# Using a neural net to make predictions

I want to use a neural net to give predictions, but I am not sure where to start. I do know that I don't want to use the high-level Predict function.

So what I have is a series of optical spectroscopies, which each corresponds to a single number from 0 to 1.

For example, in the plot attached, curves with different colors correspond to different numbers. Say, yellow curves correspond to 0.4 and red curves correspond to 0.8. I want to train the neural net so that when I input new curve data (meaning a list of numbers), it outputs a number between 0 to 1.

In short, what I want is: Input(a list of numbers) ---> Neural Net ---> Output(a single number between 0 to 1)

What kind of neural net should I use? Any examples? I didn't find any on the Neural Net Repository.

• It seems you'd have to train your network yourself...that a specific list of data corresponds to a specific number between 0 and 1...though my guess is that you'll need a lot of already defined curves since your lines only really seem to have a small area that are different than each other. – morbo Sep 22 '19 at 23:33
• It would be easier to help if you could share your data. – C. E. Sep 23 '19 at 4:33