Epoch: 0001 train_loss= 0.69922 train_acc= 0.49697 val_loss= 0.69886 val_acc= 0.45902 time= 0.12500
Epoch: 0002 train_loss= 0.69855 train_acc= 0.50606 val_loss= 0.69858 val_acc= 0.45902 time= 0.00000
Epoch: 0003 train_loss= 0.69794 train_acc= 0.50606 val_loss= 0.69823 val_acc= 0.45902 time= 0.01563
Epoch: 0004 train_loss= 0.69767 train_acc= 0.50000 val_loss= 0.69788 val_acc= 0.45902 time= 0.01563
Epoch: 0005 train_loss= 0.69745 train_acc= 0.50606 val_loss= 0.69745 val_acc= 0.45902 time= 0.01563
Epoch: 0006 train_loss= 0.69692 train_acc= 0.50606 val_loss= 0.69702 val_acc= 0.45902 time= 0.01563
Epoch: 0007 train_loss= 0.69631 train_acc= 0.50606 val_loss= 0.69660 val_acc= 0.45902 time= 0.00000
Epoch: 0008 train_loss= 0.69591 train_acc= 0.50606 val_loss= 0.69620 val_acc= 0.45902 time= 0.01563
Epoch: 0009 train_loss= 0.69580 train_acc= 0.50606 val_loss= 0.69585 val_acc= 0.45902 time= 0.01563
Epoch: 0010 train_loss= 0.69538 train_acc= 0.50606 val_loss= 0.69551 val_acc= 0.45902 time= 0.01563
Epoch: 0011 train_loss= 0.69501 train_acc= 0.50606 val_loss= 0.69524 val_acc= 0.45902 time= 0.01563
Epoch: 0012 train_loss= 0.69474 train_acc= 0.50606 val_loss= 0.69500 val_acc= 0.45902 time= 0.00000
Epoch: 0013 train_loss= 0.69447 train_acc= 0.50909 val_loss= 0.69481 val_acc= 0.45902 time= 0.01563
Epoch: 0014 train_loss= 0.69455 train_acc= 0.50303 val_loss= 0.69462 val_acc= 0.45902 time= 0.01563
Epoch: 0015 train_loss= 0.69439 train_acc= 0.50909 val_loss= 0.69444 val_acc= 0.45902 time= 0.01563
Epoch: 0016 train_loss= 0.69398 train_acc= 0.50606 val_loss= 0.69429 val_acc= 0.45902 time= 0.00000
Epoch: 0017 train_loss= 0.69393 train_acc= 0.50606 val_loss= 0.69416 val_acc= 0.45902 time= 0.01563
Epoch: 0018 train_loss= 0.69378 train_acc= 0.50606 val_loss= 0.69405 val_acc= 0.45902 time= 0.01563
Epoch: 0019 train_loss= 0.69358 train_acc= 0.50606 val_loss= 0.69397 val_acc= 0.45902 time= 0.01563
Epoch: 0020 train_loss= 0.69364 train_acc= 0.50606 val_loss= 0.69390 val_acc= 0.45902 time= 0.01563
Epoch: 0021 train_loss= 0.69356 train_acc= 0.50606 val_loss= 0.69384 val_acc= 0.45902 time= 0.01429
Epoch: 0022 train_loss= 0.69340 train_acc= 0.50606 val_loss= 0.69379 val_acc= 0.45902 time= 0.00800
Epoch: 0023 train_loss= 0.69352 train_acc= 0.50606 val_loss= 0.69373 val_acc= 0.45902 time= 0.01567
Epoch: 0024 train_loss= 0.69345 train_acc= 0.50606 val_loss= 0.69367 val_acc= 0.45902 time= 0.00000
Epoch: 0025 train_loss= 0.69325 train_acc= 0.50606 val_loss= 0.69362 val_acc= 0.45902 time= 0.01563
Epoch: 0026 train_loss= 0.69330 train_acc= 0.50909 val_loss= 0.69358 val_acc= 0.45902 time= 0.01563
Epoch: 0027 train_loss= 0.69330 train_acc= 0.50606 val_loss= 0.69355 val_acc= 0.45902 time= 0.01563
Epoch: 0028 train_loss= 0.69314 train_acc= 0.50909 val_loss= 0.69352 val_acc= 0.45902 time= 0.01563
Epoch: 0029 train_loss= 0.69321 train_acc= 0.50606 val_loss= 0.69348 val_acc= 0.45902 time= 0.00000
Epoch: 0030 train_loss= 0.69321 train_acc= 0.50606 val_loss= 0.69345 val_acc= 0.45902 time= 0.01563
Epoch: 0031 train_loss= 0.69312 train_acc= 0.50606 val_loss= 0.69344 val_acc= 0.45902 time= 0.01563
Epoch: 0032 train_loss= 0.69327 train_acc= 0.50606 val_loss= 0.69340 val_acc= 0.45902 time= 0.01563
Epoch: 0033 train_loss= 0.69303 train_acc= 0.50606 val_loss= 0.69339 val_acc= 0.45902 time= 0.00000
Epoch: 0034 train_loss= 0.69308 train_acc= 0.50303 val_loss= 0.69340 val_acc= 0.45902 time= 0.01562
Epoch: 0035 train_loss= 0.69320 train_acc= 0.50606 val_loss= 0.69341 val_acc= 0.45902 time= 0.01563
Epoch: 0036 train_loss= 0.69308 train_acc= 0.50606 val_loss= 0.69344 val_acc= 0.45902 time= 0.01563
Epoch: 0037 train_loss= 0.69312 train_acc= 0.50909 val_loss= 0.69348 val_acc= 0.45902 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69383 accuracy= 0.45902 time= 0.01563 
