Epoch: 0001 train_loss= 0.69408 train_acc= 0.51273 val_loss= 0.69738 val_acc= 0.47541 time= 0.53162
Epoch: 0002 train_loss= 0.69333 train_acc= 0.53455 val_loss= 0.69831 val_acc= 0.47541 time= 0.00000
Epoch: 0003 train_loss= 0.69334 train_acc= 0.50727 val_loss= 0.69898 val_acc= 0.47541 time= 0.01562
Epoch: 0004 train_loss= 0.69225 train_acc= 0.50909 val_loss= 0.69934 val_acc= 0.47541 time= 0.00000
Epoch: 0005 train_loss= 0.69327 train_acc= 0.51455 val_loss= 0.69969 val_acc= 0.47541 time= 0.00000
Epoch: 0006 train_loss= 0.69432 train_acc= 0.50727 val_loss= 0.69958 val_acc= 0.47541 time= 0.01563
Epoch: 0007 train_loss= 0.69460 train_acc= 0.50182 val_loss= 0.69955 val_acc= 0.47541 time= 0.00000
Epoch: 0008 train_loss= 0.69215 train_acc= 0.51636 val_loss= 0.69965 val_acc= 0.47541 time= 0.00000
Epoch: 0009 train_loss= 0.69241 train_acc= 0.51636 val_loss= 0.69981 val_acc= 0.47541 time= 0.01562
Epoch: 0010 train_loss= 0.69372 train_acc= 0.52182 val_loss= 0.69988 val_acc= 0.47541 time= 0.00000
Epoch: 0011 train_loss= 0.69175 train_acc= 0.50909 val_loss= 0.69988 val_acc= 0.47541 time= 0.00000
Epoch: 0012 train_loss= 0.69280 train_acc= 0.50909 val_loss= 0.69978 val_acc= 0.47541 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.68740 accuracy= 0.55738 time= 0.00000 
