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gamble27/MIT_MNIST_NN

Created July 28, 2019 16:47
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Flattened 2 digit MNIST NN output
Raw
MIT_MNIST_NN
/home/olga/Projects/ML_MIT/venv/bin/python /home/olga/pycharm-2018.3.5/helpers/pydev/pydevd.py --multiproc --qt-support=auto --client 127.0.0.1 --port 35909 --file /home/olga/Projects/ML_MIT/project2_mnist/part2_twodigit/mlp.py
pydev debugger: process 2921 is connecting
Connected to pydev debugger (build 183.6156.13)
-------------
Epoch 1:
100%|██████████| 562/562 [00:49<00:00, 11.27it/s]
Train | loss1: 0.776068 accuracy1: 0.792538 | loss2: 0.798555 accuracy2: 0.777441
100%|██████████| 62/62 [00:00<00:00, 666.11it/s]
Valid | loss1: 0.430175 accuracy1: 0.878780 | loss2: 0.457375 accuracy2: 0.860887
0%| | 0/562 [00:00<?, ?it/s]-------------
Epoch 2:
100%|██████████| 562/562 [00:02<00:00, 233.25it/s]
0%| | 0/62 [00:00<?, ?it/s]Train | loss1: 0.396823 accuracy1: 0.886927 | loss2: 0.426801 accuracy2: 0.870357
100%|██████████| 62/62 [00:00<00:00, 860.15it/s]
Valid | loss1: 0.382405 accuracy1: 0.889113 | loss2: 0.403938 accuracy2: 0.875504
-------------
Epoch 3:
100%|██████████| 562/562 [00:02<00:00, 225.87it/s]
0%| | 0/62 [00:00<?, ?it/s]Train | loss1: 0.360463 accuracy1: 0.896769 | loss2: 0.390284 accuracy2: 0.883285
100%|██████████| 62/62 [00:00<00:00, 729.80it/s]
Valid | loss1: 0.367611 accuracy1: 0.894405 | loss2: 0.386381 accuracy2: 0.880544
0%| | 0/562 [00:00<?, ?it/s]-------------
Epoch 4:
97%|█████████▋| 547/562 [00:02<00:00, 218.22it/s]Train | loss1: 0.342956 accuracy1: 0.901246 | loss2: 0.372160 accuracy2: 0.889290
100%|██████████| 562/562 [00:02<00:00, 218.78it/s]
0%| | 0/62 [00:00<?, ?it/s]Valid | loss1: 0.360734 accuracy1: 0.895161 | loss2: 0.376846 accuracy2: 0.886341
100%|██████████| 62/62 [00:00<00:00, 825.23it/s]
-------------
Epoch 5:
100%|██████████| 562/562 [00:02<00:00, 221.92it/s]
Train | loss1: 0.331562 accuracy1: 0.904443 | loss2: 0.360041 accuracy2: 0.893127
0%| | 0/62 [00:00<?, ?it/s]Valid | loss1: 0.356971 accuracy1: 0.896421 | loss2: 0.370512 accuracy2: 0.887853
100%|██████████| 62/62 [00:00<00:00, 807.68it/s]
-------------
Epoch 6:
98%|█████████▊| 553/562 [00:02<00:00, 232.64it/s]Train | loss1: 0.323069 accuracy1: 0.906973 | loss2: 0.350922 accuracy2: 0.895880
100%|██████████| 562/562 [00:02<00:00, 227.98it/s]
100%|██████████| 62/62 [00:00<00:00, 829.59it/s]
Valid | loss1: 0.354749 accuracy1: 0.899950 | loss2: 0.365907 accuracy2: 0.890625
-------------
Epoch 7:
97%|█████████▋| 543/562 [00:02<00:00, 232.17it/s]Train | loss1: 0.316259 accuracy1: 0.909169 | loss2: 0.343622 accuracy2: 0.898354
