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Hands-On Natural Language Processing with Python by Rajalingappaa Shanmugamani, Rajesh Arumugam

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Softmax

Softmax normalizes or squashes a vector of arbitrary values to a probability distribution between 0 and 1. The sum of the softmax output will be equal to 1. Therefore, it is commonly used in the last layer of a neural network to predict probabilities of the possible output classes. The following is the mathematical expression for the softmax function for a vector with values:

Here zj represents the jth vector value and K represents the number of classes. As we can see the exponential function smoothens the output value while the denominator normalizes the final value between 0 and 1.

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