\section{Softmax$<$ Input\+Data\+Type, Output\+Data\+Type $>$ Class Template Reference} \label{classmlpack_1_1ann_1_1Softmax}\index{Softmax$<$ Input\+Data\+Type, Output\+Data\+Type $>$@{Softmax$<$ Input\+Data\+Type, Output\+Data\+Type $>$}} Implementation of the \doxyref{Softmax}{p.}{classmlpack_1_1ann_1_1Softmax} layer. \subsection*{Public Member Functions} \begin{DoxyCompactItemize} \item \textbf{ Softmax} () \begin{DoxyCompactList}\small\item\em Create the \doxyref{Softmax}{p.}{classmlpack_1_1ann_1_1Softmax} object. \end{DoxyCompactList}\item {\footnotesize template$<$typename eT $>$ }\\void \textbf{ Backward} (const arma\+::\+Mat$<$ eT $>$ \&input, const arma\+::\+Mat$<$ eT $>$ \&gy, arma\+::\+Mat$<$ eT $>$ \&g) \begin{DoxyCompactList}\small\item\em Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f. \end{DoxyCompactList}\item Input\+Data\+Type \& \textbf{ Delta} () const \begin{DoxyCompactList}\small\item\em Get the delta. \end{DoxyCompactList}\item Input\+Data\+Type \& \textbf{ Delta} () \begin{DoxyCompactList}\small\item\em Modify the delta. \end{DoxyCompactList}\item {\footnotesize template$<$typename Input\+Type , typename Output\+Type $>$ }\\void \textbf{ Forward} (const Input\+Type \&input, Output\+Type \&output) \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\+::\+Softmax$<$ Input\+Data\+Type, Output\+Data\+Type $>$} Implementation of the \doxyref{Softmax}{p.}{classmlpack_1_1ann_1_1Softmax} layer. The softmax function takes as input a vector of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of the input numbers. It should be used for inference only and not with N\+LL loss (use \doxyref{Log\+Soft\+Max}{p.}{classmlpack_1_1ann_1_1LogSoftMax} instead). \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 38 of file softmax.\+hpp. \subsection{Constructor \& Destructor Documentation} \mbox{\label{classmlpack_1_1ann_1_1Softmax_ada0b97a2580d50b6ea865083b353e2a3}} \index{mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}!Softmax@{Softmax}} \index{Softmax@{Softmax}!mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}} \subsubsection{Softmax()} {\footnotesize\ttfamily \textbf{ Softmax} (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} Create the \doxyref{Softmax}{p.}{classmlpack_1_1ann_1_1Softmax} object. \subsection{Member Function Documentation} \mbox{\label{classmlpack_1_1ann_1_1Softmax_a78dbad83871f43db1975e45a9a69c376}} \index{mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}!Backward@{Backward}} \index{Backward@{Backward}!mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}} \subsubsection{Backward()} {\footnotesize\ttfamily void Backward (\begin{DoxyParamCaption}\item[{const arma\+::\+Mat$<$ eT $>$ \&}]{input, }\item[{const arma\+::\+Mat$<$ eT $>$ \&}]{gy, }\item[{arma\+::\+Mat$<$ eT $>$ \&}]{g }\end{DoxyParamCaption})} Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f. Using the results from the feed forward pass. \begin{DoxyParams}{Parameters} {\em input} & The propagated input activation. \\ \hline {\em gy} & The backpropagated error. \\ \hline {\em g} & The calculated gradient. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1ann_1_1Softmax_aa02f12f0f3e5fea14c9fecf889b3103a}} \index{mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}!Delta@{Delta}} \index{Delta@{Delta}!mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}} \subsubsection{Delta()\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} {\footnotesize\ttfamily Input\+Data\+Type\& Delta (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [inline]}} Get the delta. Definition at line 76 of file softmax.\+hpp. \mbox{\label{classmlpack_1_1ann_1_1Softmax_a6201406598916738050e1b6caedea03b}} \index{mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}!Delta@{Delta}} \index{Delta@{Delta}!mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}} \subsubsection{Delta()\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} {\footnotesize\ttfamily Input\+Data\+Type\& Delta (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} Modify the delta. Definition at line 78 of file softmax.\+hpp. References Softmax$<$ Input\+Data\+Type, Output\+Data\+Type $>$\+::serialize(). \mbox{\label{classmlpack_1_1ann_1_1Softmax_a09440df0a90bdcc766e56e097d91205b}} \index{mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}!Forward@{Forward}} \index{Forward@{Forward}!mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}} \subsubsection{Forward()} {\footnotesize\ttfamily void Forward (\begin{DoxyParamCaption}\item[{const Input\+Type \&}]{input, }\item[{Output\+Type \&}]{output }\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 used for evaluating the specified function. \\ \hline {\em output} & Resulting output activation. \\ \hline \end{DoxyParams} \mbox{\label{classmlpack_1_1ann_1_1Softmax_a8bae962cc603d1cab8d80ec78f8d505d}} \index{mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}!Output\+Parameter@{Output\+Parameter}} \index{Output\+Parameter@{Output\+Parameter}!mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}} \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 71 of file softmax.\+hpp. \mbox{\label{classmlpack_1_1ann_1_1Softmax_a21d5f745f02c709625a4ee0907f004a5}} \index{mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}!Output\+Parameter@{Output\+Parameter}} \index{Output\+Parameter@{Output\+Parameter}!mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}} \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 73 of file softmax.\+hpp. \mbox{\label{classmlpack_1_1ann_1_1Softmax_aa5b3dd8336182f751ebec27167c22415}} \index{mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}!serialize@{serialize}} \index{serialize@{serialize}!mlpack\+::ann\+::\+Softmax@{mlpack\+::ann\+::\+Softmax}} \subsubsection{serialize()} {\footnotesize\ttfamily void serialize (\begin{DoxyParamCaption}\item[{Archive \&}]{, }\item[{const unsigned}]{int }\end{DoxyParamCaption})} Serialize the layer. Referenced by Softmax$<$ Input\+Data\+Type, Output\+Data\+Type $>$\+::\+Delta(). The documentation for this class was generated from the following file\+:\begin{DoxyCompactItemize} \item /var/www/mlpack.\+ratml.\+org/mlpack.\+org/\+\_\+src/mlpack-\/git/src/mlpack/methods/ann/layer/\textbf{ softmax.\+hpp}\end{DoxyCompactItemize}