Softplus¶

class paddle.nn. Softplus ( beta=1, threshold=20, name=None ) [source]

Softplus Activation

\[\begin{split}softplus(x)=\begin{cases} \frac{1}{\beta} * \log(1 + e^{\beta * x}),&x\leqslant\frac{\varepsilon}{\beta};\\ x,&x>\frac{\varepsilon}{\beta}. \end{cases}\end{split}\]

Parameters

• beta (float, optional) – The value of \(\beta\) for Softplus. Default is 1

• threshold (float, optional) – The value of \(\varepsilon\) for Softplus. Default is 20

• name (str, optional) – For details, please refer to Name. Generally, no setting is required. Default: None.

Shape:

• input: Tensor with any shape.

• output: Tensor with the same shape as input.

Examples

>>> import paddle

>>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3], dtype='float32')
>>> m = paddle.nn.Softplus()
>>> out = m(x)
>>> print(out)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.51301527, 0.59813893, 0.74439669, 0.85435522])

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.