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* tanh function been added * tanh function been added * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * tanh function is added * tanh function is added * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * tanh function added * tanh function added * tanh function is added * Apply suggestions from code review * ELU activation function is added * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * elu activation is added * ELU activation is added * Update maths/elu_activation.py Co-authored-by: Christian Clauss <cclauss@me.com> * Exponential_linear_unit activation is added * Exponential_linear_unit activation is added --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Christian Clauss <cclauss@me.com>
41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
"""
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Implements the Exponential Linear Unit or ELU function.
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The function takes a vector of K real numbers and a real number alpha as
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input and then applies the ELU function to each element of the vector.
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Script inspired from its corresponding Wikipedia article
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https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
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"""
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import numpy as np
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def exponential_linear_unit(vector: np.ndarray, alpha: float) -> np.ndarray:
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"""
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Implements the ELU activation function.
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Parameters:
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vector: the array containing input of elu activation
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alpha: hyper-parameter
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return:
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elu (np.array): The input numpy array after applying elu.
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Mathematically, f(x) = x, x>0 else (alpha * (e^x -1)), x<=0, alpha >=0
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Examples:
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>>> exponential_linear_unit(vector=np.array([2.3,0.6,-2,-3.8]), alpha=0.3)
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array([ 2.3 , 0.6 , -0.25939942, -0.29328877])
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>>> exponential_linear_unit(vector=np.array([-9.2,-0.3,0.45,-4.56]), alpha=0.067)
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array([-0.06699323, -0.01736518, 0.45 , -0.06629904])
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"""
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return np.where(vector > 0, vector, (alpha * (np.exp(vector) - 1)))
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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