nnf_bilinear

Applies a bilinear transformation to the incoming data:
\(y = x_1 A x_2 + b\)

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_bilinear function

<dl><dt>input1</dt>
<dd>\((N, *, H_{in1})\) where \(H_{in1}=\mbox{in1\_features}\)
and \(*\) means any number of additional dimensions.
All but the last dimension of the inputs should be the same.</dd>
<dt>input2</dt>
<dd>\((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\)</dd>
<dt>weight</dt>
<dd>\((\mbox{out\_features}, \mbox{in1\_features},
\mbox{in2\_features})\)</dd>
<dt>bias</dt>
<dd>\((\mbox{out\_features})\)</dd></dl>

Arguments

Bilinear — nnf_bilinear

<dl>

<dt>input1</dt>
<dd>\((N, *, H_{in1})\) where \(H_{in1}=\mbox{in1\_features}\)
and \(*\) means any number of additional dimensions.
All but the last dimension of the inputs should be the same.</dd>


<dt>input2</dt>
<dd>\((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\)</dd>


<dt>weight</dt>
<dd>\((\mbox{out\_features}, \mbox{in1\_features},
\mbox{in2\_features})\)</dd>


<dt>bias</dt>
<dd>\((\mbox{out\_features})\)</dd>

</dl>

nnf_bilinear: Bilinear

Description

Usage

Value

Arguments