int main() {
cv::Mat img = util::in();
auto vec = mat_to_vector( img );
FANN::neural_net nn;
nn.create_from_file( "neural.net" );
auto result = nn.run( vec.data() );
std::cout << "Dog index: " << result[0] << '\n';
std::cout << "Human index: " << result[1] << '\n';
}
// Train the net with all the data
void train_net(FANN::neural_net &net,std::string oppName, std::string day, std::string type, const int num_output)
{
const float desired_error = 0.001f;
const unsigned int max_iterations = 1000;
const unsigned int iterations_between_reports = 1000;
std::string trainFileName="input/"+type+"_Casino_Day-"+day+"_"+oppName+"_vs_mybotisamazing.txt";
std::string valFileName="input/"+type+"_Casino_Day-"+day+"_mybotisamazing_vs_"+oppName+".txt";
cout << endl << "Training network." << endl;
FANN::training_data data;
FANN::training_data vData;
data.read_train_from_file(trainFileName);
vData.read_train_from_file(valFileName);
// Initialize and train the network with the data
net.init_weights(data);
cout << "Max Epochs " << setw(8) << max_iterations << ". "
<< "Desired Error: " << left << desired_error << right << endl;
net.set_callback(print_callback, NULL);
clock_t start=clock();
net.train_on_data(data, max_iterations,
iterations_between_reports, desired_error);
clock_t end=clock();
cout<<"Runtime:"<< end-start<<endl;
cout << endl << "Testing network." << endl;
std::string oFileName="output/"+type+"_"+oppName+"_day_"+day+".txt";
std::string actionFile="input/action_Casino_Day-"+day+"_mybotisamazing_vs_"+oppName+".txt";
validate_net(net, oFileName,actionFile, &vData, num_output);
/////////////////////////////////////////////////////
net.train_on_data(vData,max_iterations,iterations_between_reports,desired_error);
std::string oFileName2="output/"+type+"2_"+oppName+"_day_"+day+".txt";
std::string actionFile2="input/action_Casino_Day-"+day+"_mybotisamazing_vs_"+oppName+".txt";
validate_net(net,oFileName2,actionFile,&vData,num_output);
// Save the network in floating point and fixed point
std::string netFile="output/"+type+"_"+oppName+"_day_"+day+".net";
net.save(netFile);
//unsigned int decimal_point = net.save_to_fixed("training.net");
//data.save_train_to_fixed("training_fixed.data", decimal_point);
}
// Callback function that simply prints the information to cout
int print_callback(FANN::neural_net &net, FANN::training_data &train,
unsigned int max_epochs, unsigned int epochs_between_reports,
float desired_error, unsigned int epochs, void *user_data)
{
cout << "Epochs " << setw(8) << epochs << ". "
<< "Current Error: " << left << net.get_MSE() << right << endl;
return 0;
}
// Callback function that simply prints the information to cout
int printCallback(FANN::neural_net &net, FANN::training_data &train,
unsigned int max_epochs, unsigned int epochs_between_reports,
float desired_error, unsigned int epochs, void *user_data)
{
qDebug() << "epoka: " << epochs << " " << "blad: " << net.get_MSE();
//zapisac do pliku
return 0;
}
// Callback function that simply prints the information to cout
int print_callback(FANN::neural_net &net, FANN::training_data &train,
unsigned int max_epochs, unsigned int epochs_between_reports,
float desired_error, unsigned int epochs, void *user_data)
{
ofstream &eh = *(static_cast<ofstream*>(user_data));
eh << net.get_MSE() << " ";
return 0;
}
void validate_net(FANN::neural_net &net,const std::string oFileName, const std::string actionFileName, FANN::training_data * vData, int num_output){
ofstream outputFile(oFileName.c_str());
ifstream finAction(actionFileName.c_str());
float sumMSE=0;
for (unsigned int i = 0; i < vData->length_train_data(); ++i)
{
// Run the network on the test data
fann_type * input=vData->get_input()[i];
fann_type *calc_out = net.run(input);
