Epoch: 0001 train_loss= 0.69997 train_acc= 0.47403 val_loss= 0.69819 val_acc= 0.59016 time= 0.32814
Epoch: 0002 train_loss= 0.69846 train_acc= 0.51818 val_loss= 0.69471 val_acc= 0.59016 time= 0.00000
Epoch: 0003 train_loss= 0.69756 train_acc= 0.52727 val_loss= 0.69225 val_acc= 0.59016 time= 0.01563
Epoch: 0004 train_loss= 0.69700 train_acc= 0.52468 val_loss= 0.69034 val_acc= 0.59016 time= 0.01563
Epoch: 0005 train_loss= 0.69630 train_acc= 0.52468 val_loss= 0.68880 val_acc= 0.59016 time= 0.01563
Epoch: 0006 train_loss= 0.69626 train_acc= 0.52468 val_loss= 0.68755 val_acc= 0.59016 time= 0.01563
Epoch: 0007 train_loss= 0.69586 train_acc= 0.52468 val_loss= 0.68663 val_acc= 0.59016 time= 0.01563
Epoch: 0008 train_loss= 0.69562 train_acc= 0.52468 val_loss= 0.68603 val_acc= 0.59016 time= 0.00000
Epoch: 0009 train_loss= 0.69546 train_acc= 0.52468 val_loss= 0.68573 val_acc= 0.59016 time= 0.01563
Epoch: 0010 train_loss= 0.69481 train_acc= 0.52468 val_loss= 0.68558 val_acc= 0.59016 time= 0.01563
Epoch: 0011 train_loss= 0.69481 train_acc= 0.52468 val_loss= 0.68563 val_acc= 0.59016 time= 0.01563
Epoch: 0012 train_loss= 0.69504 train_acc= 0.52468 val_loss= 0.68588 val_acc= 0.59016 time= 0.01563
Epoch: 0013 train_loss= 0.69423 train_acc= 0.52468 val_loss= 0.68619 val_acc= 0.59016 time= 0.01563
Epoch: 0014 train_loss= 0.69419 train_acc= 0.52468 val_loss= 0.68655 val_acc= 0.59016 time= 0.01563
Epoch: 0015 train_loss= 0.69410 train_acc= 0.52468 val_loss= 0.68693 val_acc= 0.59016 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.70367 accuracy= 0.44262 time= 0.01563 
