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Martin Beseda authored
Rewitten compilation to simplify CMake scripts and to be able to compile examples just linked with 4neuro without being forced to link/include other dependencies.
Martin Beseda authoredRewitten compilation to simplify CMake scripts and to be able to compile examples just linked with 4neuro without being forced to link/include other dependencies.
NeuralNetwork.h 8.47 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 "../settings.h"
#include <iostream>
#include <vector>
#include <algorithm>
#include <utility>
#include <fstream>
#include <boost/random/mersenne_twister.hpp>
#include <boost/random/uniform_real_distribution.hpp>
#include <boost/archive/text_oarchive.hpp>
#include <boost/archive/text_iarchive.hpp>
#include <boost/serialization/list.hpp>
#include <boost/serialization/string.hpp>
#include <boost/serialization/version.hpp>
#include <boost/serialization/split_member.hpp>
#include <boost/serialization/export.hpp>
#include <boost/serialization/vector.hpp>
#include <boost/serialization/utility.hpp>
#include "../src/Neuron/Neuron.h"
#include "../src/Neuron/NeuronConstant.h"
#include "../src/Neuron/NeuronBinary.h"
#include "../src/Neuron/NeuronLinear.h"
#include "../src/Neuron/NeuronLogistic.h"
#include "../src/NetConnection/ConnectionFunctionGeneral.h"
#include "../src/NetConnection/ConnectionFunctionIdentity.h"
enum class BIAS_TYPE{NEXT_BIAS, NO_BIAS, EXISTING_BIAS};
enum class SIMPLE_CONNECTION_TYPE{NEXT_WEIGHT, UNITARY_WEIGHT, EXISTING_WEIGHT};
/**
*
*/
class NeuralNetwork {
private:
friend class boost::serialization::access;
/**
*
*/
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( );
template<class Archive>
void serialize(Archive & ar, const unsigned int version) {
ar & this->neurons;
ar & this->input_neuron_indices;
ar & this->output_neuron_indices;
ar & this->connection_list;
ar & this->neuron_biases;
ar & this-> neuron_bias_indices;
ar & this->neuron_potentials;
ar & this->connection_weights;
ar & this->inward_adjacency;
ar & this->outward_adjacency;
ar & this->neuron_layers_feedforward;
ar & this->neuron_layers_feedbackward;
ar & this->layers_analyzed;
ar & this->delete_weights;
ar & this->delete_biases;
};
public:
/**
*
*/
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