Question

what is Softmax?
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Answer 1

Answer:

Softmax is a mathematical function that converts a vector of numbers into a vector of probabilities, where the probabilities of each value are proportional to the relative scale of each value in the vector. ... In this tutorial, you will discover the softmax activation function used in neural network models.

Explanation:

$hope \: you \: understand$

Answer 2

Explanation :-

The SoftMax function is a generalization of the logistic function to multiple

dimensions. It is used in multinomial logistic regression and is often used as

the last activation function of a neural network to normalize the output of a

network to a probability distribution over predicted output classes, based

on Luce's choice axiom.

The SoftMax function takes as input a vector z of K real numbers, and

normalizes it into a probability distribution consisting of K probabilities

proportional to the exponentials of the input numbers. Prior to applying

SoftMax, some vector components could be negative, or greater than one;

and might not sum to 1 but after applying SoftMax, each component will be

in the interval [0,1] and the components will add up to 1, so that they can be

interpreted as probabilities. Furthermore, the larger input components will

correspond to larger probabilities.

It applies the standard exponential function to each element of the input vector z and normalizes these values by dividing by the sum of all these exponentials, this normalization ensures that the sum of the components of the output vector σ(z) is 1.

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