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* Added Scaled Exponential Linear Unit Activation Function * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update scaled_exponential_linear_unit.py * Update scaled_exponential_linear_unit.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update scaled_exponential_linear_unit.py * Update scaled_exponential_linear_unit.py * Update scaled_exponential_linear_unit.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update scaled_exponential_linear_unit.py * Update scaled_exponential_linear_unit.py --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
"""
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Implements the Scaled Exponential Linear Unit or SELU function.
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The function takes a vector of K real numbers and two real numbers
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alpha(default = 1.6732) & lambda (default = 1.0507) as input and
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then applies the SELU function to each element of the vector.
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SELU is a self-normalizing activation function. It is a variant
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of the ELU. The main advantage of SELU is that we can be sure
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that the output will always be standardized due to its
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self-normalizing behavior. That means there is no need to
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include Batch-Normalization layers.
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References :
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https://iq.opengenus.org/scaled-exponential-linear-unit/
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"""
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import numpy as np
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def scaled_exponential_linear_unit(
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vector: np.ndarray, alpha: float = 1.6732, lambda_: float = 1.0507
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) -> np.ndarray:
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"""
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Applies the Scaled Exponential Linear Unit function to each element of the vector.
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Parameters :
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vector : np.ndarray
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alpha : float (default = 1.6732)
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lambda_ : float (default = 1.0507)
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Returns : np.ndarray
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Formula : f(x) = lambda_ * x if x > 0
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lambda_ * alpha * (e**x - 1) if x <= 0
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Examples :
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>>> scaled_exponential_linear_unit(vector=np.array([1.3, 3.7, 2.4]))
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array([1.36591, 3.88759, 2.52168])
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>>> scaled_exponential_linear_unit(vector=np.array([1.3, 4.7, 8.2]))
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array([1.36591, 4.93829, 8.61574])
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"""
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return lambda_ * 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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