I'm normalizing (or standardizing or feature scaling) my neural network training inputs and training targets. I just doing linear scaling and the formula I'm using is:
I = Imin + (Imax-Imin)*(D-Dmin)/(Dmax-Dmin)
where I
is the scaled input value, Imin
and Imax
are the desired min and max range of the scaled values, D
is the original data value, and Dmin
and Dmax
are the min and max range of the original data values. In my case I'm setting Imax
to 1 and Imin
to -1.
I'm trying to predict a continuous real-valued output. How do I scale the output of my network back into the "unscaled" range?