Epoch: 0001 train_loss= 0.69991 train_acc= 0.48961 val_loss= 0.69973 val_acc= 0.44262 time= 0.70366
Epoch: 0002 train_loss= 0.69902 train_acc= 0.48312 val_loss= 0.69714 val_acc= 0.55738 time= 0.01563
Epoch: 0003 train_loss= 0.69861 train_acc= 0.51429 val_loss= 0.69586 val_acc= 0.55738 time= 0.00000
Epoch: 0004 train_loss= 0.69800 train_acc= 0.52208 val_loss= 0.69478 val_acc= 0.55738 time= 0.01563
Epoch: 0005 train_loss= 0.69736 train_acc= 0.51299 val_loss= 0.69370 val_acc= 0.55738 time= 0.01563
Epoch: 0006 train_loss= 0.69719 train_acc= 0.51688 val_loss= 0.69280 val_acc= 0.55738 time= 0.01563
Epoch: 0007 train_loss= 0.69676 train_acc= 0.50909 val_loss= 0.69199 val_acc= 0.55738 time= 0.00000
Epoch: 0008 train_loss= 0.69635 train_acc= 0.50779 val_loss= 0.69129 val_acc= 0.55738 time= 0.01563
Epoch: 0009 train_loss= 0.69633 train_acc= 0.51299 val_loss= 0.69085 val_acc= 0.55738 time= 0.01563
Epoch: 0010 train_loss= 0.69566 train_acc= 0.51169 val_loss= 0.69045 val_acc= 0.55738 time= 0.01563
Epoch: 0011 train_loss= 0.69567 train_acc= 0.51558 val_loss= 0.69020 val_acc= 0.55738 time= 0.01563
Epoch: 0012 train_loss= 0.69522 train_acc= 0.51169 val_loss= 0.69001 val_acc= 0.55738 time= 0.01563
Epoch: 0013 train_loss= 0.69474 train_acc= 0.51429 val_loss= 0.68982 val_acc= 0.55738 time= 0.00000
Epoch: 0014 train_loss= 0.69491 train_acc= 0.51299 val_loss= 0.68972 val_acc= 0.55738 time= 0.01563
Epoch: 0015 train_loss= 0.69411 train_acc= 0.51169 val_loss= 0.68953 val_acc= 0.55738 time= 0.01563
Epoch: 0016 train_loss= 0.69417 train_acc= 0.51039 val_loss= 0.68938 val_acc= 0.55738 time= 0.01563
Epoch: 0017 train_loss= 0.69408 train_acc= 0.50779 val_loss= 0.68929 val_acc= 0.55738 time= 0.01563
Epoch: 0018 train_loss= 0.69415 train_acc= 0.51169 val_loss= 0.68924 val_acc= 0.55738 time= 0.01563
Epoch: 0019 train_loss= 0.69373 train_acc= 0.51039 val_loss= 0.68922 val_acc= 0.55738 time= 0.00000
Epoch: 0020 train_loss= 0.69350 train_acc= 0.51039 val_loss= 0.68916 val_acc= 0.55738 time= 0.03125
Epoch: 0021 train_loss= 0.69360 train_acc= 0.51299 val_loss= 0.68911 val_acc= 0.55738 time= 0.01563
Epoch: 0022 train_loss= 0.69357 train_acc= 0.50779 val_loss= 0.68910 val_acc= 0.55738 time= 0.01563
Epoch: 0023 train_loss= 0.69331 train_acc= 0.51299 val_loss= 0.68914 val_acc= 0.55738 time= 0.00000
Epoch: 0024 train_loss= 0.69334 train_acc= 0.51558 val_loss= 0.68910 val_acc= 0.55738 time= 0.01563
Epoch: 0025 train_loss= 0.69341 train_acc= 0.51039 val_loss= 0.68922 val_acc= 0.55738 time= 0.01563
Epoch: 0026 train_loss= 0.69323 train_acc= 0.50779 val_loss= 0.68923 val_acc= 0.55738 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69249 accuracy= 0.54098 time= 0.00000 
