Hardswish¶

class paddle.nn. Hardswish ( name=None ) [source]

Hardswish activation. Create a callable object of Hardswish. Hardswish is proposed in MobileNetV3, and performs better in computational stability and efficiency compared to swish function. For more details please refer to: https://arxiv.org/pdf/1905.02244.pdf

\[\begin{split}Hardswish(x)= \left\{ \begin{array}{cll} 0 &, & \text{if } x \leq -3 \\ x &, & \text{if } x \geq 3 \\ \frac{x(x+3)}{6} &, & \text{otherwise} \end{array} \right.\end{split}\]

Parameters

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

Shape:

• input: Tensor with any shape.

• output: Tensor with the same shape as input.

Examples

>>> import paddle

>>> x = paddle.to_tensor([-4., 5., 1.])
>>> m = paddle.nn.Hardswish()
>>> out = m(x)
>>> print(out)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[-0.       , 5.        , 0.66666669])

forward ( x )

forward¶

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments

extra_repr ( )

extra_repr¶

Extra representation of this layer, you can have custom implementation of your own layer.