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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

- 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.

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mindspore.numpy.squeeze

mindspore.numpy.squeeze(a, axis=None)[source]

Return the Tensor after deleting the dimension of size 1 in the specified axis.

If $a x i s = N o n e$ , it will remove all the dimensions of size 1. If axis is specified, it will remove the dimensions of size 1 in the given axis. For example, if the dimension is not specified $a x i s = N o n e$ , input shape is (A, 1, B, C, 1, D), then the shape of the output Tensor is (A, B, C, D). If the dimension is specified, the squeeze operation is only performed in the specified dimension. If input shape is (A, 1, B), when $a x i s = 0$ or $a x i s = 2$ , the input tensor is not changed, while when $a x i s = 1$ , the input tensor shape is changed to (A, B).

Parameters

a (Tensor) – Input tensor array.
axis (Union[None, int, list(int), tuple(list)], optional) – The axis(axes) to squeeze, default: None .

Returns

Tensor, with all or a subset of the dimensions of length $1$ removed.

Raises

TypeError – If input arguments have types not specified above.
ValueError – If specified axis has shape entry $> 1$ .

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> x = np.ones((1,2,2,1))
>>> x = np.squeeze(x)
>>> print(x.shape)
(2, 2)