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mindspore.ops.TensorScatterMul

class mindspore.ops.TensorScatterMul

Creates a new tensor by multiplying the values from the positions in input_x indicated by indices, with values from updates. When multiple values are provided for the same index, the result of the update will be to multiply these values respectively. The updates are applied on output Tensor instead of input Parameter.

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

Refer to mindspore.ops.tensor_scatter_mul() for more details.

Inputs:

• input_x (Tensor) - The target 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_x, and the shape of updates should be equal to $indices.shape[:-1]+input\mathrm{\_}x.shape[indices.shape[-1]:]$.

Outputs:

Tensor, has the same shape and type as input_x.

Supported Platforms:

GPU CPU

Examples

>>> 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.TensorScatterMul()
>>> # 5, Perform the multiply operation for the first time:
>>> #      first_input_x = input_x[0][0] * updates[0] = [[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]
>>> # 6, Perform the multiply operation for the second time:
>>> #      second_input_x = input_x[0][0] * updates[1] = [[-0.22, 0.3, 3.6], [0.4, 0.5, -3.2]]
>>> output = op(input_x, indices, updates)
>>> print(output)
[[-0.22  0.3   3.6  ]
 [ 0.4   0.5   -3.2 ]]