nnf_triplet_margin_with_distance_loss

See <code>nn_triplet_margin_with_distance_loss()</code>

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_triplet_margin_with_distance_loss function

<dl><dt>anchor</dt>
<dd>the anchor input tensor</dd>
<dt>positive</dt>
<dd>the positive input tensor</dd>
<dt>negative</dt>
<dd>the negative input tensor</dd>
<dt>distance_function</dt>
<dd>(callable, optional): A nonnegative, real-valued function that
quantifies the closeness of two tensors. If not specified,
<code>nn_pairwise_distance()</code> will be used. Default: <code>None</code></dd>
<dt>margin</dt>
<dd>Default: 1.</dd>
<dt>swap</dt>
<dd>The distance swap is described in detail in the paper Learning shallow
convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
Default: <code>FALSE</code>.</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

Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss

<dl>

<dt>anchor</dt>
<dd>the anchor input tensor</dd>


<dt>positive</dt>
<dd>the positive input tensor</dd>


<dt>negative</dt>
<dd>the negative input tensor</dd>


<dt>distance_function</dt>
<dd>(callable, optional): A nonnegative, real-valued function that
quantifies the closeness of two tensors. If not specified,
<code>nn_pairwise_distance()</code> will be used. Default: <code>None</code></dd>


<dt>margin</dt>
<dd>Default: 1.</dd>


<dt>swap</dt>
<dd>The distance swap is described in detail in the paper Learning shallow
convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
Default: <code>FALSE</code>.</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_triplet_margin_with_distance_loss: Triplet margin with distance loss

Description

Usage

Arguments