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------------> log file ==runs2/rte/1/log_bs32_lr3e-05_20221118_060236_793692.txt
Namespace(aug_train=False, data_dir='/home.local/jianwei/datasets/nlp/glue_data/RTE', do_eval=False, early_stop=True, early_stop_metric='accuracy', eval_step=120, gradient_accumulation_steps=1, learning_rate=3e-05, local_rank=0, lr_scheduler_type=<SchedulerType.CONSTANT_WITH_WARMUP: 'constant_with_warmup'>, max_length=128, max_train_steps=None, model_name_or_path='/home.local/jianwei/workspace/archive/SparseOptimizer/output/Layer_7_12_Hid_160_768_Head_10_12_IMRatio_3.5', num_train_epochs=30, num_warmup_steps=0, output_dir='runs2/rte/1', pad_to_max_length=False, per_device_eval_batch_size=32, per_device_train_batch_size=32, print_step=5, save_last=False, seed=None, task_name='rte', train_file=None, use_slow_tokenizer=False, validation_file=None, weight_decay=0.0)
Distributed environment: NO
Num processes: 1
Process index: 0
Local process index: 0
Device: cuda
Mixed precision type: fp16
Sample 595 of the training set: (tensor([ 101, 11929, 1010, 5553, 1012, 2570, 1006, 8418, 25311, 13860,
3388, 1007, 1011, 1011, 2019, 18410, 2140, 6187, 24887, 2080,
11183, 1010, 1037, 2280, 3539, 2704, 1010, 2180, 5978, 1005,
1055, 4883, 2602, 2006, 4465, 1012, 102, 2047, 5077, 3539,
2704, 2003, 2700, 1012, 102, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]), tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]), tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]), tensor(1)).
Sample 2375 of the training set: (tensor([ 101, 1996, 5611, 2390, 2749, 3344, 2041, 1010, 2006, 5095,
1010, 1037, 6923, 2510, 3169, 2046, 1996, 2225, 2924, 2237,
1997, 15419, 2378, 1998, 2049, 13141, 3409, 1010, 2334, 9302,
4216, 2056, 1012, 102, 1996, 5611, 2390, 3344, 2041, 1037,
6923, 3169, 1999, 15419, 2378, 1012, 102, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]), tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]), tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]), tensor(0)).
Sample 149 of the training set: (tensor([ 101, 2048, 9767, 8461, 2379, 2019, 5499, 2082, 1999, 4501,
2730, 2809, 2111, 1998, 5229, 4413, 2500, 7483, 1999, 1996,
6745, 8293, 1997, 4808, 13940, 1996, 2670, 3417, 1997, 15381,
1012, 102, 2809, 2111, 8461, 2048, 9767, 2379, 2019, 5499,
2082, 1999, 4501, 1012, 102, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]), tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]), tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]), tensor(1)).
***** Running training *****
Num examples = 2490
Num Epochs = 30
Instantaneous batch size per device = 32
Total train batch size (w. parallel, distributed & accumulation) = 32
Gradient Accumulation steps = 1
Total optimization steps = 2340
000005/002340, loss: 0.694824, avg_loss: 0.691177
000010/002340, loss: 0.707565, avg_loss: 0.693715
000015/002340, loss: 0.699615, avg_loss: 0.693022
000020/002340, loss: 0.699615, avg_loss: 0.693939
000025/002340, loss: 0.699310, avg_loss: 0.694436
000030/002340, loss: 0.698532, avg_loss: 0.694941
000035/002340, loss: 0.686935, avg_loss: 0.694372
000040/002340, loss: 0.696411, avg_loss: 0.694273
000045/002340, loss: 0.692871, avg_loss: 0.693708
000050/002340, loss: 0.687256, avg_loss: 0.693756
000055/002340, loss: 0.701004, avg_loss: 0.693827
000060/002340, loss: 0.691040, avg_loss: 0.693579
000065/002340, loss: 0.689056, avg_loss: 0.693324
000070/002340, loss: 0.696518, avg_loss: 0.693440
000075/002340, loss: 0.696930, avg_loss: 0.693460
000080/002340, loss: 0.693802, avg_loss: 0.693340
