paddle. scale ( x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None ) [source]

Scale operator.

Putting scale and bias to the input Tensor as following:

bias_after_scale is True:


bias_after_scale is False:

  • 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) – Name for the operation (optional, default is None). For more information, please refer to Name.


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

Return type



# 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)
# scale with parameter scale as a Tensor
import paddle

data = paddle.randn(shape=[2, 3], dtype='float32')
factor = paddle.to_tensor([2], dtype='float32')
res = paddle.scale(data, scale=factor, bias=1.0)