss=0.09233, over 7427.00 frames.], tot_loss[loss=0.2991, simple_loss=0.3556, pruned_loss=0.1251, over 1424690.25 frames.], batch size: 18, lr: 2.31e-03
+2022-04-28 09:11:55,377 INFO [train.py:763] (7/8) Epoch 1, batch 2150, loss[loss=0.3133, simple_loss=0.3712, pruned_loss=0.1277, over 7409.00 frames.], tot_loss[loss=0.2997, simple_loss=0.3556, pruned_loss=0.1249, over 1422334.45 frames.], batch size: 21, lr: 2.30e-03
+2022-04-28 09:13:01,264 INFO [train.py:763] (7/8) Epoch 1, batch 2200, loss[loss=0.3384, simple_loss=0.3805, pruned_loss=0.1481, over 7112.00 frames.], tot_loss[loss=0.2979, simple_loss=0.3534, pruned_loss=0.1235, over 1421431.34 frames.], batch size: 21, lr: 2.29e-03
+2022-04-28 09:14:06,876 INFO [train.py:763] (7/8) Epoch 1, batch 2250, loss[loss=0.2511, simple_loss=0.3297, pruned_loss=0.08627, over 7225.00 frames.], tot_loss[loss=0.2956, simple_loss=0.3518, pruned_loss=0.1215, over 1422841.73 frames.], batch size: 21, lr: 2.29e-03
+2022-04-28 09:15:14,118 INFO [train.py:763] (7/8) Epoch 1, batch 2300, loss[loss=0.3797, simple_loss=0.4114, pruned_loss=0.174, over 7213.00 frames.], tot_loss[loss=0.2974, simple_loss=0.3532, pruned_loss=0.1221, over 1424133.39 frames.], batch size: 22, lr: 2.28e-03
+2022-04-28 09:16:21,363 INFO [train.py:763] (7/8) Epoch 1, batch 2350, loss[loss=0.3115, simple_loss=0.3632, pruned_loss=0.13, over 7233.00 frames.], tot_loss[loss=0.2991, simple_loss=0.3545, pruned_loss=0.123, over 1422327.55 frames.], batch size: 20, lr: 2.28e-03
+2022-04-28 09:17:26,507 INFO [train.py:763] (7/8) Epoch 1, batch 2400, loss[loss=0.3048, simple_loss=0.3714, pruned_loss=0.1192, over 7315.00 frames.], tot_loss[loss=0.2987, simple_loss=0.3546, pruned_loss=0.1223, over 1422699.02 frames.], batch size: 21, lr: 2.27e-03
+2022-04-28 09:18:31,936 INFO [train.py:763] (7/8) Epoch 1, batch 2450, loss[loss=0.2611, simple_loss=0.3438, pruned_loss=0.08926, over 7324.00 frames.], tot_loss[loss=0.2986, simple_loss=0.355, pruned_loss=0.1217, over 1425707.07 frames.], batch size: 21, lr: 2.27e-03
+2022-04-28 09:19:37,105 INFO [train.py:763] (7/8) Epoch 1, batch 2500, loss[loss=0.3064, simple_loss=0.3698, pruned_loss=0.1215, over 7151.00 frames.], tot_loss[loss=0.2975, simple_loss=0.3543, pruned_loss=0.1209, over 1426246.30 frames.], batch size: 26, lr: 2.26e-03
+2022-04-28 09:20:43,307 INFO [train.py:763] (7/8) Epoch 1, batch 2550, loss[loss=0.3003, simple_loss=0.3387, pruned_loss=0.1309, over 6994.00 frames.], tot_loss[loss=0.2979, simple_loss=0.3543, pruned_loss=0.1211, over 1426673.07 frames.], batch size: 16, lr: 2.26e-03
+2022-04-28 09:21:48,834 INFO [train.py:763] (7/8) Epoch 1, batch 2600, loss[loss=0.3525, simple_loss=0.3885, pruned_loss=0.1582, over 7193.00 frames.], tot_loss[loss=0.2959, simple_loss=0.3529, pruned_loss=0.1197, over 1428526.64 frames.], batch size: 26, lr: 2.25e-03
+2022-04-28 09:22:54,022 INFO [train.py:763] (7/8) Epoch 1, batch 2650, loss[loss=0.4063, simple_loss=0.4286, pruned_loss=0.192, over 6445.00 frames.], tot_loss[loss=0.2959, simple_loss=0.3528, pruned_loss=0.1197, over 1426206.81 frames.], batch size: 38, lr: 2.25e-03
+2022-04-28 09:24:00,448 INFO [train.py:763] (7/8) Epoch 1, batch 2700, loss[loss=0.3195, simple_loss=0.3752, pruned_loss=0.1319, over 6863.00 frames.], tot_loss[loss=0.2943, simple_loss=0.352, pruned_loss=0.1185, over 1425935.47 frames.], batch size: 31, lr: 2.24e-03
+2022-04-28 09:25:06,563 INFO [train.py:763] (7/8) Epoch 1, batch 2750, loss[loss=0.3352, simple_loss=0.3796, pruned_loss=0.1454, over 7290.00 frames.], tot_loss[loss=0.2933, simple_loss=0.3508, pruned_loss=0.1181, over 1422922.88 frames.], batch size: 24, lr: 2.24e-03
+2022-04-28 09:26:12,257 INFO [train.py:763] (7/8) Epoch 1, batch 2800, loss[loss=0.2605, simple_loss=0.3397, pruned_loss=0.09064, over 7217.00 frames.], tot_loss[loss=0.2921, simple_loss=0.3503, pruned_loss=0.1171, over 1425806.56 frames.], batch size: 23, lr: 2.23e-03
+2022-04-28 09:27:17,552 INFO [train.py:763] (7/8) Epoch 1, batch 2850, loss[loss=0.2885, simple_loss=0.3696, pruned_loss=0.1037, over 7297.00 frames.], tot_loss[loss=0.2912, simple_loss=0.3498, pruned_loss=0.1164, over 1425353.87 frames.], batch size: 24, lr: 2.23e-03
+2022-04-28 09:28:22,528 INFO [train.py:763] (7/8) Epoch 1, batch 2900, loss[loss=0.2717, simple_loss=0.3392, pruned_loss=0.1021, over 7236.00 frames.], tot_loss[loss=0.2932, simple_loss=0.3513, pruned_loss=0.1177, over 1420060.45 frames.], batch size: 20, lr: 2.22e-03
+2022-04-28 09:29:27,942 INFO [train.py:763] (7/8) Epoch 1, batch 2950, loss[loss=0.2844, simple_loss=0.359, pruned_loss=0.1049, over 7229.00 frames.], tot_loss[loss=0.2931, simple_loss=0.3515, pruned_loss=0.1174, over 1421626.73 frames.], batch size: 20, lr: 2.22e-03
+2022-04-28 09:30:33,560 INFO [train.py:763] (7/8) Epoch 1, batch 3000, loss[loss=0.2149, simple_loss=0.2808, pruned_loss=0.07455, over 7276.00 frames.], tot_loss[loss=0.2911, simple_loss=0.3501, pruned_loss=0.1161, over 1425848.82 frames.], batch size: 17, lr: 2.21e-03
+2022-04-28 09:30:33,561 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-28 09:30:49,512 INFO [train.py:792] (7/8) Epoch 1, validation: loss=0.217, simple_loss=0.3099, pruned_loss=0.06207, over 698248.00 frames.
+2022-04-28 09:31:55,890 INFO [train.py:763] (7/8) Epoch 1, batch 3050, loss[loss=0.2882, simple_loss=0.3476, pruned_loss=0.1144, over 7287.00 frames.], tot_loss[loss=0.2914, simple_loss=0.3504, pruned_loss=0.1162, over 1422682.45 frames.], batch size: 18, lr: 2.20e-03
+2022-04-28 09:33:01,975 INFO [train.py:763] (7/8) Epoch 1, batch 3100, loss[loss=0.3291, simple_loss=0.3767, pruned_loss=0.1407, over 5090.00 frames.], tot_loss[loss=0.2929, simple_loss=0.3521, pruned_loss=0.1169, over 1421507.72 frames.], batch size: 54, lr: 2.20e-03
+2022-04-28 09:34:07,391 INFO [train.py:763] (7/8) Epoch 1, batch 3150, loss[loss=0.2682, simple_loss=0.3223, pruned_loss=0.1071, over 6816.00 frames.], tot_loss[loss=0.2933, simple_loss=0.3528, pruned_loss=0.1169, over 1423417.30 frames.], batch size: 15, lr: 2.19e-03
+2022-04-28 09:35:13,598 INFO [train.py:763] (7/8) Epoch 1, batch 3200, loss[loss=0.3454, simple_loss=0.3923, pruned_loss=0.1492, over 5438.00 frames.], tot_loss[loss=0.2956, simple_loss=0.3551, pruned_loss=0.118, over 1413788.76 frames.], batch size: 52, lr: 2.19e-03
+2022-04-28 09:36:19,404 INFO [train.py:763] (7/8) Epoch 1, batch 3250, loss[loss=0.2962, simple_loss=0.358, pruned_loss=0.1172, over 7210.00 frames.], tot_loss[loss=0.2941, simple_loss=0.3541, pruned_loss=0.117, over 1415972.27 frames.], batch size: 23, lr: 2.18e-03
+2022-04-28 09:37:26,029 INFO [train.py:763] (7/8) Epoch 1, batch 3300, loss[loss=0.3052, simple_loss=0.3706, pruned_loss=0.1199, over 7197.00 frames.], tot_loss[loss=0.2938, simple_loss=0.3537, pruned_loss=0.1169, over 1421105.55 frames.], batch size: 22, lr: 2.18e-03
+2022-04-28 09:38:31,151 INFO [train.py:763] (7/8) Epoch 1, batch 3350, loss[loss=0.3213, simple_loss=0.3811, pruned_loss=0.1308, over 7129.00 frames.], tot_loss[loss=0.2937, simple_loss=0.354, pruned_loss=0.1167, over 1424134.27 frames.], batch size: 26, lr: 2.18e-03
+2022-04-28 09:39:36,467 INFO [train.py:763] (7/8) Epoch 1, batch 3400, loss[loss=0.2477, simple_loss=0.3165, pruned_loss=0.08951, over 7120.00 frames.], tot_loss[loss=0.2916, simple_loss=0.352, pruned_loss=0.1156, over 1425669.79 frames.], batch size: 17, lr: 2.17e-03
+2022-04-28 09:40:52,299 INFO [train.py:763] (7/8) Epoch 1, batch 3450, loss[loss=0.308, simple_loss=0.379, pruned_loss=0.1185, over 7301.00 frames.], tot_loss[loss=0.2909, simple_loss=0.3518, pruned_loss=0.1149, over 1427928.25 frames.], batch size: 24, lr: 2.17e-03
+2022-04-28 09:41:59,078 INFO [train.py:763] (7/8) Epoch 1, batch 3500, loss[loss=0.2878, simple_loss=0.3546, pruned_loss=0.1105, over 6455.00 frames.], tot_loss[loss=0.2883, simple_loss=0.3499, pruned_loss=0.1134, over 1424477.60 frames.], batch size: 38, lr: 2.16e-03
+2022-04-28 09:43:05,851 INFO [train.py:763] (7/8) Epoch 1, batch 3550, loss[loss=0.3149, simple_loss=0.3832, pruned_loss=0.1233, over 7297.00 frames.], tot_loss[loss=0.2882, simple_loss=0.3504, pruned_loss=0.113, over 1424533.64 frames.], batch size: 25, lr: 2.16e-03
+2022-04-28 09:44:13,023 INFO [train.py:763] (7/8) Epoch 1, batch 3600, loss[loss=0.3148, simple_loss=0.3813, pruned_loss=0.1242, over 7236.00 frames.], tot_loss[loss=0.2891, simple_loss=0.3514, pruned_loss=0.1134, over 1425748.11 frames.], batch size: 20, lr: 2.15e-03
+2022-04-28 09:45:20,641 INFO [train.py:763] (7/8) Epoch 1, batch 3650, loss[loss=0.3109, simple_loss=0.3526, pruned_loss=0.1346, over 7233.00 frames.], tot_loss[loss=0.2897, simple_loss=0.3514, pruned_loss=0.114, over 1427720.31 frames.], batch size: 16, lr: 2.15e-03
+2022-04-28 09:46:27,946 INFO [train.py:763] (7/8) Epoch 1, batch 3700, loss[loss=0.2928, simple_loss=0.3582, pruned_loss=0.1137, over 7166.00 frames.], tot_loss[loss=0.2887, simple_loss=0.3508, pruned_loss=0.1133, over 1429004.98 frames.], batch size: 19, lr: 2.14e-03
+2022-04-28 09:47:33,418 INFO [train.py:763] (7/8) Epoch 1, batch 3750, loss[loss=0.3171, simple_loss=0.3704, pruned_loss=0.1318, over 7303.00 frames.], tot_loss[loss=0.2888, simple_loss=0.3509, pruned_loss=0.1133, over 1429335.78 frames.], batch size: 24, lr: 2.14e-03
+2022-04-28 09:48:38,894 INFO [train.py:763] (7/8) Epoch 1, batch 3800, loss[loss=0.27, simple_loss=0.3183, pruned_loss=0.1108, over 7243.00 frames.], tot_loss[loss=0.288, simple_loss=0.3502, pruned_loss=0.1129, over 1429212.86 frames.], batch size: 16, lr: 2.13e-03
+2022-04-28 09:49:44,153 INFO [train.py:763] (7/8) Epoch 1, batch 3850, loss[loss=0.3196, simple_loss=0.3782, pruned_loss=0.1305, over 7113.00 frames.], tot_loss[loss=0.2874, simple_loss=0.3503, pruned_loss=0.1122, over 1431071.89 frames.], batch size: 26, lr: 2.13e-03
+2022-04-28 09:50:49,552 INFO [train.py:763] (7/8) Epoch 1, batch 3900, loss[loss=0.315, simple_loss=0.3849, pruned_loss=0.1225, over 7303.00 frames.], tot_loss[loss=0.2872, simple_loss=0.3498, pruned_loss=0.1122, over 1430402.73 frames.], batch size: 24, lr: 2.12e-03
+2022-04-28 09:51:55,506 INFO [train.py:763] (7/8) Epoch 1, batch 3950, loss[loss=0.3029, simple_loss=0.3656, pruned_loss=0.1201, over 7113.00 frames.], tot_loss[loss=0.2858, simple_loss=0.3486, pruned_loss=0.1115, over 1428210.15 frames.], batch size: 21, lr: 2.12e-03
+2022-04-28 09:53:01,250 INFO [train.py:763] (7/8) Epoch 1, batch 4000, loss[loss=0.3153, simple_loss=0.3752, pruned_loss=0.1277, over 7205.00 frames.], tot_loss[loss=0.2843, simple_loss=0.3478, pruned_loss=0.1104, over 1428540.16 frames.], batch size: 22, lr: 2.11e-03
+2022-04-28 09:54:07,057 INFO [train.py:763] (7/8) Epoch 1, batch 4050, loss[loss=0.3158, simple_loss=0.3741, pruned_loss=0.1288, over 6810.00 frames.], tot_loss[loss=0.2833, simple_loss=0.3469, pruned_loss=0.1098, over 1425972.68 frames.], batch size: 31, lr: 2.11e-03
+2022-04-28 09:55:12,323 INFO [train.py:763] (7/8) Epoch 1, batch 4100, loss[loss=0.287, simple_loss=0.3465, pruned_loss=0.1138, over 7212.00 frames.], tot_loss[loss=0.2843, simple_loss=0.3478, pruned_loss=0.1104, over 1421555.77 frames.], batch size: 21, lr: 2.10e-03
+2022-04-28 09:56:17,401 INFO [train.py:763] (7/8) Epoch 1, batch 4150, loss[loss=0.3456, simple_loss=0.3835, pruned_loss=0.1539, over 6805.00 frames.], tot_loss[loss=0.2841, simple_loss=0.3476, pruned_loss=0.1103, over 1420221.28 frames.], batch size: 31, lr: 2.10e-03
+2022-04-28 09:57:22,853 INFO [train.py:763] (7/8) Epoch 1, batch 4200, loss[loss=0.2273, simple_loss=0.2911, pruned_loss=0.08176, over 7272.00 frames.], tot_loss[loss=0.2836, simple_loss=0.3466, pruned_loss=0.1103, over 1418786.82 frames.], batch size: 18, lr: 2.10e-03
+2022-04-28 09:58:27,899 INFO [train.py:763] (7/8) Epoch 1, batch 4250, loss[loss=0.2609, simple_loss=0.3311, pruned_loss=0.09535, over 7291.00 frames.], tot_loss[loss=0.2855, simple_loss=0.3481, pruned_loss=0.1115, over 1413766.52 frames.], batch size: 18, lr: 2.09e-03
+2022-04-28 09:59:34,320 INFO [train.py:763] (7/8) Epoch 1, batch 4300, loss[loss=0.2708, simple_loss=0.3299, pruned_loss=0.1059, over 7279.00 frames.], tot_loss[loss=0.285, simple_loss=0.3477, pruned_loss=0.1111, over 1412256.75 frames.], batch size: 25, lr: 2.09e-03
+2022-04-28 10:00:39,978 INFO [train.py:763] (7/8) Epoch 1, batch 4350, loss[loss=0.2352, simple_loss=0.3016, pruned_loss=0.08435, over 7005.00 frames.], tot_loss[loss=0.2862, simple_loss=0.3489, pruned_loss=0.1118, over 1412861.02 frames.], batch size: 16, lr: 2.08e-03
+2022-04-28 10:01:45,344 INFO [train.py:763] (7/8) Epoch 1, batch 4400, loss[loss=0.3007, simple_loss=0.3626, pruned_loss=0.1194, over 7313.00 frames.], tot_loss[loss=0.2853, simple_loss=0.3481, pruned_loss=0.1112, over 1408388.83 frames.], batch size: 21, lr: 2.08e-03
+2022-04-28 10:02:50,316 INFO [train.py:763] (7/8) Epoch 1, batch 4450, loss[loss=0.2936, simple_loss=0.356, pruned_loss=0.1156, over 6334.00 frames.], tot_loss[loss=0.2849, simple_loss=0.348, pruned_loss=0.1109, over 1400646.85 frames.], batch size: 37, lr: 2.07e-03
+2022-04-28 10:03:55,342 INFO [train.py:763] (7/8) Epoch 1, batch 4500, loss[loss=0.3169, simple_loss=0.3707, pruned_loss=0.1315, over 6399.00 frames.], tot_loss[loss=0.2839, simple_loss=0.3464, pruned_loss=0.1106, over 1385830.92 frames.], batch size: 38, lr: 2.07e-03
+2022-04-28 10:04:59,443 INFO [train.py:763] (7/8) Epoch 1, batch 4550, loss[loss=0.3371, simple_loss=0.3725, pruned_loss=0.1509, over 4889.00 frames.], tot_loss[loss=0.2878, simple_loss=0.3496, pruned_loss=0.113, over 1355173.69 frames.], batch size: 52, lr: 2.06e-03
+2022-04-28 10:06:27,058 INFO [train.py:763] (7/8) Epoch 2, batch 0, loss[loss=0.2387, simple_loss=0.2982, pruned_loss=0.08958, over 7295.00 frames.], tot_loss[loss=0.2387, simple_loss=0.2982, pruned_loss=0.08958, over 7295.00 frames.], batch size: 17, lr: 2.02e-03
+2022-04-28 10:07:33,527 INFO [train.py:763] (7/8) Epoch 2, batch 50, loss[loss=0.318, simple_loss=0.3827, pruned_loss=0.1266, over 7308.00 frames.], tot_loss[loss=0.2813, simple_loss=0.3454, pruned_loss=0.1086, over 321179.08 frames.], batch size: 25, lr: 2.02e-03
+2022-04-28 10:08:39,172 INFO [train.py:763] (7/8) Epoch 2, batch 100, loss[loss=0.2725, simple_loss=0.3314, pruned_loss=0.1068, over 7009.00 frames.], tot_loss[loss=0.2752, simple_loss=0.3422, pruned_loss=0.1041, over 568659.51 frames.], batch size: 16, lr: 2.01e-03
+2022-04-28 10:09:45,131 INFO [train.py:763] (7/8) Epoch 2, batch 150, loss[loss=0.325, simple_loss=0.3812, pruned_loss=0.1344, over 6663.00 frames.], tot_loss[loss=0.2729, simple_loss=0.3402, pruned_loss=0.1028, over 761385.70 frames.], batch size: 31, lr: 2.01e-03
+2022-04-28 10:10:50,708 INFO [train.py:763] (7/8) Epoch 2, batch 200, loss[loss=0.2498, simple_loss=0.3096, pruned_loss=0.09496, over 7274.00 frames.], tot_loss[loss=0.2738, simple_loss=0.3406, pruned_loss=0.1035, over 900899.79 frames.], batch size: 16, lr: 2.00e-03
+2022-04-28 10:11:56,050 INFO [train.py:763] (7/8) Epoch 2, batch 250, loss[loss=0.2597, simple_loss=0.3254, pruned_loss=0.09696, over 7361.00 frames.], tot_loss[loss=0.2759, simple_loss=0.3426, pruned_loss=0.1046, over 1011619.47 frames.], batch size: 19, lr: 2.00e-03
+2022-04-28 10:13:01,585 INFO [train.py:763] (7/8) Epoch 2, batch 300, loss[loss=0.3051, simple_loss=0.3718, pruned_loss=0.1192, over 6807.00 frames.], tot_loss[loss=0.2777, simple_loss=0.3448, pruned_loss=0.1053, over 1102071.45 frames.], batch size: 31, lr: 2.00e-03
+2022-04-28 10:14:07,034 INFO [train.py:763] (7/8) Epoch 2, batch 350, loss[loss=0.2715, simple_loss=0.3541, pruned_loss=0.0945, over 7315.00 frames.], tot_loss[loss=0.2766, simple_loss=0.3446, pruned_loss=0.1043, over 1172924.88 frames.], batch size: 21, lr: 1.99e-03
+2022-04-28 10:15:12,751 INFO [train.py:763] (7/8) Epoch 2, batch 400, loss[loss=0.2774, simple_loss=0.3511, pruned_loss=0.1018, over 7271.00 frames.], tot_loss[loss=0.2777, simple_loss=0.3449, pruned_loss=0.1053, over 1224252.87 frames.], batch size: 24, lr: 1.99e-03
+2022-04-28 10:16:17,712 INFO [train.py:763] (7/8) Epoch 2, batch 450, loss[loss=0.281, simple_loss=0.3527, pruned_loss=0.1047, over 7217.00 frames.], tot_loss[loss=0.2768, simple_loss=0.3443, pruned_loss=0.1046, over 1264752.69 frames.], batch size: 22, lr: 1.98e-03
+2022-04-28 10:17:41,025 INFO [train.py:763] (7/8) Epoch 2, batch 500, loss[loss=0.2389, simple_loss=0.3068, pruned_loss=0.08544, over 7016.00 frames.], tot_loss[loss=0.2769, simple_loss=0.3439, pruned_loss=0.105, over 1302275.53 frames.], batch size: 16, lr: 1.98e-03
+2022-04-28 10:19:24,498 INFO [train.py:763] (7/8) Epoch 2, batch 550, loss[loss=0.2571, simple_loss=0.337, pruned_loss=0.08867, over 7226.00 frames.], tot_loss[loss=0.276, simple_loss=0.3437, pruned_loss=0.1042, over 1332402.48 frames.], batch size: 21, lr: 1.98e-03
+2022-04-28 10:20:31,156 INFO [train.py:763] (7/8) Epoch 2, batch 600, loss[loss=0.3312, simple_loss=0.3968, pruned_loss=0.1328, over 7292.00 frames.], tot_loss[loss=0.274, simple_loss=0.3421, pruned_loss=0.1029, over 1354117.83 frames.], batch size: 25, lr: 1.97e-03
+2022-04-28 10:21:56,869 INFO [train.py:763] (7/8) Epoch 2, batch 650, loss[loss=0.2665, simple_loss=0.3424, pruned_loss=0.09537, over 7356.00 frames.], tot_loss[loss=0.2738, simple_loss=0.3418, pruned_loss=0.1029, over 1368307.73 frames.], batch size: 19, lr: 1.97e-03
+2022-04-28 10:23:03,999 INFO [train.py:763] (7/8) Epoch 2, batch 700, loss[loss=0.2631, simple_loss=0.3371, pruned_loss=0.09459, over 7215.00 frames.], tot_loss[loss=0.2738, simple_loss=0.342, pruned_loss=0.1028, over 1378324.89 frames.], batch size: 21, lr: 1.96e-03
+2022-04-28 10:24:09,357 INFO [train.py:763] (7/8) Epoch 2, batch 750, loss[loss=0.2572, simple_loss=0.3397, pruned_loss=0.08731, over 7204.00 frames.], tot_loss[loss=0.274, simple_loss=0.3424, pruned_loss=0.1028, over 1391746.28 frames.], batch size: 23, lr: 1.96e-03
+2022-04-28 10:25:14,631 INFO [train.py:763] (7/8) Epoch 2, batch 800, loss[loss=0.2963, simple_loss=0.3722, pruned_loss=0.1101, over 7196.00 frames.], tot_loss[loss=0.2749, simple_loss=0.3432, pruned_loss=0.1033, over 1402657.98 frames.], batch size: 23, lr: 1.96e-03
+2022-04-28 10:26:20,190 INFO [train.py:763] (7/8) Epoch 2, batch 850, loss[loss=0.3238, simple_loss=0.3841, pruned_loss=0.1317, over 7330.00 frames.], tot_loss[loss=0.2732, simple_loss=0.3417, pruned_loss=0.1024, over 1410377.31 frames.], batch size: 25, lr: 1.95e-03
+2022-04-28 10:27:26,285 INFO [train.py:763] (7/8) Epoch 2, batch 900, loss[loss=0.2285, simple_loss=0.3087, pruned_loss=0.07417, over 7059.00 frames.], tot_loss[loss=0.2753, simple_loss=0.3429, pruned_loss=0.1039, over 1412465.75 frames.], batch size: 18, lr: 1.95e-03
+2022-04-28 10:28:31,608 INFO [train.py:763] (7/8) Epoch 2, batch 950, loss[loss=0.2916, simple_loss=0.36, pruned_loss=0.1116, over 7154.00 frames.], tot_loss[loss=0.2731, simple_loss=0.3415, pruned_loss=0.1024, over 1417508.33 frames.], batch size: 20, lr: 1.94e-03
+2022-04-28 10:29:36,679 INFO [train.py:763] (7/8) Epoch 2, batch 1000, loss[loss=0.3258, simple_loss=0.3836, pruned_loss=0.134, over 6741.00 frames.], tot_loss[loss=0.2746, simple_loss=0.3428, pruned_loss=0.1032, over 1415946.32 frames.], batch size: 31, lr: 1.94e-03
+2022-04-28 10:30:41,960 INFO [train.py:763] (7/8) Epoch 2, batch 1050, loss[loss=0.2305, simple_loss=0.3057, pruned_loss=0.07761, over 7288.00 frames.], tot_loss[loss=0.2735, simple_loss=0.3424, pruned_loss=0.1023, over 1415259.36 frames.], batch size: 18, lr: 1.94e-03
+2022-04-28 10:31:48,370 INFO [train.py:763] (7/8) Epoch 2, batch 1100, loss[loss=0.2673, simple_loss=0.3463, pruned_loss=0.09414, over 7212.00 frames.], tot_loss[loss=0.2742, simple_loss=0.3432, pruned_loss=0.1026, over 1420054.05 frames.], batch size: 21, lr: 1.93e-03
+2022-04-28 10:32:55,868 INFO [train.py:763] (7/8) Epoch 2, batch 1150, loss[loss=0.326, simple_loss=0.3762, pruned_loss=0.1379, over 7228.00 frames.], tot_loss[loss=0.2738, simple_loss=0.3423, pruned_loss=0.1026, over 1420488.88 frames.], batch size: 20, lr: 1.93e-03
+2022-04-28 10:34:03,613 INFO [train.py:763] (7/8) Epoch 2, batch 1200, loss[loss=0.2671, simple_loss=0.3288, pruned_loss=0.1027, over 7424.00 frames.], tot_loss[loss=0.272, simple_loss=0.3407, pruned_loss=0.1016, over 1423387.92 frames.], batch size: 20, lr: 1.93e-03
+2022-04-28 10:35:11,274 INFO [train.py:763] (7/8) Epoch 2, batch 1250, loss[loss=0.2568, simple_loss=0.3319, pruned_loss=0.09087, over 7408.00 frames.], tot_loss[loss=0.2705, simple_loss=0.3394, pruned_loss=0.1007, over 1424137.18 frames.], batch size: 21, lr: 1.92e-03
+2022-04-28 10:36:17,285 INFO [train.py:763] (7/8) Epoch 2, batch 1300, loss[loss=0.2321, simple_loss=0.312, pruned_loss=0.07608, over 7323.00 frames.], tot_loss[loss=0.2703, simple_loss=0.3394, pruned_loss=0.1006, over 1425848.34 frames.], batch size: 21, lr: 1.92e-03
+2022-04-28 10:37:22,339 INFO [train.py:763] (7/8) Epoch 2, batch 1350, loss[loss=0.2967, simple_loss=0.3489, pruned_loss=0.1222, over 7433.00 frames.], tot_loss[loss=0.272, simple_loss=0.341, pruned_loss=0.1014, over 1426034.07 frames.], batch size: 20, lr: 1.91e-03
+2022-04-28 10:38:27,411 INFO [train.py:763] (7/8) Epoch 2, batch 1400, loss[loss=0.2259, simple_loss=0.3077, pruned_loss=0.07207, over 7159.00 frames.], tot_loss[loss=0.272, simple_loss=0.3414, pruned_loss=0.1012, over 1423666.81 frames.], batch size: 19, lr: 1.91e-03
+2022-04-28 10:39:32,830 INFO [train.py:763] (7/8) Epoch 2, batch 1450, loss[loss=0.2562, simple_loss=0.3229, pruned_loss=0.0948, over 7149.00 frames.], tot_loss[loss=0.2713, simple_loss=0.3412, pruned_loss=0.1007, over 1420431.11 frames.], batch size: 17, lr: 1.91e-03
+2022-04-28 10:40:38,398 INFO [train.py:763] (7/8) Epoch 2, batch 1500, loss[loss=0.2988, simple_loss=0.3622, pruned_loss=0.1177, over 7320.00 frames.], tot_loss[loss=0.2726, simple_loss=0.3418, pruned_loss=0.1017, over 1418109.67 frames.], batch size: 21, lr: 1.90e-03
+2022-04-28 10:41:43,981 INFO [train.py:763] (7/8) Epoch 2, batch 1550, loss[loss=0.2695, simple_loss=0.3465, pruned_loss=0.09624, over 7166.00 frames.], tot_loss[loss=0.271, simple_loss=0.3407, pruned_loss=0.1007, over 1422357.20 frames.], batch size: 19, lr: 1.90e-03
+2022-04-28 10:42:49,549 INFO [train.py:763] (7/8) Epoch 2, batch 1600, loss[loss=0.225, simple_loss=0.3151, pruned_loss=0.06744, over 7158.00 frames.], tot_loss[loss=0.2688, simple_loss=0.3389, pruned_loss=0.09939, over 1424107.32 frames.], batch size: 19, lr: 1.90e-03
+2022-04-28 10:43:56,352 INFO [train.py:763] (7/8) Epoch 2, batch 1650, loss[loss=0.2396, simple_loss=0.32, pruned_loss=0.07963, over 7430.00 frames.], tot_loss[loss=0.267, simple_loss=0.3378, pruned_loss=0.09815, over 1426783.92 frames.], batch size: 20, lr: 1.89e-03
+2022-04-28 10:45:02,829 INFO [train.py:763] (7/8) Epoch 2, batch 1700, loss[loss=0.2548, simple_loss=0.3486, pruned_loss=0.08046, over 7146.00 frames.], tot_loss[loss=0.267, simple_loss=0.3378, pruned_loss=0.0981, over 1417347.18 frames.], batch size: 20, lr: 1.89e-03
+2022-04-28 10:46:08,598 INFO [train.py:763] (7/8) Epoch 2, batch 1750, loss[loss=0.2697, simple_loss=0.3384, pruned_loss=0.1005, over 7225.00 frames.], tot_loss[loss=0.2655, simple_loss=0.3366, pruned_loss=0.09724, over 1424343.45 frames.], batch size: 20, lr: 1.88e-03
+2022-04-28 10:47:13,956 INFO [train.py:763] (7/8) Epoch 2, batch 1800, loss[loss=0.2347, simple_loss=0.3098, pruned_loss=0.07977, over 7124.00 frames.], tot_loss[loss=0.2658, simple_loss=0.3361, pruned_loss=0.09773, over 1417218.29 frames.], batch size: 21, lr: 1.88e-03
+2022-04-28 10:48:20,976 INFO [train.py:763] (7/8) Epoch 2, batch 1850, loss[loss=0.2746, simple_loss=0.3557, pruned_loss=0.09674, over 7415.00 frames.], tot_loss[loss=0.2666, simple_loss=0.3366, pruned_loss=0.09834, over 1418636.68 frames.], batch size: 21, lr: 1.88e-03
+2022-04-28 10:49:26,586 INFO [train.py:763] (7/8) Epoch 2, batch 1900, loss[loss=0.2357, simple_loss=0.3055, pruned_loss=0.08292, over 7162.00 frames.], tot_loss[loss=0.2671, simple_loss=0.3369, pruned_loss=0.09871, over 1416657.43 frames.], batch size: 18, lr: 1.87e-03
+2022-04-28 10:50:31,930 INFO [train.py:763] (7/8) Epoch 2, batch 1950, loss[loss=0.2883, simple_loss=0.3583, pruned_loss=0.1091, over 6793.00 frames.], tot_loss[loss=0.2684, simple_loss=0.3378, pruned_loss=0.0995, over 1418463.24 frames.], batch size: 31, lr: 1.87e-03
+2022-04-28 10:51:37,347 INFO [train.py:763] (7/8) Epoch 2, batch 2000, loss[loss=0.2422, simple_loss=0.3113, pruned_loss=0.08658, over 7148.00 frames.], tot_loss[loss=0.266, simple_loss=0.336, pruned_loss=0.09804, over 1422729.03 frames.], batch size: 19, lr: 1.87e-03
+2022-04-28 10:52:43,647 INFO [train.py:763] (7/8) Epoch 2, batch 2050, loss[loss=0.3214, simple_loss=0.3649, pruned_loss=0.139, over 5035.00 frames.], tot_loss[loss=0.2679, simple_loss=0.3379, pruned_loss=0.09897, over 1422066.27 frames.], batch size: 52, lr: 1.86e-03
+2022-04-28 10:53:49,758 INFO [train.py:763] (7/8) Epoch 2, batch 2100, loss[loss=0.259, simple_loss=0.3443, pruned_loss=0.08688, over 7315.00 frames.], tot_loss[loss=0.2676, simple_loss=0.338, pruned_loss=0.09864, over 1424972.53 frames.], batch size: 21, lr: 1.86e-03
+2022-04-28 10:54:55,198 INFO [train.py:763] (7/8) Epoch 2, batch 2150, loss[loss=0.2623, simple_loss=0.3433, pruned_loss=0.09067, over 7230.00 frames.], tot_loss[loss=0.267, simple_loss=0.3377, pruned_loss=0.09809, over 1426990.47 frames.], batch size: 20, lr: 1.86e-03
+2022-04-28 10:56:00,724 INFO [train.py:763] (7/8) Epoch 2, batch 2200, loss[loss=0.2914, simple_loss=0.3529, pruned_loss=0.1149, over 7147.00 frames.], tot_loss[loss=0.2664, simple_loss=0.3368, pruned_loss=0.09799, over 1425902.47 frames.], batch size: 20, lr: 1.85e-03
+2022-04-28 10:57:05,947 INFO [train.py:763] (7/8) Epoch 2, batch 2250, loss[loss=0.2672, simple_loss=0.3429, pruned_loss=0.09568, over 7337.00 frames.], tot_loss[loss=0.2669, simple_loss=0.3374, pruned_loss=0.0982, over 1425396.14 frames.], batch size: 20, lr: 1.85e-03
+2022-04-28 10:58:11,392 INFO [train.py:763] (7/8) Epoch 2, batch 2300, loss[loss=0.2503, simple_loss=0.3259, pruned_loss=0.08738, over 7370.00 frames.], tot_loss[loss=0.2669, simple_loss=0.3372, pruned_loss=0.09832, over 1414031.05 frames.], batch size: 19, lr: 1.85e-03
+2022-04-28 10:59:16,575 INFO [train.py:763] (7/8) Epoch 2, batch 2350, loss[loss=0.2791, simple_loss=0.3367, pruned_loss=0.1107, over 7252.00 frames.], tot_loss[loss=0.2675, simple_loss=0.3373, pruned_loss=0.0988, over 1415612.48 frames.], batch size: 19, lr: 1.84e-03
+2022-04-28 11:00:21,748 INFO [train.py:763] (7/8) Epoch 2, batch 2400, loss[loss=0.2255, simple_loss=0.2988, pruned_loss=0.07609, over 7258.00 frames.], tot_loss[loss=0.2676, simple_loss=0.3379, pruned_loss=0.09865, over 1418706.87 frames.], batch size: 19, lr: 1.84e-03
+2022-04-28 11:01:26,812 INFO [train.py:763] (7/8) Epoch 2, batch 2450, loss[loss=0.3152, simple_loss=0.3747, pruned_loss=0.1279, over 7244.00 frames.], tot_loss[loss=0.2674, simple_loss=0.3386, pruned_loss=0.0981, over 1415902.86 frames.], batch size: 20, lr: 1.84e-03
+2022-04-28 11:02:32,504 INFO [train.py:763] (7/8) Epoch 2, batch 2500, loss[loss=0.2794, simple_loss=0.3471, pruned_loss=0.1058, over 7147.00 frames.], tot_loss[loss=0.2667, simple_loss=0.3374, pruned_loss=0.09796, over 1414186.18 frames.], batch size: 19, lr: 1.83e-03
+2022-04-28 11:03:38,321 INFO [train.py:763] (7/8) Epoch 2, batch 2550, loss[loss=0.2655, simple_loss=0.3501, pruned_loss=0.09049, over 7218.00 frames.], tot_loss[loss=0.2655, simple_loss=0.3366, pruned_loss=0.09724, over 1414043.00 frames.], batch size: 21, lr: 1.83e-03
+2022-04-28 11:04:44,230 INFO [train.py:763] (7/8) Epoch 2, batch 2600, loss[loss=0.2346, simple_loss=0.3098, pruned_loss=0.07965, over 7278.00 frames.], tot_loss[loss=0.264, simple_loss=0.3353, pruned_loss=0.09637, over 1420360.81 frames.], batch size: 18, lr: 1.83e-03
+2022-04-28 11:05:50,142 INFO [train.py:763] (7/8) Epoch 2, batch 2650, loss[loss=0.2743, simple_loss=0.348, pruned_loss=0.1003, over 7320.00 frames.], tot_loss[loss=0.2614, simple_loss=0.3337, pruned_loss=0.09453, over 1419626.63 frames.], batch size: 20, lr: 1.82e-03
+2022-04-28 11:06:55,500 INFO [train.py:763] (7/8) Epoch 2, batch 2700, loss[loss=0.2464, simple_loss=0.3142, pruned_loss=0.08934, over 7052.00 frames.], tot_loss[loss=0.2621, simple_loss=0.3346, pruned_loss=0.09485, over 1420275.94 frames.], batch size: 18, lr: 1.82e-03
+2022-04-28 11:08:01,958 INFO [train.py:763] (7/8) Epoch 2, batch 2750, loss[loss=0.2623, simple_loss=0.3398, pruned_loss=0.09241, over 7185.00 frames.], tot_loss[loss=0.2632, simple_loss=0.3353, pruned_loss=0.09553, over 1419155.72 frames.], batch size: 26, lr: 1.82e-03
+2022-04-28 11:09:07,558 INFO [train.py:763] (7/8) Epoch 2, batch 2800, loss[loss=0.3449, simple_loss=0.3859, pruned_loss=0.1519, over 5338.00 frames.], tot_loss[loss=0.263, simple_loss=0.335, pruned_loss=0.09547, over 1418920.80 frames.], batch size: 53, lr: 1.81e-03
+2022-04-28 11:10:13,398 INFO [train.py:763] (7/8) Epoch 2, batch 2850, loss[loss=0.2515, simple_loss=0.336, pruned_loss=0.08343, over 7222.00 frames.], tot_loss[loss=0.2628, simple_loss=0.3344, pruned_loss=0.09559, over 1421594.10 frames.], batch size: 21, lr: 1.81e-03
+2022-04-28 11:11:19,198 INFO [train.py:763] (7/8) Epoch 2, batch 2900, loss[loss=0.2896, simple_loss=0.3614, pruned_loss=0.1089, over 6393.00 frames.], tot_loss[loss=0.2626, simple_loss=0.3344, pruned_loss=0.09545, over 1417063.07 frames.], batch size: 38, lr: 1.81e-03
+2022-04-28 11:12:24,877 INFO [train.py:763] (7/8) Epoch 2, batch 2950, loss[loss=0.2755, simple_loss=0.3537, pruned_loss=0.09863, over 7127.00 frames.], tot_loss[loss=0.2649, simple_loss=0.3365, pruned_loss=0.09662, over 1415867.27 frames.], batch size: 26, lr: 1.80e-03
+2022-04-28 11:13:30,386 INFO [train.py:763] (7/8) Epoch 2, batch 3000, loss[loss=0.221, simple_loss=0.3119, pruned_loss=0.06503, over 7330.00 frames.], tot_loss[loss=0.2629, simple_loss=0.3349, pruned_loss=0.0954, over 1419586.07 frames.], batch size: 22, lr: 1.80e-03
+2022-04-28 11:13:30,387 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-28 11:13:45,774 INFO [train.py:792] (7/8) Epoch 2, validation: loss=0.2017, simple_loss=0.3052, pruned_loss=0.04915, over 698248.00 frames.
+2022-04-28 11:14:51,532 INFO [train.py:763] (7/8) Epoch 2, batch 3050, loss[loss=0.2629, simple_loss=0.3407, pruned_loss=0.09253, over 7409.00 frames.], tot_loss[loss=0.263, simple_loss=0.3354, pruned_loss=0.0953, over 1424465.80 frames.], batch size: 21, lr: 1.80e-03
+2022-04-28 11:15:57,122 INFO [train.py:763] (7/8) Epoch 2, batch 3100, loss[loss=0.2735, simple_loss=0.3454, pruned_loss=0.1008, over 7281.00 frames.], tot_loss[loss=0.263, simple_loss=0.3352, pruned_loss=0.0954, over 1427611.27 frames.], batch size: 18, lr: 1.79e-03
+2022-04-28 11:17:02,762 INFO [train.py:763] (7/8) Epoch 2, batch 3150, loss[loss=0.2465, simple_loss=0.3271, pruned_loss=0.08297, over 7215.00 frames.], tot_loss[loss=0.2631, simple_loss=0.3351, pruned_loss=0.09549, over 1422692.81 frames.], batch size: 21, lr: 1.79e-03
+2022-04-28 11:18:08,979 INFO [train.py:763] (7/8) Epoch 2, batch 3200, loss[loss=0.3203, simple_loss=0.3825, pruned_loss=0.129, over 7393.00 frames.], tot_loss[loss=0.2631, simple_loss=0.3356, pruned_loss=0.09526, over 1425635.28 frames.], batch size: 23, lr: 1.79e-03
+2022-04-28 11:19:14,944 INFO [train.py:763] (7/8) Epoch 2, batch 3250, loss[loss=0.2542, simple_loss=0.3308, pruned_loss=0.08875, over 7161.00 frames.], tot_loss[loss=0.2631, simple_loss=0.3359, pruned_loss=0.0952, over 1426740.53 frames.], batch size: 19, lr: 1.79e-03
+2022-04-28 11:20:20,960 INFO [train.py:763] (7/8) Epoch 2, batch 3300, loss[loss=0.2785, simple_loss=0.353, pruned_loss=0.102, over 7124.00 frames.], tot_loss[loss=0.2609, simple_loss=0.3342, pruned_loss=0.09384, over 1428883.50 frames.], batch size: 26, lr: 1.78e-03
+2022-04-28 11:21:25,818 INFO [train.py:763] (7/8) Epoch 2, batch 3350, loss[loss=0.2304, simple_loss=0.3073, pruned_loss=0.07677, over 7274.00 frames.], tot_loss[loss=0.2623, simple_loss=0.3351, pruned_loss=0.09475, over 1425187.50 frames.], batch size: 18, lr: 1.78e-03
+2022-04-28 11:22:30,858 INFO [train.py:763] (7/8) Epoch 2, batch 3400, loss[loss=0.2755, simple_loss=0.324, pruned_loss=0.1135, over 7411.00 frames.], tot_loss[loss=0.2626, simple_loss=0.3354, pruned_loss=0.09492, over 1422883.47 frames.], batch size: 18, lr: 1.78e-03
+2022-04-28 11:23:36,225 INFO [train.py:763] (7/8) Epoch 2, batch 3450, loss[loss=0.2394, simple_loss=0.3161, pruned_loss=0.08133, over 7263.00 frames.], tot_loss[loss=0.2618, simple_loss=0.3347, pruned_loss=0.09447, over 1420053.80 frames.], batch size: 19, lr: 1.77e-03
+2022-04-28 11:24:41,587 INFO [train.py:763] (7/8) Epoch 2, batch 3500, loss[loss=0.246, simple_loss=0.3243, pruned_loss=0.08383, over 7284.00 frames.], tot_loss[loss=0.261, simple_loss=0.3337, pruned_loss=0.09416, over 1420975.63 frames.], batch size: 25, lr: 1.77e-03
+2022-04-28 11:25:47,033 INFO [train.py:763] (7/8) Epoch 2, batch 3550, loss[loss=0.2229, simple_loss=0.3086, pruned_loss=0.06859, over 7220.00 frames.], tot_loss[loss=0.2636, simple_loss=0.3358, pruned_loss=0.09566, over 1419837.48 frames.], batch size: 21, lr: 1.77e-03
+2022-04-28 11:26:52,378 INFO [train.py:763] (7/8) Epoch 2, batch 3600, loss[loss=0.2631, simple_loss=0.3513, pruned_loss=0.08742, over 7292.00 frames.], tot_loss[loss=0.2623, simple_loss=0.3348, pruned_loss=0.09491, over 1421408.57 frames.], batch size: 24, lr: 1.76e-03
+2022-04-28 11:27:57,963 INFO [train.py:763] (7/8) Epoch 2, batch 3650, loss[loss=0.2821, simple_loss=0.3578, pruned_loss=0.1032, over 7387.00 frames.], tot_loss[loss=0.2607, simple_loss=0.3333, pruned_loss=0.09401, over 1421003.68 frames.], batch size: 23, lr: 1.76e-03
+2022-04-28 11:29:03,188 INFO [train.py:763] (7/8) Epoch 2, batch 3700, loss[loss=0.2355, simple_loss=0.3043, pruned_loss=0.08338, over 7420.00 frames.], tot_loss[loss=0.2613, simple_loss=0.3342, pruned_loss=0.09415, over 1415807.51 frames.], batch size: 18, lr: 1.76e-03
+2022-04-28 11:30:08,707 INFO [train.py:763] (7/8) Epoch 2, batch 3750, loss[loss=0.208, simple_loss=0.2765, pruned_loss=0.06973, over 7273.00 frames.], tot_loss[loss=0.2601, simple_loss=0.3334, pruned_loss=0.09345, over 1421921.32 frames.], batch size: 18, lr: 1.76e-03
+2022-04-28 11:31:14,714 INFO [train.py:763] (7/8) Epoch 2, batch 3800, loss[loss=0.2455, simple_loss=0.3176, pruned_loss=0.0867, over 7167.00 frames.], tot_loss[loss=0.2593, simple_loss=0.3327, pruned_loss=0.09289, over 1422218.28 frames.], batch size: 18, lr: 1.75e-03
+2022-04-28 11:32:20,656 INFO [train.py:763] (7/8) Epoch 2, batch 3850, loss[loss=0.2724, simple_loss=0.3508, pruned_loss=0.09697, over 7326.00 frames.], tot_loss[loss=0.2604, simple_loss=0.3333, pruned_loss=0.09377, over 1421479.98 frames.], batch size: 22, lr: 1.75e-03
+2022-04-28 11:33:26,584 INFO [train.py:763] (7/8) Epoch 2, batch 3900, loss[loss=0.3245, simple_loss=0.3796, pruned_loss=0.1347, over 7317.00 frames.], tot_loss[loss=0.2603, simple_loss=0.3331, pruned_loss=0.09379, over 1423175.30 frames.], batch size: 20, lr: 1.75e-03
+2022-04-28 11:34:32,002 INFO [train.py:763] (7/8) Epoch 2, batch 3950, loss[loss=0.2643, simple_loss=0.3364, pruned_loss=0.09613, over 7332.00 frames.], tot_loss[loss=0.2588, simple_loss=0.3321, pruned_loss=0.09276, over 1420607.25 frames.], batch size: 21, lr: 1.74e-03
+2022-04-28 11:35:37,608 INFO [train.py:763] (7/8) Epoch 2, batch 4000, loss[loss=0.2814, simple_loss=0.3646, pruned_loss=0.09913, over 7337.00 frames.], tot_loss[loss=0.2583, simple_loss=0.3318, pruned_loss=0.09238, over 1425518.61 frames.], batch size: 22, lr: 1.74e-03
+2022-04-28 11:36:44,087 INFO [train.py:763] (7/8) Epoch 2, batch 4050, loss[loss=0.267, simple_loss=0.3496, pruned_loss=0.0922, over 7418.00 frames.], tot_loss[loss=0.2576, simple_loss=0.3315, pruned_loss=0.09186, over 1426594.70 frames.], batch size: 20, lr: 1.74e-03
+2022-04-28 11:37:49,250 INFO [train.py:763] (7/8) Epoch 2, batch 4100, loss[loss=0.2945, simple_loss=0.3433, pruned_loss=0.1228, over 7082.00 frames.], tot_loss[loss=0.2589, simple_loss=0.3323, pruned_loss=0.09273, over 1417672.93 frames.], batch size: 18, lr: 1.73e-03
+2022-04-28 11:38:54,196 INFO [train.py:763] (7/8) Epoch 2, batch 4150, loss[loss=0.2307, simple_loss=0.3092, pruned_loss=0.07608, over 7109.00 frames.], tot_loss[loss=0.2593, simple_loss=0.3328, pruned_loss=0.09294, over 1422004.83 frames.], batch size: 21, lr: 1.73e-03
+2022-04-28 11:40:00,873 INFO [train.py:763] (7/8) Epoch 2, batch 4200, loss[loss=0.2389, simple_loss=0.3265, pruned_loss=0.07565, over 7130.00 frames.], tot_loss[loss=0.2587, simple_loss=0.3323, pruned_loss=0.09252, over 1421380.56 frames.], batch size: 28, lr: 1.73e-03
+2022-04-28 11:41:07,998 INFO [train.py:763] (7/8) Epoch 2, batch 4250, loss[loss=0.267, simple_loss=0.3463, pruned_loss=0.0938, over 7209.00 frames.], tot_loss[loss=0.2589, simple_loss=0.3323, pruned_loss=0.09277, over 1422261.45 frames.], batch size: 22, lr: 1.73e-03
+2022-04-28 11:42:14,762 INFO [train.py:763] (7/8) Epoch 2, batch 4300, loss[loss=0.2304, simple_loss=0.3065, pruned_loss=0.07713, over 7076.00 frames.], tot_loss[loss=0.2585, simple_loss=0.3324, pruned_loss=0.09233, over 1424693.84 frames.], batch size: 18, lr: 1.72e-03
+2022-04-28 11:43:21,909 INFO [train.py:763] (7/8) Epoch 2, batch 4350, loss[loss=0.3379, simple_loss=0.3939, pruned_loss=0.141, over 7144.00 frames.], tot_loss[loss=0.2587, simple_loss=0.3328, pruned_loss=0.09227, over 1426583.99 frames.], batch size: 20, lr: 1.72e-03
+2022-04-28 11:44:27,752 INFO [train.py:763] (7/8) Epoch 2, batch 4400, loss[loss=0.3072, simple_loss=0.383, pruned_loss=0.1157, over 7292.00 frames.], tot_loss[loss=0.2596, simple_loss=0.3332, pruned_loss=0.09302, over 1421187.30 frames.], batch size: 25, lr: 1.72e-03
+2022-04-28 11:45:33,300 INFO [train.py:763] (7/8) Epoch 2, batch 4450, loss[loss=0.2499, simple_loss=0.3361, pruned_loss=0.08189, over 7327.00 frames.], tot_loss[loss=0.2608, simple_loss=0.3344, pruned_loss=0.09359, over 1412977.34 frames.], batch size: 22, lr: 1.71e-03
+2022-04-28 11:46:38,411 INFO [train.py:763] (7/8) Epoch 2, batch 4500, loss[loss=0.2356, simple_loss=0.3218, pruned_loss=0.07472, over 7125.00 frames.], tot_loss[loss=0.261, simple_loss=0.3344, pruned_loss=0.09378, over 1406291.01 frames.], batch size: 21, lr: 1.71e-03
+2022-04-28 11:47:42,641 INFO [train.py:763] (7/8) Epoch 2, batch 4550, loss[loss=0.2695, simple_loss=0.3351, pruned_loss=0.102, over 6319.00 frames.], tot_loss[loss=0.2633, simple_loss=0.3363, pruned_loss=0.09514, over 1378307.81 frames.], batch size: 38, lr: 1.71e-03
+2022-04-28 11:49:10,871 INFO [train.py:763] (7/8) Epoch 3, batch 0, loss[loss=0.2578, simple_loss=0.3443, pruned_loss=0.08564, over 7211.00 frames.], tot_loss[loss=0.2578, simple_loss=0.3443, pruned_loss=0.08564, over 7211.00 frames.], batch size: 23, lr: 1.66e-03
+2022-04-28 11:50:17,413 INFO [train.py:763] (7/8) Epoch 3, batch 50, loss[loss=0.2224, simple_loss=0.2993, pruned_loss=0.07278, over 7264.00 frames.], tot_loss[loss=0.2554, simple_loss=0.328, pruned_loss=0.0914, over 318408.50 frames.], batch size: 17, lr: 1.66e-03
+2022-04-28 11:51:23,930 INFO [train.py:763] (7/8) Epoch 3, batch 100, loss[loss=0.2206, simple_loss=0.2989, pruned_loss=0.07118, over 7272.00 frames.], tot_loss[loss=0.2528, simple_loss=0.327, pruned_loss=0.0893, over 564960.05 frames.], batch size: 17, lr: 1.65e-03
+2022-04-28 11:52:29,503 INFO [train.py:763] (7/8) Epoch 3, batch 150, loss[loss=0.2617, simple_loss=0.3475, pruned_loss=0.08791, over 7339.00 frames.], tot_loss[loss=0.2552, simple_loss=0.3296, pruned_loss=0.09041, over 755663.26 frames.], batch size: 22, lr: 1.65e-03
+2022-04-28 11:53:34,983 INFO [train.py:763] (7/8) Epoch 3, batch 200, loss[loss=0.2728, simple_loss=0.3461, pruned_loss=0.09977, over 7205.00 frames.], tot_loss[loss=0.253, simple_loss=0.3285, pruned_loss=0.0887, over 904028.41 frames.], batch size: 23, lr: 1.65e-03
+2022-04-28 11:54:40,995 INFO [train.py:763] (7/8) Epoch 3, batch 250, loss[loss=0.2618, simple_loss=0.3376, pruned_loss=0.09297, over 7332.00 frames.], tot_loss[loss=0.2561, simple_loss=0.3314, pruned_loss=0.09037, over 1015826.65 frames.], batch size: 22, lr: 1.64e-03
+2022-04-28 11:55:46,618 INFO [train.py:763] (7/8) Epoch 3, batch 300, loss[loss=0.2649, simple_loss=0.3586, pruned_loss=0.08562, over 7386.00 frames.], tot_loss[loss=0.2551, simple_loss=0.3313, pruned_loss=0.08943, over 1110516.49 frames.], batch size: 23, lr: 1.64e-03
+2022-04-28 11:56:52,039 INFO [train.py:763] (7/8) Epoch 3, batch 350, loss[loss=0.2543, simple_loss=0.3485, pruned_loss=0.08011, over 7319.00 frames.], tot_loss[loss=0.254, simple_loss=0.3303, pruned_loss=0.08881, over 1181664.37 frames.], batch size: 21, lr: 1.64e-03
+2022-04-28 11:57:57,853 INFO [train.py:763] (7/8) Epoch 3, batch 400, loss[loss=0.2394, simple_loss=0.3152, pruned_loss=0.08179, over 7228.00 frames.], tot_loss[loss=0.2514, simple_loss=0.3274, pruned_loss=0.08771, over 1232112.84 frames.], batch size: 20, lr: 1.64e-03
+2022-04-28 11:59:03,278 INFO [train.py:763] (7/8) Epoch 3, batch 450, loss[loss=0.313, simple_loss=0.374, pruned_loss=0.126, over 7135.00 frames.], tot_loss[loss=0.2502, simple_loss=0.3263, pruned_loss=0.08701, over 1274212.32 frames.], batch size: 20, lr: 1.63e-03
+2022-04-28 12:00:09,029 INFO [train.py:763] (7/8) Epoch 3, batch 500, loss[loss=0.2176, simple_loss=0.3, pruned_loss=0.06756, over 7157.00 frames.], tot_loss[loss=0.2511, simple_loss=0.3275, pruned_loss=0.08739, over 1303823.77 frames.], batch size: 19, lr: 1.63e-03
+2022-04-28 12:01:14,935 INFO [train.py:763] (7/8) Epoch 3, batch 550, loss[loss=0.1947, simple_loss=0.2895, pruned_loss=0.04996, over 7159.00 frames.], tot_loss[loss=0.2521, simple_loss=0.3284, pruned_loss=0.08788, over 1329176.40 frames.], batch size: 18, lr: 1.63e-03
+2022-04-28 12:02:20,855 INFO [train.py:763] (7/8) Epoch 3, batch 600, loss[loss=0.2777, simple_loss=0.3487, pruned_loss=0.1034, over 6262.00 frames.], tot_loss[loss=0.252, simple_loss=0.3279, pruned_loss=0.08806, over 1346800.45 frames.], batch size: 37, lr: 1.63e-03
+2022-04-28 12:03:27,793 INFO [train.py:763] (7/8) Epoch 3, batch 650, loss[loss=0.2836, simple_loss=0.3496, pruned_loss=0.1088, over 7429.00 frames.], tot_loss[loss=0.2525, simple_loss=0.3282, pruned_loss=0.08845, over 1367059.12 frames.], batch size: 20, lr: 1.62e-03
+2022-04-28 12:04:35,125 INFO [train.py:763] (7/8) Epoch 3, batch 700, loss[loss=0.2187, simple_loss=0.3169, pruned_loss=0.06024, over 7283.00 frames.], tot_loss[loss=0.2512, simple_loss=0.3272, pruned_loss=0.0876, over 1384316.09 frames.], batch size: 24, lr: 1.62e-03
+2022-04-28 12:05:41,319 INFO [train.py:763] (7/8) Epoch 3, batch 750, loss[loss=0.2599, simple_loss=0.3409, pruned_loss=0.08947, over 7298.00 frames.], tot_loss[loss=0.2513, simple_loss=0.3267, pruned_loss=0.08799, over 1392523.85 frames.], batch size: 24, lr: 1.62e-03
+2022-04-28 12:06:46,999 INFO [train.py:763] (7/8) Epoch 3, batch 800, loss[loss=0.2465, simple_loss=0.3177, pruned_loss=0.08766, over 7267.00 frames.], tot_loss[loss=0.2522, simple_loss=0.328, pruned_loss=0.08824, over 1397463.72 frames.], batch size: 19, lr: 1.62e-03
+2022-04-28 12:07:53,467 INFO [train.py:763] (7/8) Epoch 3, batch 850, loss[loss=0.2223, simple_loss=0.3179, pruned_loss=0.06329, over 7068.00 frames.], tot_loss[loss=0.2509, simple_loss=0.3276, pruned_loss=0.08716, over 1407714.77 frames.], batch size: 18, lr: 1.61e-03
+2022-04-28 12:09:00,235 INFO [train.py:763] (7/8) Epoch 3, batch 900, loss[loss=0.2364, simple_loss=0.3249, pruned_loss=0.07401, over 7116.00 frames.], tot_loss[loss=0.2508, simple_loss=0.3276, pruned_loss=0.08702, over 1415765.04 frames.], batch size: 21, lr: 1.61e-03
+2022-04-28 12:10:06,517 INFO [train.py:763] (7/8) Epoch 3, batch 950, loss[loss=0.2741, simple_loss=0.3382, pruned_loss=0.105, over 7227.00 frames.], tot_loss[loss=0.2512, simple_loss=0.3277, pruned_loss=0.08731, over 1420481.51 frames.], batch size: 26, lr: 1.61e-03
+2022-04-28 12:11:12,755 INFO [train.py:763] (7/8) Epoch 3, batch 1000, loss[loss=0.2076, simple_loss=0.2906, pruned_loss=0.06233, over 7284.00 frames.], tot_loss[loss=0.2501, simple_loss=0.327, pruned_loss=0.08659, over 1421183.67 frames.], batch size: 18, lr: 1.61e-03
+2022-04-28 12:12:18,780 INFO [train.py:763] (7/8) Epoch 3, batch 1050, loss[loss=0.3049, simple_loss=0.3632, pruned_loss=0.1234, over 6756.00 frames.], tot_loss[loss=0.2495, simple_loss=0.3267, pruned_loss=0.08613, over 1419461.95 frames.], batch size: 31, lr: 1.60e-03
+2022-04-28 12:13:24,405 INFO [train.py:763] (7/8) Epoch 3, batch 1100, loss[loss=0.2434, simple_loss=0.3278, pruned_loss=0.07946, over 7409.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3256, pruned_loss=0.08577, over 1420830.30 frames.], batch size: 21, lr: 1.60e-03
+2022-04-28 12:14:28,849 INFO [train.py:763] (7/8) Epoch 3, batch 1150, loss[loss=0.2089, simple_loss=0.2994, pruned_loss=0.05916, over 7320.00 frames.], tot_loss[loss=0.2513, simple_loss=0.3281, pruned_loss=0.08723, over 1418661.98 frames.], batch size: 21, lr: 1.60e-03
+2022-04-28 12:15:35,095 INFO [train.py:763] (7/8) Epoch 3, batch 1200, loss[loss=0.2744, simple_loss=0.355, pruned_loss=0.09685, over 7319.00 frames.], tot_loss[loss=0.2521, simple_loss=0.3289, pruned_loss=0.08769, over 1416491.17 frames.], batch size: 21, lr: 1.60e-03
+2022-04-28 12:16:40,643 INFO [train.py:763] (7/8) Epoch 3, batch 1250, loss[loss=0.2096, simple_loss=0.2834, pruned_loss=0.06789, over 7243.00 frames.], tot_loss[loss=0.2507, simple_loss=0.3275, pruned_loss=0.08693, over 1414582.61 frames.], batch size: 16, lr: 1.59e-03
+2022-04-28 12:17:46,154 INFO [train.py:763] (7/8) Epoch 3, batch 1300, loss[loss=0.3057, simple_loss=0.3805, pruned_loss=0.1155, over 7192.00 frames.], tot_loss[loss=0.2511, simple_loss=0.3276, pruned_loss=0.08728, over 1417303.25 frames.], batch size: 23, lr: 1.59e-03
+2022-04-28 12:18:51,898 INFO [train.py:763] (7/8) Epoch 3, batch 1350, loss[loss=0.2265, simple_loss=0.3127, pruned_loss=0.07018, over 7243.00 frames.], tot_loss[loss=0.2512, simple_loss=0.3278, pruned_loss=0.08733, over 1416681.98 frames.], batch size: 20, lr: 1.59e-03
+2022-04-28 12:19:57,910 INFO [train.py:763] (7/8) Epoch 3, batch 1400, loss[loss=0.2391, simple_loss=0.3177, pruned_loss=0.0802, over 7223.00 frames.], tot_loss[loss=0.2501, simple_loss=0.3267, pruned_loss=0.08675, over 1420053.45 frames.], batch size: 22, lr: 1.59e-03
+2022-04-28 12:21:03,065 INFO [train.py:763] (7/8) Epoch 3, batch 1450, loss[loss=0.2329, simple_loss=0.3216, pruned_loss=0.07213, over 7292.00 frames.], tot_loss[loss=0.2505, simple_loss=0.3274, pruned_loss=0.08677, over 1421740.89 frames.], batch size: 24, lr: 1.59e-03
+2022-04-28 12:22:08,511 INFO [train.py:763] (7/8) Epoch 3, batch 1500, loss[loss=0.2598, simple_loss=0.3441, pruned_loss=0.08772, over 7303.00 frames.], tot_loss[loss=0.2503, simple_loss=0.3275, pruned_loss=0.08652, over 1419348.07 frames.], batch size: 24, lr: 1.58e-03
+2022-04-28 12:23:14,006 INFO [train.py:763] (7/8) Epoch 3, batch 1550, loss[loss=0.3027, simple_loss=0.3612, pruned_loss=0.1221, over 5070.00 frames.], tot_loss[loss=0.2501, simple_loss=0.3274, pruned_loss=0.08645, over 1418563.77 frames.], batch size: 53, lr: 1.58e-03
+2022-04-28 12:24:20,156 INFO [train.py:763] (7/8) Epoch 3, batch 1600, loss[loss=0.2872, simple_loss=0.3569, pruned_loss=0.1088, over 7281.00 frames.], tot_loss[loss=0.2523, simple_loss=0.3289, pruned_loss=0.08788, over 1414906.90 frames.], batch size: 25, lr: 1.58e-03
+2022-04-28 12:25:26,877 INFO [train.py:763] (7/8) Epoch 3, batch 1650, loss[loss=0.2516, simple_loss=0.3313, pruned_loss=0.08591, over 7326.00 frames.], tot_loss[loss=0.2495, simple_loss=0.3264, pruned_loss=0.08635, over 1416770.27 frames.], batch size: 20, lr: 1.58e-03
+2022-04-28 12:26:34,046 INFO [train.py:763] (7/8) Epoch 3, batch 1700, loss[loss=0.2322, simple_loss=0.3214, pruned_loss=0.0715, over 7144.00 frames.], tot_loss[loss=0.2488, simple_loss=0.326, pruned_loss=0.08579, over 1419957.60 frames.], batch size: 20, lr: 1.57e-03
+2022-04-28 12:27:40,159 INFO [train.py:763] (7/8) Epoch 3, batch 1750, loss[loss=0.3114, simple_loss=0.365, pruned_loss=0.1289, over 7219.00 frames.], tot_loss[loss=0.2503, simple_loss=0.327, pruned_loss=0.0868, over 1418938.78 frames.], batch size: 22, lr: 1.57e-03
+2022-04-28 12:28:45,196 INFO [train.py:763] (7/8) Epoch 3, batch 1800, loss[loss=0.2421, simple_loss=0.3244, pruned_loss=0.07995, over 7212.00 frames.], tot_loss[loss=0.2513, simple_loss=0.3282, pruned_loss=0.08723, over 1420734.80 frames.], batch size: 21, lr: 1.57e-03
+2022-04-28 12:29:50,467 INFO [train.py:763] (7/8) Epoch 3, batch 1850, loss[loss=0.2722, simple_loss=0.3373, pruned_loss=0.1035, over 7141.00 frames.], tot_loss[loss=0.2513, simple_loss=0.3284, pruned_loss=0.0871, over 1419564.32 frames.], batch size: 17, lr: 1.57e-03
+2022-04-28 12:30:57,303 INFO [train.py:763] (7/8) Epoch 3, batch 1900, loss[loss=0.2505, simple_loss=0.3287, pruned_loss=0.08613, over 7159.00 frames.], tot_loss[loss=0.2511, simple_loss=0.3286, pruned_loss=0.08684, over 1422759.87 frames.], batch size: 19, lr: 1.56e-03
+2022-04-28 12:32:03,232 INFO [train.py:763] (7/8) Epoch 3, batch 1950, loss[loss=0.2706, simple_loss=0.3457, pruned_loss=0.0978, over 6624.00 frames.], tot_loss[loss=0.2493, simple_loss=0.3275, pruned_loss=0.08561, over 1427766.41 frames.], batch size: 38, lr: 1.56e-03
+2022-04-28 12:33:17,832 INFO [train.py:763] (7/8) Epoch 3, batch 2000, loss[loss=0.2683, simple_loss=0.3548, pruned_loss=0.09092, over 7107.00 frames.], tot_loss[loss=0.2505, simple_loss=0.3286, pruned_loss=0.08622, over 1425010.10 frames.], batch size: 21, lr: 1.56e-03
+2022-04-28 12:35:10,051 INFO [train.py:763] (7/8) Epoch 3, batch 2050, loss[loss=0.2353, simple_loss=0.3255, pruned_loss=0.07252, over 6885.00 frames.], tot_loss[loss=0.2504, simple_loss=0.3282, pruned_loss=0.0863, over 1422084.44 frames.], batch size: 31, lr: 1.56e-03
+2022-04-28 12:36:15,510 INFO [train.py:763] (7/8) Epoch 3, batch 2100, loss[loss=0.2513, simple_loss=0.3318, pruned_loss=0.08538, over 7313.00 frames.], tot_loss[loss=0.2491, simple_loss=0.3272, pruned_loss=0.08553, over 1420576.31 frames.], batch size: 21, lr: 1.56e-03
+2022-04-28 12:37:29,649 INFO [train.py:763] (7/8) Epoch 3, batch 2150, loss[loss=0.2452, simple_loss=0.3351, pruned_loss=0.07771, over 7316.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3259, pruned_loss=0.08438, over 1422748.87 frames.], batch size: 22, lr: 1.55e-03
+2022-04-28 12:38:44,727 INFO [train.py:763] (7/8) Epoch 3, batch 2200, loss[loss=0.2465, simple_loss=0.3361, pruned_loss=0.07849, over 7218.00 frames.], tot_loss[loss=0.2472, simple_loss=0.3255, pruned_loss=0.08447, over 1425369.44 frames.], batch size: 21, lr: 1.55e-03
+2022-04-28 12:40:02,470 INFO [train.py:763] (7/8) Epoch 3, batch 2250, loss[loss=0.2897, simple_loss=0.3483, pruned_loss=0.1156, over 4981.00 frames.], tot_loss[loss=0.2481, simple_loss=0.3265, pruned_loss=0.08483, over 1426899.28 frames.], batch size: 52, lr: 1.55e-03
+2022-04-28 12:41:07,764 INFO [train.py:763] (7/8) Epoch 3, batch 2300, loss[loss=0.2073, simple_loss=0.285, pruned_loss=0.06483, over 7158.00 frames.], tot_loss[loss=0.2478, simple_loss=0.326, pruned_loss=0.08477, over 1429791.14 frames.], batch size: 19, lr: 1.55e-03
+2022-04-28 12:42:14,650 INFO [train.py:763] (7/8) Epoch 3, batch 2350, loss[loss=0.234, simple_loss=0.3134, pruned_loss=0.07737, over 7319.00 frames.], tot_loss[loss=0.2466, simple_loss=0.325, pruned_loss=0.08413, over 1430800.41 frames.], batch size: 20, lr: 1.54e-03
+2022-04-28 12:43:20,041 INFO [train.py:763] (7/8) Epoch 3, batch 2400, loss[loss=0.2556, simple_loss=0.3342, pruned_loss=0.08847, over 7332.00 frames.], tot_loss[loss=0.247, simple_loss=0.3263, pruned_loss=0.08388, over 1433175.45 frames.], batch size: 25, lr: 1.54e-03
+2022-04-28 12:44:25,922 INFO [train.py:763] (7/8) Epoch 3, batch 2450, loss[loss=0.2672, simple_loss=0.3479, pruned_loss=0.09326, over 7377.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3264, pruned_loss=0.08414, over 1436059.14 frames.], batch size: 23, lr: 1.54e-03
+2022-04-28 12:45:31,572 INFO [train.py:763] (7/8) Epoch 3, batch 2500, loss[loss=0.2268, simple_loss=0.3185, pruned_loss=0.0675, over 7169.00 frames.], tot_loss[loss=0.2471, simple_loss=0.3259, pruned_loss=0.08411, over 1434332.13 frames.], batch size: 19, lr: 1.54e-03
+2022-04-28 12:46:36,902 INFO [train.py:763] (7/8) Epoch 3, batch 2550, loss[loss=0.2295, simple_loss=0.304, pruned_loss=0.07747, over 7407.00 frames.], tot_loss[loss=0.2477, simple_loss=0.3262, pruned_loss=0.0846, over 1426122.99 frames.], batch size: 18, lr: 1.54e-03
+2022-04-28 12:47:42,416 INFO [train.py:763] (7/8) Epoch 3, batch 2600, loss[loss=0.2448, simple_loss=0.3172, pruned_loss=0.0862, over 7229.00 frames.], tot_loss[loss=0.25, simple_loss=0.3283, pruned_loss=0.08585, over 1426507.16 frames.], batch size: 20, lr: 1.53e-03
+2022-04-28 12:48:47,829 INFO [train.py:763] (7/8) Epoch 3, batch 2650, loss[loss=0.2137, simple_loss=0.2961, pruned_loss=0.06558, over 7002.00 frames.], tot_loss[loss=0.2507, simple_loss=0.3287, pruned_loss=0.08631, over 1420428.41 frames.], batch size: 16, lr: 1.53e-03
+2022-04-28 12:49:52,914 INFO [train.py:763] (7/8) Epoch 3, batch 2700, loss[loss=0.2105, simple_loss=0.2788, pruned_loss=0.07116, over 6827.00 frames.], tot_loss[loss=0.2494, simple_loss=0.328, pruned_loss=0.08543, over 1418646.13 frames.], batch size: 15, lr: 1.53e-03
+2022-04-28 12:50:58,287 INFO [train.py:763] (7/8) Epoch 3, batch 2750, loss[loss=0.238, simple_loss=0.3158, pruned_loss=0.08005, over 7267.00 frames.], tot_loss[loss=0.2484, simple_loss=0.3273, pruned_loss=0.08472, over 1421997.91 frames.], batch size: 19, lr: 1.53e-03
+2022-04-28 12:52:03,680 INFO [train.py:763] (7/8) Epoch 3, batch 2800, loss[loss=0.2503, simple_loss=0.328, pruned_loss=0.08625, over 7163.00 frames.], tot_loss[loss=0.2466, simple_loss=0.3258, pruned_loss=0.08371, over 1424159.91 frames.], batch size: 19, lr: 1.53e-03
+2022-04-28 12:53:09,255 INFO [train.py:763] (7/8) Epoch 3, batch 2850, loss[loss=0.3152, simple_loss=0.3672, pruned_loss=0.1316, over 5048.00 frames.], tot_loss[loss=0.2463, simple_loss=0.3253, pruned_loss=0.08367, over 1422608.45 frames.], batch size: 53, lr: 1.52e-03
+2022-04-28 12:54:14,585 INFO [train.py:763] (7/8) Epoch 3, batch 2900, loss[loss=0.2649, simple_loss=0.3464, pruned_loss=0.0917, over 6885.00 frames.], tot_loss[loss=0.2454, simple_loss=0.3245, pruned_loss=0.08314, over 1422707.41 frames.], batch size: 31, lr: 1.52e-03
+2022-04-28 12:55:20,298 INFO [train.py:763] (7/8) Epoch 3, batch 2950, loss[loss=0.284, simple_loss=0.36, pruned_loss=0.104, over 7106.00 frames.], tot_loss[loss=0.2462, simple_loss=0.3252, pruned_loss=0.08358, over 1427054.81 frames.], batch size: 28, lr: 1.52e-03
+2022-04-28 12:56:25,622 INFO [train.py:763] (7/8) Epoch 3, batch 3000, loss[loss=0.2489, simple_loss=0.3372, pruned_loss=0.08034, over 7156.00 frames.], tot_loss[loss=0.2464, simple_loss=0.3252, pruned_loss=0.08378, over 1425613.56 frames.], batch size: 20, lr: 1.52e-03
+2022-04-28 12:56:25,623 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-28 12:56:40,878 INFO [train.py:792] (7/8) Epoch 3, validation: loss=0.1917, simple_loss=0.2967, pruned_loss=0.04336, over 698248.00 frames.
+2022-04-28 12:57:46,590 INFO [train.py:763] (7/8) Epoch 3, batch 3050, loss[loss=0.2441, simple_loss=0.3338, pruned_loss=0.07716, over 7117.00 frames.], tot_loss[loss=0.2467, simple_loss=0.3256, pruned_loss=0.0839, over 1420524.71 frames.], batch size: 21, lr: 1.51e-03
+2022-04-28 12:58:52,525 INFO [train.py:763] (7/8) Epoch 3, batch 3100, loss[loss=0.2287, simple_loss=0.3163, pruned_loss=0.07057, over 7286.00 frames.], tot_loss[loss=0.2461, simple_loss=0.3245, pruned_loss=0.08389, over 1417285.16 frames.], batch size: 24, lr: 1.51e-03
+2022-04-28 12:59:58,127 INFO [train.py:763] (7/8) Epoch 3, batch 3150, loss[loss=0.2619, simple_loss=0.3475, pruned_loss=0.08814, over 7310.00 frames.], tot_loss[loss=0.2444, simple_loss=0.3233, pruned_loss=0.08277, over 1421479.11 frames.], batch size: 25, lr: 1.51e-03
+2022-04-28 13:01:03,473 INFO [train.py:763] (7/8) Epoch 3, batch 3200, loss[loss=0.2157, simple_loss=0.2951, pruned_loss=0.06814, over 7069.00 frames.], tot_loss[loss=0.243, simple_loss=0.3225, pruned_loss=0.08172, over 1422320.94 frames.], batch size: 18, lr: 1.51e-03
+2022-04-28 13:02:09,460 INFO [train.py:763] (7/8) Epoch 3, batch 3250, loss[loss=0.2289, simple_loss=0.3142, pruned_loss=0.07175, over 7259.00 frames.], tot_loss[loss=0.2446, simple_loss=0.3239, pruned_loss=0.08269, over 1422914.64 frames.], batch size: 19, lr: 1.51e-03
+2022-04-28 13:03:16,243 INFO [train.py:763] (7/8) Epoch 3, batch 3300, loss[loss=0.2124, simple_loss=0.3035, pruned_loss=0.06063, over 7200.00 frames.], tot_loss[loss=0.2442, simple_loss=0.3235, pruned_loss=0.08247, over 1421762.23 frames.], batch size: 23, lr: 1.50e-03
+2022-04-28 13:04:22,934 INFO [train.py:763] (7/8) Epoch 3, batch 3350, loss[loss=0.3444, simple_loss=0.397, pruned_loss=0.1459, over 6225.00 frames.], tot_loss[loss=0.2439, simple_loss=0.3227, pruned_loss=0.08255, over 1419980.07 frames.], batch size: 37, lr: 1.50e-03
+2022-04-28 13:05:28,649 INFO [train.py:763] (7/8) Epoch 3, batch 3400, loss[loss=0.1724, simple_loss=0.258, pruned_loss=0.04344, over 6990.00 frames.], tot_loss[loss=0.2435, simple_loss=0.322, pruned_loss=0.08255, over 1421287.87 frames.], batch size: 16, lr: 1.50e-03
+2022-04-28 13:06:35,014 INFO [train.py:763] (7/8) Epoch 3, batch 3450, loss[loss=0.2527, simple_loss=0.3226, pruned_loss=0.09137, over 7155.00 frames.], tot_loss[loss=0.2427, simple_loss=0.3214, pruned_loss=0.08196, over 1426677.45 frames.], batch size: 18, lr: 1.50e-03
+2022-04-28 13:07:42,200 INFO [train.py:763] (7/8) Epoch 3, batch 3500, loss[loss=0.2525, simple_loss=0.3411, pruned_loss=0.08201, over 7384.00 frames.], tot_loss[loss=0.2445, simple_loss=0.3227, pruned_loss=0.08314, over 1428604.93 frames.], batch size: 23, lr: 1.50e-03
+2022-04-28 13:08:48,571 INFO [train.py:763] (7/8) Epoch 3, batch 3550, loss[loss=0.27, simple_loss=0.334, pruned_loss=0.103, over 7299.00 frames.], tot_loss[loss=0.2429, simple_loss=0.3213, pruned_loss=0.08227, over 1429790.06 frames.], batch size: 24, lr: 1.49e-03
+2022-04-28 13:09:55,538 INFO [train.py:763] (7/8) Epoch 3, batch 3600, loss[loss=0.1977, simple_loss=0.2779, pruned_loss=0.05873, over 6985.00 frames.], tot_loss[loss=0.244, simple_loss=0.3222, pruned_loss=0.08288, over 1427857.52 frames.], batch size: 16, lr: 1.49e-03
+2022-04-28 13:11:02,057 INFO [train.py:763] (7/8) Epoch 3, batch 3650, loss[loss=0.2036, simple_loss=0.2765, pruned_loss=0.06534, over 7129.00 frames.], tot_loss[loss=0.2438, simple_loss=0.3222, pruned_loss=0.08273, over 1428042.81 frames.], batch size: 17, lr: 1.49e-03
+2022-04-28 13:12:07,911 INFO [train.py:763] (7/8) Epoch 3, batch 3700, loss[loss=0.2047, simple_loss=0.282, pruned_loss=0.0637, over 7003.00 frames.], tot_loss[loss=0.2423, simple_loss=0.321, pruned_loss=0.08183, over 1427428.75 frames.], batch size: 16, lr: 1.49e-03
+2022-04-28 13:13:15,367 INFO [train.py:763] (7/8) Epoch 3, batch 3750, loss[loss=0.2513, simple_loss=0.3193, pruned_loss=0.09164, over 7429.00 frames.], tot_loss[loss=0.2417, simple_loss=0.3201, pruned_loss=0.08162, over 1425669.65 frames.], batch size: 20, lr: 1.49e-03
+2022-04-28 13:14:22,365 INFO [train.py:763] (7/8) Epoch 3, batch 3800, loss[loss=0.2711, simple_loss=0.3339, pruned_loss=0.1042, over 7451.00 frames.], tot_loss[loss=0.243, simple_loss=0.3214, pruned_loss=0.08227, over 1422303.62 frames.], batch size: 19, lr: 1.48e-03
+2022-04-28 13:15:29,724 INFO [train.py:763] (7/8) Epoch 3, batch 3850, loss[loss=0.2073, simple_loss=0.2842, pruned_loss=0.06521, over 7414.00 frames.], tot_loss[loss=0.2427, simple_loss=0.3212, pruned_loss=0.08209, over 1426221.41 frames.], batch size: 18, lr: 1.48e-03
+2022-04-28 13:16:35,245 INFO [train.py:763] (7/8) Epoch 3, batch 3900, loss[loss=0.319, simple_loss=0.3676, pruned_loss=0.1352, over 4715.00 frames.], tot_loss[loss=0.2414, simple_loss=0.3206, pruned_loss=0.08112, over 1427280.56 frames.], batch size: 52, lr: 1.48e-03
+2022-04-28 13:17:41,260 INFO [train.py:763] (7/8) Epoch 3, batch 3950, loss[loss=0.1857, simple_loss=0.2666, pruned_loss=0.05238, over 6786.00 frames.], tot_loss[loss=0.2413, simple_loss=0.3201, pruned_loss=0.0812, over 1425879.89 frames.], batch size: 15, lr: 1.48e-03
+2022-04-28 13:18:46,794 INFO [train.py:763] (7/8) Epoch 3, batch 4000, loss[loss=0.2571, simple_loss=0.3326, pruned_loss=0.09084, over 7217.00 frames.], tot_loss[loss=0.2426, simple_loss=0.3211, pruned_loss=0.08202, over 1418114.49 frames.], batch size: 21, lr: 1.48e-03
+2022-04-28 13:19:52,137 INFO [train.py:763] (7/8) Epoch 3, batch 4050, loss[loss=0.2684, simple_loss=0.3577, pruned_loss=0.08957, over 7406.00 frames.], tot_loss[loss=0.2434, simple_loss=0.322, pruned_loss=0.08242, over 1419834.63 frames.], batch size: 21, lr: 1.47e-03
+2022-04-28 13:20:58,253 INFO [train.py:763] (7/8) Epoch 3, batch 4100, loss[loss=0.2961, simple_loss=0.3586, pruned_loss=0.1168, over 6511.00 frames.], tot_loss[loss=0.2442, simple_loss=0.3225, pruned_loss=0.08297, over 1421582.65 frames.], batch size: 38, lr: 1.47e-03
+2022-04-28 13:22:04,077 INFO [train.py:763] (7/8) Epoch 3, batch 4150, loss[loss=0.2087, simple_loss=0.2753, pruned_loss=0.07103, over 7443.00 frames.], tot_loss[loss=0.243, simple_loss=0.3216, pruned_loss=0.08224, over 1423746.63 frames.], batch size: 17, lr: 1.47e-03
+2022-04-28 13:23:11,051 INFO [train.py:763] (7/8) Epoch 3, batch 4200, loss[loss=0.2219, simple_loss=0.3132, pruned_loss=0.06533, over 7153.00 frames.], tot_loss[loss=0.2416, simple_loss=0.3206, pruned_loss=0.08135, over 1422170.61 frames.], batch size: 19, lr: 1.47e-03
+2022-04-28 13:24:18,373 INFO [train.py:763] (7/8) Epoch 3, batch 4250, loss[loss=0.1958, simple_loss=0.2732, pruned_loss=0.05914, over 7361.00 frames.], tot_loss[loss=0.2413, simple_loss=0.3196, pruned_loss=0.08152, over 1414097.88 frames.], batch size: 19, lr: 1.47e-03
+2022-04-28 13:25:24,095 INFO [train.py:763] (7/8) Epoch 3, batch 4300, loss[loss=0.2141, simple_loss=0.2944, pruned_loss=0.06687, over 7346.00 frames.], tot_loss[loss=0.2405, simple_loss=0.3183, pruned_loss=0.08137, over 1412749.70 frames.], batch size: 19, lr: 1.47e-03
+2022-04-28 13:26:29,903 INFO [train.py:763] (7/8) Epoch 3, batch 4350, loss[loss=0.2617, simple_loss=0.3386, pruned_loss=0.09241, over 6209.00 frames.], tot_loss[loss=0.2395, simple_loss=0.317, pruned_loss=0.08104, over 1410940.94 frames.], batch size: 37, lr: 1.46e-03
+2022-04-28 13:27:35,691 INFO [train.py:763] (7/8) Epoch 3, batch 4400, loss[loss=0.2163, simple_loss=0.2985, pruned_loss=0.06708, over 7463.00 frames.], tot_loss[loss=0.2395, simple_loss=0.3166, pruned_loss=0.08121, over 1410657.08 frames.], batch size: 19, lr: 1.46e-03
+2022-04-28 13:28:41,571 INFO [train.py:763] (7/8) Epoch 3, batch 4450, loss[loss=0.2503, simple_loss=0.3356, pruned_loss=0.08245, over 7388.00 frames.], tot_loss[loss=0.2399, simple_loss=0.3166, pruned_loss=0.08163, over 1402414.26 frames.], batch size: 23, lr: 1.46e-03
+2022-04-28 13:29:46,957 INFO [train.py:763] (7/8) Epoch 3, batch 4500, loss[loss=0.3127, simple_loss=0.3793, pruned_loss=0.123, over 6211.00 frames.], tot_loss[loss=0.2403, simple_loss=0.3169, pruned_loss=0.08189, over 1397112.56 frames.], batch size: 37, lr: 1.46e-03
+2022-04-28 13:30:51,053 INFO [train.py:763] (7/8) Epoch 3, batch 4550, loss[loss=0.2664, simple_loss=0.3395, pruned_loss=0.09661, over 5102.00 frames.], tot_loss[loss=0.2455, simple_loss=0.3216, pruned_loss=0.0847, over 1362259.53 frames.], batch size: 53, lr: 1.46e-03
+2022-04-28 13:32:20,231 INFO [train.py:763] (7/8) Epoch 4, batch 0, loss[loss=0.2294, simple_loss=0.3181, pruned_loss=0.07041, over 7198.00 frames.], tot_loss[loss=0.2294, simple_loss=0.3181, pruned_loss=0.07041, over 7198.00 frames.], batch size: 23, lr: 1.40e-03
+2022-04-28 13:33:26,511 INFO [train.py:763] (7/8) Epoch 4, batch 50, loss[loss=0.2312, simple_loss=0.3283, pruned_loss=0.06706, over 7343.00 frames.], tot_loss[loss=0.2394, simple_loss=0.3194, pruned_loss=0.07964, over 321662.76 frames.], batch size: 22, lr: 1.40e-03
+2022-04-28 13:34:31,948 INFO [train.py:763] (7/8) Epoch 4, batch 100, loss[loss=0.2307, simple_loss=0.3279, pruned_loss=0.06678, over 7344.00 frames.], tot_loss[loss=0.2379, simple_loss=0.3193, pruned_loss=0.07827, over 567286.24 frames.], batch size: 22, lr: 1.40e-03
+2022-04-28 13:35:37,389 INFO [train.py:763] (7/8) Epoch 4, batch 150, loss[loss=0.3037, simple_loss=0.3526, pruned_loss=0.1275, over 5061.00 frames.], tot_loss[loss=0.2383, simple_loss=0.3198, pruned_loss=0.07842, over 755312.06 frames.], batch size: 52, lr: 1.40e-03
+2022-04-28 13:36:43,021 INFO [train.py:763] (7/8) Epoch 4, batch 200, loss[loss=0.2748, simple_loss=0.347, pruned_loss=0.1013, over 7158.00 frames.], tot_loss[loss=0.2395, simple_loss=0.3205, pruned_loss=0.07925, over 903659.59 frames.], batch size: 19, lr: 1.40e-03
+2022-04-28 13:37:48,987 INFO [train.py:763] (7/8) Epoch 4, batch 250, loss[loss=0.2913, simple_loss=0.3762, pruned_loss=0.1032, over 7334.00 frames.], tot_loss[loss=0.2418, simple_loss=0.3233, pruned_loss=0.08015, over 1021990.85 frames.], batch size: 22, lr: 1.39e-03
+2022-04-28 13:38:55,660 INFO [train.py:763] (7/8) Epoch 4, batch 300, loss[loss=0.212, simple_loss=0.2807, pruned_loss=0.07165, over 7280.00 frames.], tot_loss[loss=0.2377, simple_loss=0.3195, pruned_loss=0.07796, over 1114101.97 frames.], batch size: 17, lr: 1.39e-03
+2022-04-28 13:40:02,801 INFO [train.py:763] (7/8) Epoch 4, batch 350, loss[loss=0.2434, simple_loss=0.3273, pruned_loss=0.0797, over 7160.00 frames.], tot_loss[loss=0.236, simple_loss=0.3177, pruned_loss=0.07714, over 1182708.51 frames.], batch size: 19, lr: 1.39e-03
+2022-04-28 13:41:09,489 INFO [train.py:763] (7/8) Epoch 4, batch 400, loss[loss=0.2775, simple_loss=0.3441, pruned_loss=0.1055, over 7124.00 frames.], tot_loss[loss=0.2362, simple_loss=0.3176, pruned_loss=0.07742, over 1233421.10 frames.], batch size: 28, lr: 1.39e-03
+2022-04-28 13:42:15,472 INFO [train.py:763] (7/8) Epoch 4, batch 450, loss[loss=0.2747, simple_loss=0.3499, pruned_loss=0.09979, over 7042.00 frames.], tot_loss[loss=0.2367, simple_loss=0.3175, pruned_loss=0.07791, over 1274332.31 frames.], batch size: 28, lr: 1.39e-03
+2022-04-28 13:43:21,280 INFO [train.py:763] (7/8) Epoch 4, batch 500, loss[loss=0.266, simple_loss=0.3532, pruned_loss=0.08938, over 7325.00 frames.], tot_loss[loss=0.235, simple_loss=0.3166, pruned_loss=0.07668, over 1309187.20 frames.], batch size: 21, lr: 1.39e-03
+2022-04-28 13:44:28,343 INFO [train.py:763] (7/8) Epoch 4, batch 550, loss[loss=0.2615, simple_loss=0.3432, pruned_loss=0.08987, over 6710.00 frames.], tot_loss[loss=0.2353, simple_loss=0.3165, pruned_loss=0.07707, over 1334513.86 frames.], batch size: 31, lr: 1.38e-03
+2022-04-28 13:45:33,798 INFO [train.py:763] (7/8) Epoch 4, batch 600, loss[loss=0.2463, simple_loss=0.3072, pruned_loss=0.09268, over 7009.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3158, pruned_loss=0.07673, over 1355843.33 frames.], batch size: 16, lr: 1.38e-03
+2022-04-28 13:46:39,065 INFO [train.py:763] (7/8) Epoch 4, batch 650, loss[loss=0.2148, simple_loss=0.3048, pruned_loss=0.06242, over 7317.00 frames.], tot_loss[loss=0.2345, simple_loss=0.3156, pruned_loss=0.07674, over 1370901.94 frames.], batch size: 20, lr: 1.38e-03
+2022-04-28 13:47:44,012 INFO [train.py:763] (7/8) Epoch 4, batch 700, loss[loss=0.2722, simple_loss=0.3796, pruned_loss=0.0824, over 7290.00 frames.], tot_loss[loss=0.236, simple_loss=0.3168, pruned_loss=0.07757, over 1380336.71 frames.], batch size: 25, lr: 1.38e-03
+2022-04-28 13:48:49,487 INFO [train.py:763] (7/8) Epoch 4, batch 750, loss[loss=0.2213, simple_loss=0.2952, pruned_loss=0.07368, over 7070.00 frames.], tot_loss[loss=0.2362, simple_loss=0.3164, pruned_loss=0.07798, over 1384891.37 frames.], batch size: 18, lr: 1.38e-03
+2022-04-28 13:49:55,011 INFO [train.py:763] (7/8) Epoch 4, batch 800, loss[loss=0.1948, simple_loss=0.2763, pruned_loss=0.05667, over 7074.00 frames.], tot_loss[loss=0.234, simple_loss=0.3139, pruned_loss=0.07701, over 1395800.12 frames.], batch size: 18, lr: 1.38e-03
+2022-04-28 13:50:59,974 INFO [train.py:763] (7/8) Epoch 4, batch 850, loss[loss=0.2217, simple_loss=0.3043, pruned_loss=0.06954, over 7073.00 frames.], tot_loss[loss=0.234, simple_loss=0.3139, pruned_loss=0.07708, over 1395618.97 frames.], batch size: 18, lr: 1.37e-03
+2022-04-28 13:52:05,765 INFO [train.py:763] (7/8) Epoch 4, batch 900, loss[loss=0.2544, simple_loss=0.336, pruned_loss=0.08642, over 7317.00 frames.], tot_loss[loss=0.2337, simple_loss=0.314, pruned_loss=0.07667, over 1403112.96 frames.], batch size: 21, lr: 1.37e-03
+2022-04-28 13:53:12,240 INFO [train.py:763] (7/8) Epoch 4, batch 950, loss[loss=0.2438, simple_loss=0.3311, pruned_loss=0.0783, over 7007.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3146, pruned_loss=0.07655, over 1406006.36 frames.], batch size: 28, lr: 1.37e-03
+2022-04-28 13:54:19,390 INFO [train.py:763] (7/8) Epoch 4, batch 1000, loss[loss=0.1986, simple_loss=0.2729, pruned_loss=0.06222, over 7062.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3135, pruned_loss=0.07591, over 1410382.27 frames.], batch size: 18, lr: 1.37e-03
+2022-04-28 13:55:24,913 INFO [train.py:763] (7/8) Epoch 4, batch 1050, loss[loss=0.2066, simple_loss=0.2993, pruned_loss=0.05694, over 7294.00 frames.], tot_loss[loss=0.2334, simple_loss=0.3145, pruned_loss=0.07614, over 1416296.63 frames.], batch size: 24, lr: 1.37e-03
+2022-04-28 13:56:29,990 INFO [train.py:763] (7/8) Epoch 4, batch 1100, loss[loss=0.2734, simple_loss=0.3392, pruned_loss=0.1038, over 6379.00 frames.], tot_loss[loss=0.2352, simple_loss=0.3162, pruned_loss=0.0771, over 1412160.06 frames.], batch size: 37, lr: 1.37e-03
+2022-04-28 13:57:36,098 INFO [train.py:763] (7/8) Epoch 4, batch 1150, loss[loss=0.2627, simple_loss=0.3399, pruned_loss=0.09275, over 7432.00 frames.], tot_loss[loss=0.2365, simple_loss=0.3177, pruned_loss=0.07764, over 1415235.00 frames.], batch size: 20, lr: 1.36e-03
+2022-04-28 13:58:41,151 INFO [train.py:763] (7/8) Epoch 4, batch 1200, loss[loss=0.2716, simple_loss=0.3462, pruned_loss=0.09853, over 6296.00 frames.], tot_loss[loss=0.2365, simple_loss=0.3173, pruned_loss=0.07788, over 1417048.23 frames.], batch size: 37, lr: 1.36e-03
+2022-04-28 13:59:46,366 INFO [train.py:763] (7/8) Epoch 4, batch 1250, loss[loss=0.2096, simple_loss=0.2972, pruned_loss=0.06101, over 7257.00 frames.], tot_loss[loss=0.2382, simple_loss=0.3184, pruned_loss=0.07902, over 1413209.27 frames.], batch size: 19, lr: 1.36e-03
+2022-04-28 14:00:51,538 INFO [train.py:763] (7/8) Epoch 4, batch 1300, loss[loss=0.2451, simple_loss=0.3263, pruned_loss=0.08191, over 7330.00 frames.], tot_loss[loss=0.2375, simple_loss=0.3188, pruned_loss=0.07808, over 1416536.34 frames.], batch size: 20, lr: 1.36e-03
+2022-04-28 14:01:57,430 INFO [train.py:763] (7/8) Epoch 4, batch 1350, loss[loss=0.1982, simple_loss=0.281, pruned_loss=0.05765, over 7146.00 frames.], tot_loss[loss=0.2359, simple_loss=0.3174, pruned_loss=0.07715, over 1423412.35 frames.], batch size: 17, lr: 1.36e-03
+2022-04-28 14:03:02,796 INFO [train.py:763] (7/8) Epoch 4, batch 1400, loss[loss=0.2311, simple_loss=0.3218, pruned_loss=0.07022, over 7245.00 frames.], tot_loss[loss=0.2365, simple_loss=0.3181, pruned_loss=0.0774, over 1419229.54 frames.], batch size: 20, lr: 1.36e-03
+2022-04-28 14:04:07,971 INFO [train.py:763] (7/8) Epoch 4, batch 1450, loss[loss=0.2305, simple_loss=0.3038, pruned_loss=0.0786, over 6995.00 frames.], tot_loss[loss=0.2361, simple_loss=0.3182, pruned_loss=0.07702, over 1419545.57 frames.], batch size: 16, lr: 1.35e-03
+2022-04-28 14:05:14,098 INFO [train.py:763] (7/8) Epoch 4, batch 1500, loss[loss=0.2384, simple_loss=0.3247, pruned_loss=0.076, over 7326.00 frames.], tot_loss[loss=0.2349, simple_loss=0.317, pruned_loss=0.07639, over 1422844.31 frames.], batch size: 20, lr: 1.35e-03
+2022-04-28 14:06:19,714 INFO [train.py:763] (7/8) Epoch 4, batch 1550, loss[loss=0.2432, simple_loss=0.3307, pruned_loss=0.07781, over 7380.00 frames.], tot_loss[loss=0.233, simple_loss=0.3148, pruned_loss=0.0756, over 1424747.97 frames.], batch size: 23, lr: 1.35e-03
+2022-04-28 14:07:24,985 INFO [train.py:763] (7/8) Epoch 4, batch 1600, loss[loss=0.2503, simple_loss=0.3338, pruned_loss=0.08335, over 7312.00 frames.], tot_loss[loss=0.2329, simple_loss=0.3147, pruned_loss=0.07555, over 1424061.31 frames.], batch size: 25, lr: 1.35e-03
+2022-04-28 14:08:30,213 INFO [train.py:763] (7/8) Epoch 4, batch 1650, loss[loss=0.2284, simple_loss=0.329, pruned_loss=0.06387, over 7125.00 frames.], tot_loss[loss=0.234, simple_loss=0.3159, pruned_loss=0.07607, over 1421811.75 frames.], batch size: 21, lr: 1.35e-03
+2022-04-28 14:09:35,815 INFO [train.py:763] (7/8) Epoch 4, batch 1700, loss[loss=0.2544, simple_loss=0.3285, pruned_loss=0.0902, over 7343.00 frames.], tot_loss[loss=0.2349, simple_loss=0.3164, pruned_loss=0.07674, over 1423689.52 frames.], batch size: 22, lr: 1.35e-03
+2022-04-28 14:10:42,778 INFO [train.py:763] (7/8) Epoch 4, batch 1750, loss[loss=0.2538, simple_loss=0.3366, pruned_loss=0.08555, over 7289.00 frames.], tot_loss[loss=0.2357, simple_loss=0.3168, pruned_loss=0.07729, over 1423330.72 frames.], batch size: 24, lr: 1.34e-03
+2022-04-28 14:11:49,101 INFO [train.py:763] (7/8) Epoch 4, batch 1800, loss[loss=0.2193, simple_loss=0.308, pruned_loss=0.06535, over 7325.00 frames.], tot_loss[loss=0.2344, simple_loss=0.316, pruned_loss=0.07643, over 1425371.90 frames.], batch size: 21, lr: 1.34e-03
+2022-04-28 14:12:54,657 INFO [train.py:763] (7/8) Epoch 4, batch 1850, loss[loss=0.2639, simple_loss=0.3401, pruned_loss=0.09382, over 6322.00 frames.], tot_loss[loss=0.2349, simple_loss=0.3165, pruned_loss=0.0766, over 1425912.23 frames.], batch size: 37, lr: 1.34e-03
+2022-04-28 14:13:59,958 INFO [train.py:763] (7/8) Epoch 4, batch 1900, loss[loss=0.2645, simple_loss=0.3319, pruned_loss=0.0986, over 7127.00 frames.], tot_loss[loss=0.2346, simple_loss=0.3166, pruned_loss=0.0763, over 1427315.99 frames.], batch size: 21, lr: 1.34e-03
+2022-04-28 14:15:05,370 INFO [train.py:763] (7/8) Epoch 4, batch 1950, loss[loss=0.2192, simple_loss=0.2937, pruned_loss=0.07234, over 7162.00 frames.], tot_loss[loss=0.2329, simple_loss=0.3155, pruned_loss=0.07516, over 1428305.59 frames.], batch size: 18, lr: 1.34e-03
+2022-04-28 14:16:10,990 INFO [train.py:763] (7/8) Epoch 4, batch 2000, loss[loss=0.2626, simple_loss=0.3499, pruned_loss=0.08768, over 7281.00 frames.], tot_loss[loss=0.2323, simple_loss=0.3149, pruned_loss=0.07482, over 1426189.01 frames.], batch size: 25, lr: 1.34e-03
+2022-04-28 14:17:16,778 INFO [train.py:763] (7/8) Epoch 4, batch 2050, loss[loss=0.2208, simple_loss=0.3127, pruned_loss=0.06446, over 7302.00 frames.], tot_loss[loss=0.2316, simple_loss=0.3146, pruned_loss=0.0743, over 1431258.23 frames.], batch size: 24, lr: 1.34e-03
+2022-04-28 14:18:22,266 INFO [train.py:763] (7/8) Epoch 4, batch 2100, loss[loss=0.2086, simple_loss=0.2784, pruned_loss=0.06942, over 7408.00 frames.], tot_loss[loss=0.2306, simple_loss=0.314, pruned_loss=0.07359, over 1433949.22 frames.], batch size: 18, lr: 1.33e-03
+2022-04-28 14:19:27,844 INFO [train.py:763] (7/8) Epoch 4, batch 2150, loss[loss=0.2672, simple_loss=0.3324, pruned_loss=0.101, over 7071.00 frames.], tot_loss[loss=0.2325, simple_loss=0.3159, pruned_loss=0.07454, over 1432479.51 frames.], batch size: 18, lr: 1.33e-03
+2022-04-28 14:20:34,215 INFO [train.py:763] (7/8) Epoch 4, batch 2200, loss[loss=0.2574, simple_loss=0.3478, pruned_loss=0.0835, over 7337.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3155, pruned_loss=0.07495, over 1434211.33 frames.], batch size: 22, lr: 1.33e-03
+2022-04-28 14:21:39,767 INFO [train.py:763] (7/8) Epoch 4, batch 2250, loss[loss=0.2427, simple_loss=0.323, pruned_loss=0.08119, over 7371.00 frames.], tot_loss[loss=0.2325, simple_loss=0.315, pruned_loss=0.07497, over 1431807.20 frames.], batch size: 23, lr: 1.33e-03
+2022-04-28 14:22:45,305 INFO [train.py:763] (7/8) Epoch 4, batch 2300, loss[loss=0.2012, simple_loss=0.2822, pruned_loss=0.06011, over 7272.00 frames.], tot_loss[loss=0.2327, simple_loss=0.315, pruned_loss=0.0752, over 1430814.15 frames.], batch size: 17, lr: 1.33e-03
+2022-04-28 14:23:50,809 INFO [train.py:763] (7/8) Epoch 4, batch 2350, loss[loss=0.2179, simple_loss=0.294, pruned_loss=0.07093, over 7429.00 frames.], tot_loss[loss=0.2328, simple_loss=0.3149, pruned_loss=0.07539, over 1434164.29 frames.], batch size: 18, lr: 1.33e-03
+2022-04-28 14:24:56,462 INFO [train.py:763] (7/8) Epoch 4, batch 2400, loss[loss=0.2063, simple_loss=0.2944, pruned_loss=0.05908, over 7223.00 frames.], tot_loss[loss=0.232, simple_loss=0.3141, pruned_loss=0.07494, over 1436460.12 frames.], batch size: 21, lr: 1.32e-03
+2022-04-28 14:26:01,955 INFO [train.py:763] (7/8) Epoch 4, batch 2450, loss[loss=0.1843, simple_loss=0.2748, pruned_loss=0.04692, over 7277.00 frames.], tot_loss[loss=0.2324, simple_loss=0.3147, pruned_loss=0.07504, over 1435707.93 frames.], batch size: 18, lr: 1.32e-03
+2022-04-28 14:27:09,076 INFO [train.py:763] (7/8) Epoch 4, batch 2500, loss[loss=0.2104, simple_loss=0.2891, pruned_loss=0.06589, over 7189.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3146, pruned_loss=0.07493, over 1433106.42 frames.], batch size: 22, lr: 1.32e-03
+2022-04-28 14:28:15,005 INFO [train.py:763] (7/8) Epoch 4, batch 2550, loss[loss=0.2453, simple_loss=0.3311, pruned_loss=0.07972, over 7143.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3141, pruned_loss=0.07472, over 1433406.44 frames.], batch size: 20, lr: 1.32e-03
+2022-04-28 14:29:20,326 INFO [train.py:763] (7/8) Epoch 4, batch 2600, loss[loss=0.2196, simple_loss=0.3221, pruned_loss=0.05853, over 7319.00 frames.], tot_loss[loss=0.2331, simple_loss=0.3153, pruned_loss=0.07549, over 1431441.75 frames.], batch size: 21, lr: 1.32e-03
+2022-04-28 14:30:26,105 INFO [train.py:763] (7/8) Epoch 4, batch 2650, loss[loss=0.2191, simple_loss=0.2979, pruned_loss=0.07009, over 6999.00 frames.], tot_loss[loss=0.2325, simple_loss=0.3147, pruned_loss=0.07519, over 1429915.42 frames.], batch size: 16, lr: 1.32e-03
+2022-04-28 14:31:31,714 INFO [train.py:763] (7/8) Epoch 4, batch 2700, loss[loss=0.2055, simple_loss=0.2936, pruned_loss=0.05867, over 7286.00 frames.], tot_loss[loss=0.2312, simple_loss=0.3136, pruned_loss=0.07434, over 1432174.87 frames.], batch size: 18, lr: 1.32e-03
+2022-04-28 14:32:38,245 INFO [train.py:763] (7/8) Epoch 4, batch 2750, loss[loss=0.2283, simple_loss=0.3044, pruned_loss=0.0761, over 7356.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3129, pruned_loss=0.07427, over 1433402.18 frames.], batch size: 19, lr: 1.31e-03
+2022-04-28 14:33:43,926 INFO [train.py:763] (7/8) Epoch 4, batch 2800, loss[loss=0.1797, simple_loss=0.2641, pruned_loss=0.04765, over 7128.00 frames.], tot_loss[loss=0.2292, simple_loss=0.3118, pruned_loss=0.07326, over 1434304.05 frames.], batch size: 17, lr: 1.31e-03
+2022-04-28 14:34:49,333 INFO [train.py:763] (7/8) Epoch 4, batch 2850, loss[loss=0.248, simple_loss=0.342, pruned_loss=0.07701, over 6661.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3129, pruned_loss=0.07368, over 1430843.93 frames.], batch size: 31, lr: 1.31e-03
+2022-04-28 14:35:55,994 INFO [train.py:763] (7/8) Epoch 4, batch 2900, loss[loss=0.266, simple_loss=0.3454, pruned_loss=0.09324, over 7288.00 frames.], tot_loss[loss=0.23, simple_loss=0.3128, pruned_loss=0.07361, over 1429009.78 frames.], batch size: 24, lr: 1.31e-03
+2022-04-28 14:37:01,953 INFO [train.py:763] (7/8) Epoch 4, batch 2950, loss[loss=0.2217, simple_loss=0.3189, pruned_loss=0.06224, over 7342.00 frames.], tot_loss[loss=0.2276, simple_loss=0.3111, pruned_loss=0.07208, over 1429196.59 frames.], batch size: 22, lr: 1.31e-03
+2022-04-28 14:38:07,801 INFO [train.py:763] (7/8) Epoch 4, batch 3000, loss[loss=0.2133, simple_loss=0.303, pruned_loss=0.06185, over 7165.00 frames.], tot_loss[loss=0.2291, simple_loss=0.312, pruned_loss=0.07308, over 1425093.04 frames.], batch size: 26, lr: 1.31e-03
+2022-04-28 14:38:07,801 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-28 14:38:23,245 INFO [train.py:792] (7/8) Epoch 4, validation: loss=0.1809, simple_loss=0.2865, pruned_loss=0.03766, over 698248.00 frames.
+2022-04-28 14:39:28,683 INFO [train.py:763] (7/8) Epoch 4, batch 3050, loss[loss=0.2597, simple_loss=0.3481, pruned_loss=0.0856, over 7193.00 frames.], tot_loss[loss=0.2304, simple_loss=0.3133, pruned_loss=0.07372, over 1428672.33 frames.], batch size: 22, lr: 1.31e-03
+2022-04-28 14:40:34,118 INFO [train.py:763] (7/8) Epoch 4, batch 3100, loss[loss=0.207, simple_loss=0.3078, pruned_loss=0.05312, over 7247.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3137, pruned_loss=0.07397, over 1428260.73 frames.], batch size: 20, lr: 1.30e-03
+2022-04-28 14:41:39,926 INFO [train.py:763] (7/8) Epoch 4, batch 3150, loss[loss=0.2118, simple_loss=0.3054, pruned_loss=0.05911, over 7294.00 frames.], tot_loss[loss=0.2309, simple_loss=0.3139, pruned_loss=0.07397, over 1429065.93 frames.], batch size: 25, lr: 1.30e-03
+2022-04-28 14:42:46,556 INFO [train.py:763] (7/8) Epoch 4, batch 3200, loss[loss=0.207, simple_loss=0.2913, pruned_loss=0.06136, over 7372.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3134, pruned_loss=0.07336, over 1429671.33 frames.], batch size: 19, lr: 1.30e-03
+2022-04-28 14:43:52,385 INFO [train.py:763] (7/8) Epoch 4, batch 3250, loss[loss=0.2661, simple_loss=0.3333, pruned_loss=0.09946, over 7180.00 frames.], tot_loss[loss=0.2293, simple_loss=0.3125, pruned_loss=0.07305, over 1427011.01 frames.], batch size: 18, lr: 1.30e-03
+2022-04-28 14:44:57,973 INFO [train.py:763] (7/8) Epoch 4, batch 3300, loss[loss=0.2569, simple_loss=0.3361, pruned_loss=0.08887, over 7117.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3134, pruned_loss=0.07414, over 1422112.84 frames.], batch size: 26, lr: 1.30e-03
+2022-04-28 14:46:03,562 INFO [train.py:763] (7/8) Epoch 4, batch 3350, loss[loss=0.2685, simple_loss=0.336, pruned_loss=0.1005, over 7125.00 frames.], tot_loss[loss=0.2314, simple_loss=0.314, pruned_loss=0.07438, over 1424441.79 frames.], batch size: 21, lr: 1.30e-03
+2022-04-28 14:47:08,823 INFO [train.py:763] (7/8) Epoch 4, batch 3400, loss[loss=0.2473, simple_loss=0.3216, pruned_loss=0.08652, over 7236.00 frames.], tot_loss[loss=0.2322, simple_loss=0.315, pruned_loss=0.07473, over 1426797.13 frames.], batch size: 20, lr: 1.30e-03
+2022-04-28 14:48:14,165 INFO [train.py:763] (7/8) Epoch 4, batch 3450, loss[loss=0.2762, simple_loss=0.3684, pruned_loss=0.09201, over 7191.00 frames.], tot_loss[loss=0.233, simple_loss=0.3153, pruned_loss=0.07537, over 1426865.89 frames.], batch size: 23, lr: 1.29e-03
+2022-04-28 14:49:37,455 INFO [train.py:763] (7/8) Epoch 4, batch 3500, loss[loss=0.2675, simple_loss=0.342, pruned_loss=0.09653, over 7329.00 frames.], tot_loss[loss=0.2331, simple_loss=0.3156, pruned_loss=0.07527, over 1430253.06 frames.], batch size: 20, lr: 1.29e-03
+2022-04-28 14:50:52,153 INFO [train.py:763] (7/8) Epoch 4, batch 3550, loss[loss=0.2719, simple_loss=0.3567, pruned_loss=0.09354, over 7416.00 frames.], tot_loss[loss=0.2344, simple_loss=0.3165, pruned_loss=0.07613, over 1424971.23 frames.], batch size: 21, lr: 1.29e-03
+2022-04-28 14:51:57,848 INFO [train.py:763] (7/8) Epoch 4, batch 3600, loss[loss=0.2051, simple_loss=0.2915, pruned_loss=0.05937, over 7269.00 frames.], tot_loss[loss=0.2335, simple_loss=0.3154, pruned_loss=0.07579, over 1421149.24 frames.], batch size: 19, lr: 1.29e-03
+2022-04-28 14:53:23,238 INFO [train.py:763] (7/8) Epoch 4, batch 3650, loss[loss=0.2345, simple_loss=0.3178, pruned_loss=0.07559, over 6733.00 frames.], tot_loss[loss=0.233, simple_loss=0.3151, pruned_loss=0.07542, over 1415259.93 frames.], batch size: 31, lr: 1.29e-03
+2022-04-28 14:54:39,021 INFO [train.py:763] (7/8) Epoch 4, batch 3700, loss[loss=0.2333, simple_loss=0.3196, pruned_loss=0.07343, over 7141.00 frames.], tot_loss[loss=0.2303, simple_loss=0.3121, pruned_loss=0.07424, over 1419159.66 frames.], batch size: 18, lr: 1.29e-03
+2022-04-28 14:55:53,489 INFO [train.py:763] (7/8) Epoch 4, batch 3750, loss[loss=0.1814, simple_loss=0.2731, pruned_loss=0.04479, over 7265.00 frames.], tot_loss[loss=0.2297, simple_loss=0.3122, pruned_loss=0.07365, over 1420810.62 frames.], batch size: 16, lr: 1.29e-03
+2022-04-28 14:56:59,189 INFO [train.py:763] (7/8) Epoch 4, batch 3800, loss[loss=0.1871, simple_loss=0.2722, pruned_loss=0.051, over 7287.00 frames.], tot_loss[loss=0.231, simple_loss=0.3137, pruned_loss=0.07409, over 1421680.77 frames.], batch size: 18, lr: 1.28e-03
+2022-04-28 14:58:05,513 INFO [train.py:763] (7/8) Epoch 4, batch 3850, loss[loss=0.2401, simple_loss=0.3255, pruned_loss=0.07736, over 7417.00 frames.], tot_loss[loss=0.2311, simple_loss=0.3137, pruned_loss=0.07425, over 1420760.20 frames.], batch size: 21, lr: 1.28e-03
+2022-04-28 14:59:11,132 INFO [train.py:763] (7/8) Epoch 4, batch 3900, loss[loss=0.2005, simple_loss=0.29, pruned_loss=0.05545, over 7155.00 frames.], tot_loss[loss=0.2291, simple_loss=0.3118, pruned_loss=0.07322, over 1418067.73 frames.], batch size: 18, lr: 1.28e-03
+2022-04-28 15:00:16,501 INFO [train.py:763] (7/8) Epoch 4, batch 3950, loss[loss=0.218, simple_loss=0.3056, pruned_loss=0.06522, over 7411.00 frames.], tot_loss[loss=0.2295, simple_loss=0.3121, pruned_loss=0.07343, over 1415540.59 frames.], batch size: 21, lr: 1.28e-03
+2022-04-28 15:01:21,857 INFO [train.py:763] (7/8) Epoch 4, batch 4000, loss[loss=0.2061, simple_loss=0.2828, pruned_loss=0.06469, over 7435.00 frames.], tot_loss[loss=0.2302, simple_loss=0.313, pruned_loss=0.07376, over 1418351.62 frames.], batch size: 20, lr: 1.28e-03
+2022-04-28 15:02:27,503 INFO [train.py:763] (7/8) Epoch 4, batch 4050, loss[loss=0.2379, simple_loss=0.3265, pruned_loss=0.07463, over 7214.00 frames.], tot_loss[loss=0.2299, simple_loss=0.3128, pruned_loss=0.07348, over 1419733.17 frames.], batch size: 21, lr: 1.28e-03
+2022-04-28 15:03:34,104 INFO [train.py:763] (7/8) Epoch 4, batch 4100, loss[loss=0.1867, simple_loss=0.276, pruned_loss=0.04872, over 7263.00 frames.], tot_loss[loss=0.2319, simple_loss=0.3149, pruned_loss=0.07445, over 1417122.70 frames.], batch size: 18, lr: 1.28e-03
+2022-04-28 15:04:40,956 INFO [train.py:763] (7/8) Epoch 4, batch 4150, loss[loss=0.2478, simple_loss=0.3236, pruned_loss=0.08597, over 7215.00 frames.], tot_loss[loss=0.2322, simple_loss=0.315, pruned_loss=0.0747, over 1415180.01 frames.], batch size: 22, lr: 1.27e-03
+2022-04-28 15:05:47,262 INFO [train.py:763] (7/8) Epoch 4, batch 4200, loss[loss=0.2421, simple_loss=0.3119, pruned_loss=0.08619, over 7140.00 frames.], tot_loss[loss=0.2315, simple_loss=0.3145, pruned_loss=0.07427, over 1413709.14 frames.], batch size: 17, lr: 1.27e-03
+2022-04-28 15:06:53,149 INFO [train.py:763] (7/8) Epoch 4, batch 4250, loss[loss=0.2267, simple_loss=0.3067, pruned_loss=0.07333, over 7081.00 frames.], tot_loss[loss=0.2326, simple_loss=0.3155, pruned_loss=0.07484, over 1414860.03 frames.], batch size: 18, lr: 1.27e-03
+2022-04-28 15:07:59,477 INFO [train.py:763] (7/8) Epoch 4, batch 4300, loss[loss=0.2248, simple_loss=0.3099, pruned_loss=0.06983, over 7139.00 frames.], tot_loss[loss=0.2334, simple_loss=0.3162, pruned_loss=0.07528, over 1415691.04 frames.], batch size: 20, lr: 1.27e-03
+2022-04-28 15:09:04,581 INFO [train.py:763] (7/8) Epoch 4, batch 4350, loss[loss=0.2339, simple_loss=0.3247, pruned_loss=0.07159, over 7420.00 frames.], tot_loss[loss=0.2332, simple_loss=0.3162, pruned_loss=0.07506, over 1414991.40 frames.], batch size: 21, lr: 1.27e-03
+2022-04-28 15:10:09,742 INFO [train.py:763] (7/8) Epoch 4, batch 4400, loss[loss=0.2159, simple_loss=0.3019, pruned_loss=0.06491, over 7259.00 frames.], tot_loss[loss=0.2326, simple_loss=0.3161, pruned_loss=0.07455, over 1411279.89 frames.], batch size: 19, lr: 1.27e-03
+2022-04-28 15:11:14,757 INFO [train.py:763] (7/8) Epoch 4, batch 4450, loss[loss=0.2337, simple_loss=0.3235, pruned_loss=0.07188, over 6665.00 frames.], tot_loss[loss=0.2319, simple_loss=0.3156, pruned_loss=0.07408, over 1405514.15 frames.], batch size: 31, lr: 1.27e-03
+2022-04-28 15:12:19,739 INFO [train.py:763] (7/8) Epoch 4, batch 4500, loss[loss=0.3113, simple_loss=0.3778, pruned_loss=0.1224, over 5142.00 frames.], tot_loss[loss=0.2345, simple_loss=0.3177, pruned_loss=0.0757, over 1395239.79 frames.], batch size: 52, lr: 1.27e-03
+2022-04-28 15:13:25,340 INFO [train.py:763] (7/8) Epoch 4, batch 4550, loss[loss=0.3014, simple_loss=0.3585, pruned_loss=0.1221, over 4651.00 frames.], tot_loss[loss=0.24, simple_loss=0.3214, pruned_loss=0.07934, over 1339486.28 frames.], batch size: 52, lr: 1.26e-03
+2022-04-28 15:14:53,622 INFO [train.py:763] (7/8) Epoch 5, batch 0, loss[loss=0.2193, simple_loss=0.3035, pruned_loss=0.06762, over 7150.00 frames.], tot_loss[loss=0.2193, simple_loss=0.3035, pruned_loss=0.06762, over 7150.00 frames.], batch size: 19, lr: 1.21e-03
+2022-04-28 15:15:59,885 INFO [train.py:763] (7/8) Epoch 5, batch 50, loss[loss=0.3105, simple_loss=0.3674, pruned_loss=0.1268, over 4988.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3123, pruned_loss=0.07391, over 319285.96 frames.], batch size: 52, lr: 1.21e-03
+2022-04-28 15:17:05,491 INFO [train.py:763] (7/8) Epoch 5, batch 100, loss[loss=0.2293, simple_loss=0.3207, pruned_loss=0.06896, over 7146.00 frames.], tot_loss[loss=0.2286, simple_loss=0.3123, pruned_loss=0.07247, over 562402.31 frames.], batch size: 20, lr: 1.21e-03
+2022-04-28 15:18:11,211 INFO [train.py:763] (7/8) Epoch 5, batch 150, loss[loss=0.2412, simple_loss=0.3272, pruned_loss=0.0776, over 6779.00 frames.], tot_loss[loss=0.2286, simple_loss=0.3126, pruned_loss=0.0723, over 750213.10 frames.], batch size: 31, lr: 1.21e-03
+2022-04-28 15:19:17,545 INFO [train.py:763] (7/8) Epoch 5, batch 200, loss[loss=0.1951, simple_loss=0.272, pruned_loss=0.05908, over 7390.00 frames.], tot_loss[loss=0.2285, simple_loss=0.3128, pruned_loss=0.07207, over 899208.16 frames.], batch size: 18, lr: 1.21e-03
+2022-04-28 15:20:23,021 INFO [train.py:763] (7/8) Epoch 5, batch 250, loss[loss=0.2417, simple_loss=0.3318, pruned_loss=0.07583, over 7328.00 frames.], tot_loss[loss=0.2266, simple_loss=0.3115, pruned_loss=0.07082, over 1019282.18 frames.], batch size: 22, lr: 1.21e-03
+2022-04-28 15:21:29,020 INFO [train.py:763] (7/8) Epoch 5, batch 300, loss[loss=0.1902, simple_loss=0.3021, pruned_loss=0.03921, over 7247.00 frames.], tot_loss[loss=0.2258, simple_loss=0.3111, pruned_loss=0.07026, over 1112097.38 frames.], batch size: 20, lr: 1.21e-03
+2022-04-28 15:22:35,205 INFO [train.py:763] (7/8) Epoch 5, batch 350, loss[loss=0.2426, simple_loss=0.3295, pruned_loss=0.07781, over 7326.00 frames.], tot_loss[loss=0.225, simple_loss=0.3101, pruned_loss=0.06997, over 1185036.93 frames.], batch size: 20, lr: 1.20e-03
+2022-04-28 15:23:40,942 INFO [train.py:763] (7/8) Epoch 5, batch 400, loss[loss=0.2389, simple_loss=0.3326, pruned_loss=0.07262, over 7376.00 frames.], tot_loss[loss=0.2275, simple_loss=0.3123, pruned_loss=0.07132, over 1236768.47 frames.], batch size: 23, lr: 1.20e-03
+2022-04-28 15:24:46,904 INFO [train.py:763] (7/8) Epoch 5, batch 450, loss[loss=0.2279, simple_loss=0.2969, pruned_loss=0.0795, over 6826.00 frames.], tot_loss[loss=0.2275, simple_loss=0.3123, pruned_loss=0.07139, over 1279257.68 frames.], batch size: 15, lr: 1.20e-03
+2022-04-28 15:25:52,445 INFO [train.py:763] (7/8) Epoch 5, batch 500, loss[loss=0.2479, simple_loss=0.3252, pruned_loss=0.0853, over 4866.00 frames.], tot_loss[loss=0.2275, simple_loss=0.312, pruned_loss=0.07148, over 1308122.70 frames.], batch size: 52, lr: 1.20e-03
+2022-04-28 15:26:57,650 INFO [train.py:763] (7/8) Epoch 5, batch 550, loss[loss=0.2666, simple_loss=0.3571, pruned_loss=0.08806, over 6135.00 frames.], tot_loss[loss=0.2278, simple_loss=0.3125, pruned_loss=0.07159, over 1331809.57 frames.], batch size: 37, lr: 1.20e-03
+2022-04-28 15:28:04,527 INFO [train.py:763] (7/8) Epoch 5, batch 600, loss[loss=0.2149, simple_loss=0.3068, pruned_loss=0.06151, over 7141.00 frames.], tot_loss[loss=0.2254, simple_loss=0.31, pruned_loss=0.0704, over 1351108.04 frames.], batch size: 20, lr: 1.20e-03
+2022-04-28 15:29:09,675 INFO [train.py:763] (7/8) Epoch 5, batch 650, loss[loss=0.2385, simple_loss=0.3326, pruned_loss=0.07223, over 7412.00 frames.], tot_loss[loss=0.2254, simple_loss=0.31, pruned_loss=0.07044, over 1365520.28 frames.], batch size: 21, lr: 1.20e-03
+2022-04-28 15:30:15,017 INFO [train.py:763] (7/8) Epoch 5, batch 700, loss[loss=0.1927, simple_loss=0.2741, pruned_loss=0.05561, over 7184.00 frames.], tot_loss[loss=0.2245, simple_loss=0.3092, pruned_loss=0.0699, over 1378076.90 frames.], batch size: 16, lr: 1.20e-03
+2022-04-28 15:31:20,306 INFO [train.py:763] (7/8) Epoch 5, batch 750, loss[loss=0.2447, simple_loss=0.3317, pruned_loss=0.07889, over 7227.00 frames.], tot_loss[loss=0.2254, simple_loss=0.3105, pruned_loss=0.07018, over 1388262.77 frames.], batch size: 21, lr: 1.19e-03
+2022-04-28 15:32:25,899 INFO [train.py:763] (7/8) Epoch 5, batch 800, loss[loss=0.2189, simple_loss=0.3177, pruned_loss=0.06007, over 7224.00 frames.], tot_loss[loss=0.2245, simple_loss=0.3097, pruned_loss=0.06971, over 1398734.03 frames.], batch size: 21, lr: 1.19e-03
+2022-04-28 15:33:31,221 INFO [train.py:763] (7/8) Epoch 5, batch 850, loss[loss=0.2158, simple_loss=0.3095, pruned_loss=0.06104, over 7198.00 frames.], tot_loss[loss=0.2242, simple_loss=0.3098, pruned_loss=0.06933, over 1404188.40 frames.], batch size: 23, lr: 1.19e-03
+2022-04-28 15:34:36,550 INFO [train.py:763] (7/8) Epoch 5, batch 900, loss[loss=0.1943, simple_loss=0.2913, pruned_loss=0.04866, over 7417.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3096, pruned_loss=0.06957, over 1406060.54 frames.], batch size: 21, lr: 1.19e-03
+2022-04-28 15:35:42,340 INFO [train.py:763] (7/8) Epoch 5, batch 950, loss[loss=0.1981, simple_loss=0.2714, pruned_loss=0.06241, over 7129.00 frames.], tot_loss[loss=0.2232, simple_loss=0.3084, pruned_loss=0.06901, over 1407307.35 frames.], batch size: 17, lr: 1.19e-03
+2022-04-28 15:36:47,770 INFO [train.py:763] (7/8) Epoch 5, batch 1000, loss[loss=0.2194, simple_loss=0.3118, pruned_loss=0.06349, over 7417.00 frames.], tot_loss[loss=0.2251, simple_loss=0.3098, pruned_loss=0.07017, over 1408488.19 frames.], batch size: 21, lr: 1.19e-03
+2022-04-28 15:37:53,899 INFO [train.py:763] (7/8) Epoch 5, batch 1050, loss[loss=0.2579, simple_loss=0.3411, pruned_loss=0.08733, over 7346.00 frames.], tot_loss[loss=0.2252, simple_loss=0.3098, pruned_loss=0.07035, over 1413332.66 frames.], batch size: 20, lr: 1.19e-03
+2022-04-28 15:39:10,240 INFO [train.py:763] (7/8) Epoch 5, batch 1100, loss[loss=0.2614, simple_loss=0.3565, pruned_loss=0.08315, over 7311.00 frames.], tot_loss[loss=0.227, simple_loss=0.3111, pruned_loss=0.07149, over 1408382.81 frames.], batch size: 21, lr: 1.19e-03
+2022-04-28 15:40:16,770 INFO [train.py:763] (7/8) Epoch 5, batch 1150, loss[loss=0.1978, simple_loss=0.2899, pruned_loss=0.0529, over 7141.00 frames.], tot_loss[loss=0.226, simple_loss=0.311, pruned_loss=0.07054, over 1413207.87 frames.], batch size: 20, lr: 1.19e-03
+2022-04-28 15:41:22,509 INFO [train.py:763] (7/8) Epoch 5, batch 1200, loss[loss=0.2207, simple_loss=0.3117, pruned_loss=0.06481, over 7201.00 frames.], tot_loss[loss=0.2254, simple_loss=0.3101, pruned_loss=0.07033, over 1414357.45 frames.], batch size: 26, lr: 1.18e-03
+2022-04-28 15:42:29,002 INFO [train.py:763] (7/8) Epoch 5, batch 1250, loss[loss=0.2209, simple_loss=0.3127, pruned_loss=0.06454, over 7147.00 frames.], tot_loss[loss=0.2258, simple_loss=0.3106, pruned_loss=0.07046, over 1413564.29 frames.], batch size: 20, lr: 1.18e-03
+2022-04-28 15:43:35,966 INFO [train.py:763] (7/8) Epoch 5, batch 1300, loss[loss=0.1909, simple_loss=0.2799, pruned_loss=0.05095, over 7362.00 frames.], tot_loss[loss=0.2251, simple_loss=0.3098, pruned_loss=0.07021, over 1411632.17 frames.], batch size: 19, lr: 1.18e-03
+2022-04-28 15:44:42,302 INFO [train.py:763] (7/8) Epoch 5, batch 1350, loss[loss=0.2595, simple_loss=0.3392, pruned_loss=0.08989, over 7032.00 frames.], tot_loss[loss=0.2248, simple_loss=0.3094, pruned_loss=0.07011, over 1415051.08 frames.], batch size: 28, lr: 1.18e-03
+2022-04-28 15:45:48,516 INFO [train.py:763] (7/8) Epoch 5, batch 1400, loss[loss=0.213, simple_loss=0.3002, pruned_loss=0.06289, over 7315.00 frames.], tot_loss[loss=0.2243, simple_loss=0.3092, pruned_loss=0.06972, over 1419141.13 frames.], batch size: 20, lr: 1.18e-03
+2022-04-28 15:46:53,773 INFO [train.py:763] (7/8) Epoch 5, batch 1450, loss[loss=0.1946, simple_loss=0.2841, pruned_loss=0.05252, over 7429.00 frames.], tot_loss[loss=0.224, simple_loss=0.3088, pruned_loss=0.06963, over 1420458.06 frames.], batch size: 20, lr: 1.18e-03
+2022-04-28 15:47:59,056 INFO [train.py:763] (7/8) Epoch 5, batch 1500, loss[loss=0.2146, simple_loss=0.2937, pruned_loss=0.0677, over 7142.00 frames.], tot_loss[loss=0.2242, simple_loss=0.309, pruned_loss=0.06972, over 1420151.54 frames.], batch size: 20, lr: 1.18e-03
+2022-04-28 15:49:04,615 INFO [train.py:763] (7/8) Epoch 5, batch 1550, loss[loss=0.2129, simple_loss=0.2956, pruned_loss=0.06509, over 7289.00 frames.], tot_loss[loss=0.2246, simple_loss=0.3091, pruned_loss=0.07003, over 1422241.50 frames.], batch size: 17, lr: 1.18e-03
+2022-04-28 15:50:09,907 INFO [train.py:763] (7/8) Epoch 5, batch 1600, loss[loss=0.1908, simple_loss=0.2909, pruned_loss=0.04534, over 7427.00 frames.], tot_loss[loss=0.2238, simple_loss=0.3086, pruned_loss=0.0695, over 1414934.67 frames.], batch size: 20, lr: 1.17e-03
+2022-04-28 15:51:15,395 INFO [train.py:763] (7/8) Epoch 5, batch 1650, loss[loss=0.254, simple_loss=0.339, pruned_loss=0.08451, over 7269.00 frames.], tot_loss[loss=0.2228, simple_loss=0.3075, pruned_loss=0.06902, over 1414778.41 frames.], batch size: 25, lr: 1.17e-03
+2022-04-28 15:52:21,483 INFO [train.py:763] (7/8) Epoch 5, batch 1700, loss[loss=0.2497, simple_loss=0.3535, pruned_loss=0.07299, over 7206.00 frames.], tot_loss[loss=0.2228, simple_loss=0.3078, pruned_loss=0.06884, over 1412665.54 frames.], batch size: 22, lr: 1.17e-03
+2022-04-28 15:53:26,981 INFO [train.py:763] (7/8) Epoch 5, batch 1750, loss[loss=0.2232, simple_loss=0.2922, pruned_loss=0.07707, over 7276.00 frames.], tot_loss[loss=0.2243, simple_loss=0.309, pruned_loss=0.0698, over 1410109.13 frames.], batch size: 18, lr: 1.17e-03
+2022-04-28 15:54:32,254 INFO [train.py:763] (7/8) Epoch 5, batch 1800, loss[loss=0.2821, simple_loss=0.3434, pruned_loss=0.1104, over 4865.00 frames.], tot_loss[loss=0.2248, simple_loss=0.3096, pruned_loss=0.07004, over 1411832.66 frames.], batch size: 52, lr: 1.17e-03
+2022-04-28 15:55:37,888 INFO [train.py:763] (7/8) Epoch 5, batch 1850, loss[loss=0.2343, simple_loss=0.3065, pruned_loss=0.08106, over 7150.00 frames.], tot_loss[loss=0.2247, simple_loss=0.3089, pruned_loss=0.07028, over 1414817.26 frames.], batch size: 18, lr: 1.17e-03
+2022-04-28 15:56:43,247 INFO [train.py:763] (7/8) Epoch 5, batch 1900, loss[loss=0.2133, simple_loss=0.2869, pruned_loss=0.06979, over 7133.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3089, pruned_loss=0.07001, over 1413526.33 frames.], batch size: 17, lr: 1.17e-03
+2022-04-28 15:57:48,613 INFO [train.py:763] (7/8) Epoch 5, batch 1950, loss[loss=0.2172, simple_loss=0.3125, pruned_loss=0.06095, over 7099.00 frames.], tot_loss[loss=0.2251, simple_loss=0.3097, pruned_loss=0.07023, over 1418964.44 frames.], batch size: 21, lr: 1.17e-03
+2022-04-28 15:58:54,751 INFO [train.py:763] (7/8) Epoch 5, batch 2000, loss[loss=0.2232, simple_loss=0.2948, pruned_loss=0.07578, over 7276.00 frames.], tot_loss[loss=0.2249, simple_loss=0.3092, pruned_loss=0.07031, over 1423568.22 frames.], batch size: 18, lr: 1.17e-03
+2022-04-28 15:59:59,961 INFO [train.py:763] (7/8) Epoch 5, batch 2050, loss[loss=0.1845, simple_loss=0.2851, pruned_loss=0.04193, over 7088.00 frames.], tot_loss[loss=0.2246, simple_loss=0.3096, pruned_loss=0.06983, over 1424709.50 frames.], batch size: 28, lr: 1.16e-03
+2022-04-28 16:01:06,591 INFO [train.py:763] (7/8) Epoch 5, batch 2100, loss[loss=0.2478, simple_loss=0.3175, pruned_loss=0.08902, over 6565.00 frames.], tot_loss[loss=0.225, simple_loss=0.3102, pruned_loss=0.06987, over 1426129.50 frames.], batch size: 38, lr: 1.16e-03
+2022-04-28 16:02:12,125 INFO [train.py:763] (7/8) Epoch 5, batch 2150, loss[loss=0.2116, simple_loss=0.308, pruned_loss=0.05758, over 7146.00 frames.], tot_loss[loss=0.2238, simple_loss=0.3095, pruned_loss=0.06901, over 1431240.24 frames.], batch size: 20, lr: 1.16e-03
+2022-04-28 16:03:17,466 INFO [train.py:763] (7/8) Epoch 5, batch 2200, loss[loss=0.2445, simple_loss=0.3247, pruned_loss=0.0821, over 7131.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3089, pruned_loss=0.06905, over 1427516.04 frames.], batch size: 20, lr: 1.16e-03
+2022-04-28 16:04:22,925 INFO [train.py:763] (7/8) Epoch 5, batch 2250, loss[loss=0.2367, simple_loss=0.3187, pruned_loss=0.07731, over 7349.00 frames.], tot_loss[loss=0.2243, simple_loss=0.3095, pruned_loss=0.06951, over 1425488.13 frames.], batch size: 19, lr: 1.16e-03
+2022-04-28 16:05:29,065 INFO [train.py:763] (7/8) Epoch 5, batch 2300, loss[loss=0.2687, simple_loss=0.3479, pruned_loss=0.09477, over 7300.00 frames.], tot_loss[loss=0.2245, simple_loss=0.3096, pruned_loss=0.06967, over 1422286.49 frames.], batch size: 24, lr: 1.16e-03
+2022-04-28 16:06:35,250 INFO [train.py:763] (7/8) Epoch 5, batch 2350, loss[loss=0.2365, simple_loss=0.3201, pruned_loss=0.07652, over 7217.00 frames.], tot_loss[loss=0.2249, simple_loss=0.3092, pruned_loss=0.07029, over 1422021.09 frames.], batch size: 21, lr: 1.16e-03
+2022-04-28 16:07:41,483 INFO [train.py:763] (7/8) Epoch 5, batch 2400, loss[loss=0.1933, simple_loss=0.2943, pruned_loss=0.04613, over 7329.00 frames.], tot_loss[loss=0.2237, simple_loss=0.3083, pruned_loss=0.06954, over 1422404.44 frames.], batch size: 20, lr: 1.16e-03
+2022-04-28 16:08:47,673 INFO [train.py:763] (7/8) Epoch 5, batch 2450, loss[loss=0.2043, simple_loss=0.3035, pruned_loss=0.05251, over 6896.00 frames.], tot_loss[loss=0.2228, simple_loss=0.3078, pruned_loss=0.06889, over 1421684.10 frames.], batch size: 15, lr: 1.16e-03
+2022-04-28 16:09:52,921 INFO [train.py:763] (7/8) Epoch 5, batch 2500, loss[loss=0.2692, simple_loss=0.3436, pruned_loss=0.09743, over 7344.00 frames.], tot_loss[loss=0.2232, simple_loss=0.3082, pruned_loss=0.06904, over 1421240.61 frames.], batch size: 22, lr: 1.15e-03
+2022-04-28 16:10:59,318 INFO [train.py:763] (7/8) Epoch 5, batch 2550, loss[loss=0.2103, simple_loss=0.2803, pruned_loss=0.07014, over 6882.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3092, pruned_loss=0.06976, over 1423004.03 frames.], batch size: 15, lr: 1.15e-03
+2022-04-28 16:12:05,363 INFO [train.py:763] (7/8) Epoch 5, batch 2600, loss[loss=0.2675, simple_loss=0.3559, pruned_loss=0.08956, over 7320.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3092, pruned_loss=0.06945, over 1425982.73 frames.], batch size: 21, lr: 1.15e-03
+2022-04-28 16:13:10,886 INFO [train.py:763] (7/8) Epoch 5, batch 2650, loss[loss=0.247, simple_loss=0.334, pruned_loss=0.08005, over 7298.00 frames.], tot_loss[loss=0.225, simple_loss=0.3101, pruned_loss=0.06999, over 1424350.55 frames.], batch size: 25, lr: 1.15e-03
+2022-04-28 16:14:16,446 INFO [train.py:763] (7/8) Epoch 5, batch 2700, loss[loss=0.2169, simple_loss=0.288, pruned_loss=0.07287, over 6819.00 frames.], tot_loss[loss=0.2245, simple_loss=0.31, pruned_loss=0.06952, over 1425911.15 frames.], batch size: 15, lr: 1.15e-03
+2022-04-28 16:15:15,076 INFO [train.py:763] (7/8) Epoch 5, batch 2750, loss[loss=0.2089, simple_loss=0.2934, pruned_loss=0.06215, over 7238.00 frames.], tot_loss[loss=0.2243, simple_loss=0.3097, pruned_loss=0.06948, over 1423296.71 frames.], batch size: 20, lr: 1.15e-03
+2022-04-28 16:16:11,925 INFO [train.py:763] (7/8) Epoch 5, batch 2800, loss[loss=0.2127, simple_loss=0.2882, pruned_loss=0.06857, over 7280.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3089, pruned_loss=0.069, over 1421240.07 frames.], batch size: 18, lr: 1.15e-03
+2022-04-28 16:17:08,607 INFO [train.py:763] (7/8) Epoch 5, batch 2850, loss[loss=0.19, simple_loss=0.2742, pruned_loss=0.05293, over 7277.00 frames.], tot_loss[loss=0.224, simple_loss=0.3093, pruned_loss=0.06933, over 1418576.76 frames.], batch size: 17, lr: 1.15e-03
+2022-04-28 16:18:06,409 INFO [train.py:763] (7/8) Epoch 5, batch 2900, loss[loss=0.2414, simple_loss=0.3219, pruned_loss=0.08047, over 6644.00 frames.], tot_loss[loss=0.2245, simple_loss=0.3098, pruned_loss=0.06954, over 1420486.17 frames.], batch size: 31, lr: 1.15e-03
+2022-04-28 16:19:04,276 INFO [train.py:763] (7/8) Epoch 5, batch 2950, loss[loss=0.2146, simple_loss=0.3057, pruned_loss=0.06173, over 7146.00 frames.], tot_loss[loss=0.2237, simple_loss=0.309, pruned_loss=0.06923, over 1419772.65 frames.], batch size: 20, lr: 1.14e-03
+2022-04-28 16:19:58,119 INFO [train.py:763] (7/8) Epoch 5, batch 3000, loss[loss=0.2346, simple_loss=0.3208, pruned_loss=0.07418, over 7231.00 frames.], tot_loss[loss=0.2248, simple_loss=0.31, pruned_loss=0.0698, over 1419172.12 frames.], batch size: 20, lr: 1.14e-03
+2022-04-28 16:19:58,120 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-28 16:20:13,356 INFO [train.py:792] (7/8) Epoch 5, validation: loss=0.1791, simple_loss=0.2847, pruned_loss=0.03677, over 698248.00 frames.
