neural_network.activation_functions.leaky_rectified_linear_unit

Leaky Rectified Linear Unit (Leaky ReLU)

Use Case: Leaky ReLU addresses the problem of the vanishing gradient. For more detailed information, you can refer to the following link: https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Leaky_ReLU

Functions

leaky_rectified_linear_unit(→ numpy.ndarray)

Implements the LeakyReLU activation function.

Module Contents

neural_network.activation_functions.leaky_rectified_linear_unit.leaky_rectified_linear_unit(vector: numpy.ndarray, alpha: float) → numpy.ndarray

Implements the LeakyReLU activation function.

Parameters:

vector (np.ndarray): The input array for LeakyReLU activation. alpha (float): The slope for negative values.

Returns:

np.ndarray: The input array after applying the LeakyReLU activation.

Formula: f(x) = x if x > 0 else f(x) = alpha * x

Examples: >>> leaky_rectified_linear_unit(vector=np.array([2.3,0.6,-2,-3.8]), alpha=0.3) array([ 2.3 , 0.6 , -0.6 , -1.14])

>>> leaky_rectified_linear_unit(np.array([-9.2, -0.3, 0.45, -4.56]), alpha=0.067)
array([-0.6164 , -0.0201 ,  0.45   , -0.30552])