Epoch: 0001 train_loss= 2.08719 train_acc= 0.14555 val_loss= 2.08515 val_acc= 0.13793 time= 0.37503
Epoch: 0002 train_loss= 2.08468 train_acc= 0.15903 val_loss= 2.08371 val_acc= 0.13793 time= 0.00000
Epoch: 0003 train_loss= 2.08231 train_acc= 0.15903 val_loss= 2.08264 val_acc= 0.13793 time= 0.01562
Epoch: 0004 train_loss= 2.08008 train_acc= 0.15903 val_loss= 2.08190 val_acc= 0.13793 time= 0.01562
Epoch: 0005 train_loss= 2.07846 train_acc= 0.15903 val_loss= 2.08147 val_acc= 0.13793 time= 0.00000
Epoch: 0006 train_loss= 2.07651 train_acc= 0.15903 val_loss= 2.08131 val_acc= 0.13793 time= 0.01563
Epoch: 0007 train_loss= 2.07516 train_acc= 0.15903 val_loss= 2.08135 val_acc= 0.13793 time= 0.01563
Epoch: 0008 train_loss= 2.07413 train_acc= 0.15903 val_loss= 2.08154 val_acc= 0.13793 time= 0.00000
Epoch: 0009 train_loss= 2.07310 train_acc= 0.15903 val_loss= 2.08189 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.07175 train_acc= 0.15903 val_loss= 2.08230 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.07106 train_acc= 0.15903 val_loss= 2.08287 val_acc= 0.13793 time= 0.01562
Epoch: 0012 train_loss= 2.07110 train_acc= 0.15903 val_loss= 2.08344 val_acc= 0.13793 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07383 accuracy= 0.13559 time= 0.00000 
