My question is simple actually, I have two features that have big difference in scale. So I used a simple normalization by dividing the scale=np.max(array) for both data and lables. Then after prediction, I mulitiplied this scale value back.
But since I used a DNN, wouldn't the nonlinear change the scale so make the multiply not valid? e.g.
given input data: X, label: y; y' = y/scale X' = X/scale predicted = f(X') predicted_update = predicted * scale
Anyone could provide some advice on whether I could do this or it's actually not correct? How do we handle this kind of problem?