Epoch: 0001 train_loss= 0.70112 train_acc= 0.51818 val_loss= 0.69814 val_acc= 0.47541 time= 0.14064
Epoch: 0002 train_loss= 0.69798 train_acc= 0.51515 val_loss= 0.69605 val_acc= 0.47541 time= 0.01562
Epoch: 0003 train_loss= 0.69578 train_acc= 0.51515 val_loss= 0.69469 val_acc= 0.47541 time= 0.00000
Epoch: 0004 train_loss= 0.69420 train_acc= 0.52121 val_loss= 0.69388 val_acc= 0.47541 time= 0.01563
Epoch: 0005 train_loss= 0.69295 train_acc= 0.51818 val_loss= 0.69349 val_acc= 0.47541 time= 0.00000
Epoch: 0006 train_loss= 0.69262 train_acc= 0.51212 val_loss= 0.69335 val_acc= 0.47541 time= 0.01563
Epoch: 0007 train_loss= 0.69230 train_acc= 0.52424 val_loss= 0.69342 val_acc= 0.47541 time= 0.01563
Epoch: 0008 train_loss= 0.69180 train_acc= 0.51818 val_loss= 0.69359 val_acc= 0.49180 time= 0.00000
Epoch: 0009 train_loss= 0.69172 train_acc= 0.55152 val_loss= 0.69381 val_acc= 0.49180 time= 0.01563
Epoch: 0010 train_loss= 0.69183 train_acc= 0.54545 val_loss= 0.69402 val_acc= 0.52459 time= 0.01563
Epoch: 0011 train_loss= 0.69175 train_acc= 0.55152 val_loss= 0.69415 val_acc= 0.52459 time= 0.00000
Epoch: 0012 train_loss= 0.69126 train_acc= 0.59394 val_loss= 0.69421 val_acc= 0.52459 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69406 accuracy= 0.50000 time= 0.00000 
