Function scale

python/paddle/tensor/math.py:237–358

Scale operator. Putting scale and bias to the input Tensor as following: ``bias_after_scale`` is True: .. math:: Out=scale*X+bias ``bias_after_scale`` is False: .. math:: Out=scale*(X+bias) Args: x (Te

(
    x: Tensor,
    scale: float | Tensor = 1.0,
    bias: float = 0.0,
    bias_after_scale: bool = True,
    act: str | None = None,
    name: str | None = None,
    *,
    out: Tensor | None = None,
)

def scale(
    x: Tensor,
    scale: float | Tensor = 1.0,
    bias: float = 0.0,
    bias_after_scale: bool = True,
    act: str | None = None,
    name: str | None = None,
    *,
    out: Tensor | None = None,
) -> Tensor:
    """
    Scale operator.

    Putting scale and bias to the input Tensor as following:

    ``bias_after_scale`` is True:

    .. math::
                            Out=scale*X+bias

    ``bias_after_scale`` is False:

    .. math::
                            Out=scale*(X+bias)

    Args:
        x (Tensor): Input N-D Tensor of scale operator. Data type can be bfloat16, float16, float32, float64, int8, int16, int32,
            int64, uint8, complex64, complex128.
        scale (float|Tensor): The scale factor of the input, it should be a float number or a 0-D Tensor with shape [] and data type as float32.
        bias (float): The bias to be put on the input.
        bias_after_scale (bool): Apply bias addition after or before scaling. It is useful for numeric stability in some circumstances.
        act (str|None, optional): Activation applied to the output such as tanh, softmax, sigmoid, relu.
        name (str|None, optional): Name for the operation. Default: None. For more information, please refer to :ref:`api_guide_Name`.

    Keyword Args:
        out (Tensor|None, optional): The output tensor. If set, the result will be stored in this Tensor. Default: None.

    Returns:
        Tensor: Output Tensor of scale operator, with shape and data type same as input.

    Examples:
        .. code-block:: pycon

            >>> # scale as a float32 number
            >>> import paddle

            >>> data = paddle.arange(6).astype("float32").reshape([2, 3])
            >>> print(data)
            Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
            [[0., 1., 2.],
             [3., 4., 5.]])
            >>> res = paddle.scale(data, scale=2.0, bias=1.0)
            >>> print(res)
            Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
            [[1. , 3. , 5. ],
             [7. , 9. , 11.]])

        .. code-block:: pycon