mindspore.ops.TensorScatterMax

class mindspore.ops.TensorScatterMax[源代码]

根据指定的更新值和输入索引,通过最大值运算将结果赋值到输出Tensor中。

更多参考详见 mindspore.ops.tensor_scatter_max()

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的。其rank必须至少为2。

  • updates (Tensor) - 指定与 input_x 取最大值操作的Tensor,其数据类型与输入相同。updates.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:

GPU CPU

样例:

>>> 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.TensorScatterMax()
>>> # 5, Perform the max operation for the first time:
>>> #      first_input_x = Max(input_x[0][0], updates[0]) = [[1.0, 0.3, 3.6], [0.4, 0.5, -3.2]]
>>> # 6, Perform the max operation for the second time:
>>> #      second_input_x = Max(input_x[0][0], updates[1]) = [[2.2, 0.3, 3.6], [0.4, 0.5, -3.2]]
>>> output = op(input_x, indices, updates)
>>> print(output)
[[ 2.2  0.3  3.6]
 [ 0.4  0.5 -3.2]]