Epoch: 0001 train_loss= 1.39458 train_acc= 0.24860 val_loss= 1.39182 val_acc= 0.30357 time= 0.80806
Epoch: 0002 train_loss= 1.39164 train_acc= 0.30866 val_loss= 1.38974 val_acc= 0.30357 time= 0.01563
Epoch: 0003 train_loss= 1.38951 train_acc= 0.30726 val_loss= 1.38824 val_acc= 0.30357 time= 0.01563
Epoch: 0004 train_loss= 1.38786 train_acc= 0.30726 val_loss= 1.38724 val_acc= 0.30357 time= 0.01562
Epoch: 0005 train_loss= 1.38657 train_acc= 0.30726 val_loss= 1.38667 val_acc= 0.30357 time= 0.01563
Epoch: 0006 train_loss= 1.38591 train_acc= 0.30726 val_loss= 1.38640 val_acc= 0.30357 time= 0.00000
Epoch: 0007 train_loss= 1.38541 train_acc= 0.30726 val_loss= 1.38632 val_acc= 0.30357 time= 0.01563
Epoch: 0008 train_loss= 1.38488 train_acc= 0.30726 val_loss= 1.38638 val_acc= 0.30357 time= 0.01563
Epoch: 0009 train_loss= 1.38453 train_acc= 0.30726 val_loss= 1.38649 val_acc= 0.30357 time= 0.01563
Epoch: 0010 train_loss= 1.38458 train_acc= 0.30726 val_loss= 1.38661 val_acc= 0.30357 time= 0.01563
Epoch: 0011 train_loss= 1.38461 train_acc= 0.30726 val_loss= 1.38671 val_acc= 0.30357 time= 0.01563
Epoch: 0012 train_loss= 1.38454 train_acc= 0.30726 val_loss= 1.38677 val_acc= 0.30357 time= 0.00000
Epoch: 0013 train_loss= 1.38405 train_acc= 0.30726 val_loss= 1.38681 val_acc= 0.30357 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38191 accuracy= 0.30973 time= 0.01563 
