\section{V\+R\+Class\+Reward$<$ Input\+Data\+Type, Output\+Data\+Type $>$ Class Template Reference} \label{classmlpack_1_1ann_1_1VRClassReward}\index{V\+R\+Class\+Reward$<$ Input\+Data\+Type, Output\+Data\+Type $>$@{V\+R\+Class\+Reward$<$ Input\+Data\+Type, Output\+Data\+Type $>$}} Implementation of the variance reduced classification reinforcement layer. \subsection*{Public Member Functions} \begin{DoxyCompactItemize} \item \textbf{ V\+R\+Class\+Reward} (const double scale=1, const bool size\+Average=true) \begin{DoxyCompactList}\small\item\em Create the \doxyref{V\+R\+Class\+Reward}{p.}{classmlpack_1_1ann_1_1VRClassReward} object. \end{DoxyCompactList}\item {\footnotesize template$<$class Layer\+Type , class... Args$>$ }\\void \textbf{ Add} (Args... args) \item void \textbf{ Add} (\textbf{ Layer\+Types}$<$$>$ layer) \item {\footnotesize template$<$typename Input\+Type , typename Target\+Type , typename Output\+Type $>$ }\\void \textbf{ Backward} (const Input\+Type \&input, const Target\+Type \&target, Output\+Type \&output) \begin{DoxyCompactList}\small\item\em Ordinary feed backward pass of a neural network. \end{DoxyCompactList}\item Output\+Data\+Type \& \textbf{ Delta} () const \begin{DoxyCompactList}\small\item\em Get the delta. \end{DoxyCompactList}\item Output\+Data\+Type \& \textbf{ Delta} () \begin{DoxyCompactList}\small\item\em Modify the delta. \end{DoxyCompactList}\item bool \textbf{ Deterministic} () const \begin{DoxyCompactList}\small\item\em Get the value of the deterministic parameter. \end{DoxyCompactList}\item bool \& \textbf{ Deterministic} () \begin{DoxyCompactList}\small\item\em Modify the value of the deterministic parameter. \end{DoxyCompactList}\item {\footnotesize template$<$typename Input\+Type , typename Target\+Type $>$ }\\double \textbf{ Forward} (const Input\+Type \&input, const Target\+Type \&target) \begin{DoxyCompactList}\small\item\em Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. \end{DoxyCompactList}\item Output\+Data\+Type \& \textbf{ Output\+Parameter} () const \begin{DoxyCompactList}\small\item\em Get the output parameter. \end{DoxyCompactList}\item Output\+Data\+Type \& \textbf{ Output\+Parameter} () \begin{DoxyCompactList}\small\item\em Modify the output parameter. \end{DoxyCompactList}\item {\footnotesize template$<$typename Archive $>$ }\\void \textbf{ serialize} (Archive \&, const unsigned int) \begin{DoxyCompactList}\small\item\em Serialize the layer. \end{DoxyCompactList}\end{DoxyCompactItemize} \subsection{Detailed Description} \subsubsection*{template$<$typename Input\+Data\+Type = arma\+::mat, typename Output\+Data\+Type = arma\+::mat$>$\newline class mlpack\+::ann\+::\+V\+R\+Class\+Reward$<$ Input\+Data\+Type, Output\+Data\+Type $>$} Implementation of the variance reduced classification reinforcement layer. This layer is meant to be used in combination with the reinforce normal layer (Reinforce\+Normal\+Layer), which expects that an reward\+: (1 for success, 0 otherwise). \begin{DoxyTemplParams}{Template Parameters} {\em Input\+Data\+Type} & Type of the input data (arma\+::colvec, arma\+::mat, arma\+::sp\+\_\+mat or arma\+::cube). \\ \hline {\em Output\+Data\+Type} & Type of the output data (arma\+::colvec, arma\+::mat, arma\+::sp\+\_\+mat or arma\+::cube). \\ \hline \end{DoxyTemplParams} Definition at line 70 of file layer\+\_\+types.\+hpp. \subsection{Constructor \& Destructor Documentation} \mbox{\label{classmlpack_1_1ann_1_1VRClassReward_aa16e0d7bec79c3c855df3f5b6ec45af9}} \index{mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}!V\+R\+Class\+Reward@{V\+R\+Class\+Reward}} \index{V\+R\+Class\+Reward@{V\+R\+Class\+Reward}!mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}} \subsubsection{V\+R\+Class\+Reward()} {\footnotesize\ttfamily \textbf{ V\+R\+Class\+Reward} (\begin{DoxyParamCaption}\item[{const double}]{scale = {\ttfamily 1}, }\item[{const bool}]{size\+Average = {\ttfamily true} }\end{DoxyParamCaption})} Create the \doxyref{V\+R\+Class\+Reward}{p.}{classmlpack_1_1ann_1_1VRClassReward} object. \begin{DoxyParams}{Parameters} {\em scale} & Parameter used to scale the reward. \\ \hline {\em size\+Average} & Take the average over all batches. \\ \hline \end{DoxyParams} \subsection{Member Function Documentation} \mbox{\label{classmlpack_1_1ann_1_1VRClassReward_a8b5234495846c00f6b2c8296ca6bc718}} \index{mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}!Add@{Add}} \index{Add@{Add}!mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}} \subsubsection{Add()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily void \textbf{ Add} (\begin{DoxyParamCaption}\item[{Args...}]{args }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Definition at line 97 of file vr\+\_\+class\+\_\+reward.\+hpp. \mbox{\label{classmlpack_1_1ann_1_1VRClassReward_a1812979d65642be34dd28899d312ce19}} \index{mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}!Add@{Add}} \index{Add@{Add}!mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}} \subsubsection{Add()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily void \textbf{ Add} (\begin{DoxyParamCaption}\item[{\textbf{ Layer\+Types}$<$$>$}]{layer }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Definition at line 104 of file vr\+\_\+class\+\_\+reward.