mindspore.ops.tensor_scatter_min

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

By comparing the value at the position indicated by indices in input_x with the value in the updates, the value at the index will eventually be equal to the smallest one to create a new tensor.

The last axis of the index is the depth of each index vector. 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 case below.

Note

If some values of the indices are out of range, instead of raising an index error, the corresponding updates will not be hw to input_x.

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 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.

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import 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, 2.2]), mindspore.float32)
>>> output = ops.tensor_scatter_min(input_x, indices, updates)
>>> print(output)
[[ -0.1  0.3  3.6]
[ 0.4  0.5 -3.2]]