Epoch: 0001 train_loss= 2.08754 train_acc= 0.09434 val_loss= 2.08547 val_acc= 0.13793 time= 0.77392
Epoch: 0002 train_loss= 2.08510 train_acc= 0.18059 val_loss= 2.08375 val_acc= 0.13793 time= 0.00000
Epoch: 0003 train_loss= 2.08311 train_acc= 0.18329 val_loss= 2.08208 val_acc= 0.13793 time= 0.01563
Epoch: 0004 train_loss= 2.08134 train_acc= 0.18329 val_loss= 2.08053 val_acc= 0.13793 time= 0.00000
Epoch: 0005 train_loss= 2.07966 train_acc= 0.18329 val_loss= 2.07900 val_acc= 0.13793 time= 0.01563
Epoch: 0006 train_loss= 2.07880 train_acc= 0.18059 val_loss= 2.07756 val_acc= 0.13793 time= 0.01563
Epoch: 0007 train_loss= 2.07669 train_acc= 0.18329 val_loss= 2.07622 val_acc= 0.13793 time= 0.00000
Epoch: 0008 train_loss= 2.07591 train_acc= 0.18329 val_loss= 2.07490 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.07510 train_acc= 0.18329 val_loss= 2.07355 val_acc= 0.13793 time= 0.00000
Epoch: 0010 train_loss= 2.07331 train_acc= 0.18329 val_loss= 2.07203 val_acc= 0.13793 time= 0.01563
Epoch: 0011 train_loss= 2.07262 train_acc= 0.18329 val_loss= 2.07055 val_acc= 0.13793 time= 0.01563
Epoch: 0012 train_loss= 2.07087 train_acc= 0.18329 val_loss= 2.06905 val_acc= 0.13793 time= 0.00000
Epoch: 0013 train_loss= 2.07164 train_acc= 0.18329 val_loss= 2.06749 val_acc= 0.13793 time= 0.01563
Epoch: 0014 train_loss= 2.07116 train_acc= 0.18059 val_loss= 2.06581 val_acc= 0.13793 time= 0.00000
Epoch: 0015 train_loss= 2.06815 train_acc= 0.18059 val_loss= 2.06407 val_acc= 0.13793 time= 0.01562
Epoch: 0016 train_loss= 2.06665 train_acc= 0.18329 val_loss= 2.06233 val_acc= 0.13793 time= 0.00000
Epoch: 0017 train_loss= 2.06697 train_acc= 0.18329 val_loss= 2.06056 val_acc= 0.13793 time= 0.01563
Epoch: 0018 train_loss= 2.06739 train_acc= 0.18329 val_loss= 2.05881 val_acc= 0.13793 time= 0.01563
Epoch: 0019 train_loss= 2.06481 train_acc= 0.18329 val_loss= 2.05706 val_acc= 0.13793 time= 0.00000
Epoch: 0020 train_loss= 2.06474 train_acc= 0.18059 val_loss= 2.05537 val_acc= 0.13793 time= 0.01563
Epoch: 0021 train_loss= 2.06321 train_acc= 0.18059 val_loss= 2.05373 val_acc= 0.13793 time= 0.00000
Epoch: 0022 train_loss= 2.06307 train_acc= 0.18329 val_loss= 2.05217 val_acc= 0.13793 time= 0.01563
Epoch: 0023 train_loss= 2.06368 train_acc= 0.18329 val_loss= 2.05061 val_acc= 0.13793 time= 0.01563
Epoch: 0024 train_loss= 2.06275 train_acc= 0.18329 val_loss= 2.04902 val_acc= 0.13793 time= 0.00000
Epoch: 0025 train_loss= 2.06007 train_acc= 0.18329 val_loss= 2.04745 val_acc= 0.13793 time= 0.01563
Epoch: 0026 train_loss= 2.05993 train_acc= 0.18329 val_loss= 2.04595 val_acc= 0.13793 time= 0.00000
