title: Combine cross-entropy and mse in loss function
body: <p>I am working on a regression problem. My dataset has labels ranging from <code>[0,1]</code>. Due to the design purpose, the label with the value over <code>0.3</code> is converted to the negative, i.e., <code>0.35</code> is converted to <code>-0.35</code>. </p>
  
  <p>In keras, I first tried <code>mse</code> as the loss function, but the performance is not good. After I realize the sign of labels, I tried binary cross-entropy as well. But the performance is still not good.</p>
  
  <p>As I explained above, it seems we can utilize two loss functions and sum them up. But I don't know how to write the code. Besides, if you have any other suggestion for this specific dataset, please let me know.</p>