Document feedback

Question document fragment

When a question document fragment contains a formula, it is displayed as a space.

Submission type

issue

It's a little complicated...

I'd like to ask someone.

Please select the submission type

Problem type

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

- Usability:

- 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

- Correctness:

- 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.

- Copyright infringement.

Please select the type of question

Problem description

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

Submit

Document feedback

mindspore.nn.ClipByNorm

class mindspore.nn.ClipByNorm(axis=None)

Clips tensor values to a maximum $L_2$-norm.

The output of this layer remains the same if the $L_2$-norm of the input tensor is not greater than the argument clip_norm. Otherwise the tensor will be normalized as:

$$\text{output}(X)=\frac{\text{clip\_norm}\ast X}{L_2(X)},$$

where $L_2(X)$ is the $L_2$-norm of $X$.

Parameters

axis (Union[None, int, tuple(int)]) – Compute the L2-norm along the Specific dimension. Default: None, all dimensions to calculate.

Inputs:

• x (Tensor) - Tensor of shape N-D. The type must be float32 or float16.

• clip_norm (Tensor) - A scalar Tensor of shape $()$ or $(1)$. Or a tensor shape can be broadcast to input shape.

Outputs:

Tensor, clipped tensor with the same shape as the x, whose type is float32.

Raises

• TypeError – If axis is not one of None, int, tuple.

• TypeError – If dtype of x is neither float32 nor float16.

Supported Platforms:

Ascend GPU CPU

Examples

>>> net = nn.ClipByNorm()
>>> x = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
>>> clip_norm = Tensor(np.array([100]).astype(np.float32))
>>> output = net(x, clip_norm)
>>> print(output.shape)
(4, 16)