Epoch: 0001 train_loss= 1.40070 train_acc= 0.20508 val_loss= 1.37573 val_acc= 0.37500 time= 0.52519
Epoch: 0002 train_loss= 1.39865 train_acc= 0.24023 val_loss= 1.37437 val_acc= 0.37500 time= 0.01563
Epoch: 0003 train_loss= 1.39468 train_acc= 0.30859 val_loss= 1.37344 val_acc= 0.37500 time= 0.00000
Epoch: 0004 train_loss= 1.39130 train_acc= 0.30273 val_loss= 1.37301 val_acc= 0.37500 time= 0.01563
Epoch: 0005 train_loss= 1.38713 train_acc= 0.30469 val_loss= 1.37293 val_acc= 0.37500 time= 0.00000
Epoch: 0006 train_loss= 1.38471 train_acc= 0.30469 val_loss= 1.37332 val_acc= 0.37500 time= 0.01563
Epoch: 0007 train_loss= 1.38277 train_acc= 0.30859 val_loss= 1.37401 val_acc= 0.37500 time= 0.00000
Epoch: 0008 train_loss= 1.38110 train_acc= 0.30469 val_loss= 1.37507 val_acc= 0.37500 time= 0.01563
Epoch: 0009 train_loss= 1.37941 train_acc= 0.30664 val_loss= 1.37645 val_acc= 0.37500 time= 0.00496
Epoch: 0010 train_loss= 1.37791 train_acc= 0.30469 val_loss= 1.37814 val_acc= 0.37500 time= 0.01100
Epoch: 0011 train_loss= 1.37569 train_acc= 0.30469 val_loss= 1.38003 val_acc= 0.37500 time= 0.00000
Epoch: 0012 train_loss= 1.37644 train_acc= 0.30469 val_loss= 1.38207 val_acc= 0.37500 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 1.38769 accuracy= 0.31858 time= 0.00000 
