mindspore.ops.TensorScatterMul
- class mindspore.ops.TensorScatterMul[源代码]
根据指定的更新值 updates 和输入索引 indices ,使用乘法运算更新输入Tensor的值。当同一索引有不同更新值时,更新的结果将是累积乘法的结果。此操作与
mindspore.ops.ScatterNdMul
类似,但更新后的结果是返回一个新的输出Tensor,而不是直接更新 input_x 。\[output\left [indices \right ] = input\_x\times update\]更多参考相见
mindspore.ops.tensor_scatter_mul()
。- 输入:
input_x (Tensor) - 输入Tensor。 input_x 的维度必须大于等于indices.shape[-1]。
indices (Tensor) - input_x 执行scatter操作的目标索引,数据类型为int32或int64,rank必须大于等于2。
updates (Tensor) - 指定与 input_x 相乘操作的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:
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.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 ]]