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train.out
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train.out
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2019-08-15 01:11:57.661154: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-08-15 01:11:57.957431: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1392] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:03:00.0
totalMemory: 11.90GiB freeMemory: 11.74GiB
2019-08-15 01:11:57.957467: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1471] Adding visible gpu devices: 0
2019-08-15 01:11:58.189101: I tensorflow/core/common_runtime/gpu/gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-08-15 01:11:58.189143: I tensorflow/core/common_runtime/gpu/gpu_device.cc:958] 0
2019-08-15 01:11:58.189150: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: N
2019-08-15 01:11:58.189414: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11360 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:03:00.0, compute capability: 6.1)
# --------------- start making data --------------- #
answer mismatch number 754
char2idx number: 1259
# --------------- start making data --------------- #
answer mismatch number 309
char2idx number: 1311
---------
Variables: name (type shape) [size]
---------
word_emb/glove_table:0 (float32_ref 95205x300) [28561500, bytes: 114246000]
char_emb/char_embed:0 (float32_ref 1311x8) [10488, bytes: 41952]
char_emb/TDNN/filter_5/filters:0 (float32_ref 1x5x8x100) [4000, bytes: 16000]
char_emb/TDNN/filter_5/filters_bias:0 (float32_ref 100) [100, bytes: 400]
highway_net/transgate/highway_matrix:0 (float32_ref 400x400) [160000, bytes: 640000]
highway_net/transgate/highway_bias:0 (float32_ref 400) [400, bytes: 1600]
highway_net/MLP/highway_matrix:0 (float32_ref 400x400) [160000, bytes: 640000]
highway_net/MLP/highway_bias:0 (float32_ref 400) [400, bytes: 1600]
context_cont/bidirectional_rnn/fw/multi_rnn_cell/cell_0/lstm_cell/kernel:0 (float32_ref 800x1600) [1280000, bytes: 5120000]
context_cont/bidirectional_rnn/fw/multi_rnn_cell/cell_0/lstm_cell/bias:0 (float32_ref 1600) [1600, bytes: 6400]
context_cont/bidirectional_rnn/bw/multi_rnn_cell/cell_0/lstm_cell/kernel:0 (float32_ref 800x1600) [1280000, bytes: 5120000]
context_cont/bidirectional_rnn/bw/multi_rnn_cell/cell_0/lstm_cell/bias:0 (float32_ref 1600) [1600, bytes: 6400]
context_ques/bidirectional_rnn/fw/multi_rnn_cell/cell_0/lstm_cell/kernel:0 (float32_ref 800x1600) [1280000, bytes: 5120000]
context_ques/bidirectional_rnn/fw/multi_rnn_cell/cell_0/lstm_cell/bias:0 (float32_ref 1600) [1600, bytes: 6400]
context_ques/bidirectional_rnn/bw/multi_rnn_cell/cell_0/lstm_cell/kernel:0 (float32_ref 800x1600) [1280000, bytes: 5120000]
context_ques/bidirectional_rnn/bw/multi_rnn_cell/cell_0/lstm_cell/bias:0 (float32_ref 1600) [1600, bytes: 6400]
similarity_matrix/similarity_weight:0 (float32_ref 2400x1) [2400, bytes: 9600]
