Epoch: 0001 train_loss= 2.08155 train_acc= 0.13208 val_loss= 2.07591 val_acc= 0.06897 time= 0.25002
Epoch: 0002 train_loss= 2.07750 train_acc= 0.15094 val_loss= 2.07428 val_acc= 0.13793 time= 0.00000
Epoch: 0003 train_loss= 2.07340 train_acc= 0.14465 val_loss= 2.07278 val_acc= 0.13793 time= 0.01563
Epoch: 0004 train_loss= 2.07409 train_acc= 0.18239 val_loss= 2.07138 val_acc= 0.13793 time= 0.00000
Epoch: 0005 train_loss= 2.07234 train_acc= 0.19497 val_loss= 2.07008 val_acc= 0.13793 time= 0.00000
Epoch: 0006 train_loss= 2.06701 train_acc= 0.18868 val_loss= 2.06894 val_acc= 0.13793 time= 0.01563
Epoch: 0007 train_loss= 2.07042 train_acc= 0.18239 val_loss= 2.06793 val_acc= 0.13793 time= 0.00000
Epoch: 0008 train_loss= 2.06598 train_acc= 0.19497 val_loss= 2.06712 val_acc= 0.13793 time= 0.00000
Epoch: 0009 train_loss= 2.06680 train_acc= 0.18239 val_loss= 2.06636 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.06253 train_acc= 0.18239 val_loss= 2.06569 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.06073 train_acc= 0.18868 val_loss= 2.06518 val_acc= 0.13793 time= 0.01563
Epoch: 0012 train_loss= 2.05799 train_acc= 0.18868 val_loss= 2.06487 val_acc= 0.13793 time= 0.00000
Epoch: 0013 train_loss= 2.05812 train_acc= 0.18868 val_loss= 2.06469 val_acc= 0.13793 time= 0.00000
Epoch: 0014 train_loss= 2.05788 train_acc= 0.18868 val_loss= 2.06461 val_acc= 0.13793 time= 0.01563
Epoch: 0015 train_loss= 2.05652 train_acc= 0.18868 val_loss= 2.06477 val_acc= 0.13793 time= 0.00000
Epoch: 0016 train_loss= 2.04791 train_acc= 0.18868 val_loss= 2.06516 val_acc= 0.13793 time= 0.01563
Epoch: 0017 train_loss= 2.05087 train_acc= 0.18868 val_loss= 2.06576 val_acc= 0.13793 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.08402 accuracy= 0.10169 time= 0.00000 
