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Martin Beseda authored
ENH: Only classes belonging to API are now contained in 4neuro.h. All those classes were enclosed in 'lib4neuro' namespace.
Martin Beseda authoredENH: Only classes belonging to API are now contained in 4neuro.h. All those classes were enclosed in 'lib4neuro' namespace.
NeuralNetwork.h 8.50 KiB
/**
* This file contains the header for the NeuralNetwork class representing a function in the form of a directed graph,
* in which the vertices are called Neurons (with activation functions) and the edges Connections (with transfer functions)
*
* @author Michal Kravčenko
* @date 13.6.18 -
*/
//TODO preprocess the feed-forward and backward passes for more efficient parallelism
#ifndef INC_4NEURO_NEURALNETWORK_H
#define INC_4NEURO_NEURALNETWORK_H
#include <iostream>
#include <vector>
#include <algorithm>
#include <utility>
#include <fstream>
#include "../settings.h"
#include "../Neuron/Neuron.h"
#include "../Neuron/NeuronConstant.h"
#include "../Neuron/NeuronBinary.h"
#include "../Neuron/NeuronLinear.h"
#include "../Neuron/NeuronLogistic.h"
#include "../NetConnection/ConnectionFunctionGeneral.h"
#include "../NetConnection/ConnectionFunctionIdentity.h"
namespace lib4neuro {
enum class BIAS_TYPE {
NEXT_BIAS, NO_BIAS, EXISTING_BIAS
};
enum class SIMPLE_CONNECTION_TYPE {
NEXT_WEIGHT, UNITARY_WEIGHT, EXISTING_WEIGHT
};
/**
*
*/
class NeuralNetwork {
private:
/**
*
*/
std::vector<Neuron *> *neurons = nullptr;
/**
*
*/
std::vector<size_t> *input_neuron_indices = nullptr;
/**
*
*/
std::vector<size_t> *output_neuron_indices = nullptr;
/**
*
*/
std::vector<double> *connection_weights = nullptr;
/**
*
*/ std::vector<double> *neuron_biases = nullptr;
/**
*
*/
std::vector<int> *neuron_bias_indices = nullptr;
/**
*
*/
std::vector<double> *neuron_potentials = nullptr;
/**
*
*/
std::vector<ConnectionFunctionGeneral *> *connection_list = nullptr;
/**
*
*/
std::vector<std::vector<std::pair<size_t, size_t>> *> *inward_adjacency = nullptr;
/**
*
*/
std::vector<std::vector<std::pair<size_t, size_t>> *> *outward_adjacency = nullptr;
/**
*
*/
std::vector<std::vector<size_t> *> *neuron_layers_feedforward = nullptr;
/**
*
*/
std::vector<std::vector<size_t> *> *neuron_layers_feedbackward = nullptr;
/**
*
*/
bool layers_analyzed = false;
/**
*
*/
bool delete_weights = true;
/**
*
*/
bool delete_biases = true;
/**
* Adds a new connection to the local list of connections
* @param con Connection object to be added
* @return Returns the index of the added connection among all the connections
*/
size_t add_new_connection_to_list(ConnectionFunctionGeneral *con);
/**
* Adds a new entry (oriented edge s -> t) to the adjacency list of this network
* @param s Index of the source neuron
* @param t Index of the target neuron
* @param con_idx Index of the connection representing the edge
*/
void add_outward_connection(size_t s, size_t t, size_t con_idx);
/**
* Adds a new entry (oriented edge s <- t) to the adjacency list of this network
* @param s Index of the source neuron * @param t Index of the target neuron
* @param con_idx Index of the connection representing the edge
*/
void add_inward_connection(size_t s, size_t t, size_t con_idx);
/**
* Performs one feedforward pass and feedbackward pass during which determines the layers of this neural network
* for simpler use during evaluation and learning
*/
void analyze_layer_structure();
public:
/**
* Struct used to access private properties from
* the serialization function
*/
struct access;
/**
*
*/
LIB4NEURO_API explicit NeuralNetwork();
/**
*
*/
LIB4NEURO_API explicit NeuralNetwork(std::string filepath);
/**
*
*/
LIB4NEURO_API virtual ~NeuralNetwork();
/**
* If possible, returns a neural net with 'input_neuron_indices' neurons as inputs and 'output_neuron_indices' as
* outputs, otherwise returns nullptr. The returned object shares adjustable weights with this network. All
* neurons are coppied (new instances), edges also. Uses a breadth-first search as the underlying algorithm.
