# scale¶

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:

\[Out=scale*X+bias\]

`bias_after_scale` is False:

\[Out=scale*(X+bias)\]
Parameters
• 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 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, 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.

Returns

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

Return type

Tensor

Examples

```>>> # 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.]])
```
```>>> # scale with parameter scale as a Tensor
>>> 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.]])
>>> factor = paddle.to_tensor([2], dtype='float32')
>>> res = paddle.scale(data, scale=factor, bias=1.0)
>>> print(res)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[1. , 3. , 5. ],
[7. , 9. , 11.]])
```