Epoch: 0001 train_loss= 0.70042 train_acc= 0.50909 val_loss= 0.69812 val_acc= 0.54098 time= 0.59813
Epoch: 0002 train_loss= 0.69834 train_acc= 0.51455 val_loss= 0.69689 val_acc= 0.54098 time= 0.00600
Epoch: 0003 train_loss= 0.69789 train_acc= 0.52000 val_loss= 0.69621 val_acc= 0.54098 time= 0.00600
Epoch: 0004 train_loss= 0.69793 train_acc= 0.52000 val_loss= 0.69574 val_acc= 0.54098 time= 0.00700
Epoch: 0005 train_loss= 0.69498 train_acc= 0.51818 val_loss= 0.69539 val_acc= 0.54098 time= 0.00600
Epoch: 0006 train_loss= 0.69662 train_acc= 0.50000 val_loss= 0.69516 val_acc= 0.54098 time= 0.00700
Epoch: 0007 train_loss= 0.69603 train_acc= 0.49636 val_loss= 0.69504 val_acc= 0.54098 time= 0.00600
Epoch: 0008 train_loss= 0.69355 train_acc= 0.52909 val_loss= 0.69484 val_acc= 0.54098 time= 0.00700
Epoch: 0009 train_loss= 0.69711 train_acc= 0.51091 val_loss= 0.69475 val_acc= 0.54098 time= 0.00600
Epoch: 0010 train_loss= 0.69556 train_acc= 0.46909 val_loss= 0.69458 val_acc= 0.55738 time= 0.00600
Epoch: 0011 train_loss= 0.69356 train_acc= 0.53091 val_loss= 0.69434 val_acc= 0.54098 time= 0.00600
Epoch: 0012 train_loss= 0.69814 train_acc= 0.48182 val_loss= 0.69398 val_acc= 0.50820 time= 0.00700
Epoch: 0013 train_loss= 0.69317 train_acc= 0.51273 val_loss= 0.69353 val_acc= 0.54098 time= 0.00600
Epoch: 0014 train_loss= 0.69278 train_acc= 0.51273 val_loss= 0.69308 val_acc= 0.57377 time= 0.00600
Epoch: 0015 train_loss= 0.69396 train_acc= 0.49455 val_loss= 0.69284 val_acc= 0.57377 time= 0.00700
Epoch: 0016 train_loss= 0.69535 train_acc= 0.48182 val_loss= 0.69265 val_acc= 0.55738 time= 0.00600
Epoch: 0017 train_loss= 0.69492 train_acc= 0.52727 val_loss= 0.69253 val_acc= 0.55738 time= 0.00600
Epoch: 0018 train_loss= 0.69178 train_acc= 0.55818 val_loss= 0.69236 val_acc= 0.55738 time= 0.00700
Epoch: 0019 train_loss= 0.69465 train_acc= 0.51273 val_loss= 0.69231 val_acc= 0.55738 time= 0.00600
Epoch: 0020 train_loss= 0.69230 train_acc= 0.52545 val_loss= 0.69234 val_acc= 0.52459 time= 0.00600
Epoch: 0021 train_loss= 0.69587 train_acc= 0.49818 val_loss= 0.69242 val_acc= 0.50820 time= 0.00700
Epoch: 0022 train_loss= 0.69379 train_acc= 0.50182 val_loss= 0.69258 val_acc= 0.49180 time= 0.00700
Epoch: 0023 train_loss= 0.69234 train_acc= 0.49636 val_loss= 0.69267 val_acc= 0.50820 time= 0.00600
Early stopping...
Optimization Finished!
Test set results: cost= 0.69268 accuracy= 0.48361 time= 0.00200 