100%|██████████| 562/562 [00:02<00:00, 222.53it/s]
0%| | 0/62 [00:00<?, ?it/s]Valid | loss1: 0.353413 accuracy1: 0.900202 | loss2: 0.362405 accuracy2: 0.893397
100%|██████████| 62/62 [00:00<00:00, 798.77it/s]
-------------
Epoch 8:
100%|██████████| 562/562 [00:02<00:00, 226.29it/s]
0%| | 0/62 [00:00<?, ?it/s]Train | loss1: 0.310557 accuracy1: 0.910698 | loss2: 0.337544 accuracy2: 0.900328
Valid | loss1: 0.352648 accuracy1: 0.899698 | loss2: 0.359679 accuracy2: 0.894153
100%|██████████| 62/62 [00:00<00:00, 792.77it/s]
-------------
Epoch 9:
100%|██████████| 562/562 [00:02<00:00, 226.48it/s]
Train | loss1: 0.305646 accuracy1: 0.912172 | loss2: 0.332348 accuracy2: 0.901968
100%|██████████| 62/62 [00:00<00:00, 701.14it/s]
Valid | loss1: 0.352283 accuracy1: 0.898942 | loss2: 0.357529 accuracy2: 0.894909
-------------
Epoch 10:
100%|██████████| 562/562 [00:02<00:00, 213.11it/s]
0%| | 0/62 [00:00<?, ?it/s]Train | loss1: 0.301333 accuracy1: 0.913201 | loss2: 0.327817 accuracy2: 0.903025
100%|██████████| 62/62 [00:00<00:00, 860.38it/s]
Valid | loss1: 0.352215 accuracy1: 0.898438 | loss2: 0.355823 accuracy2: 0.895665
-------------
Epoch 11:
97%|█████████▋| 546/562 [00:02<00:00, 222.32it/s]Train | loss1: 0.297492 accuracy1: 0.914202 | loss2: 0.323807 accuracy2: 0.904220
100%|██████████| 562/562 [00:02<00:00, 224.67it/s]
100%|██████████| 62/62 [00:00<00:00, 737.55it/s]
Valid | loss1: 0.352375 accuracy1: 0.899194 | loss2: 0.354468 accuracy2: 0.897429
0%| | 0/562 [00:00<?, ?it/s]-------------
Epoch 12:
99%|█████████▉| 558/562 [00:02<00:00, 229.69it/s]Train | loss1: 0.294033 accuracy1: 0.915036 | loss2: 0.320212 accuracy2: 0.904665
100%|██████████| 562/562 [00:02<00:00, 229.34it/s]
0%| | 0/62 [00:00<?, ?it/s]Valid | loss1: 0.352714 accuracy1: 0.898438 | loss2: 0.353398 accuracy2: 0.896925
100%|██████████| 62/62 [00:00<00:00, 826.28it/s]
-------------
Epoch 13:
100%|█████████▉| 560/562 [00:02<00:00, 234.91it/s]Train | loss1: 0.290891 accuracy1: 0.916342 | loss2: 0.316959 accuracy2: 0.905333
100%|██████████| 562/562 [00:02<00:00, 231.68it/s]
0%| | 0/62 [00:00<?, ?it/s]Valid | loss1: 0.353195 accuracy1: 0.898185 | loss2: 0.352561 accuracy2: 0.898185
100%|██████████| 62/62 [00:00<00:00, 808.02it/s]
-------------
Epoch 14:
100%|██████████| 562/562 [00:02<00:00, 236.14it/s]
Train | loss1: 0.288017 accuracy1: 0.916787 | loss2: 0.313989 accuracy2: 0.906611
0%| | 0/62 [00:00<?, ?it/s]Valid | loss1: 0.353787 accuracy1: 0.898942 | loss2: 0.351919 accuracy2: 0.898185
100%|██████████| 62/62 [00:00<00:00, 834.89it/s]
-------------
Epoch 15:
99%|█████████▉| 556/562 [00:02<00:00, 232.56it/s]Train | loss1: 0.285370 accuracy1: 0.917427 | loss2: 0.311259 accuracy2: 0.907724
