# mx.nd.LeakyReLU¶

## Description¶

Applies Leaky rectified linear unit activation element-wise to the input.

Leaky ReLUs attempt to fix the “dying ReLU” problem by allowing a small slope when the input is negative and has a slope of one when input is positive.

The following modified ReLU Activation functions are supported:

• elu: Exponential Linear Unit. y = x > 0 ? x : slope * (exp(x)-1)

• selu: Scaled Exponential Linear Unit. y = lambda * (x > 0 ? x : alpha * (exp(x) - 1)) where

lambda = 1.0507009873554804934193349852946 and alpha = 1.6732632423543772848170429916717.

• leaky: Leaky ReLU. y = x > 0 ? x : slope * x

• prelu: Parametric ReLU. This is same as leaky except that slope is learnt during training.

• rrelu: Randomized ReLU. same as leaky but the slope is uniformly and randomly chosen from [lower_bound, upper_bound) for training, while fixed to be (lower_bound+upper_bound)/2 for inference.

## Arguments¶

Argument

Description

data

NDArray-or-Symbol.

Input data to activation function.

gamma

NDArray-or-Symbol.

Input data to activation function.

act.type

{‘elu’, ‘gelu’, ‘leaky’, ‘prelu’, ‘rrelu’, ‘selu’},optional, default=’leaky’.

Activation function to be applied.

slope

float, optional, default=0.25.

Init slope for the activation. (For leaky and elu only)

lower.bound

float, optional, default=0.125.

Lower bound of random slope. (For rrelu only)

upper.bound

float, optional, default=0.333999991.

Upper bound of random slope. (For rrelu only)

## Value¶

out The result mx.ndarray