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. More...
virtual void	save (boost::property_tree::ptree &pt) const override
	Save mechanism to store Options in a xml tree. More...
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. More...

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. More...
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. More...
unsigned int	m_test_rate
	Error on validation is compared with the one before. More...
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 31 of file FANN.h.

Member Function Documentation

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 103 of file FANNOptions.cc.

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 31 of file FANNOptions.cc.

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 58 of file FANNOptions.cc.

Member Data Documentation

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 64 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 72 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 74 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