Epoch: 0001 train_loss= 1.39790 train_acc= 0.19693 val_loss= 1.39306 val_acc= 0.19643 time= 0.81266
Epoch: 0002 train_loss= 1.39363 train_acc= 0.20950 val_loss= 1.38852 val_acc= 0.30357 time= 0.01563
Epoch: 0003 train_loss= 1.39048 train_acc= 0.26117 val_loss= 1.38462 val_acc= 0.30357 time= 0.01563
Epoch: 0004 train_loss= 1.38805 train_acc= 0.30447 val_loss= 1.38118 val_acc= 0.30357 time= 0.00000
Epoch: 0005 train_loss= 1.38558 train_acc= 0.28492 val_loss= 1.37814 val_acc= 0.30357 time= 0.01563
Epoch: 0006 train_loss= 1.38452 train_acc= 0.30168 val_loss= 1.37547 val_acc= 0.30357 time= 0.00000
Epoch: 0007 train_loss= 1.38315 train_acc= 0.29609 val_loss= 1.37317 val_acc= 0.30357 time= 0.01563
Epoch: 0008 train_loss= 1.38200 train_acc= 0.29469 val_loss= 1.37122 val_acc= 0.30357 time= 0.00000
Epoch: 0009 train_loss= 1.37993 train_acc= 0.29469 val_loss= 1.36970 val_acc= 0.30357 time= 0.01563
Epoch: 0010 train_loss= 1.37950 train_acc= 0.29330 val_loss= 1.36861 val_acc= 0.30357 time= 0.00000
Epoch: 0011 train_loss= 1.37904 train_acc= 0.29469 val_loss= 1.36792 val_acc= 0.30357 time= 0.01563
Epoch: 0012 train_loss= 1.37858 train_acc= 0.29469 val_loss= 1.36775 val_acc= 0.30357 time= 0.00000
Epoch: 0013 train_loss= 1.37784 train_acc= 0.29330 val_loss= 1.36772 val_acc= 0.30357 time= 0.01563
Epoch: 0014 train_loss= 1.37911 train_acc= 0.29330 val_loss= 1.36785 val_acc= 0.30357 time= 0.00000
Epoch: 0015 train_loss= 1.37742 train_acc= 0.29330 val_loss= 1.36810 val_acc= 0.30357 time= 0.01563
Epoch: 0016 train_loss= 1.37852 train_acc= 0.29190 val_loss= 1.36830 val_acc= 0.30357 time= 0.01562
Epoch: 0017 train_loss= 1.37765 train_acc= 0.29330 val_loss= 1.36832 val_acc= 0.30357 time= 0.00000
Epoch: 0018 train_loss= 1.37840 train_acc= 0.29330 val_loss= 1.36822 val_acc= 0.30357 time= 0.01563
Epoch: 0019 train_loss= 1.37902 train_acc= 0.29190 val_loss= 1.36800 val_acc= 0.30357 time= 0.00000
Epoch: 0020 train_loss= 1.37747 train_acc= 0.29330 val_loss= 1.36769 val_acc= 0.30357 time= 0.01563
Epoch: 0021 train_loss= 1.37834 train_acc= 0.29330 val_loss= 1.36740 val_acc= 0.30357 time= 0.00000
Epoch: 0022 train_loss= 1.37960 train_acc= 0.29190 val_loss= 1.36726 val_acc= 0.30357 time= 0.01563
Epoch: 0023 train_loss= 1.37683 train_acc= 0.29330 val_loss= 1.36724 val_acc= 0.30357 time= 0.00000
Epoch: 0024 train_loss= 1.37916 train_acc= 0.29330 val_loss= 1.36729 val_acc= 0.30357 time= 0.01563
Epoch: 0025 train_loss= 1.37690 train_acc= 0.29050 val_loss= 1.36744 val_acc= 0.30357 time= 0.00000
Epoch: 0026 train_loss= 1.37740 train_acc= 0.29190 val_loss= 1.36772 val_acc= 0.30357 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.34883 accuracy= 0.36283 time= 0.00000 
