Epoch: 0001 train_loss= 2.07856 train_acc= 0.16981 val_loss= 2.10346 val_acc= 0.17241 time= 0.20004
Epoch: 0002 train_loss= 2.07052 train_acc= 0.16981 val_loss= 2.10942 val_acc= 0.17241 time= 0.00600
Epoch: 0003 train_loss= 2.06433 train_acc= 0.17610 val_loss= 2.11632 val_acc= 0.17241 time= 0.00500
Epoch: 0004 train_loss= 2.06158 train_acc= 0.16352 val_loss= 2.12437 val_acc= 0.17241 time= 0.00600
Epoch: 0005 train_loss= 2.05299 train_acc= 0.16981 val_loss= 2.13323 val_acc= 0.17241 time= 0.00400
Epoch: 0006 train_loss= 2.05418 train_acc= 0.16352 val_loss= 2.14260 val_acc= 0.17241 time= 0.00500
Epoch: 0007 train_loss= 2.04907 train_acc= 0.15723 val_loss= 2.15274 val_acc= 0.17241 time= 0.00500
Epoch: 0008 train_loss= 2.04437 train_acc= 0.20126 val_loss= 2.16363 val_acc= 0.10345 time= 0.00500
Epoch: 0009 train_loss= 2.03834 train_acc= 0.17610 val_loss= 2.17533 val_acc= 0.10345 time= 0.00500
Epoch: 0010 train_loss= 2.03581 train_acc= 0.18239 val_loss= 2.18721 val_acc= 0.10345 time= 0.00524
Epoch: 0011 train_loss= 2.03203 train_acc= 0.20755 val_loss= 2.19905 val_acc= 0.10345 time= 0.00500
Epoch: 0012 train_loss= 2.03011 train_acc= 0.16352 val_loss= 2.21133 val_acc= 0.10345 time= 0.00500
Early stopping...
Optimization Finished!
Test set results: cost= 2.10472 accuracy= 0.18644 time= 0.00300 