+2022-04-28 16:21:19,347 INFO [train.py:763] (7/8) Epoch 5, batch 3050, loss[loss=0.2606, simple_loss=0.341, pruned_loss=0.09007, over 7198.00 frames.], tot_loss[loss=0.2239, simple_loss=0.3093, pruned_loss=0.06926, over 1424784.95 frames.], batch size: 23, lr: 1.14e-03
+2022-04-28 16:22:24,947 INFO [train.py:763] (7/8) Epoch 5, batch 3100, loss[loss=0.2218, simple_loss=0.3031, pruned_loss=0.07026, over 7345.00 frames.], tot_loss[loss=0.2219, simple_loss=0.3072, pruned_loss=0.0683, over 1422829.11 frames.], batch size: 22, lr: 1.14e-03
+2022-04-28 16:23:30,179 INFO [train.py:763] (7/8) Epoch 5, batch 3150, loss[loss=0.2336, simple_loss=0.3249, pruned_loss=0.07119, over 7209.00 frames.], tot_loss[loss=0.223, simple_loss=0.3086, pruned_loss=0.06874, over 1422677.96 frames.], batch size: 23, lr: 1.14e-03
+2022-04-28 16:24:36,696 INFO [train.py:763] (7/8) Epoch 5, batch 3200, loss[loss=0.2193, simple_loss=0.3117, pruned_loss=0.06342, over 7229.00 frames.], tot_loss[loss=0.2225, simple_loss=0.3081, pruned_loss=0.06843, over 1424829.24 frames.], batch size: 21, lr: 1.14e-03
+2022-04-28 16:25:42,642 INFO [train.py:763] (7/8) Epoch 5, batch 3250, loss[loss=0.19, simple_loss=0.2789, pruned_loss=0.05056, over 7360.00 frames.], tot_loss[loss=0.2223, simple_loss=0.3081, pruned_loss=0.06823, over 1424251.68 frames.], batch size: 19, lr: 1.14e-03
+2022-04-28 16:26:48,956 INFO [train.py:763] (7/8) Epoch 5, batch 3300, loss[loss=0.2404, simple_loss=0.3232, pruned_loss=0.07881, over 7193.00 frames.], tot_loss[loss=0.2232, simple_loss=0.3092, pruned_loss=0.06861, over 1421061.25 frames.], batch size: 23, lr: 1.14e-03
+2022-04-28 16:27:54,267 INFO [train.py:763] (7/8) Epoch 5, batch 3350, loss[loss=0.2447, simple_loss=0.3233, pruned_loss=0.08309, over 7249.00 frames.], tot_loss[loss=0.2221, simple_loss=0.3083, pruned_loss=0.06799, over 1425609.11 frames.], batch size: 19, lr: 1.14e-03
+2022-04-28 16:28:59,521 INFO [train.py:763] (7/8) Epoch 5, batch 3400, loss[loss=0.1952, simple_loss=0.2923, pruned_loss=0.04902, over 7320.00 frames.], tot_loss[loss=0.2203, simple_loss=0.3065, pruned_loss=0.06705, over 1425097.42 frames.], batch size: 24, lr: 1.14e-03
+2022-04-28 16:30:05,202 INFO [train.py:763] (7/8) Epoch 5, batch 3450, loss[loss=0.2379, simple_loss=0.3132, pruned_loss=0.08126, over 7410.00 frames.], tot_loss[loss=0.2217, simple_loss=0.308, pruned_loss=0.06765, over 1427933.43 frames.], batch size: 21, lr: 1.13e-03
+2022-04-28 16:31:11,013 INFO [train.py:763] (7/8) Epoch 5, batch 3500, loss[loss=0.2273, simple_loss=0.3185, pruned_loss=0.06801, over 7198.00 frames.], tot_loss[loss=0.2208, simple_loss=0.307, pruned_loss=0.06728, over 1425334.71 frames.], batch size: 22, lr: 1.13e-03
+2022-04-28 16:32:16,138 INFO [train.py:763] (7/8) Epoch 5, batch 3550, loss[loss=0.2196, simple_loss=0.3051, pruned_loss=0.06704, over 7329.00 frames.], tot_loss[loss=0.2227, simple_loss=0.3084, pruned_loss=0.0685, over 1427717.34 frames.], batch size: 21, lr: 1.13e-03
+2022-04-28 16:33:21,415 INFO [train.py:763] (7/8) Epoch 5, batch 3600, loss[loss=0.1778, simple_loss=0.2696, pruned_loss=0.04303, over 7155.00 frames.], tot_loss[loss=0.2236, simple_loss=0.309, pruned_loss=0.06909, over 1428908.91 frames.], batch size: 18, lr: 1.13e-03
+2022-04-28 16:34:27,119 INFO [train.py:763] (7/8) Epoch 5, batch 3650, loss[loss=0.2126, simple_loss=0.3029, pruned_loss=0.06122, over 7404.00 frames.], tot_loss[loss=0.2228, simple_loss=0.3085, pruned_loss=0.06852, over 1427634.28 frames.], batch size: 21, lr: 1.13e-03
+2022-04-28 16:35:34,080 INFO [train.py:763] (7/8) Epoch 5, batch 3700, loss[loss=0.2052, simple_loss=0.2954, pruned_loss=0.05748, over 7233.00 frames.], tot_loss[loss=0.2225, simple_loss=0.3081, pruned_loss=0.06845, over 1426332.89 frames.], batch size: 20, lr: 1.13e-03
+2022-04-28 16:36:39,380 INFO [train.py:763] (7/8) Epoch 5, batch 3750, loss[loss=0.2007, simple_loss=0.2937, pruned_loss=0.05383, over 7384.00 frames.], tot_loss[loss=0.2217, simple_loss=0.3073, pruned_loss=0.06804, over 1423911.92 frames.], batch size: 23, lr: 1.13e-03
+2022-04-28 16:37:46,345 INFO [train.py:763] (7/8) Epoch 5, batch 3800, loss[loss=0.2365, simple_loss=0.3278, pruned_loss=0.0726, over 7231.00 frames.], tot_loss[loss=0.2213, simple_loss=0.3067, pruned_loss=0.06799, over 1419750.88 frames.], batch size: 20, lr: 1.13e-03
+2022-04-28 16:38:51,778 INFO [train.py:763] (7/8) Epoch 5, batch 3850, loss[loss=0.213, simple_loss=0.3094, pruned_loss=0.05828, over 7427.00 frames.], tot_loss[loss=0.222, simple_loss=0.3077, pruned_loss=0.06818, over 1420132.34 frames.], batch size: 20, lr: 1.13e-03
+2022-04-28 16:39:57,109 INFO [train.py:763] (7/8) Epoch 5, batch 3900, loss[loss=0.1884, simple_loss=0.2657, pruned_loss=0.05555, over 7405.00 frames.], tot_loss[loss=0.2223, simple_loss=0.3079, pruned_loss=0.06831, over 1424548.97 frames.], batch size: 18, lr: 1.13e-03
+2022-04-28 16:41:04,051 INFO [train.py:763] (7/8) Epoch 5, batch 3950, loss[loss=0.2277, simple_loss=0.3031, pruned_loss=0.07618, over 7295.00 frames.], tot_loss[loss=0.2201, simple_loss=0.3058, pruned_loss=0.06716, over 1423810.19 frames.], batch size: 24, lr: 1.12e-03
+2022-04-28 16:42:10,964 INFO [train.py:763] (7/8) Epoch 5, batch 4000, loss[loss=0.2579, simple_loss=0.3315, pruned_loss=0.0922, over 7196.00 frames.], tot_loss[loss=0.2204, simple_loss=0.3061, pruned_loss=0.06734, over 1426189.12 frames.], batch size: 23, lr: 1.12e-03
+2022-04-28 16:43:18,257 INFO [train.py:763] (7/8) Epoch 5, batch 4050, loss[loss=0.2306, simple_loss=0.3199, pruned_loss=0.07059, over 7305.00 frames.], tot_loss[loss=0.2192, simple_loss=0.305, pruned_loss=0.06671, over 1427219.98 frames.], batch size: 24, lr: 1.12e-03
+2022-04-28 16:44:25,542 INFO [train.py:763] (7/8) Epoch 5, batch 4100, loss[loss=0.2002, simple_loss=0.2811, pruned_loss=0.0596, over 7406.00 frames.], tot_loss[loss=0.2191, simple_loss=0.3046, pruned_loss=0.06678, over 1427326.55 frames.], batch size: 18, lr: 1.12e-03
+2022-04-28 16:45:32,385 INFO [train.py:763] (7/8) Epoch 5, batch 4150, loss[loss=0.2149, simple_loss=0.3179, pruned_loss=0.05595, over 6808.00 frames.], tot_loss[loss=0.2182, simple_loss=0.3035, pruned_loss=0.06646, over 1427166.97 frames.], batch size: 31, lr: 1.12e-03
+2022-04-28 16:46:39,141 INFO [train.py:763] (7/8) Epoch 5, batch 4200, loss[loss=0.2468, simple_loss=0.3371, pruned_loss=0.07819, over 7113.00 frames.], tot_loss[loss=0.2183, simple_loss=0.3037, pruned_loss=0.06641, over 1428227.15 frames.], batch size: 21, lr: 1.12e-03
+2022-04-28 16:47:45,521 INFO [train.py:763] (7/8) Epoch 5, batch 4250, loss[loss=0.2602, simple_loss=0.3556, pruned_loss=0.08245, over 7386.00 frames.], tot_loss[loss=0.2197, simple_loss=0.3046, pruned_loss=0.06735, over 1429118.68 frames.], batch size: 23, lr: 1.12e-03
+2022-04-28 16:48:52,251 INFO [train.py:763] (7/8) Epoch 5, batch 4300, loss[loss=0.1972, simple_loss=0.2766, pruned_loss=0.05892, over 7079.00 frames.], tot_loss[loss=0.2198, simple_loss=0.3049, pruned_loss=0.06733, over 1424665.10 frames.], batch size: 18, lr: 1.12e-03
+2022-04-28 16:49:59,937 INFO [train.py:763] (7/8) Epoch 5, batch 4350, loss[loss=0.1926, simple_loss=0.2878, pruned_loss=0.04873, over 7214.00 frames.], tot_loss[loss=0.2181, simple_loss=0.3034, pruned_loss=0.06638, over 1424351.28 frames.], batch size: 21, lr: 1.12e-03
+2022-04-28 16:51:07,605 INFO [train.py:763] (7/8) Epoch 5, batch 4400, loss[loss=0.2003, simple_loss=0.2888, pruned_loss=0.0559, over 7426.00 frames.], tot_loss[loss=0.2175, simple_loss=0.3027, pruned_loss=0.06612, over 1422770.82 frames.], batch size: 20, lr: 1.12e-03
+2022-04-28 16:52:13,257 INFO [train.py:763] (7/8) Epoch 5, batch 4450, loss[loss=0.1853, simple_loss=0.2668, pruned_loss=0.05186, over 7271.00 frames.], tot_loss[loss=0.218, simple_loss=0.303, pruned_loss=0.06649, over 1409976.92 frames.], batch size: 17, lr: 1.11e-03
+2022-04-28 16:53:19,258 INFO [train.py:763] (7/8) Epoch 5, batch 4500, loss[loss=0.2341, simple_loss=0.3273, pruned_loss=0.07041, over 7233.00 frames.], tot_loss[loss=0.2161, simple_loss=0.3006, pruned_loss=0.06583, over 1408749.81 frames.], batch size: 20, lr: 1.11e-03
+2022-04-28 16:54:23,906 INFO [train.py:763] (7/8) Epoch 5, batch 4550, loss[loss=0.3103, simple_loss=0.3906, pruned_loss=0.115, over 5067.00 frames.], tot_loss[loss=0.2208, simple_loss=0.304, pruned_loss=0.06879, over 1358788.16 frames.], batch size: 52, lr: 1.11e-03
+2022-04-28 16:55:51,910 INFO [train.py:763] (7/8) Epoch 6, batch 0, loss[loss=0.185, simple_loss=0.2732, pruned_loss=0.0484, over 7416.00 frames.], tot_loss[loss=0.185, simple_loss=0.2732, pruned_loss=0.0484, over 7416.00 frames.], batch size: 18, lr: 1.07e-03
+2022-04-28 16:56:58,102 INFO [train.py:763] (7/8) Epoch 6, batch 50, loss[loss=0.2133, simple_loss=0.29, pruned_loss=0.06831, over 7405.00 frames.], tot_loss[loss=0.2171, simple_loss=0.3041, pruned_loss=0.06507, over 322465.78 frames.], batch size: 18, lr: 1.07e-03
+2022-04-28 16:58:04,034 INFO [train.py:763] (7/8) Epoch 6, batch 100, loss[loss=0.1872, simple_loss=0.2773, pruned_loss=0.04857, over 7159.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3018, pruned_loss=0.06454, over 567589.31 frames.], batch size: 19, lr: 1.06e-03
+2022-04-28 16:59:09,776 INFO [train.py:763] (7/8) Epoch 6, batch 150, loss[loss=0.2055, simple_loss=0.2902, pruned_loss=0.06043, over 7150.00 frames.], tot_loss[loss=0.2167, simple_loss=0.3035, pruned_loss=0.06492, over 757293.96 frames.], batch size: 19, lr: 1.06e-03
+2022-04-28 17:00:15,507 INFO [train.py:763] (7/8) Epoch 6, batch 200, loss[loss=0.2381, simple_loss=0.3246, pruned_loss=0.07576, over 7355.00 frames.], tot_loss[loss=0.2174, simple_loss=0.3041, pruned_loss=0.06538, over 906100.67 frames.], batch size: 23, lr: 1.06e-03
+2022-04-28 17:01:29,835 INFO [train.py:763] (7/8) Epoch 6, batch 250, loss[loss=0.2121, simple_loss=0.3101, pruned_loss=0.0571, over 7142.00 frames.], tot_loss[loss=0.2176, simple_loss=0.3049, pruned_loss=0.06511, over 1021055.50 frames.], batch size: 20, lr: 1.06e-03
+2022-04-28 17:02:45,521 INFO [train.py:763] (7/8) Epoch 6, batch 300, loss[loss=0.1855, simple_loss=0.2636, pruned_loss=0.05375, over 7221.00 frames.], tot_loss[loss=0.2182, simple_loss=0.3054, pruned_loss=0.06554, over 1107104.46 frames.], batch size: 16, lr: 1.06e-03
+2022-04-28 17:03:59,812 INFO [train.py:763] (7/8) Epoch 6, batch 350, loss[loss=0.193, simple_loss=0.2919, pruned_loss=0.04701, over 7117.00 frames.], tot_loss[loss=0.2173, simple_loss=0.3048, pruned_loss=0.06491, over 1178789.37 frames.], batch size: 21, lr: 1.06e-03
+2022-04-28 17:05:05,105 INFO [train.py:763] (7/8) Epoch 6, batch 400, loss[loss=0.1779, simple_loss=0.2715, pruned_loss=0.04211, over 7174.00 frames.], tot_loss[loss=0.2176, simple_loss=0.3052, pruned_loss=0.065, over 1231631.67 frames.], batch size: 18, lr: 1.06e-03
+2022-04-28 17:06:20,546 INFO [train.py:763] (7/8) Epoch 6, batch 450, loss[loss=0.2065, simple_loss=0.2918, pruned_loss=0.06061, over 7365.00 frames.], tot_loss[loss=0.2167, simple_loss=0.3043, pruned_loss=0.06459, over 1277140.84 frames.], batch size: 19, lr: 1.06e-03
+2022-04-28 17:07:44,123 INFO [train.py:763] (7/8) Epoch 6, batch 500, loss[loss=0.2381, simple_loss=0.3276, pruned_loss=0.07433, over 6513.00 frames.], tot_loss[loss=0.2176, simple_loss=0.3052, pruned_loss=0.06497, over 1306265.58 frames.], batch size: 38, lr: 1.06e-03
+2022-04-28 17:08:59,121 INFO [train.py:763] (7/8) Epoch 6, batch 550, loss[loss=0.237, simple_loss=0.3158, pruned_loss=0.07907, over 7119.00 frames.], tot_loss[loss=0.216, simple_loss=0.3034, pruned_loss=0.06429, over 1331036.63 frames.], batch size: 21, lr: 1.06e-03
+2022-04-28 17:10:13,654 INFO [train.py:763] (7/8) Epoch 6, batch 600, loss[loss=0.2224, simple_loss=0.3159, pruned_loss=0.06447, over 7032.00 frames.], tot_loss[loss=0.2161, simple_loss=0.3033, pruned_loss=0.06439, over 1349557.60 frames.], batch size: 28, lr: 1.06e-03
+2022-04-28 17:11:19,498 INFO [train.py:763] (7/8) Epoch 6, batch 650, loss[loss=0.2808, simple_loss=0.3401, pruned_loss=0.1107, over 4610.00 frames.], tot_loss[loss=0.2158, simple_loss=0.3026, pruned_loss=0.0645, over 1364019.53 frames.], batch size: 52, lr: 1.05e-03
+2022-04-28 17:12:25,185 INFO [train.py:763] (7/8) Epoch 6, batch 700, loss[loss=0.2194, simple_loss=0.3055, pruned_loss=0.06666, over 7161.00 frames.], tot_loss[loss=0.2158, simple_loss=0.3028, pruned_loss=0.06441, over 1378176.99 frames.], batch size: 18, lr: 1.05e-03
+2022-04-28 17:13:31,506 INFO [train.py:763] (7/8) Epoch 6, batch 750, loss[loss=0.2623, simple_loss=0.3335, pruned_loss=0.09554, over 6670.00 frames.], tot_loss[loss=0.2157, simple_loss=0.3029, pruned_loss=0.06426, over 1390789.21 frames.], batch size: 31, lr: 1.05e-03
+2022-04-28 17:14:37,108 INFO [train.py:763] (7/8) Epoch 6, batch 800, loss[loss=0.2176, simple_loss=0.3063, pruned_loss=0.06446, over 7335.00 frames.], tot_loss[loss=0.2168, simple_loss=0.3034, pruned_loss=0.0651, over 1391232.50 frames.], batch size: 20, lr: 1.05e-03
+2022-04-28 17:15:43,500 INFO [train.py:763] (7/8) Epoch 6, batch 850, loss[loss=0.2151, simple_loss=0.3091, pruned_loss=0.06057, over 7303.00 frames.], tot_loss[loss=0.2162, simple_loss=0.3028, pruned_loss=0.06482, over 1398903.01 frames.], batch size: 24, lr: 1.05e-03
+2022-04-28 17:16:48,960 INFO [train.py:763] (7/8) Epoch 6, batch 900, loss[loss=0.2302, simple_loss=0.3247, pruned_loss=0.06787, over 7373.00 frames.], tot_loss[loss=0.218, simple_loss=0.3044, pruned_loss=0.06576, over 1404755.13 frames.], batch size: 23, lr: 1.05e-03
+2022-04-28 17:17:54,052 INFO [train.py:763] (7/8) Epoch 6, batch 950, loss[loss=0.2523, simple_loss=0.3368, pruned_loss=0.08397, over 7371.00 frames.], tot_loss[loss=0.2175, simple_loss=0.3044, pruned_loss=0.06529, over 1408578.24 frames.], batch size: 23, lr: 1.05e-03
+2022-04-28 17:18:59,571 INFO [train.py:763] (7/8) Epoch 6, batch 1000, loss[loss=0.2124, simple_loss=0.3036, pruned_loss=0.0606, over 7388.00 frames.], tot_loss[loss=0.2168, simple_loss=0.3034, pruned_loss=0.06508, over 1408950.34 frames.], batch size: 23, lr: 1.05e-03
+2022-04-28 17:20:06,068 INFO [train.py:763] (7/8) Epoch 6, batch 1050, loss[loss=0.2027, simple_loss=0.2967, pruned_loss=0.05432, over 7165.00 frames.], tot_loss[loss=0.2161, simple_loss=0.3028, pruned_loss=0.06467, over 1416002.97 frames.], batch size: 19, lr: 1.05e-03
+2022-04-28 17:21:12,159 INFO [train.py:763] (7/8) Epoch 6, batch 1100, loss[loss=0.221, simple_loss=0.3121, pruned_loss=0.06494, over 7289.00 frames.], tot_loss[loss=0.2163, simple_loss=0.3031, pruned_loss=0.0647, over 1420242.26 frames.], batch size: 25, lr: 1.05e-03
+2022-04-28 17:22:18,776 INFO [train.py:763] (7/8) Epoch 6, batch 1150, loss[loss=0.1802, simple_loss=0.2591, pruned_loss=0.05065, over 7129.00 frames.], tot_loss[loss=0.2156, simple_loss=0.3025, pruned_loss=0.06436, over 1418083.98 frames.], batch size: 17, lr: 1.05e-03
+2022-04-28 17:23:26,162 INFO [train.py:763] (7/8) Epoch 6, batch 1200, loss[loss=0.2, simple_loss=0.2782, pruned_loss=0.06087, over 6790.00 frames.], tot_loss[loss=0.2154, simple_loss=0.3025, pruned_loss=0.06415, over 1412567.64 frames.], batch size: 15, lr: 1.04e-03
+2022-04-28 17:24:33,323 INFO [train.py:763] (7/8) Epoch 6, batch 1250, loss[loss=0.2095, simple_loss=0.2896, pruned_loss=0.06468, over 7235.00 frames.], tot_loss[loss=0.2156, simple_loss=0.3026, pruned_loss=0.06427, over 1414310.15 frames.], batch size: 20, lr: 1.04e-03
+2022-04-28 17:25:39,235 INFO [train.py:763] (7/8) Epoch 6, batch 1300, loss[loss=0.2096, simple_loss=0.2826, pruned_loss=0.06833, over 7294.00 frames.], tot_loss[loss=0.2152, simple_loss=0.3019, pruned_loss=0.06425, over 1415775.97 frames.], batch size: 17, lr: 1.04e-03
+2022-04-28 17:26:44,451 INFO [train.py:763] (7/8) Epoch 6, batch 1350, loss[loss=0.229, simple_loss=0.3201, pruned_loss=0.06896, over 7413.00 frames.], tot_loss[loss=0.2143, simple_loss=0.3019, pruned_loss=0.06339, over 1420933.33 frames.], batch size: 21, lr: 1.04e-03
+2022-04-28 17:27:49,626 INFO [train.py:763] (7/8) Epoch 6, batch 1400, loss[loss=0.2039, simple_loss=0.298, pruned_loss=0.05495, over 7149.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3029, pruned_loss=0.06403, over 1419150.89 frames.], batch size: 19, lr: 1.04e-03
+2022-04-28 17:28:55,371 INFO [train.py:763] (7/8) Epoch 6, batch 1450, loss[loss=0.2233, simple_loss=0.3165, pruned_loss=0.065, over 6751.00 frames.], tot_loss[loss=0.2164, simple_loss=0.3038, pruned_loss=0.06447, over 1419166.27 frames.], batch size: 31, lr: 1.04e-03
+2022-04-28 17:30:00,757 INFO [train.py:763] (7/8) Epoch 6, batch 1500, loss[loss=0.23, simple_loss=0.3255, pruned_loss=0.06723, over 7414.00 frames.], tot_loss[loss=0.2147, simple_loss=0.3024, pruned_loss=0.06352, over 1423593.70 frames.], batch size: 21, lr: 1.04e-03
+2022-04-28 17:31:05,977 INFO [train.py:763] (7/8) Epoch 6, batch 1550, loss[loss=0.2253, simple_loss=0.3195, pruned_loss=0.06551, over 7184.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3027, pruned_loss=0.06417, over 1418450.97 frames.], batch size: 26, lr: 1.04e-03
+2022-04-28 17:32:11,546 INFO [train.py:763] (7/8) Epoch 6, batch 1600, loss[loss=0.1789, simple_loss=0.2846, pruned_loss=0.03666, over 7097.00 frames.], tot_loss[loss=0.2153, simple_loss=0.3028, pruned_loss=0.06391, over 1424554.51 frames.], batch size: 21, lr: 1.04e-03
+2022-04-28 17:33:16,944 INFO [train.py:763] (7/8) Epoch 6, batch 1650, loss[loss=0.1941, simple_loss=0.2868, pruned_loss=0.05066, over 7061.00 frames.], tot_loss[loss=0.2161, simple_loss=0.3031, pruned_loss=0.06457, over 1417921.47 frames.], batch size: 18, lr: 1.04e-03
+2022-04-28 17:34:24,092 INFO [train.py:763] (7/8) Epoch 6, batch 1700, loss[loss=0.2271, simple_loss=0.3175, pruned_loss=0.06833, over 7187.00 frames.], tot_loss[loss=0.2144, simple_loss=0.3014, pruned_loss=0.06377, over 1416300.45 frames.], batch size: 22, lr: 1.04e-03
+2022-04-28 17:35:30,128 INFO [train.py:763] (7/8) Epoch 6, batch 1750, loss[loss=0.2484, simple_loss=0.3187, pruned_loss=0.08904, over 7330.00 frames.], tot_loss[loss=0.2156, simple_loss=0.3022, pruned_loss=0.06451, over 1412362.61 frames.], batch size: 22, lr: 1.04e-03
+2022-04-28 17:36:35,232 INFO [train.py:763] (7/8) Epoch 6, batch 1800, loss[loss=0.233, simple_loss=0.3242, pruned_loss=0.07088, over 7296.00 frames.], tot_loss[loss=0.2167, simple_loss=0.3037, pruned_loss=0.06487, over 1414820.72 frames.], batch size: 25, lr: 1.03e-03
+2022-04-28 17:37:41,019 INFO [train.py:763] (7/8) Epoch 6, batch 1850, loss[loss=0.2206, simple_loss=0.2948, pruned_loss=0.07315, over 6991.00 frames.], tot_loss[loss=0.2165, simple_loss=0.3033, pruned_loss=0.06487, over 1417092.89 frames.], batch size: 16, lr: 1.03e-03
+2022-04-28 17:38:46,210 INFO [train.py:763] (7/8) Epoch 6, batch 1900, loss[loss=0.2087, simple_loss=0.3053, pruned_loss=0.05602, over 7054.00 frames.], tot_loss[loss=0.2178, simple_loss=0.3046, pruned_loss=0.06547, over 1413612.12 frames.], batch size: 18, lr: 1.03e-03
+2022-04-28 17:39:52,676 INFO [train.py:763] (7/8) Epoch 6, batch 1950, loss[loss=0.2027, simple_loss=0.285, pruned_loss=0.0602, over 7274.00 frames.], tot_loss[loss=0.2173, simple_loss=0.3041, pruned_loss=0.06529, over 1417105.92 frames.], batch size: 18, lr: 1.03e-03
+2022-04-28 17:40:59,189 INFO [train.py:763] (7/8) Epoch 6, batch 2000, loss[loss=0.2213, simple_loss=0.3113, pruned_loss=0.0656, over 7289.00 frames.], tot_loss[loss=0.2166, simple_loss=0.3037, pruned_loss=0.06473, over 1417754.46 frames.], batch size: 25, lr: 1.03e-03
+2022-04-28 17:42:06,063 INFO [train.py:763] (7/8) Epoch 6, batch 2050, loss[loss=0.26, simple_loss=0.3526, pruned_loss=0.08373, over 7270.00 frames.], tot_loss[loss=0.2177, simple_loss=0.3046, pruned_loss=0.06545, over 1414942.21 frames.], batch size: 24, lr: 1.03e-03
+2022-04-28 17:43:12,561 INFO [train.py:763] (7/8) Epoch 6, batch 2100, loss[loss=0.1881, simple_loss=0.267, pruned_loss=0.05462, over 7005.00 frames.], tot_loss[loss=0.2169, simple_loss=0.3036, pruned_loss=0.06512, over 1417908.43 frames.], batch size: 16, lr: 1.03e-03
+2022-04-28 17:44:19,367 INFO [train.py:763] (7/8) Epoch 6, batch 2150, loss[loss=0.1858, simple_loss=0.2808, pruned_loss=0.04542, over 7411.00 frames.], tot_loss[loss=0.2156, simple_loss=0.3026, pruned_loss=0.06431, over 1423404.62 frames.], batch size: 21, lr: 1.03e-03
+2022-04-28 17:45:25,703 INFO [train.py:763] (7/8) Epoch 6, batch 2200, loss[loss=0.2409, simple_loss=0.3122, pruned_loss=0.08473, over 7147.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3023, pruned_loss=0.06436, over 1422130.63 frames.], batch size: 17, lr: 1.03e-03
+2022-04-28 17:46:32,106 INFO [train.py:763] (7/8) Epoch 6, batch 2250, loss[loss=0.2247, simple_loss=0.3023, pruned_loss=0.07357, over 7284.00 frames.], tot_loss[loss=0.2168, simple_loss=0.3031, pruned_loss=0.06528, over 1416625.60 frames.], batch size: 17, lr: 1.03e-03
+2022-04-28 17:47:38,693 INFO [train.py:763] (7/8) Epoch 6, batch 2300, loss[loss=0.2428, simple_loss=0.3244, pruned_loss=0.08057, over 7215.00 frames.], tot_loss[loss=0.2171, simple_loss=0.3034, pruned_loss=0.06539, over 1419901.71 frames.], batch size: 23, lr: 1.03e-03
+2022-04-28 17:48:44,950 INFO [train.py:763] (7/8) Epoch 6, batch 2350, loss[loss=0.2098, simple_loss=0.3053, pruned_loss=0.0572, over 7417.00 frames.], tot_loss[loss=0.2159, simple_loss=0.3028, pruned_loss=0.06452, over 1416720.51 frames.], batch size: 21, lr: 1.02e-03
+2022-04-28 17:49:50,857 INFO [train.py:763] (7/8) Epoch 6, batch 2400, loss[loss=0.1676, simple_loss=0.2639, pruned_loss=0.03571, over 7291.00 frames.], tot_loss[loss=0.2146, simple_loss=0.3016, pruned_loss=0.06377, over 1420587.29 frames.], batch size: 18, lr: 1.02e-03
+2022-04-28 17:50:56,977 INFO [train.py:763] (7/8) Epoch 6, batch 2450, loss[loss=0.2346, simple_loss=0.3275, pruned_loss=0.0708, over 7408.00 frames.], tot_loss[loss=0.2152, simple_loss=0.3025, pruned_loss=0.06396, over 1416988.85 frames.], batch size: 21, lr: 1.02e-03
+2022-04-28 17:52:02,809 INFO [train.py:763] (7/8) Epoch 6, batch 2500, loss[loss=0.2802, simple_loss=0.359, pruned_loss=0.1007, over 7322.00 frames.], tot_loss[loss=0.2153, simple_loss=0.3027, pruned_loss=0.0639, over 1417132.09 frames.], batch size: 21, lr: 1.02e-03
+2022-04-28 17:53:08,650 INFO [train.py:763] (7/8) Epoch 6, batch 2550, loss[loss=0.2263, simple_loss=0.3214, pruned_loss=0.06565, over 7439.00 frames.], tot_loss[loss=0.2158, simple_loss=0.3033, pruned_loss=0.06414, over 1423691.64 frames.], batch size: 20, lr: 1.02e-03
+2022-04-28 17:54:14,775 INFO [train.py:763] (7/8) Epoch 6, batch 2600, loss[loss=0.177, simple_loss=0.2605, pruned_loss=0.04679, over 7161.00 frames.], tot_loss[loss=0.2156, simple_loss=0.3028, pruned_loss=0.06418, over 1418212.44 frames.], batch size: 18, lr: 1.02e-03
+2022-04-28 17:55:21,052 INFO [train.py:763] (7/8) Epoch 6, batch 2650, loss[loss=0.2228, simple_loss=0.2984, pruned_loss=0.07359, over 7157.00 frames.], tot_loss[loss=0.2144, simple_loss=0.3015, pruned_loss=0.06361, over 1417932.33 frames.], batch size: 18, lr: 1.02e-03
+2022-04-28 17:56:26,538 INFO [train.py:763] (7/8) Epoch 6, batch 2700, loss[loss=0.1857, simple_loss=0.2856, pruned_loss=0.04292, over 6807.00 frames.], tot_loss[loss=0.2136, simple_loss=0.3012, pruned_loss=0.06303, over 1419453.01 frames.], batch size: 15, lr: 1.02e-03
+2022-04-28 17:57:32,636 INFO [train.py:763] (7/8) Epoch 6, batch 2750, loss[loss=0.1975, simple_loss=0.2734, pruned_loss=0.06079, over 7402.00 frames.], tot_loss[loss=0.213, simple_loss=0.3006, pruned_loss=0.06268, over 1419976.52 frames.], batch size: 18, lr: 1.02e-03
+2022-04-28 17:58:39,128 INFO [train.py:763] (7/8) Epoch 6, batch 2800, loss[loss=0.22, simple_loss=0.2888, pruned_loss=0.07563, over 6995.00 frames.], tot_loss[loss=0.2138, simple_loss=0.3009, pruned_loss=0.06333, over 1418007.31 frames.], batch size: 16, lr: 1.02e-03
+2022-04-28 17:59:46,056 INFO [train.py:763] (7/8) Epoch 6, batch 2850, loss[loss=0.2388, simple_loss=0.3147, pruned_loss=0.08149, over 7318.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2999, pruned_loss=0.06281, over 1422572.07 frames.], batch size: 21, lr: 1.02e-03
+2022-04-28 18:00:52,215 INFO [train.py:763] (7/8) Epoch 6, batch 2900, loss[loss=0.2943, simple_loss=0.3482, pruned_loss=0.1202, over 5041.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2997, pruned_loss=0.06266, over 1424373.41 frames.], batch size: 55, lr: 1.02e-03
+2022-04-28 18:01:57,570 INFO [train.py:763] (7/8) Epoch 6, batch 2950, loss[loss=0.257, simple_loss=0.3253, pruned_loss=0.0944, over 7273.00 frames.], tot_loss[loss=0.2129, simple_loss=0.3004, pruned_loss=0.06274, over 1424190.44 frames.], batch size: 25, lr: 1.01e-03
+2022-04-28 18:03:03,527 INFO [train.py:763] (7/8) Epoch 6, batch 3000, loss[loss=0.2402, simple_loss=0.3365, pruned_loss=0.0719, over 7201.00 frames.], tot_loss[loss=0.2138, simple_loss=0.3015, pruned_loss=0.06308, over 1425880.87 frames.], batch size: 26, lr: 1.01e-03
+2022-04-28 18:03:03,528 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-28 18:03:18,817 INFO [train.py:792] (7/8) Epoch 6, validation: loss=0.1749, simple_loss=0.2793, pruned_loss=0.03525, over 698248.00 frames.
+2022-04-28 18:04:24,355 INFO [train.py:763] (7/8) Epoch 6, batch 3050, loss[loss=0.2244, simple_loss=0.311, pruned_loss=0.06889, over 7150.00 frames.], tot_loss[loss=0.214, simple_loss=0.3017, pruned_loss=0.06318, over 1426239.67 frames.], batch size: 26, lr: 1.01e-03
+2022-04-28 18:05:30,266 INFO [train.py:763] (7/8) Epoch 6, batch 3100, loss[loss=0.2263, simple_loss=0.3132, pruned_loss=0.06977, over 7131.00 frames.], tot_loss[loss=0.2145, simple_loss=0.3023, pruned_loss=0.06338, over 1423478.09 frames.], batch size: 26, lr: 1.01e-03
+2022-04-28 18:06:36,928 INFO [train.py:763] (7/8) Epoch 6, batch 3150, loss[loss=0.207, simple_loss=0.2945, pruned_loss=0.05974, over 7090.00 frames.], tot_loss[loss=0.2137, simple_loss=0.3017, pruned_loss=0.06288, over 1426828.24 frames.], batch size: 28, lr: 1.01e-03
+2022-04-28 18:07:42,741 INFO [train.py:763] (7/8) Epoch 6, batch 3200, loss[loss=0.2074, simple_loss=0.2956, pruned_loss=0.05956, over 7332.00 frames.], tot_loss[loss=0.2153, simple_loss=0.3031, pruned_loss=0.06369, over 1423076.98 frames.], batch size: 22, lr: 1.01e-03
+2022-04-28 18:08:48,617 INFO [train.py:763] (7/8) Epoch 6, batch 3250, loss[loss=0.2311, simple_loss=0.3198, pruned_loss=0.07121, over 7069.00 frames.], tot_loss[loss=0.2138, simple_loss=0.3017, pruned_loss=0.06292, over 1422423.61 frames.], batch size: 28, lr: 1.01e-03
+2022-04-28 18:09:54,863 INFO [train.py:763] (7/8) Epoch 6, batch 3300, loss[loss=0.2027, simple_loss=0.293, pruned_loss=0.05617, over 7153.00 frames.], tot_loss[loss=0.214, simple_loss=0.302, pruned_loss=0.06299, over 1417820.21 frames.], batch size: 20, lr: 1.01e-03
+2022-04-28 18:11:00,649 INFO [train.py:763] (7/8) Epoch 6, batch 3350, loss[loss=0.2039, simple_loss=0.2958, pruned_loss=0.05599, over 7171.00 frames.], tot_loss[loss=0.2142, simple_loss=0.302, pruned_loss=0.0632, over 1419299.21 frames.], batch size: 19, lr: 1.01e-03
+2022-04-28 18:12:05,981 INFO [train.py:763] (7/8) Epoch 6, batch 3400, loss[loss=0.2217, simple_loss=0.3152, pruned_loss=0.06411, over 7111.00 frames.], tot_loss[loss=0.2145, simple_loss=0.3025, pruned_loss=0.06331, over 1422750.74 frames.], batch size: 21, lr: 1.01e-03
+2022-04-28 18:13:11,478 INFO [train.py:763] (7/8) Epoch 6, batch 3450, loss[loss=0.2113, simple_loss=0.3109, pruned_loss=0.05583, over 7295.00 frames.], tot_loss[loss=0.2142, simple_loss=0.3024, pruned_loss=0.06295, over 1420347.16 frames.], batch size: 24, lr: 1.01e-03
+2022-04-28 18:14:16,748 INFO [train.py:763] (7/8) Epoch 6, batch 3500, loss[loss=0.2335, simple_loss=0.3286, pruned_loss=0.06913, over 7225.00 frames.], tot_loss[loss=0.2146, simple_loss=0.3029, pruned_loss=0.06317, over 1422369.33 frames.], batch size: 21, lr: 1.01e-03
+2022-04-28 18:15:22,315 INFO [train.py:763] (7/8) Epoch 6, batch 3550, loss[loss=0.2445, simple_loss=0.3363, pruned_loss=0.07631, over 7376.00 frames.], tot_loss[loss=0.2137, simple_loss=0.302, pruned_loss=0.06269, over 1423507.29 frames.], batch size: 23, lr: 1.01e-03
+2022-04-28 18:16:27,543 INFO [train.py:763] (7/8) Epoch 6, batch 3600, loss[loss=0.2288, simple_loss=0.3205, pruned_loss=0.06854, over 7218.00 frames.], tot_loss[loss=0.2132, simple_loss=0.3016, pruned_loss=0.06238, over 1424786.48 frames.], batch size: 21, lr: 1.00e-03
+2022-04-28 18:17:32,799 INFO [train.py:763] (7/8) Epoch 6, batch 3650, loss[loss=0.2128, simple_loss=0.2999, pruned_loss=0.06291, over 7026.00 frames.], tot_loss[loss=0.2132, simple_loss=0.3016, pruned_loss=0.06241, over 1421278.24 frames.], batch size: 28, lr: 1.00e-03
+2022-04-28 18:18:39,451 INFO [train.py:763] (7/8) Epoch 6, batch 3700, loss[loss=0.2033, simple_loss=0.2924, pruned_loss=0.05706, over 7426.00 frames.], tot_loss[loss=0.213, simple_loss=0.3011, pruned_loss=0.06244, over 1422471.73 frames.], batch size: 20, lr: 1.00e-03
+2022-04-28 18:19:44,877 INFO [train.py:763] (7/8) Epoch 6, batch 3750, loss[loss=0.3114, simple_loss=0.3709, pruned_loss=0.1259, over 4772.00 frames.], tot_loss[loss=0.2131, simple_loss=0.3009, pruned_loss=0.06263, over 1423129.97 frames.], batch size: 52, lr: 1.00e-03
+2022-04-28 18:20:50,229 INFO [train.py:763] (7/8) Epoch 6, batch 3800, loss[loss=0.1849, simple_loss=0.2814, pruned_loss=0.04417, over 7358.00 frames.], tot_loss[loss=0.2124, simple_loss=0.3003, pruned_loss=0.06228, over 1420137.95 frames.], batch size: 19, lr: 1.00e-03
+2022-04-28 18:21:56,480 INFO [train.py:763] (7/8) Epoch 6, batch 3850, loss[loss=0.205, simple_loss=0.2933, pruned_loss=0.05838, over 7115.00 frames.], tot_loss[loss=0.2118, simple_loss=0.2997, pruned_loss=0.06194, over 1423437.79 frames.], batch size: 17, lr: 1.00e-03
+2022-04-28 18:23:02,755 INFO [train.py:763] (7/8) Epoch 6, batch 3900, loss[loss=0.2004, simple_loss=0.2824, pruned_loss=0.05921, over 7164.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2997, pruned_loss=0.06189, over 1424436.83 frames.], batch size: 18, lr: 1.00e-03
+2022-04-28 18:24:08,633 INFO [train.py:763] (7/8) Epoch 6, batch 3950, loss[loss=0.2229, simple_loss=0.3151, pruned_loss=0.06534, over 7322.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2985, pruned_loss=0.06118, over 1427064.64 frames.], batch size: 22, lr: 9.99e-04
+2022-04-28 18:25:14,078 INFO [train.py:763] (7/8) Epoch 6, batch 4000, loss[loss=0.221, simple_loss=0.3143, pruned_loss=0.06387, over 6726.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2982, pruned_loss=0.06095, over 1431318.44 frames.], batch size: 31, lr: 9.98e-04
+2022-04-28 18:26:19,674 INFO [train.py:763] (7/8) Epoch 6, batch 4050, loss[loss=0.1993, simple_loss=0.2834, pruned_loss=0.05759, over 7161.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2982, pruned_loss=0.0608, over 1429067.92 frames.], batch size: 18, lr: 9.98e-04
+2022-04-28 18:27:25,505 INFO [train.py:763] (7/8) Epoch 6, batch 4100, loss[loss=0.2315, simple_loss=0.3323, pruned_loss=0.06533, over 7119.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2991, pruned_loss=0.06105, over 1424432.66 frames.], batch size: 21, lr: 9.97e-04
+2022-04-28 18:28:32,069 INFO [train.py:763] (7/8) Epoch 6, batch 4150, loss[loss=0.2165, simple_loss=0.3095, pruned_loss=0.06178, over 7203.00 frames.], tot_loss[loss=0.2107, simple_loss=0.2989, pruned_loss=0.06128, over 1425700.24 frames.], batch size: 23, lr: 9.96e-04
+2022-04-28 18:29:37,841 INFO [train.py:763] (7/8) Epoch 6, batch 4200, loss[loss=0.1744, simple_loss=0.2521, pruned_loss=0.04835, over 7266.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2982, pruned_loss=0.06056, over 1428515.89 frames.], batch size: 17, lr: 9.95e-04
+2022-04-28 18:30:43,258 INFO [train.py:763] (7/8) Epoch 6, batch 4250, loss[loss=0.2089, simple_loss=0.2978, pruned_loss=0.05998, over 7436.00 frames.], tot_loss[loss=0.212, simple_loss=0.3002, pruned_loss=0.06192, over 1423360.07 frames.], batch size: 20, lr: 9.95e-04
+2022-04-28 18:31:48,736 INFO [train.py:763] (7/8) Epoch 6, batch 4300, loss[loss=0.2325, simple_loss=0.3384, pruned_loss=0.06329, over 7231.00 frames.], tot_loss[loss=0.2144, simple_loss=0.3023, pruned_loss=0.06323, over 1416452.67 frames.], batch size: 20, lr: 9.94e-04
+2022-04-28 18:32:54,893 INFO [train.py:763] (7/8) Epoch 6, batch 4350, loss[loss=0.2276, simple_loss=0.3184, pruned_loss=0.06834, over 6306.00 frames.], tot_loss[loss=0.2138, simple_loss=0.3024, pruned_loss=0.0626, over 1409902.30 frames.], batch size: 37, lr: 9.93e-04
+2022-04-28 18:34:00,605 INFO [train.py:763] (7/8) Epoch 6, batch 4400, loss[loss=0.2403, simple_loss=0.3243, pruned_loss=0.07821, over 6753.00 frames.], tot_loss[loss=0.2138, simple_loss=0.3022, pruned_loss=0.0627, over 1411182.02 frames.], batch size: 31, lr: 9.92e-04
+2022-04-28 18:35:07,320 INFO [train.py:763] (7/8) Epoch 6, batch 4450, loss[loss=0.1903, simple_loss=0.2799, pruned_loss=0.05033, over 7211.00 frames.], tot_loss[loss=0.2144, simple_loss=0.3027, pruned_loss=0.06302, over 1406247.16 frames.], batch size: 22, lr: 9.92e-04
+2022-04-28 18:36:23,327 INFO [train.py:763] (7/8) Epoch 6, batch 4500, loss[loss=0.2511, simple_loss=0.3438, pruned_loss=0.07915, over 7214.00 frames.], tot_loss[loss=0.2156, simple_loss=0.3037, pruned_loss=0.06371, over 1403711.66 frames.], batch size: 22, lr: 9.91e-04
+2022-04-28 18:37:28,297 INFO [train.py:763] (7/8) Epoch 6, batch 4550, loss[loss=0.2625, simple_loss=0.337, pruned_loss=0.094, over 5102.00 frames.], tot_loss[loss=0.2172, simple_loss=0.305, pruned_loss=0.06466, over 1388535.96 frames.], batch size: 52, lr: 9.90e-04
+2022-04-28 18:38:57,453 INFO [train.py:763] (7/8) Epoch 7, batch 0, loss[loss=0.2548, simple_loss=0.3299, pruned_loss=0.08982, over 7339.00 frames.], tot_loss[loss=0.2548, simple_loss=0.3299, pruned_loss=0.08982, over 7339.00 frames.], batch size: 22, lr: 9.49e-04
+2022-04-28 18:40:02,648 INFO [train.py:763] (7/8) Epoch 7, batch 50, loss[loss=0.2314, simple_loss=0.308, pruned_loss=0.07739, over 7158.00 frames.], tot_loss[loss=0.2146, simple_loss=0.3033, pruned_loss=0.06293, over 320998.08 frames.], batch size: 17, lr: 9.48e-04
+2022-04-28 18:41:07,858 INFO [train.py:763] (7/8) Epoch 7, batch 100, loss[loss=0.2335, simple_loss=0.3162, pruned_loss=0.07537, over 7315.00 frames.], tot_loss[loss=0.2127, simple_loss=0.3019, pruned_loss=0.06175, over 569071.34 frames.], batch size: 25, lr: 9.48e-04
+2022-04-28 18:42:13,281 INFO [train.py:763] (7/8) Epoch 7, batch 150, loss[loss=0.2096, simple_loss=0.3052, pruned_loss=0.05696, over 7120.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2996, pruned_loss=0.0607, over 758457.83 frames.], batch size: 21, lr: 9.47e-04
+2022-04-28 18:43:19,118 INFO [train.py:763] (7/8) Epoch 7, batch 200, loss[loss=0.2043, simple_loss=0.2931, pruned_loss=0.05771, over 7226.00 frames.], tot_loss[loss=0.2115, simple_loss=0.3004, pruned_loss=0.06125, over 907557.82 frames.], batch size: 22, lr: 9.46e-04
+2022-04-28 18:44:24,622 INFO [train.py:763] (7/8) Epoch 7, batch 250, loss[loss=0.2338, simple_loss=0.323, pruned_loss=0.0723, over 7112.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2999, pruned_loss=0.06052, over 1020626.43 frames.], batch size: 21, lr: 9.46e-04
+2022-04-28 18:45:29,832 INFO [train.py:763] (7/8) Epoch 7, batch 300, loss[loss=0.2064, simple_loss=0.2911, pruned_loss=0.06087, over 7081.00 frames.], tot_loss[loss=0.2111, simple_loss=0.3001, pruned_loss=0.06106, over 1106004.21 frames.], batch size: 18, lr: 9.45e-04
+2022-04-28 18:46:35,556 INFO [train.py:763] (7/8) Epoch 7, batch 350, loss[loss=0.2199, simple_loss=0.3156, pruned_loss=0.06212, over 7124.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2979, pruned_loss=0.06041, over 1177475.83 frames.], batch size: 21, lr: 9.44e-04
+2022-04-28 18:47:40,831 INFO [train.py:763] (7/8) Epoch 7, batch 400, loss[loss=0.2577, simple_loss=0.3294, pruned_loss=0.09302, over 5056.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2984, pruned_loss=0.06034, over 1230705.23 frames.], batch size: 52, lr: 9.43e-04
+2022-04-28 18:48:46,405 INFO [train.py:763] (7/8) Epoch 7, batch 450, loss[loss=0.197, simple_loss=0.2778, pruned_loss=0.05809, over 7259.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2979, pruned_loss=0.06049, over 1272392.75 frames.], batch size: 16, lr: 9.43e-04
+2022-04-28 18:49:51,773 INFO [train.py:763] (7/8) Epoch 7, batch 500, loss[loss=0.2094, simple_loss=0.3017, pruned_loss=0.05855, over 7186.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2978, pruned_loss=0.06018, over 1304869.27 frames.], batch size: 23, lr: 9.42e-04
+2022-04-28 18:50:57,369 INFO [train.py:763] (7/8) Epoch 7, batch 550, loss[loss=0.2099, simple_loss=0.297, pruned_loss=0.06139, over 7216.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2984, pruned_loss=0.0603, over 1332569.38 frames.], batch size: 23, lr: 9.41e-04
+2022-04-28 18:52:02,640 INFO [train.py:763] (7/8) Epoch 7, batch 600, loss[loss=0.1905, simple_loss=0.2975, pruned_loss=0.04172, over 7215.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2992, pruned_loss=0.06029, over 1352481.89 frames.], batch size: 21, lr: 9.41e-04
+2022-04-28 18:53:08,470 INFO [train.py:763] (7/8) Epoch 7, batch 650, loss[loss=0.1845, simple_loss=0.2789, pruned_loss=0.04502, over 7263.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2983, pruned_loss=0.06024, over 1367234.03 frames.], batch size: 19, lr: 9.40e-04
+2022-04-28 18:54:13,825 INFO [train.py:763] (7/8) Epoch 7, batch 700, loss[loss=0.2991, simple_loss=0.3509, pruned_loss=0.1237, over 5003.00 frames.], tot_loss[loss=0.2116, simple_loss=0.3002, pruned_loss=0.06149, over 1376484.21 frames.], batch size: 52, lr: 9.39e-04
+2022-04-28 18:55:19,489 INFO [train.py:763] (7/8) Epoch 7, batch 750, loss[loss=0.2182, simple_loss=0.3009, pruned_loss=0.06775, over 7361.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2997, pruned_loss=0.06075, over 1385206.94 frames.], batch size: 19, lr: 9.39e-04
+2022-04-28 18:56:26,117 INFO [train.py:763] (7/8) Epoch 7, batch 800, loss[loss=0.2171, simple_loss=0.3109, pruned_loss=0.06168, over 6308.00 frames.], tot_loss[loss=0.2117, simple_loss=0.3008, pruned_loss=0.06129, over 1390281.40 frames.], batch size: 38, lr: 9.38e-04
+2022-04-28 18:57:33,285 INFO [train.py:763] (7/8) Epoch 7, batch 850, loss[loss=0.1948, simple_loss=0.2764, pruned_loss=0.0566, over 7404.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2985, pruned_loss=0.06036, over 1399351.81 frames.], batch size: 18, lr: 9.37e-04
+2022-04-28 18:58:40,234 INFO [train.py:763] (7/8) Epoch 7, batch 900, loss[loss=0.2523, simple_loss=0.3368, pruned_loss=0.0839, over 6787.00 frames.], tot_loss[loss=0.2104, simple_loss=0.299, pruned_loss=0.06087, over 1399062.99 frames.], batch size: 31, lr: 9.36e-04
+2022-04-28 18:59:46,954 INFO [train.py:763] (7/8) Epoch 7, batch 950, loss[loss=0.1874, simple_loss=0.2876, pruned_loss=0.04357, over 7226.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2992, pruned_loss=0.06053, over 1405215.78 frames.], batch size: 20, lr: 9.36e-04
+2022-04-28 19:00:52,060 INFO [train.py:763] (7/8) Epoch 7, batch 1000, loss[loss=0.2293, simple_loss=0.3187, pruned_loss=0.06997, over 7225.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2995, pruned_loss=0.06061, over 1409759.13 frames.], batch size: 21, lr: 9.35e-04
+2022-04-28 19:01:58,585 INFO [train.py:763] (7/8) Epoch 7, batch 1050, loss[loss=0.1776, simple_loss=0.2589, pruned_loss=0.04815, over 7162.00 frames.], tot_loss[loss=0.2113, simple_loss=0.3003, pruned_loss=0.06116, over 1408223.89 frames.], batch size: 17, lr: 9.34e-04
+2022-04-28 19:03:05,235 INFO [train.py:763] (7/8) Epoch 7, batch 1100, loss[loss=0.2553, simple_loss=0.3448, pruned_loss=0.08287, over 7203.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2996, pruned_loss=0.06077, over 1412892.15 frames.], batch size: 22, lr: 9.34e-04
+2022-04-28 19:04:11,937 INFO [train.py:763] (7/8) Epoch 7, batch 1150, loss[loss=0.276, simple_loss=0.3379, pruned_loss=0.1071, over 5380.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2999, pruned_loss=0.0602, over 1418466.28 frames.], batch size: 53, lr: 9.33e-04
+2022-04-28 19:05:18,448 INFO [train.py:763] (7/8) Epoch 7, batch 1200, loss[loss=0.1768, simple_loss=0.2758, pruned_loss=0.03894, over 7150.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2992, pruned_loss=0.0599, over 1421410.84 frames.], batch size: 20, lr: 9.32e-04
+2022-04-28 19:06:24,039 INFO [train.py:763] (7/8) Epoch 7, batch 1250, loss[loss=0.1905, simple_loss=0.2778, pruned_loss=0.05156, over 7287.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2985, pruned_loss=0.06008, over 1420738.21 frames.], batch size: 18, lr: 9.32e-04
+2022-04-28 19:07:30,207 INFO [train.py:763] (7/8) Epoch 7, batch 1300, loss[loss=0.1807, simple_loss=0.2852, pruned_loss=0.03814, over 7137.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2991, pruned_loss=0.06017, over 1417613.81 frames.], batch size: 20, lr: 9.31e-04
+2022-04-28 19:08:35,489 INFO [train.py:763] (7/8) Epoch 7, batch 1350, loss[loss=0.1894, simple_loss=0.2673, pruned_loss=0.05578, over 7165.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2992, pruned_loss=0.06086, over 1416768.14 frames.], batch size: 19, lr: 9.30e-04
+2022-04-28 19:09:41,327 INFO [train.py:763] (7/8) Epoch 7, batch 1400, loss[loss=0.202, simple_loss=0.2823, pruned_loss=0.06085, over 7271.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2991, pruned_loss=0.06056, over 1418149.06 frames.], batch size: 18, lr: 9.30e-04
+2022-04-28 19:10:48,160 INFO [train.py:763] (7/8) Epoch 7, batch 1450, loss[loss=0.1988, simple_loss=0.2904, pruned_loss=0.05358, over 7166.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2993, pruned_loss=0.06047, over 1417231.83 frames.], batch size: 18, lr: 9.29e-04
+2022-04-28 19:11:54,407 INFO [train.py:763] (7/8) Epoch 7, batch 1500, loss[loss=0.172, simple_loss=0.2509, pruned_loss=0.04655, over 7401.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2991, pruned_loss=0.0608, over 1416534.07 frames.], batch size: 18, lr: 9.28e-04
+2022-04-28 19:12:59,484 INFO [train.py:763] (7/8) Epoch 7, batch 1550, loss[loss=0.1956, simple_loss=0.2913, pruned_loss=0.04991, over 7212.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2985, pruned_loss=0.06031, over 1421224.97 frames.], batch size: 22, lr: 9.28e-04
+2022-04-28 19:14:04,522 INFO [train.py:763] (7/8) Epoch 7, batch 1600, loss[loss=0.1951, simple_loss=0.2954, pruned_loss=0.04739, over 6229.00 frames.], tot_loss[loss=0.2112, simple_loss=0.3001, pruned_loss=0.06113, over 1421538.26 frames.], batch size: 37, lr: 9.27e-04
+2022-04-28 19:15:09,651 INFO [train.py:763] (7/8) Epoch 7, batch 1650, loss[loss=0.1983, simple_loss=0.2998, pruned_loss=0.04837, over 7264.00 frames.], tot_loss[loss=0.2107, simple_loss=0.3, pruned_loss=0.06071, over 1420396.05 frames.], batch size: 24, lr: 9.26e-04
+2022-04-28 19:16:15,830 INFO [train.py:763] (7/8) Epoch 7, batch 1700, loss[loss=0.2434, simple_loss=0.3336, pruned_loss=0.07665, over 7325.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2999, pruned_loss=0.06015, over 1420949.04 frames.], batch size: 21, lr: 9.26e-04
+2022-04-28 19:17:22,182 INFO [train.py:763] (7/8) Epoch 7, batch 1750, loss[loss=0.2038, simple_loss=0.301, pruned_loss=0.05334, over 7332.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2994, pruned_loss=0.05992, over 1421137.88 frames.], batch size: 22, lr: 9.25e-04
+2022-04-28 19:18:45,826 INFO [train.py:763] (7/8) Epoch 7, batch 1800, loss[loss=0.1857, simple_loss=0.2902, pruned_loss=0.04057, over 7334.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2977, pruned_loss=0.05892, over 1422244.50 frames.], batch size: 22, lr: 9.24e-04
+2022-04-28 19:19:59,999 INFO [train.py:763] (7/8) Epoch 7, batch 1850, loss[loss=0.2082, simple_loss=0.2946, pruned_loss=0.06092, over 7232.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2986, pruned_loss=0.05954, over 1424113.62 frames.], batch size: 20, lr: 9.24e-04
+2022-04-28 19:21:23,418 INFO [train.py:763] (7/8) Epoch 7, batch 1900, loss[loss=0.228, simple_loss=0.3254, pruned_loss=0.06528, over 7312.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2973, pruned_loss=0.05955, over 1423061.56 frames.], batch size: 25, lr: 9.23e-04
+2022-04-28 19:22:40,118 INFO [train.py:763] (7/8) Epoch 7, batch 1950, loss[loss=0.1903, simple_loss=0.2609, pruned_loss=0.05984, over 6988.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2975, pruned_loss=0.05982, over 1426834.90 frames.], batch size: 16, lr: 9.22e-04
+2022-04-28 19:23:47,502 INFO [train.py:763] (7/8) Epoch 7, batch 2000, loss[loss=0.2231, simple_loss=0.3264, pruned_loss=0.05992, over 7117.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2977, pruned_loss=0.05977, over 1426650.75 frames.], batch size: 21, lr: 9.22e-04
+2022-04-28 19:25:02,877 INFO [train.py:763] (7/8) Epoch 7, batch 2050, loss[loss=0.2945, simple_loss=0.3471, pruned_loss=0.121, over 4759.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2986, pruned_loss=0.06024, over 1420593.08 frames.], batch size: 53, lr: 9.21e-04
+2022-04-28 19:26:07,945 INFO [train.py:763] (7/8) Epoch 7, batch 2100, loss[loss=0.2159, simple_loss=0.3139, pruned_loss=0.05893, over 7236.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2991, pruned_loss=0.06052, over 1417125.35 frames.], batch size: 20, lr: 9.20e-04
+2022-04-28 19:27:22,255 INFO [train.py:763] (7/8) Epoch 7, batch 2150, loss[loss=0.2203, simple_loss=0.3112, pruned_loss=0.06474, over 7212.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2982, pruned_loss=0.06023, over 1418965.21 frames.], batch size: 22, lr: 9.20e-04
+2022-04-28 19:28:27,696 INFO [train.py:763] (7/8) Epoch 7, batch 2200, loss[loss=0.2333, simple_loss=0.3231, pruned_loss=0.07175, over 7295.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2967, pruned_loss=0.05976, over 1417712.13 frames.], batch size: 24, lr: 9.19e-04
+2022-04-28 19:29:32,850 INFO [train.py:763] (7/8) Epoch 7, batch 2250, loss[loss=0.2079, simple_loss=0.2984, pruned_loss=0.05867, over 7191.00 frames.], tot_loss[loss=0.209, simple_loss=0.2971, pruned_loss=0.06044, over 1412290.92 frames.], batch size: 23, lr: 9.18e-04
+2022-04-28 19:30:38,178 INFO [train.py:763] (7/8) Epoch 7, batch 2300, loss[loss=0.1889, simple_loss=0.2902, pruned_loss=0.0438, over 7427.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2967, pruned_loss=0.06004, over 1412657.84 frames.], batch size: 18, lr: 9.18e-04
+2022-04-28 19:31:43,921 INFO [train.py:763] (7/8) Epoch 7, batch 2350, loss[loss=0.1946, simple_loss=0.2788, pruned_loss=0.05523, over 7071.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2968, pruned_loss=0.0602, over 1412472.86 frames.], batch size: 18, lr: 9.17e-04
+2022-04-28 19:32:50,601 INFO [train.py:763] (7/8) Epoch 7, batch 2400, loss[loss=0.2037, simple_loss=0.2948, pruned_loss=0.05634, over 7257.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2962, pruned_loss=0.05942, over 1415921.49 frames.], batch size: 19, lr: 9.16e-04
+2022-04-28 19:33:55,910 INFO [train.py:763] (7/8) Epoch 7, batch 2450, loss[loss=0.2037, simple_loss=0.2909, pruned_loss=0.05826, over 7309.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2962, pruned_loss=0.05965, over 1422469.87 frames.], batch size: 24, lr: 9.16e-04
+2022-04-28 19:35:01,310 INFO [train.py:763] (7/8) Epoch 7, batch 2500, loss[loss=0.2244, simple_loss=0.3196, pruned_loss=0.06461, over 7318.00 frames.], tot_loss[loss=0.2089, simple_loss=0.2972, pruned_loss=0.06027, over 1420180.58 frames.], batch size: 21, lr: 9.15e-04
+2022-04-28 19:36:06,933 INFO [train.py:763] (7/8) Epoch 7, batch 2550, loss[loss=0.2106, simple_loss=0.2941, pruned_loss=0.06353, over 7362.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2965, pruned_loss=0.06013, over 1424446.94 frames.], batch size: 19, lr: 9.14e-04
+2022-04-28 19:37:12,491 INFO [train.py:763] (7/8) Epoch 7, batch 2600, loss[loss=0.1853, simple_loss=0.2637, pruned_loss=0.05342, over 7225.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2968, pruned_loss=0.06029, over 1425232.42 frames.], batch size: 16, lr: 9.14e-04
+2022-04-28 19:38:17,722 INFO [train.py:763] (7/8) Epoch 7, batch 2650, loss[loss=0.1922, simple_loss=0.2925, pruned_loss=0.04593, over 7121.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2966, pruned_loss=0.05961, over 1426137.46 frames.], batch size: 21, lr: 9.13e-04
+2022-04-28 19:39:23,699 INFO [train.py:763] (7/8) Epoch 7, batch 2700, loss[loss=0.1705, simple_loss=0.2601, pruned_loss=0.04045, over 6861.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2963, pruned_loss=0.05913, over 1428447.93 frames.], batch size: 15, lr: 9.12e-04
+2022-04-28 19:40:30,727 INFO [train.py:763] (7/8) Epoch 7, batch 2750, loss[loss=0.1597, simple_loss=0.249, pruned_loss=0.03517, over 6998.00 frames.], tot_loss[loss=0.207, simple_loss=0.2956, pruned_loss=0.05919, over 1426532.13 frames.], batch size: 16, lr: 9.12e-04
+2022-04-28 19:41:36,737 INFO [train.py:763] (7/8) Epoch 7, batch 2800, loss[loss=0.204, simple_loss=0.2939, pruned_loss=0.05711, over 7145.00 frames.], tot_loss[loss=0.207, simple_loss=0.2961, pruned_loss=0.05895, over 1427167.26 frames.], batch size: 20, lr: 9.11e-04
+2022-04-28 19:42:43,494 INFO [train.py:763] (7/8) Epoch 7, batch 2850, loss[loss=0.214, simple_loss=0.3107, pruned_loss=0.05863, over 7206.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2955, pruned_loss=0.05819, over 1425651.00 frames.], batch size: 22, lr: 9.11e-04
+2022-04-28 19:43:49,301 INFO [train.py:763] (7/8) Epoch 7, batch 2900, loss[loss=0.1893, simple_loss=0.2717, pruned_loss=0.05344, over 7136.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2956, pruned_loss=0.05795, over 1424890.25 frames.], batch size: 17, lr: 9.10e-04
+2022-04-28 19:44:55,763 INFO [train.py:763] (7/8) Epoch 7, batch 2950, loss[loss=0.16, simple_loss=0.26, pruned_loss=0.03001, over 7058.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2936, pruned_loss=0.0571, over 1424663.90 frames.], batch size: 18, lr: 9.09e-04
+2022-04-28 19:46:01,166 INFO [train.py:763] (7/8) Epoch 7, batch 3000, loss[loss=0.2528, simple_loss=0.3286, pruned_loss=0.0885, over 4975.00 frames.], tot_loss[loss=0.204, simple_loss=0.2937, pruned_loss=0.0571, over 1421559.17 frames.], batch size: 52, lr: 9.09e-04
+2022-04-28 19:46:01,167 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-28 19:46:16,423 INFO [train.py:792] (7/8) Epoch 7, validation: loss=0.1713, simple_loss=0.2754, pruned_loss=0.03361, over 698248.00 frames.
+2022-04-28 19:47:23,046 INFO [train.py:763] (7/8) Epoch 7, batch 3050, loss[loss=0.2065, simple_loss=0.2993, pruned_loss=0.0568, over 6340.00 frames.], tot_loss[loss=0.2049, simple_loss=0.294, pruned_loss=0.05793, over 1414807.87 frames.], batch size: 38, lr: 9.08e-04
+2022-04-28 19:48:28,741 INFO [train.py:763] (7/8) Epoch 7, batch 3100, loss[loss=0.1996, simple_loss=0.286, pruned_loss=0.0566, over 7257.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2931, pruned_loss=0.05723, over 1419068.78 frames.], batch size: 19, lr: 9.07e-04
+2022-04-28 19:49:34,321 INFO [train.py:763] (7/8) Epoch 7, batch 3150, loss[loss=0.1992, simple_loss=0.2768, pruned_loss=0.06075, over 7422.00 frames.], tot_loss[loss=0.2041, simple_loss=0.293, pruned_loss=0.05758, over 1420054.32 frames.], batch size: 20, lr: 9.07e-04
+2022-04-28 19:50:39,926 INFO [train.py:763] (7/8) Epoch 7, batch 3200, loss[loss=0.1957, simple_loss=0.2948, pruned_loss=0.04828, over 7421.00 frames.], tot_loss[loss=0.2045, simple_loss=0.2934, pruned_loss=0.05779, over 1422503.97 frames.], batch size: 20, lr: 9.06e-04
+2022-04-28 19:51:45,175 INFO [train.py:763] (7/8) Epoch 7, batch 3250, loss[loss=0.1916, simple_loss=0.2856, pruned_loss=0.04879, over 7076.00 frames.], tot_loss[loss=0.2048, simple_loss=0.294, pruned_loss=0.05776, over 1422309.37 frames.], batch size: 28, lr: 9.05e-04
+2022-04-28 19:52:50,683 INFO [train.py:763] (7/8) Epoch 7, batch 3300, loss[loss=0.2046, simple_loss=0.2935, pruned_loss=0.05786, over 6719.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2939, pruned_loss=0.0576, over 1421254.83 frames.], batch size: 31, lr: 9.05e-04
+2022-04-28 19:53:56,165 INFO [train.py:763] (7/8) Epoch 7, batch 3350, loss[loss=0.216, simple_loss=0.305, pruned_loss=0.06353, over 7425.00 frames.], tot_loss[loss=0.2047, simple_loss=0.294, pruned_loss=0.05768, over 1419461.51 frames.], batch size: 20, lr: 9.04e-04
+2022-04-28 19:55:01,749 INFO [train.py:763] (7/8) Epoch 7, batch 3400, loss[loss=0.2133, simple_loss=0.3073, pruned_loss=0.05965, over 6845.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2941, pruned_loss=0.05785, over 1418238.53 frames.], batch size: 32, lr: 9.04e-04
+2022-04-28 19:56:08,435 INFO [train.py:763] (7/8) Epoch 7, batch 3450, loss[loss=0.2164, simple_loss=0.2912, pruned_loss=0.07077, over 7430.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2961, pruned_loss=0.05934, over 1421065.58 frames.], batch size: 18, lr: 9.03e-04
+2022-04-28 19:57:15,837 INFO [train.py:763] (7/8) Epoch 7, batch 3500, loss[loss=0.2294, simple_loss=0.3161, pruned_loss=0.07133, over 7369.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2971, pruned_loss=0.05983, over 1421105.60 frames.], batch size: 23, lr: 9.02e-04
+2022-04-28 19:58:22,794 INFO [train.py:763] (7/8) Epoch 7, batch 3550, loss[loss=0.1846, simple_loss=0.2843, pruned_loss=0.04245, over 7254.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2971, pruned_loss=0.05962, over 1422661.38 frames.], batch size: 19, lr: 9.02e-04
+2022-04-28 19:59:29,964 INFO [train.py:763] (7/8) Epoch 7, batch 3600, loss[loss=0.163, simple_loss=0.2512, pruned_loss=0.0374, over 7288.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2966, pruned_loss=0.05938, over 1421553.03 frames.], batch size: 17, lr: 9.01e-04
+2022-04-28 20:00:37,047 INFO [train.py:763] (7/8) Epoch 7, batch 3650, loss[loss=0.1952, simple_loss=0.287, pruned_loss=0.05172, over 7420.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2982, pruned_loss=0.06012, over 1415106.01 frames.], batch size: 21, lr: 9.01e-04
+2022-04-28 20:01:42,552 INFO [train.py:763] (7/8) Epoch 7, batch 3700, loss[loss=0.216, simple_loss=0.3092, pruned_loss=0.06147, over 7216.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2962, pruned_loss=0.0588, over 1419248.56 frames.], batch size: 21, lr: 9.00e-04
+2022-04-28 20:02:49,198 INFO [train.py:763] (7/8) Epoch 7, batch 3750, loss[loss=0.1968, simple_loss=0.2961, pruned_loss=0.04879, over 7167.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2957, pruned_loss=0.05821, over 1416135.53 frames.], batch size: 19, lr: 8.99e-04
+2022-04-28 20:03:54,770 INFO [train.py:763] (7/8) Epoch 7, batch 3800, loss[loss=0.2647, simple_loss=0.3243, pruned_loss=0.1025, over 7297.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2972, pruned_loss=0.05879, over 1419773.50 frames.], batch size: 24, lr: 8.99e-04
+2022-04-28 20:05:00,516 INFO [train.py:763] (7/8) Epoch 7, batch 3850, loss[loss=0.2123, simple_loss=0.3133, pruned_loss=0.0557, over 7219.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2986, pruned_loss=0.05947, over 1417326.84 frames.], batch size: 21, lr: 8.98e-04
+2022-04-28 20:06:06,744 INFO [train.py:763] (7/8) Epoch 7, batch 3900, loss[loss=0.1826, simple_loss=0.2736, pruned_loss=0.04575, over 7421.00 frames.], tot_loss[loss=0.207, simple_loss=0.2967, pruned_loss=0.05867, over 1421379.06 frames.], batch size: 20, lr: 8.97e-04
+2022-04-28 20:07:13,256 INFO [train.py:763] (7/8) Epoch 7, batch 3950, loss[loss=0.1859, simple_loss=0.278, pruned_loss=0.04691, over 6982.00 frames.], tot_loss[loss=0.206, simple_loss=0.2957, pruned_loss=0.05821, over 1423907.80 frames.], batch size: 16, lr: 8.97e-04
+2022-04-28 20:08:18,744 INFO [train.py:763] (7/8) Epoch 7, batch 4000, loss[loss=0.236, simple_loss=0.326, pruned_loss=0.07299, over 7138.00 frames.], tot_loss[loss=0.2075, simple_loss=0.297, pruned_loss=0.05896, over 1423286.30 frames.], batch size: 20, lr: 8.96e-04
+2022-04-28 20:09:23,877 INFO [train.py:763] (7/8) Epoch 7, batch 4050, loss[loss=0.2327, simple_loss=0.3225, pruned_loss=0.07147, over 7409.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2962, pruned_loss=0.05883, over 1425559.10 frames.], batch size: 21, lr: 8.96e-04
+2022-04-28 20:10:29,420 INFO [train.py:763] (7/8) Epoch 7, batch 4100, loss[loss=0.1884, simple_loss=0.2633, pruned_loss=0.05677, over 7283.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2972, pruned_loss=0.05929, over 1418526.54 frames.], batch size: 17, lr: 8.95e-04
+2022-04-28 20:11:34,149 INFO [train.py:763] (7/8) Epoch 7, batch 4150, loss[loss=0.1811, simple_loss=0.2874, pruned_loss=0.03742, over 7328.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2972, pruned_loss=0.059, over 1412289.61 frames.], batch size: 22, lr: 8.94e-04
+2022-04-28 20:12:39,373 INFO [train.py:763] (7/8) Epoch 7, batch 4200, loss[loss=0.2031, simple_loss=0.3005, pruned_loss=0.05286, over 7157.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2976, pruned_loss=0.05872, over 1415132.28 frames.], batch size: 20, lr: 8.94e-04
+2022-04-28 20:13:44,897 INFO [train.py:763] (7/8) Epoch 7, batch 4250, loss[loss=0.2026, simple_loss=0.2967, pruned_loss=0.05426, over 7194.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2976, pruned_loss=0.05902, over 1419712.80 frames.], batch size: 22, lr: 8.93e-04
+2022-04-28 20:14:50,397 INFO [train.py:763] (7/8) Epoch 7, batch 4300, loss[loss=0.1999, simple_loss=0.2963, pruned_loss=0.05179, over 7318.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2966, pruned_loss=0.05901, over 1418245.36 frames.], batch size: 21, lr: 8.93e-04
+2022-04-28 20:15:55,691 INFO [train.py:763] (7/8) Epoch 7, batch 4350, loss[loss=0.2075, simple_loss=0.3071, pruned_loss=0.05399, over 7121.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2962, pruned_loss=0.05915, over 1413521.93 frames.], batch size: 21, lr: 8.92e-04
+2022-04-28 20:17:01,788 INFO [train.py:763] (7/8) Epoch 7, batch 4400, loss[loss=0.1722, simple_loss=0.275, pruned_loss=0.03466, over 7073.00 frames.], tot_loss[loss=0.206, simple_loss=0.2947, pruned_loss=0.0586, over 1416311.96 frames.], batch size: 28, lr: 8.91e-04
+2022-04-28 20:18:08,988 INFO [train.py:763] (7/8) Epoch 7, batch 4450, loss[loss=0.2139, simple_loss=0.299, pruned_loss=0.06439, over 7325.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2951, pruned_loss=0.05873, over 1416160.81 frames.], batch size: 20, lr: 8.91e-04
+2022-04-28 20:19:16,364 INFO [train.py:763] (7/8) Epoch 7, batch 4500, loss[loss=0.181, simple_loss=0.2735, pruned_loss=0.04431, over 7160.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2947, pruned_loss=0.05854, over 1413554.87 frames.], batch size: 18, lr: 8.90e-04
+2022-04-28 20:20:24,258 INFO [train.py:763] (7/8) Epoch 7, batch 4550, loss[loss=0.1717, simple_loss=0.254, pruned_loss=0.04469, over 7271.00 frames.], tot_loss[loss=0.206, simple_loss=0.2936, pruned_loss=0.05916, over 1395799.80 frames.], batch size: 17, lr: 8.90e-04
+2022-04-28 20:21:52,813 INFO [train.py:763] (7/8) Epoch 8, batch 0, loss[loss=0.2037, simple_loss=0.3002, pruned_loss=0.05359, over 7212.00 frames.], tot_loss[loss=0.2037, simple_loss=0.3002, pruned_loss=0.05359, over 7212.00 frames.], batch size: 23, lr: 8.54e-04
+2022-04-28 20:22:58,576 INFO [train.py:763] (7/8) Epoch 8, batch 50, loss[loss=0.2063, simple_loss=0.3021, pruned_loss=0.05524, over 7029.00 frames.], tot_loss[loss=0.2075, simple_loss=0.298, pruned_loss=0.05845, over 319705.71 frames.], batch size: 28, lr: 8.53e-04
+2022-04-28 20:24:03,952 INFO [train.py:763] (7/8) Epoch 8, batch 100, loss[loss=0.2096, simple_loss=0.3017, pruned_loss=0.05882, over 7240.00 frames.], tot_loss[loss=0.2024, simple_loss=0.293, pruned_loss=0.05583, over 567515.76 frames.], batch size: 20, lr: 8.53e-04
+2022-04-28 20:25:10,098 INFO [train.py:763] (7/8) Epoch 8, batch 150, loss[loss=0.2364, simple_loss=0.3146, pruned_loss=0.07908, over 5120.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2948, pruned_loss=0.05692, over 754660.51 frames.], batch size: 52, lr: 8.52e-04
+2022-04-28 20:26:16,016 INFO [train.py:763] (7/8) Epoch 8, batch 200, loss[loss=0.2116, simple_loss=0.307, pruned_loss=0.05812, over 7204.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2955, pruned_loss=0.05694, over 903557.94 frames.], batch size: 22, lr: 8.51e-04
+2022-04-28 20:27:21,282 INFO [train.py:763] (7/8) Epoch 8, batch 250, loss[loss=0.1971, simple_loss=0.2883, pruned_loss=0.05294, over 7429.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2955, pruned_loss=0.05704, over 1020333.79 frames.], batch size: 20, lr: 8.51e-04
+2022-04-28 20:28:27,042 INFO [train.py:763] (7/8) Epoch 8, batch 300, loss[loss=0.2172, simple_loss=0.3157, pruned_loss=0.05934, over 7318.00 frames.], tot_loss[loss=0.205, simple_loss=0.2953, pruned_loss=0.05734, over 1104650.84 frames.], batch size: 22, lr: 8.50e-04
+2022-04-28 20:29:32,805 INFO [train.py:763] (7/8) Epoch 8, batch 350, loss[loss=0.192, simple_loss=0.287, pruned_loss=0.04846, over 7159.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2935, pruned_loss=0.05648, over 1178618.67 frames.], batch size: 19, lr: 8.50e-04
+2022-04-28 20:30:38,293 INFO [train.py:763] (7/8) Epoch 8, batch 400, loss[loss=0.1874, simple_loss=0.271, pruned_loss=0.05186, over 7146.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2946, pruned_loss=0.05744, over 1237695.80 frames.], batch size: 17, lr: 8.49e-04
+2022-04-28 20:31:43,720 INFO [train.py:763] (7/8) Epoch 8, batch 450, loss[loss=0.1778, simple_loss=0.2711, pruned_loss=0.04225, over 7258.00 frames.], tot_loss[loss=0.2031, simple_loss=0.293, pruned_loss=0.05658, over 1277799.18 frames.], batch size: 19, lr: 8.49e-04
+2022-04-28 20:32:50,569 INFO [train.py:763] (7/8) Epoch 8, batch 500, loss[loss=0.1742, simple_loss=0.255, pruned_loss=0.04673, over 7397.00 frames.], tot_loss[loss=0.2057, simple_loss=0.295, pruned_loss=0.05818, over 1310396.80 frames.], batch size: 18, lr: 8.48e-04
+2022-04-28 20:33:57,719 INFO [train.py:763] (7/8) Epoch 8, batch 550, loss[loss=0.2097, simple_loss=0.2885, pruned_loss=0.06546, over 7067.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2926, pruned_loss=0.05686, over 1338156.14 frames.], batch size: 18, lr: 8.48e-04
+2022-04-28 20:35:03,806 INFO [train.py:763] (7/8) Epoch 8, batch 600, loss[loss=0.1935, simple_loss=0.2801, pruned_loss=0.05351, over 7069.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2926, pruned_loss=0.05654, over 1360008.88 frames.], batch size: 18, lr: 8.47e-04
+2022-04-28 20:36:09,120 INFO [train.py:763] (7/8) Epoch 8, batch 650, loss[loss=0.1773, simple_loss=0.2761, pruned_loss=0.03923, over 7369.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2931, pruned_loss=0.05655, over 1373683.59 frames.], batch size: 19, lr: 8.46e-04
+2022-04-28 20:37:14,561 INFO [train.py:763] (7/8) Epoch 8, batch 700, loss[loss=0.1893, simple_loss=0.2787, pruned_loss=0.04998, over 7430.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2932, pruned_loss=0.05608, over 1386453.65 frames.], batch size: 20, lr: 8.46e-04
+2022-04-28 20:38:20,322 INFO [train.py:763] (7/8) Epoch 8, batch 750, loss[loss=0.1762, simple_loss=0.2676, pruned_loss=0.04246, over 7174.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2935, pruned_loss=0.05595, over 1389238.49 frames.], batch size: 18, lr: 8.45e-04
+2022-04-28 20:39:25,920 INFO [train.py:763] (7/8) Epoch 8, batch 800, loss[loss=0.2142, simple_loss=0.3067, pruned_loss=0.06089, over 7368.00 frames.], tot_loss[loss=0.2026, simple_loss=0.293, pruned_loss=0.05606, over 1395594.02 frames.], batch size: 23, lr: 8.45e-04
+2022-04-28 20:40:32,537 INFO [train.py:763] (7/8) Epoch 8, batch 850, loss[loss=0.197, simple_loss=0.295, pruned_loss=0.04957, over 7315.00 frames.], tot_loss[loss=0.2039, simple_loss=0.294, pruned_loss=0.05685, over 1400703.56 frames.], batch size: 21, lr: 8.44e-04
+2022-04-28 20:41:39,534 INFO [train.py:763] (7/8) Epoch 8, batch 900, loss[loss=0.2177, simple_loss=0.3168, pruned_loss=0.05928, over 7225.00 frames.], tot_loss[loss=0.2037, simple_loss=0.294, pruned_loss=0.05673, over 1409694.37 frames.], batch size: 21, lr: 8.44e-04
+2022-04-28 20:42:46,682 INFO [train.py:763] (7/8) Epoch 8, batch 950, loss[loss=0.1956, simple_loss=0.2912, pruned_loss=0.05001, over 7333.00 frames.], tot_loss[loss=0.2039, simple_loss=0.294, pruned_loss=0.0569, over 1408234.06 frames.], batch size: 20, lr: 8.43e-04
+2022-04-28 20:43:53,800 INFO [train.py:763] (7/8) Epoch 8, batch 1000, loss[loss=0.1803, simple_loss=0.2802, pruned_loss=0.04015, over 7422.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2932, pruned_loss=0.05686, over 1412387.81 frames.], batch size: 20, lr: 8.43e-04
+2022-04-28 20:45:00,945 INFO [train.py:763] (7/8) Epoch 8, batch 1050, loss[loss=0.1799, simple_loss=0.2736, pruned_loss=0.04313, over 7262.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2933, pruned_loss=0.05644, over 1416922.57 frames.], batch size: 19, lr: 8.42e-04
+2022-04-28 20:46:07,172 INFO [train.py:763] (7/8) Epoch 8, batch 1100, loss[loss=0.1796, simple_loss=0.248, pruned_loss=0.05559, over 7279.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2954, pruned_loss=0.05744, over 1420044.92 frames.], batch size: 17, lr: 8.41e-04
+2022-04-28 20:47:12,913 INFO [train.py:763] (7/8) Epoch 8, batch 1150, loss[loss=0.2112, simple_loss=0.3079, pruned_loss=0.05725, over 7337.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2943, pruned_loss=0.05698, over 1421337.60 frames.], batch size: 25, lr: 8.41e-04
+2022-04-28 20:48:18,248 INFO [train.py:763] (7/8) Epoch 8, batch 1200, loss[loss=0.1641, simple_loss=0.2611, pruned_loss=0.03351, over 7429.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2934, pruned_loss=0.05603, over 1421835.01 frames.], batch size: 20, lr: 8.40e-04
+2022-04-28 20:49:23,435 INFO [train.py:763] (7/8) Epoch 8, batch 1250, loss[loss=0.2181, simple_loss=0.2985, pruned_loss=0.06886, over 6792.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2928, pruned_loss=0.05596, over 1417155.65 frames.], batch size: 15, lr: 8.40e-04
+2022-04-28 20:50:29,929 INFO [train.py:763] (7/8) Epoch 8, batch 1300, loss[loss=0.2344, simple_loss=0.3296, pruned_loss=0.06963, over 7172.00 frames.], tot_loss[loss=0.203, simple_loss=0.2934, pruned_loss=0.05626, over 1413551.64 frames.], batch size: 19, lr: 8.39e-04
+2022-04-28 20:51:37,156 INFO [train.py:763] (7/8) Epoch 8, batch 1350, loss[loss=0.1748, simple_loss=0.2625, pruned_loss=0.04355, over 7431.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2938, pruned_loss=0.05622, over 1418808.58 frames.], batch size: 20, lr: 8.39e-04
+2022-04-28 20:52:43,223 INFO [train.py:763] (7/8) Epoch 8, batch 1400, loss[loss=0.2244, simple_loss=0.3164, pruned_loss=0.06618, over 7227.00 frames.], tot_loss[loss=0.2037, simple_loss=0.294, pruned_loss=0.05674, over 1415025.75 frames.], batch size: 21, lr: 8.38e-04
+2022-04-28 20:53:48,899 INFO [train.py:763] (7/8) Epoch 8, batch 1450, loss[loss=0.217, simple_loss=0.314, pruned_loss=0.06, over 7312.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2932, pruned_loss=0.05602, over 1419778.15 frames.], batch size: 21, lr: 8.38e-04
+2022-04-28 20:54:55,530 INFO [train.py:763] (7/8) Epoch 8, batch 1500, loss[loss=0.2121, simple_loss=0.3156, pruned_loss=0.05427, over 7238.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2936, pruned_loss=0.05637, over 1423138.29 frames.], batch size: 20, lr: 8.37e-04
+2022-04-28 20:56:02,367 INFO [train.py:763] (7/8) Epoch 8, batch 1550, loss[loss=0.2393, simple_loss=0.3282, pruned_loss=0.07524, over 7205.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2933, pruned_loss=0.05641, over 1422812.22 frames.], batch size: 22, lr: 8.37e-04
+2022-04-28 20:57:08,591 INFO [train.py:763] (7/8) Epoch 8, batch 1600, loss[loss=0.1753, simple_loss=0.2675, pruned_loss=0.04155, over 7065.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2929, pruned_loss=0.05587, over 1420830.62 frames.], batch size: 18, lr: 8.36e-04
+2022-04-28 20:58:15,588 INFO [train.py:763] (7/8) Epoch 8, batch 1650, loss[loss=0.2125, simple_loss=0.307, pruned_loss=0.05897, over 7115.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2935, pruned_loss=0.05636, over 1421458.83 frames.], batch size: 21, lr: 8.35e-04
+2022-04-28 20:59:22,343 INFO [train.py:763] (7/8) Epoch 8, batch 1700, loss[loss=0.2522, simple_loss=0.3212, pruned_loss=0.09156, over 7151.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2943, pruned_loss=0.05601, over 1419846.09 frames.], batch size: 20, lr: 8.35e-04
+2022-04-28 21:00:28,785 INFO [train.py:763] (7/8) Epoch 8, batch 1750, loss[loss=0.2099, simple_loss=0.2966, pruned_loss=0.06162, over 7315.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2933, pruned_loss=0.05585, over 1420974.80 frames.], batch size: 21, lr: 8.34e-04
+2022-04-28 21:01:33,987 INFO [train.py:763] (7/8) Epoch 8, batch 1800, loss[loss=0.1959, simple_loss=0.2968, pruned_loss=0.04747, over 7233.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2932, pruned_loss=0.05545, over 1417320.29 frames.], batch size: 20, lr: 8.34e-04
+2022-04-28 21:02:39,290 INFO [train.py:763] (7/8) Epoch 8, batch 1850, loss[loss=0.2035, simple_loss=0.2973, pruned_loss=0.05484, over 7225.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2935, pruned_loss=0.05511, over 1420893.54 frames.], batch size: 20, lr: 8.33e-04
+2022-04-28 21:03:44,680 INFO [train.py:763] (7/8) Epoch 8, batch 1900, loss[loss=0.1872, simple_loss=0.2869, pruned_loss=0.0438, over 7169.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2946, pruned_loss=0.05576, over 1419882.93 frames.], batch size: 19, lr: 8.33e-04
+2022-04-28 21:04:50,213 INFO [train.py:763] (7/8) Epoch 8, batch 1950, loss[loss=0.2306, simple_loss=0.3232, pruned_loss=0.06895, over 7118.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2947, pruned_loss=0.05584, over 1420907.63 frames.], batch size: 21, lr: 8.32e-04
+2022-04-28 21:05:55,505 INFO [train.py:763] (7/8) Epoch 8, batch 2000, loss[loss=0.2013, simple_loss=0.2908, pruned_loss=0.05596, over 7282.00 frames.], tot_loss[loss=0.2029, simple_loss=0.294, pruned_loss=0.05591, over 1422431.05 frames.], batch size: 24, lr: 8.32e-04
+2022-04-28 21:07:00,737 INFO [train.py:763] (7/8) Epoch 8, batch 2050, loss[loss=0.1908, simple_loss=0.2779, pruned_loss=0.05184, over 7270.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2946, pruned_loss=0.05651, over 1420690.71 frames.], batch size: 17, lr: 8.31e-04
+2022-04-28 21:08:05,947 INFO [train.py:763] (7/8) Epoch 8, batch 2100, loss[loss=0.1928, simple_loss=0.2796, pruned_loss=0.05298, over 7250.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2946, pruned_loss=0.05684, over 1422786.79 frames.], batch size: 19, lr: 8.31e-04
+2022-04-28 21:09:08,032 INFO [train.py:763] (7/8) Epoch 8, batch 2150, loss[loss=0.2205, simple_loss=0.2923, pruned_loss=0.0743, over 7062.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2946, pruned_loss=0.05665, over 1424936.23 frames.], batch size: 18, lr: 8.30e-04
+2022-04-28 21:10:14,564 INFO [train.py:763] (7/8) Epoch 8, batch 2200, loss[loss=0.1483, simple_loss=0.2364, pruned_loss=0.03013, over 7277.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2942, pruned_loss=0.05668, over 1423326.88 frames.], batch size: 17, lr: 8.30e-04
+2022-04-28 21:11:21,406 INFO [train.py:763] (7/8) Epoch 8, batch 2250, loss[loss=0.1913, simple_loss=0.272, pruned_loss=0.05531, over 7158.00 frames.], tot_loss[loss=0.2038, simple_loss=0.294, pruned_loss=0.0568, over 1423759.76 frames.], batch size: 18, lr: 8.29e-04
+2022-04-28 21:12:26,815 INFO [train.py:763] (7/8) Epoch 8, batch 2300, loss[loss=0.2056, simple_loss=0.3042, pruned_loss=0.05353, over 7144.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2943, pruned_loss=0.0572, over 1425340.32 frames.], batch size: 20, lr: 8.29e-04
+2022-04-28 21:13:32,132 INFO [train.py:763] (7/8) Epoch 8, batch 2350, loss[loss=0.2189, simple_loss=0.3164, pruned_loss=0.06072, over 6752.00 frames.], tot_loss[loss=0.205, simple_loss=0.295, pruned_loss=0.05748, over 1424079.48 frames.], batch size: 31, lr: 8.28e-04
+2022-04-28 21:14:37,460 INFO [train.py:763] (7/8) Epoch 8, batch 2400, loss[loss=0.1884, simple_loss=0.272, pruned_loss=0.05236, over 7272.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2945, pruned_loss=0.05702, over 1423962.19 frames.], batch size: 18, lr: 8.28e-04
+2022-04-28 21:15:42,885 INFO [train.py:763] (7/8) Epoch 8, batch 2450, loss[loss=0.1681, simple_loss=0.261, pruned_loss=0.03762, over 7405.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2941, pruned_loss=0.05639, over 1425743.36 frames.], batch size: 18, lr: 8.27e-04
+2022-04-28 21:16:48,172 INFO [train.py:763] (7/8) Epoch 8, batch 2500, loss[loss=0.2169, simple_loss=0.3061, pruned_loss=0.06387, over 7205.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2937, pruned_loss=0.05641, over 1423949.78 frames.], batch size: 22, lr: 8.27e-04
+2022-04-28 21:17:53,470 INFO [train.py:763] (7/8) Epoch 8, batch 2550, loss[loss=0.1758, simple_loss=0.2595, pruned_loss=0.04607, over 7138.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2935, pruned_loss=0.0566, over 1420601.95 frames.], batch size: 17, lr: 8.26e-04
+2022-04-28 21:18:58,791 INFO [train.py:763] (7/8) Epoch 8, batch 2600, loss[loss=0.2768, simple_loss=0.3645, pruned_loss=0.09452, over 7377.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2942, pruned_loss=0.05732, over 1418222.33 frames.], batch size: 23, lr: 8.25e-04
+2022-04-28 21:20:03,886 INFO [train.py:763] (7/8) Epoch 8, batch 2650, loss[loss=0.2278, simple_loss=0.3008, pruned_loss=0.0774, over 4968.00 frames.], tot_loss[loss=0.2037, simple_loss=0.294, pruned_loss=0.0567, over 1417381.42 frames.], batch size: 54, lr: 8.25e-04
+2022-04-28 21:21:09,321 INFO [train.py:763] (7/8) Epoch 8, batch 2700, loss[loss=0.2052, simple_loss=0.3097, pruned_loss=0.05036, over 7339.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2949, pruned_loss=0.05714, over 1418437.66 frames.], batch size: 22, lr: 8.24e-04
+2022-04-28 21:22:14,627 INFO [train.py:763] (7/8) Epoch 8, batch 2750, loss[loss=0.1938, simple_loss=0.2844, pruned_loss=0.05161, over 7333.00 frames.], tot_loss[loss=0.203, simple_loss=0.2934, pruned_loss=0.05631, over 1422768.03 frames.], batch size: 20, lr: 8.24e-04
+2022-04-28 21:23:20,627 INFO [train.py:763] (7/8) Epoch 8, batch 2800, loss[loss=0.1948, simple_loss=0.2962, pruned_loss=0.04669, over 7204.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2935, pruned_loss=0.05616, over 1426074.89 frames.], batch size: 22, lr: 8.23e-04
+2022-04-28 21:24:26,784 INFO [train.py:763] (7/8) Epoch 8, batch 2850, loss[loss=0.1871, simple_loss=0.2839, pruned_loss=0.04512, over 7165.00 frames.], tot_loss[loss=0.2022, simple_loss=0.293, pruned_loss=0.05576, over 1428984.02 frames.], batch size: 19, lr: 8.23e-04
+2022-04-28 21:25:32,050 INFO [train.py:763] (7/8) Epoch 8, batch 2900, loss[loss=0.2249, simple_loss=0.3156, pruned_loss=0.06707, over 7321.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2924, pruned_loss=0.05555, over 1427700.81 frames.], batch size: 21, lr: 8.22e-04
+2022-04-28 21:26:37,478 INFO [train.py:763] (7/8) Epoch 8, batch 2950, loss[loss=0.1936, simple_loss=0.2869, pruned_loss=0.05012, over 7289.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2941, pruned_loss=0.0562, over 1424001.91 frames.], batch size: 18, lr: 8.22e-04
+2022-04-28 21:27:43,097 INFO [train.py:763] (7/8) Epoch 8, batch 3000, loss[loss=0.215, simple_loss=0.3049, pruned_loss=0.06256, over 7267.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2935, pruned_loss=0.05608, over 1422046.86 frames.], batch size: 24, lr: 8.21e-04
+2022-04-28 21:27:43,098 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-28 21:27:58,489 INFO [train.py:792] (7/8) Epoch 8, validation: loss=0.1715, simple_loss=0.2766, pruned_loss=0.03324, over 698248.00 frames.
+2022-04-28 21:29:04,159 INFO [train.py:763] (7/8) Epoch 8, batch 3050, loss[loss=0.2138, simple_loss=0.3019, pruned_loss=0.06284, over 7320.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2931, pruned_loss=0.05611, over 1418970.27 frames.], batch size: 20, lr: 8.21e-04
+2022-04-28 21:30:09,338 INFO [train.py:763] (7/8) Epoch 8, batch 3100, loss[loss=0.2099, simple_loss=0.3035, pruned_loss=0.05816, over 6839.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2947, pruned_loss=0.05695, over 1414461.38 frames.], batch size: 31, lr: 8.20e-04
+2022-04-28 21:31:14,885 INFO [train.py:763] (7/8) Epoch 8, batch 3150, loss[loss=0.1987, simple_loss=0.2928, pruned_loss=0.05233, over 7163.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2939, pruned_loss=0.05657, over 1418144.67 frames.], batch size: 19, lr: 8.20e-04
+2022-04-28 21:32:20,541 INFO [train.py:763] (7/8) Epoch 8, batch 3200, loss[loss=0.2168, simple_loss=0.3103, pruned_loss=0.06163, over 7139.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2935, pruned_loss=0.05672, over 1422067.62 frames.], batch size: 20, lr: 8.19e-04
+2022-04-28 21:33:34,642 INFO [train.py:763] (7/8) Epoch 8, batch 3250, loss[loss=0.2817, simple_loss=0.3562, pruned_loss=0.1036, over 5381.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2943, pruned_loss=0.05676, over 1420050.52 frames.], batch size: 52, lr: 8.19e-04
+2022-04-28 21:34:51,679 INFO [train.py:763] (7/8) Epoch 8, batch 3300, loss[loss=0.2124, simple_loss=0.31, pruned_loss=0.05746, over 7207.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2934, pruned_loss=0.05641, over 1419545.84 frames.], batch size: 22, lr: 8.18e-04
+2022-04-28 21:36:05,904 INFO [train.py:763] (7/8) Epoch 8, batch 3350, loss[loss=0.1815, simple_loss=0.2828, pruned_loss=0.04015, over 7272.00 frames.], tot_loss[loss=0.2024, simple_loss=0.293, pruned_loss=0.05593, over 1422765.84 frames.], batch size: 19, lr: 8.18e-04
+2022-04-28 21:37:39,086 INFO [train.py:763] (7/8) Epoch 8, batch 3400, loss[loss=0.2618, simple_loss=0.3523, pruned_loss=0.08563, over 6801.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2933, pruned_loss=0.05583, over 1421223.84 frames.], batch size: 31, lr: 8.17e-04
+2022-04-28 21:38:45,193 INFO [train.py:763] (7/8) Epoch 8, batch 3450, loss[loss=0.2063, simple_loss=0.285, pruned_loss=0.06382, over 7427.00 frames.], tot_loss[loss=0.2035, simple_loss=0.294, pruned_loss=0.05646, over 1423486.91 frames.], batch size: 18, lr: 8.17e-04
+2022-04-28 21:40:00,486 INFO [train.py:763] (7/8) Epoch 8, batch 3500, loss[loss=0.1858, simple_loss=0.2767, pruned_loss=0.0474, over 7173.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2936, pruned_loss=0.05602, over 1424211.02 frames.], batch size: 19, lr: 8.16e-04
+2022-04-28 21:41:15,124 INFO [train.py:763] (7/8) Epoch 8, batch 3550, loss[loss=0.1577, simple_loss=0.2518, pruned_loss=0.0318, over 7164.00 frames.], tot_loss[loss=0.2021, simple_loss=0.293, pruned_loss=0.05562, over 1426096.66 frames.], batch size: 18, lr: 8.16e-04
+2022-04-28 21:42:20,514 INFO [train.py:763] (7/8) Epoch 8, batch 3600, loss[loss=0.2181, simple_loss=0.2891, pruned_loss=0.07353, over 7277.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2933, pruned_loss=0.05587, over 1424546.01 frames.], batch size: 18, lr: 8.15e-04
+2022-04-28 21:43:26,020 INFO [train.py:763] (7/8) Epoch 8, batch 3650, loss[loss=0.185, simple_loss=0.264, pruned_loss=0.05304, over 7143.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2925, pruned_loss=0.05538, over 1426045.51 frames.], batch size: 17, lr: 8.15e-04
+2022-04-28 21:44:39,942 INFO [train.py:763] (7/8) Epoch 8, batch 3700, loss[loss=0.2051, simple_loss=0.2994, pruned_loss=0.0554, over 7335.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2933, pruned_loss=0.05564, over 1426644.75 frames.], batch size: 25, lr: 8.14e-04
+2022-04-28 21:45:45,266 INFO [train.py:763] (7/8) Epoch 8, batch 3750, loss[loss=0.2258, simple_loss=0.3148, pruned_loss=0.06843, over 7430.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2936, pruned_loss=0.0554, over 1425604.96 frames.], batch size: 20, lr: 8.14e-04
+2022-04-28 21:46:51,598 INFO [train.py:763] (7/8) Epoch 8, batch 3800, loss[loss=0.1807, simple_loss=0.2777, pruned_loss=0.04183, over 7411.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2931, pruned_loss=0.05478, over 1428440.21 frames.], batch size: 18, lr: 8.13e-04
+2022-04-28 21:47:57,512 INFO [train.py:763] (7/8) Epoch 8, batch 3850, loss[loss=0.1933, simple_loss=0.2777, pruned_loss=0.05444, over 7275.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2933, pruned_loss=0.05492, over 1430053.67 frames.], batch size: 17, lr: 8.13e-04
+2022-04-28 21:49:03,324 INFO [train.py:763] (7/8) Epoch 8, batch 3900, loss[loss=0.2492, simple_loss=0.3187, pruned_loss=0.08989, over 5079.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2943, pruned_loss=0.05544, over 1427684.48 frames.], batch size: 52, lr: 8.12e-04
+2022-04-28 21:50:08,729 INFO [train.py:763] (7/8) Epoch 8, batch 3950, loss[loss=0.2248, simple_loss=0.3153, pruned_loss=0.06717, over 6821.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2935, pruned_loss=0.05497, over 1428904.48 frames.], batch size: 31, lr: 8.12e-04
+2022-04-28 21:51:14,804 INFO [train.py:763] (7/8) Epoch 8, batch 4000, loss[loss=0.206, simple_loss=0.2994, pruned_loss=0.05626, over 7231.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2942, pruned_loss=0.05522, over 1428331.61 frames.], batch size: 21, lr: 8.11e-04
+2022-04-28 21:52:21,960 INFO [train.py:763] (7/8) Epoch 8, batch 4050, loss[loss=0.1938, simple_loss=0.288, pruned_loss=0.0498, over 7406.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2928, pruned_loss=0.05518, over 1426624.17 frames.], batch size: 18, lr: 8.11e-04
+2022-04-28 21:53:28,745 INFO [train.py:763] (7/8) Epoch 8, batch 4100, loss[loss=0.1703, simple_loss=0.2544, pruned_loss=0.04315, over 7116.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2919, pruned_loss=0.05466, over 1427184.57 frames.], batch size: 17, lr: 8.10e-04
+2022-04-28 21:54:34,097 INFO [train.py:763] (7/8) Epoch 8, batch 4150, loss[loss=0.2213, simple_loss=0.3062, pruned_loss=0.06817, over 7130.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2926, pruned_loss=0.05541, over 1422325.83 frames.], batch size: 28, lr: 8.10e-04
+2022-04-28 21:55:39,796 INFO [train.py:763] (7/8) Epoch 8, batch 4200, loss[loss=0.2293, simple_loss=0.313, pruned_loss=0.07278, over 7319.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2911, pruned_loss=0.05493, over 1423214.05 frames.], batch size: 20, lr: 8.09e-04
+2022-04-28 21:56:45,204 INFO [train.py:763] (7/8) Epoch 8, batch 4250, loss[loss=0.1648, simple_loss=0.2481, pruned_loss=0.04072, over 7139.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2905, pruned_loss=0.05457, over 1419567.87 frames.], batch size: 17, lr: 8.09e-04
+2022-04-28 21:57:50,940 INFO [train.py:763] (7/8) Epoch 8, batch 4300, loss[loss=0.2215, simple_loss=0.3122, pruned_loss=0.06544, over 7405.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2906, pruned_loss=0.05478, over 1415262.84 frames.], batch size: 21, lr: 8.08e-04
+2022-04-28 21:58:56,630 INFO [train.py:763] (7/8) Epoch 8, batch 4350, loss[loss=0.1826, simple_loss=0.2654, pruned_loss=0.04989, over 7263.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2907, pruned_loss=0.05493, over 1420940.67 frames.], batch size: 17, lr: 8.08e-04
+2022-04-28 22:00:02,333 INFO [train.py:763] (7/8) Epoch 8, batch 4400, loss[loss=0.1803, simple_loss=0.2784, pruned_loss=0.04112, over 7099.00 frames.], tot_loss[loss=0.1997, simple_loss=0.29, pruned_loss=0.05474, over 1417056.17 frames.], batch size: 28, lr: 8.07e-04
+2022-04-28 22:01:09,631 INFO [train.py:763] (7/8) Epoch 8, batch 4450, loss[loss=0.2175, simple_loss=0.3256, pruned_loss=0.05472, over 7093.00 frames.], tot_loss[loss=0.198, simple_loss=0.288, pruned_loss=0.05405, over 1412312.52 frames.], batch size: 28, lr: 8.07e-04
+2022-04-28 22:02:15,962 INFO [train.py:763] (7/8) Epoch 8, batch 4500, loss[loss=0.1969, simple_loss=0.2939, pruned_loss=0.04995, over 7126.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2905, pruned_loss=0.05587, over 1395410.68 frames.], batch size: 28, lr: 8.07e-04
+2022-04-28 22:03:19,887 INFO [train.py:763] (7/8) Epoch 8, batch 4550, loss[loss=0.2038, simple_loss=0.2995, pruned_loss=0.05402, over 6355.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2946, pruned_loss=0.05836, over 1355792.81 frames.], batch size: 37, lr: 8.06e-04
+2022-04-28 22:04:39,805 INFO [train.py:763] (7/8) Epoch 9, batch 0, loss[loss=0.1788, simple_loss=0.2819, pruned_loss=0.03787, over 7421.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2819, pruned_loss=0.03787, over 7421.00 frames.], batch size: 21, lr: 7.75e-04
+2022-04-28 22:05:45,919 INFO [train.py:763] (7/8) Epoch 9, batch 50, loss[loss=0.2198, simple_loss=0.3164, pruned_loss=0.06163, over 7190.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2946, pruned_loss=0.05543, over 321247.25 frames.], batch size: 23, lr: 7.74e-04
+2022-04-28 22:06:51,605 INFO [train.py:763] (7/8) Epoch 9, batch 100, loss[loss=0.2392, simple_loss=0.3212, pruned_loss=0.07858, over 5215.00 frames.], tot_loss[loss=0.1985, simple_loss=0.29, pruned_loss=0.05348, over 556666.27 frames.], batch size: 53, lr: 7.74e-04
+2022-04-28 22:07:57,288 INFO [train.py:763] (7/8) Epoch 9, batch 150, loss[loss=0.1861, simple_loss=0.2699, pruned_loss=0.05116, over 7426.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2899, pruned_loss=0.05255, over 750059.00 frames.], batch size: 20, lr: 7.73e-04
+2022-04-28 22:09:03,722 INFO [train.py:763] (7/8) Epoch 9, batch 200, loss[loss=0.213, simple_loss=0.3083, pruned_loss=0.05884, over 7435.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2898, pruned_loss=0.05151, over 898723.69 frames.], batch size: 20, lr: 7.73e-04
+2022-04-28 22:10:10,406 INFO [train.py:763] (7/8) Epoch 9, batch 250, loss[loss=0.1685, simple_loss=0.2587, pruned_loss=0.03916, over 7169.00 frames.], tot_loss[loss=0.198, simple_loss=0.2914, pruned_loss=0.0523, over 1011813.48 frames.], batch size: 18, lr: 7.72e-04
+2022-04-28 22:11:16,236 INFO [train.py:763] (7/8) Epoch 9, batch 300, loss[loss=0.2082, simple_loss=0.2872, pruned_loss=0.06461, over 7327.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2912, pruned_loss=0.05268, over 1104575.31 frames.], batch size: 20, lr: 7.72e-04
+2022-04-28 22:12:21,611 INFO [train.py:763] (7/8) Epoch 9, batch 350, loss[loss=0.242, simple_loss=0.323, pruned_loss=0.08049, over 7198.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2913, pruned_loss=0.05347, over 1173439.98 frames.], batch size: 23, lr: 7.71e-04
+2022-04-28 22:13:26,952 INFO [train.py:763] (7/8) Epoch 9, batch 400, loss[loss=0.2277, simple_loss=0.3161, pruned_loss=0.06962, over 7207.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2919, pruned_loss=0.05381, over 1223245.07 frames.], batch size: 26, lr: 7.71e-04
+2022-04-28 22:14:32,134 INFO [train.py:763] (7/8) Epoch 9, batch 450, loss[loss=0.189, simple_loss=0.2927, pruned_loss=0.04271, over 6503.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2929, pruned_loss=0.05433, over 1261111.10 frames.], batch size: 38, lr: 7.71e-04
+2022-04-28 22:15:37,764 INFO [train.py:763] (7/8) Epoch 9, batch 500, loss[loss=0.2085, simple_loss=0.2907, pruned_loss=0.06315, over 7159.00 frames.], tot_loss[loss=0.201, simple_loss=0.293, pruned_loss=0.05454, over 1296213.70 frames.], batch size: 19, lr: 7.70e-04
+2022-04-28 22:16:43,400 INFO [train.py:763] (7/8) Epoch 9, batch 550, loss[loss=0.1588, simple_loss=0.2409, pruned_loss=0.0384, over 7152.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2916, pruned_loss=0.05355, over 1324727.29 frames.], batch size: 17, lr: 7.70e-04
+2022-04-28 22:17:49,466 INFO [train.py:763] (7/8) Epoch 9, batch 600, loss[loss=0.1689, simple_loss=0.2528, pruned_loss=0.04256, over 7282.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2917, pruned_loss=0.05354, over 1346321.38 frames.], batch size: 18, lr: 7.69e-04
+2022-04-28 22:18:54,920 INFO [train.py:763] (7/8) Epoch 9, batch 650, loss[loss=0.2071, simple_loss=0.2939, pruned_loss=0.06012, over 7142.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2914, pruned_loss=0.05322, over 1362610.31 frames.], batch size: 26, lr: 7.69e-04
+2022-04-28 22:20:00,493 INFO [train.py:763] (7/8) Epoch 9, batch 700, loss[loss=0.222, simple_loss=0.3208, pruned_loss=0.06163, over 7308.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2917, pruned_loss=0.05361, over 1377041.14 frames.], batch size: 25, lr: 7.68e-04
+2022-04-28 22:21:06,849 INFO [train.py:763] (7/8) Epoch 9, batch 750, loss[loss=0.1908, simple_loss=0.2913, pruned_loss=0.0452, over 7431.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2912, pruned_loss=0.05349, over 1387617.11 frames.], batch size: 20, lr: 7.68e-04
+2022-04-28 22:22:12,206 INFO [train.py:763] (7/8) Epoch 9, batch 800, loss[loss=0.2147, simple_loss=0.3164, pruned_loss=0.05651, over 7292.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2915, pruned_loss=0.05365, over 1394286.58 frames.], batch size: 24, lr: 7.67e-04
+2022-04-28 22:23:17,421 INFO [train.py:763] (7/8) Epoch 9, batch 850, loss[loss=0.2199, simple_loss=0.301, pruned_loss=0.06938, over 6324.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2916, pruned_loss=0.05348, over 1397192.84 frames.], batch size: 38, lr: 7.67e-04
+2022-04-28 22:24:22,763 INFO [train.py:763] (7/8) Epoch 9, batch 900, loss[loss=0.1953, simple_loss=0.2994, pruned_loss=0.04563, over 7322.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2911, pruned_loss=0.05339, over 1406936.05 frames.], batch size: 21, lr: 7.66e-04
+2022-04-28 22:25:27,961 INFO [train.py:763] (7/8) Epoch 9, batch 950, loss[loss=0.2034, simple_loss=0.3081, pruned_loss=0.0494, over 7202.00 frames.], tot_loss[loss=0.1998, simple_loss=0.292, pruned_loss=0.05377, over 1407235.93 frames.], batch size: 26, lr: 7.66e-04
+2022-04-28 22:26:34,006 INFO [train.py:763] (7/8) Epoch 9, batch 1000, loss[loss=0.1932, simple_loss=0.293, pruned_loss=0.04671, over 7330.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2909, pruned_loss=0.0534, over 1414562.01 frames.], batch size: 20, lr: 7.66e-04
+2022-04-28 22:27:40,370 INFO [train.py:763] (7/8) Epoch 9, batch 1050, loss[loss=0.1934, simple_loss=0.2879, pruned_loss=0.04948, over 7041.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2903, pruned_loss=0.05325, over 1416709.01 frames.], batch size: 28, lr: 7.65e-04
+2022-04-28 22:28:46,052 INFO [train.py:763] (7/8) Epoch 9, batch 1100, loss[loss=0.1979, simple_loss=0.2843, pruned_loss=0.05577, over 7040.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2912, pruned_loss=0.05388, over 1417141.06 frames.], batch size: 28, lr: 7.65e-04
+2022-04-28 22:29:52,338 INFO [train.py:763] (7/8) Epoch 9, batch 1150, loss[loss=0.1835, simple_loss=0.2833, pruned_loss=0.04187, over 7325.00 frames.], tot_loss[loss=0.199, simple_loss=0.291, pruned_loss=0.05352, over 1421273.19 frames.], batch size: 20, lr: 7.64e-04
+2022-04-28 22:30:57,653 INFO [train.py:763] (7/8) Epoch 9, batch 1200, loss[loss=0.1915, simple_loss=0.2947, pruned_loss=0.04411, over 7207.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2918, pruned_loss=0.05401, over 1420200.73 frames.], batch size: 23, lr: 7.64e-04
+2022-04-28 22:32:04,412 INFO [train.py:763] (7/8) Epoch 9, batch 1250, loss[loss=0.1829, simple_loss=0.2659, pruned_loss=0.04994, over 7285.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2919, pruned_loss=0.05448, over 1419210.22 frames.], batch size: 17, lr: 7.63e-04
+2022-04-28 22:33:11,163 INFO [train.py:763] (7/8) Epoch 9, batch 1300, loss[loss=0.1738, simple_loss=0.2496, pruned_loss=0.04904, over 7009.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2905, pruned_loss=0.05389, over 1417449.10 frames.], batch size: 16, lr: 7.63e-04
+2022-04-28 22:34:16,580 INFO [train.py:763] (7/8) Epoch 9, batch 1350, loss[loss=0.1884, simple_loss=0.2792, pruned_loss=0.0488, over 7316.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2904, pruned_loss=0.05395, over 1415953.96 frames.], batch size: 21, lr: 7.62e-04
+2022-04-28 22:35:21,685 INFO [train.py:763] (7/8) Epoch 9, batch 1400, loss[loss=0.1927, simple_loss=0.2835, pruned_loss=0.05094, over 7111.00 frames.], tot_loss[loss=0.2, simple_loss=0.2917, pruned_loss=0.05411, over 1419195.56 frames.], batch size: 21, lr: 7.62e-04
+2022-04-28 22:36:27,468 INFO [train.py:763] (7/8) Epoch 9, batch 1450, loss[loss=0.2374, simple_loss=0.3317, pruned_loss=0.07148, over 7302.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2912, pruned_loss=0.05372, over 1420172.33 frames.], batch size: 25, lr: 7.62e-04
+2022-04-28 22:37:33,411 INFO [train.py:763] (7/8) Epoch 9, batch 1500, loss[loss=0.2316, simple_loss=0.3137, pruned_loss=0.07471, over 4970.00 frames.], tot_loss[loss=0.2, simple_loss=0.2921, pruned_loss=0.05399, over 1414911.08 frames.], batch size: 52, lr: 7.61e-04
+2022-04-28 22:38:38,716 INFO [train.py:763] (7/8) Epoch 9, batch 1550, loss[loss=0.1626, simple_loss=0.259, pruned_loss=0.03306, over 7354.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2918, pruned_loss=0.05335, over 1418020.49 frames.], batch size: 19, lr: 7.61e-04
+2022-04-28 22:39:43,996 INFO [train.py:763] (7/8) Epoch 9, batch 1600, loss[loss=0.1995, simple_loss=0.3034, pruned_loss=0.04777, over 7256.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2916, pruned_loss=0.05365, over 1417491.85 frames.], batch size: 19, lr: 7.60e-04
+2022-04-28 22:40:50,105 INFO [train.py:763] (7/8) Epoch 9, batch 1650, loss[loss=0.1862, simple_loss=0.2878, pruned_loss=0.04225, over 7404.00 frames.], tot_loss[loss=0.1991, simple_loss=0.291, pruned_loss=0.05363, over 1415651.64 frames.], batch size: 21, lr: 7.60e-04
+2022-04-28 22:41:56,349 INFO [train.py:763] (7/8) Epoch 9, batch 1700, loss[loss=0.2022, simple_loss=0.3009, pruned_loss=0.05171, over 7265.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2901, pruned_loss=0.05305, over 1414187.08 frames.], batch size: 24, lr: 7.59e-04
+2022-04-28 22:43:01,527 INFO [train.py:763] (7/8) Epoch 9, batch 1750, loss[loss=0.1974, simple_loss=0.2799, pruned_loss=0.05746, over 6745.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2913, pruned_loss=0.05391, over 1405689.85 frames.], batch size: 15, lr: 7.59e-04
+2022-04-28 22:44:07,094 INFO [train.py:763] (7/8) Epoch 9, batch 1800, loss[loss=0.1885, simple_loss=0.2846, pruned_loss=0.04625, over 7352.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2906, pruned_loss=0.05363, over 1410320.18 frames.], batch size: 19, lr: 7.59e-04
+2022-04-28 22:45:14,154 INFO [train.py:763] (7/8) Epoch 9, batch 1850, loss[loss=0.2248, simple_loss=0.3128, pruned_loss=0.06844, over 7353.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2907, pruned_loss=0.05352, over 1411301.16 frames.], batch size: 19, lr: 7.58e-04
+2022-04-28 22:46:21,704 INFO [train.py:763] (7/8) Epoch 9, batch 1900, loss[loss=0.1825, simple_loss=0.2684, pruned_loss=0.04833, over 7276.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2908, pruned_loss=0.0535, over 1415276.97 frames.], batch size: 18, lr: 7.58e-04
+2022-04-28 22:47:28,663 INFO [train.py:763] (7/8) Epoch 9, batch 1950, loss[loss=0.2007, simple_loss=0.2951, pruned_loss=0.05309, over 7195.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2903, pruned_loss=0.05321, over 1414711.54 frames.], batch size: 23, lr: 7.57e-04
+2022-04-28 22:48:34,063 INFO [train.py:763] (7/8) Epoch 9, batch 2000, loss[loss=0.2352, simple_loss=0.3299, pruned_loss=0.07028, over 7231.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2892, pruned_loss=0.05236, over 1418300.23 frames.], batch size: 20, lr: 7.57e-04
+2022-04-28 22:49:39,707 INFO [train.py:763] (7/8) Epoch 9, batch 2050, loss[loss=0.209, simple_loss=0.3065, pruned_loss=0.05581, over 7175.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2897, pruned_loss=0.05281, over 1420296.90 frames.], batch size: 23, lr: 7.56e-04
+2022-04-28 22:50:45,173 INFO [train.py:763] (7/8) Epoch 9, batch 2100, loss[loss=0.1928, simple_loss=0.287, pruned_loss=0.04929, over 7147.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2901, pruned_loss=0.05332, over 1424950.20 frames.], batch size: 20, lr: 7.56e-04
+2022-04-28 22:51:50,844 INFO [train.py:763] (7/8) Epoch 9, batch 2150, loss[loss=0.1837, simple_loss=0.2735, pruned_loss=0.04696, over 7417.00 frames.], tot_loss[loss=0.197, simple_loss=0.2887, pruned_loss=0.05263, over 1426960.46 frames.], batch size: 18, lr: 7.56e-04
+2022-04-28 22:52:56,065 INFO [train.py:763] (7/8) Epoch 9, batch 2200, loss[loss=0.2031, simple_loss=0.2967, pruned_loss=0.05476, over 6048.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2895, pruned_loss=0.0528, over 1426775.46 frames.], batch size: 37, lr: 7.55e-04
+2022-04-28 22:54:01,595 INFO [train.py:763] (7/8) Epoch 9, batch 2250, loss[loss=0.2103, simple_loss=0.3079, pruned_loss=0.05636, over 7326.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2893, pruned_loss=0.05288, over 1428442.95 frames.], batch size: 21, lr: 7.55e-04
+2022-04-28 22:55:07,227 INFO [train.py:763] (7/8) Epoch 9, batch 2300, loss[loss=0.1766, simple_loss=0.2734, pruned_loss=0.03993, over 7153.00 frames.], tot_loss[loss=0.1981, simple_loss=0.29, pruned_loss=0.05312, over 1426688.58 frames.], batch size: 20, lr: 7.54e-04
+2022-04-28 22:56:13,154 INFO [train.py:763] (7/8) Epoch 9, batch 2350, loss[loss=0.2189, simple_loss=0.3099, pruned_loss=0.06391, over 7217.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2894, pruned_loss=0.05301, over 1425643.53 frames.], batch size: 22, lr: 7.54e-04
+2022-04-28 22:57:18,367 INFO [train.py:763] (7/8) Epoch 9, batch 2400, loss[loss=0.2, simple_loss=0.2874, pruned_loss=0.05633, over 7278.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2889, pruned_loss=0.05272, over 1427049.26 frames.], batch size: 18, lr: 7.53e-04
+2022-04-28 22:58:24,902 INFO [train.py:763] (7/8) Epoch 9, batch 2450, loss[loss=0.1857, simple_loss=0.2779, pruned_loss=0.04676, over 7067.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2884, pruned_loss=0.05265, over 1430776.88 frames.], batch size: 18, lr: 7.53e-04
+2022-04-28 22:59:30,595 INFO [train.py:763] (7/8) Epoch 9, batch 2500, loss[loss=0.212, simple_loss=0.3044, pruned_loss=0.05976, over 7320.00 frames.], tot_loss[loss=0.1975, simple_loss=0.289, pruned_loss=0.053, over 1428526.47 frames.], batch size: 21, lr: 7.53e-04
+2022-04-28 23:00:35,857 INFO [train.py:763] (7/8) Epoch 9, batch 2550, loss[loss=0.2066, simple_loss=0.3074, pruned_loss=0.05285, over 7221.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2891, pruned_loss=0.05307, over 1426165.52 frames.], batch size: 21, lr: 7.52e-04
+2022-04-28 23:01:42,069 INFO [train.py:763] (7/8) Epoch 9, batch 2600, loss[loss=0.2359, simple_loss=0.3355, pruned_loss=0.0681, over 7151.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2898, pruned_loss=0.05375, over 1429443.73 frames.], batch size: 26, lr: 7.52e-04
+2022-04-28 23:02:47,166 INFO [train.py:763] (7/8) Epoch 9, batch 2650, loss[loss=0.2208, simple_loss=0.321, pruned_loss=0.06035, over 7338.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2905, pruned_loss=0.05361, over 1425487.40 frames.], batch size: 22, lr: 7.51e-04
+2022-04-28 23:03:53,452 INFO [train.py:763] (7/8) Epoch 9, batch 2700, loss[loss=0.2178, simple_loss=0.3045, pruned_loss=0.06557, over 6771.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2892, pruned_loss=0.05317, over 1426476.42 frames.], batch size: 31, lr: 7.51e-04
+2022-04-28 23:04:58,891 INFO [train.py:763] (7/8) Epoch 9, batch 2750, loss[loss=0.191, simple_loss=0.2898, pruned_loss=0.04612, over 6684.00 frames.], tot_loss[loss=0.1975, simple_loss=0.289, pruned_loss=0.05294, over 1424156.84 frames.], batch size: 31, lr: 7.50e-04
+2022-04-28 23:06:04,512 INFO [train.py:763] (7/8) Epoch 9, batch 2800, loss[loss=0.2181, simple_loss=0.3064, pruned_loss=0.06492, over 7385.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2884, pruned_loss=0.05246, over 1429588.52 frames.], batch size: 23, lr: 7.50e-04
+2022-04-28 23:07:09,879 INFO [train.py:763] (7/8) Epoch 9, batch 2850, loss[loss=0.2227, simple_loss=0.314, pruned_loss=0.06569, over 7345.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2889, pruned_loss=0.05246, over 1427872.81 frames.], batch size: 22, lr: 7.50e-04
+2022-04-28 23:08:15,562 INFO [train.py:763] (7/8) Epoch 9, batch 2900, loss[loss=0.2009, simple_loss=0.303, pruned_loss=0.04944, over 7103.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2887, pruned_loss=0.05235, over 1427082.75 frames.], batch size: 21, lr: 7.49e-04
+2022-04-28 23:09:22,023 INFO [train.py:763] (7/8) Epoch 9, batch 2950, loss[loss=0.1577, simple_loss=0.2486, pruned_loss=0.03341, over 7277.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2878, pruned_loss=0.05205, over 1426841.41 frames.], batch size: 18, lr: 7.49e-04
+2022-04-28 23:10:29,012 INFO [train.py:763] (7/8) Epoch 9, batch 3000, loss[loss=0.18, simple_loss=0.2617, pruned_loss=0.04913, over 7292.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2883, pruned_loss=0.05227, over 1425949.12 frames.], batch size: 17, lr: 7.48e-04
+2022-04-28 23:10:29,013 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-28 23:10:44,551 INFO [train.py:792] (7/8) Epoch 9, validation: loss=0.1713, simple_loss=0.276, pruned_loss=0.03324, over 698248.00 frames.
+2022-04-28 23:11:50,386 INFO [train.py:763] (7/8) Epoch 9, batch 3050, loss[loss=0.1842, simple_loss=0.2823, pruned_loss=0.04307, over 7161.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2878, pruned_loss=0.05232, over 1426474.88 frames.], batch size: 19, lr: 7.48e-04
+2022-04-28 23:12:55,867 INFO [train.py:763] (7/8) Epoch 9, batch 3100, loss[loss=0.1894, simple_loss=0.2904, pruned_loss=0.04416, over 7111.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2889, pruned_loss=0.05241, over 1429588.81 frames.], batch size: 21, lr: 7.47e-04
+2022-04-28 23:14:01,353 INFO [train.py:763] (7/8) Epoch 9, batch 3150, loss[loss=0.2499, simple_loss=0.3421, pruned_loss=0.07887, over 7316.00 frames.], tot_loss[loss=0.196, simple_loss=0.2882, pruned_loss=0.05184, over 1425493.78 frames.], batch size: 21, lr: 7.47e-04
+2022-04-28 23:15:07,624 INFO [train.py:763] (7/8) Epoch 9, batch 3200, loss[loss=0.2051, simple_loss=0.2936, pruned_loss=0.05835, over 7237.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2876, pruned_loss=0.05149, over 1426354.04 frames.], batch size: 20, lr: 7.47e-04
+2022-04-28 23:16:13,894 INFO [train.py:763] (7/8) Epoch 9, batch 3250, loss[loss=0.1893, simple_loss=0.2862, pruned_loss=0.04622, over 7400.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2881, pruned_loss=0.0512, over 1428013.65 frames.], batch size: 21, lr: 7.46e-04
+2022-04-28 23:17:19,398 INFO [train.py:763] (7/8) Epoch 9, batch 3300, loss[loss=0.1989, simple_loss=0.2963, pruned_loss=0.05076, over 7208.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2878, pruned_loss=0.05102, over 1428699.25 frames.], batch size: 22, lr: 7.46e-04
+2022-04-28 23:18:25,161 INFO [train.py:763] (7/8) Epoch 9, batch 3350, loss[loss=0.2062, simple_loss=0.303, pruned_loss=0.05474, over 7197.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2881, pruned_loss=0.05116, over 1430186.05 frames.], batch size: 23, lr: 7.45e-04
+2022-04-28 23:19:31,237 INFO [train.py:763] (7/8) Epoch 9, batch 3400, loss[loss=0.1602, simple_loss=0.244, pruned_loss=0.03824, over 7282.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2882, pruned_loss=0.05169, over 1426128.24 frames.], batch size: 17, lr: 7.45e-04
+2022-04-28 23:20:36,544 INFO [train.py:763] (7/8) Epoch 9, batch 3450, loss[loss=0.2042, simple_loss=0.3029, pruned_loss=0.05276, over 7279.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2886, pruned_loss=0.05224, over 1425671.37 frames.], batch size: 24, lr: 7.45e-04
+2022-04-28 23:21:42,181 INFO [train.py:763] (7/8) Epoch 9, batch 3500, loss[loss=0.2314, simple_loss=0.3231, pruned_loss=0.06982, over 7403.00 frames.], tot_loss[loss=0.198, simple_loss=0.2897, pruned_loss=0.05317, over 1425678.58 frames.], batch size: 21, lr: 7.44e-04
+2022-04-28 23:22:49,861 INFO [train.py:763] (7/8) Epoch 9, batch 3550, loss[loss=0.2167, simple_loss=0.3178, pruned_loss=0.05779, over 7029.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2887, pruned_loss=0.05299, over 1428311.97 frames.], batch size: 28, lr: 7.44e-04
+2022-04-28 23:23:55,565 INFO [train.py:763] (7/8) Epoch 9, batch 3600, loss[loss=0.2304, simple_loss=0.3155, pruned_loss=0.07265, over 7131.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2874, pruned_loss=0.05204, over 1428039.96 frames.], batch size: 28, lr: 7.43e-04
+2022-04-28 23:25:02,080 INFO [train.py:763] (7/8) Epoch 9, batch 3650, loss[loss=0.1789, simple_loss=0.2701, pruned_loss=0.04391, over 7064.00 frames.], tot_loss[loss=0.196, simple_loss=0.2877, pruned_loss=0.0522, over 1424235.98 frames.], batch size: 18, lr: 7.43e-04
+2022-04-28 23:26:07,314 INFO [train.py:763] (7/8) Epoch 9, batch 3700, loss[loss=0.1642, simple_loss=0.2454, pruned_loss=0.04151, over 7308.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2884, pruned_loss=0.05205, over 1426581.74 frames.], batch size: 17, lr: 7.43e-04
+2022-04-28 23:27:12,613 INFO [train.py:763] (7/8) Epoch 9, batch 3750, loss[loss=0.2114, simple_loss=0.2901, pruned_loss=0.06639, over 7154.00 frames.], tot_loss[loss=0.1978, simple_loss=0.29, pruned_loss=0.05274, over 1428509.39 frames.], batch size: 19, lr: 7.42e-04
+2022-04-28 23:28:17,831 INFO [train.py:763] (7/8) Epoch 9, batch 3800, loss[loss=0.2004, simple_loss=0.293, pruned_loss=0.05393, over 7430.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2897, pruned_loss=0.05296, over 1426341.90 frames.], batch size: 20, lr: 7.42e-04
+2022-04-28 23:29:23,017 INFO [train.py:763] (7/8) Epoch 9, batch 3850, loss[loss=0.1665, simple_loss=0.2607, pruned_loss=0.03614, over 7054.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2911, pruned_loss=0.05373, over 1425586.75 frames.], batch size: 18, lr: 7.41e-04
+2022-04-28 23:30:28,561 INFO [train.py:763] (7/8) Epoch 9, batch 3900, loss[loss=0.1598, simple_loss=0.2612, pruned_loss=0.02924, over 7157.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2901, pruned_loss=0.05306, over 1426826.73 frames.], batch size: 19, lr: 7.41e-04
+2022-04-28 23:31:35,230 INFO [train.py:763] (7/8) Epoch 9, batch 3950, loss[loss=0.2812, simple_loss=0.3508, pruned_loss=0.1058, over 5246.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2891, pruned_loss=0.05227, over 1420766.27 frames.], batch size: 52, lr: 7.41e-04
+2022-04-28 23:32:42,040 INFO [train.py:763] (7/8) Epoch 9, batch 4000, loss[loss=0.2073, simple_loss=0.2906, pruned_loss=0.06201, over 7267.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2897, pruned_loss=0.05244, over 1421660.21 frames.], batch size: 19, lr: 7.40e-04
+2022-04-28 23:33:47,295 INFO [train.py:763] (7/8) Epoch 9, batch 4050, loss[loss=0.2088, simple_loss=0.2865, pruned_loss=0.06556, over 7138.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2903, pruned_loss=0.05274, over 1422021.59 frames.], batch size: 17, lr: 7.40e-04
+2022-04-28 23:34:53,534 INFO [train.py:763] (7/8) Epoch 9, batch 4100, loss[loss=0.2084, simple_loss=0.3108, pruned_loss=0.053, over 7316.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2903, pruned_loss=0.05274, over 1424247.10 frames.], batch size: 21, lr: 7.39e-04
+2022-04-28 23:35:59,487 INFO [train.py:763] (7/8) Epoch 9, batch 4150, loss[loss=0.2047, simple_loss=0.2856, pruned_loss=0.06189, over 7424.00 frames.], tot_loss[loss=0.197, simple_loss=0.2893, pruned_loss=0.05238, over 1424909.78 frames.], batch size: 18, lr: 7.39e-04
+2022-04-28 23:37:04,706 INFO [train.py:763] (7/8) Epoch 9, batch 4200, loss[loss=0.2128, simple_loss=0.3166, pruned_loss=0.05449, over 7290.00 frames.], tot_loss[loss=0.197, simple_loss=0.2896, pruned_loss=0.05221, over 1426947.62 frames.], batch size: 24, lr: 7.39e-04
+2022-04-28 23:38:10,563 INFO [train.py:763] (7/8) Epoch 9, batch 4250, loss[loss=0.1619, simple_loss=0.2488, pruned_loss=0.03752, over 7286.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2896, pruned_loss=0.05245, over 1422077.87 frames.], batch size: 17, lr: 7.38e-04
+2022-04-28 23:39:16,472 INFO [train.py:763] (7/8) Epoch 9, batch 4300, loss[loss=0.1935, simple_loss=0.3035, pruned_loss=0.04174, over 7303.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2908, pruned_loss=0.05305, over 1416507.51 frames.], batch size: 24, lr: 7.38e-04
+2022-04-28 23:40:22,460 INFO [train.py:763] (7/8) Epoch 9, batch 4350, loss[loss=0.223, simple_loss=0.307, pruned_loss=0.06948, over 5404.00 frames.], tot_loss[loss=0.199, simple_loss=0.2915, pruned_loss=0.05325, over 1407682.78 frames.], batch size: 52, lr: 7.37e-04
+2022-04-28 23:41:28,481 INFO [train.py:763] (7/8) Epoch 9, batch 4400, loss[loss=0.2206, simple_loss=0.3188, pruned_loss=0.06116, over 7215.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2929, pruned_loss=0.05402, over 1410249.96 frames.], batch size: 22, lr: 7.37e-04
+2022-04-28 23:42:35,229 INFO [train.py:763] (7/8) Epoch 9, batch 4450, loss[loss=0.2056, simple_loss=0.293, pruned_loss=0.05909, over 4921.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2931, pruned_loss=0.05451, over 1394715.27 frames.], batch size: 52, lr: 7.37e-04
+2022-04-28 23:43:41,400 INFO [train.py:763] (7/8) Epoch 9, batch 4500, loss[loss=0.2182, simple_loss=0.3126, pruned_loss=0.06187, over 7141.00 frames.], tot_loss[loss=0.201, simple_loss=0.2925, pruned_loss=0.05474, over 1391289.89 frames.], batch size: 20, lr: 7.36e-04
+2022-04-28 23:44:47,999 INFO [train.py:763] (7/8) Epoch 9, batch 4550, loss[loss=0.2022, simple_loss=0.2994, pruned_loss=0.05252, over 7143.00 frames.], tot_loss[loss=0.203, simple_loss=0.2934, pruned_loss=0.05627, over 1370738.35 frames.], batch size: 26, lr: 7.36e-04
+2022-04-28 23:46:26,288 INFO [train.py:763] (7/8) Epoch 10, batch 0, loss[loss=0.2052, simple_loss=0.2929, pruned_loss=0.05873, over 7425.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2929, pruned_loss=0.05873, over 7425.00 frames.], batch size: 20, lr: 7.08e-04
+2022-04-28 23:47:32,332 INFO [train.py:763] (7/8) Epoch 10, batch 50, loss[loss=0.1852, simple_loss=0.2823, pruned_loss=0.04408, over 7431.00 frames.], tot_loss[loss=0.1893, simple_loss=0.285, pruned_loss=0.04683, over 322539.39 frames.], batch size: 20, lr: 7.08e-04
+2022-04-28 23:48:38,945 INFO [train.py:763] (7/8) Epoch 10, batch 100, loss[loss=0.1771, simple_loss=0.2611, pruned_loss=0.04651, over 7288.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2847, pruned_loss=0.04859, over 566650.01 frames.], batch size: 18, lr: 7.08e-04
+2022-04-28 23:49:55,141 INFO [train.py:763] (7/8) Epoch 10, batch 150, loss[loss=0.1685, simple_loss=0.2573, pruned_loss=0.03988, over 7228.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2891, pruned_loss=0.05136, over 759993.12 frames.], batch size: 16, lr: 7.07e-04
+2022-04-28 23:51:18,554 INFO [train.py:763] (7/8) Epoch 10, batch 200, loss[loss=0.1687, simple_loss=0.2557, pruned_loss=0.04081, over 7409.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2892, pruned_loss=0.05153, over 907402.39 frames.], batch size: 18, lr: 7.07e-04
+2022-04-28 23:52:32,874 INFO [train.py:763] (7/8) Epoch 10, batch 250, loss[loss=0.2387, simple_loss=0.3293, pruned_loss=0.07402, over 6436.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2876, pruned_loss=0.05105, over 1022840.39 frames.], batch size: 38, lr: 7.06e-04
+2022-04-28 23:53:48,233 INFO [train.py:763] (7/8) Epoch 10, batch 300, loss[loss=0.2465, simple_loss=0.3376, pruned_loss=0.07771, over 5263.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2865, pruned_loss=0.05007, over 1114909.88 frames.], batch size: 52, lr: 7.06e-04
+2022-04-28 23:54:53,625 INFO [train.py:763] (7/8) Epoch 10, batch 350, loss[loss=0.2337, simple_loss=0.334, pruned_loss=0.06668, over 6802.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2878, pruned_loss=0.05073, over 1187145.70 frames.], batch size: 31, lr: 7.06e-04
+2022-04-28 23:56:17,508 INFO [train.py:763] (7/8) Epoch 10, batch 400, loss[loss=0.1875, simple_loss=0.2912, pruned_loss=0.04188, over 7433.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2883, pruned_loss=0.05138, over 1240387.65 frames.], batch size: 20, lr: 7.05e-04
+2022-04-28 23:57:23,260 INFO [train.py:763] (7/8) Epoch 10, batch 450, loss[loss=0.216, simple_loss=0.311, pruned_loss=0.06051, over 7229.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2867, pruned_loss=0.05049, over 1280736.59 frames.], batch size: 20, lr: 7.05e-04
+2022-04-28 23:58:37,649 INFO [train.py:763] (7/8) Epoch 10, batch 500, loss[loss=0.2244, simple_loss=0.3204, pruned_loss=0.06414, over 7328.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2864, pruned_loss=0.0502, over 1315833.37 frames.], batch size: 20, lr: 7.04e-04
+2022-04-28 23:59:42,734 INFO [train.py:763] (7/8) Epoch 10, batch 550, loss[loss=0.1909, simple_loss=0.2899, pruned_loss=0.0459, over 7059.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2863, pruned_loss=0.04995, over 1340821.74 frames.], batch size: 18, lr: 7.04e-04
+2022-04-29 00:00:47,820 INFO [train.py:763] (7/8) Epoch 10, batch 600, loss[loss=0.1548, simple_loss=0.235, pruned_loss=0.03733, over 6991.00 frames.], tot_loss[loss=0.1929, simple_loss=0.286, pruned_loss=0.04988, over 1359943.73 frames.], batch size: 16, lr: 7.04e-04
+2022-04-29 00:01:53,020 INFO [train.py:763] (7/8) Epoch 10, batch 650, loss[loss=0.1555, simple_loss=0.2503, pruned_loss=0.03038, over 7127.00 frames.], tot_loss[loss=0.194, simple_loss=0.2869, pruned_loss=0.0506, over 1365757.24 frames.], batch size: 17, lr: 7.03e-04
+2022-04-29 00:02:58,044 INFO [train.py:763] (7/8) Epoch 10, batch 700, loss[loss=0.1684, simple_loss=0.2529, pruned_loss=0.04195, over 6800.00 frames.], tot_loss[loss=0.195, simple_loss=0.2883, pruned_loss=0.05086, over 1375533.35 frames.], batch size: 15, lr: 7.03e-04
+2022-04-29 00:04:03,202 INFO [train.py:763] (7/8) Epoch 10, batch 750, loss[loss=0.2492, simple_loss=0.3332, pruned_loss=0.08265, over 7148.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2882, pruned_loss=0.05138, over 1382668.27 frames.], batch size: 20, lr: 7.03e-04
+2022-04-29 00:05:08,481 INFO [train.py:763] (7/8) Epoch 10, batch 800, loss[loss=0.2147, simple_loss=0.3079, pruned_loss=0.06072, over 7166.00 frames.], tot_loss[loss=0.195, simple_loss=0.2878, pruned_loss=0.05112, over 1394062.51 frames.], batch size: 26, lr: 7.02e-04
+2022-04-29 00:06:13,833 INFO [train.py:763] (7/8) Epoch 10, batch 850, loss[loss=0.1773, simple_loss=0.2822, pruned_loss=0.03617, over 7319.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2867, pruned_loss=0.05056, over 1398051.45 frames.], batch size: 20, lr: 7.02e-04
+2022-04-29 00:07:19,249 INFO [train.py:763] (7/8) Epoch 10, batch 900, loss[loss=0.1663, simple_loss=0.2683, pruned_loss=0.03215, over 7435.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2862, pruned_loss=0.0497, over 1406797.97 frames.], batch size: 20, lr: 7.02e-04
+2022-04-29 00:08:24,548 INFO [train.py:763] (7/8) Epoch 10, batch 950, loss[loss=0.1797, simple_loss=0.2661, pruned_loss=0.04667, over 7008.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2862, pruned_loss=0.05015, over 1409242.87 frames.], batch size: 16, lr: 7.01e-04
+2022-04-29 00:09:29,927 INFO [train.py:763] (7/8) Epoch 10, batch 1000, loss[loss=0.2517, simple_loss=0.3435, pruned_loss=0.07995, over 7303.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2867, pruned_loss=0.05014, over 1413390.21 frames.], batch size: 25, lr: 7.01e-04
+2022-04-29 00:10:35,525 INFO [train.py:763] (7/8) Epoch 10, batch 1050, loss[loss=0.1697, simple_loss=0.2632, pruned_loss=0.0381, over 7249.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2888, pruned_loss=0.05127, over 1408787.58 frames.], batch size: 19, lr: 7.00e-04
+2022-04-29 00:11:41,124 INFO [train.py:763] (7/8) Epoch 10, batch 1100, loss[loss=0.1576, simple_loss=0.2605, pruned_loss=0.02731, over 7165.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2887, pruned_loss=0.05124, over 1413428.06 frames.], batch size: 18, lr: 7.00e-04
+2022-04-29 00:12:46,593 INFO [train.py:763] (7/8) Epoch 10, batch 1150, loss[loss=0.2424, simple_loss=0.3103, pruned_loss=0.08726, over 7063.00 frames.], tot_loss[loss=0.195, simple_loss=0.2879, pruned_loss=0.05106, over 1417591.87 frames.], batch size: 18, lr: 7.00e-04
+2022-04-29 00:13:53,268 INFO [train.py:763] (7/8) Epoch 10, batch 1200, loss[loss=0.1865, simple_loss=0.2629, pruned_loss=0.05508, over 7219.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2856, pruned_loss=0.05034, over 1421077.37 frames.], batch size: 16, lr: 6.99e-04
+2022-04-29 00:14:58,987 INFO [train.py:763] (7/8) Epoch 10, batch 1250, loss[loss=0.1372, simple_loss=0.2252, pruned_loss=0.02461, over 7134.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2849, pruned_loss=0.04996, over 1424488.34 frames.], batch size: 17, lr: 6.99e-04
+2022-04-29 00:16:04,770 INFO [train.py:763] (7/8) Epoch 10, batch 1300, loss[loss=0.1793, simple_loss=0.2705, pruned_loss=0.04404, over 7324.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2848, pruned_loss=0.04996, over 1421295.05 frames.], batch size: 21, lr: 6.99e-04
+2022-04-29 00:17:11,818 INFO [train.py:763] (7/8) Epoch 10, batch 1350, loss[loss=0.1999, simple_loss=0.3014, pruned_loss=0.04919, over 7314.00 frames.], tot_loss[loss=0.1937, simple_loss=0.286, pruned_loss=0.05065, over 1424344.29 frames.], batch size: 21, lr: 6.98e-04
+2022-04-29 00:18:18,362 INFO [train.py:763] (7/8) Epoch 10, batch 1400, loss[loss=0.192, simple_loss=0.2855, pruned_loss=0.04922, over 7162.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2863, pruned_loss=0.05045, over 1427797.87 frames.], batch size: 19, lr: 6.98e-04
+2022-04-29 00:19:25,287 INFO [train.py:763] (7/8) Epoch 10, batch 1450, loss[loss=0.1588, simple_loss=0.2497, pruned_loss=0.03394, over 7278.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2874, pruned_loss=0.05071, over 1428264.88 frames.], batch size: 17, lr: 6.97e-04
+2022-04-29 00:20:30,758 INFO [train.py:763] (7/8) Epoch 10, batch 1500, loss[loss=0.1993, simple_loss=0.2954, pruned_loss=0.05164, over 7076.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2876, pruned_loss=0.0508, over 1425916.53 frames.], batch size: 28, lr: 6.97e-04
+2022-04-29 00:21:36,437 INFO [train.py:763] (7/8) Epoch 10, batch 1550, loss[loss=0.178, simple_loss=0.2771, pruned_loss=0.03938, over 7443.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2871, pruned_loss=0.05063, over 1424090.85 frames.], batch size: 20, lr: 6.97e-04
+2022-04-29 00:22:41,615 INFO [train.py:763] (7/8) Epoch 10, batch 1600, loss[loss=0.2286, simple_loss=0.3269, pruned_loss=0.06514, over 6706.00 frames.], tot_loss[loss=0.194, simple_loss=0.2871, pruned_loss=0.05046, over 1418315.95 frames.], batch size: 31, lr: 6.96e-04
+2022-04-29 00:23:47,735 INFO [train.py:763] (7/8) Epoch 10, batch 1650, loss[loss=0.1686, simple_loss=0.2493, pruned_loss=0.04391, over 7225.00 frames.], tot_loss[loss=0.1933, simple_loss=0.286, pruned_loss=0.05031, over 1418154.01 frames.], batch size: 16, lr: 6.96e-04
+2022-04-29 00:24:52,744 INFO [train.py:763] (7/8) Epoch 10, batch 1700, loss[loss=0.1785, simple_loss=0.2693, pruned_loss=0.04388, over 6788.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2873, pruned_loss=0.05097, over 1417070.28 frames.], batch size: 15, lr: 6.96e-04
+2022-04-29 00:25:58,409 INFO [train.py:763] (7/8) Epoch 10, batch 1750, loss[loss=0.2079, simple_loss=0.3015, pruned_loss=0.05712, over 7106.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2855, pruned_loss=0.05045, over 1413461.07 frames.], batch size: 21, lr: 6.95e-04
+2022-04-29 00:27:03,857 INFO [train.py:763] (7/8) Epoch 10, batch 1800, loss[loss=0.2238, simple_loss=0.3189, pruned_loss=0.06436, over 5155.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2849, pruned_loss=0.04991, over 1413117.78 frames.], batch size: 52, lr: 6.95e-04
+2022-04-29 00:28:10,772 INFO [train.py:763] (7/8) Epoch 10, batch 1850, loss[loss=0.192, simple_loss=0.2854, pruned_loss=0.04932, over 6387.00 frames.], tot_loss[loss=0.192, simple_loss=0.2849, pruned_loss=0.04957, over 1417105.65 frames.], batch size: 37, lr: 6.95e-04
+2022-04-29 00:29:17,830 INFO [train.py:763] (7/8) Epoch 10, batch 1900, loss[loss=0.195, simple_loss=0.2989, pruned_loss=0.04555, over 7324.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2852, pruned_loss=0.04963, over 1421853.34 frames.], batch size: 21, lr: 6.94e-04
+2022-04-29 00:30:24,820 INFO [train.py:763] (7/8) Epoch 10, batch 1950, loss[loss=0.1737, simple_loss=0.2641, pruned_loss=0.04171, over 7345.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2866, pruned_loss=0.05085, over 1422185.42 frames.], batch size: 19, lr: 6.94e-04
+2022-04-29 00:31:31,802 INFO [train.py:763] (7/8) Epoch 10, batch 2000, loss[loss=0.1839, simple_loss=0.2778, pruned_loss=0.04503, over 7160.00 frames.], tot_loss[loss=0.195, simple_loss=0.2877, pruned_loss=0.05115, over 1423687.76 frames.], batch size: 18, lr: 6.93e-04
+2022-04-29 00:32:38,668 INFO [train.py:763] (7/8) Epoch 10, batch 2050, loss[loss=0.1527, simple_loss=0.2371, pruned_loss=0.03412, over 7292.00 frames.], tot_loss[loss=0.194, simple_loss=0.2868, pruned_loss=0.0506, over 1425277.44 frames.], batch size: 17, lr: 6.93e-04
+2022-04-29 00:33:45,463 INFO [train.py:763] (7/8) Epoch 10, batch 2100, loss[loss=0.2079, simple_loss=0.3054, pruned_loss=0.05514, over 7366.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2865, pruned_loss=0.05012, over 1425788.26 frames.], batch size: 23, lr: 6.93e-04
+2022-04-29 00:35:01,078 INFO [train.py:763] (7/8) Epoch 10, batch 2150, loss[loss=0.1748, simple_loss=0.2693, pruned_loss=0.04013, over 7145.00 frames.], tot_loss[loss=0.194, simple_loss=0.2868, pruned_loss=0.05061, over 1426233.04 frames.], batch size: 18, lr: 6.92e-04
+2022-04-29 00:36:06,573 INFO [train.py:763] (7/8) Epoch 10, batch 2200, loss[loss=0.2046, simple_loss=0.3068, pruned_loss=0.05119, over 7235.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2863, pruned_loss=0.05059, over 1423588.20 frames.], batch size: 20, lr: 6.92e-04
+2022-04-29 00:37:11,934 INFO [train.py:763] (7/8) Epoch 10, batch 2250, loss[loss=0.2038, simple_loss=0.3037, pruned_loss=0.05198, over 7344.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2872, pruned_loss=0.05092, over 1427561.74 frames.], batch size: 22, lr: 6.92e-04
+2022-04-29 00:38:17,418 INFO [train.py:763] (7/8) Epoch 10, batch 2300, loss[loss=0.2058, simple_loss=0.3044, pruned_loss=0.05363, over 7138.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2861, pruned_loss=0.05016, over 1427629.60 frames.], batch size: 26, lr: 6.91e-04
+2022-04-29 00:39:22,713 INFO [train.py:763] (7/8) Epoch 10, batch 2350, loss[loss=0.2105, simple_loss=0.2973, pruned_loss=0.06188, over 6691.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2855, pruned_loss=0.04997, over 1429444.26 frames.], batch size: 31, lr: 6.91e-04
+2022-04-29 00:40:27,873 INFO [train.py:763] (7/8) Epoch 10, batch 2400, loss[loss=0.2195, simple_loss=0.3206, pruned_loss=0.05924, over 7314.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2857, pruned_loss=0.05005, over 1422749.97 frames.], batch size: 21, lr: 6.91e-04
+2022-04-29 00:41:33,307 INFO [train.py:763] (7/8) Epoch 10, batch 2450, loss[loss=0.1971, simple_loss=0.2745, pruned_loss=0.05986, over 7021.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2853, pruned_loss=0.05044, over 1423509.23 frames.], batch size: 16, lr: 6.90e-04
+2022-04-29 00:42:38,521 INFO [train.py:763] (7/8) Epoch 10, batch 2500, loss[loss=0.1812, simple_loss=0.2793, pruned_loss=0.04152, over 7154.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2854, pruned_loss=0.05078, over 1422712.71 frames.], batch size: 19, lr: 6.90e-04
+2022-04-29 00:43:44,300 INFO [train.py:763] (7/8) Epoch 10, batch 2550, loss[loss=0.2072, simple_loss=0.28, pruned_loss=0.06717, over 6774.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2855, pruned_loss=0.05052, over 1426486.83 frames.], batch size: 15, lr: 6.90e-04
+2022-04-29 00:44:51,078 INFO [train.py:763] (7/8) Epoch 10, batch 2600, loss[loss=0.2103, simple_loss=0.3016, pruned_loss=0.05947, over 7377.00 frames.], tot_loss[loss=0.1931, simple_loss=0.285, pruned_loss=0.05058, over 1428343.86 frames.], batch size: 23, lr: 6.89e-04
+2022-04-29 00:45:56,190 INFO [train.py:763] (7/8) Epoch 10, batch 2650, loss[loss=0.1826, simple_loss=0.2691, pruned_loss=0.0481, over 7007.00 frames.], tot_loss[loss=0.1936, simple_loss=0.286, pruned_loss=0.05062, over 1423906.92 frames.], batch size: 16, lr: 6.89e-04
+2022-04-29 00:47:01,620 INFO [train.py:763] (7/8) Epoch 10, batch 2700, loss[loss=0.2078, simple_loss=0.2977, pruned_loss=0.05896, over 7418.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2874, pruned_loss=0.05073, over 1427067.31 frames.], batch size: 21, lr: 6.89e-04
+2022-04-29 00:48:08,172 INFO [train.py:763] (7/8) Epoch 10, batch 2750, loss[loss=0.2077, simple_loss=0.292, pruned_loss=0.06168, over 7288.00 frames.], tot_loss[loss=0.1932, simple_loss=0.286, pruned_loss=0.05026, over 1425629.22 frames.], batch size: 18, lr: 6.88e-04
+2022-04-29 00:49:13,520 INFO [train.py:763] (7/8) Epoch 10, batch 2800, loss[loss=0.1646, simple_loss=0.2641, pruned_loss=0.03254, over 7152.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2858, pruned_loss=0.05004, over 1424286.44 frames.], batch size: 19, lr: 6.88e-04
+2022-04-29 00:50:19,059 INFO [train.py:763] (7/8) Epoch 10, batch 2850, loss[loss=0.1971, simple_loss=0.282, pruned_loss=0.05607, over 7315.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2855, pruned_loss=0.0499, over 1425280.43 frames.], batch size: 21, lr: 6.87e-04
+2022-04-29 00:51:24,566 INFO [train.py:763] (7/8) Epoch 10, batch 2900, loss[loss=0.2204, simple_loss=0.3122, pruned_loss=0.06423, over 7195.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2847, pruned_loss=0.0499, over 1427895.87 frames.], batch size: 23, lr: 6.87e-04
+2022-04-29 00:52:30,314 INFO [train.py:763] (7/8) Epoch 10, batch 2950, loss[loss=0.2358, simple_loss=0.3322, pruned_loss=0.06971, over 7201.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2864, pruned_loss=0.05043, over 1425520.62 frames.], batch size: 22, lr: 6.87e-04
+2022-04-29 00:53:36,058 INFO [train.py:763] (7/8) Epoch 10, batch 3000, loss[loss=0.1838, simple_loss=0.2667, pruned_loss=0.05047, over 7166.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2873, pruned_loss=0.05059, over 1423977.41 frames.], batch size: 18, lr: 6.86e-04
+2022-04-29 00:53:36,059 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 00:53:51,271 INFO [train.py:792] (7/8) Epoch 10, validation: loss=0.1689, simple_loss=0.2722, pruned_loss=0.03283, over 698248.00 frames.
