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maths/softmax.py
56 строк
1 KB
Christian Clauss
Upgrade to Python 3.13 (#11588)
01 окт 2024, 00:01
Не верифицирован
01 окт 2024, 00:01
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""" 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 >>> 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.]) """ # Calculate e^x for each x in your vector where e is Euler's # number (approximately 2.718) exponent_vector = np.exp(vector) # Add up the all the exponentials sum_of_exponents = np.sum(exponent_vector) # Divide every exponent by the sum of all exponents softmax_vector = exponent_vector / sum_of_exponents return softmax_vector if __name__ == "__main__": print(softmax((0,)))