Epoch: 0001 train_loss= 2.08706 train_acc= 0.09164 val_loss= 2.08599 val_acc= 0.10345 time= 0.66015
Epoch: 0002 train_loss= 2.08530 train_acc= 0.12399 val_loss= 2.08581 val_acc= 0.17241 time= 0.00800
Epoch: 0003 train_loss= 2.08418 train_acc= 0.12129 val_loss= 2.08582 val_acc= 0.06897 time= 0.00900
Epoch: 0004 train_loss= 2.08307 train_acc= 0.15903 val_loss= 2.08592 val_acc= 0.06897 time= 0.00800
Epoch: 0005 train_loss= 2.08193 train_acc= 0.15903 val_loss= 2.08597 val_acc= 0.06897 time= 0.00800
Epoch: 0006 train_loss= 2.08083 train_acc= 0.15903 val_loss= 2.08614 val_acc= 0.06897 time= 0.00800
Epoch: 0007 train_loss= 2.07918 train_acc= 0.15903 val_loss= 2.08643 val_acc= 0.06897 time= 0.00800
Epoch: 0008 train_loss= 2.07889 train_acc= 0.15903 val_loss= 2.08684 val_acc= 0.06897 time= 0.00800
Epoch: 0009 train_loss= 2.07682 train_acc= 0.15903 val_loss= 2.08729 val_acc= 0.06897 time= 0.00800
Epoch: 0010 train_loss= 2.07527 train_acc= 0.15903 val_loss= 2.08780 val_acc= 0.06897 time= 0.00900
Epoch: 0011 train_loss= 2.07435 train_acc= 0.15903 val_loss= 2.08840 val_acc= 0.06897 time= 0.00800
Epoch: 0012 train_loss= 2.07193 train_acc= 0.16173 val_loss= 2.08921 val_acc= 0.06897 time= 0.00800
Early stopping...
Optimization Finished!
Test set results: cost= 2.05255 accuracy= 0.16949 time= 0.00300 
