mindspore.ops.ScatterNdMin

class mindspore.ops.ScatterNdMin(use_locking=False)[source]

Applies sparse minimum to individual values or slices in a tensor.

Using given values to update tensor value through the minimum operation, along with the input indices. This operation outputs the input_x after the update is done, which makes it convenient to use the updated value.

Refer to mindspore.ops.scatter_nd_min() for more details.

Parameters: use_locking (bool, optional) – Whether to protect the assignment by a lock. Default: False .

Inputs:

input_x (Parameter) -The target tensor, with data type of Parameter.
indices (Tensor) - The index to do minimum operation whose data type must be int32 or int64. The rank of indices must be at least 2 and indices.shape[-1] <= len(shape).
updates (Tensor) - The tensor to do the max operation with input_x. The data type is same as input_x, and the shape is indices.shape[:-1] + x.shape[indices.shape[-1]:].

Outputs:

Tensor, the updated input_x, has the same shape and type as input_x.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops, Parameter
>>> input_x = Parameter(Tensor(np.ones(8) * 10, mindspore.float32), name="x")
>>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32)
>>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32)
>>> use_locking = False
>>> scatter_nd_min = ops.ScatterNdMin(use_locking)
>>> output = scatter_nd_min(input_x, indices, updates)
>>> print(output)
[10.  8.  6. 10.  7. 10. 10.  9.]
>>> input_x = Parameter(Tensor(np.ones((4, 4, 4)) * 10, mindspore.int32))
>>> indices = Tensor(np.array([[0], [2]]), mindspore.int32)
>>> updates = Tensor(np.array([[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
...                            [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]), mindspore.int32)
>>> use_locking = False
>>> scatter_nd_min = ops.ScatterNdMin(use_locking)
>>> output = scatter_nd_min(input_x, indices, updates)
>>> print(output)
[[[ 1  1  1  1]
  [ 2  2  2  2]
  [ 3  3  3  3]
  [ 4  4  4  4]]
 [[10 10 10 10]
  [10 10 10 10]
  [10 10 10 10]
  [10 10 10 10]]
 [[ 5  5  5  5]
  [ 6  6  6  6]
  [ 7  7  7  7]
  [ 8  8  8  8]]
 [[10 10 10 10]
  [10 10 10 10]
  [10 10 10 10]
  [10 10 10 10]]]