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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

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- Minor inconsistencies between the UI and descriptions.

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mindspore.mint.nn.functional.elu

mindspore.mint.nn.functional.elu(input, alpha=1.0)

Exponential Linear Unit activation function.

Applies the exponential linear unit function element-wise. The activation function is defined as:

$$\begin{array}{r}\text{ELU}(x)=\{\begin{array}{ll}\alpha (e^x-1)& \text{if }x\le 0\\ x& \text{if }x>0\end{array}\end{array}$$

Where $x$ is the element of input Tensor input, $\alpha$ is param alpha, it determines the smoothness of ELU.

ELU function graph:

Parameters

• input (Tensor) – The input of ELU is a Tensor of any dimension.

• alpha (float, optional) – The alpha value of ELU, the data type is float. Default: 1.0 .

Returns

Tensor, has the same shape and data type as input.

Raises

TypeError – If alpha is not a float.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
>>> output = mint.nn.functional.elu(x)
>>> print(output)
[[-0.63212055  4.         -0.99966455]
 [ 2.         -0.99326205  9.        ]]