Collaboration diagram for machine_learning::neural_network::NeuralNetwork:

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Public Member Functions
	NeuralNetwork ()=default

	NeuralNetwork (const std::vector< std::pair< int, std::string > > &config)

	NeuralNetwork (const NeuralNetwork &model)=default

	~NeuralNetwork ()=default

NeuralNetwork &	operator= (const NeuralNetwork &model)=default

	NeuralNetwork (NeuralNetwork &&)=default

NeuralNetwork &	operator= (NeuralNetwork &&)=default

std::pair< std::vector< std::vector< std::valarray< double > > >, std::vector< std::vector< std::valarray< double > > > >	get_XY_from_csv (const std::string &file_name, const bool &last_label, const bool &normalize, const int &slip_lines=1)

std::vector< std::valarray< double > >	single_predict (const std::vector< std::valarray< double > > &X)

std::vector< std::vector< std::valarray< double > > >	batch_predict (const std::vector< std::vector< std::valarray< double > > > &X)

void	fit (const std::vector< std::vector< std::valarray< double > > > &X_, const std::vector< std::vector< std::valarray< double > > > &Y_, const int &epochs=100, const double &learning_rate=0.01, const size_t &batch_size=32, const bool &shuffle=true)

void	fit_from_csv (const std::string &file_name, const bool &last_label, const int &epochs, const double &learning_rate, const bool &normalize, const int &slip_lines=1, const size_t &batch_size=32, const bool &shuffle=true)

void	evaluate (const std::vector< std::vector< std::valarray< double > > > &X, const std::vector< std::vector< std::valarray< double > > > &Y)

void	evaluate_from_csv (const std::string &file_name, const bool &last_label, const bool &normalize, const int &slip_lines=1)

void	save_model (const std::string &_file_name)

NeuralNetwork	load_model (const std::string &file_name)

void	summary ()

Private Member Functions
	NeuralNetwork (const std::vector< std::pair< int, std::string > > &config, const std::vector< std::vector< std::valarray< double > > > &kernels)

std::vector< std::vector< std::valarray< double > > >	__detailed_single_prediction (const std::vector< std::valarray< double > > &X)

Private Attributes
std::vector< neural_network::layers::DenseLayer >	layers

Detailed Description

NeuralNetwork class is implements MLP. This class is used by actual user to create and train networks.

Constructor & Destructor Documentation

◆ NeuralNetwork() [1/5]

machine_learning::neural_network::NeuralNetwork::NeuralNetwork	(	const std::vector< std::pair< int, std::string > > &	config,
		const std::vector< std::vector< std::valarray< double > > > &	kernels )

inlineprivate

Private Constructor for class NeuralNetwork. This constructor is used internally to load model.

Parameters

config	vector containing pair (neurons, activation)
kernels	vector containing all pretrained kernels

                                                                  {
        // First layer should not have activation
        if (config.begin()->second != "none") {
            std::cerr << "ERROR (" << __func__ << ") : ";
            std::cerr
                << "First layer can't have activation other than none got "
                << config.begin()->second;
            std::cerr << std::endl;
            std::exit(EXIT_FAILURE);
        }
        // Network should have atleast two layers
        if (config.size() <= 1) {
            std::cerr << "ERROR (" << __func__ << ") : ";
            std::cerr << "Invalid size of network, ";
            std::cerr << "Atleast two layers are required";
            std::exit(EXIT_FAILURE);
        }
        // Reconstructing all pretrained layers
        for (size_t i = 0; i < config.size(); i++) {
            layers.emplace_back(neural_network::layers::DenseLayer(
                config[i].first, config[i].second, kernels[i]));
        }
        std::cout << "INFO: Network constructed successfully" << std::endl;
    }

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◆ NeuralNetwork() [2/5]

machine_learning::neural_network::NeuralNetwork::NeuralNetwork ( )

default

Default Constructor for class NeuralNetwork. This constructor is used to create empty variable of type NeuralNetwork class.

◆ NeuralNetwork() [3/5]

machine_learning::neural_network::NeuralNetwork::NeuralNetwork ( const std::vector< std::pair< int, std::string > > & config )

inlineexplicit

Constructor for class NeuralNetwork. This constructor is used by user.

