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Applies the randomized leaky rectified liner unit function, element-wise, as described in the paper:

Usage

nn_rrelu(lower = 1/8, upper = 1/3, inplace = FALSE)

Arguments

lower

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

upper

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

inplace

can optionally do the operation in-place. Default: FALSE

Details

Empirical Evaluation of Rectified Activations in Convolutional Network.

The function is defined as:

$$ \mbox{RReLU}(x) = \left\{ \begin{array}{ll} x & \mbox{if } x \geq 0 \\ ax & \mbox{ otherwise } \end{array} \right. $$

where \(a\) is randomly sampled from uniform distribution \(\mathcal{U}(\mbox{lower}, \mbox{upper})\). See: https://arxiv.org/pdf/1505.00853.pdf

Shape

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

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

Examples

if (torch_is_installed()) {
m <- nn_rrelu(0.1, 0.3)
input <- torch_randn(2)
m(input)
}
#> torch_tensor
#> 0.01 *
#> -1.9460
#> -29.5220
#> [ CPUFloatType{2} ]