neural_network.activation_functions.softsign

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

softsign(→ numpy.ndarray)

Implements the softsign activation function

Module Contents

neural_network.activation_functions.softsign.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])