Epoch: 0001 train_loss= 1.41904 train_acc= 0.23453 val_loss= 1.38236 val_acc= 0.23214 time= 0.14063
Epoch: 0002 train_loss= 1.41769 train_acc= 0.24104 val_loss= 1.38294 val_acc= 0.30357 time= 0.01562
Epoch: 0003 train_loss= 1.41076 train_acc= 0.25733 val_loss= 1.38514 val_acc= 0.26786 time= 0.01563
Epoch: 0004 train_loss= 1.39380 train_acc= 0.27036 val_loss= 1.38804 val_acc= 0.26786 time= 0.00000
Epoch: 0005 train_loss= 1.39398 train_acc= 0.26710 val_loss= 1.39097 val_acc= 0.25000 time= 0.01563
Epoch: 0006 train_loss= 1.39022 train_acc= 0.28013 val_loss= 1.39441 val_acc= 0.21429 time= 0.01563
Epoch: 0007 train_loss= 1.39867 train_acc= 0.26384 val_loss= 1.39807 val_acc= 0.21429 time= 0.01563
Epoch: 0008 train_loss= 1.38473 train_acc= 0.27687 val_loss= 1.40198 val_acc= 0.21429 time= 0.00000
Epoch: 0009 train_loss= 1.38720 train_acc= 0.29316 val_loss= 1.40459 val_acc= 0.19643 time= 0.01563
Epoch: 0010 train_loss= 1.38299 train_acc= 0.28339 val_loss= 1.40550 val_acc= 0.19643 time= 0.01563
Epoch: 0011 train_loss= 1.39706 train_acc= 0.26384 val_loss= 1.40534 val_acc= 0.16071 time= 0.01562
Epoch: 0012 train_loss= 1.38784 train_acc= 0.24756 val_loss= 1.40435 val_acc= 0.19643 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.36977 accuracy= 0.37168 time= 0.00000 
