Epoch: 0001 train_loss= 2.03132 train_acc= 0.46667 val_loss= 1.06140 val_acc= 0.57377 time= 0.32907
Epoch: 0002 train_loss= 4.34722 train_acc= 0.50606 val_loss= 1.17335 val_acc= 0.57377 time= 0.02200
Epoch: 0003 train_loss= 2.24048 train_acc= 0.52121 val_loss= 1.05069 val_acc= 0.57377 time= 0.02201
Epoch: 0004 train_loss= 2.61226 train_acc= 0.51212 val_loss= 0.72054 val_acc= 0.54098 time= 0.02100
Epoch: 0005 train_loss= 4.12219 train_acc= 0.49697 val_loss= 0.69339 val_acc= 0.52459 time= 0.02201
Epoch: 0006 train_loss= 1.99774 train_acc= 0.52121 val_loss= 0.98240 val_acc= 0.44262 time= 0.02301
Epoch: 0007 train_loss= 1.96923 train_acc= 0.54242 val_loss= 1.62046 val_acc= 0.44262 time= 0.02401
Epoch: 0008 train_loss= 4.39335 train_acc= 0.51515 val_loss= 1.94890 val_acc= 0.44262 time= 0.02100
Epoch: 0009 train_loss= 1.63795 train_acc= 0.49394 val_loss= 2.01559 val_acc= 0.44262 time= 0.02200
Epoch: 0010 train_loss= 0.86638 train_acc= 0.50606 val_loss= 1.98555 val_acc= 0.44262 time= 0.02000
Epoch: 0011 train_loss= 2.08460 train_acc= 0.48788 val_loss= 1.96697 val_acc= 0.44262 time= 0.02000
Epoch: 0012 train_loss= 5.82520 train_acc= 0.50909 val_loss= 1.79500 val_acc= 0.44262 time= 0.02101
Early stopping...
Optimization Finished!
Test set results: cost= 1.56692 accuracy= 0.43443 time= 0.01100 