000085/002340, loss: 0.688171, avg_loss: 0.693318
000090/002340, loss: 0.698029, avg_loss: 0.693154
000095/002340, loss: 0.689453, avg_loss: 0.692949
000100/002340, loss: 0.690857, avg_loss: 0.692921
000105/002340, loss: 0.689819, avg_loss: 0.692827
000110/002340, loss: 0.682220, avg_loss: 0.692768
000115/002340, loss: 0.700806, avg_loss: 0.692803
000120/002340, loss: 0.701385, avg_loss: 0.692652
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 1, step 120/2340: {'accuracy': 0.5523465703971119}
000125/002340, loss: 0.693527, avg_loss: 0.692706
000130/002340, loss: 0.689957, avg_loss: 0.692658
000135/002340, loss: 0.685425, avg_loss: 0.692536
000140/002340, loss: 0.690201, avg_loss: 0.692434
000145/002340, loss: 0.686600, avg_loss: 0.692396
000150/002340, loss: 0.678986, avg_loss: 0.692177
000155/002340, loss: 0.679138, avg_loss: 0.691975
000160/002340, loss: 0.694275, avg_loss: 0.691769
000165/002340, loss: 0.692368, avg_loss: 0.691443
000170/002340, loss: 0.680664, avg_loss: 0.691252
000175/002340, loss: 0.666016, avg_loss: 0.690698
000180/002340, loss: 0.671844, avg_loss: 0.690296
000185/002340, loss: 0.651184, avg_loss: 0.689748
000190/002340, loss: 0.659752, avg_loss: 0.688919
000195/002340, loss: 0.662926, avg_loss: 0.688697
000200/002340, loss: 0.643776, avg_loss: 0.688136
000205/002340, loss: 0.693794, avg_loss: 0.687406
000210/002340, loss: 0.716675, avg_loss: 0.686937
000215/002340, loss: 0.665474, avg_loss: 0.686136
000220/002340, loss: 0.625298, avg_loss: 0.685308
000225/002340, loss: 0.656639, avg_loss: 0.685019
000230/002340, loss: 0.673508, avg_loss: 0.684550
000235/002340, loss: 0.575394, avg_loss: 0.682954
000240/002340, loss: 0.615173, avg_loss: 0.681390
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 3, step 240/2340: {'accuracy': 0.5884476534296029}
000245/002340, loss: 0.566116, avg_loss: 0.679216
000250/002340, loss: 0.662231, avg_loss: 0.677990
000255/002340, loss: 0.742844, avg_loss: 0.677457
000260/002340, loss: 0.744896, avg_loss: 0.677289
000265/002340, loss: 0.524788, avg_loss: 0.675974
000270/002340, loss: 0.573128, avg_loss: 0.674871
000275/002340, loss: 0.698616, avg_loss: 0.674028
000280/002340, loss: 0.661125, avg_loss: 0.672997
000285/002340, loss: 0.577705, avg_loss: 0.671527
000290/002340, loss: 0.529144, avg_loss: 0.669498
000295/002340, loss: 0.548820, avg_loss: 0.668429
000300/002340, loss: 0.533775, avg_loss: 0.667589
000305/002340, loss: 0.724682, avg_loss: 0.666549
000310/002340, loss: 0.618702, avg_loss: 0.667052
000315/002340, loss: 0.600662, avg_loss: 0.666212
000320/002340, loss: 0.560127, avg_loss: 0.665015
000325/002340, loss: 0.667423, avg_loss: 0.663344
000330/002340, loss: 0.520096, avg_loss: 0.661692
000335/002340, loss: 0.589901, avg_loss: 0.659812
000340/002340, loss: 0.718616, avg_loss: 0.658405
000345/002340, loss: 0.523731, avg_loss: 0.657693
000350/002340, loss: 0.597912, avg_loss: 0.656364
000355/002340, loss: 0.510841, avg_loss: 0.654704
000360/002340, loss: 0.598392, avg_loss: 0.652629
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 4, step 360/2340: {'accuracy': 0.6137184115523465}
000365/002340, loss: 0.509396, avg_loss: 0.650652
000370/002340, loss: 0.625957, avg_loss: 0.649372
000375/002340, loss: 0.632420, avg_loss: 0.648425
000380/002340, loss: 0.562641, avg_loss: 0.647222
000385/002340, loss: 0.649609, avg_loss: 0.645501
000390/002340, loss: 0.361694, avg_loss: 0.643182
000395/002340, loss: 0.425430, avg_loss: 0.642246
000400/002340, loss: 0.577938, avg_loss: 0.640067
000405/002340, loss: 0.554668, avg_loss: 0.638333
