Epoch: 0001 train_loss= 1.39395 train_acc= 0.32263 val_loss= 1.39246 val_acc= 0.21667 time= 0.75005
Epoch: 0002 train_loss= 1.39056 train_acc= 0.32542 val_loss= 1.39142 val_acc= 0.21667 time= 0.00000
Epoch: 0003 train_loss= 1.38792 train_acc= 0.32821 val_loss= 1.39117 val_acc= 0.21667 time= 0.01563
Epoch: 0004 train_loss= 1.38563 train_acc= 0.32682 val_loss= 1.39159 val_acc= 0.21667 time= 0.01562
Epoch: 0005 train_loss= 1.38387 train_acc= 0.32682 val_loss= 1.39255 val_acc= 0.21667 time= 0.01563
Epoch: 0006 train_loss= 1.38293 train_acc= 0.32682 val_loss= 1.39374 val_acc= 0.21667 time= 0.01563
Epoch: 0007 train_loss= 1.38171 train_acc= 0.32682 val_loss= 1.39510 val_acc= 0.21667 time= 0.00000
Epoch: 0008 train_loss= 1.38079 train_acc= 0.32682 val_loss= 1.39647 val_acc= 0.21667 time= 0.01563
Epoch: 0009 train_loss= 1.38076 train_acc= 0.32682 val_loss= 1.39770 val_acc= 0.21667 time= 0.01563
Epoch: 0010 train_loss= 1.38017 train_acc= 0.32682 val_loss= 1.39871 val_acc= 0.21667 time= 0.01563
Epoch: 0011 train_loss= 1.37967 train_acc= 0.32682 val_loss= 1.39942 val_acc= 0.21667 time= 0.01563
Epoch: 0012 train_loss= 1.37961 train_acc= 0.32682 val_loss= 1.39993 val_acc= 0.21667 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37703 accuracy= 0.31667 time= 0.00000 
