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.
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) )
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