Epoch: 0001 train_loss= 0.70067 train_acc= 0.49091 val_loss= 0.69858 val_acc= 0.47541 time= 0.34376
Epoch: 0002 train_loss= 0.69942 train_acc= 0.49091 val_loss= 0.69849 val_acc= 0.47541 time= 0.00000
Epoch: 0003 train_loss= 0.69864 train_acc= 0.50130 val_loss= 0.69851 val_acc= 0.52459 time= 0.01563
Epoch: 0004 train_loss= 0.69806 train_acc= 0.51169 val_loss= 0.69843 val_acc= 0.52459 time= 0.01563
Epoch: 0005 train_loss= 0.69764 train_acc= 0.51039 val_loss= 0.69823 val_acc= 0.52459 time= 0.01562
Epoch: 0006 train_loss= 0.69733 train_acc= 0.50909 val_loss= 0.69796 val_acc= 0.52459 time= 0.01563
Epoch: 0007 train_loss= 0.69700 train_acc= 0.50909 val_loss= 0.69768 val_acc= 0.52459 time= 0.01563
Epoch: 0008 train_loss= 0.69663 train_acc= 0.50909 val_loss= 0.69739 val_acc= 0.52459 time= 0.01563
Epoch: 0009 train_loss= 0.69621 train_acc= 0.50909 val_loss= 0.69710 val_acc= 0.52459 time= 0.00000
Epoch: 0010 train_loss= 0.69602 train_acc= 0.50909 val_loss= 0.69681 val_acc= 0.52459 time= 0.01563
Epoch: 0011 train_loss= 0.69585 train_acc= 0.50909 val_loss= 0.69651 val_acc= 0.52459 time= 0.01563
Epoch: 0012 train_loss= 0.69550 train_acc= 0.50909 val_loss= 0.69622 val_acc= 0.52459 time= 0.01563
Epoch: 0013 train_loss= 0.69528 train_acc= 0.50909 val_loss= 0.69593 val_acc= 0.52459 time= 0.01563
Epoch: 0014 train_loss= 0.69502 train_acc= 0.50909 val_loss= 0.69566 val_acc= 0.52459 time= 0.01563
Epoch: 0015 train_loss= 0.69479 train_acc= 0.50909 val_loss= 0.69542 val_acc= 0.52459 time= 0.00000
Epoch: 0016 train_loss= 0.69473 train_acc= 0.50909 val_loss= 0.69520 val_acc= 0.52459 time= 0.01563
Epoch: 0017 train_loss= 0.69449 train_acc= 0.50909 val_loss= 0.69499 val_acc= 0.52459 time= 0.01563
Epoch: 0018 train_loss= 0.69432 train_acc= 0.50909 val_loss= 0.69480 val_acc= 0.52459 time= 0.01563
Epoch: 0019 train_loss= 0.69419 train_acc= 0.50909 val_loss= 0.69464 val_acc= 0.52459 time= 0.01563
Epoch: 0020 train_loss= 0.69402 train_acc= 0.50909 val_loss= 0.69449 val_acc= 0.52459 time= 0.01563
Epoch: 0021 train_loss= 0.69402 train_acc= 0.50909 val_loss= 0.69435 val_acc= 0.52459 time= 0.01563
Epoch: 0022 train_loss= 0.69380 train_acc= 0.50909 val_loss= 0.69423 val_acc= 0.52459 time= 0.01563
Epoch: 0023 train_loss= 0.69376 train_acc= 0.50909 val_loss= 0.69413 val_acc= 0.52459 time= 0.00000
Epoch: 0024 train_loss= 0.69363 train_acc= 0.50909 val_loss= 0.69404 val_acc= 0.52459 time= 0.01563
Epoch: 0025 train_loss= 0.69358 train_acc= 0.50909 val_loss= 0.69397 val_acc= 0.52459 time= 0.01563
Epoch: 0026 train_loss= 0.69342 train_acc= 0.50909 val_loss= 0.69393 val_acc= 0.52459 time= 0.01563
Epoch: 0027 train_loss= 0.69342 train_acc= 0.50909 val_loss= 0.69390 val_acc= 0.52459 time= 0.01563
Epoch: 0028 train_loss= 0.69332 train_acc= 0.50909 val_loss= 0.69389 val_acc= 0.52459 time= 0.01563
Epoch: 0029 train_loss= 0.69333 train_acc= 0.50909 val_loss= 0.69390 val_acc= 0.52459 time= 0.01563
Epoch: 0030 train_loss= 0.69333 train_acc= 0.51039 val_loss= 0.69393 val_acc= 0.52459 time= 0.01563
Epoch: 0031 train_loss= 0.69328 train_acc= 0.50909 val_loss= 0.69393 val_acc= 0.52459 time= 0.00000
Epoch: 0032 train_loss= 0.69315 train_acc= 0.50909 val_loss= 0.69396 val_acc= 0.52459 time= 0.01563
Epoch: 0033 train_loss= 0.69321 train_acc= 0.50909 val_loss= 0.69397 val_acc= 0.52459 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69205 accuracy= 0.54918 time= 0.01563 
