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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.ops.unstack

mindspore.ops.unstack(input_x, axis=0)

Unstacks tensor in specified axis, this is the opposite of mindspore.ops.stack(). Assuming input is a tensor of rank R, output tensors will have rank (R-1).

Parameters

• input_x (Tensor) – The shape is $(x_1,x_2,...,x_R)$. A tensor to be unstacked and the rank of the tensor must be greater than 0.

• axis (int) – Dimension along which to unpack. Default: 0 . Negative values wrap around. The range is [-R, R).

Returns

A tuple of tensors, the shape of each objects is the same. Given a tensor of shape $(x_1,x_2,...,x_R)$. If $0\le axis$, the shape of tensor in output is $(x_1,x_2,...,x_{axis},x_{axis+2},...,x_R)$.

Raises

ValueError – If axis is out of the range [-len(input_x.shape), len(input_x.shape)).

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))
>>> output = ops.unstack(input_x, 0)
>>> print(output)
(Tensor(shape=[4], dtype=Int64, value= [1, 1, 1, 1]), Tensor(shape=[4], dtype=Int64, value= [2, 2, 2, 2]))