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ReLU#

Rectified Linear Units (ReLU) only output the positive signal of the input. They have the benefit of having a monotonic derivative and are cheap to compute.

\[ {\displaystyle ReLU = {\begin{aligned}&{\begin{cases}0&{\text{if }}x\leq 0\\x&{\text{if }}x>0\end{cases}}=&\max\{0,x\}\end{aligned}}} \]

Parameters#

This activation function does not have any parameters.

Example#

use Rubix\ML\NeuralNet\ActivationFunctions\ReLU;

$activationFunction = new ReLU(0.1);

References#

  1. A. L. Maas et al. (2013). Rectifier Nonlinearities Improve Neural Network Acoustic Models. ↩

  2. K. Konda et al. (2015). Zero-bias Autoencoders and the Benefits of Co-adapting Features. ↩