mindspore.ops.unsorted_segment_max

mindspore.ops.unsorted_segment_max(x, segment_ids, num_segments)[source]

Computes the maximum along segments of a tensor.

The following figure shows the calculation process of unsorted_segment_max:

../../_images/UnsortedSegmentMax.png
\[\text { output }_i=\text{max}_{j \ldots} \text { data }[j \ldots]\]

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 shape is \((x_1, x_2, ..., x_R)\). With float16, float32 or int32 data type.

  • segment_ids (Tensor) – A 1-D tensor whose shape is \((x_1)\), the value must be non-negative tensor. The data type must be int32.

  • num_segments (int) – The value specifies the number of distinct segment_ids.

Returns

Tensor, set the number of num_segments as N, the shape is \((N, x_2, ..., x_R)\).

Raises

  • TypeError – If num_segments is not an int.

  • ValueError – If length of shape of segment_ids is not equal to 1.

Supported Platforms:

Ascend GPU CPU

Examples

>>> from mindspore import Tensor
>>> from mindspore import ops
>>> import numpy as np
>>> x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [4, 2, 1]]).astype(np.float32))
>>> segment_ids = Tensor(np.array([0, 1, 1]).astype(np.int32))
>>> num_segments = 2
>>> output = ops.unsorted_segment_max(x, segment_ids, num_segments)
>>> print(output)
[[1. 2. 3.]
 [4. 5. 6.]]