Epoch: 0001 train_loss= 0.69941 train_acc= 0.49481 val_loss= 0.69912 val_acc= 0.47541 time= 0.32989
Epoch: 0002 train_loss= 0.69866 train_acc= 0.51818 val_loss= 0.69916 val_acc= 0.47541 time= 0.01563
Epoch: 0003 train_loss= 0.69804 train_acc= 0.51169 val_loss= 0.69909 val_acc= 0.47541 time= 0.01563
Epoch: 0004 train_loss= 0.69766 train_acc= 0.51299 val_loss= 0.69880 val_acc= 0.47541 time= 0.00000
Epoch: 0005 train_loss= 0.69714 train_acc= 0.51169 val_loss= 0.69843 val_acc= 0.47541 time= 0.00000
Epoch: 0006 train_loss= 0.69683 train_acc= 0.51169 val_loss= 0.69797 val_acc= 0.47541 time= 0.01563
Epoch: 0007 train_loss= 0.69639 train_acc= 0.51039 val_loss= 0.69749 val_acc= 0.47541 time= 0.01563
Epoch: 0008 train_loss= 0.69605 train_acc= 0.51169 val_loss= 0.69700 val_acc= 0.47541 time= 0.01563
Epoch: 0009 train_loss= 0.69574 train_acc= 0.51169 val_loss= 0.69653 val_acc= 0.47541 time= 0.01563
Epoch: 0010 train_loss= 0.69532 train_acc= 0.51169 val_loss= 0.69612 val_acc= 0.47541 time= 0.00000
Epoch: 0011 train_loss= 0.69511 train_acc= 0.51169 val_loss= 0.69577 val_acc= 0.47541 time= 0.01562
Epoch: 0012 train_loss= 0.69491 train_acc= 0.51169 val_loss= 0.69545 val_acc= 0.47541 time= 0.01563
Epoch: 0013 train_loss= 0.69452 train_acc= 0.51429 val_loss= 0.69522 val_acc= 0.47541 time= 0.01563
Epoch: 0014 train_loss= 0.69439 train_acc= 0.51169 val_loss= 0.69505 val_acc= 0.47541 time= 0.01563
Epoch: 0015 train_loss= 0.69430 train_acc= 0.50909 val_loss= 0.69488 val_acc= 0.47541 time= 0.01563
Epoch: 0016 train_loss= 0.69405 train_acc= 0.51299 val_loss= 0.69475 val_acc= 0.47541 time= 0.00000
Epoch: 0017 train_loss= 0.69387 train_acc= 0.51039 val_loss= 0.69473 val_acc= 0.47541 time= 0.01563
Epoch: 0018 train_loss= 0.69375 train_acc= 0.51169 val_loss= 0.69470 val_acc= 0.47541 time= 0.01563
Epoch: 0019 train_loss= 0.69367 train_acc= 0.51169 val_loss= 0.69458 val_acc= 0.47541 time= 0.01563
Epoch: 0020 train_loss= 0.69355 train_acc= 0.51169 val_loss= 0.69449 val_acc= 0.47541 time= 0.01563
Epoch: 0021 train_loss= 0.69348 train_acc= 0.51169 val_loss= 0.69444 val_acc= 0.47541 time= 0.01563
Epoch: 0022 train_loss= 0.69334 train_acc= 0.51169 val_loss= 0.69439 val_acc= 0.47541 time= 0.01562
Epoch: 0023 train_loss= 0.69329 train_acc= 0.51169 val_loss= 0.69437 val_acc= 0.47541 time= 0.00000
Epoch: 0024 train_loss= 0.69330 train_acc= 0.51169 val_loss= 0.69435 val_acc= 0.47541 time= 0.01563
Epoch: 0025 train_loss= 0.69317 train_acc= 0.51169 val_loss= 0.69434 val_acc= 0.47541 time= 0.01563
Epoch: 0026 train_loss= 0.69322 train_acc= 0.51169 val_loss= 0.69434 val_acc= 0.47541 time= 0.01563
Epoch: 0027 train_loss= 0.69301 train_acc= 0.51169 val_loss= 0.69443 val_acc= 0.47541 time= 0.01563
Epoch: 0028 train_loss= 0.69313 train_acc= 0.51169 val_loss= 0.69446 val_acc= 0.47541 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69200 accuracy= 0.54918 time= 0.02043 
