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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.

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- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

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:

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

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

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mindspore.mint.nn.functional.gelu

mindspore.mint.nn.functional.gelu(input, approximate='none')

Gaussian Error Linear Units activation function.

GeLU is described in the paper Gaussian Error Linear Units (GELUs). And also please refer to BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

When approximate argument is none, GeLU is defined as follows:

$$GELU(x_i)=x_i\ast P(X<x_i),$$

where $P$ is the cumulative distribution function of the standard Gaussian distribution, $x_i$ is the input element.

When approximate argument is tanh, GeLU is estimated with:

$$GELU(x_i)=0.5\ast x_i\ast (1+\tanh (\sqrt{(}2/\pi )\ast (x_i+0.044715\ast x_i^3)))$$

GELU Activation Function Graph:

Parameters

• input (Tensor) – The input of the activation function GeLU, the data type is float16, float32 or float64.

• approximate (str) – the gelu approximation algorithm to use. Acceptable vaslues are 'none' and 'tanh' . Default: 'none' .

Returns

Tensor, with the same type and shape as input.

Raises

• TypeError – If input is not a Tensor.

• TypeError – If dtype of input is not bfloat16, float16, float32 or float64.

• ValueError – If approximate value is neither none nor tanh.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> x = Tensor([1.0, 2.0, 3.0], mindspore.float32)
>>> result = mint.nn.functional.gelu(x)
>>> print(result)
[0.8413447 1.9544997 2.9959505]