Epoch: 0001 train_loss= 0.69900 train_acc= 0.50909 val_loss= 0.69927 val_acc= 0.54098 time= 0.21877
Epoch: 0002 train_loss= 0.69825 train_acc= 0.51273 val_loss= 0.69916 val_acc= 0.54098 time= 0.01563
Epoch: 0003 train_loss= 0.69767 train_acc= 0.51455 val_loss= 0.69903 val_acc= 0.54098 time= 0.01563
Epoch: 0004 train_loss= 0.69702 train_acc= 0.51273 val_loss= 0.69892 val_acc= 0.54098 time= 0.01563
Epoch: 0005 train_loss= 0.69651 train_acc= 0.51273 val_loss= 0.69879 val_acc= 0.54098 time= 0.01563
Epoch: 0006 train_loss= 0.69627 train_acc= 0.51273 val_loss= 0.69852 val_acc= 0.54098 time= 0.00000
Epoch: 0007 train_loss= 0.69582 train_acc= 0.51273 val_loss= 0.69820 val_acc= 0.54098 time= 0.01563
Epoch: 0008 train_loss= 0.69570 train_acc= 0.51273 val_loss= 0.69775 val_acc= 0.54098 time= 0.01563
Epoch: 0009 train_loss= 0.69516 train_acc= 0.51273 val_loss= 0.69728 val_acc= 0.54098 time= 0.01563
Epoch: 0010 train_loss= 0.69481 train_acc= 0.51273 val_loss= 0.69690 val_acc= 0.54098 time= 0.01794
Epoch: 0011 train_loss= 0.69484 train_acc= 0.51273 val_loss= 0.69653 val_acc= 0.54098 time= 0.01563
Epoch: 0012 train_loss= 0.69439 train_acc= 0.51273 val_loss= 0.69617 val_acc= 0.54098 time= 0.01563
Epoch: 0013 train_loss= 0.69409 train_acc= 0.51273 val_loss= 0.69588 val_acc= 0.54098 time= 0.01563
Epoch: 0014 train_loss= 0.69417 train_acc= 0.51273 val_loss= 0.69561 val_acc= 0.54098 time= 0.01563
Epoch: 0015 train_loss= 0.69362 train_acc= 0.51273 val_loss= 0.69537 val_acc= 0.54098 time= 0.01563
Epoch: 0016 train_loss= 0.69359 train_acc= 0.51273 val_loss= 0.69520 val_acc= 0.54098 time= 0.00000
Epoch: 0017 train_loss= 0.69335 train_acc= 0.51273 val_loss= 0.69508 val_acc= 0.54098 time= 0.01563
Epoch: 0018 train_loss= 0.69306 train_acc= 0.51273 val_loss= 0.69506 val_acc= 0.54098 time= 0.01563
Epoch: 0019 train_loss= 0.69297 train_acc= 0.51273 val_loss= 0.69509 val_acc= 0.54098 time= 0.01563
Epoch: 0020 train_loss= 0.69296 train_acc= 0.51273 val_loss= 0.69516 val_acc= 0.54098 time= 0.01563
Epoch: 0021 train_loss= 0.69296 train_acc= 0.51273 val_loss= 0.69521 val_acc= 0.54098 time= 0.01563
Epoch: 0022 train_loss= 0.69293 train_acc= 0.51273 val_loss= 0.69524 val_acc= 0.54098 time= 0.01563
Epoch: 0023 train_loss= 0.69268 train_acc= 0.51273 val_loss= 0.69521 val_acc= 0.54098 time= 0.01563
Epoch: 0024 train_loss= 0.69261 train_acc= 0.51273 val_loss= 0.69510 val_acc= 0.54098 time= 0.01562
Epoch: 0025 train_loss= 0.69260 train_acc= 0.51273 val_loss= 0.69490 val_acc= 0.54098 time= 0.00000
Epoch: 0026 train_loss= 0.69266 train_acc= 0.51273 val_loss= 0.69470 val_acc= 0.54098 time= 0.01563
Epoch: 0027 train_loss= 0.69257 train_acc= 0.51273 val_loss= 0.69449 val_acc= 0.54098 time= 0.01563
Epoch: 0028 train_loss= 0.69234 train_acc= 0.51273 val_loss= 0.69434 val_acc= 0.54098 time= 0.01563
Epoch: 0029 train_loss= 0.69245 train_acc= 0.51273 val_loss= 0.69429 val_acc= 0.54098 time= 0.01563
Epoch: 0030 train_loss= 0.69241 train_acc= 0.51273 val_loss= 0.69434 val_acc= 0.54098 time= 0.01563
Epoch: 0031 train_loss= 0.69258 train_acc= 0.51273 val_loss= 0.69443 val_acc= 0.54098 time= 0.01563
Epoch: 0032 train_loss= 0.69262 train_acc= 0.51273 val_loss= 0.69456 val_acc= 0.54098 time= 0.02326
Epoch: 0033 train_loss= 0.69238 train_acc= 0.51273 val_loss= 0.69467 val_acc= 0.54098 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69165 accuracy= 0.52459 time= 0.01563 
