Document feedback

Question document fragment

When a question document fragment contains a formula, it is displayed as a space.

Submission type

issue

It's a little complicated...

I'd like to ask someone.

Please select the submission type

Problem type

Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

- Copyright infringement.

Please select the type of question

Problem description

Describe the bug so that we can quickly locate the problem.

Submit

Document feedback

mindspore.ops.tensor_scatter_div

mindspore.ops.tensor_scatter_div(input_x, indices, updates)

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 Examples.

$$output\left[indices\right]=input\mathrm{\_}x÷update$$

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.

• 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 input 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_x tensor, has the same type as input_x. And the shape of updates should be equal to $indices.shape[:-1]+input\mathrm{\_}x.shape[indices.shape[-1]:]$.

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.

• RuntimeError – If a value of indices is not in input_x.

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 ]]