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/**
* DESCRIPTION OF THE FILE
*
* @author Michal Kravčenko
* @date 4.2.19 -
*/
#include "GradientDescentBB.h"
#include "message.h"
namespace lib4neuro {
GradientDescentBB::GradientDescentBB(double epsilon, size_t n_to_restart, int max_iters, size_t batch) {
this->tolerance = epsilon;
this->restart_frequency = n_to_restart;
this->optimal_parameters = new std::vector<double>(0);
this->maximum_niters = max_iters;
this->batch = batch;
}
GradientDescentBB::~GradientDescentBB() {
if (this->optimal_parameters) {
delete this->optimal_parameters;
this->optimal_parameters = nullptr;
}
}
void GradientDescentBB::optimize(lib4neuro::ErrorFunction &ef, std::ofstream* ofs) {
/* Copy data set max and min values, if it's normalized */
if(ef.get_dataset()->is_normalized()) {
ef.get_network_instance()->set_normalization_strategy_instance(
ef.get_dataset()->get_normalization_strategy());
}
COUT_INFO("Finding a solution via a Gradient Descent method with adaptive step-length..." << std::endl);
COUT_INFO("Initial error: " << ef.eval() << std::endl);
if(ofs && ofs->is_open()) {
*ofs << "Finding a solution via a Gradient Descent method with adaptive step-length..." << std::endl;
*ofs << "Initial error: " << ef.eval() << std::endl;
}
double grad_norm = this->tolerance * 10.0, gamma, sx, beta;
double grad_norm_prev;
size_t i;
long long int iter_idx = this->maximum_niters;
size_t iter_counter = 0;
gamma = 1.0;
double prev_val, val = 0.0, c = 1.25, val_best;
size_t n_parameters = ef.get_dimension();
std::vector<double> *gradient_current = new std::vector<double>(n_parameters);
std::vector<double> *gradient_prev = new std::vector<double>(n_parameters);
std::vector<double> *params_current = ef.get_parameters();
std::vector<double> *params_prev = new std::vector<double>(n_parameters);
std::vector<double> *params_best = new std::vector<double>(*params_current);
std::vector<double> *ptr_mem;
double alpha = -1.0, cc, gg;
std::vector<double> dot__( 3 );
double d1 = 0.0, d2 = 0.0, d3 = 0.0;
std::fill(gradient_current->begin(), gradient_current->end(), 0.0);
std::fill(gradient_prev->begin(), gradient_prev->end(), 0.0);
val = ef.eval(params_current);
val_best = val;
// this-> batch = 0;
double cooling_factor = 1.0;
while (grad_norm > this->tolerance && (iter_idx != 0)) {
iter_idx--;
iter_counter++;
prev_val = val;
grad_norm_prev = grad_norm;
/* reset of the current gradient */
std::fill(gradient_current->begin(), gradient_current->end(), 0.0);
// std::fill(gradient_mem.begin(), gradient_mem.end(), 0.0);
ef.calculate_error_gradient(*params_current, *gradient_current, 1.0, this->batch);
// double error_analytical = this->calculate_gradient( ef.get_dataset()->get_data(), (size_t)2, params_current, gradient_current );
// for(size_t k = 0; k < gradient_mem.size(); ++k){
// printf("%f : %f\n", gradient_mem[ k ], gradient_current->at( k ));
// }
// printf("---------------------\n");
grad_norm = 0.0;
for (auto v: *gradient_current) {
grad_norm += v * v;
//COUT_DEBUG( grad_norm << std::endl );
}
grad_norm = std::sqrt(grad_norm);
/* Update of the parameters */
/* step length calculation */
if (iter_counter < 10 || iter_counter % this->restart_frequency < 10 ) {
/* fixed step length */
gamma = 0.1 * this->tolerance;
// gamma = 1 / grad_norm;
// gamma = 1e-4;
cooling_factor = 1.0;
} else {
std::fill( dot__.begin( ), dot__.end( ), 0.0 );
d1 = d2 = d3 = 0.0;
for ( int d = 0; d < gradient_current->size(); d++ ) {
cc = params_current->at( d ) - params_prev->at( d );
gg = gradient_current->at( d ) - gradient_prev->at( d );
d1 += cc * cc;
d2 += cc * gg;
d3 += gg * gg;
}
dot__[0] = d1;
dot__[1] = d2;
dot__[2] = d3;
gamma = 1;
if ( fabs( dot__[1] ) > 0.0 ) {
gamma = 0.25*( dot__[0] / dot__[1] );
}
}
for (i = 0; i < gradient_current->size(); ++i) {
(*params_prev)[i] = (*params_current)[i] - cooling_factor * gamma * (*gradient_current)[i];
}
/* switcheroo */
ptr_mem = gradient_prev;
gradient_prev = gradient_current;
gradient_current = ptr_mem;
ptr_mem = params_prev;
params_prev = params_current;
params_current = ptr_mem;
val = ef.eval(params_current);
if( val < val_best ){
val_best = val;
for(i = 0; i < gradient_current->size(); ++i){
params_best->at( i ) = params_current->at( i );
}
}
COUT_DEBUG(std::string("Iteration: ") << (unsigned int)(iter_counter)
<< ". Step size: " << gamma*cooling_factor
<< ". C: " << c
<< ". Gradient norm: " << grad_norm
<< ". Total error: " << val << ". the lowest error: " << val_best
<< ".\r" );
WRITE_TO_OFS_DEBUG(ofs, "Iteration: " << (unsigned int)(iter_counter)
<< ". Step size: " << gamma*cooling_factor
<< ". C: " << c
<< ". Gradient norm: " << grad_norm
<< ". Total error: " << val << ". the lowest error: " << val_best
<< "." << std::endl);
// if(iter_counter % 100 == 0){
// COUT_INFO(std::string("Iteration: ") << (unsigned int)(iter_counter)
// << ". Step size: " << gamma
// << ". C: " << c
// << ". Gradient norm: " << grad_norm
// << ". Total error: " << val
// << ".\r");
// }
cooling_factor *= 0.99999;
}
COUT_DEBUG(std::string("Iteration: ") << (unsigned int)(iter_counter)
<< ". Step size: " << gamma*cooling_factor
<< ". C: " << c
<< ". Gradient norm: " << grad_norm
<< ". Total error: " << val
<< "." << std::endl);
if(iter_idx == 0) {
COUT_INFO(std::endl << "Maximum number of iterations (" << this->maximum_niters << ") was reached! Final error: " << val_best << std::endl);
if(ofs && ofs->is_open()) {
*ofs << "Maximum number of iterations (" << this->maximum_niters << ") was reached! Final error: " << val_best << std::endl;
}
} else {
COUT_INFO(std::endl << "Gradient Descent method converged after "
<< this->maximum_niters - iter_idx
<< " iterations. Final error:" << val_best
<< std::endl);
#ifdef L4N_DEBUG
if(ofs && ofs->is_open()) {
*ofs << "Gradient Descent method converged after "
<< this->maximum_niters-iter_idx
<< " iterations."
<< std::endl;
}
#endif
}
*this->optimal_parameters = *params_best;
// ef.analyze_error_gradient(*params_current, *gradient_current, 1.0, this->batch);
ef.get_network_instance()->copy_parameter_space(this->optimal_parameters);
delete gradient_current;
delete gradient_prev;
delete params_current;
delete params_prev;
delete params_best;
}
std::vector<double> *GradientDescentBB::get_parameters() {
return this->optimal_parameters;
}
}