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Add logging header file.

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davidjacnogueira
2016-11-03 23:05:54 +00:00
parent 7965d7b748
commit f647b05f70
9 changed files with 6812 additions and 134 deletions
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134
src/MLP.cpp
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@@ -10,6 +10,7 @@
#include <fstream>
#include <vector>
#include <algorithm>
#include "easylogging++.h"
bool MLP::ExportNNWeights(std::vector<double> *weights) const {
return true;
@@ -86,29 +87,26 @@ void MLP::UpdateMiniBatch(const std::vector<TrainingSample> &training_sample_set
int num_examples = training_sample_set_with_bias.size();
int num_features = training_sample_set_with_bias[0].GetInputVectorSize();
{
int layer_i = -1;
int node_i = -1;
std::cout << "Starting weights:" << std::endl;
for (const auto & layer : m_layers) {
layer_i++;
node_i = -1;
std::cout << "Layer " << layer_i << " :" << std::endl;
for (const auto & node : layer.GetNodes()) {
node_i++;
std::cout << "\tNode " << node_i << " :\t";
for (auto m_weightselement : node.GetWeights()) {
std::cout << m_weightselement << "\t";
}
std::cout << std::endl;
}
}
}
//{
// int layer_i = -1;
// int node_i = -1;
// std::cout << "Starting weights:" << std::endl;
// for (const auto & layer : m_layers) {
// layer_i++;
// node_i = -1;
// std::cout << "Layer " << layer_i << " :" << std::endl;
// for (const auto & node : layer.GetNodes()) {
// node_i++;
// std::cout << "\tNode " << node_i << " :\t";
// for (auto m_weightselement : node.GetWeights()) {
// std::cout << m_weightselement << "\t";
// }
// std::cout << std::endl;
// }
// }
//}
size_t i = 0;
for ( i = 0; i < max_iterations; i++) {
//std::cout << "******************************" << std::endl;
//std::cout << "******** ITER " << i << std::endl;
//std::cout << "******************************" << std::endl;
for (i = 0; i < max_iterations; i++) {
double current_iteration_cost_function = 0.0;
for (auto & training_sample_with_bias : training_sample_set_with_bias) {
std::vector<double> predicted_output;
@@ -122,16 +120,19 @@ void MLP::UpdateMiniBatch(const std::vector<TrainingSample> &training_sample_set
assert(correct_output.size() == predicted_output.size());
std::vector<double> deriv_error_output(predicted_output.size());
//std::cout << training_sample_with_bias << "\t\t";
//{
// std::cout << "Predicted output: [";
// for (int i = 0; i < predicted_output.size(); i++) {
// if (i != 0)
// std::cout << ", ";
// std::cout << predicted_output[i];
// }
// std::cout << "]" << std::endl;
//}
if ((i % (max_iterations / 100)) == 0) {
std::stringstream temp_training;
temp_training << training_sample_with_bias << "\t\t";
temp_training << "Predicted output: [";
for (int i = 0; i < predicted_output.size(); i++) {
if (i != 0)
temp_training << ", ";
temp_training << predicted_output[i];
}
temp_training << "]";
LOG(INFO) << temp_training.str();
}
for (int j = 0; j < predicted_output.size(); j++) {
current_iteration_cost_function +=
@@ -145,54 +146,35 @@ void MLP::UpdateMiniBatch(const std::vector<TrainingSample> &training_sample_set
learning_rate);
}
if((i% (max_iterations/100))==0)
std::cout << "Iteration "<< i << " cost function f(error): "
<< current_iteration_cost_function << std::endl;
if ((i % (max_iterations / 100)) == 0)
LOG(INFO) << "Iteration " << i << " cost function f(error): "
<< current_iteration_cost_function;
if (current_iteration_cost_function < min_error_cost)
break;
//{
// int layer_i = -1;
// int node_i = -1;
// std::cout << "Current weights:" << std::endl;
// for (const auto & layer : m_layers) {
// layer_i++;
// node_i = -1;
// std::cout << "Layer " << layer_i << " :" << std::endl;
// for (const auto & node : layer.GetNodes()) {
// node_i++;
// std::cout << "\tNode " << node_i << " :\t";
// for (auto m_weightselement : node.GetWeights()) {
// std::cout << m_weightselement << "\t";
// }
// std::cout << std::endl;
// }
// }
//}
}
std::cout << "******************************" << std::endl;
std::cout << "******* TRAINING ENDED *******" << std::endl;
std::cout << "******* " << i << " iters *******" << std::endl;
std::cout << "******************************" << std::endl;
{
int layer_i = -1;
int node_i = -1;
std::cout << "Final weights:" << std::endl;
for (const auto & layer : m_layers) {
layer_i++;
node_i = -1;
std::cout << "Layer " << layer_i << " :" << std::endl;
for (const auto & node : layer.GetNodes()) {
node_i++;
std::cout << "\tNode " << node_i << " :\t";
for (auto m_weightselement : node.GetWeights()) {
std::cout << m_weightselement << "\t";
}
std::cout << std::endl;
}
}
}
LOG(INFO) << "******************************" ;
LOG(INFO) << "******* TRAINING ENDED *******";
LOG(INFO) << "******* " << i << " iters *******";
LOG(INFO) << "******************************";
//{
// int layer_i = -1;
// int node_i = -1;
// std::cout << "Final weights:" << std::endl;
// for (const auto & layer : m_layers) {
// layer_i++;
// node_i = -1;
// std::cout << "Layer " << layer_i << " :" << std::endl;
// for (const auto & node : layer.GetNodes()) {
// node_i++;
// std::cout << "\tNode " << node_i << " :\t";
// for (auto m_weightselement : node.GetWeights()) {
// std::cout << m_weightselement << "\t";
// }
// std::cout << std::endl;
// }
// }
//}
};