Epoch: 0001 train_loss= 0.70036 train_acc= 0.53333 val_loss= 0.69687 val_acc= 0.52459 time= 0.21876
Epoch: 0002 train_loss= 0.69920 train_acc= 0.52727 val_loss= 0.69651 val_acc= 0.52459 time= 0.01562
Epoch: 0003 train_loss= 0.69861 train_acc= 0.47879 val_loss= 0.69601 val_acc= 0.52459 time= 0.00000
Epoch: 0004 train_loss= 0.69732 train_acc= 0.49697 val_loss= 0.69552 val_acc= 0.50820 time= 0.00000
Epoch: 0005 train_loss= 0.69733 train_acc= 0.49697 val_loss= 0.69514 val_acc= 0.52459 time= 0.01563
Epoch: 0006 train_loss= 0.69815 train_acc= 0.49394 val_loss= 0.69491 val_acc= 0.52459 time= 0.00000
Epoch: 0007 train_loss= 0.69549 train_acc= 0.51515 val_loss= 0.69480 val_acc= 0.50820 time= 0.00000
Epoch: 0008 train_loss= 0.69397 train_acc= 0.52727 val_loss= 0.69476 val_acc= 0.50820 time= 0.01563
Epoch: 0009 train_loss= 0.69655 train_acc= 0.52424 val_loss= 0.69463 val_acc= 0.50820 time= 0.00000
Epoch: 0010 train_loss= 0.69418 train_acc= 0.51818 val_loss= 0.69441 val_acc= 0.50820 time= 0.00000
Epoch: 0011 train_loss= 0.69510 train_acc= 0.51515 val_loss= 0.69407 val_acc= 0.50820 time= 0.01563
Epoch: 0012 train_loss= 0.69709 train_acc= 0.49697 val_loss= 0.69374 val_acc= 0.52459 time= 0.00000
Epoch: 0013 train_loss= 0.69445 train_acc= 0.50606 val_loss= 0.69346 val_acc= 0.54098 time= 0.00000
Epoch: 0014 train_loss= 0.69436 train_acc= 0.52424 val_loss= 0.69330 val_acc= 0.52459 time= 0.00000
Epoch: 0015 train_loss= 0.69500 train_acc= 0.48182 val_loss= 0.69325 val_acc= 0.54098 time= 0.01563
Epoch: 0016 train_loss= 0.69412 train_acc= 0.53333 val_loss= 0.69321 val_acc= 0.54098 time= 0.00000
Epoch: 0017 train_loss= 0.69387 train_acc= 0.53636 val_loss= 0.69318 val_acc= 0.52459 time= 0.00000
Epoch: 0018 train_loss= 0.69308 train_acc= 0.51212 val_loss= 0.69318 val_acc= 0.52459 time= 0.01563
Epoch: 0019 train_loss= 0.69381 train_acc= 0.50606 val_loss= 0.69321 val_acc= 0.54098 time= 0.00000
Epoch: 0020 train_loss= 0.69295 train_acc= 0.50606 val_loss= 0.69325 val_acc= 0.52459 time= 0.01563
Epoch: 0021 train_loss= 0.69396 train_acc= 0.52727 val_loss= 0.69329 val_acc= 0.52459 time= 0.00000
Epoch: 0022 train_loss= 0.69431 train_acc= 0.50303 val_loss= 0.69330 val_acc= 0.52459 time= 0.00000
Epoch: 0023 train_loss= 0.69290 train_acc= 0.51818 val_loss= 0.69332 val_acc= 0.52459 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.68941 accuracy= 0.54918 time= 0.00000 
