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Swish is a parametric activation layer that utilizes smooth rectified activation functions. The trainable beta parameter allows each activation function in the layer to tailor its output to the training set by interpolating between the linear function and ReLU.


# Name Default Type Description
1 initializer Constant Initializer The initializer of the beta parameter.


use Rubix\ML\NeuralNet\Layers\Swish;
use Rubix\ML\NeuralNet\Initializers\Constant;

$layer = new Swish(new Constant(1.0));


  1. P. Ramachandran er al. (2017). Swish: A Self-gated Activation Function. 

  2. P. Ramachandran et al. (2017). Searching for Activation Functions.