nnf_multilabel_soft_margin_loss

Creates a criterion that optimizes a multi-label one-versus-all loss based on
max-entropy, between input x and target y of size (N, C).

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_multilabel_soft_margin_loss function

<dl><dt>input</dt>
<dd>tensor (N,*) where ** means, any number of additional dimensions</dd>
<dt>target</dt>
<dd>tensor (N,*) , same shape as the input</dd>
<dt>weight</dt>
<dd>weight tensor to apply on the loss.</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

Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss

<dl>

<dt>input</dt>
<dd>tensor (N,*) where ** means, any number of additional dimensions</dd>


<dt>target</dt>
<dd>tensor (N,*) , same shape as the input</dd>


<dt>weight</dt>
<dd>weight tensor to apply on the loss.</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>

The Learning Leader's Guide to AI Literacy

nnf_multilabel_soft_margin_loss: Multilabel_soft_margin_loss

Description

Usage

Arguments