Epoch: 0001 train_loss= 2.08265 train_acc= 0.13836 val_loss= 2.08295 val_acc= 0.13793 time= 0.10967
Epoch: 0002 train_loss= 2.07089 train_acc= 0.15723 val_loss= 2.08461 val_acc= 0.17241 time= 0.01562
Epoch: 0003 train_loss= 2.06614 train_acc= 0.15723 val_loss= 2.08623 val_acc= 0.10345 time= 0.00000
Epoch: 0004 train_loss= 2.06116 train_acc= 0.16352 val_loss= 2.08837 val_acc= 0.10345 time= 0.01563
Epoch: 0005 train_loss= 2.04774 train_acc= 0.17610 val_loss= 2.09110 val_acc= 0.10345 time= 0.00000
Epoch: 0006 train_loss= 2.05745 train_acc= 0.14465 val_loss= 2.09438 val_acc= 0.10345 time= 0.01563
Epoch: 0007 train_loss= 2.04363 train_acc= 0.16981 val_loss= 2.09788 val_acc= 0.10345 time= 0.00000
Epoch: 0008 train_loss= 2.04811 train_acc= 0.15723 val_loss= 2.10115 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.04234 train_acc= 0.17610 val_loss= 2.10411 val_acc= 0.13793 time= 0.00000
Epoch: 0010 train_loss= 2.03079 train_acc= 0.18239 val_loss= 2.10728 val_acc= 0.13793 time= 0.01563
Epoch: 0011 train_loss= 2.02982 train_acc= 0.18868 val_loss= 2.10968 val_acc= 0.13793 time= 0.00000
Epoch: 0012 train_loss= 2.03310 train_acc= 0.16981 val_loss= 2.11158 val_acc= 0.13793 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.10222 accuracy= 0.08475 time= 0.00000 