000410/002340, loss: 0.505466, avg_loss: 0.636457
000415/002340, loss: 0.531124, avg_loss: 0.634969
000420/002340, loss: 0.425911, avg_loss: 0.633147
000425/002340, loss: 0.532368, avg_loss: 0.632082
000430/002340, loss: 0.569756, avg_loss: 0.630961
000435/002340, loss: 0.451645, avg_loss: 0.629107
000440/002340, loss: 0.459530, avg_loss: 0.627486
000445/002340, loss: 0.380501, avg_loss: 0.625123
000450/002340, loss: 0.565880, avg_loss: 0.624122
000455/002340, loss: 0.422201, avg_loss: 0.621911
000460/002340, loss: 0.671333, avg_loss: 0.620993
000465/002340, loss: 0.427799, avg_loss: 0.618575
000470/002340, loss: 0.301590, avg_loss: 0.616753
000475/002340, loss: 0.517204, avg_loss: 0.614735
000480/002340, loss: 0.473822, avg_loss: 0.612666
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 6, step 480/2340: {'accuracy': 0.6209386281588448}
000485/002340, loss: 0.235840, avg_loss: 0.610187
000490/002340, loss: 0.535803, avg_loss: 0.608769
000495/002340, loss: 0.447842, avg_loss: 0.606833
000500/002340, loss: 0.359915, avg_loss: 0.604468
000505/002340, loss: 0.473944, avg_loss: 0.601928
000510/002340, loss: 0.487707, avg_loss: 0.600405
000515/002340, loss: 0.280029, avg_loss: 0.599008
000520/002340, loss: 0.509848, avg_loss: 0.597484
000525/002340, loss: 0.646320, avg_loss: 0.596454
000530/002340, loss: 0.350674, avg_loss: 0.594710
000535/002340, loss: 0.480106, avg_loss: 0.593436
000540/002340, loss: 0.560251, avg_loss: 0.593214
000545/002340, loss: 0.387239, avg_loss: 0.591432
000550/002340, loss: 0.277430, avg_loss: 0.589320
000555/002340, loss: 0.280695, avg_loss: 0.587417
000560/002340, loss: 0.330351, avg_loss: 0.585310
000565/002340, loss: 0.391579, avg_loss: 0.583662
000570/002340, loss: 0.280355, avg_loss: 0.582107
000575/002340, loss: 0.359081, avg_loss: 0.580171
000580/002340, loss: 0.367201, avg_loss: 0.578450
000585/002340, loss: 0.430851, avg_loss: 0.577231
000590/002340, loss: 0.331879, avg_loss: 0.575557
000595/002340, loss: 0.333700, avg_loss: 0.573829
000600/002340, loss: 0.309275, avg_loss: 0.571686
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 7, step 600/2340: {'accuracy': 0.6425992779783394}
000605/002340, loss: 0.461454, avg_loss: 0.570168
000610/002340, loss: 0.434152, avg_loss: 0.568408
000615/002340, loss: 0.565701, avg_loss: 0.567013
000620/002340, loss: 0.281487, avg_loss: 0.564378
000625/002340, loss: 0.183996, avg_loss: 0.562576
000630/002340, loss: 0.308249, avg_loss: 0.560548
000635/002340, loss: 0.492087, avg_loss: 0.558905
000640/002340, loss: 0.276144, avg_loss: 0.556907
000645/002340, loss: 0.379016, avg_loss: 0.555011
000650/002340, loss: 0.257240, avg_loss: 0.553119
000655/002340, loss: 0.260510, avg_loss: 0.550735
000660/002340, loss: 0.482807, avg_loss: 0.549067
000665/002340, loss: 0.313425, avg_loss: 0.547653
000670/002340, loss: 0.244961, avg_loss: 0.545744
000675/002340, loss: 0.386663, avg_loss: 0.544380
000680/002340, loss: 0.137331, avg_loss: 0.541812
000685/002340, loss: 0.301256, avg_loss: 0.539778
000690/002340, loss: 0.284186, avg_loss: 0.537928
000695/002340, loss: 0.521972, avg_loss: 0.536261
000700/002340, loss: 0.718600, avg_loss: 0.535717
000705/002340, loss: 0.237306, avg_loss: 0.534266
000710/002340, loss: 0.164028, avg_loss: 0.532027
000715/002340, loss: 0.235560, avg_loss: 0.530920
000720/002340, loss: 0.224425, avg_loss: 0.529428
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 9, step 720/2340: {'accuracy': 0.6462093862815884}
000725/002340, loss: 0.250054, avg_loss: 0.527996
000730/002340, loss: 0.213790, avg_loss: 0.526521
000735/002340, loss: 0.339844, avg_loss: 0.525346
