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* from __future__ import annotations
* fixup! from __future__ import annotations
* fixup! from __future__ import annotations
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41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
"""
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This script demonstrates the implementation of the ReLU function.
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It's a kind of activation function defined as the positive part of its argument in the
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context of neural network.
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The function takes a vector of K real numbers as input and then argmax(x, 0).
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After through ReLU, the element of the vector always 0 or real number.
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Script inspired from its corresponding Wikipedia article
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https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
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"""
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from __future__ import annotations
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import numpy as np
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def relu(vector: list[float]):
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"""
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Implements the relu function
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Parameters:
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vector (np.array,list,tuple): A numpy array of shape (1,n)
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consisting of real values or a similar list,tuple
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Returns:
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relu_vec (np.array): The input numpy array, after applying
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relu.
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>>> vec = np.array([-1, 0, 5])
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>>> relu(vec)
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array([0, 0, 5])
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"""
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# compare two arrays and then return element-wise maxima.
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return np.maximum(0, vector)
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if __name__ == "__main__":
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print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
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