Epoch: 0001 train_loss= 1.39169 train_acc= 0.28906 val_loss= 1.38828 val_acc= 0.25000 time= 0.34808
Epoch: 0002 train_loss= 1.38919 train_acc= 0.28906 val_loss= 1.38384 val_acc= 0.25000 time= 0.01400
Epoch: 0003 train_loss= 1.38772 train_acc= 0.29102 val_loss= 1.38046 val_acc= 0.25000 time= 0.01500
Epoch: 0004 train_loss= 1.38675 train_acc= 0.27734 val_loss= 1.37775 val_acc= 0.25000 time= 0.01400
Epoch: 0005 train_loss= 1.38551 train_acc= 0.28320 val_loss= 1.37592 val_acc= 0.25000 time= 0.01700
Epoch: 0006 train_loss= 1.38455 train_acc= 0.24805 val_loss= 1.37446 val_acc= 0.25000 time= 0.01500
Epoch: 0007 train_loss= 1.38405 train_acc= 0.29688 val_loss= 1.37341 val_acc= 0.25000 time= 0.01500
Epoch: 0008 train_loss= 1.38382 train_acc= 0.27734 val_loss= 1.37277 val_acc= 0.25000 time= 0.01400
Epoch: 0009 train_loss= 1.38196 train_acc= 0.30664 val_loss= 1.37260 val_acc= 0.25000 time= 0.01600
Epoch: 0010 train_loss= 1.38346 train_acc= 0.28711 val_loss= 1.37287 val_acc= 0.25000 time= 0.00579
Epoch: 0011 train_loss= 1.38205 train_acc= 0.28711 val_loss= 1.37332 val_acc= 0.25000 time= 0.01563
Epoch: 0012 train_loss= 1.38256 train_acc= 0.29102 val_loss= 1.37393 val_acc= 0.25000 time= 0.01563
Epoch: 0013 train_loss= 1.38282 train_acc= 0.28906 val_loss= 1.37458 val_acc= 0.25000 time= 0.01563
Epoch: 0014 train_loss= 1.38164 train_acc= 0.29102 val_loss= 1.37531 val_acc= 0.25000 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37647 accuracy= 0.30088 time= 0.00000 
