Epoch: 0001 train_loss= 1.39286 train_acc= 0.21173 val_loss= 1.39077 val_acc= 0.35714 time= 0.09376
Epoch: 0002 train_loss= 1.39052 train_acc= 0.31922 val_loss= 1.38809 val_acc= 0.35714 time= 0.00000
Epoch: 0003 train_loss= 1.38980 train_acc= 0.32248 val_loss= 1.38589 val_acc= 0.35714 time= 0.01563
Epoch: 0004 train_loss= 1.38801 train_acc= 0.32248 val_loss= 1.38398 val_acc= 0.35714 time= 0.01563
Epoch: 0005 train_loss= 1.38648 train_acc= 0.32248 val_loss= 1.38228 val_acc= 0.35714 time= 0.01563
Epoch: 0006 train_loss= 1.38585 train_acc= 0.32248 val_loss= 1.38065 val_acc= 0.35714 time= 0.00000
Epoch: 0007 train_loss= 1.38484 train_acc= 0.32248 val_loss= 1.37884 val_acc= 0.35714 time= 0.01562
Epoch: 0008 train_loss= 1.38337 train_acc= 0.32248 val_loss= 1.37702 val_acc= 0.35714 time= 0.01563
Epoch: 0009 train_loss= 1.38342 train_acc= 0.32248 val_loss= 1.37523 val_acc= 0.35714 time= 0.01563
Epoch: 0010 train_loss= 1.38301 train_acc= 0.32248 val_loss= 1.37351 val_acc= 0.35714 time= 0.00000
Epoch: 0011 train_loss= 1.38261 train_acc= 0.32248 val_loss= 1.37191 val_acc= 0.35714 time= 0.01563
Epoch: 0012 train_loss= 1.38293 train_acc= 0.32248 val_loss= 1.37046 val_acc= 0.35714 time= 0.01563
Epoch: 0013 train_loss= 1.38320 train_acc= 0.32248 val_loss= 1.36915 val_acc= 0.35714 time= 0.01563
Epoch: 0014 train_loss= 1.38209 train_acc= 0.32248 val_loss= 1.36807 val_acc= 0.35714 time= 0.00000
Epoch: 0015 train_loss= 1.38244 train_acc= 0.32248 val_loss= 1.36725 val_acc= 0.35714 time= 0.01563
Epoch: 0016 train_loss= 1.38038 train_acc= 0.32248 val_loss= 1.36656 val_acc= 0.35714 time= 0.01563
Epoch: 0017 train_loss= 1.38125 train_acc= 0.32248 val_loss= 1.36607 val_acc= 0.35714 time= 0.01563
Epoch: 0018 train_loss= 1.38114 train_acc= 0.32248 val_loss= 1.36577 val_acc= 0.35714 time= 0.00000
Epoch: 0019 train_loss= 1.38007 train_acc= 0.32248 val_loss= 1.36560 val_acc= 0.35714 time= 0.01563
Epoch: 0020 train_loss= 1.38048 train_acc= 0.32248 val_loss= 1.36551 val_acc= 0.35714 time= 0.01563
Epoch: 0021 train_loss= 1.37991 train_acc= 0.32248 val_loss= 1.36548 val_acc= 0.35714 time= 0.01563
Epoch: 0022 train_loss= 1.38130 train_acc= 0.32248 val_loss= 1.36555 val_acc= 0.35714 time= 0.00000
Epoch: 0023 train_loss= 1.37949 train_acc= 0.32248 val_loss= 1.36566 val_acc= 0.35714 time= 0.01563
Epoch: 0024 train_loss= 1.37863 train_acc= 0.32248 val_loss= 1.36575 val_acc= 0.35714 time= 0.01563
Epoch: 0025 train_loss= 1.38025 train_acc= 0.32248 val_loss= 1.36587 val_acc= 0.35714 time= 0.01563
Epoch: 0026 train_loss= 1.37934 train_acc= 0.32248 val_loss= 1.36596 val_acc= 0.35714 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 1.38308 accuracy= 0.29204 time= 0.00000 