100%|██████████| 562/562 [00:02<00:00, 218.65it/s]
100%|██████████| 62/62 [00:00<00:00, 836.58it/s]
Valid | loss1: 0.354469 accuracy1: 0.898942 | loss2: 0.351440 accuracy2: 0.898185
-------------
Epoch 16:
100%|██████████| 562/562 [00:02<00:00, 228.32it/s]
Train | loss1: 0.282918 accuracy1: 0.918038 | loss2: 0.308734 accuracy2: 0.908697
100%|██████████| 62/62 [00:00<00:00, 791.85it/s]
Valid | loss1: 0.355223 accuracy1: 0.897933 | loss2: 0.351099 accuracy2: 0.897933
-------------
Epoch 17:
100%|██████████| 562/562 [00:02<00:00, 227.09it/s]
0%| | 0/62 [00:00<?, ?it/s]Train | loss1: 0.280636 accuracy1: 0.918789 | loss2: 0.306384 accuracy2: 0.909419
100%|██████████| 62/62 [00:00<00:00, 807.76it/s]
Valid | loss1: 0.356033 accuracy1: 0.899194 | loss2: 0.350877 accuracy2: 0.898438
-------------
Epoch 18:
100%|██████████| 562/562 [00:02<00:00, 228.76it/s]
0%| | 0/62 [00:00<?, ?it/s]Train | loss1: 0.278502 accuracy1: 0.919623 | loss2: 0.304189 accuracy2: 0.910365
100%|██████████| 62/62 [00:00<00:00, 821.62it/s]
Valid | loss1: 0.356888 accuracy1: 0.897933 | loss2: 0.350755 accuracy2: 0.899194
-------------
Epoch 19:
97%|█████████▋| 547/562 [00:02<00:00, 234.20it/s]Train | loss1: 0.276497 accuracy1: 0.920485 | loss2: 0.302129 accuracy2: 0.911032
100%|██████████| 562/562 [00:02<00:00, 225.42it/s]
0%| | 0/62 [00:00<?, ?it/s]Valid | loss1: 0.357780 accuracy1: 0.896925 | loss2: 0.350721 accuracy2: 0.899446
100%|██████████| 62/62 [00:00<00:00, 785.82it/s]
-------------
Epoch 20:
98%|█████████▊| 553/562 [00:02<00:00, 229.68it/s]Train | loss1: 0.274608 accuracy1: 0.920930 | loss2: 0.300188 accuracy2: 0.911727
100%|██████████| 562/562 [00:02<00:00, 229.49it/s]
0%| | 0/62 [00:00<?, ?it/s]Valid | loss1: 0.358699 accuracy1: 0.897429 | loss2: 0.350763 accuracy2: 0.899194
100%|██████████| 62/62 [00:00<00:00, 664.15it/s]
-------------
Epoch 21:
96%|█████████▌| 540/562 [00:02<00:00, 220.97it/s]Train | loss1: 0.272821 accuracy1: 0.921319 | loss2: 0.298353 accuracy2: 0.912200
100%|██████████| 562/562 [00:02<00:00, 219.93it/s]
0%| | 0/62 [00:00<?, ?it/s]Valid | loss1: 0.359640 accuracy1: 0.896169 | loss2: 0.350871 accuracy2: 0.898942
100%|██████████| 62/62 [00:00<00:00, 824.02it/s]
-------------
Epoch 22:
100%|██████████| 562/562 [00:02<00:00, 236.54it/s]
0%| | 0/62 [00:00<?, ?it/s]Train | loss1: 0.271126 accuracy1: 0.921625 | loss2: 0.296614 accuracy2: 0.912978
68%|██████▊ | 42/62 [00:00<00:00, 416.70it/s]Valid | loss1: 0.360598 accuracy1: 0.895917 | loss2: 0.351037 accuracy2: 0.899446
100%|██████████| 62/62 [00:00<00:00, 499.19it/s]
-------------
Epoch 23:
100%|██████████| 562/562 [00:02<00:00, 218.98it/s]
