mindspore.Tensor.scatter_div

Tensor.scatter_div(indices, updates)[source]

Creates a new tensor by dividing the values from the positions in self tensor 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], the variable input_x refers to self tensor. 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

  • 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 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:

GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> input_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, 2.0]).astype('float32'))
>>> output = input_x.scatter_div(indices, updates)
>>> print(output)
[[-0.05  0.3  3.6  ]
 [ 0.4   0.5  -3.2 ]]