Epoch: 0001 train_loss= 2.08754 train_acc= 0.10692 val_loss= 2.08600 val_acc= 0.03448 time= 0.14796
Epoch: 0002 train_loss= 2.08533 train_acc= 0.14465 val_loss= 2.08468 val_acc= 0.03448 time= 0.01562
Epoch: 0003 train_loss= 2.08366 train_acc= 0.14465 val_loss= 2.08375 val_acc= 0.03448 time= 0.01542
Epoch: 0004 train_loss= 2.08233 train_acc= 0.14465 val_loss= 2.08321 val_acc= 0.03448 time= 0.01000
Epoch: 0005 train_loss= 2.08121 train_acc= 0.14465 val_loss= 2.08312 val_acc= 0.03448 time= 0.00108
Epoch: 0006 train_loss= 2.08021 train_acc= 0.14465 val_loss= 2.08344 val_acc= 0.03448 time= 0.01563
Epoch: 0007 train_loss= 2.07915 train_acc= 0.14465 val_loss= 2.08401 val_acc= 0.03448 time= 0.00000
Epoch: 0008 train_loss= 2.07851 train_acc= 0.14465 val_loss= 2.08470 val_acc= 0.03448 time= 0.01563
Epoch: 0009 train_loss= 2.07749 train_acc= 0.13836 val_loss= 2.08546 val_acc= 0.03448 time= 0.00000
Epoch: 0010 train_loss= 2.07685 train_acc= 0.10063 val_loss= 2.08618 val_acc= 0.10345 time= 0.01562
Epoch: 0011 train_loss= 2.07533 train_acc= 0.16981 val_loss= 2.08695 val_acc= 0.10345 time= 0.00000
Epoch: 0012 train_loss= 2.07421 train_acc= 0.16981 val_loss= 2.08775 val_acc= 0.10345 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07797 accuracy= 0.13559 time= 0.00000 
