FANNOptions Class Reference

Options for the FANN MVA method. More...

#include <FANN.h>

Inheritance diagram for FANNOptions:

Collaboration diagram for FANNOptions:

Public Member Functions
virtual void	load (const boost::property_tree::ptree &pt) override
	Load mechanism to load Options from a xml tree.
virtual void	save (boost::property_tree::ptree &pt) const override
	Save mechanism to store Options in a xml tree.
virtual po::options_description	getDescription () override
	Returns a program options description for all available options.
virtual std::string	getMethod () const override
	Return method name.
std::vector< unsigned int >	getHiddenLayerNeurons (unsigned int nf) const
	Returns the internal vector parameter with the number of hidden neurons per layer.

Public Attributes
unsigned int	m_max_epochs = 10000
	Maximum number of epochs.
bool	m_verbose_mode = true
	Sets to report training status or not.
std::string	m_hidden_layers_architecture = "3*N"
	String containing the architecture of hidden neurons.
std::string	m_hidden_activiation_function = "FANN_SIGMOID_SYMMETRIC"
	Activation function in hidden layer.
std::string	m_output_activiation_function = "FANN_SIGMOID_SYMMETRIC"
	Activation function in output layer.
std::string	m_error_function = "FANN_ERRORFUNC_LINEAR"
	Loss function.
std::string	m_training_method = "FANN_TRAIN_RPROP"
	Training method for back propagation.
double	m_validation_fraction = 0.5
	Fraction of training sample used for validation in order to avoid overtraining.
unsigned int	m_random_seeds
	Number of times the training is repeated with a new weight random seed.
unsigned int	m_test_rate
	Error on validation is compared with the one before.
unsigned int	m_number_of_threads = 8
	Number of threads for parallel training.
bool	m_scale_features = true
	Scale features before training.
bool	m_scale_target = true
	Scale target before training.

Detailed Description

Options for the FANN MVA method.

Definition at line 29 of file FANN.h.

Member Function Documentation

getDescription()

po::options_description getDescription ( )

overridevirtual

Returns a program options description for all available options.

Implements Options.

Definition at line 69 of file FANNOptions.cc.

    {
      po::options_description description("FANN options");
      description.add_options()
      ("max_epochs", po::value<unsigned int>(&m_max_epochs), "Number of iEpochs")
      ("verbose_mode", po::value<bool>(&m_verbose_mode), "Prints out the training status or not")
      ("hidden_layers_architecture", po::value<std::string>(&m_hidden_layers_architecture),
       "Architecture with number of neurons in each hidden layer")
      ("hidden_activiation_function", po::value<std::string>(&m_hidden_activiation_function),
       "Name of acitvation function used for hidden layers")
      ("output_activiation_function", po::value<std::string>(&m_output_activiation_function),
       "Name of acitvation function used for output layer")
      ("error_function", po::value<std::string>(&m_error_function), "Name of error function")
      ("training_method", po::value<std::string>(&m_training_method), "Method used for backpropagation")
      ("validation_fraction", po::value<double>(&m_validation_fraction), "Fraction of training sample used for validation.")
      ("random_seeds", po::value<unsigned int>(&m_random_seeds),
       "Number of times the training is repeated with a new weight random seed.")
      ("test_rate", po::value<unsigned int>(&m_test_rate), "Rate of iEpochs to check the validation error")
      ("number_of_threads", po::value<unsigned int>(&m_number_of_threads), "Number of threads for parallel training")
      ("scale_features", po::value<bool>(&m_scale_features), "Boolean indicating if features should be scaled or not")
      ("scale_target", po::value<bool>(&m_scale_target), "Boolean indicating if target should be scaled or not");
      return description;
    }

getHiddenLayerNeurons()

std::vector< unsigned int > getHiddenLayerNeurons

(

unsigned int

nf

)

const

Returns the internal vector parameter with the number of hidden neurons per layer.

Parameters

nf	number of features (input nodes).

Definition at line 93 of file FANNOptions.cc.

    {
      std::vector<unsigned int> hiddenLayers;
      std::stringstream iLayers(m_hidden_layers_architecture);
      std::string layer;
      while (std::getline(iLayers, layer, ',')) {
        for (auto& character : layer) {
          if (character == 'N') character = 'x';
        }
        auto* iLayerSize = new TFormula("iLayerSize", layer.c_str());
        hiddenLayers.push_back(iLayerSize->Eval(nf));
      }
      return hiddenLayers;
    }

getMethod()

virtual std::string getMethod ( ) const

inlineoverridevirtual

Return method name.

Implements SpecificOptions.

Definition at line 52 of file FANN.h.

{ return "FANN"; }

load()

void load ( const boost::property_tree::ptree &  pt )

overridevirtual

Load mechanism to load Options from a xml tree.

