Epoch: 0001 train_loss= 2.11427 train_acc= 0.08302 val_loss= 2.10236 val_acc= 0.10345 time= 0.36608
Epoch: 0002 train_loss= 2.10994 train_acc= 0.07547 val_loss= 2.10192 val_acc= 0.13793 time= 0.00800
Epoch: 0003 train_loss= 2.09392 train_acc= 0.11698 val_loss= 2.10327 val_acc= 0.13793 time= 0.00900
Epoch: 0004 train_loss= 2.08581 train_acc= 0.15094 val_loss= 2.10621 val_acc= 0.13793 time= 0.00900
Epoch: 0005 train_loss= 2.07906 train_acc= 0.15094 val_loss= 2.10861 val_acc= 0.17241 time= 0.00800
Epoch: 0006 train_loss= 2.07476 train_acc= 0.14717 val_loss= 2.11128 val_acc= 0.17241 time= 0.00800
Epoch: 0007 train_loss= 2.07876 train_acc= 0.14717 val_loss= 2.11247 val_acc= 0.13793 time= 0.00700
Epoch: 0008 train_loss= 2.06576 train_acc= 0.16604 val_loss= 2.11326 val_acc= 0.13793 time= 0.00900
Epoch: 0009 train_loss= 2.06220 train_acc= 0.18868 val_loss= 2.11496 val_acc= 0.13793 time= 0.00800
Epoch: 0010 train_loss= 2.07261 train_acc= 0.13962 val_loss= 2.11676 val_acc= 0.10345 time= 0.00800
Epoch: 0011 train_loss= 2.06613 train_acc= 0.16604 val_loss= 2.11824 val_acc= 0.10345 time= 0.00800
Epoch: 0012 train_loss= 2.07112 train_acc= 0.17736 val_loss= 2.11969 val_acc= 0.10345 time= 0.00900
Early stopping...
Optimization Finished!
Test set results: cost= 2.07625 accuracy= 0.15254 time= 0.00400 
