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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,

Gesetzt

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    $\begingroup$ 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
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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.

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