Epoch: 0001 train_loss= 0.69178 train_acc= 0.52468 val_loss= 0.69887 val_acc= 0.49180 time= 0.88484
Epoch: 0002 train_loss= 0.69423 train_acc= 0.51688 val_loss= 0.69942 val_acc= 0.49180 time= 0.00000
Epoch: 0003 train_loss= 0.69117 train_acc= 0.50909 val_loss= 0.69953 val_acc= 0.49180 time= 0.01563
Epoch: 0004 train_loss= 0.69250 train_acc= 0.51948 val_loss= 0.69919 val_acc= 0.49180 time= 0.00000
Epoch: 0005 train_loss= 0.69237 train_acc= 0.52597 val_loss= 0.69864 val_acc= 0.49180 time= 0.00000
Epoch: 0006 train_loss= 0.69303 train_acc= 0.52468 val_loss= 0.69803 val_acc= 0.49180 time= 0.01562
Epoch: 0007 train_loss= 0.69313 train_acc= 0.51818 val_loss= 0.69774 val_acc= 0.49180 time= 0.00000
Epoch: 0008 train_loss= 0.69336 train_acc= 0.50779 val_loss= 0.69751 val_acc= 0.49180 time= 0.01563
Epoch: 0009 train_loss= 0.69207 train_acc= 0.51818 val_loss= 0.69734 val_acc= 0.49180 time= 0.00000
Epoch: 0010 train_loss= 0.69454 train_acc= 0.51169 val_loss= 0.69721 val_acc= 0.49180 time= 0.01563
Epoch: 0011 train_loss= 0.69267 train_acc= 0.52338 val_loss= 0.69718 val_acc= 0.49180 time= 0.00000
Epoch: 0012 train_loss= 0.69231 train_acc= 0.51948 val_loss= 0.69720 val_acc= 0.49180 time= 0.00000
Epoch: 0013 train_loss= 0.69161 train_acc= 0.52338 val_loss= 0.69728 val_acc= 0.49180 time= 0.01563
Epoch: 0014 train_loss= 0.69166 train_acc= 0.52338 val_loss= 0.69744 val_acc= 0.49180 time= 0.00000
Epoch: 0015 train_loss= 0.69300 train_acc= 0.52727 val_loss= 0.69756 val_acc= 0.49180 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69124 accuracy= 0.54098 time= 0.00000 
