netdem::GeneralNet Class Reference

A class representing a general neural network. More...

#include <general_net.hpp>

Public Member Functions
void	ResetModel ()
	Resets the current neural network model.
void	AddLayer (MLPackUtils::LayerType layer_name,...)
	Adds a layer to the current neural network model.
void	Train (const arma::mat &data_x, const arma::mat &data_y)
	Trains the current neural network model using the given input and output data.
arma::mat	Predict (const arma::mat &data_x)
	Predicts output values based on input data.
arma::mat	Classify (const arma::mat &data_x)
	Classifies input data based on the current neural network model.
arma::mat	Regress (const arma::mat &data_x)
	Performs regression on input data using the current neural network model.
void	Load (std::string const &filename, std::string const &label)
	Loads a previously saved neural network model from a file.
void	Save (std::string const &filename, std::string const &label)
	Saves the current neural network model to a file.

Public Attributes
mlpack::FFN	model
	The ANN model.
double	step_size {0.01}
	Machine learning hyper-parameters.
int	batch_size {32}
	Machine learning hyper-parameters.
double	decay_rate_moment {0.9}
	Machine learning hyper-parameters.
double	decay_rate_norm {0.9}
	Machine learning hyper-parameters.
double	gradient_init_param {1e-8}
	Machine learning hyper-parameters.
int	epochs {100}
	Machine learning hyper-parameters.
double	stop_tol {1e-8}
	Machine learning hyper-parameters.
bool	enable_logging {true}
	A flag indicating whether to log info.

Detailed Description

A class representing a general neural network.

The GeneralNet class is used to represent a general neural network, and provides methods for adding layers, training the network, and making predictions.

Examples

02_train_classifier_trimesh_plane.cpp, 03_test_classifier_trimesh_plane.cpp, 06_test_ann_vs_geom_trimesh_plane.cpp, 12_train_classifier_trimesh.cpp, 13_test_classifier_trimesh.cpp, 16_test_ann_vs_geom_trimesh.cpp, 17_test_ann_vs_geom_trimesh.cpp, 22_train_classifier_ellipsoid_plane.cpp, 23_test_classifier_ellipsoid_plane.cpp, 26_test_ann_vs_geom_ellipsoid_plane.cpp, 32_train_classifier_ellipsoid.cpp, 33_test_classifier_ellipsoid.cpp, 36_test_ann_vs_geom_ellipsoid.cpp, and 37_test_ann_vs_geom_ellipsoid.cpp.

Member Function Documentation

AddLayer()

void netdem::GeneralNet::AddLayer	(	MLPackUtils::LayerType	layer_name,
			... )

Adds a layer to the current neural network model.

Parameters

layer_name	The type of layer to add.
...	Additional parameters required for the layer to be added.

For MLPackUtils::LayerType::Linear, an additional parameter indicating the dimension of outputs is required.

Examples

02_train_classifier_trimesh_plane.cpp, 12_train_classifier_trimesh.cpp, 22_train_classifier_ellipsoid_plane.cpp, and 32_train_classifier_ellipsoid.cpp.

Classify()

arma::mat netdem::GeneralNet::Classify

(

const arma::mat &

data_x

)

Classifies input data based on the current neural network model.

Parameters

data_x

The input data to classify.

Returns

The classification results.

Examples

02_train_classifier_trimesh_plane.cpp, 03_test_classifier_trimesh_plane.cpp, 06_test_ann_vs_geom_trimesh_plane.cpp, 12_train_classifier_trimesh.cpp, 13_test_classifier_trimesh.cpp, 16_test_ann_vs_geom_trimesh.cpp, 17_test_ann_vs_geom_trimesh.cpp, 22_train_classifier_ellipsoid_plane.cpp, 23_test_classifier_ellipsoid_plane.cpp, 26_test_ann_vs_geom_ellipsoid_plane.cpp, 32_train_classifier_ellipsoid.cpp, 33_test_classifier_ellipsoid.cpp, 36_test_ann_vs_geom_ellipsoid.cpp, and 37_test_ann_vs_geom_ellipsoid.cpp.

Load()

void netdem::GeneralNet::Load	(	std::string const &	filename,
		std::string const &	label )

Loads a previously saved neural network model from a file.

Parameters

filename	The name of the file containing the saved model.
label	The label for the saved model.