modeling_layer_0/bidirectional_rnn/fw/multi_rnn_cell/cell_0/lstm_cell/kernel:0 (float32_ref 3600x1600) [5760000, bytes: 23040000]
modeling_layer_0/bidirectional_rnn/fw/multi_rnn_cell/cell_0/lstm_cell/bias:0 (float32_ref 1600) [1600, bytes: 6400]
modeling_layer_0/bidirectional_rnn/bw/multi_rnn_cell/cell_0/lstm_cell/kernel:0 (float32_ref 3600x1600) [5760000, bytes: 23040000]
modeling_layer_0/bidirectional_rnn/bw/multi_rnn_cell/cell_0/lstm_cell/bias:0 (float32_ref 1600) [1600, bytes: 6400]
modeling_layer_1/bidirectional_rnn/fw/multi_rnn_cell/cell_0/lstm_cell/kernel:0 (float32_ref 1200x1600) [1920000, bytes: 7680000]
modeling_layer_1/bidirectional_rnn/fw/multi_rnn_cell/cell_0/lstm_cell/bias:0 (float32_ref 1600) [1600, bytes: 6400]
modeling_layer_1/bidirectional_rnn/bw/multi_rnn_cell/cell_0/lstm_cell/kernel:0 (float32_ref 1200x1600) [1920000, bytes: 7680000]
modeling_layer_1/bidirectional_rnn/bw/multi_rnn_cell/cell_0/lstm_cell/bias:0 (float32_ref 1600) [1600, bytes: 6400]
output_layer/start_output_weight:0 (float32_ref 4000x1) [4000, bytes: 16000]
output_layer/end_output_weight:0 (float32_ref 4000x1) [4000, bytes: 16000]
output_layer/bidirectional_rnn/fw/multi_rnn_cell/cell_0/lstm_cell/kernel:0 (float32_ref 1200x1600) [1920000, bytes: 7680000]
output_layer/bidirectional_rnn/fw/multi_rnn_cell/cell_0/lstm_cell/bias:0 (float32_ref 1600) [1600, bytes: 6400]
output_layer/bidirectional_rnn/bw/multi_rnn_cell/cell_0/lstm_cell/kernel:0 (float32_ref 1200x1600) [1920000, bytes: 7680000]
output_layer/bidirectional_rnn/bw/multi_rnn_cell/cell_0/lstm_cell/bias:0 (float32_ref 1600) [1600, bytes: 6400]
Total size of variables: 53243288
Total bytes of variables: 212973152
-------------------- Epoch 0 is ongoing --------------------
Epoch 0, train_step 99: loss : 9.3667
Epoch 0, train_step 199: loss : 8.9406
Epoch 0, train_step 299: loss : 10.5820
Epoch 0, train_step 399: loss : 9.5474
Epoch 0, train_step 499: loss : 8.7880
Epoch 0, train_step 599: loss : 8.8106
Epoch 0, train_step 699: loss : 8.5788
Epoch 0, train_step 799: loss : 8.8438
Epoch 0, train_step 899: loss : 8.8164
Epoch 0, train_step 999: loss : 9.7059
Epoch 0, train_step 1099: loss : 8.6720
Epoch 0, train_step 1199: loss : 9.1311
Epoch 0, train_step 1299: loss : 9.3325
Epoch 0, train_step 1399: loss : 10.3372
Epoch 0, train_step 1499: loss : 8.9439
Epoch 0, train_step 1599: loss : 9.2526
Epoch 0, train_step 1699: loss : 9.3014
Epoch 0, train_step 1799: loss : 9.4277
Epoch 0, train_step 1899: loss : 9.8124
Epoch 0, train_step 1999: loss : 9.5694
Epoch 0, train_step 2099: loss : 8.4685
Epoch 0, train_step 2199: loss : 9.9917
Epoch 0, train_step 2299: loss : 8.5506
Epoch 0, train_step 2399: loss : 9.8625
Epoch 0, train_step 2499: loss : 9.7341
Epoch 0, train_step 2599: loss : 9.9889
Epoch 0, train_step 2699: loss : 9.4325
Epoch 0, train_step 2799: loss : 9.2664
Epoch 0, train_step 2899: loss : 9.2799