000740/002340, loss: 0.192316, avg_loss: 0.523399
000745/002340, loss: 0.322181, avg_loss: 0.521820
000750/002340, loss: 0.114270, avg_loss: 0.519722
000755/002340, loss: 0.242498, avg_loss: 0.517846
000760/002340, loss: 0.234197, avg_loss: 0.515497
000765/002340, loss: 0.332447, avg_loss: 0.513969
000770/002340, loss: 0.163693, avg_loss: 0.512496
000775/002340, loss: 0.260910, avg_loss: 0.511088
000780/002340, loss: 0.236919, avg_loss: 0.509495
000785/002340, loss: 0.151022, avg_loss: 0.507580
000790/002340, loss: 0.489914, avg_loss: 0.506298
000795/002340, loss: 0.175525, avg_loss: 0.504419
000800/002340, loss: 0.274471, avg_loss: 0.502310
000805/002340, loss: 0.308759, avg_loss: 0.500468
000810/002340, loss: 0.227170, avg_loss: 0.498888
000815/002340, loss: 0.112951, avg_loss: 0.496910
000820/002340, loss: 0.168542, avg_loss: 0.495333
000825/002340, loss: 0.163078, avg_loss: 0.493526
000830/002340, loss: 0.208418, avg_loss: 0.492144
000835/002340, loss: 0.204179, avg_loss: 0.490463
000840/002340, loss: 0.262290, avg_loss: 0.488488
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 10, step 840/2340: {'accuracy': 0.6245487364620939}
000845/002340, loss: 0.166388, avg_loss: 0.486870
000850/002340, loss: 0.221429, avg_loss: 0.485510
000855/002340, loss: 0.376082, avg_loss: 0.484030
000860/002340, loss: 0.083231, avg_loss: 0.482307
000865/002340, loss: 0.161541, avg_loss: 0.480355
000870/002340, loss: 0.180701, avg_loss: 0.478405
000875/002340, loss: 0.175531, avg_loss: 0.476498
000880/002340, loss: 0.148172, avg_loss: 0.475174
000885/002340, loss: 0.110148, avg_loss: 0.473676
000890/002340, loss: 0.177225, avg_loss: 0.472175
000895/002340, loss: 0.051785, avg_loss: 0.470479
000900/002340, loss: 0.239419, avg_loss: 0.469122
000905/002340, loss: 0.294643, avg_loss: 0.467460
000910/002340, loss: 0.372546, avg_loss: 0.466119
000915/002340, loss: 0.160401, avg_loss: 0.464562
000920/002340, loss: 0.389829, avg_loss: 0.463444
000925/002340, loss: 0.461596, avg_loss: 0.462050
000930/002340, loss: 0.169349, avg_loss: 0.460443
000935/002340, loss: 0.274192, avg_loss: 0.459206
000940/002340, loss: 0.245536, avg_loss: 0.457409
000945/002340, loss: 0.124900, avg_loss: 0.455669
000950/002340, loss: 0.258810, avg_loss: 0.453951
000955/002340, loss: 0.328007, avg_loss: 0.452289
000960/002340, loss: 0.243825, avg_loss: 0.450600
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 12, step 960/2340: {'accuracy': 0.6389891696750902}
000965/002340, loss: 0.201036, avg_loss: 0.449321
000970/002340, loss: 0.091728, avg_loss: 0.447797
000975/002340, loss: 0.182425, avg_loss: 0.446324
000980/002340, loss: 0.159452, avg_loss: 0.444909
000985/002340, loss: 0.142912, avg_loss: 0.443522
000990/002340, loss: 0.304327, avg_loss: 0.442004
000995/002340, loss: 0.117483, avg_loss: 0.440452
001000/002340, loss: 0.156437, avg_loss: 0.438837
001005/002340, loss: 0.032182, avg_loss: 0.437682
001010/002340, loss: 0.063084, avg_loss: 0.436744
001015/002340, loss: 0.258552, avg_loss: 0.435504
001020/002340, loss: 0.091414, avg_loss: 0.434340
001025/002340, loss: 0.100409, avg_loss: 0.432843
001030/002340, loss: 0.064708, avg_loss: 0.431516
001035/002340, loss: 0.459350, avg_loss: 0.430340
001040/002340, loss: 0.195770, avg_loss: 0.428896
001045/002340, loss: 0.101108, avg_loss: 0.427430
001050/002340, loss: 0.162723, avg_loss: 0.425868
001055/002340, loss: 0.170199, avg_loss: 0.424800
001060/002340, loss: 0.066082, avg_loss: 0.423415
001065/002340, loss: 0.139599, avg_loss: 0.422219
001070/002340, loss: 0.089475, avg_loss: 0.420665
