I have learned so far how to linear regression with one or multiple features. So far, so good, everything seems to work fine, at least for my first simple examples.
However, I now need to normalise my features for training. I'm doing this by calculating the mean and the standard deviation per feature, and then calculate the normalised feature by subtracting the mean, taking the absolute value, and dividing by the standard deviation. Again, so far, so good, the results of my tensors which I use for training look good.
I understand why I need to normalise input data, and I also understand why one can do it like this (I know that there are other ways as well, e.g. to map values to a 0-1 interval).
Now I was wondering about two things:
- First, after having trained my network, when I want to make a prediction for a specific input – do I need to normalise this as well, or do I take use the un-normalised data? Does it make a difference? My gut feeling says, I should normalise it, as it should make a difference, but I'm not sure. What should I do here, and why?
- Second, either way, I get a result. Now I was wondering whether I need to denormalise this? I mean, it should make a difference, shouldn't it? If so, how? How do I get from the normalised result value to a denormalised one? Do I just need to reverse the calculation with mean and standard deviation, to get the actual value?
It would be great if someone could shed some light on this.