Epoch: 0001 train_loss= 2.08464 train_acc= 0.11950 val_loss= 2.08101 val_acc= 0.31034 time= 0.09376
Epoch: 0002 train_loss= 2.08264 train_acc= 0.11950 val_loss= 2.08035 val_acc= 0.10345 time= 0.00000
Epoch: 0003 train_loss= 2.08041 train_acc= 0.20126 val_loss= 2.07962 val_acc= 0.10345 time= 0.01563
Epoch: 0004 train_loss= 2.07829 train_acc= 0.20126 val_loss= 2.07917 val_acc= 0.10345 time= 0.01563
Epoch: 0005 train_loss= 2.07587 train_acc= 0.20126 val_loss= 2.07883 val_acc= 0.10345 time= 0.00000
Epoch: 0006 train_loss= 2.07320 train_acc= 0.20126 val_loss= 2.07884 val_acc= 0.10345 time= 0.01563
Epoch: 0007 train_loss= 2.06978 train_acc= 0.20126 val_loss= 2.07931 val_acc= 0.10345 time= 0.00000
Epoch: 0008 train_loss= 2.06685 train_acc= 0.20126 val_loss= 2.08034 val_acc= 0.10345 time= 0.01563
Epoch: 0009 train_loss= 2.06316 train_acc= 0.20126 val_loss= 2.08196 val_acc= 0.10345 time= 0.01563
Epoch: 0010 train_loss= 2.06037 train_acc= 0.20126 val_loss= 2.08426 val_acc= 0.10345 time= 0.00000
Epoch: 0011 train_loss= 2.05678 train_acc= 0.20126 val_loss= 2.08736 val_acc= 0.10345 time= 0.01563
Epoch: 0012 train_loss= 2.05418 train_acc= 0.20126 val_loss= 2.09120 val_acc= 0.10345 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.05654 accuracy= 0.11864 time= 0.01563 
