mindspore.ops.ScatterNdSub
- class mindspore.ops.ScatterNdSub(use_locking=False)[source]
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]]]