mindspore.Tensor.scatter_sub

View Source On Gitee
Tensor.scatter_sub(indices, updates)[source]

Creates a new tensor by subtracting the values from the positions in self tensor indicated by indices, with values from updates. When multiple values are provided for the same index, the result of the update will be to subtract these values respectively. This operation is almost equivalent to using mindspore.ops.ScatterNdSub , except that the updates are applied on output Tensor instead of input Parameter.

The last axis of indices is the depth of each index vectors. For each index vector, there must be a corresponding value in updates. The shape of updates should be equal to the shape of self[indices]. For more details, see use cases.

Note

On GPU, if some values of the indices are out of bound, instead of raising an index error, the corresponding updates will not be updated to self tensor. On CPU, if some values of the indices are out of bound, raising an index error. On Ascend, out of bound checking is not supported, if some values of the indices are out of bound, unknown errors may be caused.

Parameters

  • indices (Tensor) – The index of input tensor whose data type is int32 or int64. The rank must be at least 2.

  • updates (Tensor) – The tensor to update the input tensor, has the same type as input, and updates.shape should be equal to indices.shape[:-1] + self.shape[indices.shape[-1]:].

Returns

Tensor, has the same shape and type as self tensor.

Raises

  • TypeError – If dtype of indices is neither int32 nor int64.

  • ValueError – If length of shape of self tensor is less than the last dimension of shape of indices.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]).astype('float32'))
>>> indices = Tensor(np.array([[0, 0], [0, 0]]).astype('int32'))
>>> updates = Tensor(np.array([1.0, 2.2]).astype('float32'))
>>> output = x.scatter_sub(indices, updates)
>>> print(output)
[[-3.3000002  0.3        3.6      ]
[ 0.4        0.5       -3.2      ]]