machine_learning::neural_network::layers::DenseLayer Class Reference

Collaboration diagram for machine_learning::neural_network::layers::DenseLayer:

Public Member Functions
	DenseLayer (const int &neurons, const std::string &activation, const std::pair< size_t, size_t > &kernel_shape, const bool &random_kernel)
	DenseLayer (const int &neurons, const std::string &activation, const std::vector< std::valarray< double > > &kernel)
	DenseLayer (const DenseLayer &layer)=default
	~DenseLayer ()=default
DenseLayer &	operator= (const DenseLayer &layer)=default
	DenseLayer (DenseLayer &&)=default
DenseLayer &	operator= (DenseLayer &&)=default

Public Attributes
double(*	activation_function )(const double &)
double(*	dactivation_function )(const double &)
int	neurons
std::string	activation
std::vector< std::valarray< double > >	kernel

Detailed Description

neural_network::layers::DenseLayer class is used to store all necessary information about the layers (i.e. neurons, activation and kernel). This class is used by NeuralNetwork class to store layers.

Constructor & Destructor Documentation

DenseLayer() [1/4]

machine_learning::neural_network::layers::DenseLayer::DenseLayer ( const int & neurons, const std::string & activation, const std::pair< size_t, size_t > & kernel_shape, const bool & random_kernel )

inline

Constructor for neural_network::layers::DenseLayer class

Parameters

neurons	number of neurons
activation	activation function for layer
kernel_shape	shape of kernel
random_kernel	flag for whether to initialize kernel randomly

                                          {
        // Choosing activation (and it's derivative)
        if (activation == "sigmoid") {
            activation_function = neural_network::activations::sigmoid;
            dactivation_function = neural_network::activations::sigmoid;
        } else if (activation == "relu") {
            activation_function = neural_network::activations::relu;
            dactivation_function = neural_network::activations::drelu;
        } else if (activation == "tanh") {
            activation_function = neural_network::activations::tanh;
            dactivation_function = neural_network::activations::dtanh;
        } else if (activation == "none") {
            // Set identity function in casse of none is supplied
            activation_function =
                neural_network::util_functions::identity_function;
            dactivation_function =
                neural_network::util_functions::identity_function;
        } else {
            // If supplied activation is invalid
            std::cerr << "ERROR (" << __func__ << ") : ";
            std::cerr << "Invalid argument. Expected {none, sigmoid, relu, "
                         "tanh} got ";
            std::cerr << activation << std::endl;
            std::exit(EXIT_FAILURE);
        }
        this->activation = activation;  // Setting activation name
        this->neurons = neurons;        // Setting number of neurons
        // Initialize kernel according to flag
        if (random_kernel) {
            uniform_random_initialization(kernel, kernel_shape, -1.0, 1.0);
        } else {
            unit_matrix_initialization(kernel, kernel_shape);
        }
    }

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DenseLayer() [2/4]

machine_learning::neural_network::layers::DenseLayer::DenseLayer ( const int & neurons, const std::string & activation, const std::vector< std::valarray< double > > & kernel )

inline

Constructor for neural_network::layers::DenseLayer class

Parameters

neurons	number of neurons
activation	activation function for layer
kernel	values of kernel (useful in loading model)

                                                             {
        // Choosing activation (and it's derivative)
        if (activation == "sigmoid") {
            activation_function = neural_network::activations::sigmoid;
            dactivation_function = neural_network::activations::sigmoid;
        } else if (activation == "relu") {
            activation_function = neural_network::activations::relu;
            dactivation_function = neural_network::activations::drelu;
        } else if (activation == "tanh") {
            activation_function = neural_network::activations::tanh;
            dactivation_function = neural_network::activations::dtanh;
        } else if (activation == "none") {
            // Set identity function in casse of none is supplied
            activation_function =
                neural_network::util_functions::identity_function;
            dactivation_function =
                neural_network::util_functions::identity_function;
        } else {
            // If supplied activation is invalid
            std::cerr << "ERROR (" << __func__ << ") : ";
            std::cerr << "Invalid argument. Expected {none, sigmoid, relu, "
                         "tanh} got ";
            std::cerr << activation << std::endl;
            std::exit(EXIT_FAILURE);
        }
        this->activation = activation;  // Setting activation name
        this->neurons = neurons;        // Setting number of neurons
        this->kernel = kernel;          // Setting supplied kernel values
    }

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DenseLayer() [3/4]

machine_learning::neural_network::layers::DenseLayer::DenseLayer ( const DenseLayer & layer )

default

Copy Constructor for class DenseLayer.

Parameters

model

instance of class to be copied.

~DenseLayer()

machine_learning::neural_network::layers::DenseLayer::~DenseLayer ( )

default

Destructor for class DenseLayer.

DenseLayer() [4/4]

machine_learning::neural_network::layers::DenseLayer::DenseLayer ( DenseLayer && )

default

Move constructor for class DenseLayer

Member Function Documentation

operator=() [1/2]

DenseLayer & machine_learning::neural_network::layers::DenseLayer::operator= ( const DenseLayer & layer )

default

Copy assignment operator for class DenseLayer

operator=() [2/2]

DenseLayer & machine_learning::neural_network::layers::DenseLayer::operator= ( DenseLayer && )

default

Move assignment operator for class DenseLayer

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

• machine_learning/neural_network.cpp