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* Added Leaky ReLU activation function * Added Leaky ReLU activation function * Added Leaky ReLU activation function * Formatting and spelling fixes done
40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
"""
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Leaky Rectified Linear Unit (Leaky ReLU)
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Use Case: Leaky ReLU addresses the problem of the vanishing gradient.
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For more detailed information, you can refer to the following link:
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https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Leaky_ReLU
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"""
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import numpy as np
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def leaky_rectified_linear_unit(vector: np.ndarray, alpha: float) -> np.ndarray:
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"""
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Implements the LeakyReLU activation function.
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Parameters:
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vector (np.ndarray): The input array for LeakyReLU activation.
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alpha (float): The slope for negative values.
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Returns:
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np.ndarray: The input array after applying the LeakyReLU activation.
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Formula: f(x) = x if x > 0 else f(x) = alpha * x
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Examples:
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>>> leaky_rectified_linear_unit(vector=np.array([2.3,0.6,-2,-3.8]), alpha=0.3)
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array([ 2.3 , 0.6 , -0.6 , -1.14])
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>>> leaky_rectified_linear_unit(np.array([-9.2, -0.3, 0.45, -4.56]), alpha=0.067)
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array([-0.6164 , -0.0201 , 0.45 , -0.30552])
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"""
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return np.where(vector > 0, vector, alpha * vector)
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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