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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.

- Low writing fluency that does not affect understanding.

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

Usability

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Correctness

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- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

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

Risk Warnings

- Risk Warnings:

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Content Compliance

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Problem description

Describe the bug so that we can quickly locate the problem.

mindspore.nn.GLU

class mindspore.nn.GLU(axis=- 1)[source]

The gated linear unit function.

GLU(a,b)=aσ(b)

where a is the first half of the input matrices and b is the second half.

Here σ is the sigmoid function, and is the Hadamard product.

Parameters

axis (int) – the axis to split the input. Default: -1, the last axis in x.

Inputs:
  • x (Tensor) - (1,N,2) where * means, any number of additional dimensions.

Outputs:

Tensor, the same dtype as the x, with the shape (1,M,2) where M=N/2.

Supported Platforms:

Ascend GPU CPU

Examples

>>> m = nn.GLU()
>>> input = Tensor([[0.1,0.2,0.3,0.4],[0.5,0.6,0.7,0.8]])
>>> output = m(input)
>>> print(output)
[[0.05744425 0.11973753]
 [0.33409387 0.41398472]]