Epoch: 0001 train_loss= 0.69915 train_acc= 0.50779 val_loss= 0.70005 val_acc= 0.39344 time= 0.31253
Epoch: 0002 train_loss= 0.69851 train_acc= 0.50519 val_loss= 0.70045 val_acc= 0.39344 time= 0.01562
Epoch: 0003 train_loss= 0.69783 train_acc= 0.50130 val_loss= 0.70090 val_acc= 0.39344 time= 0.01563
Epoch: 0004 train_loss= 0.69724 train_acc= 0.50260 val_loss= 0.70141 val_acc= 0.39344 time= 0.01562
Epoch: 0005 train_loss= 0.69682 train_acc= 0.50260 val_loss= 0.70194 val_acc= 0.39344 time= 0.01563
Epoch: 0006 train_loss= 0.69631 train_acc= 0.50260 val_loss= 0.70244 val_acc= 0.39344 time= 0.00000
Epoch: 0007 train_loss= 0.69590 train_acc= 0.50260 val_loss= 0.70294 val_acc= 0.39344 time= 0.01563
Epoch: 0008 train_loss= 0.69550 train_acc= 0.50260 val_loss= 0.70338 val_acc= 0.39344 time= 0.01563
Epoch: 0009 train_loss= 0.69537 train_acc= 0.50260 val_loss= 0.70368 val_acc= 0.39344 time= 0.01563
Epoch: 0010 train_loss= 0.69461 train_acc= 0.50260 val_loss= 0.70400 val_acc= 0.39344 time= 0.01563
Epoch: 0011 train_loss= 0.69469 train_acc= 0.50260 val_loss= 0.70419 val_acc= 0.39344 time= 0.01563
Epoch: 0012 train_loss= 0.69425 train_acc= 0.50260 val_loss= 0.70424 val_acc= 0.39344 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69937 accuracy= 0.47541 time= 0.00000 
