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The softmax function, also known as softargmax^[1]^: 184 or normalized exponential function,^[2]^: 198 converts a vector of $K$ real numbers into a probability distribution of $K$ possible outcomes. It is a generalization of the logistic function to multiple dimensions, and is used in multinomial logistic regression. The softmax function 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.

^ Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). "6.2.2.3 Softmax Units for Multinoulli Output Distributions". Deep Learning. MIT Press. pp. 180–184. ISBN 978-0-26203561-3.
^ Cite error: The named reference bishop was invoked but never defined (see the help page).

Softmax function