Epoch: 0027 train_loss= 2.06023 train_acc= 0.18329 val_loss= 2.04445 val_acc= 0.13793 time= 0.01563
Epoch: 0028 train_loss= 2.05781 train_acc= 0.18329 val_loss= 2.04313 val_acc= 0.13793 time= 0.01563
Epoch: 0029 train_loss= 2.05754 train_acc= 0.18329 val_loss= 2.04191 val_acc= 0.13793 time= 0.00000
Epoch: 0030 train_loss= 2.05705 train_acc= 0.18329 val_loss= 2.04074 val_acc= 0.13793 time= 0.01563
Epoch: 0031 train_loss= 2.05929 train_acc= 0.18329 val_loss= 2.03956 val_acc= 0.13793 time= 0.00000
Epoch: 0032 train_loss= 2.05882 train_acc= 0.18329 val_loss= 2.03856 val_acc= 0.13793 time= 0.01563
Epoch: 0033 train_loss= 2.05724 train_acc= 0.18329 val_loss= 2.03767 val_acc= 0.13793 time= 0.00000
Epoch: 0034 train_loss= 2.05791 train_acc= 0.18329 val_loss= 2.03669 val_acc= 0.13793 time= 0.01563
Epoch: 0035 train_loss= 2.05870 train_acc= 0.18329 val_loss= 2.03583 val_acc= 0.13793 time= 0.01563
Epoch: 0036 train_loss= 2.05711 train_acc= 0.18329 val_loss= 2.03511 val_acc= 0.13793 time= 0.00000
Epoch: 0037 train_loss= 2.05607 train_acc= 0.18329 val_loss= 2.03462 val_acc= 0.13793 time= 0.01563
Epoch: 0038 train_loss= 2.05757 train_acc= 0.18329 val_loss= 2.03406 val_acc= 0.13793 time= 0.01563
Epoch: 0039 train_loss= 2.05645 train_acc= 0.18329 val_loss= 2.03360 val_acc= 0.13793 time= 0.00000
Epoch: 0040 train_loss= 2.05587 train_acc= 0.18329 val_loss= 2.03334 val_acc= 0.13793 time= 0.01563
Epoch: 0041 train_loss= 2.05513 train_acc= 0.18329 val_loss= 2.03303 val_acc= 0.13793 time= 0.00000
Epoch: 0042 train_loss= 2.05478 train_acc= 0.18329 val_loss= 2.03245 val_acc= 0.13793 time= 0.01563
Epoch: 0043 train_loss= 2.05443 train_acc= 0.18329 val_loss= 2.03208 val_acc= 0.13793 time= 0.01563
Epoch: 0044 train_loss= 2.05403 train_acc= 0.18329 val_loss= 2.03186 val_acc= 0.13793 time= 0.00000
Epoch: 0045 train_loss= 2.05528 train_acc= 0.18329 val_loss= 2.03176 val_acc= 0.13793 time= 0.01563
Epoch: 0046 train_loss= 2.05578 train_acc= 0.18329 val_loss= 2.03164 val_acc= 0.13793 time= 0.01563
Epoch: 0047 train_loss= 2.05473 train_acc= 0.18329 val_loss= 2.03109 val_acc= 0.13793 time= 0.00000
Epoch: 0048 train_loss= 2.05319 train_acc= 0.18329 val_loss= 2.03016 val_acc= 0.13793 time= 0.01563
Epoch: 0049 train_loss= 2.05322 train_acc= 0.18329 val_loss= 2.02958 val_acc= 0.13793 time= 0.00000
Epoch: 0050 train_loss= 2.05119 train_acc= 0.18329 val_loss= 2.02886 val_acc= 0.13793 time= 0.01563
Epoch: 0051 train_loss= 2.05333 train_acc= 0.18329 val_loss= 2.02818 val_acc= 0.13793 time= 0.01563
Epoch: 0052 train_loss= 2.05227 train_acc= 0.18329 val_loss= 2.02765 val_acc= 0.13793 time= 0.00000
Epoch: 0053 train_loss= 2.05329 train_acc= 0.18329 val_loss= 2.02730 val_acc= 0.13793 time= 0.01563
