\name{autopar}
\alias{autopar}
\title{parameters for automatic hyperparameters optimization}
\description{
List of parameters to allow multi deep neural network automatic hyperparameters tuning with Particle Swarm Optimization\cr
Not mandatory (the list is preset and all arguments are initialized with default value) but it is advisable to adjust some important arguments for performance reasons (including processing time)
}
\arguments{
\item{psopartpopsize}{ number of particles in swarm, the main argument that should be tuned (default value 8, which is quite low)\cr
#tuning priority 1}

\item{psoxxx}{ see \link{pso} for other PSO specific arguments details}

\item{numiterations}{ number of convergence steps between particles (hyperparameters), default value 3)\cr
#tuning priority 1}

\item{auto_modexec}{ if \sQuote{TRUE} the type of Neural Net optimization will be randomly choosen between \sQuote{trainwgrad} and \sQuote{trainwpso} for each particle\cr
default value is \sQuote{FALSE} (so default value of argument \sQuote{modexec} in \link{automl_train_manual} function)\cr
the value can be forced if defined in \link{hpar} list}

\item{auto_minibatchsize}{ see below}
\item{auto_minibatchsize_min}{ see below}
\item{auto_minibatchsize_max}{ \sQuote{auto_minibatch} default value \sQuote{TRUE} for automatic adjustment of \sQuote{minibatchsize} argument in \link{automl_train_manual} function\cr
the minimum and maximum value for \sQuote{minibatchsize} corespond to 2 to the power value (default 0 for \sQuote{auto_minibatchsize_min} and 9 for \sQuote{auto_minibatchsize_max})}

\item{auto_learningrate}{ see below}
\item{auto_learningrate_min}{ see below}
\item{auto_learningrate_max}{ \sQuote{auto_learningrate} default value \sQuote{TRUE} for automatic adjustment of \sQuote{learningrate} argument in \link{automl_train_manual} function\cr
the minimum and maximum value for \sQuote{learningrate} correspond to 10 to the power negative value (default -5 for \sQuote{auto_learningrate_min} and -2 for \sQuote{auto_learningrate_max})}

\item{auto_beta1}{ see below}
\item{auto_beta2}{ \sQuote{auto_beta1} and \sQuote{auto_beta2} default value \sQuote{TRUE} for automatic adjustment of \sQuote{beta1} and \sQuote{beta2} argument in \link{automl_train_manual} function}

\item{auto_psopartpopsize}{ see below}
\item{auto_psopartpopsize_min}{ see below}
\item{auto_psopartpopsize_max}{ \sQuote{auto_psopartpopsize} default value \sQuote{TRUE} for automatic adjustment of \sQuote{psopartpopsize} argument in \link{automl_train_manual} function\cr
the minimum and maximum value for \sQuote{learningrate} ; default 2 for \sQuote{auto_psopartpopsize_min} and 50 for \sQuote{auto_psopartpopsize_max})}

\item{auto_lambda}{ see below}
\item{auto_lambda_min}{ see below}
\item{auto_lambda_max}{ \sQuote{auto_lambda} default value \sQuote{FALSE} for automatic adjustment of \sQuote{lambda} regularization argument in \link{automl_train_manual} function\cr
the minimum and maximum value for \sQuote{lambda} correspond to 10 to the power value  (default -2) for \sQuote{auto_lambda_min} and (default 3) for \sQuote{auto_lambda_max})}

\item{seed}{ seed for reproductibility (default 4)}

\item{nbcores}{ number of cores used to parallelize particles optimization, not available on Windows (default 1, automatically reduced if not enough cores)}

\item{verbose}{ to display or not the costs at each iteration for each particle (default TRUE)}\cr

\emph{back to \link{automl_train}}
}