Epoch: 0001 train_loss= 2.09172 train_acc= 0.11321 val_loss= 2.09669 val_acc= 0.00000 time= 0.07814
Epoch: 0002 train_loss= 2.09053 train_acc= 0.08805 val_loss= 2.09349 val_acc= 0.10345 time= 0.01562
Epoch: 0003 train_loss= 2.08470 train_acc= 0.08176 val_loss= 2.09125 val_acc= 0.13793 time= 0.00000
Epoch: 0004 train_loss= 2.07298 train_acc= 0.19497 val_loss= 2.08888 val_acc= 0.13793 time= 0.01563
Epoch: 0005 train_loss= 2.06644 train_acc= 0.19497 val_loss= 2.08815 val_acc= 0.13793 time= 0.01563
Epoch: 0006 train_loss= 2.05781 train_acc= 0.19497 val_loss= 2.08786 val_acc= 0.13793 time= 0.00000
Epoch: 0007 train_loss= 2.05635 train_acc= 0.19497 val_loss= 2.08815 val_acc= 0.13793 time= 0.01562
Epoch: 0008 train_loss= 2.05338 train_acc= 0.19497 val_loss= 2.08962 val_acc= 0.13793 time= 0.00000
Epoch: 0009 train_loss= 2.04315 train_acc= 0.20755 val_loss= 2.09074 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.04816 train_acc= 0.19497 val_loss= 2.09235 val_acc= 0.13793 time= 0.01563
Epoch: 0011 train_loss= 2.04791 train_acc= 0.20126 val_loss= 2.09456 val_acc= 0.13793 time= 0.00000
Epoch: 0012 train_loss= 2.03957 train_acc= 0.20126 val_loss= 2.09736 val_acc= 0.13793 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.02632 accuracy= 0.10169 time= 0.00000 
