Epoch: 0001 train_loss= 2.08640 train_acc= 0.11051 val_loss= 2.08468 val_acc= 0.13793 time= 0.31252
Epoch: 0002 train_loss= 2.08528 train_acc= 0.07817 val_loss= 2.08412 val_acc= 0.10345 time= 0.00000
Epoch: 0003 train_loss= 2.08412 train_acc= 0.13747 val_loss= 2.08375 val_acc= 0.10345 time= 0.01563
Epoch: 0004 train_loss= 2.08318 train_acc= 0.13208 val_loss= 2.08345 val_acc= 0.10345 time= 0.01562
Epoch: 0005 train_loss= 2.08245 train_acc= 0.13747 val_loss= 2.08338 val_acc= 0.10345 time= 0.00000
Epoch: 0006 train_loss= 2.08151 train_acc= 0.13477 val_loss= 2.08350 val_acc= 0.10345 time= 0.01562
Epoch: 0007 train_loss= 2.08083 train_acc= 0.13477 val_loss= 2.08375 val_acc= 0.10345 time= 0.00000
Epoch: 0008 train_loss= 2.08001 train_acc= 0.13477 val_loss= 2.08413 val_acc= 0.10345 time= 0.01563
Epoch: 0009 train_loss= 2.07903 train_acc= 0.13477 val_loss= 2.08463 val_acc= 0.10345 time= 0.00000
Epoch: 0010 train_loss= 2.07870 train_acc= 0.13477 val_loss= 2.08526 val_acc= 0.10345 time= 0.01563
Epoch: 0011 train_loss= 2.07682 train_acc= 0.13477 val_loss= 2.08608 val_acc= 0.10345 time= 0.01562
Epoch: 0012 train_loss= 2.07619 train_acc= 0.13477 val_loss= 2.08699 val_acc= 0.10345 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.06807 accuracy= 0.25424 time= 0.00000 
