nnf_conv_tbc

Applies a 1-dimensional sequence convolution over an input sequence.
Input and output dimensions are (Time, Batch, Channels) - hence TBC.

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_conv_tbc function

<dl><dt>input</dt>
<dd>input tensor of shape \((\mbox{sequence length} \times
batch \times \mbox{in\_channels})\)</dd>
<dt>weight</dt>
<dd>filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels}
\times \mbox{out\_channels}\))</dd>
<dt>bias</dt>
<dd>bias of shape (\(\mbox{out\_channels}\))</dd>
<dt>pad</dt>
<dd>number of timesteps to pad. Default: 0</dd></dl>

Arguments

Conv_tbc — nnf_conv_tbc

<dl>

<dt>input</dt>
<dd>input tensor of shape \((\mbox{sequence length} \times
batch \times \mbox{in\_channels})\)</dd>


<dt>weight</dt>
<dd>filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels}
\times \mbox{out\_channels}\))</dd>


<dt>bias</dt>
<dd>bias of shape (\(\mbox{out\_channels}\))</dd>


<dt>pad</dt>
<dd>number of timesteps to pad. Default: 0</dd>

</dl>

Conv_tbc

The Learning Leader's Guide to AI Literacy

nnf_conv_tbc: Conv_tbc

Description

Usage

Arguments