Epoch: 0001 train_loss= 1.39121 train_acc= 0.24860 val_loss= 1.39298 val_acc= 0.30357 time= 0.60942
Epoch: 0002 train_loss= 1.38921 train_acc= 0.29749 val_loss= 1.39044 val_acc= 0.30357 time= 0.01562
Epoch: 0003 train_loss= 1.38728 train_acc= 0.30447 val_loss= 1.38863 val_acc= 0.30357 time= 0.01563
Epoch: 0004 train_loss= 1.38519 train_acc= 0.30587 val_loss= 1.38712 val_acc= 0.30357 time= 0.01563
Epoch: 0005 train_loss= 1.38369 train_acc= 0.30587 val_loss= 1.38595 val_acc= 0.30357 time= 0.01563
Epoch: 0006 train_loss= 1.38307 train_acc= 0.30587 val_loss= 1.38516 val_acc= 0.30357 time= 0.01563
Epoch: 0007 train_loss= 1.38200 train_acc= 0.30587 val_loss= 1.38491 val_acc= 0.30357 time= 0.01563
Epoch: 0008 train_loss= 1.38189 train_acc= 0.30587 val_loss= 1.38502 val_acc= 0.30357 time= 0.01563
Epoch: 0009 train_loss= 1.38191 train_acc= 0.30587 val_loss= 1.38546 val_acc= 0.30357 time= 0.01563
Epoch: 0010 train_loss= 1.38224 train_acc= 0.30587 val_loss= 1.38602 val_acc= 0.30357 time= 0.01563
Epoch: 0011 train_loss= 1.38153 train_acc= 0.30587 val_loss= 1.38653 val_acc= 0.30357 time= 0.01563
Epoch: 0012 train_loss= 1.38134 train_acc= 0.30587 val_loss= 1.38688 val_acc= 0.30357 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 1.39100 accuracy= 0.31858 time= 0.00000 
