Function vector_norm

python/paddle/tensor/linalg.py:398–654

Calculate the p-order vector norm for certain dimension of Tensor `input`. Returns the vector norm (the 1-norm, the Euclidean or 2-norm, and in general the p-norm) of a given tensor. .. note:: Alias Support: The parameter name ``ord`` can be used as an alias for ``p``, and

(
    x: Tensor,
    p: float = 2.0,
    axis: int | Sequence[int] | None = None,
    keepdim: bool = False,
    name: str | None = None,
    *,
    dtype: paddle._typing.DTypeLike | None = None,
    out: Tensor | None = None,
)

@param_two_alias(["p", "ord"], ["axis", "dim"])
def vector_norm(
    x: Tensor,
    p: float = 2.0,
    axis: int | Sequence[int] | None = None,
    keepdim: bool = False,
    name: str | None = None,
    *,
    dtype: paddle._typing.DTypeLike | None = None,
    out: Tensor | None = None,
) -> Tensor:
    """
    Calculate the p-order vector norm for certain  dimension of Tensor `input`.
    Returns the vector norm (the 1-norm, the Euclidean or 2-norm, and in general the p-norm)
    of a given tensor.

    .. note::
        Alias Support: The parameter name ``ord`` can be used as an alias for ``p``, and ``dim`` can be used as an alias for ``axis``.

    Args:
        x (Tensor): Tensor, data type float32, float64.
        p (int|float, optional): None for porder=2.0. Default None.
        axis (int|list|tuple, optional): None for last dimension. Default None.
        keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.
        name (str|None, optional): The default value is None. Normally there is no need for
            user to set this property. For more information, please refer to :ref:`api_guide_Name`.
        dtype (paddle._typing.DTypeLike, optional): It may be used to perform the computation in a more precise dtype. It is semantically equivalent to calling linalg.vector_norm(x.to(dtype)) but it is faster in some cases. Default None.
        out (Tensor| None, optional): output tensor. Ignored if None. Default: None.

    Returns:
        Tensor: results of vector_norm operation on the specified axis of input tensor,
        it's data type is the same as input's Tensor.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> import numpy as np
            >>> x = paddle.arange(24, dtype="float32").reshape([2, 3, 4]) - 12
            >>> print(x)
            Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
            [[[-12., -11., -10., -9. ],
              [-8. , -7. , -6. , -5. ],
              [-4. , -3. , -2. , -1. ]],
             [[ 0. ,  1. ,  2. ,  3. ],
              [ 4. ,  5. ,  6. ,  7. ],
              [ 8. ,  9. ,  10.,  11.]]])
            >>> out_vector_norm = paddle.linalg.vector_norm(x=x, p=2, axis=None, keepdim=False)
            >>> print(out_vector_norm)
            Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
            34.)
            >>> out_vector_norm = paddle.linalg.vector_norm(x=x, p=0, axis=[0, 1], keepdim=False)
            >>> print(out_vector_norm)
            Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
            [5., 6., 6., 6.])
            >>> out_vector_norm = paddle.linalg.vector_norm(x=x, p=float("inf"), axis=[1, 2], keepdim=False)
            >>> print(out_vector_norm)
            Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
            [12., 11.])