[source] # PReLU Parametric Rectified Linear Units are leaky rectifiers whose *leakage* coefficient is learned during training. Unlike standard [Leaky ReLUs](../activation-functions/leaky-relu.md) whose leakage remains constant, PReLU layers can adjust the leakage to better suite the model on a per node basis. $$ {\displaystyle PReLU = {\begin{cases}\alpha x&{\text{if }}x<0\\x&{\text{if }}x\geq 0\end{cases}}} $$ ## Parameters | # | Name | Default | Type | Description | |---|---|---|---|---| | 1 | initializer | Constant | Initializer | The initializer of the leakage parameter. | ## Example ```php use Rubix\ML\NeuralNet\Layers\PReLU; use Rubix\ML\NeuralNet\Initializers\Normal; $layer = new PReLU(new Normal(0.5)); ``` ## References [^1]: K. He et al. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.