mindspore.Tensor.topk

Tensor.topk(k, dim=- 1, largest=True, sorted=True)

Finds the k largest or smallest element along the given dimension and returns its value and corresponding index.

Warning

  • 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 self 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 self, 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

  • 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: -1 .

  • 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 or ascending order according to largest. If False , the obtained elements will not be sorted. Default: True .

Returns

A tuple consisting of values and indices.

  • 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 self.

Raises
Supported Platforms:

Ascend

Examples

>>> import mindspore as ms
>>> from mindspore import Tensor
>>> 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 = Tensor.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 = Tensor.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]]))
Tensor.topk(k, dim=None, largest=True, sorted=True)

For more details, please refer to mindspore.ops.topk().