Question

I have seen a few times people using -1 as opposed to 0 when working with neural networks for the input data. How is this better and does it effect any of the mathematics to implement it?

Edit: Using feedforward and back prop

Edit 2: I gave it a go but the network stopped learning so I assume the maths would have to change somewhere?

Edit 3: Finally found the answer. The mathematics for binary is different to bipolar. See my answer below.

Answer 1

Recently found that the sigmoid and sigmoid derivative formula needs to change if using bipolar over binary.

Bipolar Sigmoid Function: f(x) = -1 + 2 / (1 + e^-x)

Bipolar Sigmoid Derivative: f’(x) = 0.5 * (1 + f(x)) * (1 – f(x) )