Epoch: 0001 train_loss= 1.39016 train_acc= 0.28664 val_loss= 1.38449 val_acc= 0.35714 time= 0.10938
Epoch: 0002 train_loss= 1.38991 train_acc= 0.28664 val_loss= 1.38255 val_acc= 0.35714 time= 0.00000
Epoch: 0003 train_loss= 1.38877 train_acc= 0.28664 val_loss= 1.38127 val_acc= 0.35714 time= 0.01563
Epoch: 0004 train_loss= 1.38764 train_acc= 0.28664 val_loss= 1.38024 val_acc= 0.35714 time= 0.01562
Epoch: 0005 train_loss= 1.38813 train_acc= 0.28664 val_loss= 1.37976 val_acc= 0.35714 time= 0.01563
Epoch: 0006 train_loss= 1.38695 train_acc= 0.28664 val_loss= 1.37972 val_acc= 0.35714 time= 0.01563
Epoch: 0007 train_loss= 1.38615 train_acc= 0.28664 val_loss= 1.37988 val_acc= 0.35714 time= 0.01563
Epoch: 0008 train_loss= 1.38535 train_acc= 0.28664 val_loss= 1.38032 val_acc= 0.35714 time= 0.00000
Epoch: 0009 train_loss= 1.38525 train_acc= 0.28664 val_loss= 1.38109 val_acc= 0.35714 time= 0.01563
Epoch: 0010 train_loss= 1.38563 train_acc= 0.28664 val_loss= 1.38176 val_acc= 0.35714 time= 0.01563
Epoch: 0011 train_loss= 1.38564 train_acc= 0.28664 val_loss= 1.38263 val_acc= 0.35714 time= 0.01563
Epoch: 0012 train_loss= 1.38488 train_acc= 0.28664 val_loss= 1.38367 val_acc= 0.35714 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38264 accuracy= 0.31858 time= 0.00000 
