Epoch: 0001 train_loss= 0.98006 train_acc= 0.51558 val_loss= 0.77459 val_acc= 0.45902 time= 0.29690
Epoch: 0002 train_loss= 1.34993 train_acc= 0.51948 val_loss= 0.73454 val_acc= 0.49180 time= 0.01563
Epoch: 0003 train_loss= 1.00673 train_acc= 0.52857 val_loss= 0.79426 val_acc= 0.59016 time= 0.01563
Epoch: 0004 train_loss= 1.12014 train_acc= 0.52727 val_loss= 0.95298 val_acc= 0.59016 time= 0.01563
Epoch: 0005 train_loss= 1.96673 train_acc= 0.46623 val_loss= 0.99895 val_acc= 0.59016 time= 0.01563
Epoch: 0006 train_loss= 1.30974 train_acc= 0.47532 val_loss= 0.96997 val_acc= 0.59016 time= 0.01563
Epoch: 0007 train_loss= 0.86082 train_acc= 0.50130 val_loss= 0.95775 val_acc= 0.59016 time= 0.01563
Epoch: 0008 train_loss= 1.85013 train_acc= 0.47403 val_loss= 0.90400 val_acc= 0.59016 time= 0.01410
Epoch: 0009 train_loss= 1.01681 train_acc= 0.46883 val_loss= 0.84767 val_acc= 0.59016 time= 0.00700
Epoch: 0010 train_loss= 1.26183 train_acc= 0.46753 val_loss= 0.79073 val_acc= 0.59016 time= 0.01567
Epoch: 0011 train_loss= 1.00425 train_acc= 0.52597 val_loss= 0.75943 val_acc= 0.59016 time= 0.01563
Epoch: 0012 train_loss= 1.08026 train_acc= 0.49091 val_loss= 0.73650 val_acc= 0.59016 time= 0.00000
Epoch: 0013 train_loss= 0.92713 train_acc= 0.46753 val_loss= 0.72265 val_acc= 0.55738 time= 0.01563
Epoch: 0014 train_loss= 0.86177 train_acc= 0.48442 val_loss= 0.72292 val_acc= 0.42623 time= 0.01563
Epoch: 0015 train_loss= 0.95255 train_acc= 0.53506 val_loss= 0.72852 val_acc= 0.37705 time= 0.01563
Epoch: 0016 train_loss= 0.94212 train_acc= 0.53117 val_loss= 0.73135 val_acc= 0.42623 time= 0.01562
Epoch: 0017 train_loss= 1.05111 train_acc= 0.54675 val_loss= 0.73111 val_acc= 0.39344 time= 0.01563
Epoch: 0018 train_loss= 1.19223 train_acc= 0.52987 val_loss= 0.72675 val_acc= 0.42623 time= 0.00000
Epoch: 0019 train_loss= 0.79818 train_acc= 0.51039 val_loss= 0.72426 val_acc= 0.47541 time= 0.01563
Epoch: 0020 train_loss= 1.03733 train_acc= 0.53247 val_loss= 0.72355 val_acc= 0.52459 time= 0.01563
Epoch: 0021 train_loss= 0.95646 train_acc= 0.52597 val_loss= 0.72697 val_acc= 0.59016 time= 0.01563
Epoch: 0022 train_loss= 0.78067 train_acc= 0.50649 val_loss= 0.73332 val_acc= 0.59016 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.76508 accuracy= 0.51639 time= 0.00000 
