Neural networks typically produce class probabilities by using a
softmaxoutput layer that converts the logit,
zi, computed for each class into a probability,
qi, by comparing
ziwith the other logits where
Tis a temperature that is normally set to 1. Using a higher value for
Tproduces a softer probability distribution over classes.
What does that mean intuitively?