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16 | 16 |
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17 | 17 |
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18 | 18 | def scaled_exponential_linear_unit(
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19 |
| - vector: np.ndarray, alpha: float = 1.6732, _lambda: float = 1.0507 |
| 19 | + vector: np.ndarray, alpha: float = 1.6732, lambda_: float = 1.0507 |
20 | 20 | ) -> np.ndarray:
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21 | 21 | """
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22 | 22 | Applies the Scaled Exponential Linear Unit function to each element of the vector.
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23 | 23 | Parameters :
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24 | 24 | vector : np.ndarray
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25 | 25 | alpha : float (default = 1.6732)
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26 |
| - _lambda : float (default = 1.0507) |
| 26 | + lambda_ : float (default = 1.0507) |
27 | 27 |
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28 | 28 | Returns : np.ndarray
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29 |
| - Formula : f(x) = _lambda * x if x > 0 |
30 |
| - _lambda * alpha * (e**x - 1) if x <= 0 |
| 29 | + Formula : f(x) = lambda_ * x if x > 0 |
| 30 | + lambda_ * alpha * (e**x - 1) if x <= 0 |
31 | 31 | Examples :
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32 | 32 | >>> scaled_exponential_linear_unit(vector=np.array([1.3, 3.7, 2.4]))
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33 | 33 | array([1.36591, 3.88759, 2.52168])
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34 | 34 |
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35 | 35 | >>> scaled_exponential_linear_unit(vector=np.array([1.3, 4.7, 8.2]))
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36 | 36 | array([1.36591, 4.93829, 8.61574])
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37 | 37 | """
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38 |
| - return _lambda * np.where(vector > 0, vector, alpha * (np.exp(vector) - 1)) |
| 38 | + return lambda_ * np.where(vector > 0, vector, alpha * (np.exp(vector) - 1)) |
39 | 39 |
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40 | 40 |
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41 | 41 | if __name__ == "__main__":
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