hi everybody,
i’m trying to learn parameters for forward and inverse gamma correction:
input->gamma_correction->network->another_gamma_correction->loss

what i do is simply define a nn.Parameter() for the initial correction and another nn.Parameter() for the final correction.

when i only try to learn the initial gamma correction there’s no problem but when i try to learn both initial and final gamma corrections i get nan values for the parameters after the first backpropagation.

I had a similar problem, you might be getting nan's because some of the inputs to the second gamma correction layer are very close to 0 and you raise them to a power which leads to problems with gradients. If I am not mistaken about how it works in PyTorch (roughly), the derivative of x^a is being evaluated with respect to a and with respect to x, either one could cause problems (say, a<1).

Now, I hope this small bit info helps you, because in my case I was unable to resolve my issue.

There are two tricks here. The first is to handle negative inputs by odd symmetry; f(-x) is computed as -f(x). The second is to handle zero or close-to-zero inputs by adding a small ‘epsilon’ value to ensure numerical stability.

I tested this layer and was able to get it to train whereas without these tricks the self.gamma parameter would end up NaN.