Epoch: 0001 train_loss= 2.08556 train_acc= 0.11860 val_loss= 2.08746 val_acc= 0.10345 time= 0.32815
Epoch: 0002 train_loss= 2.08310 train_acc= 0.14555 val_loss= 2.08821 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.08072 train_acc= 0.16442 val_loss= 2.08952 val_acc= 0.10345 time= 0.00000
Epoch: 0004 train_loss= 2.07893 train_acc= 0.16442 val_loss= 2.09110 val_acc= 0.10345 time= 0.01563
Epoch: 0005 train_loss= 2.07742 train_acc= 0.16442 val_loss= 2.09293 val_acc= 0.10345 time= 0.01562
Epoch: 0006 train_loss= 2.07475 train_acc= 0.16442 val_loss= 2.09505 val_acc= 0.10345 time= 0.00000
Epoch: 0007 train_loss= 2.07308 train_acc= 0.16442 val_loss= 2.09750 val_acc= 0.10345 time= 0.01563
Epoch: 0008 train_loss= 2.07105 train_acc= 0.16442 val_loss= 2.10029 val_acc= 0.10345 time= 0.00000
Epoch: 0009 train_loss= 2.06939 train_acc= 0.16442 val_loss= 2.10343 val_acc= 0.10345 time= 0.01562
Epoch: 0010 train_loss= 2.06823 train_acc= 0.16442 val_loss= 2.10692 val_acc= 0.10345 time= 0.01563
Epoch: 0011 train_loss= 2.06616 train_acc= 0.16442 val_loss= 2.11069 val_acc= 0.10345 time= 0.00000
Epoch: 0012 train_loss= 2.06514 train_acc= 0.16442 val_loss= 2.11468 val_acc= 0.10345 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.06853 accuracy= 0.18644 time= 0.00000 
