Epoch: 0001 train_loss= 1.39304 train_acc= 0.24860 val_loss= 1.39457 val_acc= 0.17857 time= 0.35940
Epoch: 0002 train_loss= 1.39176 train_acc= 0.24860 val_loss= 1.39392 val_acc= 0.17857 time= 0.01563
Epoch: 0003 train_loss= 1.39069 train_acc= 0.24860 val_loss= 1.39351 val_acc= 0.17857 time= 0.01563
Epoch: 0004 train_loss= 1.38981 train_acc= 0.24860 val_loss= 1.39373 val_acc= 0.17857 time= 0.01563
Epoch: 0005 train_loss= 1.38903 train_acc= 0.24860 val_loss= 1.39380 val_acc= 0.17857 time= 0.01563
Epoch: 0006 train_loss= 1.38840 train_acc= 0.24860 val_loss= 1.39383 val_acc= 0.17857 time= 0.01563
Epoch: 0007 train_loss= 1.38785 train_acc= 0.24860 val_loss= 1.39383 val_acc= 0.17857 time= 0.01563
Epoch: 0008 train_loss= 1.38695 train_acc= 0.24860 val_loss= 1.39382 val_acc= 0.17857 time= 0.01563
Epoch: 0009 train_loss= 1.38644 train_acc= 0.24860 val_loss= 1.39380 val_acc= 0.17857 time= 0.01563
Epoch: 0010 train_loss= 1.38593 train_acc= 0.24860 val_loss= 1.39376 val_acc= 0.17857 time= 0.01563
Epoch: 0011 train_loss= 1.38509 train_acc= 0.25140 val_loss= 1.39370 val_acc= 0.17857 time= 0.01563
Epoch: 0012 train_loss= 1.38446 train_acc= 0.25978 val_loss= 1.39383 val_acc= 0.30357 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38977 accuracy= 0.31858 time= 0.01563 
