maths.softmax

This script demonstrates the implementation of the Softmax function.

Its a function that takes as input a vector of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of the input numbers. After softmax, the elements of the vector always sum up to 1.

Script inspired from its corresponding Wikipedia article https://en.wikipedia.org/wiki/Softmax_function

Functions

softmax(vector)

Implements the softmax function

Module Contents

maths.softmax.softmax(vector)

Implements the softmax function

Parameters:

vector (np.array,list,tuple): A numpy array of shape (1,n) consisting of real values or a similar list,tuple

Returns:

softmax_vec (np.array): The input numpy array after applying softmax.

The softmax vector adds up to one. We need to ceil to mitigate for precision >>> float(np.ceil(np.sum(softmax([1,2,3,4])))) 1.0

>>> vec = np.array([5,5])
>>> softmax(vec)
array([0.5, 0.5])

>>> softmax([0])
array([1.])