Epoch: 0001 train_loss= 0.69877 train_acc= 0.52597 val_loss= 0.70194 val_acc= 0.40984 time= 0.63010
Epoch: 0002 train_loss= 0.69788 train_acc= 0.52338 val_loss= 0.70255 val_acc= 0.40984 time= 0.01563
Epoch: 0003 train_loss= 0.69757 train_acc= 0.52338 val_loss= 0.70305 val_acc= 0.40984 time= 0.00000
Epoch: 0004 train_loss= 0.69717 train_acc= 0.52597 val_loss= 0.70337 val_acc= 0.40984 time= 0.01563
Epoch: 0005 train_loss= 0.69705 train_acc= 0.52597 val_loss= 0.70333 val_acc= 0.40984 time= 0.01563
Epoch: 0006 train_loss= 0.69634 train_acc= 0.52468 val_loss= 0.70310 val_acc= 0.40984 time= 0.01563
Epoch: 0007 train_loss= 0.69623 train_acc= 0.52338 val_loss= 0.70271 val_acc= 0.40984 time= 0.01563
Epoch: 0008 train_loss= 0.69519 train_acc= 0.52597 val_loss= 0.70251 val_acc= 0.40984 time= 0.00000
Epoch: 0009 train_loss= 0.69511 train_acc= 0.52468 val_loss= 0.70247 val_acc= 0.40984 time= 0.02348
Epoch: 0010 train_loss= 0.69497 train_acc= 0.52208 val_loss= 0.70234 val_acc= 0.40984 time= 0.01500
Epoch: 0011 train_loss= 0.69459 train_acc= 0.52338 val_loss= 0.70215 val_acc= 0.40984 time= 0.01400
Epoch: 0012 train_loss= 0.69447 train_acc= 0.52468 val_loss= 0.70180 val_acc= 0.40984 time= 0.01500
Epoch: 0013 train_loss= 0.69409 train_acc= 0.52468 val_loss= 0.70143 val_acc= 0.40984 time= 0.01500
Epoch: 0014 train_loss= 0.69411 train_acc= 0.52338 val_loss= 0.70100 val_acc= 0.40984 time= 0.01500
Epoch: 0015 train_loss= 0.69387 train_acc= 0.52338 val_loss= 0.70052 val_acc= 0.40984 time= 0.01300
Epoch: 0016 train_loss= 0.69389 train_acc= 0.52338 val_loss= 0.69995 val_acc= 0.40984 time= 0.01300
Epoch: 0017 train_loss= 0.69323 train_acc= 0.52338 val_loss= 0.69962 val_acc= 0.40984 time= 0.01300
Epoch: 0018 train_loss= 0.69318 train_acc= 0.52468 val_loss= 0.69937 val_acc= 0.40984 time= 0.01400
Epoch: 0019 train_loss= 0.69300 train_acc= 0.52468 val_loss= 0.69938 val_acc= 0.40984 time= 0.01100
Epoch: 0020 train_loss= 0.69304 train_acc= 0.52468 val_loss= 0.69963 val_acc= 0.40984 time= 0.01300
Epoch: 0021 train_loss= 0.69275 train_acc= 0.52468 val_loss= 0.70012 val_acc= 0.40984 time= 0.01200
Epoch: 0022 train_loss= 0.69294 train_acc= 0.52468 val_loss= 0.70049 val_acc= 0.40984 time= 0.01200
Early stopping...
Optimization Finished!
Test set results: cost= 0.69598 accuracy= 0.48361 time= 0.00600 
