Epoch: 0001 train_loss= 1.39421 train_acc= 0.20117 val_loss= 1.39170 val_acc= 0.26667 time= 0.28127
Epoch: 0002 train_loss= 1.39111 train_acc= 0.29102 val_loss= 1.38998 val_acc= 0.26667 time= 0.01563
Epoch: 0003 train_loss= 1.38862 train_acc= 0.29102 val_loss= 1.38898 val_acc= 0.26667 time= 0.01563
Epoch: 0004 train_loss= 1.38665 train_acc= 0.29102 val_loss= 1.38868 val_acc= 0.26667 time= 0.01562
Epoch: 0005 train_loss= 1.38538 train_acc= 0.29102 val_loss= 1.38896 val_acc= 0.26667 time= 0.01563
Epoch: 0006 train_loss= 1.38458 train_acc= 0.29102 val_loss= 1.38964 val_acc= 0.26667 time= 0.01563
Epoch: 0007 train_loss= 1.38417 train_acc= 0.29102 val_loss= 1.39045 val_acc= 0.26667 time= 0.01563
Epoch: 0008 train_loss= 1.38366 train_acc= 0.29102 val_loss= 1.39127 val_acc= 0.26667 time= 0.01563
Epoch: 0009 train_loss= 1.38352 train_acc= 0.29102 val_loss= 1.39202 val_acc= 0.26667 time= 0.01563
Epoch: 0010 train_loss= 1.38362 train_acc= 0.29102 val_loss= 1.39268 val_acc= 0.26667 time= 0.01563
Epoch: 0011 train_loss= 1.38386 train_acc= 0.29102 val_loss= 1.39315 val_acc= 0.26667 time= 0.01563
Epoch: 0012 train_loss= 1.38343 train_acc= 0.29102 val_loss= 1.39344 val_acc= 0.26667 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38317 accuracy= 0.31667 time= 0.01563 