Parameters

config vector containing pair (neurons, activation)

                                                          {
        // First layer should not have activation
        if (config.begin()->second != "none") {
            std::cerr << "ERROR (" << __func__ << ") : ";
            std::cerr
                << "First layer can't have activation other than none got "
                << config.begin()->second;
            std::cerr << std::endl;
            std::exit(EXIT_FAILURE);
        }
        // Network should have atleast two layers
        if (config.size() <= 1) {
            std::cerr << "ERROR (" << __func__ << ") : ";
            std::cerr << "Invalid size of network, ";
            std::cerr << "Atleast two layers are required";
            std::exit(EXIT_FAILURE);
        }
        // Separately creating first layer so it can have unit matrix
        // as kernel.
        layers.push_back(neural_network::layers::DenseLayer(
            config[0].first, config[0].second,
            {config[0].first, config[0].first}, false));
        // Creating remaining layers
        for (size_t i = 1; i < config.size(); i++) {
            layers.push_back(neural_network::layers::DenseLayer(
                config[i].first, config[i].second,
                {config[i - 1].first, config[i].first}, true));
        }
        std::cout << "INFO: Network constructed successfully" << std::endl;
    }

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◆ NeuralNetwork() [4/5]

machine_learning::neural_network::NeuralNetwork::NeuralNetwork ( const NeuralNetwork & model )

default

Copy Constructor for class NeuralNetwork.

Parameters

model instance of class to be copied.

◆ ~NeuralNetwork()

machine_learning::neural_network::NeuralNetwork::~NeuralNetwork ( )

default

Destructor for class NeuralNetwork.

◆ NeuralNetwork() [5/5]

machine_learning::neural_network::NeuralNetwork::NeuralNetwork ( NeuralNetwork && )

default

Move constructor for class NeuralNetwork

Member Function Documentation

◆ __detailed_single_prediction()

std::vector< std::vector< std::valarray< double > > > machine_learning::neural_network::NeuralNetwork::__detailed_single_prediction ( const std::vector< std::valarray< double > > & X )

inlineprivate

Private function to get detailed predictions (i.e. activated neuron values). This function is used in backpropagation, single predict and batch predict.

Parameters

X	input vector

                                                                          {
        std::vector<std::vector<std::valarray<double>>> details;
        std::vector<std::valarray<double>> current_pass = X;
        details.emplace_back(X);
        for (const auto &l : layers) {
            current_pass = multiply(current_pass, l.kernel);
            current_pass = apply_function(current_pass, l.activation_function);
            details.emplace_back(current_pass);
        }
        return details;
    }

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◆ batch_predict()

std::vector< std::vector< std::valarray< double > > > machine_learning::neural_network::NeuralNetwork::batch_predict ( const std::vector< std::vector< std::valarray< double > > > & X )

inline

Function to get prediction of model on batch

Parameters

X	array of feature vectors

Returns: returns predicted values as vector

                                                            {
        // Store predicted values
        std::vector<std::vector<std::valarray<double>>> predicted_batch(
            X.size());
        for (size_t i = 0; i < X.size(); i++) {  // For every sample
            // Push predicted values
            predicted_batch[i] = this->single_predict(X[i]);
        }
        return predicted_batch;  // Return predicted values
    }

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◆ evaluate()

void machine_learning::neural_network::NeuralNetwork::evaluate	(	const std::vector< std::vector< std::valarray< double > > > &	X,
		const std::vector< std::vector< std::valarray< double > > > &	Y )

inline

Function to evaluate model on supplied data

Parameters

X	array of feature vectors (input data)
Y	array of target values (label)

                                                                      {
        std::cout << "INFO: Evaluation Started" << std::endl;
        double acc = 0, loss = 0;  // intialize performance metrics with zero
        for (size_t i = 0; i < X.size(); i++) {  // For every sample in input
            // Get predictions
            std::vector<std::valarray<double>> pred =
                this->single_predict(X[i]);
            // If predicted class is correct
            if (argmax(pred) == argmax(Y[i])) {
                acc += 1;  // Increment accuracy
            }
            // Calculating loss - Mean Squared Error
            loss += sum(apply_function((Y[i] - pred),
                                       neural_network::util_functions::square) *
                        0.5);
        }
        acc /= X.size();   // Averaging accuracy
        loss /= X.size();  // Averaging loss
        // Prinitng performance of the model
        std::cout << "Evaluation: Loss: " << loss;
        std::cout << ", Accuracy: " << acc << std::endl;
        return;
    }

