nnf_dropout3d

Randomly zero out entire channels (a channel is a 3D feature map,
e.g., the \(j\)-th channel of the \(i\)-th sample in the
batched input is a 3D tensor \(input[i, j]\)) of the input tensor).
Each channel will be zeroed out independently on every forward call 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) <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

 RStudio

nnf_dropout3d function

<dl><dt>input</dt>
<dd>the input tensor</dd>
<dt>p</dt>
<dd>probability of a channel 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

Dropout3d — nnf_dropout3d

<dl>

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


<dt>p</dt>
<dd>probability of a channel 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>

nnf_dropout3d: Dropout3d

Description

Usage

Arguments