mindspore.Tensor.scatter_min
- Tensor.scatter_min(indices, updates)[source]
By comparing the value at the position indicated by indices in self tensor 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 updated to input_x.
- Parameters
- 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:
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_min(indices, updates) >>> print(output) [[ -0.1 0.3 3.6] [ 0.4 0.5 -3.2]]