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Martin Beseda authored
FIX: Removed 'normalized' and 'de-normalized' evaluation as there is just 'de-normalized' natively from now.
Martin Beseda authoredFIX: Removed 'normalized' and 'de-normalized' evaluation as there is just 'de-normalized' natively from now.
simulator.cpp 7.11 KiB
//
// Created by martin on 25.11.18.
//
#include <iostream>
#include <cstdio>
#include <fstream>
#include <vector>
#include <utility>
#include <algorithm>
#include <assert.h>
#include "4neuro.h"
#include "../CrossValidator/CrossValidator.h"
int main(int argc, char** argv) {
try {
/* PHASE 1 - TRAINING DATA LOADING, NETWORK ASSEMBLY AND PARTICLE SWARM OPTIMIZATION */
l4n::CSVReader reader1("/home/martin/Desktop/lib4neuro_test2/simulator_input.txt", "\t", true); // File, separator, skip 1st line
reader1.read(); // Read from the file
/* PHASE 1 - NEURAL NETWORK SPECIFICATION */
/* Create data set for both the first training of the neural network */
/* Specify which columns are inputs or outputs */
std::vector<unsigned int> inputs1 = { 0 }; // Possible multiple inputs, e.g. {0,3}, column indices starting from 0
std::vector<unsigned int> outputs1 = { 1 }; // Possible multiple outputs, e.g. {1,2}
l4n::DataSet ds1 = reader1.get_data_set(&inputs1, &outputs1); // Creation of data-set for NN
ds1.normalize(); // Normalization of data to prevent numerical problems
/* Numbers of neurons in layers (including input and output layers) */
std::vector<unsigned int> neuron_numbers_in_layers = { 1, 3, 1 };
/* Fully connected feed-forward network with linear activation functions for input and output */
/* layers and the specified activation fns for the hidden ones (each entry = layer)*/
std::vector<l4n::NEURON_TYPE> hidden_type_v = { l4n::NEURON_TYPE::LOGISTIC}; // hidden_type_v = {l4n::NEURON_TYPE::LOGISTIC, l4n::NEURON_TYPE::LINEAR}
l4n::FullyConnectedFFN nn1(&neuron_numbers_in_layers, &hidden_type_v);
/* Error function */
l4n::MSE mse1(&nn1, &ds1); // First parameter - neural network, second parameter - data-set
/* Particle Swarm method domain*/
std::vector<double> domain_bounds(2 * (nn1.get_n_weights() + nn1.get_n_biases()));
for (size_t i = 0; i < domain_bounds.size() / 2; ++i) {
domain_bounds[2 * i] = -10;
domain_bounds[2 * i + 1] = 10;
}
// Parameters of the Particle Swarm
// 1) domain_bounds Bounds for every optimized parameter (p1_lower, p1_upper, p2_lower, p2_upper...)
// 2) c1 Cognitive parameter
// 3) c2 Social parameter
// 4) w Inertia weight
// 5) gamma Threshold value for particle velocity - all particles must posses the same or slower velocity for the algorithm to end
// 6) epsilon Radius of the cluster area (Euclidean distance)
// 7) delta Amount of particles, which has to be in the cluster for the algorithm to stop (0-1)
// 8) n_particles Number of particles in the swarm
// 9) iter_max Maximal number of iterations - optimization will stop after that, even if not converged
l4n::ParticleSwarm ps(&domain_bounds,
1.711897,
1.711897,
0.711897,
0.5,
0.3,
0.7,
150,
1500);
/* Weight and bias randomization in the network accordingly to the uniform distribution */
nn1.randomize_parameters();
/* Particle Swarm optimization */
ps.optimize(mse1);
/* Save Neural network parameters to file */
nn1.save_text("test_net_Particle_Swarm.4n");
/* PHASE 3 - LOADING NN FROM FILE AND TRAINING NO 2 - GRADIENT DESCENT */
l4n::NeuralNetwork nn2("test_net_Particle_Swarm.4n");
/* Training data loading for the second phase */
l4n::CSVReader reader2("/home/martin/Desktop/lib4neuro_test2/simulator_input.txt", "\t", true); // File, separator, skip 1st line
reader2.read(); // Read from the file
/* Create data set for both the first training of the neural network */
/* Specify which columns are inputs or outputs */
std::vector<unsigned int> inputs2 = { 0 }; // Possible multiple inputs, e.g. {0,3}, column indices starting from 0
std::vector<unsigned int> outputs2 = { 1 }; // Possible multiple outputs, e.g. {1,2}
l4n::DataSet ds2 = reader2.get_data_set(&inputs2, &outputs2); // Creation of data-set for NN
ds2.normalize(); // Normalization of data to prevent numerical problems
/* Error function */
l4n::MSE mse2(&nn2, &ds2); // First parameter - neural network, second parameter - data-set
// Parameters of the gradient descent
// 1) Threshold for the successful ending of the optimization - deviation from minima
// 2) Number of iterations to reset step size to tolerance/10.0
// 3) Maximal number of iterations - optimization will stop after that, even if not converged
l4n::GradientDescent gs(1e-4, 150, 200000);
/* Gradient Descent Optimization */
gs.optimize(mse2); // Network training
/* Save Neural network parameters to file */
nn2.save_text("test_net_Gradient_Descent.4n");
/* PHASE 4 - TESTING DATA */
/* Output file specification */
std::string filename = "simulator_output.txt";
std::ofstream output_file(filename);
if (!output_file.is_open()) {
throw std::runtime_error("File '" + filename + "' can't be opened!");
}
/* Neural network loading */
l4n::NeuralNetwork nn3("test_net_Gradient_Descent.4n");
/* Check of the saved network - write to the file */
output_file << std::endl << "The loaded network info:" << std::endl;
nn3.write_stats(&output_file);
nn3.write_weights(&output_file);
nn3.write_biases(&output_file);
/* Evaluate network on an arbitrary data-set and save results into the file */
l4n::CSVReader reader3("/home/martin/Desktop/lib4neuro_test2/simulator_input.txt", "\t", true); // File, separator, skip 1st line
reader3.read(); // Read from the file
/* Create data set for both the testing of the neural network */
/* Specify which columns are inputs or outputs */
std::vector<unsigned int> inputs3 = { 0 }; // Possible multiple inputs, e.g. {0,3}, column indices starting from 0
std::vector<unsigned int> outputs3 = { 1 }; // Possible multiple outputs, e.g. {1,2}
l4n::DataSet ds3 = reader3.get_data_set(&inputs3, &outputs3); // Creation of data-set for NN
ds3.normalize(); // Normalization of data to prevent numerical problems
output_file << std::endl << "Evaluating network on the dataset: " << std::endl;
ds3.store_data_text(&output_file);
output_file << "Output and the error:" << std::endl;
/* Error function */
l4n::MSE mse3(&nn3, &ds3); // First parameter - neural network, second parameter - data-set
mse3.eval_on_data_set(&ds3, &output_file);
/* Close the output file for writing */
output_file.close();
return 0;
}
catch (const std::exception& e) {
std::cerr << e.what() << std::endl;
exit(EXIT_FAILURE);
}
}