Epoch 0, train_step 2999: loss : 8.7135
Epoch 0, train_step 3099: loss : 9.6714
Epoch 0, train_step 3199: loss : 9.3795
Epoch 0, train_step 3299: loss : 9.3620
Epoch 0, train_step 3399: loss : 9.4037
Epoch 0, train_step 3499: loss : 9.8726
Epoch 0, train_step 3599: loss : 9.0864
Epoch 0, train_step 3699: loss : 8.9808
Epoch 0, train_step 3799: loss : 8.7680
Epoch 0, train_step 3899: loss : 9.4035
Epoch 0, train_step 3999: loss : 9.5569
Epoch 0, train_step 4099: loss : 9.2218
Epoch 0, train_step 4199: loss : 9.9071
Epoch 0, train_step 4299: loss : 9.5133
Successfully saved model
Epoch 0, dev_step 99
Epoch 0, dev_step 199
Epoch 0, dev_step 299
Epoch 0, dev_step 399
Epoch 0, dev_step 499
Epoch 0, dev_step 599
Epoch 0, dev_step 699
Epoch 0, dev_step 799
Epoch 0, dev_step 899
Epoch 0, dev_step 999
Epoch 0, dev_step 1099
Epoch 0, dev_step 1199
Epoch 0, dev_step 1299
Epoch 0, dev_step 1399
Epoch 0, dev_step 1499
Epoch 0, dev_step 1599
Epoch 0, dev_step 1699
exact_match
0.4824976348155156
f1 score
5.165427831979965
-------------------- Epoch 1 is ongoing --------------------
Epoch 1, train_step 99: loss : 8.4507
Epoch 1, train_step 199: loss : 8.3525
Epoch 1, train_step 299: loss : 9.5626
Epoch 1, train_step 399: loss : 9.1725
Epoch 1, train_step 499: loss : 8.3416
Epoch 1, train_step 599: loss : 8.7611
Epoch 1, train_step 699: loss : 7.6492
Epoch 1, train_step 799: loss : 8.3373
Epoch 1, train_step 899: loss : 8.3364
Epoch 1, train_step 999: loss : 9.4457
Epoch 1, train_step 1099: loss : 8.1755
Epoch 1, train_step 1199: loss : 8.7220
Epoch 1, train_step 1299: loss : 8.1256
Epoch 1, train_step 1399: loss : 9.4164
Epoch 1, train_step 1499: loss : 8.9319
Epoch 1, train_step 1599: loss : 9.2808
Epoch 1, train_step 1699: loss : 8.7814
Epoch 1, train_step 1799: loss : 9.2913
Epoch 1, train_step 1899: loss : 9.8081
Epoch 1, train_step 1999: loss : 8.4074
Epoch 1, train_step 2099: loss : 7.6830
Epoch 1, train_step 2199: loss : 9.1310
Epoch 1, train_step 2299: loss : 8.7073
Epoch 1, train_step 2399: loss : 9.3901
Epoch 1, train_step 2499: loss : 9.3614
Epoch 1, train_step 2599: loss : 9.8400
Epoch 1, train_step 2699: loss : 9.4544
Epoch 1, train_step 2799: loss : 9.3218
Epoch 1, train_step 2899: loss : 9.1751
Epoch 1, train_step 2999: loss : 8.7333
Epoch 1, train_step 3099: loss : 9.5557
Epoch 1, train_step 3199: loss : 9.5697
Epoch 1, train_step 3299: loss : 8.8671
Epoch 1, train_step 3399: loss : 8.8276
Epoch 1, train_step 3499: loss : 10.0914
Epoch 1, train_step 3599: loss : 9.0769
Epoch 1, train_step 3699: loss : 9.1669
Epoch 1, train_step 3799: loss : 8.8043
Epoch 1, train_step 3899: loss : 9.5824
Epoch 1, train_step 3999: loss : 9.1617
Epoch 1, train_step 4099: loss : 8.7793
Epoch 1, train_step 4199: loss : 9.7479
Epoch 1, train_step 4299: loss : 9.2555
Successfully saved model
Epoch 1, dev_step 99
Epoch 1, dev_step 199
Epoch 1, dev_step 299
