# How to normalize the output of a neural network [duplicate]

We have a VGG16 network trained from scratch with a Sigmoid output function. We have 6 classes and the usual output looks like this:

scores': [6.494849458249519e-08, 1.8738395510808914e-06, 3.010111981893715e-07, 0.0, 0.0, 0.8633317947387695]


The problem is that the output value is very low in each class, I would like to have a normalized output that sums to 1.0 Thanks

• Don't you have softmax layer? – gunes Mar 9 '20 at 12:32

Sigmoid outputs will each vary between 0 and 1, but if you have $$k$$ sigmoid units, then the total can vary between 0 and $$k$$. By contrast, a softmax function sums to 1 and has non-negative values.