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57 lines
1.4 KiB
Python
57 lines
1.4 KiB
Python
"""
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This script demonstrates the implementation of the Softmax function.
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Its a function that takes as input a vector of K real numbers, and normalizes
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it into a probability distribution consisting of K probabilities proportional
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to the exponentials of the input numbers. After softmax, the elements of the
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vector always sum up to 1.
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Script inspired from its corresponding Wikipedia article
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https://en.wikipedia.org/wiki/Softmax_function
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"""
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import numpy as np
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def softmax(vector):
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"""
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Implements the softmax function
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Parameters:
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vector (np.array,list,tuple): A numpy array of shape (1,n)
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consisting of real values or a similar list,tuple
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Returns:
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softmax_vec (np.array): The input numpy array after applying
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softmax.
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The softmax vector adds up to one. We need to ceil to mitigate for
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precision
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>>> np.ceil(np.sum(softmax([1,2,3,4])))
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1.0
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>>> vec = np.array([5,5])
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>>> softmax(vec)
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array([0.5, 0.5])
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>>> softmax([0])
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array([1.])
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"""
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# Calculate e^x for each x in your vector where e is Euler's
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# number (approximately 2.718)
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exponentVector = np.exp(vector)
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# Add up the all the exponentials
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sumOfExponents = np.sum(exponentVector)
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# Divide every exponent by the sum of all exponents
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softmax_vector = exponentVector / sumOfExponents
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return softmax_vector
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if __name__ == "__main__":
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print(softmax((0,)))
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