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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.ops.unsorted_segment_max

View Source On Gitee
mindspore.ops.unsorted_segment_max(x, segment_ids, num_segments)[source]

Compute the maximum of the input tensor along segments.

The following figure shows the calculation process of unsorted_segment_max:

../../_images/UnsortedSegmentMax.png
 output i=maxj data [j]

where max over tuples j... such that segment_ids[j...]==i.

Note

  • If the segment_id i is absent in the segment_ids, then output[i] will be filled with the minimum value of the x's type.

  • The segment_ids must be non-negative tensor.

Parameters
  • x (Tensor) – The input tensor.

  • segment_ids (Tensor) – Indicate the segment to which each element belongs. Set the shape as (x1,x2,...,xN), where 0 < N <= R.

  • num_segments (Union[int, Tensor]) – Number of segments, it can be an int or 0-D tensor.

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> x = mindspore.tensor([[1, 2, 3], [4, 5, 6], [4, 2, 1]])
>>> segment_ids = mindspore.tensor([0, 1, 1])
>>> num_segments = 2
>>> mindspore.ops.unsorted_segment_max(x, segment_ids, num_segments)
Tensor(shape=[2, 3], dtype=Int64, value=
[[1, 2, 3],
 [4, 5, 6]])