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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.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

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- Incorrect version numbers, including software package names and version numbers on the UI.

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- Incorrect schematic or architecture diagrams.

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- Incorrect code.

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- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

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mindspore.nn.LeakyReLU

class mindspore.nn.LeakyReLU(alpha=0.2)

Leaky ReLU activation function.

LeakyReLU is similar to ReLU, but LeakyReLU has a slope that makes it not equal to 0 at x < 0. The activation function is defined as:

$$\text{leaky\_relu}(x)=\{\begin{array}{ll}x,& \text{if }x\ge 0;\\ \text{alpha}\ast x,& \text{otherwise.}\end{array}$$

See https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf

Parameters

alpha (Union[int, float]) – Slope of the activation function at x < 0. Default: 0.2.

Inputs:

• x (Tensor) - The input of LeakyReLU. The shape is $(N,\ast )$ where $\ast$ means, any number of additional dimensions.

Outputs:

Tensor, has the same type and shape as the x.

Raises

TypeError – If alpha is not a float or an int.

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
>>> leaky_relu = nn.LeakyReLU()
>>> output = leaky_relu(x)
>>> print(output)
[[-0.2  4.  -1.6]
 [ 2.  -1.   9. ]]