+2022-04-29 00:54:57,783 INFO [train.py:763] (7/8) Epoch 10, batch 3050, loss[loss=0.194, simple_loss=0.2947, pruned_loss=0.04662, over 7168.00 frames.], tot_loss[loss=0.1938, simple_loss=0.287, pruned_loss=0.05034, over 1427836.42 frames.], batch size: 26, lr: 6.86e-04
+2022-04-29 00:56:03,594 INFO [train.py:763] (7/8) Epoch 10, batch 3100, loss[loss=0.1781, simple_loss=0.2678, pruned_loss=0.04413, over 7407.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2874, pruned_loss=0.05105, over 1425395.20 frames.], batch size: 18, lr: 6.86e-04
+2022-04-29 00:57:10,806 INFO [train.py:763] (7/8) Epoch 10, batch 3150, loss[loss=0.1639, simple_loss=0.2531, pruned_loss=0.03733, over 7277.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2863, pruned_loss=0.05046, over 1428322.52 frames.], batch size: 18, lr: 6.85e-04
+2022-04-29 00:58:16,979 INFO [train.py:763] (7/8) Epoch 10, batch 3200, loss[loss=0.1632, simple_loss=0.2558, pruned_loss=0.03533, over 7175.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2844, pruned_loss=0.04963, over 1430102.56 frames.], batch size: 18, lr: 6.85e-04
+2022-04-29 00:59:22,569 INFO [train.py:763] (7/8) Epoch 10, batch 3250, loss[loss=0.1893, simple_loss=0.2883, pruned_loss=0.04515, over 7064.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2844, pruned_loss=0.04951, over 1432101.65 frames.], batch size: 18, lr: 6.85e-04
+2022-04-29 01:00:29,384 INFO [train.py:763] (7/8) Epoch 10, batch 3300, loss[loss=0.2035, simple_loss=0.2976, pruned_loss=0.05474, over 6296.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2854, pruned_loss=0.05001, over 1431027.04 frames.], batch size: 37, lr: 6.84e-04
+2022-04-29 01:01:36,455 INFO [train.py:763] (7/8) Epoch 10, batch 3350, loss[loss=0.2063, simple_loss=0.2996, pruned_loss=0.0565, over 7118.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2857, pruned_loss=0.05034, over 1425655.92 frames.], batch size: 21, lr: 6.84e-04
+2022-04-29 01:02:41,930 INFO [train.py:763] (7/8) Epoch 10, batch 3400, loss[loss=0.1693, simple_loss=0.2565, pruned_loss=0.04102, over 7000.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2858, pruned_loss=0.05032, over 1422259.51 frames.], batch size: 16, lr: 6.84e-04
+2022-04-29 01:03:47,419 INFO [train.py:763] (7/8) Epoch 10, batch 3450, loss[loss=0.2102, simple_loss=0.318, pruned_loss=0.05117, over 7116.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2863, pruned_loss=0.05036, over 1425689.75 frames.], batch size: 21, lr: 6.83e-04
+2022-04-29 01:04:52,729 INFO [train.py:763] (7/8) Epoch 10, batch 3500, loss[loss=0.1722, simple_loss=0.255, pruned_loss=0.04471, over 7412.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2858, pruned_loss=0.04999, over 1426118.22 frames.], batch size: 18, lr: 6.83e-04
+2022-04-29 01:05:58,220 INFO [train.py:763] (7/8) Epoch 10, batch 3550, loss[loss=0.2029, simple_loss=0.2961, pruned_loss=0.05485, over 6137.00 frames.], tot_loss[loss=0.1931, simple_loss=0.286, pruned_loss=0.0501, over 1424470.76 frames.], batch size: 37, lr: 6.83e-04
+2022-04-29 01:07:03,440 INFO [train.py:763] (7/8) Epoch 10, batch 3600, loss[loss=0.1752, simple_loss=0.2795, pruned_loss=0.03542, over 6329.00 frames.], tot_loss[loss=0.194, simple_loss=0.2866, pruned_loss=0.05071, over 1420625.99 frames.], batch size: 37, lr: 6.82e-04
+2022-04-29 01:08:09,042 INFO [train.py:763] (7/8) Epoch 10, batch 3650, loss[loss=0.2099, simple_loss=0.3006, pruned_loss=0.05963, over 7118.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2867, pruned_loss=0.05025, over 1422465.12 frames.], batch size: 21, lr: 6.82e-04
+2022-04-29 01:09:14,325 INFO [train.py:763] (7/8) Epoch 10, batch 3700, loss[loss=0.1765, simple_loss=0.2755, pruned_loss=0.0387, over 7113.00 frames.], tot_loss[loss=0.193, simple_loss=0.2858, pruned_loss=0.05011, over 1419182.15 frames.], batch size: 21, lr: 6.82e-04
+2022-04-29 01:10:20,250 INFO [train.py:763] (7/8) Epoch 10, batch 3750, loss[loss=0.1864, simple_loss=0.2793, pruned_loss=0.04676, over 7427.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2865, pruned_loss=0.0503, over 1424427.28 frames.], batch size: 20, lr: 6.81e-04
+2022-04-29 01:11:26,048 INFO [train.py:763] (7/8) Epoch 10, batch 3800, loss[loss=0.2039, simple_loss=0.2889, pruned_loss=0.05945, over 7298.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2871, pruned_loss=0.05059, over 1423187.26 frames.], batch size: 24, lr: 6.81e-04
+2022-04-29 01:12:32,967 INFO [train.py:763] (7/8) Epoch 10, batch 3850, loss[loss=0.2232, simple_loss=0.3186, pruned_loss=0.06392, over 7208.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2867, pruned_loss=0.05035, over 1427840.73 frames.], batch size: 22, lr: 6.81e-04
+2022-04-29 01:13:40,351 INFO [train.py:763] (7/8) Epoch 10, batch 3900, loss[loss=0.2021, simple_loss=0.2913, pruned_loss=0.05648, over 7380.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2861, pruned_loss=0.04987, over 1427538.63 frames.], batch size: 23, lr: 6.80e-04
+2022-04-29 01:14:47,724 INFO [train.py:763] (7/8) Epoch 10, batch 3950, loss[loss=0.2218, simple_loss=0.3093, pruned_loss=0.06714, over 7434.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2856, pruned_loss=0.04964, over 1426478.05 frames.], batch size: 20, lr: 6.80e-04
+2022-04-29 01:15:53,621 INFO [train.py:763] (7/8) Epoch 10, batch 4000, loss[loss=0.2206, simple_loss=0.3178, pruned_loss=0.06171, over 7234.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2862, pruned_loss=0.05054, over 1418233.51 frames.], batch size: 21, lr: 6.80e-04
+2022-04-29 01:17:00,555 INFO [train.py:763] (7/8) Epoch 10, batch 4050, loss[loss=0.1843, simple_loss=0.2834, pruned_loss=0.04266, over 7193.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2867, pruned_loss=0.05076, over 1417849.21 frames.], batch size: 22, lr: 6.79e-04
+2022-04-29 01:18:07,371 INFO [train.py:763] (7/8) Epoch 10, batch 4100, loss[loss=0.2073, simple_loss=0.3029, pruned_loss=0.05585, over 7193.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2863, pruned_loss=0.05054, over 1417439.08 frames.], batch size: 22, lr: 6.79e-04
+2022-04-29 01:19:14,037 INFO [train.py:763] (7/8) Epoch 10, batch 4150, loss[loss=0.2209, simple_loss=0.3144, pruned_loss=0.06365, over 6671.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2866, pruned_loss=0.05041, over 1413931.68 frames.], batch size: 31, lr: 6.79e-04
+2022-04-29 01:20:19,809 INFO [train.py:763] (7/8) Epoch 10, batch 4200, loss[loss=0.2093, simple_loss=0.2987, pruned_loss=0.05989, over 6989.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2872, pruned_loss=0.05048, over 1414833.52 frames.], batch size: 28, lr: 6.78e-04
+2022-04-29 01:21:26,038 INFO [train.py:763] (7/8) Epoch 10, batch 4250, loss[loss=0.2039, simple_loss=0.2888, pruned_loss=0.05945, over 5168.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2863, pruned_loss=0.05012, over 1414720.73 frames.], batch size: 53, lr: 6.78e-04
+2022-04-29 01:22:31,083 INFO [train.py:763] (7/8) Epoch 10, batch 4300, loss[loss=0.265, simple_loss=0.3329, pruned_loss=0.09857, over 5251.00 frames.], tot_loss[loss=0.1944, simple_loss=0.287, pruned_loss=0.05091, over 1411828.05 frames.], batch size: 52, lr: 6.78e-04
+2022-04-29 01:23:36,205 INFO [train.py:763] (7/8) Epoch 10, batch 4350, loss[loss=0.2044, simple_loss=0.3019, pruned_loss=0.0535, over 7227.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2881, pruned_loss=0.05142, over 1410309.14 frames.], batch size: 20, lr: 6.77e-04
+2022-04-29 01:24:41,268 INFO [train.py:763] (7/8) Epoch 10, batch 4400, loss[loss=0.2203, simple_loss=0.3132, pruned_loss=0.0637, over 7204.00 frames.], tot_loss[loss=0.195, simple_loss=0.2879, pruned_loss=0.05108, over 1415704.25 frames.], batch size: 22, lr: 6.77e-04
+2022-04-29 01:25:46,632 INFO [train.py:763] (7/8) Epoch 10, batch 4450, loss[loss=0.1973, simple_loss=0.2931, pruned_loss=0.05081, over 7232.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2891, pruned_loss=0.05151, over 1418467.71 frames.], batch size: 20, lr: 6.77e-04
+2022-04-29 01:26:52,309 INFO [train.py:763] (7/8) Epoch 10, batch 4500, loss[loss=0.2308, simple_loss=0.3067, pruned_loss=0.0775, over 5111.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2897, pruned_loss=0.05201, over 1411054.59 frames.], batch size: 52, lr: 6.76e-04
+2022-04-29 01:27:57,109 INFO [train.py:763] (7/8) Epoch 10, batch 4550, loss[loss=0.211, simple_loss=0.296, pruned_loss=0.06303, over 5039.00 frames.], tot_loss[loss=0.2007, simple_loss=0.2922, pruned_loss=0.05457, over 1346226.58 frames.], batch size: 52, lr: 6.76e-04
+2022-04-29 01:29:26,063 INFO [train.py:763] (7/8) Epoch 11, batch 0, loss[loss=0.2245, simple_loss=0.3172, pruned_loss=0.06597, over 7429.00 frames.], tot_loss[loss=0.2245, simple_loss=0.3172, pruned_loss=0.06597, over 7429.00 frames.], batch size: 21, lr: 6.52e-04
+2022-04-29 01:30:32,273 INFO [train.py:763] (7/8) Epoch 11, batch 50, loss[loss=0.2301, simple_loss=0.3227, pruned_loss=0.0687, over 4736.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2874, pruned_loss=0.05064, over 319342.82 frames.], batch size: 52, lr: 6.52e-04
+2022-04-29 01:31:38,390 INFO [train.py:763] (7/8) Epoch 11, batch 100, loss[loss=0.1786, simple_loss=0.2839, pruned_loss=0.03658, over 6444.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2868, pruned_loss=0.04931, over 558857.05 frames.], batch size: 38, lr: 6.51e-04
+2022-04-29 01:32:44,344 INFO [train.py:763] (7/8) Epoch 11, batch 150, loss[loss=0.1632, simple_loss=0.2533, pruned_loss=0.03656, over 7283.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2876, pruned_loss=0.04957, over 748936.17 frames.], batch size: 17, lr: 6.51e-04
+2022-04-29 01:33:50,256 INFO [train.py:763] (7/8) Epoch 11, batch 200, loss[loss=0.1993, simple_loss=0.2932, pruned_loss=0.05274, over 7204.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2879, pruned_loss=0.04974, over 896336.83 frames.], batch size: 22, lr: 6.51e-04
+2022-04-29 01:34:55,816 INFO [train.py:763] (7/8) Epoch 11, batch 250, loss[loss=0.1871, simple_loss=0.288, pruned_loss=0.04305, over 6697.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2859, pruned_loss=0.04864, over 1014852.34 frames.], batch size: 31, lr: 6.50e-04
+2022-04-29 01:36:01,210 INFO [train.py:763] (7/8) Epoch 11, batch 300, loss[loss=0.204, simple_loss=0.2986, pruned_loss=0.05472, over 7209.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2861, pruned_loss=0.04857, over 1098065.47 frames.], batch size: 22, lr: 6.50e-04
+2022-04-29 01:37:06,913 INFO [train.py:763] (7/8) Epoch 11, batch 350, loss[loss=0.1796, simple_loss=0.2846, pruned_loss=0.03731, over 7341.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2859, pruned_loss=0.04858, over 1164846.08 frames.], batch size: 22, lr: 6.50e-04
+2022-04-29 01:38:12,686 INFO [train.py:763] (7/8) Epoch 11, batch 400, loss[loss=0.1842, simple_loss=0.2865, pruned_loss=0.04095, over 7342.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2849, pruned_loss=0.04827, over 1219364.48 frames.], batch size: 22, lr: 6.49e-04
+2022-04-29 01:39:18,310 INFO [train.py:763] (7/8) Epoch 11, batch 450, loss[loss=0.1933, simple_loss=0.2865, pruned_loss=0.05002, over 7157.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2842, pruned_loss=0.0481, over 1268145.71 frames.], batch size: 19, lr: 6.49e-04
+2022-04-29 01:40:24,063 INFO [train.py:763] (7/8) Epoch 11, batch 500, loss[loss=0.1992, simple_loss=0.3012, pruned_loss=0.04865, over 7379.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2844, pruned_loss=0.04813, over 1302544.69 frames.], batch size: 23, lr: 6.49e-04
+2022-04-29 01:41:30,088 INFO [train.py:763] (7/8) Epoch 11, batch 550, loss[loss=0.1909, simple_loss=0.2907, pruned_loss=0.0455, over 7414.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2838, pruned_loss=0.04751, over 1329864.57 frames.], batch size: 21, lr: 6.48e-04
+2022-04-29 01:42:36,726 INFO [train.py:763] (7/8) Epoch 11, batch 600, loss[loss=0.1902, simple_loss=0.2899, pruned_loss=0.04524, over 7343.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2832, pruned_loss=0.04732, over 1349427.73 frames.], batch size: 22, lr: 6.48e-04
+2022-04-29 01:43:44,075 INFO [train.py:763] (7/8) Epoch 11, batch 650, loss[loss=0.207, simple_loss=0.3191, pruned_loss=0.04741, over 7381.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2826, pruned_loss=0.04733, over 1370759.33 frames.], batch size: 23, lr: 6.48e-04
+2022-04-29 01:44:51,075 INFO [train.py:763] (7/8) Epoch 11, batch 700, loss[loss=0.203, simple_loss=0.2983, pruned_loss=0.05384, over 7296.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2833, pruned_loss=0.04749, over 1381094.70 frames.], batch size: 24, lr: 6.47e-04
+2022-04-29 01:45:57,545 INFO [train.py:763] (7/8) Epoch 11, batch 750, loss[loss=0.2048, simple_loss=0.287, pruned_loss=0.06129, over 7322.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2842, pruned_loss=0.04827, over 1386563.73 frames.], batch size: 20, lr: 6.47e-04
+2022-04-29 01:47:03,490 INFO [train.py:763] (7/8) Epoch 11, batch 800, loss[loss=0.1545, simple_loss=0.2385, pruned_loss=0.03524, over 7399.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2824, pruned_loss=0.0474, over 1398876.22 frames.], batch size: 18, lr: 6.47e-04
+2022-04-29 01:48:08,971 INFO [train.py:763] (7/8) Epoch 11, batch 850, loss[loss=0.1859, simple_loss=0.2862, pruned_loss=0.04283, over 6914.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2834, pruned_loss=0.04789, over 1402920.72 frames.], batch size: 31, lr: 6.46e-04
+2022-04-29 01:49:14,797 INFO [train.py:763] (7/8) Epoch 11, batch 900, loss[loss=0.1609, simple_loss=0.2709, pruned_loss=0.02545, over 7337.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2844, pruned_loss=0.04829, over 1407624.99 frames.], batch size: 22, lr: 6.46e-04
+2022-04-29 01:50:20,611 INFO [train.py:763] (7/8) Epoch 11, batch 950, loss[loss=0.1822, simple_loss=0.2753, pruned_loss=0.0446, over 7435.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2839, pruned_loss=0.04818, over 1412171.94 frames.], batch size: 20, lr: 6.46e-04
+2022-04-29 01:51:27,144 INFO [train.py:763] (7/8) Epoch 11, batch 1000, loss[loss=0.1986, simple_loss=0.2784, pruned_loss=0.05937, over 7171.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2852, pruned_loss=0.04869, over 1414816.38 frames.], batch size: 19, lr: 6.46e-04
+2022-04-29 01:52:32,496 INFO [train.py:763] (7/8) Epoch 11, batch 1050, loss[loss=0.162, simple_loss=0.242, pruned_loss=0.04104, over 7407.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2848, pruned_loss=0.04838, over 1415283.36 frames.], batch size: 17, lr: 6.45e-04
+2022-04-29 01:53:38,696 INFO [train.py:763] (7/8) Epoch 11, batch 1100, loss[loss=0.1801, simple_loss=0.2738, pruned_loss=0.04322, over 7155.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2853, pruned_loss=0.04867, over 1419374.41 frames.], batch size: 19, lr: 6.45e-04
+2022-04-29 01:54:45,805 INFO [train.py:763] (7/8) Epoch 11, batch 1150, loss[loss=0.2302, simple_loss=0.3125, pruned_loss=0.07395, over 4882.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2843, pruned_loss=0.04804, over 1421322.77 frames.], batch size: 52, lr: 6.45e-04
+2022-04-29 01:55:51,965 INFO [train.py:763] (7/8) Epoch 11, batch 1200, loss[loss=0.1995, simple_loss=0.292, pruned_loss=0.05355, over 7118.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2847, pruned_loss=0.04834, over 1424019.70 frames.], batch size: 21, lr: 6.44e-04
+2022-04-29 01:56:57,806 INFO [train.py:763] (7/8) Epoch 11, batch 1250, loss[loss=0.1908, simple_loss=0.2646, pruned_loss=0.05851, over 7001.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2846, pruned_loss=0.04856, over 1424551.60 frames.], batch size: 16, lr: 6.44e-04
+2022-04-29 01:58:03,710 INFO [train.py:763] (7/8) Epoch 11, batch 1300, loss[loss=0.167, simple_loss=0.2692, pruned_loss=0.03245, over 7319.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2837, pruned_loss=0.04779, over 1426149.86 frames.], batch size: 20, lr: 6.44e-04
+2022-04-29 01:59:10,173 INFO [train.py:763] (7/8) Epoch 11, batch 1350, loss[loss=0.2042, simple_loss=0.2965, pruned_loss=0.05595, over 7330.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2836, pruned_loss=0.04778, over 1423310.24 frames.], batch size: 21, lr: 6.43e-04
+2022-04-29 02:00:15,534 INFO [train.py:763] (7/8) Epoch 11, batch 1400, loss[loss=0.1706, simple_loss=0.2752, pruned_loss=0.03302, over 7323.00 frames.], tot_loss[loss=0.1891, simple_loss=0.283, pruned_loss=0.04766, over 1420268.51 frames.], batch size: 21, lr: 6.43e-04
+2022-04-29 02:01:21,173 INFO [train.py:763] (7/8) Epoch 11, batch 1450, loss[loss=0.1844, simple_loss=0.28, pruned_loss=0.04436, over 7062.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2837, pruned_loss=0.04823, over 1420565.75 frames.], batch size: 18, lr: 6.43e-04
+2022-04-29 02:02:28,463 INFO [train.py:763] (7/8) Epoch 11, batch 1500, loss[loss=0.2318, simple_loss=0.3235, pruned_loss=0.07, over 7200.00 frames.], tot_loss[loss=0.19, simple_loss=0.2838, pruned_loss=0.04811, over 1424638.54 frames.], batch size: 23, lr: 6.42e-04
+2022-04-29 02:03:33,967 INFO [train.py:763] (7/8) Epoch 11, batch 1550, loss[loss=0.2098, simple_loss=0.2935, pruned_loss=0.06307, over 7229.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2833, pruned_loss=0.04816, over 1423403.11 frames.], batch size: 20, lr: 6.42e-04
+2022-04-29 02:04:39,643 INFO [train.py:763] (7/8) Epoch 11, batch 1600, loss[loss=0.156, simple_loss=0.2456, pruned_loss=0.03324, over 7357.00 frames.], tot_loss[loss=0.1904, simple_loss=0.284, pruned_loss=0.04836, over 1424572.31 frames.], batch size: 19, lr: 6.42e-04
+2022-04-29 02:06:04,023 INFO [train.py:763] (7/8) Epoch 11, batch 1650, loss[loss=0.1787, simple_loss=0.2833, pruned_loss=0.0371, over 7378.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2838, pruned_loss=0.04836, over 1425200.87 frames.], batch size: 23, lr: 6.42e-04
+2022-04-29 02:07:17,974 INFO [train.py:763] (7/8) Epoch 11, batch 1700, loss[loss=0.1911, simple_loss=0.2823, pruned_loss=0.04992, over 7211.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2845, pruned_loss=0.04849, over 1426917.23 frames.], batch size: 21, lr: 6.41e-04
+2022-04-29 02:08:33,282 INFO [train.py:763] (7/8) Epoch 11, batch 1750, loss[loss=0.216, simple_loss=0.3145, pruned_loss=0.05876, over 7136.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2849, pruned_loss=0.04836, over 1427100.22 frames.], batch size: 26, lr: 6.41e-04
+2022-04-29 02:09:47,993 INFO [train.py:763] (7/8) Epoch 11, batch 1800, loss[loss=0.1334, simple_loss=0.2184, pruned_loss=0.02419, over 6989.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2842, pruned_loss=0.04823, over 1427329.78 frames.], batch size: 16, lr: 6.41e-04
+2022-04-29 02:11:03,178 INFO [train.py:763] (7/8) Epoch 11, batch 1850, loss[loss=0.2208, simple_loss=0.3069, pruned_loss=0.06732, over 7160.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2836, pruned_loss=0.0484, over 1426144.63 frames.], batch size: 26, lr: 6.40e-04
+2022-04-29 02:12:18,085 INFO [train.py:763] (7/8) Epoch 11, batch 1900, loss[loss=0.1893, simple_loss=0.2823, pruned_loss=0.04813, over 7432.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2838, pruned_loss=0.04862, over 1428149.55 frames.], batch size: 20, lr: 6.40e-04
+2022-04-29 02:13:32,355 INFO [train.py:763] (7/8) Epoch 11, batch 1950, loss[loss=0.1583, simple_loss=0.2449, pruned_loss=0.0359, over 7012.00 frames.], tot_loss[loss=0.191, simple_loss=0.2842, pruned_loss=0.04893, over 1426957.73 frames.], batch size: 16, lr: 6.40e-04
+2022-04-29 02:14:38,133 INFO [train.py:763] (7/8) Epoch 11, batch 2000, loss[loss=0.1969, simple_loss=0.2973, pruned_loss=0.0483, over 6126.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2841, pruned_loss=0.04874, over 1424624.19 frames.], batch size: 37, lr: 6.39e-04
+2022-04-29 02:15:44,454 INFO [train.py:763] (7/8) Epoch 11, batch 2050, loss[loss=0.1816, simple_loss=0.2742, pruned_loss=0.04451, over 7377.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2829, pruned_loss=0.04827, over 1422623.35 frames.], batch size: 23, lr: 6.39e-04
+2022-04-29 02:16:50,760 INFO [train.py:763] (7/8) Epoch 11, batch 2100, loss[loss=0.2413, simple_loss=0.3212, pruned_loss=0.08071, over 6769.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2833, pruned_loss=0.04818, over 1426799.31 frames.], batch size: 31, lr: 6.39e-04
+2022-04-29 02:17:57,173 INFO [train.py:763] (7/8) Epoch 11, batch 2150, loss[loss=0.1492, simple_loss=0.238, pruned_loss=0.03022, over 6761.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2835, pruned_loss=0.04861, over 1421864.61 frames.], batch size: 15, lr: 6.38e-04
+2022-04-29 02:19:03,279 INFO [train.py:763] (7/8) Epoch 11, batch 2200, loss[loss=0.1815, simple_loss=0.2771, pruned_loss=0.04295, over 7449.00 frames.], tot_loss[loss=0.1908, simple_loss=0.284, pruned_loss=0.04882, over 1425814.05 frames.], batch size: 20, lr: 6.38e-04
+2022-04-29 02:20:09,541 INFO [train.py:763] (7/8) Epoch 11, batch 2250, loss[loss=0.1707, simple_loss=0.263, pruned_loss=0.03915, over 7137.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2835, pruned_loss=0.0486, over 1425092.51 frames.], batch size: 17, lr: 6.38e-04
+2022-04-29 02:21:16,315 INFO [train.py:763] (7/8) Epoch 11, batch 2300, loss[loss=0.1708, simple_loss=0.2661, pruned_loss=0.03777, over 7361.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2851, pruned_loss=0.04894, over 1423837.67 frames.], batch size: 19, lr: 6.38e-04
+2022-04-29 02:22:22,098 INFO [train.py:763] (7/8) Epoch 11, batch 2350, loss[loss=0.2117, simple_loss=0.306, pruned_loss=0.05864, over 7280.00 frames.], tot_loss[loss=0.191, simple_loss=0.2849, pruned_loss=0.04859, over 1425127.19 frames.], batch size: 24, lr: 6.37e-04
+2022-04-29 02:23:28,192 INFO [train.py:763] (7/8) Epoch 11, batch 2400, loss[loss=0.1926, simple_loss=0.2942, pruned_loss=0.04552, over 7110.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2857, pruned_loss=0.04868, over 1427288.58 frames.], batch size: 21, lr: 6.37e-04
+2022-04-29 02:24:33,629 INFO [train.py:763] (7/8) Epoch 11, batch 2450, loss[loss=0.1987, simple_loss=0.2976, pruned_loss=0.04989, over 7230.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2854, pruned_loss=0.04878, over 1425018.05 frames.], batch size: 20, lr: 6.37e-04
+2022-04-29 02:25:39,235 INFO [train.py:763] (7/8) Epoch 11, batch 2500, loss[loss=0.1897, simple_loss=0.2738, pruned_loss=0.05282, over 7075.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2856, pruned_loss=0.04915, over 1425002.98 frames.], batch size: 18, lr: 6.36e-04
+2022-04-29 02:26:45,654 INFO [train.py:763] (7/8) Epoch 11, batch 2550, loss[loss=0.1532, simple_loss=0.2371, pruned_loss=0.03467, over 7283.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2854, pruned_loss=0.04851, over 1428262.51 frames.], batch size: 17, lr: 6.36e-04
+2022-04-29 02:27:50,874 INFO [train.py:763] (7/8) Epoch 11, batch 2600, loss[loss=0.225, simple_loss=0.3015, pruned_loss=0.07425, over 7278.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2847, pruned_loss=0.04855, over 1422707.93 frames.], batch size: 24, lr: 6.36e-04
+2022-04-29 02:28:56,403 INFO [train.py:763] (7/8) Epoch 11, batch 2650, loss[loss=0.2189, simple_loss=0.2948, pruned_loss=0.07152, over 7262.00 frames.], tot_loss[loss=0.1912, simple_loss=0.285, pruned_loss=0.04869, over 1419220.71 frames.], batch size: 19, lr: 6.36e-04
+2022-04-29 02:30:03,356 INFO [train.py:763] (7/8) Epoch 11, batch 2700, loss[loss=0.2075, simple_loss=0.3005, pruned_loss=0.05722, over 7285.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2846, pruned_loss=0.04838, over 1422705.35 frames.], batch size: 25, lr: 6.35e-04
+2022-04-29 02:31:08,828 INFO [train.py:763] (7/8) Epoch 11, batch 2750, loss[loss=0.173, simple_loss=0.2624, pruned_loss=0.04179, over 7428.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2838, pruned_loss=0.0482, over 1425990.13 frames.], batch size: 20, lr: 6.35e-04
+2022-04-29 02:32:14,656 INFO [train.py:763] (7/8) Epoch 11, batch 2800, loss[loss=0.2275, simple_loss=0.3141, pruned_loss=0.07044, over 7109.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2845, pruned_loss=0.04884, over 1427082.89 frames.], batch size: 21, lr: 6.35e-04
+2022-04-29 02:33:21,163 INFO [train.py:763] (7/8) Epoch 11, batch 2850, loss[loss=0.1883, simple_loss=0.2927, pruned_loss=0.04194, over 7322.00 frames.], tot_loss[loss=0.191, simple_loss=0.2846, pruned_loss=0.04874, over 1429440.11 frames.], batch size: 21, lr: 6.34e-04
+2022-04-29 02:34:28,452 INFO [train.py:763] (7/8) Epoch 11, batch 2900, loss[loss=0.1754, simple_loss=0.2819, pruned_loss=0.0345, over 7276.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2852, pruned_loss=0.04884, over 1426250.88 frames.], batch size: 24, lr: 6.34e-04
+2022-04-29 02:35:35,079 INFO [train.py:763] (7/8) Epoch 11, batch 2950, loss[loss=0.1922, simple_loss=0.294, pruned_loss=0.04522, over 7228.00 frames.], tot_loss[loss=0.192, simple_loss=0.2857, pruned_loss=0.04917, over 1421444.84 frames.], batch size: 21, lr: 6.34e-04
+2022-04-29 02:36:40,654 INFO [train.py:763] (7/8) Epoch 11, batch 3000, loss[loss=0.2172, simple_loss=0.3114, pruned_loss=0.06153, over 7308.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2849, pruned_loss=0.04914, over 1422848.61 frames.], batch size: 25, lr: 6.33e-04
+2022-04-29 02:36:40,655 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 02:36:55,968 INFO [train.py:792] (7/8) Epoch 11, validation: loss=0.1677, simple_loss=0.2702, pruned_loss=0.03262, over 698248.00 frames.
+2022-04-29 02:38:01,332 INFO [train.py:763] (7/8) Epoch 11, batch 3050, loss[loss=0.1924, simple_loss=0.292, pruned_loss=0.04641, over 7375.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2863, pruned_loss=0.04934, over 1420167.70 frames.], batch size: 23, lr: 6.33e-04
+2022-04-29 02:39:07,047 INFO [train.py:763] (7/8) Epoch 11, batch 3100, loss[loss=0.1707, simple_loss=0.2584, pruned_loss=0.04153, over 7336.00 frames.], tot_loss[loss=0.191, simple_loss=0.2845, pruned_loss=0.0487, over 1422248.68 frames.], batch size: 20, lr: 6.33e-04
+2022-04-29 02:40:14,574 INFO [train.py:763] (7/8) Epoch 11, batch 3150, loss[loss=0.2011, simple_loss=0.2984, pruned_loss=0.05194, over 7377.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2843, pruned_loss=0.0487, over 1423959.65 frames.], batch size: 23, lr: 6.33e-04
+2022-04-29 02:41:19,868 INFO [train.py:763] (7/8) Epoch 11, batch 3200, loss[loss=0.1882, simple_loss=0.2936, pruned_loss=0.04145, over 7113.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2849, pruned_loss=0.04916, over 1423822.49 frames.], batch size: 21, lr: 6.32e-04
+2022-04-29 02:42:26,211 INFO [train.py:763] (7/8) Epoch 11, batch 3250, loss[loss=0.1681, simple_loss=0.2669, pruned_loss=0.03469, over 7425.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2844, pruned_loss=0.04888, over 1424669.30 frames.], batch size: 21, lr: 6.32e-04
+2022-04-29 02:43:31,325 INFO [train.py:763] (7/8) Epoch 11, batch 3300, loss[loss=0.1682, simple_loss=0.2633, pruned_loss=0.03658, over 7002.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2863, pruned_loss=0.04978, over 1425129.90 frames.], batch size: 16, lr: 6.32e-04
+2022-04-29 02:44:36,757 INFO [train.py:763] (7/8) Epoch 11, batch 3350, loss[loss=0.1878, simple_loss=0.2786, pruned_loss=0.04851, over 7286.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2859, pruned_loss=0.04877, over 1426208.46 frames.], batch size: 18, lr: 6.31e-04
+2022-04-29 02:45:42,406 INFO [train.py:763] (7/8) Epoch 11, batch 3400, loss[loss=0.1881, simple_loss=0.2812, pruned_loss=0.04752, over 6347.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2859, pruned_loss=0.04868, over 1421265.69 frames.], batch size: 37, lr: 6.31e-04
+2022-04-29 02:46:49,536 INFO [train.py:763] (7/8) Epoch 11, batch 3450, loss[loss=0.1852, simple_loss=0.2821, pruned_loss=0.0442, over 7132.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2854, pruned_loss=0.04862, over 1419449.04 frames.], batch size: 21, lr: 6.31e-04
+2022-04-29 02:47:56,131 INFO [train.py:763] (7/8) Epoch 11, batch 3500, loss[loss=0.1978, simple_loss=0.2969, pruned_loss=0.04933, over 7322.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2851, pruned_loss=0.04811, over 1425586.50 frames.], batch size: 21, lr: 6.31e-04
+2022-04-29 02:49:02,219 INFO [train.py:763] (7/8) Epoch 11, batch 3550, loss[loss=0.163, simple_loss=0.2488, pruned_loss=0.03861, over 7002.00 frames.], tot_loss[loss=0.1907, simple_loss=0.285, pruned_loss=0.04826, over 1424531.46 frames.], batch size: 16, lr: 6.30e-04
+2022-04-29 02:50:08,015 INFO [train.py:763] (7/8) Epoch 11, batch 3600, loss[loss=0.2089, simple_loss=0.2996, pruned_loss=0.05914, over 7236.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2859, pruned_loss=0.04854, over 1426060.27 frames.], batch size: 20, lr: 6.30e-04
+2022-04-29 02:51:13,371 INFO [train.py:763] (7/8) Epoch 11, batch 3650, loss[loss=0.2001, simple_loss=0.2917, pruned_loss=0.05427, over 7428.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2855, pruned_loss=0.04861, over 1424900.88 frames.], batch size: 20, lr: 6.30e-04
+2022-04-29 02:52:20,073 INFO [train.py:763] (7/8) Epoch 11, batch 3700, loss[loss=0.1855, simple_loss=0.28, pruned_loss=0.04552, over 6825.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2852, pruned_loss=0.04881, over 1421646.59 frames.], batch size: 31, lr: 6.29e-04
+2022-04-29 02:53:25,487 INFO [train.py:763] (7/8) Epoch 11, batch 3750, loss[loss=0.2182, simple_loss=0.3192, pruned_loss=0.0586, over 7359.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2839, pruned_loss=0.04777, over 1425555.19 frames.], batch size: 23, lr: 6.29e-04
+2022-04-29 02:54:30,959 INFO [train.py:763] (7/8) Epoch 11, batch 3800, loss[loss=0.1923, simple_loss=0.2902, pruned_loss=0.04718, over 7169.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2832, pruned_loss=0.04734, over 1428191.81 frames.], batch size: 26, lr: 6.29e-04
+2022-04-29 02:55:36,112 INFO [train.py:763] (7/8) Epoch 11, batch 3850, loss[loss=0.1607, simple_loss=0.2734, pruned_loss=0.02403, over 7107.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2829, pruned_loss=0.04674, over 1428918.12 frames.], batch size: 21, lr: 6.29e-04
+2022-04-29 02:56:41,392 INFO [train.py:763] (7/8) Epoch 11, batch 3900, loss[loss=0.1618, simple_loss=0.2551, pruned_loss=0.03421, over 7429.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2828, pruned_loss=0.0468, over 1429882.51 frames.], batch size: 20, lr: 6.28e-04
+2022-04-29 02:57:46,967 INFO [train.py:763] (7/8) Epoch 11, batch 3950, loss[loss=0.1896, simple_loss=0.2871, pruned_loss=0.04606, over 7228.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2829, pruned_loss=0.04722, over 1431240.48 frames.], batch size: 20, lr: 6.28e-04
+2022-04-29 02:58:52,100 INFO [train.py:763] (7/8) Epoch 11, batch 4000, loss[loss=0.1772, simple_loss=0.2785, pruned_loss=0.03795, over 7413.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2839, pruned_loss=0.04797, over 1425651.52 frames.], batch size: 21, lr: 6.28e-04
+2022-04-29 02:59:57,364 INFO [train.py:763] (7/8) Epoch 11, batch 4050, loss[loss=0.1769, simple_loss=0.2758, pruned_loss=0.03897, over 7433.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2834, pruned_loss=0.04818, over 1424185.41 frames.], batch size: 20, lr: 6.27e-04
+2022-04-29 03:01:03,199 INFO [train.py:763] (7/8) Epoch 11, batch 4100, loss[loss=0.189, simple_loss=0.2885, pruned_loss=0.04471, over 7333.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2838, pruned_loss=0.04801, over 1421841.28 frames.], batch size: 20, lr: 6.27e-04
+2022-04-29 03:02:08,255 INFO [train.py:763] (7/8) Epoch 11, batch 4150, loss[loss=0.2038, simple_loss=0.2978, pruned_loss=0.05489, over 7241.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2836, pruned_loss=0.04769, over 1421452.61 frames.], batch size: 20, lr: 6.27e-04
+2022-04-29 03:03:14,704 INFO [train.py:763] (7/8) Epoch 11, batch 4200, loss[loss=0.1811, simple_loss=0.2762, pruned_loss=0.04301, over 7329.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2836, pruned_loss=0.04757, over 1421291.09 frames.], batch size: 22, lr: 6.27e-04
+2022-04-29 03:04:21,500 INFO [train.py:763] (7/8) Epoch 11, batch 4250, loss[loss=0.1748, simple_loss=0.2636, pruned_loss=0.04307, over 7414.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2839, pruned_loss=0.04814, over 1424569.65 frames.], batch size: 18, lr: 6.26e-04
+2022-04-29 03:05:27,643 INFO [train.py:763] (7/8) Epoch 11, batch 4300, loss[loss=0.1874, simple_loss=0.2821, pruned_loss=0.04634, over 7220.00 frames.], tot_loss[loss=0.189, simple_loss=0.2827, pruned_loss=0.04765, over 1418430.08 frames.], batch size: 20, lr: 6.26e-04
+2022-04-29 03:06:35,261 INFO [train.py:763] (7/8) Epoch 11, batch 4350, loss[loss=0.1951, simple_loss=0.2951, pruned_loss=0.04761, over 7209.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2814, pruned_loss=0.04725, over 1420186.45 frames.], batch size: 22, lr: 6.26e-04
+2022-04-29 03:07:41,515 INFO [train.py:763] (7/8) Epoch 11, batch 4400, loss[loss=0.2812, simple_loss=0.3564, pruned_loss=0.103, over 7326.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2816, pruned_loss=0.04725, over 1418542.66 frames.], batch size: 21, lr: 6.25e-04
+2022-04-29 03:08:47,814 INFO [train.py:763] (7/8) Epoch 11, batch 4450, loss[loss=0.2158, simple_loss=0.3146, pruned_loss=0.05849, over 6396.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2807, pruned_loss=0.0474, over 1405119.98 frames.], batch size: 38, lr: 6.25e-04
+2022-04-29 03:09:54,267 INFO [train.py:763] (7/8) Epoch 11, batch 4500, loss[loss=0.2016, simple_loss=0.2973, pruned_loss=0.05294, over 6631.00 frames.], tot_loss[loss=0.19, simple_loss=0.282, pruned_loss=0.04898, over 1389717.87 frames.], batch size: 38, lr: 6.25e-04
+2022-04-29 03:10:59,848 INFO [train.py:763] (7/8) Epoch 11, batch 4550, loss[loss=0.2303, simple_loss=0.3019, pruned_loss=0.07941, over 5285.00 frames.], tot_loss[loss=0.1927, simple_loss=0.284, pruned_loss=0.05068, over 1350689.41 frames.], batch size: 52, lr: 6.25e-04
+2022-04-29 03:12:38,236 INFO [train.py:763] (7/8) Epoch 12, batch 0, loss[loss=0.1717, simple_loss=0.2707, pruned_loss=0.03633, over 7142.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2707, pruned_loss=0.03633, over 7142.00 frames.], batch size: 20, lr: 6.03e-04
+2022-04-29 03:13:44,625 INFO [train.py:763] (7/8) Epoch 12, batch 50, loss[loss=0.1767, simple_loss=0.274, pruned_loss=0.03973, over 7243.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2802, pruned_loss=0.04606, over 319155.80 frames.], batch size: 20, lr: 6.03e-04
+2022-04-29 03:14:50,365 INFO [train.py:763] (7/8) Epoch 12, batch 100, loss[loss=0.2053, simple_loss=0.2913, pruned_loss=0.05965, over 7207.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2828, pruned_loss=0.04719, over 565153.00 frames.], batch size: 23, lr: 6.03e-04
+2022-04-29 03:15:56,454 INFO [train.py:763] (7/8) Epoch 12, batch 150, loss[loss=0.1845, simple_loss=0.2801, pruned_loss=0.04444, over 7149.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2845, pruned_loss=0.0472, over 754313.06 frames.], batch size: 20, lr: 6.03e-04
+2022-04-29 03:17:02,809 INFO [train.py:763] (7/8) Epoch 12, batch 200, loss[loss=0.1872, simple_loss=0.2845, pruned_loss=0.04498, over 7143.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2833, pruned_loss=0.04728, over 901064.06 frames.], batch size: 20, lr: 6.02e-04
+2022-04-29 03:18:09,065 INFO [train.py:763] (7/8) Epoch 12, batch 250, loss[loss=0.1661, simple_loss=0.246, pruned_loss=0.04305, over 7218.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2827, pruned_loss=0.04708, over 1014887.78 frames.], batch size: 16, lr: 6.02e-04
+2022-04-29 03:19:15,288 INFO [train.py:763] (7/8) Epoch 12, batch 300, loss[loss=0.1661, simple_loss=0.2563, pruned_loss=0.03795, over 7142.00 frames.], tot_loss[loss=0.187, simple_loss=0.2814, pruned_loss=0.04631, over 1105078.90 frames.], batch size: 20, lr: 6.02e-04
+2022-04-29 03:20:20,578 INFO [train.py:763] (7/8) Epoch 12, batch 350, loss[loss=0.185, simple_loss=0.2843, pruned_loss=0.04288, over 7071.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2815, pruned_loss=0.04569, over 1177004.27 frames.], batch size: 28, lr: 6.01e-04
+2022-04-29 03:21:26,182 INFO [train.py:763] (7/8) Epoch 12, batch 400, loss[loss=0.1621, simple_loss=0.268, pruned_loss=0.02816, over 7355.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2819, pruned_loss=0.04593, over 1234118.66 frames.], batch size: 19, lr: 6.01e-04
+2022-04-29 03:22:31,844 INFO [train.py:763] (7/8) Epoch 12, batch 450, loss[loss=0.1904, simple_loss=0.2945, pruned_loss=0.0431, over 7328.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2819, pruned_loss=0.04617, over 1277563.78 frames.], batch size: 21, lr: 6.01e-04
+2022-04-29 03:23:38,042 INFO [train.py:763] (7/8) Epoch 12, batch 500, loss[loss=0.205, simple_loss=0.2955, pruned_loss=0.05729, over 6286.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2809, pruned_loss=0.04629, over 1310451.50 frames.], batch size: 37, lr: 6.01e-04
+2022-04-29 03:24:43,952 INFO [train.py:763] (7/8) Epoch 12, batch 550, loss[loss=0.2035, simple_loss=0.3074, pruned_loss=0.0498, over 7403.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2819, pruned_loss=0.0469, over 1332533.15 frames.], batch size: 23, lr: 6.00e-04
+2022-04-29 03:25:49,975 INFO [train.py:763] (7/8) Epoch 12, batch 600, loss[loss=0.1487, simple_loss=0.2418, pruned_loss=0.02781, over 7197.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2806, pruned_loss=0.04638, over 1346753.46 frames.], batch size: 16, lr: 6.00e-04
+2022-04-29 03:26:55,899 INFO [train.py:763] (7/8) Epoch 12, batch 650, loss[loss=0.19, simple_loss=0.2744, pruned_loss=0.05285, over 7275.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2812, pruned_loss=0.04652, over 1366348.88 frames.], batch size: 18, lr: 6.00e-04
+2022-04-29 03:28:02,308 INFO [train.py:763] (7/8) Epoch 12, batch 700, loss[loss=0.1597, simple_loss=0.2442, pruned_loss=0.03764, over 6840.00 frames.], tot_loss[loss=0.187, simple_loss=0.2817, pruned_loss=0.04615, over 1383907.05 frames.], batch size: 15, lr: 6.00e-04
+2022-04-29 03:29:08,001 INFO [train.py:763] (7/8) Epoch 12, batch 750, loss[loss=0.1977, simple_loss=0.2967, pruned_loss=0.04938, over 7216.00 frames.], tot_loss[loss=0.187, simple_loss=0.2822, pruned_loss=0.0459, over 1396250.19 frames.], batch size: 23, lr: 5.99e-04
+2022-04-29 03:30:14,236 INFO [train.py:763] (7/8) Epoch 12, batch 800, loss[loss=0.1885, simple_loss=0.2868, pruned_loss=0.04509, over 7214.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2821, pruned_loss=0.04608, over 1405154.25 frames.], batch size: 22, lr: 5.99e-04
+2022-04-29 03:31:20,660 INFO [train.py:763] (7/8) Epoch 12, batch 850, loss[loss=0.1658, simple_loss=0.2478, pruned_loss=0.04187, over 7126.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2831, pruned_loss=0.04676, over 1412440.44 frames.], batch size: 17, lr: 5.99e-04
+2022-04-29 03:32:27,851 INFO [train.py:763] (7/8) Epoch 12, batch 900, loss[loss=0.1809, simple_loss=0.2764, pruned_loss=0.04269, over 7324.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2821, pruned_loss=0.04618, over 1415567.47 frames.], batch size: 20, lr: 5.99e-04
+2022-04-29 03:33:44,143 INFO [train.py:763] (7/8) Epoch 12, batch 950, loss[loss=0.1875, simple_loss=0.2943, pruned_loss=0.04041, over 7135.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2824, pruned_loss=0.04646, over 1415750.45 frames.], batch size: 26, lr: 5.98e-04
+2022-04-29 03:34:49,711 INFO [train.py:763] (7/8) Epoch 12, batch 1000, loss[loss=0.2013, simple_loss=0.2944, pruned_loss=0.05408, over 6524.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2831, pruned_loss=0.04686, over 1416249.56 frames.], batch size: 38, lr: 5.98e-04
+2022-04-29 03:35:56,187 INFO [train.py:763] (7/8) Epoch 12, batch 1050, loss[loss=0.1956, simple_loss=0.2829, pruned_loss=0.05418, over 7246.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2818, pruned_loss=0.04629, over 1417040.55 frames.], batch size: 19, lr: 5.98e-04
+2022-04-29 03:37:02,305 INFO [train.py:763] (7/8) Epoch 12, batch 1100, loss[loss=0.1899, simple_loss=0.2878, pruned_loss=0.04605, over 7380.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2818, pruned_loss=0.04635, over 1423023.42 frames.], batch size: 23, lr: 5.97e-04
+2022-04-29 03:38:08,863 INFO [train.py:763] (7/8) Epoch 12, batch 1150, loss[loss=0.1993, simple_loss=0.2964, pruned_loss=0.05113, over 7333.00 frames.], tot_loss[loss=0.188, simple_loss=0.2824, pruned_loss=0.04683, over 1425421.18 frames.], batch size: 20, lr: 5.97e-04
+2022-04-29 03:39:15,127 INFO [train.py:763] (7/8) Epoch 12, batch 1200, loss[loss=0.2195, simple_loss=0.3073, pruned_loss=0.06579, over 5281.00 frames.], tot_loss[loss=0.1878, simple_loss=0.282, pruned_loss=0.04685, over 1422473.78 frames.], batch size: 53, lr: 5.97e-04
+2022-04-29 03:40:21,640 INFO [train.py:763] (7/8) Epoch 12, batch 1250, loss[loss=0.1574, simple_loss=0.2662, pruned_loss=0.02428, over 7161.00 frames.], tot_loss[loss=0.1875, simple_loss=0.282, pruned_loss=0.04649, over 1419540.88 frames.], batch size: 19, lr: 5.97e-04
+2022-04-29 03:41:28,277 INFO [train.py:763] (7/8) Epoch 12, batch 1300, loss[loss=0.1792, simple_loss=0.2651, pruned_loss=0.04665, over 7071.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2811, pruned_loss=0.04593, over 1420337.36 frames.], batch size: 18, lr: 5.96e-04
+2022-04-29 03:42:33,975 INFO [train.py:763] (7/8) Epoch 12, batch 1350, loss[loss=0.2109, simple_loss=0.2958, pruned_loss=0.063, over 4934.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2826, pruned_loss=0.04681, over 1417590.36 frames.], batch size: 52, lr: 5.96e-04
+2022-04-29 03:43:39,840 INFO [train.py:763] (7/8) Epoch 12, batch 1400, loss[loss=0.1873, simple_loss=0.2797, pruned_loss=0.04742, over 7252.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2834, pruned_loss=0.04747, over 1416729.55 frames.], batch size: 25, lr: 5.96e-04
+2022-04-29 03:44:45,274 INFO [train.py:763] (7/8) Epoch 12, batch 1450, loss[loss=0.206, simple_loss=0.3098, pruned_loss=0.0511, over 7320.00 frames.], tot_loss[loss=0.188, simple_loss=0.2828, pruned_loss=0.0466, over 1414083.05 frames.], batch size: 21, lr: 5.96e-04
+2022-04-29 03:45:51,855 INFO [train.py:763] (7/8) Epoch 12, batch 1500, loss[loss=0.192, simple_loss=0.2856, pruned_loss=0.04914, over 7216.00 frames.], tot_loss[loss=0.1872, simple_loss=0.282, pruned_loss=0.04621, over 1417571.55 frames.], batch size: 23, lr: 5.95e-04
+2022-04-29 03:46:59,232 INFO [train.py:763] (7/8) Epoch 12, batch 1550, loss[loss=0.2038, simple_loss=0.3055, pruned_loss=0.05109, over 7036.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2812, pruned_loss=0.04584, over 1419147.19 frames.], batch size: 28, lr: 5.95e-04
+2022-04-29 03:48:05,690 INFO [train.py:763] (7/8) Epoch 12, batch 1600, loss[loss=0.1866, simple_loss=0.2797, pruned_loss=0.04677, over 7311.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2815, pruned_loss=0.04604, over 1417896.86 frames.], batch size: 25, lr: 5.95e-04
+2022-04-29 03:49:11,835 INFO [train.py:763] (7/8) Epoch 12, batch 1650, loss[loss=0.2183, simple_loss=0.3152, pruned_loss=0.06072, over 7315.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2811, pruned_loss=0.04562, over 1421148.98 frames.], batch size: 24, lr: 5.95e-04
+2022-04-29 03:50:17,601 INFO [train.py:763] (7/8) Epoch 12, batch 1700, loss[loss=0.1663, simple_loss=0.2516, pruned_loss=0.04044, over 7124.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2811, pruned_loss=0.04589, over 1417897.29 frames.], batch size: 17, lr: 5.94e-04
+2022-04-29 03:51:23,283 INFO [train.py:763] (7/8) Epoch 12, batch 1750, loss[loss=0.2426, simple_loss=0.3218, pruned_loss=0.08173, over 7180.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2805, pruned_loss=0.04596, over 1421478.33 frames.], batch size: 26, lr: 5.94e-04
+2022-04-29 03:52:29,201 INFO [train.py:763] (7/8) Epoch 12, batch 1800, loss[loss=0.1614, simple_loss=0.2609, pruned_loss=0.03097, over 7006.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2818, pruned_loss=0.0468, over 1426716.50 frames.], batch size: 16, lr: 5.94e-04
+2022-04-29 03:53:35,394 INFO [train.py:763] (7/8) Epoch 12, batch 1850, loss[loss=0.1797, simple_loss=0.2842, pruned_loss=0.03762, over 7339.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2817, pruned_loss=0.04667, over 1427343.41 frames.], batch size: 22, lr: 5.94e-04
+2022-04-29 03:54:41,524 INFO [train.py:763] (7/8) Epoch 12, batch 1900, loss[loss=0.176, simple_loss=0.2743, pruned_loss=0.03883, over 7226.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2819, pruned_loss=0.04665, over 1427902.38 frames.], batch size: 20, lr: 5.93e-04
+2022-04-29 03:55:47,387 INFO [train.py:763] (7/8) Epoch 12, batch 1950, loss[loss=0.1495, simple_loss=0.245, pruned_loss=0.02697, over 7286.00 frames.], tot_loss[loss=0.1876, simple_loss=0.282, pruned_loss=0.04656, over 1428009.88 frames.], batch size: 17, lr: 5.93e-04
+2022-04-29 03:56:53,894 INFO [train.py:763] (7/8) Epoch 12, batch 2000, loss[loss=0.1732, simple_loss=0.2597, pruned_loss=0.04331, over 6993.00 frames.], tot_loss[loss=0.1867, simple_loss=0.281, pruned_loss=0.04624, over 1427961.38 frames.], batch size: 16, lr: 5.93e-04
+2022-04-29 03:57:59,765 INFO [train.py:763] (7/8) Epoch 12, batch 2050, loss[loss=0.1761, simple_loss=0.2692, pruned_loss=0.04146, over 7165.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2803, pruned_loss=0.04591, over 1421204.24 frames.], batch size: 19, lr: 5.93e-04
+2022-04-29 03:59:05,508 INFO [train.py:763] (7/8) Epoch 12, batch 2100, loss[loss=0.1932, simple_loss=0.2906, pruned_loss=0.0479, over 7159.00 frames.], tot_loss[loss=0.1857, simple_loss=0.28, pruned_loss=0.04564, over 1422112.85 frames.], batch size: 19, lr: 5.92e-04
+2022-04-29 04:00:11,338 INFO [train.py:763] (7/8) Epoch 12, batch 2150, loss[loss=0.157, simple_loss=0.2475, pruned_loss=0.03322, over 7265.00 frames.], tot_loss[loss=0.1851, simple_loss=0.28, pruned_loss=0.04513, over 1421986.80 frames.], batch size: 18, lr: 5.92e-04
+2022-04-29 04:01:17,168 INFO [train.py:763] (7/8) Epoch 12, batch 2200, loss[loss=0.1857, simple_loss=0.2883, pruned_loss=0.04159, over 7324.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2802, pruned_loss=0.04536, over 1422794.01 frames.], batch size: 20, lr: 5.92e-04
+2022-04-29 04:02:23,234 INFO [train.py:763] (7/8) Epoch 12, batch 2250, loss[loss=0.209, simple_loss=0.3077, pruned_loss=0.05512, over 7041.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2799, pruned_loss=0.04524, over 1420819.10 frames.], batch size: 28, lr: 5.91e-04
+2022-04-29 04:03:29,743 INFO [train.py:763] (7/8) Epoch 12, batch 2300, loss[loss=0.1903, simple_loss=0.2892, pruned_loss=0.04571, over 7104.00 frames.], tot_loss[loss=0.186, simple_loss=0.2807, pruned_loss=0.04568, over 1424788.73 frames.], batch size: 21, lr: 5.91e-04
+2022-04-29 04:04:36,303 INFO [train.py:763] (7/8) Epoch 12, batch 2350, loss[loss=0.1709, simple_loss=0.2673, pruned_loss=0.03727, over 7160.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2805, pruned_loss=0.0455, over 1425854.45 frames.], batch size: 19, lr: 5.91e-04
+2022-04-29 04:05:42,061 INFO [train.py:763] (7/8) Epoch 12, batch 2400, loss[loss=0.1616, simple_loss=0.2607, pruned_loss=0.03121, over 7148.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2801, pruned_loss=0.04555, over 1426242.77 frames.], batch size: 17, lr: 5.91e-04
+2022-04-29 04:06:47,911 INFO [train.py:763] (7/8) Epoch 12, batch 2450, loss[loss=0.1935, simple_loss=0.2975, pruned_loss=0.04477, over 7226.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2808, pruned_loss=0.04577, over 1425391.32 frames.], batch size: 21, lr: 5.90e-04
+2022-04-29 04:07:54,993 INFO [train.py:763] (7/8) Epoch 12, batch 2500, loss[loss=0.2071, simple_loss=0.2991, pruned_loss=0.05758, over 7275.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2814, pruned_loss=0.04588, over 1426166.47 frames.], batch size: 18, lr: 5.90e-04
+2022-04-29 04:09:01,339 INFO [train.py:763] (7/8) Epoch 12, batch 2550, loss[loss=0.1733, simple_loss=0.2585, pruned_loss=0.04402, over 6843.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2814, pruned_loss=0.04642, over 1428224.18 frames.], batch size: 15, lr: 5.90e-04
+2022-04-29 04:10:08,009 INFO [train.py:763] (7/8) Epoch 12, batch 2600, loss[loss=0.1905, simple_loss=0.2753, pruned_loss=0.05286, over 7221.00 frames.], tot_loss[loss=0.1868, simple_loss=0.281, pruned_loss=0.04635, over 1424571.65 frames.], batch size: 16, lr: 5.90e-04
+2022-04-29 04:11:13,667 INFO [train.py:763] (7/8) Epoch 12, batch 2650, loss[loss=0.1701, simple_loss=0.2538, pruned_loss=0.04316, over 7000.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2805, pruned_loss=0.04582, over 1423049.80 frames.], batch size: 16, lr: 5.89e-04
+2022-04-29 04:12:19,570 INFO [train.py:763] (7/8) Epoch 12, batch 2700, loss[loss=0.1624, simple_loss=0.2544, pruned_loss=0.03516, over 6991.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2812, pruned_loss=0.04615, over 1424176.39 frames.], batch size: 16, lr: 5.89e-04
+2022-04-29 04:13:25,138 INFO [train.py:763] (7/8) Epoch 12, batch 2750, loss[loss=0.175, simple_loss=0.2757, pruned_loss=0.03717, over 7115.00 frames.], tot_loss[loss=0.186, simple_loss=0.2805, pruned_loss=0.04572, over 1421687.54 frames.], batch size: 21, lr: 5.89e-04
+2022-04-29 04:14:30,853 INFO [train.py:763] (7/8) Epoch 12, batch 2800, loss[loss=0.1562, simple_loss=0.245, pruned_loss=0.03365, over 7124.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2815, pruned_loss=0.04589, over 1421395.94 frames.], batch size: 17, lr: 5.89e-04
+2022-04-29 04:15:37,568 INFO [train.py:763] (7/8) Epoch 12, batch 2850, loss[loss=0.1889, simple_loss=0.292, pruned_loss=0.04292, over 7377.00 frames.], tot_loss[loss=0.1869, simple_loss=0.282, pruned_loss=0.0459, over 1426800.65 frames.], batch size: 23, lr: 5.88e-04
+2022-04-29 04:16:43,210 INFO [train.py:763] (7/8) Epoch 12, batch 2900, loss[loss=0.1802, simple_loss=0.2768, pruned_loss=0.0418, over 7363.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2828, pruned_loss=0.04586, over 1425196.78 frames.], batch size: 19, lr: 5.88e-04
+2022-04-29 04:17:49,209 INFO [train.py:763] (7/8) Epoch 12, batch 2950, loss[loss=0.1827, simple_loss=0.2802, pruned_loss=0.04257, over 7123.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2819, pruned_loss=0.04545, over 1426709.33 frames.], batch size: 21, lr: 5.88e-04
+2022-04-29 04:18:54,879 INFO [train.py:763] (7/8) Epoch 12, batch 3000, loss[loss=0.1603, simple_loss=0.2531, pruned_loss=0.03377, over 7274.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2816, pruned_loss=0.04553, over 1428159.47 frames.], batch size: 17, lr: 5.88e-04
+2022-04-29 04:18:54,880 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 04:19:10,344 INFO [train.py:792] (7/8) Epoch 12, validation: loss=0.1673, simple_loss=0.27, pruned_loss=0.03225, over 698248.00 frames.
+2022-04-29 04:20:16,212 INFO [train.py:763] (7/8) Epoch 12, batch 3050, loss[loss=0.172, simple_loss=0.2581, pruned_loss=0.04299, over 7116.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2805, pruned_loss=0.04489, over 1428735.95 frames.], batch size: 17, lr: 5.87e-04
+2022-04-29 04:21:32,115 INFO [train.py:763] (7/8) Epoch 12, batch 3100, loss[loss=0.182, simple_loss=0.2864, pruned_loss=0.03878, over 7114.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2801, pruned_loss=0.04473, over 1428207.01 frames.], batch size: 21, lr: 5.87e-04
+2022-04-29 04:22:37,476 INFO [train.py:763] (7/8) Epoch 12, batch 3150, loss[loss=0.1911, simple_loss=0.2854, pruned_loss=0.04839, over 7276.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2814, pruned_loss=0.04497, over 1425481.33 frames.], batch size: 25, lr: 5.87e-04
+2022-04-29 04:23:52,370 INFO [train.py:763] (7/8) Epoch 12, batch 3200, loss[loss=0.222, simple_loss=0.3016, pruned_loss=0.07116, over 5319.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2816, pruned_loss=0.04503, over 1426803.04 frames.], batch size: 52, lr: 5.87e-04
+2022-04-29 04:25:17,152 INFO [train.py:763] (7/8) Epoch 12, batch 3250, loss[loss=0.1741, simple_loss=0.2552, pruned_loss=0.04649, over 7278.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2809, pruned_loss=0.045, over 1428298.93 frames.], batch size: 17, lr: 5.86e-04
+2022-04-29 04:26:23,083 INFO [train.py:763] (7/8) Epoch 12, batch 3300, loss[loss=0.1822, simple_loss=0.2742, pruned_loss=0.04512, over 7330.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2807, pruned_loss=0.04532, over 1428056.82 frames.], batch size: 20, lr: 5.86e-04
+2022-04-29 04:27:37,941 INFO [train.py:763] (7/8) Epoch 12, batch 3350, loss[loss=0.1541, simple_loss=0.2387, pruned_loss=0.03471, over 7012.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2802, pruned_loss=0.04538, over 1420279.78 frames.], batch size: 16, lr: 5.86e-04
+2022-04-29 04:29:03,567 INFO [train.py:763] (7/8) Epoch 12, batch 3400, loss[loss=0.1749, simple_loss=0.2715, pruned_loss=0.03919, over 7378.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2808, pruned_loss=0.04524, over 1424607.14 frames.], batch size: 23, lr: 5.86e-04
+2022-04-29 04:30:18,603 INFO [train.py:763] (7/8) Epoch 12, batch 3450, loss[loss=0.1868, simple_loss=0.2766, pruned_loss=0.04851, over 7420.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2818, pruned_loss=0.04581, over 1414207.96 frames.], batch size: 18, lr: 5.85e-04
+2022-04-29 04:31:24,827 INFO [train.py:763] (7/8) Epoch 12, batch 3500, loss[loss=0.1932, simple_loss=0.289, pruned_loss=0.0487, over 6768.00 frames.], tot_loss[loss=0.1866, simple_loss=0.282, pruned_loss=0.04557, over 1416504.41 frames.], batch size: 31, lr: 5.85e-04
+2022-04-29 04:32:31,893 INFO [train.py:763] (7/8) Epoch 12, batch 3550, loss[loss=0.2064, simple_loss=0.2813, pruned_loss=0.06575, over 6989.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2824, pruned_loss=0.04564, over 1421815.40 frames.], batch size: 16, lr: 5.85e-04
+2022-04-29 04:33:38,550 INFO [train.py:763] (7/8) Epoch 12, batch 3600, loss[loss=0.1841, simple_loss=0.2757, pruned_loss=0.04626, over 7290.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2822, pruned_loss=0.04602, over 1421838.86 frames.], batch size: 18, lr: 5.85e-04
+2022-04-29 04:34:44,031 INFO [train.py:763] (7/8) Epoch 12, batch 3650, loss[loss=0.2364, simple_loss=0.3366, pruned_loss=0.06806, over 7409.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2818, pruned_loss=0.04577, over 1424459.26 frames.], batch size: 21, lr: 5.84e-04
+2022-04-29 04:35:49,789 INFO [train.py:763] (7/8) Epoch 12, batch 3700, loss[loss=0.1879, simple_loss=0.2827, pruned_loss=0.04652, over 7260.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2804, pruned_loss=0.04528, over 1425231.76 frames.], batch size: 19, lr: 5.84e-04
+2022-04-29 04:36:55,389 INFO [train.py:763] (7/8) Epoch 12, batch 3750, loss[loss=0.1936, simple_loss=0.2926, pruned_loss=0.04727, over 7419.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2809, pruned_loss=0.04577, over 1424980.87 frames.], batch size: 21, lr: 5.84e-04
+2022-04-29 04:38:01,443 INFO [train.py:763] (7/8) Epoch 12, batch 3800, loss[loss=0.2117, simple_loss=0.3064, pruned_loss=0.05848, over 7008.00 frames.], tot_loss[loss=0.1864, simple_loss=0.281, pruned_loss=0.04586, over 1428695.54 frames.], batch size: 28, lr: 5.84e-04
+2022-04-29 04:39:06,796 INFO [train.py:763] (7/8) Epoch 12, batch 3850, loss[loss=0.2232, simple_loss=0.3208, pruned_loss=0.06284, over 7197.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2834, pruned_loss=0.04695, over 1426058.47 frames.], batch size: 22, lr: 5.83e-04
+2022-04-29 04:40:13,137 INFO [train.py:763] (7/8) Epoch 12, batch 3900, loss[loss=0.2143, simple_loss=0.3023, pruned_loss=0.06315, over 7300.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2826, pruned_loss=0.04659, over 1424953.51 frames.], batch size: 24, lr: 5.83e-04
+2022-04-29 04:41:18,543 INFO [train.py:763] (7/8) Epoch 12, batch 3950, loss[loss=0.172, simple_loss=0.2674, pruned_loss=0.03828, over 7183.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2822, pruned_loss=0.04644, over 1423814.39 frames.], batch size: 23, lr: 5.83e-04
+2022-04-29 04:42:24,209 INFO [train.py:763] (7/8) Epoch 12, batch 4000, loss[loss=0.1437, simple_loss=0.2397, pruned_loss=0.02379, over 7138.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2824, pruned_loss=0.0463, over 1423058.45 frames.], batch size: 17, lr: 5.83e-04
+2022-04-29 04:43:29,497 INFO [train.py:763] (7/8) Epoch 12, batch 4050, loss[loss=0.1914, simple_loss=0.2835, pruned_loss=0.04962, over 7239.00 frames.], tot_loss[loss=0.1872, simple_loss=0.282, pruned_loss=0.04616, over 1424997.53 frames.], batch size: 20, lr: 5.82e-04
+2022-04-29 04:44:35,696 INFO [train.py:763] (7/8) Epoch 12, batch 4100, loss[loss=0.2024, simple_loss=0.3099, pruned_loss=0.04744, over 7138.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2811, pruned_loss=0.04594, over 1424591.42 frames.], batch size: 20, lr: 5.82e-04
+2022-04-29 04:45:41,170 INFO [train.py:763] (7/8) Epoch 12, batch 4150, loss[loss=0.16, simple_loss=0.2605, pruned_loss=0.02973, over 7444.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2814, pruned_loss=0.04587, over 1419367.33 frames.], batch size: 20, lr: 5.82e-04
+2022-04-29 04:46:48,363 INFO [train.py:763] (7/8) Epoch 12, batch 4200, loss[loss=0.182, simple_loss=0.2823, pruned_loss=0.04085, over 7148.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2802, pruned_loss=0.04543, over 1420664.13 frames.], batch size: 20, lr: 5.82e-04
+2022-04-29 04:47:54,435 INFO [train.py:763] (7/8) Epoch 12, batch 4250, loss[loss=0.1921, simple_loss=0.2855, pruned_loss=0.04937, over 7189.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2801, pruned_loss=0.04568, over 1418472.50 frames.], batch size: 26, lr: 5.81e-04
+2022-04-29 04:49:00,811 INFO [train.py:763] (7/8) Epoch 12, batch 4300, loss[loss=0.1645, simple_loss=0.2674, pruned_loss=0.03083, over 7432.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2812, pruned_loss=0.04602, over 1415108.85 frames.], batch size: 20, lr: 5.81e-04
+2022-04-29 04:50:06,816 INFO [train.py:763] (7/8) Epoch 12, batch 4350, loss[loss=0.1373, simple_loss=0.2315, pruned_loss=0.02158, over 6991.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2807, pruned_loss=0.04602, over 1409119.68 frames.], batch size: 16, lr: 5.81e-04
+2022-04-29 04:51:13,425 INFO [train.py:763] (7/8) Epoch 12, batch 4400, loss[loss=0.2425, simple_loss=0.3124, pruned_loss=0.0863, over 5231.00 frames.], tot_loss[loss=0.185, simple_loss=0.2791, pruned_loss=0.04546, over 1408184.51 frames.], batch size: 53, lr: 5.81e-04
+2022-04-29 04:52:19,282 INFO [train.py:763] (7/8) Epoch 12, batch 4450, loss[loss=0.2193, simple_loss=0.3212, pruned_loss=0.05868, over 7293.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2796, pruned_loss=0.04545, over 1405335.15 frames.], batch size: 24, lr: 5.81e-04
+2022-04-29 04:53:25,185 INFO [train.py:763] (7/8) Epoch 12, batch 4500, loss[loss=0.1755, simple_loss=0.2773, pruned_loss=0.03686, over 7406.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2813, pruned_loss=0.04665, over 1386071.55 frames.], batch size: 21, lr: 5.80e-04
+2022-04-29 04:54:31,144 INFO [train.py:763] (7/8) Epoch 12, batch 4550, loss[loss=0.2432, simple_loss=0.3117, pruned_loss=0.08732, over 4875.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2838, pruned_loss=0.04824, over 1352018.31 frames.], batch size: 52, lr: 5.80e-04
+2022-04-29 04:56:09,902 INFO [train.py:763] (7/8) Epoch 13, batch 0, loss[loss=0.2052, simple_loss=0.3127, pruned_loss=0.0489, over 7378.00 frames.], tot_loss[loss=0.2052, simple_loss=0.3127, pruned_loss=0.0489, over 7378.00 frames.], batch size: 23, lr: 5.61e-04
+2022-04-29 04:57:15,979 INFO [train.py:763] (7/8) Epoch 13, batch 50, loss[loss=0.2263, simple_loss=0.3193, pruned_loss=0.06665, over 7111.00 frames.], tot_loss[loss=0.1802, simple_loss=0.274, pruned_loss=0.04321, over 322084.90 frames.], batch size: 21, lr: 5.61e-04
+2022-04-29 04:58:22,271 INFO [train.py:763] (7/8) Epoch 13, batch 100, loss[loss=0.199, simple_loss=0.2951, pruned_loss=0.05151, over 7148.00 frames.], tot_loss[loss=0.1825, simple_loss=0.278, pruned_loss=0.04345, over 572264.25 frames.], batch size: 20, lr: 5.61e-04
+2022-04-29 04:59:28,149 INFO [train.py:763] (7/8) Epoch 13, batch 150, loss[loss=0.167, simple_loss=0.2553, pruned_loss=0.03935, over 7010.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2779, pruned_loss=0.04337, over 763162.37 frames.], batch size: 16, lr: 5.61e-04
+2022-04-29 05:00:33,593 INFO [train.py:763] (7/8) Epoch 13, batch 200, loss[loss=0.1817, simple_loss=0.2837, pruned_loss=0.03987, over 7217.00 frames.], tot_loss[loss=0.1835, simple_loss=0.279, pruned_loss=0.04396, over 910067.59 frames.], batch size: 22, lr: 5.60e-04
+2022-04-29 05:01:39,410 INFO [train.py:763] (7/8) Epoch 13, batch 250, loss[loss=0.1951, simple_loss=0.2943, pruned_loss=0.04797, over 7208.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2796, pruned_loss=0.04444, over 1026403.15 frames.], batch size: 22, lr: 5.60e-04
+2022-04-29 05:02:44,830 INFO [train.py:763] (7/8) Epoch 13, batch 300, loss[loss=0.1802, simple_loss=0.2901, pruned_loss=0.03519, over 7400.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2807, pruned_loss=0.04432, over 1113120.42 frames.], batch size: 21, lr: 5.60e-04
+2022-04-29 05:03:50,343 INFO [train.py:763] (7/8) Epoch 13, batch 350, loss[loss=0.1952, simple_loss=0.2988, pruned_loss=0.04583, over 7433.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2794, pruned_loss=0.04399, over 1180498.93 frames.], batch size: 20, lr: 5.60e-04
+2022-04-29 05:04:55,876 INFO [train.py:763] (7/8) Epoch 13, batch 400, loss[loss=0.1699, simple_loss=0.281, pruned_loss=0.02943, over 7050.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2792, pruned_loss=0.0438, over 1230622.30 frames.], batch size: 28, lr: 5.59e-04
+2022-04-29 05:06:01,976 INFO [train.py:763] (7/8) Epoch 13, batch 450, loss[loss=0.1821, simple_loss=0.283, pruned_loss=0.04061, over 6341.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2799, pruned_loss=0.04444, over 1272956.43 frames.], batch size: 37, lr: 5.59e-04
+2022-04-29 05:07:07,992 INFO [train.py:763] (7/8) Epoch 13, batch 500, loss[loss=0.229, simple_loss=0.3227, pruned_loss=0.06769, over 7057.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2797, pruned_loss=0.04463, over 1300653.44 frames.], batch size: 28, lr: 5.59e-04
+2022-04-29 05:08:13,596 INFO [train.py:763] (7/8) Epoch 13, batch 550, loss[loss=0.1867, simple_loss=0.2907, pruned_loss=0.04131, over 6614.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2806, pruned_loss=0.0448, over 1326745.88 frames.], batch size: 38, lr: 5.59e-04
+2022-04-29 05:09:19,626 INFO [train.py:763] (7/8) Epoch 13, batch 600, loss[loss=0.194, simple_loss=0.2911, pruned_loss=0.04845, over 7326.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2798, pruned_loss=0.04477, over 1349495.45 frames.], batch size: 21, lr: 5.59e-04
+2022-04-29 05:10:25,770 INFO [train.py:763] (7/8) Epoch 13, batch 650, loss[loss=0.193, simple_loss=0.2821, pruned_loss=0.05196, over 7459.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2804, pruned_loss=0.04505, over 1362224.34 frames.], batch size: 19, lr: 5.58e-04
+2022-04-29 05:11:32,556 INFO [train.py:763] (7/8) Epoch 13, batch 700, loss[loss=0.1442, simple_loss=0.2433, pruned_loss=0.02259, over 7276.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2796, pruned_loss=0.04436, over 1377463.34 frames.], batch size: 18, lr: 5.58e-04
+2022-04-29 05:12:37,753 INFO [train.py:763] (7/8) Epoch 13, batch 750, loss[loss=0.2091, simple_loss=0.3044, pruned_loss=0.05691, over 7212.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2801, pruned_loss=0.04444, over 1383802.39 frames.], batch size: 23, lr: 5.58e-04
+2022-04-29 05:13:44,383 INFO [train.py:763] (7/8) Epoch 13, batch 800, loss[loss=0.2238, simple_loss=0.3151, pruned_loss=0.06626, over 7309.00 frames.], tot_loss[loss=0.1851, simple_loss=0.281, pruned_loss=0.04464, over 1393009.68 frames.], batch size: 25, lr: 5.58e-04
+2022-04-29 05:14:50,899 INFO [train.py:763] (7/8) Epoch 13, batch 850, loss[loss=0.1775, simple_loss=0.2828, pruned_loss=0.03616, over 7220.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2802, pruned_loss=0.04438, over 1400667.42 frames.], batch size: 21, lr: 5.57e-04
+2022-04-29 05:15:57,555 INFO [train.py:763] (7/8) Epoch 13, batch 900, loss[loss=0.1712, simple_loss=0.2637, pruned_loss=0.03933, over 7174.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2801, pruned_loss=0.04462, over 1403288.45 frames.], batch size: 18, lr: 5.57e-04
+2022-04-29 05:17:04,254 INFO [train.py:763] (7/8) Epoch 13, batch 950, loss[loss=0.1697, simple_loss=0.2804, pruned_loss=0.02953, over 7221.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2804, pruned_loss=0.04507, over 1403779.19 frames.], batch size: 21, lr: 5.57e-04
+2022-04-29 05:18:11,100 INFO [train.py:763] (7/8) Epoch 13, batch 1000, loss[loss=0.2281, simple_loss=0.315, pruned_loss=0.07061, over 7201.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2797, pruned_loss=0.04504, over 1411194.74 frames.], batch size: 22, lr: 5.57e-04
+2022-04-29 05:19:17,022 INFO [train.py:763] (7/8) Epoch 13, batch 1050, loss[loss=0.1957, simple_loss=0.2897, pruned_loss=0.0508, over 7413.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2791, pruned_loss=0.04467, over 1412168.26 frames.], batch size: 21, lr: 5.56e-04
+2022-04-29 05:20:22,753 INFO [train.py:763] (7/8) Epoch 13, batch 1100, loss[loss=0.2144, simple_loss=0.3063, pruned_loss=0.06122, over 6803.00 frames.], tot_loss[loss=0.184, simple_loss=0.2785, pruned_loss=0.04471, over 1411503.18 frames.], batch size: 31, lr: 5.56e-04
+2022-04-29 05:21:28,702 INFO [train.py:763] (7/8) Epoch 13, batch 1150, loss[loss=0.1644, simple_loss=0.2656, pruned_loss=0.03159, over 7331.00 frames.], tot_loss[loss=0.185, simple_loss=0.2803, pruned_loss=0.04485, over 1411332.96 frames.], batch size: 22, lr: 5.56e-04
+2022-04-29 05:22:34,616 INFO [train.py:763] (7/8) Epoch 13, batch 1200, loss[loss=0.1835, simple_loss=0.2751, pruned_loss=0.04598, over 4869.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2801, pruned_loss=0.0448, over 1409890.62 frames.], batch size: 53, lr: 5.56e-04
+2022-04-29 05:23:40,307 INFO [train.py:763] (7/8) Epoch 13, batch 1250, loss[loss=0.1581, simple_loss=0.2636, pruned_loss=0.02632, over 7429.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2808, pruned_loss=0.04474, over 1415207.77 frames.], batch size: 20, lr: 5.56e-04
+2022-04-29 05:24:45,583 INFO [train.py:763] (7/8) Epoch 13, batch 1300, loss[loss=0.1671, simple_loss=0.2624, pruned_loss=0.03594, over 7267.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2819, pruned_loss=0.04489, over 1418531.29 frames.], batch size: 19, lr: 5.55e-04
+2022-04-29 05:25:51,465 INFO [train.py:763] (7/8) Epoch 13, batch 1350, loss[loss=0.165, simple_loss=0.258, pruned_loss=0.03596, over 7277.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2813, pruned_loss=0.0451, over 1422124.87 frames.], batch size: 18, lr: 5.55e-04
+2022-04-29 05:26:57,114 INFO [train.py:763] (7/8) Epoch 13, batch 1400, loss[loss=0.1545, simple_loss=0.249, pruned_loss=0.02997, over 7166.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2821, pruned_loss=0.04573, over 1418509.78 frames.], batch size: 18, lr: 5.55e-04
+2022-04-29 05:28:02,598 INFO [train.py:763] (7/8) Epoch 13, batch 1450, loss[loss=0.163, simple_loss=0.2625, pruned_loss=0.03171, over 7272.00 frames.], tot_loss[loss=0.1863, simple_loss=0.282, pruned_loss=0.04532, over 1422037.34 frames.], batch size: 17, lr: 5.55e-04
+2022-04-29 05:29:08,116 INFO [train.py:763] (7/8) Epoch 13, batch 1500, loss[loss=0.1711, simple_loss=0.2523, pruned_loss=0.04492, over 7266.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2805, pruned_loss=0.04485, over 1423156.90 frames.], batch size: 17, lr: 5.54e-04
+2022-04-29 05:30:14,053 INFO [train.py:763] (7/8) Epoch 13, batch 1550, loss[loss=0.1843, simple_loss=0.2839, pruned_loss=0.04233, over 6395.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2811, pruned_loss=0.04517, over 1418823.72 frames.], batch size: 37, lr: 5.54e-04
+2022-04-29 05:31:19,500 INFO [train.py:763] (7/8) Epoch 13, batch 1600, loss[loss=0.1705, simple_loss=0.2763, pruned_loss=0.03236, over 7416.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2811, pruned_loss=0.04534, over 1418569.35 frames.], batch size: 21, lr: 5.54e-04
+2022-04-29 05:32:25,615 INFO [train.py:763] (7/8) Epoch 13, batch 1650, loss[loss=0.205, simple_loss=0.3064, pruned_loss=0.05186, over 7230.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2817, pruned_loss=0.04543, over 1420711.04 frames.], batch size: 20, lr: 5.54e-04
+2022-04-29 05:33:31,245 INFO [train.py:763] (7/8) Epoch 13, batch 1700, loss[loss=0.1846, simple_loss=0.2788, pruned_loss=0.04524, over 6461.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2817, pruned_loss=0.04523, over 1419579.12 frames.], batch size: 38, lr: 5.54e-04
+2022-04-29 05:34:36,779 INFO [train.py:763] (7/8) Epoch 13, batch 1750, loss[loss=0.1646, simple_loss=0.2583, pruned_loss=0.03544, over 7284.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2807, pruned_loss=0.04456, over 1422143.66 frames.], batch size: 17, lr: 5.53e-04
+2022-04-29 05:35:42,709 INFO [train.py:763] (7/8) Epoch 13, batch 1800, loss[loss=0.1835, simple_loss=0.274, pruned_loss=0.04657, over 7148.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2798, pruned_loss=0.04428, over 1427162.58 frames.], batch size: 20, lr: 5.53e-04
+2022-04-29 05:36:48,193 INFO [train.py:763] (7/8) Epoch 13, batch 1850, loss[loss=0.1932, simple_loss=0.2903, pruned_loss=0.04806, over 7287.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2807, pruned_loss=0.04495, over 1426533.73 frames.], batch size: 25, lr: 5.53e-04
+2022-04-29 05:37:54,126 INFO [train.py:763] (7/8) Epoch 13, batch 1900, loss[loss=0.2141, simple_loss=0.3138, pruned_loss=0.05721, over 6412.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2804, pruned_loss=0.04484, over 1421690.67 frames.], batch size: 38, lr: 5.53e-04
+2022-04-29 05:39:00,696 INFO [train.py:763] (7/8) Epoch 13, batch 1950, loss[loss=0.1609, simple_loss=0.2518, pruned_loss=0.03499, over 7251.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2814, pruned_loss=0.04465, over 1422883.17 frames.], batch size: 19, lr: 5.52e-04
+2022-04-29 05:40:07,439 INFO [train.py:763] (7/8) Epoch 13, batch 2000, loss[loss=0.1903, simple_loss=0.2917, pruned_loss=0.04448, over 7348.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2813, pruned_loss=0.04489, over 1424758.31 frames.], batch size: 22, lr: 5.52e-04
+2022-04-29 05:41:13,033 INFO [train.py:763] (7/8) Epoch 13, batch 2050, loss[loss=0.213, simple_loss=0.3104, pruned_loss=0.05777, over 7377.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2808, pruned_loss=0.04469, over 1426186.02 frames.], batch size: 23, lr: 5.52e-04
+2022-04-29 05:42:18,161 INFO [train.py:763] (7/8) Epoch 13, batch 2100, loss[loss=0.1985, simple_loss=0.2965, pruned_loss=0.05025, over 7228.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2818, pruned_loss=0.04469, over 1426261.58 frames.], batch size: 20, lr: 5.52e-04
+2022-04-29 05:43:24,249 INFO [train.py:763] (7/8) Epoch 13, batch 2150, loss[loss=0.2003, simple_loss=0.2913, pruned_loss=0.05465, over 7214.00 frames.], tot_loss[loss=0.185, simple_loss=0.2812, pruned_loss=0.0444, over 1428964.56 frames.], batch size: 26, lr: 5.52e-04
+2022-04-29 05:44:29,765 INFO [train.py:763] (7/8) Epoch 13, batch 2200, loss[loss=0.1683, simple_loss=0.2678, pruned_loss=0.03443, over 7428.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2811, pruned_loss=0.0446, over 1427718.88 frames.], batch size: 20, lr: 5.51e-04
+2022-04-29 05:45:35,375 INFO [train.py:763] (7/8) Epoch 13, batch 2250, loss[loss=0.176, simple_loss=0.2737, pruned_loss=0.03915, over 7234.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2802, pruned_loss=0.04422, over 1428130.06 frames.], batch size: 20, lr: 5.51e-04
+2022-04-29 05:46:41,467 INFO [train.py:763] (7/8) Epoch 13, batch 2300, loss[loss=0.2035, simple_loss=0.3, pruned_loss=0.05351, over 7102.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2787, pruned_loss=0.04377, over 1428624.78 frames.], batch size: 28, lr: 5.51e-04
+2022-04-29 05:47:46,900 INFO [train.py:763] (7/8) Epoch 13, batch 2350, loss[loss=0.2105, simple_loss=0.3046, pruned_loss=0.05817, over 5114.00 frames.], tot_loss[loss=0.1842, simple_loss=0.28, pruned_loss=0.04418, over 1427671.78 frames.], batch size: 52, lr: 5.51e-04
+2022-04-29 05:48:52,777 INFO [train.py:763] (7/8) Epoch 13, batch 2400, loss[loss=0.18, simple_loss=0.2635, pruned_loss=0.04827, over 7289.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2799, pruned_loss=0.04419, over 1428807.08 frames.], batch size: 17, lr: 5.50e-04
+2022-04-29 05:49:58,380 INFO [train.py:763] (7/8) Epoch 13, batch 2450, loss[loss=0.1876, simple_loss=0.2825, pruned_loss=0.04635, over 6789.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2806, pruned_loss=0.04443, over 1431041.49 frames.], batch size: 31, lr: 5.50e-04
+2022-04-29 05:51:03,657 INFO [train.py:763] (7/8) Epoch 13, batch 2500, loss[loss=0.1836, simple_loss=0.2649, pruned_loss=0.05114, over 7288.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2809, pruned_loss=0.04475, over 1427270.20 frames.], batch size: 17, lr: 5.50e-04
+2022-04-29 05:52:08,897 INFO [train.py:763] (7/8) Epoch 13, batch 2550, loss[loss=0.2178, simple_loss=0.3084, pruned_loss=0.06363, over 7286.00 frames.], tot_loss[loss=0.186, simple_loss=0.2813, pruned_loss=0.04537, over 1424348.64 frames.], batch size: 25, lr: 5.50e-04
+2022-04-29 05:53:14,615 INFO [train.py:763] (7/8) Epoch 13, batch 2600, loss[loss=0.1845, simple_loss=0.2829, pruned_loss=0.0431, over 7405.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2813, pruned_loss=0.04519, over 1420417.46 frames.], batch size: 21, lr: 5.50e-04
+2022-04-29 05:54:20,027 INFO [train.py:763] (7/8) Epoch 13, batch 2650, loss[loss=0.1995, simple_loss=0.296, pruned_loss=0.0515, over 7109.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2812, pruned_loss=0.0452, over 1418279.94 frames.], batch size: 21, lr: 5.49e-04
+2022-04-29 05:55:25,835 INFO [train.py:763] (7/8) Epoch 13, batch 2700, loss[loss=0.1605, simple_loss=0.2534, pruned_loss=0.0338, over 6998.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2807, pruned_loss=0.04499, over 1422828.95 frames.], batch size: 16, lr: 5.49e-04
+2022-04-29 05:56:31,333 INFO [train.py:763] (7/8) Epoch 13, batch 2750, loss[loss=0.193, simple_loss=0.2947, pruned_loss=0.04568, over 7304.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2797, pruned_loss=0.0446, over 1427318.82 frames.], batch size: 24, lr: 5.49e-04
+2022-04-29 05:57:36,865 INFO [train.py:763] (7/8) Epoch 13, batch 2800, loss[loss=0.1467, simple_loss=0.2349, pruned_loss=0.02926, over 7145.00 frames.], tot_loss[loss=0.1838, simple_loss=0.279, pruned_loss=0.04434, over 1425282.82 frames.], batch size: 17, lr: 5.49e-04
+2022-04-29 05:58:42,732 INFO [train.py:763] (7/8) Epoch 13, batch 2850, loss[loss=0.1984, simple_loss=0.3004, pruned_loss=0.04821, over 7414.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2786, pruned_loss=0.04408, over 1426259.74 frames.], batch size: 21, lr: 5.48e-04
+2022-04-29 05:59:48,444 INFO [train.py:763] (7/8) Epoch 13, batch 2900, loss[loss=0.1916, simple_loss=0.2869, pruned_loss=0.04814, over 7110.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2793, pruned_loss=0.0442, over 1427283.10 frames.], batch size: 21, lr: 5.48e-04
+2022-04-29 06:00:53,888 INFO [train.py:763] (7/8) Epoch 13, batch 2950, loss[loss=0.2358, simple_loss=0.3225, pruned_loss=0.07454, over 7206.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2796, pruned_loss=0.04397, over 1428724.28 frames.], batch size: 23, lr: 5.48e-04
+2022-04-29 06:01:59,754 INFO [train.py:763] (7/8) Epoch 13, batch 3000, loss[loss=0.2156, simple_loss=0.3088, pruned_loss=0.06123, over 7299.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2789, pruned_loss=0.04417, over 1430353.41 frames.], batch size: 24, lr: 5.48e-04
+2022-04-29 06:01:59,755 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 06:02:15,158 INFO [train.py:792] (7/8) Epoch 13, validation: loss=0.1677, simple_loss=0.2714, pruned_loss=0.03198, over 698248.00 frames.
+2022-04-29 06:03:21,975 INFO [train.py:763] (7/8) Epoch 13, batch 3050, loss[loss=0.1416, simple_loss=0.2353, pruned_loss=0.02397, over 7277.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2788, pruned_loss=0.04409, over 1430807.20 frames.], batch size: 17, lr: 5.48e-04
+2022-04-29 06:04:29,190 INFO [train.py:763] (7/8) Epoch 13, batch 3100, loss[loss=0.183, simple_loss=0.2777, pruned_loss=0.04409, over 7203.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2799, pruned_loss=0.04474, over 1431876.21 frames.], batch size: 23, lr: 5.47e-04
+2022-04-29 06:05:35,712 INFO [train.py:763] (7/8) Epoch 13, batch 3150, loss[loss=0.2428, simple_loss=0.3193, pruned_loss=0.0832, over 5083.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2788, pruned_loss=0.04423, over 1430931.76 frames.], batch size: 53, lr: 5.47e-04
+2022-04-29 06:06:41,352 INFO [train.py:763] (7/8) Epoch 13, batch 3200, loss[loss=0.1941, simple_loss=0.2927, pruned_loss=0.04776, over 7332.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2788, pruned_loss=0.04385, over 1430520.30 frames.], batch size: 22, lr: 5.47e-04
+2022-04-29 06:07:46,891 INFO [train.py:763] (7/8) Epoch 13, batch 3250, loss[loss=0.1948, simple_loss=0.292, pruned_loss=0.04882, over 7181.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2795, pruned_loss=0.04456, over 1427131.04 frames.], batch size: 26, lr: 5.47e-04
+2022-04-29 06:08:52,454 INFO [train.py:763] (7/8) Epoch 13, batch 3300, loss[loss=0.1522, simple_loss=0.2422, pruned_loss=0.03117, over 7173.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2791, pruned_loss=0.04435, over 1424461.27 frames.], batch size: 18, lr: 5.46e-04
+2022-04-29 06:09:57,835 INFO [train.py:763] (7/8) Epoch 13, batch 3350, loss[loss=0.1683, simple_loss=0.2627, pruned_loss=0.03689, over 7409.00 frames.], tot_loss[loss=0.1836, simple_loss=0.279, pruned_loss=0.04407, over 1426056.04 frames.], batch size: 18, lr: 5.46e-04
+2022-04-29 06:11:03,355 INFO [train.py:763] (7/8) Epoch 13, batch 3400, loss[loss=0.1897, simple_loss=0.2814, pruned_loss=0.04906, over 7164.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2789, pruned_loss=0.04407, over 1426862.01 frames.], batch size: 18, lr: 5.46e-04
+2022-04-29 06:12:10,263 INFO [train.py:763] (7/8) Epoch 13, batch 3450, loss[loss=0.17, simple_loss=0.268, pruned_loss=0.03594, over 7125.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2789, pruned_loss=0.04404, over 1425849.67 frames.], batch size: 21, lr: 5.46e-04
+2022-04-29 06:13:16,591 INFO [train.py:763] (7/8) Epoch 13, batch 3500, loss[loss=0.2158, simple_loss=0.3162, pruned_loss=0.05772, over 7333.00 frames.], tot_loss[loss=0.1838, simple_loss=0.279, pruned_loss=0.04437, over 1427182.79 frames.], batch size: 22, lr: 5.46e-04
+2022-04-29 06:14:22,087 INFO [train.py:763] (7/8) Epoch 13, batch 3550, loss[loss=0.1913, simple_loss=0.2976, pruned_loss=0.04246, over 7312.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2791, pruned_loss=0.04427, over 1427410.42 frames.], batch size: 21, lr: 5.45e-04
+2022-04-29 06:15:27,786 INFO [train.py:763] (7/8) Epoch 13, batch 3600, loss[loss=0.1701, simple_loss=0.264, pruned_loss=0.03813, over 7364.00 frames.], tot_loss[loss=0.183, simple_loss=0.2782, pruned_loss=0.04388, over 1431013.53 frames.], batch size: 19, lr: 5.45e-04
+2022-04-29 06:16:33,714 INFO [train.py:763] (7/8) Epoch 13, batch 3650, loss[loss=0.2055, simple_loss=0.3046, pruned_loss=0.05319, over 7235.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2781, pruned_loss=0.04408, over 1430250.11 frames.], batch size: 20, lr: 5.45e-04
+2022-04-29 06:17:39,189 INFO [train.py:763] (7/8) Epoch 13, batch 3700, loss[loss=0.1981, simple_loss=0.3, pruned_loss=0.04816, over 7298.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2795, pruned_loss=0.04437, over 1422741.19 frames.], batch size: 24, lr: 5.45e-04
+2022-04-29 06:18:44,885 INFO [train.py:763] (7/8) Epoch 13, batch 3750, loss[loss=0.1886, simple_loss=0.2732, pruned_loss=0.05202, over 5065.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2799, pruned_loss=0.04463, over 1421094.53 frames.], batch size: 52, lr: 5.45e-04
+2022-04-29 06:19:51,478 INFO [train.py:763] (7/8) Epoch 13, batch 3800, loss[loss=0.1706, simple_loss=0.2574, pruned_loss=0.0419, over 7434.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2798, pruned_loss=0.04447, over 1420806.60 frames.], batch size: 17, lr: 5.44e-04
+2022-04-29 06:20:57,078 INFO [train.py:763] (7/8) Epoch 13, batch 3850, loss[loss=0.2163, simple_loss=0.3021, pruned_loss=0.06527, over 7198.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2798, pruned_loss=0.0445, over 1420790.97 frames.], batch size: 22, lr: 5.44e-04
+2022-04-29 06:22:02,382 INFO [train.py:763] (7/8) Epoch 13, batch 3900, loss[loss=0.1712, simple_loss=0.2705, pruned_loss=0.03593, over 7309.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2808, pruned_loss=0.04499, over 1421914.84 frames.], batch size: 21, lr: 5.44e-04
+2022-04-29 06:23:08,137 INFO [train.py:763] (7/8) Epoch 13, batch 3950, loss[loss=0.224, simple_loss=0.3068, pruned_loss=0.07066, over 5183.00 frames.], tot_loss[loss=0.184, simple_loss=0.2794, pruned_loss=0.04429, over 1420627.36 frames.], batch size: 52, lr: 5.44e-04
+2022-04-29 06:24:13,277 INFO [train.py:763] (7/8) Epoch 13, batch 4000, loss[loss=0.2019, simple_loss=0.3082, pruned_loss=0.04782, over 7342.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2799, pruned_loss=0.04419, over 1422548.72 frames.], batch size: 22, lr: 5.43e-04
+2022-04-29 06:25:19,020 INFO [train.py:763] (7/8) Epoch 13, batch 4050, loss[loss=0.1461, simple_loss=0.2314, pruned_loss=0.03039, over 6797.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2789, pruned_loss=0.04413, over 1423484.17 frames.], batch size: 15, lr: 5.43e-04
+2022-04-29 06:26:24,361 INFO [train.py:763] (7/8) Epoch 13, batch 4100, loss[loss=0.1931, simple_loss=0.2827, pruned_loss=0.05172, over 6706.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2788, pruned_loss=0.04436, over 1421717.27 frames.], batch size: 31, lr: 5.43e-04
+2022-04-29 06:27:29,940 INFO [train.py:763] (7/8) Epoch 13, batch 4150, loss[loss=0.181, simple_loss=0.2866, pruned_loss=0.03775, over 7228.00 frames.], tot_loss[loss=0.183, simple_loss=0.2782, pruned_loss=0.04392, over 1421093.43 frames.], batch size: 21, lr: 5.43e-04
+2022-04-29 06:28:36,043 INFO [train.py:763] (7/8) Epoch 13, batch 4200, loss[loss=0.1606, simple_loss=0.2517, pruned_loss=0.03472, over 7273.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2772, pruned_loss=0.04369, over 1422109.86 frames.], batch size: 17, lr: 5.43e-04
+2022-04-29 06:29:41,284 INFO [train.py:763] (7/8) Epoch 13, batch 4250, loss[loss=0.1912, simple_loss=0.2875, pruned_loss=0.0475, over 6251.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2777, pruned_loss=0.04386, over 1416042.67 frames.], batch size: 37, lr: 5.42e-04
+2022-04-29 06:30:47,752 INFO [train.py:763] (7/8) Epoch 13, batch 4300, loss[loss=0.1826, simple_loss=0.2948, pruned_loss=0.03524, over 7226.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2784, pruned_loss=0.04432, over 1411389.52 frames.], batch size: 21, lr: 5.42e-04
+2022-04-29 06:31:53,168 INFO [train.py:763] (7/8) Epoch 13, batch 4350, loss[loss=0.1655, simple_loss=0.2526, pruned_loss=0.03917, over 6803.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2784, pruned_loss=0.04432, over 1407760.28 frames.], batch size: 15, lr: 5.42e-04
+2022-04-29 06:33:10,024 INFO [train.py:763] (7/8) Epoch 13, batch 4400, loss[loss=0.1683, simple_loss=0.2652, pruned_loss=0.03566, over 7147.00 frames.], tot_loss[loss=0.183, simple_loss=0.2777, pruned_loss=0.04409, over 1401296.32 frames.], batch size: 20, lr: 5.42e-04
+2022-04-29 06:34:14,937 INFO [train.py:763] (7/8) Epoch 13, batch 4450, loss[loss=0.2105, simple_loss=0.3045, pruned_loss=0.05824, over 5265.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2794, pruned_loss=0.04457, over 1393299.78 frames.], batch size: 52, lr: 5.42e-04
+2022-04-29 06:35:30,501 INFO [train.py:763] (7/8) Epoch 13, batch 4500, loss[loss=0.2064, simple_loss=0.2877, pruned_loss=0.06253, over 5033.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2795, pruned_loss=0.04483, over 1379366.63 frames.], batch size: 53, lr: 5.41e-04
+2022-04-29 06:36:35,415 INFO [train.py:763] (7/8) Epoch 13, batch 4550, loss[loss=0.2065, simple_loss=0.3039, pruned_loss=0.05454, over 6807.00 frames.], tot_loss[loss=0.185, simple_loss=0.2797, pruned_loss=0.04509, over 1369367.44 frames.], batch size: 31, lr: 5.41e-04
+2022-04-29 06:38:13,970 INFO [train.py:763] (7/8) Epoch 14, batch 0, loss[loss=0.1927, simple_loss=0.2881, pruned_loss=0.04869, over 7087.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2881, pruned_loss=0.04869, over 7087.00 frames.], batch size: 28, lr: 5.25e-04
+2022-04-29 06:39:20,743 INFO [train.py:763] (7/8) Epoch 14, batch 50, loss[loss=0.2306, simple_loss=0.3107, pruned_loss=0.07523, over 5202.00 frames.], tot_loss[loss=0.1838, simple_loss=0.28, pruned_loss=0.0438, over 321987.79 frames.], batch size: 52, lr: 5.24e-04
+2022-04-29 06:40:45,792 INFO [train.py:763] (7/8) Epoch 14, batch 100, loss[loss=0.1955, simple_loss=0.2808, pruned_loss=0.05514, over 7172.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2803, pruned_loss=0.04433, over 568306.83 frames.], batch size: 18, lr: 5.24e-04
+2022-04-29 06:41:59,843 INFO [train.py:763] (7/8) Epoch 14, batch 150, loss[loss=0.1988, simple_loss=0.2884, pruned_loss=0.05455, over 7110.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2814, pruned_loss=0.04379, over 758265.12 frames.], batch size: 21, lr: 5.24e-04
+2022-04-29 06:43:06,564 INFO [train.py:763] (7/8) Epoch 14, batch 200, loss[loss=0.1873, simple_loss=0.2791, pruned_loss=0.04776, over 7325.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2823, pruned_loss=0.0441, over 902911.17 frames.], batch size: 20, lr: 5.24e-04
+2022-04-29 06:44:23,273 INFO [train.py:763] (7/8) Epoch 14, batch 250, loss[loss=0.1814, simple_loss=0.2879, pruned_loss=0.03744, over 6396.00 frames.], tot_loss[loss=0.1842, simple_loss=0.281, pruned_loss=0.04367, over 1020647.93 frames.], batch size: 37, lr: 5.24e-04
+2022-04-29 06:45:48,441 INFO [train.py:763] (7/8) Epoch 14, batch 300, loss[loss=0.1535, simple_loss=0.2415, pruned_loss=0.03277, over 7141.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2793, pruned_loss=0.04281, over 1110630.63 frames.], batch size: 17, lr: 5.23e-04
+2022-04-29 06:46:55,951 INFO [train.py:763] (7/8) Epoch 14, batch 350, loss[loss=0.1644, simple_loss=0.2577, pruned_loss=0.03557, over 6857.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2784, pruned_loss=0.04288, over 1173365.96 frames.], batch size: 15, lr: 5.23e-04
+2022-04-29 06:48:03,009 INFO [train.py:763] (7/8) Epoch 14, batch 400, loss[loss=0.1785, simple_loss=0.2798, pruned_loss=0.03861, over 7139.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2773, pruned_loss=0.0427, over 1228755.53 frames.], batch size: 20, lr: 5.23e-04
+2022-04-29 06:49:01,694 INFO [train.py:763] (7/8) Epoch 14, batch 450, loss[loss=0.1743, simple_loss=0.2575, pruned_loss=0.04552, over 7160.00 frames.], tot_loss[loss=0.1811, simple_loss=0.277, pruned_loss=0.04259, over 1273168.97 frames.], batch size: 19, lr: 5.23e-04
+2022-04-29 06:50:05,446 INFO [train.py:763] (7/8) Epoch 14, batch 500, loss[loss=0.1708, simple_loss=0.2664, pruned_loss=0.0376, over 7438.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2778, pruned_loss=0.04291, over 1304947.12 frames.], batch size: 20, lr: 5.23e-04
+2022-04-29 06:51:07,468 INFO [train.py:763] (7/8) Epoch 14, batch 550, loss[loss=0.1644, simple_loss=0.2585, pruned_loss=0.03512, over 7276.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2772, pruned_loss=0.04257, over 1332797.71 frames.], batch size: 18, lr: 5.22e-04
+2022-04-29 06:52:12,679 INFO [train.py:763] (7/8) Epoch 14, batch 600, loss[loss=0.1783, simple_loss=0.2675, pruned_loss=0.04458, over 7229.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2765, pruned_loss=0.04206, over 1355503.52 frames.], batch size: 20, lr: 5.22e-04
+2022-04-29 06:53:18,175 INFO [train.py:763] (7/8) Epoch 14, batch 650, loss[loss=0.2028, simple_loss=0.3059, pruned_loss=0.0498, over 7319.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2772, pruned_loss=0.0425, over 1369785.63 frames.], batch size: 22, lr: 5.22e-04
+2022-04-29 06:54:23,439 INFO [train.py:763] (7/8) Epoch 14, batch 700, loss[loss=0.1792, simple_loss=0.2706, pruned_loss=0.04385, over 7327.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2773, pruned_loss=0.04255, over 1382453.82 frames.], batch size: 20, lr: 5.22e-04
+2022-04-29 06:55:28,873 INFO [train.py:763] (7/8) Epoch 14, batch 750, loss[loss=0.1632, simple_loss=0.2704, pruned_loss=0.02803, over 7353.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2776, pruned_loss=0.04287, over 1391185.57 frames.], batch size: 22, lr: 5.22e-04
+2022-04-29 06:56:34,185 INFO [train.py:763] (7/8) Epoch 14, batch 800, loss[loss=0.1542, simple_loss=0.2612, pruned_loss=0.02362, over 7337.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2777, pruned_loss=0.04252, over 1398963.46 frames.], batch size: 22, lr: 5.21e-04
+2022-04-29 06:57:40,715 INFO [train.py:763] (7/8) Epoch 14, batch 850, loss[loss=0.158, simple_loss=0.2475, pruned_loss=0.03428, over 7116.00 frames.], tot_loss[loss=0.1808, simple_loss=0.277, pruned_loss=0.0423, over 1402193.56 frames.], batch size: 17, lr: 5.21e-04
+2022-04-29 06:58:46,061 INFO [train.py:763] (7/8) Epoch 14, batch 900, loss[loss=0.1775, simple_loss=0.2709, pruned_loss=0.0421, over 7266.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2777, pruned_loss=0.04286, over 1397672.17 frames.], batch size: 19, lr: 5.21e-04
+2022-04-29 06:59:51,300 INFO [train.py:763] (7/8) Epoch 14, batch 950, loss[loss=0.1934, simple_loss=0.2954, pruned_loss=0.04575, over 7330.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2776, pruned_loss=0.04294, over 1406541.75 frames.], batch size: 22, lr: 5.21e-04
+2022-04-29 07:00:56,961 INFO [train.py:763] (7/8) Epoch 14, batch 1000, loss[loss=0.194, simple_loss=0.3019, pruned_loss=0.04307, over 7029.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2782, pruned_loss=0.04347, over 1407197.72 frames.], batch size: 28, lr: 5.21e-04
+2022-04-29 07:02:02,204 INFO [train.py:763] (7/8) Epoch 14, batch 1050, loss[loss=0.1975, simple_loss=0.2794, pruned_loss=0.05783, over 7288.00 frames.], tot_loss[loss=0.1822, simple_loss=0.278, pruned_loss=0.04317, over 1413409.53 frames.], batch size: 18, lr: 5.20e-04
+2022-04-29 07:03:07,581 INFO [train.py:763] (7/8) Epoch 14, batch 1100, loss[loss=0.1542, simple_loss=0.2493, pruned_loss=0.02952, over 7286.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2782, pruned_loss=0.04348, over 1416916.01 frames.], batch size: 17, lr: 5.20e-04
+2022-04-29 07:04:13,195 INFO [train.py:763] (7/8) Epoch 14, batch 1150, loss[loss=0.1694, simple_loss=0.279, pruned_loss=0.02989, over 7412.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2772, pruned_loss=0.04296, over 1421812.85 frames.], batch size: 21, lr: 5.20e-04
+2022-04-29 07:05:18,955 INFO [train.py:763] (7/8) Epoch 14, batch 1200, loss[loss=0.151, simple_loss=0.2486, pruned_loss=0.02672, over 7426.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2767, pruned_loss=0.04282, over 1423064.62 frames.], batch size: 20, lr: 5.20e-04
+2022-04-29 07:06:24,254 INFO [train.py:763] (7/8) Epoch 14, batch 1250, loss[loss=0.1879, simple_loss=0.2805, pruned_loss=0.04764, over 7359.00 frames.], tot_loss[loss=0.181, simple_loss=0.2767, pruned_loss=0.04265, over 1425716.78 frames.], batch size: 19, lr: 5.20e-04
+2022-04-29 07:07:29,941 INFO [train.py:763] (7/8) Epoch 14, batch 1300, loss[loss=0.1886, simple_loss=0.2872, pruned_loss=0.04505, over 6298.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2772, pruned_loss=0.04295, over 1420374.25 frames.], batch size: 37, lr: 5.19e-04
+2022-04-29 07:08:35,862 INFO [train.py:763] (7/8) Epoch 14, batch 1350, loss[loss=0.149, simple_loss=0.24, pruned_loss=0.029, over 7005.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2777, pruned_loss=0.04307, over 1421687.57 frames.], batch size: 16, lr: 5.19e-04
+2022-04-29 07:09:40,894 INFO [train.py:763] (7/8) Epoch 14, batch 1400, loss[loss=0.2027, simple_loss=0.3061, pruned_loss=0.04967, over 7267.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2769, pruned_loss=0.04281, over 1420734.34 frames.], batch size: 24, lr: 5.19e-04
+2022-04-29 07:10:46,121 INFO [train.py:763] (7/8) Epoch 14, batch 1450, loss[loss=0.1872, simple_loss=0.2799, pruned_loss=0.04724, over 7394.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2777, pruned_loss=0.04292, over 1417297.55 frames.], batch size: 23, lr: 5.19e-04
+2022-04-29 07:11:52,463 INFO [train.py:763] (7/8) Epoch 14, batch 1500, loss[loss=0.1612, simple_loss=0.2623, pruned_loss=0.03004, over 7150.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2788, pruned_loss=0.04343, over 1411957.25 frames.], batch size: 20, lr: 5.19e-04
+2022-04-29 07:12:59,683 INFO [train.py:763] (7/8) Epoch 14, batch 1550, loss[loss=0.1737, simple_loss=0.2796, pruned_loss=0.03393, over 7126.00 frames.], tot_loss[loss=0.181, simple_loss=0.2769, pruned_loss=0.04253, over 1416556.35 frames.], batch size: 21, lr: 5.18e-04
+2022-04-29 07:14:06,944 INFO [train.py:763] (7/8) Epoch 14, batch 1600, loss[loss=0.1663, simple_loss=0.2659, pruned_loss=0.03337, over 7419.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2769, pruned_loss=0.04274, over 1418985.55 frames.], batch size: 21, lr: 5.18e-04
+2022-04-29 07:15:13,447 INFO [train.py:763] (7/8) Epoch 14, batch 1650, loss[loss=0.2033, simple_loss=0.3021, pruned_loss=0.05226, over 7198.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2765, pruned_loss=0.04213, over 1424342.67 frames.], batch size: 23, lr: 5.18e-04
+2022-04-29 07:16:19,652 INFO [train.py:763] (7/8) Epoch 14, batch 1700, loss[loss=0.1856, simple_loss=0.2922, pruned_loss=0.0395, over 7298.00 frames.], tot_loss[loss=0.18, simple_loss=0.2762, pruned_loss=0.04189, over 1428216.85 frames.], batch size: 25, lr: 5.18e-04
+2022-04-29 07:17:25,769 INFO [train.py:763] (7/8) Epoch 14, batch 1750, loss[loss=0.1771, simple_loss=0.2823, pruned_loss=0.03593, over 7137.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2766, pruned_loss=0.04207, over 1431440.85 frames.], batch size: 28, lr: 5.18e-04
+2022-04-29 07:18:31,004 INFO [train.py:763] (7/8) Epoch 14, batch 1800, loss[loss=0.149, simple_loss=0.2349, pruned_loss=0.03154, over 7275.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2762, pruned_loss=0.04211, over 1428298.79 frames.], batch size: 17, lr: 5.17e-04
+2022-04-29 07:19:36,660 INFO [train.py:763] (7/8) Epoch 14, batch 1850, loss[loss=0.1643, simple_loss=0.2641, pruned_loss=0.03226, over 7168.00 frames.], tot_loss[loss=0.181, simple_loss=0.2767, pruned_loss=0.04269, over 1432590.79 frames.], batch size: 18, lr: 5.17e-04
+2022-04-29 07:20:42,283 INFO [train.py:763] (7/8) Epoch 14, batch 1900, loss[loss=0.2129, simple_loss=0.2952, pruned_loss=0.0653, over 7123.00 frames.], tot_loss[loss=0.1807, simple_loss=0.276, pruned_loss=0.04271, over 1431393.29 frames.], batch size: 21, lr: 5.17e-04
+2022-04-29 07:21:47,870 INFO [train.py:763] (7/8) Epoch 14, batch 1950, loss[loss=0.1948, simple_loss=0.2905, pruned_loss=0.04958, over 7292.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2755, pruned_loss=0.04245, over 1431956.41 frames.], batch size: 18, lr: 5.17e-04
+2022-04-29 07:22:53,282 INFO [train.py:763] (7/8) Epoch 14, batch 2000, loss[loss=0.1856, simple_loss=0.2877, pruned_loss=0.0417, over 6494.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2758, pruned_loss=0.04273, over 1428252.27 frames.], batch size: 38, lr: 5.17e-04
+2022-04-29 07:23:58,409 INFO [train.py:763] (7/8) Epoch 14, batch 2050, loss[loss=0.2048, simple_loss=0.3054, pruned_loss=0.05209, over 7313.00 frames.], tot_loss[loss=0.1819, simple_loss=0.277, pruned_loss=0.04337, over 1429491.64 frames.], batch size: 25, lr: 5.16e-04
+2022-04-29 07:25:03,748 INFO [train.py:763] (7/8) Epoch 14, batch 2100, loss[loss=0.1677, simple_loss=0.2587, pruned_loss=0.03834, over 7395.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2767, pruned_loss=0.04326, over 1422665.23 frames.], batch size: 18, lr: 5.16e-04
+2022-04-29 07:26:09,026 INFO [train.py:763] (7/8) Epoch 14, batch 2150, loss[loss=0.187, simple_loss=0.2869, pruned_loss=0.04359, over 7204.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2758, pruned_loss=0.0427, over 1420195.25 frames.], batch size: 22, lr: 5.16e-04
+2022-04-29 07:27:14,558 INFO [train.py:763] (7/8) Epoch 14, batch 2200, loss[loss=0.1717, simple_loss=0.274, pruned_loss=0.03473, over 7431.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2771, pruned_loss=0.04301, over 1420401.27 frames.], batch size: 20, lr: 5.16e-04
+2022-04-29 07:28:19,758 INFO [train.py:763] (7/8) Epoch 14, batch 2250, loss[loss=0.2046, simple_loss=0.3043, pruned_loss=0.05241, over 7080.00 frames.], tot_loss[loss=0.1812, simple_loss=0.277, pruned_loss=0.04275, over 1421455.24 frames.], batch size: 28, lr: 5.16e-04
+2022-04-29 07:29:24,993 INFO [train.py:763] (7/8) Epoch 14, batch 2300, loss[loss=0.1382, simple_loss=0.2263, pruned_loss=0.02507, over 6791.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2766, pruned_loss=0.04232, over 1420852.38 frames.], batch size: 15, lr: 5.15e-04
+2022-04-29 07:30:30,174 INFO [train.py:763] (7/8) Epoch 14, batch 2350, loss[loss=0.1569, simple_loss=0.2517, pruned_loss=0.03101, over 7403.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2767, pruned_loss=0.04236, over 1423800.92 frames.], batch size: 18, lr: 5.15e-04
+2022-04-29 07:31:35,500 INFO [train.py:763] (7/8) Epoch 14, batch 2400, loss[loss=0.1642, simple_loss=0.2589, pruned_loss=0.03476, over 7413.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2781, pruned_loss=0.04305, over 1422227.86 frames.], batch size: 18, lr: 5.15e-04
+2022-04-29 07:32:40,937 INFO [train.py:763] (7/8) Epoch 14, batch 2450, loss[loss=0.1914, simple_loss=0.2925, pruned_loss=0.04518, over 7410.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2789, pruned_loss=0.04326, over 1423772.66 frames.], batch size: 21, lr: 5.15e-04
+2022-04-29 07:33:46,243 INFO [train.py:763] (7/8) Epoch 14, batch 2500, loss[loss=0.1707, simple_loss=0.2721, pruned_loss=0.03459, over 7327.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2796, pruned_loss=0.04375, over 1425696.97 frames.], batch size: 21, lr: 5.15e-04
+2022-04-29 07:34:51,438 INFO [train.py:763] (7/8) Epoch 14, batch 2550, loss[loss=0.1868, simple_loss=0.288, pruned_loss=0.04279, over 7171.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2789, pruned_loss=0.0433, over 1428073.60 frames.], batch size: 18, lr: 5.14e-04
+2022-04-29 07:35:56,554 INFO [train.py:763] (7/8) Epoch 14, batch 2600, loss[loss=0.1728, simple_loss=0.2695, pruned_loss=0.03812, over 7201.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2793, pruned_loss=0.04384, over 1422349.53 frames.], batch size: 23, lr: 5.14e-04
+2022-04-29 07:37:01,627 INFO [train.py:763] (7/8) Epoch 14, batch 2650, loss[loss=0.1919, simple_loss=0.3025, pruned_loss=0.04061, over 7252.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2787, pruned_loss=0.04381, over 1421980.76 frames.], batch size: 25, lr: 5.14e-04
+2022-04-29 07:38:06,943 INFO [train.py:763] (7/8) Epoch 14, batch 2700, loss[loss=0.1765, simple_loss=0.2759, pruned_loss=0.03853, over 7320.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2788, pruned_loss=0.04345, over 1424658.02 frames.], batch size: 21, lr: 5.14e-04
+2022-04-29 07:39:12,140 INFO [train.py:763] (7/8) Epoch 14, batch 2750, loss[loss=0.1886, simple_loss=0.2971, pruned_loss=0.03998, over 7282.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2784, pruned_loss=0.04303, over 1424686.45 frames.], batch size: 24, lr: 5.14e-04
+2022-04-29 07:40:17,451 INFO [train.py:763] (7/8) Epoch 14, batch 2800, loss[loss=0.1632, simple_loss=0.2536, pruned_loss=0.03641, over 7145.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2782, pruned_loss=0.04279, over 1427226.62 frames.], batch size: 20, lr: 5.14e-04
+2022-04-29 07:41:22,768 INFO [train.py:763] (7/8) Epoch 14, batch 2850, loss[loss=0.1741, simple_loss=0.2617, pruned_loss=0.04325, over 6809.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2785, pruned_loss=0.04268, over 1427051.09 frames.], batch size: 15, lr: 5.13e-04
+2022-04-29 07:42:28,532 INFO [train.py:763] (7/8) Epoch 14, batch 2900, loss[loss=0.1956, simple_loss=0.2811, pruned_loss=0.05502, over 7365.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2789, pruned_loss=0.04309, over 1423307.86 frames.], batch size: 23, lr: 5.13e-04
+2022-04-29 07:43:34,062 INFO [train.py:763] (7/8) Epoch 14, batch 2950, loss[loss=0.1614, simple_loss=0.257, pruned_loss=0.0329, over 7443.00 frames.], tot_loss[loss=0.1821, simple_loss=0.278, pruned_loss=0.0431, over 1424690.51 frames.], batch size: 20, lr: 5.13e-04
+2022-04-29 07:44:39,588 INFO [train.py:763] (7/8) Epoch 14, batch 3000, loss[loss=0.1703, simple_loss=0.2635, pruned_loss=0.03852, over 7163.00 frames.], tot_loss[loss=0.182, simple_loss=0.2779, pruned_loss=0.04303, over 1422456.77 frames.], batch size: 19, lr: 5.13e-04
+2022-04-29 07:44:39,589 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 07:44:54,980 INFO [train.py:792] (7/8) Epoch 14, validation: loss=0.1687, simple_loss=0.2708, pruned_loss=0.03331, over 698248.00 frames.
+2022-04-29 07:46:00,338 INFO [train.py:763] (7/8) Epoch 14, batch 3050, loss[loss=0.1634, simple_loss=0.2564, pruned_loss=0.0352, over 6741.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2781, pruned_loss=0.04314, over 1425411.85 frames.], batch size: 15, lr: 5.13e-04
+2022-04-29 07:47:05,883 INFO [train.py:763] (7/8) Epoch 14, batch 3100, loss[loss=0.1627, simple_loss=0.2675, pruned_loss=0.0289, over 7323.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2792, pruned_loss=0.04334, over 1421803.83 frames.], batch size: 20, lr: 5.12e-04
+2022-04-29 07:48:12,220 INFO [train.py:763] (7/8) Epoch 14, batch 3150, loss[loss=0.1486, simple_loss=0.233, pruned_loss=0.03207, over 7279.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2776, pruned_loss=0.0427, over 1426347.47 frames.], batch size: 17, lr: 5.12e-04
+2022-04-29 07:49:18,818 INFO [train.py:763] (7/8) Epoch 14, batch 3200, loss[loss=0.1879, simple_loss=0.2962, pruned_loss=0.03981, over 7036.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2765, pruned_loss=0.04237, over 1427494.57 frames.], batch size: 28, lr: 5.12e-04
+2022-04-29 07:50:24,268 INFO [train.py:763] (7/8) Epoch 14, batch 3250, loss[loss=0.1946, simple_loss=0.2877, pruned_loss=0.05072, over 7452.00 frames.], tot_loss[loss=0.1801, simple_loss=0.276, pruned_loss=0.04213, over 1427792.63 frames.], batch size: 19, lr: 5.12e-04
+2022-04-29 07:51:29,746 INFO [train.py:763] (7/8) Epoch 14, batch 3300, loss[loss=0.1751, simple_loss=0.2581, pruned_loss=0.04602, over 7274.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2757, pruned_loss=0.04228, over 1426305.18 frames.], batch size: 17, lr: 5.12e-04
+2022-04-29 07:52:35,065 INFO [train.py:763] (7/8) Epoch 14, batch 3350, loss[loss=0.2286, simple_loss=0.3034, pruned_loss=0.0769, over 7213.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2761, pruned_loss=0.04247, over 1426743.53 frames.], batch size: 23, lr: 5.11e-04
+2022-04-29 07:53:40,786 INFO [train.py:763] (7/8) Epoch 14, batch 3400, loss[loss=0.1834, simple_loss=0.2836, pruned_loss=0.04156, over 7223.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2765, pruned_loss=0.04268, over 1423575.47 frames.], batch size: 21, lr: 5.11e-04
+2022-04-29 07:54:46,000 INFO [train.py:763] (7/8) Epoch 14, batch 3450, loss[loss=0.1948, simple_loss=0.3, pruned_loss=0.04482, over 7058.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2783, pruned_loss=0.04336, over 1420992.16 frames.], batch size: 28, lr: 5.11e-04
+2022-04-29 07:55:51,610 INFO [train.py:763] (7/8) Epoch 14, batch 3500, loss[loss=0.2055, simple_loss=0.3011, pruned_loss=0.05499, over 7162.00 frames.], tot_loss[loss=0.182, simple_loss=0.2776, pruned_loss=0.04317, over 1425842.44 frames.], batch size: 26, lr: 5.11e-04
+2022-04-29 07:56:57,030 INFO [train.py:763] (7/8) Epoch 14, batch 3550, loss[loss=0.2109, simple_loss=0.3108, pruned_loss=0.05547, over 7229.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2779, pruned_loss=0.04321, over 1427797.13 frames.], batch size: 20, lr: 5.11e-04
+2022-04-29 07:58:03,516 INFO [train.py:763] (7/8) Epoch 14, batch 3600, loss[loss=0.1848, simple_loss=0.276, pruned_loss=0.04674, over 7317.00 frames.], tot_loss[loss=0.1824, simple_loss=0.278, pruned_loss=0.04343, over 1423704.71 frames.], batch size: 21, lr: 5.11e-04
+2022-04-29 07:59:08,925 INFO [train.py:763] (7/8) Epoch 14, batch 3650, loss[loss=0.1759, simple_loss=0.2737, pruned_loss=0.0391, over 7264.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2775, pruned_loss=0.04319, over 1424826.98 frames.], batch size: 19, lr: 5.10e-04
+2022-04-29 08:00:14,243 INFO [train.py:763] (7/8) Epoch 14, batch 3700, loss[loss=0.1657, simple_loss=0.2563, pruned_loss=0.03757, over 7424.00 frames.], tot_loss[loss=0.1821, simple_loss=0.278, pruned_loss=0.04315, over 1422266.73 frames.], batch size: 20, lr: 5.10e-04
+2022-04-29 08:01:20,002 INFO [train.py:763] (7/8) Epoch 14, batch 3750, loss[loss=0.2258, simple_loss=0.3156, pruned_loss=0.06799, over 5199.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2774, pruned_loss=0.04278, over 1424092.46 frames.], batch size: 52, lr: 5.10e-04
+2022-04-29 08:02:27,032 INFO [train.py:763] (7/8) Epoch 14, batch 3800, loss[loss=0.1639, simple_loss=0.257, pruned_loss=0.03536, over 7447.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2773, pruned_loss=0.04253, over 1426307.73 frames.], batch size: 19, lr: 5.10e-04
+2022-04-29 08:03:33,829 INFO [train.py:763] (7/8) Epoch 14, batch 3850, loss[loss=0.2002, simple_loss=0.3005, pruned_loss=0.05, over 7239.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2772, pruned_loss=0.04202, over 1428958.98 frames.], batch size: 20, lr: 5.10e-04
+2022-04-29 08:04:40,273 INFO [train.py:763] (7/8) Epoch 14, batch 3900, loss[loss=0.1782, simple_loss=0.2799, pruned_loss=0.03823, over 7256.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2767, pruned_loss=0.04192, over 1426551.53 frames.], batch size: 19, lr: 5.09e-04
+2022-04-29 08:05:46,506 INFO [train.py:763] (7/8) Epoch 14, batch 3950, loss[loss=0.1606, simple_loss=0.2602, pruned_loss=0.03047, over 7355.00 frames.], tot_loss[loss=0.18, simple_loss=0.2762, pruned_loss=0.04187, over 1423428.62 frames.], batch size: 19, lr: 5.09e-04
+2022-04-29 08:06:52,818 INFO [train.py:763] (7/8) Epoch 14, batch 4000, loss[loss=0.2062, simple_loss=0.3005, pruned_loss=0.05588, over 7218.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2765, pruned_loss=0.0418, over 1423478.86 frames.], batch size: 21, lr: 5.09e-04
+2022-04-29 08:07:58,001 INFO [train.py:763] (7/8) Epoch 14, batch 4050, loss[loss=0.1966, simple_loss=0.3096, pruned_loss=0.04179, over 7208.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2779, pruned_loss=0.04224, over 1427245.46 frames.], batch size: 21, lr: 5.09e-04
+2022-04-29 08:09:03,259 INFO [train.py:763] (7/8) Epoch 14, batch 4100, loss[loss=0.199, simple_loss=0.2927, pruned_loss=0.05269, over 7200.00 frames.], tot_loss[loss=0.182, simple_loss=0.2781, pruned_loss=0.04289, over 1419052.94 frames.], batch size: 23, lr: 5.09e-04
+2022-04-29 08:10:08,505 INFO [train.py:763] (7/8) Epoch 14, batch 4150, loss[loss=0.2027, simple_loss=0.2826, pruned_loss=0.06135, over 5249.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2782, pruned_loss=0.04318, over 1413678.41 frames.], batch size: 52, lr: 5.08e-04
+2022-04-29 08:11:13,739 INFO [train.py:763] (7/8) Epoch 14, batch 4200, loss[loss=0.181, simple_loss=0.2843, pruned_loss=0.03882, over 7234.00 frames.], tot_loss[loss=0.1812, simple_loss=0.277, pruned_loss=0.04269, over 1412479.55 frames.], batch size: 20, lr: 5.08e-04
+2022-04-29 08:12:19,802 INFO [train.py:763] (7/8) Epoch 14, batch 4250, loss[loss=0.159, simple_loss=0.2536, pruned_loss=0.03217, over 7069.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2765, pruned_loss=0.04251, over 1410009.15 frames.], batch size: 18, lr: 5.08e-04
+2022-04-29 08:13:25,936 INFO [train.py:763] (7/8) Epoch 14, batch 4300, loss[loss=0.1559, simple_loss=0.2324, pruned_loss=0.03969, over 7269.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2769, pruned_loss=0.04289, over 1405705.40 frames.], batch size: 16, lr: 5.08e-04
+2022-04-29 08:14:30,955 INFO [train.py:763] (7/8) Epoch 14, batch 4350, loss[loss=0.1836, simple_loss=0.2797, pruned_loss=0.04371, over 7311.00 frames.], tot_loss[loss=0.1814, simple_loss=0.277, pruned_loss=0.04293, over 1409133.00 frames.], batch size: 21, lr: 5.08e-04
+2022-04-29 08:15:37,016 INFO [train.py:763] (7/8) Epoch 14, batch 4400, loss[loss=0.1749, simple_loss=0.2772, pruned_loss=0.03634, over 7146.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2757, pruned_loss=0.04227, over 1411597.41 frames.], batch size: 19, lr: 5.08e-04
+2022-04-29 08:16:42,699 INFO [train.py:763] (7/8) Epoch 14, batch 4450, loss[loss=0.1757, simple_loss=0.2751, pruned_loss=0.03813, over 7158.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2734, pruned_loss=0.04163, over 1403796.19 frames.], batch size: 18, lr: 5.07e-04
+2022-04-29 08:17:47,618 INFO [train.py:763] (7/8) Epoch 14, batch 4500, loss[loss=0.1656, simple_loss=0.2566, pruned_loss=0.03735, over 7071.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2739, pruned_loss=0.04184, over 1395128.57 frames.], batch size: 18, lr: 5.07e-04
+2022-04-29 08:18:51,951 INFO [train.py:763] (7/8) Epoch 14, batch 4550, loss[loss=0.2349, simple_loss=0.3286, pruned_loss=0.07057, over 4762.00 frames.], tot_loss[loss=0.1813, simple_loss=0.276, pruned_loss=0.04326, over 1367538.48 frames.], batch size: 52, lr: 5.07e-04
+2022-04-29 08:20:20,840 INFO [train.py:763] (7/8) Epoch 15, batch 0, loss[loss=0.1817, simple_loss=0.2815, pruned_loss=0.04096, over 7302.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2815, pruned_loss=0.04096, over 7302.00 frames.], batch size: 24, lr: 4.92e-04
+2022-04-29 08:21:27,553 INFO [train.py:763] (7/8) Epoch 15, batch 50, loss[loss=0.1578, simple_loss=0.2549, pruned_loss=0.03038, over 7425.00 frames.], tot_loss[loss=0.1789, simple_loss=0.276, pruned_loss=0.04093, over 321200.36 frames.], batch size: 18, lr: 4.92e-04
+2022-04-29 08:22:33,682 INFO [train.py:763] (7/8) Epoch 15, batch 100, loss[loss=0.1721, simple_loss=0.2675, pruned_loss=0.03837, over 7324.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2764, pruned_loss=0.04237, over 563777.89 frames.], batch size: 20, lr: 4.92e-04
+2022-04-29 08:23:40,368 INFO [train.py:763] (7/8) Epoch 15, batch 150, loss[loss=0.2052, simple_loss=0.2996, pruned_loss=0.05542, over 7146.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2762, pruned_loss=0.04231, over 753422.84 frames.], batch size: 20, lr: 4.92e-04
+2022-04-29 08:24:46,776 INFO [train.py:763] (7/8) Epoch 15, batch 200, loss[loss=0.1938, simple_loss=0.2999, pruned_loss=0.04384, over 7130.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2747, pruned_loss=0.04181, over 897300.78 frames.], batch size: 21, lr: 4.91e-04
+2022-04-29 08:25:52,235 INFO [train.py:763] (7/8) Epoch 15, batch 250, loss[loss=0.1789, simple_loss=0.2638, pruned_loss=0.047, over 7162.00 frames.], tot_loss[loss=0.179, simple_loss=0.2749, pruned_loss=0.04153, over 1014632.29 frames.], batch size: 19, lr: 4.91e-04
+2022-04-29 08:26:57,846 INFO [train.py:763] (7/8) Epoch 15, batch 300, loss[loss=0.171, simple_loss=0.2714, pruned_loss=0.0353, over 7159.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2746, pruned_loss=0.04161, over 1108651.07 frames.], batch size: 19, lr: 4.91e-04
+2022-04-29 08:28:03,225 INFO [train.py:763] (7/8) Epoch 15, batch 350, loss[loss=0.1652, simple_loss=0.2451, pruned_loss=0.04266, over 7293.00 frames.], tot_loss[loss=0.178, simple_loss=0.2739, pruned_loss=0.04103, over 1180615.60 frames.], batch size: 18, lr: 4.91e-04
+2022-04-29 08:29:08,697 INFO [train.py:763] (7/8) Epoch 15, batch 400, loss[loss=0.1614, simple_loss=0.2659, pruned_loss=0.02839, over 7264.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2748, pruned_loss=0.04109, over 1234462.56 frames.], batch size: 19, lr: 4.91e-04
+2022-04-29 08:30:14,247 INFO [train.py:763] (7/8) Epoch 15, batch 450, loss[loss=0.1867, simple_loss=0.2855, pruned_loss=0.04398, over 7418.00 frames.], tot_loss[loss=0.179, simple_loss=0.2752, pruned_loss=0.04137, over 1281681.00 frames.], batch size: 20, lr: 4.91e-04
+2022-04-29 08:31:19,789 INFO [train.py:763] (7/8) Epoch 15, batch 500, loss[loss=0.2128, simple_loss=0.3064, pruned_loss=0.0596, over 7198.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2762, pruned_loss=0.0412, over 1319015.90 frames.], batch size: 23, lr: 4.90e-04
+2022-04-29 08:32:25,953 INFO [train.py:763] (7/8) Epoch 15, batch 550, loss[loss=0.1681, simple_loss=0.2597, pruned_loss=0.03824, over 7269.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2755, pruned_loss=0.04076, over 1346285.61 frames.], batch size: 18, lr: 4.90e-04
+2022-04-29 08:33:31,121 INFO [train.py:763] (7/8) Epoch 15, batch 600, loss[loss=0.1959, simple_loss=0.2822, pruned_loss=0.05485, over 7159.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2757, pruned_loss=0.04131, over 1361333.71 frames.], batch size: 19, lr: 4.90e-04
+2022-04-29 08:34:36,406 INFO [train.py:763] (7/8) Epoch 15, batch 650, loss[loss=0.1894, simple_loss=0.2827, pruned_loss=0.04809, over 6541.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2765, pruned_loss=0.04172, over 1374255.35 frames.], batch size: 38, lr: 4.90e-04
+2022-04-29 08:35:42,067 INFO [train.py:763] (7/8) Epoch 15, batch 700, loss[loss=0.1751, simple_loss=0.2764, pruned_loss=0.03693, over 7051.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2771, pruned_loss=0.04211, over 1385901.12 frames.], batch size: 28, lr: 4.90e-04
+2022-04-29 08:36:47,200 INFO [train.py:763] (7/8) Epoch 15, batch 750, loss[loss=0.1618, simple_loss=0.2587, pruned_loss=0.03249, over 7142.00 frames.], tot_loss[loss=0.18, simple_loss=0.2764, pruned_loss=0.04174, over 1395216.56 frames.], batch size: 19, lr: 4.89e-04
+2022-04-29 08:37:53,222 INFO [train.py:763] (7/8) Epoch 15, batch 800, loss[loss=0.1907, simple_loss=0.285, pruned_loss=0.04824, over 7266.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2763, pruned_loss=0.04172, over 1402555.69 frames.], batch size: 19, lr: 4.89e-04
+2022-04-29 08:39:00,111 INFO [train.py:763] (7/8) Epoch 15, batch 850, loss[loss=0.192, simple_loss=0.3032, pruned_loss=0.04043, over 7157.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2766, pruned_loss=0.04177, over 1405168.31 frames.], batch size: 20, lr: 4.89e-04
+2022-04-29 08:40:05,810 INFO [train.py:763] (7/8) Epoch 15, batch 900, loss[loss=0.172, simple_loss=0.2639, pruned_loss=0.04001, over 7356.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2767, pruned_loss=0.04201, over 1404054.99 frames.], batch size: 19, lr: 4.89e-04
+2022-04-29 08:41:11,046 INFO [train.py:763] (7/8) Epoch 15, batch 950, loss[loss=0.1768, simple_loss=0.2671, pruned_loss=0.04329, over 7436.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2759, pruned_loss=0.0422, over 1407582.71 frames.], batch size: 20, lr: 4.89e-04
+2022-04-29 08:42:16,450 INFO [train.py:763] (7/8) Epoch 15, batch 1000, loss[loss=0.1861, simple_loss=0.287, pruned_loss=0.04267, over 7301.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2755, pruned_loss=0.04178, over 1412064.85 frames.], batch size: 25, lr: 4.89e-04
+2022-04-29 08:43:21,679 INFO [train.py:763] (7/8) Epoch 15, batch 1050, loss[loss=0.1524, simple_loss=0.2446, pruned_loss=0.03007, over 7330.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2758, pruned_loss=0.04166, over 1417625.42 frames.], batch size: 20, lr: 4.88e-04
+2022-04-29 08:44:28,821 INFO [train.py:763] (7/8) Epoch 15, batch 1100, loss[loss=0.1703, simple_loss=0.2671, pruned_loss=0.03672, over 7345.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2751, pruned_loss=0.04115, over 1420476.90 frames.], batch size: 19, lr: 4.88e-04
+2022-04-29 08:45:35,107 INFO [train.py:763] (7/8) Epoch 15, batch 1150, loss[loss=0.192, simple_loss=0.2924, pruned_loss=0.04577, over 4867.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2746, pruned_loss=0.04099, over 1421503.71 frames.], batch size: 53, lr: 4.88e-04
+2022-04-29 08:46:40,380 INFO [train.py:763] (7/8) Epoch 15, batch 1200, loss[loss=0.159, simple_loss=0.2641, pruned_loss=0.02699, over 7117.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2745, pruned_loss=0.04086, over 1419242.83 frames.], batch size: 21, lr: 4.88e-04
+2022-04-29 08:47:45,868 INFO [train.py:763] (7/8) Epoch 15, batch 1250, loss[loss=0.1602, simple_loss=0.2539, pruned_loss=0.03325, over 6805.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2737, pruned_loss=0.04078, over 1420853.65 frames.], batch size: 15, lr: 4.88e-04
+2022-04-29 08:48:51,156 INFO [train.py:763] (7/8) Epoch 15, batch 1300, loss[loss=0.1852, simple_loss=0.2823, pruned_loss=0.04408, over 7201.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2744, pruned_loss=0.04088, over 1426544.99 frames.], batch size: 22, lr: 4.88e-04
+2022-04-29 08:49:56,777 INFO [train.py:763] (7/8) Epoch 15, batch 1350, loss[loss=0.1426, simple_loss=0.2418, pruned_loss=0.02173, over 7163.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2745, pruned_loss=0.04102, over 1419366.21 frames.], batch size: 19, lr: 4.87e-04
+2022-04-29 08:51:13,208 INFO [train.py:763] (7/8) Epoch 15, batch 1400, loss[loss=0.1956, simple_loss=0.2924, pruned_loss=0.04939, over 7349.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2757, pruned_loss=0.04143, over 1417011.99 frames.], batch size: 22, lr: 4.87e-04
+2022-04-29 08:52:20,217 INFO [train.py:763] (7/8) Epoch 15, batch 1450, loss[loss=0.2005, simple_loss=0.3009, pruned_loss=0.05001, over 7419.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2758, pruned_loss=0.04125, over 1422818.60 frames.], batch size: 21, lr: 4.87e-04
+2022-04-29 08:53:25,692 INFO [train.py:763] (7/8) Epoch 15, batch 1500, loss[loss=0.1746, simple_loss=0.2744, pruned_loss=0.03737, over 7199.00 frames.], tot_loss[loss=0.1806, simple_loss=0.277, pruned_loss=0.04207, over 1422410.39 frames.], batch size: 23, lr: 4.87e-04
+2022-04-29 08:54:40,097 INFO [train.py:763] (7/8) Epoch 15, batch 1550, loss[loss=0.1668, simple_loss=0.2566, pruned_loss=0.03848, over 6824.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2758, pruned_loss=0.04176, over 1420421.40 frames.], batch size: 15, lr: 4.87e-04
+2022-04-29 08:56:04,003 INFO [train.py:763] (7/8) Epoch 15, batch 1600, loss[loss=0.191, simple_loss=0.2664, pruned_loss=0.0578, over 7241.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2765, pruned_loss=0.0421, over 1423180.81 frames.], batch size: 16, lr: 4.87e-04
+2022-04-29 08:57:19,962 INFO [train.py:763] (7/8) Epoch 15, batch 1650, loss[loss=0.1632, simple_loss=0.2682, pruned_loss=0.02915, over 7150.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2758, pruned_loss=0.04174, over 1424428.73 frames.], batch size: 20, lr: 4.86e-04
+2022-04-29 08:58:25,688 INFO [train.py:763] (7/8) Epoch 15, batch 1700, loss[loss=0.1855, simple_loss=0.2679, pruned_loss=0.05153, over 7414.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2753, pruned_loss=0.04177, over 1423968.70 frames.], batch size: 18, lr: 4.86e-04
+2022-04-29 08:59:40,122 INFO [train.py:763] (7/8) Epoch 15, batch 1750, loss[loss=0.2142, simple_loss=0.3091, pruned_loss=0.05962, over 7373.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2757, pruned_loss=0.04206, over 1423759.42 frames.], batch size: 23, lr: 4.86e-04
+2022-04-29 09:00:47,103 INFO [train.py:763] (7/8) Epoch 15, batch 1800, loss[loss=0.1606, simple_loss=0.2604, pruned_loss=0.03036, over 7360.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2758, pruned_loss=0.04194, over 1422008.75 frames.], batch size: 19, lr: 4.86e-04
+2022-04-29 09:02:11,311 INFO [train.py:763] (7/8) Epoch 15, batch 1850, loss[loss=0.1891, simple_loss=0.2902, pruned_loss=0.04396, over 7139.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2754, pruned_loss=0.0419, over 1425077.39 frames.], batch size: 20, lr: 4.86e-04
+2022-04-29 09:03:16,756 INFO [train.py:763] (7/8) Epoch 15, batch 1900, loss[loss=0.2158, simple_loss=0.3127, pruned_loss=0.05942, over 7316.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2753, pruned_loss=0.04183, over 1429166.78 frames.], batch size: 25, lr: 4.86e-04
+2022-04-29 09:04:23,881 INFO [train.py:763] (7/8) Epoch 15, batch 1950, loss[loss=0.2058, simple_loss=0.302, pruned_loss=0.05483, over 7197.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2765, pruned_loss=0.04243, over 1430490.40 frames.], batch size: 23, lr: 4.85e-04
+2022-04-29 09:05:29,704 INFO [train.py:763] (7/8) Epoch 15, batch 2000, loss[loss=0.214, simple_loss=0.2976, pruned_loss=0.06525, over 5115.00 frames.], tot_loss[loss=0.181, simple_loss=0.277, pruned_loss=0.0425, over 1423306.56 frames.], batch size: 52, lr: 4.85e-04
+2022-04-29 09:06:36,286 INFO [train.py:763] (7/8) Epoch 15, batch 2050, loss[loss=0.1775, simple_loss=0.2805, pruned_loss=0.03724, over 6593.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2773, pruned_loss=0.04281, over 1422358.59 frames.], batch size: 38, lr: 4.85e-04
+2022-04-29 09:07:41,972 INFO [train.py:763] (7/8) Epoch 15, batch 2100, loss[loss=0.1985, simple_loss=0.2908, pruned_loss=0.05311, over 7448.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2768, pruned_loss=0.04271, over 1423582.89 frames.], batch size: 22, lr: 4.85e-04
+2022-04-29 09:08:48,753 INFO [train.py:763] (7/8) Epoch 15, batch 2150, loss[loss=0.1718, simple_loss=0.2867, pruned_loss=0.02847, over 7253.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2763, pruned_loss=0.04227, over 1418907.82 frames.], batch size: 19, lr: 4.85e-04
+2022-04-29 09:09:53,847 INFO [train.py:763] (7/8) Epoch 15, batch 2200, loss[loss=0.1923, simple_loss=0.2926, pruned_loss=0.04602, over 7203.00 frames.], tot_loss[loss=0.1801, simple_loss=0.276, pruned_loss=0.04215, over 1416210.47 frames.], batch size: 22, lr: 4.84e-04
+2022-04-29 09:10:59,464 INFO [train.py:763] (7/8) Epoch 15, batch 2250, loss[loss=0.196, simple_loss=0.2916, pruned_loss=0.05017, over 7410.00 frames.], tot_loss[loss=0.181, simple_loss=0.2768, pruned_loss=0.04259, over 1418173.89 frames.], batch size: 21, lr: 4.84e-04
+2022-04-29 09:12:05,790 INFO [train.py:763] (7/8) Epoch 15, batch 2300, loss[loss=0.2055, simple_loss=0.2928, pruned_loss=0.05913, over 7197.00 frames.], tot_loss[loss=0.181, simple_loss=0.277, pruned_loss=0.04247, over 1419805.62 frames.], batch size: 23, lr: 4.84e-04
+2022-04-29 09:13:13,324 INFO [train.py:763] (7/8) Epoch 15, batch 2350, loss[loss=0.1846, simple_loss=0.2797, pruned_loss=0.04474, over 7302.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2773, pruned_loss=0.04257, over 1421947.97 frames.], batch size: 25, lr: 4.84e-04
+2022-04-29 09:14:19,348 INFO [train.py:763] (7/8) Epoch 15, batch 2400, loss[loss=0.1911, simple_loss=0.2934, pruned_loss=0.04434, over 7290.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2765, pruned_loss=0.04244, over 1425024.98 frames.], batch size: 25, lr: 4.84e-04
+2022-04-29 09:15:24,442 INFO [train.py:763] (7/8) Epoch 15, batch 2450, loss[loss=0.1911, simple_loss=0.2883, pruned_loss=0.04696, over 6759.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2772, pruned_loss=0.04271, over 1424492.65 frames.], batch size: 31, lr: 4.84e-04
+2022-04-29 09:16:31,171 INFO [train.py:763] (7/8) Epoch 15, batch 2500, loss[loss=0.1807, simple_loss=0.2792, pruned_loss=0.04113, over 7221.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2765, pruned_loss=0.04227, over 1427573.34 frames.], batch size: 21, lr: 4.83e-04
+2022-04-29 09:17:37,375 INFO [train.py:763] (7/8) Epoch 15, batch 2550, loss[loss=0.1662, simple_loss=0.2659, pruned_loss=0.03321, over 7146.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2766, pruned_loss=0.04218, over 1424202.23 frames.], batch size: 20, lr: 4.83e-04
+2022-04-29 09:18:44,508 INFO [train.py:763] (7/8) Epoch 15, batch 2600, loss[loss=0.1641, simple_loss=0.2579, pruned_loss=0.03519, over 7366.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2761, pruned_loss=0.042, over 1422777.32 frames.], batch size: 19, lr: 4.83e-04
+2022-04-29 09:19:51,217 INFO [train.py:763] (7/8) Epoch 15, batch 2650, loss[loss=0.1931, simple_loss=0.2869, pruned_loss=0.04965, over 7391.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2762, pruned_loss=0.04177, over 1423385.47 frames.], batch size: 23, lr: 4.83e-04
+2022-04-29 09:20:56,500 INFO [train.py:763] (7/8) Epoch 15, batch 2700, loss[loss=0.2205, simple_loss=0.3128, pruned_loss=0.06408, over 7164.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2761, pruned_loss=0.04177, over 1420827.02 frames.], batch size: 26, lr: 4.83e-04
+2022-04-29 09:22:02,826 INFO [train.py:763] (7/8) Epoch 15, batch 2750, loss[loss=0.1896, simple_loss=0.283, pruned_loss=0.0481, over 7288.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2762, pruned_loss=0.04154, over 1424979.90 frames.], batch size: 18, lr: 4.83e-04
+2022-04-29 09:23:10,094 INFO [train.py:763] (7/8) Epoch 15, batch 2800, loss[loss=0.1715, simple_loss=0.2745, pruned_loss=0.03422, over 7211.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2762, pruned_loss=0.04174, over 1427301.48 frames.], batch size: 21, lr: 4.82e-04
+2022-04-29 09:24:17,273 INFO [train.py:763] (7/8) Epoch 15, batch 2850, loss[loss=0.1633, simple_loss=0.2595, pruned_loss=0.0336, over 7158.00 frames.], tot_loss[loss=0.1806, simple_loss=0.277, pruned_loss=0.04208, over 1425911.23 frames.], batch size: 18, lr: 4.82e-04
+2022-04-29 09:25:24,207 INFO [train.py:763] (7/8) Epoch 15, batch 2900, loss[loss=0.1852, simple_loss=0.285, pruned_loss=0.04266, over 7174.00 frames.], tot_loss[loss=0.18, simple_loss=0.2765, pruned_loss=0.0418, over 1428112.75 frames.], batch size: 18, lr: 4.82e-04
+2022-04-29 09:26:29,789 INFO [train.py:763] (7/8) Epoch 15, batch 2950, loss[loss=0.1835, simple_loss=0.287, pruned_loss=0.03999, over 7336.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2761, pruned_loss=0.04178, over 1425063.29 frames.], batch size: 22, lr: 4.82e-04
+2022-04-29 09:27:35,051 INFO [train.py:763] (7/8) Epoch 15, batch 3000, loss[loss=0.1698, simple_loss=0.2743, pruned_loss=0.03269, over 7408.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2763, pruned_loss=0.04173, over 1429250.38 frames.], batch size: 21, lr: 4.82e-04
+2022-04-29 09:27:35,052 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 09:27:50,493 INFO [train.py:792] (7/8) Epoch 15, validation: loss=0.1668, simple_loss=0.2684, pruned_loss=0.03254, over 698248.00 frames.
