mindspore.ops.TensorScatterMin

class mindspore.ops.TensorScatterMin[源代码]

根据指定的更新值 updates 和输入索引 indices ，计算原值与更新值的较小值并更新原值，返回更新后的Tensor。

更多参考详见 mindspore.ops.tensor_scatter_min()。

\[output\left [indices \right ] = \min(input\_x, update)\]
输入：

  • input_x (Tensor) - 输入Tensor。 input_x 的维度必须不小于indices.shape[-1]。

  • indices (Tensor) - 输入Tensor的索引，数据类型为int32或int64。其rank必须至少为2。

  • updates (Tensor) - 指定与 input_x 取最小值的Tensor，其数据类型与 input_x 相同，并且shape应等于 \(indices.shape[:-1] + input\_x.shape[indices.shape[-1]:]\) 。

输出：

Tensor，shape和数据类型与输入 input_x 相同。

支持平台：

Ascend GPU CPU

样例：

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, 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)
>>> # Next, demonstrate the approximate operation process of this operator:
>>> # 1, indices[0] = [0, 0], indices[1] = [0, 0]
>>> # 2, And input_x[0, 0] = -0.1
>>> # 3, So input_x[indices] = [-0.1, -0.1]
>>> # 4, Satisfy the above formula: input_x[indices].shape=(2) == updates.shape=(2)
>>> op = ops.TensorScatterMin()
>>> # 5, Perform the min operation for the first time:
>>> #      first_input_x = Min(input_x[0][0], updates[0]) = [[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]
>>> # 6, Perform the min operation for the second time:
>>> #      second_input_x = Min(input_x[0][0], updates[1]) = [[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]
>>> output = op(input_x, indices, updates)
>>> print(output)
[[ -0.1  0.3  3.6]
 [ 0.4  0.5 -3.2]]