float mse=calc_MSE(calc_out, vData->get_output()[i],num_output);
sumMSE+=mse;
std::string line;
getline(finAction, line);
outputFile<< "***********"<<mse<<endl;
if((int)input[19]==1){
outputFile<<"PREFLOP"<<endl;
}else if((int)input[20]==1){
outputFile<<"FLOP"<<endl;
}else if((int)input[21]==1){
outputFile<<"TURN"<<endl;
}else if((int)input[22]==1){
outputFile<<"RIVER"<<endl;
}
outputFile<<line<<endl;
outputFile<<"nn output"<<endl;
for(int k=0; k<num_output; k++){
outputFile<<calc_out[k]<<" ";
}
outputFile<<endl;
for(int j=0; j<num_output;j++){
outputFile<<vData->get_output()[i][j]<<" ";
}
outputFile<<endl;
}
outputFile<<"Average MSE: "<<sumMSE/vData->length_train_data()<<endl;
outputFile.close();
}
//Creates a neural net with 1 hidden layer
FANN::neural_net create_net(float learning_rate, unsigned int num_layers,unsigned int num_input, unsigned int num_hidden, unsigned int num_output){
FANN::neural_net net;
unsigned int layers[3]={num_input,num_hidden,num_output};
net.create_standard_array(num_layers,layers);
net.set_learning_rate(learning_rate);
net.set_activation_steepness_hidden(.1);
net.set_activation_steepness_output(.1);
net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE);
net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE);
//net.set_training_algorithm(FANN::TRAIN_INCREMENTAL);
// Set additional properties such as the training algorithm
//net.set_training_algorithm(FANN::TRAIN_QUICKPROP);
return net;
}
void FannTest::AssertCreate(FANN::neural_net &net, unsigned int numLayers, unsigned int *layers,
unsigned int neurons, unsigned int connections) {
EXPECT_EQ(numLayers, net.get_num_layers());
EXPECT_EQ(layers[0], net.get_num_input());
EXPECT_EQ(layers[numLayers - 1], net.get_num_output());
unsigned int *layers_res = new unsigned int[numLayers];
net.get_layer_array(layers_res);
for (unsigned int i = 0; i < numLayers; i++) {
EXPECT_EQ(layers[i], layers_res[i]);
}
delete layers_res;
EXPECT_EQ(neurons, net.get_total_neurons());
EXPECT_EQ(connections, net.get_total_connections());
AssertWeights(-0.09, 0.09, 0.0);
}
int trainingThread::print_callback(FANN::neural_net &net, FANN::training_data &train,
unsigned int max_epochs, unsigned int epochs_between_reports,
float desired_error, unsigned int epochs, void *user_data)
{
static int i_old = 0;
std::stringstream log;
log << "Epochs " << std::setw(8) << epochs << ". "
<< "Current Error: " << std::left << net.get_MSE() << std::right << std::endl;
//
int i = (epochs * 100 )/ max_epochs;
if (i != i_old )
{
qDebug() << "i ma : " << i << " max_epochs " << max_epochs;
//emit updateProgressBar(i);
static_this->updateProgressBar(i);
static_this->updateLog(QString::fromStdString(log.str()));
i_old = i;
}
return 0;
}
int main(int argc, char *argv[])
{
if (argv[1][0] == 'r') {
WAVFile inp(argv[2]);
translate_wav(inp);
return 0;
}
if (argc == 1 || argc % 2 != 1) {
std::cout << "bad number of training examples\n";
return -1;
}
int to_open = (argc - 1)/2;
for (int i = 0; i < to_open; i++) {
WAVFile inp(argv[2*i+1]);
WAVFile out(argv[2*i+2]);
add_training_sound(inp, out);
}
float *train_in[input_training.size()];
float *train_out[output_training.size()];
for (int i = 0; i < input_training.size(); i++) {
train_in[i] = input_training[i];
train_out[i] = output_training[i];
}
FANN::training_data training;
training.set_train_data(input_training.size(), (samples_per_segment/2+1)*2,
train_in, (samples_per_segment/2+1)*2, train_out);
FANN::neural_net net;
const unsigned int layers[] = {(samples_per_segment/2+1)*2, (samples_per_segment/2+1)*2, (samples_per_segment/2+1)*2};
net.create_standard_array(3, (unsigned int*)layers);
net.set_activation_function_output(FANN::LINEAR);
//net.set_activation_function_hidden(FANN::LINEAR);
net.set_learning_rate(1.2f);
net.train_on_data(training, 50000, 1, 3.0f);