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◆ evaluate_from_csv()

void machine_learning::neural_network::NeuralNetwork::evaluate_from_csv	(	const std::string &	file_name,
		const bool &	last_label,
		const bool &	normalize,
		const int &	slip_lines = 1 )

inline

Function to evaluate model on data stored in csv file

Parameters

file_name	csv file name
last_label	flag for whether label is in first or last column
normalize	flag for whether to normalize data
slip_lines	number of lines to skip

                                                                             {
        // Getting training data from csv file
        auto data =
            this->get_XY_from_csv(file_name, last_label, normalize, slip_lines);
        // Evaluating model
        this->evaluate(data.first, data.second);
        return;
    }

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◆ fit()

void machine_learning::neural_network::NeuralNetwork::fit	(	const std::vector< std::vector< std::valarray< double > > > &	X_,
		const std::vector< std::vector< std::valarray< double > > > &	Y_,
		const int &	epochs = 100,
		const double &	learning_rate = 0.01,
		const size_t &	batch_size = 32,
		const bool &	shuffle = true )

inline

Function to fit model on supplied data

Parameters

X	array of feature vectors
Y	array of target values
epochs	number of epochs (default = 100)
learning_rate	learning rate (default = 0.01)
batch_size	batch size for gradient descent (default = 32)
shuffle	flag for whether to shuffle data (default = true)

                                                                        {
        std::vector<std::vector<std::valarray<double>>> X = X_, Y = Y_;
        // Both label and input data should have same size
        if (X.size() != Y.size()) {
            std::cerr << "ERROR (" << __func__ << ") : ";
            std::cerr << "X and Y in fit have different sizes" << std::endl;
            std::exit(EXIT_FAILURE);
        }
        std::cout << "INFO: Training Started" << std::endl;
        for (int epoch = 1; epoch <= epochs; epoch++) {  // For every epoch
            // Shuffle X and Y if flag is set
            if (shuffle) {
                equal_shuffle(X, Y);
            }
            auto start =
                std::chrono::high_resolution_clock::now();  // Start clock
            double loss = 0,
                   acc = 0;  // Intialize performance metrics with zero
            // For each starting index of batch
            for (size_t batch_start = 0; batch_start < X.size();
                 batch_start += batch_size) {
                for (size_t i = batch_start;
                     i < std::min(X.size(), batch_start + batch_size); i++) {
                    std::vector<std::valarray<double>> grad, cur_error,
                        predicted;
                    auto activations = this->__detailed_single_prediction(X[i]);
                    // Gradients vector to store gradients for all layers
                    // They will be averaged and applied to kernel
                    std::vector<std::vector<std::valarray<double>>> gradients;
                    gradients.resize(this->layers.size());
                    // First intialize gradients to zero
                    for (size_t i = 0; i < gradients.size(); i++) {
                        zeroes_initialization(
                            gradients[i], get_shape(this->layers[i].kernel));
                    }
                    predicted = activations.back();  // Predicted vector
                    cur_error = predicted - Y[i];    // Absoulute error
                    // Calculating loss with MSE
                    loss += sum(apply_function(
                        cur_error, neural_network::util_functions::square));
                    // If prediction is correct
                    if (argmax(predicted) == argmax(Y[i])) {
                        acc += 1;
                    }
                    // For every layer (except first) starting from last one
                    for (size_t j = this->layers.size() - 1; j >= 1; j--) {
                        // Backpropogating errors
                        cur_error = hadamard_product(
                            cur_error,
                            apply_function(
                                activations[j + 1],
                                this->layers[j].dactivation_function));
                        // Calculating gradient for current layer
                        grad = multiply(transpose(activations[j]), cur_error);
                        // Change error according to current kernel values
                        cur_error = multiply(cur_error,
                                             transpose(this->layers[j].kernel));
                        // Adding gradient values to collection of gradients
                        gradients[j] = gradients[j] + grad / double(batch_size);
                    }
                    // Applying gradients
                    for (size_t j = this->layers.size() - 1; j >= 1; j--) {
                        // Updating kernel (aka weights)
                        this->layers[j].kernel = this->layers[j].kernel -
                                                 gradients[j] * learning_rate;
                    }
                }
            }
            auto stop =
                std::chrono::high_resolution_clock::now();  // Stoping the clock
            // Calculate time taken by epoch
            auto duration =
                std::chrono::duration_cast<std::chrono::microseconds>(stop -
                                                                      start);
            loss /= X.size();        // Averaging loss
            acc /= X.size();         // Averaging accuracy
            std::cout.precision(4);  // set output precision to 4
            // Printing training stats
            std::cout << "Training: Epoch " << epoch << '/' << epochs;
            std::cout << ", Loss: " << loss;
            std::cout << ", Accuracy: " << acc;
            std::cout << ", Taken time: " << duration.count() / 1e6
                      << " seconds";
            std::cout << std::endl;
        }
        return;
    }

