Epoch: 0001 train_loss= 1.40145 train_acc= 0.27793 val_loss= 1.39544 val_acc= 0.26667 time= 0.70317
Epoch: 0002 train_loss= 1.38318 train_acc= 0.29609 val_loss= 1.39854 val_acc= 0.30000 time= 0.01562
Epoch: 0003 train_loss= 1.38243 train_acc= 0.30866 val_loss= 1.40281 val_acc= 0.30000 time= 0.01562
Epoch: 0004 train_loss= 1.37464 train_acc= 0.28771 val_loss= 1.40918 val_acc= 0.26667 time= 0.01563
Epoch: 0005 train_loss= 1.40895 train_acc= 0.31704 val_loss= 1.40979 val_acc= 0.26667 time= 0.00000
Epoch: 0006 train_loss= 1.38180 train_acc= 0.29190 val_loss= 1.41190 val_acc= 0.26667 time= 0.01563
Epoch: 0007 train_loss= 1.38883 train_acc= 0.30307 val_loss= 1.41228 val_acc= 0.26667 time= 0.01563
Epoch: 0008 train_loss= 1.37495 train_acc= 0.31844 val_loss= 1.41364 val_acc= 0.26667 time= 0.01563
Epoch: 0009 train_loss= 1.38448 train_acc= 0.31285 val_loss= 1.41445 val_acc= 0.26667 time= 0.00000
Epoch: 0010 train_loss= 1.37468 train_acc= 0.31704 val_loss= 1.41382 val_acc= 0.26667 time= 0.01563
Epoch: 0011 train_loss= 1.37906 train_acc= 0.31145 val_loss= 1.41321 val_acc= 0.28333 time= 0.01563
Epoch: 0012 train_loss= 1.37671 train_acc= 0.30587 val_loss= 1.41290 val_acc= 0.28333 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.40133 accuracy= 0.26667 time= 0.00000 
