Epoch: 0001 train_loss= 0.70074 train_acc= 0.47273 val_loss= 0.69846 val_acc= 0.52459 time= 0.20314
Epoch: 0002 train_loss= 0.69920 train_acc= 0.49091 val_loss= 0.69788 val_acc= 0.52459 time= 0.00000
Epoch: 0003 train_loss= 0.69868 train_acc= 0.51515 val_loss= 0.69737 val_acc= 0.52459 time= 0.01563
Epoch: 0004 train_loss= 0.69679 train_acc= 0.51818 val_loss= 0.69691 val_acc= 0.52459 time= 0.00000
Epoch: 0005 train_loss= 0.69615 train_acc= 0.50909 val_loss= 0.69651 val_acc= 0.52459 time= 0.00000
Epoch: 0006 train_loss= 0.69608 train_acc= 0.51212 val_loss= 0.69616 val_acc= 0.52459 time= 0.00000
Epoch: 0007 train_loss= 0.69629 train_acc= 0.50606 val_loss= 0.69585 val_acc= 0.52459 time= 0.01563
Epoch: 0008 train_loss= 0.69628 train_acc= 0.46667 val_loss= 0.69556 val_acc= 0.52459 time= 0.00000
Epoch: 0009 train_loss= 0.69529 train_acc= 0.51818 val_loss= 0.69529 val_acc= 0.50820 time= 0.00000
Epoch: 0010 train_loss= 0.69498 train_acc= 0.49394 val_loss= 0.69506 val_acc= 0.50820 time= 0.01563
Epoch: 0011 train_loss= 0.69432 train_acc= 0.51212 val_loss= 0.69485 val_acc= 0.50820 time= 0.00000
Epoch: 0012 train_loss= 0.69489 train_acc= 0.50909 val_loss= 0.69466 val_acc= 0.50820 time= 0.00000
Epoch: 0013 train_loss= 0.69469 train_acc= 0.50303 val_loss= 0.69448 val_acc= 0.52459 time= 0.01563
Epoch: 0014 train_loss= 0.69438 train_acc= 0.50000 val_loss= 0.69433 val_acc= 0.52459 time= 0.00000
Epoch: 0015 train_loss= 0.69299 train_acc= 0.50606 val_loss= 0.69421 val_acc= 0.52459 time= 0.00000
Epoch: 0016 train_loss= 0.69411 train_acc= 0.50909 val_loss= 0.69412 val_acc= 0.52459 time= 0.01563
Epoch: 0017 train_loss= 0.69400 train_acc= 0.50909 val_loss= 0.69404 val_acc= 0.52459 time= 0.00000
Epoch: 0018 train_loss= 0.69369 train_acc= 0.50303 val_loss= 0.69397 val_acc= 0.52459 time= 0.00000
Epoch: 0019 train_loss= 0.69434 train_acc= 0.50909 val_loss= 0.69390 val_acc= 0.52459 time= 0.01563
Epoch: 0020 train_loss= 0.69299 train_acc= 0.51515 val_loss= 0.69384 val_acc= 0.52459 time= 0.00000
Epoch: 0021 train_loss= 0.69334 train_acc= 0.51212 val_loss= 0.69378 val_acc= 0.50820 time= 0.00000
Epoch: 0022 train_loss= 0.69267 train_acc= 0.50909 val_loss= 0.69373 val_acc= 0.50820 time= 0.01562
Epoch: 0023 train_loss= 0.69322 train_acc= 0.51818 val_loss= 0.69368 val_acc= 0.50820 time= 0.00000
Epoch: 0024 train_loss= 0.69296 train_acc= 0.51212 val_loss= 0.69364 val_acc= 0.50820 time= 0.00000
Epoch: 0025 train_loss= 0.69387 train_acc= 0.52424 val_loss= 0.69360 val_acc= 0.50820 time= 0.01563
Epoch: 0026 train_loss= 0.69388 train_acc= 0.49697 val_loss= 0.69356 val_acc= 0.50820 time= 0.00000
Epoch: 0027 train_loss= 0.69186 train_acc= 0.52424 val_loss= 0.69353 val_acc= 0.50820 time= 0.00000
Epoch: 0028 train_loss= 0.69309 train_acc= 0.49394 val_loss= 0.69351 val_acc= 0.49180 time= 0.01563
Epoch: 0029 train_loss= 0.69374 train_acc= 0.46364 val_loss= 0.69350 val_acc= 0.49180 time= 0.00000
Epoch: 0030 train_loss= 0.69339 train_acc= 0.50000 val_loss= 0.69350 val_acc= 0.47541 time= 0.00000
Epoch: 0031 train_loss= 0.69506 train_acc= 0.51212 val_loss= 0.69350 val_acc= 0.47541 time= 0.01563
Epoch: 0032 train_loss= 0.69327 train_acc= 0.50000 val_loss= 0.69350 val_acc= 0.47541 time= 0.00000
Epoch: 0033 train_loss= 0.69302 train_acc= 0.51515 val_loss= 0.69350 val_acc= 0.49180 time= 0.00000
Epoch: 0034 train_loss= 0.69177 train_acc= 0.51818 val_loss= 0.69351 val_acc= 0.49180 time= 0.01563
Epoch: 0035 train_loss= 0.69372 train_acc= 0.48485 val_loss= 0.69352 val_acc= 0.49180 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69145 accuracy= 0.53279 time= 0.00000 
