Alternative to softmax function for Neural Network predicting fractions of a whole

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Hi, I created a feed forward Regression Neural Network to predict variables which are fractions of a whole (i.e. they sum up to 1). In order to have the network fullfil this criterion perfectly, I am using the softmax transfer function. Unfortunately, I realize that the network predicts smaller fractions very poorly, and I think this is due to the fact that the softmax transfer function normalizes my target fractions by dividing exponent of the fractions minus the largest fraction by its sum (exp(n-nmax)/sum(exp(n-nmax))), which results in much larger values for very small fractions. It wouldn't have to do that, since my fractions are already between 0 and 1. Can I change that somehow in the softmax transfer function, or is there an alternative to it that doesn't do this normailzation?
Ajay Pattassery
Ajay Pattassery on 29 Aug 2019
Did you tried creating a custom layer which can force your output to one like the one I mentioned above instead of having a softmax layer.
Please refer the following link for creating custom layers.

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