nnf_dropout

During training, randomly zeroes some of the elements of the input
tensor with probability <code>p</code> using samples from a Bernoulli
distribution.

Provides functionality to define and train neural networks similar to
'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R
using the 'libtorch' library. Also supports low-level tensor operations and
'GPU' acceleration.

Daniel Falbel

torch

Tensors and Neural Networks with 'GPU' Acceleration

Javier Luraschi

Dmitriy Selivanov

Athos Damiani

Christophe Regouby

Krzysztof Joachimiak

Hamada S. Badr

Sebastian Fischer

Maximilian Pichler

 RStudio

nnf_dropout function

<dl><dt>input</dt>
<dd>the input tensor</dd>
<dt>p</dt>
<dd>probability of an element to be zeroed. Default: 0.5</dd>
<dt>training</dt>
<dd>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></dd>
<dt>inplace</dt>
<dd>If set to <code>TRUE</code>, will do this operation in-place.
Default: <code>FALSE</code></dd></dl>

Arguments

Dropout — nnf_dropout

<dl>

<dt>input</dt>
<dd>the input tensor</dd>


<dt>p</dt>
<dd>probability of an element to be zeroed. Default: 0.5</dd>


<dt>training</dt>
<dd>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></dd>


<dt>inplace</dt>
<dd>If set to <code>TRUE</code>, will do this operation in-place.
Default: <code>FALSE</code></dd>

</dl>

Dropout

nnf_dropout: Dropout

Description

Usage

Arguments