Epoch: 0001 train_loss= 0.70041 train_acc= 0.50545 val_loss= 0.68828 val_acc= 0.55738 time= 0.59407
Epoch: 0002 train_loss= 0.69536 train_acc= 0.51455 val_loss= 0.68923 val_acc= 0.55738 time= 0.00600
Epoch: 0003 train_loss= 0.69772 train_acc= 0.50000 val_loss= 0.69030 val_acc= 0.55738 time= 0.00500
Epoch: 0004 train_loss= 0.69600 train_acc= 0.50545 val_loss= 0.69136 val_acc= 0.55738 time= 0.00600
Epoch: 0005 train_loss= 0.69601 train_acc= 0.50545 val_loss= 0.69254 val_acc= 0.55738 time= 0.00500
Epoch: 0006 train_loss= 0.69564 train_acc= 0.51636 val_loss= 0.69364 val_acc= 0.47541 time= 0.00500
Epoch: 0007 train_loss= 0.69522 train_acc= 0.48909 val_loss= 0.69473 val_acc= 0.47541 time= 0.00500
Epoch: 0008 train_loss= 0.69211 train_acc= 0.50727 val_loss= 0.69571 val_acc= 0.44262 time= 0.00500
Epoch: 0009 train_loss= 0.69417 train_acc= 0.49636 val_loss= 0.69659 val_acc= 0.44262 time= 0.00500
Epoch: 0010 train_loss= 0.69281 train_acc= 0.50182 val_loss= 0.69714 val_acc= 0.44262 time= 0.00600
Epoch: 0011 train_loss= 0.69599 train_acc= 0.47636 val_loss= 0.69743 val_acc= 0.44262 time= 0.00600
Epoch: 0012 train_loss= 0.69503 train_acc= 0.48364 val_loss= 0.69753 val_acc= 0.44262 time= 0.00500
Early stopping...
Optimization Finished!
Test set results: cost= 0.69561 accuracy= 0.45902 time= 0.00200 
