parameters for automatic hyperparameters optimization

List of parameters to allow multi deep neural network automatic hyperparameters tuning with Particle Swarm Optimization
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

psopartpopsize	number of particles in swarm, the main argument that should be tuned (default value 8, which is quite low) #tuning priority 1
psoxxx	see pso for other PSO specific arguments details
numiterations	number of convergence steps between particles (hyperparameters), default value 3) #tuning priority 1
auto_modexec	if ‘TRUE’ the type of Neural Net optimization will be randomly choosen between ‘trainwgrad’ and ‘trainwpso’ for each particle default value is ‘FALSE’ (so default value of argument ‘modexec’ in automl_train_manual function, actually ‘trainwgrad’ as default is more suited to large data volume) the value can be forced if defined in hpar list
auto_runtype	if ‘2steps’ the 2 following steps will be run automatically (default value is ‘normal’): 1st overfitting, the goal is performance 2nd regularization, the goal is generalization nb: ‘overfitting’ or ‘regularization’ may be directly specified to avoid the 2 steps
auto_minibatchsize	see below
auto_minibatchsize_min	see below
auto_minibatchsize_max	‘auto_minibatch’ default value ‘TRUE’ for automatic adjustment of ‘minibatchsize’ argument in automl_train_manual function the minimum and maximum value for ‘minibatchsize’ corespond to 2 to the power value (default 0 for ‘auto_minibatchsize_min’ and 9 for ‘auto_minibatchsize_max’)
auto_learningrate	see below
auto_learningrate_min	see below
auto_learningrate_max	‘auto_learningrate’ default value ‘TRUE’ for automatic adjustment of ‘learningrate’ argument in automl_train_manual function the minimum and maximum value for ‘learningrate’ correspond to 10 to the power negative value (default -5 for ‘auto_learningrate_min’ and -2 for ‘auto_learningrate_max’)
auto_beta1	see below
auto_beta2	‘auto_beta1’ and ‘auto_beta2’ default value ‘TRUE’ for automatic adjustment of ‘beta1’ and ‘beta2’ argument in automl_train_manual function
auto_psopartpopsize	see below
auto_psopartpopsize_min	see below
auto_psopartpopsize_max	‘auto_psopartpopsize’ default value ‘TRUE’ for automatic adjustment of ‘psopartpopsize’ argument in automl_train_manual function (concern only ‘modexec’ set to ‘trainwpso’) the minimum and maximum value for ‘psopartpopsize’ ; default 2 for ‘auto_psopartpopsize_min’ and 50 for ‘auto_psopartpopsize_max’)
auto_lambda	see below
auto_lambda_min	see below
auto_lambda_max	‘auto_lambda’ default value ‘FALSE’ for automatic adjustment of ‘lambda’ regularization argument in automl_train_manual function the minimum and maximum value for ‘lambda’ correspond to 10 to the power value (default -2) for ‘auto_lambda_min’ and (default 4) for ‘auto_lambda_max’)
auto_psovelocitymaxratio	see below
auto_psovelocitymaxratio_min	see below
auto_psovelocitymaxratio_max	‘auto_psovelocitymaxratio’ default value ‘TRUE’ for automatic adjustment of ‘psovelocitymaxratio’ PSO velocity max ratio argument in automl_train_manual function the minimum and maximum value for ‘psovelocitymaxratio’; default 0.01 for ‘auto_psovelocitymaxratio_min’ and 0.5 for ‘auto_psovelocitymaxratio_max’
auto_layers	see below (‘auto_layers’ default value ‘TRUE’ for automatic adjustment of layers shape in automl_train_manual function)
auto_layers_min	(linked to ‘auto_layers’ above, set hpar ‘layersshape’ and ‘layersacttype’) the minimum number of hidden layers (default 1 no hidden layer)
auto_layers_max	(linked to ‘auto_layers’ above, set hpar ‘layersshape’ and ‘layersacttype’) the maximum number of hidden layers (default 2)
auto_layersnodes_min	(linked to ‘auto_layers’ above, set hpar ‘layersshape’ and ‘layersacttype’) the minimum number of nodes per layer (default 3)
auto_layersnodes_max	(linked to ‘auto_layers’ above, set hpar ‘layersshape’ and ‘layersacttype’) the maximum number of nodes per layer (default 33)
auto_layersdropo	see below
auto_layersdropoprob_min	see below
auto_layersdropoprob_max	‘auto_layersdropo’ default value ‘FALSE’ for automatic adjustment of hpar ‘layersdropoprob’ in automl_train_manual function) the minimum and maximum value for ‘layersdropoprob’; default 0.05 for ‘auto_layersdropoprob_min’ and 0.75 for ‘auto_layersdropoprob_max’
seed	seed for reproductibility (default 4)
nbcores	number of cores used to parallelize particles optimization, not available on Windows (default 1, automatically reduced if not enough cores)
verbose	to display or not the costs at each iteration for each particle (default TRUE)
subtimelimit	time limit in seconds for sub modelizations to avoid waiting too long for a specific particle to finish its modelization (default 3600)