mindspore.ops.ScatterNdSub

class mindspore.ops.ScatterNdSub(use_locking=False)

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

Using given values to update tensor value through the subtraction 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_sub() 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. The shape is \((N, *)\) where \(*\) means any number of additional dimensions.

• indices (Tensor) - The index to do sub operation whose data type must be mindspore.int32. The rank of indices must be at least 2 and indices.shape[-1] <= len(shape).

• updates (Tensor) - The tensor doing the sub operation with input_x, the data type is same as input_x, 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.array([1, 2, 3, 4, 5, 6, 7, 8]), 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_sub = ops.ScatterNdSub(use_locking)
>>> output = scatter_nd_sub(input_x, indices, updates)
>>> print(output)
[ 1. -6. -3.  4. -2.  6.  7. -1.]
>>> input_x = Parameter(Tensor(np.zeros((4, 4, 4)), 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_sub = ops.ScatterNdSub(use_locking)
>>> output = scatter_nd_sub(input_x, indices, updates)
>>> print(output)
[[[-1 -1 -1 -1]
  [-2 -2 -2 -2]
  [-3 -3 -3 -3]
  [-4 -4 -4 -4]]
 [[ 0  0  0  0]
  [ 0  0  0  0]
  [ 0  0  0  0]
  [ 0  0  0  0]]
 [[-5 -5 -5 -5]
  [-6 -6 -6 -6]
  [-7 -7 -7 -7]
  [-8 -8 -8 -8]]
 [[ 0  0  0  0]
  [ 0  0  0  0]
  [ 0  0  0  0]
  [ 0  0  0  0]]]