Epoch: 0001 train_loss= 0.79211 train_acc= 0.50727 val_loss= 1.11304 val_acc= 0.47541 time= 0.66004
Epoch: 0002 train_loss= 0.77238 train_acc= 0.51455 val_loss= 0.83916 val_acc= 0.54098 time= 0.03125
Epoch: 0003 train_loss= 0.84802 train_acc= 0.47455 val_loss= 0.88856 val_acc= 0.54098 time= 0.03125
Epoch: 0004 train_loss= 0.82450 train_acc= 0.51455 val_loss= 0.86707 val_acc= 0.54098 time= 0.01563
Epoch: 0005 train_loss= 1.55757 train_acc= 0.51455 val_loss= 0.73073 val_acc= 0.54098 time= 0.03646
Epoch: 0006 train_loss= 1.06586 train_acc= 0.51273 val_loss= 0.68861 val_acc= 0.54098 time= 0.01050
Epoch: 0007 train_loss= 0.83785 train_acc= 0.51091 val_loss= 0.89824 val_acc= 0.50820 time= 0.03126
Epoch: 0008 train_loss= 0.94349 train_acc= 0.51636 val_loss= 1.22465 val_acc= 0.49180 time= 0.03125
Epoch: 0009 train_loss= 0.87503 train_acc= 0.48545 val_loss= 1.65980 val_acc= 0.50820 time= 0.02415
Epoch: 0010 train_loss= 1.95362 train_acc= 0.51091 val_loss= 1.67319 val_acc= 0.52459 time= 0.02300
Epoch: 0011 train_loss= 1.62450 train_acc= 0.48364 val_loss= 1.57270 val_acc= 0.52459 time= 0.02701
Epoch: 0012 train_loss= 1.41835 train_acc= 0.50545 val_loss= 1.36844 val_acc= 0.52459 time= 0.02501
Early stopping...
Optimization Finished!
Test set results: cost= 0.77673 accuracy= 0.45082 time= 0.01100 
