mindspore.Tensor.unsorted_segment_max

Tensor.unsorted_segment_max(segment_ids, num_segments)[source]

Computes the maximum along segments of a tensor.

Note

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

  • The segment_ids must be non-negative tensor.

Parameters
  • 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
>>> 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 = x.unsorted_segment_max(segment_ids, num_segments)
>>> print(output)
[[1. 2. 3.]
 [4. 5. 6.]]