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)
}