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The Softmax function is a generalization of the Sigmoid function that squashes each activation between 0 and 1 with the addition that all activations add up to 1. Together, these properties allow the output of the Softmax function to be interpretable as a joint probability distribution.

\[ {\displaystyle Softmax = {\frac {e^{x_{i}}}{\sum _{j=1}^{J}e^{x_{j}}}}} \]


This activation function does not have any parameters.


use Rubix\ML\NeuralNet\ActivationFunctions\Softmax;

$activationFunction = new Softmax();