001075/002340, loss: 0.115157, avg_loss: 0.419250
001080/002340, loss: 0.085939, avg_loss: 0.417821
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 13, step 1080/2340: {'accuracy': 0.6173285198555957}
001085/002340, loss: 0.138964, avg_loss: 0.416740
001090/002340, loss: 0.385725, avg_loss: 0.415552
001095/002340, loss: 0.173466, avg_loss: 0.414612
001100/002340, loss: 0.101382, avg_loss: 0.413397
001105/002340, loss: 0.098917, avg_loss: 0.412091
001110/002340, loss: 0.088198, avg_loss: 0.410518
001115/002340, loss: 0.039977, avg_loss: 0.409207
001120/002340, loss: 0.126413, avg_loss: 0.407805
001125/002340, loss: 0.154641, avg_loss: 0.406540
001130/002340, loss: 0.221717, avg_loss: 0.405238
001135/002340, loss: 0.155590, avg_loss: 0.403870
001140/002340, loss: 0.072533, avg_loss: 0.402521
001145/002340, loss: 0.148947, avg_loss: 0.401401
001150/002340, loss: 0.202878, avg_loss: 0.400165
001155/002340, loss: 0.054971, avg_loss: 0.399305
001160/002340, loss: 0.058926, avg_loss: 0.398088
001165/002340, loss: 0.187665, avg_loss: 0.396901
001170/002340, loss: 0.091442, avg_loss: 0.395624
001175/002340, loss: 0.339817, avg_loss: 0.394529
001180/002340, loss: 0.029183, avg_loss: 0.393430
001185/002340, loss: 0.052091, avg_loss: 0.392348
001190/002340, loss: 0.175309, avg_loss: 0.391464
001195/002340, loss: 0.269615, avg_loss: 0.390438
001200/002340, loss: 0.042982, avg_loss: 0.389416
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 15, step 1200/2340: {'accuracy': 0.6353790613718412}
001205/002340, loss: 0.029362, avg_loss: 0.388045
001210/002340, loss: 0.106356, avg_loss: 0.386842
001215/002340, loss: 0.055282, avg_loss: 0.385720
001220/002340, loss: 0.025587, avg_loss: 0.384474
001225/002340, loss: 0.017830, avg_loss: 0.383314
001230/002340, loss: 0.156192, avg_loss: 0.382166
001235/002340, loss: 0.017268, avg_loss: 0.381167
001240/002340, loss: 0.015908, avg_loss: 0.379919
001245/002340, loss: 0.024442, avg_loss: 0.378661
001250/002340, loss: 0.016508, avg_loss: 0.377585
001255/002340, loss: 0.021355, avg_loss: 0.376479
001260/002340, loss: 0.024076, avg_loss: 0.375165
001265/002340, loss: 0.202033, avg_loss: 0.374116
001270/002340, loss: 0.027793, avg_loss: 0.372882
001275/002340, loss: 0.027369, avg_loss: 0.372247
001280/002340, loss: 0.021813, avg_loss: 0.371052
001285/002340, loss: 0.021163, avg_loss: 0.370046
001290/002340, loss: 0.046603, avg_loss: 0.369336
001295/002340, loss: 0.076338, avg_loss: 0.368328
001300/002340, loss: 0.183380, avg_loss: 0.367225
001305/002340, loss: 0.169317, avg_loss: 0.366140
001310/002340, loss: 0.020987, avg_loss: 0.365018
001315/002340, loss: 0.169484, avg_loss: 0.364127
001320/002340, loss: 0.044023, avg_loss: 0.363106
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 16, step 1320/2340: {'accuracy': 0.6462093862815884}
001325/002340, loss: 0.146640, avg_loss: 0.361943
001330/002340, loss: 0.053370, avg_loss: 0.360778
001335/002340, loss: 0.024849, avg_loss: 0.359785
001340/002340, loss: 0.040356, avg_loss: 0.358545
001345/002340, loss: 0.216520, avg_loss: 0.357564
001350/002340, loss: 0.020188, avg_loss: 0.356442
001355/002340, loss: 0.050854, avg_loss: 0.355434
001360/002340, loss: 0.013922, avg_loss: 0.354336
001365/002340, loss: 0.034302, avg_loss: 0.353537
001370/002340, loss: 0.083984, avg_loss: 0.352530
001375/002340, loss: 0.044313, avg_loss: 0.351671
001380/002340, loss: 0.197178, avg_loss: 0.350656
001385/002340, loss: 0.087372, avg_loss: 0.349721
001390/002340, loss: 0.122292, avg_loss: 0.348657
001395/002340, loss: 0.161705, avg_loss: 0.347780
