ThresholdedReLU¶

class paddle.nn. ThresholdedReLU ( threshold: float = 1.0, value: float = 0.0, name: str | None = None ) [source]

Thresholded ReLU Activation

\[\begin{split}ThresholdedReLU(x) = \left\{ \begin{array}{rl} x,& \text{if } \ x > threshold \\ value,& \text{otherwise} \end{array} \right.\end{split}\]

Parameters

• threshold (float, optional) – The value of threshold for ThresholdedReLU. Default is 1.0

• value (float, optional) – The value to replace with when x is less than threshold. Default is 0.0

• name (str|None, 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([2., 0., 1.])
>>> m = paddle.nn.ThresholdedReLU()
>>> out = m(x)
>>> print(out)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[2., 0., 0.])

forward ( x: Tensor ) → Tensor

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 ( ) → str

extra_repr¶

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