mindspore.Tensor.addmv

Tensor.addmv(mat, vec, beta=1, alpha=1)[source]

Multiplies matrix mat and vector vec. Input vector is added to the final result.

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

The optional values beta and alpha are the matrix-vector product between mat and vec and the scale factor for the added tensor x respectively. If beta is 0, then x will be ignored.

\[output = β x + α (mat @ vec)\]
Parameters

  • mat (Tensor) – The first tensor to be multiplied. The shape of the tensor is \((N, M)\).

  • vec (Tensor) – The second tensor to be multiplied. The shape of the tensor is \((M,)\).

  • beta (scalar[int, float, bool], optional) – Multiplier for x (β). The beta must be int or float or bool, Default: 1.

  • alpha (scalar[int, float, bool], optional) – Multiplier for mat @ vec (α). The alpha must be int or float or bool, Default: 1.

Returns

Tensor, the shape of the output tensor is \((N,)\), has the same dtype as x.

Raises

  • TypeError – If mat, vec, x is not a Tensor.

  • TypeError – If input tensor and x, mat, ‘vec’ are not the same dtype.

  • ValueError – If mat is not a 2-D Tensor. If x, vec is not a 1-D Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.array([2., 3.]).astype(np.float32))
>>> mat = Tensor(np.array([[2., 5., 3.], [4., 2., 2.]]).astype(np.float32))
>>> vec = Tensor(np.array([3., 2., 4.]).astype(np.float32))
>>> output = x.addmv(mat, vec)
>>> print(output)
[30. 27.]