Epoch: 0001 train_loss= 1.39382 train_acc= 0.21173 val_loss= 1.39151 val_acc= 0.23214 time= 0.10908
Epoch: 0002 train_loss= 1.39168 train_acc= 0.27036 val_loss= 1.38887 val_acc= 0.35714 time= 0.01563
Epoch: 0003 train_loss= 1.39010 train_acc= 0.31596 val_loss= 1.38633 val_acc= 0.35714 time= 0.01563
Epoch: 0004 train_loss= 1.38872 train_acc= 0.31596 val_loss= 1.38405 val_acc= 0.35714 time= 0.01563
Epoch: 0005 train_loss= 1.38750 train_acc= 0.31596 val_loss= 1.38182 val_acc= 0.35714 time= 0.01563
Epoch: 0006 train_loss= 1.38610 train_acc= 0.31596 val_loss= 1.37968 val_acc= 0.35714 time= 0.01563
Epoch: 0007 train_loss= 1.38438 train_acc= 0.31596 val_loss= 1.37761 val_acc= 0.35714 time= 0.01563
Epoch: 0008 train_loss= 1.38385 train_acc= 0.31596 val_loss= 1.37553 val_acc= 0.35714 time= 0.01563
Epoch: 0009 train_loss= 1.38365 train_acc= 0.31596 val_loss= 1.37348 val_acc= 0.35714 time= 0.01563
Epoch: 0010 train_loss= 1.38197 train_acc= 0.31596 val_loss= 1.37146 val_acc= 0.35714 time= 0.00000
Epoch: 0011 train_loss= 1.38109 train_acc= 0.31596 val_loss= 1.36948 val_acc= 0.35714 time= 0.01563
Epoch: 0012 train_loss= 1.38097 train_acc= 0.31596 val_loss= 1.36757 val_acc= 0.35714 time= 0.01563
Epoch: 0013 train_loss= 1.37954 train_acc= 0.31596 val_loss= 1.36573 val_acc= 0.35714 time= 0.01563
Epoch: 0014 train_loss= 1.37904 train_acc= 0.31596 val_loss= 1.36398 val_acc= 0.35714 time= 0.01563
Epoch: 0015 train_loss= 1.37946 train_acc= 0.31596 val_loss= 1.36234 val_acc= 0.35714 time= 0.01563
Epoch: 0016 train_loss= 1.37890 train_acc= 0.31596 val_loss= 1.36085 val_acc= 0.35714 time= 0.01563
Epoch: 0017 train_loss= 1.37954 train_acc= 0.31596 val_loss= 1.35955 val_acc= 0.35714 time= 0.01563
Epoch: 0018 train_loss= 1.37774 train_acc= 0.31596 val_loss= 1.35841 val_acc= 0.35714 time= 0.01563
Epoch: 0019 train_loss= 1.37937 train_acc= 0.31596 val_loss= 1.35751 val_acc= 0.35714 time= 0.01562
Epoch: 0020 train_loss= 1.37712 train_acc= 0.31596 val_loss= 1.35677 val_acc= 0.35714 time= 0.01563
Epoch: 0021 train_loss= 1.37917 train_acc= 0.31596 val_loss= 1.35622 val_acc= 0.35714 time= 0.01563
Epoch: 0022 train_loss= 1.37766 train_acc= 0.31596 val_loss= 1.35585 val_acc= 0.35714 time= 0.01563
Epoch: 0023 train_loss= 1.37771 train_acc= 0.31596 val_loss= 1.35568 val_acc= 0.35714 time= 0.01563
Epoch: 0024 train_loss= 1.37688 train_acc= 0.31596 val_loss= 1.35567 val_acc= 0.35714 time= 0.01563
Epoch: 0025 train_loss= 1.37844 train_acc= 0.31596 val_loss= 1.35581 val_acc= 0.35714 time= 0.01563
Epoch: 0026 train_loss= 1.37847 train_acc= 0.31596 val_loss= 1.35607 val_acc= 0.35714 time= 0.01562
Epoch: 0027 train_loss= 1.37713 train_acc= 0.31596 val_loss= 1.35641 val_acc= 0.35714 time= 0.00000
Epoch: 0028 train_loss= 1.37693 train_acc= 0.31596 val_loss= 1.35683 val_acc= 0.35714 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.35054 accuracy= 0.36283 time= 0.01563 