+2022-04-29 09:28:57,629 INFO [train.py:763] (7/8) Epoch 15, batch 3050, loss[loss=0.1501, simple_loss=0.2381, pruned_loss=0.0311, over 7397.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2765, pruned_loss=0.04222, over 1427964.54 frames.], batch size: 18, lr: 4.82e-04
+2022-04-29 09:30:04,547 INFO [train.py:763] (7/8) Epoch 15, batch 3100, loss[loss=0.1944, simple_loss=0.3035, pruned_loss=0.04268, over 7204.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2759, pruned_loss=0.0419, over 1427101.92 frames.], batch size: 23, lr: 4.81e-04
+2022-04-29 09:31:11,574 INFO [train.py:763] (7/8) Epoch 15, batch 3150, loss[loss=0.1622, simple_loss=0.2505, pruned_loss=0.03695, over 7167.00 frames.], tot_loss[loss=0.179, simple_loss=0.2749, pruned_loss=0.04153, over 1424446.28 frames.], batch size: 18, lr: 4.81e-04
+2022-04-29 09:32:29,240 INFO [train.py:763] (7/8) Epoch 15, batch 3200, loss[loss=0.1696, simple_loss=0.2789, pruned_loss=0.03012, over 7317.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2758, pruned_loss=0.04157, over 1424344.83 frames.], batch size: 24, lr: 4.81e-04
+2022-04-29 09:33:36,701 INFO [train.py:763] (7/8) Epoch 15, batch 3250, loss[loss=0.2081, simple_loss=0.3047, pruned_loss=0.0557, over 7329.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2746, pruned_loss=0.04099, over 1425997.41 frames.], batch size: 21, lr: 4.81e-04
+2022-04-29 09:34:43,467 INFO [train.py:763] (7/8) Epoch 15, batch 3300, loss[loss=0.2099, simple_loss=0.3096, pruned_loss=0.05506, over 7245.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2755, pruned_loss=0.0414, over 1429632.01 frames.], batch size: 25, lr: 4.81e-04
+2022-04-29 09:35:50,333 INFO [train.py:763] (7/8) Epoch 15, batch 3350, loss[loss=0.2109, simple_loss=0.3149, pruned_loss=0.05341, over 7231.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2758, pruned_loss=0.04166, over 1431926.74 frames.], batch size: 20, lr: 4.81e-04
+2022-04-29 09:36:57,584 INFO [train.py:763] (7/8) Epoch 15, batch 3400, loss[loss=0.1661, simple_loss=0.2623, pruned_loss=0.0349, over 7066.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2761, pruned_loss=0.04159, over 1428769.62 frames.], batch size: 28, lr: 4.80e-04
+2022-04-29 09:38:05,073 INFO [train.py:763] (7/8) Epoch 15, batch 3450, loss[loss=0.1616, simple_loss=0.2602, pruned_loss=0.03148, over 7359.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2757, pruned_loss=0.04124, over 1430257.68 frames.], batch size: 19, lr: 4.80e-04
+2022-04-29 09:39:11,466 INFO [train.py:763] (7/8) Epoch 15, batch 3500, loss[loss=0.1913, simple_loss=0.2906, pruned_loss=0.046, over 7333.00 frames.], tot_loss[loss=0.179, simple_loss=0.2755, pruned_loss=0.04123, over 1428674.02 frames.], batch size: 21, lr: 4.80e-04
+2022-04-29 09:40:16,442 INFO [train.py:763] (7/8) Epoch 15, batch 3550, loss[loss=0.184, simple_loss=0.29, pruned_loss=0.03901, over 7170.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2767, pruned_loss=0.04205, over 1423694.72 frames.], batch size: 26, lr: 4.80e-04
+2022-04-29 09:41:21,627 INFO [train.py:763] (7/8) Epoch 15, batch 3600, loss[loss=0.2089, simple_loss=0.3082, pruned_loss=0.05475, over 7315.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2764, pruned_loss=0.04151, over 1425519.00 frames.], batch size: 21, lr: 4.80e-04
+2022-04-29 09:42:26,940 INFO [train.py:763] (7/8) Epoch 15, batch 3650, loss[loss=0.1705, simple_loss=0.2588, pruned_loss=0.04111, over 7288.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2759, pruned_loss=0.04135, over 1425686.84 frames.], batch size: 18, lr: 4.80e-04
+2022-04-29 09:43:33,163 INFO [train.py:763] (7/8) Epoch 15, batch 3700, loss[loss=0.1463, simple_loss=0.233, pruned_loss=0.02977, over 6786.00 frames.], tot_loss[loss=0.1788, simple_loss=0.275, pruned_loss=0.0413, over 1424010.14 frames.], batch size: 15, lr: 4.79e-04
+2022-04-29 09:44:39,845 INFO [train.py:763] (7/8) Epoch 15, batch 3750, loss[loss=0.188, simple_loss=0.2938, pruned_loss=0.04115, over 7253.00 frames.], tot_loss[loss=0.1786, simple_loss=0.275, pruned_loss=0.04112, over 1422010.44 frames.], batch size: 25, lr: 4.79e-04
+2022-04-29 09:45:46,808 INFO [train.py:763] (7/8) Epoch 15, batch 3800, loss[loss=0.1492, simple_loss=0.2423, pruned_loss=0.02803, over 7137.00 frames.], tot_loss[loss=0.179, simple_loss=0.2757, pruned_loss=0.04111, over 1425684.85 frames.], batch size: 17, lr: 4.79e-04
+2022-04-29 09:46:53,791 INFO [train.py:763] (7/8) Epoch 15, batch 3850, loss[loss=0.1491, simple_loss=0.2443, pruned_loss=0.02699, over 7270.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2755, pruned_loss=0.04148, over 1421175.34 frames.], batch size: 18, lr: 4.79e-04
+2022-04-29 09:48:00,493 INFO [train.py:763] (7/8) Epoch 15, batch 3900, loss[loss=0.1696, simple_loss=0.2731, pruned_loss=0.03302, over 7219.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2761, pruned_loss=0.04168, over 1422800.74 frames.], batch size: 21, lr: 4.79e-04
+2022-04-29 09:49:06,586 INFO [train.py:763] (7/8) Epoch 15, batch 3950, loss[loss=0.1813, simple_loss=0.2843, pruned_loss=0.03915, over 7252.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2764, pruned_loss=0.04199, over 1421191.45 frames.], batch size: 20, lr: 4.79e-04
+2022-04-29 09:50:13,637 INFO [train.py:763] (7/8) Epoch 15, batch 4000, loss[loss=0.196, simple_loss=0.295, pruned_loss=0.0485, over 7312.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2765, pruned_loss=0.042, over 1419092.75 frames.], batch size: 21, lr: 4.79e-04
+2022-04-29 09:51:19,323 INFO [train.py:763] (7/8) Epoch 15, batch 4050, loss[loss=0.1636, simple_loss=0.2571, pruned_loss=0.035, over 7157.00 frames.], tot_loss[loss=0.18, simple_loss=0.2765, pruned_loss=0.04172, over 1418258.84 frames.], batch size: 18, lr: 4.78e-04
+2022-04-29 09:52:24,929 INFO [train.py:763] (7/8) Epoch 15, batch 4100, loss[loss=0.1833, simple_loss=0.2797, pruned_loss=0.04344, over 7159.00 frames.], tot_loss[loss=0.179, simple_loss=0.2755, pruned_loss=0.04123, over 1423234.86 frames.], batch size: 18, lr: 4.78e-04
+2022-04-29 09:53:30,124 INFO [train.py:763] (7/8) Epoch 15, batch 4150, loss[loss=0.1824, simple_loss=0.2788, pruned_loss=0.04299, over 7132.00 frames.], tot_loss[loss=0.18, simple_loss=0.2764, pruned_loss=0.04174, over 1418280.90 frames.], batch size: 28, lr: 4.78e-04
+2022-04-29 09:54:36,335 INFO [train.py:763] (7/8) Epoch 15, batch 4200, loss[loss=0.1661, simple_loss=0.2479, pruned_loss=0.04219, over 7421.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2757, pruned_loss=0.04166, over 1417737.60 frames.], batch size: 17, lr: 4.78e-04
+2022-04-29 09:55:43,473 INFO [train.py:763] (7/8) Epoch 15, batch 4250, loss[loss=0.1729, simple_loss=0.2707, pruned_loss=0.03754, over 7169.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2744, pruned_loss=0.04155, over 1417617.73 frames.], batch size: 18, lr: 4.78e-04
+2022-04-29 09:56:48,669 INFO [train.py:763] (7/8) Epoch 15, batch 4300, loss[loss=0.1772, simple_loss=0.2817, pruned_loss=0.03638, over 6783.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2736, pruned_loss=0.0407, over 1412857.71 frames.], batch size: 31, lr: 4.78e-04
+2022-04-29 09:57:53,928 INFO [train.py:763] (7/8) Epoch 15, batch 4350, loss[loss=0.1794, simple_loss=0.274, pruned_loss=0.04242, over 7167.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2743, pruned_loss=0.04071, over 1415999.69 frames.], batch size: 18, lr: 4.77e-04
+2022-04-29 09:59:00,579 INFO [train.py:763] (7/8) Epoch 15, batch 4400, loss[loss=0.1794, simple_loss=0.2853, pruned_loss=0.03679, over 7112.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2739, pruned_loss=0.0403, over 1416151.47 frames.], batch size: 21, lr: 4.77e-04
+2022-04-29 10:00:06,766 INFO [train.py:763] (7/8) Epoch 15, batch 4450, loss[loss=0.1981, simple_loss=0.2943, pruned_loss=0.05093, over 7207.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2743, pruned_loss=0.04055, over 1410880.72 frames.], batch size: 22, lr: 4.77e-04
+2022-04-29 10:01:11,560 INFO [train.py:763] (7/8) Epoch 15, batch 4500, loss[loss=0.1635, simple_loss=0.2521, pruned_loss=0.03739, over 7125.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2747, pruned_loss=0.04113, over 1401696.19 frames.], batch size: 17, lr: 4.77e-04
+2022-04-29 10:02:15,691 INFO [train.py:763] (7/8) Epoch 15, batch 4550, loss[loss=0.2196, simple_loss=0.3085, pruned_loss=0.06539, over 5195.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2782, pruned_loss=0.0436, over 1350714.15 frames.], batch size: 52, lr: 4.77e-04
+2022-04-29 10:03:53,504 INFO [train.py:763] (7/8) Epoch 16, batch 0, loss[loss=0.1843, simple_loss=0.2863, pruned_loss=0.0411, over 7107.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2863, pruned_loss=0.0411, over 7107.00 frames.], batch size: 21, lr: 4.63e-04
+2022-04-29 10:04:59,101 INFO [train.py:763] (7/8) Epoch 16, batch 50, loss[loss=0.167, simple_loss=0.2738, pruned_loss=0.03008, over 7308.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2803, pruned_loss=0.04379, over 317397.58 frames.], batch size: 21, lr: 4.63e-04
+2022-04-29 10:06:04,347 INFO [train.py:763] (7/8) Epoch 16, batch 100, loss[loss=0.1634, simple_loss=0.2641, pruned_loss=0.03136, over 7138.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2782, pruned_loss=0.04216, over 559321.53 frames.], batch size: 20, lr: 4.63e-04
+2022-04-29 10:07:09,689 INFO [train.py:763] (7/8) Epoch 16, batch 150, loss[loss=0.1619, simple_loss=0.2595, pruned_loss=0.03214, over 7012.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2768, pruned_loss=0.04106, over 747409.98 frames.], batch size: 16, lr: 4.63e-04
+2022-04-29 10:08:15,071 INFO [train.py:763] (7/8) Epoch 16, batch 200, loss[loss=0.1593, simple_loss=0.2493, pruned_loss=0.03463, over 7145.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2778, pruned_loss=0.0415, over 896257.74 frames.], batch size: 17, lr: 4.63e-04
+2022-04-29 10:09:20,562 INFO [train.py:763] (7/8) Epoch 16, batch 250, loss[loss=0.1699, simple_loss=0.2691, pruned_loss=0.03534, over 7268.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2783, pruned_loss=0.04208, over 1015523.11 frames.], batch size: 19, lr: 4.63e-04
+2022-04-29 10:10:25,854 INFO [train.py:763] (7/8) Epoch 16, batch 300, loss[loss=0.1673, simple_loss=0.256, pruned_loss=0.03927, over 7070.00 frames.], tot_loss[loss=0.182, simple_loss=0.2789, pruned_loss=0.04252, over 1102092.17 frames.], batch size: 18, lr: 4.62e-04
+2022-04-29 10:11:32,028 INFO [train.py:763] (7/8) Epoch 16, batch 350, loss[loss=0.1587, simple_loss=0.2501, pruned_loss=0.03364, over 6811.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2774, pruned_loss=0.04203, over 1173142.82 frames.], batch size: 15, lr: 4.62e-04
+2022-04-29 10:12:37,997 INFO [train.py:763] (7/8) Epoch 16, batch 400, loss[loss=0.1783, simple_loss=0.2677, pruned_loss=0.0444, over 5208.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2775, pruned_loss=0.04158, over 1228941.88 frames.], batch size: 52, lr: 4.62e-04
+2022-04-29 10:13:43,453 INFO [train.py:763] (7/8) Epoch 16, batch 450, loss[loss=0.1631, simple_loss=0.2636, pruned_loss=0.03128, over 7359.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2764, pruned_loss=0.04067, over 1269702.75 frames.], batch size: 19, lr: 4.62e-04
+2022-04-29 10:14:49,063 INFO [train.py:763] (7/8) Epoch 16, batch 500, loss[loss=0.1497, simple_loss=0.2496, pruned_loss=0.02483, over 7161.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2757, pruned_loss=0.04024, over 1303156.05 frames.], batch size: 18, lr: 4.62e-04
+2022-04-29 10:15:54,732 INFO [train.py:763] (7/8) Epoch 16, batch 550, loss[loss=0.1627, simple_loss=0.2521, pruned_loss=0.0366, over 7155.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2751, pruned_loss=0.04033, over 1328398.36 frames.], batch size: 17, lr: 4.62e-04
+2022-04-29 10:17:00,212 INFO [train.py:763] (7/8) Epoch 16, batch 600, loss[loss=0.182, simple_loss=0.2856, pruned_loss=0.03917, over 7118.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2753, pruned_loss=0.04115, over 1342951.68 frames.], batch size: 28, lr: 4.62e-04
+2022-04-29 10:18:05,539 INFO [train.py:763] (7/8) Epoch 16, batch 650, loss[loss=0.1786, simple_loss=0.2781, pruned_loss=0.03957, over 7326.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2765, pruned_loss=0.04159, over 1361661.97 frames.], batch size: 20, lr: 4.61e-04
+2022-04-29 10:19:10,740 INFO [train.py:763] (7/8) Epoch 16, batch 700, loss[loss=0.1748, simple_loss=0.2736, pruned_loss=0.038, over 7252.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2769, pruned_loss=0.04175, over 1368173.74 frames.], batch size: 19, lr: 4.61e-04
+2022-04-29 10:20:16,748 INFO [train.py:763] (7/8) Epoch 16, batch 750, loss[loss=0.1597, simple_loss=0.2481, pruned_loss=0.0356, over 7154.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2774, pruned_loss=0.04206, over 1376177.06 frames.], batch size: 20, lr: 4.61e-04
+2022-04-29 10:21:21,866 INFO [train.py:763] (7/8) Epoch 16, batch 800, loss[loss=0.1646, simple_loss=0.2593, pruned_loss=0.03496, over 7165.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2772, pruned_loss=0.04187, over 1387421.21 frames.], batch size: 19, lr: 4.61e-04
+2022-04-29 10:22:27,315 INFO [train.py:763] (7/8) Epoch 16, batch 850, loss[loss=0.1927, simple_loss=0.294, pruned_loss=0.04571, over 6363.00 frames.], tot_loss[loss=0.18, simple_loss=0.2767, pruned_loss=0.04165, over 1395790.23 frames.], batch size: 38, lr: 4.61e-04
+2022-04-29 10:23:32,973 INFO [train.py:763] (7/8) Epoch 16, batch 900, loss[loss=0.2044, simple_loss=0.2951, pruned_loss=0.05691, over 7327.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2767, pruned_loss=0.04145, over 1407172.09 frames.], batch size: 20, lr: 4.61e-04
+2022-04-29 10:24:38,454 INFO [train.py:763] (7/8) Epoch 16, batch 950, loss[loss=0.1485, simple_loss=0.242, pruned_loss=0.02746, over 7123.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2761, pruned_loss=0.04116, over 1412078.51 frames.], batch size: 17, lr: 4.60e-04
+2022-04-29 10:25:44,706 INFO [train.py:763] (7/8) Epoch 16, batch 1000, loss[loss=0.1644, simple_loss=0.2714, pruned_loss=0.02864, over 7113.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2762, pruned_loss=0.04095, over 1415986.04 frames.], batch size: 21, lr: 4.60e-04
+2022-04-29 10:26:51,183 INFO [train.py:763] (7/8) Epoch 16, batch 1050, loss[loss=0.1871, simple_loss=0.2893, pruned_loss=0.04241, over 7342.00 frames.], tot_loss[loss=0.178, simple_loss=0.2748, pruned_loss=0.0406, over 1420801.50 frames.], batch size: 22, lr: 4.60e-04
+2022-04-29 10:27:57,461 INFO [train.py:763] (7/8) Epoch 16, batch 1100, loss[loss=0.1893, simple_loss=0.2898, pruned_loss=0.04443, over 7282.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2749, pruned_loss=0.04036, over 1421807.46 frames.], batch size: 24, lr: 4.60e-04
+2022-04-29 10:29:02,480 INFO [train.py:763] (7/8) Epoch 16, batch 1150, loss[loss=0.1994, simple_loss=0.3063, pruned_loss=0.04622, over 7301.00 frames.], tot_loss[loss=0.1775, simple_loss=0.275, pruned_loss=0.04001, over 1422626.91 frames.], batch size: 24, lr: 4.60e-04
+2022-04-29 10:30:08,059 INFO [train.py:763] (7/8) Epoch 16, batch 1200, loss[loss=0.2693, simple_loss=0.3611, pruned_loss=0.08872, over 7299.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2751, pruned_loss=0.04066, over 1420056.63 frames.], batch size: 25, lr: 4.60e-04
+2022-04-29 10:31:13,275 INFO [train.py:763] (7/8) Epoch 16, batch 1250, loss[loss=0.1526, simple_loss=0.2482, pruned_loss=0.0285, over 7261.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2755, pruned_loss=0.04088, over 1415970.76 frames.], batch size: 18, lr: 4.60e-04
+2022-04-29 10:32:19,093 INFO [train.py:763] (7/8) Epoch 16, batch 1300, loss[loss=0.1731, simple_loss=0.2799, pruned_loss=0.03313, over 7333.00 frames.], tot_loss[loss=0.179, simple_loss=0.2756, pruned_loss=0.04115, over 1415725.18 frames.], batch size: 22, lr: 4.59e-04
+2022-04-29 10:33:25,817 INFO [train.py:763] (7/8) Epoch 16, batch 1350, loss[loss=0.1564, simple_loss=0.2378, pruned_loss=0.03752, over 6982.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2756, pruned_loss=0.04084, over 1420967.99 frames.], batch size: 16, lr: 4.59e-04
+2022-04-29 10:34:32,896 INFO [train.py:763] (7/8) Epoch 16, batch 1400, loss[loss=0.1707, simple_loss=0.2648, pruned_loss=0.03831, over 7152.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2738, pruned_loss=0.0402, over 1422060.63 frames.], batch size: 20, lr: 4.59e-04
+2022-04-29 10:35:38,359 INFO [train.py:763] (7/8) Epoch 16, batch 1450, loss[loss=0.1859, simple_loss=0.2847, pruned_loss=0.04357, over 7333.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2736, pruned_loss=0.04002, over 1420749.71 frames.], batch size: 22, lr: 4.59e-04
+2022-04-29 10:36:44,006 INFO [train.py:763] (7/8) Epoch 16, batch 1500, loss[loss=0.1607, simple_loss=0.2582, pruned_loss=0.0316, over 7258.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2724, pruned_loss=0.03953, over 1426536.08 frames.], batch size: 19, lr: 4.59e-04
+2022-04-29 10:37:49,285 INFO [train.py:763] (7/8) Epoch 16, batch 1550, loss[loss=0.1841, simple_loss=0.2834, pruned_loss=0.04242, over 7221.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2731, pruned_loss=0.04, over 1423444.54 frames.], batch size: 21, lr: 4.59e-04
+2022-04-29 10:38:55,276 INFO [train.py:763] (7/8) Epoch 16, batch 1600, loss[loss=0.1689, simple_loss=0.2745, pruned_loss=0.03161, over 7438.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2722, pruned_loss=0.03921, over 1427351.28 frames.], batch size: 20, lr: 4.58e-04
+2022-04-29 10:40:00,461 INFO [train.py:763] (7/8) Epoch 16, batch 1650, loss[loss=0.1882, simple_loss=0.2811, pruned_loss=0.04763, over 7423.00 frames.], tot_loss[loss=0.176, simple_loss=0.2732, pruned_loss=0.03945, over 1428959.05 frames.], batch size: 21, lr: 4.58e-04
+2022-04-29 10:41:05,551 INFO [train.py:763] (7/8) Epoch 16, batch 1700, loss[loss=0.2599, simple_loss=0.3282, pruned_loss=0.09575, over 4702.00 frames.], tot_loss[loss=0.1781, simple_loss=0.275, pruned_loss=0.0406, over 1421549.56 frames.], batch size: 52, lr: 4.58e-04
+2022-04-29 10:42:10,609 INFO [train.py:763] (7/8) Epoch 16, batch 1750, loss[loss=0.188, simple_loss=0.2863, pruned_loss=0.04485, over 7371.00 frames.], tot_loss[loss=0.179, simple_loss=0.2762, pruned_loss=0.0409, over 1414154.01 frames.], batch size: 23, lr: 4.58e-04
+2022-04-29 10:43:15,532 INFO [train.py:763] (7/8) Epoch 16, batch 1800, loss[loss=0.1774, simple_loss=0.2751, pruned_loss=0.03986, over 7180.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2764, pruned_loss=0.04074, over 1414853.77 frames.], batch size: 23, lr: 4.58e-04
+2022-04-29 10:44:20,693 INFO [train.py:763] (7/8) Epoch 16, batch 1850, loss[loss=0.1945, simple_loss=0.2951, pruned_loss=0.04697, over 6483.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2754, pruned_loss=0.04011, over 1416045.70 frames.], batch size: 37, lr: 4.58e-04
+2022-04-29 10:45:26,195 INFO [train.py:763] (7/8) Epoch 16, batch 1900, loss[loss=0.1602, simple_loss=0.2427, pruned_loss=0.03882, over 7428.00 frames.], tot_loss[loss=0.1777, simple_loss=0.275, pruned_loss=0.04023, over 1419792.00 frames.], batch size: 20, lr: 4.58e-04
+2022-04-29 10:46:31,355 INFO [train.py:763] (7/8) Epoch 16, batch 1950, loss[loss=0.1804, simple_loss=0.2833, pruned_loss=0.03875, over 7321.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2747, pruned_loss=0.04016, over 1422480.20 frames.], batch size: 21, lr: 4.57e-04
+2022-04-29 10:47:36,633 INFO [train.py:763] (7/8) Epoch 16, batch 2000, loss[loss=0.1672, simple_loss=0.2625, pruned_loss=0.03595, over 7265.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2753, pruned_loss=0.0406, over 1423933.22 frames.], batch size: 19, lr: 4.57e-04
+2022-04-29 10:48:44,164 INFO [train.py:763] (7/8) Epoch 16, batch 2050, loss[loss=0.1578, simple_loss=0.2494, pruned_loss=0.03308, over 7401.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2738, pruned_loss=0.04023, over 1426824.03 frames.], batch size: 18, lr: 4.57e-04
+2022-04-29 10:49:51,127 INFO [train.py:763] (7/8) Epoch 16, batch 2100, loss[loss=0.1702, simple_loss=0.2676, pruned_loss=0.03639, over 7430.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2737, pruned_loss=0.03989, over 1428064.76 frames.], batch size: 21, lr: 4.57e-04
+2022-04-29 10:50:58,001 INFO [train.py:763] (7/8) Epoch 16, batch 2150, loss[loss=0.1665, simple_loss=0.265, pruned_loss=0.03405, over 7363.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2743, pruned_loss=0.04015, over 1424345.78 frames.], batch size: 19, lr: 4.57e-04
+2022-04-29 10:52:04,711 INFO [train.py:763] (7/8) Epoch 16, batch 2200, loss[loss=0.1883, simple_loss=0.2994, pruned_loss=0.0386, over 7337.00 frames.], tot_loss[loss=0.1768, simple_loss=0.274, pruned_loss=0.03979, over 1422090.53 frames.], batch size: 22, lr: 4.57e-04
+2022-04-29 10:53:10,684 INFO [train.py:763] (7/8) Epoch 16, batch 2250, loss[loss=0.1834, simple_loss=0.2856, pruned_loss=0.04064, over 7409.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2747, pruned_loss=0.04043, over 1423993.91 frames.], batch size: 21, lr: 4.56e-04
+2022-04-29 10:54:16,242 INFO [train.py:763] (7/8) Epoch 16, batch 2300, loss[loss=0.1728, simple_loss=0.2682, pruned_loss=0.03871, over 7305.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2737, pruned_loss=0.03988, over 1423524.82 frames.], batch size: 24, lr: 4.56e-04
+2022-04-29 10:55:22,556 INFO [train.py:763] (7/8) Epoch 16, batch 2350, loss[loss=0.1871, simple_loss=0.2922, pruned_loss=0.04102, over 7381.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2736, pruned_loss=0.0395, over 1426556.71 frames.], batch size: 23, lr: 4.56e-04
+2022-04-29 10:56:28,594 INFO [train.py:763] (7/8) Epoch 16, batch 2400, loss[loss=0.1629, simple_loss=0.2559, pruned_loss=0.03491, over 7016.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2726, pruned_loss=0.03914, over 1424601.76 frames.], batch size: 16, lr: 4.56e-04
+2022-04-29 10:57:34,915 INFO [train.py:763] (7/8) Epoch 16, batch 2450, loss[loss=0.18, simple_loss=0.2832, pruned_loss=0.03846, over 7330.00 frames.], tot_loss[loss=0.1753, simple_loss=0.272, pruned_loss=0.03927, over 1422735.82 frames.], batch size: 22, lr: 4.56e-04
+2022-04-29 10:58:41,500 INFO [train.py:763] (7/8) Epoch 16, batch 2500, loss[loss=0.1802, simple_loss=0.2896, pruned_loss=0.0354, over 7217.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2713, pruned_loss=0.03909, over 1422743.50 frames.], batch size: 21, lr: 4.56e-04
+2022-04-29 10:59:48,430 INFO [train.py:763] (7/8) Epoch 16, batch 2550, loss[loss=0.1577, simple_loss=0.2611, pruned_loss=0.02713, over 7218.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2718, pruned_loss=0.03943, over 1418375.62 frames.], batch size: 21, lr: 4.56e-04
+2022-04-29 11:00:54,067 INFO [train.py:763] (7/8) Epoch 16, batch 2600, loss[loss=0.1733, simple_loss=0.2799, pruned_loss=0.03333, over 7134.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2741, pruned_loss=0.04045, over 1421083.29 frames.], batch size: 28, lr: 4.55e-04
+2022-04-29 11:01:59,332 INFO [train.py:763] (7/8) Epoch 16, batch 2650, loss[loss=0.1608, simple_loss=0.2532, pruned_loss=0.03416, over 7357.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2751, pruned_loss=0.04102, over 1419083.50 frames.], batch size: 19, lr: 4.55e-04
+2022-04-29 11:03:04,689 INFO [train.py:763] (7/8) Epoch 16, batch 2700, loss[loss=0.211, simple_loss=0.3124, pruned_loss=0.0548, over 7347.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2738, pruned_loss=0.04061, over 1421694.86 frames.], batch size: 22, lr: 4.55e-04
+2022-04-29 11:04:10,098 INFO [train.py:763] (7/8) Epoch 16, batch 2750, loss[loss=0.168, simple_loss=0.2676, pruned_loss=0.03417, over 7164.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2728, pruned_loss=0.04036, over 1421765.06 frames.], batch size: 19, lr: 4.55e-04
+2022-04-29 11:05:15,596 INFO [train.py:763] (7/8) Epoch 16, batch 2800, loss[loss=0.1902, simple_loss=0.2839, pruned_loss=0.04824, over 5078.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2728, pruned_loss=0.04076, over 1421533.01 frames.], batch size: 52, lr: 4.55e-04
+2022-04-29 11:06:20,613 INFO [train.py:763] (7/8) Epoch 16, batch 2850, loss[loss=0.1915, simple_loss=0.2992, pruned_loss=0.0419, over 7312.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2734, pruned_loss=0.04037, over 1421804.60 frames.], batch size: 21, lr: 4.55e-04
+2022-04-29 11:07:35,858 INFO [train.py:763] (7/8) Epoch 16, batch 2900, loss[loss=0.1667, simple_loss=0.271, pruned_loss=0.03124, over 7238.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2737, pruned_loss=0.04063, over 1416852.35 frames.], batch size: 20, lr: 4.55e-04
+2022-04-29 11:08:42,378 INFO [train.py:763] (7/8) Epoch 16, batch 2950, loss[loss=0.1691, simple_loss=0.2585, pruned_loss=0.03981, over 7279.00 frames.], tot_loss[loss=0.1786, simple_loss=0.275, pruned_loss=0.0411, over 1417844.55 frames.], batch size: 18, lr: 4.54e-04
+2022-04-29 11:09:49,133 INFO [train.py:763] (7/8) Epoch 16, batch 3000, loss[loss=0.1851, simple_loss=0.2871, pruned_loss=0.04153, over 7154.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2758, pruned_loss=0.04116, over 1423022.79 frames.], batch size: 20, lr: 4.54e-04
+2022-04-29 11:09:49,134 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 11:10:05,042 INFO [train.py:792] (7/8) Epoch 16, validation: loss=0.1677, simple_loss=0.2693, pruned_loss=0.03309, over 698248.00 frames.
+2022-04-29 11:11:10,317 INFO [train.py:763] (7/8) Epoch 16, batch 3050, loss[loss=0.2032, simple_loss=0.3038, pruned_loss=0.05127, over 6379.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2755, pruned_loss=0.04102, over 1422744.15 frames.], batch size: 37, lr: 4.54e-04
+2022-04-29 11:12:42,608 INFO [train.py:763] (7/8) Epoch 16, batch 3100, loss[loss=0.179, simple_loss=0.2813, pruned_loss=0.0384, over 7264.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2756, pruned_loss=0.04095, over 1418956.42 frames.], batch size: 25, lr: 4.54e-04
+2022-04-29 11:13:48,028 INFO [train.py:763] (7/8) Epoch 16, batch 3150, loss[loss=0.1675, simple_loss=0.2609, pruned_loss=0.03703, over 7335.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2749, pruned_loss=0.04074, over 1418776.99 frames.], batch size: 20, lr: 4.54e-04
+2022-04-29 11:15:03,467 INFO [train.py:763] (7/8) Epoch 16, batch 3200, loss[loss=0.1669, simple_loss=0.2661, pruned_loss=0.03386, over 7362.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2745, pruned_loss=0.04064, over 1418652.95 frames.], batch size: 19, lr: 4.54e-04
+2022-04-29 11:16:27,104 INFO [train.py:763] (7/8) Epoch 16, batch 3250, loss[loss=0.1635, simple_loss=0.2573, pruned_loss=0.03488, over 7062.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2744, pruned_loss=0.04033, over 1424256.70 frames.], batch size: 18, lr: 4.54e-04
+2022-04-29 11:17:32,429 INFO [train.py:763] (7/8) Epoch 16, batch 3300, loss[loss=0.1921, simple_loss=0.2869, pruned_loss=0.04862, over 7166.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2751, pruned_loss=0.04093, over 1424969.79 frames.], batch size: 19, lr: 4.53e-04
+2022-04-29 11:18:47,361 INFO [train.py:763] (7/8) Epoch 16, batch 3350, loss[loss=0.1747, simple_loss=0.2734, pruned_loss=0.03804, over 7337.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2752, pruned_loss=0.04111, over 1425822.40 frames.], batch size: 22, lr: 4.53e-04
+2022-04-29 11:19:54,008 INFO [train.py:763] (7/8) Epoch 16, batch 3400, loss[loss=0.1744, simple_loss=0.2772, pruned_loss=0.03576, over 7151.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2749, pruned_loss=0.04083, over 1422500.27 frames.], batch size: 20, lr: 4.53e-04
+2022-04-29 11:21:00,504 INFO [train.py:763] (7/8) Epoch 16, batch 3450, loss[loss=0.1804, simple_loss=0.27, pruned_loss=0.04535, over 7339.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2734, pruned_loss=0.04013, over 1423894.02 frames.], batch size: 20, lr: 4.53e-04
+2022-04-29 11:22:05,844 INFO [train.py:763] (7/8) Epoch 16, batch 3500, loss[loss=0.1814, simple_loss=0.2725, pruned_loss=0.04514, over 7191.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2729, pruned_loss=0.03985, over 1423592.71 frames.], batch size: 22, lr: 4.53e-04
+2022-04-29 11:23:11,006 INFO [train.py:763] (7/8) Epoch 16, batch 3550, loss[loss=0.1793, simple_loss=0.2755, pruned_loss=0.04154, over 7123.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2738, pruned_loss=0.03988, over 1425854.52 frames.], batch size: 21, lr: 4.53e-04
+2022-04-29 11:24:16,273 INFO [train.py:763] (7/8) Epoch 16, batch 3600, loss[loss=0.1428, simple_loss=0.2383, pruned_loss=0.02366, over 7288.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2737, pruned_loss=0.03964, over 1427556.64 frames.], batch size: 18, lr: 4.52e-04
+2022-04-29 11:25:21,860 INFO [train.py:763] (7/8) Epoch 16, batch 3650, loss[loss=0.1693, simple_loss=0.2741, pruned_loss=0.03221, over 7315.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2726, pruned_loss=0.03923, over 1431426.51 frames.], batch size: 21, lr: 4.52e-04
+2022-04-29 11:26:27,143 INFO [train.py:763] (7/8) Epoch 16, batch 3700, loss[loss=0.1762, simple_loss=0.2733, pruned_loss=0.03949, over 7154.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2725, pruned_loss=0.03928, over 1430361.06 frames.], batch size: 20, lr: 4.52e-04
+2022-04-29 11:27:34,335 INFO [train.py:763] (7/8) Epoch 16, batch 3750, loss[loss=0.1861, simple_loss=0.2852, pruned_loss=0.04347, over 6188.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2726, pruned_loss=0.03934, over 1427522.67 frames.], batch size: 37, lr: 4.52e-04
+2022-04-29 11:28:40,560 INFO [train.py:763] (7/8) Epoch 16, batch 3800, loss[loss=0.1829, simple_loss=0.2758, pruned_loss=0.04496, over 6411.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2733, pruned_loss=0.03965, over 1425915.84 frames.], batch size: 37, lr: 4.52e-04
+2022-04-29 11:29:46,878 INFO [train.py:763] (7/8) Epoch 16, batch 3850, loss[loss=0.1584, simple_loss=0.2494, pruned_loss=0.03366, over 7002.00 frames.], tot_loss[loss=0.176, simple_loss=0.273, pruned_loss=0.03948, over 1425618.41 frames.], batch size: 16, lr: 4.52e-04
+2022-04-29 11:30:53,563 INFO [train.py:763] (7/8) Epoch 16, batch 3900, loss[loss=0.1772, simple_loss=0.2794, pruned_loss=0.03748, over 7216.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2713, pruned_loss=0.03894, over 1428219.13 frames.], batch size: 22, lr: 4.52e-04
+2022-04-29 11:32:00,335 INFO [train.py:763] (7/8) Epoch 16, batch 3950, loss[loss=0.215, simple_loss=0.3052, pruned_loss=0.06236, over 7205.00 frames.], tot_loss[loss=0.1762, simple_loss=0.273, pruned_loss=0.03967, over 1427513.96 frames.], batch size: 23, lr: 4.51e-04
+2022-04-29 11:33:05,776 INFO [train.py:763] (7/8) Epoch 16, batch 4000, loss[loss=0.1669, simple_loss=0.2552, pruned_loss=0.03927, over 7277.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2729, pruned_loss=0.03976, over 1428424.68 frames.], batch size: 18, lr: 4.51e-04
+2022-04-29 11:34:12,348 INFO [train.py:763] (7/8) Epoch 16, batch 4050, loss[loss=0.182, simple_loss=0.2874, pruned_loss=0.03832, over 6661.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2731, pruned_loss=0.03998, over 1425409.79 frames.], batch size: 31, lr: 4.51e-04
+2022-04-29 11:35:18,259 INFO [train.py:763] (7/8) Epoch 16, batch 4100, loss[loss=0.2273, simple_loss=0.3121, pruned_loss=0.07128, over 6357.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2742, pruned_loss=0.04028, over 1423991.39 frames.], batch size: 37, lr: 4.51e-04
+2022-04-29 11:36:24,682 INFO [train.py:763] (7/8) Epoch 16, batch 4150, loss[loss=0.1751, simple_loss=0.2724, pruned_loss=0.03892, over 7130.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2736, pruned_loss=0.03988, over 1423168.84 frames.], batch size: 17, lr: 4.51e-04
+2022-04-29 11:37:30,250 INFO [train.py:763] (7/8) Epoch 16, batch 4200, loss[loss=0.2047, simple_loss=0.308, pruned_loss=0.0507, over 7116.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2745, pruned_loss=0.04036, over 1421848.12 frames.], batch size: 26, lr: 4.51e-04
+2022-04-29 11:38:36,675 INFO [train.py:763] (7/8) Epoch 16, batch 4250, loss[loss=0.1807, simple_loss=0.2662, pruned_loss=0.04763, over 7265.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2744, pruned_loss=0.04009, over 1423458.42 frames.], batch size: 18, lr: 4.51e-04
+2022-04-29 11:39:43,739 INFO [train.py:763] (7/8) Epoch 16, batch 4300, loss[loss=0.1688, simple_loss=0.2571, pruned_loss=0.0403, over 7063.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2733, pruned_loss=0.03965, over 1421842.89 frames.], batch size: 18, lr: 4.50e-04
+2022-04-29 11:40:49,819 INFO [train.py:763] (7/8) Epoch 16, batch 4350, loss[loss=0.1772, simple_loss=0.2653, pruned_loss=0.04455, over 7166.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2736, pruned_loss=0.04003, over 1420629.53 frames.], batch size: 18, lr: 4.50e-04
+2022-04-29 11:41:55,146 INFO [train.py:763] (7/8) Epoch 16, batch 4400, loss[loss=0.2044, simple_loss=0.3006, pruned_loss=0.0541, over 7214.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2736, pruned_loss=0.04039, over 1418981.19 frames.], batch size: 21, lr: 4.50e-04
+2022-04-29 11:43:00,297 INFO [train.py:763] (7/8) Epoch 16, batch 4450, loss[loss=0.1917, simple_loss=0.273, pruned_loss=0.05519, over 7156.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2761, pruned_loss=0.04116, over 1415059.19 frames.], batch size: 17, lr: 4.50e-04
+2022-04-29 11:44:06,071 INFO [train.py:763] (7/8) Epoch 16, batch 4500, loss[loss=0.1811, simple_loss=0.2742, pruned_loss=0.04395, over 7238.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2743, pruned_loss=0.04077, over 1413028.69 frames.], batch size: 20, lr: 4.50e-04
+2022-04-29 11:45:13,655 INFO [train.py:763] (7/8) Epoch 16, batch 4550, loss[loss=0.2213, simple_loss=0.3076, pruned_loss=0.06754, over 5184.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2737, pruned_loss=0.04144, over 1379676.87 frames.], batch size: 52, lr: 4.50e-04
+2022-04-29 11:46:42,235 INFO [train.py:763] (7/8) Epoch 17, batch 0, loss[loss=0.1773, simple_loss=0.2747, pruned_loss=0.03988, over 7236.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2747, pruned_loss=0.03988, over 7236.00 frames.], batch size: 20, lr: 4.38e-04
+2022-04-29 11:47:48,733 INFO [train.py:763] (7/8) Epoch 17, batch 50, loss[loss=0.1534, simple_loss=0.2552, pruned_loss=0.02583, over 6994.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2709, pruned_loss=0.03827, over 324085.98 frames.], batch size: 16, lr: 4.38e-04
+2022-04-29 11:48:54,542 INFO [train.py:763] (7/8) Epoch 17, batch 100, loss[loss=0.141, simple_loss=0.2409, pruned_loss=0.02051, over 7171.00 frames.], tot_loss[loss=0.176, simple_loss=0.2737, pruned_loss=0.0392, over 565684.93 frames.], batch size: 18, lr: 4.37e-04
+2022-04-29 11:50:00,293 INFO [train.py:763] (7/8) Epoch 17, batch 150, loss[loss=0.2035, simple_loss=0.2987, pruned_loss=0.05412, over 7149.00 frames.], tot_loss[loss=0.178, simple_loss=0.2754, pruned_loss=0.04029, over 753691.86 frames.], batch size: 20, lr: 4.37e-04
+2022-04-29 11:51:07,284 INFO [train.py:763] (7/8) Epoch 17, batch 200, loss[loss=0.1696, simple_loss=0.2571, pruned_loss=0.04103, over 7161.00 frames.], tot_loss[loss=0.179, simple_loss=0.2762, pruned_loss=0.0409, over 904543.96 frames.], batch size: 18, lr: 4.37e-04
+2022-04-29 11:52:14,170 INFO [train.py:763] (7/8) Epoch 17, batch 250, loss[loss=0.1694, simple_loss=0.2749, pruned_loss=0.032, over 6791.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2755, pruned_loss=0.04012, over 1022062.73 frames.], batch size: 31, lr: 4.37e-04
+2022-04-29 11:53:19,807 INFO [train.py:763] (7/8) Epoch 17, batch 300, loss[loss=0.1818, simple_loss=0.2817, pruned_loss=0.0409, over 7057.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2753, pruned_loss=0.0402, over 1106585.93 frames.], batch size: 28, lr: 4.37e-04
+2022-04-29 11:54:25,521 INFO [train.py:763] (7/8) Epoch 17, batch 350, loss[loss=0.1675, simple_loss=0.273, pruned_loss=0.03104, over 7331.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2739, pruned_loss=0.03983, over 1173979.75 frames.], batch size: 22, lr: 4.37e-04
+2022-04-29 11:55:31,581 INFO [train.py:763] (7/8) Epoch 17, batch 400, loss[loss=0.1649, simple_loss=0.2538, pruned_loss=0.03803, over 7237.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2735, pruned_loss=0.03935, over 1234092.56 frames.], batch size: 16, lr: 4.37e-04
+2022-04-29 11:56:37,256 INFO [train.py:763] (7/8) Epoch 17, batch 450, loss[loss=0.1876, simple_loss=0.2875, pruned_loss=0.04382, over 7216.00 frames.], tot_loss[loss=0.1761, simple_loss=0.274, pruned_loss=0.03912, over 1276966.24 frames.], batch size: 22, lr: 4.36e-04
+2022-04-29 11:57:42,955 INFO [train.py:763] (7/8) Epoch 17, batch 500, loss[loss=0.1845, simple_loss=0.2878, pruned_loss=0.04063, over 7335.00 frames.], tot_loss[loss=0.1764, simple_loss=0.274, pruned_loss=0.0394, over 1313474.70 frames.], batch size: 22, lr: 4.36e-04
+2022-04-29 11:58:48,668 INFO [train.py:763] (7/8) Epoch 17, batch 550, loss[loss=0.166, simple_loss=0.2667, pruned_loss=0.03265, over 7139.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2735, pruned_loss=0.03936, over 1340350.72 frames.], batch size: 17, lr: 4.36e-04
+2022-04-29 11:59:54,504 INFO [train.py:763] (7/8) Epoch 17, batch 600, loss[loss=0.1649, simple_loss=0.2681, pruned_loss=0.03081, over 6408.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2744, pruned_loss=0.0396, over 1356937.45 frames.], batch size: 37, lr: 4.36e-04
+2022-04-29 12:01:00,189 INFO [train.py:763] (7/8) Epoch 17, batch 650, loss[loss=0.2195, simple_loss=0.2991, pruned_loss=0.06995, over 5403.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2743, pruned_loss=0.03963, over 1370010.86 frames.], batch size: 52, lr: 4.36e-04
+2022-04-29 12:02:07,708 INFO [train.py:763] (7/8) Epoch 17, batch 700, loss[loss=0.1801, simple_loss=0.278, pruned_loss=0.04113, over 7311.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2738, pruned_loss=0.03968, over 1380987.22 frames.], batch size: 21, lr: 4.36e-04
+2022-04-29 12:03:15,631 INFO [train.py:763] (7/8) Epoch 17, batch 750, loss[loss=0.1489, simple_loss=0.2339, pruned_loss=0.03197, over 7417.00 frames.], tot_loss[loss=0.1752, simple_loss=0.272, pruned_loss=0.03916, over 1391324.76 frames.], batch size: 18, lr: 4.36e-04
+2022-04-29 12:04:22,605 INFO [train.py:763] (7/8) Epoch 17, batch 800, loss[loss=0.2155, simple_loss=0.3046, pruned_loss=0.06326, over 7315.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2723, pruned_loss=0.03892, over 1403573.90 frames.], batch size: 21, lr: 4.36e-04
+2022-04-29 12:05:28,627 INFO [train.py:763] (7/8) Epoch 17, batch 850, loss[loss=0.1858, simple_loss=0.2919, pruned_loss=0.03988, over 7420.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2716, pruned_loss=0.03863, over 1407178.79 frames.], batch size: 21, lr: 4.35e-04
+2022-04-29 12:06:34,127 INFO [train.py:763] (7/8) Epoch 17, batch 900, loss[loss=0.1871, simple_loss=0.2848, pruned_loss=0.04472, over 7197.00 frames.], tot_loss[loss=0.1766, simple_loss=0.274, pruned_loss=0.0396, over 1406369.46 frames.], batch size: 22, lr: 4.35e-04
+2022-04-29 12:07:40,041 INFO [train.py:763] (7/8) Epoch 17, batch 950, loss[loss=0.178, simple_loss=0.2809, pruned_loss=0.03757, over 7253.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2738, pruned_loss=0.0394, over 1409716.29 frames.], batch size: 19, lr: 4.35e-04
+2022-04-29 12:08:46,281 INFO [train.py:763] (7/8) Epoch 17, batch 1000, loss[loss=0.2169, simple_loss=0.3109, pruned_loss=0.06146, over 7289.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2733, pruned_loss=0.03895, over 1415143.93 frames.], batch size: 24, lr: 4.35e-04
+2022-04-29 12:09:52,075 INFO [train.py:763] (7/8) Epoch 17, batch 1050, loss[loss=0.1536, simple_loss=0.2345, pruned_loss=0.03633, over 7286.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2727, pruned_loss=0.03875, over 1418233.60 frames.], batch size: 17, lr: 4.35e-04
+2022-04-29 12:10:57,977 INFO [train.py:763] (7/8) Epoch 17, batch 1100, loss[loss=0.2041, simple_loss=0.3053, pruned_loss=0.05148, over 7265.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2736, pruned_loss=0.03926, over 1421204.68 frames.], batch size: 25, lr: 4.35e-04
+2022-04-29 12:12:04,952 INFO [train.py:763] (7/8) Epoch 17, batch 1150, loss[loss=0.1906, simple_loss=0.2804, pruned_loss=0.05042, over 7381.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2735, pruned_loss=0.03938, over 1419264.21 frames.], batch size: 23, lr: 4.35e-04
+2022-04-29 12:13:12,232 INFO [train.py:763] (7/8) Epoch 17, batch 1200, loss[loss=0.1982, simple_loss=0.2862, pruned_loss=0.05506, over 7285.00 frames.], tot_loss[loss=0.1758, simple_loss=0.273, pruned_loss=0.03926, over 1416023.18 frames.], batch size: 18, lr: 4.34e-04
+2022-04-29 12:14:19,352 INFO [train.py:763] (7/8) Epoch 17, batch 1250, loss[loss=0.2012, simple_loss=0.293, pruned_loss=0.05469, over 7401.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2724, pruned_loss=0.03923, over 1418681.93 frames.], batch size: 21, lr: 4.34e-04
+2022-04-29 12:15:25,182 INFO [train.py:763] (7/8) Epoch 17, batch 1300, loss[loss=0.1825, simple_loss=0.2897, pruned_loss=0.03768, over 7180.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2726, pruned_loss=0.0396, over 1420160.20 frames.], batch size: 26, lr: 4.34e-04
+2022-04-29 12:16:30,508 INFO [train.py:763] (7/8) Epoch 17, batch 1350, loss[loss=0.1296, simple_loss=0.2176, pruned_loss=0.02083, over 6996.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2726, pruned_loss=0.03929, over 1422267.16 frames.], batch size: 16, lr: 4.34e-04
+2022-04-29 12:17:36,054 INFO [train.py:763] (7/8) Epoch 17, batch 1400, loss[loss=0.185, simple_loss=0.2944, pruned_loss=0.03777, over 7116.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2742, pruned_loss=0.03974, over 1424096.42 frames.], batch size: 21, lr: 4.34e-04
+2022-04-29 12:18:41,496 INFO [train.py:763] (7/8) Epoch 17, batch 1450, loss[loss=0.1896, simple_loss=0.2911, pruned_loss=0.04407, over 7146.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2742, pruned_loss=0.03975, over 1422098.99 frames.], batch size: 20, lr: 4.34e-04
+2022-04-29 12:19:47,547 INFO [train.py:763] (7/8) Epoch 17, batch 1500, loss[loss=0.2129, simple_loss=0.3107, pruned_loss=0.05751, over 7304.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2746, pruned_loss=0.04013, over 1413731.76 frames.], batch size: 25, lr: 4.34e-04
+2022-04-29 12:20:53,508 INFO [train.py:763] (7/8) Epoch 17, batch 1550, loss[loss=0.1683, simple_loss=0.2618, pruned_loss=0.03742, over 7156.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2736, pruned_loss=0.03938, over 1421075.11 frames.], batch size: 19, lr: 4.33e-04
+2022-04-29 12:21:59,204 INFO [train.py:763] (7/8) Epoch 17, batch 1600, loss[loss=0.1579, simple_loss=0.2521, pruned_loss=0.03184, over 7448.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2735, pruned_loss=0.03931, over 1421911.69 frames.], batch size: 20, lr: 4.33e-04
+2022-04-29 12:23:04,511 INFO [train.py:763] (7/8) Epoch 17, batch 1650, loss[loss=0.1414, simple_loss=0.2446, pruned_loss=0.01909, over 7283.00 frames.], tot_loss[loss=0.176, simple_loss=0.2736, pruned_loss=0.03922, over 1421708.73 frames.], batch size: 17, lr: 4.33e-04
+2022-04-29 12:24:09,906 INFO [train.py:763] (7/8) Epoch 17, batch 1700, loss[loss=0.1544, simple_loss=0.248, pruned_loss=0.0304, over 7370.00 frames.], tot_loss[loss=0.1757, simple_loss=0.273, pruned_loss=0.03918, over 1425161.15 frames.], batch size: 19, lr: 4.33e-04
+2022-04-29 12:25:15,260 INFO [train.py:763] (7/8) Epoch 17, batch 1750, loss[loss=0.1737, simple_loss=0.2766, pruned_loss=0.03539, over 7319.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2728, pruned_loss=0.03912, over 1425002.81 frames.], batch size: 21, lr: 4.33e-04
+2022-04-29 12:26:20,543 INFO [train.py:763] (7/8) Epoch 17, batch 1800, loss[loss=0.1608, simple_loss=0.2631, pruned_loss=0.02927, over 7246.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2724, pruned_loss=0.03897, over 1428356.90 frames.], batch size: 20, lr: 4.33e-04
+2022-04-29 12:27:26,292 INFO [train.py:763] (7/8) Epoch 17, batch 1850, loss[loss=0.2267, simple_loss=0.3083, pruned_loss=0.07254, over 5307.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2714, pruned_loss=0.03892, over 1426741.40 frames.], batch size: 53, lr: 4.33e-04
+2022-04-29 12:28:31,347 INFO [train.py:763] (7/8) Epoch 17, batch 1900, loss[loss=0.1602, simple_loss=0.2679, pruned_loss=0.02627, over 7320.00 frames.], tot_loss[loss=0.1755, simple_loss=0.273, pruned_loss=0.039, over 1427261.60 frames.], batch size: 21, lr: 4.33e-04
+2022-04-29 12:29:36,737 INFO [train.py:763] (7/8) Epoch 17, batch 1950, loss[loss=0.173, simple_loss=0.2725, pruned_loss=0.03673, over 7326.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2741, pruned_loss=0.03937, over 1424241.52 frames.], batch size: 21, lr: 4.32e-04
+2022-04-29 12:30:42,624 INFO [train.py:763] (7/8) Epoch 17, batch 2000, loss[loss=0.2348, simple_loss=0.3114, pruned_loss=0.07916, over 4914.00 frames.], tot_loss[loss=0.176, simple_loss=0.2732, pruned_loss=0.03942, over 1424672.89 frames.], batch size: 53, lr: 4.32e-04
+2022-04-29 12:31:59,169 INFO [train.py:763] (7/8) Epoch 17, batch 2050, loss[loss=0.1497, simple_loss=0.2561, pruned_loss=0.02169, over 7117.00 frames.], tot_loss[loss=0.177, simple_loss=0.2738, pruned_loss=0.04013, over 1420415.15 frames.], batch size: 21, lr: 4.32e-04
+2022-04-29 12:33:04,650 INFO [train.py:763] (7/8) Epoch 17, batch 2100, loss[loss=0.1945, simple_loss=0.2903, pruned_loss=0.04934, over 6809.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2741, pruned_loss=0.04029, over 1416229.70 frames.], batch size: 31, lr: 4.32e-04
+2022-04-29 12:34:11,534 INFO [train.py:763] (7/8) Epoch 17, batch 2150, loss[loss=0.2041, simple_loss=0.3044, pruned_loss=0.0519, over 7217.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2738, pruned_loss=0.03981, over 1418359.14 frames.], batch size: 21, lr: 4.32e-04
+2022-04-29 12:35:18,277 INFO [train.py:763] (7/8) Epoch 17, batch 2200, loss[loss=0.1693, simple_loss=0.2577, pruned_loss=0.04042, over 6809.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2729, pruned_loss=0.03963, over 1421094.04 frames.], batch size: 15, lr: 4.32e-04
+2022-04-29 12:36:23,948 INFO [train.py:763] (7/8) Epoch 17, batch 2250, loss[loss=0.1861, simple_loss=0.2787, pruned_loss=0.04674, over 7010.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2729, pruned_loss=0.03985, over 1424468.14 frames.], batch size: 16, lr: 4.32e-04
+2022-04-29 12:37:31,412 INFO [train.py:763] (7/8) Epoch 17, batch 2300, loss[loss=0.206, simple_loss=0.3017, pruned_loss=0.05514, over 7142.00 frames.], tot_loss[loss=0.177, simple_loss=0.2738, pruned_loss=0.04007, over 1427312.75 frames.], batch size: 20, lr: 4.31e-04
+2022-04-29 12:38:38,630 INFO [train.py:763] (7/8) Epoch 17, batch 2350, loss[loss=0.2098, simple_loss=0.3151, pruned_loss=0.05224, over 7175.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2728, pruned_loss=0.03979, over 1426786.32 frames.], batch size: 26, lr: 4.31e-04
+2022-04-29 12:39:44,071 INFO [train.py:763] (7/8) Epoch 17, batch 2400, loss[loss=0.1708, simple_loss=0.2817, pruned_loss=0.02994, over 6330.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2726, pruned_loss=0.03943, over 1425095.69 frames.], batch size: 38, lr: 4.31e-04
+2022-04-29 12:40:49,298 INFO [train.py:763] (7/8) Epoch 17, batch 2450, loss[loss=0.1563, simple_loss=0.2502, pruned_loss=0.03124, over 7159.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2721, pruned_loss=0.03914, over 1426665.11 frames.], batch size: 19, lr: 4.31e-04
+2022-04-29 12:41:54,340 INFO [train.py:763] (7/8) Epoch 17, batch 2500, loss[loss=0.1863, simple_loss=0.2955, pruned_loss=0.03853, over 7126.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2745, pruned_loss=0.04038, over 1418364.76 frames.], batch size: 21, lr: 4.31e-04
+2022-04-29 12:42:59,732 INFO [train.py:763] (7/8) Epoch 17, batch 2550, loss[loss=0.1959, simple_loss=0.2978, pruned_loss=0.047, over 7311.00 frames.], tot_loss[loss=0.1772, simple_loss=0.274, pruned_loss=0.04023, over 1418712.21 frames.], batch size: 21, lr: 4.31e-04
+2022-04-29 12:44:04,858 INFO [train.py:763] (7/8) Epoch 17, batch 2600, loss[loss=0.1425, simple_loss=0.2382, pruned_loss=0.02337, over 6809.00 frames.], tot_loss[loss=0.1772, simple_loss=0.274, pruned_loss=0.04016, over 1418137.35 frames.], batch size: 15, lr: 4.31e-04
+2022-04-29 12:45:10,707 INFO [train.py:763] (7/8) Epoch 17, batch 2650, loss[loss=0.1698, simple_loss=0.2613, pruned_loss=0.03918, over 7363.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2737, pruned_loss=0.04005, over 1419542.76 frames.], batch size: 19, lr: 4.31e-04
+2022-04-29 12:46:17,014 INFO [train.py:763] (7/8) Epoch 17, batch 2700, loss[loss=0.1604, simple_loss=0.2524, pruned_loss=0.03417, over 7289.00 frames.], tot_loss[loss=0.1766, simple_loss=0.273, pruned_loss=0.04006, over 1418801.84 frames.], batch size: 18, lr: 4.30e-04
+2022-04-29 12:47:22,086 INFO [train.py:763] (7/8) Epoch 17, batch 2750, loss[loss=0.1577, simple_loss=0.2603, pruned_loss=0.02757, over 7147.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2728, pruned_loss=0.03995, over 1416974.89 frames.], batch size: 20, lr: 4.30e-04
+2022-04-29 12:48:28,865 INFO [train.py:763] (7/8) Epoch 17, batch 2800, loss[loss=0.1574, simple_loss=0.2592, pruned_loss=0.02778, over 7319.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2721, pruned_loss=0.0395, over 1416338.57 frames.], batch size: 21, lr: 4.30e-04
+2022-04-29 12:49:34,431 INFO [train.py:763] (7/8) Epoch 17, batch 2850, loss[loss=0.1891, simple_loss=0.2788, pruned_loss=0.04974, over 7300.00 frames.], tot_loss[loss=0.1758, simple_loss=0.273, pruned_loss=0.0393, over 1418841.52 frames.], batch size: 25, lr: 4.30e-04
+2022-04-29 12:50:39,894 INFO [train.py:763] (7/8) Epoch 17, batch 2900, loss[loss=0.2006, simple_loss=0.2988, pruned_loss=0.05119, over 7185.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2731, pruned_loss=0.03933, over 1422231.19 frames.], batch size: 22, lr: 4.30e-04
+2022-04-29 12:51:46,370 INFO [train.py:763] (7/8) Epoch 17, batch 2950, loss[loss=0.2026, simple_loss=0.2933, pruned_loss=0.05591, over 6254.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2729, pruned_loss=0.03926, over 1418410.24 frames.], batch size: 37, lr: 4.30e-04
+2022-04-29 12:52:52,644 INFO [train.py:763] (7/8) Epoch 17, batch 3000, loss[loss=0.1804, simple_loss=0.2805, pruned_loss=0.04017, over 7299.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2735, pruned_loss=0.03957, over 1417077.62 frames.], batch size: 25, lr: 4.30e-04
+2022-04-29 12:52:52,645 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 12:53:07,981 INFO [train.py:792] (7/8) Epoch 17, validation: loss=0.167, simple_loss=0.268, pruned_loss=0.03296, over 698248.00 frames.
+2022-04-29 12:54:13,323 INFO [train.py:763] (7/8) Epoch 17, batch 3050, loss[loss=0.2079, simple_loss=0.3151, pruned_loss=0.05042, over 7101.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2726, pruned_loss=0.03946, over 1416847.12 frames.], batch size: 21, lr: 4.29e-04
+2022-04-29 12:55:18,443 INFO [train.py:763] (7/8) Epoch 17, batch 3100, loss[loss=0.1852, simple_loss=0.2907, pruned_loss=0.03982, over 7234.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2729, pruned_loss=0.03938, over 1417295.37 frames.], batch size: 20, lr: 4.29e-04
+2022-04-29 12:56:23,988 INFO [train.py:763] (7/8) Epoch 17, batch 3150, loss[loss=0.1727, simple_loss=0.2756, pruned_loss=0.03493, over 7272.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2724, pruned_loss=0.03907, over 1420927.88 frames.], batch size: 19, lr: 4.29e-04
+2022-04-29 12:57:29,305 INFO [train.py:763] (7/8) Epoch 17, batch 3200, loss[loss=0.1836, simple_loss=0.2933, pruned_loss=0.03701, over 6964.00 frames.], tot_loss[loss=0.176, simple_loss=0.2727, pruned_loss=0.03968, over 1419020.59 frames.], batch size: 32, lr: 4.29e-04
+2022-04-29 12:58:34,639 INFO [train.py:763] (7/8) Epoch 17, batch 3250, loss[loss=0.1721, simple_loss=0.2744, pruned_loss=0.03492, over 7393.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2724, pruned_loss=0.03937, over 1421901.65 frames.], batch size: 23, lr: 4.29e-04
+2022-04-29 12:59:42,214 INFO [train.py:763] (7/8) Epoch 17, batch 3300, loss[loss=0.1599, simple_loss=0.2539, pruned_loss=0.03296, over 7162.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2718, pruned_loss=0.03899, over 1426485.22 frames.], batch size: 18, lr: 4.29e-04
+2022-04-29 13:00:47,860 INFO [train.py:763] (7/8) Epoch 17, batch 3350, loss[loss=0.1522, simple_loss=0.2435, pruned_loss=0.03047, over 7417.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2723, pruned_loss=0.03917, over 1426170.83 frames.], batch size: 18, lr: 4.29e-04
+2022-04-29 13:01:54,353 INFO [train.py:763] (7/8) Epoch 17, batch 3400, loss[loss=0.1809, simple_loss=0.285, pruned_loss=0.03842, over 7379.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2728, pruned_loss=0.03941, over 1429943.31 frames.], batch size: 23, lr: 4.29e-04
+2022-04-29 13:02:59,894 INFO [train.py:763] (7/8) Epoch 17, batch 3450, loss[loss=0.1535, simple_loss=0.2458, pruned_loss=0.03063, over 7403.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2736, pruned_loss=0.03934, over 1430490.59 frames.], batch size: 18, lr: 4.28e-04
+2022-04-29 13:04:05,580 INFO [train.py:763] (7/8) Epoch 17, batch 3500, loss[loss=0.181, simple_loss=0.28, pruned_loss=0.04095, over 6315.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2728, pruned_loss=0.03907, over 1432684.09 frames.], batch size: 37, lr: 4.28e-04
+2022-04-29 13:05:11,610 INFO [train.py:763] (7/8) Epoch 17, batch 3550, loss[loss=0.183, simple_loss=0.2811, pruned_loss=0.04245, over 7194.00 frames.], tot_loss[loss=0.1759, simple_loss=0.273, pruned_loss=0.03942, over 1430887.58 frames.], batch size: 23, lr: 4.28e-04
+2022-04-29 13:06:17,364 INFO [train.py:763] (7/8) Epoch 17, batch 3600, loss[loss=0.1808, simple_loss=0.2814, pruned_loss=0.04007, over 7218.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2719, pruned_loss=0.03872, over 1431821.36 frames.], batch size: 21, lr: 4.28e-04
+2022-04-29 13:07:22,985 INFO [train.py:763] (7/8) Epoch 17, batch 3650, loss[loss=0.2181, simple_loss=0.3057, pruned_loss=0.06525, over 7319.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2721, pruned_loss=0.03891, over 1423232.11 frames.], batch size: 22, lr: 4.28e-04
+2022-04-29 13:08:28,140 INFO [train.py:763] (7/8) Epoch 17, batch 3700, loss[loss=0.1432, simple_loss=0.2348, pruned_loss=0.02574, over 6993.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2734, pruned_loss=0.03956, over 1424365.74 frames.], batch size: 16, lr: 4.28e-04
+2022-04-29 13:09:33,333 INFO [train.py:763] (7/8) Epoch 17, batch 3750, loss[loss=0.19, simple_loss=0.2938, pruned_loss=0.04308, over 7323.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2738, pruned_loss=0.03918, over 1426639.19 frames.], batch size: 25, lr: 4.28e-04
+2022-04-29 13:10:39,701 INFO [train.py:763] (7/8) Epoch 17, batch 3800, loss[loss=0.1757, simple_loss=0.2771, pruned_loss=0.0372, over 7351.00 frames.], tot_loss[loss=0.176, simple_loss=0.2733, pruned_loss=0.03933, over 1426743.01 frames.], batch size: 19, lr: 4.28e-04
+2022-04-29 13:11:45,025 INFO [train.py:763] (7/8) Epoch 17, batch 3850, loss[loss=0.1726, simple_loss=0.2633, pruned_loss=0.04089, over 7400.00 frames.], tot_loss[loss=0.1762, simple_loss=0.273, pruned_loss=0.03968, over 1424964.31 frames.], batch size: 18, lr: 4.27e-04
+2022-04-29 13:12:50,432 INFO [train.py:763] (7/8) Epoch 17, batch 3900, loss[loss=0.1823, simple_loss=0.2793, pruned_loss=0.04269, over 7117.00 frames.], tot_loss[loss=0.176, simple_loss=0.273, pruned_loss=0.03954, over 1421303.02 frames.], batch size: 21, lr: 4.27e-04
+2022-04-29 13:13:55,786 INFO [train.py:763] (7/8) Epoch 17, batch 3950, loss[loss=0.1862, simple_loss=0.2834, pruned_loss=0.04454, over 7044.00 frames.], tot_loss[loss=0.1761, simple_loss=0.273, pruned_loss=0.03955, over 1422710.27 frames.], batch size: 28, lr: 4.27e-04
+2022-04-29 13:15:01,132 INFO [train.py:763] (7/8) Epoch 17, batch 4000, loss[loss=0.162, simple_loss=0.2403, pruned_loss=0.04182, over 7209.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2734, pruned_loss=0.03997, over 1423806.23 frames.], batch size: 16, lr: 4.27e-04
+2022-04-29 13:16:06,989 INFO [train.py:763] (7/8) Epoch 17, batch 4050, loss[loss=0.1749, simple_loss=0.2695, pruned_loss=0.04012, over 7050.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2745, pruned_loss=0.04016, over 1426962.80 frames.], batch size: 28, lr: 4.27e-04
+2022-04-29 13:17:12,358 INFO [train.py:763] (7/8) Epoch 17, batch 4100, loss[loss=0.1956, simple_loss=0.2819, pruned_loss=0.05461, over 7149.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2731, pruned_loss=0.03977, over 1423777.03 frames.], batch size: 20, lr: 4.27e-04
+2022-04-29 13:18:18,030 INFO [train.py:763] (7/8) Epoch 17, batch 4150, loss[loss=0.1672, simple_loss=0.2689, pruned_loss=0.03269, over 7327.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2733, pruned_loss=0.03979, over 1422938.92 frames.], batch size: 20, lr: 4.27e-04
+2022-04-29 13:19:24,070 INFO [train.py:763] (7/8) Epoch 17, batch 4200, loss[loss=0.1603, simple_loss=0.2457, pruned_loss=0.03741, over 6997.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2714, pruned_loss=0.03912, over 1422199.44 frames.], batch size: 16, lr: 4.26e-04
+2022-04-29 13:20:29,211 INFO [train.py:763] (7/8) Epoch 17, batch 4250, loss[loss=0.1827, simple_loss=0.287, pruned_loss=0.03925, over 6761.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2714, pruned_loss=0.03935, over 1417453.72 frames.], batch size: 31, lr: 4.26e-04
+2022-04-29 13:21:35,169 INFO [train.py:763] (7/8) Epoch 17, batch 4300, loss[loss=0.1702, simple_loss=0.2607, pruned_loss=0.0399, over 6997.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2706, pruned_loss=0.03909, over 1418756.99 frames.], batch size: 16, lr: 4.26e-04
+2022-04-29 13:22:49,732 INFO [train.py:763] (7/8) Epoch 17, batch 4350, loss[loss=0.1814, simple_loss=0.2863, pruned_loss=0.03826, over 7225.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2711, pruned_loss=0.03978, over 1407216.40 frames.], batch size: 21, lr: 4.26e-04
+2022-04-29 13:23:54,558 INFO [train.py:763] (7/8) Epoch 17, batch 4400, loss[loss=0.1673, simple_loss=0.2588, pruned_loss=0.03786, over 7068.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2727, pruned_loss=0.04009, over 1401292.59 frames.], batch size: 18, lr: 4.26e-04
+2022-04-29 13:24:59,624 INFO [train.py:763] (7/8) Epoch 17, batch 4450, loss[loss=0.1892, simple_loss=0.2841, pruned_loss=0.04714, over 6503.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2734, pruned_loss=0.04002, over 1392615.84 frames.], batch size: 38, lr: 4.26e-04
+2022-04-29 13:26:04,081 INFO [train.py:763] (7/8) Epoch 17, batch 4500, loss[loss=0.1485, simple_loss=0.243, pruned_loss=0.02699, over 6991.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2747, pruned_loss=0.04078, over 1379733.02 frames.], batch size: 16, lr: 4.26e-04
+2022-04-29 13:27:09,483 INFO [train.py:763] (7/8) Epoch 17, batch 4550, loss[loss=0.1589, simple_loss=0.2668, pruned_loss=0.02547, over 7145.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2741, pruned_loss=0.04057, over 1370320.08 frames.], batch size: 19, lr: 4.26e-04
+2022-04-29 13:29:06,473 INFO [train.py:763] (7/8) Epoch 18, batch 0, loss[loss=0.1878, simple_loss=0.2854, pruned_loss=0.04512, over 7287.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2854, pruned_loss=0.04512, over 7287.00 frames.], batch size: 25, lr: 4.15e-04
+2022-04-29 13:30:22,095 INFO [train.py:763] (7/8) Epoch 18, batch 50, loss[loss=0.1963, simple_loss=0.2947, pruned_loss=0.04893, over 7320.00 frames.], tot_loss[loss=0.174, simple_loss=0.2722, pruned_loss=0.03791, over 325007.96 frames.], batch size: 22, lr: 4.15e-04
+2022-04-29 13:31:37,256 INFO [train.py:763] (7/8) Epoch 18, batch 100, loss[loss=0.1561, simple_loss=0.2574, pruned_loss=0.02745, over 7345.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2702, pruned_loss=0.03675, over 574680.30 frames.], batch size: 22, lr: 4.14e-04
+2022-04-29 13:32:51,559 INFO [train.py:763] (7/8) Epoch 18, batch 150, loss[loss=0.1754, simple_loss=0.2759, pruned_loss=0.03745, over 7218.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2703, pruned_loss=0.03732, over 763989.09 frames.], batch size: 21, lr: 4.14e-04
+2022-04-29 13:33:57,490 INFO [train.py:763] (7/8) Epoch 18, batch 200, loss[loss=0.1517, simple_loss=0.2403, pruned_loss=0.03155, over 7267.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2715, pruned_loss=0.03854, over 909158.44 frames.], batch size: 17, lr: 4.14e-04
+2022-04-29 13:35:11,776 INFO [train.py:763] (7/8) Epoch 18, batch 250, loss[loss=0.1649, simple_loss=0.2655, pruned_loss=0.03212, over 6737.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2715, pruned_loss=0.03873, over 1025630.85 frames.], batch size: 31, lr: 4.14e-04
+2022-04-29 13:36:17,280 INFO [train.py:763] (7/8) Epoch 18, batch 300, loss[loss=0.1723, simple_loss=0.2751, pruned_loss=0.0348, over 7234.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2712, pruned_loss=0.03864, over 1115356.16 frames.], batch size: 20, lr: 4.14e-04
+2022-04-29 13:37:24,215 INFO [train.py:763] (7/8) Epoch 18, batch 350, loss[loss=0.1765, simple_loss=0.2702, pruned_loss=0.04137, over 6702.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2702, pruned_loss=0.03823, over 1181892.35 frames.], batch size: 31, lr: 4.14e-04
+2022-04-29 13:38:31,323 INFO [train.py:763] (7/8) Epoch 18, batch 400, loss[loss=0.1666, simple_loss=0.257, pruned_loss=0.03816, over 7064.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2714, pruned_loss=0.03853, over 1234076.38 frames.], batch size: 18, lr: 4.14e-04
+2022-04-29 13:39:38,725 INFO [train.py:763] (7/8) Epoch 18, batch 450, loss[loss=0.1662, simple_loss=0.2546, pruned_loss=0.03886, over 7328.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2717, pruned_loss=0.03855, over 1275700.75 frames.], batch size: 22, lr: 4.14e-04
+2022-04-29 13:40:45,473 INFO [train.py:763] (7/8) Epoch 18, batch 500, loss[loss=0.1475, simple_loss=0.2391, pruned_loss=0.0279, over 7133.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2727, pruned_loss=0.03878, over 1307059.29 frames.], batch size: 17, lr: 4.13e-04
+2022-04-29 13:41:52,286 INFO [train.py:763] (7/8) Epoch 18, batch 550, loss[loss=0.1557, simple_loss=0.2477, pruned_loss=0.03186, over 7292.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2724, pruned_loss=0.03849, over 1336193.14 frames.], batch size: 17, lr: 4.13e-04
+2022-04-29 13:42:57,729 INFO [train.py:763] (7/8) Epoch 18, batch 600, loss[loss=0.1484, simple_loss=0.2435, pruned_loss=0.02662, over 7289.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2723, pruned_loss=0.03858, over 1357258.19 frames.], batch size: 18, lr: 4.13e-04
+2022-04-29 13:44:04,385 INFO [train.py:763] (7/8) Epoch 18, batch 650, loss[loss=0.1708, simple_loss=0.2727, pruned_loss=0.03449, over 7108.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2713, pruned_loss=0.03827, over 1376815.09 frames.], batch size: 21, lr: 4.13e-04
+2022-04-29 13:45:09,479 INFO [train.py:763] (7/8) Epoch 18, batch 700, loss[loss=0.2025, simple_loss=0.297, pruned_loss=0.05401, over 4755.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2717, pruned_loss=0.03843, over 1386509.10 frames.], batch size: 52, lr: 4.13e-04
+2022-04-29 13:46:15,223 INFO [train.py:763] (7/8) Epoch 18, batch 750, loss[loss=0.1716, simple_loss=0.2727, pruned_loss=0.0353, over 7154.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2713, pruned_loss=0.03818, over 1394481.57 frames.], batch size: 19, lr: 4.13e-04
+2022-04-29 13:47:20,156 INFO [train.py:763] (7/8) Epoch 18, batch 800, loss[loss=0.1866, simple_loss=0.2798, pruned_loss=0.04663, over 6703.00 frames.], tot_loss[loss=0.175, simple_loss=0.2724, pruned_loss=0.03882, over 1396918.83 frames.], batch size: 31, lr: 4.13e-04
+2022-04-29 13:48:26,410 INFO [train.py:763] (7/8) Epoch 18, batch 850, loss[loss=0.1617, simple_loss=0.2565, pruned_loss=0.03343, over 7072.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2723, pruned_loss=0.03863, over 1404471.81 frames.], batch size: 18, lr: 4.13e-04
+2022-04-29 13:49:33,114 INFO [train.py:763] (7/8) Epoch 18, batch 900, loss[loss=0.1444, simple_loss=0.2389, pruned_loss=0.02495, over 6797.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2733, pruned_loss=0.03855, over 1409638.63 frames.], batch size: 15, lr: 4.12e-04
+2022-04-29 13:50:38,412 INFO [train.py:763] (7/8) Epoch 18, batch 950, loss[loss=0.174, simple_loss=0.2799, pruned_loss=0.03405, over 7384.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2725, pruned_loss=0.03848, over 1412469.19 frames.], batch size: 23, lr: 4.12e-04
+2022-04-29 13:51:45,562 INFO [train.py:763] (7/8) Epoch 18, batch 1000, loss[loss=0.1642, simple_loss=0.2736, pruned_loss=0.02739, over 7140.00 frames.], tot_loss[loss=0.1752, simple_loss=0.273, pruned_loss=0.03871, over 1420140.70 frames.], batch size: 20, lr: 4.12e-04
+2022-04-29 13:52:53,039 INFO [train.py:763] (7/8) Epoch 18, batch 1050, loss[loss=0.1692, simple_loss=0.2782, pruned_loss=0.03013, over 7262.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2726, pruned_loss=0.03892, over 1418905.10 frames.], batch size: 25, lr: 4.12e-04
+2022-04-29 13:53:58,537 INFO [train.py:763] (7/8) Epoch 18, batch 1100, loss[loss=0.1611, simple_loss=0.2667, pruned_loss=0.02772, over 7322.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2717, pruned_loss=0.0387, over 1420216.86 frames.], batch size: 20, lr: 4.12e-04
+2022-04-29 13:55:03,945 INFO [train.py:763] (7/8) Epoch 18, batch 1150, loss[loss=0.2445, simple_loss=0.3247, pruned_loss=0.08215, over 7309.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2725, pruned_loss=0.03897, over 1420242.60 frames.], batch size: 24, lr: 4.12e-04
+2022-04-29 13:56:09,840 INFO [train.py:763] (7/8) Epoch 18, batch 1200, loss[loss=0.195, simple_loss=0.2915, pruned_loss=0.04921, over 4943.00 frames.], tot_loss[loss=0.174, simple_loss=0.2714, pruned_loss=0.03834, over 1413604.69 frames.], batch size: 52, lr: 4.12e-04
+2022-04-29 13:57:15,058 INFO [train.py:763] (7/8) Epoch 18, batch 1250, loss[loss=0.1574, simple_loss=0.2663, pruned_loss=0.02422, over 7119.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2719, pruned_loss=0.03826, over 1413964.69 frames.], batch size: 21, lr: 4.12e-04
+2022-04-29 13:58:20,091 INFO [train.py:763] (7/8) Epoch 18, batch 1300, loss[loss=0.1646, simple_loss=0.2668, pruned_loss=0.03121, over 7171.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2729, pruned_loss=0.03864, over 1413560.74 frames.], batch size: 19, lr: 4.12e-04
+2022-04-29 13:59:25,406 INFO [train.py:763] (7/8) Epoch 18, batch 1350, loss[loss=0.1999, simple_loss=0.2956, pruned_loss=0.05215, over 7062.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2734, pruned_loss=0.03865, over 1411885.59 frames.], batch size: 28, lr: 4.11e-04
+2022-04-29 14:00:32,455 INFO [train.py:763] (7/8) Epoch 18, batch 1400, loss[loss=0.1641, simple_loss=0.2665, pruned_loss=0.03085, over 7454.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2724, pruned_loss=0.03827, over 1410512.89 frames.], batch size: 19, lr: 4.11e-04
+2022-04-29 14:01:39,703 INFO [train.py:763] (7/8) Epoch 18, batch 1450, loss[loss=0.1835, simple_loss=0.2887, pruned_loss=0.03909, over 7325.00 frames.], tot_loss[loss=0.1743, simple_loss=0.272, pruned_loss=0.03826, over 1417774.47 frames.], batch size: 21, lr: 4.11e-04
+2022-04-29 14:02:45,990 INFO [train.py:763] (7/8) Epoch 18, batch 1500, loss[loss=0.1686, simple_loss=0.262, pruned_loss=0.03757, over 7264.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2726, pruned_loss=0.03857, over 1421638.63 frames.], batch size: 19, lr: 4.11e-04
+2022-04-29 14:03:53,126 INFO [train.py:763] (7/8) Epoch 18, batch 1550, loss[loss=0.2183, simple_loss=0.3076, pruned_loss=0.06451, over 7415.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2724, pruned_loss=0.03845, over 1425132.92 frames.], batch size: 21, lr: 4.11e-04
+2022-04-29 14:04:58,315 INFO [train.py:763] (7/8) Epoch 18, batch 1600, loss[loss=0.1989, simple_loss=0.2916, pruned_loss=0.05308, over 7205.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2716, pruned_loss=0.03813, over 1423446.74 frames.], batch size: 22, lr: 4.11e-04
+2022-04-29 14:06:03,955 INFO [train.py:763] (7/8) Epoch 18, batch 1650, loss[loss=0.1616, simple_loss=0.2546, pruned_loss=0.03431, over 7166.00 frames.], tot_loss[loss=0.1745, simple_loss=0.272, pruned_loss=0.03847, over 1422016.40 frames.], batch size: 18, lr: 4.11e-04
+2022-04-29 14:07:10,603 INFO [train.py:763] (7/8) Epoch 18, batch 1700, loss[loss=0.1699, simple_loss=0.2589, pruned_loss=0.04048, over 7182.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2723, pruned_loss=0.03857, over 1423295.50 frames.], batch size: 18, lr: 4.11e-04
+2022-04-29 14:08:17,632 INFO [train.py:763] (7/8) Epoch 18, batch 1750, loss[loss=0.1817, simple_loss=0.2883, pruned_loss=0.03749, over 7150.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2733, pruned_loss=0.03873, over 1416560.64 frames.], batch size: 20, lr: 4.10e-04
+2022-04-29 14:09:24,743 INFO [train.py:763] (7/8) Epoch 18, batch 1800, loss[loss=0.1711, simple_loss=0.2704, pruned_loss=0.03591, over 7263.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2742, pruned_loss=0.03897, over 1417439.48 frames.], batch size: 19, lr: 4.10e-04
+2022-04-29 14:10:32,275 INFO [train.py:763] (7/8) Epoch 18, batch 1850, loss[loss=0.1857, simple_loss=0.2909, pruned_loss=0.04027, over 7300.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2744, pruned_loss=0.03904, over 1422962.88 frames.], batch size: 24, lr: 4.10e-04
+2022-04-29 14:11:39,610 INFO [train.py:763] (7/8) Epoch 18, batch 1900, loss[loss=0.1873, simple_loss=0.2807, pruned_loss=0.04692, over 6958.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2747, pruned_loss=0.03942, over 1419756.50 frames.], batch size: 28, lr: 4.10e-04
+2022-04-29 14:12:46,680 INFO [train.py:763] (7/8) Epoch 18, batch 1950, loss[loss=0.1365, simple_loss=0.2265, pruned_loss=0.02321, over 6994.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2755, pruned_loss=0.03972, over 1420395.84 frames.], batch size: 16, lr: 4.10e-04
+2022-04-29 14:13:51,997 INFO [train.py:763] (7/8) Epoch 18, batch 2000, loss[loss=0.191, simple_loss=0.3024, pruned_loss=0.03977, over 7148.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2746, pruned_loss=0.03959, over 1423846.72 frames.], batch size: 20, lr: 4.10e-04
+2022-04-29 14:14:57,431 INFO [train.py:763] (7/8) Epoch 18, batch 2050, loss[loss=0.2116, simple_loss=0.3018, pruned_loss=0.06076, over 7300.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2738, pruned_loss=0.03937, over 1423701.49 frames.], batch size: 25, lr: 4.10e-04
+2022-04-29 14:16:02,577 INFO [train.py:763] (7/8) Epoch 18, batch 2100, loss[loss=0.1387, simple_loss=0.2368, pruned_loss=0.02026, over 7159.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2731, pruned_loss=0.03881, over 1424429.04 frames.], batch size: 19, lr: 4.10e-04
+2022-04-29 14:17:08,141 INFO [train.py:763] (7/8) Epoch 18, batch 2150, loss[loss=0.171, simple_loss=0.27, pruned_loss=0.03599, over 7219.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2729, pruned_loss=0.03922, over 1422042.99 frames.], batch size: 21, lr: 4.09e-04
+2022-04-29 14:18:13,405 INFO [train.py:763] (7/8) Epoch 18, batch 2200, loss[loss=0.1773, simple_loss=0.2863, pruned_loss=0.03415, over 7119.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2721, pruned_loss=0.03865, over 1425662.95 frames.], batch size: 21, lr: 4.09e-04
+2022-04-29 14:19:18,577 INFO [train.py:763] (7/8) Epoch 18, batch 2250, loss[loss=0.191, simple_loss=0.2949, pruned_loss=0.04358, over 6475.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2729, pruned_loss=0.03867, over 1423785.00 frames.], batch size: 38, lr: 4.09e-04
+2022-04-29 14:20:23,895 INFO [train.py:763] (7/8) Epoch 18, batch 2300, loss[loss=0.2111, simple_loss=0.3128, pruned_loss=0.05468, over 7376.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2726, pruned_loss=0.0384, over 1425620.99 frames.], batch size: 23, lr: 4.09e-04
+2022-04-29 14:21:28,915 INFO [train.py:763] (7/8) Epoch 18, batch 2350, loss[loss=0.153, simple_loss=0.254, pruned_loss=0.02597, over 7283.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2722, pruned_loss=0.03779, over 1422529.97 frames.], batch size: 17, lr: 4.09e-04
+2022-04-29 14:22:34,043 INFO [train.py:763] (7/8) Epoch 18, batch 2400, loss[loss=0.1817, simple_loss=0.277, pruned_loss=0.0432, over 7143.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2729, pruned_loss=0.03833, over 1419025.01 frames.], batch size: 20, lr: 4.09e-04
+2022-04-29 14:23:41,085 INFO [train.py:763] (7/8) Epoch 18, batch 2450, loss[loss=0.169, simple_loss=0.2655, pruned_loss=0.03621, over 7146.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2735, pruned_loss=0.03876, over 1421533.09 frames.], batch size: 20, lr: 4.09e-04
+2022-04-29 14:24:46,864 INFO [train.py:763] (7/8) Epoch 18, batch 2500, loss[loss=0.1615, simple_loss=0.2664, pruned_loss=0.02825, over 7185.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2725, pruned_loss=0.03859, over 1421296.37 frames.], batch size: 26, lr: 4.09e-04
+2022-04-29 14:25:51,859 INFO [train.py:763] (7/8) Epoch 18, batch 2550, loss[loss=0.1865, simple_loss=0.2846, pruned_loss=0.04419, over 7328.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2712, pruned_loss=0.03829, over 1421028.54 frames.], batch size: 24, lr: 4.08e-04
+2022-04-29 14:26:57,019 INFO [train.py:763] (7/8) Epoch 18, batch 2600, loss[loss=0.1711, simple_loss=0.2587, pruned_loss=0.04172, over 7008.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2717, pruned_loss=0.0386, over 1424613.62 frames.], batch size: 16, lr: 4.08e-04
+2022-04-29 14:28:02,339 INFO [train.py:763] (7/8) Epoch 18, batch 2650, loss[loss=0.1892, simple_loss=0.2889, pruned_loss=0.04478, over 7280.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2727, pruned_loss=0.03904, over 1427293.14 frames.], batch size: 24, lr: 4.08e-04
+2022-04-29 14:29:08,107 INFO [train.py:763] (7/8) Epoch 18, batch 2700, loss[loss=0.2388, simple_loss=0.3225, pruned_loss=0.07755, over 7316.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2717, pruned_loss=0.03876, over 1431385.44 frames.], batch size: 25, lr: 4.08e-04
+2022-04-29 14:30:14,911 INFO [train.py:763] (7/8) Epoch 18, batch 2750, loss[loss=0.1769, simple_loss=0.2939, pruned_loss=0.02998, over 7407.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2721, pruned_loss=0.03825, over 1430587.64 frames.], batch size: 21, lr: 4.08e-04
+2022-04-29 14:31:21,345 INFO [train.py:763] (7/8) Epoch 18, batch 2800, loss[loss=0.172, simple_loss=0.2588, pruned_loss=0.04258, over 7066.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2708, pruned_loss=0.03774, over 1430770.29 frames.], batch size: 18, lr: 4.08e-04
+2022-04-29 14:32:26,514 INFO [train.py:763] (7/8) Epoch 18, batch 2850, loss[loss=0.1621, simple_loss=0.2651, pruned_loss=0.02958, over 7160.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2713, pruned_loss=0.0379, over 1427921.68 frames.], batch size: 19, lr: 4.08e-04
+2022-04-29 14:33:31,788 INFO [train.py:763] (7/8) Epoch 18, batch 2900, loss[loss=0.1929, simple_loss=0.3, pruned_loss=0.04293, over 7164.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2716, pruned_loss=0.03803, over 1425294.40 frames.], batch size: 26, lr: 4.08e-04
+2022-04-29 14:34:37,299 INFO [train.py:763] (7/8) Epoch 18, batch 2950, loss[loss=0.1438, simple_loss=0.2282, pruned_loss=0.0297, over 7280.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2715, pruned_loss=0.03815, over 1431205.06 frames.], batch size: 17, lr: 4.08e-04
+2022-04-29 14:35:43,270 INFO [train.py:763] (7/8) Epoch 18, batch 3000, loss[loss=0.2237, simple_loss=0.3013, pruned_loss=0.07309, over 5213.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2707, pruned_loss=0.03805, over 1430800.61 frames.], batch size: 52, lr: 4.07e-04
+2022-04-29 14:35:43,271 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 14:35:58,559 INFO [train.py:792] (7/8) Epoch 18, validation: loss=0.1668, simple_loss=0.2671, pruned_loss=0.03324, over 698248.00 frames.
+2022-04-29 14:37:05,456 INFO [train.py:763] (7/8) Epoch 18, batch 3050, loss[loss=0.1716, simple_loss=0.2692, pruned_loss=0.03703, over 7227.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2715, pruned_loss=0.0383, over 1431628.25 frames.], batch size: 23, lr: 4.07e-04
+2022-04-29 14:38:12,652 INFO [train.py:763] (7/8) Epoch 18, batch 3100, loss[loss=0.1674, simple_loss=0.2705, pruned_loss=0.03217, over 6535.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2716, pruned_loss=0.03785, over 1432321.56 frames.], batch size: 38, lr: 4.07e-04
+2022-04-29 14:39:19,405 INFO [train.py:763] (7/8) Epoch 18, batch 3150, loss[loss=0.1696, simple_loss=0.2576, pruned_loss=0.04077, over 7288.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2719, pruned_loss=0.03817, over 1429565.29 frames.], batch size: 18, lr: 4.07e-04
+2022-04-29 14:40:26,387 INFO [train.py:763] (7/8) Epoch 18, batch 3200, loss[loss=0.1739, simple_loss=0.2749, pruned_loss=0.03649, over 7152.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2725, pruned_loss=0.03855, over 1428026.68 frames.], batch size: 19, lr: 4.07e-04
+2022-04-29 14:41:32,526 INFO [train.py:763] (7/8) Epoch 18, batch 3250, loss[loss=0.1672, simple_loss=0.2665, pruned_loss=0.03397, over 7355.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2732, pruned_loss=0.03874, over 1424737.50 frames.], batch size: 19, lr: 4.07e-04
+2022-04-29 14:42:37,752 INFO [train.py:763] (7/8) Epoch 18, batch 3300, loss[loss=0.1986, simple_loss=0.2995, pruned_loss=0.04887, over 6346.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2733, pruned_loss=0.03872, over 1424924.05 frames.], batch size: 37, lr: 4.07e-04
+2022-04-29 14:43:43,244 INFO [train.py:763] (7/8) Epoch 18, batch 3350, loss[loss=0.2116, simple_loss=0.2946, pruned_loss=0.06435, over 7108.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2724, pruned_loss=0.0384, over 1424269.04 frames.], batch size: 21, lr: 4.07e-04
+2022-04-29 14:44:48,490 INFO [train.py:763] (7/8) Epoch 18, batch 3400, loss[loss=0.1628, simple_loss=0.2615, pruned_loss=0.03206, over 7278.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2722, pruned_loss=0.0382, over 1425130.57 frames.], batch size: 18, lr: 4.06e-04
+2022-04-29 14:45:53,988 INFO [train.py:763] (7/8) Epoch 18, batch 3450, loss[loss=0.157, simple_loss=0.2468, pruned_loss=0.03363, over 7366.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2703, pruned_loss=0.03771, over 1421303.32 frames.], batch size: 19, lr: 4.06e-04
+2022-04-29 14:46:59,204 INFO [train.py:763] (7/8) Epoch 18, batch 3500, loss[loss=0.1824, simple_loss=0.2778, pruned_loss=0.04345, over 7285.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2709, pruned_loss=0.03771, over 1423557.20 frames.], batch size: 18, lr: 4.06e-04
+2022-04-29 14:48:04,608 INFO [train.py:763] (7/8) Epoch 18, batch 3550, loss[loss=0.1462, simple_loss=0.235, pruned_loss=0.02876, over 7137.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2713, pruned_loss=0.03822, over 1423130.92 frames.], batch size: 17, lr: 4.06e-04
+2022-04-29 14:49:09,825 INFO [train.py:763] (7/8) Epoch 18, batch 3600, loss[loss=0.2259, simple_loss=0.3211, pruned_loss=0.06537, over 7194.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2721, pruned_loss=0.03886, over 1420705.43 frames.], batch size: 23, lr: 4.06e-04
+2022-04-29 14:50:14,987 INFO [train.py:763] (7/8) Epoch 18, batch 3650, loss[loss=0.1575, simple_loss=0.2603, pruned_loss=0.02728, over 7330.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2719, pruned_loss=0.03867, over 1414238.35 frames.], batch size: 20, lr: 4.06e-04
+2022-04-29 14:51:20,208 INFO [train.py:763] (7/8) Epoch 18, batch 3700, loss[loss=0.1679, simple_loss=0.2628, pruned_loss=0.03647, over 7408.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2727, pruned_loss=0.03892, over 1416679.93 frames.], batch size: 21, lr: 4.06e-04
+2022-04-29 14:52:25,591 INFO [train.py:763] (7/8) Epoch 18, batch 3750, loss[loss=0.1737, simple_loss=0.2732, pruned_loss=0.03712, over 7376.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2725, pruned_loss=0.03879, over 1413018.86 frames.], batch size: 23, lr: 4.06e-04
+2022-04-29 14:53:30,901 INFO [train.py:763] (7/8) Epoch 18, batch 3800, loss[loss=0.1613, simple_loss=0.2532, pruned_loss=0.03466, over 7360.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2725, pruned_loss=0.03869, over 1418802.93 frames.], batch size: 19, lr: 4.06e-04
+2022-04-29 14:54:36,414 INFO [train.py:763] (7/8) Epoch 18, batch 3850, loss[loss=0.1636, simple_loss=0.2528, pruned_loss=0.03722, over 7165.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2723, pruned_loss=0.03901, over 1416663.89 frames.], batch size: 18, lr: 4.05e-04
+2022-04-29 14:55:41,222 INFO [train.py:763] (7/8) Epoch 18, batch 3900, loss[loss=0.1846, simple_loss=0.2907, pruned_loss=0.0392, over 7117.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2727, pruned_loss=0.039, over 1413746.43 frames.], batch size: 21, lr: 4.05e-04
+2022-04-29 14:56:46,307 INFO [train.py:763] (7/8) Epoch 18, batch 3950, loss[loss=0.1888, simple_loss=0.2868, pruned_loss=0.04543, over 7166.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2731, pruned_loss=0.03897, over 1416030.96 frames.], batch size: 18, lr: 4.05e-04
+2022-04-29 14:57:51,533 INFO [train.py:763] (7/8) Epoch 18, batch 4000, loss[loss=0.1772, simple_loss=0.2706, pruned_loss=0.0419, over 5109.00 frames.], tot_loss[loss=0.175, simple_loss=0.2723, pruned_loss=0.03881, over 1416637.86 frames.], batch size: 52, lr: 4.05e-04
+2022-04-29 14:58:57,199 INFO [train.py:763] (7/8) Epoch 18, batch 4050, loss[loss=0.1431, simple_loss=0.2282, pruned_loss=0.02894, over 7237.00 frames.], tot_loss[loss=0.1748, simple_loss=0.272, pruned_loss=0.03878, over 1415732.29 frames.], batch size: 16, lr: 4.05e-04
+2022-04-29 15:00:03,358 INFO [train.py:763] (7/8) Epoch 18, batch 4100, loss[loss=0.1996, simple_loss=0.289, pruned_loss=0.05504, over 5296.00 frames.], tot_loss[loss=0.175, simple_loss=0.2724, pruned_loss=0.03879, over 1416321.51 frames.], batch size: 52, lr: 4.05e-04
+2022-04-29 15:01:09,084 INFO [train.py:763] (7/8) Epoch 18, batch 4150, loss[loss=0.2165, simple_loss=0.3108, pruned_loss=0.06109, over 7389.00 frames.], tot_loss[loss=0.174, simple_loss=0.2717, pruned_loss=0.03815, over 1421891.81 frames.], batch size: 23, lr: 4.05e-04
+2022-04-29 15:02:16,229 INFO [train.py:763] (7/8) Epoch 18, batch 4200, loss[loss=0.2018, simple_loss=0.2963, pruned_loss=0.05364, over 7213.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2718, pruned_loss=0.03794, over 1421285.35 frames.], batch size: 23, lr: 4.05e-04
+2022-04-29 15:03:23,618 INFO [train.py:763] (7/8) Epoch 18, batch 4250, loss[loss=0.1316, simple_loss=0.222, pruned_loss=0.02062, over 6836.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2713, pruned_loss=0.0378, over 1420905.39 frames.], batch size: 15, lr: 4.04e-04
+2022-04-29 15:04:28,940 INFO [train.py:763] (7/8) Epoch 18, batch 4300, loss[loss=0.1922, simple_loss=0.2849, pruned_loss=0.04977, over 7200.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2718, pruned_loss=0.03786, over 1420533.05 frames.], batch size: 26, lr: 4.04e-04
+2022-04-29 15:05:35,129 INFO [train.py:763] (7/8) Epoch 18, batch 4350, loss[loss=0.1571, simple_loss=0.2581, pruned_loss=0.02802, over 7163.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2713, pruned_loss=0.03761, over 1417812.67 frames.], batch size: 18, lr: 4.04e-04
+2022-04-29 15:06:42,575 INFO [train.py:763] (7/8) Epoch 18, batch 4400, loss[loss=0.2059, simple_loss=0.2926, pruned_loss=0.0596, over 6476.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2707, pruned_loss=0.0377, over 1412999.87 frames.], batch size: 38, lr: 4.04e-04
+2022-04-29 15:07:48,918 INFO [train.py:763] (7/8) Epoch 18, batch 4450, loss[loss=0.1481, simple_loss=0.2436, pruned_loss=0.02629, over 6823.00 frames.], tot_loss[loss=0.1729, simple_loss=0.27, pruned_loss=0.0379, over 1407092.46 frames.], batch size: 15, lr: 4.04e-04
+2022-04-29 15:08:55,431 INFO [train.py:763] (7/8) Epoch 18, batch 4500, loss[loss=0.1909, simple_loss=0.2956, pruned_loss=0.04311, over 7144.00 frames.], tot_loss[loss=0.1743, simple_loss=0.271, pruned_loss=0.03882, over 1394010.89 frames.], batch size: 20, lr: 4.04e-04
+2022-04-29 15:10:01,689 INFO [train.py:763] (7/8) Epoch 18, batch 4550, loss[loss=0.1802, simple_loss=0.2743, pruned_loss=0.04305, over 6420.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2709, pruned_loss=0.03927, over 1366541.10 frames.], batch size: 37, lr: 4.04e-04
+2022-04-29 15:11:30,601 INFO [train.py:763] (7/8) Epoch 19, batch 0, loss[loss=0.1468, simple_loss=0.2494, pruned_loss=0.02212, over 7356.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2494, pruned_loss=0.02212, over 7356.00 frames.], batch size: 19, lr: 3.94e-04
+2022-04-29 15:12:36,746 INFO [train.py:763] (7/8) Epoch 19, batch 50, loss[loss=0.1422, simple_loss=0.2414, pruned_loss=0.02154, over 7279.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2696, pruned_loss=0.03651, over 320761.18 frames.], batch size: 18, lr: 3.94e-04
+2022-04-29 15:13:42,686 INFO [train.py:763] (7/8) Epoch 19, batch 100, loss[loss=0.2241, simple_loss=0.3072, pruned_loss=0.07051, over 5241.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2712, pruned_loss=0.03753, over 565603.27 frames.], batch size: 53, lr: 3.94e-04
+2022-04-29 15:14:48,883 INFO [train.py:763] (7/8) Epoch 19, batch 150, loss[loss=0.1836, simple_loss=0.2882, pruned_loss=0.03947, over 7320.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2734, pruned_loss=0.03772, over 755595.45 frames.], batch size: 21, lr: 3.94e-04
+2022-04-29 15:15:54,348 INFO [train.py:763] (7/8) Epoch 19, batch 200, loss[loss=0.1695, simple_loss=0.2713, pruned_loss=0.03384, over 7349.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2725, pruned_loss=0.03748, over 902738.78 frames.], batch size: 22, lr: 3.93e-04
+2022-04-29 15:17:00,306 INFO [train.py:763] (7/8) Epoch 19, batch 250, loss[loss=0.1844, simple_loss=0.29, pruned_loss=0.03938, over 7322.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2709, pruned_loss=0.03695, over 1022353.77 frames.], batch size: 22, lr: 3.93e-04
+2022-04-29 15:18:06,656 INFO [train.py:763] (7/8) Epoch 19, batch 300, loss[loss=0.2155, simple_loss=0.3111, pruned_loss=0.05989, over 7222.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2715, pruned_loss=0.03715, over 1111809.37 frames.], batch size: 23, lr: 3.93e-04
+2022-04-29 15:19:12,760 INFO [train.py:763] (7/8) Epoch 19, batch 350, loss[loss=0.1857, simple_loss=0.2891, pruned_loss=0.04114, over 7144.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2727, pruned_loss=0.03754, over 1184055.96 frames.], batch size: 20, lr: 3.93e-04
+2022-04-29 15:20:18,129 INFO [train.py:763] (7/8) Epoch 19, batch 400, loss[loss=0.1741, simple_loss=0.2711, pruned_loss=0.03852, over 7140.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2745, pruned_loss=0.03842, over 1237604.18 frames.], batch size: 20, lr: 3.93e-04
+2022-04-29 15:21:23,464 INFO [train.py:763] (7/8) Epoch 19, batch 450, loss[loss=0.1757, simple_loss=0.2775, pruned_loss=0.03698, over 7374.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2742, pruned_loss=0.03837, over 1275143.62 frames.], batch size: 23, lr: 3.93e-04
+2022-04-29 15:22:28,673 INFO [train.py:763] (7/8) Epoch 19, batch 500, loss[loss=0.1821, simple_loss=0.2952, pruned_loss=0.03448, over 7227.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2752, pruned_loss=0.03873, over 1307130.41 frames.], batch size: 21, lr: 3.93e-04
+2022-04-29 15:23:34,251 INFO [train.py:763] (7/8) Epoch 19, batch 550, loss[loss=0.2005, simple_loss=0.3072, pruned_loss=0.04686, over 6742.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2743, pruned_loss=0.03837, over 1332436.66 frames.], batch size: 31, lr: 3.93e-04
+2022-04-29 15:24:40,474 INFO [train.py:763] (7/8) Epoch 19, batch 600, loss[loss=0.1512, simple_loss=0.2468, pruned_loss=0.02774, over 7160.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2732, pruned_loss=0.03799, over 1354953.29 frames.], batch size: 18, lr: 3.93e-04
+2022-04-29 15:25:45,949 INFO [train.py:763] (7/8) Epoch 19, batch 650, loss[loss=0.1771, simple_loss=0.2643, pruned_loss=0.04491, over 7177.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2723, pruned_loss=0.03757, over 1369613.46 frames.], batch size: 18, lr: 3.92e-04
+2022-04-29 15:26:51,177 INFO [train.py:763] (7/8) Epoch 19, batch 700, loss[loss=0.1745, simple_loss=0.2865, pruned_loss=0.03123, over 7232.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2728, pruned_loss=0.03754, over 1383346.19 frames.], batch size: 20, lr: 3.92e-04
+2022-04-29 15:27:56,790 INFO [train.py:763] (7/8) Epoch 19, batch 750, loss[loss=0.1916, simple_loss=0.2894, pruned_loss=0.0469, over 7312.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2719, pruned_loss=0.0376, over 1393736.43 frames.], batch size: 25, lr: 3.92e-04
+2022-04-29 15:29:03,465 INFO [train.py:763] (7/8) Epoch 19, batch 800, loss[loss=0.1804, simple_loss=0.264, pruned_loss=0.04838, over 7410.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2711, pruned_loss=0.03761, over 1403228.96 frames.], batch size: 18, lr: 3.92e-04
+2022-04-29 15:30:19,527 INFO [train.py:763] (7/8) Epoch 19, batch 850, loss[loss=0.1803, simple_loss=0.2785, pruned_loss=0.04109, over 7100.00 frames.], tot_loss[loss=0.1731, simple_loss=0.271, pruned_loss=0.03761, over 1411168.94 frames.], batch size: 28, lr: 3.92e-04
+2022-04-29 15:31:25,298 INFO [train.py:763] (7/8) Epoch 19, batch 900, loss[loss=0.1915, simple_loss=0.2749, pruned_loss=0.054, over 7357.00 frames.], tot_loss[loss=0.1726, simple_loss=0.27, pruned_loss=0.03754, over 1416569.54 frames.], batch size: 19, lr: 3.92e-04
+2022-04-29 15:32:30,754 INFO [train.py:763] (7/8) Epoch 19, batch 950, loss[loss=0.1873, simple_loss=0.283, pruned_loss=0.04577, over 7232.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2709, pruned_loss=0.03772, over 1420166.80 frames.], batch size: 20, lr: 3.92e-04
+2022-04-29 15:33:36,040 INFO [train.py:763] (7/8) Epoch 19, batch 1000, loss[loss=0.2228, simple_loss=0.3089, pruned_loss=0.06832, over 7282.00 frames.], tot_loss[loss=0.174, simple_loss=0.2717, pruned_loss=0.03819, over 1421665.63 frames.], batch size: 24, lr: 3.92e-04
+2022-04-29 15:34:41,377 INFO [train.py:763] (7/8) Epoch 19, batch 1050, loss[loss=0.2067, simple_loss=0.2964, pruned_loss=0.05855, over 7198.00 frames.], tot_loss[loss=0.1742, simple_loss=0.272, pruned_loss=0.03819, over 1420162.42 frames.], batch size: 22, lr: 3.92e-04
+2022-04-29 15:35:47,018 INFO [train.py:763] (7/8) Epoch 19, batch 1100, loss[loss=0.1849, simple_loss=0.2837, pruned_loss=0.043, over 7210.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2722, pruned_loss=0.0382, over 1416581.81 frames.], batch size: 22, lr: 3.91e-04
+2022-04-29 15:36:52,338 INFO [train.py:763] (7/8) Epoch 19, batch 1150, loss[loss=0.1841, simple_loss=0.2892, pruned_loss=0.03956, over 7296.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2728, pruned_loss=0.03813, over 1420467.87 frames.], batch size: 24, lr: 3.91e-04
+2022-04-29 15:38:08,762 INFO [train.py:763] (7/8) Epoch 19, batch 1200, loss[loss=0.174, simple_loss=0.2845, pruned_loss=0.03169, over 7337.00 frames.], tot_loss[loss=0.174, simple_loss=0.272, pruned_loss=0.03805, over 1426228.53 frames.], batch size: 22, lr: 3.91e-04
+2022-04-29 15:39:14,197 INFO [train.py:763] (7/8) Epoch 19, batch 1250, loss[loss=0.1583, simple_loss=0.247, pruned_loss=0.03479, over 7138.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2715, pruned_loss=0.03787, over 1427474.31 frames.], batch size: 17, lr: 3.91e-04
+2022-04-29 15:40:19,886 INFO [train.py:763] (7/8) Epoch 19, batch 1300, loss[loss=0.1822, simple_loss=0.2719, pruned_loss=0.04622, over 7116.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2709, pruned_loss=0.03776, over 1429244.28 frames.], batch size: 21, lr: 3.91e-04
+2022-04-29 15:41:25,087 INFO [train.py:763] (7/8) Epoch 19, batch 1350, loss[loss=0.2203, simple_loss=0.3212, pruned_loss=0.05964, over 7208.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2717, pruned_loss=0.03793, over 1430970.82 frames.], batch size: 22, lr: 3.91e-04
+2022-04-29 15:42:30,870 INFO [train.py:763] (7/8) Epoch 19, batch 1400, loss[loss=0.1674, simple_loss=0.2719, pruned_loss=0.03147, over 7191.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2715, pruned_loss=0.03796, over 1432330.92 frames.], batch size: 26, lr: 3.91e-04
+2022-04-29 15:43:46,252 INFO [train.py:763] (7/8) Epoch 19, batch 1450, loss[loss=0.1875, simple_loss=0.2918, pruned_loss=0.04156, over 7235.00 frames.], tot_loss[loss=0.174, simple_loss=0.2722, pruned_loss=0.03789, over 1430597.22 frames.], batch size: 26, lr: 3.91e-04
+2022-04-29 15:45:09,727 INFO [train.py:763] (7/8) Epoch 19, batch 1500, loss[loss=0.2072, simple_loss=0.3095, pruned_loss=0.05242, over 7365.00 frames.], tot_loss[loss=0.1745, simple_loss=0.273, pruned_loss=0.03803, over 1427861.40 frames.], batch size: 23, lr: 3.91e-04
+2022-04-29 15:46:15,434 INFO [train.py:763] (7/8) Epoch 19, batch 1550, loss[loss=0.157, simple_loss=0.2506, pruned_loss=0.03167, over 7423.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2722, pruned_loss=0.03784, over 1429590.97 frames.], batch size: 20, lr: 3.91e-04
+2022-04-29 15:47:30,084 INFO [train.py:763] (7/8) Epoch 19, batch 1600, loss[loss=0.1702, simple_loss=0.2753, pruned_loss=0.03249, over 7324.00 frames.], tot_loss[loss=0.1739, simple_loss=0.272, pruned_loss=0.03785, over 1424468.31 frames.], batch size: 22, lr: 3.90e-04
+2022-04-29 15:48:53,942 INFO [train.py:763] (7/8) Epoch 19, batch 1650, loss[loss=0.1995, simple_loss=0.2982, pruned_loss=0.05036, over 7213.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2724, pruned_loss=0.03816, over 1421617.41 frames.], batch size: 23, lr: 3.90e-04
+2022-04-29 15:50:08,834 INFO [train.py:763] (7/8) Epoch 19, batch 1700, loss[loss=0.158, simple_loss=0.2674, pruned_loss=0.02434, over 7160.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2724, pruned_loss=0.03788, over 1420898.92 frames.], batch size: 19, lr: 3.90e-04
+2022-04-29 15:51:14,408 INFO [train.py:763] (7/8) Epoch 19, batch 1750, loss[loss=0.1837, simple_loss=0.2855, pruned_loss=0.04097, over 7327.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2719, pruned_loss=0.03776, over 1427020.47 frames.], batch size: 22, lr: 3.90e-04
+2022-04-29 15:52:20,006 INFO [train.py:763] (7/8) Epoch 19, batch 1800, loss[loss=0.1802, simple_loss=0.2824, pruned_loss=0.03896, over 7331.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2715, pruned_loss=0.03775, over 1427052.61 frames.], batch size: 25, lr: 3.90e-04
+2022-04-29 15:53:25,564 INFO [train.py:763] (7/8) Epoch 19, batch 1850, loss[loss=0.1567, simple_loss=0.2551, pruned_loss=0.02912, over 7072.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2711, pruned_loss=0.03787, over 1429792.80 frames.], batch size: 18, lr: 3.90e-04
+2022-04-29 15:54:30,877 INFO [train.py:763] (7/8) Epoch 19, batch 1900, loss[loss=0.1633, simple_loss=0.2701, pruned_loss=0.02824, over 7239.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2719, pruned_loss=0.03782, over 1430543.55 frames.], batch size: 20, lr: 3.90e-04
+2022-04-29 15:55:38,252 INFO [train.py:763] (7/8) Epoch 19, batch 1950, loss[loss=0.1789, simple_loss=0.2745, pruned_loss=0.04166, over 6484.00 frames.], tot_loss[loss=0.173, simple_loss=0.2707, pruned_loss=0.03761, over 1430542.44 frames.], batch size: 38, lr: 3.90e-04
+2022-04-29 15:56:45,566 INFO [train.py:763] (7/8) Epoch 19, batch 2000, loss[loss=0.1575, simple_loss=0.2587, pruned_loss=0.02812, over 7220.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2697, pruned_loss=0.03738, over 1431451.12 frames.], batch size: 20, lr: 3.90e-04
+2022-04-29 15:57:52,845 INFO [train.py:763] (7/8) Epoch 19, batch 2050, loss[loss=0.1573, simple_loss=0.259, pruned_loss=0.02783, over 7229.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2696, pruned_loss=0.03716, over 1430918.80 frames.], batch size: 21, lr: 3.89e-04
+2022-04-29 15:58:58,699 INFO [train.py:763] (7/8) Epoch 19, batch 2100, loss[loss=0.1526, simple_loss=0.2565, pruned_loss=0.02433, over 7421.00 frames.], tot_loss[loss=0.172, simple_loss=0.2697, pruned_loss=0.03721, over 1432832.28 frames.], batch size: 20, lr: 3.89e-04
+2022-04-29 16:00:05,512 INFO [train.py:763] (7/8) Epoch 19, batch 2150, loss[loss=0.189, simple_loss=0.2883, pruned_loss=0.04482, over 7202.00 frames.], tot_loss[loss=0.172, simple_loss=0.2695, pruned_loss=0.0372, over 1426261.46 frames.], batch size: 22, lr: 3.89e-04
+2022-04-29 16:01:11,310 INFO [train.py:763] (7/8) Epoch 19, batch 2200, loss[loss=0.1685, simple_loss=0.2561, pruned_loss=0.04045, over 6851.00 frames.], tot_loss[loss=0.172, simple_loss=0.2696, pruned_loss=0.03716, over 1421994.12 frames.], batch size: 15, lr: 3.89e-04
+2022-04-29 16:02:17,303 INFO [train.py:763] (7/8) Epoch 19, batch 2250, loss[loss=0.1616, simple_loss=0.2708, pruned_loss=0.02617, over 7144.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2697, pruned_loss=0.03738, over 1424626.12 frames.], batch size: 20, lr: 3.89e-04
+2022-04-29 16:03:23,083 INFO [train.py:763] (7/8) Epoch 19, batch 2300, loss[loss=0.1789, simple_loss=0.2784, pruned_loss=0.03965, over 7375.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2696, pruned_loss=0.03708, over 1425346.34 frames.], batch size: 23, lr: 3.89e-04
+2022-04-29 16:04:28,776 INFO [train.py:763] (7/8) Epoch 19, batch 2350, loss[loss=0.1723, simple_loss=0.2744, pruned_loss=0.03509, over 7322.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2706, pruned_loss=0.03718, over 1424052.72 frames.], batch size: 21, lr: 3.89e-04
+2022-04-29 16:05:34,133 INFO [train.py:763] (7/8) Epoch 19, batch 2400, loss[loss=0.1704, simple_loss=0.2735, pruned_loss=0.03367, over 7422.00 frames.], tot_loss[loss=0.172, simple_loss=0.2702, pruned_loss=0.03685, over 1425927.12 frames.], batch size: 20, lr: 3.89e-04
+2022-04-29 16:06:39,702 INFO [train.py:763] (7/8) Epoch 19, batch 2450, loss[loss=0.1556, simple_loss=0.2659, pruned_loss=0.02268, over 7103.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2698, pruned_loss=0.03697, over 1428113.80 frames.], batch size: 28, lr: 3.89e-04
+2022-04-29 16:07:45,469 INFO [train.py:763] (7/8) Epoch 19, batch 2500, loss[loss=0.1819, simple_loss=0.2766, pruned_loss=0.04364, over 7150.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2691, pruned_loss=0.03713, over 1425779.60 frames.], batch size: 26, lr: 3.88e-04
+2022-04-29 16:08:51,002 INFO [train.py:763] (7/8) Epoch 19, batch 2550, loss[loss=0.1791, simple_loss=0.2725, pruned_loss=0.04288, over 7338.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2691, pruned_loss=0.03741, over 1424621.93 frames.], batch size: 20, lr: 3.88e-04
+2022-04-29 16:09:56,834 INFO [train.py:763] (7/8) Epoch 19, batch 2600, loss[loss=0.1716, simple_loss=0.2715, pruned_loss=0.03588, over 6783.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2697, pruned_loss=0.03773, over 1425306.82 frames.], batch size: 31, lr: 3.88e-04
+2022-04-29 16:11:03,372 INFO [train.py:763] (7/8) Epoch 19, batch 2650, loss[loss=0.1344, simple_loss=0.2229, pruned_loss=0.02296, over 6988.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2688, pruned_loss=0.03695, over 1427193.77 frames.], batch size: 16, lr: 3.88e-04
+2022-04-29 16:12:10,060 INFO [train.py:763] (7/8) Epoch 19, batch 2700, loss[loss=0.1834, simple_loss=0.2837, pruned_loss=0.04159, over 7386.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2688, pruned_loss=0.03692, over 1428118.45 frames.], batch size: 23, lr: 3.88e-04
+2022-04-29 16:13:17,145 INFO [train.py:763] (7/8) Epoch 19, batch 2750, loss[loss=0.2226, simple_loss=0.3155, pruned_loss=0.06479, over 7191.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2698, pruned_loss=0.03784, over 1427259.47 frames.], batch size: 23, lr: 3.88e-04
+2022-04-29 16:14:22,714 INFO [train.py:763] (7/8) Epoch 19, batch 2800, loss[loss=0.1399, simple_loss=0.2353, pruned_loss=0.02221, over 7167.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2696, pruned_loss=0.03708, over 1430773.33 frames.], batch size: 18, lr: 3.88e-04
+2022-04-29 16:15:28,769 INFO [train.py:763] (7/8) Epoch 19, batch 2850, loss[loss=0.1878, simple_loss=0.2837, pruned_loss=0.04597, over 7422.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2695, pruned_loss=0.03674, over 1432529.44 frames.], batch size: 21, lr: 3.88e-04
+2022-04-29 16:16:34,854 INFO [train.py:763] (7/8) Epoch 19, batch 2900, loss[loss=0.1834, simple_loss=0.2912, pruned_loss=0.03778, over 7202.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2695, pruned_loss=0.03689, over 1427905.21 frames.], batch size: 26, lr: 3.88e-04
+2022-04-29 16:17:40,412 INFO [train.py:763] (7/8) Epoch 19, batch 2950, loss[loss=0.1685, simple_loss=0.2652, pruned_loss=0.03586, over 7227.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2695, pruned_loss=0.03653, over 1432006.00 frames.], batch size: 20, lr: 3.87e-04
+2022-04-29 16:18:45,963 INFO [train.py:763] (7/8) Epoch 19, batch 3000, loss[loss=0.1994, simple_loss=0.3058, pruned_loss=0.04653, over 7389.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2708, pruned_loss=0.03684, over 1430970.89 frames.], batch size: 23, lr: 3.87e-04
+2022-04-29 16:18:45,964 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 16:19:01,553 INFO [train.py:792] (7/8) Epoch 19, validation: loss=0.1668, simple_loss=0.2663, pruned_loss=0.03363, over 698248.00 frames.
+2022-04-29 16:20:06,927 INFO [train.py:763] (7/8) Epoch 19, batch 3050, loss[loss=0.1729, simple_loss=0.2716, pruned_loss=0.03713, over 7171.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2714, pruned_loss=0.03711, over 1432670.63 frames.], batch size: 19, lr: 3.87e-04
+2022-04-29 16:21:12,189 INFO [train.py:763] (7/8) Epoch 19, batch 3100, loss[loss=0.1641, simple_loss=0.2563, pruned_loss=0.03597, over 7100.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2714, pruned_loss=0.03699, over 1431395.83 frames.], batch size: 21, lr: 3.87e-04
+2022-04-29 16:22:17,540 INFO [train.py:763] (7/8) Epoch 19, batch 3150, loss[loss=0.1451, simple_loss=0.2371, pruned_loss=0.02652, over 7276.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2709, pruned_loss=0.03699, over 1432139.06 frames.], batch size: 18, lr: 3.87e-04
+2022-04-29 16:23:23,028 INFO [train.py:763] (7/8) Epoch 19, batch 3200, loss[loss=0.1919, simple_loss=0.2936, pruned_loss=0.04514, over 6710.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2706, pruned_loss=0.03716, over 1431265.23 frames.], batch size: 31, lr: 3.87e-04
+2022-04-29 16:24:28,073 INFO [train.py:763] (7/8) Epoch 19, batch 3250, loss[loss=0.1537, simple_loss=0.2524, pruned_loss=0.02747, over 7073.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2706, pruned_loss=0.0372, over 1428078.91 frames.], batch size: 18, lr: 3.87e-04
+2022-04-29 16:25:34,726 INFO [train.py:763] (7/8) Epoch 19, batch 3300, loss[loss=0.1476, simple_loss=0.2446, pruned_loss=0.02531, over 7134.00 frames.], tot_loss[loss=0.172, simple_loss=0.2699, pruned_loss=0.03701, over 1427623.41 frames.], batch size: 17, lr: 3.87e-04
+2022-04-29 16:26:41,790 INFO [train.py:763] (7/8) Epoch 19, batch 3350, loss[loss=0.1855, simple_loss=0.2863, pruned_loss=0.04235, over 7143.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2704, pruned_loss=0.03737, over 1427543.12 frames.], batch size: 20, lr: 3.87e-04
+2022-04-29 16:27:47,549 INFO [train.py:763] (7/8) Epoch 19, batch 3400, loss[loss=0.1446, simple_loss=0.2327, pruned_loss=0.02828, over 7276.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2704, pruned_loss=0.03719, over 1426841.51 frames.], batch size: 17, lr: 3.87e-04
+2022-04-29 16:28:53,022 INFO [train.py:763] (7/8) Epoch 19, batch 3450, loss[loss=0.1868, simple_loss=0.2878, pruned_loss=0.04291, over 7234.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2719, pruned_loss=0.03758, over 1425295.22 frames.], batch size: 20, lr: 3.86e-04
+2022-04-29 16:29:58,529 INFO [train.py:763] (7/8) Epoch 19, batch 3500, loss[loss=0.1514, simple_loss=0.2509, pruned_loss=0.02593, over 7263.00 frames.], tot_loss[loss=0.1729, simple_loss=0.271, pruned_loss=0.0374, over 1423637.52 frames.], batch size: 19, lr: 3.86e-04
+2022-04-29 16:31:03,667 INFO [train.py:763] (7/8) Epoch 19, batch 3550, loss[loss=0.1754, simple_loss=0.2831, pruned_loss=0.03384, over 7120.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2719, pruned_loss=0.03808, over 1425962.47 frames.], batch size: 21, lr: 3.86e-04
+2022-04-29 16:32:09,198 INFO [train.py:763] (7/8) Epoch 19, batch 3600, loss[loss=0.1916, simple_loss=0.3016, pruned_loss=0.04084, over 7201.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2716, pruned_loss=0.038, over 1428398.35 frames.], batch size: 23, lr: 3.86e-04
+2022-04-29 16:33:15,445 INFO [train.py:763] (7/8) Epoch 19, batch 3650, loss[loss=0.1625, simple_loss=0.2701, pruned_loss=0.02752, over 7309.00 frames.], tot_loss[loss=0.1736, simple_loss=0.271, pruned_loss=0.03805, over 1429593.12 frames.], batch size: 21, lr: 3.86e-04
+2022-04-29 16:34:21,099 INFO [train.py:763] (7/8) Epoch 19, batch 3700, loss[loss=0.1871, simple_loss=0.2803, pruned_loss=0.04693, over 7166.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2717, pruned_loss=0.03837, over 1431618.60 frames.], batch size: 18, lr: 3.86e-04
+2022-04-29 16:35:26,778 INFO [train.py:763] (7/8) Epoch 19, batch 3750, loss[loss=0.2088, simple_loss=0.3035, pruned_loss=0.05707, over 7126.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2705, pruned_loss=0.03786, over 1425735.86 frames.], batch size: 28, lr: 3.86e-04
+2022-04-29 16:36:32,315 INFO [train.py:763] (7/8) Epoch 19, batch 3800, loss[loss=0.17, simple_loss=0.2771, pruned_loss=0.03146, over 7336.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2698, pruned_loss=0.03779, over 1421620.41 frames.], batch size: 20, lr: 3.86e-04
+2022-04-29 16:37:37,916 INFO [train.py:763] (7/8) Epoch 19, batch 3850, loss[loss=0.155, simple_loss=0.2415, pruned_loss=0.03427, over 7283.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2698, pruned_loss=0.0377, over 1420843.16 frames.], batch size: 17, lr: 3.86e-04
+2022-04-29 16:38:44,173 INFO [train.py:763] (7/8) Epoch 19, batch 3900, loss[loss=0.1762, simple_loss=0.2716, pruned_loss=0.04041, over 7113.00 frames.], tot_loss[loss=0.173, simple_loss=0.2708, pruned_loss=0.03764, over 1417933.08 frames.], batch size: 21, lr: 3.85e-04
+2022-04-29 16:39:50,756 INFO [train.py:763] (7/8) Epoch 19, batch 3950, loss[loss=0.1692, simple_loss=0.2717, pruned_loss=0.03336, over 7338.00 frames.], tot_loss[loss=0.173, simple_loss=0.2706, pruned_loss=0.03769, over 1411984.18 frames.], batch size: 20, lr: 3.85e-04
+2022-04-29 16:40:57,120 INFO [train.py:763] (7/8) Epoch 19, batch 4000, loss[loss=0.1725, simple_loss=0.261, pruned_loss=0.04207, over 7166.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2701, pruned_loss=0.03749, over 1409293.55 frames.], batch size: 18, lr: 3.85e-04
+2022-04-29 16:42:03,333 INFO [train.py:763] (7/8) Epoch 19, batch 4050, loss[loss=0.1618, simple_loss=0.2653, pruned_loss=0.02908, over 7326.00 frames.], tot_loss[loss=0.172, simple_loss=0.2695, pruned_loss=0.03724, over 1406391.41 frames.], batch size: 20, lr: 3.85e-04
+2022-04-29 16:43:09,196 INFO [train.py:763] (7/8) Epoch 19, batch 4100, loss[loss=0.1835, simple_loss=0.2697, pruned_loss=0.0487, over 7294.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2695, pruned_loss=0.03745, over 1407081.11 frames.], batch size: 18, lr: 3.85e-04
+2022-04-29 16:44:14,867 INFO [train.py:763] (7/8) Epoch 19, batch 4150, loss[loss=0.1585, simple_loss=0.2703, pruned_loss=0.02336, over 7065.00 frames.], tot_loss[loss=0.171, simple_loss=0.2684, pruned_loss=0.03676, over 1411383.64 frames.], batch size: 18, lr: 3.85e-04
+2022-04-29 16:45:20,211 INFO [train.py:763] (7/8) Epoch 19, batch 4200, loss[loss=0.165, simple_loss=0.2552, pruned_loss=0.03742, over 6829.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2697, pruned_loss=0.03741, over 1405823.28 frames.], batch size: 15, lr: 3.85e-04
+2022-04-29 16:46:26,010 INFO [train.py:763] (7/8) Epoch 19, batch 4250, loss[loss=0.1966, simple_loss=0.2909, pruned_loss=0.05112, over 7198.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2687, pruned_loss=0.0371, over 1403691.62 frames.], batch size: 23, lr: 3.85e-04
+2022-04-29 16:47:31,503 INFO [train.py:763] (7/8) Epoch 19, batch 4300, loss[loss=0.1764, simple_loss=0.2805, pruned_loss=0.0362, over 7218.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2695, pruned_loss=0.03739, over 1400945.60 frames.], batch size: 21, lr: 3.85e-04
+2022-04-29 16:48:37,215 INFO [train.py:763] (7/8) Epoch 19, batch 4350, loss[loss=0.2203, simple_loss=0.3152, pruned_loss=0.06266, over 4643.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2676, pruned_loss=0.03682, over 1403024.15 frames.], batch size: 52, lr: 3.84e-04
+2022-04-29 16:49:42,600 INFO [train.py:763] (7/8) Epoch 19, batch 4400, loss[loss=0.1597, simple_loss=0.2566, pruned_loss=0.03136, over 7152.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2672, pruned_loss=0.03673, over 1398124.86 frames.], batch size: 19, lr: 3.84e-04
+2022-04-29 16:50:47,795 INFO [train.py:763] (7/8) Epoch 19, batch 4450, loss[loss=0.1499, simple_loss=0.2344, pruned_loss=0.03271, over 7213.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2675, pruned_loss=0.03734, over 1390093.59 frames.], batch size: 16, lr: 3.84e-04
+2022-04-29 16:51:52,281 INFO [train.py:763] (7/8) Epoch 19, batch 4500, loss[loss=0.1716, simple_loss=0.281, pruned_loss=0.03112, over 7210.00 frames.], tot_loss[loss=0.1725, simple_loss=0.269, pruned_loss=0.038, over 1383558.37 frames.], batch size: 23, lr: 3.84e-04
+2022-04-29 16:52:57,064 INFO [train.py:763] (7/8) Epoch 19, batch 4550, loss[loss=0.1699, simple_loss=0.2751, pruned_loss=0.03234, over 6273.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2714, pruned_loss=0.03915, over 1339897.51 frames.], batch size: 37, lr: 3.84e-04
+2022-04-29 16:54:25,888 INFO [train.py:763] (7/8) Epoch 20, batch 0, loss[loss=0.1966, simple_loss=0.2751, pruned_loss=0.05907, over 6996.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2751, pruned_loss=0.05907, over 6996.00 frames.], batch size: 16, lr: 3.75e-04
+2022-04-29 16:55:32,601 INFO [train.py:763] (7/8) Epoch 20, batch 50, loss[loss=0.1768, simple_loss=0.2773, pruned_loss=0.03814, over 6333.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2691, pruned_loss=0.03669, over 322782.37 frames.], batch size: 37, lr: 3.75e-04
+2022-04-29 16:56:38,012 INFO [train.py:763] (7/8) Epoch 20, batch 100, loss[loss=0.157, simple_loss=0.2477, pruned_loss=0.03315, over 6836.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2713, pruned_loss=0.03765, over 565797.57 frames.], batch size: 15, lr: 3.75e-04
+2022-04-29 16:57:44,570 INFO [train.py:763] (7/8) Epoch 20, batch 150, loss[loss=0.1664, simple_loss=0.2704, pruned_loss=0.03116, over 7166.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2694, pruned_loss=0.03644, over 755515.23 frames.], batch size: 18, lr: 3.75e-04
+2022-04-29 16:58:49,759 INFO [train.py:763] (7/8) Epoch 20, batch 200, loss[loss=0.166, simple_loss=0.2803, pruned_loss=0.02586, over 6801.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2702, pruned_loss=0.03681, over 900205.47 frames.], batch size: 31, lr: 3.75e-04
+2022-04-29 16:59:55,587 INFO [train.py:763] (7/8) Epoch 20, batch 250, loss[loss=0.1519, simple_loss=0.2457, pruned_loss=0.02904, over 7168.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2696, pruned_loss=0.0367, over 1012287.16 frames.], batch size: 19, lr: 3.75e-04
+2022-04-29 17:01:00,770 INFO [train.py:763] (7/8) Epoch 20, batch 300, loss[loss=0.166, simple_loss=0.2568, pruned_loss=0.03763, over 7262.00 frames.], tot_loss[loss=0.172, simple_loss=0.2697, pruned_loss=0.03716, over 1101079.51 frames.], batch size: 18, lr: 3.75e-04
+2022-04-29 17:02:05,615 INFO [train.py:763] (7/8) Epoch 20, batch 350, loss[loss=0.1477, simple_loss=0.2516, pruned_loss=0.02193, over 7249.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2704, pruned_loss=0.03738, over 1169009.63 frames.], batch size: 19, lr: 3.74e-04
+2022-04-29 17:03:10,964 INFO [train.py:763] (7/8) Epoch 20, batch 400, loss[loss=0.1804, simple_loss=0.2637, pruned_loss=0.04857, over 7076.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2702, pruned_loss=0.03744, over 1228028.90 frames.], batch size: 18, lr: 3.74e-04
+2022-04-29 17:04:16,941 INFO [train.py:763] (7/8) Epoch 20, batch 450, loss[loss=0.1762, simple_loss=0.2685, pruned_loss=0.04198, over 7058.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2704, pruned_loss=0.03715, over 1270411.56 frames.], batch size: 18, lr: 3.74e-04
+2022-04-29 17:05:22,383 INFO [train.py:763] (7/8) Epoch 20, batch 500, loss[loss=0.1959, simple_loss=0.2928, pruned_loss=0.04949, over 7013.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2707, pruned_loss=0.03745, over 1309355.17 frames.], batch size: 28, lr: 3.74e-04
+2022-04-29 17:06:27,724 INFO [train.py:763] (7/8) Epoch 20, batch 550, loss[loss=0.1617, simple_loss=0.245, pruned_loss=0.03924, over 6826.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2694, pruned_loss=0.03668, over 1335845.98 frames.], batch size: 15, lr: 3.74e-04
+2022-04-29 17:07:34,463 INFO [train.py:763] (7/8) Epoch 20, batch 600, loss[loss=0.1999, simple_loss=0.2993, pruned_loss=0.05024, over 7204.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2699, pruned_loss=0.03664, over 1355222.80 frames.], batch size: 22, lr: 3.74e-04
+2022-04-29 17:08:41,628 INFO [train.py:763] (7/8) Epoch 20, batch 650, loss[loss=0.1602, simple_loss=0.2505, pruned_loss=0.035, over 7134.00 frames.], tot_loss[loss=0.1713, simple_loss=0.269, pruned_loss=0.03677, over 1370031.40 frames.], batch size: 17, lr: 3.74e-04
+2022-04-29 17:09:47,501 INFO [train.py:763] (7/8) Epoch 20, batch 700, loss[loss=0.1788, simple_loss=0.2778, pruned_loss=0.03994, over 7234.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2706, pruned_loss=0.03734, over 1380060.11 frames.], batch size: 20, lr: 3.74e-04
+2022-04-29 17:10:53,625 INFO [train.py:763] (7/8) Epoch 20, batch 750, loss[loss=0.1366, simple_loss=0.2372, pruned_loss=0.01794, over 7410.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2712, pruned_loss=0.0377, over 1386489.55 frames.], batch size: 18, lr: 3.74e-04
+2022-04-29 17:11:58,925 INFO [train.py:763] (7/8) Epoch 20, batch 800, loss[loss=0.1782, simple_loss=0.2706, pruned_loss=0.04283, over 7236.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2709, pruned_loss=0.03747, over 1385196.02 frames.], batch size: 20, lr: 3.73e-04
+2022-04-29 17:13:05,464 INFO [train.py:763] (7/8) Epoch 20, batch 850, loss[loss=0.1861, simple_loss=0.2804, pruned_loss=0.04591, over 7317.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2701, pruned_loss=0.03733, over 1392028.62 frames.], batch size: 25, lr: 3.73e-04
+2022-04-29 17:14:10,913 INFO [train.py:763] (7/8) Epoch 20, batch 900, loss[loss=0.1542, simple_loss=0.2612, pruned_loss=0.02357, over 7237.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2697, pruned_loss=0.03728, over 1400440.42 frames.], batch size: 20, lr: 3.73e-04
+2022-04-29 17:15:15,955 INFO [train.py:763] (7/8) Epoch 20, batch 950, loss[loss=0.1771, simple_loss=0.2855, pruned_loss=0.03434, over 7333.00 frames.], tot_loss[loss=0.172, simple_loss=0.2699, pruned_loss=0.03703, over 1405889.08 frames.], batch size: 22, lr: 3.73e-04
+2022-04-29 17:16:21,999 INFO [train.py:763] (7/8) Epoch 20, batch 1000, loss[loss=0.1808, simple_loss=0.2772, pruned_loss=0.04218, over 7199.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2708, pruned_loss=0.03773, over 1405250.97 frames.], batch size: 23, lr: 3.73e-04
+2022-04-29 17:17:26,884 INFO [train.py:763] (7/8) Epoch 20, batch 1050, loss[loss=0.1735, simple_loss=0.2838, pruned_loss=0.03163, over 7410.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2716, pruned_loss=0.03774, over 1406999.73 frames.], batch size: 21, lr: 3.73e-04
+2022-04-29 17:18:32,327 INFO [train.py:763] (7/8) Epoch 20, batch 1100, loss[loss=0.1756, simple_loss=0.2665, pruned_loss=0.04237, over 6798.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2709, pruned_loss=0.03759, over 1409048.47 frames.], batch size: 15, lr: 3.73e-04
+2022-04-29 17:19:37,622 INFO [train.py:763] (7/8) Epoch 20, batch 1150, loss[loss=0.1921, simple_loss=0.3049, pruned_loss=0.03961, over 7295.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2703, pruned_loss=0.03694, over 1413826.46 frames.], batch size: 24, lr: 3.73e-04
+2022-04-29 17:20:42,606 INFO [train.py:763] (7/8) Epoch 20, batch 1200, loss[loss=0.1521, simple_loss=0.2484, pruned_loss=0.02796, over 7276.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2701, pruned_loss=0.03686, over 1415830.69 frames.], batch size: 18, lr: 3.73e-04
+2022-04-29 17:21:47,938 INFO [train.py:763] (7/8) Epoch 20, batch 1250, loss[loss=0.1961, simple_loss=0.298, pruned_loss=0.0471, over 7280.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2687, pruned_loss=0.03637, over 1417702.93 frames.], batch size: 24, lr: 3.73e-04
+2022-04-29 17:22:53,232 INFO [train.py:763] (7/8) Epoch 20, batch 1300, loss[loss=0.1611, simple_loss=0.26, pruned_loss=0.03112, over 7067.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2676, pruned_loss=0.03615, over 1417285.35 frames.], batch size: 18, lr: 3.72e-04
+2022-04-29 17:23:59,061 INFO [train.py:763] (7/8) Epoch 20, batch 1350, loss[loss=0.184, simple_loss=0.2923, pruned_loss=0.03783, over 7339.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2678, pruned_loss=0.0359, over 1424410.25 frames.], batch size: 22, lr: 3.72e-04
+2022-04-29 17:25:04,581 INFO [train.py:763] (7/8) Epoch 20, batch 1400, loss[loss=0.1901, simple_loss=0.2863, pruned_loss=0.04698, over 7391.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2675, pruned_loss=0.03601, over 1427031.51 frames.], batch size: 23, lr: 3.72e-04
+2022-04-29 17:26:11,043 INFO [train.py:763] (7/8) Epoch 20, batch 1450, loss[loss=0.21, simple_loss=0.2969, pruned_loss=0.06153, over 5061.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2672, pruned_loss=0.03596, over 1421460.80 frames.], batch size: 52, lr: 3.72e-04
+2022-04-29 17:27:17,690 INFO [train.py:763] (7/8) Epoch 20, batch 1500, loss[loss=0.1711, simple_loss=0.2709, pruned_loss=0.03563, over 7337.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2679, pruned_loss=0.0362, over 1419233.59 frames.], batch size: 22, lr: 3.72e-04
+2022-04-29 17:28:24,683 INFO [train.py:763] (7/8) Epoch 20, batch 1550, loss[loss=0.1958, simple_loss=0.2959, pruned_loss=0.04783, over 6878.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2687, pruned_loss=0.0367, over 1420968.10 frames.], batch size: 31, lr: 3.72e-04
+2022-04-29 17:29:31,799 INFO [train.py:763] (7/8) Epoch 20, batch 1600, loss[loss=0.1576, simple_loss=0.2576, pruned_loss=0.02877, over 7337.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2686, pruned_loss=0.03631, over 1422000.45 frames.], batch size: 22, lr: 3.72e-04
+2022-04-29 17:30:38,869 INFO [train.py:763] (7/8) Epoch 20, batch 1650, loss[loss=0.1595, simple_loss=0.252, pruned_loss=0.03355, over 7327.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2693, pruned_loss=0.0368, over 1422611.31 frames.], batch size: 20, lr: 3.72e-04
+2022-04-29 17:31:46,145 INFO [train.py:763] (7/8) Epoch 20, batch 1700, loss[loss=0.1661, simple_loss=0.2716, pruned_loss=0.03032, over 7345.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2685, pruned_loss=0.03653, over 1422524.24 frames.], batch size: 22, lr: 3.72e-04
+2022-04-29 17:32:52,739 INFO [train.py:763] (7/8) Epoch 20, batch 1750, loss[loss=0.145, simple_loss=0.2287, pruned_loss=0.03065, over 7384.00 frames.], tot_loss[loss=0.17, simple_loss=0.2678, pruned_loss=0.03609, over 1422551.33 frames.], batch size: 18, lr: 3.72e-04
+2022-04-29 17:33:59,664 INFO [train.py:763] (7/8) Epoch 20, batch 1800, loss[loss=0.181, simple_loss=0.2742, pruned_loss=0.04388, over 7189.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2683, pruned_loss=0.03626, over 1423814.21 frames.], batch size: 23, lr: 3.71e-04
+2022-04-29 17:35:06,950 INFO [train.py:763] (7/8) Epoch 20, batch 1850, loss[loss=0.1656, simple_loss=0.2568, pruned_loss=0.03718, over 7417.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2687, pruned_loss=0.03644, over 1423032.98 frames.], batch size: 18, lr: 3.71e-04
+2022-04-29 17:36:12,565 INFO [train.py:763] (7/8) Epoch 20, batch 1900, loss[loss=0.1525, simple_loss=0.2465, pruned_loss=0.02919, over 7159.00 frames.], tot_loss[loss=0.171, simple_loss=0.269, pruned_loss=0.03646, over 1424099.74 frames.], batch size: 19, lr: 3.71e-04
+2022-04-29 17:37:18,025 INFO [train.py:763] (7/8) Epoch 20, batch 1950, loss[loss=0.1729, simple_loss=0.2592, pruned_loss=0.04328, over 7251.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2701, pruned_loss=0.03687, over 1428262.10 frames.], batch size: 19, lr: 3.71e-04
+2022-04-29 17:38:24,309 INFO [train.py:763] (7/8) Epoch 20, batch 2000, loss[loss=0.1574, simple_loss=0.2613, pruned_loss=0.02671, over 6860.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2697, pruned_loss=0.03697, over 1424592.50 frames.], batch size: 31, lr: 3.71e-04
+2022-04-29 17:39:29,421 INFO [train.py:763] (7/8) Epoch 20, batch 2050, loss[loss=0.1814, simple_loss=0.2852, pruned_loss=0.0388, over 7220.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2699, pruned_loss=0.03732, over 1424396.45 frames.], batch size: 21, lr: 3.71e-04
+2022-04-29 17:40:35,652 INFO [train.py:763] (7/8) Epoch 20, batch 2100, loss[loss=0.1789, simple_loss=0.2805, pruned_loss=0.03864, over 7068.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2695, pruned_loss=0.03691, over 1423306.16 frames.], batch size: 18, lr: 3.71e-04
+2022-04-29 17:41:42,866 INFO [train.py:763] (7/8) Epoch 20, batch 2150, loss[loss=0.1449, simple_loss=0.2398, pruned_loss=0.02499, over 6770.00 frames.], tot_loss[loss=0.1722, simple_loss=0.27, pruned_loss=0.03722, over 1421934.66 frames.], batch size: 15, lr: 3.71e-04
+2022-04-29 17:42:49,000 INFO [train.py:763] (7/8) Epoch 20, batch 2200, loss[loss=0.2332, simple_loss=0.3321, pruned_loss=0.06718, over 7208.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2698, pruned_loss=0.03705, over 1423538.81 frames.], batch size: 22, lr: 3.71e-04
+2022-04-29 17:43:54,368 INFO [train.py:763] (7/8) Epoch 20, batch 2250, loss[loss=0.1758, simple_loss=0.278, pruned_loss=0.0368, over 7205.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2707, pruned_loss=0.03719, over 1424801.59 frames.], batch size: 22, lr: 3.71e-04
+2022-04-29 17:45:01,614 INFO [train.py:763] (7/8) Epoch 20, batch 2300, loss[loss=0.2174, simple_loss=0.3084, pruned_loss=0.06324, over 4919.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2696, pruned_loss=0.03705, over 1422245.48 frames.], batch size: 52, lr: 3.71e-04
+2022-04-29 17:46:08,275 INFO [train.py:763] (7/8) Epoch 20, batch 2350, loss[loss=0.1984, simple_loss=0.2996, pruned_loss=0.04858, over 7290.00 frames.], tot_loss[loss=0.1728, simple_loss=0.271, pruned_loss=0.03734, over 1417223.10 frames.], batch size: 24, lr: 3.70e-04
+2022-04-29 17:47:15,543 INFO [train.py:763] (7/8) Epoch 20, batch 2400, loss[loss=0.1684, simple_loss=0.2735, pruned_loss=0.03165, over 7197.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2693, pruned_loss=0.03646, over 1420352.64 frames.], batch size: 23, lr: 3.70e-04
+2022-04-29 17:48:22,386 INFO [train.py:763] (7/8) Epoch 20, batch 2450, loss[loss=0.1462, simple_loss=0.2444, pruned_loss=0.02402, over 7165.00 frames.], tot_loss[loss=0.17, simple_loss=0.2684, pruned_loss=0.03576, over 1421522.25 frames.], batch size: 19, lr: 3.70e-04
+2022-04-29 17:49:29,431 INFO [train.py:763] (7/8) Epoch 20, batch 2500, loss[loss=0.187, simple_loss=0.2819, pruned_loss=0.04605, over 7413.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2686, pruned_loss=0.03592, over 1422564.25 frames.], batch size: 21, lr: 3.70e-04
+2022-04-29 17:50:36,107 INFO [train.py:763] (7/8) Epoch 20, batch 2550, loss[loss=0.2078, simple_loss=0.2962, pruned_loss=0.05972, over 5178.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2692, pruned_loss=0.03659, over 1420181.92 frames.], batch size: 52, lr: 3.70e-04
+2022-04-29 17:51:41,452 INFO [train.py:763] (7/8) Epoch 20, batch 2600, loss[loss=0.1492, simple_loss=0.2483, pruned_loss=0.02509, over 7064.00 frames.], tot_loss[loss=0.172, simple_loss=0.2701, pruned_loss=0.03695, over 1421047.30 frames.], batch size: 18, lr: 3.70e-04
+2022-04-29 17:52:58,247 INFO [train.py:763] (7/8) Epoch 20, batch 2650, loss[loss=0.1688, simple_loss=0.2673, pruned_loss=0.03513, over 7333.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2707, pruned_loss=0.03743, over 1416367.83 frames.], batch size: 20, lr: 3.70e-04
+2022-04-29 17:54:04,069 INFO [train.py:763] (7/8) Epoch 20, batch 2700, loss[loss=0.1639, simple_loss=0.2535, pruned_loss=0.0371, over 7406.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2701, pruned_loss=0.03672, over 1419949.75 frames.], batch size: 18, lr: 3.70e-04
+2022-04-29 17:55:10,591 INFO [train.py:763] (7/8) Epoch 20, batch 2750, loss[loss=0.1741, simple_loss=0.2701, pruned_loss=0.03901, over 7157.00 frames.], tot_loss[loss=0.1718, simple_loss=0.27, pruned_loss=0.03678, over 1422329.11 frames.], batch size: 18, lr: 3.70e-04
+2022-04-29 17:56:15,909 INFO [train.py:763] (7/8) Epoch 20, batch 2800, loss[loss=0.1898, simple_loss=0.2877, pruned_loss=0.046, over 7381.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2696, pruned_loss=0.03668, over 1425756.88 frames.], batch size: 23, lr: 3.70e-04
+2022-04-29 17:57:21,244 INFO [train.py:763] (7/8) Epoch 20, batch 2850, loss[loss=0.1939, simple_loss=0.2898, pruned_loss=0.04896, over 7190.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2695, pruned_loss=0.03646, over 1421377.07 frames.], batch size: 23, lr: 3.69e-04
+2022-04-29 17:58:26,466 INFO [train.py:763] (7/8) Epoch 20, batch 2900, loss[loss=0.1798, simple_loss=0.2818, pruned_loss=0.03884, over 7103.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2694, pruned_loss=0.03656, over 1416647.38 frames.], batch size: 28, lr: 3.69e-04
+2022-04-29 17:59:31,736 INFO [train.py:763] (7/8) Epoch 20, batch 2950, loss[loss=0.1557, simple_loss=0.2564, pruned_loss=0.02755, over 7352.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2695, pruned_loss=0.03664, over 1414624.27 frames.], batch size: 19, lr: 3.69e-04
+2022-04-29 18:01:03,493 INFO [train.py:763] (7/8) Epoch 20, batch 3000, loss[loss=0.1888, simple_loss=0.2907, pruned_loss=0.04346, over 6754.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2703, pruned_loss=0.03704, over 1414175.38 frames.], batch size: 31, lr: 3.69e-04
+2022-04-29 18:01:03,493 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 18:01:18,757 INFO [train.py:792] (7/8) Epoch 20, validation: loss=0.1672, simple_loss=0.2663, pruned_loss=0.03407, over 698248.00 frames.
