Epoch: 0001 train_loss= 0.70641 train_acc= 0.49273 val_loss= 0.72340 val_acc= 0.37705 time= 0.56276
Epoch: 0002 train_loss= 0.70328 train_acc= 0.49273 val_loss= 0.71781 val_acc= 0.37705 time= 0.00000
Epoch: 0003 train_loss= 0.69750 train_acc= 0.49273 val_loss= 0.71294 val_acc= 0.37705 time= 0.01563
Epoch: 0004 train_loss= 0.70315 train_acc= 0.49273 val_loss= 0.70854 val_acc= 0.37705 time= 0.00000
Epoch: 0005 train_loss= 0.69552 train_acc= 0.49636 val_loss= 0.70478 val_acc= 0.37705 time= 0.00000
Epoch: 0006 train_loss= 0.69473 train_acc= 0.49273 val_loss= 0.70163 val_acc= 0.37705 time= 0.01563
Epoch: 0007 train_loss= 0.69437 train_acc= 0.49818 val_loss= 0.69890 val_acc= 0.37705 time= 0.00000
Epoch: 0008 train_loss= 0.69573 train_acc= 0.49455 val_loss= 0.69659 val_acc= 0.49180 time= 0.01563
Epoch: 0009 train_loss= 0.69565 train_acc= 0.49455 val_loss= 0.69455 val_acc= 0.55738 time= 0.00000
Epoch: 0010 train_loss= 0.69576 train_acc= 0.50727 val_loss= 0.69272 val_acc= 0.60656 time= 0.00000
Epoch: 0011 train_loss= 0.69267 train_acc= 0.50182 val_loss= 0.69117 val_acc= 0.63934 time= 0.01563
Epoch: 0012 train_loss= 0.69378 train_acc= 0.50182 val_loss= 0.68993 val_acc= 0.63934 time= 0.00000
Epoch: 0013 train_loss= 0.69498 train_acc= 0.49273 val_loss= 0.68897 val_acc= 0.63934 time= 0.00000
Epoch: 0014 train_loss= 0.69491 train_acc= 0.50364 val_loss= 0.68819 val_acc= 0.63934 time= 0.01563
Epoch: 0015 train_loss= 0.69502 train_acc= 0.48909 val_loss= 0.68780 val_acc= 0.63934 time= 0.00000
Epoch: 0016 train_loss= 0.69610 train_acc= 0.48727 val_loss= 0.68762 val_acc= 0.65574 time= 0.00000
Epoch: 0017 train_loss= 0.69411 train_acc= 0.51273 val_loss= 0.68759 val_acc= 0.65574 time= 0.01563
Epoch: 0018 train_loss= 0.69457 train_acc= 0.50000 val_loss= 0.68772 val_acc= 0.63934 time= 0.00000
Epoch: 0019 train_loss= 0.69411 train_acc= 0.51455 val_loss= 0.68798 val_acc= 0.63934 time= 0.00000
Epoch: 0020 train_loss= 0.69577 train_acc= 0.50364 val_loss= 0.68826 val_acc= 0.63934 time= 0.00000
Epoch: 0021 train_loss= 0.69517 train_acc= 0.48364 val_loss= 0.68861 val_acc= 0.63934 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69593 accuracy= 0.46721 time= 0.00000 