001400/002340, loss: 0.014310, avg_loss: 0.346943
001405/002340, loss: 0.096345, avg_loss: 0.345930
001410/002340, loss: 0.142292, avg_loss: 0.345120
001415/002340, loss: 0.016984, avg_loss: 0.344193
001420/002340, loss: 0.014843, avg_loss: 0.343171
001425/002340, loss: 0.054250, avg_loss: 0.342329
001430/002340, loss: 0.049341, avg_loss: 0.341417
001435/002340, loss: 0.033567, avg_loss: 0.340340
001440/002340, loss: 0.108241, avg_loss: 0.339508
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 18, step 1440/2340: {'accuracy': 0.6137184115523465}
001445/002340, loss: 0.148780, avg_loss: 0.338643
001450/002340, loss: 0.121979, avg_loss: 0.337871
001455/002340, loss: 0.015762, avg_loss: 0.337010
001460/002340, loss: 0.197943, avg_loss: 0.336178
001465/002340, loss: 0.019593, avg_loss: 0.335371
001470/002340, loss: 0.129545, avg_loss: 0.334404
001475/002340, loss: 0.015238, avg_loss: 0.333483
001480/002340, loss: 0.016869, avg_loss: 0.332625
001485/002340, loss: 0.011418, avg_loss: 0.331565
001490/002340, loss: 0.338315, avg_loss: 0.330893
001495/002340, loss: 0.288740, avg_loss: 0.330484
001500/002340, loss: 0.148870, avg_loss: 0.329575
001505/002340, loss: 0.013757, avg_loss: 0.328768
001510/002340, loss: 0.016786, avg_loss: 0.327894
001515/002340, loss: 0.013239, avg_loss: 0.326989
001520/002340, loss: 0.024581, avg_loss: 0.326006
001525/002340, loss: 0.017539, avg_loss: 0.325226
001530/002340, loss: 0.067678, avg_loss: 0.324287
001535/002340, loss: 0.024253, avg_loss: 0.323389
001540/002340, loss: 0.077925, avg_loss: 0.322495
001545/002340, loss: 0.024680, avg_loss: 0.321567
001550/002340, loss: 0.012920, avg_loss: 0.320824
001555/002340, loss: 0.023837, avg_loss: 0.320000
001560/002340, loss: 0.221982, avg_loss: 0.319304
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 19, step 1560/2340: {'accuracy': 0.6137184115523465}
001565/002340, loss: 0.013699, avg_loss: 0.318449
001570/002340, loss: 0.011844, avg_loss: 0.317610
001575/002340, loss: 0.012580, avg_loss: 0.316855
001580/002340, loss: 0.037540, avg_loss: 0.316005
001585/002340, loss: 0.019229, avg_loss: 0.315232
001590/002340, loss: 0.048232, avg_loss: 0.314477
001595/002340, loss: 0.141452, avg_loss: 0.313963
001600/002340, loss: 0.015298, avg_loss: 0.313133
001605/002340, loss: 0.013662, avg_loss: 0.312229
001610/002340, loss: 0.160849, avg_loss: 0.311404
001615/002340, loss: 0.012301, avg_loss: 0.310524
001620/002340, loss: 0.063877, avg_loss: 0.309759
001625/002340, loss: 0.032892, avg_loss: 0.309026
001630/002340, loss: 0.177563, avg_loss: 0.308279
001635/002340, loss: 0.157313, avg_loss: 0.307644
001640/002340, loss: 0.130090, avg_loss: 0.306819
001645/002340, loss: 0.021889, avg_loss: 0.306081
001650/002340, loss: 0.152882, avg_loss: 0.305300
001655/002340, loss: 0.009122, avg_loss: 0.304627
001660/002340, loss: 0.015140, avg_loss: 0.303849
001665/002340, loss: 0.164985, avg_loss: 0.303089
001670/002340, loss: 0.008990, avg_loss: 0.302396
001675/002340, loss: 0.010757, avg_loss: 0.301671
001680/002340, loss: 0.009137, avg_loss: 0.300904
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 21, step 1680/2340: {'accuracy': 0.6173285198555957}
001685/002340, loss: 0.053387, avg_loss: 0.300194
001690/002340, loss: 0.022511, avg_loss: 0.299502
001695/002340, loss: 0.105420, avg_loss: 0.298722
001700/002340, loss: 0.013549, avg_loss: 0.297988
001705/002340, loss: 0.073981, avg_loss: 0.297318
001710/002340, loss: 0.014491, avg_loss: 0.296600
001715/002340, loss: 0.154422, avg_loss: 0.295955
001720/002340, loss: 0.163267, avg_loss: 0.295310
