mindspore.ops.tensor_scatter_div

mindspore.ops.tensor_scatter_div(input_x, indices, updates)[source]

Creates a new tensor by dividing the values from the positions in input_x indicated by indices, with values from updates. When divided values are provided for the same index, the result of the update will be to divided these values respectively. 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 input_x[indices]. For more details, see use cases.

Note

If some values of the indices are out of bound, instead of raising an index error, the corresponding updates will not be updated to input_x.
The operator can’t handle division by 0 exceptions, so the user needs to make sure there is no 0 value in updates.

Parameters

input_x (Tensor) – The target tensor. The dimension of input_x must be no less than indices.shape[-1].
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] + input_x.shape[indices.shape[-1]:].

Returns

Tensor, has the same shape and type as input_x.

Raises

TypeError – If dtype of indices is neither int32 nor int64.
ValueError – If length of shape of input_x is less than the last dimension of shape of indices.

Supported Platforms:: GPU CPU

Examples

>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor, nn, ops
>>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> indices = Tensor(np.array([[0, 0], [0, 0]]), mindspore.int32)
>>> updates = Tensor(np.array([1.0, 2.0]), mindspore.float32)
>>> output = ops.tensor_scatter_div(input_x, indices, updates)
>>> print(output)
[[-0.05  0.3  3.6  ]
 [ 0.4   0.5  -3.2 ]]