mindspore.ops.addcmul

mindspore.ops.addcmul(input, tensor1, tensor2, value=1)[source]

Performs the element-wise product of tensor tensor1 and tensor tensor2, multiply the result by the scalar value and add it to input_data.

\[output[i] = input[i] + value[i] * (tensor1[i] * tensor2[i])\]
Parameters

  • input (Tensor) – The tensor to be added.

  • tensor1 (Tensor) – The tensor to be multiplied.

  • tensor2 (Tensor) – The tensor to be multiplied.

  • value (Union[Tensor, Number]) – The multiplier for tensor1*tensor2. Default: 1 .

Returns

Tensor, has the same shape and dtype as x1*x2.

Raises

  • TypeError – If dtype of tensor1, tensor2, input is not Tensor.

  • TypeError – If dtype of input is not one of: float32, float16, int32.

  • TypeError – If dtype of tensor1 or tensor2 is not one of: float32, float16, int32.

  • TypeError – If dtype of value is not one of: float32, float16, int32.

  • ValueError – If tensor1 could not be broadcast to a tensor with shape of tensor2.

  • ValueError – If value could not be broadcast to tensors with shapes of tensor1 * tensor2.

  • ValueError – If input could not be broadcast to tensors with shapes of value*(tensor1*tensor2).

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_data = Tensor(np.array([1, 1, 1]), mindspore.float32)
>>> x1 = Tensor(np.array([[1], [2], [3]]), mindspore.float32)
>>> x2 = Tensor(np.array([[1, 2, 3]]), mindspore.float32)
>>> value = Tensor([1], mindspore.float32)
>>> y = ops.addcmul(input_data, x1, x2, value)
>>> print(y)
[[ 2.  3.  4.]
 [ 3.  5.  7.]
 [ 4.  7. 10.]]