001725/002340, loss: 0.136114, avg_loss: 0.294759
001730/002340, loss: 0.015310, avg_loss: 0.294064
001735/002340, loss: 0.087005, avg_loss: 0.293422
001740/002340, loss: 0.020296, avg_loss: 0.292756
001745/002340, loss: 0.018787, avg_loss: 0.292135
001750/002340, loss: 0.034191, avg_loss: 0.291526
001755/002340, loss: 0.045470, avg_loss: 0.290987
001760/002340, loss: 0.014372, avg_loss: 0.290662
001765/002340, loss: 0.015767, avg_loss: 0.289942
001770/002340, loss: 0.039629, avg_loss: 0.289302
001775/002340, loss: 0.016410, avg_loss: 0.288527
001780/002340, loss: 0.038289, avg_loss: 0.287933
001785/002340, loss: 0.017720, avg_loss: 0.287493
001790/002340, loss: 0.033570, avg_loss: 0.286735
001795/002340, loss: 0.012522, avg_loss: 0.286079
001800/002340, loss: 0.053891, avg_loss: 0.285344
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 23, step 1800/2340: {'accuracy': 0.6245487364620939}
001805/002340, loss: 0.126177, avg_loss: 0.284716
001810/002340, loss: 0.011923, avg_loss: 0.284070
001815/002340, loss: 0.142181, avg_loss: 0.283613
001820/002340, loss: 0.010828, avg_loss: 0.282998
001825/002340, loss: 0.025087, avg_loss: 0.282492
001830/002340, loss: 0.273915, avg_loss: 0.281916
001835/002340, loss: 0.016827, avg_loss: 0.281382
001840/002340, loss: 0.010785, avg_loss: 0.280767
001845/002340, loss: 0.015339, avg_loss: 0.280337
001850/002340, loss: 0.020906, avg_loss: 0.279696
001855/002340, loss: 0.165239, avg_loss: 0.279069
001860/002340, loss: 0.053642, avg_loss: 0.278450
001865/002340, loss: 0.133574, avg_loss: 0.277862
001870/002340, loss: 0.097644, avg_loss: 0.277226
001875/002340, loss: 0.059441, avg_loss: 0.276570
001880/002340, loss: 0.016699, avg_loss: 0.275948
001885/002340, loss: 0.146401, avg_loss: 0.275488
001890/002340, loss: 0.011636, avg_loss: 0.274799
001895/002340, loss: 0.018686, avg_loss: 0.274214
001900/002340, loss: 0.026965, avg_loss: 0.273611
001905/002340, loss: 0.013933, avg_loss: 0.272935
001910/002340, loss: 0.125580, avg_loss: 0.272318
001915/002340, loss: 0.129783, avg_loss: 0.271802
001920/002340, loss: 0.116678, avg_loss: 0.271278
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 24, step 1920/2340: {'accuracy': 0.6173285198555957}
001925/002340, loss: 0.254784, avg_loss: 0.270806
001930/002340, loss: 0.157526, avg_loss: 0.270238
001935/002340, loss: 0.031608, avg_loss: 0.269644
001940/002340, loss: 0.009236, avg_loss: 0.269169
001945/002340, loss: 0.009980, avg_loss: 0.268799
001950/002340, loss: 0.033835, avg_loss: 0.268168
001955/002340, loss: 0.051771, avg_loss: 0.267547
001960/002340, loss: 0.142184, avg_loss: 0.267055
001965/002340, loss: 0.046325, avg_loss: 0.266676
001970/002340, loss: 0.041966, avg_loss: 0.266192
001975/002340, loss: 0.020202, avg_loss: 0.265597
001980/002340, loss: 0.125195, avg_loss: 0.265071
001985/002340, loss: 0.019307, avg_loss: 0.264558
001990/002340, loss: 0.011511, avg_loss: 0.263954
001995/002340, loss: 0.092994, avg_loss: 0.263384
002000/002340, loss: 0.098703, avg_loss: 0.262809
002005/002340, loss: 0.017836, avg_loss: 0.262371
002010/002340, loss: 0.047947, avg_loss: 0.261831
002015/002340, loss: 0.157151, avg_loss: 0.261291
002020/002340, loss: 0.063095, avg_loss: 0.260695
002025/002340, loss: 0.239691, avg_loss: 0.260198
002030/002340, loss: 0.008953, avg_loss: 0.259652
002035/002340, loss: 0.008303, avg_loss: 0.259056
002040/002340, loss: 0.133496, avg_loss: 0.258505
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 26, step 2040/2340: {'accuracy': 0.6173285198555957}
002045/002340, loss: 0.070495, avg_loss: 0.258069
