mindspore.Tensor.addmv

Tensor.addmv(mat, vec, *, beta=1, alpha=1) → Tensor

Performs a matrix-vector product of mat and vec, and add self to the final result.

If mat is a tensor of size $(N,M)$ , vec is a 1-D tensor of size $M$ , then self must be broadcastable with a 1-D tensor of size $N$ . In this case, output is a 1-D Tensor of size $N$ .

$$output=\beta self+\alpha (mat@vec)$$

Warning

This is an experimental API that is subject to change or deletion.

Parameters

• mat (Tensor) – The first tensor needs to be multiplied.

• vec (Tensor) – The second tensor needs to be multiplied.

Keyword Arguments

• beta (Union[float, int], optional) – Coefficient of self. Default: 1.

• alpha (Union[float, int], optional) – Coefficient of $mat@vec$ . Default: 1.

Returns

Tensor, with a shape of $(N,)$ , and its dtype is the same as self.

Raises

• TypeError – If dtype of self, mat or vec is not tensor.

• TypeError – If dtypes of mat and vec are not the same.

• ValueError – If mat is not a 2-D tensor.

• ValueError – If vec is not a 1-D tensor.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> 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.]