Epoch: 0001 train_loss= 1.39136 train_acc= 0.24860 val_loss= 1.38923 val_acc= 0.35714 time= 0.65657
Epoch: 0002 train_loss= 1.38995 train_acc= 0.25838 val_loss= 1.38711 val_acc= 0.35714 time= 0.01563
Epoch: 0003 train_loss= 1.38878 train_acc= 0.29190 val_loss= 1.38530 val_acc= 0.35714 time= 0.01563
Epoch: 0004 train_loss= 1.38775 train_acc= 0.28911 val_loss= 1.38354 val_acc= 0.35714 time= 0.01563
Epoch: 0005 train_loss= 1.38680 train_acc= 0.29190 val_loss= 1.38192 val_acc= 0.35714 time= 0.01562
Epoch: 0006 train_loss= 1.38604 train_acc= 0.29190 val_loss= 1.38035 val_acc= 0.35714 time= 0.01563
Epoch: 0007 train_loss= 1.38525 train_acc= 0.29190 val_loss= 1.37883 val_acc= 0.35714 time= 0.01562
Epoch: 0008 train_loss= 1.38511 train_acc= 0.29190 val_loss= 1.37739 val_acc= 0.35714 time= 0.01563
Epoch: 0009 train_loss= 1.38375 train_acc= 0.29190 val_loss= 1.37605 val_acc= 0.35714 time= 0.00000
Epoch: 0010 train_loss= 1.38323 train_acc= 0.29190 val_loss= 1.37484 val_acc= 0.35714 time= 0.01563
Epoch: 0011 train_loss= 1.38369 train_acc= 0.29190 val_loss= 1.37377 val_acc= 0.35714 time= 0.01563
Epoch: 0012 train_loss= 1.38373 train_acc= 0.29190 val_loss= 1.37286 val_acc= 0.35714 time= 0.02099
Epoch: 0013 train_loss= 1.38317 train_acc= 0.29190 val_loss= 1.37213 val_acc= 0.35714 time= 0.01050
Epoch: 0014 train_loss= 1.38281 train_acc= 0.29190 val_loss= 1.37154 val_acc= 0.35714 time= 0.01563
Epoch: 0015 train_loss= 1.38156 train_acc= 0.29190 val_loss= 1.37112 val_acc= 0.35714 time= 0.01563
Epoch: 0016 train_loss= 1.38234 train_acc= 0.29190 val_loss= 1.37085 val_acc= 0.35714 time= 0.01562
Epoch: 0017 train_loss= 1.38232 train_acc= 0.29190 val_loss= 1.37070 val_acc= 0.35714 time= 0.00000
Epoch: 0018 train_loss= 1.38189 train_acc= 0.29190 val_loss= 1.37064 val_acc= 0.35714 time= 0.01563
Epoch: 0019 train_loss= 1.38114 train_acc= 0.29190 val_loss= 1.37060 val_acc= 0.35714 time= 0.01562
Epoch: 0020 train_loss= 1.38156 train_acc= 0.29050 val_loss= 1.37057 val_acc= 0.35714 time= 0.01563
Epoch: 0021 train_loss= 1.38111 train_acc= 0.29190 val_loss= 1.37054 val_acc= 0.35714 time= 0.01563
Epoch: 0022 train_loss= 1.38116 train_acc= 0.29190 val_loss= 1.37052 val_acc= 0.35714 time= 0.01563
Epoch: 0023 train_loss= 1.38120 train_acc= 0.29190 val_loss= 1.37058 val_acc= 0.35714 time= 0.01563
Epoch: 0024 train_loss= 1.38041 train_acc= 0.29050 val_loss= 1.37059 val_acc= 0.35714 time= 0.01563
Epoch: 0025 train_loss= 1.38127 train_acc= 0.29190 val_loss= 1.37064 val_acc= 0.35714 time= 0.00000
Epoch: 0026 train_loss= 1.38038 train_acc= 0.29190 val_loss= 1.37067 val_acc= 0.35714 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37628 accuracy= 0.30088 time= 0.01563 
