mindspore.ops.Addcmul

class mindspore.ops.Addcmul[源代码]

Performs the element-wise product of tensor x1 and tensor x2, multiply the result by the scalar value and add it to input_data.

\[output[i] = input\_data[i] + value[i] * (x1[i] * x2[i])\]
Inputs:

  • input_data (Tensor) - The tensor to be added, with data type float16, float32 and int32.

  • x1 (Tensor) - The tensor to be multiplied, with data type float16, float32 and int32.

  • x2 (Tensor) - The tensor to be multiplied, with data type float16, float32 and int32.

  • value (Tensor) - The multiplier for tensor x1*x2, with data type float16, float32 and int32.

Outputs:

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

Raises

  • TypeError – If dtype of x1, x2, value, input_data is not tensor.

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

  • TypeError – If dtype of x1 or x2 is not one of: float32, float16, int32.

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

  • ValueError – If x1 could not be broadcast to a tensor with shape of x2.

  • ValueError – If value could not be broadcast to tensors with shapes of x1 * x2.

  • ValueError – If input_data could not be broadcast to tensors with shapes of value*(x1*x2).

Supported Platforms:

Ascend

Examples

>>> 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)
>>> addcmul = ops.Addcmul()
>>> y = addcmul(input_data, x1, x2, value)
>>> print(y)
[[ 2.  3.  4.]
 [ 3.  5.  7.]
 [ 4.  7. 10.]]