Epoch: 0001 train_loss= 1.39448 train_acc= 0.20508 val_loss= 1.39238 val_acc= 0.17857 time= 0.56647
Epoch: 0002 train_loss= 1.39145 train_acc= 0.25391 val_loss= 1.39130 val_acc= 0.17857 time= 0.01500
Epoch: 0003 train_loss= 1.38915 train_acc= 0.25586 val_loss= 1.39071 val_acc= 0.17857 time= 0.01420
Epoch: 0004 train_loss= 1.38749 train_acc= 0.25586 val_loss= 1.39059 val_acc= 0.17857 time= 0.01433
Epoch: 0005 train_loss= 1.38652 train_acc= 0.25586 val_loss= 1.39077 val_acc= 0.17857 time= 0.01314
Epoch: 0006 train_loss= 1.38553 train_acc= 0.25391 val_loss= 1.39120 val_acc= 0.17857 time= 0.00695
Epoch: 0007 train_loss= 1.38506 train_acc= 0.25586 val_loss= 1.39173 val_acc= 0.17857 time= 0.01563
Epoch: 0008 train_loss= 1.38450 train_acc= 0.25586 val_loss= 1.39233 val_acc= 0.17857 time= 0.01563
Epoch: 0009 train_loss= 1.38444 train_acc= 0.25781 val_loss= 1.39293 val_acc= 0.17857 time= 0.01563
Epoch: 0010 train_loss= 1.38424 train_acc= 0.25586 val_loss= 1.39343 val_acc= 0.17857 time= 0.01563
Epoch: 0011 train_loss= 1.38399 train_acc= 0.25391 val_loss= 1.39381 val_acc= 0.17857 time= 0.01563
Epoch: 0012 train_loss= 1.38397 train_acc= 0.25781 val_loss= 1.39394 val_acc= 0.17857 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37556 accuracy= 0.25664 time= 0.01563 