net.save("net.net");
}
void translate_wav(WAVFile input)
{
double *in;
fftw_complex *out;
fftw_complex *in_back;
double *out_back;
fftw_plan plan;
fftw_plan plan_back;
int length = input.m_data_header.sub_chunk_2_size;
in = (double*)fftw_malloc(sizeof(double)*samples_per_segment);
out = (fftw_complex*)fftw_malloc(sizeof(fftw_complex)*samples_per_segment/2+1);
in_back = (fftw_complex*)fftw_malloc(sizeof(fftw_complex)*samples_per_segment/2+1);
out_back = (double*)fftw_malloc(sizeof(double)*samples_per_segment+2);
plan = fftw_plan_dft_r2c_1d(samples_per_segment, in, out, FFTW_ESTIMATE);
plan_back = fftw_plan_dft_c2r_1d(samples_per_segment+1, in_back, out_back, FFTW_ESTIMATE);
FANN::neural_net net;
net.create_from_file("net.net");
short outbuffer[(((length/2)/samples_per_segment)+1)*samples_per_segment];
int sample_number = 0;
while (sample_number < length/2) {
for (int i = 0; i < samples_per_segment; i++) {
if (sample_number+i >= length/2) {
break;
}
in[i] = (double)input.m_data.PCM16[sample_number+i]/((double)(65536/2));
}
fftw_execute(plan);
float *input_train = (float*)malloc(sizeof(float)*samples_per_segment/2+1);
for (int i = 0; i < samples_per_segment/2+1; i++) {
input_train[i] = out[0][i];
}
// Use neural net to translate voice
std::cout << "outputting data\n";
float *out_net = net.run(input_train);
for (int i = 0; i < samples_per_segment/2+1; i++) {
in_back[0][i] = out_net[i];
in_back[1][i] = out_net[i+samples_per_segment/2+1];
//in_back[0][i] = out[0][i];
//in_back[1][i] = out[1][i];
}
memset(out_back, 0, sizeof(double)*samples_per_segment);
fftw_execute(plan_back);
for (int i = 0; i < samples_per_segment; i++) {
outbuffer[sample_number+i] = ((out_back[i])/samples_per_segment)*(65536.f/2.f);
//std::cout << ((out_back[i])/(float)samples_per_segment)*(65536.f/2.f)<<"\n";//outbuffer[sample_number+i] << "\n";
}
sample_number += samples_per_segment;
}
WAVFile output_wav(outbuffer, length, 16);
output_wav.writeToFile("output.wav");
}
void PrintBest(vector<dna>& dnas, int gen)
{
cout << "Best gen : "<< gen << "----------" << endl;
for (int i = 0; i < gen; ++i)
{
cout << endl;
cout << "-********************-" << endl;
cout << "fitness" << dnas[i].fitness << endl;
cout << "learningRate" << dnas[i].learningRate << endl;
cout << "momentum" << dnas[i].momentum << endl;
cout << "numberOfHidden" << dnas[i].numberOfHidden << endl;
cout << "maxIteration" << dnas[i].maxIteration << endl;
cout << "desiredError" << dnas[i].desiredError<< endl;
cout << "-********************-" << endl;
cout << endl;
}
FANN::neural_net net;
net.create_standard(3, data.num_input_train_data(), dnas[0].numberOfHidden, data.num_output_train_data());
net.set_learning_rate(dnas[0].learningRate);
net.set_learning_momentum(dnas[0].momentum);
net.set_activation_steepness_hidden(1.0);
net.set_activation_steepness_output(1.0);
net.set_activation_function_hidden(FANN::SIGMOID);
net.set_activation_function_output(FANN::SIGMOID);
ofstream file("ErrorHistory/" + file + ".csv");
net.set_callback(print_callback, &file);
net.set_error_log(NULL);
net.train_on_data(data, dnas[0].maxIteration,
1, dnas[0].desiredError);
cout << "----------------------" << endl;
}
void trainingThread::train()
{
std::stringstream log;
log << std::endl << " test started." << std::endl;
const float learning_rate = netConfigPTR->learning_rate ;
const unsigned int num_layers = netConfigPTR->num_layers;
const unsigned int num_input = netConfigPTR->num_input;
const unsigned int num_hidden = netConfigPTR->num_hidden;
const unsigned int num_output = netConfigPTR->num_output;
const float desired_error = netConfigPTR->desired_error;
const unsigned int max_iterations = netConfigPTR->max_iterations;
const unsigned int iterations_between_reports = netConfigPTR->iterations_between_reports;
