mindspore.ops.addmv

mindspore.ops.addmv(input, mat, vec, *, beta=1, alpha=1)[source]

Multiply the matrix mat and vector vec , and then add the result to the input .

Note

If mat is a \((N, M)\) tensor, vec is a 1-D tensor of size \(M\), then input must be broadcastable with a 1-D tensor of size \(N\), out will be a 1-D tensor of size \(N\).
If beta is 0, input will be ignored.

\[output = β input + α (mat @ vec)\]

Parameters

input (Tensor) – The input tensor.
mat (Tensor) – The matrix tensor to be multiplied.
vec (Tensor) – The vector tensor to be multiplied.

Keyword Arguments

beta (scalar[int, float, bool], optional) – Scale factor for input . Default 1 .
alpha (scalar[int, float, bool], optional) – Scale factor for ( mat @ vec ). Default 1 .

Returns

Tensor

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> input = mindspore.tensor([2., 3.])
>>> mat = mindspore.tensor([[2., 5., 3.], [4., 2., 2.]])
>>> vec = mindspore.tensor([3., 2., 4.])
>>> output = mindspore.ops.addmv(input, mat, vec)
>>> print(output)
[30. 27.]