mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-23 21:11:08 +00:00
5ffe601c86
* updating DIRECTORY.md
* Fix mypy errors in sigmoid_linear_unit.py
* updating DIRECTORY.md
* updating DIRECTORY.md
---------
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
"""
|
|
This script demonstrates the implementation of the Sigmoid Linear Unit (SiLU)
|
|
or swish function.
|
|
* https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
|
|
* https://en.wikipedia.org/wiki/Swish_function
|
|
|
|
The function takes a vector x of K real numbers as input and returns x * sigmoid(x).
|
|
Swish is a smooth, non-monotonic function defined as f(x) = x * sigmoid(x).
|
|
Extensive experiments shows that Swish consistently matches or outperforms ReLU
|
|
on deep networks applied to a variety of challenging domains such as
|
|
image classification and machine translation.
|
|
|
|
This script is inspired by a corresponding research paper.
|
|
* https://arxiv.org/abs/1710.05941
|
|
"""
|
|
|
|
import numpy as np
|
|
|
|
|
|
def sigmoid(vector: np.ndarray) -> np.ndarray:
|
|
"""
|
|
Mathematical function sigmoid takes a vector x of K real numbers as input and
|
|
returns 1/ (1 + e^-x).
|
|
https://en.wikipedia.org/wiki/Sigmoid_function
|
|
|
|
>>> sigmoid(np.array([-1.0, 1.0, 2.0]))
|
|
array([0.26894142, 0.73105858, 0.88079708])
|
|
"""
|
|
return 1 / (1 + np.exp(-vector))
|
|
|
|
|
|
def sigmoid_linear_unit(vector: np.ndarray) -> np.ndarray:
|
|
"""
|
|
Implements the Sigmoid Linear Unit (SiLU) or swish function
|
|
|
|
Parameters:
|
|
vector (np.ndarray): A numpy array consisting of real values
|
|
|
|
Returns:
|
|
swish_vec (np.ndarray): The input numpy array, after applying swish
|
|
|
|
Examples:
|
|
>>> sigmoid_linear_unit(np.array([-1.0, 1.0, 2.0]))
|
|
array([-0.26894142, 0.73105858, 1.76159416])
|
|
|
|
>>> sigmoid_linear_unit(np.array([-2]))
|
|
array([-0.23840584])
|
|
"""
|
|
return vector * sigmoid(vector)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import doctest
|
|
|
|
doctest.testmod()
|