002050/002340, loss: 0.082666, avg_loss: 0.257558
002055/002340, loss: 0.036117, avg_loss: 0.257011
002060/002340, loss: 0.018446, avg_loss: 0.256447
002065/002340, loss: 0.019938, avg_loss: 0.255982
002070/002340, loss: 0.010070, avg_loss: 0.255545
002075/002340, loss: 0.010592, avg_loss: 0.254990
002080/002340, loss: 0.047749, avg_loss: 0.254418
002085/002340, loss: 0.157273, avg_loss: 0.253991
002090/002340, loss: 0.012268, avg_loss: 0.253488
002095/002340, loss: 0.010397, avg_loss: 0.252964
002100/002340, loss: 0.152166, avg_loss: 0.252516
002105/002340, loss: 0.149034, avg_loss: 0.252077
002110/002340, loss: 0.022406, avg_loss: 0.251554
002115/002340, loss: 0.050635, avg_loss: 0.251001
002120/002340, loss: 0.101384, avg_loss: 0.250624
002125/002340, loss: 0.019535, avg_loss: 0.250064
002130/002340, loss: 0.017638, avg_loss: 0.249509
002135/002340, loss: 0.007454, avg_loss: 0.249097
002140/002340, loss: 0.170886, avg_loss: 0.248638
002145/002340, loss: 0.008658, avg_loss: 0.248148
002150/002340, loss: 0.018784, avg_loss: 0.247731
002155/002340, loss: 0.006945, avg_loss: 0.247294
002160/002340, loss: 0.149141, avg_loss: 0.246973
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 27, step 2160/2340: {'accuracy': 0.6173285198555957}
002165/002340, loss: 0.070260, avg_loss: 0.246627
002170/002340, loss: 0.018735, avg_loss: 0.246110
002175/002340, loss: 0.011750, avg_loss: 0.245641
002180/002340, loss: 0.024557, avg_loss: 0.245194
002185/002340, loss: 0.022439, avg_loss: 0.244675
002190/002340, loss: 0.009183, avg_loss: 0.244218
002195/002340, loss: 0.147473, avg_loss: 0.243797
002200/002340, loss: 0.008439, avg_loss: 0.243311
002205/002340, loss: 0.009392, avg_loss: 0.242842
002210/002340, loss: 0.007260, avg_loss: 0.242363
002215/002340, loss: 0.006505, avg_loss: 0.241869
002220/002340, loss: 0.036663, avg_loss: 0.241415
002225/002340, loss: 0.010591, avg_loss: 0.240936
002230/002340, loss: 0.008057, avg_loss: 0.240418
002235/002340, loss: 0.005135, avg_loss: 0.240005
002240/002340, loss: 0.009763, avg_loss: 0.239661
002245/002340, loss: 0.009173, avg_loss: 0.239206
002250/002340, loss: 0.015700, avg_loss: 0.238819
002255/002340, loss: 0.021340, avg_loss: 0.238346
002260/002340, loss: 0.060185, avg_loss: 0.237882
002265/002340, loss: 0.038913, avg_loss: 0.237484
002270/002340, loss: 0.016376, avg_loss: 0.237112
002275/002340, loss: 0.010828, avg_loss: 0.236714
002280/002340, loss: 0.129731, avg_loss: 0.236370
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
epoch 29, step 2280/2340: {'accuracy': 0.6064981949458483}
002285/002340, loss: 0.044581, avg_loss: 0.235897
002290/002340, loss: 0.008923, avg_loss: 0.235524
002295/002340, loss: 0.011697, avg_loss: 0.235179
002300/002340, loss: 0.020234, avg_loss: 0.234708
002305/002340, loss: 0.024606, avg_loss: 0.234225
002310/002340, loss: 0.007431, avg_loss: 0.233798
002315/002340, loss: 0.006717, avg_loss: 0.233382
002320/002340, loss: 0.017990, avg_loss: 0.232940
002325/002340, loss: 0.145197, avg_loss: 0.232597
002330/002340, loss: 0.013951, avg_loss: 0.232139
002335/002340, loss: 0.014238, avg_loss: 0.231719
002340/002340, loss: 0.019154, avg_loss: 0.231268
***** Running train evaluation *****
Num examples = 2490
Instantaneous batch size per device = 32
Train Dataset Result: {'accuracy': 0.9955823293172691}
***** Running dev evaluation *****
Num examples = 277
Instantaneous batch size per device = 32
Dev Dataset Result: {'accuracy': 0.6101083032490975}
DEV Best Result: accuracy, 0.6462093862815884
Training time 0:02:36
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