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Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
"""
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This script demonstrates the implementation of the Sigmoid Linear Unit (SiLU)
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or swish function.
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* https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
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* https://en.wikipedia.org/wiki/Swish_function
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The function takes a vector x of K real numbers as input and returns x * sigmoid(x).
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Swish is a smooth, non-monotonic function defined as f(x) = x * sigmoid(x).
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Extensive experiments shows that Swish consistently matches or outperforms ReLU
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on deep networks applied to a variety of challenging domains such as
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image classification and machine translation.
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This script is inspired by a corresponding research paper.
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* https://arxiv.org/abs/1710.05941
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"""
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import numpy as np
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def sigmoid(vector: np.ndarray) -> np.ndarray:
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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 sigmoid_linear_unit(vector: np.ndarray) -> np.ndarray:
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"""
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Implements the Sigmoid Linear Unit (SiLU) or swish function
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Parameters:
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vector (np.ndarray): A numpy array consisting of real values
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Returns:
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swish_vec (np.ndarray): The input numpy array, after applying swish
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Examples:
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>>> sigmoid_linear_unit(np.array([-1.0, 1.0, 2.0]))
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array([-0.26894142, 0.73105858, 1.76159416])
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>>> sigmoid_linear_unit(np.array([-2]))
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array([-0.23840584])
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"""
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return vector * sigmoid(vector)
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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