Epoch: 0001 train_loss= 0.69958 train_acc= 0.46364 val_loss= 0.69917 val_acc= 0.49180 time= 0.09427
Epoch: 0002 train_loss= 0.69827 train_acc= 0.55758 val_loss= 0.69939 val_acc= 0.49180 time= 0.01510
Epoch: 0003 train_loss= 0.69706 train_acc= 0.56667 val_loss= 0.69983 val_acc= 0.49180 time= 0.00000
Epoch: 0004 train_loss= 0.69605 train_acc= 0.56061 val_loss= 0.70050 val_acc= 0.49180 time= 0.01563
Epoch: 0005 train_loss= 0.69510 train_acc= 0.56364 val_loss= 0.70113 val_acc= 0.49180 time= 0.01563
Epoch: 0006 train_loss= 0.69510 train_acc= 0.56364 val_loss= 0.70176 val_acc= 0.49180 time= 0.01563
Epoch: 0007 train_loss= 0.69412 train_acc= 0.56364 val_loss= 0.70236 val_acc= 0.49180 time= 0.01563
Epoch: 0008 train_loss= 0.69376 train_acc= 0.56364 val_loss= 0.70282 val_acc= 0.49180 time= 0.01563
Epoch: 0009 train_loss= 0.69349 train_acc= 0.56364 val_loss= 0.70315 val_acc= 0.49180 time= 0.00000
Epoch: 0010 train_loss= 0.69353 train_acc= 0.56364 val_loss= 0.70321 val_acc= 0.49180 time= 0.01563
Epoch: 0011 train_loss= 0.69327 train_acc= 0.56364 val_loss= 0.70298 val_acc= 0.49180 time= 0.01562
Epoch: 0012 train_loss= 0.69256 train_acc= 0.56364 val_loss= 0.70263 val_acc= 0.49180 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.68758 accuracy= 0.55738 time= 0.00000 
