Epoch: 0001 train_loss= 0.70112 train_acc= 0.50649 val_loss= 0.69857 val_acc= 0.45902 time= 0.36195
Epoch: 0002 train_loss= 0.69804 train_acc= 0.52857 val_loss= 0.69684 val_acc= 0.45902 time= 0.00000
Epoch: 0003 train_loss= 0.69585 train_acc= 0.52597 val_loss= 0.69594 val_acc= 0.45902 time= 0.01562
Epoch: 0004 train_loss= 0.69427 train_acc= 0.52468 val_loss= 0.69573 val_acc= 0.45902 time= 0.01563
Epoch: 0005 train_loss= 0.69318 train_acc= 0.52468 val_loss= 0.69604 val_acc= 0.45902 time= 0.00000
Epoch: 0006 train_loss= 0.69270 train_acc= 0.52468 val_loss= 0.69669 val_acc= 0.45902 time= 0.01563
Epoch: 0007 train_loss= 0.69256 train_acc= 0.52597 val_loss= 0.69736 val_acc= 0.45902 time= 0.01563
Epoch: 0008 train_loss= 0.69227 train_acc= 0.52468 val_loss= 0.69814 val_acc= 0.45902 time= 0.01563
Epoch: 0009 train_loss= 0.69254 train_acc= 0.52468 val_loss= 0.69880 val_acc= 0.45902 time= 0.00000
Epoch: 0010 train_loss= 0.69268 train_acc= 0.52597 val_loss= 0.69921 val_acc= 0.45902 time= 0.01563
Epoch: 0011 train_loss= 0.69235 train_acc= 0.52468 val_loss= 0.69942 val_acc= 0.45902 time= 0.01563
Epoch: 0012 train_loss= 0.69218 train_acc= 0.52468 val_loss= 0.69937 val_acc= 0.45902 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.68932 accuracy= 0.55738 time= 0.01563 
