I have an MLP with 3072 input nodes which are for 1024 rgb pixels. My datasets is in an array with each row representing one image and looking like this:
[red_pix1, red_pix2, ..., red_pix1024, green_pix1, green_pix2, ..., green_pix1024, blue_pix1, blue_pix2, ..., blue_pix1024]
Each array value is an integer between 0 and 255.
My question is, before training the network, should I "normalize" my dataset by dividing each element by 255? That way, each input element would have values between 0 and 1. Is this better than having values between 0 and 255?