Epoch: 0054 train_loss= 2.05319 train_acc= 0.18329 val_loss= 2.02692 val_acc= 0.13793 time= 0.01562
Epoch: 0055 train_loss= 2.05121 train_acc= 0.18329 val_loss= 2.02660 val_acc= 0.13793 time= 0.00000
Epoch: 0056 train_loss= 2.05417 train_acc= 0.18329 val_loss= 2.02658 val_acc= 0.13793 time= 0.01563
Epoch: 0057 train_loss= 2.05193 train_acc= 0.18329 val_loss= 2.02677 val_acc= 0.13793 time= 0.00000
Epoch: 0058 train_loss= 2.05014 train_acc= 0.18329 val_loss= 2.02684 val_acc= 0.13793 time= 0.01563
Epoch: 0059 train_loss= 2.05029 train_acc= 0.18329 val_loss= 2.02693 val_acc= 0.13793 time= 0.01563
Epoch: 0060 train_loss= 2.04799 train_acc= 0.18329 val_loss= 2.02710 val_acc= 0.13793 time= 0.00000
Epoch: 0061 train_loss= 2.05065 train_acc= 0.18329 val_loss= 2.02649 val_acc= 0.13793 time= 0.01563
Epoch: 0062 train_loss= 2.05071 train_acc= 0.18329 val_loss= 2.02604 val_acc= 0.13793 time= 0.00000
Epoch: 0063 train_loss= 2.05122 train_acc= 0.18329 val_loss= 2.02547 val_acc= 0.13793 time= 0.01563
Epoch: 0064 train_loss= 2.04843 train_acc= 0.18329 val_loss= 2.02491 val_acc= 0.13793 time= 0.01563
Epoch: 0065 train_loss= 2.05082 train_acc= 0.18329 val_loss= 2.02438 val_acc= 0.13793 time= 0.00000
Epoch: 0066 train_loss= 2.04708 train_acc= 0.18329 val_loss= 2.02376 val_acc= 0.13793 time= 0.01563
Epoch: 0067 train_loss= 2.05003 train_acc= 0.18059 val_loss= 2.02337 val_acc= 0.13793 time= 0.01563
Epoch: 0068 train_loss= 2.04999 train_acc= 0.18329 val_loss= 2.02324 val_acc= 0.13793 time= 0.00000
Epoch: 0069 train_loss= 2.04930 train_acc= 0.18598 val_loss= 2.02337 val_acc= 0.13793 time= 0.01563
Epoch: 0070 train_loss= 2.04974 train_acc= 0.18329 val_loss= 2.02338 val_acc= 0.13793 time= 0.00000
Epoch: 0071 train_loss= 2.04715 train_acc= 0.18329 val_loss= 2.02332 val_acc= 0.13793 time= 0.01563
Epoch: 0072 train_loss= 2.04952 train_acc= 0.18329 val_loss= 2.02336 val_acc= 0.13793 time= 0.01563
Epoch: 0073 train_loss= 2.04714 train_acc= 0.18329 val_loss= 2.02309 val_acc= 0.13793 time= 0.00000
Epoch: 0074 train_loss= 2.04506 train_acc= 0.18598 val_loss= 2.02224 val_acc= 0.13793 time= 0.01563
Epoch: 0075 train_loss= 2.04661 train_acc= 0.18329 val_loss= 2.02182 val_acc= 0.13793 time= 0.00000
Epoch: 0076 train_loss= 2.04675 train_acc= 0.18329 val_loss= 2.02190 val_acc= 0.13793 time= 0.01563
Epoch: 0077 train_loss= 2.04914 train_acc= 0.18329 val_loss= 2.02238 val_acc= 0.13793 time= 0.01563
Epoch: 0078 train_loss= 2.04665 train_acc= 0.18329 val_loss= 2.02305 val_acc= 0.13793 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.09430 accuracy= 0.16949 time= 0.01563 
