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.ops.ReduceSum

class mindspore.ops.ReduceSum(keep_dims=False, skip_mode=False)

Reduces a dimension of a tensor by summing all elements in the dimension, by default. And also can reduce a dimension of x along the axis. Determine whether the dimensions of the output and input are the same by controlling keep_dims.

Note

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

Parameters

• keep_dims (bool) – If True , keep these reduced dimensions and the length is 1. If False , don't keep these dimensions. Default: False .

• skip_mode (bool) – If True and axis is empty tuple or empty list, the ReduceSum operation isn't performed, skip it. If True and axis is other values, the ReduceSum calculation is performed normally. If False , do reduce. Default: False .

Inputs:

• x (Tensor[Number]) - The input tensor.

• axis (Union[int, tuple(int), list(int), Tensor]) - The dimensions to reduce. Default: () , reduce all dimensions when skip_mode is False . Only constant value is allowed. Must be in the range [-rank(x), rank(x)).

Outputs:

Tensor, has the same dtype as the x.

• If axis is () , keep_dims is False , and skip_mode is False , the output is a 0-D tensor representing the sum of all elements in the input tensor.

• If axis is () , and skip_mode is True , the ReduceSum operation is not performed, output tensor is equal to the input tensor.

• If axis is int, set as 2, and keep_dims is False , the shape of output is $(x_1,x_3,...,x_R)$.

• If axis is tuple(int) or list(int), set as (2, 3), and keep_dims is False , the shape of output is $(x_1,x_4,...,x_R)$.

• If axis is 1-D Tensor, set as [2, 3], and keep_dims is False , the shape of output is $(x_1,x_4,...,x_R)$.

Raises

• TypeError – If keep_dims is not a bool.

• TypeError – If skip_mode is not a bool.

• TypeError – If x is not a Tensor.

• ValueError – If axis is None.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = ops.ReduceSum(keep_dims=True)
>>> output = op(x, 1)
>>> output.shape
(3, 1, 5, 6)
>>> # case 1: Reduces a dimension by summing all elements in the dimension.
>>> x = Tensor(np.array([[[1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3]],
...                      [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
...                      [[7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8], [9, 9, 9, 9, 9, 9]]]), mindspore.float32)
>>> output = op(x)
>>> print(output)
[[[270.]]]
>>> print(output.shape)
(1, 1, 1)
>>> # case 2: Reduces a dimension along axis 0.
>>> output = op(x, 0)
>>> print(output)
[[[12. 12. 12. 12. 12. 12.]
[15. 15. 15. 15. 15. 15.]
[18. 18. 18. 18. 18. 18.]]]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = op(x, 1)
>>> print(output)
[[[ 6.  6.  6.  6.  6.  6.]]
[[15. 15. 15. 15. 15. 15.]]
[[24. 24. 24. 24. 24. 24.]]]
>>> # case 4: Reduces a dimension along axis 2.
>>> output = op(x, 2)
>>> print(output)
[[[ 6.]
[12.]
[18.]]
[[24.]
[30.]
[36.]]
[[42.]
[48.]
[54.]]]