Train | loss1: 0.269513 accuracy1: 0.922070 | loss2: 0.294962 accuracy2: 0.913089
100%|██████████| 62/62 [00:00<00:00, 784.82it/s]
Valid | loss1: 0.361567 accuracy1: 0.895413 | loss2: 0.351254 accuracy2: 0.899194
-------------
Epoch 24:
100%|██████████| 562/562 [00:02<00:00, 221.77it/s]
0%| | 0/62 [00:00<?, ?it/s]Train | loss1: 0.267976 accuracy1: 0.922375 | loss2: 0.293387 accuracy2: 0.913562
Valid | loss1: 0.362544 accuracy1: 0.894909 | loss2: 0.351516 accuracy2: 0.899194
100%|██████████| 62/62 [00:00<00:00, 790.13it/s]
-------------
Epoch 25:
100%|██████████| 562/562 [00:02<00:00, 218.84it/s]
Train | loss1: 0.266507 accuracy1: 0.922848 | loss2: 0.291884 accuracy2: 0.914229
100%|██████████| 62/62 [00:00<00:00, 774.37it/s]
Valid | loss1: 0.363526 accuracy1: 0.894909 | loss2: 0.351818 accuracy2: 0.898690
-------------
Epoch 26:
100%|██████████| 562/562 [00:11<00:00, 49.87it/s]
Train | loss1: 0.265100 accuracy1: 0.923376 | loss2: 0.290447 accuracy2: 0.914591
0%| | 0/62 [00:00<?, ?it/s]Valid | loss1: 0.364511 accuracy1: 0.894153 | loss2: 0.352155 accuracy2: 0.899194
100%|██████████| 62/62 [00:00<00:00, 775.08it/s]
-------------
Epoch 27:
100%|██████████| 562/562 [00:02<00:00, 229.36it/s]
0%| | 0/62 [00:00<?, ?it/s]Train | loss1: 0.263751 accuracy1: 0.923682 | loss2: 0.289070 accuracy2: 0.915008
100%|██████████| 62/62 [00:00<00:00, 869.19it/s]
Valid | loss1: 0.365495 accuracy1: 0.893649 | loss2: 0.352524 accuracy2: 0.899194
0%| | 0/562 [00:00<?, ?it/s]-------------
Epoch 28:
98%|█████████▊| 553/562 [00:02<00:00, 224.57it/s]Train | loss1: 0.262455 accuracy1: 0.924266 | loss2: 0.287748 accuracy2: 0.915453
100%|██████████| 562/562 [00:02<00:00, 217.13it/s]
0%| | 0/62 [00:00<?, ?it/s]Valid | loss1: 0.366477 accuracy1: 0.892893 | loss2: 0.352921 accuracy2: 0.899446
100%|██████████| 62/62 [00:00<00:00, 799.10it/s]
-------------
Epoch 29:
100%|██████████| 562/562 [00:02<00:00, 212.10it/s]
0%| | 0/62 [00:00<?, ?it/s]Train | loss1: 0.261209 accuracy1: 0.924600 | loss2: 0.286478 accuracy2: 0.915897
Valid | loss1: 0.367456 accuracy1: 0.892893 | loss2: 0.353343 accuracy2: 0.898438
100%|██████████| 62/62 [00:00<00:00, 829.41it/s]
-------------
Epoch 30:
100%|██████████| 562/562 [00:02<00:00, 227.22it/s]
Train | loss1: 0.260008 accuracy1: 0.924766 | loss2: 0.285256 accuracy2: 0.916315
100%|██████████| 62/62 [00:00<00:00, 804.85it/s]
Valid | loss1: 0.368430 accuracy1: 0.892389 | loss2: 0.353787 accuracy2: 0.898185
0%| | 0/62 [00:00<?, ?it/s]Test loss1: 0.400817 accuracy1: 0.892389 loss2: 0.374367 accuracy2: 0.893901
100%|██████████| 62/62 [00:00<00:00, 844.45it/s]
Process finished with exit code 0
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