nnf_margin_ranking_loss

Creates a criterion that measures the loss given inputs x1 , x2 , two 1D
mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1).

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_margin_ranking_loss function

<dl><dt>input1</dt>
<dd>the first tensor</dd>
<dt>input2</dt>
<dd>the second input tensor</dd>
<dt>target</dt>
<dd>the target tensor</dd>
<dt>margin</dt>
<dd>Has a default value of 00 .</dd>
<dt>reduction</dt>
<dd>(string, optional) – Specifies the reduction to apply to the
output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
the sum of the output will be divided by the number of elements in the output,
'sum': the output will be summed. Default: 'mean'</dd></dl>

Arguments

Margin_ranking_loss — nnf_margin_ranking_loss

<dl>

<dt>input1</dt>
<dd>the first tensor</dd>


<dt>input2</dt>
<dd>the second input tensor</dd>


<dt>target</dt>
<dd>the target tensor</dd>


<dt>margin</dt>
<dd>Has a default value of 00 .</dd>


<dt>reduction</dt>
<dd>(string, optional) – Specifies the reduction to apply to the
output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
the sum of the output will be divided by the number of elements in the output,
'sum': the output will be summed. Default: 'mean'</dd>

</dl>

nnf_margin_ranking_loss: Margin_ranking_loss

Description

Usage

Arguments