 in 2015, in a preprint that formulated the concept and showed some results achieved in the task of image classification.Given a large model as a function of the vector variable formula_1, trained for a specific classification task, typically the final layer of the network is a softmax in the form
where formula_3 is a parameter called "temperature", that for a standard softmax is normally set to 1.The softmax operator converts the logit values formula_4 to pseudo-probabilities, and higher values of temperature have the effect of generating a softer distribution of pseudo-probabilities among the output classes.