Epoch: 0001 train_loss= 1.39234 train_acc= 0.23779 val_loss= 1.39974 val_acc= 0.26667 time= 0.30622
Epoch: 0002 train_loss= 1.39065 train_acc= 0.29316 val_loss= 1.39896 val_acc= 0.26667 time= 0.00814
Epoch: 0003 train_loss= 1.38850 train_acc= 0.29642 val_loss= 1.39823 val_acc= 0.26667 time= 0.00805
Epoch: 0004 train_loss= 1.38891 train_acc= 0.29316 val_loss= 1.39750 val_acc= 0.26667 time= 0.00811
Epoch: 0005 train_loss= 1.38743 train_acc= 0.28990 val_loss= 1.39686 val_acc= 0.26667 time= 0.00923
Epoch: 0006 train_loss= 1.38696 train_acc= 0.29316 val_loss= 1.39627 val_acc= 0.26667 time= 0.00990
Epoch: 0007 train_loss= 1.38547 train_acc= 0.28990 val_loss= 1.39572 val_acc= 0.26667 time= 0.00899
Epoch: 0008 train_loss= 1.38565 train_acc= 0.29316 val_loss= 1.39519 val_acc= 0.26667 time= 0.01009
Epoch: 0009 train_loss= 1.38600 train_acc= 0.29316 val_loss= 1.39461 val_acc= 0.26667 time= 0.00906
Epoch: 0010 train_loss= 1.38563 train_acc= 0.29642 val_loss= 1.39380 val_acc= 0.26667 time= 0.00917
Epoch: 0011 train_loss= 1.38436 train_acc= 0.28990 val_loss= 1.39297 val_acc= 0.26667 time= 0.00804
Epoch: 0012 train_loss= 1.38408 train_acc= 0.29316 val_loss= 1.39201 val_acc= 0.26667 time= 0.01009
Epoch: 0013 train_loss= 1.38328 train_acc= 0.29316 val_loss= 1.39120 val_acc= 0.26667 time= 0.00900
Epoch: 0014 train_loss= 1.38394 train_acc= 0.29316 val_loss= 1.39050 val_acc= 0.26667 time= 0.00712
Epoch: 0015 train_loss= 1.38303 train_acc= 0.29316 val_loss= 1.38996 val_acc= 0.26667 time= 0.00902
Epoch: 0016 train_loss= 1.38392 train_acc= 0.29316 val_loss= 1.38957 val_acc= 0.26667 time= 0.01140
Epoch: 0017 train_loss= 1.38289 train_acc= 0.29316 val_loss= 1.38937 val_acc= 0.26667 time= 0.00712
Epoch: 0018 train_loss= 1.38349 train_acc= 0.29316 val_loss= 1.38927 val_acc= 0.26667 time= 0.00995
Epoch: 0019 train_loss= 1.38258 train_acc= 0.29642 val_loss= 1.38937 val_acc= 0.26667 time= 0.00910
Epoch: 0020 train_loss= 1.38163 train_acc= 0.29316 val_loss= 1.38970 val_acc= 0.26667 time= 0.00905
Epoch: 0021 train_loss= 1.38254 train_acc= 0.29316 val_loss= 1.39016 val_acc= 0.26667 time= 0.00907
Epoch: 0022 train_loss= 1.38115 train_acc= 0.29642 val_loss= 1.39066 val_acc= 0.26667 time= 0.00818
Early stopping...
Optimization Finished!
Test set results: cost= 1.37848 accuracy= 0.31667 time= 0.00300 
