neural_network.activation_functions.leaky_rectified_linear_unit
===============================================================

.. py:module:: neural_network.activation_functions.leaky_rectified_linear_unit

.. autoapi-nested-parse::

   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
---------

.. autoapisummary::

   neural_network.activation_functions.leaky_rectified_linear_unit.leaky_rectified_linear_unit


Module Contents
---------------

.. py:function:: 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])