Epoch: 0001 train_loss= 2.08668 train_acc= 0.08176 val_loss= 2.08606 val_acc= 0.10345 time= 0.10938
Epoch: 0002 train_loss= 2.08424 train_acc= 0.11321 val_loss= 2.08548 val_acc= 0.10345 time= 0.00000
Epoch: 0003 train_loss= 2.08209 train_acc= 0.11321 val_loss= 2.08510 val_acc= 0.10345 time= 0.01563
Epoch: 0004 train_loss= 2.08041 train_acc= 0.11950 val_loss= 2.08491 val_acc= 0.10345 time= 0.00000
Epoch: 0005 train_loss= 2.07860 train_acc= 0.11321 val_loss= 2.08503 val_acc= 0.10345 time= 0.01563
Epoch: 0006 train_loss= 2.07705 train_acc= 0.11321 val_loss= 2.08538 val_acc= 0.10345 time= 0.00000
Epoch: 0007 train_loss= 2.07528 train_acc= 0.11321 val_loss= 2.08590 val_acc= 0.10345 time= 0.01563
Epoch: 0008 train_loss= 2.07346 train_acc= 0.11321 val_loss= 2.08657 val_acc= 0.10345 time= 0.00000
Epoch: 0009 train_loss= 2.07156 train_acc= 0.11321 val_loss= 2.08735 val_acc= 0.10345 time= 0.01563
Epoch: 0010 train_loss= 2.06828 train_acc= 0.10692 val_loss= 2.08832 val_acc= 0.10345 time= 0.00000
Epoch: 0011 train_loss= 2.06564 train_acc= 0.11950 val_loss= 2.08952 val_acc= 0.10345 time= 0.01562
Epoch: 0012 train_loss= 2.06388 train_acc= 0.14465 val_loss= 2.09093 val_acc= 0.06897 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.08519 accuracy= 0.13559 time= 0.00000 
