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.topk

mindspore.ops.topk(input, k, dim=None, largest=True, sorted=True)

Finds values and indices of the k largest or smallest entries along a given dimension.

Warning

• If sorted is set to False, it will use the aicpu operator, the performance may be reduced. In addition, due to different memory layout and traversal methods on different platforms, the display order of calculation results may be inconsistent when sorted is False.

If the input is a one-dimensional Tensor, finds the k largest or smallest entries in the Tensor, and outputs its value and index as a Tensor. values[k] is the k largest item in input, and its index is indices [k].

For a multi-dimensional matrix, calculates the first or last k entries in a given dimension, therefore:

$$values.shape=indices.shape$$

If the two compared elements are the same, the one with the smaller index value is returned first.

Parameters

• input (Tensor) – Input to be computed, data type must be float16, float32 or int32.

• k (int) – The number of top or bottom elements to be computed along the last dimension.

• dim (int, optional) – The dimension to sort along. Default: None .

• largest (bool, optional) – If largest is False then the k smallest elements are returned. Default: True .

• sorted (bool, optional) – If True , the obtained elements will be sorted by the values in descending order. If False , the obtained elements will not be sorted. Default: True .

Returns

A tuple consisting of values and indexes.

• values (Tensor): The k largest or smallest elements in each slice of the given dimension.

• indices (Tensor): The indices of values within the last dimension of input.

Raises

• TypeError – If sorted is not a bool.

• TypeError – If input is not a Tensor.

• TypeError – If k is not an int.

• TypeError – If dtype of input is not one of the following: float16, float32 or int32.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> from mindspore import ops
>>> x = ms.Tensor([[0.5368, 0.2447, 0.4302, 0.9673],
...                [0.4388, 0.6525, 0.4685, 0.1868],
...                [0.3563, 0.5152, 0.9675, 0.8230]], dtype=ms.float32)
>>> output = ops.topk(x, 2, dim=1)
>>> print(output)
(Tensor(shape=[3, 2], dtype=Float32, value=
[[ 9.67299998e-01,  5.36800027e-01],
 [ 6.52499974e-01,  4.68499988e-01],
 [ 9.67499971e-01,  8.23000014e-01]]), Tensor(shape=[3, 2], dtype=Int32, value=
[[3, 0],
 [1, 2],
 [2, 3]]))
>>> output2 = ops.topk(x, 2, dim=1, largest=False)
>>> print(output2)
(Tensor(shape=[3, 2], dtype=Float32, value=
[[ 2.44700000e-01,  4.30200011e-01],
 [ 1.86800003e-01,  4.38800007e-01],
 [ 3.56299996e-01,  5.15200019e-01]]), Tensor(shape=[3, 2], dtype=Int32, value=
[[1, 2],
 [3, 0],
 [0, 1]]))