+2022-04-29 18:02:33,651 INFO [train.py:763] (7/8) Epoch 20, batch 3050, loss[loss=0.1553, simple_loss=0.2505, pruned_loss=0.03009, over 7282.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2704, pruned_loss=0.03717, over 1414946.67 frames.], batch size: 18, lr: 3.69e-04
+2022-04-29 18:03:49,741 INFO [train.py:763] (7/8) Epoch 20, batch 3100, loss[loss=0.181, simple_loss=0.2771, pruned_loss=0.04243, over 7372.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2714, pruned_loss=0.03739, over 1413599.75 frames.], batch size: 23, lr: 3.69e-04
+2022-04-29 18:05:13,908 INFO [train.py:763] (7/8) Epoch 20, batch 3150, loss[loss=0.1972, simple_loss=0.2865, pruned_loss=0.05402, over 7303.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2705, pruned_loss=0.03704, over 1418854.14 frames.], batch size: 24, lr: 3.69e-04
+2022-04-29 18:06:18,930 INFO [train.py:763] (7/8) Epoch 20, batch 3200, loss[loss=0.1774, simple_loss=0.2867, pruned_loss=0.0341, over 7319.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2708, pruned_loss=0.03701, over 1423164.03 frames.], batch size: 21, lr: 3.69e-04
+2022-04-29 18:07:24,057 INFO [train.py:763] (7/8) Epoch 20, batch 3250, loss[loss=0.1469, simple_loss=0.248, pruned_loss=0.02285, over 7056.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2704, pruned_loss=0.0367, over 1421836.79 frames.], batch size: 18, lr: 3.69e-04
+2022-04-29 18:08:29,719 INFO [train.py:763] (7/8) Epoch 20, batch 3300, loss[loss=0.1336, simple_loss=0.2272, pruned_loss=0.01995, over 7128.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2691, pruned_loss=0.0359, over 1423729.28 frames.], batch size: 17, lr: 3.69e-04
+2022-04-29 18:09:35,981 INFO [train.py:763] (7/8) Epoch 20, batch 3350, loss[loss=0.1698, simple_loss=0.2674, pruned_loss=0.0361, over 7222.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2699, pruned_loss=0.03651, over 1420478.84 frames.], batch size: 20, lr: 3.68e-04
+2022-04-29 18:10:42,817 INFO [train.py:763] (7/8) Epoch 20, batch 3400, loss[loss=0.1904, simple_loss=0.2846, pruned_loss=0.04814, over 6308.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2698, pruned_loss=0.03628, over 1416321.95 frames.], batch size: 38, lr: 3.68e-04
+2022-04-29 18:11:49,535 INFO [train.py:763] (7/8) Epoch 20, batch 3450, loss[loss=0.1615, simple_loss=0.2661, pruned_loss=0.02846, over 7318.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2705, pruned_loss=0.03689, over 1414773.78 frames.], batch size: 21, lr: 3.68e-04
+2022-04-29 18:12:54,745 INFO [train.py:763] (7/8) Epoch 20, batch 3500, loss[loss=0.1941, simple_loss=0.289, pruned_loss=0.04962, over 7074.00 frames.], tot_loss[loss=0.1727, simple_loss=0.271, pruned_loss=0.03716, over 1410539.86 frames.], batch size: 28, lr: 3.68e-04
+2022-04-29 18:14:00,250 INFO [train.py:763] (7/8) Epoch 20, batch 3550, loss[loss=0.1597, simple_loss=0.255, pruned_loss=0.03216, over 7268.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2699, pruned_loss=0.03625, over 1414047.04 frames.], batch size: 17, lr: 3.68e-04
+2022-04-29 18:15:05,508 INFO [train.py:763] (7/8) Epoch 20, batch 3600, loss[loss=0.1741, simple_loss=0.2752, pruned_loss=0.03655, over 7362.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2705, pruned_loss=0.03638, over 1411708.35 frames.], batch size: 23, lr: 3.68e-04
+2022-04-29 18:16:10,767 INFO [train.py:763] (7/8) Epoch 20, batch 3650, loss[loss=0.1692, simple_loss=0.2817, pruned_loss=0.02833, over 7228.00 frames.], tot_loss[loss=0.171, simple_loss=0.2697, pruned_loss=0.03612, over 1413807.91 frames.], batch size: 26, lr: 3.68e-04
+2022-04-29 18:17:15,976 INFO [train.py:763] (7/8) Epoch 20, batch 3700, loss[loss=0.1904, simple_loss=0.2877, pruned_loss=0.04655, over 7314.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2703, pruned_loss=0.03642, over 1414668.99 frames.], batch size: 21, lr: 3.68e-04
+2022-04-29 18:18:22,138 INFO [train.py:763] (7/8) Epoch 20, batch 3750, loss[loss=0.2113, simple_loss=0.2963, pruned_loss=0.06314, over 7294.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2699, pruned_loss=0.03646, over 1418087.74 frames.], batch size: 25, lr: 3.68e-04
+2022-04-29 18:19:27,290 INFO [train.py:763] (7/8) Epoch 20, batch 3800, loss[loss=0.1901, simple_loss=0.291, pruned_loss=0.04464, over 7187.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2688, pruned_loss=0.03601, over 1418718.23 frames.], batch size: 26, lr: 3.68e-04
+2022-04-29 18:20:33,288 INFO [train.py:763] (7/8) Epoch 20, batch 3850, loss[loss=0.1608, simple_loss=0.262, pruned_loss=0.02981, over 7332.00 frames.], tot_loss[loss=0.169, simple_loss=0.2681, pruned_loss=0.035, over 1419374.75 frames.], batch size: 20, lr: 3.68e-04
+2022-04-29 18:21:38,675 INFO [train.py:763] (7/8) Epoch 20, batch 3900, loss[loss=0.1874, simple_loss=0.2786, pruned_loss=0.04814, over 7259.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2682, pruned_loss=0.03532, over 1422679.10 frames.], batch size: 19, lr: 3.67e-04
+2022-04-29 18:22:44,418 INFO [train.py:763] (7/8) Epoch 20, batch 3950, loss[loss=0.1618, simple_loss=0.25, pruned_loss=0.03675, over 7415.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2695, pruned_loss=0.03577, over 1417406.77 frames.], batch size: 18, lr: 3.67e-04
+2022-04-29 18:23:51,285 INFO [train.py:763] (7/8) Epoch 20, batch 4000, loss[loss=0.1488, simple_loss=0.2416, pruned_loss=0.02794, over 7368.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2688, pruned_loss=0.03543, over 1421429.73 frames.], batch size: 19, lr: 3.67e-04
+2022-04-29 18:24:58,624 INFO [train.py:763] (7/8) Epoch 20, batch 4050, loss[loss=0.233, simple_loss=0.317, pruned_loss=0.07453, over 5188.00 frames.], tot_loss[loss=0.17, simple_loss=0.2682, pruned_loss=0.03587, over 1419423.95 frames.], batch size: 52, lr: 3.67e-04
+2022-04-29 18:26:05,429 INFO [train.py:763] (7/8) Epoch 20, batch 4100, loss[loss=0.1763, simple_loss=0.28, pruned_loss=0.03629, over 7211.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2692, pruned_loss=0.03653, over 1410931.27 frames.], batch size: 21, lr: 3.67e-04
+2022-04-29 18:27:11,001 INFO [train.py:763] (7/8) Epoch 20, batch 4150, loss[loss=0.137, simple_loss=0.234, pruned_loss=0.02002, over 7070.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2698, pruned_loss=0.03669, over 1412208.93 frames.], batch size: 18, lr: 3.67e-04
+2022-04-29 18:28:16,332 INFO [train.py:763] (7/8) Epoch 20, batch 4200, loss[loss=0.1672, simple_loss=0.2696, pruned_loss=0.03239, over 6789.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2699, pruned_loss=0.03654, over 1413002.91 frames.], batch size: 31, lr: 3.67e-04
+2022-04-29 18:29:32,313 INFO [train.py:763] (7/8) Epoch 20, batch 4250, loss[loss=0.1959, simple_loss=0.2944, pruned_loss=0.04869, over 7224.00 frames.], tot_loss[loss=0.171, simple_loss=0.2693, pruned_loss=0.03636, over 1417964.69 frames.], batch size: 21, lr: 3.67e-04
+2022-04-29 18:30:39,039 INFO [train.py:763] (7/8) Epoch 20, batch 4300, loss[loss=0.1442, simple_loss=0.2542, pruned_loss=0.0171, over 7313.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2691, pruned_loss=0.03626, over 1419531.03 frames.], batch size: 24, lr: 3.67e-04
+2022-04-29 18:31:45,057 INFO [train.py:763] (7/8) Epoch 20, batch 4350, loss[loss=0.1844, simple_loss=0.2867, pruned_loss=0.04107, over 7222.00 frames.], tot_loss[loss=0.1703, simple_loss=0.269, pruned_loss=0.03582, over 1418709.01 frames.], batch size: 21, lr: 3.67e-04
+2022-04-29 18:32:52,263 INFO [train.py:763] (7/8) Epoch 20, batch 4400, loss[loss=0.1496, simple_loss=0.2386, pruned_loss=0.03033, over 7158.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2687, pruned_loss=0.03553, over 1417401.52 frames.], batch size: 18, lr: 3.66e-04
+2022-04-29 18:33:58,461 INFO [train.py:763] (7/8) Epoch 20, batch 4450, loss[loss=0.1506, simple_loss=0.2321, pruned_loss=0.03455, over 6984.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2686, pruned_loss=0.03592, over 1409271.91 frames.], batch size: 16, lr: 3.66e-04
+2022-04-29 18:35:05,730 INFO [train.py:763] (7/8) Epoch 20, batch 4500, loss[loss=0.1553, simple_loss=0.244, pruned_loss=0.03329, over 6976.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2687, pruned_loss=0.03551, over 1411168.40 frames.], batch size: 16, lr: 3.66e-04
+2022-04-29 18:36:13,277 INFO [train.py:763] (7/8) Epoch 20, batch 4550, loss[loss=0.2021, simple_loss=0.2929, pruned_loss=0.05566, over 4900.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2679, pruned_loss=0.03579, over 1395560.57 frames.], batch size: 52, lr: 3.66e-04
+2022-04-29 18:37:42,395 INFO [train.py:763] (7/8) Epoch 21, batch 0, loss[loss=0.2014, simple_loss=0.3071, pruned_loss=0.04781, over 7294.00 frames.], tot_loss[loss=0.2014, simple_loss=0.3071, pruned_loss=0.04781, over 7294.00 frames.], batch size: 25, lr: 3.58e-04
+2022-04-29 18:38:48,215 INFO [train.py:763] (7/8) Epoch 21, batch 50, loss[loss=0.1603, simple_loss=0.2629, pruned_loss=0.02886, over 7173.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2703, pruned_loss=0.03611, over 317634.73 frames.], batch size: 18, lr: 3.58e-04
+2022-04-29 18:39:53,579 INFO [train.py:763] (7/8) Epoch 21, batch 100, loss[loss=0.192, simple_loss=0.2985, pruned_loss=0.04273, over 7437.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2688, pruned_loss=0.03569, over 563948.36 frames.], batch size: 22, lr: 3.58e-04
+2022-04-29 18:41:00,391 INFO [train.py:763] (7/8) Epoch 21, batch 150, loss[loss=0.1785, simple_loss=0.274, pruned_loss=0.04144, over 7320.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2687, pruned_loss=0.03573, over 753653.63 frames.], batch size: 21, lr: 3.58e-04
+2022-04-29 18:42:07,807 INFO [train.py:763] (7/8) Epoch 21, batch 200, loss[loss=0.1831, simple_loss=0.2862, pruned_loss=0.04002, over 7342.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2689, pruned_loss=0.03589, over 901627.78 frames.], batch size: 22, lr: 3.58e-04
+2022-04-29 18:43:14,305 INFO [train.py:763] (7/8) Epoch 21, batch 250, loss[loss=0.1493, simple_loss=0.2567, pruned_loss=0.021, over 7259.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2692, pruned_loss=0.0363, over 1014368.83 frames.], batch size: 19, lr: 3.57e-04
+2022-04-29 18:44:19,591 INFO [train.py:763] (7/8) Epoch 21, batch 300, loss[loss=0.1639, simple_loss=0.2574, pruned_loss=0.03514, over 7241.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2698, pruned_loss=0.0368, over 1107205.00 frames.], batch size: 20, lr: 3.57e-04
+2022-04-29 18:45:25,089 INFO [train.py:763] (7/8) Epoch 21, batch 350, loss[loss=0.1438, simple_loss=0.2482, pruned_loss=0.0197, over 7162.00 frames.], tot_loss[loss=0.171, simple_loss=0.2694, pruned_loss=0.03629, over 1177639.92 frames.], batch size: 19, lr: 3.57e-04
+2022-04-29 18:46:30,626 INFO [train.py:763] (7/8) Epoch 21, batch 400, loss[loss=0.1704, simple_loss=0.2752, pruned_loss=0.03281, over 7218.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2693, pruned_loss=0.03601, over 1230119.19 frames.], batch size: 21, lr: 3.57e-04
+2022-04-29 18:47:36,048 INFO [train.py:763] (7/8) Epoch 21, batch 450, loss[loss=0.2176, simple_loss=0.3103, pruned_loss=0.06241, over 5051.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2682, pruned_loss=0.03564, over 1273454.12 frames.], batch size: 52, lr: 3.57e-04
+2022-04-29 18:48:41,855 INFO [train.py:763] (7/8) Epoch 21, batch 500, loss[loss=0.1722, simple_loss=0.2784, pruned_loss=0.03299, over 7320.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2688, pruned_loss=0.03569, over 1309535.29 frames.], batch size: 25, lr: 3.57e-04
+2022-04-29 18:49:47,447 INFO [train.py:763] (7/8) Epoch 21, batch 550, loss[loss=0.1767, simple_loss=0.2805, pruned_loss=0.03644, over 7413.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2691, pruned_loss=0.03586, over 1332388.76 frames.], batch size: 20, lr: 3.57e-04
+2022-04-29 18:50:53,648 INFO [train.py:763] (7/8) Epoch 21, batch 600, loss[loss=0.1704, simple_loss=0.2733, pruned_loss=0.03379, over 7346.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2675, pruned_loss=0.03515, over 1353871.51 frames.], batch size: 22, lr: 3.57e-04
+2022-04-29 18:51:58,885 INFO [train.py:763] (7/8) Epoch 21, batch 650, loss[loss=0.1646, simple_loss=0.2709, pruned_loss=0.02914, over 7328.00 frames.], tot_loss[loss=0.169, simple_loss=0.2683, pruned_loss=0.03485, over 1369865.75 frames.], batch size: 22, lr: 3.57e-04
+2022-04-29 18:53:04,519 INFO [train.py:763] (7/8) Epoch 21, batch 700, loss[loss=0.1782, simple_loss=0.285, pruned_loss=0.03573, over 7336.00 frames.], tot_loss[loss=0.169, simple_loss=0.268, pruned_loss=0.03501, over 1377738.70 frames.], batch size: 25, lr: 3.57e-04
+2022-04-29 18:54:10,377 INFO [train.py:763] (7/8) Epoch 21, batch 750, loss[loss=0.17, simple_loss=0.2561, pruned_loss=0.04193, over 7165.00 frames.], tot_loss[loss=0.1694, simple_loss=0.268, pruned_loss=0.03539, over 1386518.20 frames.], batch size: 18, lr: 3.57e-04
+2022-04-29 18:55:16,604 INFO [train.py:763] (7/8) Epoch 21, batch 800, loss[loss=0.1939, simple_loss=0.2989, pruned_loss=0.04443, over 7279.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2687, pruned_loss=0.03536, over 1399237.20 frames.], batch size: 25, lr: 3.56e-04
+2022-04-29 18:56:22,312 INFO [train.py:763] (7/8) Epoch 21, batch 850, loss[loss=0.158, simple_loss=0.2565, pruned_loss=0.0297, over 7402.00 frames.], tot_loss[loss=0.1701, simple_loss=0.269, pruned_loss=0.03557, over 1405361.63 frames.], batch size: 18, lr: 3.56e-04
+2022-04-29 18:57:27,458 INFO [train.py:763] (7/8) Epoch 21, batch 900, loss[loss=0.1696, simple_loss=0.2705, pruned_loss=0.03431, over 6173.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2695, pruned_loss=0.03592, over 1408030.27 frames.], batch size: 37, lr: 3.56e-04
+2022-04-29 18:58:32,843 INFO [train.py:763] (7/8) Epoch 21, batch 950, loss[loss=0.1665, simple_loss=0.2603, pruned_loss=0.03632, over 7288.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2682, pruned_loss=0.03547, over 1410078.92 frames.], batch size: 18, lr: 3.56e-04
+2022-04-29 18:59:38,156 INFO [train.py:763] (7/8) Epoch 21, batch 1000, loss[loss=0.1585, simple_loss=0.2562, pruned_loss=0.03046, over 7155.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2688, pruned_loss=0.03582, over 1410301.19 frames.], batch size: 19, lr: 3.56e-04
+2022-04-29 19:00:44,779 INFO [train.py:763] (7/8) Epoch 21, batch 1050, loss[loss=0.1693, simple_loss=0.2757, pruned_loss=0.03146, over 7330.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2684, pruned_loss=0.03587, over 1414315.43 frames.], batch size: 22, lr: 3.56e-04
+2022-04-29 19:01:50,760 INFO [train.py:763] (7/8) Epoch 21, batch 1100, loss[loss=0.1678, simple_loss=0.2735, pruned_loss=0.03103, over 6366.00 frames.], tot_loss[loss=0.17, simple_loss=0.2685, pruned_loss=0.03575, over 1418075.66 frames.], batch size: 38, lr: 3.56e-04
+2022-04-29 19:02:56,413 INFO [train.py:763] (7/8) Epoch 21, batch 1150, loss[loss=0.1669, simple_loss=0.2674, pruned_loss=0.03321, over 7253.00 frames.], tot_loss[loss=0.17, simple_loss=0.2686, pruned_loss=0.03569, over 1419766.51 frames.], batch size: 19, lr: 3.56e-04
+2022-04-29 19:04:02,105 INFO [train.py:763] (7/8) Epoch 21, batch 1200, loss[loss=0.1856, simple_loss=0.2835, pruned_loss=0.04385, over 7305.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2681, pruned_loss=0.03583, over 1421475.83 frames.], batch size: 25, lr: 3.56e-04
+2022-04-29 19:05:07,726 INFO [train.py:763] (7/8) Epoch 21, batch 1250, loss[loss=0.1499, simple_loss=0.2414, pruned_loss=0.0292, over 7006.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2683, pruned_loss=0.03579, over 1420780.09 frames.], batch size: 16, lr: 3.56e-04
+2022-04-29 19:06:13,277 INFO [train.py:763] (7/8) Epoch 21, batch 1300, loss[loss=0.1685, simple_loss=0.2747, pruned_loss=0.0312, over 7155.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2686, pruned_loss=0.03558, over 1419194.73 frames.], batch size: 19, lr: 3.56e-04
+2022-04-29 19:07:19,458 INFO [train.py:763] (7/8) Epoch 21, batch 1350, loss[loss=0.1843, simple_loss=0.2849, pruned_loss=0.04182, over 7418.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2682, pruned_loss=0.03568, over 1422988.12 frames.], batch size: 21, lr: 3.55e-04
+2022-04-29 19:08:24,901 INFO [train.py:763] (7/8) Epoch 21, batch 1400, loss[loss=0.1867, simple_loss=0.2849, pruned_loss=0.0443, over 7219.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2681, pruned_loss=0.0358, over 1419806.96 frames.], batch size: 22, lr: 3.55e-04
+2022-04-29 19:09:30,416 INFO [train.py:763] (7/8) Epoch 21, batch 1450, loss[loss=0.1615, simple_loss=0.2681, pruned_loss=0.02752, over 7432.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2682, pruned_loss=0.03549, over 1424577.28 frames.], batch size: 20, lr: 3.55e-04
+2022-04-29 19:10:36,226 INFO [train.py:763] (7/8) Epoch 21, batch 1500, loss[loss=0.1699, simple_loss=0.2693, pruned_loss=0.03528, over 7230.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2678, pruned_loss=0.03559, over 1425836.48 frames.], batch size: 20, lr: 3.55e-04
+2022-04-29 19:11:42,029 INFO [train.py:763] (7/8) Epoch 21, batch 1550, loss[loss=0.1758, simple_loss=0.2748, pruned_loss=0.03836, over 7234.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2675, pruned_loss=0.03556, over 1428385.47 frames.], batch size: 20, lr: 3.55e-04
+2022-04-29 19:12:47,955 INFO [train.py:763] (7/8) Epoch 21, batch 1600, loss[loss=0.1332, simple_loss=0.2216, pruned_loss=0.02236, over 6846.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2678, pruned_loss=0.03556, over 1429211.09 frames.], batch size: 15, lr: 3.55e-04
+2022-04-29 19:13:54,891 INFO [train.py:763] (7/8) Epoch 21, batch 1650, loss[loss=0.1501, simple_loss=0.2566, pruned_loss=0.02176, over 6725.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2681, pruned_loss=0.03541, over 1431308.97 frames.], batch size: 31, lr: 3.55e-04
+2022-04-29 19:15:01,806 INFO [train.py:763] (7/8) Epoch 21, batch 1700, loss[loss=0.1749, simple_loss=0.2927, pruned_loss=0.02855, over 7325.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2667, pruned_loss=0.03505, over 1433340.40 frames.], batch size: 22, lr: 3.55e-04
+2022-04-29 19:16:08,184 INFO [train.py:763] (7/8) Epoch 21, batch 1750, loss[loss=0.1734, simple_loss=0.2734, pruned_loss=0.03669, over 7232.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2675, pruned_loss=0.03576, over 1432965.14 frames.], batch size: 20, lr: 3.55e-04
+2022-04-29 19:17:14,204 INFO [train.py:763] (7/8) Epoch 21, batch 1800, loss[loss=0.1385, simple_loss=0.2327, pruned_loss=0.02214, over 7289.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2668, pruned_loss=0.03564, over 1430298.50 frames.], batch size: 17, lr: 3.55e-04
+2022-04-29 19:18:19,485 INFO [train.py:763] (7/8) Epoch 21, batch 1850, loss[loss=0.1975, simple_loss=0.2975, pruned_loss=0.04873, over 6495.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2664, pruned_loss=0.03543, over 1425868.07 frames.], batch size: 38, lr: 3.55e-04
+2022-04-29 19:19:25,211 INFO [train.py:763] (7/8) Epoch 21, batch 1900, loss[loss=0.2338, simple_loss=0.3203, pruned_loss=0.0736, over 4971.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2671, pruned_loss=0.0357, over 1424607.05 frames.], batch size: 52, lr: 3.54e-04
+2022-04-29 19:20:31,913 INFO [train.py:763] (7/8) Epoch 21, batch 1950, loss[loss=0.1532, simple_loss=0.2481, pruned_loss=0.02919, over 7267.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2667, pruned_loss=0.03541, over 1425578.91 frames.], batch size: 17, lr: 3.54e-04
+2022-04-29 19:21:37,668 INFO [train.py:763] (7/8) Epoch 21, batch 2000, loss[loss=0.1833, simple_loss=0.2805, pruned_loss=0.04298, over 7337.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2676, pruned_loss=0.03552, over 1427330.77 frames.], batch size: 20, lr: 3.54e-04
+2022-04-29 19:22:44,049 INFO [train.py:763] (7/8) Epoch 21, batch 2050, loss[loss=0.1743, simple_loss=0.2588, pruned_loss=0.04496, over 7286.00 frames.], tot_loss[loss=0.17, simple_loss=0.2683, pruned_loss=0.03587, over 1428369.10 frames.], batch size: 17, lr: 3.54e-04
+2022-04-29 19:23:50,510 INFO [train.py:763] (7/8) Epoch 21, batch 2100, loss[loss=0.1764, simple_loss=0.2653, pruned_loss=0.04376, over 7422.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2689, pruned_loss=0.03633, over 1426820.13 frames.], batch size: 18, lr: 3.54e-04
+2022-04-29 19:24:56,275 INFO [train.py:763] (7/8) Epoch 21, batch 2150, loss[loss=0.1648, simple_loss=0.2597, pruned_loss=0.03497, over 7171.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2681, pruned_loss=0.0362, over 1423437.42 frames.], batch size: 18, lr: 3.54e-04
+2022-04-29 19:26:02,256 INFO [train.py:763] (7/8) Epoch 21, batch 2200, loss[loss=0.1955, simple_loss=0.2942, pruned_loss=0.04833, over 7119.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2679, pruned_loss=0.03595, over 1426486.50 frames.], batch size: 21, lr: 3.54e-04
+2022-04-29 19:27:08,598 INFO [train.py:763] (7/8) Epoch 21, batch 2250, loss[loss=0.1736, simple_loss=0.2533, pruned_loss=0.04696, over 7201.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2686, pruned_loss=0.03633, over 1423798.20 frames.], batch size: 16, lr: 3.54e-04
+2022-04-29 19:28:14,989 INFO [train.py:763] (7/8) Epoch 21, batch 2300, loss[loss=0.2657, simple_loss=0.3347, pruned_loss=0.09834, over 5063.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2695, pruned_loss=0.03672, over 1424979.85 frames.], batch size: 53, lr: 3.54e-04
+2022-04-29 19:29:21,498 INFO [train.py:763] (7/8) Epoch 21, batch 2350, loss[loss=0.1657, simple_loss=0.2698, pruned_loss=0.03086, over 6447.00 frames.], tot_loss[loss=0.171, simple_loss=0.269, pruned_loss=0.03646, over 1427829.62 frames.], batch size: 38, lr: 3.54e-04
+2022-04-29 19:30:28,268 INFO [train.py:763] (7/8) Epoch 21, batch 2400, loss[loss=0.1326, simple_loss=0.221, pruned_loss=0.02209, over 7143.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2684, pruned_loss=0.03629, over 1426517.65 frames.], batch size: 17, lr: 3.54e-04
+2022-04-29 19:31:33,889 INFO [train.py:763] (7/8) Epoch 21, batch 2450, loss[loss=0.1543, simple_loss=0.2419, pruned_loss=0.03338, over 7278.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2685, pruned_loss=0.03606, over 1424582.70 frames.], batch size: 17, lr: 3.54e-04
+2022-04-29 19:32:39,525 INFO [train.py:763] (7/8) Epoch 21, batch 2500, loss[loss=0.1844, simple_loss=0.2812, pruned_loss=0.04386, over 7420.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2691, pruned_loss=0.03658, over 1421951.65 frames.], batch size: 21, lr: 3.53e-04
+2022-04-29 19:33:46,133 INFO [train.py:763] (7/8) Epoch 21, batch 2550, loss[loss=0.1936, simple_loss=0.2857, pruned_loss=0.05072, over 7071.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2695, pruned_loss=0.03649, over 1420225.14 frames.], batch size: 18, lr: 3.53e-04
+2022-04-29 19:34:52,142 INFO [train.py:763] (7/8) Epoch 21, batch 2600, loss[loss=0.1601, simple_loss=0.2583, pruned_loss=0.03096, over 7161.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2701, pruned_loss=0.03657, over 1416622.32 frames.], batch size: 19, lr: 3.53e-04
+2022-04-29 19:35:58,124 INFO [train.py:763] (7/8) Epoch 21, batch 2650, loss[loss=0.1572, simple_loss=0.254, pruned_loss=0.03018, over 7266.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2689, pruned_loss=0.03642, over 1420259.31 frames.], batch size: 19, lr: 3.53e-04
+2022-04-29 19:37:03,424 INFO [train.py:763] (7/8) Epoch 21, batch 2700, loss[loss=0.1662, simple_loss=0.2564, pruned_loss=0.03801, over 7155.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2681, pruned_loss=0.03618, over 1418942.34 frames.], batch size: 18, lr: 3.53e-04
+2022-04-29 19:38:08,444 INFO [train.py:763] (7/8) Epoch 21, batch 2750, loss[loss=0.1526, simple_loss=0.2563, pruned_loss=0.02442, over 7057.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2684, pruned_loss=0.03588, over 1419099.36 frames.], batch size: 18, lr: 3.53e-04
+2022-04-29 19:39:13,893 INFO [train.py:763] (7/8) Epoch 21, batch 2800, loss[loss=0.1552, simple_loss=0.2373, pruned_loss=0.0366, over 7291.00 frames.], tot_loss[loss=0.17, simple_loss=0.2683, pruned_loss=0.03586, over 1419693.88 frames.], batch size: 18, lr: 3.53e-04
+2022-04-29 19:40:19,374 INFO [train.py:763] (7/8) Epoch 21, batch 2850, loss[loss=0.1569, simple_loss=0.2604, pruned_loss=0.02672, over 7164.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2676, pruned_loss=0.03547, over 1417815.30 frames.], batch size: 19, lr: 3.53e-04
+2022-04-29 19:41:24,563 INFO [train.py:763] (7/8) Epoch 21, batch 2900, loss[loss=0.1742, simple_loss=0.2661, pruned_loss=0.04112, over 7162.00 frames.], tot_loss[loss=0.169, simple_loss=0.2672, pruned_loss=0.03545, over 1420636.03 frames.], batch size: 19, lr: 3.53e-04
+2022-04-29 19:42:30,262 INFO [train.py:763] (7/8) Epoch 21, batch 2950, loss[loss=0.174, simple_loss=0.2751, pruned_loss=0.03643, over 7401.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2669, pruned_loss=0.0355, over 1421857.02 frames.], batch size: 21, lr: 3.53e-04
+2022-04-29 19:43:36,690 INFO [train.py:763] (7/8) Epoch 21, batch 3000, loss[loss=0.1374, simple_loss=0.2294, pruned_loss=0.02268, over 7157.00 frames.], tot_loss[loss=0.169, simple_loss=0.267, pruned_loss=0.03543, over 1425683.55 frames.], batch size: 18, lr: 3.53e-04
+2022-04-29 19:43:36,691 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 19:43:52,054 INFO [train.py:792] (7/8) Epoch 21, validation: loss=0.1676, simple_loss=0.2672, pruned_loss=0.03398, over 698248.00 frames.
+2022-04-29 19:44:57,945 INFO [train.py:763] (7/8) Epoch 21, batch 3050, loss[loss=0.1755, simple_loss=0.2741, pruned_loss=0.03849, over 7047.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2666, pruned_loss=0.0353, over 1427442.24 frames.], batch size: 28, lr: 3.52e-04
+2022-04-29 19:46:03,962 INFO [train.py:763] (7/8) Epoch 21, batch 3100, loss[loss=0.202, simple_loss=0.2962, pruned_loss=0.05386, over 5465.00 frames.], tot_loss[loss=0.1694, simple_loss=0.267, pruned_loss=0.0359, over 1427469.08 frames.], batch size: 54, lr: 3.52e-04
+2022-04-29 19:47:10,174 INFO [train.py:763] (7/8) Epoch 21, batch 3150, loss[loss=0.1775, simple_loss=0.2827, pruned_loss=0.0361, over 7412.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2666, pruned_loss=0.03561, over 1426132.21 frames.], batch size: 21, lr: 3.52e-04
+2022-04-29 19:48:15,892 INFO [train.py:763] (7/8) Epoch 21, batch 3200, loss[loss=0.1537, simple_loss=0.2521, pruned_loss=0.02766, over 7062.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2662, pruned_loss=0.03546, over 1426674.72 frames.], batch size: 18, lr: 3.52e-04
+2022-04-29 19:49:21,834 INFO [train.py:763] (7/8) Epoch 21, batch 3250, loss[loss=0.1812, simple_loss=0.265, pruned_loss=0.04867, over 7428.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2672, pruned_loss=0.03559, over 1428210.70 frames.], batch size: 17, lr: 3.52e-04
+2022-04-29 19:50:27,772 INFO [train.py:763] (7/8) Epoch 21, batch 3300, loss[loss=0.1569, simple_loss=0.2527, pruned_loss=0.03057, over 7434.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2682, pruned_loss=0.03567, over 1430909.86 frames.], batch size: 20, lr: 3.52e-04
+2022-04-29 19:51:34,068 INFO [train.py:763] (7/8) Epoch 21, batch 3350, loss[loss=0.1586, simple_loss=0.2518, pruned_loss=0.03271, over 7357.00 frames.], tot_loss[loss=0.1704, simple_loss=0.269, pruned_loss=0.03595, over 1429175.24 frames.], batch size: 19, lr: 3.52e-04
+2022-04-29 19:52:40,206 INFO [train.py:763] (7/8) Epoch 21, batch 3400, loss[loss=0.1784, simple_loss=0.2669, pruned_loss=0.04499, over 7141.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2688, pruned_loss=0.03615, over 1426066.16 frames.], batch size: 17, lr: 3.52e-04
+2022-04-29 19:53:45,700 INFO [train.py:763] (7/8) Epoch 21, batch 3450, loss[loss=0.1727, simple_loss=0.2878, pruned_loss=0.02877, over 7338.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2681, pruned_loss=0.03589, over 1427223.73 frames.], batch size: 22, lr: 3.52e-04
+2022-04-29 19:54:51,967 INFO [train.py:763] (7/8) Epoch 21, batch 3500, loss[loss=0.1759, simple_loss=0.2769, pruned_loss=0.03751, over 7349.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2681, pruned_loss=0.0358, over 1429569.03 frames.], batch size: 22, lr: 3.52e-04
+2022-04-29 19:55:58,125 INFO [train.py:763] (7/8) Epoch 21, batch 3550, loss[loss=0.1771, simple_loss=0.284, pruned_loss=0.03511, over 6694.00 frames.], tot_loss[loss=0.171, simple_loss=0.2689, pruned_loss=0.03649, over 1427444.95 frames.], batch size: 31, lr: 3.52e-04
+2022-04-29 19:57:04,821 INFO [train.py:763] (7/8) Epoch 21, batch 3600, loss[loss=0.1438, simple_loss=0.2349, pruned_loss=0.02636, over 7289.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2691, pruned_loss=0.03675, over 1421647.31 frames.], batch size: 17, lr: 3.51e-04
+2022-04-29 19:58:10,369 INFO [train.py:763] (7/8) Epoch 21, batch 3650, loss[loss=0.194, simple_loss=0.2864, pruned_loss=0.05079, over 7372.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2695, pruned_loss=0.03662, over 1423844.15 frames.], batch size: 23, lr: 3.51e-04
+2022-04-29 19:59:15,690 INFO [train.py:763] (7/8) Epoch 21, batch 3700, loss[loss=0.1751, simple_loss=0.2712, pruned_loss=0.03953, over 7219.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2691, pruned_loss=0.03636, over 1426157.86 frames.], batch size: 21, lr: 3.51e-04
+2022-04-29 20:00:21,238 INFO [train.py:763] (7/8) Epoch 21, batch 3750, loss[loss=0.1539, simple_loss=0.2359, pruned_loss=0.03597, over 7005.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2691, pruned_loss=0.03623, over 1430598.87 frames.], batch size: 16, lr: 3.51e-04
+2022-04-29 20:01:26,928 INFO [train.py:763] (7/8) Epoch 21, batch 3800, loss[loss=0.1827, simple_loss=0.2808, pruned_loss=0.04228, over 4956.00 frames.], tot_loss[loss=0.1697, simple_loss=0.268, pruned_loss=0.03571, over 1424951.88 frames.], batch size: 52, lr: 3.51e-04
+2022-04-29 20:02:32,219 INFO [train.py:763] (7/8) Epoch 21, batch 3850, loss[loss=0.2166, simple_loss=0.3002, pruned_loss=0.06653, over 7238.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2676, pruned_loss=0.03542, over 1427146.36 frames.], batch size: 20, lr: 3.51e-04
+2022-04-29 20:03:37,831 INFO [train.py:763] (7/8) Epoch 21, batch 3900, loss[loss=0.1601, simple_loss=0.2629, pruned_loss=0.02865, over 6348.00 frames.], tot_loss[loss=0.1689, simple_loss=0.267, pruned_loss=0.03536, over 1427876.96 frames.], batch size: 38, lr: 3.51e-04
+2022-04-29 20:04:43,338 INFO [train.py:763] (7/8) Epoch 21, batch 3950, loss[loss=0.1719, simple_loss=0.2496, pruned_loss=0.04709, over 7291.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2673, pruned_loss=0.03569, over 1425724.18 frames.], batch size: 17, lr: 3.51e-04
+2022-04-29 20:05:50,781 INFO [train.py:763] (7/8) Epoch 21, batch 4000, loss[loss=0.1543, simple_loss=0.2682, pruned_loss=0.02022, over 7325.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2687, pruned_loss=0.03606, over 1425871.56 frames.], batch size: 21, lr: 3.51e-04
+2022-04-29 20:06:57,092 INFO [train.py:763] (7/8) Epoch 21, batch 4050, loss[loss=0.151, simple_loss=0.2483, pruned_loss=0.02686, over 7350.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2684, pruned_loss=0.03609, over 1424074.88 frames.], batch size: 19, lr: 3.51e-04
+2022-04-29 20:08:02,554 INFO [train.py:763] (7/8) Epoch 21, batch 4100, loss[loss=0.1544, simple_loss=0.2494, pruned_loss=0.02974, over 7335.00 frames.], tot_loss[loss=0.1708, simple_loss=0.269, pruned_loss=0.0363, over 1425655.42 frames.], batch size: 20, lr: 3.51e-04
+2022-04-29 20:09:08,421 INFO [train.py:763] (7/8) Epoch 21, batch 4150, loss[loss=0.1664, simple_loss=0.2581, pruned_loss=0.03739, over 7067.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2682, pruned_loss=0.03611, over 1421190.07 frames.], batch size: 18, lr: 3.51e-04
+2022-04-29 20:10:23,437 INFO [train.py:763] (7/8) Epoch 21, batch 4200, loss[loss=0.176, simple_loss=0.2712, pruned_loss=0.0404, over 7144.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2691, pruned_loss=0.03617, over 1416183.77 frames.], batch size: 20, lr: 3.50e-04
+2022-04-29 20:11:28,564 INFO [train.py:763] (7/8) Epoch 21, batch 4250, loss[loss=0.17, simple_loss=0.2719, pruned_loss=0.03399, over 6709.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2702, pruned_loss=0.03644, over 1409467.81 frames.], batch size: 31, lr: 3.50e-04
+2022-04-29 20:12:34,526 INFO [train.py:763] (7/8) Epoch 21, batch 4300, loss[loss=0.2048, simple_loss=0.302, pruned_loss=0.05378, over 7294.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2706, pruned_loss=0.03636, over 1411144.69 frames.], batch size: 24, lr: 3.50e-04
+2022-04-29 20:13:40,107 INFO [train.py:763] (7/8) Epoch 21, batch 4350, loss[loss=0.1742, simple_loss=0.2824, pruned_loss=0.03302, over 7326.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2709, pruned_loss=0.03636, over 1407530.44 frames.], batch size: 22, lr: 3.50e-04
+2022-04-29 20:14:45,325 INFO [train.py:763] (7/8) Epoch 21, batch 4400, loss[loss=0.1586, simple_loss=0.2594, pruned_loss=0.0289, over 7127.00 frames.], tot_loss[loss=0.172, simple_loss=0.271, pruned_loss=0.03648, over 1401824.92 frames.], batch size: 21, lr: 3.50e-04
+2022-04-29 20:15:50,796 INFO [train.py:763] (7/8) Epoch 21, batch 4450, loss[loss=0.1571, simple_loss=0.2664, pruned_loss=0.02393, over 7335.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2714, pruned_loss=0.03659, over 1399509.41 frames.], batch size: 22, lr: 3.50e-04
+2022-04-29 20:17:22,866 INFO [train.py:763] (7/8) Epoch 21, batch 4500, loss[loss=0.2092, simple_loss=0.3111, pruned_loss=0.05368, over 7068.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2726, pruned_loss=0.03732, over 1389409.98 frames.], batch size: 28, lr: 3.50e-04
+2022-04-29 20:18:27,317 INFO [train.py:763] (7/8) Epoch 21, batch 4550, loss[loss=0.2283, simple_loss=0.311, pruned_loss=0.07282, over 4985.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2736, pruned_loss=0.03828, over 1347233.88 frames.], batch size: 52, lr: 3.50e-04
+2022-04-29 20:20:15,486 INFO [train.py:763] (7/8) Epoch 22, batch 0, loss[loss=0.1655, simple_loss=0.25, pruned_loss=0.04051, over 6771.00 frames.], tot_loss[loss=0.1655, simple_loss=0.25, pruned_loss=0.04051, over 6771.00 frames.], batch size: 15, lr: 3.42e-04
+2022-04-29 20:21:30,532 INFO [train.py:763] (7/8) Epoch 22, batch 50, loss[loss=0.1576, simple_loss=0.2667, pruned_loss=0.02424, over 7150.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2675, pruned_loss=0.0364, over 318936.21 frames.], batch size: 19, lr: 3.42e-04
+2022-04-29 20:22:35,949 INFO [train.py:763] (7/8) Epoch 22, batch 100, loss[loss=0.1786, simple_loss=0.2752, pruned_loss=0.041, over 7279.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2677, pruned_loss=0.03565, over 565232.94 frames.], batch size: 18, lr: 3.42e-04
+2022-04-29 20:23:41,428 INFO [train.py:763] (7/8) Epoch 22, batch 150, loss[loss=0.1845, simple_loss=0.2879, pruned_loss=0.04051, over 7302.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2687, pruned_loss=0.03535, over 754018.87 frames.], batch size: 24, lr: 3.42e-04
+2022-04-29 20:24:46,890 INFO [train.py:763] (7/8) Epoch 22, batch 200, loss[loss=0.1903, simple_loss=0.2895, pruned_loss=0.04559, over 6304.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2689, pruned_loss=0.03565, over 902429.45 frames.], batch size: 37, lr: 3.42e-04
+2022-04-29 20:25:52,449 INFO [train.py:763] (7/8) Epoch 22, batch 250, loss[loss=0.1658, simple_loss=0.2691, pruned_loss=0.03121, over 7206.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2696, pruned_loss=0.0358, over 1017218.43 frames.], batch size: 23, lr: 3.42e-04
+2022-04-29 20:26:58,041 INFO [train.py:763] (7/8) Epoch 22, batch 300, loss[loss=0.1534, simple_loss=0.2583, pruned_loss=0.02421, over 7162.00 frames.], tot_loss[loss=0.1705, simple_loss=0.269, pruned_loss=0.03599, over 1102881.36 frames.], batch size: 19, lr: 3.42e-04
+2022-04-29 20:28:05,403 INFO [train.py:763] (7/8) Epoch 22, batch 350, loss[loss=0.1766, simple_loss=0.2822, pruned_loss=0.03546, over 7323.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2685, pruned_loss=0.03555, over 1177303.48 frames.], batch size: 22, lr: 3.42e-04
+2022-04-29 20:29:12,854 INFO [train.py:763] (7/8) Epoch 22, batch 400, loss[loss=0.1789, simple_loss=0.2734, pruned_loss=0.04221, over 7208.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2677, pruned_loss=0.03527, over 1230138.95 frames.], batch size: 23, lr: 3.42e-04
+2022-04-29 20:30:18,170 INFO [train.py:763] (7/8) Epoch 22, batch 450, loss[loss=0.194, simple_loss=0.2947, pruned_loss=0.0467, over 7276.00 frames.], tot_loss[loss=0.17, simple_loss=0.2684, pruned_loss=0.03576, over 1271540.78 frames.], batch size: 24, lr: 3.42e-04
+2022-04-29 20:31:24,312 INFO [train.py:763] (7/8) Epoch 22, batch 500, loss[loss=0.1629, simple_loss=0.2529, pruned_loss=0.03648, over 6759.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2692, pruned_loss=0.03594, over 1306512.83 frames.], batch size: 15, lr: 3.41e-04
+2022-04-29 20:32:31,792 INFO [train.py:763] (7/8) Epoch 22, batch 550, loss[loss=0.1786, simple_loss=0.2743, pruned_loss=0.04148, over 7320.00 frames.], tot_loss[loss=0.17, simple_loss=0.268, pruned_loss=0.03597, over 1336983.65 frames.], batch size: 24, lr: 3.41e-04
+2022-04-29 20:33:39,088 INFO [train.py:763] (7/8) Epoch 22, batch 600, loss[loss=0.1721, simple_loss=0.2795, pruned_loss=0.03233, over 7114.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2693, pruned_loss=0.03631, over 1359353.17 frames.], batch size: 21, lr: 3.41e-04
+2022-04-29 20:34:44,750 INFO [train.py:763] (7/8) Epoch 22, batch 650, loss[loss=0.1593, simple_loss=0.2601, pruned_loss=0.02923, over 6841.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2692, pruned_loss=0.03581, over 1374622.36 frames.], batch size: 31, lr: 3.41e-04
+2022-04-29 20:35:51,933 INFO [train.py:763] (7/8) Epoch 22, batch 700, loss[loss=0.1942, simple_loss=0.2854, pruned_loss=0.05151, over 4657.00 frames.], tot_loss[loss=0.1702, simple_loss=0.269, pruned_loss=0.03567, over 1380624.23 frames.], batch size: 52, lr: 3.41e-04
+2022-04-29 20:36:59,168 INFO [train.py:763] (7/8) Epoch 22, batch 750, loss[loss=0.1724, simple_loss=0.2767, pruned_loss=0.03401, over 7209.00 frames.], tot_loss[loss=0.1699, simple_loss=0.269, pruned_loss=0.03546, over 1391879.82 frames.], batch size: 23, lr: 3.41e-04
+2022-04-29 20:38:05,940 INFO [train.py:763] (7/8) Epoch 22, batch 800, loss[loss=0.16, simple_loss=0.2612, pruned_loss=0.02934, over 7361.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2694, pruned_loss=0.03574, over 1395431.72 frames.], batch size: 19, lr: 3.41e-04
+2022-04-29 20:39:11,703 INFO [train.py:763] (7/8) Epoch 22, batch 850, loss[loss=0.1695, simple_loss=0.2665, pruned_loss=0.03623, over 7431.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2692, pruned_loss=0.03558, over 1403998.98 frames.], batch size: 20, lr: 3.41e-04
+2022-04-29 20:40:16,916 INFO [train.py:763] (7/8) Epoch 22, batch 900, loss[loss=0.1693, simple_loss=0.2652, pruned_loss=0.03667, over 7144.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2689, pruned_loss=0.03539, over 1408364.14 frames.], batch size: 19, lr: 3.41e-04
+2022-04-29 20:41:22,128 INFO [train.py:763] (7/8) Epoch 22, batch 950, loss[loss=0.1993, simple_loss=0.3026, pruned_loss=0.04801, over 7050.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2694, pruned_loss=0.03564, over 1410568.31 frames.], batch size: 28, lr: 3.41e-04
+2022-04-29 20:42:27,352 INFO [train.py:763] (7/8) Epoch 22, batch 1000, loss[loss=0.1602, simple_loss=0.2491, pruned_loss=0.03564, over 7362.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2686, pruned_loss=0.03515, over 1417540.06 frames.], batch size: 19, lr: 3.41e-04
+2022-04-29 20:43:32,812 INFO [train.py:763] (7/8) Epoch 22, batch 1050, loss[loss=0.196, simple_loss=0.3009, pruned_loss=0.04555, over 5329.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2683, pruned_loss=0.0351, over 1418430.09 frames.], batch size: 52, lr: 3.41e-04
+2022-04-29 20:44:37,795 INFO [train.py:763] (7/8) Epoch 22, batch 1100, loss[loss=0.1584, simple_loss=0.2543, pruned_loss=0.03127, over 7282.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2696, pruned_loss=0.03569, over 1417995.07 frames.], batch size: 17, lr: 3.40e-04
+2022-04-29 20:45:43,161 INFO [train.py:763] (7/8) Epoch 22, batch 1150, loss[loss=0.1738, simple_loss=0.2764, pruned_loss=0.03562, over 7427.00 frames.], tot_loss[loss=0.171, simple_loss=0.2702, pruned_loss=0.03591, over 1422006.13 frames.], batch size: 20, lr: 3.40e-04
+2022-04-29 20:46:49,119 INFO [train.py:763] (7/8) Epoch 22, batch 1200, loss[loss=0.1537, simple_loss=0.2537, pruned_loss=0.02684, over 7277.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2687, pruned_loss=0.0351, over 1422058.00 frames.], batch size: 18, lr: 3.40e-04
+2022-04-29 20:47:55,643 INFO [train.py:763] (7/8) Epoch 22, batch 1250, loss[loss=0.1419, simple_loss=0.2378, pruned_loss=0.02298, over 7196.00 frames.], tot_loss[loss=0.1689, simple_loss=0.268, pruned_loss=0.03488, over 1425475.55 frames.], batch size: 16, lr: 3.40e-04
+2022-04-29 20:49:00,856 INFO [train.py:763] (7/8) Epoch 22, batch 1300, loss[loss=0.1778, simple_loss=0.2844, pruned_loss=0.03558, over 7188.00 frames.], tot_loss[loss=0.169, simple_loss=0.2681, pruned_loss=0.03492, over 1427576.69 frames.], batch size: 23, lr: 3.40e-04
+2022-04-29 20:50:07,461 INFO [train.py:763] (7/8) Epoch 22, batch 1350, loss[loss=0.1582, simple_loss=0.2596, pruned_loss=0.02839, over 7266.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2671, pruned_loss=0.03466, over 1428385.37 frames.], batch size: 18, lr: 3.40e-04
+2022-04-29 20:51:13,812 INFO [train.py:763] (7/8) Epoch 22, batch 1400, loss[loss=0.1577, simple_loss=0.2708, pruned_loss=0.02231, over 7110.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2678, pruned_loss=0.03464, over 1427559.48 frames.], batch size: 21, lr: 3.40e-04
+2022-04-29 20:52:19,592 INFO [train.py:763] (7/8) Epoch 22, batch 1450, loss[loss=0.171, simple_loss=0.2605, pruned_loss=0.0408, over 7433.00 frames.], tot_loss[loss=0.1692, simple_loss=0.268, pruned_loss=0.0352, over 1421490.11 frames.], batch size: 18, lr: 3.40e-04
+2022-04-29 20:53:25,458 INFO [train.py:763] (7/8) Epoch 22, batch 1500, loss[loss=0.1563, simple_loss=0.2555, pruned_loss=0.02857, over 7046.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2661, pruned_loss=0.0349, over 1422347.19 frames.], batch size: 28, lr: 3.40e-04
+2022-04-29 20:54:31,388 INFO [train.py:763] (7/8) Epoch 22, batch 1550, loss[loss=0.144, simple_loss=0.2429, pruned_loss=0.02258, over 7365.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2659, pruned_loss=0.03452, over 1413155.40 frames.], batch size: 19, lr: 3.40e-04
+2022-04-29 20:55:37,838 INFO [train.py:763] (7/8) Epoch 22, batch 1600, loss[loss=0.1668, simple_loss=0.2792, pruned_loss=0.02714, over 7215.00 frames.], tot_loss[loss=0.1688, simple_loss=0.267, pruned_loss=0.03527, over 1411114.69 frames.], batch size: 21, lr: 3.40e-04
+2022-04-29 20:56:43,442 INFO [train.py:763] (7/8) Epoch 22, batch 1650, loss[loss=0.1891, simple_loss=0.2921, pruned_loss=0.04303, over 7368.00 frames.], tot_loss[loss=0.1691, simple_loss=0.267, pruned_loss=0.03561, over 1414881.59 frames.], batch size: 23, lr: 3.40e-04
+2022-04-29 20:57:48,944 INFO [train.py:763] (7/8) Epoch 22, batch 1700, loss[loss=0.1474, simple_loss=0.2385, pruned_loss=0.02814, over 7397.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2668, pruned_loss=0.03545, over 1416342.08 frames.], batch size: 18, lr: 3.39e-04
+2022-04-29 20:58:54,078 INFO [train.py:763] (7/8) Epoch 22, batch 1750, loss[loss=0.1821, simple_loss=0.2895, pruned_loss=0.03736, over 7161.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2675, pruned_loss=0.03549, over 1415392.29 frames.], batch size: 26, lr: 3.39e-04
+2022-04-29 20:59:59,916 INFO [train.py:763] (7/8) Epoch 22, batch 1800, loss[loss=0.1856, simple_loss=0.2827, pruned_loss=0.04425, over 5257.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2681, pruned_loss=0.03577, over 1412108.12 frames.], batch size: 52, lr: 3.39e-04
+2022-04-29 21:01:05,548 INFO [train.py:763] (7/8) Epoch 22, batch 1850, loss[loss=0.1553, simple_loss=0.2563, pruned_loss=0.02718, over 7444.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2665, pruned_loss=0.03493, over 1417123.41 frames.], batch size: 20, lr: 3.39e-04
+2022-04-29 21:02:10,922 INFO [train.py:763] (7/8) Epoch 22, batch 1900, loss[loss=0.165, simple_loss=0.2768, pruned_loss=0.02659, over 7148.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2665, pruned_loss=0.03502, over 1420904.44 frames.], batch size: 20, lr: 3.39e-04
+2022-04-29 21:03:17,160 INFO [train.py:763] (7/8) Epoch 22, batch 1950, loss[loss=0.1651, simple_loss=0.2748, pruned_loss=0.02765, over 7152.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2669, pruned_loss=0.03541, over 1417895.69 frames.], batch size: 20, lr: 3.39e-04
+2022-04-29 21:04:22,505 INFO [train.py:763] (7/8) Epoch 22, batch 2000, loss[loss=0.1594, simple_loss=0.2531, pruned_loss=0.03285, over 7254.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2685, pruned_loss=0.03545, over 1421499.30 frames.], batch size: 19, lr: 3.39e-04
+2022-04-29 21:05:28,506 INFO [train.py:763] (7/8) Epoch 22, batch 2050, loss[loss=0.2185, simple_loss=0.3047, pruned_loss=0.06622, over 7232.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2684, pruned_loss=0.0353, over 1425430.35 frames.], batch size: 20, lr: 3.39e-04
+2022-04-29 21:06:35,607 INFO [train.py:763] (7/8) Epoch 22, batch 2100, loss[loss=0.184, simple_loss=0.2828, pruned_loss=0.04267, over 7179.00 frames.], tot_loss[loss=0.169, simple_loss=0.2678, pruned_loss=0.0351, over 1420475.00 frames.], batch size: 23, lr: 3.39e-04
+2022-04-29 21:07:42,155 INFO [train.py:763] (7/8) Epoch 22, batch 2150, loss[loss=0.1678, simple_loss=0.2691, pruned_loss=0.03326, over 7155.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2669, pruned_loss=0.03472, over 1421028.47 frames.], batch size: 19, lr: 3.39e-04
+2022-04-29 21:08:47,304 INFO [train.py:763] (7/8) Epoch 22, batch 2200, loss[loss=0.1626, simple_loss=0.2702, pruned_loss=0.02751, over 7151.00 frames.], tot_loss[loss=0.1687, simple_loss=0.267, pruned_loss=0.0352, over 1415328.16 frames.], batch size: 20, lr: 3.39e-04
+2022-04-29 21:09:53,568 INFO [train.py:763] (7/8) Epoch 22, batch 2250, loss[loss=0.1479, simple_loss=0.2522, pruned_loss=0.02176, over 7157.00 frames.], tot_loss[loss=0.1685, simple_loss=0.267, pruned_loss=0.03504, over 1411564.30 frames.], batch size: 19, lr: 3.39e-04
+2022-04-29 21:11:00,722 INFO [train.py:763] (7/8) Epoch 22, batch 2300, loss[loss=0.1729, simple_loss=0.2898, pruned_loss=0.02797, over 7314.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2671, pruned_loss=0.03523, over 1414455.66 frames.], batch size: 21, lr: 3.38e-04
+2022-04-29 21:12:07,645 INFO [train.py:763] (7/8) Epoch 22, batch 2350, loss[loss=0.1696, simple_loss=0.2686, pruned_loss=0.03531, over 7338.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2679, pruned_loss=0.03529, over 1415916.98 frames.], batch size: 22, lr: 3.38e-04
+2022-04-29 21:13:14,364 INFO [train.py:763] (7/8) Epoch 22, batch 2400, loss[loss=0.1711, simple_loss=0.2746, pruned_loss=0.0338, over 7264.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2685, pruned_loss=0.0358, over 1418141.16 frames.], batch size: 24, lr: 3.38e-04
+2022-04-29 21:14:19,611 INFO [train.py:763] (7/8) Epoch 22, batch 2450, loss[loss=0.1799, simple_loss=0.2702, pruned_loss=0.04482, over 7208.00 frames.], tot_loss[loss=0.17, simple_loss=0.2688, pruned_loss=0.03558, over 1422309.12 frames.], batch size: 22, lr: 3.38e-04
+2022-04-29 21:15:24,886 INFO [train.py:763] (7/8) Epoch 22, batch 2500, loss[loss=0.1593, simple_loss=0.2651, pruned_loss=0.0268, over 6443.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2666, pruned_loss=0.03501, over 1420465.12 frames.], batch size: 38, lr: 3.38e-04
+2022-04-29 21:16:30,052 INFO [train.py:763] (7/8) Epoch 22, batch 2550, loss[loss=0.1995, simple_loss=0.2986, pruned_loss=0.05016, over 7382.00 frames.], tot_loss[loss=0.1685, simple_loss=0.267, pruned_loss=0.03503, over 1421985.86 frames.], batch size: 23, lr: 3.38e-04
+2022-04-29 21:17:35,660 INFO [train.py:763] (7/8) Epoch 22, batch 2600, loss[loss=0.1502, simple_loss=0.2532, pruned_loss=0.02355, over 7338.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2676, pruned_loss=0.03504, over 1427368.57 frames.], batch size: 22, lr: 3.38e-04
+2022-04-29 21:18:41,163 INFO [train.py:763] (7/8) Epoch 22, batch 2650, loss[loss=0.169, simple_loss=0.2793, pruned_loss=0.02939, over 7323.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2662, pruned_loss=0.03476, over 1424156.49 frames.], batch size: 25, lr: 3.38e-04
+2022-04-29 21:19:46,649 INFO [train.py:763] (7/8) Epoch 22, batch 2700, loss[loss=0.1537, simple_loss=0.2609, pruned_loss=0.02326, over 7157.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2662, pruned_loss=0.03507, over 1422904.42 frames.], batch size: 19, lr: 3.38e-04
+2022-04-29 21:20:54,014 INFO [train.py:763] (7/8) Epoch 22, batch 2750, loss[loss=0.1586, simple_loss=0.2528, pruned_loss=0.03221, over 7170.00 frames.], tot_loss[loss=0.1679, simple_loss=0.266, pruned_loss=0.03484, over 1420893.31 frames.], batch size: 18, lr: 3.38e-04
+2022-04-29 21:22:00,031 INFO [train.py:763] (7/8) Epoch 22, batch 2800, loss[loss=0.1536, simple_loss=0.251, pruned_loss=0.02815, over 7163.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2666, pruned_loss=0.0354, over 1420159.92 frames.], batch size: 18, lr: 3.38e-04
+2022-04-29 21:23:05,444 INFO [train.py:763] (7/8) Epoch 22, batch 2850, loss[loss=0.1761, simple_loss=0.2829, pruned_loss=0.03464, over 7110.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2668, pruned_loss=0.03525, over 1421545.57 frames.], batch size: 28, lr: 3.38e-04
+2022-04-29 21:24:10,671 INFO [train.py:763] (7/8) Epoch 22, batch 2900, loss[loss=0.186, simple_loss=0.2882, pruned_loss=0.04189, over 7334.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2671, pruned_loss=0.03568, over 1423370.90 frames.], batch size: 25, lr: 3.37e-04
+2022-04-29 21:25:15,980 INFO [train.py:763] (7/8) Epoch 22, batch 2950, loss[loss=0.1889, simple_loss=0.2928, pruned_loss=0.04255, over 7199.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2679, pruned_loss=0.03576, over 1423900.72 frames.], batch size: 22, lr: 3.37e-04
+2022-04-29 21:26:20,981 INFO [train.py:763] (7/8) Epoch 22, batch 3000, loss[loss=0.1483, simple_loss=0.2432, pruned_loss=0.02664, over 6996.00 frames.], tot_loss[loss=0.1686, simple_loss=0.267, pruned_loss=0.03509, over 1423092.33 frames.], batch size: 16, lr: 3.37e-04
+2022-04-29 21:26:20,981 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 21:26:36,379 INFO [train.py:792] (7/8) Epoch 22, validation: loss=0.1681, simple_loss=0.2667, pruned_loss=0.03474, over 698248.00 frames.
+2022-04-29 21:27:41,679 INFO [train.py:763] (7/8) Epoch 22, batch 3050, loss[loss=0.1586, simple_loss=0.2487, pruned_loss=0.03429, over 7154.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2669, pruned_loss=0.0348, over 1425176.98 frames.], batch size: 19, lr: 3.37e-04
+2022-04-29 21:28:58,468 INFO [train.py:763] (7/8) Epoch 22, batch 3100, loss[loss=0.1637, simple_loss=0.27, pruned_loss=0.02872, over 7239.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2663, pruned_loss=0.035, over 1424912.35 frames.], batch size: 20, lr: 3.37e-04
+2022-04-29 21:30:03,947 INFO [train.py:763] (7/8) Epoch 22, batch 3150, loss[loss=0.1588, simple_loss=0.2666, pruned_loss=0.02552, over 7324.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2663, pruned_loss=0.035, over 1425976.82 frames.], batch size: 20, lr: 3.37e-04
+2022-04-29 21:31:09,282 INFO [train.py:763] (7/8) Epoch 22, batch 3200, loss[loss=0.1827, simple_loss=0.2905, pruned_loss=0.03747, over 7114.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2664, pruned_loss=0.03494, over 1427309.60 frames.], batch size: 21, lr: 3.37e-04
+2022-04-29 21:32:14,556 INFO [train.py:763] (7/8) Epoch 22, batch 3250, loss[loss=0.1648, simple_loss=0.2705, pruned_loss=0.02961, over 6421.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2677, pruned_loss=0.0354, over 1422518.76 frames.], batch size: 38, lr: 3.37e-04
+2022-04-29 21:33:19,835 INFO [train.py:763] (7/8) Epoch 22, batch 3300, loss[loss=0.1665, simple_loss=0.267, pruned_loss=0.033, over 7299.00 frames.], tot_loss[loss=0.169, simple_loss=0.2678, pruned_loss=0.03512, over 1422923.61 frames.], batch size: 24, lr: 3.37e-04
+2022-04-29 21:34:25,363 INFO [train.py:763] (7/8) Epoch 22, batch 3350, loss[loss=0.1774, simple_loss=0.2838, pruned_loss=0.03543, over 7120.00 frames.], tot_loss[loss=0.168, simple_loss=0.2667, pruned_loss=0.0346, over 1426894.00 frames.], batch size: 26, lr: 3.37e-04
+2022-04-29 21:35:30,557 INFO [train.py:763] (7/8) Epoch 22, batch 3400, loss[loss=0.1556, simple_loss=0.2568, pruned_loss=0.02721, over 7161.00 frames.], tot_loss[loss=0.168, simple_loss=0.2667, pruned_loss=0.0346, over 1428162.98 frames.], batch size: 19, lr: 3.37e-04
+2022-04-29 21:36:36,037 INFO [train.py:763] (7/8) Epoch 22, batch 3450, loss[loss=0.1495, simple_loss=0.2443, pruned_loss=0.02739, over 6817.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2659, pruned_loss=0.03453, over 1429902.38 frames.], batch size: 15, lr: 3.37e-04
+2022-04-29 21:37:41,474 INFO [train.py:763] (7/8) Epoch 22, batch 3500, loss[loss=0.147, simple_loss=0.2385, pruned_loss=0.02773, over 7194.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2662, pruned_loss=0.0344, over 1430844.26 frames.], batch size: 16, lr: 3.37e-04
+2022-04-29 21:38:46,769 INFO [train.py:763] (7/8) Epoch 22, batch 3550, loss[loss=0.1345, simple_loss=0.232, pruned_loss=0.01847, over 7399.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2672, pruned_loss=0.03524, over 1430641.15 frames.], batch size: 18, lr: 3.36e-04
+2022-04-29 21:39:52,013 INFO [train.py:763] (7/8) Epoch 22, batch 3600, loss[loss=0.1532, simple_loss=0.2466, pruned_loss=0.02988, over 7273.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2679, pruned_loss=0.03549, over 1431198.74 frames.], batch size: 17, lr: 3.36e-04
+2022-04-29 21:40:57,424 INFO [train.py:763] (7/8) Epoch 22, batch 3650, loss[loss=0.1643, simple_loss=0.2607, pruned_loss=0.03399, over 6521.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2676, pruned_loss=0.03528, over 1431297.71 frames.], batch size: 38, lr: 3.36e-04
+2022-04-29 21:42:03,759 INFO [train.py:763] (7/8) Epoch 22, batch 3700, loss[loss=0.1656, simple_loss=0.2648, pruned_loss=0.03319, over 7149.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2671, pruned_loss=0.035, over 1430143.85 frames.], batch size: 19, lr: 3.36e-04
+2022-04-29 21:43:09,199 INFO [train.py:763] (7/8) Epoch 22, batch 3750, loss[loss=0.1493, simple_loss=0.2466, pruned_loss=0.02595, over 7299.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2671, pruned_loss=0.03479, over 1428071.15 frames.], batch size: 17, lr: 3.36e-04
+2022-04-29 21:44:14,447 INFO [train.py:763] (7/8) Epoch 22, batch 3800, loss[loss=0.1746, simple_loss=0.2837, pruned_loss=0.03275, over 7366.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2672, pruned_loss=0.03492, over 1429265.03 frames.], batch size: 23, lr: 3.36e-04
+2022-04-29 21:45:19,898 INFO [train.py:763] (7/8) Epoch 22, batch 3850, loss[loss=0.1899, simple_loss=0.3005, pruned_loss=0.03966, over 7063.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2675, pruned_loss=0.03501, over 1430130.36 frames.], batch size: 28, lr: 3.36e-04
+2022-04-29 21:46:26,379 INFO [train.py:763] (7/8) Epoch 22, batch 3900, loss[loss=0.1518, simple_loss=0.2535, pruned_loss=0.02509, over 7117.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2672, pruned_loss=0.03523, over 1430621.02 frames.], batch size: 21, lr: 3.36e-04
+2022-04-29 21:47:31,499 INFO [train.py:763] (7/8) Epoch 22, batch 3950, loss[loss=0.1772, simple_loss=0.2782, pruned_loss=0.03812, over 7164.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2679, pruned_loss=0.03518, over 1430543.01 frames.], batch size: 19, lr: 3.36e-04
+2022-04-29 21:48:36,605 INFO [train.py:763] (7/8) Epoch 22, batch 4000, loss[loss=0.133, simple_loss=0.2195, pruned_loss=0.02323, over 7295.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2672, pruned_loss=0.0349, over 1427297.80 frames.], batch size: 17, lr: 3.36e-04
+2022-04-29 21:49:42,559 INFO [train.py:763] (7/8) Epoch 22, batch 4050, loss[loss=0.1491, simple_loss=0.24, pruned_loss=0.02909, over 6852.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2677, pruned_loss=0.03464, over 1422317.74 frames.], batch size: 15, lr: 3.36e-04
+2022-04-29 21:50:49,126 INFO [train.py:763] (7/8) Epoch 22, batch 4100, loss[loss=0.1483, simple_loss=0.2416, pruned_loss=0.02752, over 7272.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2676, pruned_loss=0.03491, over 1419225.30 frames.], batch size: 16, lr: 3.36e-04
+2022-04-29 21:51:54,125 INFO [train.py:763] (7/8) Epoch 22, batch 4150, loss[loss=0.164, simple_loss=0.2692, pruned_loss=0.0294, over 7324.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2683, pruned_loss=0.03461, over 1418509.70 frames.], batch size: 21, lr: 3.35e-04
+2022-04-29 21:52:59,308 INFO [train.py:763] (7/8) Epoch 22, batch 4200, loss[loss=0.1335, simple_loss=0.226, pruned_loss=0.02046, over 6998.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2686, pruned_loss=0.03415, over 1422511.09 frames.], batch size: 16, lr: 3.35e-04
+2022-04-29 21:54:05,498 INFO [train.py:763] (7/8) Epoch 22, batch 4250, loss[loss=0.1675, simple_loss=0.2745, pruned_loss=0.03031, over 7238.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2684, pruned_loss=0.03419, over 1424423.69 frames.], batch size: 20, lr: 3.35e-04
+2022-04-29 21:55:12,494 INFO [train.py:763] (7/8) Epoch 22, batch 4300, loss[loss=0.1458, simple_loss=0.2437, pruned_loss=0.02396, over 7155.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2669, pruned_loss=0.03415, over 1421943.39 frames.], batch size: 18, lr: 3.35e-04
+2022-04-29 21:56:19,747 INFO [train.py:763] (7/8) Epoch 22, batch 4350, loss[loss=0.174, simple_loss=0.2625, pruned_loss=0.04276, over 6822.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2657, pruned_loss=0.03389, over 1422595.20 frames.], batch size: 15, lr: 3.35e-04
+2022-04-29 21:57:26,798 INFO [train.py:763] (7/8) Epoch 22, batch 4400, loss[loss=0.1609, simple_loss=0.2559, pruned_loss=0.03292, over 7060.00 frames.], tot_loss[loss=0.1665, simple_loss=0.265, pruned_loss=0.034, over 1420074.86 frames.], batch size: 18, lr: 3.35e-04
+2022-04-29 21:58:31,952 INFO [train.py:763] (7/8) Epoch 22, batch 4450, loss[loss=0.2175, simple_loss=0.305, pruned_loss=0.06496, over 5140.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2658, pruned_loss=0.0346, over 1414198.59 frames.], batch size: 52, lr: 3.35e-04
+2022-04-29 21:59:36,925 INFO [train.py:763] (7/8) Epoch 22, batch 4500, loss[loss=0.1263, simple_loss=0.227, pruned_loss=0.01285, over 7065.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2657, pruned_loss=0.03455, over 1413328.78 frames.], batch size: 18, lr: 3.35e-04
+2022-04-29 22:00:41,218 INFO [train.py:763] (7/8) Epoch 22, batch 4550, loss[loss=0.1778, simple_loss=0.2632, pruned_loss=0.04622, over 4859.00 frames.], tot_loss[loss=0.171, simple_loss=0.2692, pruned_loss=0.03644, over 1357015.65 frames.], batch size: 52, lr: 3.35e-04
+2022-04-29 22:02:00,640 INFO [train.py:763] (7/8) Epoch 23, batch 0, loss[loss=0.1539, simple_loss=0.2493, pruned_loss=0.02928, over 6827.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2493, pruned_loss=0.02928, over 6827.00 frames.], batch size: 15, lr: 3.28e-04
+2022-04-29 22:03:02,950 INFO [train.py:763] (7/8) Epoch 23, batch 50, loss[loss=0.1555, simple_loss=0.2474, pruned_loss=0.03176, over 7286.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2685, pruned_loss=0.03499, over 316348.48 frames.], batch size: 17, lr: 3.28e-04
+2022-04-29 22:04:05,016 INFO [train.py:763] (7/8) Epoch 23, batch 100, loss[loss=0.1435, simple_loss=0.2312, pruned_loss=0.02785, over 7332.00 frames.], tot_loss[loss=0.167, simple_loss=0.2664, pruned_loss=0.03377, over 567311.41 frames.], batch size: 20, lr: 3.28e-04
+2022-04-29 22:05:10,562 INFO [train.py:763] (7/8) Epoch 23, batch 150, loss[loss=0.1774, simple_loss=0.2787, pruned_loss=0.03798, over 7370.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2675, pruned_loss=0.03477, over 753454.60 frames.], batch size: 23, lr: 3.28e-04
+2022-04-29 22:06:15,919 INFO [train.py:763] (7/8) Epoch 23, batch 200, loss[loss=0.1608, simple_loss=0.2605, pruned_loss=0.03053, over 7205.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2669, pruned_loss=0.03471, over 904150.41 frames.], batch size: 22, lr: 3.28e-04
+2022-04-29 22:07:21,272 INFO [train.py:763] (7/8) Epoch 23, batch 250, loss[loss=0.1621, simple_loss=0.2613, pruned_loss=0.0315, over 7412.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2671, pruned_loss=0.03481, over 1016462.50 frames.], batch size: 21, lr: 3.28e-04
+2022-04-29 22:08:27,025 INFO [train.py:763] (7/8) Epoch 23, batch 300, loss[loss=0.1518, simple_loss=0.2532, pruned_loss=0.02518, over 7149.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2672, pruned_loss=0.03483, over 1107346.91 frames.], batch size: 20, lr: 3.27e-04
+2022-04-29 22:09:32,889 INFO [train.py:763] (7/8) Epoch 23, batch 350, loss[loss=0.1795, simple_loss=0.2773, pruned_loss=0.04086, over 7260.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2671, pruned_loss=0.03516, over 1179047.40 frames.], batch size: 25, lr: 3.27e-04
+2022-04-29 22:10:38,052 INFO [train.py:763] (7/8) Epoch 23, batch 400, loss[loss=0.1833, simple_loss=0.286, pruned_loss=0.04029, over 7277.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2669, pruned_loss=0.03524, over 1230497.64 frames.], batch size: 24, lr: 3.27e-04
+2022-04-29 22:11:43,833 INFO [train.py:763] (7/8) Epoch 23, batch 450, loss[loss=0.1805, simple_loss=0.2817, pruned_loss=0.03963, over 7147.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2672, pruned_loss=0.03479, over 1275948.55 frames.], batch size: 20, lr: 3.27e-04
+2022-04-29 22:12:49,143 INFO [train.py:763] (7/8) Epoch 23, batch 500, loss[loss=0.1816, simple_loss=0.2686, pruned_loss=0.04734, over 7342.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2673, pruned_loss=0.03489, over 1307882.36 frames.], batch size: 19, lr: 3.27e-04
+2022-04-29 22:13:54,760 INFO [train.py:763] (7/8) Epoch 23, batch 550, loss[loss=0.2067, simple_loss=0.3014, pruned_loss=0.05601, over 7213.00 frames.], tot_loss[loss=0.168, simple_loss=0.267, pruned_loss=0.03454, over 1336136.97 frames.], batch size: 22, lr: 3.27e-04
+2022-04-29 22:15:00,608 INFO [train.py:763] (7/8) Epoch 23, batch 600, loss[loss=0.1579, simple_loss=0.2585, pruned_loss=0.02866, over 7350.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2662, pruned_loss=0.0343, over 1353163.30 frames.], batch size: 19, lr: 3.27e-04
+2022-04-29 22:16:06,061 INFO [train.py:763] (7/8) Epoch 23, batch 650, loss[loss=0.1443, simple_loss=0.2361, pruned_loss=0.02629, over 7367.00 frames.], tot_loss[loss=0.1676, simple_loss=0.266, pruned_loss=0.03459, over 1363861.96 frames.], batch size: 19, lr: 3.27e-04
+2022-04-29 22:17:12,017 INFO [train.py:763] (7/8) Epoch 23, batch 700, loss[loss=0.1689, simple_loss=0.2792, pruned_loss=0.02931, over 7189.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2648, pruned_loss=0.03417, over 1381943.52 frames.], batch size: 26, lr: 3.27e-04
+2022-04-29 22:18:17,847 INFO [train.py:763] (7/8) Epoch 23, batch 750, loss[loss=0.1351, simple_loss=0.2235, pruned_loss=0.0234, over 7416.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2659, pruned_loss=0.03418, over 1393408.53 frames.], batch size: 17, lr: 3.27e-04
+2022-04-29 22:19:23,440 INFO [train.py:763] (7/8) Epoch 23, batch 800, loss[loss=0.1669, simple_loss=0.2607, pruned_loss=0.03652, over 7249.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2653, pruned_loss=0.03447, over 1399329.92 frames.], batch size: 19, lr: 3.27e-04
+2022-04-29 22:20:28,954 INFO [train.py:763] (7/8) Epoch 23, batch 850, loss[loss=0.1602, simple_loss=0.2556, pruned_loss=0.03239, over 6678.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2651, pruned_loss=0.0342, over 1406352.66 frames.], batch size: 31, lr: 3.27e-04
+2022-04-29 22:21:34,338 INFO [train.py:763] (7/8) Epoch 23, batch 900, loss[loss=0.1494, simple_loss=0.2493, pruned_loss=0.02476, over 7418.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2653, pruned_loss=0.03386, over 1411611.00 frames.], batch size: 20, lr: 3.27e-04
+2022-04-29 22:22:49,577 INFO [train.py:763] (7/8) Epoch 23, batch 950, loss[loss=0.1714, simple_loss=0.278, pruned_loss=0.03245, over 6479.00 frames.], tot_loss[loss=0.1666, simple_loss=0.265, pruned_loss=0.03406, over 1416467.39 frames.], batch size: 38, lr: 3.26e-04
+2022-04-29 22:23:55,246 INFO [train.py:763] (7/8) Epoch 23, batch 1000, loss[loss=0.1585, simple_loss=0.2694, pruned_loss=0.02379, over 7316.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2644, pruned_loss=0.03397, over 1418065.04 frames.], batch size: 21, lr: 3.26e-04
+2022-04-29 22:25:00,711 INFO [train.py:763] (7/8) Epoch 23, batch 1050, loss[loss=0.161, simple_loss=0.2613, pruned_loss=0.03033, over 7241.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2654, pruned_loss=0.03451, over 1411410.81 frames.], batch size: 20, lr: 3.26e-04
+2022-04-29 22:26:07,035 INFO [train.py:763] (7/8) Epoch 23, batch 1100, loss[loss=0.1548, simple_loss=0.2534, pruned_loss=0.02812, over 7145.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2655, pruned_loss=0.03433, over 1411744.33 frames.], batch size: 20, lr: 3.26e-04
+2022-04-29 22:27:12,606 INFO [train.py:763] (7/8) Epoch 23, batch 1150, loss[loss=0.1686, simple_loss=0.2771, pruned_loss=0.03003, over 6251.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2644, pruned_loss=0.03388, over 1415290.79 frames.], batch size: 38, lr: 3.26e-04
+2022-04-29 22:28:17,840 INFO [train.py:763] (7/8) Epoch 23, batch 1200, loss[loss=0.1807, simple_loss=0.2886, pruned_loss=0.03642, over 7157.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2645, pruned_loss=0.03367, over 1418187.17 frames.], batch size: 18, lr: 3.26e-04
+2022-04-29 22:29:23,315 INFO [train.py:763] (7/8) Epoch 23, batch 1250, loss[loss=0.1631, simple_loss=0.2633, pruned_loss=0.03145, over 7332.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2643, pruned_loss=0.03361, over 1418979.53 frames.], batch size: 20, lr: 3.26e-04
+2022-04-29 22:30:28,901 INFO [train.py:763] (7/8) Epoch 23, batch 1300, loss[loss=0.1689, simple_loss=0.267, pruned_loss=0.03542, over 6756.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2653, pruned_loss=0.03402, over 1420558.17 frames.], batch size: 31, lr: 3.26e-04
+2022-04-29 22:31:51,707 INFO [train.py:763] (7/8) Epoch 23, batch 1350, loss[loss=0.1427, simple_loss=0.2362, pruned_loss=0.02465, over 7391.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2661, pruned_loss=0.03431, over 1426314.59 frames.], batch size: 18, lr: 3.26e-04
+2022-04-29 22:32:57,251 INFO [train.py:763] (7/8) Epoch 23, batch 1400, loss[loss=0.1787, simple_loss=0.275, pruned_loss=0.04119, over 7162.00 frames.], tot_loss[loss=0.1674, simple_loss=0.266, pruned_loss=0.03439, over 1424205.37 frames.], batch size: 26, lr: 3.26e-04
+2022-04-29 22:34:20,496 INFO [train.py:763] (7/8) Epoch 23, batch 1450, loss[loss=0.1826, simple_loss=0.2891, pruned_loss=0.03804, over 7144.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2663, pruned_loss=0.03429, over 1421637.62 frames.], batch size: 20, lr: 3.26e-04
+2022-04-29 22:35:53,270 INFO [train.py:763] (7/8) Epoch 23, batch 1500, loss[loss=0.1577, simple_loss=0.2713, pruned_loss=0.02208, over 7137.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2659, pruned_loss=0.03438, over 1419605.87 frames.], batch size: 20, lr: 3.26e-04
+2022-04-29 22:36:59,428 INFO [train.py:763] (7/8) Epoch 23, batch 1550, loss[loss=0.196, simple_loss=0.2964, pruned_loss=0.04784, over 6778.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2659, pruned_loss=0.03457, over 1420112.56 frames.], batch size: 31, lr: 3.26e-04
+2022-04-29 22:38:04,566 INFO [train.py:763] (7/8) Epoch 23, batch 1600, loss[loss=0.1826, simple_loss=0.2781, pruned_loss=0.04352, over 7333.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2661, pruned_loss=0.03465, over 1422117.79 frames.], batch size: 20, lr: 3.25e-04
+2022-04-29 22:39:10,561 INFO [train.py:763] (7/8) Epoch 23, batch 1650, loss[loss=0.1712, simple_loss=0.2466, pruned_loss=0.04784, over 6793.00 frames.], tot_loss[loss=0.168, simple_loss=0.2665, pruned_loss=0.03478, over 1414009.55 frames.], batch size: 15, lr: 3.25e-04
+2022-04-29 22:40:17,877 INFO [train.py:763] (7/8) Epoch 23, batch 1700, loss[loss=0.1419, simple_loss=0.2501, pruned_loss=0.0169, over 7321.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2655, pruned_loss=0.03418, over 1418232.98 frames.], batch size: 21, lr: 3.25e-04
+2022-04-29 22:41:24,862 INFO [train.py:763] (7/8) Epoch 23, batch 1750, loss[loss=0.1495, simple_loss=0.243, pruned_loss=0.02802, over 7065.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2659, pruned_loss=0.03462, over 1419695.58 frames.], batch size: 18, lr: 3.25e-04
+2022-04-29 22:42:30,378 INFO [train.py:763] (7/8) Epoch 23, batch 1800, loss[loss=0.1668, simple_loss=0.2691, pruned_loss=0.03228, over 7335.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2667, pruned_loss=0.03478, over 1420176.27 frames.], batch size: 22, lr: 3.25e-04
+2022-04-29 22:43:35,688 INFO [train.py:763] (7/8) Epoch 23, batch 1850, loss[loss=0.1609, simple_loss=0.2623, pruned_loss=0.0298, over 7282.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2667, pruned_loss=0.03433, over 1423772.33 frames.], batch size: 24, lr: 3.25e-04
+2022-04-29 22:44:41,110 INFO [train.py:763] (7/8) Epoch 23, batch 1900, loss[loss=0.1639, simple_loss=0.2669, pruned_loss=0.03045, over 7070.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2668, pruned_loss=0.0343, over 1421684.00 frames.], batch size: 28, lr: 3.25e-04
+2022-04-29 22:45:46,564 INFO [train.py:763] (7/8) Epoch 23, batch 1950, loss[loss=0.1656, simple_loss=0.2685, pruned_loss=0.03134, over 7121.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2669, pruned_loss=0.0342, over 1423259.84 frames.], batch size: 21, lr: 3.25e-04
+2022-04-29 22:46:52,064 INFO [train.py:763] (7/8) Epoch 23, batch 2000, loss[loss=0.1913, simple_loss=0.2875, pruned_loss=0.04756, over 5188.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2683, pruned_loss=0.03471, over 1421324.48 frames.], batch size: 52, lr: 3.25e-04
+2022-04-29 22:47:58,961 INFO [train.py:763] (7/8) Epoch 23, batch 2050, loss[loss=0.1447, simple_loss=0.2431, pruned_loss=0.02318, over 7425.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2672, pruned_loss=0.03428, over 1420753.01 frames.], batch size: 20, lr: 3.25e-04
+2022-04-29 22:49:05,156 INFO [train.py:763] (7/8) Epoch 23, batch 2100, loss[loss=0.1685, simple_loss=0.2548, pruned_loss=0.04108, over 6998.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2665, pruned_loss=0.03425, over 1422454.68 frames.], batch size: 16, lr: 3.25e-04
+2022-04-29 22:50:10,669 INFO [train.py:763] (7/8) Epoch 23, batch 2150, loss[loss=0.1999, simple_loss=0.2982, pruned_loss=0.05084, over 4984.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2654, pruned_loss=0.03395, over 1420196.68 frames.], batch size: 52, lr: 3.25e-04
+2022-04-29 22:51:16,172 INFO [train.py:763] (7/8) Epoch 23, batch 2200, loss[loss=0.1509, simple_loss=0.2333, pruned_loss=0.03428, over 7120.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2652, pruned_loss=0.03403, over 1419679.89 frames.], batch size: 17, lr: 3.25e-04
+2022-04-29 22:52:21,341 INFO [train.py:763] (7/8) Epoch 23, batch 2250, loss[loss=0.1622, simple_loss=0.2662, pruned_loss=0.02909, over 7320.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2659, pruned_loss=0.03465, over 1408410.89 frames.], batch size: 25, lr: 3.24e-04
+2022-04-29 22:53:28,321 INFO [train.py:763] (7/8) Epoch 23, batch 2300, loss[loss=0.1515, simple_loss=0.2433, pruned_loss=0.02991, over 7267.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2649, pruned_loss=0.03408, over 1415986.60 frames.], batch size: 17, lr: 3.24e-04
+2022-04-29 22:54:34,472 INFO [train.py:763] (7/8) Epoch 23, batch 2350, loss[loss=0.1737, simple_loss=0.2718, pruned_loss=0.03782, over 7355.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2658, pruned_loss=0.0347, over 1418059.13 frames.], batch size: 22, lr: 3.24e-04
+2022-04-29 22:55:39,718 INFO [train.py:763] (7/8) Epoch 23, batch 2400, loss[loss=0.1502, simple_loss=0.2402, pruned_loss=0.03014, over 7245.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2668, pruned_loss=0.03487, over 1421112.57 frames.], batch size: 16, lr: 3.24e-04
+2022-04-29 22:56:45,980 INFO [train.py:763] (7/8) Epoch 23, batch 2450, loss[loss=0.1671, simple_loss=0.2692, pruned_loss=0.03248, over 7230.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2672, pruned_loss=0.035, over 1417299.50 frames.], batch size: 20, lr: 3.24e-04
+2022-04-29 22:57:51,405 INFO [train.py:763] (7/8) Epoch 23, batch 2500, loss[loss=0.1879, simple_loss=0.2829, pruned_loss=0.04646, over 7318.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2673, pruned_loss=0.03517, over 1417589.63 frames.], batch size: 21, lr: 3.24e-04
+2022-04-29 22:58:56,893 INFO [train.py:763] (7/8) Epoch 23, batch 2550, loss[loss=0.1743, simple_loss=0.271, pruned_loss=0.03883, over 5240.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2664, pruned_loss=0.03508, over 1413858.15 frames.], batch size: 52, lr: 3.24e-04
+2022-04-29 23:00:02,970 INFO [train.py:763] (7/8) Epoch 23, batch 2600, loss[loss=0.1543, simple_loss=0.2497, pruned_loss=0.02942, over 7279.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2672, pruned_loss=0.035, over 1418041.56 frames.], batch size: 18, lr: 3.24e-04
+2022-04-29 23:01:08,578 INFO [train.py:763] (7/8) Epoch 23, batch 2650, loss[loss=0.1562, simple_loss=0.2643, pruned_loss=0.02402, over 7325.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2666, pruned_loss=0.03462, over 1417431.99 frames.], batch size: 21, lr: 3.24e-04
+2022-04-29 23:02:14,031 INFO [train.py:763] (7/8) Epoch 23, batch 2700, loss[loss=0.1608, simple_loss=0.2622, pruned_loss=0.02967, over 7326.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2672, pruned_loss=0.03489, over 1422677.70 frames.], batch size: 22, lr: 3.24e-04
+2022-04-29 23:03:19,906 INFO [train.py:763] (7/8) Epoch 23, batch 2750, loss[loss=0.1578, simple_loss=0.2535, pruned_loss=0.03103, over 7415.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2673, pruned_loss=0.03469, over 1426408.62 frames.], batch size: 21, lr: 3.24e-04
+2022-04-29 23:04:25,103 INFO [train.py:763] (7/8) Epoch 23, batch 2800, loss[loss=0.1715, simple_loss=0.2707, pruned_loss=0.03616, over 7224.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2687, pruned_loss=0.03536, over 1422235.18 frames.], batch size: 20, lr: 3.24e-04
+2022-04-29 23:05:30,282 INFO [train.py:763] (7/8) Epoch 23, batch 2850, loss[loss=0.1832, simple_loss=0.2768, pruned_loss=0.04479, over 7364.00 frames.], tot_loss[loss=0.17, simple_loss=0.2693, pruned_loss=0.03534, over 1422361.31 frames.], batch size: 19, lr: 3.24e-04
+2022-04-29 23:06:35,480 INFO [train.py:763] (7/8) Epoch 23, batch 2900, loss[loss=0.1911, simple_loss=0.2949, pruned_loss=0.04367, over 7283.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2689, pruned_loss=0.0348, over 1421300.90 frames.], batch size: 25, lr: 3.24e-04
+2022-04-29 23:07:40,693 INFO [train.py:763] (7/8) Epoch 23, batch 2950, loss[loss=0.1743, simple_loss=0.2614, pruned_loss=0.04355, over 7280.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2676, pruned_loss=0.03435, over 1425264.04 frames.], batch size: 17, lr: 3.23e-04
+2022-04-29 23:08:45,900 INFO [train.py:763] (7/8) Epoch 23, batch 3000, loss[loss=0.1696, simple_loss=0.2613, pruned_loss=0.03895, over 7109.00 frames.], tot_loss[loss=0.168, simple_loss=0.2669, pruned_loss=0.03456, over 1421585.34 frames.], batch size: 21, lr: 3.23e-04
+2022-04-29 23:08:45,901 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-29 23:09:01,228 INFO [train.py:792] (7/8) Epoch 23, validation: loss=0.1683, simple_loss=0.2665, pruned_loss=0.03509, over 698248.00 frames.
+2022-04-29 23:10:07,046 INFO [train.py:763] (7/8) Epoch 23, batch 3050, loss[loss=0.1605, simple_loss=0.2468, pruned_loss=0.03705, over 7310.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2663, pruned_loss=0.03454, over 1416454.88 frames.], batch size: 18, lr: 3.23e-04
+2022-04-29 23:11:12,536 INFO [train.py:763] (7/8) Epoch 23, batch 3100, loss[loss=0.1553, simple_loss=0.2554, pruned_loss=0.02761, over 6794.00 frames.], tot_loss[loss=0.168, simple_loss=0.2667, pruned_loss=0.03471, over 1419399.35 frames.], batch size: 31, lr: 3.23e-04
+2022-04-29 23:12:19,108 INFO [train.py:763] (7/8) Epoch 23, batch 3150, loss[loss=0.1626, simple_loss=0.2543, pruned_loss=0.03541, over 6985.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2659, pruned_loss=0.0343, over 1420653.65 frames.], batch size: 16, lr: 3.23e-04
+2022-04-29 23:13:26,842 INFO [train.py:763] (7/8) Epoch 23, batch 3200, loss[loss=0.1722, simple_loss=0.2824, pruned_loss=0.03103, over 7320.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2662, pruned_loss=0.03434, over 1425725.11 frames.], batch size: 21, lr: 3.23e-04
+2022-04-29 23:14:33,562 INFO [train.py:763] (7/8) Epoch 23, batch 3250, loss[loss=0.1597, simple_loss=0.2544, pruned_loss=0.03248, over 7156.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2654, pruned_loss=0.03405, over 1426998.50 frames.], batch size: 18, lr: 3.23e-04
+2022-04-29 23:15:38,826 INFO [train.py:763] (7/8) Epoch 23, batch 3300, loss[loss=0.1838, simple_loss=0.2968, pruned_loss=0.03539, over 7286.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2653, pruned_loss=0.03354, over 1427538.34 frames.], batch size: 24, lr: 3.23e-04
+2022-04-29 23:16:45,594 INFO [train.py:763] (7/8) Epoch 23, batch 3350, loss[loss=0.1766, simple_loss=0.273, pruned_loss=0.04007, over 7311.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2662, pruned_loss=0.03405, over 1423999.57 frames.], batch size: 24, lr: 3.23e-04
+2022-04-29 23:17:51,532 INFO [train.py:763] (7/8) Epoch 23, batch 3400, loss[loss=0.1857, simple_loss=0.277, pruned_loss=0.04717, over 7346.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2659, pruned_loss=0.03374, over 1427928.26 frames.], batch size: 19, lr: 3.23e-04
+2022-04-29 23:18:56,734 INFO [train.py:763] (7/8) Epoch 23, batch 3450, loss[loss=0.1781, simple_loss=0.2864, pruned_loss=0.03494, over 7337.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2668, pruned_loss=0.03387, over 1423475.55 frames.], batch size: 22, lr: 3.23e-04
+2022-04-29 23:20:02,258 INFO [train.py:763] (7/8) Epoch 23, batch 3500, loss[loss=0.1542, simple_loss=0.2376, pruned_loss=0.0354, over 6850.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2657, pruned_loss=0.03379, over 1422026.28 frames.], batch size: 15, lr: 3.23e-04
+2022-04-29 23:21:08,259 INFO [train.py:763] (7/8) Epoch 23, batch 3550, loss[loss=0.1995, simple_loss=0.2975, pruned_loss=0.05068, over 7111.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2662, pruned_loss=0.03437, over 1423200.89 frames.], batch size: 21, lr: 3.23e-04
+2022-04-29 23:22:13,620 INFO [train.py:763] (7/8) Epoch 23, batch 3600, loss[loss=0.1478, simple_loss=0.2482, pruned_loss=0.02374, over 7061.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2665, pruned_loss=0.03442, over 1422794.67 frames.], batch size: 18, lr: 3.22e-04
+2022-04-29 23:23:18,849 INFO [train.py:763] (7/8) Epoch 23, batch 3650, loss[loss=0.1632, simple_loss=0.2612, pruned_loss=0.03261, over 7361.00 frames.], tot_loss[loss=0.169, simple_loss=0.2679, pruned_loss=0.03502, over 1423808.79 frames.], batch size: 19, lr: 3.22e-04
+2022-04-29 23:24:24,050 INFO [train.py:763] (7/8) Epoch 23, batch 3700, loss[loss=0.163, simple_loss=0.2649, pruned_loss=0.03058, over 6589.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2683, pruned_loss=0.03501, over 1421447.41 frames.], batch size: 38, lr: 3.22e-04
+2022-04-29 23:25:30,892 INFO [train.py:763] (7/8) Epoch 23, batch 3750, loss[loss=0.1629, simple_loss=0.2552, pruned_loss=0.03537, over 7276.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2674, pruned_loss=0.0346, over 1422873.92 frames.], batch size: 18, lr: 3.22e-04
+2022-04-29 23:26:37,724 INFO [train.py:763] (7/8) Epoch 23, batch 3800, loss[loss=0.1462, simple_loss=0.2541, pruned_loss=0.01913, over 7429.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2662, pruned_loss=0.03413, over 1424741.74 frames.], batch size: 20, lr: 3.22e-04
+2022-04-29 23:27:43,272 INFO [train.py:763] (7/8) Epoch 23, batch 3850, loss[loss=0.2, simple_loss=0.2926, pruned_loss=0.05367, over 4964.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2665, pruned_loss=0.03431, over 1420530.65 frames.], batch size: 52, lr: 3.22e-04
+2022-04-29 23:28:48,642 INFO [train.py:763] (7/8) Epoch 23, batch 3900, loss[loss=0.1616, simple_loss=0.2539, pruned_loss=0.03461, over 6760.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2661, pruned_loss=0.03443, over 1417531.21 frames.], batch size: 31, lr: 3.22e-04
+2022-04-29 23:29:53,697 INFO [train.py:763] (7/8) Epoch 23, batch 3950, loss[loss=0.135, simple_loss=0.2308, pruned_loss=0.0196, over 7131.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2663, pruned_loss=0.03414, over 1417710.62 frames.], batch size: 17, lr: 3.22e-04
+2022-04-29 23:30:59,599 INFO [train.py:763] (7/8) Epoch 23, batch 4000, loss[loss=0.1573, simple_loss=0.2535, pruned_loss=0.03055, over 7201.00 frames.], tot_loss[loss=0.1679, simple_loss=0.267, pruned_loss=0.03442, over 1415447.83 frames.], batch size: 22, lr: 3.22e-04
+2022-04-29 23:32:05,454 INFO [train.py:763] (7/8) Epoch 23, batch 4050, loss[loss=0.2069, simple_loss=0.2955, pruned_loss=0.05915, over 5175.00 frames.], tot_loss[loss=0.1683, simple_loss=0.267, pruned_loss=0.03478, over 1416596.15 frames.], batch size: 55, lr: 3.22e-04
+2022-04-29 23:33:10,726 INFO [train.py:763] (7/8) Epoch 23, batch 4100, loss[loss=0.1742, simple_loss=0.2711, pruned_loss=0.0387, over 7284.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2672, pruned_loss=0.03498, over 1417163.52 frames.], batch size: 18, lr: 3.22e-04
+2022-04-29 23:34:16,161 INFO [train.py:763] (7/8) Epoch 23, batch 4150, loss[loss=0.1427, simple_loss=0.2378, pruned_loss=0.02378, over 7005.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2668, pruned_loss=0.0351, over 1418528.98 frames.], batch size: 16, lr: 3.22e-04
+2022-04-29 23:35:21,260 INFO [train.py:763] (7/8) Epoch 23, batch 4200, loss[loss=0.1543, simple_loss=0.265, pruned_loss=0.02181, over 7281.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2681, pruned_loss=0.03549, over 1418646.85 frames.], batch size: 18, lr: 3.22e-04
+2022-04-29 23:36:26,923 INFO [train.py:763] (7/8) Epoch 23, batch 4250, loss[loss=0.1868, simple_loss=0.2909, pruned_loss=0.04141, over 7383.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2674, pruned_loss=0.03509, over 1416696.35 frames.], batch size: 23, lr: 3.22e-04
+2022-04-29 23:37:32,242 INFO [train.py:763] (7/8) Epoch 23, batch 4300, loss[loss=0.1625, simple_loss=0.2433, pruned_loss=0.04085, over 6806.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2666, pruned_loss=0.035, over 1415833.75 frames.], batch size: 15, lr: 3.21e-04
+2022-04-29 23:38:37,644 INFO [train.py:763] (7/8) Epoch 23, batch 4350, loss[loss=0.1768, simple_loss=0.2779, pruned_loss=0.03785, over 6810.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2683, pruned_loss=0.03549, over 1412742.51 frames.], batch size: 31, lr: 3.21e-04
+2022-04-29 23:39:43,238 INFO [train.py:763] (7/8) Epoch 23, batch 4400, loss[loss=0.1753, simple_loss=0.2861, pruned_loss=0.03219, over 6400.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2689, pruned_loss=0.03569, over 1406482.06 frames.], batch size: 38, lr: 3.21e-04
+2022-04-29 23:40:48,382 INFO [train.py:763] (7/8) Epoch 23, batch 4450, loss[loss=0.2027, simple_loss=0.2952, pruned_loss=0.05514, over 6393.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2679, pruned_loss=0.03519, over 1408926.27 frames.], batch size: 38, lr: 3.21e-04
+2022-04-29 23:41:53,051 INFO [train.py:763] (7/8) Epoch 23, batch 4500, loss[loss=0.1619, simple_loss=0.2591, pruned_loss=0.03242, over 6154.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2683, pruned_loss=0.03536, over 1395448.46 frames.], batch size: 37, lr: 3.21e-04
+2022-04-29 23:42:58,318 INFO [train.py:763] (7/8) Epoch 23, batch 4550, loss[loss=0.1581, simple_loss=0.2679, pruned_loss=0.02414, over 7279.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2684, pruned_loss=0.03555, over 1385660.14 frames.], batch size: 24, lr: 3.21e-04
+2022-04-29 23:44:17,947 INFO [train.py:763] (7/8) Epoch 24, batch 0, loss[loss=0.1816, simple_loss=0.276, pruned_loss=0.04353, over 7061.00 frames.], tot_loss[loss=0.1816, simple_loss=0.276, pruned_loss=0.04353, over 7061.00 frames.], batch size: 18, lr: 3.15e-04
+2022-04-29 23:45:23,866 INFO [train.py:763] (7/8) Epoch 24, batch 50, loss[loss=0.154, simple_loss=0.2446, pruned_loss=0.0317, over 7245.00 frames.], tot_loss[loss=0.1677, simple_loss=0.265, pruned_loss=0.03521, over 322027.44 frames.], batch size: 19, lr: 3.15e-04
+2022-04-29 23:46:30,371 INFO [train.py:763] (7/8) Epoch 24, batch 100, loss[loss=0.1627, simple_loss=0.2649, pruned_loss=0.03026, over 7330.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2647, pruned_loss=0.03436, over 570110.52 frames.], batch size: 20, lr: 3.15e-04
+2022-04-29 23:47:35,991 INFO [train.py:763] (7/8) Epoch 24, batch 150, loss[loss=0.1696, simple_loss=0.2685, pruned_loss=0.03531, over 7319.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2651, pruned_loss=0.03459, over 761861.01 frames.], batch size: 21, lr: 3.14e-04
+2022-04-29 23:48:41,602 INFO [train.py:763] (7/8) Epoch 24, batch 200, loss[loss=0.1522, simple_loss=0.2412, pruned_loss=0.03163, over 6739.00 frames.], tot_loss[loss=0.1669, simple_loss=0.265, pruned_loss=0.03444, over 906343.63 frames.], batch size: 15, lr: 3.14e-04
+2022-04-29 23:49:46,891 INFO [train.py:763] (7/8) Epoch 24, batch 250, loss[loss=0.166, simple_loss=0.2647, pruned_loss=0.03366, over 7239.00 frames.], tot_loss[loss=0.1663, simple_loss=0.264, pruned_loss=0.03427, over 1018770.43 frames.], batch size: 20, lr: 3.14e-04
+2022-04-29 23:50:52,243 INFO [train.py:763] (7/8) Epoch 24, batch 300, loss[loss=0.1666, simple_loss=0.2587, pruned_loss=0.03726, over 7156.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2652, pruned_loss=0.03424, over 1112156.15 frames.], batch size: 19, lr: 3.14e-04
+2022-04-29 23:51:57,528 INFO [train.py:763] (7/8) Epoch 24, batch 350, loss[loss=0.179, simple_loss=0.2779, pruned_loss=0.04005, over 7211.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2655, pruned_loss=0.03416, over 1181028.96 frames.], batch size: 23, lr: 3.14e-04
+2022-04-29 23:53:03,349 INFO [train.py:763] (7/8) Epoch 24, batch 400, loss[loss=0.1702, simple_loss=0.2722, pruned_loss=0.03413, over 7244.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2648, pruned_loss=0.03396, over 1236259.88 frames.], batch size: 20, lr: 3.14e-04
+2022-04-29 23:54:08,686 INFO [train.py:763] (7/8) Epoch 24, batch 450, loss[loss=0.1713, simple_loss=0.2799, pruned_loss=0.03138, over 7079.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2645, pruned_loss=0.03362, over 1277696.02 frames.], batch size: 28, lr: 3.14e-04
+2022-04-29 23:55:14,219 INFO [train.py:763] (7/8) Epoch 24, batch 500, loss[loss=0.1764, simple_loss=0.2711, pruned_loss=0.0408, over 7164.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2652, pruned_loss=0.03358, over 1312602.96 frames.], batch size: 18, lr: 3.14e-04
+2022-04-29 23:56:20,434 INFO [train.py:763] (7/8) Epoch 24, batch 550, loss[loss=0.1462, simple_loss=0.2485, pruned_loss=0.02195, over 7167.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2657, pruned_loss=0.03379, over 1339406.59 frames.], batch size: 18, lr: 3.14e-04
+2022-04-29 23:57:26,727 INFO [train.py:763] (7/8) Epoch 24, batch 600, loss[loss=0.1865, simple_loss=0.2851, pruned_loss=0.04395, over 7203.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2651, pruned_loss=0.03371, over 1359181.66 frames.], batch size: 23, lr: 3.14e-04
+2022-04-29 23:58:32,103 INFO [train.py:763] (7/8) Epoch 24, batch 650, loss[loss=0.1363, simple_loss=0.2197, pruned_loss=0.02646, over 7259.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2646, pruned_loss=0.03381, over 1371305.62 frames.], batch size: 17, lr: 3.14e-04
+2022-04-29 23:59:38,791 INFO [train.py:763] (7/8) Epoch 24, batch 700, loss[loss=0.136, simple_loss=0.2271, pruned_loss=0.02243, over 6804.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2646, pruned_loss=0.03376, over 1387550.27 frames.], batch size: 15, lr: 3.14e-04
+2022-04-30 00:00:44,934 INFO [train.py:763] (7/8) Epoch 24, batch 750, loss[loss=0.1775, simple_loss=0.2882, pruned_loss=0.03337, over 7235.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2654, pruned_loss=0.03419, over 1398791.63 frames.], batch size: 20, lr: 3.14e-04
+2022-04-30 00:01:50,617 INFO [train.py:763] (7/8) Epoch 24, batch 800, loss[loss=0.2065, simple_loss=0.306, pruned_loss=0.05348, over 7415.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2659, pruned_loss=0.03426, over 1406942.27 frames.], batch size: 21, lr: 3.14e-04
+2022-04-30 00:02:56,138 INFO [train.py:763] (7/8) Epoch 24, batch 850, loss[loss=0.1453, simple_loss=0.2609, pruned_loss=0.01491, over 7325.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2652, pruned_loss=0.03386, over 1408229.49 frames.], batch size: 21, lr: 3.13e-04
+2022-04-30 00:04:01,376 INFO [train.py:763] (7/8) Epoch 24, batch 900, loss[loss=0.1781, simple_loss=0.2767, pruned_loss=0.03975, over 7316.00 frames.], tot_loss[loss=0.1671, simple_loss=0.266, pruned_loss=0.03405, over 1410638.35 frames.], batch size: 25, lr: 3.13e-04
+2022-04-30 00:05:07,040 INFO [train.py:763] (7/8) Epoch 24, batch 950, loss[loss=0.192, simple_loss=0.287, pruned_loss=0.0485, over 5082.00 frames.], tot_loss[loss=0.167, simple_loss=0.2658, pruned_loss=0.03413, over 1405738.89 frames.], batch size: 52, lr: 3.13e-04
+2022-04-30 00:06:12,852 INFO [train.py:763] (7/8) Epoch 24, batch 1000, loss[loss=0.172, simple_loss=0.2689, pruned_loss=0.03755, over 7411.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2658, pruned_loss=0.03402, over 1412165.45 frames.], batch size: 21, lr: 3.13e-04
+2022-04-30 00:07:18,493 INFO [train.py:763] (7/8) Epoch 24, batch 1050, loss[loss=0.1402, simple_loss=0.2342, pruned_loss=0.02308, over 7329.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2657, pruned_loss=0.03364, over 1418746.38 frames.], batch size: 20, lr: 3.13e-04
+2022-04-30 00:08:23,993 INFO [train.py:763] (7/8) Epoch 24, batch 1100, loss[loss=0.1673, simple_loss=0.2685, pruned_loss=0.03307, over 7335.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2659, pruned_loss=0.03376, over 1421008.01 frames.], batch size: 22, lr: 3.13e-04
+2022-04-30 00:09:29,785 INFO [train.py:763] (7/8) Epoch 24, batch 1150, loss[loss=0.1725, simple_loss=0.2637, pruned_loss=0.04062, over 7211.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2665, pruned_loss=0.03423, over 1423799.49 frames.], batch size: 23, lr: 3.13e-04
+2022-04-30 00:10:35,408 INFO [train.py:763] (7/8) Epoch 24, batch 1200, loss[loss=0.2023, simple_loss=0.2933, pruned_loss=0.05564, over 7379.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2655, pruned_loss=0.03387, over 1422683.82 frames.], batch size: 23, lr: 3.13e-04
+2022-04-30 00:11:41,727 INFO [train.py:763] (7/8) Epoch 24, batch 1250, loss[loss=0.1481, simple_loss=0.2461, pruned_loss=0.02501, over 7149.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2656, pruned_loss=0.03387, over 1421067.82 frames.], batch size: 20, lr: 3.13e-04
+2022-04-30 00:12:47,629 INFO [train.py:763] (7/8) Epoch 24, batch 1300, loss[loss=0.1633, simple_loss=0.2572, pruned_loss=0.03471, over 7172.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2656, pruned_loss=0.03406, over 1421181.54 frames.], batch size: 16, lr: 3.13e-04
+2022-04-30 00:13:53,412 INFO [train.py:763] (7/8) Epoch 24, batch 1350, loss[loss=0.1692, simple_loss=0.2737, pruned_loss=0.03238, over 6467.00 frames.], tot_loss[loss=0.1664, simple_loss=0.265, pruned_loss=0.03389, over 1421148.99 frames.], batch size: 38, lr: 3.13e-04
+2022-04-30 00:14:58,842 INFO [train.py:763] (7/8) Epoch 24, batch 1400, loss[loss=0.1484, simple_loss=0.246, pruned_loss=0.02543, over 7269.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2656, pruned_loss=0.03396, over 1426323.89 frames.], batch size: 17, lr: 3.13e-04
+2022-04-30 00:16:04,297 INFO [train.py:763] (7/8) Epoch 24, batch 1450, loss[loss=0.1604, simple_loss=0.2758, pruned_loss=0.02247, over 7151.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2655, pruned_loss=0.03396, over 1422756.07 frames.], batch size: 20, lr: 3.13e-04
+2022-04-30 00:17:11,233 INFO [train.py:763] (7/8) Epoch 24, batch 1500, loss[loss=0.1843, simple_loss=0.2947, pruned_loss=0.03695, over 6793.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2656, pruned_loss=0.03389, over 1421741.67 frames.], batch size: 31, lr: 3.13e-04
+2022-04-30 00:18:17,544 INFO [train.py:763] (7/8) Epoch 24, batch 1550, loss[loss=0.1513, simple_loss=0.2357, pruned_loss=0.03342, over 7277.00 frames.], tot_loss[loss=0.168, simple_loss=0.2669, pruned_loss=0.0345, over 1422971.74 frames.], batch size: 18, lr: 3.12e-04
+2022-04-30 00:19:23,713 INFO [train.py:763] (7/8) Epoch 24, batch 1600, loss[loss=0.1695, simple_loss=0.2554, pruned_loss=0.04181, over 6856.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2658, pruned_loss=0.03424, over 1421624.48 frames.], batch size: 15, lr: 3.12e-04
+2022-04-30 00:20:29,921 INFO [train.py:763] (7/8) Epoch 24, batch 1650, loss[loss=0.1719, simple_loss=0.2732, pruned_loss=0.03531, over 7215.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2654, pruned_loss=0.03401, over 1422628.42 frames.], batch size: 21, lr: 3.12e-04
+2022-04-30 00:21:35,730 INFO [train.py:763] (7/8) Epoch 24, batch 1700, loss[loss=0.1929, simple_loss=0.2907, pruned_loss=0.0476, over 7380.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2656, pruned_loss=0.03427, over 1421672.02 frames.], batch size: 23, lr: 3.12e-04
+2022-04-30 00:22:40,929 INFO [train.py:763] (7/8) Epoch 24, batch 1750, loss[loss=0.1547, simple_loss=0.2428, pruned_loss=0.0333, over 7139.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2665, pruned_loss=0.03449, over 1424033.17 frames.], batch size: 17, lr: 3.12e-04
+2022-04-30 00:23:47,093 INFO [train.py:763] (7/8) Epoch 24, batch 1800, loss[loss=0.1613, simple_loss=0.2517, pruned_loss=0.03543, over 7007.00 frames.], tot_loss[loss=0.168, simple_loss=0.2669, pruned_loss=0.03455, over 1423972.11 frames.], batch size: 16, lr: 3.12e-04
+2022-04-30 00:24:52,837 INFO [train.py:763] (7/8) Epoch 24, batch 1850, loss[loss=0.129, simple_loss=0.2211, pruned_loss=0.01841, over 6808.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2666, pruned_loss=0.0348, over 1419986.83 frames.], batch size: 15, lr: 3.12e-04
+2022-04-30 00:26:09,453 INFO [train.py:763] (7/8) Epoch 24, batch 1900, loss[loss=0.1945, simple_loss=0.3004, pruned_loss=0.04436, over 7336.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2667, pruned_loss=0.03477, over 1422111.61 frames.], batch size: 25, lr: 3.12e-04
+2022-04-30 00:27:15,231 INFO [train.py:763] (7/8) Epoch 24, batch 1950, loss[loss=0.187, simple_loss=0.2793, pruned_loss=0.04735, over 7254.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2668, pruned_loss=0.03483, over 1423697.87 frames.], batch size: 19, lr: 3.12e-04
+2022-04-30 00:28:21,035 INFO [train.py:763] (7/8) Epoch 24, batch 2000, loss[loss=0.1408, simple_loss=0.2358, pruned_loss=0.02288, over 7161.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2657, pruned_loss=0.03446, over 1423559.17 frames.], batch size: 18, lr: 3.12e-04
+2022-04-30 00:29:27,112 INFO [train.py:763] (7/8) Epoch 24, batch 2050, loss[loss=0.1871, simple_loss=0.2875, pruned_loss=0.04334, over 7314.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2645, pruned_loss=0.03419, over 1426154.14 frames.], batch size: 21, lr: 3.12e-04
+2022-04-30 00:30:32,491 INFO [train.py:763] (7/8) Epoch 24, batch 2100, loss[loss=0.1532, simple_loss=0.2473, pruned_loss=0.02953, over 7260.00 frames.], tot_loss[loss=0.1668, simple_loss=0.265, pruned_loss=0.03428, over 1422722.80 frames.], batch size: 19, lr: 3.12e-04
+2022-04-30 00:31:37,982 INFO [train.py:763] (7/8) Epoch 24, batch 2150, loss[loss=0.1547, simple_loss=0.2554, pruned_loss=0.02699, over 7448.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2661, pruned_loss=0.03407, over 1421516.64 frames.], batch size: 20, lr: 3.12e-04
+2022-04-30 00:32:43,341 INFO [train.py:763] (7/8) Epoch 24, batch 2200, loss[loss=0.1531, simple_loss=0.2355, pruned_loss=0.03528, over 7187.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2647, pruned_loss=0.03374, over 1420662.34 frames.], batch size: 16, lr: 3.12e-04
+2022-04-30 00:33:49,456 INFO [train.py:763] (7/8) Epoch 24, batch 2250, loss[loss=0.1815, simple_loss=0.2656, pruned_loss=0.04866, over 7060.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2649, pruned_loss=0.03404, over 1416884.36 frames.], batch size: 18, lr: 3.12e-04
+2022-04-30 00:34:55,320 INFO [train.py:763] (7/8) Epoch 24, batch 2300, loss[loss=0.1401, simple_loss=0.2333, pruned_loss=0.02343, over 7187.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2639, pruned_loss=0.03333, over 1418503.63 frames.], batch size: 16, lr: 3.11e-04
+2022-04-30 00:36:01,142 INFO [train.py:763] (7/8) Epoch 24, batch 2350, loss[loss=0.1698, simple_loss=0.2761, pruned_loss=0.03179, over 7330.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2631, pruned_loss=0.03316, over 1419112.63 frames.], batch size: 21, lr: 3.11e-04
+2022-04-30 00:37:06,721 INFO [train.py:763] (7/8) Epoch 24, batch 2400, loss[loss=0.1754, simple_loss=0.2757, pruned_loss=0.03753, over 7365.00 frames.], tot_loss[loss=0.1661, simple_loss=0.265, pruned_loss=0.03364, over 1423731.15 frames.], batch size: 19, lr: 3.11e-04
+2022-04-30 00:38:21,825 INFO [train.py:763] (7/8) Epoch 24, batch 2450, loss[loss=0.1605, simple_loss=0.249, pruned_loss=0.03594, over 7138.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2651, pruned_loss=0.03358, over 1423783.42 frames.], batch size: 17, lr: 3.11e-04
+2022-04-30 00:39:27,193 INFO [train.py:763] (7/8) Epoch 24, batch 2500, loss[loss=0.1726, simple_loss=0.2807, pruned_loss=0.03226, over 7407.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2653, pruned_loss=0.03354, over 1423894.29 frames.], batch size: 21, lr: 3.11e-04
+2022-04-30 00:40:32,711 INFO [train.py:763] (7/8) Epoch 24, batch 2550, loss[loss=0.1827, simple_loss=0.2784, pruned_loss=0.04348, over 7429.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2653, pruned_loss=0.03368, over 1426076.68 frames.], batch size: 20, lr: 3.11e-04
+2022-04-30 00:41:38,108 INFO [train.py:763] (7/8) Epoch 24, batch 2600, loss[loss=0.161, simple_loss=0.242, pruned_loss=0.03997, over 7131.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2657, pruned_loss=0.03352, over 1423403.77 frames.], batch size: 17, lr: 3.11e-04
+2022-04-30 00:42:43,686 INFO [train.py:763] (7/8) Epoch 24, batch 2650, loss[loss=0.177, simple_loss=0.2734, pruned_loss=0.04034, over 7216.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2667, pruned_loss=0.03376, over 1425508.73 frames.], batch size: 22, lr: 3.11e-04
+2022-04-30 00:43:49,279 INFO [train.py:763] (7/8) Epoch 24, batch 2700, loss[loss=0.1559, simple_loss=0.2572, pruned_loss=0.02733, over 7059.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2661, pruned_loss=0.03379, over 1427256.79 frames.], batch size: 18, lr: 3.11e-04
+2022-04-30 00:44:54,697 INFO [train.py:763] (7/8) Epoch 24, batch 2750, loss[loss=0.1825, simple_loss=0.282, pruned_loss=0.04151, over 7147.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2658, pruned_loss=0.03377, over 1421468.97 frames.], batch size: 20, lr: 3.11e-04
+2022-04-30 00:46:00,217 INFO [train.py:763] (7/8) Epoch 24, batch 2800, loss[loss=0.172, simple_loss=0.2714, pruned_loss=0.0363, over 7251.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2658, pruned_loss=0.03363, over 1421436.03 frames.], batch size: 19, lr: 3.11e-04
+2022-04-30 00:47:22,982 INFO [train.py:763] (7/8) Epoch 24, batch 2850, loss[loss=0.1617, simple_loss=0.258, pruned_loss=0.03273, over 7431.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2652, pruned_loss=0.03334, over 1419761.88 frames.], batch size: 20, lr: 3.11e-04
+2022-04-30 00:48:28,460 INFO [train.py:763] (7/8) Epoch 24, batch 2900, loss[loss=0.2151, simple_loss=0.3172, pruned_loss=0.05647, over 7191.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2655, pruned_loss=0.03347, over 1420151.85 frames.], batch size: 23, lr: 3.11e-04
+2022-04-30 00:49:52,269 INFO [train.py:763] (7/8) Epoch 24, batch 2950, loss[loss=0.1465, simple_loss=0.2473, pruned_loss=0.02284, over 7105.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2655, pruned_loss=0.03343, over 1425244.53 frames.], batch size: 21, lr: 3.11e-04
+2022-04-30 00:51:06,877 INFO [train.py:763] (7/8) Epoch 24, batch 3000, loss[loss=0.196, simple_loss=0.2997, pruned_loss=0.0462, over 6869.00 frames.], tot_loss[loss=0.165, simple_loss=0.2641, pruned_loss=0.03294, over 1427964.87 frames.], batch size: 31, lr: 3.10e-04
+2022-04-30 00:51:06,878 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 00:51:22,143 INFO [train.py:792] (7/8) Epoch 24, validation: loss=0.1679, simple_loss=0.2653, pruned_loss=0.03523, over 698248.00 frames.
+2022-04-30 00:52:37,069 INFO [train.py:763] (7/8) Epoch 24, batch 3050, loss[loss=0.1536, simple_loss=0.2552, pruned_loss=0.02603, over 7112.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2644, pruned_loss=0.03303, over 1427698.17 frames.], batch size: 21, lr: 3.10e-04
+2022-04-30 00:53:42,770 INFO [train.py:763] (7/8) Epoch 24, batch 3100, loss[loss=0.1859, simple_loss=0.2719, pruned_loss=0.04988, over 6832.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2641, pruned_loss=0.03302, over 1429263.67 frames.], batch size: 15, lr: 3.10e-04
+2022-04-30 00:54:48,081 INFO [train.py:763] (7/8) Epoch 24, batch 3150, loss[loss=0.1387, simple_loss=0.2401, pruned_loss=0.01868, over 7251.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2652, pruned_loss=0.03333, over 1430713.20 frames.], batch size: 19, lr: 3.10e-04
+2022-04-30 00:55:53,506 INFO [train.py:763] (7/8) Epoch 24, batch 3200, loss[loss=0.1924, simple_loss=0.2784, pruned_loss=0.05318, over 5109.00 frames.], tot_loss[loss=0.166, simple_loss=0.2649, pruned_loss=0.03355, over 1429015.16 frames.], batch size: 52, lr: 3.10e-04
+2022-04-30 00:56:59,211 INFO [train.py:763] (7/8) Epoch 24, batch 3250, loss[loss=0.2405, simple_loss=0.3307, pruned_loss=0.07509, over 7236.00 frames.], tot_loss[loss=0.167, simple_loss=0.2656, pruned_loss=0.03422, over 1426654.14 frames.], batch size: 20, lr: 3.10e-04
+2022-04-30 00:58:05,428 INFO [train.py:763] (7/8) Epoch 24, batch 3300, loss[loss=0.1732, simple_loss=0.2765, pruned_loss=0.03497, over 7170.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2657, pruned_loss=0.034, over 1426022.04 frames.], batch size: 19, lr: 3.10e-04
+2022-04-30 00:59:11,091 INFO [train.py:763] (7/8) Epoch 24, batch 3350, loss[loss=0.1351, simple_loss=0.2339, pruned_loss=0.01813, over 7248.00 frames.], tot_loss[loss=0.166, simple_loss=0.2647, pruned_loss=0.03365, over 1422634.64 frames.], batch size: 19, lr: 3.10e-04
+2022-04-30 01:00:16,812 INFO [train.py:763] (7/8) Epoch 24, batch 3400, loss[loss=0.1301, simple_loss=0.219, pruned_loss=0.0206, over 7275.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2642, pruned_loss=0.0336, over 1424111.56 frames.], batch size: 17, lr: 3.10e-04
+2022-04-30 01:01:22,336 INFO [train.py:763] (7/8) Epoch 24, batch 3450, loss[loss=0.1688, simple_loss=0.271, pruned_loss=0.03325, over 7219.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2645, pruned_loss=0.03361, over 1421299.45 frames.], batch size: 21, lr: 3.10e-04
+2022-04-30 01:02:27,617 INFO [train.py:763] (7/8) Epoch 24, batch 3500, loss[loss=0.1447, simple_loss=0.2329, pruned_loss=0.0283, over 7155.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2653, pruned_loss=0.03404, over 1422697.01 frames.], batch size: 17, lr: 3.10e-04
+2022-04-30 01:03:33,204 INFO [train.py:763] (7/8) Epoch 24, batch 3550, loss[loss=0.1619, simple_loss=0.2598, pruned_loss=0.03196, over 7335.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2662, pruned_loss=0.03454, over 1423617.74 frames.], batch size: 20, lr: 3.10e-04
+2022-04-30 01:04:38,404 INFO [train.py:763] (7/8) Epoch 24, batch 3600, loss[loss=0.1741, simple_loss=0.2732, pruned_loss=0.03751, over 7216.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2664, pruned_loss=0.03426, over 1421988.92 frames.], batch size: 23, lr: 3.10e-04
+2022-04-30 01:05:45,323 INFO [train.py:763] (7/8) Epoch 24, batch 3650, loss[loss=0.1571, simple_loss=0.2626, pruned_loss=0.02576, over 6432.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2656, pruned_loss=0.03391, over 1418122.06 frames.], batch size: 37, lr: 3.10e-04
+2022-04-30 01:06:51,868 INFO [train.py:763] (7/8) Epoch 24, batch 3700, loss[loss=0.1622, simple_loss=0.2674, pruned_loss=0.02856, over 7437.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2652, pruned_loss=0.0337, over 1420944.63 frames.], batch size: 20, lr: 3.10e-04
+2022-04-30 01:07:57,550 INFO [train.py:763] (7/8) Epoch 24, batch 3750, loss[loss=0.1884, simple_loss=0.292, pruned_loss=0.04241, over 7383.00 frames.], tot_loss[loss=0.1662, simple_loss=0.265, pruned_loss=0.03371, over 1423823.12 frames.], batch size: 23, lr: 3.09e-04
+2022-04-30 01:09:02,959 INFO [train.py:763] (7/8) Epoch 24, batch 3800, loss[loss=0.2099, simple_loss=0.3027, pruned_loss=0.05857, over 5118.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2654, pruned_loss=0.0339, over 1421653.76 frames.], batch size: 52, lr: 3.09e-04
+2022-04-30 01:10:08,037 INFO [train.py:763] (7/8) Epoch 24, batch 3850, loss[loss=0.1588, simple_loss=0.252, pruned_loss=0.03278, over 7294.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2657, pruned_loss=0.03373, over 1421719.08 frames.], batch size: 18, lr: 3.09e-04
+2022-04-30 01:11:13,761 INFO [train.py:763] (7/8) Epoch 24, batch 3900, loss[loss=0.1473, simple_loss=0.2489, pruned_loss=0.02281, over 7262.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2657, pruned_loss=0.03365, over 1421012.22 frames.], batch size: 19, lr: 3.09e-04
+2022-04-30 01:12:19,235 INFO [train.py:763] (7/8) Epoch 24, batch 3950, loss[loss=0.142, simple_loss=0.2338, pruned_loss=0.02512, over 7412.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2653, pruned_loss=0.03318, over 1423079.43 frames.], batch size: 18, lr: 3.09e-04
+2022-04-30 01:13:24,357 INFO [train.py:763] (7/8) Epoch 24, batch 4000, loss[loss=0.1636, simple_loss=0.2649, pruned_loss=0.03115, over 7326.00 frames.], tot_loss[loss=0.1666, simple_loss=0.266, pruned_loss=0.03364, over 1422746.77 frames.], batch size: 21, lr: 3.09e-04
+2022-04-30 01:14:29,879 INFO [train.py:763] (7/8) Epoch 24, batch 4050, loss[loss=0.1455, simple_loss=0.2481, pruned_loss=0.02142, over 7433.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2654, pruned_loss=0.03354, over 1421910.49 frames.], batch size: 20, lr: 3.09e-04
+2022-04-30 01:15:36,743 INFO [train.py:763] (7/8) Epoch 24, batch 4100, loss[loss=0.1771, simple_loss=0.2795, pruned_loss=0.03732, over 6127.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2669, pruned_loss=0.0344, over 1422207.79 frames.], batch size: 37, lr: 3.09e-04
+2022-04-30 01:16:43,491 INFO [train.py:763] (7/8) Epoch 24, batch 4150, loss[loss=0.1687, simple_loss=0.2764, pruned_loss=0.03051, over 7210.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2663, pruned_loss=0.0342, over 1419234.59 frames.], batch size: 21, lr: 3.09e-04
+2022-04-30 01:17:50,175 INFO [train.py:763] (7/8) Epoch 24, batch 4200, loss[loss=0.1756, simple_loss=0.281, pruned_loss=0.03507, over 7196.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2682, pruned_loss=0.03451, over 1420762.40 frames.], batch size: 23, lr: 3.09e-04
+2022-04-30 01:18:56,616 INFO [train.py:763] (7/8) Epoch 24, batch 4250, loss[loss=0.1689, simple_loss=0.2752, pruned_loss=0.03124, over 6536.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2674, pruned_loss=0.03418, over 1415685.91 frames.], batch size: 37, lr: 3.09e-04
+2022-04-30 01:20:02,379 INFO [train.py:763] (7/8) Epoch 24, batch 4300, loss[loss=0.1464, simple_loss=0.2448, pruned_loss=0.02401, over 7160.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2672, pruned_loss=0.03446, over 1415109.89 frames.], batch size: 19, lr: 3.09e-04
+2022-04-30 01:21:09,419 INFO [train.py:763] (7/8) Epoch 24, batch 4350, loss[loss=0.1985, simple_loss=0.3033, pruned_loss=0.04681, over 7274.00 frames.], tot_loss[loss=0.1675, simple_loss=0.266, pruned_loss=0.03455, over 1414716.64 frames.], batch size: 25, lr: 3.09e-04
+2022-04-30 01:22:16,110 INFO [train.py:763] (7/8) Epoch 24, batch 4400, loss[loss=0.193, simple_loss=0.2962, pruned_loss=0.04495, over 7279.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2671, pruned_loss=0.03475, over 1413603.48 frames.], batch size: 24, lr: 3.09e-04
+2022-04-30 01:23:21,730 INFO [train.py:763] (7/8) Epoch 24, batch 4450, loss[loss=0.1897, simple_loss=0.2923, pruned_loss=0.04352, over 7297.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2678, pruned_loss=0.03465, over 1405144.85 frames.], batch size: 25, lr: 3.09e-04
+2022-04-30 01:24:28,212 INFO [train.py:763] (7/8) Epoch 24, batch 4500, loss[loss=0.2004, simple_loss=0.2842, pruned_loss=0.05827, over 5330.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2694, pruned_loss=0.03542, over 1389319.14 frames.], batch size: 52, lr: 3.08e-04
+2022-04-30 01:25:32,957 INFO [train.py:763] (7/8) Epoch 24, batch 4550, loss[loss=0.1831, simple_loss=0.2788, pruned_loss=0.04366, over 4928.00 frames.], tot_loss[loss=0.172, simple_loss=0.2713, pruned_loss=0.0363, over 1352404.12 frames.], batch size: 52, lr: 3.08e-04
+2022-04-30 01:26:52,298 INFO [train.py:763] (7/8) Epoch 25, batch 0, loss[loss=0.1763, simple_loss=0.2825, pruned_loss=0.03501, over 7222.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2825, pruned_loss=0.03501, over 7222.00 frames.], batch size: 21, lr: 3.02e-04
+2022-04-30 01:27:58,479 INFO [train.py:763] (7/8) Epoch 25, batch 50, loss[loss=0.1546, simple_loss=0.2585, pruned_loss=0.02535, over 7318.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2612, pruned_loss=0.03174, over 322029.42 frames.], batch size: 21, lr: 3.02e-04
+2022-04-30 01:29:03,637 INFO [train.py:763] (7/8) Epoch 25, batch 100, loss[loss=0.2009, simple_loss=0.2915, pruned_loss=0.05512, over 5261.00 frames.], tot_loss[loss=0.1652, simple_loss=0.264, pruned_loss=0.03319, over 566462.72 frames.], batch size: 53, lr: 3.02e-04
+2022-04-30 01:30:08,890 INFO [train.py:763] (7/8) Epoch 25, batch 150, loss[loss=0.125, simple_loss=0.2107, pruned_loss=0.01963, over 7282.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2643, pruned_loss=0.03312, over 760555.85 frames.], batch size: 17, lr: 3.02e-04
+2022-04-30 01:31:14,502 INFO [train.py:763] (7/8) Epoch 25, batch 200, loss[loss=0.1925, simple_loss=0.2907, pruned_loss=0.04719, over 7378.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2639, pruned_loss=0.03309, over 907509.86 frames.], batch size: 23, lr: 3.02e-04
+2022-04-30 01:32:20,369 INFO [train.py:763] (7/8) Epoch 25, batch 250, loss[loss=0.1884, simple_loss=0.295, pruned_loss=0.04092, over 7193.00 frames.], tot_loss[loss=0.166, simple_loss=0.2651, pruned_loss=0.03349, over 1020048.20 frames.], batch size: 22, lr: 3.02e-04
+2022-04-30 01:33:26,245 INFO [train.py:763] (7/8) Epoch 25, batch 300, loss[loss=0.1673, simple_loss=0.2741, pruned_loss=0.03024, over 7326.00 frames.], tot_loss[loss=0.1664, simple_loss=0.266, pruned_loss=0.03344, over 1106618.48 frames.], batch size: 20, lr: 3.02e-04
+2022-04-30 01:34:31,524 INFO [train.py:763] (7/8) Epoch 25, batch 350, loss[loss=0.1902, simple_loss=0.2877, pruned_loss=0.04638, over 7169.00 frames.], tot_loss[loss=0.165, simple_loss=0.2644, pruned_loss=0.03277, over 1176330.71 frames.], batch size: 18, lr: 3.02e-04
+2022-04-30 01:35:36,796 INFO [train.py:763] (7/8) Epoch 25, batch 400, loss[loss=0.1497, simple_loss=0.2399, pruned_loss=0.02972, over 7411.00 frames.], tot_loss[loss=0.1655, simple_loss=0.265, pruned_loss=0.03299, over 1234380.35 frames.], batch size: 18, lr: 3.02e-04
+2022-04-30 01:36:42,362 INFO [train.py:763] (7/8) Epoch 25, batch 450, loss[loss=0.1664, simple_loss=0.2731, pruned_loss=0.02983, over 7416.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2655, pruned_loss=0.03281, over 1274943.83 frames.], batch size: 21, lr: 3.02e-04
+2022-04-30 01:37:47,510 INFO [train.py:763] (7/8) Epoch 25, batch 500, loss[loss=0.1811, simple_loss=0.2776, pruned_loss=0.04228, over 7394.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2659, pruned_loss=0.03297, over 1303111.90 frames.], batch size: 23, lr: 3.02e-04
+2022-04-30 01:38:52,816 INFO [train.py:763] (7/8) Epoch 25, batch 550, loss[loss=0.1507, simple_loss=0.2506, pruned_loss=0.0254, over 7237.00 frames.], tot_loss[loss=0.165, simple_loss=0.2645, pruned_loss=0.03273, over 1329306.90 frames.], batch size: 20, lr: 3.02e-04
+2022-04-30 01:39:58,993 INFO [train.py:763] (7/8) Epoch 25, batch 600, loss[loss=0.1672, simple_loss=0.2761, pruned_loss=0.02916, over 7050.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2649, pruned_loss=0.03285, over 1347249.94 frames.], batch size: 28, lr: 3.02e-04
+2022-04-30 01:41:04,688 INFO [train.py:763] (7/8) Epoch 25, batch 650, loss[loss=0.1799, simple_loss=0.2817, pruned_loss=0.0391, over 7330.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2642, pruned_loss=0.03263, over 1361510.32 frames.], batch size: 20, lr: 3.02e-04
+2022-04-30 01:42:10,715 INFO [train.py:763] (7/8) Epoch 25, batch 700, loss[loss=0.2288, simple_loss=0.3162, pruned_loss=0.07077, over 7146.00 frames.], tot_loss[loss=0.165, simple_loss=0.2644, pruned_loss=0.03281, over 1374835.90 frames.], batch size: 20, lr: 3.02e-04
+2022-04-30 01:43:16,104 INFO [train.py:763] (7/8) Epoch 25, batch 750, loss[loss=0.1526, simple_loss=0.2572, pruned_loss=0.02403, over 7418.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2646, pruned_loss=0.03263, over 1390204.86 frames.], batch size: 20, lr: 3.01e-04
+2022-04-30 01:44:20,972 INFO [train.py:763] (7/8) Epoch 25, batch 800, loss[loss=0.1823, simple_loss=0.2809, pruned_loss=0.04185, over 6719.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2651, pruned_loss=0.03308, over 1395317.65 frames.], batch size: 31, lr: 3.01e-04
+2022-04-30 01:45:26,288 INFO [train.py:763] (7/8) Epoch 25, batch 850, loss[loss=0.1501, simple_loss=0.2521, pruned_loss=0.02402, over 7107.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2651, pruned_loss=0.03267, over 1405710.95 frames.], batch size: 21, lr: 3.01e-04
+2022-04-30 01:46:33,111 INFO [train.py:763] (7/8) Epoch 25, batch 900, loss[loss=0.1563, simple_loss=0.2474, pruned_loss=0.03255, over 6824.00 frames.], tot_loss[loss=0.1652, simple_loss=0.265, pruned_loss=0.0327, over 1405528.69 frames.], batch size: 15, lr: 3.01e-04
+2022-04-30 01:47:40,162 INFO [train.py:763] (7/8) Epoch 25, batch 950, loss[loss=0.1459, simple_loss=0.2357, pruned_loss=0.02804, over 7285.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2649, pruned_loss=0.03281, over 1412418.29 frames.], batch size: 17, lr: 3.01e-04
+2022-04-30 01:48:46,814 INFO [train.py:763] (7/8) Epoch 25, batch 1000, loss[loss=0.1884, simple_loss=0.291, pruned_loss=0.04288, over 7119.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2651, pruned_loss=0.03285, over 1411322.64 frames.], batch size: 21, lr: 3.01e-04
+2022-04-30 01:49:52,620 INFO [train.py:763] (7/8) Epoch 25, batch 1050, loss[loss=0.1575, simple_loss=0.2524, pruned_loss=0.03132, over 5304.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2659, pruned_loss=0.03283, over 1412447.96 frames.], batch size: 52, lr: 3.01e-04
+2022-04-30 01:50:59,156 INFO [train.py:763] (7/8) Epoch 25, batch 1100, loss[loss=0.1621, simple_loss=0.2765, pruned_loss=0.02384, over 7124.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2657, pruned_loss=0.03266, over 1413576.58 frames.], batch size: 21, lr: 3.01e-04
+2022-04-30 01:52:04,521 INFO [train.py:763] (7/8) Epoch 25, batch 1150, loss[loss=0.1715, simple_loss=0.2698, pruned_loss=0.0366, over 7374.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2657, pruned_loss=0.033, over 1417214.45 frames.], batch size: 23, lr: 3.01e-04
+2022-04-30 01:53:10,916 INFO [train.py:763] (7/8) Epoch 25, batch 1200, loss[loss=0.1483, simple_loss=0.2521, pruned_loss=0.02222, over 7149.00 frames.], tot_loss[loss=0.166, simple_loss=0.2654, pruned_loss=0.03332, over 1421165.07 frames.], batch size: 17, lr: 3.01e-04
+2022-04-30 01:54:16,915 INFO [train.py:763] (7/8) Epoch 25, batch 1250, loss[loss=0.19, simple_loss=0.2873, pruned_loss=0.04634, over 7319.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2659, pruned_loss=0.03358, over 1423343.36 frames.], batch size: 21, lr: 3.01e-04
+2022-04-30 01:55:23,806 INFO [train.py:763] (7/8) Epoch 25, batch 1300, loss[loss=0.1413, simple_loss=0.2466, pruned_loss=0.01798, over 7431.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2656, pruned_loss=0.03337, over 1426343.97 frames.], batch size: 20, lr: 3.01e-04
+2022-04-30 01:56:30,382 INFO [train.py:763] (7/8) Epoch 25, batch 1350, loss[loss=0.1694, simple_loss=0.2748, pruned_loss=0.03201, over 7318.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2663, pruned_loss=0.03336, over 1426682.09 frames.], batch size: 21, lr: 3.01e-04
+2022-04-30 01:57:36,872 INFO [train.py:763] (7/8) Epoch 25, batch 1400, loss[loss=0.1647, simple_loss=0.2725, pruned_loss=0.02842, over 7340.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2661, pruned_loss=0.03353, over 1427209.78 frames.], batch size: 22, lr: 3.01e-04
+2022-04-30 01:58:42,276 INFO [train.py:763] (7/8) Epoch 25, batch 1450, loss[loss=0.1551, simple_loss=0.2349, pruned_loss=0.03771, over 6997.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2667, pruned_loss=0.03378, over 1428975.53 frames.], batch size: 16, lr: 3.01e-04
+2022-04-30 01:59:49,368 INFO [train.py:763] (7/8) Epoch 25, batch 1500, loss[loss=0.1717, simple_loss=0.2755, pruned_loss=0.03392, over 7231.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2658, pruned_loss=0.03349, over 1428105.03 frames.], batch size: 21, lr: 3.00e-04
+2022-04-30 02:00:55,054 INFO [train.py:763] (7/8) Epoch 25, batch 1550, loss[loss=0.1357, simple_loss=0.2333, pruned_loss=0.01902, over 7127.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2655, pruned_loss=0.03337, over 1427410.96 frames.], batch size: 17, lr: 3.00e-04
+2022-04-30 02:02:00,080 INFO [train.py:763] (7/8) Epoch 25, batch 1600, loss[loss=0.1713, simple_loss=0.2732, pruned_loss=0.03471, over 7143.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2664, pruned_loss=0.03387, over 1424515.64 frames.], batch size: 20, lr: 3.00e-04
+2022-04-30 02:03:05,641 INFO [train.py:763] (7/8) Epoch 25, batch 1650, loss[loss=0.1724, simple_loss=0.2832, pruned_loss=0.03079, over 7098.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2649, pruned_loss=0.03344, over 1425318.69 frames.], batch size: 28, lr: 3.00e-04
+2022-04-30 02:04:10,617 INFO [train.py:763] (7/8) Epoch 25, batch 1700, loss[loss=0.1759, simple_loss=0.279, pruned_loss=0.03637, over 7320.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2652, pruned_loss=0.03327, over 1425046.89 frames.], batch size: 21, lr: 3.00e-04
+2022-04-30 02:05:15,849 INFO [train.py:763] (7/8) Epoch 25, batch 1750, loss[loss=0.1477, simple_loss=0.2402, pruned_loss=0.02764, over 7141.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2649, pruned_loss=0.0333, over 1424706.12 frames.], batch size: 17, lr: 3.00e-04
+2022-04-30 02:06:21,048 INFO [train.py:763] (7/8) Epoch 25, batch 1800, loss[loss=0.1551, simple_loss=0.2565, pruned_loss=0.02683, over 7146.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2645, pruned_loss=0.03348, over 1420690.75 frames.], batch size: 20, lr: 3.00e-04
+2022-04-30 02:07:26,304 INFO [train.py:763] (7/8) Epoch 25, batch 1850, loss[loss=0.1708, simple_loss=0.2668, pruned_loss=0.03743, over 7443.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2645, pruned_loss=0.03342, over 1421390.05 frames.], batch size: 20, lr: 3.00e-04
+2022-04-30 02:08:31,453 INFO [train.py:763] (7/8) Epoch 25, batch 1900, loss[loss=0.1473, simple_loss=0.2303, pruned_loss=0.03217, over 7140.00 frames.], tot_loss[loss=0.166, simple_loss=0.265, pruned_loss=0.03349, over 1422104.55 frames.], batch size: 17, lr: 3.00e-04
+2022-04-30 02:09:36,792 INFO [train.py:763] (7/8) Epoch 25, batch 1950, loss[loss=0.1814, simple_loss=0.2789, pruned_loss=0.04196, over 4865.00 frames.], tot_loss[loss=0.166, simple_loss=0.265, pruned_loss=0.03354, over 1419591.55 frames.], batch size: 53, lr: 3.00e-04
+2022-04-30 02:10:42,036 INFO [train.py:763] (7/8) Epoch 25, batch 2000, loss[loss=0.1477, simple_loss=0.241, pruned_loss=0.02718, over 7155.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2648, pruned_loss=0.03345, over 1417386.78 frames.], batch size: 19, lr: 3.00e-04
+2022-04-30 02:11:47,917 INFO [train.py:763] (7/8) Epoch 25, batch 2050, loss[loss=0.1676, simple_loss=0.2759, pruned_loss=0.02964, over 7318.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2649, pruned_loss=0.03346, over 1418959.98 frames.], batch size: 20, lr: 3.00e-04
+2022-04-30 02:12:54,279 INFO [train.py:763] (7/8) Epoch 25, batch 2100, loss[loss=0.205, simple_loss=0.2871, pruned_loss=0.06143, over 7216.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2654, pruned_loss=0.03379, over 1418048.51 frames.], batch size: 22, lr: 3.00e-04
+2022-04-30 02:13:59,572 INFO [train.py:763] (7/8) Epoch 25, batch 2150, loss[loss=0.151, simple_loss=0.2466, pruned_loss=0.02766, over 7170.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2665, pruned_loss=0.03413, over 1420464.39 frames.], batch size: 18, lr: 3.00e-04
+2022-04-30 02:15:05,491 INFO [train.py:763] (7/8) Epoch 25, batch 2200, loss[loss=0.1665, simple_loss=0.2699, pruned_loss=0.03156, over 7050.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2659, pruned_loss=0.03356, over 1422629.19 frames.], batch size: 28, lr: 3.00e-04
+2022-04-30 02:16:11,392 INFO [train.py:763] (7/8) Epoch 25, batch 2250, loss[loss=0.1711, simple_loss=0.2774, pruned_loss=0.03246, over 7369.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2652, pruned_loss=0.03317, over 1424904.68 frames.], batch size: 23, lr: 3.00e-04
+2022-04-30 02:17:16,601 INFO [train.py:763] (7/8) Epoch 25, batch 2300, loss[loss=0.1506, simple_loss=0.2496, pruned_loss=0.0258, over 7064.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2659, pruned_loss=0.0332, over 1424935.44 frames.], batch size: 18, lr: 2.99e-04
+2022-04-30 02:18:23,394 INFO [train.py:763] (7/8) Epoch 25, batch 2350, loss[loss=0.1528, simple_loss=0.2588, pruned_loss=0.0234, over 7251.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2649, pruned_loss=0.03319, over 1425851.98 frames.], batch size: 19, lr: 2.99e-04
+2022-04-30 02:19:30,581 INFO [train.py:763] (7/8) Epoch 25, batch 2400, loss[loss=0.186, simple_loss=0.2887, pruned_loss=0.04167, over 7386.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2648, pruned_loss=0.03332, over 1423457.35 frames.], batch size: 23, lr: 2.99e-04
+2022-04-30 02:20:35,965 INFO [train.py:763] (7/8) Epoch 25, batch 2450, loss[loss=0.1732, simple_loss=0.2723, pruned_loss=0.03705, over 6744.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2641, pruned_loss=0.03304, over 1421897.63 frames.], batch size: 31, lr: 2.99e-04
+2022-04-30 02:21:42,830 INFO [train.py:763] (7/8) Epoch 25, batch 2500, loss[loss=0.1556, simple_loss=0.2565, pruned_loss=0.02741, over 7353.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2634, pruned_loss=0.03291, over 1424367.37 frames.], batch size: 19, lr: 2.99e-04
+2022-04-30 02:22:48,836 INFO [train.py:763] (7/8) Epoch 25, batch 2550, loss[loss=0.143, simple_loss=0.242, pruned_loss=0.02204, over 7408.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2639, pruned_loss=0.03337, over 1426554.28 frames.], batch size: 18, lr: 2.99e-04
+2022-04-30 02:23:56,426 INFO [train.py:763] (7/8) Epoch 25, batch 2600, loss[loss=0.1594, simple_loss=0.2506, pruned_loss=0.03413, over 7158.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2641, pruned_loss=0.03353, over 1423835.71 frames.], batch size: 19, lr: 2.99e-04
+2022-04-30 02:25:02,547 INFO [train.py:763] (7/8) Epoch 25, batch 2650, loss[loss=0.1674, simple_loss=0.2691, pruned_loss=0.03285, over 7090.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2649, pruned_loss=0.03373, over 1419474.03 frames.], batch size: 28, lr: 2.99e-04
+2022-04-30 02:26:07,764 INFO [train.py:763] (7/8) Epoch 25, batch 2700, loss[loss=0.1418, simple_loss=0.2424, pruned_loss=0.02062, over 7254.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2641, pruned_loss=0.03317, over 1420732.28 frames.], batch size: 19, lr: 2.99e-04
+2022-04-30 02:27:12,947 INFO [train.py:763] (7/8) Epoch 25, batch 2750, loss[loss=0.1923, simple_loss=0.2914, pruned_loss=0.04666, over 7305.00 frames.], tot_loss[loss=0.166, simple_loss=0.2647, pruned_loss=0.03366, over 1413609.73 frames.], batch size: 25, lr: 2.99e-04
+2022-04-30 02:28:19,377 INFO [train.py:763] (7/8) Epoch 25, batch 2800, loss[loss=0.1288, simple_loss=0.233, pruned_loss=0.01236, over 7279.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2643, pruned_loss=0.03347, over 1416366.99 frames.], batch size: 18, lr: 2.99e-04
+2022-04-30 02:29:24,937 INFO [train.py:763] (7/8) Epoch 25, batch 2850, loss[loss=0.1473, simple_loss=0.2497, pruned_loss=0.02238, over 7417.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2636, pruned_loss=0.03295, over 1411930.29 frames.], batch size: 21, lr: 2.99e-04
+2022-04-30 02:30:30,628 INFO [train.py:763] (7/8) Epoch 25, batch 2900, loss[loss=0.1767, simple_loss=0.2781, pruned_loss=0.03765, over 7154.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2639, pruned_loss=0.0329, over 1418370.23 frames.], batch size: 20, lr: 2.99e-04
+2022-04-30 02:31:35,895 INFO [train.py:763] (7/8) Epoch 25, batch 2950, loss[loss=0.1532, simple_loss=0.2628, pruned_loss=0.02183, over 7309.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2649, pruned_loss=0.03288, over 1418459.07 frames.], batch size: 20, lr: 2.99e-04
+2022-04-30 02:32:41,168 INFO [train.py:763] (7/8) Epoch 25, batch 3000, loss[loss=0.1662, simple_loss=0.2718, pruned_loss=0.03028, over 6516.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2653, pruned_loss=0.03284, over 1423054.60 frames.], batch size: 38, lr: 2.99e-04
+2022-04-30 02:32:41,169 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 02:32:56,273 INFO [train.py:792] (7/8) Epoch 25, validation: loss=0.1697, simple_loss=0.2684, pruned_loss=0.03548, over 698248.00 frames.
