Epoch: 0001 train_loss= 1.41217 train_acc= 0.54727 val_loss= 0.89259 val_acc= 0.42623 time= 0.40987
Epoch: 0002 train_loss= 1.19400 train_acc= 0.51091 val_loss= 0.70494 val_acc= 0.55738 time= 0.00000
Epoch: 0003 train_loss= 1.12632 train_acc= 0.53091 val_loss= 0.72574 val_acc= 0.60656 time= 0.01563
Epoch: 0004 train_loss= 0.99438 train_acc= 0.49455 val_loss= 0.78555 val_acc= 0.60656 time= 0.01563
Epoch: 0005 train_loss= 0.92867 train_acc= 0.50545 val_loss= 0.82645 val_acc= 0.60656 time= 0.00000
Epoch: 0006 train_loss= 1.44569 train_acc= 0.46545 val_loss= 0.82493 val_acc= 0.62295 time= 0.01563
Epoch: 0007 train_loss= 0.85817 train_acc= 0.49818 val_loss= 0.80773 val_acc= 0.63934 time= 0.01563
Epoch: 0008 train_loss= 0.91961 train_acc= 0.48364 val_loss= 0.79131 val_acc= 0.65574 time= 0.00000
Epoch: 0009 train_loss= 0.83929 train_acc= 0.49273 val_loss= 0.77815 val_acc= 0.65574 time= 0.01563
Epoch: 0010 train_loss= 1.14915 train_acc= 0.46364 val_loss= 0.75605 val_acc= 0.59016 time= 0.01563
Epoch: 0011 train_loss= 0.81119 train_acc= 0.49818 val_loss= 0.73620 val_acc= 0.59016 time= 0.02099
Epoch: 0012 train_loss= 0.91430 train_acc= 0.52727 val_loss= 0.72337 val_acc= 0.60656 time= 0.01050
Epoch: 0013 train_loss= 0.97722 train_acc= 0.52000 val_loss= 0.71165 val_acc= 0.60656 time= 0.01563
Epoch: 0014 train_loss= 1.19732 train_acc= 0.52909 val_loss= 0.70382 val_acc= 0.60656 time= 0.00000
Epoch: 0015 train_loss= 0.92722 train_acc= 0.48364 val_loss= 0.69509 val_acc= 0.60656 time= 0.00000
Epoch: 0016 train_loss= 1.00430 train_acc= 0.50364 val_loss= 0.68915 val_acc= 0.57377 time= 0.01563
Epoch: 0017 train_loss= 0.82628 train_acc= 0.48364 val_loss= 0.69057 val_acc= 0.54098 time= 0.01563
Epoch: 0018 train_loss= 0.77476 train_acc= 0.53455 val_loss= 0.69818 val_acc= 0.52459 time= 0.01563
Epoch: 0019 train_loss= 0.81664 train_acc= 0.51818 val_loss= 0.70748 val_acc= 0.50820 time= 0.00000
Epoch: 0020 train_loss= 0.90154 train_acc= 0.51636 val_loss= 0.71021 val_acc= 0.49180 time= 0.01563
Epoch: 0021 train_loss= 0.78199 train_acc= 0.50364 val_loss= 0.71663 val_acc= 0.45902 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.74478 accuracy= 0.50820 time= 0.00000 
