Epoch: 0001 train_loss= 2.10695 train_acc= 0.13962 val_loss= 2.09378 val_acc= 0.13793 time= 0.40109
Epoch: 0002 train_loss= 2.10483 train_acc= 0.13585 val_loss= 2.09167 val_acc= 0.13793 time= 0.00900
Epoch: 0003 train_loss= 2.08389 train_acc= 0.13585 val_loss= 2.08970 val_acc= 0.13793 time= 0.00700
Epoch: 0004 train_loss= 2.07786 train_acc= 0.16604 val_loss= 2.08780 val_acc= 0.13793 time= 0.00900
Epoch: 0005 train_loss= 2.07345 train_acc= 0.14340 val_loss= 2.08653 val_acc= 0.13793 time= 0.00800
Epoch: 0006 train_loss= 2.06882 train_acc= 0.14340 val_loss= 2.08617 val_acc= 0.06897 time= 0.00700
Epoch: 0007 train_loss= 2.06592 train_acc= 0.18113 val_loss= 2.08603 val_acc= 0.10345 time= 0.00700
Epoch: 0008 train_loss= 2.06511 train_acc= 0.21509 val_loss= 2.08677 val_acc= 0.10345 time= 0.00800
Epoch: 0009 train_loss= 2.05258 train_acc= 0.18868 val_loss= 2.08745 val_acc= 0.10345 time= 0.01000
Epoch: 0010 train_loss= 2.05542 train_acc= 0.17736 val_loss= 2.08774 val_acc= 0.10345 time= 0.00800
Epoch: 0011 train_loss= 2.05425 train_acc= 0.17358 val_loss= 2.08798 val_acc= 0.10345 time= 0.00700
Epoch: 0012 train_loss= 2.04830 train_acc= 0.18491 val_loss= 2.08837 val_acc= 0.10345 time= 0.00800
Early stopping...
Optimization Finished!
Test set results: cost= 2.06283 accuracy= 0.15254 time= 0.00400 
