Epoch: 0001 train_loss= 1.39392 train_acc= 0.30664 val_loss= 1.38213 val_acc= 0.33333 time= 0.21877
Epoch: 0002 train_loss= 1.38718 train_acc= 0.31445 val_loss= 1.38334 val_acc= 0.35000 time= 0.01563
Epoch: 0003 train_loss= 1.38127 train_acc= 0.32227 val_loss= 1.38515 val_acc= 0.35000 time= 0.01563
Epoch: 0004 train_loss= 1.37910 train_acc= 0.30469 val_loss= 1.38768 val_acc= 0.36667 time= 0.01563
Epoch: 0005 train_loss= 1.38089 train_acc= 0.31836 val_loss= 1.39137 val_acc= 0.36667 time= 0.01563
Epoch: 0006 train_loss= 1.37425 train_acc= 0.31641 val_loss= 1.39534 val_acc= 0.36667 time= 0.01563
Epoch: 0007 train_loss= 1.37371 train_acc= 0.33203 val_loss= 1.39918 val_acc= 0.36667 time= 0.01563
Epoch: 0008 train_loss= 1.38928 train_acc= 0.31641 val_loss= 1.40321 val_acc= 0.33333 time= 0.01563
Epoch: 0009 train_loss= 1.37454 train_acc= 0.31445 val_loss= 1.40594 val_acc= 0.31667 time= 0.01563
Epoch: 0010 train_loss= 1.37746 train_acc= 0.32227 val_loss= 1.40747 val_acc= 0.33333 time= 0.01562
Epoch: 0011 train_loss= 1.37360 train_acc= 0.32031 val_loss= 1.40844 val_acc= 0.33333 time= 0.03125
Epoch: 0012 train_loss= 1.37885 train_acc= 0.32422 val_loss= 1.40846 val_acc= 0.33333 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38636 accuracy= 0.28333 time= 0.00000 
