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.

PR

Just a small problem.

I can fix it online!

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.

Document feedback

mindspore.mint.mean

mindspore.mint.mean(input, *, dtype=None) → Tensor[source]

Reduces all dimension of a tensor by averaging all elements.

Parameters: input (Tensor[Number]) – The input tensor. The dtype of the tensor to be reduced is number. $(N, *)$ where $*$ means, any number of additional dimensions.
Keyword Arguments: dtype (mindspore.dtype, optional) – The desired data type of returned Tensor. Default: None .
Returns: Tensor.
Raises: TypeError – If input is not a Tensor.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([[[2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2]],
... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
... [[6, 6, 6, 6, 6, 6], [8, 8, 8, 8, 8, 8], [10, 10, 10, 10, 10, 10]]]),
... mindspore.float32)
>>> output = mint.mean(x)
>>> print(output)
5.0
>>> print(output.shape)
()

mindspore.mint.mean(input, dim, keepdim=False, *, dtype=None) → Tensor[source]

Reduces all dimension of a tensor by averaging all elements in the dimension, by default. And reduce a dimension of input along the specified dim. keepdim determines whether the dimensions of the output and input are the same.

Note

The dim with tensor type is only used for compatibility with older versions and is not recommended.

Parameters

input (Tensor[Number]) – The input tensor. The dtype of the tensor to be reduced is number. $(N, *)$ where $*$ means, any number of additional dimensions.
dim (Union[int, tuple(int), list(int), Tensor]) – The dimensions to reduce. Only constant value is allowed. Assume the rank of input is r, and the value range is [-r,r).
keepdim (bool) – If True , keep these reduced dimensions and the length is 1. If False , don't keep these dimensions. Default: False .

Keyword Arguments

dtype (mindspore.dtype, optional) – The desired data type of returned Tensor. Default: None .

Returns

Tensor.

If dim is int, set as 1, and keepdim is False , the shape of output is $(i n p u t_{0}, i n p u t_{2}, . . ., i n p u t_{R})$ .
If dim is tuple(int) or list(int), set as (1, 2), and keepdim is False , the shape of output is $(i n p u t_{0}, i n p u t_{3}, . . ., i n p u t_{R})$ .
If dim is 1-D Tensor, set as [1, 2], and keepdim is False , the shape of output is $(i n p u t_{0}, i n p u t_{3}, . . ., i n p u t_{R})$ .

Raises

TypeError – If input is not a Tensor.
TypeError – If dim is not one of the following: int, tuple, list or Tensor.
TypeError – If keepdim is not a bool.
ValueError – If dim is out of range.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([[[2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2]],
... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
... [[6, 6, 6, 6, 6, 6], [8, 8, 8, 8, 8, 8], [10, 10, 10, 10, 10, 10]]]),
... mindspore.float32)
>>> output = mint.mean(x, 0, True)
>>> print(output)
[[[4. 4. 4. 4. 4. 4.]
  [5. 5. 5. 5. 5. 5.]
  [6. 6. 6. 6. 6. 6.]]]