I'm trying to train a U-net generator using the Pix2Pix code as written in https://www.tensorflow.org/tutorials/generative/pix2pix . The original code is though to use normalized data in input and get output data going from -1 to 1. The activation function is tanh.
My goal is to give to the generator an image with normalized values in range [-1, 1], but as output I want to have an image with arbitrary values in the range [0, inf], therefore cannot be normailzed. I know that usually data, in both input images and ground truth, are normalized for optimizing the training process of the network, but in my specific case this is not possible. I want to retrieve the information of the peak value of my target image.
I tried to change the activation function at the very end of the U-net, but with ReLU and sigmoid I had no success (ReLU seems to not converge, while sigmoid keep ignoring values higher than 1).
I'm quite new in ML, do you have any suggestion?