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◆ fit_from_csv()

void machine_learning::neural_network::NeuralNetwork::fit_from_csv	(	const std::string &	file_name,
		const bool &	last_label,
		const int &	epochs,
		const double &	learning_rate,
		const bool &	normalize,
		const int &	slip_lines = 1,
		const size_t &	batch_size = 32,
		const bool &	shuffle = true )

inline

Function to fit model on data stored in csv file

Parameters

file_name	csv file name
last_label	flag for whether label is in first or last column
epochs	number of epochs
learning_rate	learning rate
normalize	flag for whether to normalize data
slip_lines	number of lines to skip
batch_size	batch size for gradient descent (default = 32)
shuffle	flag for whether to shuffle data (default = true)

                                                  {
        // Getting training data from csv file
        auto data =
            this->get_XY_from_csv(file_name, last_label, normalize, slip_lines);
        // Fit the model on training data
        this->fit(data.first, data.second, epochs, learning_rate, batch_size,
                  shuffle);
        return;
    }

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◆ get_XY_from_csv()

std::pair< std::vector< std::vector< std::valarray< double > > >, std::vector< std::vector< std::valarray< double > > > > machine_learning::neural_network::NeuralNetwork::get_XY_from_csv	(	const std::string &	file_name,
		const bool &	last_label,
		const bool &	normalize,
		const int &	slip_lines = 1 )

inline

Function to get X and Y from csv file (where X = data, Y = label)

Parameters

file_name	csv file name
last_label	flag for whether label is in first or last column
normalize	flag for whether to normalize data
slip_lines	number of lines to skip

Returns: returns pair of X and Y

                                                                      {
        std::ifstream in_file;                          // Ifstream to read file
        in_file.open(file_name.c_str(), std::ios::in);  // Open file
        // If there is any problem in opening file
        if (!in_file.is_open()) {
            std::cerr << "ERROR (" << __func__ << ") : ";
            std::cerr << "Unable to open file: " << file_name << std::endl;
            std::exit(EXIT_FAILURE);
        }
        std::vector<std::vector<std::valarray<double>>> X,
            Y;             // To store X and Y
        std::string line;  // To store each line
        // Skip lines
        for (int i = 0; i < slip_lines; i++) {
            std::getline(in_file, line, '\n');  // Ignore line
        }
        // While file has information
        while (!in_file.eof() && std::getline(in_file, line, '\n')) {
            std::valarray<double> x_data,
                y_data;                  // To store single sample and label
            std::stringstream ss(line);  // Constructing stringstream from line
            std::string token;  // To store each token in line (seprated by ',')
            while (std::getline(ss, token, ',')) {  // For each token
                // Insert numerical value of token in x_data
                x_data = insert_element(x_data, std::stod(token));
            }
            // If label is in last column
            if (last_label) {
                y_data.resize(this->layers.back().neurons);
                // If task is classification
                if (y_data.size() > 1) {
                    y_data[x_data[x_data.size() - 1]] = 1;
                }
                // If task is regrssion (of single value)
                else {
                    y_data[0] = x_data[x_data.size() - 1];
                }
                x_data = pop_back(x_data);  // Remove label from x_data
            } else {
                y_data.resize(this->layers.back().neurons);
                // If task is classification
                if (y_data.size() > 1) {
                    y_data[x_data[x_data.size() - 1]] = 1;
                }
                // If task is regrssion (of single value)
                else {
                    y_data[0] = x_data[x_data.size() - 1];
                }
                x_data = pop_front(x_data);  // Remove label from x_data
            }
            // Push collected X_data and y_data in X and Y
            X.push_back({x_data});
            Y.push_back({y_data});
        }
        // Normalize training data if flag is set
        if (normalize) {
            // Scale data between 0 and 1 using min-max scaler
            X = minmax_scaler(X, 0.01, 1.0);
        }
        in_file.close();         // Closing file
        return make_pair(X, Y);  // Return pair of X and Y
    }

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◆ load_model()

NeuralNetwork machine_learning::neural_network::NeuralNetwork::load_model ( const std::string & file_name )

inline

Function to load earlier saved model.

