Epoch: 0001 train_loss= 1.37965 train_acc= 0.32248 val_loss= 1.38876 val_acc= 0.23214 time= 0.29832
Epoch: 0002 train_loss= 1.37949 train_acc= 0.31922 val_loss= 1.38865 val_acc= 0.23214 time= 0.00713
Epoch: 0003 train_loss= 1.37885 train_acc= 0.31922 val_loss= 1.38850 val_acc= 0.23214 time= 0.00609
Epoch: 0004 train_loss= 1.37716 train_acc= 0.31596 val_loss= 1.38841 val_acc= 0.23214 time= 0.00711
Epoch: 0005 train_loss= 1.37737 train_acc= 0.31922 val_loss= 1.38831 val_acc= 0.23214 time= 0.00600
Epoch: 0006 train_loss= 1.37653 train_acc= 0.31596 val_loss= 1.38819 val_acc= 0.23214 time= 0.00600
Epoch: 0007 train_loss= 1.37415 train_acc= 0.31922 val_loss= 1.38818 val_acc= 0.23214 time= 0.00700
Epoch: 0008 train_loss= 1.37250 train_acc= 0.31596 val_loss= 1.38827 val_acc= 0.23214 time= 0.00700
Epoch: 0009 train_loss= 1.37424 train_acc= 0.31596 val_loss= 1.38850 val_acc= 0.23214 time= 0.00600
Epoch: 0010 train_loss= 1.37388 train_acc= 0.31922 val_loss= 1.38878 val_acc= 0.23214 time= 0.00600
Epoch: 0011 train_loss= 1.37166 train_acc= 0.31596 val_loss= 1.38913 val_acc= 0.23214 time= 0.00600
Epoch: 0012 train_loss= 1.37151 train_acc= 0.31596 val_loss= 1.38947 val_acc= 0.23214 time= 0.00600
Early stopping...
Optimization Finished!
Test set results: cost= 1.38386 accuracy= 0.31858 time= 0.00400 