+2022-04-30 02:34:02,080 INFO [train.py:763] (7/8) Epoch 25, batch 3050, loss[loss=0.1586, simple_loss=0.2611, pruned_loss=0.02803, over 7333.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2658, pruned_loss=0.03319, over 1421873.00 frames.], batch size: 22, lr: 2.99e-04
+2022-04-30 02:35:09,278 INFO [train.py:763] (7/8) Epoch 25, batch 3100, loss[loss=0.1455, simple_loss=0.2503, pruned_loss=0.02035, over 7257.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2649, pruned_loss=0.03278, over 1419230.38 frames.], batch size: 19, lr: 2.98e-04
+2022-04-30 02:36:16,369 INFO [train.py:763] (7/8) Epoch 25, batch 3150, loss[loss=0.147, simple_loss=0.2383, pruned_loss=0.02784, over 7161.00 frames.], tot_loss[loss=0.165, simple_loss=0.2644, pruned_loss=0.03278, over 1418333.17 frames.], batch size: 17, lr: 2.98e-04
+2022-04-30 02:37:22,265 INFO [train.py:763] (7/8) Epoch 25, batch 3200, loss[loss=0.1658, simple_loss=0.2644, pruned_loss=0.03362, over 7141.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2642, pruned_loss=0.03266, over 1421186.42 frames.], batch size: 19, lr: 2.98e-04
+2022-04-30 02:38:29,220 INFO [train.py:763] (7/8) Epoch 25, batch 3250, loss[loss=0.1467, simple_loss=0.2393, pruned_loss=0.02705, over 7267.00 frames.], tot_loss[loss=0.1637, simple_loss=0.263, pruned_loss=0.03226, over 1424812.69 frames.], batch size: 18, lr: 2.98e-04
+2022-04-30 02:39:35,765 INFO [train.py:763] (7/8) Epoch 25, batch 3300, loss[loss=0.1898, simple_loss=0.2915, pruned_loss=0.04402, over 7161.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2644, pruned_loss=0.03312, over 1417667.54 frames.], batch size: 26, lr: 2.98e-04
+2022-04-30 02:40:42,713 INFO [train.py:763] (7/8) Epoch 25, batch 3350, loss[loss=0.1714, simple_loss=0.2641, pruned_loss=0.03934, over 7319.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2644, pruned_loss=0.03341, over 1414270.47 frames.], batch size: 21, lr: 2.98e-04
+2022-04-30 02:41:49,879 INFO [train.py:763] (7/8) Epoch 25, batch 3400, loss[loss=0.1687, simple_loss=0.281, pruned_loss=0.02819, over 6460.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2637, pruned_loss=0.03321, over 1418881.47 frames.], batch size: 37, lr: 2.98e-04
+2022-04-30 02:42:55,401 INFO [train.py:763] (7/8) Epoch 25, batch 3450, loss[loss=0.1654, simple_loss=0.2483, pruned_loss=0.04129, over 7162.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2633, pruned_loss=0.03343, over 1419137.00 frames.], batch size: 18, lr: 2.98e-04
+2022-04-30 02:44:00,611 INFO [train.py:763] (7/8) Epoch 25, batch 3500, loss[loss=0.1861, simple_loss=0.2961, pruned_loss=0.0381, over 7389.00 frames.], tot_loss[loss=0.1653, simple_loss=0.264, pruned_loss=0.03336, over 1418586.16 frames.], batch size: 23, lr: 2.98e-04
+2022-04-30 02:45:06,564 INFO [train.py:763] (7/8) Epoch 25, batch 3550, loss[loss=0.1761, simple_loss=0.2798, pruned_loss=0.03618, over 7406.00 frames.], tot_loss[loss=0.165, simple_loss=0.2639, pruned_loss=0.03302, over 1420959.95 frames.], batch size: 21, lr: 2.98e-04
+2022-04-30 02:46:12,324 INFO [train.py:763] (7/8) Epoch 25, batch 3600, loss[loss=0.1725, simple_loss=0.2669, pruned_loss=0.03906, over 7196.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2633, pruned_loss=0.03308, over 1425468.51 frames.], batch size: 23, lr: 2.98e-04
+2022-04-30 02:47:18,137 INFO [train.py:763] (7/8) Epoch 25, batch 3650, loss[loss=0.1508, simple_loss=0.2473, pruned_loss=0.02722, over 7263.00 frames.], tot_loss[loss=0.165, simple_loss=0.2641, pruned_loss=0.03298, over 1426857.09 frames.], batch size: 19, lr: 2.98e-04
+2022-04-30 02:48:25,803 INFO [train.py:763] (7/8) Epoch 25, batch 3700, loss[loss=0.1697, simple_loss=0.261, pruned_loss=0.03916, over 7072.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2641, pruned_loss=0.03313, over 1424850.88 frames.], batch size: 18, lr: 2.98e-04
+2022-04-30 02:49:32,865 INFO [train.py:763] (7/8) Epoch 25, batch 3750, loss[loss=0.1524, simple_loss=0.2593, pruned_loss=0.0228, over 7155.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2642, pruned_loss=0.03306, over 1423916.13 frames.], batch size: 19, lr: 2.98e-04
+2022-04-30 02:50:38,255 INFO [train.py:763] (7/8) Epoch 25, batch 3800, loss[loss=0.1975, simple_loss=0.2804, pruned_loss=0.05733, over 6311.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2644, pruned_loss=0.03357, over 1422290.33 frames.], batch size: 37, lr: 2.98e-04
+2022-04-30 02:51:43,571 INFO [train.py:763] (7/8) Epoch 25, batch 3850, loss[loss=0.1523, simple_loss=0.2508, pruned_loss=0.02693, over 7158.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2645, pruned_loss=0.03361, over 1419592.47 frames.], batch size: 20, lr: 2.97e-04
+2022-04-30 02:52:57,818 INFO [train.py:763] (7/8) Epoch 25, batch 3900, loss[loss=0.1372, simple_loss=0.2272, pruned_loss=0.02359, over 7404.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2654, pruned_loss=0.03381, over 1421791.71 frames.], batch size: 18, lr: 2.97e-04
+2022-04-30 02:54:03,682 INFO [train.py:763] (7/8) Epoch 25, batch 3950, loss[loss=0.1664, simple_loss=0.277, pruned_loss=0.02791, over 7230.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2656, pruned_loss=0.0339, over 1426462.50 frames.], batch size: 20, lr: 2.97e-04
+2022-04-30 02:55:09,646 INFO [train.py:763] (7/8) Epoch 25, batch 4000, loss[loss=0.1403, simple_loss=0.2413, pruned_loss=0.01963, over 7433.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2655, pruned_loss=0.0341, over 1418888.56 frames.], batch size: 20, lr: 2.97e-04
+2022-04-30 02:56:14,894 INFO [train.py:763] (7/8) Epoch 25, batch 4050, loss[loss=0.1767, simple_loss=0.2679, pruned_loss=0.04273, over 7409.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2658, pruned_loss=0.03398, over 1420021.16 frames.], batch size: 21, lr: 2.97e-04
+2022-04-30 02:57:21,076 INFO [train.py:763] (7/8) Epoch 25, batch 4100, loss[loss=0.161, simple_loss=0.2697, pruned_loss=0.02614, over 7422.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2655, pruned_loss=0.03389, over 1418710.08 frames.], batch size: 21, lr: 2.97e-04
+2022-04-30 02:58:26,425 INFO [train.py:763] (7/8) Epoch 25, batch 4150, loss[loss=0.1746, simple_loss=0.2701, pruned_loss=0.03952, over 7256.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2653, pruned_loss=0.0338, over 1423468.01 frames.], batch size: 19, lr: 2.97e-04
+2022-04-30 02:59:32,231 INFO [train.py:763] (7/8) Epoch 25, batch 4200, loss[loss=0.1782, simple_loss=0.2883, pruned_loss=0.03411, over 7088.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2653, pruned_loss=0.03384, over 1419504.77 frames.], batch size: 28, lr: 2.97e-04
+2022-04-30 03:00:37,748 INFO [train.py:763] (7/8) Epoch 25, batch 4250, loss[loss=0.1395, simple_loss=0.2374, pruned_loss=0.02077, over 7157.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2653, pruned_loss=0.03375, over 1419450.36 frames.], batch size: 18, lr: 2.97e-04
+2022-04-30 03:01:43,177 INFO [train.py:763] (7/8) Epoch 25, batch 4300, loss[loss=0.1824, simple_loss=0.2968, pruned_loss=0.03402, over 7165.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2668, pruned_loss=0.03427, over 1423335.33 frames.], batch size: 26, lr: 2.97e-04
+2022-04-30 03:03:06,207 INFO [train.py:763] (7/8) Epoch 25, batch 4350, loss[loss=0.1605, simple_loss=0.263, pruned_loss=0.02899, over 7239.00 frames.], tot_loss[loss=0.1671, simple_loss=0.266, pruned_loss=0.03411, over 1415614.41 frames.], batch size: 20, lr: 2.97e-04
+2022-04-30 03:04:20,108 INFO [train.py:763] (7/8) Epoch 25, batch 4400, loss[loss=0.1417, simple_loss=0.2404, pruned_loss=0.02151, over 7074.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2667, pruned_loss=0.03397, over 1414894.55 frames.], batch size: 18, lr: 2.97e-04
+2022-04-30 03:05:34,216 INFO [train.py:763] (7/8) Epoch 25, batch 4450, loss[loss=0.1868, simple_loss=0.285, pruned_loss=0.04424, over 7288.00 frames.], tot_loss[loss=0.167, simple_loss=0.2662, pruned_loss=0.03392, over 1413647.95 frames.], batch size: 24, lr: 2.97e-04
+2022-04-30 03:06:39,200 INFO [train.py:763] (7/8) Epoch 25, batch 4500, loss[loss=0.1673, simple_loss=0.2637, pruned_loss=0.03539, over 7322.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2666, pruned_loss=0.03415, over 1398768.10 frames.], batch size: 20, lr: 2.97e-04
+2022-04-30 03:08:11,357 INFO [train.py:763] (7/8) Epoch 25, batch 4550, loss[loss=0.1973, simple_loss=0.2781, pruned_loss=0.05829, over 4887.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2674, pruned_loss=0.03491, over 1389118.44 frames.], batch size: 52, lr: 2.97e-04
+2022-04-30 03:09:39,553 INFO [train.py:763] (7/8) Epoch 26, batch 0, loss[loss=0.1634, simple_loss=0.2535, pruned_loss=0.03669, over 7181.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2535, pruned_loss=0.03669, over 7181.00 frames.], batch size: 18, lr: 2.91e-04
+2022-04-30 03:10:45,456 INFO [train.py:763] (7/8) Epoch 26, batch 50, loss[loss=0.1543, simple_loss=0.2409, pruned_loss=0.03383, over 7281.00 frames.], tot_loss[loss=0.1662, simple_loss=0.265, pruned_loss=0.03368, over 318322.74 frames.], batch size: 17, lr: 2.91e-04
+2022-04-30 03:11:50,717 INFO [train.py:763] (7/8) Epoch 26, batch 100, loss[loss=0.1348, simple_loss=0.2327, pruned_loss=0.01845, over 7274.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2634, pruned_loss=0.03247, over 562467.95 frames.], batch size: 17, lr: 2.91e-04
+2022-04-30 03:12:56,056 INFO [train.py:763] (7/8) Epoch 26, batch 150, loss[loss=0.1615, simple_loss=0.2634, pruned_loss=0.02975, over 6491.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2636, pruned_loss=0.03212, over 751142.31 frames.], batch size: 38, lr: 2.91e-04
+2022-04-30 03:14:01,254 INFO [train.py:763] (7/8) Epoch 26, batch 200, loss[loss=0.1784, simple_loss=0.2791, pruned_loss=0.03885, over 7162.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2649, pruned_loss=0.0332, over 894587.02 frames.], batch size: 26, lr: 2.91e-04
+2022-04-30 03:15:07,048 INFO [train.py:763] (7/8) Epoch 26, batch 250, loss[loss=0.1738, simple_loss=0.2712, pruned_loss=0.03817, over 6456.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2659, pruned_loss=0.03363, over 1007667.25 frames.], batch size: 38, lr: 2.91e-04
+2022-04-30 03:16:13,128 INFO [train.py:763] (7/8) Epoch 26, batch 300, loss[loss=0.1634, simple_loss=0.2739, pruned_loss=0.02648, over 6609.00 frames.], tot_loss[loss=0.166, simple_loss=0.2651, pruned_loss=0.03343, over 1102071.36 frames.], batch size: 38, lr: 2.91e-04
+2022-04-30 03:17:18,455 INFO [train.py:763] (7/8) Epoch 26, batch 350, loss[loss=0.1782, simple_loss=0.2728, pruned_loss=0.04181, over 6742.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2637, pruned_loss=0.03341, over 1169284.06 frames.], batch size: 31, lr: 2.91e-04
+2022-04-30 03:18:23,755 INFO [train.py:763] (7/8) Epoch 26, batch 400, loss[loss=0.1696, simple_loss=0.2682, pruned_loss=0.03554, over 7143.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2637, pruned_loss=0.03333, over 1229005.49 frames.], batch size: 20, lr: 2.91e-04
+2022-04-30 03:19:29,476 INFO [train.py:763] (7/8) Epoch 26, batch 450, loss[loss=0.1489, simple_loss=0.256, pruned_loss=0.02087, over 7232.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2636, pruned_loss=0.03271, over 1277303.43 frames.], batch size: 20, lr: 2.91e-04
+2022-04-30 03:20:34,852 INFO [train.py:763] (7/8) Epoch 26, batch 500, loss[loss=0.2086, simple_loss=0.2952, pruned_loss=0.061, over 5522.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2633, pruned_loss=0.03259, over 1309407.35 frames.], batch size: 52, lr: 2.91e-04
+2022-04-30 03:21:40,175 INFO [train.py:763] (7/8) Epoch 26, batch 550, loss[loss=0.1607, simple_loss=0.2613, pruned_loss=0.03, over 7204.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2633, pruned_loss=0.03273, over 1333505.11 frames.], batch size: 22, lr: 2.90e-04
+2022-04-30 03:22:45,584 INFO [train.py:763] (7/8) Epoch 26, batch 600, loss[loss=0.1364, simple_loss=0.2374, pruned_loss=0.0177, over 7263.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2636, pruned_loss=0.03261, over 1356229.63 frames.], batch size: 19, lr: 2.90e-04
+2022-04-30 03:23:51,110 INFO [train.py:763] (7/8) Epoch 26, batch 650, loss[loss=0.1479, simple_loss=0.2382, pruned_loss=0.02883, over 7286.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2633, pruned_loss=0.03246, over 1372610.83 frames.], batch size: 18, lr: 2.90e-04
+2022-04-30 03:24:56,243 INFO [train.py:763] (7/8) Epoch 26, batch 700, loss[loss=0.1733, simple_loss=0.2732, pruned_loss=0.03676, over 7116.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2646, pruned_loss=0.03293, over 1381509.14 frames.], batch size: 21, lr: 2.90e-04
+2022-04-30 03:26:12,132 INFO [train.py:763] (7/8) Epoch 26, batch 750, loss[loss=0.1527, simple_loss=0.2557, pruned_loss=0.02483, over 7155.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2641, pruned_loss=0.03281, over 1389626.62 frames.], batch size: 20, lr: 2.90e-04
+2022-04-30 03:27:17,959 INFO [train.py:763] (7/8) Epoch 26, batch 800, loss[loss=0.1811, simple_loss=0.2895, pruned_loss=0.03639, over 7241.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2642, pruned_loss=0.03272, over 1396260.84 frames.], batch size: 20, lr: 2.90e-04
+2022-04-30 03:28:23,830 INFO [train.py:763] (7/8) Epoch 26, batch 850, loss[loss=0.1914, simple_loss=0.2858, pruned_loss=0.04846, over 5115.00 frames.], tot_loss[loss=0.1652, simple_loss=0.265, pruned_loss=0.03268, over 1399004.72 frames.], batch size: 52, lr: 2.90e-04
+2022-04-30 03:29:29,385 INFO [train.py:763] (7/8) Epoch 26, batch 900, loss[loss=0.1572, simple_loss=0.2508, pruned_loss=0.03184, over 7403.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2645, pruned_loss=0.03286, over 1408594.14 frames.], batch size: 18, lr: 2.90e-04
+2022-04-30 03:30:35,262 INFO [train.py:763] (7/8) Epoch 26, batch 950, loss[loss=0.1718, simple_loss=0.2609, pruned_loss=0.04134, over 6790.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2651, pruned_loss=0.0332, over 1409453.48 frames.], batch size: 15, lr: 2.90e-04
+2022-04-30 03:31:40,722 INFO [train.py:763] (7/8) Epoch 26, batch 1000, loss[loss=0.1671, simple_loss=0.2845, pruned_loss=0.02487, over 7306.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2657, pruned_loss=0.03354, over 1413269.83 frames.], batch size: 24, lr: 2.90e-04
+2022-04-30 03:32:46,146 INFO [train.py:763] (7/8) Epoch 26, batch 1050, loss[loss=0.1791, simple_loss=0.2729, pruned_loss=0.0426, over 7205.00 frames.], tot_loss[loss=0.166, simple_loss=0.2654, pruned_loss=0.03333, over 1419048.82 frames.], batch size: 23, lr: 2.90e-04
+2022-04-30 03:33:51,504 INFO [train.py:763] (7/8) Epoch 26, batch 1100, loss[loss=0.1879, simple_loss=0.2871, pruned_loss=0.04435, over 7188.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2648, pruned_loss=0.03312, over 1422889.49 frames.], batch size: 22, lr: 2.90e-04
+2022-04-30 03:34:56,901 INFO [train.py:763] (7/8) Epoch 26, batch 1150, loss[loss=0.1561, simple_loss=0.2414, pruned_loss=0.03535, over 7165.00 frames.], tot_loss[loss=0.166, simple_loss=0.2653, pruned_loss=0.03337, over 1423901.90 frames.], batch size: 19, lr: 2.90e-04
+2022-04-30 03:36:02,481 INFO [train.py:763] (7/8) Epoch 26, batch 1200, loss[loss=0.1507, simple_loss=0.2598, pruned_loss=0.02081, over 7311.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2653, pruned_loss=0.03322, over 1427617.35 frames.], batch size: 24, lr: 2.90e-04
+2022-04-30 03:37:08,334 INFO [train.py:763] (7/8) Epoch 26, batch 1250, loss[loss=0.164, simple_loss=0.2703, pruned_loss=0.02885, over 6667.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2637, pruned_loss=0.03266, over 1426870.18 frames.], batch size: 38, lr: 2.90e-04
+2022-04-30 03:38:14,033 INFO [train.py:763] (7/8) Epoch 26, batch 1300, loss[loss=0.139, simple_loss=0.2305, pruned_loss=0.02374, over 7284.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2638, pruned_loss=0.03286, over 1423526.99 frames.], batch size: 18, lr: 2.90e-04
+2022-04-30 03:39:20,374 INFO [train.py:763] (7/8) Epoch 26, batch 1350, loss[loss=0.1336, simple_loss=0.2195, pruned_loss=0.02389, over 7416.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2623, pruned_loss=0.0325, over 1426806.63 frames.], batch size: 18, lr: 2.89e-04
+2022-04-30 03:40:25,497 INFO [train.py:763] (7/8) Epoch 26, batch 1400, loss[loss=0.1706, simple_loss=0.2714, pruned_loss=0.03491, over 7199.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2626, pruned_loss=0.03258, over 1418999.96 frames.], batch size: 23, lr: 2.89e-04
+2022-04-30 03:41:30,983 INFO [train.py:763] (7/8) Epoch 26, batch 1450, loss[loss=0.1518, simple_loss=0.2499, pruned_loss=0.0269, over 7277.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2634, pruned_loss=0.03281, over 1420863.33 frames.], batch size: 18, lr: 2.89e-04
+2022-04-30 03:42:36,434 INFO [train.py:763] (7/8) Epoch 26, batch 1500, loss[loss=0.1811, simple_loss=0.2764, pruned_loss=0.04287, over 5391.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2628, pruned_loss=0.03242, over 1418329.93 frames.], batch size: 53, lr: 2.89e-04
+2022-04-30 03:43:42,579 INFO [train.py:763] (7/8) Epoch 26, batch 1550, loss[loss=0.1853, simple_loss=0.2864, pruned_loss=0.04214, over 7104.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2627, pruned_loss=0.03209, over 1422457.04 frames.], batch size: 21, lr: 2.89e-04
+2022-04-30 03:44:49,286 INFO [train.py:763] (7/8) Epoch 26, batch 1600, loss[loss=0.1318, simple_loss=0.224, pruned_loss=0.01978, over 7252.00 frames.], tot_loss[loss=0.163, simple_loss=0.2619, pruned_loss=0.03205, over 1425847.93 frames.], batch size: 19, lr: 2.89e-04
+2022-04-30 03:45:54,882 INFO [train.py:763] (7/8) Epoch 26, batch 1650, loss[loss=0.1608, simple_loss=0.266, pruned_loss=0.02777, over 7155.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2622, pruned_loss=0.0322, over 1429733.69 frames.], batch size: 26, lr: 2.89e-04
+2022-04-30 03:47:00,382 INFO [train.py:763] (7/8) Epoch 26, batch 1700, loss[loss=0.174, simple_loss=0.283, pruned_loss=0.0325, over 7327.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2625, pruned_loss=0.03214, over 1431291.13 frames.], batch size: 22, lr: 2.89e-04
+2022-04-30 03:48:06,026 INFO [train.py:763] (7/8) Epoch 26, batch 1750, loss[loss=0.1897, simple_loss=0.2861, pruned_loss=0.04661, over 7133.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2629, pruned_loss=0.03238, over 1431536.07 frames.], batch size: 26, lr: 2.89e-04
+2022-04-30 03:49:13,280 INFO [train.py:763] (7/8) Epoch 26, batch 1800, loss[loss=0.158, simple_loss=0.2693, pruned_loss=0.02331, over 7095.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2633, pruned_loss=0.03256, over 1428836.95 frames.], batch size: 21, lr: 2.89e-04
+2022-04-30 03:50:19,942 INFO [train.py:763] (7/8) Epoch 26, batch 1850, loss[loss=0.1861, simple_loss=0.2833, pruned_loss=0.04438, over 5125.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2636, pruned_loss=0.03242, over 1429574.29 frames.], batch size: 52, lr: 2.89e-04
+2022-04-30 03:51:25,635 INFO [train.py:763] (7/8) Epoch 26, batch 1900, loss[loss=0.1492, simple_loss=0.2465, pruned_loss=0.02593, over 7361.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2628, pruned_loss=0.03198, over 1428878.22 frames.], batch size: 19, lr: 2.89e-04
+2022-04-30 03:52:30,907 INFO [train.py:763] (7/8) Epoch 26, batch 1950, loss[loss=0.1516, simple_loss=0.2605, pruned_loss=0.02135, over 6420.00 frames.], tot_loss[loss=0.1637, simple_loss=0.263, pruned_loss=0.0322, over 1424934.25 frames.], batch size: 38, lr: 2.89e-04
+2022-04-30 03:53:36,226 INFO [train.py:763] (7/8) Epoch 26, batch 2000, loss[loss=0.1786, simple_loss=0.279, pruned_loss=0.03907, over 6750.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2628, pruned_loss=0.03223, over 1422291.69 frames.], batch size: 31, lr: 2.89e-04
+2022-04-30 03:54:41,499 INFO [train.py:763] (7/8) Epoch 26, batch 2050, loss[loss=0.1529, simple_loss=0.2561, pruned_loss=0.02487, over 7105.00 frames.], tot_loss[loss=0.1646, simple_loss=0.264, pruned_loss=0.03259, over 1426037.78 frames.], batch size: 26, lr: 2.89e-04
+2022-04-30 03:55:48,191 INFO [train.py:763] (7/8) Epoch 26, batch 2100, loss[loss=0.1619, simple_loss=0.2643, pruned_loss=0.02974, over 7204.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2637, pruned_loss=0.03228, over 1424283.05 frames.], batch size: 22, lr: 2.89e-04
+2022-04-30 03:56:54,329 INFO [train.py:763] (7/8) Epoch 26, batch 2150, loss[loss=0.1804, simple_loss=0.2804, pruned_loss=0.04019, over 7293.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2651, pruned_loss=0.0326, over 1427630.27 frames.], batch size: 25, lr: 2.89e-04
+2022-04-30 03:57:59,853 INFO [train.py:763] (7/8) Epoch 26, batch 2200, loss[loss=0.1552, simple_loss=0.2582, pruned_loss=0.02611, over 7228.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2647, pruned_loss=0.03274, over 1426399.09 frames.], batch size: 20, lr: 2.88e-04
+2022-04-30 03:59:06,011 INFO [train.py:763] (7/8) Epoch 26, batch 2250, loss[loss=0.1536, simple_loss=0.24, pruned_loss=0.03362, over 6995.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2649, pruned_loss=0.03278, over 1431508.99 frames.], batch size: 16, lr: 2.88e-04
+2022-04-30 04:00:11,179 INFO [train.py:763] (7/8) Epoch 26, batch 2300, loss[loss=0.1435, simple_loss=0.2339, pruned_loss=0.02652, over 7139.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2651, pruned_loss=0.03311, over 1432851.50 frames.], batch size: 17, lr: 2.88e-04
+2022-04-30 04:01:17,255 INFO [train.py:763] (7/8) Epoch 26, batch 2350, loss[loss=0.1945, simple_loss=0.29, pruned_loss=0.04944, over 7156.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2664, pruned_loss=0.03373, over 1431485.91 frames.], batch size: 20, lr: 2.88e-04
+2022-04-30 04:02:24,656 INFO [train.py:763] (7/8) Epoch 26, batch 2400, loss[loss=0.1914, simple_loss=0.2961, pruned_loss=0.04336, over 7289.00 frames.], tot_loss[loss=0.167, simple_loss=0.2665, pruned_loss=0.03375, over 1433428.45 frames.], batch size: 24, lr: 2.88e-04
+2022-04-30 04:03:31,281 INFO [train.py:763] (7/8) Epoch 26, batch 2450, loss[loss=0.1658, simple_loss=0.2782, pruned_loss=0.02668, over 7234.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2658, pruned_loss=0.03318, over 1435802.50 frames.], batch size: 20, lr: 2.88e-04
+2022-04-30 04:04:36,630 INFO [train.py:763] (7/8) Epoch 26, batch 2500, loss[loss=0.1706, simple_loss=0.2778, pruned_loss=0.03177, over 7216.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2656, pruned_loss=0.03331, over 1437397.33 frames.], batch size: 21, lr: 2.88e-04
+2022-04-30 04:05:41,770 INFO [train.py:763] (7/8) Epoch 26, batch 2550, loss[loss=0.1638, simple_loss=0.2557, pruned_loss=0.03594, over 6754.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2645, pruned_loss=0.0326, over 1434447.74 frames.], batch size: 31, lr: 2.88e-04
+2022-04-30 04:06:47,207 INFO [train.py:763] (7/8) Epoch 26, batch 2600, loss[loss=0.1693, simple_loss=0.2605, pruned_loss=0.039, over 6799.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2651, pruned_loss=0.03273, over 1434050.31 frames.], batch size: 15, lr: 2.88e-04
+2022-04-30 04:07:52,623 INFO [train.py:763] (7/8) Epoch 26, batch 2650, loss[loss=0.1821, simple_loss=0.2755, pruned_loss=0.0443, over 7274.00 frames.], tot_loss[loss=0.1654, simple_loss=0.265, pruned_loss=0.03292, over 1430529.06 frames.], batch size: 24, lr: 2.88e-04
+2022-04-30 04:08:58,048 INFO [train.py:763] (7/8) Epoch 26, batch 2700, loss[loss=0.1616, simple_loss=0.2686, pruned_loss=0.02723, over 7327.00 frames.], tot_loss[loss=0.1653, simple_loss=0.265, pruned_loss=0.03273, over 1428137.71 frames.], batch size: 22, lr: 2.88e-04
+2022-04-30 04:10:03,931 INFO [train.py:763] (7/8) Epoch 26, batch 2750, loss[loss=0.1414, simple_loss=0.2417, pruned_loss=0.0205, over 7156.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2653, pruned_loss=0.03316, over 1426821.48 frames.], batch size: 19, lr: 2.88e-04
+2022-04-30 04:11:09,752 INFO [train.py:763] (7/8) Epoch 26, batch 2800, loss[loss=0.1871, simple_loss=0.2911, pruned_loss=0.04154, over 7283.00 frames.], tot_loss[loss=0.1654, simple_loss=0.265, pruned_loss=0.03284, over 1426752.40 frames.], batch size: 25, lr: 2.88e-04
+2022-04-30 04:12:16,493 INFO [train.py:763] (7/8) Epoch 26, batch 2850, loss[loss=0.1416, simple_loss=0.248, pruned_loss=0.01757, over 7262.00 frames.], tot_loss[loss=0.1649, simple_loss=0.265, pruned_loss=0.03243, over 1426899.49 frames.], batch size: 19, lr: 2.88e-04
+2022-04-30 04:13:21,776 INFO [train.py:763] (7/8) Epoch 26, batch 2900, loss[loss=0.1374, simple_loss=0.2343, pruned_loss=0.02027, over 7153.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2644, pruned_loss=0.03232, over 1426284.83 frames.], batch size: 19, lr: 2.88e-04
+2022-04-30 04:14:26,926 INFO [train.py:763] (7/8) Epoch 26, batch 2950, loss[loss=0.1639, simple_loss=0.2721, pruned_loss=0.02778, over 7126.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2655, pruned_loss=0.03294, over 1419436.79 frames.], batch size: 21, lr: 2.88e-04
+2022-04-30 04:15:32,490 INFO [train.py:763] (7/8) Epoch 26, batch 3000, loss[loss=0.1712, simple_loss=0.2807, pruned_loss=0.03079, over 7396.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2656, pruned_loss=0.0327, over 1418860.35 frames.], batch size: 21, lr: 2.88e-04
+2022-04-30 04:15:32,491 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 04:15:47,842 INFO [train.py:792] (7/8) Epoch 26, validation: loss=0.1682, simple_loss=0.2653, pruned_loss=0.03549, over 698248.00 frames.
+2022-04-30 04:16:54,028 INFO [train.py:763] (7/8) Epoch 26, batch 3050, loss[loss=0.172, simple_loss=0.287, pruned_loss=0.0285, over 7106.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2645, pruned_loss=0.03271, over 1410104.96 frames.], batch size: 21, lr: 2.87e-04
+2022-04-30 04:17:59,877 INFO [train.py:763] (7/8) Epoch 26, batch 3100, loss[loss=0.1796, simple_loss=0.2862, pruned_loss=0.0365, over 7334.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2654, pruned_loss=0.03269, over 1415973.44 frames.], batch size: 21, lr: 2.87e-04
+2022-04-30 04:19:05,980 INFO [train.py:763] (7/8) Epoch 26, batch 3150, loss[loss=0.1753, simple_loss=0.2773, pruned_loss=0.0367, over 7199.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2661, pruned_loss=0.03289, over 1416536.92 frames.], batch size: 22, lr: 2.87e-04
+2022-04-30 04:20:11,646 INFO [train.py:763] (7/8) Epoch 26, batch 3200, loss[loss=0.1718, simple_loss=0.2818, pruned_loss=0.03093, over 7223.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2662, pruned_loss=0.03303, over 1418628.63 frames.], batch size: 23, lr: 2.87e-04
+2022-04-30 04:21:17,174 INFO [train.py:763] (7/8) Epoch 26, batch 3250, loss[loss=0.1637, simple_loss=0.2692, pruned_loss=0.02914, over 6483.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2652, pruned_loss=0.03295, over 1420092.34 frames.], batch size: 38, lr: 2.87e-04
+2022-04-30 04:22:22,728 INFO [train.py:763] (7/8) Epoch 26, batch 3300, loss[loss=0.1538, simple_loss=0.2644, pruned_loss=0.02167, over 6656.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2648, pruned_loss=0.03278, over 1419495.56 frames.], batch size: 31, lr: 2.87e-04
+2022-04-30 04:23:27,761 INFO [train.py:763] (7/8) Epoch 26, batch 3350, loss[loss=0.1497, simple_loss=0.2555, pruned_loss=0.02195, over 7346.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2658, pruned_loss=0.03287, over 1420533.79 frames.], batch size: 22, lr: 2.87e-04
+2022-04-30 04:24:33,274 INFO [train.py:763] (7/8) Epoch 26, batch 3400, loss[loss=0.1839, simple_loss=0.2978, pruned_loss=0.03501, over 7153.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2664, pruned_loss=0.03323, over 1417537.24 frames.], batch size: 20, lr: 2.87e-04
+2022-04-30 04:25:38,633 INFO [train.py:763] (7/8) Epoch 26, batch 3450, loss[loss=0.1979, simple_loss=0.3017, pruned_loss=0.04707, over 7354.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2658, pruned_loss=0.03289, over 1420948.85 frames.], batch size: 22, lr: 2.87e-04
+2022-04-30 04:26:44,106 INFO [train.py:763] (7/8) Epoch 26, batch 3500, loss[loss=0.1552, simple_loss=0.2522, pruned_loss=0.02913, over 6851.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2651, pruned_loss=0.03295, over 1423212.05 frames.], batch size: 15, lr: 2.87e-04
+2022-04-30 04:27:49,692 INFO [train.py:763] (7/8) Epoch 26, batch 3550, loss[loss=0.1967, simple_loss=0.2832, pruned_loss=0.05505, over 4819.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2649, pruned_loss=0.03322, over 1416245.98 frames.], batch size: 52, lr: 2.87e-04
+2022-04-30 04:28:54,790 INFO [train.py:763] (7/8) Epoch 26, batch 3600, loss[loss=0.1753, simple_loss=0.2696, pruned_loss=0.0405, over 7153.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2647, pruned_loss=0.03291, over 1413831.96 frames.], batch size: 19, lr: 2.87e-04
+2022-04-30 04:30:00,892 INFO [train.py:763] (7/8) Epoch 26, batch 3650, loss[loss=0.1625, simple_loss=0.2629, pruned_loss=0.03102, over 7066.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2645, pruned_loss=0.0331, over 1413280.21 frames.], batch size: 18, lr: 2.87e-04
+2022-04-30 04:31:07,252 INFO [train.py:763] (7/8) Epoch 26, batch 3700, loss[loss=0.1358, simple_loss=0.2289, pruned_loss=0.02129, over 7272.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2638, pruned_loss=0.03316, over 1412647.85 frames.], batch size: 18, lr: 2.87e-04
+2022-04-30 04:32:12,934 INFO [train.py:763] (7/8) Epoch 26, batch 3750, loss[loss=0.2116, simple_loss=0.3116, pruned_loss=0.05585, over 7217.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2628, pruned_loss=0.03282, over 1416379.85 frames.], batch size: 21, lr: 2.87e-04
+2022-04-30 04:33:20,002 INFO [train.py:763] (7/8) Epoch 26, batch 3800, loss[loss=0.1503, simple_loss=0.2517, pruned_loss=0.02448, over 7323.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2618, pruned_loss=0.03249, over 1420369.92 frames.], batch size: 20, lr: 2.87e-04
+2022-04-30 04:34:26,378 INFO [train.py:763] (7/8) Epoch 26, batch 3850, loss[loss=0.1508, simple_loss=0.2316, pruned_loss=0.03498, over 7387.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2634, pruned_loss=0.0328, over 1413399.25 frames.], batch size: 18, lr: 2.87e-04
+2022-04-30 04:35:31,751 INFO [train.py:763] (7/8) Epoch 26, batch 3900, loss[loss=0.1895, simple_loss=0.2945, pruned_loss=0.04228, over 7051.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2637, pruned_loss=0.03274, over 1415028.20 frames.], batch size: 28, lr: 2.86e-04
+2022-04-30 04:36:37,015 INFO [train.py:763] (7/8) Epoch 26, batch 3950, loss[loss=0.16, simple_loss=0.2696, pruned_loss=0.0252, over 7368.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2646, pruned_loss=0.0329, over 1419374.56 frames.], batch size: 19, lr: 2.86e-04
+2022-04-30 04:37:42,780 INFO [train.py:763] (7/8) Epoch 26, batch 4000, loss[loss=0.2101, simple_loss=0.3035, pruned_loss=0.05838, over 7070.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2642, pruned_loss=0.03279, over 1424554.92 frames.], batch size: 28, lr: 2.86e-04
+2022-04-30 04:38:48,126 INFO [train.py:763] (7/8) Epoch 26, batch 4050, loss[loss=0.1694, simple_loss=0.272, pruned_loss=0.03338, over 7323.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2647, pruned_loss=0.03302, over 1425731.41 frames.], batch size: 20, lr: 2.86e-04
+2022-04-30 04:39:53,371 INFO [train.py:763] (7/8) Epoch 26, batch 4100, loss[loss=0.1579, simple_loss=0.2681, pruned_loss=0.02387, over 7340.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2641, pruned_loss=0.03276, over 1423644.87 frames.], batch size: 20, lr: 2.86e-04
+2022-04-30 04:40:58,518 INFO [train.py:763] (7/8) Epoch 26, batch 4150, loss[loss=0.1527, simple_loss=0.2689, pruned_loss=0.01822, over 7119.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2645, pruned_loss=0.03257, over 1421323.67 frames.], batch size: 21, lr: 2.86e-04
+2022-04-30 04:42:03,908 INFO [train.py:763] (7/8) Epoch 26, batch 4200, loss[loss=0.1749, simple_loss=0.2826, pruned_loss=0.03357, over 7335.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2647, pruned_loss=0.03284, over 1422364.20 frames.], batch size: 22, lr: 2.86e-04
+2022-04-30 04:43:08,786 INFO [train.py:763] (7/8) Epoch 26, batch 4250, loss[loss=0.1614, simple_loss=0.2627, pruned_loss=0.03001, over 7407.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2656, pruned_loss=0.03307, over 1415847.91 frames.], batch size: 21, lr: 2.86e-04
+2022-04-30 04:44:14,525 INFO [train.py:763] (7/8) Epoch 26, batch 4300, loss[loss=0.1695, simple_loss=0.273, pruned_loss=0.03304, over 6816.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2658, pruned_loss=0.03297, over 1414203.35 frames.], batch size: 31, lr: 2.86e-04
+2022-04-30 04:45:19,683 INFO [train.py:763] (7/8) Epoch 26, batch 4350, loss[loss=0.1615, simple_loss=0.2496, pruned_loss=0.03668, over 6995.00 frames.], tot_loss[loss=0.166, simple_loss=0.2659, pruned_loss=0.03305, over 1414023.96 frames.], batch size: 16, lr: 2.86e-04
+2022-04-30 04:46:24,711 INFO [train.py:763] (7/8) Epoch 26, batch 4400, loss[loss=0.157, simple_loss=0.2594, pruned_loss=0.02727, over 6545.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2654, pruned_loss=0.03307, over 1401128.19 frames.], batch size: 38, lr: 2.86e-04
+2022-04-30 04:47:29,343 INFO [train.py:763] (7/8) Epoch 26, batch 4450, loss[loss=0.1607, simple_loss=0.2686, pruned_loss=0.02641, over 7338.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2647, pruned_loss=0.03275, over 1395741.36 frames.], batch size: 22, lr: 2.86e-04
+2022-04-30 04:48:34,536 INFO [train.py:763] (7/8) Epoch 26, batch 4500, loss[loss=0.1638, simple_loss=0.2563, pruned_loss=0.03563, over 7162.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2654, pruned_loss=0.03364, over 1387052.82 frames.], batch size: 18, lr: 2.86e-04
+2022-04-30 04:49:39,415 INFO [train.py:763] (7/8) Epoch 26, batch 4550, loss[loss=0.1608, simple_loss=0.2623, pruned_loss=0.02965, over 4960.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2645, pruned_loss=0.03395, over 1369284.58 frames.], batch size: 52, lr: 2.86e-04
+2022-04-30 04:51:07,364 INFO [train.py:763] (7/8) Epoch 27, batch 0, loss[loss=0.1456, simple_loss=0.2445, pruned_loss=0.02331, over 7261.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2445, pruned_loss=0.02331, over 7261.00 frames.], batch size: 19, lr: 2.81e-04
+2022-04-30 04:52:13,094 INFO [train.py:763] (7/8) Epoch 27, batch 50, loss[loss=0.1543, simple_loss=0.2527, pruned_loss=0.02799, over 7261.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2637, pruned_loss=0.03299, over 321497.44 frames.], batch size: 19, lr: 2.81e-04
+2022-04-30 04:53:19,215 INFO [train.py:763] (7/8) Epoch 27, batch 100, loss[loss=0.1704, simple_loss=0.2731, pruned_loss=0.03381, over 7141.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2643, pruned_loss=0.03228, over 565928.55 frames.], batch size: 20, lr: 2.80e-04
+2022-04-30 04:54:25,266 INFO [train.py:763] (7/8) Epoch 27, batch 150, loss[loss=0.1609, simple_loss=0.2577, pruned_loss=0.03207, over 6443.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2638, pruned_loss=0.03177, over 754916.49 frames.], batch size: 38, lr: 2.80e-04
+2022-04-30 04:55:31,382 INFO [train.py:763] (7/8) Epoch 27, batch 200, loss[loss=0.1895, simple_loss=0.2841, pruned_loss=0.0475, over 7191.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2646, pruned_loss=0.03234, over 900907.51 frames.], batch size: 23, lr: 2.80e-04
+2022-04-30 04:56:38,009 INFO [train.py:763] (7/8) Epoch 27, batch 250, loss[loss=0.1711, simple_loss=0.2727, pruned_loss=0.03469, over 7270.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2649, pruned_loss=0.03273, over 1016580.68 frames.], batch size: 24, lr: 2.80e-04
+2022-04-30 04:57:44,226 INFO [train.py:763] (7/8) Epoch 27, batch 300, loss[loss=0.1518, simple_loss=0.2547, pruned_loss=0.02446, over 6710.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2645, pruned_loss=0.03212, over 1106687.70 frames.], batch size: 31, lr: 2.80e-04
+2022-04-30 04:58:50,099 INFO [train.py:763] (7/8) Epoch 27, batch 350, loss[loss=0.1685, simple_loss=0.2692, pruned_loss=0.03385, over 7167.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2631, pruned_loss=0.03196, over 1178609.34 frames.], batch size: 19, lr: 2.80e-04
+2022-04-30 04:59:56,376 INFO [train.py:763] (7/8) Epoch 27, batch 400, loss[loss=0.1575, simple_loss=0.2555, pruned_loss=0.02972, over 7138.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2634, pruned_loss=0.03191, over 1234408.20 frames.], batch size: 17, lr: 2.80e-04
+2022-04-30 05:01:02,262 INFO [train.py:763] (7/8) Epoch 27, batch 450, loss[loss=0.1628, simple_loss=0.2698, pruned_loss=0.02786, over 7296.00 frames.], tot_loss[loss=0.164, simple_loss=0.2637, pruned_loss=0.03209, over 1271137.86 frames.], batch size: 25, lr: 2.80e-04
+2022-04-30 05:02:08,173 INFO [train.py:763] (7/8) Epoch 27, batch 500, loss[loss=0.1642, simple_loss=0.2691, pruned_loss=0.02965, over 7311.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2648, pruned_loss=0.03212, over 1308115.39 frames.], batch size: 21, lr: 2.80e-04
+2022-04-30 05:03:14,030 INFO [train.py:763] (7/8) Epoch 27, batch 550, loss[loss=0.1512, simple_loss=0.247, pruned_loss=0.02775, over 7453.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2647, pruned_loss=0.03246, over 1330933.69 frames.], batch size: 19, lr: 2.80e-04
+2022-04-30 05:04:19,664 INFO [train.py:763] (7/8) Epoch 27, batch 600, loss[loss=0.1576, simple_loss=0.2605, pruned_loss=0.02736, over 7328.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2631, pruned_loss=0.03188, over 1348662.70 frames.], batch size: 20, lr: 2.80e-04
+2022-04-30 05:05:24,807 INFO [train.py:763] (7/8) Epoch 27, batch 650, loss[loss=0.1746, simple_loss=0.2702, pruned_loss=0.03947, over 6978.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2641, pruned_loss=0.03238, over 1365718.08 frames.], batch size: 28, lr: 2.80e-04
+2022-04-30 05:06:40,266 INFO [train.py:763] (7/8) Epoch 27, batch 700, loss[loss=0.155, simple_loss=0.2551, pruned_loss=0.02742, over 7066.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2637, pruned_loss=0.03244, over 1379888.55 frames.], batch size: 18, lr: 2.80e-04
+2022-04-30 05:07:46,101 INFO [train.py:763] (7/8) Epoch 27, batch 750, loss[loss=0.174, simple_loss=0.2806, pruned_loss=0.03368, over 7217.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2627, pruned_loss=0.03225, over 1391129.55 frames.], batch size: 21, lr: 2.80e-04
+2022-04-30 05:08:51,503 INFO [train.py:763] (7/8) Epoch 27, batch 800, loss[loss=0.1755, simple_loss=0.2779, pruned_loss=0.03656, over 7119.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2627, pruned_loss=0.03226, over 1397994.94 frames.], batch size: 28, lr: 2.80e-04
+2022-04-30 05:09:56,925 INFO [train.py:763] (7/8) Epoch 27, batch 850, loss[loss=0.1785, simple_loss=0.285, pruned_loss=0.03602, over 7317.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2631, pruned_loss=0.03213, over 1405771.08 frames.], batch size: 25, lr: 2.80e-04
+2022-04-30 05:11:02,113 INFO [train.py:763] (7/8) Epoch 27, batch 900, loss[loss=0.1559, simple_loss=0.2431, pruned_loss=0.03429, over 7003.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2641, pruned_loss=0.03263, over 1407698.55 frames.], batch size: 16, lr: 2.80e-04
+2022-04-30 05:12:07,299 INFO [train.py:763] (7/8) Epoch 27, batch 950, loss[loss=0.1522, simple_loss=0.2422, pruned_loss=0.03111, over 7159.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2647, pruned_loss=0.03284, over 1409661.06 frames.], batch size: 18, lr: 2.80e-04
+2022-04-30 05:13:12,808 INFO [train.py:763] (7/8) Epoch 27, batch 1000, loss[loss=0.1481, simple_loss=0.2479, pruned_loss=0.02418, over 7419.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2641, pruned_loss=0.03248, over 1415528.47 frames.], batch size: 20, lr: 2.79e-04
+2022-04-30 05:14:18,824 INFO [train.py:763] (7/8) Epoch 27, batch 1050, loss[loss=0.1718, simple_loss=0.275, pruned_loss=0.03435, over 7416.00 frames.], tot_loss[loss=0.165, simple_loss=0.2644, pruned_loss=0.03285, over 1415585.38 frames.], batch size: 21, lr: 2.79e-04
+2022-04-30 05:15:25,048 INFO [train.py:763] (7/8) Epoch 27, batch 1100, loss[loss=0.1694, simple_loss=0.2685, pruned_loss=0.03517, over 7067.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2647, pruned_loss=0.03299, over 1415521.34 frames.], batch size: 18, lr: 2.79e-04
+2022-04-30 05:16:31,248 INFO [train.py:763] (7/8) Epoch 27, batch 1150, loss[loss=0.1739, simple_loss=0.2704, pruned_loss=0.0387, over 7214.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2639, pruned_loss=0.03265, over 1420669.21 frames.], batch size: 23, lr: 2.79e-04
+2022-04-30 05:17:47,509 INFO [train.py:763] (7/8) Epoch 27, batch 1200, loss[loss=0.1418, simple_loss=0.2356, pruned_loss=0.02398, over 7138.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2629, pruned_loss=0.03233, over 1425468.85 frames.], batch size: 17, lr: 2.79e-04
+2022-04-30 05:19:01,892 INFO [train.py:763] (7/8) Epoch 27, batch 1250, loss[loss=0.1252, simple_loss=0.2197, pruned_loss=0.0153, over 7131.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2634, pruned_loss=0.03254, over 1423282.33 frames.], batch size: 17, lr: 2.79e-04
+2022-04-30 05:20:26,028 INFO [train.py:763] (7/8) Epoch 27, batch 1300, loss[loss=0.1396, simple_loss=0.2367, pruned_loss=0.02123, over 7283.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2636, pruned_loss=0.03272, over 1420406.74 frames.], batch size: 18, lr: 2.79e-04
+2022-04-30 05:21:31,881 INFO [train.py:763] (7/8) Epoch 27, batch 1350, loss[loss=0.1384, simple_loss=0.2307, pruned_loss=0.02307, over 7357.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2635, pruned_loss=0.03274, over 1419956.32 frames.], batch size: 19, lr: 2.79e-04
+2022-04-30 05:22:37,310 INFO [train.py:763] (7/8) Epoch 27, batch 1400, loss[loss=0.1637, simple_loss=0.2612, pruned_loss=0.0331, over 7064.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2633, pruned_loss=0.03251, over 1420155.79 frames.], batch size: 18, lr: 2.79e-04
+2022-04-30 05:24:10,250 INFO [train.py:763] (7/8) Epoch 27, batch 1450, loss[loss=0.1645, simple_loss=0.2747, pruned_loss=0.02714, over 7314.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2621, pruned_loss=0.03211, over 1422150.79 frames.], batch size: 20, lr: 2.79e-04
+2022-04-30 05:25:16,102 INFO [train.py:763] (7/8) Epoch 27, batch 1500, loss[loss=0.1706, simple_loss=0.2926, pruned_loss=0.02429, over 7104.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2636, pruned_loss=0.03206, over 1423564.90 frames.], batch size: 21, lr: 2.79e-04
+2022-04-30 05:26:22,006 INFO [train.py:763] (7/8) Epoch 27, batch 1550, loss[loss=0.1373, simple_loss=0.231, pruned_loss=0.02183, over 6808.00 frames.], tot_loss[loss=0.1635, simple_loss=0.263, pruned_loss=0.03197, over 1420377.62 frames.], batch size: 15, lr: 2.79e-04
+2022-04-30 05:27:29,042 INFO [train.py:763] (7/8) Epoch 27, batch 1600, loss[loss=0.1625, simple_loss=0.2666, pruned_loss=0.0292, over 7407.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2628, pruned_loss=0.03202, over 1424176.27 frames.], batch size: 21, lr: 2.79e-04
+2022-04-30 05:28:35,027 INFO [train.py:763] (7/8) Epoch 27, batch 1650, loss[loss=0.1648, simple_loss=0.2662, pruned_loss=0.03174, over 7059.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2624, pruned_loss=0.03208, over 1424696.03 frames.], batch size: 18, lr: 2.79e-04
+2022-04-30 05:29:41,350 INFO [train.py:763] (7/8) Epoch 27, batch 1700, loss[loss=0.1546, simple_loss=0.2555, pruned_loss=0.02682, over 7354.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2639, pruned_loss=0.03247, over 1426512.70 frames.], batch size: 19, lr: 2.79e-04
+2022-04-30 05:30:48,497 INFO [train.py:763] (7/8) Epoch 27, batch 1750, loss[loss=0.1661, simple_loss=0.2694, pruned_loss=0.03144, over 6704.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2632, pruned_loss=0.03212, over 1428147.37 frames.], batch size: 31, lr: 2.79e-04
+2022-04-30 05:31:54,578 INFO [train.py:763] (7/8) Epoch 27, batch 1800, loss[loss=0.1755, simple_loss=0.2801, pruned_loss=0.03544, over 7223.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2638, pruned_loss=0.03229, over 1427387.37 frames.], batch size: 20, lr: 2.79e-04
+2022-04-30 05:33:00,699 INFO [train.py:763] (7/8) Epoch 27, batch 1850, loss[loss=0.1686, simple_loss=0.2647, pruned_loss=0.03623, over 7164.00 frames.], tot_loss[loss=0.164, simple_loss=0.2635, pruned_loss=0.03228, over 1430662.90 frames.], batch size: 19, lr: 2.79e-04
+2022-04-30 05:34:06,847 INFO [train.py:763] (7/8) Epoch 27, batch 1900, loss[loss=0.1221, simple_loss=0.2149, pruned_loss=0.01469, over 7261.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2643, pruned_loss=0.03254, over 1430499.18 frames.], batch size: 17, lr: 2.78e-04
+2022-04-30 05:35:13,658 INFO [train.py:763] (7/8) Epoch 27, batch 1950, loss[loss=0.1558, simple_loss=0.2537, pruned_loss=0.0289, over 6190.00 frames.], tot_loss[loss=0.165, simple_loss=0.2643, pruned_loss=0.03286, over 1425274.76 frames.], batch size: 37, lr: 2.78e-04
+2022-04-30 05:36:20,344 INFO [train.py:763] (7/8) Epoch 27, batch 2000, loss[loss=0.1477, simple_loss=0.2563, pruned_loss=0.01956, over 7216.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2638, pruned_loss=0.0325, over 1424862.93 frames.], batch size: 21, lr: 2.78e-04
+2022-04-30 05:37:26,478 INFO [train.py:763] (7/8) Epoch 27, batch 2050, loss[loss=0.197, simple_loss=0.2963, pruned_loss=0.04881, over 7205.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2645, pruned_loss=0.03262, over 1423554.03 frames.], batch size: 23, lr: 2.78e-04
+2022-04-30 05:38:32,952 INFO [train.py:763] (7/8) Epoch 27, batch 2100, loss[loss=0.1727, simple_loss=0.2753, pruned_loss=0.03503, over 7297.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2641, pruned_loss=0.03219, over 1424071.62 frames.], batch size: 25, lr: 2.78e-04
+2022-04-30 05:39:38,776 INFO [train.py:763] (7/8) Epoch 27, batch 2150, loss[loss=0.1524, simple_loss=0.2435, pruned_loss=0.03065, over 7137.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2643, pruned_loss=0.03245, over 1422698.42 frames.], batch size: 17, lr: 2.78e-04
+2022-04-30 05:40:44,420 INFO [train.py:763] (7/8) Epoch 27, batch 2200, loss[loss=0.1727, simple_loss=0.2799, pruned_loss=0.03273, over 7293.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2638, pruned_loss=0.03233, over 1421233.77 frames.], batch size: 24, lr: 2.78e-04
+2022-04-30 05:41:50,165 INFO [train.py:763] (7/8) Epoch 27, batch 2250, loss[loss=0.15, simple_loss=0.255, pruned_loss=0.02254, over 7327.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2639, pruned_loss=0.03251, over 1424354.29 frames.], batch size: 22, lr: 2.78e-04
+2022-04-30 05:42:56,040 INFO [train.py:763] (7/8) Epoch 27, batch 2300, loss[loss=0.1623, simple_loss=0.2638, pruned_loss=0.03042, over 7149.00 frames.], tot_loss[loss=0.1642, simple_loss=0.264, pruned_loss=0.03223, over 1421563.18 frames.], batch size: 20, lr: 2.78e-04
+2022-04-30 05:44:01,774 INFO [train.py:763] (7/8) Epoch 27, batch 2350, loss[loss=0.1528, simple_loss=0.2547, pruned_loss=0.02548, over 7158.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2634, pruned_loss=0.0321, over 1419804.60 frames.], batch size: 19, lr: 2.78e-04
+2022-04-30 05:45:08,052 INFO [train.py:763] (7/8) Epoch 27, batch 2400, loss[loss=0.1773, simple_loss=0.2807, pruned_loss=0.03692, over 7211.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2638, pruned_loss=0.03224, over 1423043.20 frames.], batch size: 23, lr: 2.78e-04
+2022-04-30 05:46:14,172 INFO [train.py:763] (7/8) Epoch 27, batch 2450, loss[loss=0.1627, simple_loss=0.2746, pruned_loss=0.0254, over 6392.00 frames.], tot_loss[loss=0.163, simple_loss=0.2625, pruned_loss=0.03177, over 1423745.62 frames.], batch size: 37, lr: 2.78e-04
+2022-04-30 05:47:19,810 INFO [train.py:763] (7/8) Epoch 27, batch 2500, loss[loss=0.1384, simple_loss=0.233, pruned_loss=0.02197, over 6828.00 frames.], tot_loss[loss=0.1639, simple_loss=0.263, pruned_loss=0.03237, over 1420922.97 frames.], batch size: 15, lr: 2.78e-04
+2022-04-30 05:48:25,894 INFO [train.py:763] (7/8) Epoch 27, batch 2550, loss[loss=0.161, simple_loss=0.2697, pruned_loss=0.02618, over 7247.00 frames.], tot_loss[loss=0.164, simple_loss=0.2634, pruned_loss=0.03228, over 1421525.18 frames.], batch size: 19, lr: 2.78e-04
+2022-04-30 05:49:31,732 INFO [train.py:763] (7/8) Epoch 27, batch 2600, loss[loss=0.1614, simple_loss=0.2588, pruned_loss=0.03204, over 7239.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2633, pruned_loss=0.03222, over 1421787.53 frames.], batch size: 20, lr: 2.78e-04
+2022-04-30 05:50:37,404 INFO [train.py:763] (7/8) Epoch 27, batch 2650, loss[loss=0.1535, simple_loss=0.2401, pruned_loss=0.03342, over 6998.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2634, pruned_loss=0.03196, over 1420316.70 frames.], batch size: 16, lr: 2.78e-04
+2022-04-30 05:51:42,961 INFO [train.py:763] (7/8) Epoch 27, batch 2700, loss[loss=0.1852, simple_loss=0.2862, pruned_loss=0.04208, over 7313.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2643, pruned_loss=0.0326, over 1422425.14 frames.], batch size: 21, lr: 2.78e-04
+2022-04-30 05:52:49,044 INFO [train.py:763] (7/8) Epoch 27, batch 2750, loss[loss=0.1446, simple_loss=0.2413, pruned_loss=0.02392, over 7253.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2652, pruned_loss=0.03271, over 1420732.99 frames.], batch size: 19, lr: 2.78e-04
+2022-04-30 05:53:54,762 INFO [train.py:763] (7/8) Epoch 27, batch 2800, loss[loss=0.1724, simple_loss=0.28, pruned_loss=0.03247, over 7236.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2652, pruned_loss=0.0329, over 1416645.31 frames.], batch size: 20, lr: 2.77e-04
+2022-04-30 05:55:00,528 INFO [train.py:763] (7/8) Epoch 27, batch 2850, loss[loss=0.1288, simple_loss=0.2224, pruned_loss=0.01755, over 7143.00 frames.], tot_loss[loss=0.1644, simple_loss=0.264, pruned_loss=0.03245, over 1420460.67 frames.], batch size: 17, lr: 2.77e-04
+2022-04-30 05:56:06,165 INFO [train.py:763] (7/8) Epoch 27, batch 2900, loss[loss=0.1596, simple_loss=0.265, pruned_loss=0.02712, over 7286.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2648, pruned_loss=0.03265, over 1419913.40 frames.], batch size: 25, lr: 2.77e-04
+2022-04-30 05:57:11,717 INFO [train.py:763] (7/8) Epoch 27, batch 2950, loss[loss=0.1585, simple_loss=0.2644, pruned_loss=0.02629, over 7213.00 frames.], tot_loss[loss=0.165, simple_loss=0.2651, pruned_loss=0.03245, over 1423051.53 frames.], batch size: 23, lr: 2.77e-04
+2022-04-30 05:58:18,107 INFO [train.py:763] (7/8) Epoch 27, batch 3000, loss[loss=0.1795, simple_loss=0.2842, pruned_loss=0.03737, over 7078.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2649, pruned_loss=0.03221, over 1424725.80 frames.], batch size: 28, lr: 2.77e-04
+2022-04-30 05:58:18,108 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 05:58:33,165 INFO [train.py:792] (7/8) Epoch 27, validation: loss=0.1686, simple_loss=0.2648, pruned_loss=0.03621, over 698248.00 frames.
+2022-04-30 05:59:40,081 INFO [train.py:763] (7/8) Epoch 27, batch 3050, loss[loss=0.1372, simple_loss=0.2328, pruned_loss=0.02084, over 7153.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2648, pruned_loss=0.03211, over 1426235.06 frames.], batch size: 17, lr: 2.77e-04
+2022-04-30 06:00:45,841 INFO [train.py:763] (7/8) Epoch 27, batch 3100, loss[loss=0.181, simple_loss=0.272, pruned_loss=0.04493, over 7380.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2642, pruned_loss=0.03242, over 1424937.01 frames.], batch size: 23, lr: 2.77e-04
+2022-04-30 06:01:51,948 INFO [train.py:763] (7/8) Epoch 27, batch 3150, loss[loss=0.1526, simple_loss=0.2423, pruned_loss=0.03148, over 7417.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2634, pruned_loss=0.03211, over 1423304.68 frames.], batch size: 18, lr: 2.77e-04
+2022-04-30 06:02:58,139 INFO [train.py:763] (7/8) Epoch 27, batch 3200, loss[loss=0.1614, simple_loss=0.2611, pruned_loss=0.03087, over 7317.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2643, pruned_loss=0.03212, over 1423958.74 frames.], batch size: 21, lr: 2.77e-04
+2022-04-30 06:04:04,084 INFO [train.py:763] (7/8) Epoch 27, batch 3250, loss[loss=0.1513, simple_loss=0.2439, pruned_loss=0.02938, over 7170.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2637, pruned_loss=0.03233, over 1423407.42 frames.], batch size: 18, lr: 2.77e-04
+2022-04-30 06:05:10,054 INFO [train.py:763] (7/8) Epoch 27, batch 3300, loss[loss=0.154, simple_loss=0.2422, pruned_loss=0.0329, over 6983.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2637, pruned_loss=0.03248, over 1422338.53 frames.], batch size: 16, lr: 2.77e-04
+2022-04-30 06:06:16,459 INFO [train.py:763] (7/8) Epoch 27, batch 3350, loss[loss=0.1973, simple_loss=0.2902, pruned_loss=0.05215, over 7389.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2645, pruned_loss=0.03314, over 1419217.23 frames.], batch size: 23, lr: 2.77e-04
+2022-04-30 06:07:23,082 INFO [train.py:763] (7/8) Epoch 27, batch 3400, loss[loss=0.1621, simple_loss=0.2693, pruned_loss=0.02747, over 7320.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2652, pruned_loss=0.03312, over 1421642.77 frames.], batch size: 20, lr: 2.77e-04
+2022-04-30 06:08:29,082 INFO [train.py:763] (7/8) Epoch 27, batch 3450, loss[loss=0.1898, simple_loss=0.2828, pruned_loss=0.04842, over 7204.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2646, pruned_loss=0.03301, over 1423085.88 frames.], batch size: 22, lr: 2.77e-04
+2022-04-30 06:09:34,970 INFO [train.py:763] (7/8) Epoch 27, batch 3500, loss[loss=0.1574, simple_loss=0.2546, pruned_loss=0.03007, over 7061.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2642, pruned_loss=0.03309, over 1422036.35 frames.], batch size: 18, lr: 2.77e-04
+2022-04-30 06:10:40,834 INFO [train.py:763] (7/8) Epoch 27, batch 3550, loss[loss=0.168, simple_loss=0.2704, pruned_loss=0.03285, over 7342.00 frames.], tot_loss[loss=0.165, simple_loss=0.2642, pruned_loss=0.03289, over 1422603.59 frames.], batch size: 22, lr: 2.77e-04
+2022-04-30 06:11:46,454 INFO [train.py:763] (7/8) Epoch 27, batch 3600, loss[loss=0.1658, simple_loss=0.2648, pruned_loss=0.0334, over 7077.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2648, pruned_loss=0.03304, over 1421673.58 frames.], batch size: 18, lr: 2.77e-04
+2022-04-30 06:12:52,033 INFO [train.py:763] (7/8) Epoch 27, batch 3650, loss[loss=0.1769, simple_loss=0.2769, pruned_loss=0.03842, over 7411.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2634, pruned_loss=0.03257, over 1423113.43 frames.], batch size: 21, lr: 2.77e-04
+2022-04-30 06:13:58,388 INFO [train.py:763] (7/8) Epoch 27, batch 3700, loss[loss=0.1679, simple_loss=0.2625, pruned_loss=0.0367, over 7436.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2636, pruned_loss=0.03269, over 1423305.72 frames.], batch size: 20, lr: 2.77e-04
+2022-04-30 06:15:04,070 INFO [train.py:763] (7/8) Epoch 27, batch 3750, loss[loss=0.2148, simple_loss=0.3048, pruned_loss=0.06236, over 4898.00 frames.], tot_loss[loss=0.1639, simple_loss=0.263, pruned_loss=0.03241, over 1418954.41 frames.], batch size: 53, lr: 2.76e-04
+2022-04-30 06:16:10,315 INFO [train.py:763] (7/8) Epoch 27, batch 3800, loss[loss=0.1421, simple_loss=0.2327, pruned_loss=0.0258, over 7284.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2633, pruned_loss=0.03253, over 1421182.79 frames.], batch size: 17, lr: 2.76e-04
+2022-04-30 06:17:16,537 INFO [train.py:763] (7/8) Epoch 27, batch 3850, loss[loss=0.1592, simple_loss=0.2598, pruned_loss=0.02929, over 7152.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2632, pruned_loss=0.03198, over 1425561.76 frames.], batch size: 19, lr: 2.76e-04
+2022-04-30 06:18:22,958 INFO [train.py:763] (7/8) Epoch 27, batch 3900, loss[loss=0.204, simple_loss=0.299, pruned_loss=0.05454, over 7212.00 frames.], tot_loss[loss=0.164, simple_loss=0.264, pruned_loss=0.03199, over 1424605.07 frames.], batch size: 22, lr: 2.76e-04
+2022-04-30 06:19:28,547 INFO [train.py:763] (7/8) Epoch 27, batch 3950, loss[loss=0.174, simple_loss=0.2704, pruned_loss=0.03878, over 7211.00 frames.], tot_loss[loss=0.1633, simple_loss=0.263, pruned_loss=0.03179, over 1425775.05 frames.], batch size: 22, lr: 2.76e-04
+2022-04-30 06:20:34,805 INFO [train.py:763] (7/8) Epoch 27, batch 4000, loss[loss=0.1475, simple_loss=0.263, pruned_loss=0.01604, over 6700.00 frames.], tot_loss[loss=0.162, simple_loss=0.2614, pruned_loss=0.03126, over 1423926.26 frames.], batch size: 31, lr: 2.76e-04
+2022-04-30 06:21:40,924 INFO [train.py:763] (7/8) Epoch 27, batch 4050, loss[loss=0.2376, simple_loss=0.3071, pruned_loss=0.08409, over 5098.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2627, pruned_loss=0.03176, over 1416625.78 frames.], batch size: 53, lr: 2.76e-04
+2022-04-30 06:22:47,116 INFO [train.py:763] (7/8) Epoch 27, batch 4100, loss[loss=0.1577, simple_loss=0.24, pruned_loss=0.0377, over 7134.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2626, pruned_loss=0.03201, over 1418958.04 frames.], batch size: 17, lr: 2.76e-04
+2022-04-30 06:24:03,956 INFO [train.py:763] (7/8) Epoch 27, batch 4150, loss[loss=0.1663, simple_loss=0.2639, pruned_loss=0.03433, over 7159.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2636, pruned_loss=0.03233, over 1424216.14 frames.], batch size: 19, lr: 2.76e-04
+2022-04-30 06:25:09,383 INFO [train.py:763] (7/8) Epoch 27, batch 4200, loss[loss=0.1989, simple_loss=0.2896, pruned_loss=0.05412, over 5163.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2642, pruned_loss=0.03262, over 1418030.72 frames.], batch size: 54, lr: 2.76e-04
+2022-04-30 06:26:15,114 INFO [train.py:763] (7/8) Epoch 27, batch 4250, loss[loss=0.1573, simple_loss=0.2481, pruned_loss=0.0332, over 7078.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2642, pruned_loss=0.03252, over 1415412.44 frames.], batch size: 18, lr: 2.76e-04
+2022-04-30 06:27:21,147 INFO [train.py:763] (7/8) Epoch 27, batch 4300, loss[loss=0.1447, simple_loss=0.2399, pruned_loss=0.02478, over 7130.00 frames.], tot_loss[loss=0.164, simple_loss=0.2634, pruned_loss=0.03231, over 1416966.33 frames.], batch size: 17, lr: 2.76e-04
+2022-04-30 06:28:27,409 INFO [train.py:763] (7/8) Epoch 27, batch 4350, loss[loss=0.16, simple_loss=0.2622, pruned_loss=0.02893, over 7227.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2637, pruned_loss=0.0322, over 1416797.33 frames.], batch size: 21, lr: 2.76e-04
+2022-04-30 06:29:33,363 INFO [train.py:763] (7/8) Epoch 27, batch 4400, loss[loss=0.1629, simple_loss=0.266, pruned_loss=0.02994, over 6583.00 frames.], tot_loss[loss=0.164, simple_loss=0.2635, pruned_loss=0.03226, over 1409612.58 frames.], batch size: 38, lr: 2.76e-04
+2022-04-30 06:30:39,376 INFO [train.py:763] (7/8) Epoch 27, batch 4450, loss[loss=0.1538, simple_loss=0.245, pruned_loss=0.03134, over 7207.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2639, pruned_loss=0.03248, over 1403536.39 frames.], batch size: 16, lr: 2.76e-04
+2022-04-30 06:31:44,903 INFO [train.py:763] (7/8) Epoch 27, batch 4500, loss[loss=0.161, simple_loss=0.2695, pruned_loss=0.02627, over 7215.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2643, pruned_loss=0.03243, over 1391385.70 frames.], batch size: 21, lr: 2.76e-04
+2022-04-30 06:32:50,043 INFO [train.py:763] (7/8) Epoch 27, batch 4550, loss[loss=0.1825, simple_loss=0.2892, pruned_loss=0.03792, over 6475.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2649, pruned_loss=0.03292, over 1359555.10 frames.], batch size: 38, lr: 2.76e-04
+2022-04-30 06:34:19,206 INFO [train.py:763] (7/8) Epoch 28, batch 0, loss[loss=0.1754, simple_loss=0.2765, pruned_loss=0.03713, over 7084.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2765, pruned_loss=0.03713, over 7084.00 frames.], batch size: 28, lr: 2.71e-04
+2022-04-30 06:35:24,841 INFO [train.py:763] (7/8) Epoch 28, batch 50, loss[loss=0.1703, simple_loss=0.2666, pruned_loss=0.037, over 7303.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2652, pruned_loss=0.03148, over 323714.01 frames.], batch size: 24, lr: 2.71e-04
+2022-04-30 06:36:31,689 INFO [train.py:763] (7/8) Epoch 28, batch 100, loss[loss=0.1781, simple_loss=0.295, pruned_loss=0.03062, over 7320.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2636, pruned_loss=0.03209, over 570024.06 frames.], batch size: 21, lr: 2.71e-04
+2022-04-30 06:37:37,378 INFO [train.py:763] (7/8) Epoch 28, batch 150, loss[loss=0.1705, simple_loss=0.2725, pruned_loss=0.03424, over 7238.00 frames.], tot_loss[loss=0.164, simple_loss=0.2639, pruned_loss=0.032, over 760002.67 frames.], batch size: 20, lr: 2.71e-04
+2022-04-30 06:38:43,648 INFO [train.py:763] (7/8) Epoch 28, batch 200, loss[loss=0.1412, simple_loss=0.2396, pruned_loss=0.02143, over 7061.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2632, pruned_loss=0.03187, over 908907.92 frames.], batch size: 18, lr: 2.71e-04
+2022-04-30 06:39:49,247 INFO [train.py:763] (7/8) Epoch 28, batch 250, loss[loss=0.1725, simple_loss=0.2625, pruned_loss=0.04124, over 5331.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2628, pruned_loss=0.03211, over 1020292.13 frames.], batch size: 53, lr: 2.71e-04
+2022-04-30 06:40:54,490 INFO [train.py:763] (7/8) Epoch 28, batch 300, loss[loss=0.1753, simple_loss=0.2637, pruned_loss=0.0434, over 7175.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2633, pruned_loss=0.03214, over 1109839.70 frames.], batch size: 18, lr: 2.70e-04
+2022-04-30 06:41:59,631 INFO [train.py:763] (7/8) Epoch 28, batch 350, loss[loss=0.1452, simple_loss=0.2401, pruned_loss=0.02516, over 7058.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2636, pruned_loss=0.03166, over 1181426.88 frames.], batch size: 18, lr: 2.70e-04
+2022-04-30 06:43:05,893 INFO [train.py:763] (7/8) Epoch 28, batch 400, loss[loss=0.1578, simple_loss=0.2691, pruned_loss=0.02321, over 7151.00 frames.], tot_loss[loss=0.163, simple_loss=0.2629, pruned_loss=0.03152, over 1236691.09 frames.], batch size: 20, lr: 2.70e-04
+2022-04-30 06:44:12,429 INFO [train.py:763] (7/8) Epoch 28, batch 450, loss[loss=0.181, simple_loss=0.287, pruned_loss=0.03745, over 7120.00 frames.], tot_loss[loss=0.163, simple_loss=0.2625, pruned_loss=0.03171, over 1282496.90 frames.], batch size: 21, lr: 2.70e-04
+2022-04-30 06:45:17,944 INFO [train.py:763] (7/8) Epoch 28, batch 500, loss[loss=0.1992, simple_loss=0.2977, pruned_loss=0.05031, over 4957.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2634, pruned_loss=0.03218, over 1309613.48 frames.], batch size: 52, lr: 2.70e-04
+2022-04-30 06:46:23,655 INFO [train.py:763] (7/8) Epoch 28, batch 550, loss[loss=0.1648, simple_loss=0.2757, pruned_loss=0.02697, over 7230.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2635, pruned_loss=0.03272, over 1332039.04 frames.], batch size: 21, lr: 2.70e-04
+2022-04-30 06:47:29,786 INFO [train.py:763] (7/8) Epoch 28, batch 600, loss[loss=0.1597, simple_loss=0.2587, pruned_loss=0.03032, over 7244.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2629, pruned_loss=0.03273, over 1348671.29 frames.], batch size: 19, lr: 2.70e-04
+2022-04-30 06:48:35,468 INFO [train.py:763] (7/8) Epoch 28, batch 650, loss[loss=0.154, simple_loss=0.2449, pruned_loss=0.03153, over 7069.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2622, pruned_loss=0.03257, over 1366852.17 frames.], batch size: 18, lr: 2.70e-04
+2022-04-30 06:49:42,657 INFO [train.py:763] (7/8) Epoch 28, batch 700, loss[loss=0.1847, simple_loss=0.2771, pruned_loss=0.04615, over 4881.00 frames.], tot_loss[loss=0.164, simple_loss=0.2625, pruned_loss=0.03271, over 1375414.34 frames.], batch size: 54, lr: 2.70e-04
+2022-04-30 06:50:48,236 INFO [train.py:763] (7/8) Epoch 28, batch 750, loss[loss=0.1582, simple_loss=0.2629, pruned_loss=0.02674, over 7420.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2628, pruned_loss=0.0329, over 1382189.01 frames.], batch size: 20, lr: 2.70e-04
+2022-04-30 06:51:53,713 INFO [train.py:763] (7/8) Epoch 28, batch 800, loss[loss=0.1603, simple_loss=0.274, pruned_loss=0.02326, over 7115.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2634, pruned_loss=0.03293, over 1388119.46 frames.], batch size: 21, lr: 2.70e-04
+2022-04-30 06:52:59,924 INFO [train.py:763] (7/8) Epoch 28, batch 850, loss[loss=0.1547, simple_loss=0.2627, pruned_loss=0.02334, over 6512.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2637, pruned_loss=0.03306, over 1393075.27 frames.], batch size: 38, lr: 2.70e-04
+2022-04-30 06:54:06,458 INFO [train.py:763] (7/8) Epoch 28, batch 900, loss[loss=0.1524, simple_loss=0.2632, pruned_loss=0.02083, over 6953.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2641, pruned_loss=0.03311, over 1399885.60 frames.], batch size: 32, lr: 2.70e-04
+2022-04-30 06:55:12,071 INFO [train.py:763] (7/8) Epoch 28, batch 950, loss[loss=0.1684, simple_loss=0.2674, pruned_loss=0.03468, over 7203.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2637, pruned_loss=0.03299, over 1409195.20 frames.], batch size: 22, lr: 2.70e-04
+2022-04-30 06:56:17,989 INFO [train.py:763] (7/8) Epoch 28, batch 1000, loss[loss=0.153, simple_loss=0.2493, pruned_loss=0.02832, over 6853.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2626, pruned_loss=0.0324, over 1415609.13 frames.], batch size: 15, lr: 2.70e-04
+2022-04-30 06:57:23,505 INFO [train.py:763] (7/8) Epoch 28, batch 1050, loss[loss=0.1476, simple_loss=0.2494, pruned_loss=0.02284, over 7413.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2629, pruned_loss=0.03211, over 1420937.41 frames.], batch size: 21, lr: 2.70e-04
+2022-04-30 06:58:29,255 INFO [train.py:763] (7/8) Epoch 28, batch 1100, loss[loss=0.1307, simple_loss=0.2247, pruned_loss=0.01835, over 7286.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2618, pruned_loss=0.03142, over 1423866.95 frames.], batch size: 17, lr: 2.70e-04
+2022-04-30 06:59:35,667 INFO [train.py:763] (7/8) Epoch 28, batch 1150, loss[loss=0.1749, simple_loss=0.2797, pruned_loss=0.03505, over 7079.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2623, pruned_loss=0.03144, over 1422685.48 frames.], batch size: 28, lr: 2.70e-04
+2022-04-30 07:00:40,818 INFO [train.py:763] (7/8) Epoch 28, batch 1200, loss[loss=0.1564, simple_loss=0.2625, pruned_loss=0.02515, over 6991.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2633, pruned_loss=0.03149, over 1424413.70 frames.], batch size: 28, lr: 2.70e-04
+2022-04-30 07:01:47,029 INFO [train.py:763] (7/8) Epoch 28, batch 1250, loss[loss=0.1837, simple_loss=0.2876, pruned_loss=0.03996, over 7210.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2623, pruned_loss=0.03147, over 1417757.84 frames.], batch size: 22, lr: 2.70e-04
+2022-04-30 07:02:52,934 INFO [train.py:763] (7/8) Epoch 28, batch 1300, loss[loss=0.1545, simple_loss=0.2577, pruned_loss=0.02561, over 7155.00 frames.], tot_loss[loss=0.162, simple_loss=0.2615, pruned_loss=0.03125, over 1420907.58 frames.], batch size: 20, lr: 2.69e-04
+2022-04-30 07:03:58,481 INFO [train.py:763] (7/8) Epoch 28, batch 1350, loss[loss=0.1868, simple_loss=0.2968, pruned_loss=0.03841, over 7114.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2612, pruned_loss=0.03134, over 1426084.30 frames.], batch size: 21, lr: 2.69e-04
+2022-04-30 07:05:04,533 INFO [train.py:763] (7/8) Epoch 28, batch 1400, loss[loss=0.1531, simple_loss=0.2402, pruned_loss=0.03304, over 7258.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2616, pruned_loss=0.03139, over 1427727.46 frames.], batch size: 17, lr: 2.69e-04
+2022-04-30 07:06:10,015 INFO [train.py:763] (7/8) Epoch 28, batch 1450, loss[loss=0.1675, simple_loss=0.273, pruned_loss=0.03096, over 7296.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2618, pruned_loss=0.03162, over 1431821.64 frames.], batch size: 24, lr: 2.69e-04
+2022-04-30 07:07:16,036 INFO [train.py:763] (7/8) Epoch 28, batch 1500, loss[loss=0.1605, simple_loss=0.2536, pruned_loss=0.03365, over 7331.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2624, pruned_loss=0.03173, over 1428159.90 frames.], batch size: 20, lr: 2.69e-04
+2022-04-30 07:08:21,701 INFO [train.py:763] (7/8) Epoch 28, batch 1550, loss[loss=0.1889, simple_loss=0.2994, pruned_loss=0.03913, over 7222.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2627, pruned_loss=0.03146, over 1430256.41 frames.], batch size: 21, lr: 2.69e-04
+2022-04-30 07:09:26,983 INFO [train.py:763] (7/8) Epoch 28, batch 1600, loss[loss=0.1571, simple_loss=0.2423, pruned_loss=0.036, over 6880.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2628, pruned_loss=0.03134, over 1426339.43 frames.], batch size: 15, lr: 2.69e-04
+2022-04-30 07:10:32,960 INFO [train.py:763] (7/8) Epoch 28, batch 1650, loss[loss=0.1701, simple_loss=0.255, pruned_loss=0.04259, over 6769.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2626, pruned_loss=0.03142, over 1428290.93 frames.], batch size: 15, lr: 2.69e-04
+2022-04-30 07:11:39,868 INFO [train.py:763] (7/8) Epoch 28, batch 1700, loss[loss=0.1418, simple_loss=0.2303, pruned_loss=0.0267, over 7262.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2618, pruned_loss=0.03115, over 1430648.74 frames.], batch size: 19, lr: 2.69e-04
+2022-04-30 07:12:45,219 INFO [train.py:763] (7/8) Epoch 28, batch 1750, loss[loss=0.1453, simple_loss=0.2445, pruned_loss=0.02306, over 7114.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2612, pruned_loss=0.03089, over 1432985.28 frames.], batch size: 21, lr: 2.69e-04
+2022-04-30 07:13:50,845 INFO [train.py:763] (7/8) Epoch 28, batch 1800, loss[loss=0.1459, simple_loss=0.2354, pruned_loss=0.02821, over 6992.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2605, pruned_loss=0.03083, over 1422592.01 frames.], batch size: 16, lr: 2.69e-04
+2022-04-30 07:14:57,006 INFO [train.py:763] (7/8) Epoch 28, batch 1850, loss[loss=0.1577, simple_loss=0.256, pruned_loss=0.0297, over 7400.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2621, pruned_loss=0.03137, over 1425155.20 frames.], batch size: 18, lr: 2.69e-04
+2022-04-30 07:16:02,997 INFO [train.py:763] (7/8) Epoch 28, batch 1900, loss[loss=0.1599, simple_loss=0.2566, pruned_loss=0.03161, over 7152.00 frames.], tot_loss[loss=0.1622, simple_loss=0.262, pruned_loss=0.03118, over 1425556.62 frames.], batch size: 26, lr: 2.69e-04
+2022-04-30 07:17:09,685 INFO [train.py:763] (7/8) Epoch 28, batch 1950, loss[loss=0.2058, simple_loss=0.3093, pruned_loss=0.05117, over 7298.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2625, pruned_loss=0.03155, over 1427996.32 frames.], batch size: 25, lr: 2.69e-04
+2022-04-30 07:18:15,518 INFO [train.py:763] (7/8) Epoch 28, batch 2000, loss[loss=0.1685, simple_loss=0.2758, pruned_loss=0.03063, over 7198.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2621, pruned_loss=0.03162, over 1430472.06 frames.], batch size: 23, lr: 2.69e-04
+2022-04-30 07:19:21,150 INFO [train.py:763] (7/8) Epoch 28, batch 2050, loss[loss=0.1388, simple_loss=0.2556, pruned_loss=0.01099, over 7322.00 frames.], tot_loss[loss=0.163, simple_loss=0.2623, pruned_loss=0.03182, over 1424351.25 frames.], batch size: 21, lr: 2.69e-04
+2022-04-30 07:20:26,751 INFO [train.py:763] (7/8) Epoch 28, batch 2100, loss[loss=0.1861, simple_loss=0.2898, pruned_loss=0.0412, over 7318.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2618, pruned_loss=0.03146, over 1426390.07 frames.], batch size: 25, lr: 2.69e-04
+2022-04-30 07:21:33,891 INFO [train.py:763] (7/8) Epoch 28, batch 2150, loss[loss=0.1661, simple_loss=0.2766, pruned_loss=0.02776, over 7210.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2617, pruned_loss=0.03099, over 1427966.12 frames.], batch size: 21, lr: 2.69e-04
+2022-04-30 07:22:48,808 INFO [train.py:763] (7/8) Epoch 28, batch 2200, loss[loss=0.1691, simple_loss=0.2684, pruned_loss=0.03491, over 7278.00 frames.], tot_loss[loss=0.1622, simple_loss=0.262, pruned_loss=0.0312, over 1422024.06 frames.], batch size: 25, lr: 2.69e-04
+2022-04-30 07:23:56,121 INFO [train.py:763] (7/8) Epoch 28, batch 2250, loss[loss=0.1811, simple_loss=0.2834, pruned_loss=0.03945, over 7111.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2626, pruned_loss=0.03137, over 1426353.91 frames.], batch size: 21, lr: 2.68e-04
+2022-04-30 07:25:01,841 INFO [train.py:763] (7/8) Epoch 28, batch 2300, loss[loss=0.1928, simple_loss=0.2938, pruned_loss=0.04586, over 7278.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2627, pruned_loss=0.03143, over 1427300.85 frames.], batch size: 24, lr: 2.68e-04
+2022-04-30 07:26:07,556 INFO [train.py:763] (7/8) Epoch 28, batch 2350, loss[loss=0.1433, simple_loss=0.2424, pruned_loss=0.0221, over 7069.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2621, pruned_loss=0.03126, over 1424860.66 frames.], batch size: 18, lr: 2.68e-04
+2022-04-30 07:27:14,922 INFO [train.py:763] (7/8) Epoch 28, batch 2400, loss[loss=0.1518, simple_loss=0.2516, pruned_loss=0.02597, over 7350.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2622, pruned_loss=0.03154, over 1425620.97 frames.], batch size: 19, lr: 2.68e-04
+2022-04-30 07:28:20,444 INFO [train.py:763] (7/8) Epoch 28, batch 2450, loss[loss=0.1745, simple_loss=0.2748, pruned_loss=0.03712, over 7119.00 frames.], tot_loss[loss=0.1634, simple_loss=0.263, pruned_loss=0.03184, over 1415902.78 frames.], batch size: 21, lr: 2.68e-04
+2022-04-30 07:29:26,096 INFO [train.py:763] (7/8) Epoch 28, batch 2500, loss[loss=0.1345, simple_loss=0.2295, pruned_loss=0.01975, over 7404.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2619, pruned_loss=0.03143, over 1419252.37 frames.], batch size: 18, lr: 2.68e-04
+2022-04-30 07:30:32,248 INFO [train.py:763] (7/8) Epoch 28, batch 2550, loss[loss=0.1582, simple_loss=0.2519, pruned_loss=0.03223, over 7173.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2628, pruned_loss=0.03198, over 1416864.00 frames.], batch size: 18, lr: 2.68e-04
+2022-04-30 07:31:37,906 INFO [train.py:763] (7/8) Epoch 28, batch 2600, loss[loss=0.1846, simple_loss=0.285, pruned_loss=0.04211, over 7204.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2628, pruned_loss=0.03237, over 1415028.79 frames.], batch size: 23, lr: 2.68e-04
+2022-04-30 07:32:43,463 INFO [train.py:763] (7/8) Epoch 28, batch 2650, loss[loss=0.145, simple_loss=0.2444, pruned_loss=0.02277, over 7404.00 frames.], tot_loss[loss=0.1638, simple_loss=0.263, pruned_loss=0.03232, over 1418386.34 frames.], batch size: 18, lr: 2.68e-04
+2022-04-30 07:33:59,651 INFO [train.py:763] (7/8) Epoch 28, batch 2700, loss[loss=0.2022, simple_loss=0.2921, pruned_loss=0.05616, over 4944.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2623, pruned_loss=0.03242, over 1419044.84 frames.], batch size: 52, lr: 2.68e-04
+2022-04-30 07:35:13,956 INFO [train.py:763] (7/8) Epoch 28, batch 2750, loss[loss=0.1617, simple_loss=0.2687, pruned_loss=0.02733, over 7307.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2625, pruned_loss=0.03221, over 1415527.98 frames.], batch size: 21, lr: 2.68e-04
+2022-04-30 07:36:28,362 INFO [train.py:763] (7/8) Epoch 28, batch 2800, loss[loss=0.1617, simple_loss=0.2663, pruned_loss=0.02851, over 7350.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2624, pruned_loss=0.03189, over 1418178.29 frames.], batch size: 22, lr: 2.68e-04
+2022-04-30 07:37:44,250 INFO [train.py:763] (7/8) Epoch 28, batch 2850, loss[loss=0.1488, simple_loss=0.2551, pruned_loss=0.02129, over 7256.00 frames.], tot_loss[loss=0.1627, simple_loss=0.262, pruned_loss=0.03169, over 1418809.86 frames.], batch size: 19, lr: 2.68e-04
+2022-04-30 07:38:58,505 INFO [train.py:763] (7/8) Epoch 28, batch 2900, loss[loss=0.161, simple_loss=0.2467, pruned_loss=0.03769, over 7288.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2615, pruned_loss=0.03164, over 1418229.84 frames.], batch size: 17, lr: 2.68e-04
+2022-04-30 07:40:13,615 INFO [train.py:763] (7/8) Epoch 28, batch 2950, loss[loss=0.1493, simple_loss=0.2391, pruned_loss=0.02978, over 7143.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2608, pruned_loss=0.0317, over 1418510.58 frames.], batch size: 17, lr: 2.68e-04
+2022-04-30 07:41:27,535 INFO [train.py:763] (7/8) Epoch 28, batch 3000, loss[loss=0.1492, simple_loss=0.2523, pruned_loss=0.02303, over 7245.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2617, pruned_loss=0.03187, over 1419283.63 frames.], batch size: 20, lr: 2.68e-04
+2022-04-30 07:41:27,536 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 07:41:44,121 INFO [train.py:792] (7/8) Epoch 28, validation: loss=0.1685, simple_loss=0.2656, pruned_loss=0.03573, over 698248.00 frames.
+2022-04-30 07:42:49,830 INFO [train.py:763] (7/8) Epoch 28, batch 3050, loss[loss=0.1301, simple_loss=0.2294, pruned_loss=0.0154, over 7164.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2614, pruned_loss=0.03167, over 1422575.66 frames.], batch size: 18, lr: 2.68e-04
+2022-04-30 07:43:55,535 INFO [train.py:763] (7/8) Epoch 28, batch 3100, loss[loss=0.1622, simple_loss=0.2608, pruned_loss=0.03181, over 7254.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2609, pruned_loss=0.03118, over 1419507.22 frames.], batch size: 18, lr: 2.68e-04
+2022-04-30 07:45:01,635 INFO [train.py:763] (7/8) Epoch 28, batch 3150, loss[loss=0.1651, simple_loss=0.2625, pruned_loss=0.03388, over 7226.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2615, pruned_loss=0.03114, over 1422675.08 frames.], batch size: 21, lr: 2.68e-04
+2022-04-30 07:46:07,731 INFO [train.py:763] (7/8) Epoch 28, batch 3200, loss[loss=0.1583, simple_loss=0.2657, pruned_loss=0.02544, over 7440.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2617, pruned_loss=0.03097, over 1422869.07 frames.], batch size: 22, lr: 2.68e-04
+2022-04-30 07:47:14,375 INFO [train.py:763] (7/8) Epoch 28, batch 3250, loss[loss=0.135, simple_loss=0.2316, pruned_loss=0.01919, over 6778.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2614, pruned_loss=0.03099, over 1422685.09 frames.], batch size: 15, lr: 2.67e-04
+2022-04-30 07:48:20,835 INFO [train.py:763] (7/8) Epoch 28, batch 3300, loss[loss=0.1777, simple_loss=0.2843, pruned_loss=0.0355, over 7225.00 frames.], tot_loss[loss=0.1631, simple_loss=0.263, pruned_loss=0.03162, over 1421390.92 frames.], batch size: 21, lr: 2.67e-04
+2022-04-30 07:49:26,937 INFO [train.py:763] (7/8) Epoch 28, batch 3350, loss[loss=0.1559, simple_loss=0.2584, pruned_loss=0.02668, over 7047.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2627, pruned_loss=0.03193, over 1417845.91 frames.], batch size: 28, lr: 2.67e-04
+2022-04-30 07:50:33,801 INFO [train.py:763] (7/8) Epoch 28, batch 3400, loss[loss=0.1418, simple_loss=0.2341, pruned_loss=0.02474, over 7068.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2629, pruned_loss=0.03225, over 1416567.25 frames.], batch size: 18, lr: 2.67e-04
+2022-04-30 07:51:39,853 INFO [train.py:763] (7/8) Epoch 28, batch 3450, loss[loss=0.1311, simple_loss=0.2201, pruned_loss=0.02101, over 7278.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2623, pruned_loss=0.03217, over 1419713.11 frames.], batch size: 17, lr: 2.67e-04
+2022-04-30 07:52:45,413 INFO [train.py:763] (7/8) Epoch 28, batch 3500, loss[loss=0.1827, simple_loss=0.2852, pruned_loss=0.0401, over 6744.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2628, pruned_loss=0.03197, over 1419697.03 frames.], batch size: 31, lr: 2.67e-04
+2022-04-30 07:53:50,888 INFO [train.py:763] (7/8) Epoch 28, batch 3550, loss[loss=0.1844, simple_loss=0.273, pruned_loss=0.04793, over 7288.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2623, pruned_loss=0.03209, over 1423135.26 frames.], batch size: 18, lr: 2.67e-04
+2022-04-30 07:54:56,709 INFO [train.py:763] (7/8) Epoch 28, batch 3600, loss[loss=0.1546, simple_loss=0.2481, pruned_loss=0.03057, over 7208.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2625, pruned_loss=0.03204, over 1424001.26 frames.], batch size: 16, lr: 2.67e-04
+2022-04-30 07:56:02,367 INFO [train.py:763] (7/8) Epoch 28, batch 3650, loss[loss=0.1698, simple_loss=0.2787, pruned_loss=0.03041, over 7344.00 frames.], tot_loss[loss=0.1637, simple_loss=0.263, pruned_loss=0.03224, over 1427451.21 frames.], batch size: 22, lr: 2.67e-04
+2022-04-30 07:57:08,125 INFO [train.py:763] (7/8) Epoch 28, batch 3700, loss[loss=0.2045, simple_loss=0.2967, pruned_loss=0.05614, over 7209.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2621, pruned_loss=0.03189, over 1427070.84 frames.], batch size: 23, lr: 2.67e-04
+2022-04-30 07:58:13,568 INFO [train.py:763] (7/8) Epoch 28, batch 3750, loss[loss=0.1954, simple_loss=0.2892, pruned_loss=0.05084, over 4917.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2622, pruned_loss=0.03207, over 1426313.35 frames.], batch size: 52, lr: 2.67e-04
+2022-04-30 07:59:19,070 INFO [train.py:763] (7/8) Epoch 28, batch 3800, loss[loss=0.1471, simple_loss=0.2568, pruned_loss=0.01868, over 7436.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2627, pruned_loss=0.03205, over 1426797.29 frames.], batch size: 20, lr: 2.67e-04
+2022-04-30 08:00:24,617 INFO [train.py:763] (7/8) Epoch 28, batch 3850, loss[loss=0.1765, simple_loss=0.2872, pruned_loss=0.03294, over 7382.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2632, pruned_loss=0.03214, over 1427405.97 frames.], batch size: 23, lr: 2.67e-04
+2022-04-30 08:01:31,050 INFO [train.py:763] (7/8) Epoch 28, batch 3900, loss[loss=0.1715, simple_loss=0.2719, pruned_loss=0.03552, over 7288.00 frames.], tot_loss[loss=0.164, simple_loss=0.264, pruned_loss=0.03202, over 1429950.27 frames.], batch size: 24, lr: 2.67e-04
+2022-04-30 08:02:37,693 INFO [train.py:763] (7/8) Epoch 28, batch 3950, loss[loss=0.1271, simple_loss=0.2248, pruned_loss=0.01474, over 7421.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2649, pruned_loss=0.03203, over 1430620.62 frames.], batch size: 18, lr: 2.67e-04
+2022-04-30 08:03:44,070 INFO [train.py:763] (7/8) Epoch 28, batch 4000, loss[loss=0.1723, simple_loss=0.2796, pruned_loss=0.03247, over 7333.00 frames.], tot_loss[loss=0.1648, simple_loss=0.265, pruned_loss=0.03231, over 1430467.62 frames.], batch size: 22, lr: 2.67e-04
+2022-04-30 08:04:50,791 INFO [train.py:763] (7/8) Epoch 28, batch 4050, loss[loss=0.145, simple_loss=0.2333, pruned_loss=0.02828, over 7279.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2656, pruned_loss=0.0324, over 1429239.77 frames.], batch size: 17, lr: 2.67e-04
+2022-04-30 08:05:55,986 INFO [train.py:763] (7/8) Epoch 28, batch 4100, loss[loss=0.1594, simple_loss=0.272, pruned_loss=0.02338, over 7351.00 frames.], tot_loss[loss=0.165, simple_loss=0.2655, pruned_loss=0.03224, over 1430213.40 frames.], batch size: 22, lr: 2.67e-04
+2022-04-30 08:07:02,637 INFO [train.py:763] (7/8) Epoch 28, batch 4150, loss[loss=0.1561, simple_loss=0.2658, pruned_loss=0.02319, over 7320.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2647, pruned_loss=0.03215, over 1423864.18 frames.], batch size: 21, lr: 2.67e-04
+2022-04-30 08:08:09,150 INFO [train.py:763] (7/8) Epoch 28, batch 4200, loss[loss=0.1461, simple_loss=0.2469, pruned_loss=0.02263, over 7255.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2651, pruned_loss=0.03237, over 1419886.70 frames.], batch size: 19, lr: 2.66e-04
+2022-04-30 08:09:14,676 INFO [train.py:763] (7/8) Epoch 28, batch 4250, loss[loss=0.174, simple_loss=0.2803, pruned_loss=0.03384, over 6854.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2649, pruned_loss=0.0324, over 1421943.25 frames.], batch size: 31, lr: 2.66e-04
+2022-04-30 08:10:19,674 INFO [train.py:763] (7/8) Epoch 28, batch 4300, loss[loss=0.1452, simple_loss=0.2437, pruned_loss=0.0233, over 7165.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2646, pruned_loss=0.03215, over 1417962.58 frames.], batch size: 18, lr: 2.66e-04
+2022-04-30 08:11:24,977 INFO [train.py:763] (7/8) Epoch 28, batch 4350, loss[loss=0.1626, simple_loss=0.2746, pruned_loss=0.02533, over 7322.00 frames.], tot_loss[loss=0.164, simple_loss=0.2639, pruned_loss=0.0321, over 1419524.02 frames.], batch size: 21, lr: 2.66e-04
+2022-04-30 08:12:30,150 INFO [train.py:763] (7/8) Epoch 28, batch 4400, loss[loss=0.1739, simple_loss=0.2755, pruned_loss=0.03614, over 7290.00 frames.], tot_loss[loss=0.1641, simple_loss=0.264, pruned_loss=0.03209, over 1411022.88 frames.], batch size: 24, lr: 2.66e-04
+2022-04-30 08:13:35,279 INFO [train.py:763] (7/8) Epoch 28, batch 4450, loss[loss=0.1624, simple_loss=0.2668, pruned_loss=0.02903, over 6256.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2632, pruned_loss=0.03178, over 1400835.26 frames.], batch size: 38, lr: 2.66e-04
+2022-04-30 08:14:40,136 INFO [train.py:763] (7/8) Epoch 28, batch 4500, loss[loss=0.1617, simple_loss=0.2688, pruned_loss=0.0273, over 7219.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2638, pruned_loss=0.032, over 1378128.04 frames.], batch size: 22, lr: 2.66e-04
+2022-04-30 08:15:45,368 INFO [train.py:763] (7/8) Epoch 28, batch 4550, loss[loss=0.2291, simple_loss=0.3149, pruned_loss=0.07168, over 4815.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2658, pruned_loss=0.03289, over 1361104.69 frames.], batch size: 52, lr: 2.66e-04
+2022-04-30 08:17:05,896 INFO [train.py:763] (7/8) Epoch 29, batch 0, loss[loss=0.1552, simple_loss=0.2545, pruned_loss=0.02796, over 7324.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2545, pruned_loss=0.02796, over 7324.00 frames.], batch size: 20, lr: 2.62e-04
+2022-04-30 08:18:11,697 INFO [train.py:763] (7/8) Epoch 29, batch 50, loss[loss=0.164, simple_loss=0.256, pruned_loss=0.03603, over 7276.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2655, pruned_loss=0.03167, over 324433.70 frames.], batch size: 18, lr: 2.62e-04
+2022-04-30 08:19:17,266 INFO [train.py:763] (7/8) Epoch 29, batch 100, loss[loss=0.1539, simple_loss=0.2472, pruned_loss=0.03025, over 7267.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2611, pruned_loss=0.031, over 572365.03 frames.], batch size: 17, lr: 2.62e-04
+2022-04-30 08:20:22,577 INFO [train.py:763] (7/8) Epoch 29, batch 150, loss[loss=0.1996, simple_loss=0.3107, pruned_loss=0.04425, over 7315.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2631, pruned_loss=0.03191, over 750159.61 frames.], batch size: 24, lr: 2.62e-04
+2022-04-30 08:21:28,011 INFO [train.py:763] (7/8) Epoch 29, batch 200, loss[loss=0.1346, simple_loss=0.2347, pruned_loss=0.01725, over 7352.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2625, pruned_loss=0.03165, over 899847.52 frames.], batch size: 19, lr: 2.61e-04
+2022-04-30 08:22:33,086 INFO [train.py:763] (7/8) Epoch 29, batch 250, loss[loss=0.1311, simple_loss=0.2258, pruned_loss=0.01818, over 6850.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2624, pruned_loss=0.03126, over 1015334.76 frames.], batch size: 15, lr: 2.61e-04
+2022-04-30 08:23:39,545 INFO [train.py:763] (7/8) Epoch 29, batch 300, loss[loss=0.1561, simple_loss=0.2483, pruned_loss=0.03192, over 7280.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2635, pruned_loss=0.03164, over 1108726.83 frames.], batch size: 18, lr: 2.61e-04
+2022-04-30 08:24:46,648 INFO [train.py:763] (7/8) Epoch 29, batch 350, loss[loss=0.1455, simple_loss=0.24, pruned_loss=0.02549, over 7334.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2619, pruned_loss=0.03117, over 1181384.22 frames.], batch size: 20, lr: 2.61e-04
+2022-04-30 08:25:52,378 INFO [train.py:763] (7/8) Epoch 29, batch 400, loss[loss=0.1577, simple_loss=0.2599, pruned_loss=0.02773, over 7285.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2619, pruned_loss=0.03086, over 1237188.30 frames.], batch size: 24, lr: 2.61e-04
+2022-04-30 08:26:57,838 INFO [train.py:763] (7/8) Epoch 29, batch 450, loss[loss=0.1894, simple_loss=0.2953, pruned_loss=0.04175, over 7414.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2618, pruned_loss=0.03103, over 1279564.53 frames.], batch size: 21, lr: 2.61e-04
+2022-04-30 08:28:03,221 INFO [train.py:763] (7/8) Epoch 29, batch 500, loss[loss=0.1606, simple_loss=0.2666, pruned_loss=0.0273, over 7330.00 frames.], tot_loss[loss=0.1631, simple_loss=0.263, pruned_loss=0.03158, over 1307820.50 frames.], batch size: 20, lr: 2.61e-04
+2022-04-30 08:29:08,678 INFO [train.py:763] (7/8) Epoch 29, batch 550, loss[loss=0.1554, simple_loss=0.2691, pruned_loss=0.02087, over 7296.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2634, pruned_loss=0.03195, over 1335583.06 frames.], batch size: 24, lr: 2.61e-04
+2022-04-30 08:30:14,692 INFO [train.py:763] (7/8) Epoch 29, batch 600, loss[loss=0.1905, simple_loss=0.295, pruned_loss=0.04303, over 7213.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2635, pruned_loss=0.03175, over 1351542.14 frames.], batch size: 22, lr: 2.61e-04
+2022-04-30 08:31:20,880 INFO [train.py:763] (7/8) Epoch 29, batch 650, loss[loss=0.1318, simple_loss=0.2264, pruned_loss=0.01862, over 7447.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2636, pruned_loss=0.03166, over 1366728.48 frames.], batch size: 19, lr: 2.61e-04
+2022-04-30 08:32:27,051 INFO [train.py:763] (7/8) Epoch 29, batch 700, loss[loss=0.1691, simple_loss=0.2603, pruned_loss=0.03892, over 7329.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2645, pruned_loss=0.03213, over 1375611.55 frames.], batch size: 20, lr: 2.61e-04
+2022-04-30 08:33:32,290 INFO [train.py:763] (7/8) Epoch 29, batch 750, loss[loss=0.1621, simple_loss=0.262, pruned_loss=0.03115, over 7231.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2639, pruned_loss=0.03187, over 1381852.65 frames.], batch size: 20, lr: 2.61e-04
+2022-04-30 08:34:37,547 INFO [train.py:763] (7/8) Epoch 29, batch 800, loss[loss=0.1703, simple_loss=0.2801, pruned_loss=0.03024, over 7344.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2636, pruned_loss=0.03175, over 1387727.68 frames.], batch size: 22, lr: 2.61e-04
+2022-04-30 08:35:43,028 INFO [train.py:763] (7/8) Epoch 29, batch 850, loss[loss=0.1652, simple_loss=0.2706, pruned_loss=0.02983, over 7063.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2627, pruned_loss=0.03127, over 1396477.57 frames.], batch size: 18, lr: 2.61e-04
+2022-04-30 08:36:48,537 INFO [train.py:763] (7/8) Epoch 29, batch 900, loss[loss=0.1776, simple_loss=0.2853, pruned_loss=0.03498, over 7227.00 frames.], tot_loss[loss=0.162, simple_loss=0.2619, pruned_loss=0.03104, over 1400700.82 frames.], batch size: 21, lr: 2.61e-04
+2022-04-30 08:37:53,916 INFO [train.py:763] (7/8) Epoch 29, batch 950, loss[loss=0.1498, simple_loss=0.2464, pruned_loss=0.02653, over 7106.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2626, pruned_loss=0.03122, over 1406808.08 frames.], batch size: 21, lr: 2.61e-04
+2022-04-30 08:38:59,981 INFO [train.py:763] (7/8) Epoch 29, batch 1000, loss[loss=0.1593, simple_loss=0.2661, pruned_loss=0.02628, over 7142.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2636, pruned_loss=0.03136, over 1410855.90 frames.], batch size: 20, lr: 2.61e-04
+2022-04-30 08:40:06,277 INFO [train.py:763] (7/8) Epoch 29, batch 1050, loss[loss=0.1493, simple_loss=0.2389, pruned_loss=0.02988, over 7280.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2643, pruned_loss=0.03202, over 1407398.43 frames.], batch size: 18, lr: 2.61e-04
+2022-04-30 08:41:11,513 INFO [train.py:763] (7/8) Epoch 29, batch 1100, loss[loss=0.1882, simple_loss=0.2936, pruned_loss=0.04142, over 7310.00 frames.], tot_loss[loss=0.1646, simple_loss=0.265, pruned_loss=0.03207, over 1416850.76 frames.], batch size: 21, lr: 2.61e-04
+2022-04-30 08:42:16,635 INFO [train.py:763] (7/8) Epoch 29, batch 1150, loss[loss=0.145, simple_loss=0.231, pruned_loss=0.02946, over 6971.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2645, pruned_loss=0.03158, over 1417368.40 frames.], batch size: 16, lr: 2.61e-04
+2022-04-30 08:43:21,917 INFO [train.py:763] (7/8) Epoch 29, batch 1200, loss[loss=0.1774, simple_loss=0.2742, pruned_loss=0.04028, over 7161.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2639, pruned_loss=0.03135, over 1422127.46 frames.], batch size: 19, lr: 2.61e-04
+2022-04-30 08:44:27,484 INFO [train.py:763] (7/8) Epoch 29, batch 1250, loss[loss=0.1746, simple_loss=0.2746, pruned_loss=0.03724, over 4916.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2638, pruned_loss=0.03168, over 1417019.74 frames.], batch size: 52, lr: 2.60e-04
+2022-04-30 08:45:34,677 INFO [train.py:763] (7/8) Epoch 29, batch 1300, loss[loss=0.169, simple_loss=0.2831, pruned_loss=0.02746, over 7338.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2636, pruned_loss=0.03169, over 1418145.73 frames.], batch size: 22, lr: 2.60e-04
+2022-04-30 08:46:42,221 INFO [train.py:763] (7/8) Epoch 29, batch 1350, loss[loss=0.1647, simple_loss=0.2685, pruned_loss=0.03043, over 6648.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2637, pruned_loss=0.0318, over 1419616.38 frames.], batch size: 38, lr: 2.60e-04
+2022-04-30 08:47:49,037 INFO [train.py:763] (7/8) Epoch 29, batch 1400, loss[loss=0.1703, simple_loss=0.2539, pruned_loss=0.04331, over 6893.00 frames.], tot_loss[loss=0.163, simple_loss=0.2629, pruned_loss=0.03159, over 1420466.28 frames.], batch size: 15, lr: 2.60e-04
+2022-04-30 08:48:56,281 INFO [train.py:763] (7/8) Epoch 29, batch 1450, loss[loss=0.1689, simple_loss=0.2664, pruned_loss=0.03577, over 7103.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2622, pruned_loss=0.0314, over 1418930.25 frames.], batch size: 21, lr: 2.60e-04
+2022-04-30 08:50:03,384 INFO [train.py:763] (7/8) Epoch 29, batch 1500, loss[loss=0.1694, simple_loss=0.2687, pruned_loss=0.03505, over 7249.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2623, pruned_loss=0.03157, over 1418041.54 frames.], batch size: 19, lr: 2.60e-04
+2022-04-30 08:51:09,985 INFO [train.py:763] (7/8) Epoch 29, batch 1550, loss[loss=0.173, simple_loss=0.2827, pruned_loss=0.03158, over 7212.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2626, pruned_loss=0.03166, over 1418781.49 frames.], batch size: 23, lr: 2.60e-04
+2022-04-30 08:52:16,978 INFO [train.py:763] (7/8) Epoch 29, batch 1600, loss[loss=0.1604, simple_loss=0.2677, pruned_loss=0.02657, over 7323.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2632, pruned_loss=0.032, over 1419619.44 frames.], batch size: 21, lr: 2.60e-04
+2022-04-30 08:53:22,980 INFO [train.py:763] (7/8) Epoch 29, batch 1650, loss[loss=0.1586, simple_loss=0.2654, pruned_loss=0.02586, over 7157.00 frames.], tot_loss[loss=0.1633, simple_loss=0.263, pruned_loss=0.03181, over 1423872.31 frames.], batch size: 26, lr: 2.60e-04
+2022-04-30 08:54:28,302 INFO [train.py:763] (7/8) Epoch 29, batch 1700, loss[loss=0.1556, simple_loss=0.2477, pruned_loss=0.03175, over 7129.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2632, pruned_loss=0.03201, over 1426684.06 frames.], batch size: 17, lr: 2.60e-04
+2022-04-30 08:55:35,263 INFO [train.py:763] (7/8) Epoch 29, batch 1750, loss[loss=0.1725, simple_loss=0.2843, pruned_loss=0.03034, over 7151.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2633, pruned_loss=0.03212, over 1423260.75 frames.], batch size: 20, lr: 2.60e-04
+2022-04-30 08:56:42,202 INFO [train.py:763] (7/8) Epoch 29, batch 1800, loss[loss=0.226, simple_loss=0.3117, pruned_loss=0.07016, over 5072.00 frames.], tot_loss[loss=0.164, simple_loss=0.2635, pruned_loss=0.03226, over 1420983.72 frames.], batch size: 52, lr: 2.60e-04
+2022-04-30 08:57:49,270 INFO [train.py:763] (7/8) Epoch 29, batch 1850, loss[loss=0.1641, simple_loss=0.2689, pruned_loss=0.02962, over 7106.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2629, pruned_loss=0.03232, over 1424820.43 frames.], batch size: 21, lr: 2.60e-04
+2022-04-30 08:58:55,915 INFO [train.py:763] (7/8) Epoch 29, batch 1900, loss[loss=0.1775, simple_loss=0.266, pruned_loss=0.04453, over 6851.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2626, pruned_loss=0.03227, over 1427397.49 frames.], batch size: 15, lr: 2.60e-04
+2022-04-30 09:00:01,484 INFO [train.py:763] (7/8) Epoch 29, batch 1950, loss[loss=0.1253, simple_loss=0.2193, pruned_loss=0.01569, over 7267.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2628, pruned_loss=0.03216, over 1428595.73 frames.], batch size: 17, lr: 2.60e-04
+2022-04-30 09:01:06,705 INFO [train.py:763] (7/8) Epoch 29, batch 2000, loss[loss=0.1482, simple_loss=0.2578, pruned_loss=0.01925, over 7330.00 frames.], tot_loss[loss=0.1633, simple_loss=0.263, pruned_loss=0.03185, over 1431051.96 frames.], batch size: 22, lr: 2.60e-04
+2022-04-30 09:02:12,113 INFO [train.py:763] (7/8) Epoch 29, batch 2050, loss[loss=0.1877, simple_loss=0.2874, pruned_loss=0.04398, over 7179.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2629, pruned_loss=0.03191, over 1432036.93 frames.], batch size: 23, lr: 2.60e-04
+2022-04-30 09:03:17,258 INFO [train.py:763] (7/8) Epoch 29, batch 2100, loss[loss=0.1645, simple_loss=0.2725, pruned_loss=0.02821, over 7140.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2628, pruned_loss=0.0318, over 1430980.90 frames.], batch size: 20, lr: 2.60e-04
+2022-04-30 09:04:22,323 INFO [train.py:763] (7/8) Epoch 29, batch 2150, loss[loss=0.1537, simple_loss=0.2434, pruned_loss=0.03206, over 7141.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2628, pruned_loss=0.03183, over 1429403.51 frames.], batch size: 17, lr: 2.60e-04
+2022-04-30 09:05:27,768 INFO [train.py:763] (7/8) Epoch 29, batch 2200, loss[loss=0.1643, simple_loss=0.2611, pruned_loss=0.03375, over 7268.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2626, pruned_loss=0.03191, over 1424849.79 frames.], batch size: 24, lr: 2.60e-04
+2022-04-30 09:06:32,921 INFO [train.py:763] (7/8) Epoch 29, batch 2250, loss[loss=0.2106, simple_loss=0.3186, pruned_loss=0.0513, over 7175.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2624, pruned_loss=0.03173, over 1423507.27 frames.], batch size: 26, lr: 2.59e-04
+2022-04-30 09:07:38,525 INFO [train.py:763] (7/8) Epoch 29, batch 2300, loss[loss=0.1652, simple_loss=0.2671, pruned_loss=0.03162, over 7327.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2628, pruned_loss=0.03168, over 1420021.19 frames.], batch size: 20, lr: 2.59e-04
+2022-04-30 09:08:43,794 INFO [train.py:763] (7/8) Epoch 29, batch 2350, loss[loss=0.1729, simple_loss=0.2779, pruned_loss=0.03391, over 7338.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2629, pruned_loss=0.03187, over 1421632.99 frames.], batch size: 22, lr: 2.59e-04
+2022-04-30 09:09:49,535 INFO [train.py:763] (7/8) Epoch 29, batch 2400, loss[loss=0.1626, simple_loss=0.2621, pruned_loss=0.03158, over 7279.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2628, pruned_loss=0.03197, over 1423583.71 frames.], batch size: 25, lr: 2.59e-04
+2022-04-30 09:10:55,183 INFO [train.py:763] (7/8) Epoch 29, batch 2450, loss[loss=0.1817, simple_loss=0.2911, pruned_loss=0.03609, over 7150.00 frames.], tot_loss[loss=0.1624, simple_loss=0.262, pruned_loss=0.03137, over 1427521.13 frames.], batch size: 20, lr: 2.59e-04
+2022-04-30 09:12:00,719 INFO [train.py:763] (7/8) Epoch 29, batch 2500, loss[loss=0.1459, simple_loss=0.2362, pruned_loss=0.02778, over 6788.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2621, pruned_loss=0.03167, over 1430846.69 frames.], batch size: 15, lr: 2.59e-04
+2022-04-30 09:13:06,087 INFO [train.py:763] (7/8) Epoch 29, batch 2550, loss[loss=0.1558, simple_loss=0.2512, pruned_loss=0.03021, over 7411.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2618, pruned_loss=0.03165, over 1428339.20 frames.], batch size: 18, lr: 2.59e-04
+2022-04-30 09:14:11,181 INFO [train.py:763] (7/8) Epoch 29, batch 2600, loss[loss=0.1764, simple_loss=0.2873, pruned_loss=0.03273, over 7119.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2621, pruned_loss=0.03165, over 1427659.69 frames.], batch size: 21, lr: 2.59e-04
+2022-04-30 09:15:16,460 INFO [train.py:763] (7/8) Epoch 29, batch 2650, loss[loss=0.1349, simple_loss=0.2252, pruned_loss=0.02232, over 7146.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2618, pruned_loss=0.03149, over 1429741.35 frames.], batch size: 17, lr: 2.59e-04
+2022-04-30 09:16:21,507 INFO [train.py:763] (7/8) Epoch 29, batch 2700, loss[loss=0.1555, simple_loss=0.263, pruned_loss=0.02396, over 7112.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2621, pruned_loss=0.03125, over 1429464.93 frames.], batch size: 21, lr: 2.59e-04
+2022-04-30 09:17:27,772 INFO [train.py:763] (7/8) Epoch 29, batch 2750, loss[loss=0.1358, simple_loss=0.2441, pruned_loss=0.01376, over 7232.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2624, pruned_loss=0.03122, over 1425297.70 frames.], batch size: 20, lr: 2.59e-04
+2022-04-30 09:18:33,551 INFO [train.py:763] (7/8) Epoch 29, batch 2800, loss[loss=0.1579, simple_loss=0.2586, pruned_loss=0.02864, over 7331.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2621, pruned_loss=0.0313, over 1424421.73 frames.], batch size: 22, lr: 2.59e-04
+2022-04-30 09:19:39,951 INFO [train.py:763] (7/8) Epoch 29, batch 2850, loss[loss=0.1724, simple_loss=0.2726, pruned_loss=0.03613, over 7232.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2616, pruned_loss=0.03151, over 1419069.80 frames.], batch size: 20, lr: 2.59e-04
+2022-04-30 09:20:45,388 INFO [train.py:763] (7/8) Epoch 29, batch 2900, loss[loss=0.1467, simple_loss=0.2378, pruned_loss=0.02784, over 6999.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2612, pruned_loss=0.03113, over 1422162.01 frames.], batch size: 16, lr: 2.59e-04
+2022-04-30 09:22:01,692 INFO [train.py:763] (7/8) Epoch 29, batch 2950, loss[loss=0.148, simple_loss=0.2649, pruned_loss=0.01553, over 6425.00 frames.], tot_loss[loss=0.161, simple_loss=0.2607, pruned_loss=0.03064, over 1422913.92 frames.], batch size: 38, lr: 2.59e-04
+2022-04-30 09:23:07,158 INFO [train.py:763] (7/8) Epoch 29, batch 3000, loss[loss=0.1555, simple_loss=0.2538, pruned_loss=0.02864, over 7125.00 frames.], tot_loss[loss=0.1615, simple_loss=0.261, pruned_loss=0.03105, over 1425095.75 frames.], batch size: 21, lr: 2.59e-04
+2022-04-30 09:23:07,158 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 09:23:22,371 INFO [train.py:792] (7/8) Epoch 29, validation: loss=0.1693, simple_loss=0.2664, pruned_loss=0.03606, over 698248.00 frames.
+2022-04-30 09:24:27,457 INFO [train.py:763] (7/8) Epoch 29, batch 3050, loss[loss=0.1519, simple_loss=0.2559, pruned_loss=0.02392, over 7116.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2621, pruned_loss=0.03129, over 1426527.38 frames.], batch size: 21, lr: 2.59e-04
+2022-04-30 09:25:32,601 INFO [train.py:763] (7/8) Epoch 29, batch 3100, loss[loss=0.1727, simple_loss=0.2755, pruned_loss=0.03492, over 7400.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2625, pruned_loss=0.03112, over 1426939.77 frames.], batch size: 21, lr: 2.59e-04
+2022-04-30 09:26:38,430 INFO [train.py:763] (7/8) Epoch 29, batch 3150, loss[loss=0.1401, simple_loss=0.2301, pruned_loss=0.02502, over 7164.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2613, pruned_loss=0.03097, over 1422273.95 frames.], batch size: 18, lr: 2.59e-04
+2022-04-30 09:27:44,840 INFO [train.py:763] (7/8) Epoch 29, batch 3200, loss[loss=0.1716, simple_loss=0.27, pruned_loss=0.03661, over 7251.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2598, pruned_loss=0.03046, over 1424937.58 frames.], batch size: 19, lr: 2.59e-04
+2022-04-30 09:28:51,949 INFO [train.py:763] (7/8) Epoch 29, batch 3250, loss[loss=0.1558, simple_loss=0.2579, pruned_loss=0.02683, over 7082.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2607, pruned_loss=0.03096, over 1419530.15 frames.], batch size: 28, lr: 2.59e-04
+2022-04-30 09:29:57,739 INFO [train.py:763] (7/8) Epoch 29, batch 3300, loss[loss=0.1517, simple_loss=0.257, pruned_loss=0.02324, over 7324.00 frames.], tot_loss[loss=0.1613, simple_loss=0.261, pruned_loss=0.0308, over 1423202.20 frames.], batch size: 20, lr: 2.58e-04
+2022-04-30 09:31:03,719 INFO [train.py:763] (7/8) Epoch 29, batch 3350, loss[loss=0.1299, simple_loss=0.2144, pruned_loss=0.0227, over 7277.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2605, pruned_loss=0.03091, over 1427126.08 frames.], batch size: 17, lr: 2.58e-04
+2022-04-30 09:32:09,347 INFO [train.py:763] (7/8) Epoch 29, batch 3400, loss[loss=0.1854, simple_loss=0.2811, pruned_loss=0.04489, over 4614.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2606, pruned_loss=0.03112, over 1422982.15 frames.], batch size: 52, lr: 2.58e-04
+2022-04-30 09:33:15,087 INFO [train.py:763] (7/8) Epoch 29, batch 3450, loss[loss=0.1469, simple_loss=0.2541, pruned_loss=0.01989, over 7275.00 frames.], tot_loss[loss=0.161, simple_loss=0.2602, pruned_loss=0.03089, over 1420558.96 frames.], batch size: 24, lr: 2.58e-04
+2022-04-30 09:34:21,156 INFO [train.py:763] (7/8) Epoch 29, batch 3500, loss[loss=0.1872, simple_loss=0.2924, pruned_loss=0.04101, over 7163.00 frames.], tot_loss[loss=0.162, simple_loss=0.2614, pruned_loss=0.03133, over 1422758.61 frames.], batch size: 26, lr: 2.58e-04
+2022-04-30 09:35:26,543 INFO [train.py:763] (7/8) Epoch 29, batch 3550, loss[loss=0.1466, simple_loss=0.2439, pruned_loss=0.02464, over 7174.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2612, pruned_loss=0.03112, over 1422747.90 frames.], batch size: 18, lr: 2.58e-04
+2022-04-30 09:36:32,246 INFO [train.py:763] (7/8) Epoch 29, batch 3600, loss[loss=0.1714, simple_loss=0.271, pruned_loss=0.03593, over 7253.00 frames.], tot_loss[loss=0.1616, simple_loss=0.261, pruned_loss=0.03105, over 1428103.96 frames.], batch size: 19, lr: 2.58e-04
+2022-04-30 09:37:46,884 INFO [train.py:763] (7/8) Epoch 29, batch 3650, loss[loss=0.1715, simple_loss=0.2766, pruned_loss=0.03321, over 6784.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2616, pruned_loss=0.03145, over 1429352.09 frames.], batch size: 31, lr: 2.58e-04
+2022-04-30 09:38:52,220 INFO [train.py:763] (7/8) Epoch 29, batch 3700, loss[loss=0.1575, simple_loss=0.2457, pruned_loss=0.03472, over 7281.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2607, pruned_loss=0.03129, over 1429504.01 frames.], batch size: 17, lr: 2.58e-04
+2022-04-30 09:39:59,127 INFO [train.py:763] (7/8) Epoch 29, batch 3750, loss[loss=0.1731, simple_loss=0.2681, pruned_loss=0.03903, over 7049.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2616, pruned_loss=0.0311, over 1432443.71 frames.], batch size: 28, lr: 2.58e-04
+2022-04-30 09:41:05,840 INFO [train.py:763] (7/8) Epoch 29, batch 3800, loss[loss=0.1963, simple_loss=0.3, pruned_loss=0.04631, over 7209.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2626, pruned_loss=0.03121, over 1425483.01 frames.], batch size: 22, lr: 2.58e-04
+2022-04-30 09:42:11,185 INFO [train.py:763] (7/8) Epoch 29, batch 3850, loss[loss=0.1551, simple_loss=0.2481, pruned_loss=0.03107, over 6780.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2623, pruned_loss=0.03126, over 1426191.61 frames.], batch size: 15, lr: 2.58e-04
+2022-04-30 09:43:16,822 INFO [train.py:763] (7/8) Epoch 29, batch 3900, loss[loss=0.1451, simple_loss=0.2432, pruned_loss=0.02355, over 7130.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2624, pruned_loss=0.03127, over 1426778.19 frames.], batch size: 17, lr: 2.58e-04
+2022-04-30 09:44:22,555 INFO [train.py:763] (7/8) Epoch 29, batch 3950, loss[loss=0.1838, simple_loss=0.2883, pruned_loss=0.03966, over 7384.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2636, pruned_loss=0.03172, over 1420548.37 frames.], batch size: 23, lr: 2.58e-04
+2022-04-30 09:45:27,980 INFO [train.py:763] (7/8) Epoch 29, batch 4000, loss[loss=0.1761, simple_loss=0.2781, pruned_loss=0.03702, over 7305.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2642, pruned_loss=0.03199, over 1419960.20 frames.], batch size: 25, lr: 2.58e-04
+2022-04-30 09:46:33,257 INFO [train.py:763] (7/8) Epoch 29, batch 4050, loss[loss=0.1729, simple_loss=0.2772, pruned_loss=0.03429, over 7157.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2632, pruned_loss=0.03172, over 1419499.99 frames.], batch size: 28, lr: 2.58e-04
+2022-04-30 09:47:39,271 INFO [train.py:763] (7/8) Epoch 29, batch 4100, loss[loss=0.1623, simple_loss=0.2728, pruned_loss=0.02592, over 7311.00 frames.], tot_loss[loss=0.1634, simple_loss=0.263, pruned_loss=0.03186, over 1421245.52 frames.], batch size: 21, lr: 2.58e-04
+2022-04-30 09:48:45,609 INFO [train.py:763] (7/8) Epoch 29, batch 4150, loss[loss=0.1763, simple_loss=0.2931, pruned_loss=0.0297, over 7218.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2616, pruned_loss=0.03151, over 1421939.40 frames.], batch size: 21, lr: 2.58e-04
+2022-04-30 09:50:00,130 INFO [train.py:763] (7/8) Epoch 29, batch 4200, loss[loss=0.161, simple_loss=0.2769, pruned_loss=0.02251, over 7428.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2618, pruned_loss=0.03144, over 1422361.83 frames.], batch size: 20, lr: 2.58e-04
+2022-04-30 09:51:13,971 INFO [train.py:763] (7/8) Epoch 29, batch 4250, loss[loss=0.1842, simple_loss=0.285, pruned_loss=0.04167, over 7382.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2637, pruned_loss=0.03172, over 1416329.27 frames.], batch size: 23, lr: 2.58e-04
+2022-04-30 09:52:28,890 INFO [train.py:763] (7/8) Epoch 29, batch 4300, loss[loss=0.1323, simple_loss=0.2266, pruned_loss=0.01901, over 7281.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2635, pruned_loss=0.03162, over 1419966.38 frames.], batch size: 17, lr: 2.58e-04
+2022-04-30 09:53:43,989 INFO [train.py:763] (7/8) Epoch 29, batch 4350, loss[loss=0.1946, simple_loss=0.2838, pruned_loss=0.05269, over 7237.00 frames.], tot_loss[loss=0.163, simple_loss=0.2624, pruned_loss=0.03183, over 1422878.86 frames.], batch size: 20, lr: 2.58e-04
+2022-04-30 09:54:58,504 INFO [train.py:763] (7/8) Epoch 29, batch 4400, loss[loss=0.1816, simple_loss=0.2822, pruned_loss=0.0405, over 7235.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2616, pruned_loss=0.03138, over 1418837.44 frames.], batch size: 20, lr: 2.57e-04
+2022-04-30 09:56:12,794 INFO [train.py:763] (7/8) Epoch 29, batch 4450, loss[loss=0.1562, simple_loss=0.2637, pruned_loss=0.02429, over 6593.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2613, pruned_loss=0.03108, over 1413198.47 frames.], batch size: 38, lr: 2.57e-04
+2022-04-30 09:57:17,989 INFO [train.py:763] (7/8) Epoch 29, batch 4500, loss[loss=0.2017, simple_loss=0.2977, pruned_loss=0.05288, over 5323.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2626, pruned_loss=0.03144, over 1398079.06 frames.], batch size: 52, lr: 2.57e-04
+2022-04-30 09:58:32,311 INFO [train.py:763] (7/8) Epoch 29, batch 4550, loss[loss=0.1982, simple_loss=0.2884, pruned_loss=0.05401, over 5123.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2654, pruned_loss=0.03283, over 1358924.43 frames.], batch size: 52, lr: 2.57e-04
+2022-04-30 10:00:01,326 INFO [train.py:763] (7/8) Epoch 30, batch 0, loss[loss=0.1513, simple_loss=0.2468, pruned_loss=0.02789, over 7322.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2468, pruned_loss=0.02789, over 7322.00 frames.], batch size: 20, lr: 2.53e-04
+2022-04-30 10:01:07,000 INFO [train.py:763] (7/8) Epoch 30, batch 50, loss[loss=0.172, simple_loss=0.2771, pruned_loss=0.03345, over 7258.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2616, pruned_loss=0.03232, over 317125.55 frames.], batch size: 19, lr: 2.53e-04
+2022-04-30 10:02:12,192 INFO [train.py:763] (7/8) Epoch 30, batch 100, loss[loss=0.1602, simple_loss=0.2661, pruned_loss=0.02718, over 7380.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2623, pruned_loss=0.03204, over 561019.36 frames.], batch size: 23, lr: 2.53e-04
+2022-04-30 10:03:17,813 INFO [train.py:763] (7/8) Epoch 30, batch 150, loss[loss=0.1952, simple_loss=0.2983, pruned_loss=0.04608, over 7203.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2609, pruned_loss=0.03134, over 756132.66 frames.], batch size: 22, lr: 2.53e-04
+2022-04-30 10:04:23,878 INFO [train.py:763] (7/8) Epoch 30, batch 200, loss[loss=0.1604, simple_loss=0.262, pruned_loss=0.02944, over 5070.00 frames.], tot_loss[loss=0.1611, simple_loss=0.26, pruned_loss=0.03108, over 900682.88 frames.], batch size: 54, lr: 2.53e-04
+2022-04-30 10:05:30,003 INFO [train.py:763] (7/8) Epoch 30, batch 250, loss[loss=0.1608, simple_loss=0.2697, pruned_loss=0.02596, over 7268.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2627, pruned_loss=0.03147, over 1015457.18 frames.], batch size: 25, lr: 2.53e-04
+2022-04-30 10:06:35,966 INFO [train.py:763] (7/8) Epoch 30, batch 300, loss[loss=0.1783, simple_loss=0.2767, pruned_loss=0.03992, over 7330.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2626, pruned_loss=0.03203, over 1107348.92 frames.], batch size: 21, lr: 2.53e-04
+2022-04-30 10:07:41,458 INFO [train.py:763] (7/8) Epoch 30, batch 350, loss[loss=0.1524, simple_loss=0.2586, pruned_loss=0.02306, over 7161.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2628, pruned_loss=0.03179, over 1174195.03 frames.], batch size: 18, lr: 2.53e-04
+2022-04-30 10:08:46,862 INFO [train.py:763] (7/8) Epoch 30, batch 400, loss[loss=0.16, simple_loss=0.262, pruned_loss=0.02896, over 7220.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2629, pruned_loss=0.03162, over 1224087.12 frames.], batch size: 21, lr: 2.53e-04
+2022-04-30 10:09:52,327 INFO [train.py:763] (7/8) Epoch 30, batch 450, loss[loss=0.199, simple_loss=0.2962, pruned_loss=0.05092, over 7173.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2641, pruned_loss=0.03228, over 1265141.10 frames.], batch size: 26, lr: 2.53e-04
+2022-04-30 10:10:57,864 INFO [train.py:763] (7/8) Epoch 30, batch 500, loss[loss=0.1456, simple_loss=0.2386, pruned_loss=0.02625, over 7270.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2634, pruned_loss=0.03221, over 1300873.34 frames.], batch size: 17, lr: 2.53e-04
+2022-04-30 10:12:03,599 INFO [train.py:763] (7/8) Epoch 30, batch 550, loss[loss=0.1668, simple_loss=0.2781, pruned_loss=0.02772, over 7403.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2633, pruned_loss=0.03201, over 1328011.41 frames.], batch size: 21, lr: 2.53e-04
+2022-04-30 10:13:09,447 INFO [train.py:763] (7/8) Epoch 30, batch 600, loss[loss=0.1706, simple_loss=0.2673, pruned_loss=0.03697, over 7079.00 frames.], tot_loss[loss=0.164, simple_loss=0.2643, pruned_loss=0.03185, over 1347258.20 frames.], batch size: 18, lr: 2.53e-04
+2022-04-30 10:14:15,869 INFO [train.py:763] (7/8) Epoch 30, batch 650, loss[loss=0.1618, simple_loss=0.2643, pruned_loss=0.02961, over 7147.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2637, pruned_loss=0.03175, over 1368668.40 frames.], batch size: 20, lr: 2.53e-04
+2022-04-30 10:15:21,942 INFO [train.py:763] (7/8) Epoch 30, batch 700, loss[loss=0.1404, simple_loss=0.2242, pruned_loss=0.0283, over 6755.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2638, pruned_loss=0.03173, over 1378161.09 frames.], batch size: 15, lr: 2.52e-04
+2022-04-30 10:16:28,677 INFO [train.py:763] (7/8) Epoch 30, batch 750, loss[loss=0.1627, simple_loss=0.2591, pruned_loss=0.03315, over 7243.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2633, pruned_loss=0.03182, over 1386853.86 frames.], batch size: 20, lr: 2.52e-04
+2022-04-30 10:17:34,233 INFO [train.py:763] (7/8) Epoch 30, batch 800, loss[loss=0.1549, simple_loss=0.2584, pruned_loss=0.02574, over 7330.00 frames.], tot_loss[loss=0.1629, simple_loss=0.263, pruned_loss=0.0314, over 1395485.00 frames.], batch size: 20, lr: 2.52e-04
+2022-04-30 10:18:39,967 INFO [train.py:763] (7/8) Epoch 30, batch 850, loss[loss=0.1714, simple_loss=0.2697, pruned_loss=0.03651, over 7426.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2616, pruned_loss=0.03136, over 1399919.99 frames.], batch size: 20, lr: 2.52e-04
+2022-04-30 10:19:45,747 INFO [train.py:763] (7/8) Epoch 30, batch 900, loss[loss=0.1335, simple_loss=0.2331, pruned_loss=0.01697, over 6821.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2618, pruned_loss=0.03128, over 1404645.43 frames.], batch size: 15, lr: 2.52e-04
+2022-04-30 10:20:52,504 INFO [train.py:763] (7/8) Epoch 30, batch 950, loss[loss=0.1608, simple_loss=0.2618, pruned_loss=0.02993, over 7050.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2612, pruned_loss=0.0312, over 1406321.67 frames.], batch size: 28, lr: 2.52e-04
+2022-04-30 10:21:58,507 INFO [train.py:763] (7/8) Epoch 30, batch 1000, loss[loss=0.1518, simple_loss=0.2585, pruned_loss=0.02253, over 7336.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2616, pruned_loss=0.03099, over 1408872.22 frames.], batch size: 22, lr: 2.52e-04
+2022-04-30 10:23:03,979 INFO [train.py:763] (7/8) Epoch 30, batch 1050, loss[loss=0.1732, simple_loss=0.2782, pruned_loss=0.0341, over 7044.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2611, pruned_loss=0.03069, over 1410780.17 frames.], batch size: 28, lr: 2.52e-04
+2022-04-30 10:24:09,744 INFO [train.py:763] (7/8) Epoch 30, batch 1100, loss[loss=0.1529, simple_loss=0.262, pruned_loss=0.02194, over 7069.00 frames.], tot_loss[loss=0.161, simple_loss=0.2608, pruned_loss=0.03064, over 1415268.69 frames.], batch size: 18, lr: 2.52e-04
+2022-04-30 10:25:15,767 INFO [train.py:763] (7/8) Epoch 30, batch 1150, loss[loss=0.1455, simple_loss=0.241, pruned_loss=0.02499, over 7055.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2608, pruned_loss=0.03078, over 1416941.79 frames.], batch size: 18, lr: 2.52e-04
+2022-04-30 10:26:21,670 INFO [train.py:763] (7/8) Epoch 30, batch 1200, loss[loss=0.1761, simple_loss=0.2789, pruned_loss=0.03664, over 7203.00 frames.], tot_loss[loss=0.1616, simple_loss=0.261, pruned_loss=0.0311, over 1419467.12 frames.], batch size: 22, lr: 2.52e-04
+2022-04-30 10:27:27,473 INFO [train.py:763] (7/8) Epoch 30, batch 1250, loss[loss=0.1588, simple_loss=0.2562, pruned_loss=0.03068, over 7408.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2614, pruned_loss=0.03123, over 1418761.14 frames.], batch size: 18, lr: 2.52e-04
+2022-04-30 10:28:33,987 INFO [train.py:763] (7/8) Epoch 30, batch 1300, loss[loss=0.1777, simple_loss=0.2799, pruned_loss=0.03775, over 7168.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2621, pruned_loss=0.03129, over 1417584.04 frames.], batch size: 26, lr: 2.52e-04
+2022-04-30 10:29:40,226 INFO [train.py:763] (7/8) Epoch 30, batch 1350, loss[loss=0.1577, simple_loss=0.2463, pruned_loss=0.0346, over 7138.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2629, pruned_loss=0.03146, over 1415165.01 frames.], batch size: 17, lr: 2.52e-04
+2022-04-30 10:30:45,710 INFO [train.py:763] (7/8) Epoch 30, batch 1400, loss[loss=0.2004, simple_loss=0.2919, pruned_loss=0.05438, over 7332.00 frames.], tot_loss[loss=0.163, simple_loss=0.263, pruned_loss=0.03147, over 1419122.99 frames.], batch size: 22, lr: 2.52e-04
+2022-04-30 10:31:51,075 INFO [train.py:763] (7/8) Epoch 30, batch 1450, loss[loss=0.1733, simple_loss=0.2741, pruned_loss=0.03625, over 7144.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2628, pruned_loss=0.03129, over 1420337.82 frames.], batch size: 20, lr: 2.52e-04
+2022-04-30 10:32:56,524 INFO [train.py:763] (7/8) Epoch 30, batch 1500, loss[loss=0.1692, simple_loss=0.2744, pruned_loss=0.03199, over 7300.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2632, pruned_loss=0.031, over 1425947.40 frames.], batch size: 25, lr: 2.52e-04
+2022-04-30 10:34:02,187 INFO [train.py:763] (7/8) Epoch 30, batch 1550, loss[loss=0.1737, simple_loss=0.2759, pruned_loss=0.03579, over 7297.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2622, pruned_loss=0.03112, over 1427167.21 frames.], batch size: 25, lr: 2.52e-04
+2022-04-30 10:35:07,686 INFO [train.py:763] (7/8) Epoch 30, batch 1600, loss[loss=0.1567, simple_loss=0.2629, pruned_loss=0.02524, over 7257.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2609, pruned_loss=0.03076, over 1428109.54 frames.], batch size: 19, lr: 2.52e-04
+2022-04-30 10:36:13,958 INFO [train.py:763] (7/8) Epoch 30, batch 1650, loss[loss=0.1651, simple_loss=0.2724, pruned_loss=0.02885, over 7113.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2616, pruned_loss=0.03085, over 1428312.08 frames.], batch size: 21, lr: 2.52e-04
+2022-04-30 10:37:20,422 INFO [train.py:763] (7/8) Epoch 30, batch 1700, loss[loss=0.183, simple_loss=0.2925, pruned_loss=0.0367, over 7282.00 frames.], tot_loss[loss=0.162, simple_loss=0.2611, pruned_loss=0.03143, over 1425247.64 frames.], batch size: 24, lr: 2.52e-04
+2022-04-30 10:38:27,163 INFO [train.py:763] (7/8) Epoch 30, batch 1750, loss[loss=0.175, simple_loss=0.2814, pruned_loss=0.03432, over 7373.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2618, pruned_loss=0.03143, over 1427417.63 frames.], batch size: 23, lr: 2.52e-04
+2022-04-30 10:39:33,039 INFO [train.py:763] (7/8) Epoch 30, batch 1800, loss[loss=0.1601, simple_loss=0.27, pruned_loss=0.02513, over 7442.00 frames.], tot_loss[loss=0.1625, simple_loss=0.262, pruned_loss=0.03146, over 1423048.78 frames.], batch size: 20, lr: 2.51e-04
+2022-04-30 10:40:39,000 INFO [train.py:763] (7/8) Epoch 30, batch 1850, loss[loss=0.1309, simple_loss=0.2264, pruned_loss=0.0177, over 7134.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2611, pruned_loss=0.03103, over 1421790.24 frames.], batch size: 17, lr: 2.51e-04
+2022-04-30 10:41:45,834 INFO [train.py:763] (7/8) Epoch 30, batch 1900, loss[loss=0.1864, simple_loss=0.2852, pruned_loss=0.04378, over 7333.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2614, pruned_loss=0.03107, over 1425270.70 frames.], batch size: 20, lr: 2.51e-04
+2022-04-30 10:42:51,859 INFO [train.py:763] (7/8) Epoch 30, batch 1950, loss[loss=0.1731, simple_loss=0.2695, pruned_loss=0.03832, over 7382.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2614, pruned_loss=0.03096, over 1424837.84 frames.], batch size: 23, lr: 2.51e-04
+2022-04-30 10:43:59,524 INFO [train.py:763] (7/8) Epoch 30, batch 2000, loss[loss=0.17, simple_loss=0.2666, pruned_loss=0.03666, over 7159.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2602, pruned_loss=0.03075, over 1426218.01 frames.], batch size: 18, lr: 2.51e-04
+2022-04-30 10:45:05,779 INFO [train.py:763] (7/8) Epoch 30, batch 2050, loss[loss=0.1735, simple_loss=0.2657, pruned_loss=0.0407, over 7189.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2597, pruned_loss=0.03078, over 1423639.35 frames.], batch size: 22, lr: 2.51e-04
+2022-04-30 10:46:11,299 INFO [train.py:763] (7/8) Epoch 30, batch 2100, loss[loss=0.1689, simple_loss=0.2656, pruned_loss=0.03609, over 7168.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2611, pruned_loss=0.0313, over 1422548.28 frames.], batch size: 19, lr: 2.51e-04
+2022-04-30 10:47:17,309 INFO [train.py:763] (7/8) Epoch 30, batch 2150, loss[loss=0.1468, simple_loss=0.253, pruned_loss=0.02027, over 7165.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2612, pruned_loss=0.03106, over 1426220.20 frames.], batch size: 18, lr: 2.51e-04
+2022-04-30 10:48:22,854 INFO [train.py:763] (7/8) Epoch 30, batch 2200, loss[loss=0.1748, simple_loss=0.2736, pruned_loss=0.038, over 7061.00 frames.], tot_loss[loss=0.1623, simple_loss=0.262, pruned_loss=0.03132, over 1427775.78 frames.], batch size: 18, lr: 2.51e-04
+2022-04-30 10:49:28,462 INFO [train.py:763] (7/8) Epoch 30, batch 2250, loss[loss=0.1842, simple_loss=0.2755, pruned_loss=0.04642, over 7188.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2631, pruned_loss=0.03168, over 1427009.44 frames.], batch size: 23, lr: 2.51e-04
+2022-04-30 10:50:34,522 INFO [train.py:763] (7/8) Epoch 30, batch 2300, loss[loss=0.1512, simple_loss=0.2465, pruned_loss=0.02797, over 7256.00 frames.], tot_loss[loss=0.163, simple_loss=0.2628, pruned_loss=0.03162, over 1429409.76 frames.], batch size: 19, lr: 2.51e-04
+2022-04-30 10:51:40,510 INFO [train.py:763] (7/8) Epoch 30, batch 2350, loss[loss=0.163, simple_loss=0.2659, pruned_loss=0.03007, over 7070.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2628, pruned_loss=0.03179, over 1429023.94 frames.], batch size: 18, lr: 2.51e-04
+2022-04-30 10:52:46,211 INFO [train.py:763] (7/8) Epoch 30, batch 2400, loss[loss=0.1646, simple_loss=0.2681, pruned_loss=0.03052, over 7223.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2632, pruned_loss=0.03174, over 1428481.15 frames.], batch size: 21, lr: 2.51e-04
+2022-04-30 10:53:51,760 INFO [train.py:763] (7/8) Epoch 30, batch 2450, loss[loss=0.1715, simple_loss=0.2794, pruned_loss=0.03182, over 7208.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2635, pruned_loss=0.03136, over 1423957.74 frames.], batch size: 21, lr: 2.51e-04
+2022-04-30 10:54:57,040 INFO [train.py:763] (7/8) Epoch 30, batch 2500, loss[loss=0.1678, simple_loss=0.2746, pruned_loss=0.03046, over 7327.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2634, pruned_loss=0.0312, over 1426538.65 frames.], batch size: 22, lr: 2.51e-04
+2022-04-30 10:56:03,564 INFO [train.py:763] (7/8) Epoch 30, batch 2550, loss[loss=0.1645, simple_loss=0.2668, pruned_loss=0.03109, over 7220.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2626, pruned_loss=0.03118, over 1428330.84 frames.], batch size: 23, lr: 2.51e-04
+2022-04-30 10:57:09,414 INFO [train.py:763] (7/8) Epoch 30, batch 2600, loss[loss=0.1428, simple_loss=0.2328, pruned_loss=0.02636, over 7406.00 frames.], tot_loss[loss=0.1621, simple_loss=0.262, pruned_loss=0.0311, over 1427399.99 frames.], batch size: 18, lr: 2.51e-04
+2022-04-30 10:58:15,111 INFO [train.py:763] (7/8) Epoch 30, batch 2650, loss[loss=0.1715, simple_loss=0.2784, pruned_loss=0.03231, over 7410.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2622, pruned_loss=0.03119, over 1424778.96 frames.], batch size: 21, lr: 2.51e-04
+2022-04-30 10:59:20,437 INFO [train.py:763] (7/8) Epoch 30, batch 2700, loss[loss=0.1673, simple_loss=0.2708, pruned_loss=0.03187, over 7311.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2625, pruned_loss=0.03121, over 1418636.70 frames.], batch size: 25, lr: 2.51e-04
+2022-04-30 11:00:26,183 INFO [train.py:763] (7/8) Epoch 30, batch 2750, loss[loss=0.1849, simple_loss=0.2865, pruned_loss=0.04171, over 7148.00 frames.], tot_loss[loss=0.1631, simple_loss=0.263, pruned_loss=0.03163, over 1419259.45 frames.], batch size: 20, lr: 2.51e-04
+2022-04-30 11:01:31,742 INFO [train.py:763] (7/8) Epoch 30, batch 2800, loss[loss=0.149, simple_loss=0.2459, pruned_loss=0.02604, over 7172.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2629, pruned_loss=0.03129, over 1421665.54 frames.], batch size: 18, lr: 2.51e-04
+2022-04-30 11:02:36,845 INFO [train.py:763] (7/8) Epoch 30, batch 2850, loss[loss=0.171, simple_loss=0.2743, pruned_loss=0.03379, over 7210.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2633, pruned_loss=0.03142, over 1419352.78 frames.], batch size: 22, lr: 2.51e-04
+2022-04-30 11:03:42,119 INFO [train.py:763] (7/8) Epoch 30, batch 2900, loss[loss=0.1618, simple_loss=0.261, pruned_loss=0.03133, over 7118.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2627, pruned_loss=0.03146, over 1423185.78 frames.], batch size: 21, lr: 2.51e-04
+2022-04-30 11:04:47,466 INFO [train.py:763] (7/8) Epoch 30, batch 2950, loss[loss=0.1529, simple_loss=0.2502, pruned_loss=0.02785, over 7256.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2623, pruned_loss=0.03102, over 1423056.18 frames.], batch size: 19, lr: 2.50e-04
+2022-04-30 11:05:53,073 INFO [train.py:763] (7/8) Epoch 30, batch 3000, loss[loss=0.1569, simple_loss=0.2636, pruned_loss=0.02513, over 7320.00 frames.], tot_loss[loss=0.162, simple_loss=0.2622, pruned_loss=0.03091, over 1423444.84 frames.], batch size: 20, lr: 2.50e-04
+2022-04-30 11:05:53,073 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 11:06:08,153 INFO [train.py:792] (7/8) Epoch 30, validation: loss=0.1701, simple_loss=0.2661, pruned_loss=0.03704, over 698248.00 frames.
