torch: nn_dropout – R documentation

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nn_dropout

Dropout module

Description

During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution. Each channel will be zeroed out independently on every forward call.

Usage

nn_dropout(p = 0.5, inplace = FALSE)

Arguments

`p`	probability of an element to be zeroed. Default: 0.5
`inplace`	If set to `TRUE`, will do this operation in-place. Default: `FALSE`.

Details

This has proven to be an effective technique for regularization and preventing the co-adaptation of neurons as described in the paper Improving neural networks by preventing co-adaptation of feature detectors.

Furthermore, the outputs are scaled by a factor of :math:\frac{1}{1-p} during training. This means that during evaluation the module simply computes an identity function.

Shape

Input: (*). Input can be of any shape
Output: (*). Output is of the same shape as input

Examples

if (torch_is_installed()) {
m <- nn_dropout(p = 0.2)
input <- torch_randn(20, 16)
output <- m(input)

}

torch

Tensors and Neural Networks with 'GPU' Acceleration

v0.3.0

MIT + file LICENSE

Authors

Daniel Falbel [aut, cre, cph], Javier Luraschi [aut], Dmitriy Selivanov [ctb], Athos Damiani [ctb], Christophe Regouby [ctb], Krzysztof Joachimiak [ctb], RStudio [cph]

Initial release