Epoch: 0001 train_loss= 2.28746 train_acc= 0.48909 val_loss= 1.28438 val_acc= 0.44262 time= 0.35508
Epoch: 0002 train_loss= 2.58253 train_acc= 0.45455 val_loss= 1.37845 val_acc= 0.45902 time= 0.01400
Epoch: 0003 train_loss= 1.40393 train_acc= 0.51091 val_loss= 1.48166 val_acc= 0.44262 time= 0.01100
Epoch: 0004 train_loss= 4.36794 train_acc= 0.52909 val_loss= 1.17445 val_acc= 0.44262 time= 0.01100
Epoch: 0005 train_loss= 1.62237 train_acc= 0.55818 val_loss= 0.84738 val_acc= 0.52459 time= 0.01200
Epoch: 0006 train_loss= 3.34333 train_acc= 0.52364 val_loss= 0.84252 val_acc= 0.59016 time= 0.01100
Epoch: 0007 train_loss= 1.47256 train_acc= 0.50000 val_loss= 1.15374 val_acc= 0.57377 time= 0.01200
Epoch: 0008 train_loss= 2.52486 train_acc= 0.51091 val_loss= 1.61274 val_acc= 0.54098 time= 0.01200
Epoch: 0009 train_loss= 2.05064 train_acc= 0.48182 val_loss= 1.91302 val_acc= 0.55738 time= 0.01100
Epoch: 0010 train_loss= 3.34520 train_acc= 0.47273 val_loss= 2.03292 val_acc= 0.55738 time= 0.01200
Epoch: 0011 train_loss= 1.96063 train_acc= 0.49273 val_loss= 2.04404 val_acc= 0.55738 time= 0.01200
Epoch: 0012 train_loss= 1.69441 train_acc= 0.49091 val_loss= 1.93179 val_acc= 0.55738 time= 0.01200
Early stopping...
Optimization Finished!
Test set results: cost= 0.84388 accuracy= 0.49180 time= 0.00400 
