neural_network.activation_functions.softsign
============================================

.. py:module:: neural_network.activation_functions.softsign

.. autoapi-nested-parse::

   This script demonstrates the implementation of the Softsign activation function.

   Softsign is a smooth activation function defined as:

       f(x) = x / (1 + |x|)

   It maps input values into the range (-1, 1), similar to the hyperbolic tangent (tanh)
   function but with a polynomial decay instead of exponential.

   https://en.wikipedia.org/wiki/Activation_function
   https://www.gabormelli.com/RKB/Softsign_Activation_Function



Functions
---------

.. autoapisummary::

   neural_network.activation_functions.softsign.softsign


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

.. py:function:: softsign(vector: numpy.ndarray) -> numpy.ndarray

   Implements the softsign activation function

   Parameters:
       vector (ndarray): A vector that consists of numeric values

   Returns:
       vector (ndarray): Input vector after applying softsign function

   >>> vector = np.array([-5, -1, 0, 1, 5])
   >>> softsign(vector)
   array([-0.83333333, -0.5       ,  0.        ,  0.5       ,  0.83333333])