Parameters

pt

xml tree

Implements Options.

Definition at line 21 of file FANNOptions.cc.

    {
 
      int version = pt.get<int>("FANN_version");
      if (version != 1) {
        B2ERROR("Unknown weightfile version " << std::to_string(version));
        throw std::runtime_error("Unknown weightfile version " + std::to_string(version));
      }
      m_max_epochs = pt.get<unsigned int>("FANN_max_epochs");
      m_verbose_mode = pt.get<bool>("FANN_verbose_mode");
 
      m_hidden_layers_architecture = pt.get<std::string>("FANN_hidden_layers_architecture");
 
      m_hidden_activiation_function = pt.get<std::string>("FANN_hidden_activation_function");
      m_output_activiation_function = pt.get<std::string>("FANN_output_activation_function");
      m_error_function = pt.get<std::string>("FANN_error_function");
      m_training_method = pt.get<std::string>("FANN_training_method");
      m_validation_fraction = pt.get<double>("FANN_validation_fraction");
      m_random_seeds = pt.get<unsigned int>("FANN_random_seeds");
      m_test_rate = pt.get<unsigned int>("FANN_test_rate");
      m_number_of_threads = pt.get<unsigned int>("FANN_number_of_threads");
 
      m_scale_features = pt.get<bool>("FANN_scale_features");
      m_scale_target = pt.get<bool>("FANN_scale_target");
 
    }

save()

void save ( boost::property_tree::ptree &  pt ) const

overridevirtual

Save mechanism to store Options in a xml tree.

Parameters

pt

xml tree

Implements Options.

Definition at line 48 of file FANNOptions.cc.

    {
      pt.put("FANN_version", 1);
      pt.put("FANN_max_epochs", m_max_epochs);
      pt.put("FANN_verbose_mode", m_verbose_mode);
      pt.put("FANN_hidden_layers_architecture", m_hidden_layers_architecture);
      pt.put("FANN_hidden_activation_function", m_hidden_activiation_function);
      pt.put("FANN_output_activation_function", m_output_activiation_function);
      pt.put("FANN_error_function", m_error_function);
      pt.put("FANN_training_method", m_training_method);
      pt.put("FANN_validation_fraction", m_validation_fraction);
      pt.put("FANN_random_seeds", m_random_seeds);
      pt.put("FANN_test_rate", m_test_rate);
      pt.put("FANN_number_of_threads", m_number_of_threads);
 
      pt.put("FANN_scale_features", m_scale_features);
      pt.put("FANN_scale_target", m_scale_target);
 
 
    }

Member Data Documentation

m_error_function

std::string m_error_function = "FANN_ERRORFUNC_LINEAR"

Loss function.

Definition at line 66 of file FANN.h.

m_hidden_activiation_function

std::string m_hidden_activiation_function = "FANN_SIGMOID_SYMMETRIC"

Activation function in hidden layer.

Definition at line 64 of file FANN.h.

m_hidden_layers_architecture

std::string m_hidden_layers_architecture = "3*N"

String containing the architecture of hidden neurons.

Ex. "3,3*N,3*(N-1)

Definition at line 62 of file FANN.h.

m_max_epochs

unsigned int m_max_epochs = 10000

Maximum number of epochs.

Definition at line 60 of file FANN.h.

m_number_of_threads

unsigned int m_number_of_threads = 8

Number of threads for parallel training.

Definition at line 74 of file FANN.h.

m_output_activiation_function

std::string m_output_activiation_function = "FANN_SIGMOID_SYMMETRIC"

Activation function in output layer.

Definition at line 65 of file FANN.h.

m_random_seeds

unsigned int m_random_seeds

Initial value:

=
        3

Number of times the training is repeated with a new weight random seed.

The one with the best result is saved.

Definition at line 70 of file FANN.h.

m_scale_features

bool m_scale_features = true

Scale features before training.

Definition at line 77 of file FANN.h.

m_scale_target

bool m_scale_target = true

Scale target before training.

Definition at line 78 of file FANN.h.

m_test_rate

unsigned int m_test_rate

Initial value:

=
        500

Error on validation is compared with the one before.

The number of epochs before is given by this parameter.

Definition at line 72 of file FANN.h.

m_training_method

std::string m_training_method = "FANN_TRAIN_RPROP"

Training method for back propagation.

Definition at line 67 of file FANN.h.

m_validation_fraction

double m_validation_fraction = 0.5

Fraction of training sample used for validation in order to avoid overtraining.

Definition at line 69 of file FANN.h.

m_verbose_mode

bool m_verbose_mode = true

Sets to report training status or not.

Definition at line 61 of file FANN.h.

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The documentation for this class was generated from the following files:

• mva/methods/include/FANN.h
• mva/methods/src/FANNOptions.cc