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.Moments

class mindspore.nn.Moments(axis=None, keep_dims=None)

Calculates the mean and variance of x.

Parameters

• axis (Union[int, tuple(int)]) – Calculates the mean and variance along the specified axis. Default: None.

• keep_dims (bool) – If true, The dimension of mean and variance are identical with input’s. If false, don’t keep these dimensions. Default: None.

Inputs:

• x (Tensor) - The tensor to be calculated. Only float16 and float32 are supported. $(N,\ast )$ where $\ast$ means,any number of additional dimensions.

Outputs:

• mean (Tensor) - The mean of x, with the same date type as input x.

• variance (Tensor) - The variance of x, with the same date type as input x.

Raises

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

• TypeError – If keep_dims is neither bool nor None.

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.array([[[[1, 2, 3, 4], [3, 4, 5, 6]]]]), mindspore.float32)
>>> net = nn.Moments(axis=0, keep_dims=True)
>>> output = net(x)
>>> print(output)
(Tensor(shape=[1, 1, 2, 4], dtype=Float32, value=
[[[[ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00, 4.00000000e+00],
   [ 3.00000000e+00, 4.00000000e+00, 5.00000000e+00, 6.00000000e+00]]]]),
Tensor(shape=[1, 1, 2, 4], dtype=Float32, value=
[[[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
   [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]]]))
>>> net = nn.Moments(axis=1, keep_dims=True)
>>> output = net(x)
>>> print(output)
(Tensor(shape=[1, 1, 2, 4], dtype=Float32, value=
[[[[ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00, 4.00000000e+00],
   [ 3.00000000e+00, 4.00000000e+00, 5.00000000e+00, 6.00000000e+00]]]]),
Tensor(shape=[1, 1, 2, 4], dtype=Float32, value=
[[[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
   [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]]]))
>>> net = nn.Moments(axis=2, keep_dims=True)
>>> output = net(x)
>>> print(output)
(Tensor(shape=[1, 1, 1, 4], dtype=Float32, value=
[[[[ 2.00000000e+00, 3.00000000e+00, 4.00000000e+00, 5.00000000e+00]]]]),
Tensor(shape=[1, 1, 1, 4], dtype=Float32, value=
[[[[ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]]]]))
>>> net = nn.Moments(axis=3, keep_dims=True)
>>> output = net(x)
>>> print(output)
(Tensor(shape=[1, 1, 2, 1], dtype=Float32, value=
[[[[ 2.50000000e+00],
   [ 4.50000000e+00]]]]), Tensor(shape=[1, 1, 2, 1], dtype=Float32, value=
[[[[ 1.25000000e+00],
   [ 1.25000000e+00]]]]))