Examples

03_test_classifier_trimesh_plane.cpp, 06_test_ann_vs_geom_trimesh_plane.cpp, 13_test_classifier_trimesh.cpp, 16_test_ann_vs_geom_trimesh.cpp, 17_test_ann_vs_geom_trimesh.cpp, 23_test_classifier_ellipsoid_plane.cpp, 26_test_ann_vs_geom_ellipsoid_plane.cpp, 33_test_classifier_ellipsoid.cpp, 36_test_ann_vs_geom_ellipsoid.cpp, and 37_test_ann_vs_geom_ellipsoid.cpp.

Predict()

arma::mat netdem::GeneralNet::Predict

(

const arma::mat &

data_x

)

Predicts output values based on input data.

Parameters

data_x

The input data to use for prediction.

Returns

The predicted output values.

Regress()

arma::mat netdem::GeneralNet::Regress

(

const arma::mat &

data_x

)

Performs regression on input data using the current neural network model.

Parameters

data_x

The input data to use for regression.

Returns

The regression results.

ResetModel()

void netdem::GeneralNet::ResetModel

(

)

Resets the current neural network model.

Save()

void netdem::GeneralNet::Save	(	std::string const &	filename,
		std::string const &	label )

Saves the current neural network model to a file.

Parameters

filename	The name of the file to save the model to.
label	The label for the saved model.

Examples

02_train_classifier_trimesh_plane.cpp, 12_train_classifier_trimesh.cpp, 22_train_classifier_ellipsoid_plane.cpp, and 32_train_classifier_ellipsoid.cpp.

Train()

void netdem::GeneralNet::Train	(	const arma::mat &	data_x,
		const arma::mat &	data_y )

Trains the current neural network model using the given input and output data.

Parameters

data_x	The input data to use for training.
data_y	The output data to use for training.

Examples

02_train_classifier_trimesh_plane.cpp, 12_train_classifier_trimesh.cpp, 22_train_classifier_ellipsoid_plane.cpp, and 32_train_classifier_ellipsoid.cpp.

Member Data Documentation

batch_size

int netdem::GeneralNet::batch_size {32}

Machine learning hyper-parameters.

Examples

02_train_classifier_trimesh_plane.cpp, 12_train_classifier_trimesh.cpp, 22_train_classifier_ellipsoid_plane.cpp, and 32_train_classifier_ellipsoid.cpp.

decay_rate_moment

double netdem::GeneralNet::decay_rate_moment {0.9}

Machine learning hyper-parameters.

Examples

32_train_classifier_ellipsoid.cpp.

decay_rate_norm

double netdem::GeneralNet::decay_rate_norm {0.9}

Machine learning hyper-parameters.

Examples

32_train_classifier_ellipsoid.cpp.

enable_logging

bool netdem::GeneralNet::enable_logging {true}

A flag indicating whether to log info.

epochs

int netdem::GeneralNet::epochs {100}

Machine learning hyper-parameters.

Examples

02_train_classifier_trimesh_plane.cpp, 12_train_classifier_trimesh.cpp, 22_train_classifier_ellipsoid_plane.cpp, and 32_train_classifier_ellipsoid.cpp.

gradient_init_param

double netdem::GeneralNet::gradient_init_param {1e-8}

Machine learning hyper-parameters.

model

mlpack::FFN netdem::GeneralNet::model

The ANN model.

step_size

double netdem::GeneralNet::step_size {0.01}

Machine learning hyper-parameters.

Examples

02_train_classifier_trimesh_plane.cpp, 12_train_classifier_trimesh.cpp, 22_train_classifier_ellipsoid_plane.cpp, and 32_train_classifier_ellipsoid.cpp.

stop_tol

double netdem::GeneralNet::stop_tol {1e-8}

Machine learning hyper-parameters.

Examples

02_train_classifier_trimesh_plane.cpp, 12_train_classifier_trimesh.cpp, 22_train_classifier_ellipsoid_plane.cpp, and 32_train_classifier_ellipsoid.cpp.

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

• /Users/lzhshou/Documents/Research/myProjects/apaam/repo/netdem/src/mlpack/general_net.hpp
• /Users/lzhshou/Documents/Research/myProjects/apaam/repo/netdem/src/mlpack/general_net.cpp