Epoch: 0001 train_loss= 1.39452 train_acc= 0.20391 val_loss= 1.39175 val_acc= 0.28571 time= 0.79693
Epoch: 0002 train_loss= 1.39153 train_acc= 0.30866 val_loss= 1.38988 val_acc= 0.28571 time= 0.01562
Epoch: 0003 train_loss= 1.38935 train_acc= 0.31006 val_loss= 1.38862 val_acc= 0.28571 time= 0.01563
Epoch: 0004 train_loss= 1.38764 train_acc= 0.31006 val_loss= 1.38793 val_acc= 0.28571 time= 0.01563
Epoch: 0005 train_loss= 1.38641 train_acc= 0.31006 val_loss= 1.38760 val_acc= 0.28571 time= 0.01563
Epoch: 0006 train_loss= 1.38538 train_acc= 0.31006 val_loss= 1.38759 val_acc= 0.28571 time= 0.00000
Epoch: 0007 train_loss= 1.38491 train_acc= 0.31006 val_loss= 1.38775 val_acc= 0.28571 time= 0.01563
Epoch: 0008 train_loss= 1.38448 train_acc= 0.31006 val_loss= 1.38796 val_acc= 0.28571 time= 0.01563
Epoch: 0009 train_loss= 1.38398 train_acc= 0.31006 val_loss= 1.38819 val_acc= 0.28571 time= 0.01563
Epoch: 0010 train_loss= 1.38380 train_acc= 0.31006 val_loss= 1.38835 val_acc= 0.28571 time= 0.01562
Epoch: 0011 train_loss= 1.38381 train_acc= 0.31006 val_loss= 1.38839 val_acc= 0.28571 time= 0.01563
Epoch: 0012 train_loss= 1.38340 train_acc= 0.30866 val_loss= 1.38829 val_acc= 0.28571 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 1.37405 accuracy= 0.36283 time= 0.01562 
