Epoch: 0001 train_loss= 0.70102 train_acc= 0.49697 val_loss= 0.69837 val_acc= 0.47541 time= 0.17204
Epoch: 0002 train_loss= 0.69791 train_acc= 0.54848 val_loss= 0.69650 val_acc= 0.47541 time= 0.00800
Epoch: 0003 train_loss= 0.69578 train_acc= 0.55455 val_loss= 0.69525 val_acc= 0.47541 time= 0.00800
Epoch: 0004 train_loss= 0.69403 train_acc= 0.56061 val_loss= 0.69450 val_acc= 0.47541 time= 0.00800
Epoch: 0005 train_loss= 0.69286 train_acc= 0.56667 val_loss= 0.69408 val_acc= 0.47541 time= 0.00800
Epoch: 0006 train_loss= 0.69192 train_acc= 0.58182 val_loss= 0.69387 val_acc= 0.47541 time= 0.00900
Epoch: 0007 train_loss= 0.69095 train_acc= 0.60303 val_loss= 0.69387 val_acc= 0.47541 time= 0.00900
Epoch: 0008 train_loss= 0.69034 train_acc= 0.60606 val_loss= 0.69400 val_acc= 0.49180 time= 0.00800
Epoch: 0009 train_loss= 0.69078 train_acc= 0.63333 val_loss= 0.69419 val_acc= 0.50820 time= 0.00800
Epoch: 0010 train_loss= 0.68963 train_acc= 0.59394 val_loss= 0.69434 val_acc= 0.50820 time= 0.00700
Epoch: 0011 train_loss= 0.68954 train_acc= 0.62424 val_loss= 0.69446 val_acc= 0.50820 time= 0.00800
Epoch: 0012 train_loss= 0.68858 train_acc= 0.64242 val_loss= 0.69452 val_acc= 0.50820 time= 0.00700
Early stopping...
Optimization Finished!
Test set results: cost= 0.69146 accuracy= 0.59016 time= 0.00400 
