Epoch: 0001 train_loss= 0.69872 train_acc= 0.52545 val_loss= 0.69908 val_acc= 0.52459 time= 0.21877
Epoch: 0002 train_loss= 0.69813 train_acc= 0.52545 val_loss= 0.69891 val_acc= 0.52459 time= 0.01562
Epoch: 0003 train_loss= 0.69773 train_acc= 0.52545 val_loss= 0.69867 val_acc= 0.52459 time= 0.01563
Epoch: 0004 train_loss= 0.69712 train_acc= 0.52545 val_loss= 0.69836 val_acc= 0.52459 time= 0.01563
Epoch: 0005 train_loss= 0.69679 train_acc= 0.52545 val_loss= 0.69801 val_acc= 0.52459 time= 0.01563
Epoch: 0006 train_loss= 0.69641 train_acc= 0.52545 val_loss= 0.69761 val_acc= 0.52459 time= 0.01563
Epoch: 0007 train_loss= 0.69590 train_acc= 0.52545 val_loss= 0.69723 val_acc= 0.52459 time= 0.01563
Epoch: 0008 train_loss= 0.69571 train_acc= 0.52545 val_loss= 0.69682 val_acc= 0.52459 time= 0.01563
Epoch: 0009 train_loss= 0.69525 train_acc= 0.52545 val_loss= 0.69642 val_acc= 0.52459 time= 0.01563
Epoch: 0010 train_loss= 0.69485 train_acc= 0.52545 val_loss= 0.69607 val_acc= 0.52459 time= 0.00000
Epoch: 0011 train_loss= 0.69473 train_acc= 0.52545 val_loss= 0.69575 val_acc= 0.52459 time= 0.01563
Epoch: 0012 train_loss= 0.69432 train_acc= 0.52545 val_loss= 0.69547 val_acc= 0.52459 time= 0.01563
Epoch: 0013 train_loss= 0.69427 train_acc= 0.52545 val_loss= 0.69524 val_acc= 0.52459 time= 0.01563
Epoch: 0014 train_loss= 0.69393 train_acc= 0.52545 val_loss= 0.69508 val_acc= 0.52459 time= 0.01563
Epoch: 0015 train_loss= 0.69386 train_acc= 0.52545 val_loss= 0.69496 val_acc= 0.52459 time= 0.01563
Epoch: 0016 train_loss= 0.69364 train_acc= 0.52545 val_loss= 0.69486 val_acc= 0.52459 time= 0.01563
Epoch: 0017 train_loss= 0.69339 train_acc= 0.52545 val_loss= 0.69482 val_acc= 0.52459 time= 0.01563
Epoch: 0018 train_loss= 0.69338 train_acc= 0.52545 val_loss= 0.69476 val_acc= 0.52459 time= 0.01563
Epoch: 0019 train_loss= 0.69318 train_acc= 0.52545 val_loss= 0.69470 val_acc= 0.52459 time= 0.00000
Epoch: 0020 train_loss= 0.69317 train_acc= 0.52545 val_loss= 0.69464 val_acc= 0.52459 time= 0.01563
Epoch: 0021 train_loss= 0.69310 train_acc= 0.52545 val_loss= 0.69457 val_acc= 0.52459 time= 0.01563
Epoch: 0022 train_loss= 0.69302 train_acc= 0.52545 val_loss= 0.69453 val_acc= 0.52459 time= 0.01563
Epoch: 0023 train_loss= 0.69301 train_acc= 0.52545 val_loss= 0.69446 val_acc= 0.52459 time= 0.01563
Epoch: 0024 train_loss= 0.69276 train_acc= 0.52545 val_loss= 0.69441 val_acc= 0.52459 time= 0.01563
Epoch: 0025 train_loss= 0.69294 train_acc= 0.52545 val_loss= 0.69436 val_acc= 0.52459 time= 0.01562
Epoch: 0026 train_loss= 0.69281 train_acc= 0.52545 val_loss= 0.69432 val_acc= 0.52459 time= 0.01563
Epoch: 0027 train_loss= 0.69286 train_acc= 0.52545 val_loss= 0.69426 val_acc= 0.52459 time= 0.01563
Epoch: 0028 train_loss= 0.69288 train_acc= 0.52545 val_loss= 0.69420 val_acc= 0.52459 time= 0.00000
Epoch: 0029 train_loss= 0.69256 train_acc= 0.52545 val_loss= 0.69418 val_acc= 0.52459 time= 0.01563
Epoch: 0030 train_loss= 0.69274 train_acc= 0.52545 val_loss= 0.69416 val_acc= 0.52459 time= 0.01562
Epoch: 0031 train_loss= 0.69285 train_acc= 0.52545 val_loss= 0.69409 val_acc= 0.52459 time= 0.01563
Epoch: 0032 train_loss= 0.69266 train_acc= 0.52545 val_loss= 0.69406 val_acc= 0.52459 time= 0.01563
Epoch: 0033 train_loss= 0.69258 train_acc= 0.52545 val_loss= 0.69410 val_acc= 0.52459 time= 0.01562
Epoch: 0034 train_loss= 0.69267 train_acc= 0.52545 val_loss= 0.69412 val_acc= 0.52459 time= 0.01563
Epoch: 0035 train_loss= 0.69267 train_acc= 0.52545 val_loss= 0.69417 val_acc= 0.52459 time= 0.00000
Epoch: 0036 train_loss= 0.69265 train_acc= 0.52545 val_loss= 0.69423 val_acc= 0.52459 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 0.69495 accuracy= 0.48361 time= 0.01563 