+2022-04-30 11:07:13,680 INFO [train.py:763] (7/8) Epoch 30, batch 3050, loss[loss=0.1553, simple_loss=0.2383, pruned_loss=0.03614, over 7021.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2627, pruned_loss=0.03138, over 1422911.89 frames.], batch size: 16, lr: 2.50e-04
+2022-04-30 11:08:19,239 INFO [train.py:763] (7/8) Epoch 30, batch 3100, loss[loss=0.1784, simple_loss=0.2739, pruned_loss=0.04145, over 7307.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2617, pruned_loss=0.03097, over 1426201.38 frames.], batch size: 25, lr: 2.50e-04
+2022-04-30 11:09:24,933 INFO [train.py:763] (7/8) Epoch 30, batch 3150, loss[loss=0.1398, simple_loss=0.2442, pruned_loss=0.01775, over 7008.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2615, pruned_loss=0.03089, over 1425485.45 frames.], batch size: 16, lr: 2.50e-04
+2022-04-30 11:10:31,201 INFO [train.py:763] (7/8) Epoch 30, batch 3200, loss[loss=0.1731, simple_loss=0.2796, pruned_loss=0.0333, over 7180.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2613, pruned_loss=0.03076, over 1416141.86 frames.], batch size: 23, lr: 2.50e-04
+2022-04-30 11:11:37,943 INFO [train.py:763] (7/8) Epoch 30, batch 3250, loss[loss=0.1632, simple_loss=0.2852, pruned_loss=0.02062, over 7149.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2614, pruned_loss=0.03061, over 1416040.65 frames.], batch size: 20, lr: 2.50e-04
+2022-04-30 11:12:45,407 INFO [train.py:763] (7/8) Epoch 30, batch 3300, loss[loss=0.1622, simple_loss=0.2464, pruned_loss=0.03904, over 7285.00 frames.], tot_loss[loss=0.162, simple_loss=0.2619, pruned_loss=0.03107, over 1422290.08 frames.], batch size: 17, lr: 2.50e-04
+2022-04-30 11:13:51,992 INFO [train.py:763] (7/8) Epoch 30, batch 3350, loss[loss=0.1593, simple_loss=0.2563, pruned_loss=0.03116, over 7223.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2613, pruned_loss=0.03058, over 1421256.06 frames.], batch size: 21, lr: 2.50e-04
+2022-04-30 11:14:57,148 INFO [train.py:763] (7/8) Epoch 30, batch 3400, loss[loss=0.1526, simple_loss=0.2573, pruned_loss=0.02398, over 7300.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2609, pruned_loss=0.03063, over 1421705.75 frames.], batch size: 25, lr: 2.50e-04
+2022-04-30 11:16:02,387 INFO [train.py:763] (7/8) Epoch 30, batch 3450, loss[loss=0.1673, simple_loss=0.2684, pruned_loss=0.03306, over 6516.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2617, pruned_loss=0.03077, over 1425618.83 frames.], batch size: 38, lr: 2.50e-04
+2022-04-30 11:17:08,612 INFO [train.py:763] (7/8) Epoch 30, batch 3500, loss[loss=0.1697, simple_loss=0.2751, pruned_loss=0.03214, over 7383.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2611, pruned_loss=0.03033, over 1427370.91 frames.], batch size: 23, lr: 2.50e-04
+2022-04-30 11:18:14,748 INFO [train.py:763] (7/8) Epoch 30, batch 3550, loss[loss=0.1713, simple_loss=0.2723, pruned_loss=0.0351, over 7423.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2616, pruned_loss=0.03029, over 1428702.76 frames.], batch size: 20, lr: 2.50e-04
+2022-04-30 11:19:20,444 INFO [train.py:763] (7/8) Epoch 30, batch 3600, loss[loss=0.1599, simple_loss=0.2652, pruned_loss=0.02731, over 7293.00 frames.], tot_loss[loss=0.1624, simple_loss=0.263, pruned_loss=0.03091, over 1423289.98 frames.], batch size: 24, lr: 2.50e-04
+2022-04-30 11:20:25,893 INFO [train.py:763] (7/8) Epoch 30, batch 3650, loss[loss=0.1455, simple_loss=0.2368, pruned_loss=0.02715, over 7153.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2616, pruned_loss=0.03035, over 1422921.74 frames.], batch size: 17, lr: 2.50e-04
+2022-04-30 11:21:32,108 INFO [train.py:763] (7/8) Epoch 30, batch 3700, loss[loss=0.1463, simple_loss=0.2327, pruned_loss=0.02991, over 7274.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2601, pruned_loss=0.0302, over 1425374.15 frames.], batch size: 17, lr: 2.50e-04
+2022-04-30 11:22:38,035 INFO [train.py:763] (7/8) Epoch 30, batch 3750, loss[loss=0.142, simple_loss=0.2418, pruned_loss=0.02113, over 7252.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2605, pruned_loss=0.03043, over 1423898.51 frames.], batch size: 19, lr: 2.50e-04
+2022-04-30 11:23:45,251 INFO [train.py:763] (7/8) Epoch 30, batch 3800, loss[loss=0.1503, simple_loss=0.2419, pruned_loss=0.02939, over 7261.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2604, pruned_loss=0.03051, over 1426755.69 frames.], batch size: 18, lr: 2.50e-04
+2022-04-30 11:24:50,566 INFO [train.py:763] (7/8) Epoch 30, batch 3850, loss[loss=0.1614, simple_loss=0.2574, pruned_loss=0.03267, over 7069.00 frames.], tot_loss[loss=0.1621, simple_loss=0.262, pruned_loss=0.03107, over 1426495.71 frames.], batch size: 18, lr: 2.50e-04
+2022-04-30 11:25:56,102 INFO [train.py:763] (7/8) Epoch 30, batch 3900, loss[loss=0.1863, simple_loss=0.2958, pruned_loss=0.03839, over 7284.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2612, pruned_loss=0.0308, over 1430169.98 frames.], batch size: 24, lr: 2.50e-04
+2022-04-30 11:27:01,592 INFO [train.py:763] (7/8) Epoch 30, batch 3950, loss[loss=0.1661, simple_loss=0.2658, pruned_loss=0.0332, over 7362.00 frames.], tot_loss[loss=0.1612, simple_loss=0.261, pruned_loss=0.03074, over 1430370.47 frames.], batch size: 19, lr: 2.50e-04
+2022-04-30 11:28:06,978 INFO [train.py:763] (7/8) Epoch 30, batch 4000, loss[loss=0.1495, simple_loss=0.2484, pruned_loss=0.02529, over 7154.00 frames.], tot_loss[loss=0.1633, simple_loss=0.263, pruned_loss=0.03184, over 1427833.57 frames.], batch size: 18, lr: 2.50e-04
+2022-04-30 11:29:11,964 INFO [train.py:763] (7/8) Epoch 30, batch 4050, loss[loss=0.1802, simple_loss=0.2879, pruned_loss=0.03627, over 7313.00 frames.], tot_loss[loss=0.1632, simple_loss=0.263, pruned_loss=0.03166, over 1426833.41 frames.], batch size: 24, lr: 2.49e-04
+2022-04-30 11:30:18,174 INFO [train.py:763] (7/8) Epoch 30, batch 4100, loss[loss=0.1545, simple_loss=0.2595, pruned_loss=0.02472, over 7151.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2628, pruned_loss=0.03145, over 1427985.80 frames.], batch size: 19, lr: 2.49e-04
+2022-04-30 11:31:24,166 INFO [train.py:763] (7/8) Epoch 30, batch 4150, loss[loss=0.1644, simple_loss=0.2785, pruned_loss=0.02516, over 7101.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2628, pruned_loss=0.03129, over 1430494.26 frames.], batch size: 21, lr: 2.49e-04
+2022-04-30 11:32:29,738 INFO [train.py:763] (7/8) Epoch 30, batch 4200, loss[loss=0.1336, simple_loss=0.2209, pruned_loss=0.02312, over 6804.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2617, pruned_loss=0.03085, over 1432945.84 frames.], batch size: 15, lr: 2.49e-04
+2022-04-30 11:33:35,014 INFO [train.py:763] (7/8) Epoch 30, batch 4250, loss[loss=0.1945, simple_loss=0.311, pruned_loss=0.039, over 7210.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2616, pruned_loss=0.03064, over 1429361.53 frames.], batch size: 26, lr: 2.49e-04
+2022-04-30 11:34:41,242 INFO [train.py:763] (7/8) Epoch 30, batch 4300, loss[loss=0.1722, simple_loss=0.2744, pruned_loss=0.03498, over 7307.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2613, pruned_loss=0.03074, over 1431553.31 frames.], batch size: 24, lr: 2.49e-04
+2022-04-30 11:35:46,148 INFO [train.py:763] (7/8) Epoch 30, batch 4350, loss[loss=0.1487, simple_loss=0.2548, pruned_loss=0.0213, over 7116.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2616, pruned_loss=0.0309, over 1422569.02 frames.], batch size: 21, lr: 2.49e-04
+2022-04-30 11:36:51,036 INFO [train.py:763] (7/8) Epoch 30, batch 4400, loss[loss=0.1701, simple_loss=0.2802, pruned_loss=0.02998, over 7106.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2617, pruned_loss=0.03097, over 1412444.15 frames.], batch size: 21, lr: 2.49e-04
+2022-04-30 11:37:56,314 INFO [train.py:763] (7/8) Epoch 30, batch 4450, loss[loss=0.189, simple_loss=0.2759, pruned_loss=0.05109, over 6536.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2621, pruned_loss=0.0311, over 1411944.90 frames.], batch size: 38, lr: 2.49e-04
+2022-04-30 11:39:02,215 INFO [train.py:763] (7/8) Epoch 30, batch 4500, loss[loss=0.1495, simple_loss=0.2547, pruned_loss=0.02216, over 6412.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2627, pruned_loss=0.03148, over 1387672.32 frames.], batch size: 38, lr: 2.49e-04
+2022-04-30 11:40:07,234 INFO [train.py:763] (7/8) Epoch 30, batch 4550, loss[loss=0.1757, simple_loss=0.2814, pruned_loss=0.03495, over 5103.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2639, pruned_loss=0.03221, over 1357850.00 frames.], batch size: 54, lr: 2.49e-04
+2022-04-30 11:41:35,701 INFO [train.py:763] (7/8) Epoch 31, batch 0, loss[loss=0.2047, simple_loss=0.303, pruned_loss=0.05323, over 4804.00 frames.], tot_loss[loss=0.2047, simple_loss=0.303, pruned_loss=0.05323, over 4804.00 frames.], batch size: 52, lr: 2.45e-04
+2022-04-30 11:42:41,164 INFO [train.py:763] (7/8) Epoch 31, batch 50, loss[loss=0.1736, simple_loss=0.2741, pruned_loss=0.03656, over 6420.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2695, pruned_loss=0.03312, over 319227.21 frames.], batch size: 38, lr: 2.45e-04
+2022-04-30 11:43:46,476 INFO [train.py:763] (7/8) Epoch 31, batch 100, loss[loss=0.1614, simple_loss=0.2691, pruned_loss=0.02683, over 7282.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2671, pruned_loss=0.03251, over 566328.33 frames.], batch size: 25, lr: 2.45e-04
+2022-04-30 11:44:52,577 INFO [train.py:763] (7/8) Epoch 31, batch 150, loss[loss=0.1683, simple_loss=0.2744, pruned_loss=0.03111, over 7200.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2648, pruned_loss=0.03141, over 758268.19 frames.], batch size: 26, lr: 2.45e-04
+2022-04-30 11:45:58,822 INFO [train.py:763] (7/8) Epoch 31, batch 200, loss[loss=0.1531, simple_loss=0.2426, pruned_loss=0.03176, over 6999.00 frames.], tot_loss[loss=0.1624, simple_loss=0.263, pruned_loss=0.03086, over 903230.52 frames.], batch size: 16, lr: 2.45e-04
+2022-04-30 11:47:04,088 INFO [train.py:763] (7/8) Epoch 31, batch 250, loss[loss=0.1822, simple_loss=0.2841, pruned_loss=0.04015, over 7274.00 frames.], tot_loss[loss=0.1624, simple_loss=0.263, pruned_loss=0.03087, over 1022916.48 frames.], batch size: 24, lr: 2.45e-04
+2022-04-30 11:48:09,442 INFO [train.py:763] (7/8) Epoch 31, batch 300, loss[loss=0.1771, simple_loss=0.2706, pruned_loss=0.04182, over 7284.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2635, pruned_loss=0.03138, over 1113481.60 frames.], batch size: 24, lr: 2.45e-04
+2022-04-30 11:49:14,703 INFO [train.py:763] (7/8) Epoch 31, batch 350, loss[loss=0.1607, simple_loss=0.2682, pruned_loss=0.02661, over 7094.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2621, pruned_loss=0.03103, over 1181741.05 frames.], batch size: 28, lr: 2.45e-04
+2022-04-30 11:50:20,243 INFO [train.py:763] (7/8) Epoch 31, batch 400, loss[loss=0.1825, simple_loss=0.2946, pruned_loss=0.03521, over 7187.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2628, pruned_loss=0.03149, over 1236599.71 frames.], batch size: 26, lr: 2.45e-04
+2022-04-30 11:51:25,634 INFO [train.py:763] (7/8) Epoch 31, batch 450, loss[loss=0.1424, simple_loss=0.2517, pruned_loss=0.0166, over 7318.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2615, pruned_loss=0.03052, over 1277434.39 frames.], batch size: 21, lr: 2.45e-04
+2022-04-30 11:52:41,069 INFO [train.py:763] (7/8) Epoch 31, batch 500, loss[loss=0.1649, simple_loss=0.2713, pruned_loss=0.02919, over 7334.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2612, pruned_loss=0.03094, over 1313002.71 frames.], batch size: 22, lr: 2.45e-04
+2022-04-30 11:53:47,770 INFO [train.py:763] (7/8) Epoch 31, batch 550, loss[loss=0.159, simple_loss=0.2695, pruned_loss=0.02421, over 7330.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2603, pruned_loss=0.03031, over 1341697.24 frames.], batch size: 22, lr: 2.45e-04
+2022-04-30 11:54:53,982 INFO [train.py:763] (7/8) Epoch 31, batch 600, loss[loss=0.1571, simple_loss=0.2549, pruned_loss=0.02959, over 7142.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2605, pruned_loss=0.03049, over 1364062.58 frames.], batch size: 17, lr: 2.45e-04
+2022-04-30 11:55:59,916 INFO [train.py:763] (7/8) Epoch 31, batch 650, loss[loss=0.1459, simple_loss=0.2301, pruned_loss=0.03088, over 6988.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2606, pruned_loss=0.03042, over 1379321.55 frames.], batch size: 16, lr: 2.45e-04
+2022-04-30 11:57:06,469 INFO [train.py:763] (7/8) Epoch 31, batch 700, loss[loss=0.1793, simple_loss=0.27, pruned_loss=0.04426, over 7186.00 frames.], tot_loss[loss=0.161, simple_loss=0.2607, pruned_loss=0.03062, over 1388537.90 frames.], batch size: 23, lr: 2.45e-04
+2022-04-30 11:58:13,276 INFO [train.py:763] (7/8) Epoch 31, batch 750, loss[loss=0.1465, simple_loss=0.2583, pruned_loss=0.01735, over 7105.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2603, pruned_loss=0.03052, over 1397021.40 frames.], batch size: 21, lr: 2.44e-04
+2022-04-30 11:59:18,742 INFO [train.py:763] (7/8) Epoch 31, batch 800, loss[loss=0.1504, simple_loss=0.245, pruned_loss=0.02792, over 7276.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2604, pruned_loss=0.0306, over 1401466.01 frames.], batch size: 18, lr: 2.44e-04
+2022-04-30 12:00:24,043 INFO [train.py:763] (7/8) Epoch 31, batch 850, loss[loss=0.1717, simple_loss=0.2814, pruned_loss=0.03099, over 7284.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2611, pruned_loss=0.03058, over 1409205.03 frames.], batch size: 25, lr: 2.44e-04
+2022-04-30 12:01:28,737 INFO [train.py:763] (7/8) Epoch 31, batch 900, loss[loss=0.175, simple_loss=0.2818, pruned_loss=0.03411, over 7326.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2626, pruned_loss=0.03095, over 1411611.37 frames.], batch size: 22, lr: 2.44e-04
+2022-04-30 12:02:34,066 INFO [train.py:763] (7/8) Epoch 31, batch 950, loss[loss=0.1303, simple_loss=0.2137, pruned_loss=0.02348, over 7199.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2613, pruned_loss=0.03098, over 1413662.60 frames.], batch size: 16, lr: 2.44e-04
+2022-04-30 12:03:39,312 INFO [train.py:763] (7/8) Epoch 31, batch 1000, loss[loss=0.1576, simple_loss=0.2575, pruned_loss=0.02891, over 7420.00 frames.], tot_loss[loss=0.1613, simple_loss=0.261, pruned_loss=0.0308, over 1417403.99 frames.], batch size: 20, lr: 2.44e-04
+2022-04-30 12:04:53,746 INFO [train.py:763] (7/8) Epoch 31, batch 1050, loss[loss=0.1639, simple_loss=0.2774, pruned_loss=0.02524, over 7229.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2603, pruned_loss=0.03043, over 1420913.78 frames.], batch size: 20, lr: 2.44e-04
+2022-04-30 12:05:59,163 INFO [train.py:763] (7/8) Epoch 31, batch 1100, loss[loss=0.184, simple_loss=0.2771, pruned_loss=0.04549, over 7204.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2597, pruned_loss=0.03035, over 1419178.03 frames.], batch size: 22, lr: 2.44e-04
+2022-04-30 12:07:23,569 INFO [train.py:763] (7/8) Epoch 31, batch 1150, loss[loss=0.164, simple_loss=0.2633, pruned_loss=0.03232, over 7144.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2606, pruned_loss=0.03065, over 1422670.09 frames.], batch size: 17, lr: 2.44e-04
+2022-04-30 12:08:30,110 INFO [train.py:763] (7/8) Epoch 31, batch 1200, loss[loss=0.1544, simple_loss=0.269, pruned_loss=0.0199, over 7422.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2596, pruned_loss=0.03008, over 1425000.41 frames.], batch size: 21, lr: 2.44e-04
+2022-04-30 12:09:54,604 INFO [train.py:763] (7/8) Epoch 31, batch 1250, loss[loss=0.1686, simple_loss=0.2825, pruned_loss=0.02732, over 7198.00 frames.], tot_loss[loss=0.16, simple_loss=0.2598, pruned_loss=0.03015, over 1418713.23 frames.], batch size: 23, lr: 2.44e-04
+2022-04-30 12:11:00,233 INFO [train.py:763] (7/8) Epoch 31, batch 1300, loss[loss=0.1826, simple_loss=0.2844, pruned_loss=0.04045, over 7143.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2603, pruned_loss=0.03028, over 1423958.09 frames.], batch size: 20, lr: 2.44e-04
+2022-04-30 12:12:14,917 INFO [train.py:763] (7/8) Epoch 31, batch 1350, loss[loss=0.1591, simple_loss=0.2613, pruned_loss=0.0285, over 7331.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2605, pruned_loss=0.03053, over 1421275.86 frames.], batch size: 20, lr: 2.44e-04
+2022-04-30 12:13:22,527 INFO [train.py:763] (7/8) Epoch 31, batch 1400, loss[loss=0.1344, simple_loss=0.2327, pruned_loss=0.01801, over 7246.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2599, pruned_loss=0.03027, over 1421333.84 frames.], batch size: 20, lr: 2.44e-04
+2022-04-30 12:14:38,831 INFO [train.py:763] (7/8) Epoch 31, batch 1450, loss[loss=0.1396, simple_loss=0.2372, pruned_loss=0.02099, over 7340.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2601, pruned_loss=0.03022, over 1423750.47 frames.], batch size: 20, lr: 2.44e-04
+2022-04-30 12:15:46,119 INFO [train.py:763] (7/8) Epoch 31, batch 1500, loss[loss=0.1701, simple_loss=0.2651, pruned_loss=0.03758, over 5441.00 frames.], tot_loss[loss=0.16, simple_loss=0.26, pruned_loss=0.02997, over 1423113.66 frames.], batch size: 52, lr: 2.44e-04
+2022-04-30 12:16:51,636 INFO [train.py:763] (7/8) Epoch 31, batch 1550, loss[loss=0.1421, simple_loss=0.2248, pruned_loss=0.02972, over 7405.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2601, pruned_loss=0.03005, over 1422443.97 frames.], batch size: 18, lr: 2.44e-04
+2022-04-30 12:17:56,946 INFO [train.py:763] (7/8) Epoch 31, batch 1600, loss[loss=0.1752, simple_loss=0.2673, pruned_loss=0.04155, over 7195.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2603, pruned_loss=0.03033, over 1418413.60 frames.], batch size: 23, lr: 2.44e-04
+2022-04-30 12:19:02,310 INFO [train.py:763] (7/8) Epoch 31, batch 1650, loss[loss=0.1629, simple_loss=0.2746, pruned_loss=0.02558, over 7406.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2614, pruned_loss=0.03071, over 1417913.46 frames.], batch size: 21, lr: 2.44e-04
+2022-04-30 12:20:07,958 INFO [train.py:763] (7/8) Epoch 31, batch 1700, loss[loss=0.174, simple_loss=0.2749, pruned_loss=0.03654, over 7110.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2617, pruned_loss=0.0309, over 1412896.95 frames.], batch size: 21, lr: 2.44e-04
+2022-04-30 12:21:14,758 INFO [train.py:763] (7/8) Epoch 31, batch 1750, loss[loss=0.2364, simple_loss=0.314, pruned_loss=0.07933, over 5200.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2618, pruned_loss=0.03096, over 1410321.44 frames.], batch size: 52, lr: 2.44e-04
+2022-04-30 12:22:33,267 INFO [train.py:763] (7/8) Epoch 31, batch 1800, loss[loss=0.1404, simple_loss=0.2397, pruned_loss=0.02058, over 7229.00 frames.], tot_loss[loss=0.162, simple_loss=0.2621, pruned_loss=0.03091, over 1412012.56 frames.], batch size: 20, lr: 2.44e-04
+2022-04-30 12:23:40,158 INFO [train.py:763] (7/8) Epoch 31, batch 1850, loss[loss=0.1459, simple_loss=0.2348, pruned_loss=0.02851, over 6992.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2629, pruned_loss=0.03145, over 1406833.52 frames.], batch size: 16, lr: 2.44e-04
+2022-04-30 12:24:46,010 INFO [train.py:763] (7/8) Epoch 31, batch 1900, loss[loss=0.1343, simple_loss=0.2273, pruned_loss=0.02069, over 7362.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2611, pruned_loss=0.03099, over 1413670.48 frames.], batch size: 19, lr: 2.44e-04
+2022-04-30 12:25:51,356 INFO [train.py:763] (7/8) Epoch 31, batch 1950, loss[loss=0.1485, simple_loss=0.2587, pruned_loss=0.01918, over 7361.00 frames.], tot_loss[loss=0.161, simple_loss=0.2602, pruned_loss=0.03086, over 1419302.55 frames.], batch size: 19, lr: 2.43e-04
+2022-04-30 12:26:56,764 INFO [train.py:763] (7/8) Epoch 31, batch 2000, loss[loss=0.1507, simple_loss=0.243, pruned_loss=0.02925, over 7278.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2608, pruned_loss=0.03098, over 1420232.65 frames.], batch size: 18, lr: 2.43e-04
+2022-04-30 12:28:01,925 INFO [train.py:763] (7/8) Epoch 31, batch 2050, loss[loss=0.1661, simple_loss=0.2617, pruned_loss=0.03526, over 7153.00 frames.], tot_loss[loss=0.1606, simple_loss=0.26, pruned_loss=0.03057, over 1416875.48 frames.], batch size: 20, lr: 2.43e-04
+2022-04-30 12:29:07,881 INFO [train.py:763] (7/8) Epoch 31, batch 2100, loss[loss=0.1436, simple_loss=0.2353, pruned_loss=0.02589, over 7189.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2611, pruned_loss=0.03091, over 1417234.50 frames.], batch size: 16, lr: 2.43e-04
+2022-04-30 12:30:13,159 INFO [train.py:763] (7/8) Epoch 31, batch 2150, loss[loss=0.1744, simple_loss=0.2859, pruned_loss=0.03152, over 7213.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2609, pruned_loss=0.03046, over 1420751.43 frames.], batch size: 21, lr: 2.43e-04
+2022-04-30 12:31:18,654 INFO [train.py:763] (7/8) Epoch 31, batch 2200, loss[loss=0.1673, simple_loss=0.2735, pruned_loss=0.03053, over 7183.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2611, pruned_loss=0.03075, over 1423488.09 frames.], batch size: 26, lr: 2.43e-04
+2022-04-30 12:32:23,992 INFO [train.py:763] (7/8) Epoch 31, batch 2250, loss[loss=0.1481, simple_loss=0.2456, pruned_loss=0.02528, over 7069.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2614, pruned_loss=0.03076, over 1424488.91 frames.], batch size: 18, lr: 2.43e-04
+2022-04-30 12:33:30,751 INFO [train.py:763] (7/8) Epoch 31, batch 2300, loss[loss=0.1747, simple_loss=0.2785, pruned_loss=0.0355, over 7343.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2613, pruned_loss=0.03082, over 1422382.47 frames.], batch size: 22, lr: 2.43e-04
+2022-04-30 12:34:36,648 INFO [train.py:763] (7/8) Epoch 31, batch 2350, loss[loss=0.1364, simple_loss=0.224, pruned_loss=0.0244, over 7268.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2625, pruned_loss=0.03132, over 1425916.11 frames.], batch size: 17, lr: 2.43e-04
+2022-04-30 12:35:41,763 INFO [train.py:763] (7/8) Epoch 31, batch 2400, loss[loss=0.1701, simple_loss=0.267, pruned_loss=0.03664, over 7327.00 frames.], tot_loss[loss=0.163, simple_loss=0.2627, pruned_loss=0.03165, over 1421576.08 frames.], batch size: 20, lr: 2.43e-04
+2022-04-30 12:36:47,288 INFO [train.py:763] (7/8) Epoch 31, batch 2450, loss[loss=0.1755, simple_loss=0.2872, pruned_loss=0.03185, over 7176.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2624, pruned_loss=0.03143, over 1423304.13 frames.], batch size: 26, lr: 2.43e-04
+2022-04-30 12:37:52,791 INFO [train.py:763] (7/8) Epoch 31, batch 2500, loss[loss=0.1453, simple_loss=0.2429, pruned_loss=0.02391, over 7301.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2621, pruned_loss=0.03124, over 1426031.23 frames.], batch size: 17, lr: 2.43e-04
+2022-04-30 12:38:58,024 INFO [train.py:763] (7/8) Epoch 31, batch 2550, loss[loss=0.148, simple_loss=0.2602, pruned_loss=0.01788, over 7322.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2622, pruned_loss=0.0312, over 1423567.64 frames.], batch size: 20, lr: 2.43e-04
+2022-04-30 12:40:03,285 INFO [train.py:763] (7/8) Epoch 31, batch 2600, loss[loss=0.1377, simple_loss=0.2214, pruned_loss=0.02702, over 7137.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2621, pruned_loss=0.03078, over 1421656.96 frames.], batch size: 17, lr: 2.43e-04
+2022-04-30 12:41:08,499 INFO [train.py:763] (7/8) Epoch 31, batch 2650, loss[loss=0.185, simple_loss=0.276, pruned_loss=0.04698, over 7180.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2613, pruned_loss=0.03066, over 1423698.51 frames.], batch size: 26, lr: 2.43e-04
+2022-04-30 12:42:15,319 INFO [train.py:763] (7/8) Epoch 31, batch 2700, loss[loss=0.1464, simple_loss=0.2414, pruned_loss=0.02574, over 7331.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2614, pruned_loss=0.03097, over 1421913.31 frames.], batch size: 20, lr: 2.43e-04
+2022-04-30 12:43:20,602 INFO [train.py:763] (7/8) Epoch 31, batch 2750, loss[loss=0.1683, simple_loss=0.2703, pruned_loss=0.03318, over 7159.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2611, pruned_loss=0.03081, over 1423762.07 frames.], batch size: 28, lr: 2.43e-04
+2022-04-30 12:44:27,128 INFO [train.py:763] (7/8) Epoch 31, batch 2800, loss[loss=0.14, simple_loss=0.2335, pruned_loss=0.02328, over 7400.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2605, pruned_loss=0.03065, over 1422951.04 frames.], batch size: 18, lr: 2.43e-04
+2022-04-30 12:45:34,102 INFO [train.py:763] (7/8) Epoch 31, batch 2850, loss[loss=0.165, simple_loss=0.2744, pruned_loss=0.02776, over 6264.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2616, pruned_loss=0.03123, over 1420077.45 frames.], batch size: 37, lr: 2.43e-04
+2022-04-30 12:46:39,735 INFO [train.py:763] (7/8) Epoch 31, batch 2900, loss[loss=0.1481, simple_loss=0.25, pruned_loss=0.02308, over 7233.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2627, pruned_loss=0.03124, over 1424090.60 frames.], batch size: 20, lr: 2.43e-04
+2022-04-30 12:47:44,753 INFO [train.py:763] (7/8) Epoch 31, batch 2950, loss[loss=0.1624, simple_loss=0.2596, pruned_loss=0.03258, over 7204.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2627, pruned_loss=0.03119, over 1417535.68 frames.], batch size: 23, lr: 2.43e-04
+2022-04-30 12:48:50,672 INFO [train.py:763] (7/8) Epoch 31, batch 3000, loss[loss=0.1761, simple_loss=0.2752, pruned_loss=0.03852, over 7427.00 frames.], tot_loss[loss=0.1627, simple_loss=0.263, pruned_loss=0.03123, over 1418209.19 frames.], batch size: 20, lr: 2.43e-04
+2022-04-30 12:48:50,673 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 12:49:05,872 INFO [train.py:792] (7/8) Epoch 31, validation: loss=0.1686, simple_loss=0.2652, pruned_loss=0.03603, over 698248.00 frames.
+2022-04-30 12:50:12,213 INFO [train.py:763] (7/8) Epoch 31, batch 3050, loss[loss=0.1847, simple_loss=0.2862, pruned_loss=0.04155, over 7305.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2631, pruned_loss=0.03132, over 1422404.33 frames.], batch size: 25, lr: 2.43e-04
+2022-04-30 12:51:18,212 INFO [train.py:763] (7/8) Epoch 31, batch 3100, loss[loss=0.1709, simple_loss=0.2787, pruned_loss=0.03154, over 7090.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2637, pruned_loss=0.03172, over 1425731.29 frames.], batch size: 28, lr: 2.42e-04
+2022-04-30 12:52:23,646 INFO [train.py:763] (7/8) Epoch 31, batch 3150, loss[loss=0.1347, simple_loss=0.227, pruned_loss=0.02123, over 7277.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2624, pruned_loss=0.03136, over 1423434.01 frames.], batch size: 17, lr: 2.42e-04
+2022-04-30 12:53:29,092 INFO [train.py:763] (7/8) Epoch 31, batch 3200, loss[loss=0.1735, simple_loss=0.278, pruned_loss=0.03452, over 7111.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2625, pruned_loss=0.03111, over 1426699.50 frames.], batch size: 21, lr: 2.42e-04
+2022-04-30 12:54:36,159 INFO [train.py:763] (7/8) Epoch 31, batch 3250, loss[loss=0.1608, simple_loss=0.2644, pruned_loss=0.0286, over 7332.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2628, pruned_loss=0.03121, over 1427837.97 frames.], batch size: 22, lr: 2.42e-04
+2022-04-30 12:55:42,945 INFO [train.py:763] (7/8) Epoch 31, batch 3300, loss[loss=0.1519, simple_loss=0.2481, pruned_loss=0.02788, over 7431.00 frames.], tot_loss[loss=0.1622, simple_loss=0.262, pruned_loss=0.03121, over 1424156.78 frames.], batch size: 20, lr: 2.42e-04
+2022-04-30 12:56:50,150 INFO [train.py:763] (7/8) Epoch 31, batch 3350, loss[loss=0.1715, simple_loss=0.2727, pruned_loss=0.03515, over 7337.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2601, pruned_loss=0.03061, over 1425237.52 frames.], batch size: 21, lr: 2.42e-04
+2022-04-30 12:57:56,839 INFO [train.py:763] (7/8) Epoch 31, batch 3400, loss[loss=0.1836, simple_loss=0.2798, pruned_loss=0.04371, over 7321.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2618, pruned_loss=0.03102, over 1421791.85 frames.], batch size: 20, lr: 2.42e-04
+2022-04-30 12:59:03,251 INFO [train.py:763] (7/8) Epoch 31, batch 3450, loss[loss=0.177, simple_loss=0.281, pruned_loss=0.03645, over 7204.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2629, pruned_loss=0.03099, over 1424854.62 frames.], batch size: 22, lr: 2.42e-04
+2022-04-30 13:00:08,929 INFO [train.py:763] (7/8) Epoch 31, batch 3500, loss[loss=0.1651, simple_loss=0.2652, pruned_loss=0.03255, over 7296.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2632, pruned_loss=0.03094, over 1428308.37 frames.], batch size: 24, lr: 2.42e-04
+2022-04-30 13:01:14,849 INFO [train.py:763] (7/8) Epoch 31, batch 3550, loss[loss=0.1767, simple_loss=0.2785, pruned_loss=0.03739, over 7376.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2629, pruned_loss=0.03135, over 1430895.77 frames.], batch size: 23, lr: 2.42e-04
+2022-04-30 13:02:21,343 INFO [train.py:763] (7/8) Epoch 31, batch 3600, loss[loss=0.164, simple_loss=0.2746, pruned_loss=0.02666, over 6376.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2631, pruned_loss=0.03155, over 1428051.58 frames.], batch size: 38, lr: 2.42e-04
+2022-04-30 13:03:26,543 INFO [train.py:763] (7/8) Epoch 31, batch 3650, loss[loss=0.1602, simple_loss=0.2633, pruned_loss=0.02858, over 7225.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2632, pruned_loss=0.0311, over 1428780.29 frames.], batch size: 20, lr: 2.42e-04
+2022-04-30 13:04:32,089 INFO [train.py:763] (7/8) Epoch 31, batch 3700, loss[loss=0.143, simple_loss=0.235, pruned_loss=0.0255, over 7121.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2619, pruned_loss=0.03059, over 1429999.40 frames.], batch size: 17, lr: 2.42e-04
+2022-04-30 13:05:36,821 INFO [train.py:763] (7/8) Epoch 31, batch 3750, loss[loss=0.1681, simple_loss=0.2653, pruned_loss=0.03544, over 7203.00 frames.], tot_loss[loss=0.1616, simple_loss=0.262, pruned_loss=0.03054, over 1423833.86 frames.], batch size: 23, lr: 2.42e-04
+2022-04-30 13:06:42,593 INFO [train.py:763] (7/8) Epoch 31, batch 3800, loss[loss=0.1555, simple_loss=0.2562, pruned_loss=0.02739, over 7366.00 frames.], tot_loss[loss=0.161, simple_loss=0.2614, pruned_loss=0.03033, over 1424984.99 frames.], batch size: 23, lr: 2.42e-04
+2022-04-30 13:07:47,984 INFO [train.py:763] (7/8) Epoch 31, batch 3850, loss[loss=0.1518, simple_loss=0.2592, pruned_loss=0.02219, over 7419.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2606, pruned_loss=0.03003, over 1427455.45 frames.], batch size: 20, lr: 2.42e-04
+2022-04-30 13:08:53,265 INFO [train.py:763] (7/8) Epoch 31, batch 3900, loss[loss=0.1696, simple_loss=0.2652, pruned_loss=0.03695, over 7168.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2606, pruned_loss=0.03008, over 1428526.21 frames.], batch size: 18, lr: 2.42e-04
+2022-04-30 13:09:58,656 INFO [train.py:763] (7/8) Epoch 31, batch 3950, loss[loss=0.1588, simple_loss=0.2656, pruned_loss=0.02602, over 7214.00 frames.], tot_loss[loss=0.161, simple_loss=0.2611, pruned_loss=0.03047, over 1423800.73 frames.], batch size: 21, lr: 2.42e-04
+2022-04-30 13:11:04,255 INFO [train.py:763] (7/8) Epoch 31, batch 4000, loss[loss=0.1345, simple_loss=0.2293, pruned_loss=0.01979, over 7408.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2606, pruned_loss=0.03052, over 1420902.09 frames.], batch size: 18, lr: 2.42e-04
+2022-04-30 13:12:09,639 INFO [train.py:763] (7/8) Epoch 31, batch 4050, loss[loss=0.1913, simple_loss=0.2968, pruned_loss=0.04287, over 7387.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2611, pruned_loss=0.03111, over 1419831.72 frames.], batch size: 23, lr: 2.42e-04
+2022-04-30 13:13:15,768 INFO [train.py:763] (7/8) Epoch 31, batch 4100, loss[loss=0.1601, simple_loss=0.265, pruned_loss=0.02762, over 7202.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2615, pruned_loss=0.03109, over 1417381.50 frames.], batch size: 22, lr: 2.42e-04
+2022-04-30 13:14:21,782 INFO [train.py:763] (7/8) Epoch 31, batch 4150, loss[loss=0.1863, simple_loss=0.2973, pruned_loss=0.03764, over 7226.00 frames.], tot_loss[loss=0.1613, simple_loss=0.261, pruned_loss=0.03079, over 1421723.39 frames.], batch size: 21, lr: 2.42e-04
+2022-04-30 13:15:28,694 INFO [train.py:763] (7/8) Epoch 31, batch 4200, loss[loss=0.1435, simple_loss=0.2411, pruned_loss=0.0229, over 7327.00 frames.], tot_loss[loss=0.16, simple_loss=0.2597, pruned_loss=0.03014, over 1421229.88 frames.], batch size: 20, lr: 2.42e-04
+2022-04-30 13:16:35,581 INFO [train.py:763] (7/8) Epoch 31, batch 4250, loss[loss=0.1617, simple_loss=0.2596, pruned_loss=0.03194, over 7255.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2602, pruned_loss=0.03059, over 1420001.98 frames.], batch size: 19, lr: 2.42e-04
+2022-04-30 13:17:40,858 INFO [train.py:763] (7/8) Epoch 31, batch 4300, loss[loss=0.1589, simple_loss=0.2472, pruned_loss=0.03534, over 7433.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2591, pruned_loss=0.03021, over 1419221.66 frames.], batch size: 18, lr: 2.42e-04
+2022-04-30 13:18:46,154 INFO [train.py:763] (7/8) Epoch 31, batch 4350, loss[loss=0.1891, simple_loss=0.2821, pruned_loss=0.04804, over 7160.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2606, pruned_loss=0.03104, over 1419847.22 frames.], batch size: 18, lr: 2.41e-04
+2022-04-30 13:19:51,349 INFO [train.py:763] (7/8) Epoch 31, batch 4400, loss[loss=0.1626, simple_loss=0.2619, pruned_loss=0.0317, over 7290.00 frames.], tot_loss[loss=0.162, simple_loss=0.2615, pruned_loss=0.03129, over 1405826.02 frames.], batch size: 25, lr: 2.41e-04
+2022-04-30 13:20:56,974 INFO [train.py:763] (7/8) Epoch 31, batch 4450, loss[loss=0.1319, simple_loss=0.2237, pruned_loss=0.02008, over 6793.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2615, pruned_loss=0.03147, over 1403214.27 frames.], batch size: 15, lr: 2.41e-04
+2022-04-30 13:22:02,221 INFO [train.py:763] (7/8) Epoch 31, batch 4500, loss[loss=0.159, simple_loss=0.2607, pruned_loss=0.02872, over 6845.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2621, pruned_loss=0.03177, over 1395018.53 frames.], batch size: 31, lr: 2.41e-04
+2022-04-30 13:23:07,084 INFO [train.py:763] (7/8) Epoch 31, batch 4550, loss[loss=0.1938, simple_loss=0.2885, pruned_loss=0.04949, over 4806.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2618, pruned_loss=0.0323, over 1355340.24 frames.], batch size: 52, lr: 2.41e-04
+2022-04-30 13:24:35,160 INFO [train.py:763] (7/8) Epoch 32, batch 0, loss[loss=0.177, simple_loss=0.271, pruned_loss=0.04148, over 6677.00 frames.], tot_loss[loss=0.177, simple_loss=0.271, pruned_loss=0.04148, over 6677.00 frames.], batch size: 31, lr: 2.38e-04
+2022-04-30 13:25:38,917 INFO [train.py:763] (7/8) Epoch 32, batch 50, loss[loss=0.1816, simple_loss=0.2813, pruned_loss=0.04091, over 4837.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2635, pruned_loss=0.03162, over 313986.19 frames.], batch size: 52, lr: 2.38e-04
+2022-04-30 13:26:41,327 INFO [train.py:763] (7/8) Epoch 32, batch 100, loss[loss=0.1825, simple_loss=0.2905, pruned_loss=0.03726, over 6476.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2633, pruned_loss=0.031, over 559218.92 frames.], batch size: 38, lr: 2.38e-04
+2022-04-30 13:27:47,098 INFO [train.py:763] (7/8) Epoch 32, batch 150, loss[loss=0.1713, simple_loss=0.2774, pruned_loss=0.03257, over 7189.00 frames.], tot_loss[loss=0.1622, simple_loss=0.263, pruned_loss=0.03068, over 751996.92 frames.], batch size: 23, lr: 2.37e-04
+2022-04-30 13:28:52,470 INFO [train.py:763] (7/8) Epoch 32, batch 200, loss[loss=0.1742, simple_loss=0.2668, pruned_loss=0.04079, over 7007.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2614, pruned_loss=0.03007, over 895736.83 frames.], batch size: 16, lr: 2.37e-04
+2022-04-30 13:29:57,600 INFO [train.py:763] (7/8) Epoch 32, batch 250, loss[loss=0.1465, simple_loss=0.2518, pruned_loss=0.02066, over 7229.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2622, pruned_loss=0.03054, over 1010757.62 frames.], batch size: 20, lr: 2.37e-04
+2022-04-30 13:31:03,094 INFO [train.py:763] (7/8) Epoch 32, batch 300, loss[loss=0.1727, simple_loss=0.2873, pruned_loss=0.02904, over 6911.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2632, pruned_loss=0.03064, over 1094276.25 frames.], batch size: 32, lr: 2.37e-04
+2022-04-30 13:32:10,115 INFO [train.py:763] (7/8) Epoch 32, batch 350, loss[loss=0.156, simple_loss=0.2479, pruned_loss=0.03207, over 7398.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2634, pruned_loss=0.03124, over 1164004.26 frames.], batch size: 18, lr: 2.37e-04
+2022-04-30 13:33:15,984 INFO [train.py:763] (7/8) Epoch 32, batch 400, loss[loss=0.1499, simple_loss=0.251, pruned_loss=0.02444, over 7438.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2616, pruned_loss=0.03084, over 1220725.62 frames.], batch size: 20, lr: 2.37e-04
+2022-04-30 13:34:21,571 INFO [train.py:763] (7/8) Epoch 32, batch 450, loss[loss=0.1868, simple_loss=0.2816, pruned_loss=0.04596, over 6898.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2605, pruned_loss=0.0306, over 1263132.32 frames.], batch size: 31, lr: 2.37e-04
+2022-04-30 13:35:26,880 INFO [train.py:763] (7/8) Epoch 32, batch 500, loss[loss=0.1656, simple_loss=0.2634, pruned_loss=0.0339, over 7205.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2617, pruned_loss=0.03083, over 1300995.87 frames.], batch size: 23, lr: 2.37e-04
+2022-04-30 13:36:32,833 INFO [train.py:763] (7/8) Epoch 32, batch 550, loss[loss=0.1786, simple_loss=0.2705, pruned_loss=0.04337, over 7323.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2632, pruned_loss=0.03129, over 1329266.83 frames.], batch size: 21, lr: 2.37e-04
+2022-04-30 13:37:38,150 INFO [train.py:763] (7/8) Epoch 32, batch 600, loss[loss=0.203, simple_loss=0.2905, pruned_loss=0.0577, over 7270.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2632, pruned_loss=0.03109, over 1346925.76 frames.], batch size: 24, lr: 2.37e-04
+2022-04-30 13:38:43,409 INFO [train.py:763] (7/8) Epoch 32, batch 650, loss[loss=0.1776, simple_loss=0.2897, pruned_loss=0.0328, over 7204.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2628, pruned_loss=0.03112, over 1364427.23 frames.], batch size: 26, lr: 2.37e-04
+2022-04-30 13:39:48,627 INFO [train.py:763] (7/8) Epoch 32, batch 700, loss[loss=0.1402, simple_loss=0.2308, pruned_loss=0.02485, over 7125.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2616, pruned_loss=0.03063, over 1375136.92 frames.], batch size: 17, lr: 2.37e-04
+2022-04-30 13:40:55,081 INFO [train.py:763] (7/8) Epoch 32, batch 750, loss[loss=0.1902, simple_loss=0.2983, pruned_loss=0.04104, over 7215.00 frames.], tot_loss[loss=0.162, simple_loss=0.2622, pruned_loss=0.03091, over 1380719.15 frames.], batch size: 21, lr: 2.37e-04
+2022-04-30 13:42:02,257 INFO [train.py:763] (7/8) Epoch 32, batch 800, loss[loss=0.1517, simple_loss=0.2599, pruned_loss=0.02175, over 7429.00 frames.], tot_loss[loss=0.1612, simple_loss=0.261, pruned_loss=0.03073, over 1392812.06 frames.], batch size: 20, lr: 2.37e-04
+2022-04-30 13:43:08,545 INFO [train.py:763] (7/8) Epoch 32, batch 850, loss[loss=0.1746, simple_loss=0.2706, pruned_loss=0.03931, over 7363.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2619, pruned_loss=0.03077, over 1400025.83 frames.], batch size: 23, lr: 2.37e-04
+2022-04-30 13:44:14,354 INFO [train.py:763] (7/8) Epoch 32, batch 900, loss[loss=0.1718, simple_loss=0.2801, pruned_loss=0.03176, over 7204.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2607, pruned_loss=0.03036, over 1409995.39 frames.], batch size: 23, lr: 2.37e-04
+2022-04-30 13:45:21,102 INFO [train.py:763] (7/8) Epoch 32, batch 950, loss[loss=0.1396, simple_loss=0.2307, pruned_loss=0.02421, over 7426.00 frames.], tot_loss[loss=0.161, simple_loss=0.261, pruned_loss=0.0305, over 1414402.43 frames.], batch size: 20, lr: 2.37e-04
+2022-04-30 13:46:27,371 INFO [train.py:763] (7/8) Epoch 32, batch 1000, loss[loss=0.1865, simple_loss=0.2876, pruned_loss=0.04271, over 7189.00 frames.], tot_loss[loss=0.1603, simple_loss=0.26, pruned_loss=0.03028, over 1414033.72 frames.], batch size: 23, lr: 2.37e-04
+2022-04-30 13:47:33,319 INFO [train.py:763] (7/8) Epoch 32, batch 1050, loss[loss=0.1534, simple_loss=0.2642, pruned_loss=0.02126, over 7036.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2607, pruned_loss=0.03061, over 1413355.26 frames.], batch size: 28, lr: 2.37e-04
+2022-04-30 13:48:38,625 INFO [train.py:763] (7/8) Epoch 32, batch 1100, loss[loss=0.1652, simple_loss=0.277, pruned_loss=0.02667, over 7279.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2609, pruned_loss=0.03072, over 1418150.18 frames.], batch size: 24, lr: 2.37e-04
+2022-04-30 13:49:45,248 INFO [train.py:763] (7/8) Epoch 32, batch 1150, loss[loss=0.1746, simple_loss=0.271, pruned_loss=0.03908, over 7201.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2609, pruned_loss=0.03068, over 1420601.29 frames.], batch size: 23, lr: 2.37e-04
+2022-04-30 13:50:50,728 INFO [train.py:763] (7/8) Epoch 32, batch 1200, loss[loss=0.1827, simple_loss=0.2928, pruned_loss=0.03626, over 7242.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2617, pruned_loss=0.03079, over 1423538.07 frames.], batch size: 26, lr: 2.37e-04
+2022-04-30 13:51:56,756 INFO [train.py:763] (7/8) Epoch 32, batch 1250, loss[loss=0.1734, simple_loss=0.2768, pruned_loss=0.03502, over 6595.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2614, pruned_loss=0.03058, over 1422342.45 frames.], batch size: 38, lr: 2.37e-04
+2022-04-30 13:53:02,550 INFO [train.py:763] (7/8) Epoch 32, batch 1300, loss[loss=0.167, simple_loss=0.2714, pruned_loss=0.03125, over 7214.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2601, pruned_loss=0.03002, over 1422927.67 frames.], batch size: 21, lr: 2.37e-04
+2022-04-30 13:54:10,251 INFO [train.py:763] (7/8) Epoch 32, batch 1350, loss[loss=0.156, simple_loss=0.2533, pruned_loss=0.02932, over 7282.00 frames.], tot_loss[loss=0.161, simple_loss=0.2611, pruned_loss=0.03046, over 1421618.17 frames.], batch size: 17, lr: 2.37e-04
+2022-04-30 13:55:17,157 INFO [train.py:763] (7/8) Epoch 32, batch 1400, loss[loss=0.1615, simple_loss=0.2639, pruned_loss=0.02958, over 7139.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2606, pruned_loss=0.03049, over 1423179.65 frames.], batch size: 20, lr: 2.36e-04
+2022-04-30 13:56:22,427 INFO [train.py:763] (7/8) Epoch 32, batch 1450, loss[loss=0.1502, simple_loss=0.2496, pruned_loss=0.02543, over 6837.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2602, pruned_loss=0.03018, over 1426079.75 frames.], batch size: 31, lr: 2.36e-04
+2022-04-30 13:57:27,833 INFO [train.py:763] (7/8) Epoch 32, batch 1500, loss[loss=0.1792, simple_loss=0.2681, pruned_loss=0.04517, over 5130.00 frames.], tot_loss[loss=0.161, simple_loss=0.261, pruned_loss=0.03052, over 1423170.42 frames.], batch size: 53, lr: 2.36e-04
+2022-04-30 13:58:33,085 INFO [train.py:763] (7/8) Epoch 32, batch 1550, loss[loss=0.1643, simple_loss=0.2655, pruned_loss=0.03153, over 7223.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2622, pruned_loss=0.03082, over 1419573.83 frames.], batch size: 21, lr: 2.36e-04
+2022-04-30 13:59:38,334 INFO [train.py:763] (7/8) Epoch 32, batch 1600, loss[loss=0.1591, simple_loss=0.2611, pruned_loss=0.0286, over 7410.00 frames.], tot_loss[loss=0.1618, simple_loss=0.262, pruned_loss=0.0308, over 1420993.72 frames.], batch size: 21, lr: 2.36e-04
+2022-04-30 14:00:43,693 INFO [train.py:763] (7/8) Epoch 32, batch 1650, loss[loss=0.1466, simple_loss=0.2495, pruned_loss=0.02186, over 7227.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2618, pruned_loss=0.03079, over 1421519.42 frames.], batch size: 21, lr: 2.36e-04
+2022-04-30 14:01:48,795 INFO [train.py:763] (7/8) Epoch 32, batch 1700, loss[loss=0.2007, simple_loss=0.2979, pruned_loss=0.05173, over 7301.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2618, pruned_loss=0.03065, over 1423339.00 frames.], batch size: 24, lr: 2.36e-04
+2022-04-30 14:02:54,192 INFO [train.py:763] (7/8) Epoch 32, batch 1750, loss[loss=0.1685, simple_loss=0.2758, pruned_loss=0.03056, over 7108.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2622, pruned_loss=0.03108, over 1416917.11 frames.], batch size: 28, lr: 2.36e-04
+2022-04-30 14:03:59,648 INFO [train.py:763] (7/8) Epoch 32, batch 1800, loss[loss=0.1637, simple_loss=0.2636, pruned_loss=0.03188, over 7259.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2616, pruned_loss=0.03062, over 1421007.34 frames.], batch size: 19, lr: 2.36e-04
+2022-04-30 14:05:06,123 INFO [train.py:763] (7/8) Epoch 32, batch 1850, loss[loss=0.1617, simple_loss=0.2654, pruned_loss=0.02899, over 7325.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2614, pruned_loss=0.03033, over 1423234.45 frames.], batch size: 21, lr: 2.36e-04
+2022-04-30 14:06:21,250 INFO [train.py:763] (7/8) Epoch 32, batch 1900, loss[loss=0.2026, simple_loss=0.2961, pruned_loss=0.05458, over 7376.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2615, pruned_loss=0.03035, over 1425675.97 frames.], batch size: 23, lr: 2.36e-04
+2022-04-30 14:07:26,678 INFO [train.py:763] (7/8) Epoch 32, batch 1950, loss[loss=0.1998, simple_loss=0.3013, pruned_loss=0.04917, over 7286.00 frames.], tot_loss[loss=0.161, simple_loss=0.2613, pruned_loss=0.03032, over 1423934.71 frames.], batch size: 24, lr: 2.36e-04
+2022-04-30 14:08:33,727 INFO [train.py:763] (7/8) Epoch 32, batch 2000, loss[loss=0.1564, simple_loss=0.2648, pruned_loss=0.02398, over 6371.00 frames.], tot_loss[loss=0.1612, simple_loss=0.261, pruned_loss=0.03065, over 1424922.48 frames.], batch size: 37, lr: 2.36e-04
+2022-04-30 14:09:39,835 INFO [train.py:763] (7/8) Epoch 32, batch 2050, loss[loss=0.1702, simple_loss=0.2702, pruned_loss=0.0351, over 7156.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2615, pruned_loss=0.03062, over 1426114.42 frames.], batch size: 18, lr: 2.36e-04
+2022-04-30 14:10:45,538 INFO [train.py:763] (7/8) Epoch 32, batch 2100, loss[loss=0.1451, simple_loss=0.2392, pruned_loss=0.02553, over 7152.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2607, pruned_loss=0.03034, over 1426758.31 frames.], batch size: 19, lr: 2.36e-04
+2022-04-30 14:11:52,562 INFO [train.py:763] (7/8) Epoch 32, batch 2150, loss[loss=0.1523, simple_loss=0.2518, pruned_loss=0.0264, over 7398.00 frames.], tot_loss[loss=0.1607, simple_loss=0.261, pruned_loss=0.03018, over 1427282.21 frames.], batch size: 18, lr: 2.36e-04
+2022-04-30 14:12:58,741 INFO [train.py:763] (7/8) Epoch 32, batch 2200, loss[loss=0.2147, simple_loss=0.2995, pruned_loss=0.06494, over 5036.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2615, pruned_loss=0.03073, over 1421299.76 frames.], batch size: 52, lr: 2.36e-04
+2022-04-30 14:14:05,708 INFO [train.py:763] (7/8) Epoch 32, batch 2250, loss[loss=0.1795, simple_loss=0.2867, pruned_loss=0.03613, over 7126.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2613, pruned_loss=0.03091, over 1419126.52 frames.], batch size: 26, lr: 2.36e-04
+2022-04-30 14:15:12,732 INFO [train.py:763] (7/8) Epoch 32, batch 2300, loss[loss=0.2066, simple_loss=0.3076, pruned_loss=0.05283, over 7191.00 frames.], tot_loss[loss=0.1604, simple_loss=0.26, pruned_loss=0.03034, over 1418083.07 frames.], batch size: 22, lr: 2.36e-04
+2022-04-30 14:16:18,553 INFO [train.py:763] (7/8) Epoch 32, batch 2350, loss[loss=0.1444, simple_loss=0.2278, pruned_loss=0.0305, over 6819.00 frames.], tot_loss[loss=0.16, simple_loss=0.2595, pruned_loss=0.03024, over 1421017.60 frames.], batch size: 15, lr: 2.36e-04
+2022-04-30 14:17:26,004 INFO [train.py:763] (7/8) Epoch 32, batch 2400, loss[loss=0.1693, simple_loss=0.2693, pruned_loss=0.03467, over 7435.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2587, pruned_loss=0.0299, over 1423783.74 frames.], batch size: 20, lr: 2.36e-04
+2022-04-30 14:18:32,885 INFO [train.py:763] (7/8) Epoch 32, batch 2450, loss[loss=0.1498, simple_loss=0.2517, pruned_loss=0.02396, over 7259.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2589, pruned_loss=0.02982, over 1425640.08 frames.], batch size: 19, lr: 2.36e-04
+2022-04-30 14:19:38,461 INFO [train.py:763] (7/8) Epoch 32, batch 2500, loss[loss=0.1496, simple_loss=0.2528, pruned_loss=0.02316, over 7322.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2589, pruned_loss=0.02976, over 1427125.89 frames.], batch size: 21, lr: 2.36e-04
+2022-04-30 14:20:45,076 INFO [train.py:763] (7/8) Epoch 32, batch 2550, loss[loss=0.1869, simple_loss=0.2972, pruned_loss=0.03828, over 7355.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2595, pruned_loss=0.02989, over 1426579.98 frames.], batch size: 23, lr: 2.36e-04
+2022-04-30 14:21:59,930 INFO [train.py:763] (7/8) Epoch 32, batch 2600, loss[loss=0.1861, simple_loss=0.2802, pruned_loss=0.04601, over 7182.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2594, pruned_loss=0.02983, over 1426999.12 frames.], batch size: 23, lr: 2.36e-04
+2022-04-30 14:23:23,018 INFO [train.py:763] (7/8) Epoch 32, batch 2650, loss[loss=0.1518, simple_loss=0.2393, pruned_loss=0.0321, over 7243.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2602, pruned_loss=0.03018, over 1422132.37 frames.], batch size: 16, lr: 2.35e-04
+2022-04-30 14:24:36,945 INFO [train.py:763] (7/8) Epoch 32, batch 2700, loss[loss=0.1505, simple_loss=0.2538, pruned_loss=0.02358, over 7428.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2606, pruned_loss=0.03021, over 1423765.37 frames.], batch size: 20, lr: 2.35e-04
+2022-04-30 14:25:51,361 INFO [train.py:763] (7/8) Epoch 32, batch 2750, loss[loss=0.1526, simple_loss=0.2436, pruned_loss=0.03075, over 7286.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2609, pruned_loss=0.03014, over 1424964.32 frames.], batch size: 18, lr: 2.35e-04
+2022-04-30 14:26:57,668 INFO [train.py:763] (7/8) Epoch 32, batch 2800, loss[loss=0.1904, simple_loss=0.2938, pruned_loss=0.04349, over 7213.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2607, pruned_loss=0.03028, over 1424019.58 frames.], batch size: 23, lr: 2.35e-04
+2022-04-30 14:28:12,050 INFO [train.py:763] (7/8) Epoch 32, batch 2850, loss[loss=0.1817, simple_loss=0.2915, pruned_loss=0.036, over 7327.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2601, pruned_loss=0.03038, over 1425319.19 frames.], batch size: 21, lr: 2.35e-04
+2022-04-30 14:29:27,089 INFO [train.py:763] (7/8) Epoch 32, batch 2900, loss[loss=0.1739, simple_loss=0.2789, pruned_loss=0.03444, over 7321.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2606, pruned_loss=0.03064, over 1424828.32 frames.], batch size: 25, lr: 2.35e-04
+2022-04-30 14:30:33,981 INFO [train.py:763] (7/8) Epoch 32, batch 2950, loss[loss=0.1778, simple_loss=0.2727, pruned_loss=0.04139, over 7425.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2618, pruned_loss=0.03067, over 1426948.90 frames.], batch size: 20, lr: 2.35e-04
+2022-04-30 14:31:40,127 INFO [train.py:763] (7/8) Epoch 32, batch 3000, loss[loss=0.1486, simple_loss=0.2522, pruned_loss=0.02254, over 7063.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2615, pruned_loss=0.03066, over 1425486.45 frames.], batch size: 18, lr: 2.35e-04
+2022-04-30 14:31:40,127 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 14:31:55,319 INFO [train.py:792] (7/8) Epoch 32, validation: loss=0.1696, simple_loss=0.2645, pruned_loss=0.0374, over 698248.00 frames.
+2022-04-30 14:33:01,771 INFO [train.py:763] (7/8) Epoch 32, batch 3050, loss[loss=0.1682, simple_loss=0.2723, pruned_loss=0.03205, over 6435.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2608, pruned_loss=0.03067, over 1422503.47 frames.], batch size: 38, lr: 2.35e-04
+2022-04-30 14:34:07,513 INFO [train.py:763] (7/8) Epoch 32, batch 3100, loss[loss=0.1833, simple_loss=0.287, pruned_loss=0.03985, over 7368.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2612, pruned_loss=0.03067, over 1422985.72 frames.], batch size: 23, lr: 2.35e-04
+2022-04-30 14:35:13,896 INFO [train.py:763] (7/8) Epoch 32, batch 3150, loss[loss=0.1676, simple_loss=0.271, pruned_loss=0.03207, over 7066.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2611, pruned_loss=0.03054, over 1420825.50 frames.], batch size: 18, lr: 2.35e-04
+2022-04-30 14:36:20,362 INFO [train.py:763] (7/8) Epoch 32, batch 3200, loss[loss=0.1555, simple_loss=0.2399, pruned_loss=0.0356, over 6771.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2613, pruned_loss=0.03073, over 1420461.18 frames.], batch size: 15, lr: 2.35e-04
+2022-04-30 14:37:25,792 INFO [train.py:763] (7/8) Epoch 32, batch 3250, loss[loss=0.1335, simple_loss=0.2245, pruned_loss=0.02124, over 7281.00 frames.], tot_loss[loss=0.162, simple_loss=0.2616, pruned_loss=0.03121, over 1418446.52 frames.], batch size: 18, lr: 2.35e-04
+2022-04-30 14:38:31,364 INFO [train.py:763] (7/8) Epoch 32, batch 3300, loss[loss=0.1704, simple_loss=0.2803, pruned_loss=0.03029, over 7238.00 frames.], tot_loss[loss=0.1614, simple_loss=0.261, pruned_loss=0.0309, over 1423787.50 frames.], batch size: 20, lr: 2.35e-04
+2022-04-30 14:39:37,091 INFO [train.py:763] (7/8) Epoch 32, batch 3350, loss[loss=0.1502, simple_loss=0.2506, pruned_loss=0.02488, over 7321.00 frames.], tot_loss[loss=0.161, simple_loss=0.2609, pruned_loss=0.03055, over 1427674.60 frames.], batch size: 21, lr: 2.35e-04
+2022-04-30 14:40:43,419 INFO [train.py:763] (7/8) Epoch 32, batch 3400, loss[loss=0.1614, simple_loss=0.2625, pruned_loss=0.03014, over 7273.00 frames.], tot_loss[loss=0.161, simple_loss=0.2606, pruned_loss=0.03067, over 1428399.95 frames.], batch size: 18, lr: 2.35e-04
+2022-04-30 14:41:50,124 INFO [train.py:763] (7/8) Epoch 32, batch 3450, loss[loss=0.1756, simple_loss=0.2783, pruned_loss=0.03649, over 7328.00 frames.], tot_loss[loss=0.1611, simple_loss=0.261, pruned_loss=0.03059, over 1432246.05 frames.], batch size: 20, lr: 2.35e-04
+2022-04-30 14:42:56,331 INFO [train.py:763] (7/8) Epoch 32, batch 3500, loss[loss=0.1914, simple_loss=0.2999, pruned_loss=0.04143, over 7377.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2617, pruned_loss=0.03083, over 1428576.75 frames.], batch size: 23, lr: 2.35e-04
+2022-04-30 14:44:01,654 INFO [train.py:763] (7/8) Epoch 32, batch 3550, loss[loss=0.1618, simple_loss=0.2512, pruned_loss=0.03617, over 7426.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2617, pruned_loss=0.03099, over 1427549.53 frames.], batch size: 18, lr: 2.35e-04
+2022-04-30 14:45:07,001 INFO [train.py:763] (7/8) Epoch 32, batch 3600, loss[loss=0.1525, simple_loss=0.2583, pruned_loss=0.02332, over 7319.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2614, pruned_loss=0.03071, over 1423835.27 frames.], batch size: 20, lr: 2.35e-04
+2022-04-30 14:46:12,683 INFO [train.py:763] (7/8) Epoch 32, batch 3650, loss[loss=0.1606, simple_loss=0.2615, pruned_loss=0.02991, over 7326.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2598, pruned_loss=0.03035, over 1424060.31 frames.], batch size: 20, lr: 2.35e-04
+2022-04-30 14:47:18,409 INFO [train.py:763] (7/8) Epoch 32, batch 3700, loss[loss=0.1534, simple_loss=0.2504, pruned_loss=0.02819, over 7290.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2611, pruned_loss=0.03076, over 1427675.61 frames.], batch size: 17, lr: 2.35e-04
+2022-04-30 14:48:25,084 INFO [train.py:763] (7/8) Epoch 32, batch 3750, loss[loss=0.1761, simple_loss=0.2757, pruned_loss=0.03825, over 7219.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2608, pruned_loss=0.03065, over 1427498.71 frames.], batch size: 21, lr: 2.35e-04
+2022-04-30 14:49:30,598 INFO [train.py:763] (7/8) Epoch 32, batch 3800, loss[loss=0.1859, simple_loss=0.2829, pruned_loss=0.0444, over 7193.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2607, pruned_loss=0.03102, over 1428288.90 frames.], batch size: 23, lr: 2.35e-04
+2022-04-30 14:50:35,847 INFO [train.py:763] (7/8) Epoch 32, batch 3850, loss[loss=0.1492, simple_loss=0.2562, pruned_loss=0.02105, over 7317.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2603, pruned_loss=0.03074, over 1429790.81 frames.], batch size: 21, lr: 2.35e-04
+2022-04-30 14:51:41,199 INFO [train.py:763] (7/8) Epoch 32, batch 3900, loss[loss=0.1496, simple_loss=0.2408, pruned_loss=0.02917, over 6792.00 frames.], tot_loss[loss=0.1619, simple_loss=0.262, pruned_loss=0.03087, over 1429659.87 frames.], batch size: 15, lr: 2.35e-04
+2022-04-30 14:52:46,613 INFO [train.py:763] (7/8) Epoch 32, batch 3950, loss[loss=0.1754, simple_loss=0.2619, pruned_loss=0.04443, over 7425.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2626, pruned_loss=0.0312, over 1431474.64 frames.], batch size: 18, lr: 2.34e-04
+2022-04-30 14:53:52,273 INFO [train.py:763] (7/8) Epoch 32, batch 4000, loss[loss=0.1547, simple_loss=0.2635, pruned_loss=0.02295, over 6519.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2614, pruned_loss=0.03064, over 1431728.15 frames.], batch size: 38, lr: 2.34e-04
+2022-04-30 14:54:57,666 INFO [train.py:763] (7/8) Epoch 32, batch 4050, loss[loss=0.1532, simple_loss=0.2494, pruned_loss=0.02849, over 7282.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2616, pruned_loss=0.03054, over 1427937.87 frames.], batch size: 18, lr: 2.34e-04
+2022-04-30 14:56:02,800 INFO [train.py:763] (7/8) Epoch 32, batch 4100, loss[loss=0.1541, simple_loss=0.2553, pruned_loss=0.02639, over 7158.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2611, pruned_loss=0.03036, over 1421830.51 frames.], batch size: 26, lr: 2.34e-04
+2022-04-30 14:57:08,467 INFO [train.py:763] (7/8) Epoch 32, batch 4150, loss[loss=0.1598, simple_loss=0.2422, pruned_loss=0.03874, over 6802.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2605, pruned_loss=0.02989, over 1421828.02 frames.], batch size: 15, lr: 2.34e-04
+2022-04-30 14:58:14,272 INFO [train.py:763] (7/8) Epoch 32, batch 4200, loss[loss=0.1676, simple_loss=0.2707, pruned_loss=0.03225, over 7261.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2602, pruned_loss=0.02967, over 1420126.40 frames.], batch size: 19, lr: 2.34e-04
+2022-04-30 14:59:19,688 INFO [train.py:763] (7/8) Epoch 32, batch 4250, loss[loss=0.1496, simple_loss=0.2393, pruned_loss=0.0299, over 7425.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2608, pruned_loss=0.02996, over 1420262.28 frames.], batch size: 20, lr: 2.34e-04
+2022-04-30 15:00:26,358 INFO [train.py:763] (7/8) Epoch 32, batch 4300, loss[loss=0.1882, simple_loss=0.2944, pruned_loss=0.04105, over 6936.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2609, pruned_loss=0.02999, over 1419508.85 frames.], batch size: 32, lr: 2.34e-04
+2022-04-30 15:01:33,001 INFO [train.py:763] (7/8) Epoch 32, batch 4350, loss[loss=0.1638, simple_loss=0.271, pruned_loss=0.02834, over 7221.00 frames.], tot_loss[loss=0.1597, simple_loss=0.26, pruned_loss=0.02969, over 1414801.47 frames.], batch size: 21, lr: 2.34e-04
+2022-04-30 15:02:38,284 INFO [train.py:763] (7/8) Epoch 32, batch 4400, loss[loss=0.1673, simple_loss=0.2771, pruned_loss=0.02878, over 7137.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2596, pruned_loss=0.02941, over 1414125.43 frames.], batch size: 20, lr: 2.34e-04
+2022-04-30 15:03:43,372 INFO [train.py:763] (7/8) Epoch 32, batch 4450, loss[loss=0.1686, simple_loss=0.2765, pruned_loss=0.03034, over 7330.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2603, pruned_loss=0.02962, over 1407124.64 frames.], batch size: 22, lr: 2.34e-04
+2022-04-30 15:04:48,254 INFO [train.py:763] (7/8) Epoch 32, batch 4500, loss[loss=0.1571, simple_loss=0.264, pruned_loss=0.02507, over 7144.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2602, pruned_loss=0.0297, over 1396830.19 frames.], batch size: 20, lr: 2.34e-04
+2022-04-30 15:05:53,080 INFO [train.py:763] (7/8) Epoch 32, batch 4550, loss[loss=0.1937, simple_loss=0.2873, pruned_loss=0.05011, over 4860.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2606, pruned_loss=0.0301, over 1375209.99 frames.], batch size: 52, lr: 2.34e-04
+2022-04-30 15:07:21,099 INFO [train.py:763] (7/8) Epoch 33, batch 0, loss[loss=0.1492, simple_loss=0.2529, pruned_loss=0.02276, over 7436.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2529, pruned_loss=0.02276, over 7436.00 frames.], batch size: 20, lr: 2.31e-04
+2022-04-30 15:08:26,680 INFO [train.py:763] (7/8) Epoch 33, batch 50, loss[loss=0.1576, simple_loss=0.2572, pruned_loss=0.02903, over 7069.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2578, pruned_loss=0.02923, over 324630.75 frames.], batch size: 28, lr: 2.30e-04
+2022-04-30 15:09:31,889 INFO [train.py:763] (7/8) Epoch 33, batch 100, loss[loss=0.1668, simple_loss=0.2737, pruned_loss=0.02995, over 7452.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2607, pruned_loss=0.02989, over 566765.47 frames.], batch size: 22, lr: 2.30e-04
+2022-04-30 15:10:37,382 INFO [train.py:763] (7/8) Epoch 33, batch 150, loss[loss=0.1429, simple_loss=0.2465, pruned_loss=0.01965, over 7068.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2582, pruned_loss=0.02934, over 756792.23 frames.], batch size: 18, lr: 2.30e-04
+2022-04-30 15:11:42,904 INFO [train.py:763] (7/8) Epoch 33, batch 200, loss[loss=0.1678, simple_loss=0.2713, pruned_loss=0.03215, over 7277.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2571, pruned_loss=0.02871, over 905939.80 frames.], batch size: 17, lr: 2.30e-04
+2022-04-30 15:12:48,581 INFO [train.py:763] (7/8) Epoch 33, batch 250, loss[loss=0.205, simple_loss=0.2939, pruned_loss=0.05804, over 5318.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2584, pruned_loss=0.02946, over 1012965.02 frames.], batch size: 52, lr: 2.30e-04
+2022-04-30 15:13:55,827 INFO [train.py:763] (7/8) Epoch 33, batch 300, loss[loss=0.172, simple_loss=0.2708, pruned_loss=0.03655, over 7382.00 frames.], tot_loss[loss=0.16, simple_loss=0.2598, pruned_loss=0.03014, over 1103954.36 frames.], batch size: 23, lr: 2.30e-04
+2022-04-30 15:15:01,954 INFO [train.py:763] (7/8) Epoch 33, batch 350, loss[loss=0.146, simple_loss=0.2416, pruned_loss=0.0252, over 7109.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2617, pruned_loss=0.03072, over 1169170.23 frames.], batch size: 17, lr: 2.30e-04
+2022-04-30 15:16:08,901 INFO [train.py:763] (7/8) Epoch 33, batch 400, loss[loss=0.1624, simple_loss=0.2687, pruned_loss=0.028, over 7414.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2608, pruned_loss=0.03038, over 1229839.69 frames.], batch size: 21, lr: 2.30e-04
+2022-04-30 15:17:14,706 INFO [train.py:763] (7/8) Epoch 33, batch 450, loss[loss=0.155, simple_loss=0.251, pruned_loss=0.02949, over 7410.00 frames.], tot_loss[loss=0.1606, simple_loss=0.261, pruned_loss=0.03006, over 1274178.54 frames.], batch size: 18, lr: 2.30e-04
+2022-04-30 15:18:21,059 INFO [train.py:763] (7/8) Epoch 33, batch 500, loss[loss=0.1891, simple_loss=0.2856, pruned_loss=0.04627, over 7290.00 frames.], tot_loss[loss=0.1619, simple_loss=0.262, pruned_loss=0.03094, over 1307453.16 frames.], batch size: 24, lr: 2.30e-04
+2022-04-30 15:19:26,294 INFO [train.py:763] (7/8) Epoch 33, batch 550, loss[loss=0.1637, simple_loss=0.2725, pruned_loss=0.02742, over 6419.00 frames.], tot_loss[loss=0.162, simple_loss=0.2617, pruned_loss=0.03111, over 1331056.48 frames.], batch size: 37, lr: 2.30e-04
+2022-04-30 15:20:43,093 INFO [train.py:763] (7/8) Epoch 33, batch 600, loss[loss=0.1848, simple_loss=0.2876, pruned_loss=0.04101, over 7308.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2622, pruned_loss=0.03105, over 1353427.54 frames.], batch size: 25, lr: 2.30e-04
+2022-04-30 15:21:48,338 INFO [train.py:763] (7/8) Epoch 33, batch 650, loss[loss=0.1462, simple_loss=0.243, pruned_loss=0.02466, over 7164.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2623, pruned_loss=0.03081, over 1371565.00 frames.], batch size: 18, lr: 2.30e-04
+2022-04-30 15:22:53,619 INFO [train.py:763] (7/8) Epoch 33, batch 700, loss[loss=0.1156, simple_loss=0.2031, pruned_loss=0.01403, over 7135.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2602, pruned_loss=0.0305, over 1378846.58 frames.], batch size: 17, lr: 2.30e-04
+2022-04-30 15:23:58,791 INFO [train.py:763] (7/8) Epoch 33, batch 750, loss[loss=0.1505, simple_loss=0.2504, pruned_loss=0.0253, over 7193.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2603, pruned_loss=0.02996, over 1389910.12 frames.], batch size: 23, lr: 2.30e-04
+2022-04-30 15:25:05,671 INFO [train.py:763] (7/8) Epoch 33, batch 800, loss[loss=0.1485, simple_loss=0.2478, pruned_loss=0.02463, over 7280.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2604, pruned_loss=0.02988, over 1395017.63 frames.], batch size: 18, lr: 2.30e-04
+2022-04-30 15:26:11,933 INFO [train.py:763] (7/8) Epoch 33, batch 850, loss[loss=0.1788, simple_loss=0.279, pruned_loss=0.03927, over 6397.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2606, pruned_loss=0.03004, over 1403972.35 frames.], batch size: 38, lr: 2.30e-04
+2022-04-30 15:27:17,428 INFO [train.py:763] (7/8) Epoch 33, batch 900, loss[loss=0.1868, simple_loss=0.2721, pruned_loss=0.05078, over 4989.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2592, pruned_loss=0.02997, over 1409332.09 frames.], batch size: 52, lr: 2.30e-04
+2022-04-30 15:28:22,830 INFO [train.py:763] (7/8) Epoch 33, batch 950, loss[loss=0.1723, simple_loss=0.2588, pruned_loss=0.04287, over 7288.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2592, pruned_loss=0.03022, over 1407696.26 frames.], batch size: 18, lr: 2.30e-04
+2022-04-30 15:29:28,261 INFO [train.py:763] (7/8) Epoch 33, batch 1000, loss[loss=0.1514, simple_loss=0.2534, pruned_loss=0.02473, over 7441.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2589, pruned_loss=0.0299, over 1409803.25 frames.], batch size: 20, lr: 2.30e-04
+2022-04-30 15:30:33,678 INFO [train.py:763] (7/8) Epoch 33, batch 1050, loss[loss=0.1413, simple_loss=0.248, pruned_loss=0.01728, over 7156.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2589, pruned_loss=0.02967, over 1416260.75 frames.], batch size: 19, lr: 2.30e-04
+2022-04-30 15:31:40,476 INFO [train.py:763] (7/8) Epoch 33, batch 1100, loss[loss=0.1618, simple_loss=0.2705, pruned_loss=0.02657, over 6314.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2598, pruned_loss=0.02978, over 1414897.73 frames.], batch size: 38, lr: 2.30e-04
+2022-04-30 15:32:45,935 INFO [train.py:763] (7/8) Epoch 33, batch 1150, loss[loss=0.1472, simple_loss=0.243, pruned_loss=0.02573, over 7423.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2593, pruned_loss=0.02954, over 1417505.89 frames.], batch size: 20, lr: 2.30e-04
+2022-04-30 15:33:51,350 INFO [train.py:763] (7/8) Epoch 33, batch 1200, loss[loss=0.1776, simple_loss=0.2838, pruned_loss=0.03574, over 7194.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2594, pruned_loss=0.02954, over 1421813.80 frames.], batch size: 23, lr: 2.30e-04
+2022-04-30 15:34:56,640 INFO [train.py:763] (7/8) Epoch 33, batch 1250, loss[loss=0.1807, simple_loss=0.2858, pruned_loss=0.03782, over 7335.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2594, pruned_loss=0.02961, over 1419555.33 frames.], batch size: 22, lr: 2.30e-04
+2022-04-30 15:36:02,624 INFO [train.py:763] (7/8) Epoch 33, batch 1300, loss[loss=0.1546, simple_loss=0.2684, pruned_loss=0.02044, over 7165.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2591, pruned_loss=0.0296, over 1418846.57 frames.], batch size: 26, lr: 2.30e-04
+2022-04-30 15:37:09,771 INFO [train.py:763] (7/8) Epoch 33, batch 1350, loss[loss=0.1757, simple_loss=0.2795, pruned_loss=0.03596, over 7219.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2586, pruned_loss=0.02934, over 1419133.46 frames.], batch size: 21, lr: 2.29e-04
+2022-04-30 15:38:16,833 INFO [train.py:763] (7/8) Epoch 33, batch 1400, loss[loss=0.1531, simple_loss=0.2614, pruned_loss=0.02244, over 7258.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2582, pruned_loss=0.02944, over 1422275.74 frames.], batch size: 19, lr: 2.29e-04
+2022-04-30 15:39:22,850 INFO [train.py:763] (7/8) Epoch 33, batch 1450, loss[loss=0.166, simple_loss=0.2703, pruned_loss=0.03087, over 7415.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2586, pruned_loss=0.02934, over 1426146.08 frames.], batch size: 21, lr: 2.29e-04
+2022-04-30 15:40:28,346 INFO [train.py:763] (7/8) Epoch 33, batch 1500, loss[loss=0.2023, simple_loss=0.3005, pruned_loss=0.05202, over 7367.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2591, pruned_loss=0.02934, over 1424409.78 frames.], batch size: 23, lr: 2.29e-04
+2022-04-30 15:41:33,832 INFO [train.py:763] (7/8) Epoch 33, batch 1550, loss[loss=0.148, simple_loss=0.2461, pruned_loss=0.02499, over 7277.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2601, pruned_loss=0.02982, over 1422247.23 frames.], batch size: 24, lr: 2.29e-04
+2022-04-30 15:42:39,074 INFO [train.py:763] (7/8) Epoch 33, batch 1600, loss[loss=0.1373, simple_loss=0.2453, pruned_loss=0.01462, over 7335.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2607, pruned_loss=0.03003, over 1423841.10 frames.], batch size: 20, lr: 2.29e-04
+2022-04-30 15:43:46,218 INFO [train.py:763] (7/8) Epoch 33, batch 1650, loss[loss=0.1574, simple_loss=0.2584, pruned_loss=0.02821, over 7206.00 frames.], tot_loss[loss=0.161, simple_loss=0.2614, pruned_loss=0.03032, over 1422773.85 frames.], batch size: 22, lr: 2.29e-04
+2022-04-30 15:44:53,568 INFO [train.py:763] (7/8) Epoch 33, batch 1700, loss[loss=0.1594, simple_loss=0.2669, pruned_loss=0.02598, over 7375.00 frames.], tot_loss[loss=0.161, simple_loss=0.2616, pruned_loss=0.03024, over 1426617.79 frames.], batch size: 23, lr: 2.29e-04
+2022-04-30 15:46:00,139 INFO [train.py:763] (7/8) Epoch 33, batch 1750, loss[loss=0.1725, simple_loss=0.2692, pruned_loss=0.03792, over 7022.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2617, pruned_loss=0.03036, over 1420780.00 frames.], batch size: 28, lr: 2.29e-04
+2022-04-30 15:47:05,300 INFO [train.py:763] (7/8) Epoch 33, batch 1800, loss[loss=0.1458, simple_loss=0.2322, pruned_loss=0.02973, over 7268.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2611, pruned_loss=0.03029, over 1422438.02 frames.], batch size: 17, lr: 2.29e-04
+2022-04-30 15:48:11,946 INFO [train.py:763] (7/8) Epoch 33, batch 1850, loss[loss=0.1676, simple_loss=0.2752, pruned_loss=0.03003, over 7322.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2613, pruned_loss=0.03053, over 1414535.64 frames.], batch size: 21, lr: 2.29e-04
+2022-04-30 15:49:17,349 INFO [train.py:763] (7/8) Epoch 33, batch 1900, loss[loss=0.1542, simple_loss=0.2659, pruned_loss=0.02127, over 6684.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2608, pruned_loss=0.03038, over 1409980.38 frames.], batch size: 31, lr: 2.29e-04
+2022-04-30 15:50:23,834 INFO [train.py:763] (7/8) Epoch 33, batch 1950, loss[loss=0.1522, simple_loss=0.2414, pruned_loss=0.03146, over 6983.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2607, pruned_loss=0.03053, over 1416241.50 frames.], batch size: 16, lr: 2.29e-04
+2022-04-30 15:51:31,082 INFO [train.py:763] (7/8) Epoch 33, batch 2000, loss[loss=0.1576, simple_loss=0.2493, pruned_loss=0.03297, over 7415.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2605, pruned_loss=0.03035, over 1420939.87 frames.], batch size: 18, lr: 2.29e-04
+2022-04-30 15:52:37,450 INFO [train.py:763] (7/8) Epoch 33, batch 2050, loss[loss=0.1973, simple_loss=0.2909, pruned_loss=0.05182, over 7176.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2602, pruned_loss=0.03032, over 1420009.00 frames.], batch size: 26, lr: 2.29e-04
+2022-04-30 15:53:42,716 INFO [train.py:763] (7/8) Epoch 33, batch 2100, loss[loss=0.1603, simple_loss=0.2532, pruned_loss=0.03368, over 7206.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2608, pruned_loss=0.03033, over 1423051.62 frames.], batch size: 23, lr: 2.29e-04
+2022-04-30 15:54:47,949 INFO [train.py:763] (7/8) Epoch 33, batch 2150, loss[loss=0.1624, simple_loss=0.2588, pruned_loss=0.03305, over 7282.00 frames.], tot_loss[loss=0.16, simple_loss=0.26, pruned_loss=0.03004, over 1422660.11 frames.], batch size: 24, lr: 2.29e-04
+2022-04-30 15:55:53,187 INFO [train.py:763] (7/8) Epoch 33, batch 2200, loss[loss=0.1552, simple_loss=0.2599, pruned_loss=0.02529, over 7318.00 frames.], tot_loss[loss=0.16, simple_loss=0.2604, pruned_loss=0.02974, over 1426016.88 frames.], batch size: 21, lr: 2.29e-04
+2022-04-30 15:56:58,881 INFO [train.py:763] (7/8) Epoch 33, batch 2250, loss[loss=0.1256, simple_loss=0.223, pruned_loss=0.0141, over 7278.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2604, pruned_loss=0.02997, over 1422966.18 frames.], batch size: 18, lr: 2.29e-04
+2022-04-30 15:58:05,279 INFO [train.py:763] (7/8) Epoch 33, batch 2300, loss[loss=0.1291, simple_loss=0.2196, pruned_loss=0.0193, over 7162.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2614, pruned_loss=0.0301, over 1423423.68 frames.], batch size: 19, lr: 2.29e-04
+2022-04-30 15:59:10,715 INFO [train.py:763] (7/8) Epoch 33, batch 2350, loss[loss=0.1645, simple_loss=0.2565, pruned_loss=0.03623, over 7165.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2612, pruned_loss=0.03017, over 1424512.77 frames.], batch size: 19, lr: 2.29e-04
+2022-04-30 16:00:16,838 INFO [train.py:763] (7/8) Epoch 33, batch 2400, loss[loss=0.1705, simple_loss=0.2753, pruned_loss=0.03284, over 7384.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2601, pruned_loss=0.02992, over 1425604.20 frames.], batch size: 23, lr: 2.29e-04
+2022-04-30 16:01:22,900 INFO [train.py:763] (7/8) Epoch 33, batch 2450, loss[loss=0.1694, simple_loss=0.2696, pruned_loss=0.03458, over 7226.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2612, pruned_loss=0.03026, over 1420162.09 frames.], batch size: 21, lr: 2.29e-04
+2022-04-30 16:02:28,053 INFO [train.py:763] (7/8) Epoch 33, batch 2500, loss[loss=0.1464, simple_loss=0.2352, pruned_loss=0.02878, over 6987.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2615, pruned_loss=0.03042, over 1418324.20 frames.], batch size: 16, lr: 2.29e-04
+2022-04-30 16:03:33,230 INFO [train.py:763] (7/8) Epoch 33, batch 2550, loss[loss=0.1653, simple_loss=0.2723, pruned_loss=0.02912, over 7341.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2615, pruned_loss=0.03084, over 1419350.35 frames.], batch size: 22, lr: 2.29e-04
+2022-04-30 16:04:38,859 INFO [train.py:763] (7/8) Epoch 33, batch 2600, loss[loss=0.1592, simple_loss=0.2558, pruned_loss=0.03136, over 7076.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2616, pruned_loss=0.03105, over 1419913.89 frames.], batch size: 18, lr: 2.29e-04
+2022-04-30 16:05:45,696 INFO [train.py:763] (7/8) Epoch 33, batch 2650, loss[loss=0.1589, simple_loss=0.2687, pruned_loss=0.02454, over 7337.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2603, pruned_loss=0.03024, over 1420070.29 frames.], batch size: 22, lr: 2.29e-04
+2022-04-30 16:06:52,541 INFO [train.py:763] (7/8) Epoch 33, batch 2700, loss[loss=0.1458, simple_loss=0.243, pruned_loss=0.02433, over 7266.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2606, pruned_loss=0.03018, over 1424828.61 frames.], batch size: 18, lr: 2.28e-04
+2022-04-30 16:07:59,674 INFO [train.py:763] (7/8) Epoch 33, batch 2750, loss[loss=0.1706, simple_loss=0.2711, pruned_loss=0.03501, over 7323.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2608, pruned_loss=0.03041, over 1423801.48 frames.], batch size: 21, lr: 2.28e-04
+2022-04-30 16:09:06,761 INFO [train.py:763] (7/8) Epoch 33, batch 2800, loss[loss=0.1674, simple_loss=0.2527, pruned_loss=0.04107, over 7414.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2618, pruned_loss=0.03073, over 1428606.17 frames.], batch size: 18, lr: 2.28e-04
+2022-04-30 16:10:13,303 INFO [train.py:763] (7/8) Epoch 33, batch 2850, loss[loss=0.1849, simple_loss=0.2822, pruned_loss=0.04378, over 7196.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2617, pruned_loss=0.03032, over 1429499.93 frames.], batch size: 23, lr: 2.28e-04
+2022-04-30 16:11:18,336 INFO [train.py:763] (7/8) Epoch 33, batch 2900, loss[loss=0.166, simple_loss=0.2758, pruned_loss=0.02814, over 7155.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2613, pruned_loss=0.03005, over 1426725.54 frames.], batch size: 20, lr: 2.28e-04
+2022-04-30 16:12:24,343 INFO [train.py:763] (7/8) Epoch 33, batch 2950, loss[loss=0.1722, simple_loss=0.2824, pruned_loss=0.03097, over 7139.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2598, pruned_loss=0.02955, over 1427188.07 frames.], batch size: 20, lr: 2.28e-04
+2022-04-30 16:13:31,378 INFO [train.py:763] (7/8) Epoch 33, batch 3000, loss[loss=0.1506, simple_loss=0.2494, pruned_loss=0.02588, over 7364.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2599, pruned_loss=0.02971, over 1427774.51 frames.], batch size: 19, lr: 2.28e-04
+2022-04-30 16:13:31,378 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 16:13:46,765 INFO [train.py:792] (7/8) Epoch 33, validation: loss=0.1701, simple_loss=0.2653, pruned_loss=0.03746, over 698248.00 frames.
+2022-04-30 16:14:51,755 INFO [train.py:763] (7/8) Epoch 33, batch 3050, loss[loss=0.1511, simple_loss=0.2594, pruned_loss=0.0214, over 7355.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2613, pruned_loss=0.02992, over 1427836.62 frames.], batch size: 19, lr: 2.28e-04
+2022-04-30 16:15:58,071 INFO [train.py:763] (7/8) Epoch 33, batch 3100, loss[loss=0.1309, simple_loss=0.2214, pruned_loss=0.02019, over 7256.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2619, pruned_loss=0.03034, over 1430108.30 frames.], batch size: 16, lr: 2.28e-04
+2022-04-30 16:17:04,959 INFO [train.py:763] (7/8) Epoch 33, batch 3150, loss[loss=0.1371, simple_loss=0.226, pruned_loss=0.02408, over 7294.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2608, pruned_loss=0.02978, over 1429756.78 frames.], batch size: 17, lr: 2.28e-04
+2022-04-30 16:18:11,852 INFO [train.py:763] (7/8) Epoch 33, batch 3200, loss[loss=0.1873, simple_loss=0.2738, pruned_loss=0.05039, over 5133.00 frames.], tot_loss[loss=0.16, simple_loss=0.26, pruned_loss=0.02997, over 1425404.94 frames.], batch size: 52, lr: 2.28e-04
+2022-04-30 16:19:17,479 INFO [train.py:763] (7/8) Epoch 33, batch 3250, loss[loss=0.1739, simple_loss=0.2689, pruned_loss=0.03948, over 7140.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2602, pruned_loss=0.03006, over 1422559.27 frames.], batch size: 17, lr: 2.28e-04
+2022-04-30 16:20:22,906 INFO [train.py:763] (7/8) Epoch 33, batch 3300, loss[loss=0.1599, simple_loss=0.2609, pruned_loss=0.02945, over 7025.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2605, pruned_loss=0.0304, over 1419178.94 frames.], batch size: 28, lr: 2.28e-04
+2022-04-30 16:21:28,699 INFO [train.py:763] (7/8) Epoch 33, batch 3350, loss[loss=0.1653, simple_loss=0.2669, pruned_loss=0.03181, over 7154.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2597, pruned_loss=0.03, over 1422235.68 frames.], batch size: 20, lr: 2.28e-04
+2022-04-30 16:22:44,388 INFO [train.py:763] (7/8) Epoch 33, batch 3400, loss[loss=0.1764, simple_loss=0.2772, pruned_loss=0.03782, over 7206.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2606, pruned_loss=0.03016, over 1422495.03 frames.], batch size: 23, lr: 2.28e-04
+2022-04-30 16:23:50,296 INFO [train.py:763] (7/8) Epoch 33, batch 3450, loss[loss=0.1342, simple_loss=0.2241, pruned_loss=0.02213, over 7008.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2595, pruned_loss=0.0299, over 1427959.99 frames.], batch size: 16, lr: 2.28e-04
+2022-04-30 16:24:55,500 INFO [train.py:763] (7/8) Epoch 33, batch 3500, loss[loss=0.1765, simple_loss=0.2692, pruned_loss=0.04194, over 7170.00 frames.], tot_loss[loss=0.161, simple_loss=0.2614, pruned_loss=0.03031, over 1429488.56 frames.], batch size: 23, lr: 2.28e-04
+2022-04-30 16:26:01,151 INFO [train.py:763] (7/8) Epoch 33, batch 3550, loss[loss=0.1381, simple_loss=0.2275, pruned_loss=0.0244, over 7288.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2604, pruned_loss=0.02987, over 1430510.17 frames.], batch size: 17, lr: 2.28e-04
+2022-04-30 16:27:06,633 INFO [train.py:763] (7/8) Epoch 33, batch 3600, loss[loss=0.1624, simple_loss=0.2771, pruned_loss=0.02389, over 7317.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2605, pruned_loss=0.03029, over 1432486.00 frames.], batch size: 21, lr: 2.28e-04
+2022-04-30 16:28:13,483 INFO [train.py:763] (7/8) Epoch 33, batch 3650, loss[loss=0.159, simple_loss=0.2623, pruned_loss=0.02784, over 6398.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2611, pruned_loss=0.03035, over 1427746.00 frames.], batch size: 37, lr: 2.28e-04
+2022-04-30 16:29:20,530 INFO [train.py:763] (7/8) Epoch 33, batch 3700, loss[loss=0.1655, simple_loss=0.2791, pruned_loss=0.0259, over 7235.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2603, pruned_loss=0.03024, over 1423105.02 frames.], batch size: 20, lr: 2.28e-04
+2022-04-30 16:30:26,047 INFO [train.py:763] (7/8) Epoch 33, batch 3750, loss[loss=0.1622, simple_loss=0.2703, pruned_loss=0.02706, over 7298.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2604, pruned_loss=0.03034, over 1420211.55 frames.], batch size: 24, lr: 2.28e-04
+2022-04-30 16:31:31,707 INFO [train.py:763] (7/8) Epoch 33, batch 3800, loss[loss=0.1442, simple_loss=0.2496, pruned_loss=0.01941, over 7149.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2605, pruned_loss=0.03007, over 1425014.41 frames.], batch size: 20, lr: 2.28e-04
+2022-04-30 16:32:38,588 INFO [train.py:763] (7/8) Epoch 33, batch 3850, loss[loss=0.1663, simple_loss=0.2659, pruned_loss=0.03329, over 7204.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2599, pruned_loss=0.03043, over 1427155.75 frames.], batch size: 23, lr: 2.28e-04
+2022-04-30 16:33:45,467 INFO [train.py:763] (7/8) Epoch 33, batch 3900, loss[loss=0.1607, simple_loss=0.2696, pruned_loss=0.02595, over 7216.00 frames.], tot_loss[loss=0.1605, simple_loss=0.26, pruned_loss=0.03047, over 1425659.12 frames.], batch size: 23, lr: 2.28e-04
+2022-04-30 16:34:52,429 INFO [train.py:763] (7/8) Epoch 33, batch 3950, loss[loss=0.1398, simple_loss=0.238, pruned_loss=0.02077, over 7334.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2597, pruned_loss=0.03008, over 1423028.89 frames.], batch size: 20, lr: 2.28e-04
+2022-04-30 16:35:59,198 INFO [train.py:763] (7/8) Epoch 33, batch 4000, loss[loss=0.1748, simple_loss=0.2648, pruned_loss=0.04241, over 7064.00 frames.], tot_loss[loss=0.1601, simple_loss=0.26, pruned_loss=0.03008, over 1423365.04 frames.], batch size: 18, lr: 2.28e-04
+2022-04-30 16:37:13,149 INFO [train.py:763] (7/8) Epoch 33, batch 4050, loss[loss=0.1857, simple_loss=0.281, pruned_loss=0.04524, over 7170.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2612, pruned_loss=0.03048, over 1418863.14 frames.], batch size: 26, lr: 2.27e-04
+2022-04-30 16:38:27,108 INFO [train.py:763] (7/8) Epoch 33, batch 4100, loss[loss=0.1443, simple_loss=0.2535, pruned_loss=0.01756, over 6267.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2617, pruned_loss=0.03043, over 1419274.05 frames.], batch size: 37, lr: 2.27e-04
+2022-04-30 16:39:41,400 INFO [train.py:763] (7/8) Epoch 33, batch 4150, loss[loss=0.1428, simple_loss=0.2429, pruned_loss=0.02134, over 7413.00 frames.], tot_loss[loss=0.161, simple_loss=0.2615, pruned_loss=0.03026, over 1417392.81 frames.], batch size: 18, lr: 2.27e-04
+2022-04-30 16:40:55,337 INFO [train.py:763] (7/8) Epoch 33, batch 4200, loss[loss=0.1498, simple_loss=0.2523, pruned_loss=0.02369, over 7237.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2613, pruned_loss=0.03023, over 1420303.02 frames.], batch size: 20, lr: 2.27e-04
+2022-04-30 16:42:02,055 INFO [train.py:763] (7/8) Epoch 33, batch 4250, loss[loss=0.1536, simple_loss=0.2443, pruned_loss=0.03147, over 7116.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2613, pruned_loss=0.03052, over 1420798.53 frames.], batch size: 17, lr: 2.27e-04
+2022-04-30 16:43:17,925 INFO [train.py:763] (7/8) Epoch 33, batch 4300, loss[loss=0.1487, simple_loss=0.2398, pruned_loss=0.02874, over 7012.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2613, pruned_loss=0.03047, over 1421413.09 frames.], batch size: 16, lr: 2.27e-04
+2022-04-30 16:44:24,678 INFO [train.py:763] (7/8) Epoch 33, batch 4350, loss[loss=0.139, simple_loss=0.2223, pruned_loss=0.0279, over 7208.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2616, pruned_loss=0.03044, over 1416505.88 frames.], batch size: 16, lr: 2.27e-04
+2022-04-30 16:45:48,501 INFO [train.py:763] (7/8) Epoch 33, batch 4400, loss[loss=0.157, simple_loss=0.2506, pruned_loss=0.03172, over 7160.00 frames.], tot_loss[loss=0.16, simple_loss=0.2601, pruned_loss=0.02993, over 1416420.64 frames.], batch size: 18, lr: 2.27e-04
+2022-04-30 16:46:53,561 INFO [train.py:763] (7/8) Epoch 33, batch 4450, loss[loss=0.1651, simple_loss=0.2691, pruned_loss=0.03054, over 7204.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2603, pruned_loss=0.02997, over 1401414.18 frames.], batch size: 23, lr: 2.27e-04
+2022-04-30 16:48:00,209 INFO [train.py:763] (7/8) Epoch 33, batch 4500, loss[loss=0.1887, simple_loss=0.2761, pruned_loss=0.05066, over 5247.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2602, pruned_loss=0.03006, over 1391938.13 frames.], batch size: 52, lr: 2.27e-04
+2022-04-30 16:49:05,836 INFO [train.py:763] (7/8) Epoch 33, batch 4550, loss[loss=0.1915, simple_loss=0.2908, pruned_loss=0.0461, over 4947.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2621, pruned_loss=0.03073, over 1351039.63 frames.], batch size: 52, lr: 2.27e-04
+2022-04-30 16:50:25,389 INFO [train.py:763] (7/8) Epoch 34, batch 0, loss[loss=0.1539, simple_loss=0.2597, pruned_loss=0.02404, over 7231.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2597, pruned_loss=0.02404, over 7231.00 frames.], batch size: 20, lr: 2.24e-04
+2022-04-30 16:51:31,611 INFO [train.py:763] (7/8) Epoch 34, batch 50, loss[loss=0.1807, simple_loss=0.2742, pruned_loss=0.04358, over 7295.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2607, pruned_loss=0.0298, over 318131.52 frames.], batch size: 24, lr: 2.24e-04
+2022-04-30 16:52:37,611 INFO [train.py:763] (7/8) Epoch 34, batch 100, loss[loss=0.1723, simple_loss=0.2754, pruned_loss=0.03457, over 7162.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2594, pruned_loss=0.02921, over 567694.99 frames.], batch size: 26, lr: 2.24e-04
+2022-04-30 16:53:43,318 INFO [train.py:763] (7/8) Epoch 34, batch 150, loss[loss=0.1701, simple_loss=0.2742, pruned_loss=0.03304, over 7371.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2612, pruned_loss=0.02921, over 760119.39 frames.], batch size: 23, lr: 2.24e-04
+2022-04-30 16:54:49,449 INFO [train.py:763] (7/8) Epoch 34, batch 200, loss[loss=0.131, simple_loss=0.2344, pruned_loss=0.01379, over 7064.00 frames.], tot_loss[loss=0.159, simple_loss=0.2594, pruned_loss=0.02923, over 910208.91 frames.], batch size: 18, lr: 2.24e-04
+2022-04-30 16:55:56,564 INFO [train.py:763] (7/8) Epoch 34, batch 250, loss[loss=0.1559, simple_loss=0.2567, pruned_loss=0.0276, over 7232.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2593, pruned_loss=0.02914, over 1027812.85 frames.], batch size: 20, lr: 2.24e-04
+2022-04-30 16:57:03,061 INFO [train.py:763] (7/8) Epoch 34, batch 300, loss[loss=0.1557, simple_loss=0.2565, pruned_loss=0.02742, over 7150.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2591, pruned_loss=0.02924, over 1114795.44 frames.], batch size: 19, lr: 2.24e-04
+2022-04-30 16:58:08,944 INFO [train.py:763] (7/8) Epoch 34, batch 350, loss[loss=0.1758, simple_loss=0.2664, pruned_loss=0.04264, over 7197.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2587, pruned_loss=0.02942, over 1186563.60 frames.], batch size: 23, lr: 2.24e-04
+2022-04-30 16:59:14,466 INFO [train.py:763] (7/8) Epoch 34, batch 400, loss[loss=0.1624, simple_loss=0.2702, pruned_loss=0.02723, over 7329.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2595, pruned_loss=0.02969, over 1241177.72 frames.], batch size: 20, lr: 2.24e-04
+2022-04-30 17:00:20,025 INFO [train.py:763] (7/8) Epoch 34, batch 450, loss[loss=0.161, simple_loss=0.2628, pruned_loss=0.0296, over 6719.00 frames.], tot_loss[loss=0.159, simple_loss=0.2585, pruned_loss=0.02977, over 1285534.03 frames.], batch size: 31, lr: 2.24e-04
+2022-04-30 17:01:26,968 INFO [train.py:763] (7/8) Epoch 34, batch 500, loss[loss=0.1372, simple_loss=0.2385, pruned_loss=0.01794, over 7329.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2588, pruned_loss=0.02981, over 1313699.56 frames.], batch size: 20, lr: 2.23e-04
+2022-04-30 17:02:32,710 INFO [train.py:763] (7/8) Epoch 34, batch 550, loss[loss=0.1348, simple_loss=0.2371, pruned_loss=0.01623, over 7054.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2584, pruned_loss=0.02967, over 1334539.89 frames.], batch size: 18, lr: 2.23e-04
+2022-04-30 17:03:38,769 INFO [train.py:763] (7/8) Epoch 34, batch 600, loss[loss=0.1554, simple_loss=0.253, pruned_loss=0.02886, over 7334.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2592, pruned_loss=0.02988, over 1353569.28 frames.], batch size: 22, lr: 2.23e-04
+2022-04-30 17:04:44,673 INFO [train.py:763] (7/8) Epoch 34, batch 650, loss[loss=0.1389, simple_loss=0.2347, pruned_loss=0.0216, over 7174.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2597, pruned_loss=0.02984, over 1372700.01 frames.], batch size: 18, lr: 2.23e-04
+2022-04-30 17:05:50,835 INFO [train.py:763] (7/8) Epoch 34, batch 700, loss[loss=0.153, simple_loss=0.2451, pruned_loss=0.03047, over 7282.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2598, pruned_loss=0.02973, over 1387219.09 frames.], batch size: 17, lr: 2.23e-04
+2022-04-30 17:06:58,030 INFO [train.py:763] (7/8) Epoch 34, batch 750, loss[loss=0.162, simple_loss=0.2597, pruned_loss=0.03219, over 7260.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2594, pruned_loss=0.02977, over 1394122.73 frames.], batch size: 19, lr: 2.23e-04
+2022-04-30 17:08:04,376 INFO [train.py:763] (7/8) Epoch 34, batch 800, loss[loss=0.1563, simple_loss=0.2669, pruned_loss=0.02288, over 7226.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2594, pruned_loss=0.0297, over 1403158.04 frames.], batch size: 21, lr: 2.23e-04
+2022-04-30 17:09:09,702 INFO [train.py:763] (7/8) Epoch 34, batch 850, loss[loss=0.1743, simple_loss=0.2791, pruned_loss=0.03472, over 7300.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2598, pruned_loss=0.02971, over 1402983.21 frames.], batch size: 24, lr: 2.23e-04
+2022-04-30 17:10:15,230 INFO [train.py:763] (7/8) Epoch 34, batch 900, loss[loss=0.1706, simple_loss=0.2731, pruned_loss=0.03409, over 5088.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2595, pruned_loss=0.02958, over 1406571.91 frames.], batch size: 52, lr: 2.23e-04
+2022-04-30 17:11:21,173 INFO [train.py:763] (7/8) Epoch 34, batch 950, loss[loss=0.1377, simple_loss=0.2401, pruned_loss=0.01767, over 7250.00 frames.], tot_loss[loss=0.159, simple_loss=0.259, pruned_loss=0.02949, over 1410280.00 frames.], batch size: 19, lr: 2.23e-04
+2022-04-30 17:12:27,462 INFO [train.py:763] (7/8) Epoch 34, batch 1000, loss[loss=0.1634, simple_loss=0.2775, pruned_loss=0.02463, over 6882.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2592, pruned_loss=0.02979, over 1411746.88 frames.], batch size: 31, lr: 2.23e-04
+2022-04-30 17:13:34,607 INFO [train.py:763] (7/8) Epoch 34, batch 1050, loss[loss=0.1752, simple_loss=0.2846, pruned_loss=0.03288, over 7412.00 frames.], tot_loss[loss=0.159, simple_loss=0.2586, pruned_loss=0.02968, over 1416530.99 frames.], batch size: 21, lr: 2.23e-04
+2022-04-30 17:14:40,043 INFO [train.py:763] (7/8) Epoch 34, batch 1100, loss[loss=0.1365, simple_loss=0.2389, pruned_loss=0.01702, over 7356.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2581, pruned_loss=0.02924, over 1420254.07 frames.], batch size: 19, lr: 2.23e-04
+2022-04-30 17:15:45,157 INFO [train.py:763] (7/8) Epoch 34, batch 1150, loss[loss=0.1764, simple_loss=0.2819, pruned_loss=0.03547, over 7191.00 frames.], tot_loss[loss=0.159, simple_loss=0.2592, pruned_loss=0.02937, over 1421656.66 frames.], batch size: 23, lr: 2.23e-04
+2022-04-30 17:16:50,479 INFO [train.py:763] (7/8) Epoch 34, batch 1200, loss[loss=0.1446, simple_loss=0.239, pruned_loss=0.02512, over 7279.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2591, pruned_loss=0.02924, over 1425387.36 frames.], batch size: 18, lr: 2.23e-04
+2022-04-30 17:17:56,089 INFO [train.py:763] (7/8) Epoch 34, batch 1250, loss[loss=0.1658, simple_loss=0.2666, pruned_loss=0.0325, over 7340.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2588, pruned_loss=0.02931, over 1424126.92 frames.], batch size: 22, lr: 2.23e-04
+2022-04-30 17:19:02,080 INFO [train.py:763] (7/8) Epoch 34, batch 1300, loss[loss=0.1762, simple_loss=0.2786, pruned_loss=0.03686, over 7019.00 frames.], tot_loss[loss=0.1599, simple_loss=0.26, pruned_loss=0.02994, over 1419177.76 frames.], batch size: 28, lr: 2.23e-04
+2022-04-30 17:20:07,327 INFO [train.py:763] (7/8) Epoch 34, batch 1350, loss[loss=0.1795, simple_loss=0.2877, pruned_loss=0.03562, over 7042.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2598, pruned_loss=0.02967, over 1422480.89 frames.], batch size: 28, lr: 2.23e-04
+2022-04-30 17:21:12,470 INFO [train.py:763] (7/8) Epoch 34, batch 1400, loss[loss=0.1544, simple_loss=0.2625, pruned_loss=0.02313, over 7332.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2601, pruned_loss=0.03016, over 1420948.77 frames.], batch size: 20, lr: 2.23e-04
+2022-04-30 17:22:17,963 INFO [train.py:763] (7/8) Epoch 34, batch 1450, loss[loss=0.1459, simple_loss=0.2411, pruned_loss=0.02539, over 7247.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2599, pruned_loss=0.0303, over 1418696.46 frames.], batch size: 19, lr: 2.23e-04
+2022-04-30 17:23:24,442 INFO [train.py:763] (7/8) Epoch 34, batch 1500, loss[loss=0.1344, simple_loss=0.227, pruned_loss=0.02086, over 7115.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2598, pruned_loss=0.03036, over 1419436.31 frames.], batch size: 17, lr: 2.23e-04
+2022-04-30 17:24:29,706 INFO [train.py:763] (7/8) Epoch 34, batch 1550, loss[loss=0.1746, simple_loss=0.2817, pruned_loss=0.03372, over 7223.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2607, pruned_loss=0.03047, over 1419869.52 frames.], batch size: 21, lr: 2.23e-04
+2022-04-30 17:25:36,480 INFO [train.py:763] (7/8) Epoch 34, batch 1600, loss[loss=0.1578, simple_loss=0.2655, pruned_loss=0.02504, over 7021.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2603, pruned_loss=0.03029, over 1421200.98 frames.], batch size: 28, lr: 2.23e-04
+2022-04-30 17:26:43,367 INFO [train.py:763] (7/8) Epoch 34, batch 1650, loss[loss=0.1659, simple_loss=0.2599, pruned_loss=0.03598, over 7408.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2597, pruned_loss=0.02976, over 1426465.80 frames.], batch size: 18, lr: 2.23e-04
+2022-04-30 17:27:48,842 INFO [train.py:763] (7/8) Epoch 34, batch 1700, loss[loss=0.1955, simple_loss=0.2831, pruned_loss=0.05398, over 5146.00 frames.], tot_loss[loss=0.16, simple_loss=0.2601, pruned_loss=0.02998, over 1426047.29 frames.], batch size: 53, lr: 2.23e-04
+2022-04-30 17:28:54,329 INFO [train.py:763] (7/8) Epoch 34, batch 1750, loss[loss=0.1579, simple_loss=0.2572, pruned_loss=0.02925, over 7160.00 frames.], tot_loss[loss=0.1599, simple_loss=0.26, pruned_loss=0.02993, over 1424820.53 frames.], batch size: 18, lr: 2.23e-04
+2022-04-30 17:29:59,731 INFO [train.py:763] (7/8) Epoch 34, batch 1800, loss[loss=0.1745, simple_loss=0.28, pruned_loss=0.03448, over 7295.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2595, pruned_loss=0.02962, over 1428641.06 frames.], batch size: 25, lr: 2.23e-04
+2022-04-30 17:31:04,989 INFO [train.py:763] (7/8) Epoch 34, batch 1850, loss[loss=0.1552, simple_loss=0.2541, pruned_loss=0.02813, over 7073.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2599, pruned_loss=0.02961, over 1424617.16 frames.], batch size: 18, lr: 2.23e-04
+2022-04-30 17:32:10,326 INFO [train.py:763] (7/8) Epoch 34, batch 1900, loss[loss=0.1671, simple_loss=0.2707, pruned_loss=0.0317, over 7366.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2602, pruned_loss=0.03017, over 1424079.87 frames.], batch size: 23, lr: 2.22e-04
+2022-04-30 17:33:15,849 INFO [train.py:763] (7/8) Epoch 34, batch 1950, loss[loss=0.1369, simple_loss=0.2314, pruned_loss=0.02119, over 7160.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2594, pruned_loss=0.02988, over 1422902.65 frames.], batch size: 18, lr: 2.22e-04
+2022-04-30 17:34:22,087 INFO [train.py:763] (7/8) Epoch 34, batch 2000, loss[loss=0.1789, simple_loss=0.2818, pruned_loss=0.03805, over 6592.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2601, pruned_loss=0.03007, over 1418430.51 frames.], batch size: 38, lr: 2.22e-04
+2022-04-30 17:35:27,887 INFO [train.py:763] (7/8) Epoch 34, batch 2050, loss[loss=0.1477, simple_loss=0.2546, pruned_loss=0.02037, over 7123.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2605, pruned_loss=0.03024, over 1420810.32 frames.], batch size: 21, lr: 2.22e-04
+2022-04-30 17:36:33,100 INFO [train.py:763] (7/8) Epoch 34, batch 2100, loss[loss=0.1616, simple_loss=0.2549, pruned_loss=0.03411, over 7420.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2605, pruned_loss=0.02981, over 1423601.14 frames.], batch size: 21, lr: 2.22e-04
+2022-04-30 17:37:40,132 INFO [train.py:763] (7/8) Epoch 34, batch 2150, loss[loss=0.1467, simple_loss=0.2552, pruned_loss=0.0191, over 6612.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2598, pruned_loss=0.02959, over 1427420.91 frames.], batch size: 38, lr: 2.22e-04
+2022-04-30 17:38:46,202 INFO [train.py:763] (7/8) Epoch 34, batch 2200, loss[loss=0.1382, simple_loss=0.2474, pruned_loss=0.01446, over 7434.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2597, pruned_loss=0.02961, over 1423546.59 frames.], batch size: 20, lr: 2.22e-04
+2022-04-30 17:39:51,392 INFO [train.py:763] (7/8) Epoch 34, batch 2250, loss[loss=0.1392, simple_loss=0.2328, pruned_loss=0.02275, over 7277.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2593, pruned_loss=0.02974, over 1421681.80 frames.], batch size: 18, lr: 2.22e-04
+2022-04-30 17:40:56,565 INFO [train.py:763] (7/8) Epoch 34, batch 2300, loss[loss=0.1536, simple_loss=0.2599, pruned_loss=0.02359, over 7189.00 frames.], tot_loss[loss=0.1595, simple_loss=0.259, pruned_loss=0.02994, over 1418163.42 frames.], batch size: 26, lr: 2.22e-04
+2022-04-30 17:42:01,786 INFO [train.py:763] (7/8) Epoch 34, batch 2350, loss[loss=0.1548, simple_loss=0.2575, pruned_loss=0.02608, over 7087.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2588, pruned_loss=0.02967, over 1416748.92 frames.], batch size: 28, lr: 2.22e-04
+2022-04-30 17:43:08,021 INFO [train.py:763] (7/8) Epoch 34, batch 2400, loss[loss=0.1441, simple_loss=0.236, pruned_loss=0.02614, over 6982.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2585, pruned_loss=0.02919, over 1422084.90 frames.], batch size: 16, lr: 2.22e-04
+2022-04-30 17:44:15,071 INFO [train.py:763] (7/8) Epoch 34, batch 2450, loss[loss=0.1612, simple_loss=0.2517, pruned_loss=0.03538, over 7437.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2583, pruned_loss=0.02897, over 1422255.78 frames.], batch size: 20, lr: 2.22e-04
+2022-04-30 17:45:22,388 INFO [train.py:763] (7/8) Epoch 34, batch 2500, loss[loss=0.1569, simple_loss=0.2585, pruned_loss=0.02761, over 6243.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2576, pruned_loss=0.02904, over 1423942.87 frames.], batch size: 37, lr: 2.22e-04
+2022-04-30 17:46:28,721 INFO [train.py:763] (7/8) Epoch 34, batch 2550, loss[loss=0.1703, simple_loss=0.2716, pruned_loss=0.03445, over 7119.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2582, pruned_loss=0.02915, over 1423326.33 frames.], batch size: 21, lr: 2.22e-04
+2022-04-30 17:47:35,763 INFO [train.py:763] (7/8) Epoch 34, batch 2600, loss[loss=0.1674, simple_loss=0.2746, pruned_loss=0.03017, over 7212.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2586, pruned_loss=0.0294, over 1423939.05 frames.], batch size: 22, lr: 2.22e-04
+2022-04-30 17:48:40,946 INFO [train.py:763] (7/8) Epoch 34, batch 2650, loss[loss=0.1689, simple_loss=0.2771, pruned_loss=0.03036, over 7222.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2594, pruned_loss=0.02986, over 1422352.86 frames.], batch size: 23, lr: 2.22e-04
+2022-04-30 17:49:46,287 INFO [train.py:763] (7/8) Epoch 34, batch 2700, loss[loss=0.1501, simple_loss=0.2505, pruned_loss=0.02484, over 7117.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2593, pruned_loss=0.02991, over 1424827.03 frames.], batch size: 21, lr: 2.22e-04
+2022-04-30 17:50:51,551 INFO [train.py:763] (7/8) Epoch 34, batch 2750, loss[loss=0.1641, simple_loss=0.2608, pruned_loss=0.03366, over 7318.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2594, pruned_loss=0.02983, over 1424287.63 frames.], batch size: 21, lr: 2.22e-04
+2022-04-30 17:51:57,735 INFO [train.py:763] (7/8) Epoch 34, batch 2800, loss[loss=0.1657, simple_loss=0.2654, pruned_loss=0.03299, over 7321.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2596, pruned_loss=0.02991, over 1425533.08 frames.], batch size: 20, lr: 2.22e-04
+2022-04-30 17:53:04,505 INFO [train.py:763] (7/8) Epoch 34, batch 2850, loss[loss=0.1367, simple_loss=0.2351, pruned_loss=0.01916, over 7168.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2603, pruned_loss=0.03013, over 1424074.34 frames.], batch size: 19, lr: 2.22e-04
+2022-04-30 17:54:11,631 INFO [train.py:763] (7/8) Epoch 34, batch 2900, loss[loss=0.1701, simple_loss=0.2739, pruned_loss=0.03311, over 6442.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2606, pruned_loss=0.03013, over 1423218.14 frames.], batch size: 37, lr: 2.22e-04
+2022-04-30 17:55:17,500 INFO [train.py:763] (7/8) Epoch 34, batch 2950, loss[loss=0.1525, simple_loss=0.2418, pruned_loss=0.03153, over 7249.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2612, pruned_loss=0.03057, over 1416599.59 frames.], batch size: 16, lr: 2.22e-04
+2022-04-30 17:56:22,963 INFO [train.py:763] (7/8) Epoch 34, batch 3000, loss[loss=0.1606, simple_loss=0.2646, pruned_loss=0.02833, over 7395.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2607, pruned_loss=0.03027, over 1420548.35 frames.], batch size: 23, lr: 2.22e-04
+2022-04-30 17:56:22,964 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 17:56:38,270 INFO [train.py:792] (7/8) Epoch 34, validation: loss=0.1686, simple_loss=0.2638, pruned_loss=0.03669, over 698248.00 frames.
