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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.

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

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

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- Incorrect commands or command parameters.

- 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.

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mindspore.ops.celu

View Source On Gitee
mindspore.ops.celu(x, alpha=1.0)[source]

celu activation function, computes celu (Continuously differentiable exponential linear units) of input tensors element-wise. The formula is defined as follows:

CeLU(x)=max(0,x)+min(0,α(exp(x/α)1))

For more details, please refer to celu.

Warning

This is an experimental API that is subject to change or deletion.

CELU Activation Function Graph:

../../_images/CELU.png
Parameters
  • x (Tensor) – The input of celu with data type of float16 or float32.

  • alpha (float, optional) – The α value for the Celu formulation. Default: 1.0

Returns

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

Raises
Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([-2.0, -1.0, 1.0, 2.0]), mindspore.float32)
>>> output = ops.celu(x, alpha=1.0)
>>> print(output)
[-0.86466473 -0.63212055  1.          2.        ]