Imagine I want to consider the temperature for a process given several input varibales. The temperature can be anywhere between 400 and 500 K. Consider I have experimental data to train the network and then I want to predict the temperature for a test point.

As I understand the theory of NN activation function are needed to bound the value between 0 and 1. How could I proceed in my example? Should I just scale my data between 0 and 1? Are there good and bad methods to scale (of course there will be?). Or should I modify the activation function?

However, what if one of the training point's is at 600 K. If I bound the values it would be impossible for the NN to reach this value. I hope someone can give me clarity on this issues.

Best regards,


  • 1
    $\begingroup$ For scaling your data, see stats.stackexchange.com/questions/70801/…. For questions involving data, machine-learning, and neural-networks, you would probably have better luck on the statistics stack exchange. $\endgroup$
    – amarney
    Jul 2 '18 at 20:45

NN activation functions don't need to be between 0 and 1. That's only done for classification problems. Many times you want them continuously differentiable and monotonic, though that isn't even required. RELU activation functions which are $\sigma(x)=max(0,x)$ are quite common for these kinds of scenarios.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.