Epoch: 0001 train_loss= 1.39546 train_acc= 0.25407 val_loss= 1.39544 val_acc= 0.21429 time= 0.07854
Epoch: 0002 train_loss= 1.39163 train_acc= 0.25081 val_loss= 1.39888 val_acc= 0.21429 time= 0.01521
Epoch: 0003 train_loss= 1.38339 train_acc= 0.28990 val_loss= 1.40302 val_acc= 0.21429 time= 0.01563
Epoch: 0004 train_loss= 1.38283 train_acc= 0.28013 val_loss= 1.40807 val_acc= 0.21429 time= 0.01563
Epoch: 0005 train_loss= 1.37074 train_acc= 0.31596 val_loss= 1.41401 val_acc= 0.21429 time= 0.01563
Epoch: 0006 train_loss= 1.36535 train_acc= 0.32573 val_loss= 1.42026 val_acc= 0.21429 time= 0.01563
Epoch: 0007 train_loss= 1.36625 train_acc= 0.34528 val_loss= 1.42704 val_acc= 0.21429 time= 0.01563
Epoch: 0008 train_loss= 1.36617 train_acc= 0.33876 val_loss= 1.43437 val_acc= 0.21429 time= 0.01562
Epoch: 0009 train_loss= 1.36599 train_acc= 0.34528 val_loss= 1.44049 val_acc= 0.21429 time= 0.01563
Epoch: 0010 train_loss= 1.36529 train_acc= 0.34528 val_loss= 1.44639 val_acc= 0.21429 time= 0.01563
Epoch: 0011 train_loss= 1.36461 train_acc= 0.34528 val_loss= 1.45093 val_acc= 0.21429 time= 0.01563
Epoch: 0012 train_loss= 1.35991 train_acc= 0.34528 val_loss= 1.45335 val_acc= 0.21429 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38991 accuracy= 0.28319 time= 0.00000 
