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 float32, float64, int8, int16, int32, int64, uint8.
scale(float|Tensor): The scale factor of the input, it should be a float number or a Tensor with shape [1] 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, optional): Activation applied to the output such as tanh, softmax, sigmoid, relu.
name(str, 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`
Returns:
Tensor: Output tensor of scale operator, with shape and data type same as input.
Examples:
.. code-block:: python
# scale as a float32 number
import paddle
data = paddle.randn(shape=[2,3], dtype='float32')
res = paddle.scale(data, scale=2.0, bias=1.0)
.. code-block:: python
# scale with parameter scale as a Tensor
import paddle
data = paddle.randn(shape=[2, 3], dtype='float32')