Epoch 1, dev_step 399
Epoch 1, dev_step 499
Epoch 1, dev_step 599
Epoch 1, dev_step 699
Epoch 1, dev_step 799
Epoch 1, dev_step 899
Epoch 1, dev_step 999
Epoch 1, dev_step 1099
Epoch 1, dev_step 1199
Epoch 1, dev_step 1299
Epoch 1, dev_step 1399
Epoch 1, dev_step 1499
Epoch 1, dev_step 1599
/home/minbyul/anaconda3/envs/re_env/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/minbyul/anaconda3/envs/re_env/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/minbyul/anaconda3/envs/re_env/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/minbyul/anaconda3/envs/re_env/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/minbyul/anaconda3/envs/re_env/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/minbyul/anaconda3/envs/re_env/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
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Epoch 1, dev_step 1699
exact_match
0.6527909176915799
f1 score
5.499596954931828
-------------------- Epoch 2 is ongoing --------------------
Epoch 2, train_step 99: loss : 8.7059
Epoch 2, train_step 199: loss : 8.4105
Epoch 2, train_step 299: loss : 9.9306
Epoch 2, train_step 399: loss : 8.8816
Epoch 2, train_step 499: loss : 8.1977
Epoch 2, train_step 599: loss : 7.7744
Epoch 2, train_step 699: loss : 8.1223
Epoch 2, train_step 799: loss : 7.7577
Epoch 2, train_step 899: loss : 8.4382
Epoch 2, train_step 999: loss : 10.0887
Epoch 2, train_step 1099: loss : 8.4526
Epoch 2, train_step 1199: loss : 9.0323
Epoch 2, train_step 1299: loss : 8.1537
Epoch 2, train_step 1399: loss : 9.2853
Epoch 2, train_step 1499: loss : 9.0330
Epoch 2, train_step 1599: loss : 9.1107
Epoch 2, train_step 1699: loss : 8.7744
Epoch 2, train_step 1799: loss : 9.0855
Epoch 2, train_step 1899: loss : 9.4632
Epoch 2, train_step 1999: loss : 8.6066
Epoch 2, train_step 2099: loss : 7.9641
Epoch 2, train_step 2199: loss : 8.7538
Epoch 2, train_step 2299: loss : 8.5454
Epoch 2, train_step 2399: loss : 9.3807
Epoch 2, train_step 2499: loss : 9.2497
Epoch 2, train_step 2599: loss : 10.2608
Epoch 2, train_step 2699: loss : 9.6505
Epoch 2, train_step 2799: loss : 9.2954
Epoch 2, train_step 2899: loss : 8.6534
Epoch 2, train_step 2999: loss : 8.2745
Epoch 2, train_step 3099: loss : 9.7048
Epoch 2, train_step 3199: loss : 8.9962
Epoch 2, train_step 3299: loss : 8.9817
Epoch 2, train_step 3399: loss : 8.8753
Epoch 2, train_step 3499: loss : 9.8590
Epoch 2, train_step 3599: loss : 9.0675
Epoch 2, train_step 3699: loss : 9.1319
Epoch 2, train_step 3799: loss : 8.7876
Epoch 2, train_step 3899: loss : 9.3642
Epoch 2, train_step 3999: loss : 9.3495
Epoch 2, train_step 4099: loss : 8.4553
Epoch 2, train_step 4199: loss : 10.3462
Epoch 2, train_step 4299: loss : 9.3365
Successfully saved model
Epoch 2, dev_step 99
Epoch 2, dev_step 199
Epoch 2, dev_step 299
Epoch 2, dev_step 399
Epoch 2, dev_step 499
Epoch 2, dev_step 599
Epoch 2, dev_step 699
Epoch 2, dev_step 799
