[source] # Softmax The Softmax function is a generalization of the [Sigmoid](sigmoid.md) 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. $$ {\displaystyle Softmax = {\frac {e^{x_{i}}}{\sum _{j=1}^{J}e^{x_{j}}}}} $$ ## Parameters This activation function does not have any parameters. ## Example ```php use Rubix\ML\NeuralNet\ActivationFunctions\Softmax; $activationFunction = new Softmax(); ```