Parameters

file_name file from which model will be loaded (*.model)

Returns: instance of NeuralNetwork class with pretrained weights

                                                         {
        std::ifstream in_file;            // Ifstream to read file
        in_file.open(file_name.c_str());  // Openinig file
        // If there is any problem in opening file
        if (!in_file.is_open()) {
            std::cerr << "ERROR (" << __func__ << ") : ";
            std::cerr << "Unable to open file: " << file_name << std::endl;
            std::exit(EXIT_FAILURE);
        }
        std::vector<std::pair<int, std::string>> config;  // To store config
        std::vector<std::vector<std::valarray<double>>>
            kernels;  // To store pretrained kernels
        // Loading model from saved file format
        size_t total_layers = 0;
        in_file >> total_layers;
        for (size_t i = 0; i < total_layers; i++) {
            int neurons = 0;
            std::string activation;
            size_t shape_a = 0, shape_b = 0;
            std::vector<std::valarray<double>> kernel;
            in_file >> neurons >> activation >> shape_a >> shape_b;
            for (size_t r = 0; r < shape_a; r++) {
                std::valarray<double> row(shape_b);
                for (size_t c = 0; c < shape_b; c++) {
                    in_file >> row[c];
                }
                kernel.push_back(row);
            }
            config.emplace_back(make_pair(neurons, activation));
            ;
            kernels.emplace_back(kernel);
        }
        std::cout << "INFO: Model loaded successfully" << std::endl;
        in_file.close();  // Closing file
        return NeuralNetwork(
            config, kernels);  // Return instance of NeuralNetwork class
    }

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◆ operator=() [1/2]

NeuralNetwork & machine_learning::neural_network::NeuralNetwork::operator= ( const NeuralNetwork & model )

default

Copy assignment operator for class NeuralNetwork

◆ operator=() [2/2]

NeuralNetwork & machine_learning::neural_network::NeuralNetwork::operator= ( NeuralNetwork && )

default

Move assignment operator for class NeuralNetwork

◆ save_model()

void machine_learning::neural_network::NeuralNetwork::save_model ( const std::string & _file_name )

inline

Function to save current model.

Parameters

file_name file name to save model (*.model)

Format in which model is saved:

total_layers neurons(1st neural_network::layers::DenseLayer) activation_name(1st neural_network::layers::DenseLayer) kernel_shape(1st neural_network::layers::DenseLayer) kernel_values neurons(Nth neural_network::layers::DenseLayer) activation_name(Nth neural_network::layers::DenseLayer) kernel_shape(Nth neural_network::layers::DenseLayer) kernel_value

For Example, pretrained model with 3 layers:

 3
 4 none
 4 4
 1 0 0 0
 0 1 0 0
 0 0 1 0
 0 0 0 1
 6 relu
 4 6
 -1.88963 -3.61165 1.30757 -0.443906 -2.41039 -2.69653
 -0.684753 0.0891452 0.795294 -2.39619 2.73377 0.318202
 -2.91451 -4.43249 -0.804187 2.51995 -6.97524 -1.07049
 -0.571531 -1.81689 -1.24485 1.92264 -2.81322 1.01741
 3 sigmoid
 6 3
 0.390267 -0.391703 -0.0989607
 0.499234 -0.564539 -0.28097
 0.553386 -0.153974 -1.92493
 -2.01336 -0.0219682 1.44145
 1.72853 -0.465264 -0.705373
 -0.908409 -0.740547 0.376416

