RReLU

class torch.nn.RReLU(lower=0.125, upper=0.3333333333333333, inplace=False)

Applies the randomized leaky rectified linear unit function, element-wise.

Method described in the paper: Empirical Evaluation of Rectified Activations in Convolutional Network.

The function is defined as:

\[\text{RReLU}(x) = \begin{cases} x & \text{if } x \geq 0 \\ ax & \text{ otherwise } \end{cases} \]

where \(a\) is randomly sampled from uniform distribution \(\mathcal{U}(\text{lower}, \text{upper})\) during training while during evaluation \(a\) is fixed with \(a = \frac{\text{lower} + \text{upper}}{2}\).

Parameters:

• lower (float) – lower bound of the uniform distribution. Default: \(\frac{1}{8}\)

• upper (float) – upper bound of the uniform distribution. Default: \(\frac{1}{3}\)

• inplace (bool) – can optionally do the operation in-place. Default: False

Shape:

• Input: \((*)\), where \(*\) means any number of dimensions.

• Output: \((*)\), same shape as the input.

Examples:

>>> m = nn.RReLU(0.1, 0.3)
>>> input = torch.randn(2)
>>> output = m(input)