Epoch: 0001 train_loss= 1.39384 train_acc= 0.32031 val_loss= 1.39040 val_acc= 0.33929 time= 0.45545
Epoch: 0002 train_loss= 1.38962 train_acc= 0.32617 val_loss= 1.38705 val_acc= 0.33929 time= 0.00000
Epoch: 0003 train_loss= 1.38543 train_acc= 0.32617 val_loss= 1.38441 val_acc= 0.33929 time= 0.01563
Epoch: 0004 train_loss= 1.38205 train_acc= 0.32617 val_loss= 1.38251 val_acc= 0.33929 time= 0.01563
Epoch: 0005 train_loss= 1.37957 train_acc= 0.32617 val_loss= 1.38144 val_acc= 0.33929 time= 0.01562
Epoch: 0006 train_loss= 1.37801 train_acc= 0.32617 val_loss= 1.38114 val_acc= 0.33929 time= 0.01563
Epoch: 0007 train_loss= 1.37622 train_acc= 0.32617 val_loss= 1.38146 val_acc= 0.33929 time= 0.01563
Epoch: 0008 train_loss= 1.37614 train_acc= 0.32617 val_loss= 1.38228 val_acc= 0.33929 time= 0.01562
Epoch: 0009 train_loss= 1.37545 train_acc= 0.32617 val_loss= 1.38349 val_acc= 0.33929 time= 0.01563
Epoch: 0010 train_loss= 1.37513 train_acc= 0.32617 val_loss= 1.38463 val_acc= 0.33929 time= 0.02035
Epoch: 0011 train_loss= 1.37511 train_acc= 0.32617 val_loss= 1.38555 val_acc= 0.33929 time= 0.01100
Epoch: 0012 train_loss= 1.37465 train_acc= 0.32617 val_loss= 1.38639 val_acc= 0.33929 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.39004 accuracy= 0.31858 time= 0.01563 
