mindspore.ops.scatter_nd_sub

mindspore.ops.scatter_nd_sub(input_x, indices, updates, use_locking=False)[source]

Perform a sparse subtraction update on input_x based on the specified indices and update values.

\[\text{input_x}[\text{indices}[i, ..., j]] \mathrel{-}= \text{updates}[i, ..., j]\]

Note

Support implicit type conversion and type promotion.
The dimension of indices is at least 2, and its shape must be indices.shape[-1] <= len(indices.shape).
The shape of updates is indices.shape[:-1] + input_x.shape[indices.shape[-1]:].

Parameters

input_x (Union[Parameter, Tensor]) – The input parameter or tensor.
indices (Tensor) – The specified indices.
updates (Tensor) – The update values.
use_locking (bool) – Whether to protect the assignment by a lock. Default: False .

Returns

Tensor

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> input_x = mindspore.Parameter(mindspore.tensor([1, 2, 3, 4, 5, 6, 7, 8],
...                               mindspore.float32), name="x")
>>> indices = mindspore.tensor([[2], [4], [1], [7]], mindspore.int32)
>>> updates = mindspore.tensor([6, 7, 8, 9], mindspore.float32)
>>> output = mindspore.ops.scatter_nd_sub(input_x, indices, updates, False)
>>> print(output)
[ 1. -6. -3.  4. -2.  6.  7. -1.]
>>> input_x = mindspore.Parameter(mindspore.tensor(mindspore.ops.zeros((4, 4, 4)), mindspore.int32))
>>> indices = mindspore.tensor([[0], [2]], mindspore.int32)
>>> updates = mindspore.tensor([[[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)
>>> output = mindspore.ops.scatter_nd_sub(input_x, indices, updates, False)
>>> 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]]]