Epoch 2, dev_step 899
Epoch 2, dev_step 999
Epoch 2, dev_step 1099
Epoch 2, dev_step 1199
Epoch 2, dev_step 1299
Epoch 2, dev_step 1399
Epoch 2, dev_step 1499
Epoch 2, dev_step 1599
Epoch 2, dev_step 1699
exact_match
0.6717123935666982
f1 score
5.781498654775652
-------------------- Epoch 3 is ongoing --------------------
Epoch 3, train_step 99: loss : 8.3667
Epoch 3, train_step 199: loss : 8.1225
Epoch 3, train_step 299: loss : 9.8917
Epoch 3, train_step 399: loss : 8.6744
Epoch 3, train_step 499: loss : 7.4891
Epoch 3, train_step 599: loss : 7.7412
Epoch 3, train_step 699: loss : 7.2495
Epoch 3, train_step 799: loss : 7.6681
Epoch 3, train_step 899: loss : 8.3270
Epoch 3, train_step 999: loss : 9.8978
Epoch 3, train_step 1099: loss : 8.5658
Epoch 3, train_step 1199: loss : 8.5385
Epoch 3, train_step 1299: loss : 8.0493
Epoch 3, train_step 1399: loss : 8.3840
Epoch 3, train_step 1499: loss : 9.1341
Epoch 3, train_step 1599: loss : 9.0886
Epoch 3, train_step 1699: loss : 8.2670
Epoch 3, train_step 1799: loss : 8.5396
Epoch 3, train_step 1899: loss : 9.1983
Epoch 3, train_step 1999: loss : 8.1674
Epoch 3, train_step 2099: loss : 7.4263
Epoch 3, train_step 2199: loss : 8.2953
Epoch 3, train_step 2299: loss : 7.5807
Epoch 3, train_step 2399: loss : 9.2021
Epoch 3, train_step 2499: loss : 9.4969
Epoch 3, train_step 2599: loss : 9.5261
Epoch 3, train_step 2699: loss : 9.1125
Epoch 3, train_step 2799: loss : 8.8251
Epoch 3, train_step 2899: loss : 8.7452
Epoch 3, train_step 2999: loss : 7.5698
Epoch 3, train_step 3099: loss : 9.4644
Epoch 3, train_step 3199: loss : 9.0390
Epoch 3, train_step 3299: loss : 8.3843
Epoch 3, train_step 3399: loss : 8.3265
Epoch 3, train_step 3499: loss : 9.7625
Epoch 3, train_step 3599: loss : 8.3701
Epoch 3, train_step 3699: loss : 9.1320
Epoch 3, train_step 3799: loss : 8.5138
Epoch 3, train_step 3899: loss : 8.6524
Epoch 3, train_step 3999: loss : 9.3195
Epoch 3, train_step 4099: loss : 8.7275
Epoch 3, train_step 4199: loss : 9.8199
Epoch 3, train_step 4299: loss : 8.8400
Successfully saved model
Epoch 3, dev_step 99
Epoch 3, dev_step 199
Epoch 3, dev_step 299
Epoch 3, dev_step 399
Epoch 3, dev_step 499
Epoch 3, dev_step 599
Epoch 3, dev_step 699
Epoch 3, dev_step 799
Epoch 3, dev_step 899
Epoch 3, dev_step 999
Epoch 3, dev_step 1099
Epoch 3, dev_step 1199
Epoch 3, dev_step 1299
Epoch 3, dev_step 1399
Epoch 3, dev_step 1499
Epoch 3, dev_step 1599
Epoch 3, dev_step 1699
exact_match
1.390728476821192
f1 score
7.039338612040506
-------------------- Epoch 4 is ongoing --------------------
Epoch 4, train_step 99: loss : 8.3878
Epoch 4, train_step 199: loss : 7.9856
Epoch 4, train_step 299: loss : 9.9021
Epoch 4, train_step 399: loss : 8.2444
Epoch 4, train_step 499: loss : 7.7362
Epoch 4, train_step 599: loss : 7.5569
Epoch 4, train_step 699: loss : 7.5450
Epoch 4, train_step 799: loss : 7.3450
Epoch 4, train_step 899: loss : 8.2079
