Epoch: 0001 train_loss= 1.38459 train_acc= 0.30726 val_loss= 1.38054 val_acc= 0.28571 time= 0.82818
Epoch: 0002 train_loss= 1.38173 train_acc= 0.30726 val_loss= 1.38118 val_acc= 0.28571 time= 0.01563
Epoch: 0003 train_loss= 1.38120 train_acc= 0.30726 val_loss= 1.38213 val_acc= 0.28571 time= 0.00000
Epoch: 0004 train_loss= 1.38067 train_acc= 0.30866 val_loss= 1.38321 val_acc= 0.28571 time= 0.01563
Epoch: 0005 train_loss= 1.37867 train_acc= 0.30866 val_loss= 1.38420 val_acc= 0.28571 time= 0.00000
Epoch: 0006 train_loss= 1.37757 train_acc= 0.30726 val_loss= 1.38509 val_acc= 0.28571 time= 0.00000
Epoch: 0007 train_loss= 1.37906 train_acc= 0.30726 val_loss= 1.38562 val_acc= 0.28571 time= 0.01563
Epoch: 0008 train_loss= 1.37659 train_acc= 0.30866 val_loss= 1.38572 val_acc= 0.28571 time= 0.00000
Epoch: 0009 train_loss= 1.37730 train_acc= 0.30726 val_loss= 1.38545 val_acc= 0.28571 time= 0.01563
Epoch: 0010 train_loss= 1.37708 train_acc= 0.31006 val_loss= 1.38496 val_acc= 0.28571 time= 0.00000
Epoch: 0011 train_loss= 1.37709 train_acc= 0.30726 val_loss= 1.38422 val_acc= 0.28571 time= 0.01563
Epoch: 0012 train_loss= 1.37729 train_acc= 0.30726 val_loss= 1.38335 val_acc= 0.28571 time= 0.00000
Epoch: 0013 train_loss= 1.37551 train_acc= 0.30726 val_loss= 1.38243 val_acc= 0.28571 time= 0.01563
Epoch: 0014 train_loss= 1.37522 train_acc= 0.30866 val_loss= 1.38151 val_acc= 0.28571 time= 0.00000
Epoch: 0015 train_loss= 1.37526 train_acc= 0.30726 val_loss= 1.38061 val_acc= 0.28571 time= 0.01563
Epoch: 0016 train_loss= 1.37758 train_acc= 0.30726 val_loss= 1.37983 val_acc= 0.28571 time= 0.00000
Epoch: 0017 train_loss= 1.37380 train_acc= 0.30726 val_loss= 1.37915 val_acc= 0.28571 time= 0.01563
Epoch: 0018 train_loss= 1.37472 train_acc= 0.30726 val_loss= 1.37873 val_acc= 0.28571 time= 0.00000
Epoch: 0019 train_loss= 1.37567 train_acc= 0.30726 val_loss= 1.37838 val_acc= 0.28571 time= 0.00000
Epoch: 0020 train_loss= 1.37599 train_acc= 0.30726 val_loss= 1.37812 val_acc= 0.28571 time= 0.02092
Epoch: 0021 train_loss= 1.37700 train_acc= 0.30726 val_loss= 1.37797 val_acc= 0.28571 time= 0.00000
Epoch: 0022 train_loss= 1.37366 train_acc= 0.30726 val_loss= 1.37793 val_acc= 0.28571 time= 0.01050
Epoch: 0023 train_loss= 1.37508 train_acc= 0.30866 val_loss= 1.37796 val_acc= 0.28571 time= 0.01563
Epoch: 0024 train_loss= 1.37563 train_acc= 0.30866 val_loss= 1.37807 val_acc= 0.28571 time= 0.00000
Epoch: 0025 train_loss= 1.37373 train_acc= 0.30587 val_loss= 1.37829 val_acc= 0.28571 time= 0.00000
Epoch: 0026 train_loss= 1.37453 train_acc= 0.30726 val_loss= 1.37834 val_acc= 0.28571 time= 0.01563
Epoch: 0027 train_loss= 1.37574 train_acc= 0.30726 val_loss= 1.37849 val_acc= 0.28571 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 1.38413 accuracy= 0.31858 time= 0.00000 
