Epoch: 0001 train_loss= 1.39387 train_acc= 0.32617 val_loss= 1.39399 val_acc= 0.21429 time= 0.51366
Epoch: 0002 train_loss= 1.38899 train_acc= 0.33203 val_loss= 1.39471 val_acc= 0.21429 time= 0.01700
Epoch: 0003 train_loss= 1.38479 train_acc= 0.33203 val_loss= 1.39642 val_acc= 0.21429 time= 0.01115
Epoch: 0004 train_loss= 1.38152 train_acc= 0.33203 val_loss= 1.39898 val_acc= 0.21429 time= 0.01823
Epoch: 0005 train_loss= 1.37870 train_acc= 0.33203 val_loss= 1.40223 val_acc= 0.21429 time= 0.01725
Epoch: 0006 train_loss= 1.37673 train_acc= 0.33203 val_loss= 1.40596 val_acc= 0.21429 time= 0.01198
Epoch: 0007 train_loss= 1.37566 train_acc= 0.33203 val_loss= 1.40991 val_acc= 0.21429 time= 0.01824
Epoch: 0008 train_loss= 1.37432 train_acc= 0.33203 val_loss= 1.41384 val_acc= 0.21429 time= 0.01599
Epoch: 0009 train_loss= 1.37374 train_acc= 0.33203 val_loss= 1.41739 val_acc= 0.21429 time= 0.01533
Epoch: 0010 train_loss= 1.37345 train_acc= 0.33203 val_loss= 1.42034 val_acc= 0.21429 time= 0.01428
Epoch: 0011 train_loss= 1.37273 train_acc= 0.33203 val_loss= 1.42260 val_acc= 0.21429 time= 0.04529
Epoch: 0012 train_loss= 1.37273 train_acc= 0.33203 val_loss= 1.42413 val_acc= 0.21429 time= 0.03326
Early stopping...
Optimization Finished!
Test set results: cost= 1.37975 accuracy= 0.30088 time= 0.01000 
