Epoch: 0001 train_loss= 1.39279 train_acc= 0.20112 val_loss= 1.39208 val_acc= 0.28571 time= 0.35940
Epoch: 0002 train_loss= 1.39141 train_acc= 0.29888 val_loss= 1.39037 val_acc= 0.28571 time= 0.01563
Epoch: 0003 train_loss= 1.39032 train_acc= 0.29888 val_loss= 1.38905 val_acc= 0.28571 time= 0.01563
Epoch: 0004 train_loss= 1.38932 train_acc= 0.28771 val_loss= 1.38790 val_acc= 0.28571 time= 0.01563
Epoch: 0005 train_loss= 1.38835 train_acc= 0.29749 val_loss= 1.38642 val_acc= 0.28571 time= 0.01563
Epoch: 0006 train_loss= 1.38761 train_acc= 0.30168 val_loss= 1.38492 val_acc= 0.28571 time= 0.01563
Epoch: 0007 train_loss= 1.38653 train_acc= 0.29609 val_loss= 1.38347 val_acc= 0.28571 time= 0.01563
Epoch: 0008 train_loss= 1.38541 train_acc= 0.29888 val_loss= 1.38200 val_acc= 0.28571 time= 0.01563
Epoch: 0009 train_loss= 1.38461 train_acc= 0.29609 val_loss= 1.38051 val_acc= 0.28571 time= 0.01563
Epoch: 0010 train_loss= 1.38405 train_acc= 0.29888 val_loss= 1.37895 val_acc= 0.28571 time= 0.01563
Epoch: 0011 train_loss= 1.38249 train_acc= 0.30028 val_loss= 1.37740 val_acc= 0.28571 time= 0.01563
Epoch: 0012 train_loss= 1.38245 train_acc= 0.29749 val_loss= 1.37588 val_acc= 0.28571 time= 0.01563
Epoch: 0013 train_loss= 1.38187 train_acc= 0.29749 val_loss= 1.37435 val_acc= 0.28571 time= 0.01563
Epoch: 0014 train_loss= 1.38074 train_acc= 0.29749 val_loss= 1.37294 val_acc= 0.28571 time= 0.01563
Epoch: 0015 train_loss= 1.38036 train_acc= 0.29749 val_loss= 1.37172 val_acc= 0.28571 time= 0.03125
Epoch: 0016 train_loss= 1.37970 train_acc= 0.29749 val_loss= 1.37061 val_acc= 0.28571 time= 0.01563
Epoch: 0017 train_loss= 1.38021 train_acc= 0.29749 val_loss= 1.36954 val_acc= 0.28571 time= 0.01563
Epoch: 0018 train_loss= 1.38012 train_acc= 0.29749 val_loss= 1.36843 val_acc= 0.28571 time= 0.01563
Epoch: 0019 train_loss= 1.37988 train_acc= 0.29749 val_loss= 1.36739 val_acc= 0.28571 time= 0.01563
Epoch: 0020 train_loss= 1.38061 train_acc= 0.29749 val_loss= 1.36649 val_acc= 0.28571 time= 0.01563
Epoch: 0021 train_loss= 1.38004 train_acc= 0.29749 val_loss= 1.36588 val_acc= 0.28571 time= 0.01563
Epoch: 0022 train_loss= 1.38007 train_acc= 0.29749 val_loss= 1.36545 val_acc= 0.28571 time= 0.03125
Epoch: 0023 train_loss= 1.38000 train_acc= 0.29749 val_loss= 1.36538 val_acc= 0.28571 time= 0.01563
Epoch: 0024 train_loss= 1.37950 train_acc= 0.29749 val_loss= 1.36559 val_acc= 0.28571 time= 0.01563
Epoch: 0025 train_loss= 1.37960 train_acc= 0.29749 val_loss= 1.36603 val_acc= 0.28571 time= 0.01562
Epoch: 0026 train_loss= 1.37887 train_acc= 0.29749 val_loss= 1.36666 val_acc= 0.28571 time= 0.01563
Epoch: 0027 train_loss= 1.37894 train_acc= 0.29749 val_loss= 1.36747 val_acc= 0.28571 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.34845 accuracy= 0.36283 time= 0.01563 