                                                 {
        std::string file_name = _file_name;
        // Adding ".model" extension if it is not already there in name
        if (file_name.find(".model") == file_name.npos) {
            file_name += ".model";
        }
        std::ofstream out_file;  // Ofstream to write in file
        // Open file in out|trunc mode
        out_file.open(file_name.c_str(),
                      std::ofstream::out | std::ofstream::trunc);
        // If there is any problem in opening file
        if (!out_file.is_open()) {
            std::cerr << "ERROR (" << __func__ << ") : ";
            std::cerr << "Unable to open file: " << file_name << std::endl;
            std::exit(EXIT_FAILURE);
        }
        /**
            Format in which model is saved:
 
            total_layers
            neurons(1st neural_network::layers::DenseLayer) activation_name(1st
           neural_network::layers::DenseLayer) kernel_shape(1st
           neural_network::layers::DenseLayer) kernel_values
            .
            .
            .
            neurons(Nth neural_network::layers::DenseLayer) activation_name(Nth
           neural_network::layers::DenseLayer) kernel_shape(Nth
           neural_network::layers::DenseLayer) kernel_value
 
            For Example, pretrained model with 3 layers:
            <pre>
            3
            4 none
            4 4
            1 0 0 0
            0 1 0 0
            0 0 1 0
            0 0 0 1
            6 relu
            4 6
            -1.88963 -3.61165 1.30757 -0.443906 -2.41039 -2.69653
            -0.684753 0.0891452 0.795294 -2.39619 2.73377 0.318202
            -2.91451 -4.43249 -0.804187 2.51995 -6.97524 -1.07049
            -0.571531 -1.81689 -1.24485 1.92264 -2.81322 1.01741
            3 sigmoid
            6 3
            0.390267 -0.391703 -0.0989607
            0.499234 -0.564539 -0.28097
            0.553386 -0.153974 -1.92493
            -2.01336 -0.0219682 1.44145
            1.72853 -0.465264 -0.705373
            -0.908409 -0.740547 0.376416
            </pre>
        */
        // Saving model in the same format
        out_file << layers.size();
        out_file << std::endl;
        for (const auto &layer : this->layers) {
            out_file << layer.neurons << ' ' << layer.activation << std::endl;
            const auto shape = get_shape(layer.kernel);
            out_file << shape.first << ' ' << shape.second << std::endl;
            for (const auto &row : layer.kernel) {
                for (const auto &val : row) {
                    out_file << val << ' ';
                }
                out_file << std::endl;
            }
        }
        std::cout << "INFO: Model saved successfully with name : ";
        std::cout << file_name << std::endl;
        out_file.close();  // Closing file
        return;
    }

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◆ single_predict()

std::vector< std::valarray< double > > machine_learning::neural_network::NeuralNetwork::single_predict ( const std::vector< std::valarray< double > > & X )

inline

Function to get prediction of model on single sample.

Parameters

X	array of feature vectors

Returns: returns predictions as vector

                                                 {
        // Get activations of all layers
        auto activations = this->__detailed_single_prediction(X);
        // Return activations of last layer (actual predicted values)
        return activations.back();
    }

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◆ summary()

void machine_learning::neural_network::NeuralNetwork::summary ( )

inline

Function to print summary of the network.

                   {
        // Printing Summary
        std::cout
            << "==============================================================="
            << std::endl;
        std::cout << "\t\t+ MODEL SUMMARY +\t\t\n";
        std::cout
            << "==============================================================="
            << std::endl;
        for (size_t i = 1; i <= layers.size(); i++) {  // For every layer
            std::cout << i << ")";
            std::cout << " Neurons : "
                      << layers[i - 1].neurons;  // number of neurons
            std::cout << ", Activation : "
                      << layers[i - 1].activation;  // activation
            std::cout << ", kernel Shape : "
                      << get_shape(layers[i - 1].kernel);  // kernel shape
            std::cout << std::endl;
        }
        std::cout
            << "==============================================================="
            << std::endl;
        return;
    }

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

machine_learning/neural_network.cpp

Public Member Functions

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ NeuralNetwork() [1/5]

◆ NeuralNetwork() [2/5]

◆ NeuralNetwork() [3/5]

◆ NeuralNetwork() [4/5]

◆ ~NeuralNetwork()

◆ NeuralNetwork() [5/5]

Member Function Documentation

◆ __detailed_single_prediction()

◆ batch_predict()

◆ evaluate()

◆ evaluate_from_csv()

◆ fit()

◆ fit_from_csv()

◆ get_XY_from_csv()

◆ load_model()

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ save_model()

◆ single_predict()

◆ summary()