torch_conv_tbc

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

torch_conv_tbc function

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

Arguments

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

TEST 

Conv_tbc — torch_conv_tbc

<dl>

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


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


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


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

</dl>

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

torch_conv_tbc: Conv_tbc

Description

Usage

Arguments

TEST