mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-23 21:11:08 +00:00
57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
"""
|
|
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
|
|
"""
|
|
|
|
import numpy as np
|
|
|
|
|
|
def 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
|
|
>>> 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.])
|
|
"""
|
|
|
|
# Calculate e^x for each x in your vector where e is Euler's
|
|
# number (approximately 2.718)
|
|
exponentVector = np.exp(vector)
|
|
|
|
# Add up the all the exponentials
|
|
sumOfExponents = np.sum(exponentVector)
|
|
|
|
# Divide every exponent by the sum of all exponents
|
|
softmax_vector = exponentVector / sumOfExponents
|
|
|
|
return softmax_vector
|
|
|
|
|
|
if __name__ == "__main__":
|
|
print(softmax((0,)))
|