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48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
"""
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Leaky Rectified Linear Unit (LeakyReLU)
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Input: vector (type: np.ndarray) , alpha (type: float)
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Output: vector (type: np.ndarray)
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UseCase: LeakyReLU solves the issue of dead neurons or vanishing gradient problem.
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Refer the below link for more information:
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https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Leaky_ReLU
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Applications:
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Generative Adversarial Networks (GANs)
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Object Detection and Image Segmentation
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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(vector=np.array([-9.2,-0.3,0.45,-4.56]), \
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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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