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54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
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"""
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This script demonstrates an implementation of the Gaussian Error Linear Unit function.
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* https://en.wikipedia.org/wiki/Activation_function#Comparison_of_activation_functions
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The function takes a vector of K real numbers as input and returns x * sigmoid(1.702*x).
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Gaussian Error Linear Unit (GELU) is a high-performing neural network activation
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function.
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This script is inspired by a corresponding research paper.
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* https://arxiv.org/abs/1606.08415
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"""
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import numpy as np
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def sigmoid(vector: np.array) -> np.array:
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"""
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Mathematical function sigmoid takes a vector x of K real numbers as input and
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returns 1/ (1 + e^-x).
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https://en.wikipedia.org/wiki/Sigmoid_function
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>>> sigmoid(np.array([-1.0, 1.0, 2.0]))
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array([0.26894142, 0.73105858, 0.88079708])
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"""
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return 1 / (1 + np.exp(-vector))
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def gaussian_error_linear_unit(vector: np.array) -> np.array:
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"""
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Implements the Gaussian Error Linear Unit (GELU) function
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Parameters:
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vector (np.array): A numpy array of shape (1,n)
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consisting of real values
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Returns:
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gelu_vec (np.array): The input numpy array, after applying
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gelu.
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Examples:
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>>> gaussian_error_linear_unit(np.array([-1.0, 1.0, 2.0]))
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array([-0.15420423, 0.84579577, 1.93565862])
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>>> gaussian_error_linear_unit(np.array([-3]))
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array([-0.01807131])
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"""
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return vector * sigmoid(1.702 * vector)
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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