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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.

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Problem description

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

mindspore.mint.mv

View Source On Gitee
mindspore.mint.mv(input, vec)[source]

Multiply matrix input and vector vec. If input is a tensor with shape (N,M) and vec is a tensor with shape (M,), The output is a 1-D tensor which shape is (N,).

Warning

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

Parameters
  • input (Tensor) – The input matrix which shape is (N,M) and the rank must be 2-D.

  • vec (Tensor) – The input vector which shape is (M,) and the rank is 1-D.

Returns

Tensor, the shape is (N,).

Raises
  • TypeError – If input or vec is not a tensor.

  • TypeError – If the dtype of input or vec is not float16 or float32.

  • TypeError – If the dtypes of input and vec are different.

  • ValueError – If the input is not a 2-D tensor or the vec is not a 1-D tensor.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> input = Tensor(np.array([[3., 4.], [1., 6.], [1., 3.]]).astype(np.float32))
>>> vec = Tensor(np.array([1., 2.]).astype(np.float32))
>>> output = mint.mv(input, vec)
>>> print(output)
[11. 13. 7.]