Epoch: 0001 train_loss= 1.39432 train_acc= 0.18241 val_loss= 1.39157 val_acc= 0.21429 time= 0.15626
Epoch: 0002 train_loss= 1.39060 train_acc= 0.31922 val_loss= 1.38971 val_acc= 0.21429 time= 0.01563
Epoch: 0003 train_loss= 1.38724 train_acc= 0.32573 val_loss= 1.38858 val_acc= 0.21429 time= 0.01563
Epoch: 0004 train_loss= 1.38425 train_acc= 0.32573 val_loss= 1.38827 val_acc= 0.21429 time= 0.01563
Epoch: 0005 train_loss= 1.38225 train_acc= 0.32573 val_loss= 1.38856 val_acc= 0.21429 time= 0.01563
Epoch: 0006 train_loss= 1.38047 train_acc= 0.32573 val_loss= 1.38929 val_acc= 0.21429 time= 0.01563
Epoch: 0007 train_loss= 1.37933 train_acc= 0.32573 val_loss= 1.39015 val_acc= 0.21429 time= 0.01563
Epoch: 0008 train_loss= 1.37853 train_acc= 0.32573 val_loss= 1.39097 val_acc= 0.21429 time= 0.01563
Epoch: 0009 train_loss= 1.37755 train_acc= 0.32573 val_loss= 1.39179 val_acc= 0.21429 time= 0.01563
Epoch: 0010 train_loss= 1.37755 train_acc= 0.32573 val_loss= 1.39245 val_acc= 0.21429 time= 0.01563
Epoch: 0011 train_loss= 1.37749 train_acc= 0.32573 val_loss= 1.39288 val_acc= 0.21429 time= 0.01563
Epoch: 0012 train_loss= 1.37677 train_acc= 0.32573 val_loss= 1.39304 val_acc= 0.21429 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38969 accuracy= 0.28319 time= 0.00000 
