Epoch: 0001 train_loss= 0.69965 train_acc= 0.51169 val_loss= 0.69538 val_acc= 0.54098 time= 0.84406
Epoch: 0002 train_loss= 0.69844 train_acc= 0.53377 val_loss= 0.69469 val_acc= 0.54098 time= 0.00000
Epoch: 0003 train_loss= 0.69747 train_acc= 0.50519 val_loss= 0.69378 val_acc= 0.54098 time= 0.00000
Epoch: 0004 train_loss= 0.69786 train_acc= 0.52338 val_loss= 0.69340 val_acc= 0.54098 time= 0.00000
Epoch: 0005 train_loss= 0.69612 train_acc= 0.52857 val_loss= 0.69326 val_acc= 0.54098 time= 0.01563
Epoch: 0006 train_loss= 0.69704 train_acc= 0.51299 val_loss= 0.69327 val_acc= 0.54098 time= 0.00000
Epoch: 0007 train_loss= 0.69499 train_acc= 0.51169 val_loss= 0.69276 val_acc= 0.54098 time= 0.00000
Epoch: 0008 train_loss= 0.69574 train_acc= 0.52468 val_loss= 0.69204 val_acc= 0.54098 time= 0.01563
Epoch: 0009 train_loss= 0.69538 train_acc= 0.51299 val_loss= 0.69134 val_acc= 0.54098 time= 0.00000
Epoch: 0010 train_loss= 0.69420 train_acc= 0.53377 val_loss= 0.69067 val_acc= 0.54098 time= 0.01563
Epoch: 0011 train_loss= 0.69288 train_acc= 0.52338 val_loss= 0.68999 val_acc= 0.54098 time= 0.00000
Epoch: 0012 train_loss= 0.69341 train_acc= 0.53506 val_loss= 0.68945 val_acc= 0.54098 time= 0.01563
Epoch: 0013 train_loss= 0.69511 train_acc= 0.51169 val_loss= 0.68912 val_acc= 0.54098 time= 0.00000
Epoch: 0014 train_loss= 0.69248 train_acc= 0.52208 val_loss= 0.68889 val_acc= 0.54098 time= 0.00000
Epoch: 0015 train_loss= 0.69385 train_acc= 0.52078 val_loss= 0.68878 val_acc= 0.54098 time= 0.01563
Epoch: 0016 train_loss= 0.69329 train_acc= 0.51948 val_loss= 0.68889 val_acc= 0.54098 time= 0.00000
Epoch: 0017 train_loss= 0.69437 train_acc= 0.50519 val_loss= 0.68916 val_acc= 0.54098 time= 0.00000
Epoch: 0018 train_loss= 0.69348 train_acc= 0.53117 val_loss= 0.68944 val_acc= 0.54098 time= 0.01563
Epoch: 0019 train_loss= 0.69294 train_acc= 0.52078 val_loss= 0.68978 val_acc= 0.54098 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69214 accuracy= 0.54098 time= 0.00000 
