Epoch: 0001 train_loss= 0.70608 train_acc= 0.49697 val_loss= 0.68802 val_acc= 0.52459 time= 0.23912
Epoch: 0002 train_loss= 0.70502 train_acc= 0.50000 val_loss= 0.68891 val_acc= 0.52459 time= 0.01100
Epoch: 0003 train_loss= 0.70202 train_acc= 0.50000 val_loss= 0.68981 val_acc= 0.52459 time= 0.00000
Epoch: 0004 train_loss= 0.69976 train_acc= 0.49697 val_loss= 0.69072 val_acc= 0.52459 time= 0.00000
Epoch: 0005 train_loss= 0.69831 train_acc= 0.50000 val_loss= 0.69165 val_acc= 0.52459 time= 0.01563
Epoch: 0006 train_loss= 0.69896 train_acc= 0.49697 val_loss= 0.69260 val_acc= 0.52459 time= 0.00000
Epoch: 0007 train_loss= 0.69756 train_acc= 0.49091 val_loss= 0.69359 val_acc= 0.52459 time= 0.01563
Epoch: 0008 train_loss= 0.69602 train_acc= 0.50000 val_loss= 0.69462 val_acc= 0.52459 time= 0.00000
Epoch: 0009 train_loss= 0.69627 train_acc= 0.49091 val_loss= 0.69570 val_acc= 0.42623 time= 0.00000
Epoch: 0010 train_loss= 0.69522 train_acc= 0.50909 val_loss= 0.69679 val_acc= 0.47541 time= 0.01563
Epoch: 0011 train_loss= 0.69519 train_acc= 0.50000 val_loss= 0.69783 val_acc= 0.47541 time= 0.00000
Epoch: 0012 train_loss= 0.69458 train_acc= 0.50909 val_loss= 0.69879 val_acc= 0.47541 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69625 accuracy= 0.46721 time= 0.01563 