Epoch 4, train_step 999: loss : 9.7011
Epoch 4, train_step 1099: loss : 7.3276
Epoch 4, train_step 1199: loss : 8.0726
Epoch 4, train_step 1299: loss : 8.4462
Epoch 4, train_step 1399: loss : 8.3119
Epoch 4, train_step 1499: loss : 8.6387
Epoch 4, train_step 1599: loss : 8.9880
Epoch 4, train_step 1699: loss : 8.5957
Epoch 4, train_step 1799: loss : 8.5092
Epoch 4, train_step 1899: loss : 9.3919
Epoch 4, train_step 1999: loss : 8.0932
Epoch 4, train_step 2099: loss : 7.2629
Epoch 4, train_step 2199: loss : 8.6560
Epoch 4, train_step 2299: loss : 8.0384
Epoch 4, train_step 2399: loss : 9.1642
Epoch 4, train_step 2499: loss : 9.3657
Epoch 4, train_step 2599: loss : 9.5813
Epoch 4, train_step 2699: loss : 8.9009
Epoch 4, train_step 2799: loss : 8.3828
Epoch 4, train_step 2899: loss : 8.5083
Epoch 4, train_step 2999: loss : 8.7914
Epoch 4, train_step 3099: loss : 9.3395
Epoch 4, train_step 3199: loss : 9.3672
Epoch 4, train_step 3299: loss : 8.4946
Epoch 4, train_step 3399: loss : 7.8342
Epoch 4, train_step 3499: loss : 9.7166
Epoch 4, train_step 3599: loss : 8.6207
Epoch 4, train_step 3699: loss : 8.7548
Epoch 4, train_step 3799: loss : 8.2380
Epoch 4, train_step 3899: loss : 8.8694
Epoch 4, train_step 3999: loss : 8.4813
Epoch 4, train_step 4099: loss : 9.2479
Epoch 4, train_step 4199: loss : 9.7217
Epoch 4, train_step 4299: loss : 9.2689
Successfully saved model
Epoch 4, dev_step 99
Epoch 4, dev_step 199
Epoch 4, dev_step 299
Epoch 4, dev_step 399
Epoch 4, dev_step 499
Epoch 4, dev_step 599
Epoch 4, dev_step 699
Epoch 4, dev_step 799
Epoch 4, dev_step 899
Epoch 4, dev_step 999
Epoch 4, dev_step 1099
Epoch 4, dev_step 1199
Epoch 4, dev_step 1299
Epoch 4, dev_step 1399
Epoch 4, dev_step 1499
Epoch 4, dev_step 1599
Epoch 4, dev_step 1699
exact_match
1.4474929044465468
f1 score
7.288634348332177
-------------------- Epoch 5 is ongoing --------------------
Epoch 5, train_step 99: loss : 8.1367
Epoch 5, train_step 199: loss : 7.7150
Epoch 5, train_step 299: loss : 9.4728
Epoch 5, train_step 399: loss : 7.9397
Epoch 5, train_step 499: loss : 7.9583
Epoch 5, train_step 599: loss : 6.6027
Epoch 5, train_step 699: loss : 7.5990
Epoch 5, train_step 799: loss : 7.1500
Epoch 5, train_step 899: loss : 7.5400
Epoch 5, train_step 999: loss : 9.7213
Epoch 5, train_step 1099: loss : 7.7455
Epoch 5, train_step 1199: loss : 7.8379
Epoch 5, train_step 1299: loss : 7.1090
Epoch 5, train_step 1399: loss : 8.5112
Epoch 5, train_step 1499: loss : 8.7798
Epoch 5, train_step 1599: loss : 8.6509
Epoch 5, train_step 1699: loss : 7.7914
Epoch 5, train_step 1799: loss : 7.8385
Epoch 5, train_step 1899: loss : 9.0045
Epoch 5, train_step 1999: loss : 7.3826
Epoch 5, train_step 2099: loss : 7.9768
Epoch 5, train_step 2199: loss : 8.0147
Epoch 5, train_step 2299: loss : 8.0889
Epoch 5, train_step 2399: loss : 9.0786
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