+2022-04-30 17:57:44,339 INFO [train.py:763] (7/8) Epoch 34, batch 3050, loss[loss=0.1524, simple_loss=0.2528, pruned_loss=0.02599, over 7233.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2611, pruned_loss=0.03054, over 1423146.16 frames.], batch size: 20, lr: 2.22e-04
+2022-04-30 17:58:51,178 INFO [train.py:763] (7/8) Epoch 34, batch 3100, loss[loss=0.1746, simple_loss=0.2806, pruned_loss=0.03427, over 7390.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2605, pruned_loss=0.0302, over 1420598.81 frames.], batch size: 23, lr: 2.22e-04
+2022-04-30 17:59:56,675 INFO [train.py:763] (7/8) Epoch 34, batch 3150, loss[loss=0.1915, simple_loss=0.2898, pruned_loss=0.04657, over 7209.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2596, pruned_loss=0.03028, over 1422848.83 frames.], batch size: 22, lr: 2.22e-04
+2022-04-30 18:01:02,266 INFO [train.py:763] (7/8) Epoch 34, batch 3200, loss[loss=0.1627, simple_loss=0.2664, pruned_loss=0.0295, over 7207.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2607, pruned_loss=0.03025, over 1427600.11 frames.], batch size: 22, lr: 2.22e-04
+2022-04-30 18:02:09,386 INFO [train.py:763] (7/8) Epoch 34, batch 3250, loss[loss=0.1762, simple_loss=0.2588, pruned_loss=0.04684, over 7436.00 frames.], tot_loss[loss=0.1611, simple_loss=0.261, pruned_loss=0.03056, over 1425627.44 frames.], batch size: 20, lr: 2.22e-04
+2022-04-30 18:03:15,767 INFO [train.py:763] (7/8) Epoch 34, batch 3300, loss[loss=0.1294, simple_loss=0.2222, pruned_loss=0.01829, over 7427.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2607, pruned_loss=0.03014, over 1426537.53 frames.], batch size: 20, lr: 2.22e-04
+2022-04-30 18:04:21,132 INFO [train.py:763] (7/8) Epoch 34, batch 3350, loss[loss=0.1421, simple_loss=0.2465, pruned_loss=0.01883, over 7430.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2608, pruned_loss=0.03031, over 1429425.34 frames.], batch size: 20, lr: 2.21e-04
+2022-04-30 18:05:26,514 INFO [train.py:763] (7/8) Epoch 34, batch 3400, loss[loss=0.1431, simple_loss=0.2295, pruned_loss=0.02832, over 7305.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2607, pruned_loss=0.03029, over 1426180.12 frames.], batch size: 18, lr: 2.21e-04
+2022-04-30 18:06:31,943 INFO [train.py:763] (7/8) Epoch 34, batch 3450, loss[loss=0.1291, simple_loss=0.2207, pruned_loss=0.01873, over 6996.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2606, pruned_loss=0.03017, over 1429006.83 frames.], batch size: 16, lr: 2.21e-04
+2022-04-30 18:07:37,456 INFO [train.py:763] (7/8) Epoch 34, batch 3500, loss[loss=0.1495, simple_loss=0.2569, pruned_loss=0.02107, over 7349.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2603, pruned_loss=0.02956, over 1427751.39 frames.], batch size: 22, lr: 2.21e-04
+2022-04-30 18:08:42,517 INFO [train.py:763] (7/8) Epoch 34, batch 3550, loss[loss=0.1495, simple_loss=0.2519, pruned_loss=0.02359, over 6835.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2609, pruned_loss=0.02995, over 1420943.54 frames.], batch size: 31, lr: 2.21e-04
+2022-04-30 18:09:48,199 INFO [train.py:763] (7/8) Epoch 34, batch 3600, loss[loss=0.1498, simple_loss=0.2545, pruned_loss=0.02252, over 7205.00 frames.], tot_loss[loss=0.16, simple_loss=0.2608, pruned_loss=0.02961, over 1419993.28 frames.], batch size: 22, lr: 2.21e-04
+2022-04-30 18:10:55,375 INFO [train.py:763] (7/8) Epoch 34, batch 3650, loss[loss=0.1584, simple_loss=0.2646, pruned_loss=0.02613, over 7313.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2608, pruned_loss=0.0299, over 1421164.52 frames.], batch size: 25, lr: 2.21e-04
+2022-04-30 18:12:01,505 INFO [train.py:763] (7/8) Epoch 34, batch 3700, loss[loss=0.1662, simple_loss=0.2764, pruned_loss=0.02803, over 6303.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2609, pruned_loss=0.02998, over 1420346.71 frames.], batch size: 37, lr: 2.21e-04
+2022-04-30 18:13:06,712 INFO [train.py:763] (7/8) Epoch 34, batch 3750, loss[loss=0.1744, simple_loss=0.2682, pruned_loss=0.04029, over 4494.00 frames.], tot_loss[loss=0.1604, simple_loss=0.261, pruned_loss=0.02984, over 1417217.99 frames.], batch size: 52, lr: 2.21e-04
+2022-04-30 18:14:11,992 INFO [train.py:763] (7/8) Epoch 34, batch 3800, loss[loss=0.1814, simple_loss=0.2838, pruned_loss=0.03951, over 6764.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2603, pruned_loss=0.0295, over 1418159.87 frames.], batch size: 31, lr: 2.21e-04
+2022-04-30 18:15:17,342 INFO [train.py:763] (7/8) Epoch 34, batch 3850, loss[loss=0.1626, simple_loss=0.2634, pruned_loss=0.03093, over 7297.00 frames.], tot_loss[loss=0.1594, simple_loss=0.26, pruned_loss=0.02936, over 1421209.22 frames.], batch size: 24, lr: 2.21e-04
+2022-04-30 18:16:23,817 INFO [train.py:763] (7/8) Epoch 34, batch 3900, loss[loss=0.1428, simple_loss=0.2366, pruned_loss=0.0245, over 6836.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2599, pruned_loss=0.02929, over 1418672.63 frames.], batch size: 15, lr: 2.21e-04
+2022-04-30 18:17:30,974 INFO [train.py:763] (7/8) Epoch 34, batch 3950, loss[loss=0.1661, simple_loss=0.2656, pruned_loss=0.03328, over 7149.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2597, pruned_loss=0.02931, over 1418993.26 frames.], batch size: 17, lr: 2.21e-04
+2022-04-30 18:18:37,960 INFO [train.py:763] (7/8) Epoch 34, batch 4000, loss[loss=0.1449, simple_loss=0.2345, pruned_loss=0.02764, over 7010.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2595, pruned_loss=0.02945, over 1418111.84 frames.], batch size: 16, lr: 2.21e-04
+2022-04-30 18:19:54,720 INFO [train.py:763] (7/8) Epoch 34, batch 4050, loss[loss=0.1628, simple_loss=0.2688, pruned_loss=0.02836, over 6297.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2602, pruned_loss=0.02954, over 1420987.62 frames.], batch size: 37, lr: 2.21e-04
+2022-04-30 18:21:01,780 INFO [train.py:763] (7/8) Epoch 34, batch 4100, loss[loss=0.1624, simple_loss=0.2681, pruned_loss=0.02833, over 7217.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2606, pruned_loss=0.02988, over 1426084.37 frames.], batch size: 21, lr: 2.21e-04
+2022-04-30 18:22:08,658 INFO [train.py:763] (7/8) Epoch 34, batch 4150, loss[loss=0.1595, simple_loss=0.2588, pruned_loss=0.0301, over 7319.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2593, pruned_loss=0.02953, over 1424702.04 frames.], batch size: 21, lr: 2.21e-04
+2022-04-30 18:23:15,047 INFO [train.py:763] (7/8) Epoch 34, batch 4200, loss[loss=0.1591, simple_loss=0.2623, pruned_loss=0.02797, over 7322.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2596, pruned_loss=0.02955, over 1423240.96 frames.], batch size: 21, lr: 2.21e-04
+2022-04-30 18:24:20,545 INFO [train.py:763] (7/8) Epoch 34, batch 4250, loss[loss=0.1428, simple_loss=0.2408, pruned_loss=0.02238, over 7283.00 frames.], tot_loss[loss=0.1585, simple_loss=0.259, pruned_loss=0.02898, over 1427370.42 frames.], batch size: 17, lr: 2.21e-04
+2022-04-30 18:25:25,969 INFO [train.py:763] (7/8) Epoch 34, batch 4300, loss[loss=0.1732, simple_loss=0.2771, pruned_loss=0.03462, over 7162.00 frames.], tot_loss[loss=0.1579, simple_loss=0.258, pruned_loss=0.02893, over 1418396.02 frames.], batch size: 26, lr: 2.21e-04
+2022-04-30 18:26:32,716 INFO [train.py:763] (7/8) Epoch 34, batch 4350, loss[loss=0.1662, simple_loss=0.2758, pruned_loss=0.02832, over 7277.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2596, pruned_loss=0.02961, over 1414685.92 frames.], batch size: 24, lr: 2.21e-04
+2022-04-30 18:27:38,212 INFO [train.py:763] (7/8) Epoch 34, batch 4400, loss[loss=0.137, simple_loss=0.2325, pruned_loss=0.02076, over 7157.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2606, pruned_loss=0.02989, over 1408651.28 frames.], batch size: 19, lr: 2.21e-04
+2022-04-30 18:28:42,665 INFO [train.py:763] (7/8) Epoch 34, batch 4450, loss[loss=0.1616, simple_loss=0.2726, pruned_loss=0.02536, over 6798.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2611, pruned_loss=0.03012, over 1392618.20 frames.], batch size: 31, lr: 2.21e-04
+2022-04-30 18:29:47,251 INFO [train.py:763] (7/8) Epoch 34, batch 4500, loss[loss=0.1621, simple_loss=0.2766, pruned_loss=0.02374, over 7153.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2611, pruned_loss=0.03031, over 1378777.89 frames.], batch size: 26, lr: 2.21e-04
+2022-04-30 18:30:51,781 INFO [train.py:763] (7/8) Epoch 34, batch 4550, loss[loss=0.1894, simple_loss=0.2928, pruned_loss=0.04298, over 5204.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2629, pruned_loss=0.03135, over 1353791.87 frames.], batch size: 53, lr: 2.21e-04
+2022-04-30 18:32:11,398 INFO [train.py:763] (7/8) Epoch 35, batch 0, loss[loss=0.1597, simple_loss=0.2609, pruned_loss=0.02931, over 7342.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2609, pruned_loss=0.02931, over 7342.00 frames.], batch size: 20, lr: 2.18e-04
+2022-04-30 18:33:17,382 INFO [train.py:763] (7/8) Epoch 35, batch 50, loss[loss=0.1623, simple_loss=0.2598, pruned_loss=0.03239, over 7440.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2613, pruned_loss=0.03112, over 316454.21 frames.], batch size: 20, lr: 2.18e-04
+2022-04-30 18:34:22,751 INFO [train.py:763] (7/8) Epoch 35, batch 100, loss[loss=0.1636, simple_loss=0.2621, pruned_loss=0.03253, over 4890.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2604, pruned_loss=0.02963, over 561223.73 frames.], batch size: 52, lr: 2.17e-04
+2022-04-30 18:35:28,410 INFO [train.py:763] (7/8) Epoch 35, batch 150, loss[loss=0.1525, simple_loss=0.2524, pruned_loss=0.02624, over 7247.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2586, pruned_loss=0.02955, over 750569.56 frames.], batch size: 20, lr: 2.17e-04
+2022-04-30 18:36:34,135 INFO [train.py:763] (7/8) Epoch 35, batch 200, loss[loss=0.1658, simple_loss=0.2766, pruned_loss=0.0275, over 7324.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2576, pruned_loss=0.02899, over 900508.25 frames.], batch size: 21, lr: 2.17e-04
+2022-04-30 18:37:50,891 INFO [train.py:763] (7/8) Epoch 35, batch 250, loss[loss=0.157, simple_loss=0.2566, pruned_loss=0.02873, over 7157.00 frames.], tot_loss[loss=0.1571, simple_loss=0.257, pruned_loss=0.02862, over 1020223.06 frames.], batch size: 19, lr: 2.17e-04
+2022-04-30 18:38:58,290 INFO [train.py:763] (7/8) Epoch 35, batch 300, loss[loss=0.2049, simple_loss=0.2982, pruned_loss=0.0558, over 7132.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2576, pruned_loss=0.02891, over 1105437.22 frames.], batch size: 26, lr: 2.17e-04
+2022-04-30 18:40:05,593 INFO [train.py:763] (7/8) Epoch 35, batch 350, loss[loss=0.1847, simple_loss=0.2862, pruned_loss=0.04156, over 6749.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2588, pruned_loss=0.02908, over 1174441.71 frames.], batch size: 31, lr: 2.17e-04
+2022-04-30 18:41:12,810 INFO [train.py:763] (7/8) Epoch 35, batch 400, loss[loss=0.1782, simple_loss=0.2788, pruned_loss=0.03883, over 7217.00 frames.], tot_loss[loss=0.159, simple_loss=0.2598, pruned_loss=0.02914, over 1229719.17 frames.], batch size: 22, lr: 2.17e-04
+2022-04-30 18:42:19,856 INFO [train.py:763] (7/8) Epoch 35, batch 450, loss[loss=0.1669, simple_loss=0.2665, pruned_loss=0.03367, over 7148.00 frames.], tot_loss[loss=0.159, simple_loss=0.2598, pruned_loss=0.02914, over 1277702.38 frames.], batch size: 26, lr: 2.17e-04
+2022-04-30 18:43:25,160 INFO [train.py:763] (7/8) Epoch 35, batch 500, loss[loss=0.165, simple_loss=0.2685, pruned_loss=0.03072, over 7191.00 frames.], tot_loss[loss=0.159, simple_loss=0.2598, pruned_loss=0.02904, over 1309690.71 frames.], batch size: 23, lr: 2.17e-04
+2022-04-30 18:44:30,974 INFO [train.py:763] (7/8) Epoch 35, batch 550, loss[loss=0.1545, simple_loss=0.2549, pruned_loss=0.02699, over 7426.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2608, pruned_loss=0.02925, over 1336154.21 frames.], batch size: 20, lr: 2.17e-04
+2022-04-30 18:45:37,276 INFO [train.py:763] (7/8) Epoch 35, batch 600, loss[loss=0.1747, simple_loss=0.2737, pruned_loss=0.03782, over 7204.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2601, pruned_loss=0.02923, over 1358953.74 frames.], batch size: 23, lr: 2.17e-04
+2022-04-30 18:46:44,947 INFO [train.py:763] (7/8) Epoch 35, batch 650, loss[loss=0.1463, simple_loss=0.2469, pruned_loss=0.02284, over 7156.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2597, pruned_loss=0.02937, over 1372949.29 frames.], batch size: 19, lr: 2.17e-04
+2022-04-30 18:47:52,784 INFO [train.py:763] (7/8) Epoch 35, batch 700, loss[loss=0.1467, simple_loss=0.2421, pruned_loss=0.02569, over 7261.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2588, pruned_loss=0.02914, over 1383857.47 frames.], batch size: 19, lr: 2.17e-04
+2022-04-30 18:48:58,279 INFO [train.py:763] (7/8) Epoch 35, batch 750, loss[loss=0.1459, simple_loss=0.2424, pruned_loss=0.02473, over 7325.00 frames.], tot_loss[loss=0.1589, simple_loss=0.259, pruned_loss=0.0294, over 1384599.02 frames.], batch size: 20, lr: 2.17e-04
+2022-04-30 18:50:03,741 INFO [train.py:763] (7/8) Epoch 35, batch 800, loss[loss=0.1718, simple_loss=0.2802, pruned_loss=0.03169, over 7421.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2595, pruned_loss=0.02937, over 1393414.51 frames.], batch size: 21, lr: 2.17e-04
+2022-04-30 18:51:09,208 INFO [train.py:763] (7/8) Epoch 35, batch 850, loss[loss=0.1673, simple_loss=0.2706, pruned_loss=0.03202, over 7212.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2597, pruned_loss=0.02921, over 1394811.96 frames.], batch size: 21, lr: 2.17e-04
+2022-04-30 18:52:23,437 INFO [train.py:763] (7/8) Epoch 35, batch 900, loss[loss=0.1791, simple_loss=0.285, pruned_loss=0.0366, over 6757.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2603, pruned_loss=0.02966, over 1401897.74 frames.], batch size: 31, lr: 2.17e-04
+2022-04-30 18:53:37,774 INFO [train.py:763] (7/8) Epoch 35, batch 950, loss[loss=0.1282, simple_loss=0.2149, pruned_loss=0.02077, over 6997.00 frames.], tot_loss[loss=0.1596, simple_loss=0.26, pruned_loss=0.02962, over 1404911.75 frames.], batch size: 16, lr: 2.17e-04
+2022-04-30 18:54:42,884 INFO [train.py:763] (7/8) Epoch 35, batch 1000, loss[loss=0.1472, simple_loss=0.2376, pruned_loss=0.02841, over 7271.00 frames.], tot_loss[loss=0.16, simple_loss=0.2603, pruned_loss=0.02982, over 1407326.65 frames.], batch size: 17, lr: 2.17e-04
+2022-04-30 18:55:57,248 INFO [train.py:763] (7/8) Epoch 35, batch 1050, loss[loss=0.1679, simple_loss=0.2605, pruned_loss=0.03769, over 7361.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2599, pruned_loss=0.02986, over 1407100.84 frames.], batch size: 19, lr: 2.17e-04
+2022-04-30 18:57:20,306 INFO [train.py:763] (7/8) Epoch 35, batch 1100, loss[loss=0.1687, simple_loss=0.271, pruned_loss=0.03319, over 7213.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2601, pruned_loss=0.02967, over 1407674.79 frames.], batch size: 22, lr: 2.17e-04
+2022-04-30 18:58:25,992 INFO [train.py:763] (7/8) Epoch 35, batch 1150, loss[loss=0.1678, simple_loss=0.2769, pruned_loss=0.02934, over 7288.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2593, pruned_loss=0.02951, over 1413322.94 frames.], batch size: 24, lr: 2.17e-04
+2022-04-30 18:59:32,082 INFO [train.py:763] (7/8) Epoch 35, batch 1200, loss[loss=0.1396, simple_loss=0.2316, pruned_loss=0.02384, over 7289.00 frames.], tot_loss[loss=0.1606, simple_loss=0.261, pruned_loss=0.03007, over 1408889.46 frames.], batch size: 17, lr: 2.17e-04
+2022-04-30 19:00:55,262 INFO [train.py:763] (7/8) Epoch 35, batch 1250, loss[loss=0.1333, simple_loss=0.22, pruned_loss=0.02328, over 7000.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2599, pruned_loss=0.02977, over 1410666.23 frames.], batch size: 16, lr: 2.17e-04
+2022-04-30 19:02:00,732 INFO [train.py:763] (7/8) Epoch 35, batch 1300, loss[loss=0.1403, simple_loss=0.2325, pruned_loss=0.02401, over 7150.00 frames.], tot_loss[loss=0.1601, simple_loss=0.26, pruned_loss=0.03005, over 1414279.05 frames.], batch size: 17, lr: 2.17e-04
+2022-04-30 19:03:07,774 INFO [train.py:763] (7/8) Epoch 35, batch 1350, loss[loss=0.1586, simple_loss=0.2578, pruned_loss=0.02964, over 7271.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2596, pruned_loss=0.02982, over 1419076.05 frames.], batch size: 19, lr: 2.17e-04
+2022-04-30 19:04:12,921 INFO [train.py:763] (7/8) Epoch 35, batch 1400, loss[loss=0.1465, simple_loss=0.2418, pruned_loss=0.02559, over 7005.00 frames.], tot_loss[loss=0.1598, simple_loss=0.26, pruned_loss=0.0298, over 1417768.52 frames.], batch size: 16, lr: 2.17e-04
+2022-04-30 19:05:18,842 INFO [train.py:763] (7/8) Epoch 35, batch 1450, loss[loss=0.1369, simple_loss=0.2386, pruned_loss=0.01757, over 6796.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2599, pruned_loss=0.02972, over 1414940.42 frames.], batch size: 15, lr: 2.17e-04
+2022-04-30 19:06:24,734 INFO [train.py:763] (7/8) Epoch 35, batch 1500, loss[loss=0.1629, simple_loss=0.2752, pruned_loss=0.02526, over 7319.00 frames.], tot_loss[loss=0.1602, simple_loss=0.261, pruned_loss=0.02975, over 1419011.41 frames.], batch size: 21, lr: 2.17e-04
+2022-04-30 19:07:30,587 INFO [train.py:763] (7/8) Epoch 35, batch 1550, loss[loss=0.1527, simple_loss=0.2537, pruned_loss=0.02587, over 7224.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2599, pruned_loss=0.02925, over 1420132.86 frames.], batch size: 20, lr: 2.17e-04
+2022-04-30 19:08:36,025 INFO [train.py:763] (7/8) Epoch 35, batch 1600, loss[loss=0.1707, simple_loss=0.271, pruned_loss=0.03524, over 7357.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2595, pruned_loss=0.02894, over 1420080.52 frames.], batch size: 23, lr: 2.16e-04
+2022-04-30 19:09:42,632 INFO [train.py:763] (7/8) Epoch 35, batch 1650, loss[loss=0.1536, simple_loss=0.2595, pruned_loss=0.02378, over 7163.00 frames.], tot_loss[loss=0.1584, simple_loss=0.259, pruned_loss=0.0289, over 1421566.91 frames.], batch size: 19, lr: 2.16e-04
+2022-04-30 19:10:49,572 INFO [train.py:763] (7/8) Epoch 35, batch 1700, loss[loss=0.1774, simple_loss=0.2864, pruned_loss=0.03421, over 7282.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2599, pruned_loss=0.02888, over 1424299.05 frames.], batch size: 25, lr: 2.16e-04
+2022-04-30 19:11:56,511 INFO [train.py:763] (7/8) Epoch 35, batch 1750, loss[loss=0.1413, simple_loss=0.2413, pruned_loss=0.0206, over 7268.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2596, pruned_loss=0.0291, over 1420386.20 frames.], batch size: 18, lr: 2.16e-04
+2022-04-30 19:13:03,556 INFO [train.py:763] (7/8) Epoch 35, batch 1800, loss[loss=0.1983, simple_loss=0.2989, pruned_loss=0.04883, over 7216.00 frames.], tot_loss[loss=0.1594, simple_loss=0.26, pruned_loss=0.02936, over 1422219.59 frames.], batch size: 23, lr: 2.16e-04
+2022-04-30 19:14:09,388 INFO [train.py:763] (7/8) Epoch 35, batch 1850, loss[loss=0.1829, simple_loss=0.2853, pruned_loss=0.04022, over 7116.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2599, pruned_loss=0.02926, over 1424520.12 frames.], batch size: 21, lr: 2.16e-04
+2022-04-30 19:15:15,159 INFO [train.py:763] (7/8) Epoch 35, batch 1900, loss[loss=0.1543, simple_loss=0.2665, pruned_loss=0.0211, over 6785.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2591, pruned_loss=0.02897, over 1424408.76 frames.], batch size: 31, lr: 2.16e-04
+2022-04-30 19:16:21,447 INFO [train.py:763] (7/8) Epoch 35, batch 1950, loss[loss=0.1476, simple_loss=0.2537, pruned_loss=0.02075, over 7226.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2584, pruned_loss=0.02915, over 1421615.02 frames.], batch size: 20, lr: 2.16e-04
+2022-04-30 19:17:27,468 INFO [train.py:763] (7/8) Epoch 35, batch 2000, loss[loss=0.1627, simple_loss=0.239, pruned_loss=0.04322, over 6994.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2597, pruned_loss=0.02963, over 1418376.82 frames.], batch size: 16, lr: 2.16e-04
+2022-04-30 19:18:34,505 INFO [train.py:763] (7/8) Epoch 35, batch 2050, loss[loss=0.1947, simple_loss=0.2992, pruned_loss=0.04508, over 7319.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2591, pruned_loss=0.02957, over 1423099.64 frames.], batch size: 21, lr: 2.16e-04
+2022-04-30 19:19:40,359 INFO [train.py:763] (7/8) Epoch 35, batch 2100, loss[loss=0.1558, simple_loss=0.254, pruned_loss=0.02877, over 7414.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2582, pruned_loss=0.02897, over 1422563.80 frames.], batch size: 21, lr: 2.16e-04
+2022-04-30 19:20:47,333 INFO [train.py:763] (7/8) Epoch 35, batch 2150, loss[loss=0.14, simple_loss=0.2368, pruned_loss=0.02163, over 7258.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2579, pruned_loss=0.02893, over 1424913.58 frames.], batch size: 19, lr: 2.16e-04
+2022-04-30 19:21:54,107 INFO [train.py:763] (7/8) Epoch 35, batch 2200, loss[loss=0.1406, simple_loss=0.2335, pruned_loss=0.02382, over 7400.00 frames.], tot_loss[loss=0.158, simple_loss=0.2582, pruned_loss=0.02893, over 1425164.51 frames.], batch size: 18, lr: 2.16e-04
+2022-04-30 19:23:01,311 INFO [train.py:763] (7/8) Epoch 35, batch 2250, loss[loss=0.175, simple_loss=0.2743, pruned_loss=0.03789, over 7335.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2589, pruned_loss=0.02911, over 1422188.39 frames.], batch size: 22, lr: 2.16e-04
+2022-04-30 19:24:07,993 INFO [train.py:763] (7/8) Epoch 35, batch 2300, loss[loss=0.1357, simple_loss=0.2311, pruned_loss=0.02015, over 7137.00 frames.], tot_loss[loss=0.1582, simple_loss=0.258, pruned_loss=0.02916, over 1424413.89 frames.], batch size: 17, lr: 2.16e-04
+2022-04-30 19:25:12,956 INFO [train.py:763] (7/8) Epoch 35, batch 2350, loss[loss=0.1901, simple_loss=0.2913, pruned_loss=0.04441, over 4989.00 frames.], tot_loss[loss=0.16, simple_loss=0.26, pruned_loss=0.02996, over 1422711.37 frames.], batch size: 52, lr: 2.16e-04
+2022-04-30 19:26:18,896 INFO [train.py:763] (7/8) Epoch 35, batch 2400, loss[loss=0.1354, simple_loss=0.2264, pruned_loss=0.02217, over 7420.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2594, pruned_loss=0.02983, over 1425858.44 frames.], batch size: 18, lr: 2.16e-04
+2022-04-30 19:27:24,054 INFO [train.py:763] (7/8) Epoch 35, batch 2450, loss[loss=0.1383, simple_loss=0.232, pruned_loss=0.02231, over 7156.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2591, pruned_loss=0.02978, over 1421704.27 frames.], batch size: 18, lr: 2.16e-04
+2022-04-30 19:28:30,206 INFO [train.py:763] (7/8) Epoch 35, batch 2500, loss[loss=0.1488, simple_loss=0.2492, pruned_loss=0.02422, over 7141.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2593, pruned_loss=0.02988, over 1425397.41 frames.], batch size: 20, lr: 2.16e-04
+2022-04-30 19:29:36,791 INFO [train.py:763] (7/8) Epoch 35, batch 2550, loss[loss=0.1605, simple_loss=0.2563, pruned_loss=0.03236, over 7351.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2598, pruned_loss=0.02998, over 1423678.71 frames.], batch size: 19, lr: 2.16e-04
+2022-04-30 19:30:41,929 INFO [train.py:763] (7/8) Epoch 35, batch 2600, loss[loss=0.1413, simple_loss=0.242, pruned_loss=0.02028, over 7161.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2597, pruned_loss=0.02998, over 1424283.96 frames.], batch size: 19, lr: 2.16e-04
+2022-04-30 19:31:47,702 INFO [train.py:763] (7/8) Epoch 35, batch 2650, loss[loss=0.2098, simple_loss=0.3061, pruned_loss=0.05677, over 5269.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2597, pruned_loss=0.02987, over 1423759.92 frames.], batch size: 52, lr: 2.16e-04
+2022-04-30 19:32:53,220 INFO [train.py:763] (7/8) Epoch 35, batch 2700, loss[loss=0.1734, simple_loss=0.2716, pruned_loss=0.03763, over 7312.00 frames.], tot_loss[loss=0.1593, simple_loss=0.259, pruned_loss=0.02976, over 1423832.90 frames.], batch size: 21, lr: 2.16e-04
+2022-04-30 19:33:59,318 INFO [train.py:763] (7/8) Epoch 35, batch 2750, loss[loss=0.1648, simple_loss=0.2671, pruned_loss=0.03123, over 7116.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2592, pruned_loss=0.02969, over 1426029.77 frames.], batch size: 21, lr: 2.16e-04
+2022-04-30 19:35:05,463 INFO [train.py:763] (7/8) Epoch 35, batch 2800, loss[loss=0.1868, simple_loss=0.2836, pruned_loss=0.04499, over 7196.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2586, pruned_loss=0.02981, over 1428221.18 frames.], batch size: 22, lr: 2.16e-04
+2022-04-30 19:36:12,147 INFO [train.py:763] (7/8) Epoch 35, batch 2850, loss[loss=0.1291, simple_loss=0.2249, pruned_loss=0.01666, over 7285.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2583, pruned_loss=0.0297, over 1429197.83 frames.], batch size: 17, lr: 2.16e-04
+2022-04-30 19:37:18,083 INFO [train.py:763] (7/8) Epoch 35, batch 2900, loss[loss=0.1364, simple_loss=0.2376, pruned_loss=0.01765, over 7261.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2577, pruned_loss=0.02946, over 1427937.07 frames.], batch size: 19, lr: 2.16e-04
+2022-04-30 19:38:23,367 INFO [train.py:763] (7/8) Epoch 35, batch 2950, loss[loss=0.1706, simple_loss=0.2632, pruned_loss=0.03904, over 7175.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2589, pruned_loss=0.02976, over 1425723.45 frames.], batch size: 18, lr: 2.16e-04
+2022-04-30 19:39:28,878 INFO [train.py:763] (7/8) Epoch 35, batch 3000, loss[loss=0.1657, simple_loss=0.2754, pruned_loss=0.02796, over 7162.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2603, pruned_loss=0.0302, over 1422535.70 frames.], batch size: 19, lr: 2.16e-04
+2022-04-30 19:39:28,879 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 19:39:43,929 INFO [train.py:792] (7/8) Epoch 35, validation: loss=0.1681, simple_loss=0.2634, pruned_loss=0.03644, over 698248.00 frames.
+2022-04-30 19:40:49,424 INFO [train.py:763] (7/8) Epoch 35, batch 3050, loss[loss=0.1655, simple_loss=0.2771, pruned_loss=0.02697, over 7275.00 frames.], tot_loss[loss=0.1603, simple_loss=0.26, pruned_loss=0.03028, over 1425177.69 frames.], batch size: 24, lr: 2.16e-04
+2022-04-30 19:41:55,480 INFO [train.py:763] (7/8) Epoch 35, batch 3100, loss[loss=0.1697, simple_loss=0.2679, pruned_loss=0.03576, over 7307.00 frames.], tot_loss[loss=0.16, simple_loss=0.2599, pruned_loss=0.03006, over 1429306.49 frames.], batch size: 25, lr: 2.15e-04
+2022-04-30 19:43:02,585 INFO [train.py:763] (7/8) Epoch 35, batch 3150, loss[loss=0.1875, simple_loss=0.2946, pruned_loss=0.04019, over 7379.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2605, pruned_loss=0.03032, over 1427707.47 frames.], batch size: 23, lr: 2.15e-04
+2022-04-30 19:44:09,434 INFO [train.py:763] (7/8) Epoch 35, batch 3200, loss[loss=0.1525, simple_loss=0.2495, pruned_loss=0.02772, over 7144.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2608, pruned_loss=0.03023, over 1421169.46 frames.], batch size: 17, lr: 2.15e-04
+2022-04-30 19:45:15,574 INFO [train.py:763] (7/8) Epoch 35, batch 3250, loss[loss=0.156, simple_loss=0.2606, pruned_loss=0.02573, over 4917.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2609, pruned_loss=0.03033, over 1418498.14 frames.], batch size: 53, lr: 2.15e-04
+2022-04-30 19:46:21,020 INFO [train.py:763] (7/8) Epoch 35, batch 3300, loss[loss=0.1716, simple_loss=0.2719, pruned_loss=0.03564, over 7202.00 frames.], tot_loss[loss=0.1605, simple_loss=0.261, pruned_loss=0.03003, over 1421984.33 frames.], batch size: 23, lr: 2.15e-04
+2022-04-30 19:47:26,306 INFO [train.py:763] (7/8) Epoch 35, batch 3350, loss[loss=0.1818, simple_loss=0.2782, pruned_loss=0.04265, over 7198.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2611, pruned_loss=0.02997, over 1425717.33 frames.], batch size: 23, lr: 2.15e-04
+2022-04-30 19:48:32,229 INFO [train.py:763] (7/8) Epoch 35, batch 3400, loss[loss=0.1386, simple_loss=0.2369, pruned_loss=0.02018, over 7246.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2605, pruned_loss=0.02995, over 1423849.38 frames.], batch size: 19, lr: 2.15e-04
+2022-04-30 19:49:37,625 INFO [train.py:763] (7/8) Epoch 35, batch 3450, loss[loss=0.1558, simple_loss=0.2415, pruned_loss=0.03506, over 7281.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2601, pruned_loss=0.03004, over 1421741.14 frames.], batch size: 17, lr: 2.15e-04
+2022-04-30 19:50:43,224 INFO [train.py:763] (7/8) Epoch 35, batch 3500, loss[loss=0.1786, simple_loss=0.2768, pruned_loss=0.04023, over 7410.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2596, pruned_loss=0.0301, over 1419163.64 frames.], batch size: 21, lr: 2.15e-04
+2022-04-30 19:51:48,963 INFO [train.py:763] (7/8) Epoch 35, batch 3550, loss[loss=0.1802, simple_loss=0.2901, pruned_loss=0.0351, over 7063.00 frames.], tot_loss[loss=0.1594, simple_loss=0.259, pruned_loss=0.02986, over 1423434.42 frames.], batch size: 28, lr: 2.15e-04
+2022-04-30 19:52:54,524 INFO [train.py:763] (7/8) Epoch 35, batch 3600, loss[loss=0.1878, simple_loss=0.285, pruned_loss=0.04529, over 7294.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2603, pruned_loss=0.03001, over 1422472.83 frames.], batch size: 25, lr: 2.15e-04
+2022-04-30 19:54:00,490 INFO [train.py:763] (7/8) Epoch 35, batch 3650, loss[loss=0.2051, simple_loss=0.3054, pruned_loss=0.05237, over 7282.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2596, pruned_loss=0.02995, over 1424361.49 frames.], batch size: 24, lr: 2.15e-04
+2022-04-30 19:55:05,862 INFO [train.py:763] (7/8) Epoch 35, batch 3700, loss[loss=0.15, simple_loss=0.258, pruned_loss=0.02098, over 7114.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2592, pruned_loss=0.02978, over 1426508.65 frames.], batch size: 21, lr: 2.15e-04
+2022-04-30 19:56:11,436 INFO [train.py:763] (7/8) Epoch 35, batch 3750, loss[loss=0.1776, simple_loss=0.2755, pruned_loss=0.03986, over 7330.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2593, pruned_loss=0.02976, over 1426515.87 frames.], batch size: 22, lr: 2.15e-04
+2022-04-30 19:57:16,653 INFO [train.py:763] (7/8) Epoch 35, batch 3800, loss[loss=0.1417, simple_loss=0.239, pruned_loss=0.02216, over 7357.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2607, pruned_loss=0.03007, over 1428583.89 frames.], batch size: 19, lr: 2.15e-04
+2022-04-30 19:58:21,853 INFO [train.py:763] (7/8) Epoch 35, batch 3850, loss[loss=0.1531, simple_loss=0.2421, pruned_loss=0.03207, over 6987.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2612, pruned_loss=0.03047, over 1424731.74 frames.], batch size: 16, lr: 2.15e-04
+2022-04-30 19:59:27,378 INFO [train.py:763] (7/8) Epoch 35, batch 3900, loss[loss=0.1426, simple_loss=0.2452, pruned_loss=0.01998, over 7196.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2602, pruned_loss=0.03051, over 1426748.31 frames.], batch size: 23, lr: 2.15e-04
+2022-04-30 20:00:33,699 INFO [train.py:763] (7/8) Epoch 35, batch 3950, loss[loss=0.1782, simple_loss=0.2835, pruned_loss=0.03647, over 6767.00 frames.], tot_loss[loss=0.1612, simple_loss=0.261, pruned_loss=0.0307, over 1424598.87 frames.], batch size: 31, lr: 2.15e-04
+2022-04-30 20:01:41,050 INFO [train.py:763] (7/8) Epoch 35, batch 4000, loss[loss=0.169, simple_loss=0.2713, pruned_loss=0.03339, over 7020.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2613, pruned_loss=0.03068, over 1424633.93 frames.], batch size: 28, lr: 2.15e-04
+2022-04-30 20:02:46,179 INFO [train.py:763] (7/8) Epoch 35, batch 4050, loss[loss=0.1644, simple_loss=0.2723, pruned_loss=0.02826, over 7225.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2604, pruned_loss=0.03019, over 1426627.17 frames.], batch size: 21, lr: 2.15e-04
+2022-04-30 20:03:51,653 INFO [train.py:763] (7/8) Epoch 35, batch 4100, loss[loss=0.1417, simple_loss=0.2345, pruned_loss=0.02442, over 7134.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2604, pruned_loss=0.03017, over 1426781.05 frames.], batch size: 17, lr: 2.15e-04
+2022-04-30 20:04:57,471 INFO [train.py:763] (7/8) Epoch 35, batch 4150, loss[loss=0.1573, simple_loss=0.2635, pruned_loss=0.02552, over 7201.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2599, pruned_loss=0.02992, over 1419040.45 frames.], batch size: 23, lr: 2.15e-04
+2022-04-30 20:06:03,156 INFO [train.py:763] (7/8) Epoch 35, batch 4200, loss[loss=0.1532, simple_loss=0.2563, pruned_loss=0.02503, over 7237.00 frames.], tot_loss[loss=0.16, simple_loss=0.2605, pruned_loss=0.02975, over 1417291.16 frames.], batch size: 20, lr: 2.15e-04
+2022-04-30 20:07:09,107 INFO [train.py:763] (7/8) Epoch 35, batch 4250, loss[loss=0.1564, simple_loss=0.2521, pruned_loss=0.03038, over 7202.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2599, pruned_loss=0.02943, over 1415448.30 frames.], batch size: 22, lr: 2.15e-04
+2022-04-30 20:08:14,305 INFO [train.py:763] (7/8) Epoch 35, batch 4300, loss[loss=0.1983, simple_loss=0.2976, pruned_loss=0.04952, over 7199.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2597, pruned_loss=0.02955, over 1412187.11 frames.], batch size: 22, lr: 2.15e-04
+2022-04-30 20:09:20,414 INFO [train.py:763] (7/8) Epoch 35, batch 4350, loss[loss=0.1358, simple_loss=0.24, pruned_loss=0.01583, over 7424.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2584, pruned_loss=0.02902, over 1411396.35 frames.], batch size: 20, lr: 2.15e-04
+2022-04-30 20:10:26,465 INFO [train.py:763] (7/8) Epoch 35, batch 4400, loss[loss=0.162, simple_loss=0.258, pruned_loss=0.03298, over 7358.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2579, pruned_loss=0.02885, over 1416130.92 frames.], batch size: 19, lr: 2.15e-04
+2022-04-30 20:11:33,072 INFO [train.py:763] (7/8) Epoch 35, batch 4450, loss[loss=0.1843, simple_loss=0.289, pruned_loss=0.03981, over 7217.00 frames.], tot_loss[loss=0.158, simple_loss=0.2578, pruned_loss=0.02906, over 1406908.20 frames.], batch size: 21, lr: 2.15e-04
+2022-04-30 20:12:39,704 INFO [train.py:763] (7/8) Epoch 35, batch 4500, loss[loss=0.1459, simple_loss=0.2501, pruned_loss=0.02087, over 7217.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2581, pruned_loss=0.0291, over 1394804.51 frames.], batch size: 21, lr: 2.15e-04
+2022-04-30 20:13:46,221 INFO [train.py:763] (7/8) Epoch 35, batch 4550, loss[loss=0.1279, simple_loss=0.2297, pruned_loss=0.01306, over 7254.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2592, pruned_loss=0.03012, over 1356211.42 frames.], batch size: 19, lr: 2.15e-04
+2022-04-30 20:15:13,854 INFO [train.py:763] (7/8) Epoch 36, batch 0, loss[loss=0.1671, simple_loss=0.2737, pruned_loss=0.03028, over 7351.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2737, pruned_loss=0.03028, over 7351.00 frames.], batch size: 22, lr: 2.12e-04
+2022-04-30 20:16:19,187 INFO [train.py:763] (7/8) Epoch 36, batch 50, loss[loss=0.1531, simple_loss=0.2555, pruned_loss=0.02532, over 7065.00 frames.], tot_loss[loss=0.1583, simple_loss=0.259, pruned_loss=0.02878, over 320955.43 frames.], batch size: 18, lr: 2.12e-04
+2022-04-30 20:17:24,387 INFO [train.py:763] (7/8) Epoch 36, batch 100, loss[loss=0.14, simple_loss=0.2438, pruned_loss=0.01813, over 7330.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2574, pruned_loss=0.02798, over 566735.12 frames.], batch size: 20, lr: 2.12e-04
+2022-04-30 20:18:29,504 INFO [train.py:763] (7/8) Epoch 36, batch 150, loss[loss=0.1573, simple_loss=0.2492, pruned_loss=0.03266, over 7036.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2578, pruned_loss=0.02822, over 754306.88 frames.], batch size: 28, lr: 2.11e-04
+2022-04-30 20:19:34,482 INFO [train.py:763] (7/8) Epoch 36, batch 200, loss[loss=0.1611, simple_loss=0.2702, pruned_loss=0.02605, over 7310.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2613, pruned_loss=0.02922, over 905677.81 frames.], batch size: 21, lr: 2.11e-04
+2022-04-30 20:20:39,738 INFO [train.py:763] (7/8) Epoch 36, batch 250, loss[loss=0.1519, simple_loss=0.2516, pruned_loss=0.02607, over 7261.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2597, pruned_loss=0.02866, over 1017921.33 frames.], batch size: 19, lr: 2.11e-04
+2022-04-30 20:21:45,232 INFO [train.py:763] (7/8) Epoch 36, batch 300, loss[loss=0.1859, simple_loss=0.2896, pruned_loss=0.0411, over 7353.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2597, pruned_loss=0.02888, over 1104581.91 frames.], batch size: 22, lr: 2.11e-04
+2022-04-30 20:22:50,524 INFO [train.py:763] (7/8) Epoch 36, batch 350, loss[loss=0.1624, simple_loss=0.2593, pruned_loss=0.03276, over 7152.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2601, pruned_loss=0.02915, over 1174018.95 frames.], batch size: 18, lr: 2.11e-04
+2022-04-30 20:23:55,941 INFO [train.py:763] (7/8) Epoch 36, batch 400, loss[loss=0.1497, simple_loss=0.2501, pruned_loss=0.0247, over 7230.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2599, pruned_loss=0.02918, over 1232729.17 frames.], batch size: 20, lr: 2.11e-04
+2022-04-30 20:25:01,054 INFO [train.py:763] (7/8) Epoch 36, batch 450, loss[loss=0.1572, simple_loss=0.2726, pruned_loss=0.02094, over 7144.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2598, pruned_loss=0.02892, over 1276979.76 frames.], batch size: 20, lr: 2.11e-04
+2022-04-30 20:26:07,126 INFO [train.py:763] (7/8) Epoch 36, batch 500, loss[loss=0.15, simple_loss=0.2489, pruned_loss=0.0255, over 7231.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2595, pruned_loss=0.0289, over 1306891.61 frames.], batch size: 20, lr: 2.11e-04
+2022-04-30 20:27:14,456 INFO [train.py:763] (7/8) Epoch 36, batch 550, loss[loss=0.1506, simple_loss=0.2476, pruned_loss=0.02682, over 7446.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2603, pruned_loss=0.02954, over 1321936.37 frames.], batch size: 19, lr: 2.11e-04
+2022-04-30 20:28:22,126 INFO [train.py:763] (7/8) Epoch 36, batch 600, loss[loss=0.1429, simple_loss=0.2461, pruned_loss=0.01979, over 7429.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2588, pruned_loss=0.02952, over 1346961.91 frames.], batch size: 20, lr: 2.11e-04
+2022-04-30 20:29:29,947 INFO [train.py:763] (7/8) Epoch 36, batch 650, loss[loss=0.1425, simple_loss=0.222, pruned_loss=0.03149, over 7129.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2575, pruned_loss=0.02932, over 1366635.87 frames.], batch size: 17, lr: 2.11e-04
+2022-04-30 20:30:35,977 INFO [train.py:763] (7/8) Epoch 36, batch 700, loss[loss=0.1509, simple_loss=0.248, pruned_loss=0.02694, over 7230.00 frames.], tot_loss[loss=0.1587, simple_loss=0.258, pruned_loss=0.02969, over 1379937.78 frames.], batch size: 20, lr: 2.11e-04
+2022-04-30 20:31:41,370 INFO [train.py:763] (7/8) Epoch 36, batch 750, loss[loss=0.1562, simple_loss=0.2653, pruned_loss=0.02357, over 7163.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2575, pruned_loss=0.02934, over 1388162.71 frames.], batch size: 19, lr: 2.11e-04
+2022-04-30 20:32:47,565 INFO [train.py:763] (7/8) Epoch 36, batch 800, loss[loss=0.1352, simple_loss=0.2327, pruned_loss=0.01888, over 7408.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2571, pruned_loss=0.02913, over 1399635.70 frames.], batch size: 18, lr: 2.11e-04
+2022-04-30 20:33:53,524 INFO [train.py:763] (7/8) Epoch 36, batch 850, loss[loss=0.1526, simple_loss=0.2634, pruned_loss=0.02089, over 7262.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2581, pruned_loss=0.02922, over 1399347.62 frames.], batch size: 19, lr: 2.11e-04
+2022-04-30 20:34:59,133 INFO [train.py:763] (7/8) Epoch 36, batch 900, loss[loss=0.1516, simple_loss=0.2454, pruned_loss=0.02885, over 7071.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2573, pruned_loss=0.02861, over 1407657.18 frames.], batch size: 18, lr: 2.11e-04
+2022-04-30 20:36:04,444 INFO [train.py:763] (7/8) Epoch 36, batch 950, loss[loss=0.1444, simple_loss=0.24, pruned_loss=0.02442, over 7282.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2577, pruned_loss=0.02883, over 1411042.39 frames.], batch size: 17, lr: 2.11e-04
+2022-04-30 20:37:09,737 INFO [train.py:763] (7/8) Epoch 36, batch 1000, loss[loss=0.1716, simple_loss=0.269, pruned_loss=0.03712, over 6857.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2582, pruned_loss=0.0292, over 1414185.62 frames.], batch size: 31, lr: 2.11e-04
+2022-04-30 20:38:15,280 INFO [train.py:763] (7/8) Epoch 36, batch 1050, loss[loss=0.1677, simple_loss=0.2798, pruned_loss=0.02777, over 7388.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2579, pruned_loss=0.02918, over 1417756.78 frames.], batch size: 23, lr: 2.11e-04
+2022-04-30 20:39:20,512 INFO [train.py:763] (7/8) Epoch 36, batch 1100, loss[loss=0.1488, simple_loss=0.2534, pruned_loss=0.02212, over 7212.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2582, pruned_loss=0.02921, over 1418771.87 frames.], batch size: 21, lr: 2.11e-04
+2022-04-30 20:40:26,430 INFO [train.py:763] (7/8) Epoch 36, batch 1150, loss[loss=0.1778, simple_loss=0.2729, pruned_loss=0.04132, over 5302.00 frames.], tot_loss[loss=0.1579, simple_loss=0.258, pruned_loss=0.02887, over 1418170.30 frames.], batch size: 52, lr: 2.11e-04
+2022-04-30 20:41:32,761 INFO [train.py:763] (7/8) Epoch 36, batch 1200, loss[loss=0.1566, simple_loss=0.2636, pruned_loss=0.02484, over 7144.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2586, pruned_loss=0.02881, over 1420332.88 frames.], batch size: 20, lr: 2.11e-04
+2022-04-30 20:42:37,816 INFO [train.py:763] (7/8) Epoch 36, batch 1250, loss[loss=0.1735, simple_loss=0.269, pruned_loss=0.03895, over 7183.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2595, pruned_loss=0.02908, over 1420249.49 frames.], batch size: 22, lr: 2.11e-04
+2022-04-30 20:43:42,989 INFO [train.py:763] (7/8) Epoch 36, batch 1300, loss[loss=0.1351, simple_loss=0.2299, pruned_loss=0.02015, over 7141.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2593, pruned_loss=0.02911, over 1422387.62 frames.], batch size: 17, lr: 2.11e-04
+2022-04-30 20:44:48,216 INFO [train.py:763] (7/8) Epoch 36, batch 1350, loss[loss=0.1391, simple_loss=0.2332, pruned_loss=0.02254, over 7070.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2592, pruned_loss=0.02928, over 1418416.55 frames.], batch size: 18, lr: 2.11e-04
+2022-04-30 20:45:54,988 INFO [train.py:763] (7/8) Epoch 36, batch 1400, loss[loss=0.1528, simple_loss=0.2494, pruned_loss=0.02808, over 6980.00 frames.], tot_loss[loss=0.159, simple_loss=0.2592, pruned_loss=0.02943, over 1418379.62 frames.], batch size: 16, lr: 2.11e-04
+2022-04-30 20:47:00,124 INFO [train.py:763] (7/8) Epoch 36, batch 1450, loss[loss=0.1848, simple_loss=0.2907, pruned_loss=0.03945, over 7304.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2595, pruned_loss=0.02988, over 1420023.19 frames.], batch size: 24, lr: 2.11e-04
+2022-04-30 20:48:05,230 INFO [train.py:763] (7/8) Epoch 36, batch 1500, loss[loss=0.1536, simple_loss=0.2566, pruned_loss=0.02535, over 7292.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2597, pruned_loss=0.03001, over 1416522.41 frames.], batch size: 24, lr: 2.11e-04
+2022-04-30 20:49:10,957 INFO [train.py:763] (7/8) Epoch 36, batch 1550, loss[loss=0.1777, simple_loss=0.2768, pruned_loss=0.03933, over 6891.00 frames.], tot_loss[loss=0.16, simple_loss=0.2596, pruned_loss=0.03023, over 1411157.51 frames.], batch size: 31, lr: 2.11e-04
+2022-04-30 20:50:16,855 INFO [train.py:763] (7/8) Epoch 36, batch 1600, loss[loss=0.1867, simple_loss=0.2827, pruned_loss=0.04533, over 7385.00 frames.], tot_loss[loss=0.159, simple_loss=0.2585, pruned_loss=0.02982, over 1411781.46 frames.], batch size: 23, lr: 2.11e-04
+2022-04-30 20:51:24,019 INFO [train.py:763] (7/8) Epoch 36, batch 1650, loss[loss=0.18, simple_loss=0.2736, pruned_loss=0.0432, over 7196.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2588, pruned_loss=0.02984, over 1415259.28 frames.], batch size: 22, lr: 2.11e-04
+2022-04-30 20:52:38,237 INFO [train.py:763] (7/8) Epoch 36, batch 1700, loss[loss=0.1601, simple_loss=0.2551, pruned_loss=0.0325, over 7160.00 frames.], tot_loss[loss=0.16, simple_loss=0.2598, pruned_loss=0.03008, over 1414085.88 frames.], batch size: 19, lr: 2.11e-04
+2022-04-30 20:53:43,590 INFO [train.py:763] (7/8) Epoch 36, batch 1750, loss[loss=0.139, simple_loss=0.2374, pruned_loss=0.02027, over 7355.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2594, pruned_loss=0.02975, over 1408978.19 frames.], batch size: 19, lr: 2.10e-04
+2022-04-30 20:54:48,738 INFO [train.py:763] (7/8) Epoch 36, batch 1800, loss[loss=0.1772, simple_loss=0.2801, pruned_loss=0.03716, over 7300.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2605, pruned_loss=0.02979, over 1410755.59 frames.], batch size: 24, lr: 2.10e-04
+2022-04-30 20:55:54,043 INFO [train.py:763] (7/8) Epoch 36, batch 1850, loss[loss=0.1506, simple_loss=0.2507, pruned_loss=0.02528, over 7258.00 frames.], tot_loss[loss=0.159, simple_loss=0.2593, pruned_loss=0.02938, over 1410615.69 frames.], batch size: 19, lr: 2.10e-04
+2022-04-30 20:56:59,708 INFO [train.py:763] (7/8) Epoch 36, batch 1900, loss[loss=0.1826, simple_loss=0.2905, pruned_loss=0.03736, over 6744.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2602, pruned_loss=0.02951, over 1416616.64 frames.], batch size: 31, lr: 2.10e-04
+2022-04-30 20:58:07,283 INFO [train.py:763] (7/8) Epoch 36, batch 1950, loss[loss=0.1522, simple_loss=0.258, pruned_loss=0.0232, over 7223.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2596, pruned_loss=0.02928, over 1420652.20 frames.], batch size: 21, lr: 2.10e-04
+2022-04-30 20:59:14,682 INFO [train.py:763] (7/8) Epoch 36, batch 2000, loss[loss=0.1752, simple_loss=0.2845, pruned_loss=0.03299, over 7414.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2595, pruned_loss=0.0294, over 1417273.85 frames.], batch size: 21, lr: 2.10e-04
+2022-04-30 21:00:22,243 INFO [train.py:763] (7/8) Epoch 36, batch 2050, loss[loss=0.16, simple_loss=0.2678, pruned_loss=0.02612, over 7226.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2591, pruned_loss=0.02931, over 1420300.01 frames.], batch size: 20, lr: 2.10e-04
+2022-04-30 21:01:28,539 INFO [train.py:763] (7/8) Epoch 36, batch 2100, loss[loss=0.1606, simple_loss=0.2638, pruned_loss=0.02871, over 7147.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2598, pruned_loss=0.02969, over 1420269.03 frames.], batch size: 20, lr: 2.10e-04
+2022-04-30 21:02:35,097 INFO [train.py:763] (7/8) Epoch 36, batch 2150, loss[loss=0.1619, simple_loss=0.2628, pruned_loss=0.03051, over 7415.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2603, pruned_loss=0.02977, over 1417081.34 frames.], batch size: 21, lr: 2.10e-04
+2022-04-30 21:03:42,390 INFO [train.py:763] (7/8) Epoch 36, batch 2200, loss[loss=0.1434, simple_loss=0.241, pruned_loss=0.02284, over 7245.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2588, pruned_loss=0.02931, over 1419889.35 frames.], batch size: 19, lr: 2.10e-04
+2022-04-30 21:04:49,060 INFO [train.py:763] (7/8) Epoch 36, batch 2250, loss[loss=0.1622, simple_loss=0.2688, pruned_loss=0.02779, over 7144.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2599, pruned_loss=0.02976, over 1421050.56 frames.], batch size: 20, lr: 2.10e-04
+2022-04-30 21:05:54,019 INFO [train.py:763] (7/8) Epoch 36, batch 2300, loss[loss=0.1739, simple_loss=0.2818, pruned_loss=0.033, over 7177.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2606, pruned_loss=0.03008, over 1420058.76 frames.], batch size: 23, lr: 2.10e-04
+2022-04-30 21:06:59,119 INFO [train.py:763] (7/8) Epoch 36, batch 2350, loss[loss=0.1383, simple_loss=0.2261, pruned_loss=0.02528, over 7275.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2613, pruned_loss=0.03053, over 1413701.15 frames.], batch size: 17, lr: 2.10e-04
+2022-04-30 21:08:06,487 INFO [train.py:763] (7/8) Epoch 36, batch 2400, loss[loss=0.1758, simple_loss=0.2747, pruned_loss=0.03839, over 7304.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2601, pruned_loss=0.02988, over 1420991.69 frames.], batch size: 25, lr: 2.10e-04
+2022-04-30 21:09:12,598 INFO [train.py:763] (7/8) Epoch 36, batch 2450, loss[loss=0.1483, simple_loss=0.2472, pruned_loss=0.02471, over 7143.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2592, pruned_loss=0.02979, over 1425607.65 frames.], batch size: 26, lr: 2.10e-04
+2022-04-30 21:10:36,037 INFO [train.py:763] (7/8) Epoch 36, batch 2500, loss[loss=0.1433, simple_loss=0.2449, pruned_loss=0.0209, over 7158.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2586, pruned_loss=0.02935, over 1427960.61 frames.], batch size: 19, lr: 2.10e-04
+2022-04-30 21:11:41,260 INFO [train.py:763] (7/8) Epoch 36, batch 2550, loss[loss=0.2032, simple_loss=0.285, pruned_loss=0.06072, over 7290.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2588, pruned_loss=0.02927, over 1428530.29 frames.], batch size: 24, lr: 2.10e-04
+2022-04-30 21:12:55,230 INFO [train.py:763] (7/8) Epoch 36, batch 2600, loss[loss=0.1647, simple_loss=0.2598, pruned_loss=0.03479, over 7237.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2597, pruned_loss=0.02966, over 1424618.04 frames.], batch size: 16, lr: 2.10e-04
+2022-04-30 21:14:18,390 INFO [train.py:763] (7/8) Epoch 36, batch 2650, loss[loss=0.1889, simple_loss=0.2776, pruned_loss=0.0501, over 7196.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2603, pruned_loss=0.03019, over 1428217.49 frames.], batch size: 22, lr: 2.10e-04
+2022-04-30 21:15:32,428 INFO [train.py:763] (7/8) Epoch 36, batch 2700, loss[loss=0.1512, simple_loss=0.2594, pruned_loss=0.02146, over 6413.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2599, pruned_loss=0.03011, over 1423885.97 frames.], batch size: 37, lr: 2.10e-04
+2022-04-30 21:16:46,246 INFO [train.py:763] (7/8) Epoch 36, batch 2750, loss[loss=0.1624, simple_loss=0.2517, pruned_loss=0.03655, over 5036.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2602, pruned_loss=0.02971, over 1424335.44 frames.], batch size: 52, lr: 2.10e-04
+2022-04-30 21:17:52,041 INFO [train.py:763] (7/8) Epoch 36, batch 2800, loss[loss=0.134, simple_loss=0.2225, pruned_loss=0.02272, over 7273.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2592, pruned_loss=0.02951, over 1429459.31 frames.], batch size: 18, lr: 2.10e-04
+2022-04-30 21:19:07,522 INFO [train.py:763] (7/8) Epoch 36, batch 2850, loss[loss=0.1467, simple_loss=0.2526, pruned_loss=0.02045, over 6168.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2592, pruned_loss=0.02973, over 1428078.55 frames.], batch size: 37, lr: 2.10e-04
+2022-04-30 21:20:13,042 INFO [train.py:763] (7/8) Epoch 36, batch 2900, loss[loss=0.1504, simple_loss=0.2335, pruned_loss=0.03364, over 7013.00 frames.], tot_loss[loss=0.16, simple_loss=0.26, pruned_loss=0.02997, over 1429217.61 frames.], batch size: 16, lr: 2.10e-04
+2022-04-30 21:21:20,801 INFO [train.py:763] (7/8) Epoch 36, batch 2950, loss[loss=0.1669, simple_loss=0.2591, pruned_loss=0.03735, over 7423.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2596, pruned_loss=0.02981, over 1426219.89 frames.], batch size: 20, lr: 2.10e-04
+2022-04-30 21:22:27,906 INFO [train.py:763] (7/8) Epoch 36, batch 3000, loss[loss=0.1538, simple_loss=0.257, pruned_loss=0.02528, over 7215.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2603, pruned_loss=0.03031, over 1422593.98 frames.], batch size: 21, lr: 2.10e-04
+2022-04-30 21:22:27,906 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 21:22:43,065 INFO [train.py:792] (7/8) Epoch 36, validation: loss=0.1683, simple_loss=0.2628, pruned_loss=0.03692, over 698248.00 frames.
+2022-04-30 21:23:48,286 INFO [train.py:763] (7/8) Epoch 36, batch 3050, loss[loss=0.1358, simple_loss=0.2302, pruned_loss=0.02069, over 7249.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2598, pruned_loss=0.03024, over 1420916.46 frames.], batch size: 16, lr: 2.10e-04
+2022-04-30 21:24:54,047 INFO [train.py:763] (7/8) Epoch 36, batch 3100, loss[loss=0.1596, simple_loss=0.2568, pruned_loss=0.03121, over 7064.00 frames.], tot_loss[loss=0.159, simple_loss=0.259, pruned_loss=0.02954, over 1417968.73 frames.], batch size: 18, lr: 2.10e-04
+2022-04-30 21:26:01,274 INFO [train.py:763] (7/8) Epoch 36, batch 3150, loss[loss=0.135, simple_loss=0.2255, pruned_loss=0.02226, over 6998.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2582, pruned_loss=0.02919, over 1417635.53 frames.], batch size: 16, lr: 2.10e-04
+2022-04-30 21:27:07,754 INFO [train.py:763] (7/8) Epoch 36, batch 3200, loss[loss=0.1841, simple_loss=0.2738, pruned_loss=0.04719, over 5332.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2585, pruned_loss=0.02953, over 1417795.47 frames.], batch size: 52, lr: 2.10e-04
+2022-04-30 21:28:14,755 INFO [train.py:763] (7/8) Epoch 36, batch 3250, loss[loss=0.1738, simple_loss=0.2778, pruned_loss=0.03487, over 7203.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2592, pruned_loss=0.02985, over 1416941.11 frames.], batch size: 22, lr: 2.10e-04
+2022-04-30 21:29:20,192 INFO [train.py:763] (7/8) Epoch 36, batch 3300, loss[loss=0.1485, simple_loss=0.2477, pruned_loss=0.02467, over 7423.00 frames.], tot_loss[loss=0.16, simple_loss=0.2598, pruned_loss=0.03005, over 1415355.73 frames.], batch size: 21, lr: 2.10e-04
+2022-04-30 21:30:25,151 INFO [train.py:763] (7/8) Epoch 36, batch 3350, loss[loss=0.1675, simple_loss=0.2613, pruned_loss=0.03689, over 7380.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2616, pruned_loss=0.0306, over 1411752.29 frames.], batch size: 23, lr: 2.09e-04
+2022-04-30 21:31:31,796 INFO [train.py:763] (7/8) Epoch 36, batch 3400, loss[loss=0.1135, simple_loss=0.2076, pruned_loss=0.009683, over 7142.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2606, pruned_loss=0.03037, over 1416031.68 frames.], batch size: 17, lr: 2.09e-04
+2022-04-30 21:32:37,227 INFO [train.py:763] (7/8) Epoch 36, batch 3450, loss[loss=0.1355, simple_loss=0.2226, pruned_loss=0.02421, over 7289.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2599, pruned_loss=0.0299, over 1418668.96 frames.], batch size: 17, lr: 2.09e-04
+2022-04-30 21:33:42,456 INFO [train.py:763] (7/8) Epoch 36, batch 3500, loss[loss=0.1494, simple_loss=0.2533, pruned_loss=0.02275, over 7351.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2596, pruned_loss=0.0297, over 1416429.38 frames.], batch size: 19, lr: 2.09e-04
+2022-04-30 21:34:47,635 INFO [train.py:763] (7/8) Epoch 36, batch 3550, loss[loss=0.1359, simple_loss=0.2316, pruned_loss=0.02007, over 6831.00 frames.], tot_loss[loss=0.159, simple_loss=0.2594, pruned_loss=0.02933, over 1413944.78 frames.], batch size: 15, lr: 2.09e-04
+2022-04-30 21:35:54,825 INFO [train.py:763] (7/8) Epoch 36, batch 3600, loss[loss=0.1564, simple_loss=0.2468, pruned_loss=0.03297, over 6977.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2586, pruned_loss=0.0292, over 1420729.56 frames.], batch size: 16, lr: 2.09e-04
+2022-04-30 21:37:01,781 INFO [train.py:763] (7/8) Epoch 36, batch 3650, loss[loss=0.1622, simple_loss=0.2607, pruned_loss=0.03183, over 7168.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2579, pruned_loss=0.02884, over 1422530.74 frames.], batch size: 19, lr: 2.09e-04
+2022-04-30 21:38:08,991 INFO [train.py:763] (7/8) Epoch 36, batch 3700, loss[loss=0.1392, simple_loss=0.2382, pruned_loss=0.02011, over 7235.00 frames.], tot_loss[loss=0.158, simple_loss=0.2577, pruned_loss=0.02918, over 1426162.96 frames.], batch size: 20, lr: 2.09e-04
+2022-04-30 21:39:14,218 INFO [train.py:763] (7/8) Epoch 36, batch 3750, loss[loss=0.1929, simple_loss=0.2935, pruned_loss=0.04617, over 7265.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2588, pruned_loss=0.02932, over 1422664.82 frames.], batch size: 24, lr: 2.09e-04
+2022-04-30 21:40:19,613 INFO [train.py:763] (7/8) Epoch 36, batch 3800, loss[loss=0.137, simple_loss=0.2279, pruned_loss=0.02309, over 7282.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2575, pruned_loss=0.0288, over 1424418.86 frames.], batch size: 17, lr: 2.09e-04
+2022-04-30 21:41:25,011 INFO [train.py:763] (7/8) Epoch 36, batch 3850, loss[loss=0.1894, simple_loss=0.2907, pruned_loss=0.0441, over 5271.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2578, pruned_loss=0.02919, over 1424010.81 frames.], batch size: 53, lr: 2.09e-04
+2022-04-30 21:42:30,264 INFO [train.py:763] (7/8) Epoch 36, batch 3900, loss[loss=0.14, simple_loss=0.2448, pruned_loss=0.01757, over 7342.00 frames.], tot_loss[loss=0.158, simple_loss=0.2576, pruned_loss=0.0292, over 1426268.45 frames.], batch size: 20, lr: 2.09e-04
+2022-04-30 21:43:35,828 INFO [train.py:763] (7/8) Epoch 36, batch 3950, loss[loss=0.1612, simple_loss=0.2522, pruned_loss=0.03512, over 7282.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2583, pruned_loss=0.02965, over 1427638.84 frames.], batch size: 18, lr: 2.09e-04
+2022-04-30 21:44:41,526 INFO [train.py:763] (7/8) Epoch 36, batch 4000, loss[loss=0.148, simple_loss=0.2698, pruned_loss=0.01307, over 7151.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2582, pruned_loss=0.02926, over 1428408.97 frames.], batch size: 20, lr: 2.09e-04
+2022-04-30 21:45:48,408 INFO [train.py:763] (7/8) Epoch 36, batch 4050, loss[loss=0.1577, simple_loss=0.2699, pruned_loss=0.02276, over 7141.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2581, pruned_loss=0.02905, over 1427480.91 frames.], batch size: 20, lr: 2.09e-04
+2022-04-30 21:46:54,637 INFO [train.py:763] (7/8) Epoch 36, batch 4100, loss[loss=0.1601, simple_loss=0.2569, pruned_loss=0.0317, over 7297.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2589, pruned_loss=0.02911, over 1424896.14 frames.], batch size: 25, lr: 2.09e-04
+2022-04-30 21:48:00,289 INFO [train.py:763] (7/8) Epoch 36, batch 4150, loss[loss=0.1682, simple_loss=0.2794, pruned_loss=0.02849, over 7223.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2584, pruned_loss=0.02889, over 1426509.91 frames.], batch size: 21, lr: 2.09e-04
+2022-04-30 21:49:06,733 INFO [train.py:763] (7/8) Epoch 36, batch 4200, loss[loss=0.1711, simple_loss=0.2709, pruned_loss=0.03563, over 7325.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2583, pruned_loss=0.029, over 1428566.91 frames.], batch size: 22, lr: 2.09e-04
+2022-04-30 21:50:13,185 INFO [train.py:763] (7/8) Epoch 36, batch 4250, loss[loss=0.1621, simple_loss=0.2663, pruned_loss=0.02898, over 7195.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2585, pruned_loss=0.02897, over 1431340.96 frames.], batch size: 22, lr: 2.09e-04
+2022-04-30 21:51:18,755 INFO [train.py:763] (7/8) Epoch 36, batch 4300, loss[loss=0.1463, simple_loss=0.2509, pruned_loss=0.02086, over 7323.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2586, pruned_loss=0.02927, over 1425833.88 frames.], batch size: 20, lr: 2.09e-04
+2022-04-30 21:52:24,312 INFO [train.py:763] (7/8) Epoch 36, batch 4350, loss[loss=0.1704, simple_loss=0.2794, pruned_loss=0.03072, over 7325.00 frames.], tot_loss[loss=0.159, simple_loss=0.259, pruned_loss=0.02946, over 1430509.07 frames.], batch size: 22, lr: 2.09e-04
+2022-04-30 21:53:30,955 INFO [train.py:763] (7/8) Epoch 36, batch 4400, loss[loss=0.1668, simple_loss=0.2733, pruned_loss=0.0302, over 7337.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2596, pruned_loss=0.0296, over 1422410.73 frames.], batch size: 22, lr: 2.09e-04
+2022-04-30 21:54:38,275 INFO [train.py:763] (7/8) Epoch 36, batch 4450, loss[loss=0.1462, simple_loss=0.2373, pruned_loss=0.02756, over 7419.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2599, pruned_loss=0.02969, over 1420900.62 frames.], batch size: 18, lr: 2.09e-04
+2022-04-30 21:55:43,453 INFO [train.py:763] (7/8) Epoch 36, batch 4500, loss[loss=0.1421, simple_loss=0.2324, pruned_loss=0.02593, over 7270.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2593, pruned_loss=0.02972, over 1416054.17 frames.], batch size: 18, lr: 2.09e-04
+2022-04-30 21:56:48,000 INFO [train.py:763] (7/8) Epoch 36, batch 4550, loss[loss=0.1383, simple_loss=0.2447, pruned_loss=0.01592, over 6367.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2606, pruned_loss=0.03024, over 1392200.17 frames.], batch size: 38, lr: 2.09e-04
+2022-04-30 21:58:07,240 INFO [train.py:763] (7/8) Epoch 37, batch 0, loss[loss=0.1451, simple_loss=0.2469, pruned_loss=0.02166, over 7370.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2469, pruned_loss=0.02166, over 7370.00 frames.], batch size: 19, lr: 2.06e-04
+2022-04-30 21:59:13,887 INFO [train.py:763] (7/8) Epoch 37, batch 50, loss[loss=0.1573, simple_loss=0.2608, pruned_loss=0.02687, over 6497.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2538, pruned_loss=0.02691, over 322170.77 frames.], batch size: 37, lr: 2.06e-04
+2022-04-30 22:00:20,511 INFO [train.py:763] (7/8) Epoch 37, batch 100, loss[loss=0.1507, simple_loss=0.2504, pruned_loss=0.02549, over 7268.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2572, pruned_loss=0.02804, over 559924.71 frames.], batch size: 19, lr: 2.06e-04
+2022-04-30 22:01:27,287 INFO [train.py:763] (7/8) Epoch 37, batch 150, loss[loss=0.1577, simple_loss=0.2541, pruned_loss=0.03061, over 7381.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2578, pruned_loss=0.0284, over 747991.84 frames.], batch size: 23, lr: 2.06e-04
+2022-04-30 22:02:34,181 INFO [train.py:763] (7/8) Epoch 37, batch 200, loss[loss=0.1435, simple_loss=0.2525, pruned_loss=0.01726, over 7404.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2574, pruned_loss=0.02838, over 897019.99 frames.], batch size: 21, lr: 2.06e-04
+2022-04-30 22:03:39,638 INFO [train.py:763] (7/8) Epoch 37, batch 250, loss[loss=0.144, simple_loss=0.2421, pruned_loss=0.02299, over 7364.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2567, pruned_loss=0.02839, over 1015326.60 frames.], batch size: 19, lr: 2.06e-04
+2022-04-30 22:04:45,214 INFO [train.py:763] (7/8) Epoch 37, batch 300, loss[loss=0.1547, simple_loss=0.2644, pruned_loss=0.02253, over 7238.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2576, pruned_loss=0.02869, over 1105619.86 frames.], batch size: 20, lr: 2.06e-04
+2022-04-30 22:05:51,663 INFO [train.py:763] (7/8) Epoch 37, batch 350, loss[loss=0.1561, simple_loss=0.2589, pruned_loss=0.02662, over 7258.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2586, pruned_loss=0.02923, over 1172608.92 frames.], batch size: 19, lr: 2.06e-04
+2022-04-30 22:06:57,568 INFO [train.py:763] (7/8) Epoch 37, batch 400, loss[loss=0.1396, simple_loss=0.2316, pruned_loss=0.02379, over 7271.00 frames.], tot_loss[loss=0.158, simple_loss=0.2584, pruned_loss=0.02884, over 1232269.20 frames.], batch size: 17, lr: 2.06e-04
+2022-04-30 22:08:03,022 INFO [train.py:763] (7/8) Epoch 37, batch 450, loss[loss=0.1594, simple_loss=0.2692, pruned_loss=0.02479, over 7119.00 frames.], tot_loss[loss=0.157, simple_loss=0.257, pruned_loss=0.02846, over 1275261.77 frames.], batch size: 21, lr: 2.06e-04
+2022-04-30 22:09:09,226 INFO [train.py:763] (7/8) Epoch 37, batch 500, loss[loss=0.1588, simple_loss=0.257, pruned_loss=0.03029, over 7277.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2566, pruned_loss=0.02833, over 1311324.03 frames.], batch size: 18, lr: 2.06e-04
+2022-04-30 22:10:16,152 INFO [train.py:763] (7/8) Epoch 37, batch 550, loss[loss=0.1485, simple_loss=0.2458, pruned_loss=0.02559, over 7322.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2575, pruned_loss=0.02871, over 1335207.26 frames.], batch size: 20, lr: 2.06e-04
+2022-04-30 22:11:23,010 INFO [train.py:763] (7/8) Epoch 37, batch 600, loss[loss=0.1534, simple_loss=0.2533, pruned_loss=0.02679, over 7362.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2586, pruned_loss=0.02886, over 1356548.45 frames.], batch size: 23, lr: 2.06e-04
+2022-04-30 22:12:30,682 INFO [train.py:763] (7/8) Epoch 37, batch 650, loss[loss=0.1502, simple_loss=0.2562, pruned_loss=0.02208, over 7356.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2595, pruned_loss=0.02934, over 1373220.71 frames.], batch size: 22, lr: 2.06e-04
+2022-04-30 22:13:38,208 INFO [train.py:763] (7/8) Epoch 37, batch 700, loss[loss=0.1394, simple_loss=0.2379, pruned_loss=0.02043, over 7166.00 frames.], tot_loss[loss=0.159, simple_loss=0.2595, pruned_loss=0.02923, over 1386233.69 frames.], batch size: 18, lr: 2.06e-04
+2022-04-30 22:14:45,777 INFO [train.py:763] (7/8) Epoch 37, batch 750, loss[loss=0.1603, simple_loss=0.272, pruned_loss=0.02425, over 7374.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2598, pruned_loss=0.02903, over 1400828.17 frames.], batch size: 23, lr: 2.05e-04
+2022-04-30 22:15:51,462 INFO [train.py:763] (7/8) Epoch 37, batch 800, loss[loss=0.141, simple_loss=0.2318, pruned_loss=0.02508, over 7394.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2599, pruned_loss=0.02884, over 1409572.28 frames.], batch size: 18, lr: 2.05e-04
+2022-04-30 22:16:56,747 INFO [train.py:763] (7/8) Epoch 37, batch 850, loss[loss=0.1495, simple_loss=0.248, pruned_loss=0.0255, over 7354.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2595, pruned_loss=0.02865, over 1411449.25 frames.], batch size: 19, lr: 2.05e-04
+2022-04-30 22:18:02,426 INFO [train.py:763] (7/8) Epoch 37, batch 900, loss[loss=0.2297, simple_loss=0.3146, pruned_loss=0.07242, over 7270.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2586, pruned_loss=0.02845, over 1413523.07 frames.], batch size: 24, lr: 2.05e-04
+2022-04-30 22:19:07,703 INFO [train.py:763] (7/8) Epoch 37, batch 950, loss[loss=0.1476, simple_loss=0.2468, pruned_loss=0.0242, over 7254.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2595, pruned_loss=0.02867, over 1418725.90 frames.], batch size: 19, lr: 2.05e-04
+2022-04-30 22:20:12,883 INFO [train.py:763] (7/8) Epoch 37, batch 1000, loss[loss=0.1784, simple_loss=0.2688, pruned_loss=0.04403, over 7200.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2602, pruned_loss=0.02921, over 1421818.49 frames.], batch size: 22, lr: 2.05e-04
+2022-04-30 22:21:18,165 INFO [train.py:763] (7/8) Epoch 37, batch 1050, loss[loss=0.1529, simple_loss=0.2549, pruned_loss=0.02548, over 7339.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2601, pruned_loss=0.02919, over 1421840.60 frames.], batch size: 20, lr: 2.05e-04
+2022-04-30 22:22:25,782 INFO [train.py:763] (7/8) Epoch 37, batch 1100, loss[loss=0.1524, simple_loss=0.2482, pruned_loss=0.02827, over 7175.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2604, pruned_loss=0.02928, over 1424504.53 frames.], batch size: 16, lr: 2.05e-04
+2022-04-30 22:23:31,650 INFO [train.py:763] (7/8) Epoch 37, batch 1150, loss[loss=0.1489, simple_loss=0.2377, pruned_loss=0.03002, over 7285.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2608, pruned_loss=0.02917, over 1421407.04 frames.], batch size: 18, lr: 2.05e-04
+2022-04-30 22:24:36,983 INFO [train.py:763] (7/8) Epoch 37, batch 1200, loss[loss=0.1846, simple_loss=0.2937, pruned_loss=0.03779, over 7232.00 frames.], tot_loss[loss=0.1594, simple_loss=0.261, pruned_loss=0.02893, over 1423101.70 frames.], batch size: 26, lr: 2.05e-04
+2022-04-30 22:25:43,873 INFO [train.py:763] (7/8) Epoch 37, batch 1250, loss[loss=0.1591, simple_loss=0.2636, pruned_loss=0.02736, over 6344.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2604, pruned_loss=0.02901, over 1426337.06 frames.], batch size: 37, lr: 2.05e-04
+2022-04-30 22:26:50,678 INFO [train.py:763] (7/8) Epoch 37, batch 1300, loss[loss=0.1528, simple_loss=0.2444, pruned_loss=0.03058, over 7277.00 frames.], tot_loss[loss=0.16, simple_loss=0.2612, pruned_loss=0.02942, over 1426900.18 frames.], batch size: 17, lr: 2.05e-04
+2022-04-30 22:27:56,068 INFO [train.py:763] (7/8) Epoch 37, batch 1350, loss[loss=0.1682, simple_loss=0.2731, pruned_loss=0.03166, over 7103.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2601, pruned_loss=0.02941, over 1420872.86 frames.], batch size: 21, lr: 2.05e-04
+2022-04-30 22:29:02,065 INFO [train.py:763] (7/8) Epoch 37, batch 1400, loss[loss=0.1688, simple_loss=0.2718, pruned_loss=0.03292, over 7299.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2596, pruned_loss=0.02933, over 1420558.74 frames.], batch size: 24, lr: 2.05e-04
+2022-04-30 22:30:07,335 INFO [train.py:763] (7/8) Epoch 37, batch 1450, loss[loss=0.1658, simple_loss=0.2636, pruned_loss=0.03401, over 7199.00 frames.], tot_loss[loss=0.1595, simple_loss=0.26, pruned_loss=0.02955, over 1425210.44 frames.], batch size: 22, lr: 2.05e-04
+2022-04-30 22:31:13,189 INFO [train.py:763] (7/8) Epoch 37, batch 1500, loss[loss=0.1674, simple_loss=0.2684, pruned_loss=0.03315, over 7292.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2596, pruned_loss=0.02931, over 1425390.13 frames.], batch size: 25, lr: 2.05e-04
+2022-04-30 22:32:18,528 INFO [train.py:763] (7/8) Epoch 37, batch 1550, loss[loss=0.1418, simple_loss=0.2397, pruned_loss=0.02198, over 7243.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2593, pruned_loss=0.02898, over 1422775.70 frames.], batch size: 20, lr: 2.05e-04
+2022-04-30 22:33:23,890 INFO [train.py:763] (7/8) Epoch 37, batch 1600, loss[loss=0.1453, simple_loss=0.2391, pruned_loss=0.02575, over 7263.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2599, pruned_loss=0.0289, over 1425147.38 frames.], batch size: 19, lr: 2.05e-04
+2022-04-30 22:34:29,226 INFO [train.py:763] (7/8) Epoch 37, batch 1650, loss[loss=0.1696, simple_loss=0.2677, pruned_loss=0.03572, over 7083.00 frames.], tot_loss[loss=0.158, simple_loss=0.2589, pruned_loss=0.02859, over 1424344.29 frames.], batch size: 28, lr: 2.05e-04
+2022-04-30 22:35:34,598 INFO [train.py:763] (7/8) Epoch 37, batch 1700, loss[loss=0.1362, simple_loss=0.2212, pruned_loss=0.0256, over 7161.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2578, pruned_loss=0.02881, over 1422356.15 frames.], batch size: 18, lr: 2.05e-04
+2022-04-30 22:36:40,251 INFO [train.py:763] (7/8) Epoch 37, batch 1750, loss[loss=0.1874, simple_loss=0.2894, pruned_loss=0.04267, over 5386.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2585, pruned_loss=0.02884, over 1421979.31 frames.], batch size: 52, lr: 2.05e-04
+2022-04-30 22:37:45,576 INFO [train.py:763] (7/8) Epoch 37, batch 1800, loss[loss=0.1613, simple_loss=0.2604, pruned_loss=0.03105, over 7328.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2576, pruned_loss=0.02859, over 1419680.09 frames.], batch size: 20, lr: 2.05e-04
+2022-04-30 22:38:50,835 INFO [train.py:763] (7/8) Epoch 37, batch 1850, loss[loss=0.1378, simple_loss=0.2281, pruned_loss=0.02379, over 7282.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2576, pruned_loss=0.02873, over 1421663.33 frames.], batch size: 18, lr: 2.05e-04
+2022-04-30 22:39:57,229 INFO [train.py:763] (7/8) Epoch 37, batch 1900, loss[loss=0.1437, simple_loss=0.243, pruned_loss=0.0222, over 6793.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2593, pruned_loss=0.02918, over 1424392.81 frames.], batch size: 15, lr: 2.05e-04
+2022-04-30 22:41:04,648 INFO [train.py:763] (7/8) Epoch 37, batch 1950, loss[loss=0.1607, simple_loss=0.2527, pruned_loss=0.03439, over 7267.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2598, pruned_loss=0.02928, over 1427369.55 frames.], batch size: 19, lr: 2.05e-04
+2022-04-30 22:42:12,282 INFO [train.py:763] (7/8) Epoch 37, batch 2000, loss[loss=0.1281, simple_loss=0.216, pruned_loss=0.02013, over 7413.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2585, pruned_loss=0.02892, over 1426150.13 frames.], batch size: 18, lr: 2.05e-04
+2022-04-30 22:43:17,409 INFO [train.py:763] (7/8) Epoch 37, batch 2050, loss[loss=0.1359, simple_loss=0.241, pruned_loss=0.01536, over 7249.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2589, pruned_loss=0.02921, over 1423388.67 frames.], batch size: 19, lr: 2.05e-04
+2022-04-30 22:44:22,380 INFO [train.py:763] (7/8) Epoch 37, batch 2100, loss[loss=0.1688, simple_loss=0.2676, pruned_loss=0.035, over 7193.00 frames.], tot_loss[loss=0.1595, simple_loss=0.26, pruned_loss=0.02948, over 1417678.43 frames.], batch size: 26, lr: 2.05e-04
+2022-04-30 22:45:27,594 INFO [train.py:763] (7/8) Epoch 37, batch 2150, loss[loss=0.1551, simple_loss=0.2507, pruned_loss=0.02972, over 7068.00 frames.], tot_loss[loss=0.1596, simple_loss=0.26, pruned_loss=0.02962, over 1418576.95 frames.], batch size: 18, lr: 2.05e-04
+2022-04-30 22:46:32,472 INFO [train.py:763] (7/8) Epoch 37, batch 2200, loss[loss=0.1389, simple_loss=0.2418, pruned_loss=0.01801, over 7061.00 frames.], tot_loss[loss=0.16, simple_loss=0.2609, pruned_loss=0.02954, over 1419532.04 frames.], batch size: 18, lr: 2.05e-04
+2022-04-30 22:47:37,577 INFO [train.py:763] (7/8) Epoch 37, batch 2250, loss[loss=0.1557, simple_loss=0.2716, pruned_loss=0.01988, over 6596.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2608, pruned_loss=0.02966, over 1417893.31 frames.], batch size: 38, lr: 2.05e-04
+2022-04-30 22:48:44,714 INFO [train.py:763] (7/8) Epoch 37, batch 2300, loss[loss=0.1352, simple_loss=0.2339, pruned_loss=0.0182, over 7060.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2611, pruned_loss=0.0298, over 1421477.00 frames.], batch size: 18, lr: 2.05e-04
+2022-04-30 22:49:50,017 INFO [train.py:763] (7/8) Epoch 37, batch 2350, loss[loss=0.1557, simple_loss=0.2465, pruned_loss=0.03245, over 7325.00 frames.], tot_loss[loss=0.16, simple_loss=0.2605, pruned_loss=0.02972, over 1419812.28 frames.], batch size: 20, lr: 2.05e-04
+2022-04-30 22:50:55,509 INFO [train.py:763] (7/8) Epoch 37, batch 2400, loss[loss=0.1571, simple_loss=0.2496, pruned_loss=0.03232, over 7416.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2601, pruned_loss=0.02951, over 1424779.32 frames.], batch size: 18, lr: 2.05e-04
+2022-04-30 22:52:02,213 INFO [train.py:763] (7/8) Epoch 37, batch 2450, loss[loss=0.1423, simple_loss=0.2413, pruned_loss=0.02164, over 7337.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2602, pruned_loss=0.02925, over 1427138.46 frames.], batch size: 20, lr: 2.04e-04
+2022-04-30 22:53:07,507 INFO [train.py:763] (7/8) Epoch 37, batch 2500, loss[loss=0.1424, simple_loss=0.2423, pruned_loss=0.02119, over 7162.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2596, pruned_loss=0.02913, over 1426978.43 frames.], batch size: 18, lr: 2.04e-04
+2022-04-30 22:54:13,325 INFO [train.py:763] (7/8) Epoch 37, batch 2550, loss[loss=0.1388, simple_loss=0.2355, pruned_loss=0.02105, over 7167.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2603, pruned_loss=0.02953, over 1424331.77 frames.], batch size: 18, lr: 2.04e-04
+2022-04-30 22:55:19,547 INFO [train.py:763] (7/8) Epoch 37, batch 2600, loss[loss=0.1594, simple_loss=0.2648, pruned_loss=0.02696, over 7434.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2598, pruned_loss=0.02944, over 1424233.75 frames.], batch size: 20, lr: 2.04e-04
+2022-04-30 22:56:24,712 INFO [train.py:763] (7/8) Epoch 37, batch 2650, loss[loss=0.1508, simple_loss=0.2493, pruned_loss=0.02615, over 7176.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2594, pruned_loss=0.02938, over 1425529.01 frames.], batch size: 23, lr: 2.04e-04
+2022-04-30 22:57:30,435 INFO [train.py:763] (7/8) Epoch 37, batch 2700, loss[loss=0.1478, simple_loss=0.2543, pruned_loss=0.02067, over 7233.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2584, pruned_loss=0.02894, over 1424141.07 frames.], batch size: 20, lr: 2.04e-04
+2022-04-30 22:58:35,700 INFO [train.py:763] (7/8) Epoch 37, batch 2750, loss[loss=0.1585, simple_loss=0.2522, pruned_loss=0.03243, over 7368.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2587, pruned_loss=0.02899, over 1425679.95 frames.], batch size: 19, lr: 2.04e-04
+2022-04-30 22:59:42,065 INFO [train.py:763] (7/8) Epoch 37, batch 2800, loss[loss=0.184, simple_loss=0.2854, pruned_loss=0.04136, over 7294.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2589, pruned_loss=0.02883, over 1424201.52 frames.], batch size: 24, lr: 2.04e-04
+2022-04-30 23:00:49,145 INFO [train.py:763] (7/8) Epoch 37, batch 2850, loss[loss=0.1496, simple_loss=0.2569, pruned_loss=0.02115, over 7431.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2581, pruned_loss=0.02884, over 1424209.60 frames.], batch size: 21, lr: 2.04e-04
+2022-04-30 23:01:56,143 INFO [train.py:763] (7/8) Epoch 37, batch 2900, loss[loss=0.1336, simple_loss=0.2194, pruned_loss=0.02392, over 7135.00 frames.], tot_loss[loss=0.158, simple_loss=0.2579, pruned_loss=0.02909, over 1423912.99 frames.], batch size: 17, lr: 2.04e-04
+2022-04-30 23:03:03,266 INFO [train.py:763] (7/8) Epoch 37, batch 2950, loss[loss=0.1295, simple_loss=0.2229, pruned_loss=0.01807, over 7422.00 frames.], tot_loss[loss=0.158, simple_loss=0.2581, pruned_loss=0.029, over 1428552.60 frames.], batch size: 18, lr: 2.04e-04
+2022-04-30 23:04:10,185 INFO [train.py:763] (7/8) Epoch 37, batch 3000, loss[loss=0.1739, simple_loss=0.2656, pruned_loss=0.04112, over 7194.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2589, pruned_loss=0.02926, over 1428138.20 frames.], batch size: 23, lr: 2.04e-04
+2022-04-30 23:04:10,185 INFO [train.py:783] (7/8) Computing validation loss
+2022-04-30 23:04:25,433 INFO [train.py:792] (7/8) Epoch 37, validation: loss=0.1692, simple_loss=0.2632, pruned_loss=0.03757, over 698248.00 frames.
+2022-04-30 23:05:32,522 INFO [train.py:763] (7/8) Epoch 37, batch 3050, loss[loss=0.1331, simple_loss=0.2334, pruned_loss=0.01644, over 7163.00 frames.], tot_loss[loss=0.159, simple_loss=0.2595, pruned_loss=0.02923, over 1428970.42 frames.], batch size: 18, lr: 2.04e-04
+2022-04-30 23:06:38,302 INFO [train.py:763] (7/8) Epoch 37, batch 3100, loss[loss=0.1782, simple_loss=0.2829, pruned_loss=0.03675, over 7200.00 frames.], tot_loss[loss=0.1594, simple_loss=0.26, pruned_loss=0.02943, over 1422447.20 frames.], batch size: 22, lr: 2.04e-04
+2022-04-30 23:07:53,035 INFO [train.py:763] (7/8) Epoch 37, batch 3150, loss[loss=0.1702, simple_loss=0.271, pruned_loss=0.03467, over 7396.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2596, pruned_loss=0.02941, over 1420406.55 frames.], batch size: 23, lr: 2.04e-04
+2022-04-30 23:08:58,943 INFO [train.py:763] (7/8) Epoch 37, batch 3200, loss[loss=0.175, simple_loss=0.2816, pruned_loss=0.03419, over 7099.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2597, pruned_loss=0.02943, over 1425256.90 frames.], batch size: 21, lr: 2.04e-04
+2022-04-30 23:10:06,337 INFO [train.py:763] (7/8) Epoch 37, batch 3250, loss[loss=0.1326, simple_loss=0.225, pruned_loss=0.02011, over 7278.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2589, pruned_loss=0.02898, over 1425945.06 frames.], batch size: 18, lr: 2.04e-04
+2022-04-30 23:11:13,130 INFO [train.py:763] (7/8) Epoch 37, batch 3300, loss[loss=0.1518, simple_loss=0.2515, pruned_loss=0.02608, over 7238.00 frames.], tot_loss[loss=0.158, simple_loss=0.2583, pruned_loss=0.02884, over 1425416.04 frames.], batch size: 20, lr: 2.04e-04
+2022-04-30 23:12:18,281 INFO [train.py:763] (7/8) Epoch 37, batch 3350, loss[loss=0.1877, simple_loss=0.2899, pruned_loss=0.04277, over 7202.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2605, pruned_loss=0.02948, over 1426329.33 frames.], batch size: 22, lr: 2.04e-04
+2022-04-30 23:13:23,573 INFO [train.py:763] (7/8) Epoch 37, batch 3400, loss[loss=0.1661, simple_loss=0.2693, pruned_loss=0.03145, over 6763.00 frames.], tot_loss[loss=0.1591, simple_loss=0.26, pruned_loss=0.02906, over 1430307.06 frames.], batch size: 31, lr: 2.04e-04
+2022-04-30 23:14:28,977 INFO [train.py:763] (7/8) Epoch 37, batch 3450, loss[loss=0.1431, simple_loss=0.2358, pruned_loss=0.0252, over 7427.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2598, pruned_loss=0.02898, over 1431328.24 frames.], batch size: 20, lr: 2.04e-04
+2022-04-30 23:15:34,304 INFO [train.py:763] (7/8) Epoch 37, batch 3500, loss[loss=0.1563, simple_loss=0.2673, pruned_loss=0.02268, over 7239.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2595, pruned_loss=0.02905, over 1429878.51 frames.], batch size: 20, lr: 2.04e-04
+2022-04-30 23:16:39,554 INFO [train.py:763] (7/8) Epoch 37, batch 3550, loss[loss=0.1636, simple_loss=0.2746, pruned_loss=0.02634, over 7154.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2607, pruned_loss=0.02935, over 1430038.79 frames.], batch size: 20, lr: 2.04e-04
+2022-04-30 23:17:44,647 INFO [train.py:763] (7/8) Epoch 37, batch 3600, loss[loss=0.1687, simple_loss=0.2722, pruned_loss=0.03263, over 6885.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2602, pruned_loss=0.02921, over 1428604.73 frames.], batch size: 31, lr: 2.04e-04
+2022-04-30 23:18:50,226 INFO [train.py:763] (7/8) Epoch 37, batch 3650, loss[loss=0.1674, simple_loss=0.2775, pruned_loss=0.02865, over 7061.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2598, pruned_loss=0.029, over 1431259.47 frames.], batch size: 28, lr: 2.04e-04
+2022-04-30 23:19:55,920 INFO [train.py:763] (7/8) Epoch 37, batch 3700, loss[loss=0.1878, simple_loss=0.296, pruned_loss=0.0398, over 7317.00 frames.], tot_loss[loss=0.159, simple_loss=0.2595, pruned_loss=0.02924, over 1422569.80 frames.], batch size: 24, lr: 2.04e-04
+2022-04-30 23:21:00,955 INFO [train.py:763] (7/8) Epoch 37, batch 3750, loss[loss=0.1697, simple_loss=0.271, pruned_loss=0.03424, over 7158.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2595, pruned_loss=0.02917, over 1418425.73 frames.], batch size: 19, lr: 2.04e-04
+2022-04-30 23:22:07,069 INFO [train.py:763] (7/8) Epoch 37, batch 3800, loss[loss=0.1636, simple_loss=0.2697, pruned_loss=0.02871, over 7374.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2591, pruned_loss=0.029, over 1418393.51 frames.], batch size: 23, lr: 2.04e-04
+2022-04-30 23:23:12,325 INFO [train.py:763] (7/8) Epoch 37, batch 3850, loss[loss=0.1582, simple_loss=0.2706, pruned_loss=0.02284, over 7125.00 frames.], tot_loss[loss=0.1583, simple_loss=0.259, pruned_loss=0.02884, over 1420712.96 frames.], batch size: 21, lr: 2.04e-04
+2022-04-30 23:24:18,029 INFO [train.py:763] (7/8) Epoch 37, batch 3900, loss[loss=0.1531, simple_loss=0.2581, pruned_loss=0.02406, over 7324.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2586, pruned_loss=0.02934, over 1422556.24 frames.], batch size: 20, lr: 2.04e-04
+2022-04-30 23:25:32,666 INFO [train.py:763] (7/8) Epoch 37, batch 3950, loss[loss=0.152, simple_loss=0.2571, pruned_loss=0.02344, over 7214.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2587, pruned_loss=0.02926, over 1417674.22 frames.], batch size: 22, lr: 2.04e-04
+2022-04-30 23:26:37,884 INFO [train.py:763] (7/8) Epoch 37, batch 4000, loss[loss=0.1214, simple_loss=0.2195, pruned_loss=0.01163, over 7162.00 frames.], tot_loss[loss=0.1579, simple_loss=0.258, pruned_loss=0.02885, over 1418122.94 frames.], batch size: 19, lr: 2.04e-04
+2022-04-30 23:28:01,977 INFO [train.py:763] (7/8) Epoch 37, batch 4050, loss[loss=0.1459, simple_loss=0.2389, pruned_loss=0.0265, over 7270.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2581, pruned_loss=0.02907, over 1410717.14 frames.], batch size: 17, lr: 2.04e-04
+2022-04-30 23:29:07,122 INFO [train.py:763] (7/8) Epoch 37, batch 4100, loss[loss=0.1534, simple_loss=0.2533, pruned_loss=0.02679, over 7212.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2587, pruned_loss=0.02897, over 1412859.85 frames.], batch size: 21, lr: 2.04e-04
+2022-04-30 23:30:21,714 INFO [train.py:763] (7/8) Epoch 37, batch 4150, loss[loss=0.158, simple_loss=0.2609, pruned_loss=0.02755, over 7246.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2576, pruned_loss=0.02873, over 1412237.61 frames.], batch size: 19, lr: 2.03e-04
+2022-04-30 23:31:36,377 INFO [train.py:763] (7/8) Epoch 37, batch 4200, loss[loss=0.1799, simple_loss=0.2824, pruned_loss=0.0387, over 7293.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2574, pruned_loss=0.02849, over 1413650.19 frames.], batch size: 24, lr: 2.03e-04
+2022-04-30 23:32:51,958 INFO [train.py:763] (7/8) Epoch 37, batch 4250, loss[loss=0.1404, simple_loss=0.2412, pruned_loss=0.01974, over 7220.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2566, pruned_loss=0.02809, over 1413758.88 frames.], batch size: 20, lr: 2.03e-04
+2022-04-30 23:33:58,673 INFO [train.py:763] (7/8) Epoch 37, batch 4300, loss[loss=0.2087, simple_loss=0.3067, pruned_loss=0.05535, over 4727.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2561, pruned_loss=0.02816, over 1411259.55 frames.], batch size: 52, lr: 2.03e-04
+2022-04-30 23:35:04,842 INFO [train.py:763] (7/8) Epoch 37, batch 4350, loss[loss=0.1447, simple_loss=0.2344, pruned_loss=0.02746, over 7416.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2561, pruned_loss=0.02854, over 1413379.64 frames.], batch size: 17, lr: 2.03e-04
+2022-04-30 23:36:10,344 INFO [train.py:763] (7/8) Epoch 37, batch 4400, loss[loss=0.1349, simple_loss=0.2233, pruned_loss=0.02323, over 7194.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2562, pruned_loss=0.02829, over 1414706.43 frames.], batch size: 16, lr: 2.03e-04
+2022-04-30 23:37:17,185 INFO [train.py:763] (7/8) Epoch 37, batch 4450, loss[loss=0.135, simple_loss=0.232, pruned_loss=0.01901, over 6746.00 frames.], tot_loss[loss=0.156, simple_loss=0.2553, pruned_loss=0.02836, over 1406466.24 frames.], batch size: 15, lr: 2.03e-04
+2022-04-30 23:38:22,793 INFO [train.py:763] (7/8) Epoch 37, batch 4500, loss[loss=0.1697, simple_loss=0.2706, pruned_loss=0.03444, over 6615.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2556, pruned_loss=0.02868, over 1381209.80 frames.], batch size: 38, lr: 2.03e-04
+2022-04-30 23:39:28,663 INFO [train.py:763] (7/8) Epoch 37, batch 4550, loss[loss=0.2093, simple_loss=0.3112, pruned_loss=0.05371, over 5047.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2554, pruned_loss=0.02912, over 1353793.00 frames.], batch size: 52, lr: 2.03e-04
+2022-04-30 23:40:56,603 INFO [train.py:763] (7/8) Epoch 38, batch 0, loss[loss=0.1362, simple_loss=0.2368, pruned_loss=0.01777, over 7255.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2368, pruned_loss=0.01777, over 7255.00 frames.], batch size: 19, lr: 2.01e-04
+2022-04-30 23:42:03,211 INFO [train.py:763] (7/8) Epoch 38, batch 50, loss[loss=0.1488, simple_loss=0.2583, pruned_loss=0.01967, over 7143.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2614, pruned_loss=0.02797, over 320006.93 frames.], batch size: 20, lr: 2.01e-04
+2022-04-30 23:43:10,057 INFO [train.py:763] (7/8) Epoch 38, batch 100, loss[loss=0.1536, simple_loss=0.2632, pruned_loss=0.02205, over 6779.00 frames.], tot_loss[loss=0.1592, simple_loss=0.261, pruned_loss=0.02867, over 565470.54 frames.], batch size: 31, lr: 2.01e-04
+2022-04-30 23:44:16,797 INFO [train.py:763] (7/8) Epoch 38, batch 150, loss[loss=0.1336, simple_loss=0.2352, pruned_loss=0.01602, over 7155.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2577, pruned_loss=0.02855, over 754430.22 frames.], batch size: 18, lr: 2.01e-04
+2022-04-30 23:45:22,780 INFO [train.py:763] (7/8) Epoch 38, batch 200, loss[loss=0.1313, simple_loss=0.233, pruned_loss=0.01479, over 7439.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2587, pruned_loss=0.02907, over 900731.70 frames.], batch size: 20, lr: 2.00e-04
+2022-04-30 23:46:29,142 INFO [train.py:763] (7/8) Epoch 38, batch 250, loss[loss=0.172, simple_loss=0.2704, pruned_loss=0.03678, over 6259.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2602, pruned_loss=0.0297, over 1016352.24 frames.], batch size: 38, lr: 2.00e-04
+2022-04-30 23:47:35,372 INFO [train.py:763] (7/8) Epoch 38, batch 300, loss[loss=0.166, simple_loss=0.2738, pruned_loss=0.02912, over 7437.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2598, pruned_loss=0.0292, over 1111373.48 frames.], batch size: 20, lr: 2.00e-04
+2022-04-30 23:48:41,453 INFO [train.py:763] (7/8) Epoch 38, batch 350, loss[loss=0.1796, simple_loss=0.2841, pruned_loss=0.03761, over 7302.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2594, pruned_loss=0.02908, over 1178286.72 frames.], batch size: 24, lr: 2.00e-04
+2022-04-30 23:49:47,452 INFO [train.py:763] (7/8) Epoch 38, batch 400, loss[loss=0.1508, simple_loss=0.2609, pruned_loss=0.02033, over 7214.00 frames.], tot_loss[loss=0.1585, simple_loss=0.259, pruned_loss=0.02902, over 1227516.89 frames.], batch size: 21, lr: 2.00e-04
+2022-04-30 23:50:53,882 INFO [train.py:763] (7/8) Epoch 38, batch 450, loss[loss=0.177, simple_loss=0.2807, pruned_loss=0.03661, over 7186.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2585, pruned_loss=0.02913, over 1272597.70 frames.], batch size: 23, lr: 2.00e-04
+2022-04-30 23:52:00,170 INFO [train.py:763] (7/8) Epoch 38, batch 500, loss[loss=0.1513, simple_loss=0.2513, pruned_loss=0.02565, over 7144.00 frames.], tot_loss[loss=0.1581, simple_loss=0.258, pruned_loss=0.02907, over 1299926.68 frames.], batch size: 20, lr: 2.00e-04
+2022-04-30 23:53:06,421 INFO [train.py:763] (7/8) Epoch 38, batch 550, loss[loss=0.145, simple_loss=0.2458, pruned_loss=0.02209, over 7429.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2578, pruned_loss=0.02891, over 1326176.38 frames.], batch size: 20, lr: 2.00e-04
+2022-04-30 23:54:12,153 INFO [train.py:763] (7/8) Epoch 38, batch 600, loss[loss=0.1619, simple_loss=0.2566, pruned_loss=0.0336, over 7165.00 frames.], tot_loss[loss=0.158, simple_loss=0.2582, pruned_loss=0.02892, over 1344691.99 frames.], batch size: 18, lr: 2.00e-04
+2022-04-30 23:55:17,895 INFO [train.py:763] (7/8) Epoch 38, batch 650, loss[loss=0.1428, simple_loss=0.2324, pruned_loss=0.02661, over 7285.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2579, pruned_loss=0.02865, over 1364527.30 frames.], batch size: 17, lr: 2.00e-04
+2022-04-30 23:56:23,420 INFO [train.py:763] (7/8) Epoch 38, batch 700, loss[loss=0.1485, simple_loss=0.2382, pruned_loss=0.02943, over 6813.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2564, pruned_loss=0.02844, over 1377112.88 frames.], batch size: 15, lr: 2.00e-04
+2022-04-30 23:57:28,968 INFO [train.py:763] (7/8) Epoch 38, batch 750, loss[loss=0.1595, simple_loss=0.2626, pruned_loss=0.02817, over 6488.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2553, pruned_loss=0.02796, over 1386163.64 frames.], batch size: 38, lr: 2.00e-04
+2022-04-30 23:58:35,125 INFO [train.py:763] (7/8) Epoch 38, batch 800, loss[loss=0.1833, simple_loss=0.2943, pruned_loss=0.03611, over 7218.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2566, pruned_loss=0.02823, over 1399093.32 frames.], batch size: 20, lr: 2.00e-04
+2022-04-30 23:59:41,189 INFO [train.py:763] (7/8) Epoch 38, batch 850, loss[loss=0.1681, simple_loss=0.2665, pruned_loss=0.03486, over 7049.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2562, pruned_loss=0.02818, over 1405309.73 frames.], batch size: 28, lr: 2.00e-04
+2022-05-01 00:00:47,062 INFO [train.py:763] (7/8) Epoch 38, batch 900, loss[loss=0.1668, simple_loss=0.2708, pruned_loss=0.0314, over 7407.00 frames.], tot_loss[loss=0.157, simple_loss=0.2573, pruned_loss=0.02834, over 1403486.22 frames.], batch size: 21, lr: 2.00e-04
+2022-05-01 00:01:52,982 INFO [train.py:763] (7/8) Epoch 38, batch 950, loss[loss=0.124, simple_loss=0.2148, pruned_loss=0.01658, over 7146.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2578, pruned_loss=0.02867, over 1405882.78 frames.], batch size: 17, lr: 2.00e-04
+2022-05-01 00:02:58,576 INFO [train.py:763] (7/8) Epoch 38, batch 1000, loss[loss=0.147, simple_loss=0.2528, pruned_loss=0.02063, over 7358.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2578, pruned_loss=0.02892, over 1409312.46 frames.], batch size: 19, lr: 2.00e-04
+2022-05-01 00:04:04,003 INFO [train.py:763] (7/8) Epoch 38, batch 1050, loss[loss=0.1713, simple_loss=0.2763, pruned_loss=0.03311, over 6766.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2577, pruned_loss=0.02899, over 1411781.06 frames.], batch size: 31, lr: 2.00e-04
+2022-05-01 00:05:09,991 INFO [train.py:763] (7/8) Epoch 38, batch 1100, loss[loss=0.1592, simple_loss=0.2584, pruned_loss=0.03002, over 7382.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2576, pruned_loss=0.02901, over 1416001.03 frames.], batch size: 23, lr: 2.00e-04
+2022-05-01 00:06:15,685 INFO [train.py:763] (7/8) Epoch 38, batch 1150, loss[loss=0.1351, simple_loss=0.2229, pruned_loss=0.02368, over 7274.00 frames.], tot_loss[loss=0.1572, simple_loss=0.257, pruned_loss=0.02872, over 1419615.98 frames.], batch size: 18, lr: 2.00e-04
+2022-05-01 00:07:21,232 INFO [train.py:763] (7/8) Epoch 38, batch 1200, loss[loss=0.1654, simple_loss=0.2664, pruned_loss=0.03222, over 6739.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2571, pruned_loss=0.02886, over 1420838.09 frames.], batch size: 31, lr: 2.00e-04
+2022-05-01 00:08:27,099 INFO [train.py:763] (7/8) Epoch 38, batch 1250, loss[loss=0.1705, simple_loss=0.2719, pruned_loss=0.03448, over 7431.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2571, pruned_loss=0.02865, over 1422458.29 frames.], batch size: 20, lr: 2.00e-04
+2022-05-01 00:09:34,192 INFO [train.py:763] (7/8) Epoch 38, batch 1300, loss[loss=0.144, simple_loss=0.2345, pruned_loss=0.02673, over 7271.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2565, pruned_loss=0.02862, over 1425319.32 frames.], batch size: 17, lr: 2.00e-04
+2022-05-01 00:10:39,863 INFO [train.py:763] (7/8) Epoch 38, batch 1350, loss[loss=0.1711, simple_loss=0.2743, pruned_loss=0.03394, over 7328.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2573, pruned_loss=0.02874, over 1425144.06 frames.], batch size: 20, lr: 2.00e-04
+2022-05-01 00:11:45,183 INFO [train.py:763] (7/8) Epoch 38, batch 1400, loss[loss=0.1469, simple_loss=0.2447, pruned_loss=0.02452, over 7151.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2576, pruned_loss=0.02869, over 1424904.63 frames.], batch size: 19, lr: 2.00e-04
+2022-05-01 00:12:50,409 INFO [train.py:763] (7/8) Epoch 38, batch 1450, loss[loss=0.1793, simple_loss=0.2849, pruned_loss=0.03685, over 7282.00 frames.], tot_loss[loss=0.158, simple_loss=0.2588, pruned_loss=0.02861, over 1425688.80 frames.], batch size: 25, lr: 2.00e-04
+2022-05-01 00:13:55,963 INFO [train.py:763] (7/8) Epoch 38, batch 1500, loss[loss=0.1562, simple_loss=0.2568, pruned_loss=0.02778, over 7112.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2591, pruned_loss=0.02882, over 1424836.12 frames.], batch size: 21, lr: 2.00e-04
+2022-05-01 00:15:03,023 INFO [train.py:763] (7/8) Epoch 38, batch 1550, loss[loss=0.1588, simple_loss=0.2786, pruned_loss=0.0195, over 7217.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2581, pruned_loss=0.02868, over 1424008.74 frames.], batch size: 22, lr: 2.00e-04
+2022-05-01 00:16:09,267 INFO [train.py:763] (7/8) Epoch 38, batch 1600, loss[loss=0.1935, simple_loss=0.305, pruned_loss=0.041, over 6754.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2581, pruned_loss=0.02856, over 1425767.27 frames.], batch size: 31, lr: 2.00e-04
+2022-05-01 00:17:15,078 INFO [train.py:763] (7/8) Epoch 38, batch 1650, loss[loss=0.1766, simple_loss=0.2806, pruned_loss=0.03632, over 7221.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2582, pruned_loss=0.02868, over 1425216.75 frames.], batch size: 21, lr: 2.00e-04
+2022-05-01 00:18:31,390 INFO [train.py:763] (7/8) Epoch 38, batch 1700, loss[loss=0.1422, simple_loss=0.2486, pruned_loss=0.01794, over 7077.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2586, pruned_loss=0.02841, over 1426651.62 frames.], batch size: 28, lr: 2.00e-04
+2022-05-01 00:19:36,548 INFO [train.py:763] (7/8) Epoch 38, batch 1750, loss[loss=0.1488, simple_loss=0.2517, pruned_loss=0.02294, over 7428.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2588, pruned_loss=0.02855, over 1425558.18 frames.], batch size: 20, lr: 2.00e-04
+2022-05-01 00:20:42,270 INFO [train.py:763] (7/8) Epoch 38, batch 1800, loss[loss=0.1787, simple_loss=0.2888, pruned_loss=0.03429, over 7208.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2593, pruned_loss=0.02855, over 1423345.20 frames.], batch size: 23, lr: 2.00e-04
+2022-05-01 00:21:47,728 INFO [train.py:763] (7/8) Epoch 38, batch 1850, loss[loss=0.1466, simple_loss=0.2456, pruned_loss=0.02382, over 7145.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2583, pruned_loss=0.02839, over 1420609.37 frames.], batch size: 19, lr: 2.00e-04
+2022-05-01 00:22:54,657 INFO [train.py:763] (7/8) Epoch 38, batch 1900, loss[loss=0.1288, simple_loss=0.2222, pruned_loss=0.01767, over 7285.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2579, pruned_loss=0.02869, over 1424154.95 frames.], batch size: 18, lr: 2.00e-04
+2022-05-01 00:24:00,347 INFO [train.py:763] (7/8) Epoch 38, batch 1950, loss[loss=0.1725, simple_loss=0.2816, pruned_loss=0.03168, over 7324.00 frames.], tot_loss[loss=0.158, simple_loss=0.2586, pruned_loss=0.02869, over 1424113.82 frames.], batch size: 21, lr: 1.99e-04
+2022-05-01 00:25:06,488 INFO [train.py:763] (7/8) Epoch 38, batch 2000, loss[loss=0.1548, simple_loss=0.2587, pruned_loss=0.02543, over 7251.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2586, pruned_loss=0.02881, over 1423096.07 frames.], batch size: 19, lr: 1.99e-04
+2022-05-01 00:26:13,061 INFO [train.py:763] (7/8) Epoch 38, batch 2050, loss[loss=0.1621, simple_loss=0.2609, pruned_loss=0.03169, over 7327.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2587, pruned_loss=0.02882, over 1421249.42 frames.], batch size: 20, lr: 1.99e-04
+2022-05-01 00:27:18,306 INFO [train.py:763] (7/8) Epoch 38, batch 2100, loss[loss=0.1495, simple_loss=0.2336, pruned_loss=0.03265, over 7235.00 frames.], tot_loss[loss=0.1578, simple_loss=0.258, pruned_loss=0.02876, over 1422338.79 frames.], batch size: 16, lr: 1.99e-04
+2022-05-01 00:28:25,371 INFO [train.py:763] (7/8) Epoch 38, batch 2150, loss[loss=0.1526, simple_loss=0.2495, pruned_loss=0.02782, over 7267.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2583, pruned_loss=0.02862, over 1420235.15 frames.], batch size: 19, lr: 1.99e-04
+2022-05-01 00:29:31,408 INFO [train.py:763] (7/8) Epoch 38, batch 2200, loss[loss=0.1465, simple_loss=0.2475, pruned_loss=0.02276, over 7210.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2582, pruned_loss=0.02859, over 1420800.53 frames.], batch size: 22, lr: 1.99e-04
+2022-05-01 00:30:38,818 INFO [train.py:763] (7/8) Epoch 38, batch 2250, loss[loss=0.1641, simple_loss=0.2796, pruned_loss=0.02433, over 7138.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2583, pruned_loss=0.02855, over 1423090.44 frames.], batch size: 20, lr: 1.99e-04
+2022-05-01 00:31:44,030 INFO [train.py:763] (7/8) Epoch 38, batch 2300, loss[loss=0.1523, simple_loss=0.2494, pruned_loss=0.02754, over 7158.00 frames.], tot_loss[loss=0.1573, simple_loss=0.258, pruned_loss=0.02833, over 1422559.47 frames.], batch size: 19, lr: 1.99e-04
+2022-05-01 00:32:50,165 INFO [train.py:763] (7/8) Epoch 38, batch 2350, loss[loss=0.1922, simple_loss=0.2792, pruned_loss=0.05262, over 7231.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2578, pruned_loss=0.02861, over 1424568.42 frames.], batch size: 20, lr: 1.99e-04
+2022-05-01 00:33:55,531 INFO [train.py:763] (7/8) Epoch 38, batch 2400, loss[loss=0.1697, simple_loss=0.2746, pruned_loss=0.03236, over 7140.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2573, pruned_loss=0.02869, over 1427403.15 frames.], batch size: 20, lr: 1.99e-04
+2022-05-01 00:35:01,026 INFO [train.py:763] (7/8) Epoch 38, batch 2450, loss[loss=0.1251, simple_loss=0.2194, pruned_loss=0.01545, over 7406.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2564, pruned_loss=0.02834, over 1428175.75 frames.], batch size: 18, lr: 1.99e-04
+2022-05-01 00:36:07,001 INFO [train.py:763] (7/8) Epoch 38, batch 2500, loss[loss=0.136, simple_loss=0.2325, pruned_loss=0.01978, over 7406.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2562, pruned_loss=0.02813, over 1426891.85 frames.], batch size: 18, lr: 1.99e-04
+2022-05-01 00:37:12,692 INFO [train.py:763] (7/8) Epoch 38, batch 2550, loss[loss=0.1409, simple_loss=0.2446, pruned_loss=0.01861, over 7433.00 frames.], tot_loss[loss=0.156, simple_loss=0.256, pruned_loss=0.02801, over 1431542.10 frames.], batch size: 20, lr: 1.99e-04
+2022-05-01 00:38:18,043 INFO [train.py:763] (7/8) Epoch 38, batch 2600, loss[loss=0.1627, simple_loss=0.2682, pruned_loss=0.02859, over 7156.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2569, pruned_loss=0.02842, over 1429557.52 frames.], batch size: 26, lr: 1.99e-04
+2022-05-01 00:39:23,363 INFO [train.py:763] (7/8) Epoch 38, batch 2650, loss[loss=0.146, simple_loss=0.2526, pruned_loss=0.01969, over 7084.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2568, pruned_loss=0.0281, over 1430807.43 frames.], batch size: 28, lr: 1.99e-04
+2022-05-01 00:40:27,520 INFO [train.py:763] (7/8) Epoch 38, batch 2700, loss[loss=0.2064, simple_loss=0.311, pruned_loss=0.05089, over 7294.00 frames.], tot_loss[loss=0.157, simple_loss=0.2573, pruned_loss=0.02833, over 1428830.13 frames.], batch size: 25, lr: 1.99e-04
+2022-05-01 00:41:33,249 INFO [train.py:763] (7/8) Epoch 38, batch 2750, loss[loss=0.1377, simple_loss=0.2342, pruned_loss=0.02056, over 7162.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2573, pruned_loss=0.02847, over 1429525.77 frames.], batch size: 19, lr: 1.99e-04
+2022-05-01 00:42:38,773 INFO [train.py:763] (7/8) Epoch 38, batch 2800, loss[loss=0.1583, simple_loss=0.2658, pruned_loss=0.0254, over 7342.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2576, pruned_loss=0.02868, over 1426072.92 frames.], batch size: 22, lr: 1.99e-04
+2022-05-01 00:43:44,135 INFO [train.py:763] (7/8) Epoch 38, batch 2850, loss[loss=0.1548, simple_loss=0.2602, pruned_loss=0.02471, over 6289.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2571, pruned_loss=0.02852, over 1426048.66 frames.], batch size: 38, lr: 1.99e-04
+2022-05-01 00:44:49,680 INFO [train.py:763] (7/8) Epoch 38, batch 2900, loss[loss=0.1615, simple_loss=0.2681, pruned_loss=0.0274, over 7316.00 frames.], tot_loss[loss=0.157, simple_loss=0.257, pruned_loss=0.02843, over 1425934.08 frames.], batch size: 21, lr: 1.99e-04
+2022-05-01 00:45:55,148 INFO [train.py:763] (7/8) Epoch 38, batch 2950, loss[loss=0.1598, simple_loss=0.2588, pruned_loss=0.03039, over 7339.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2567, pruned_loss=0.02854, over 1428769.58 frames.], batch size: 22, lr: 1.99e-04
+2022-05-01 00:47:00,422 INFO [train.py:763] (7/8) Epoch 38, batch 3000, loss[loss=0.1498, simple_loss=0.2618, pruned_loss=0.01888, over 7232.00 frames.], tot_loss[loss=0.158, simple_loss=0.2579, pruned_loss=0.02899, over 1429297.69 frames.], batch size: 20, lr: 1.99e-04
+2022-05-01 00:47:00,423 INFO [train.py:783] (7/8) Computing validation loss
+2022-05-01 00:47:15,873 INFO [train.py:792] (7/8) Epoch 38, validation: loss=0.1707, simple_loss=0.2648, pruned_loss=0.03834, over 698248.00 frames.
+2022-05-01 00:48:21,037 INFO [train.py:763] (7/8) Epoch 38, batch 3050, loss[loss=0.155, simple_loss=0.2478, pruned_loss=0.03111, over 7123.00 frames.], tot_loss[loss=0.1579, simple_loss=0.258, pruned_loss=0.02897, over 1426549.00 frames.], batch size: 17, lr: 1.99e-04
+2022-05-01 00:49:26,210 INFO [train.py:763] (7/8) Epoch 38, batch 3100, loss[loss=0.1685, simple_loss=0.2662, pruned_loss=0.03545, over 6296.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2582, pruned_loss=0.02916, over 1418397.27 frames.], batch size: 38, lr: 1.99e-04
+2022-05-01 00:50:31,513 INFO [train.py:763] (7/8) Epoch 38, batch 3150, loss[loss=0.1744, simple_loss=0.2743, pruned_loss=0.03721, over 7407.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2585, pruned_loss=0.02882, over 1423793.12 frames.], batch size: 21, lr: 1.99e-04
+2022-05-01 00:51:36,882 INFO [train.py:763] (7/8) Epoch 38, batch 3200, loss[loss=0.1575, simple_loss=0.2565, pruned_loss=0.02919, over 6340.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2583, pruned_loss=0.02862, over 1424350.64 frames.], batch size: 38, lr: 1.99e-04
+2022-05-01 00:52:42,227 INFO [train.py:763] (7/8) Epoch 38, batch 3250, loss[loss=0.1604, simple_loss=0.2705, pruned_loss=0.02517, over 6363.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2584, pruned_loss=0.02836, over 1424568.84 frames.], batch size: 38, lr: 1.99e-04
+2022-05-01 00:53:47,537 INFO [train.py:763] (7/8) Epoch 38, batch 3300, loss[loss=0.1775, simple_loss=0.2705, pruned_loss=0.04224, over 7159.00 frames.], tot_loss[loss=0.1574, simple_loss=0.258, pruned_loss=0.02838, over 1423923.23 frames.], batch size: 19, lr: 1.99e-04
+2022-05-01 00:54:52,921 INFO [train.py:763] (7/8) Epoch 38, batch 3350, loss[loss=0.1308, simple_loss=0.2269, pruned_loss=0.01735, over 7132.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2572, pruned_loss=0.02808, over 1425959.75 frames.], batch size: 17, lr: 1.99e-04
+2022-05-01 00:55:59,028 INFO [train.py:763] (7/8) Epoch 38, batch 3400, loss[loss=0.175, simple_loss=0.2719, pruned_loss=0.03907, over 7352.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2575, pruned_loss=0.0286, over 1427188.29 frames.], batch size: 19, lr: 1.99e-04
+2022-05-01 00:57:06,555 INFO [train.py:763] (7/8) Epoch 38, batch 3450, loss[loss=0.1867, simple_loss=0.2862, pruned_loss=0.04354, over 7181.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2575, pruned_loss=0.02861, over 1419953.33 frames.], batch size: 23, lr: 1.99e-04
+2022-05-01 00:58:13,615 INFO [train.py:763] (7/8) Epoch 38, batch 3500, loss[loss=0.1469, simple_loss=0.2483, pruned_loss=0.02269, over 7153.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2586, pruned_loss=0.0292, over 1421080.63 frames.], batch size: 19, lr: 1.99e-04
+2022-05-01 00:59:19,220 INFO [train.py:763] (7/8) Epoch 38, batch 3550, loss[loss=0.1485, simple_loss=0.263, pruned_loss=0.01696, over 7333.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2581, pruned_loss=0.02907, over 1423166.50 frames.], batch size: 22, lr: 1.99e-04
+2022-05-01 01:00:25,370 INFO [train.py:763] (7/8) Epoch 38, batch 3600, loss[loss=0.13, simple_loss=0.2246, pruned_loss=0.01766, over 7281.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2578, pruned_loss=0.02933, over 1423905.86 frames.], batch size: 18, lr: 1.99e-04
+2022-05-01 01:01:30,608 INFO [train.py:763] (7/8) Epoch 38, batch 3650, loss[loss=0.1532, simple_loss=0.2628, pruned_loss=0.0218, over 7078.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2579, pruned_loss=0.02918, over 1425358.77 frames.], batch size: 28, lr: 1.99e-04
+2022-05-01 01:02:35,714 INFO [train.py:763] (7/8) Epoch 38, batch 3700, loss[loss=0.1615, simple_loss=0.2732, pruned_loss=0.02489, over 6442.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2585, pruned_loss=0.02907, over 1421983.15 frames.], batch size: 38, lr: 1.99e-04
+2022-05-01 01:03:41,354 INFO [train.py:763] (7/8) Epoch 38, batch 3750, loss[loss=0.1805, simple_loss=0.2905, pruned_loss=0.03531, over 7203.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2582, pruned_loss=0.02905, over 1415391.38 frames.], batch size: 23, lr: 1.98e-04
+2022-05-01 01:04:46,834 INFO [train.py:763] (7/8) Epoch 38, batch 3800, loss[loss=0.156, simple_loss=0.2464, pruned_loss=0.03281, over 7371.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2579, pruned_loss=0.02873, over 1421850.05 frames.], batch size: 19, lr: 1.98e-04
+2022-05-01 01:05:52,030 INFO [train.py:763] (7/8) Epoch 38, batch 3850, loss[loss=0.1909, simple_loss=0.2788, pruned_loss=0.05146, over 4696.00 frames.], tot_loss[loss=0.1581, simple_loss=0.258, pruned_loss=0.02904, over 1418170.29 frames.], batch size: 52, lr: 1.98e-04
+2022-05-01 01:06:57,240 INFO [train.py:763] (7/8) Epoch 38, batch 3900, loss[loss=0.184, simple_loss=0.2885, pruned_loss=0.03975, over 7019.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2578, pruned_loss=0.02861, over 1419075.00 frames.], batch size: 28, lr: 1.98e-04
+2022-05-01 01:08:02,838 INFO [train.py:763] (7/8) Epoch 38, batch 3950, loss[loss=0.1743, simple_loss=0.2748, pruned_loss=0.03695, over 7321.00 frames.], tot_loss[loss=0.1575, simple_loss=0.258, pruned_loss=0.02854, over 1421305.78 frames.], batch size: 25, lr: 1.98e-04
+2022-05-01 01:09:08,091 INFO [train.py:763] (7/8) Epoch 38, batch 4000, loss[loss=0.1449, simple_loss=0.2464, pruned_loss=0.02165, over 6721.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2577, pruned_loss=0.02844, over 1423100.15 frames.], batch size: 31, lr: 1.98e-04
+2022-05-01 01:10:13,459 INFO [train.py:763] (7/8) Epoch 38, batch 4050, loss[loss=0.1936, simple_loss=0.2961, pruned_loss=0.04551, over 6819.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2583, pruned_loss=0.02845, over 1422262.44 frames.], batch size: 31, lr: 1.98e-04
+2022-05-01 01:11:18,894 INFO [train.py:763] (7/8) Epoch 38, batch 4100, loss[loss=0.1555, simple_loss=0.2624, pruned_loss=0.02428, over 7220.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2584, pruned_loss=0.02865, over 1422103.14 frames.], batch size: 21, lr: 1.98e-04
+2022-05-01 01:12:24,235 INFO [train.py:763] (7/8) Epoch 38, batch 4150, loss[loss=0.1494, simple_loss=0.2505, pruned_loss=0.02415, over 7214.00 frames.], tot_loss[loss=0.157, simple_loss=0.2578, pruned_loss=0.02813, over 1420069.61 frames.], batch size: 21, lr: 1.98e-04
+2022-05-01 01:13:30,542 INFO [train.py:763] (7/8) Epoch 38, batch 4200, loss[loss=0.1545, simple_loss=0.2586, pruned_loss=0.0252, over 6709.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2581, pruned_loss=0.02802, over 1420116.03 frames.], batch size: 31, lr: 1.98e-04
+2022-05-01 01:14:35,842 INFO [train.py:763] (7/8) Epoch 38, batch 4250, loss[loss=0.1464, simple_loss=0.2384, pruned_loss=0.02716, over 7154.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2581, pruned_loss=0.02835, over 1417457.08 frames.], batch size: 17, lr: 1.98e-04
+2022-05-01 01:15:41,260 INFO [train.py:763] (7/8) Epoch 38, batch 4300, loss[loss=0.1881, simple_loss=0.291, pruned_loss=0.04262, over 7299.00 frames.], tot_loss[loss=0.158, simple_loss=0.2591, pruned_loss=0.02849, over 1418859.29 frames.], batch size: 25, lr: 1.98e-04
+2022-05-01 01:16:46,663 INFO [train.py:763] (7/8) Epoch 38, batch 4350, loss[loss=0.1534, simple_loss=0.2566, pruned_loss=0.02513, over 7431.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2595, pruned_loss=0.02864, over 1415455.08 frames.], batch size: 20, lr: 1.98e-04
+2022-05-01 01:17:51,735 INFO [train.py:763] (7/8) Epoch 38, batch 4400, loss[loss=0.1536, simple_loss=0.2642, pruned_loss=0.02151, over 7329.00 frames.], tot_loss[loss=0.1598, simple_loss=0.261, pruned_loss=0.02928, over 1412020.40 frames.], batch size: 22, lr: 1.98e-04
+2022-05-01 01:18:57,810 INFO [train.py:763] (7/8) Epoch 38, batch 4450, loss[loss=0.1289, simple_loss=0.2269, pruned_loss=0.01541, over 7416.00 frames.], tot_loss[loss=0.16, simple_loss=0.2613, pruned_loss=0.02932, over 1399005.94 frames.], batch size: 17, lr: 1.98e-04
+2022-05-01 01:20:03,895 INFO [train.py:763] (7/8) Epoch 38, batch 4500, loss[loss=0.1496, simple_loss=0.2484, pruned_loss=0.02538, over 7171.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2617, pruned_loss=0.02984, over 1387604.57 frames.], batch size: 18, lr: 1.98e-04
+2022-05-01 01:21:09,328 INFO [train.py:763] (7/8) Epoch 38, batch 4550, loss[loss=0.1743, simple_loss=0.2774, pruned_loss=0.03564, over 5073.00 frames.], tot_loss[loss=0.163, simple_loss=0.2639, pruned_loss=0.03107, over 1348534.40 frames.], batch size: 52, lr: 1.98e-04
+2022-05-01 01:22:39,313 INFO [train.py:763] (7/8) Epoch 39, batch 0, loss[loss=0.1773, simple_loss=0.2829, pruned_loss=0.03589, over 7298.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2829, pruned_loss=0.03589, over 7298.00 frames.], batch size: 24, lr: 1.96e-04
+2022-05-01 01:23:45,009 INFO [train.py:763] (7/8) Epoch 39, batch 50, loss[loss=0.1304, simple_loss=0.2194, pruned_loss=0.02064, over 7277.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2612, pruned_loss=0.03099, over 317620.78 frames.], batch size: 17, lr: 1.95e-04
+2022-05-01 01:24:50,362 INFO [train.py:763] (7/8) Epoch 39, batch 100, loss[loss=0.1583, simple_loss=0.2629, pruned_loss=0.02691, over 7359.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2582, pruned_loss=0.02903, over 563258.61 frames.], batch size: 19, lr: 1.95e-04
+2022-05-01 01:25:56,225 INFO [train.py:763] (7/8) Epoch 39, batch 150, loss[loss=0.1771, simple_loss=0.2798, pruned_loss=0.03713, over 7239.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2563, pruned_loss=0.02895, over 755801.07 frames.], batch size: 20, lr: 1.95e-04
+2022-05-01 01:27:01,302 INFO [train.py:763] (7/8) Epoch 39, batch 200, loss[loss=0.1307, simple_loss=0.2239, pruned_loss=0.01875, over 7426.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2583, pruned_loss=0.02946, over 903616.72 frames.], batch size: 18, lr: 1.95e-04
+2022-05-01 01:28:06,667 INFO [train.py:763] (7/8) Epoch 39, batch 250, loss[loss=0.1553, simple_loss=0.2558, pruned_loss=0.0274, over 7442.00 frames.], tot_loss[loss=0.158, simple_loss=0.2578, pruned_loss=0.02905, over 1016985.49 frames.], batch size: 22, lr: 1.95e-04
+2022-05-01 01:29:11,535 INFO [train.py:763] (7/8) Epoch 39, batch 300, loss[loss=0.1761, simple_loss=0.2713, pruned_loss=0.04046, over 7294.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2576, pruned_loss=0.02866, over 1106944.13 frames.], batch size: 24, lr: 1.95e-04
+2022-05-01 01:30:16,882 INFO [train.py:763] (7/8) Epoch 39, batch 350, loss[loss=0.1634, simple_loss=0.2701, pruned_loss=0.02833, over 7149.00 frames.], tot_loss[loss=0.1567, simple_loss=0.257, pruned_loss=0.02814, over 1172477.33 frames.], batch size: 20, lr: 1.95e-04
+2022-05-01 01:31:22,227 INFO [train.py:763] (7/8) Epoch 39, batch 400, loss[loss=0.1713, simple_loss=0.2677, pruned_loss=0.03745, over 7207.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2578, pruned_loss=0.02843, over 1229437.45 frames.], batch size: 26, lr: 1.95e-04
+2022-05-01 01:32:27,460 INFO [train.py:763] (7/8) Epoch 39, batch 450, loss[loss=0.1667, simple_loss=0.2651, pruned_loss=0.03412, over 7305.00 frames.], tot_loss[loss=0.1564, simple_loss=0.257, pruned_loss=0.02791, over 1273387.35 frames.], batch size: 25, lr: 1.95e-04
+2022-05-01 01:33:32,874 INFO [train.py:763] (7/8) Epoch 39, batch 500, loss[loss=0.1583, simple_loss=0.2698, pruned_loss=0.0234, over 7314.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2568, pruned_loss=0.02788, over 1306206.85 frames.], batch size: 21, lr: 1.95e-04
+2022-05-01 01:34:38,288 INFO [train.py:763] (7/8) Epoch 39, batch 550, loss[loss=0.1475, simple_loss=0.248, pruned_loss=0.02354, over 7243.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2569, pruned_loss=0.02836, over 1328180.93 frames.], batch size: 20, lr: 1.95e-04
+2022-05-01 01:35:43,505 INFO [train.py:763] (7/8) Epoch 39, batch 600, loss[loss=0.1464, simple_loss=0.241, pruned_loss=0.02592, over 7270.00 frames.], tot_loss[loss=0.156, simple_loss=0.2561, pruned_loss=0.02794, over 1349565.65 frames.], batch size: 19, lr: 1.95e-04
+2022-05-01 01:36:48,744 INFO [train.py:763] (7/8) Epoch 39, batch 650, loss[loss=0.1486, simple_loss=0.2529, pruned_loss=0.02218, over 7230.00 frames.], tot_loss[loss=0.1559, simple_loss=0.256, pruned_loss=0.02788, over 1368142.11 frames.], batch size: 20, lr: 1.95e-04
+2022-05-01 01:37:53,929 INFO [train.py:763] (7/8) Epoch 39, batch 700, loss[loss=0.1349, simple_loss=0.2271, pruned_loss=0.02135, over 7273.00 frames.], tot_loss[loss=0.156, simple_loss=0.256, pruned_loss=0.02798, over 1381394.54 frames.], batch size: 18, lr: 1.95e-04
+2022-05-01 01:38:59,271 INFO [train.py:763] (7/8) Epoch 39, batch 750, loss[loss=0.152, simple_loss=0.2565, pruned_loss=0.02372, over 7360.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2561, pruned_loss=0.02803, over 1387082.75 frames.], batch size: 19, lr: 1.95e-04
+2022-05-01 01:40:04,499 INFO [train.py:763] (7/8) Epoch 39, batch 800, loss[loss=0.1644, simple_loss=0.2746, pruned_loss=0.02709, over 7111.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2564, pruned_loss=0.02804, over 1396302.11 frames.], batch size: 21, lr: 1.95e-04
+2022-05-01 01:41:18,509 INFO [train.py:763] (7/8) Epoch 39, batch 850, loss[loss=0.14, simple_loss=0.2337, pruned_loss=0.0232, over 7129.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2572, pruned_loss=0.02809, over 1402907.22 frames.], batch size: 17, lr: 1.95e-04
+2022-05-01 01:42:32,271 INFO [train.py:763] (7/8) Epoch 39, batch 900, loss[loss=0.1806, simple_loss=0.2802, pruned_loss=0.04045, over 7183.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2578, pruned_loss=0.02823, over 1408614.19 frames.], batch size: 23, lr: 1.95e-04
+2022-05-01 01:43:55,217 INFO [train.py:763] (7/8) Epoch 39, batch 950, loss[loss=0.1701, simple_loss=0.267, pruned_loss=0.03654, over 5008.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2579, pruned_loss=0.02839, over 1411350.44 frames.], batch size: 52, lr: 1.95e-04
+2022-05-01 01:45:01,229 INFO [train.py:763] (7/8) Epoch 39, batch 1000, loss[loss=0.1477, simple_loss=0.2574, pruned_loss=0.01904, over 7126.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2578, pruned_loss=0.02848, over 1409823.02 frames.], batch size: 21, lr: 1.95e-04
+2022-05-01 01:46:06,278 INFO [train.py:763] (7/8) Epoch 39, batch 1050, loss[loss=0.1627, simple_loss=0.2743, pruned_loss=0.02553, over 7226.00 frames.], tot_loss[loss=0.158, simple_loss=0.2586, pruned_loss=0.02866, over 1408631.64 frames.], batch size: 21, lr: 1.95e-04
+2022-05-01 01:47:29,500 INFO [train.py:763] (7/8) Epoch 39, batch 1100, loss[loss=0.1551, simple_loss=0.252, pruned_loss=0.02907, over 7161.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2584, pruned_loss=0.02863, over 1408054.45 frames.], batch size: 18, lr: 1.95e-04
+2022-05-01 01:48:43,949 INFO [train.py:763] (7/8) Epoch 39, batch 1150, loss[loss=0.1703, simple_loss=0.2761, pruned_loss=0.03222, over 6694.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2582, pruned_loss=0.02823, over 1415246.40 frames.], batch size: 31, lr: 1.95e-04
+2022-05-01 01:49:48,910 INFO [train.py:763] (7/8) Epoch 39, batch 1200, loss[loss=0.1493, simple_loss=0.2597, pruned_loss=0.01944, over 6379.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2589, pruned_loss=0.02822, over 1417827.03 frames.], batch size: 37, lr: 1.95e-04
+2022-05-01 01:50:54,376 INFO [train.py:763] (7/8) Epoch 39, batch 1250, loss[loss=0.1805, simple_loss=0.2915, pruned_loss=0.03475, over 7295.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2581, pruned_loss=0.02805, over 1421663.70 frames.], batch size: 25, lr: 1.95e-04
+2022-05-01 01:51:59,447 INFO [train.py:763] (7/8) Epoch 39, batch 1300, loss[loss=0.1687, simple_loss=0.2745, pruned_loss=0.03148, over 7431.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2588, pruned_loss=0.02852, over 1421989.59 frames.], batch size: 20, lr: 1.95e-04
+2022-05-01 01:53:04,823 INFO [train.py:763] (7/8) Epoch 39, batch 1350, loss[loss=0.1599, simple_loss=0.2681, pruned_loss=0.02583, over 6451.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2585, pruned_loss=0.02843, over 1421372.99 frames.], batch size: 38, lr: 1.95e-04
+2022-05-01 01:54:11,096 INFO [train.py:763] (7/8) Epoch 39, batch 1400, loss[loss=0.1551, simple_loss=0.2623, pruned_loss=0.02394, over 6553.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2585, pruned_loss=0.02829, over 1423629.06 frames.], batch size: 38, lr: 1.95e-04
+2022-05-01 01:55:16,359 INFO [train.py:763] (7/8) Epoch 39, batch 1450, loss[loss=0.1781, simple_loss=0.2749, pruned_loss=0.04063, over 7227.00 frames.], tot_loss[loss=0.157, simple_loss=0.2578, pruned_loss=0.02803, over 1425107.32 frames.], batch size: 23, lr: 1.95e-04
+2022-05-01 01:56:21,450 INFO [train.py:763] (7/8) Epoch 39, batch 1500, loss[loss=0.1569, simple_loss=0.251, pruned_loss=0.03142, over 7144.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2584, pruned_loss=0.02805, over 1425406.55 frames.], batch size: 17, lr: 1.95e-04
+2022-05-01 01:57:28,673 INFO [train.py:763] (7/8) Epoch 39, batch 1550, loss[loss=0.1713, simple_loss=0.2863, pruned_loss=0.02813, over 7199.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2576, pruned_loss=0.0281, over 1423682.78 frames.], batch size: 23, lr: 1.95e-04
+2022-05-01 01:58:35,236 INFO [train.py:763] (7/8) Epoch 39, batch 1600, loss[loss=0.1589, simple_loss=0.2623, pruned_loss=0.02778, over 7054.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2577, pruned_loss=0.02832, over 1426627.05 frames.], batch size: 28, lr: 1.95e-04
+2022-05-01 01:59:41,406 INFO [train.py:763] (7/8) Epoch 39, batch 1650, loss[loss=0.1798, simple_loss=0.2873, pruned_loss=0.03611, over 5111.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2582, pruned_loss=0.02847, over 1420051.50 frames.], batch size: 52, lr: 1.95e-04
+2022-05-01 02:00:47,170 INFO [train.py:763] (7/8) Epoch 39, batch 1700, loss[loss=0.1434, simple_loss=0.2325, pruned_loss=0.02717, over 7022.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2581, pruned_loss=0.02863, over 1413001.59 frames.], batch size: 16, lr: 1.95e-04
+2022-05-01 02:01:53,367 INFO [train.py:763] (7/8) Epoch 39, batch 1750, loss[loss=0.1486, simple_loss=0.2518, pruned_loss=0.02274, over 7318.00 frames.], tot_loss[loss=0.1576, simple_loss=0.258, pruned_loss=0.02859, over 1414685.48 frames.], batch size: 21, lr: 1.95e-04
+2022-05-01 02:02:58,288 INFO [train.py:763] (7/8) Epoch 39, batch 1800, loss[loss=0.1524, simple_loss=0.2588, pruned_loss=0.02302, over 7321.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2581, pruned_loss=0.02869, over 1416973.73 frames.], batch size: 22, lr: 1.95e-04
+2022-05-01 02:04:03,608 INFO [train.py:763] (7/8) Epoch 39, batch 1850, loss[loss=0.1605, simple_loss=0.2498, pruned_loss=0.03557, over 7055.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2583, pruned_loss=0.02853, over 1420257.82 frames.], batch size: 18, lr: 1.95e-04
+2022-05-01 02:05:08,891 INFO [train.py:763] (7/8) Epoch 39, batch 1900, loss[loss=0.1495, simple_loss=0.2545, pruned_loss=0.02225, over 7158.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2588, pruned_loss=0.02854, over 1423617.34 frames.], batch size: 19, lr: 1.94e-04
+2022-05-01 02:06:14,317 INFO [train.py:763] (7/8) Epoch 39, batch 1950, loss[loss=0.1808, simple_loss=0.2858, pruned_loss=0.03788, over 5213.00 frames.], tot_loss[loss=0.1584, simple_loss=0.259, pruned_loss=0.02897, over 1418187.92 frames.], batch size: 52, lr: 1.94e-04
+2022-05-01 02:07:19,655 INFO [train.py:763] (7/8) Epoch 39, batch 2000, loss[loss=0.1487, simple_loss=0.256, pruned_loss=0.02068, over 7458.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2585, pruned_loss=0.02859, over 1422304.92 frames.], batch size: 19, lr: 1.94e-04
+2022-05-01 02:08:24,818 INFO [train.py:763] (7/8) Epoch 39, batch 2050, loss[loss=0.1228, simple_loss=0.2165, pruned_loss=0.0146, over 7421.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2586, pruned_loss=0.02857, over 1426222.83 frames.], batch size: 20, lr: 1.94e-04
+2022-05-01 02:09:30,537 INFO [train.py:763] (7/8) Epoch 39, batch 2100, loss[loss=0.1255, simple_loss=0.2196, pruned_loss=0.01572, over 7412.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2576, pruned_loss=0.02805, over 1425837.79 frames.], batch size: 18, lr: 1.94e-04
+2022-05-01 02:10:35,981 INFO [train.py:763] (7/8) Epoch 39, batch 2150, loss[loss=0.1411, simple_loss=0.2503, pruned_loss=0.01592, over 7144.00 frames.], tot_loss[loss=0.156, simple_loss=0.2568, pruned_loss=0.02761, over 1429633.89 frames.], batch size: 20, lr: 1.94e-04
+2022-05-01 02:11:43,135 INFO [train.py:763] (7/8) Epoch 39, batch 2200, loss[loss=0.153, simple_loss=0.2579, pruned_loss=0.02398, over 7237.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2573, pruned_loss=0.02784, over 1432467.54 frames.], batch size: 20, lr: 1.94e-04
+2022-05-01 02:12:48,277 INFO [train.py:763] (7/8) Epoch 39, batch 2250, loss[loss=0.1857, simple_loss=0.2859, pruned_loss=0.04276, over 7213.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2578, pruned_loss=0.02841, over 1430616.31 frames.], batch size: 22, lr: 1.94e-04
+2022-05-01 02:13:53,534 INFO [train.py:763] (7/8) Epoch 39, batch 2300, loss[loss=0.1443, simple_loss=0.2475, pruned_loss=0.02059, over 7428.00 frames.], tot_loss[loss=0.1567, simple_loss=0.257, pruned_loss=0.02818, over 1426439.23 frames.], batch size: 20, lr: 1.94e-04
+2022-05-01 02:15:00,693 INFO [train.py:763] (7/8) Epoch 39, batch 2350, loss[loss=0.1271, simple_loss=0.2274, pruned_loss=0.01341, over 7333.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2563, pruned_loss=0.02837, over 1426281.61 frames.], batch size: 22, lr: 1.94e-04
+2022-05-01 02:16:07,741 INFO [train.py:763] (7/8) Epoch 39, batch 2400, loss[loss=0.1677, simple_loss=0.273, pruned_loss=0.03115, over 7204.00 frames.], tot_loss[loss=0.157, simple_loss=0.2569, pruned_loss=0.02857, over 1426882.07 frames.], batch size: 22, lr: 1.94e-04
+2022-05-01 02:17:13,335 INFO [train.py:763] (7/8) Epoch 39, batch 2450, loss[loss=0.1788, simple_loss=0.2867, pruned_loss=0.03547, over 7038.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2582, pruned_loss=0.02894, over 1422468.61 frames.], batch size: 28, lr: 1.94e-04
+2022-05-01 02:18:19,527 INFO [train.py:763] (7/8) Epoch 39, batch 2500, loss[loss=0.1433, simple_loss=0.2518, pruned_loss=0.01743, over 7409.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2578, pruned_loss=0.02868, over 1419150.48 frames.], batch size: 21, lr: 1.94e-04
+2022-05-01 02:19:24,729 INFO [train.py:763] (7/8) Epoch 39, batch 2550, loss[loss=0.1821, simple_loss=0.2892, pruned_loss=0.03745, over 6986.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2591, pruned_loss=0.02933, over 1418666.30 frames.], batch size: 28, lr: 1.94e-04
+2022-05-01 02:20:31,612 INFO [train.py:763] (7/8) Epoch 39, batch 2600, loss[loss=0.1651, simple_loss=0.2683, pruned_loss=0.03098, over 7335.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2582, pruned_loss=0.02908, over 1418504.47 frames.], batch size: 22, lr: 1.94e-04
+2022-05-01 02:21:37,541 INFO [train.py:763] (7/8) Epoch 39, batch 2650, loss[loss=0.1374, simple_loss=0.2347, pruned_loss=0.02004, over 7155.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2582, pruned_loss=0.02911, over 1420858.99 frames.], batch size: 18, lr: 1.94e-04
+2022-05-01 02:22:43,394 INFO [train.py:763] (7/8) Epoch 39, batch 2700, loss[loss=0.173, simple_loss=0.2782, pruned_loss=0.03387, over 7197.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2591, pruned_loss=0.02928, over 1423071.38 frames.], batch size: 26, lr: 1.94e-04
+2022-05-01 02:23:48,618 INFO [train.py:763] (7/8) Epoch 39, batch 2750, loss[loss=0.161, simple_loss=0.2665, pruned_loss=0.02773, over 7297.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2594, pruned_loss=0.02923, over 1426293.70 frames.], batch size: 24, lr: 1.94e-04
+2022-05-01 02:24:53,696 INFO [train.py:763] (7/8) Epoch 39, batch 2800, loss[loss=0.1352, simple_loss=0.2278, pruned_loss=0.02128, over 7056.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2595, pruned_loss=0.02914, over 1423191.62 frames.], batch size: 18, lr: 1.94e-04
+2022-05-01 02:25:58,661 INFO [train.py:763] (7/8) Epoch 39, batch 2850, loss[loss=0.1583, simple_loss=0.2628, pruned_loss=0.02694, over 6352.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2592, pruned_loss=0.0291, over 1419505.15 frames.], batch size: 38, lr: 1.94e-04
+2022-05-01 02:27:03,587 INFO [train.py:763] (7/8) Epoch 39, batch 2900, loss[loss=0.1484, simple_loss=0.2481, pruned_loss=0.02435, over 7054.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2582, pruned_loss=0.02859, over 1419702.64 frames.], batch size: 18, lr: 1.94e-04
+2022-05-01 02:28:08,514 INFO [train.py:763] (7/8) Epoch 39, batch 2950, loss[loss=0.1656, simple_loss=0.2699, pruned_loss=0.03067, over 7280.00 frames.], tot_loss[loss=0.1583, simple_loss=0.259, pruned_loss=0.02883, over 1418473.77 frames.], batch size: 24, lr: 1.94e-04
+2022-05-01 02:29:13,397 INFO [train.py:763] (7/8) Epoch 39, batch 3000, loss[loss=0.1789, simple_loss=0.2712, pruned_loss=0.04337, over 7339.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2597, pruned_loss=0.02954, over 1412780.82 frames.], batch size: 22, lr: 1.94e-04
+2022-05-01 02:29:13,397 INFO [train.py:783] (7/8) Computing validation loss
+2022-05-01 02:29:28,415 INFO [train.py:792] (7/8) Epoch 39, validation: loss=0.1688, simple_loss=0.2638, pruned_loss=0.03694, over 698248.00 frames.
+2022-05-01 02:30:33,964 INFO [train.py:763] (7/8) Epoch 39, batch 3050, loss[loss=0.1619, simple_loss=0.2582, pruned_loss=0.03281, over 7369.00 frames.], tot_loss[loss=0.159, simple_loss=0.2593, pruned_loss=0.02938, over 1414901.74 frames.], batch size: 19, lr: 1.94e-04
+2022-05-01 02:31:41,203 INFO [train.py:763] (7/8) Epoch 39, batch 3100, loss[loss=0.1517, simple_loss=0.2623, pruned_loss=0.02056, over 7206.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2595, pruned_loss=0.02942, over 1417431.55 frames.], batch size: 26, lr: 1.94e-04
+2022-05-01 02:32:47,809 INFO [train.py:763] (7/8) Epoch 39, batch 3150, loss[loss=0.1528, simple_loss=0.267, pruned_loss=0.0193, over 7158.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2591, pruned_loss=0.029, over 1421406.93 frames.], batch size: 20, lr: 1.94e-04
+2022-05-01 02:33:53,393 INFO [train.py:763] (7/8) Epoch 39, batch 3200, loss[loss=0.1846, simple_loss=0.2763, pruned_loss=0.04642, over 5274.00 frames.], tot_loss[loss=0.158, simple_loss=0.2587, pruned_loss=0.02868, over 1421644.45 frames.], batch size: 52, lr: 1.94e-04
+2022-05-01 02:34:58,495 INFO [train.py:763] (7/8) Epoch 39, batch 3250, loss[loss=0.1587, simple_loss=0.2574, pruned_loss=0.02997, over 7381.00 frames.], tot_loss[loss=0.159, simple_loss=0.26, pruned_loss=0.02893, over 1420629.84 frames.], batch size: 23, lr: 1.94e-04
+2022-05-01 02:36:03,625 INFO [train.py:763] (7/8) Epoch 39, batch 3300, loss[loss=0.1729, simple_loss=0.2767, pruned_loss=0.03454, over 7107.00 frames.], tot_loss[loss=0.1583, simple_loss=0.259, pruned_loss=0.02876, over 1419784.89 frames.], batch size: 21, lr: 1.94e-04
+2022-05-01 02:37:08,807 INFO [train.py:763] (7/8) Epoch 39, batch 3350, loss[loss=0.1524, simple_loss=0.2703, pruned_loss=0.01719, over 7110.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2592, pruned_loss=0.02862, over 1417368.64 frames.], batch size: 21, lr: 1.94e-04
+2022-05-01 02:38:14,804 INFO [train.py:763] (7/8) Epoch 39, batch 3400, loss[loss=0.1574, simple_loss=0.259, pruned_loss=0.02784, over 7148.00 frames.], tot_loss[loss=0.158, simple_loss=0.259, pruned_loss=0.02846, over 1418338.20 frames.], batch size: 19, lr: 1.94e-04
+2022-05-01 02:39:20,477 INFO [train.py:763] (7/8) Epoch 39, batch 3450, loss[loss=0.1406, simple_loss=0.2335, pruned_loss=0.02384, over 7289.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2594, pruned_loss=0.02863, over 1416995.34 frames.], batch size: 17, lr: 1.94e-04
+2022-05-01 02:40:25,657 INFO [train.py:763] (7/8) Epoch 39, batch 3500, loss[loss=0.144, simple_loss=0.2524, pruned_loss=0.01777, over 7324.00 frames.], tot_loss[loss=0.1579, simple_loss=0.259, pruned_loss=0.02843, over 1418149.75 frames.], batch size: 21, lr: 1.94e-04
+2022-05-01 02:41:31,495 INFO [train.py:763] (7/8) Epoch 39, batch 3550, loss[loss=0.1443, simple_loss=0.2327, pruned_loss=0.02799, over 7066.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2576, pruned_loss=0.02825, over 1419323.71 frames.], batch size: 18, lr: 1.94e-04
+2022-05-01 02:42:37,815 INFO [train.py:763] (7/8) Epoch 39, batch 3600, loss[loss=0.1687, simple_loss=0.2716, pruned_loss=0.03289, over 5309.00 frames.], tot_loss[loss=0.1576, simple_loss=0.258, pruned_loss=0.02859, over 1416784.00 frames.], batch size: 52, lr: 1.94e-04
+2022-05-01 02:43:44,830 INFO [train.py:763] (7/8) Epoch 39, batch 3650, loss[loss=0.176, simple_loss=0.2729, pruned_loss=0.03956, over 6291.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2577, pruned_loss=0.02835, over 1418522.32 frames.], batch size: 37, lr: 1.94e-04
+2022-05-01 02:44:50,061 INFO [train.py:763] (7/8) Epoch 39, batch 3700, loss[loss=0.1617, simple_loss=0.2517, pruned_loss=0.03581, over 7134.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2574, pruned_loss=0.02817, over 1422183.67 frames.], batch size: 17, lr: 1.94e-04
+2022-05-01 02:45:55,102 INFO [train.py:763] (7/8) Epoch 39, batch 3750, loss[loss=0.1399, simple_loss=0.244, pruned_loss=0.01789, over 7341.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2571, pruned_loss=0.028, over 1418778.44 frames.], batch size: 19, lr: 1.93e-04
+2022-05-01 02:47:00,713 INFO [train.py:763] (7/8) Epoch 39, batch 3800, loss[loss=0.1384, simple_loss=0.2303, pruned_loss=0.02329, over 7002.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2567, pruned_loss=0.02739, over 1422622.01 frames.], batch size: 16, lr: 1.93e-04
+2022-05-01 02:48:07,775 INFO [train.py:763] (7/8) Epoch 39, batch 3850, loss[loss=0.1956, simple_loss=0.294, pruned_loss=0.0486, over 7407.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2564, pruned_loss=0.02749, over 1419238.77 frames.], batch size: 21, lr: 1.93e-04
+2022-05-01 02:49:13,641 INFO [train.py:763] (7/8) Epoch 39, batch 3900, loss[loss=0.1824, simple_loss=0.2848, pruned_loss=0.04003, over 7212.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2559, pruned_loss=0.02723, over 1419830.86 frames.], batch size: 23, lr: 1.93e-04
+2022-05-01 02:50:20,019 INFO [train.py:763] (7/8) Epoch 39, batch 3950, loss[loss=0.15, simple_loss=0.2406, pruned_loss=0.02968, over 7057.00 frames.], tot_loss[loss=0.156, simple_loss=0.2561, pruned_loss=0.0279, over 1414961.54 frames.], batch size: 18, lr: 1.93e-04
+2022-05-01 02:51:25,353 INFO [train.py:763] (7/8) Epoch 39, batch 4000, loss[loss=0.1319, simple_loss=0.2221, pruned_loss=0.02082, over 7128.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2563, pruned_loss=0.0279, over 1415718.69 frames.], batch size: 17, lr: 1.93e-04
+2022-05-01 02:52:30,802 INFO [train.py:763] (7/8) Epoch 39, batch 4050, loss[loss=0.1594, simple_loss=0.2592, pruned_loss=0.0298, over 7208.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2568, pruned_loss=0.02782, over 1420327.37 frames.], batch size: 22, lr: 1.93e-04
+2022-05-01 02:53:35,953 INFO [train.py:763] (7/8) Epoch 39, batch 4100, loss[loss=0.1738, simple_loss=0.2731, pruned_loss=0.03724, over 7238.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2569, pruned_loss=0.02791, over 1420472.52 frames.], batch size: 20, lr: 1.93e-04
+2022-05-01 02:54:41,377 INFO [train.py:763] (7/8) Epoch 39, batch 4150, loss[loss=0.1589, simple_loss=0.2512, pruned_loss=0.03327, over 7288.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2568, pruned_loss=0.02788, over 1422717.47 frames.], batch size: 18, lr: 1.93e-04
+2022-05-01 02:55:46,816 INFO [train.py:763] (7/8) Epoch 39, batch 4200, loss[loss=0.1598, simple_loss=0.2533, pruned_loss=0.03316, over 7165.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2572, pruned_loss=0.02786, over 1423890.29 frames.], batch size: 18, lr: 1.93e-04
+2022-05-01 02:56:52,123 INFO [train.py:763] (7/8) Epoch 39, batch 4250, loss[loss=0.1446, simple_loss=0.248, pruned_loss=0.02062, over 7330.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2579, pruned_loss=0.02851, over 1419858.99 frames.], batch size: 21, lr: 1.93e-04
+2022-05-01 02:57:57,431 INFO [train.py:763] (7/8) Epoch 39, batch 4300, loss[loss=0.1467, simple_loss=0.2405, pruned_loss=0.02649, over 7161.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2578, pruned_loss=0.0286, over 1420325.40 frames.], batch size: 18, lr: 1.93e-04
+2022-05-01 02:59:02,819 INFO [train.py:763] (7/8) Epoch 39, batch 4350, loss[loss=0.1682, simple_loss=0.2669, pruned_loss=0.03473, over 7317.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2576, pruned_loss=0.02843, over 1421952.59 frames.], batch size: 20, lr: 1.93e-04
+2022-05-01 03:00:09,040 INFO [train.py:763] (7/8) Epoch 39, batch 4400, loss[loss=0.1761, simple_loss=0.2842, pruned_loss=0.03402, over 6702.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2586, pruned_loss=0.02864, over 1421643.32 frames.], batch size: 31, lr: 1.93e-04
+2022-05-01 03:01:14,013 INFO [train.py:763] (7/8) Epoch 39, batch 4450, loss[loss=0.1376, simple_loss=0.2362, pruned_loss=0.01953, over 7171.00 frames.], tot_loss[loss=0.158, simple_loss=0.2583, pruned_loss=0.02887, over 1409346.34 frames.], batch size: 18, lr: 1.93e-04
+2022-05-01 03:02:19,228 INFO [train.py:763] (7/8) Epoch 39, batch 4500, loss[loss=0.165, simple_loss=0.2699, pruned_loss=0.03003, over 7223.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2591, pruned_loss=0.0292, over 1400407.67 frames.], batch size: 21, lr: 1.93e-04
+2022-05-01 03:03:25,876 INFO [train.py:763] (7/8) Epoch 39, batch 4550, loss[loss=0.1519, simple_loss=0.2495, pruned_loss=0.02721, over 7198.00 frames.], tot_loss[loss=0.157, simple_loss=0.2562, pruned_loss=0.0289, over 1393058.70 frames.], batch size: 16, lr: 1.93e-04
+2022-05-01 03:04:15,373 INFO [train.py:971] (7/8) Done!