* @param input_neuron_indices
* @param output_neuron_indices
* @return
*/
LIB4NEURO_API NeuralNetwork *
get_subnet(std::vector<size_t> &input_neuron_indices, std::vector<size_t> &output_neuron_indices);
/**
* Replaces the values in @{this->connection_weights} and @{this->neuron_biases} by the provided values
* @param parameters
*/
LIB4NEURO_API virtual void copy_parameter_space(std::vector<double> *parameters);
/**
* Copies the pointers @{this->connection_weights} and @{this->neuron_biases} from the parental network, sets
* flags to not delete the vectors in this object
* @param parent_network
*/
LIB4NEURO_API virtual void set_parameter_space_pointers(NeuralNetwork &parent_network);
/**
*
* @param input
* @param output
* @param custom_weights_and_biases
*/
LIB4NEURO_API virtual void eval_single(std::vector<double> &input, std::vector<double> &output,
std::vector<double> *custom_weights_and_biases = nullptr);
/**
* Adds a new neuron to the list of neurons. Also assigns a valid bias value to its activation function
* @param[in] n * @return
*/
LIB4NEURO_API size_t add_neuron(Neuron *n, BIAS_TYPE bt = BIAS_TYPE::NEXT_BIAS, size_t bias_idx = 0);
/**
*
* @param n1_idx
* @param n2_idx
* @return
*/
LIB4NEURO_API size_t add_connection_simple(size_t n1_idx, size_t n2_idx,
SIMPLE_CONNECTION_TYPE sct = SIMPLE_CONNECTION_TYPE::NEXT_WEIGHT,
size_t weight_idx = 0);
/**
* Take the existing connection with index 'connection_idx' in 'parent_network' and adds it to the structure of this
* object
* @param n1_idx
* @param n2_idx
* @param connection_idx
* @param parent_network
*/
LIB4NEURO_API void
add_existing_connection(size_t n1_idx, size_t n2_idx, size_t connection_idx, NeuralNetwork &parent_network);
/**
*
*/
LIB4NEURO_API void randomize_weights();
/**
*
*/
LIB4NEURO_API void randomize_biases();
/**
*
* @return
*/
LIB4NEURO_API virtual size_t get_n_inputs();
/**
*
* @return
*/
LIB4NEURO_API virtual size_t get_n_outputs();
/**
*
* @return
*/
LIB4NEURO_API virtual size_t get_n_weights();
/**
*
* @return
*/
LIB4NEURO_API virtual size_t get_n_biases();
/**
*
* @return
*/
LIB4NEURO_API virtual int get_neuron_bias_index(size_t neuron_idx);
/**
*
* @return
*/ LIB4NEURO_API virtual size_t get_n_neurons();
/**
*
* @param input_neurons_indices
*/
LIB4NEURO_API void specify_input_neurons(std::vector<size_t> &input_neurons_indices);
/**
*
* @param output_neurons_indices
*/
LIB4NEURO_API void specify_output_neurons(std::vector<size_t> &output_neurons_indices);
/**
*
*/
LIB4NEURO_API void print_weights();
/**
*
*/
LIB4NEURO_API void print_stats();
/**
*
* @return
*/
LIB4NEURO_API std::vector<double> *get_parameter_ptr_weights();
/**
*
* @return
*/
LIB4NEURO_API std::vector<double> *get_parameter_ptr_biases();
/**
*
* @param filepath
*/
LIB4NEURO_API void save_text(std::string filepath);
};
}
#endif //INC_4NEURO_NEURALNETWORK_H