Epoch: 0001 train_loss= 0.69461 train_acc= 0.49394 val_loss= 0.69369 val_acc= 0.50820 time= 0.23439
Epoch: 0002 train_loss= 0.69479 train_acc= 0.48788 val_loss= 0.69225 val_acc= 0.54098 time= 0.01562
Epoch: 0003 train_loss= 0.69274 train_acc= 0.52424 val_loss= 0.69095 val_acc= 0.55738 time= 0.00000
Epoch: 0004 train_loss= 0.69436 train_acc= 0.51515 val_loss= 0.68986 val_acc= 0.55738 time= 0.00000
Epoch: 0005 train_loss= 0.69196 train_acc= 0.52121 val_loss= 0.68889 val_acc= 0.55738 time= 0.01563
Epoch: 0006 train_loss= 0.69064 train_acc= 0.53939 val_loss= 0.68802 val_acc= 0.55738 time= 0.00000
Epoch: 0007 train_loss= 0.69110 train_acc= 0.52727 val_loss= 0.68724 val_acc= 0.55738 time= 0.00000
Epoch: 0008 train_loss= 0.68945 train_acc= 0.54242 val_loss= 0.68659 val_acc= 0.55738 time= 0.01563
Epoch: 0009 train_loss= 0.69122 train_acc= 0.52121 val_loss= 0.68605 val_acc= 0.55738 time= 0.00000
Epoch: 0010 train_loss= 0.69122 train_acc= 0.53333 val_loss= 0.68562 val_acc= 0.55738 time= 0.00000
Epoch: 0011 train_loss= 0.69143 train_acc= 0.52727 val_loss= 0.68533 val_acc= 0.55738 time= 0.01563
Epoch: 0012 train_loss= 0.69103 train_acc= 0.53030 val_loss= 0.68513 val_acc= 0.55738 time= 0.00000
Epoch: 0013 train_loss= 0.69086 train_acc= 0.53030 val_loss= 0.68497 val_acc= 0.55738 time= 0.00000
Epoch: 0014 train_loss= 0.68881 train_acc= 0.53333 val_loss= 0.68484 val_acc= 0.55738 time= 0.00000
Epoch: 0015 train_loss= 0.68957 train_acc= 0.53333 val_loss= 0.68476 val_acc= 0.55738 time= 0.01563
Epoch: 0016 train_loss= 0.69318 train_acc= 0.53030 val_loss= 0.68471 val_acc= 0.55738 time= 0.00000
Epoch: 0017 train_loss= 0.68910 train_acc= 0.52727 val_loss= 0.68468 val_acc= 0.55738 time= 0.01563
Epoch: 0018 train_loss= 0.69275 train_acc= 0.53030 val_loss= 0.68469 val_acc= 0.55738 time= 0.00000
Epoch: 0019 train_loss= 0.69332 train_acc= 0.53030 val_loss= 0.68474 val_acc= 0.55738 time= 0.00000
Epoch: 0020 train_loss= 0.69033 train_acc= 0.52727 val_loss= 0.68482 val_acc= 0.55738 time= 0.01563
Epoch: 0021 train_loss= 0.68985 train_acc= 0.52727 val_loss= 0.68492 val_acc= 0.55738 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.68265 accuracy= 0.55738 time= 0.00000 