\+hpp. References V\+R\+Class\+Reward$<$ Input\+Data\+Type, Output\+Data\+Type $>$\+::serialize(). \mbox{\label{classmlpack_1_1ann_1_1VRClassReward_a7a5e88245fe9cf5644f846902393e97a}} \index{mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}!Backward@{Backward}} \index{Backward@{Backward}!mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}} \subsubsection{Backward()} {\footnotesize\ttfamily void Backward (\begin{DoxyParamCaption}\item[{const Input\+Type \&}]{input, }\item[{const Target\+Type \&}]{target, }\item[{Output\+Type \&}]{output }\end{DoxyParamCaption})} Ordinary feed backward pass of a neural network. The negative log likelihood layer expectes that the input contains log-\/probabilities for each class. The layer also expects a class index, in the range between 1 and the number of classes, as target when calling the Forward function. \begin{DoxyParams}{Parameters} {\em input} & The propagated input activation. \\ \hline {\em target} & The target vector, that contains the class index in the range between 1 and the number of classes. \\ \hline {\em output} & The calculated error. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1ann_1_1VRClassReward_ae7c8eba5764f021cd93e30efe638e63c}} \index{mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}!Delta@{Delta}} \index{Delta@{Delta}!mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}} \subsubsection{Delta()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily Output\+Data\+Type\& Delta (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the delta. Definition at line 82 of file vr\+\_\+class\+\_\+reward.\+hpp. \mbox{\label{classmlpack_1_1ann_1_1VRClassReward_ad6601342d560219ce951d554e69e5e87}} \index{mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}!Delta@{Delta}} \index{Delta@{Delta}!mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}} \subsubsection{Delta()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily Output\+Data\+Type\& Delta (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Modify the delta. Definition at line 84 of file vr\+\_\+class\+\_\+reward.\+hpp. \mbox{\label{classmlpack_1_1ann_1_1VRClassReward_a9f4103707f4d199ce5594d239b60443e}} \index{mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}!Deterministic@{Deterministic}} \index{Deterministic@{Deterministic}!mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}} \subsubsection{Deterministic()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily bool Deterministic (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the value of the deterministic parameter. Definition at line 87 of file vr\+\_\+class\+\_\+reward.\+hpp. \mbox{\label{classmlpack_1_1ann_1_1VRClassReward_a42d4ee3da432cff20d3a41b8b1ec801c}} \index{mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}!Deterministic@{Deterministic}} \index{Deterministic@{Deterministic}!mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}} \subsubsection{Deterministic()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily bool\& Deterministic (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Modify the value of the deterministic parameter. Definition at line 89 of file vr\+\_\+class\+\_\+reward.\+hpp. \mbox{\label{classmlpack_1_1ann_1_1VRClassReward_ab29558c4430cc17c1f76f92a470fb17a}} \index{mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}!Forward@{Forward}} \index{Forward@{Forward}!mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}} \subsubsection{Forward()} {\footnotesize\ttfamily double Forward (\begin{DoxyParamCaption}\item[{const Input\+Type \&}]{input, }\item[{const Target\+Type \&}]{target }\end{DoxyParamCaption})} Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. \begin{DoxyParams}{Parameters} {\em input} & Input data that contains the log-\/probabilities for each class. \\ \hline {\em target} & The target vector, that contains the class index in the range between 1 and the number of classes. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1ann_1_1VRClassReward_a8bae962cc603d1cab8d80ec78f8d505d}} \index{mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}!Output\+Parameter@{Output\+Parameter}} \index{Output\+Parameter@{Output\+Parameter}!mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}} \subsubsection{Output\+Parameter()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily Output\+Data\+Type\& Output\+Parameter (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the output parameter. Definition at line 77 of file vr\+\_\+class\+\_\+reward.\+hpp. \mbox{\label{classmlpack_1_1ann_1_1VRClassReward_a21d5f745f02c709625a4ee0907f004a5}} \index{mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}!Output\+Parameter@{Output\+Parameter}} \index{Output\+Parameter@{Output\+Parameter}!mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}} \subsubsection{Output\+Parameter()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily Output\+Data\+Type\& Output\+Parameter (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Modify the output parameter. Definition at line 79 of file vr\+\_\+class\+\_\+reward.\+hpp. \mbox{\label{classmlpack_1_1ann_1_1VRClassReward_aa5b3dd8336182f751ebec27167c22415}} \index{mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}!serialize@{serialize}} \index{serialize@{serialize}!mlpack\+::ann\+::\+V\+R\+Class\+Reward@{mlpack\+::ann\+::\+V\+R\+Class\+Reward}} \subsubsection{serialize()} {\footnotesize\ttfamily void serialize (\begin{DoxyParamCaption}\item[{Archive \&}]{, }\item[{const unsigned}]{int }\end{DoxyParamCaption})} Serialize the layer. Referenced by V\+R\+Class\+Reward$<$ Input\+Data\+Type, Output\+Data\+Type $>$\+::\+Add(). The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} \item /var/www/mlpack.\+ratml.\+org/mlpack.\+org/\+\_\+src/mlpack-\/3.\+3.\+0/src/mlpack/methods/ann/layer/\textbf{ layer\+\_\+types.\+hpp}\item /var/www/mlpack.\+ratml.\+org/mlpack.\+org/\+\_\+src/mlpack-\/3.\+3.\+0/src/mlpack/methods/ann/layer/\textbf{ vr\+\_\+class\+\_\+reward.\+hpp}\end{DoxyCompactItemize}