log << std::endl << "Creating network." << std::endl;
FANN::neural_net net;
if (netConfigPTR->leyersVector.size() > 1)
{
unsigned int vectorSize = netConfigPTR->leyersVector.size();
unsigned int* leyers = new unsigned int[vectorSize+2];
leyers[0] = num_input;
for (unsigned int i = 0; i < vectorSize; ++i)
{
leyers[i+1] = netConfigPTR->leyersVector.at(i);
}
leyers[num_layers-1] = num_output;
for ( unsigned int i = 0 ; i< vectorSize+2 ; ++i)
{
qDebug() << "vector size: "<< vectorSize+2<<" i:"<<i<< " leyers "<< leyers[i];
}
net.create_standard_array(vectorSize+2, leyers);
//net.create_standard(vectorSize+2, leyers[0], leyers[2],leyers[3], leyers[1]);
delete[] leyers;
}
else
{
net.create_standard(num_layers, num_input, num_hidden, num_output);
}
net.set_learning_rate(learning_rate);
net.set_activation_steepness_hidden(1.0);
net.set_activation_steepness_output(1.0);
net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE);
net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE);
// Set additional properties such as the training algorithm
net.set_training_algorithm(netConfigPTR->trainingAlgo);
// Output network type and parameters
log << std::endl << "Network Type : ";
switch (net.get_network_type())
{
case FANN::LAYER:
log << "LAYER" << std::endl;
break;
case FANN::SHORTCUT:
log << "SHORTCUT" << std::endl;
break;
default:
log << "UNKNOWN" << std::endl;
break;
}
//net.print_parameters();
log << std::endl << "Training network." << std::endl;
FANN::training_data data;
if (data.read_train_from_file(netConfigPTR->trainingDataPatch))
{
// Initialize and train the network with the data
net.init_weights(data);
log << "Max Epochs " << std::setw(8) << max_iterations << ". "
<< "Desired Error: " << std::left << desired_error << std::right << std::endl;
emit updateLog(QString::fromStdString(log.str()));
log << "dupa";
log.str("");
log.clear();
net.set_callback(print_callback, nullptr);
net.train_on_data(data, max_iterations,
iterations_between_reports, desired_error);
log << std::endl << "Testing network." << std::endl;
for (unsigned int i = 0; i < data.length_train_data(); ++i)
{
// Run the network on the test data
fann_type *calc_out = net.run(data.get_input()[i]);
log << "test (";
for (unsigned int j = 0; j < num_input; ++j)
{
log << std::showpos << data.get_input()[i][j] << ", ";
//qDebug()<< "jestem w log<<";
}
log << ") -> " ;
for(unsigned int k = 0 ; k < num_output ; ++k)
{
log << calc_out[k] <<", ";
}
log << ",\t should be ";
for(unsigned int k = 0 ; k < num_output ; ++k)
{
log << data.get_output()[i][k] <<", ";
}
log << std::endl ;
}
log << std::endl << "Saving network." << std::endl;
// Save the network in floating point and fixed point
net.save(netConfigPTR->netFloat);
unsigned int decimal_point = net.save_to_fixed(netConfigPTR->netFixed);
std::string path = netConfigPTR->trainingDataPatch.substr(0,netConfigPTR->trainingDataPatch.size()-5);
data.save_train_to_fixed(path +"_fixed.data", decimal_point);
log << std::endl << "test completed." << std::endl;
emit updateLog(QString::fromStdString(log.str()));
emit updateProgressBar(100);
}
}
int callback_html(FANN::neural_net &rede_neural,
training_data &dados,
unsigned int turnos_max,
unsigned int turnos_por_amostra,
float margem_de_erro,
unsigned int turnos,
void *_cerebro){
static FILE *plot = NULL;
static float total = 0;
static float last_MSE = 0;
static float last_bit_fail = 0;
static int loops = 0;
static int MSE_counter = 0;
static int bit_fail_counter = 0;
static float steepness = 0;
static float aprendizagem = 0;
static float last_aprendizagem = 0;
static float aprendizagem_inicial = 0;
static bool set_steepness = false;
static bool set_aprendizagem = false;
static training_data *dados_teste = NULL;
char buffer[20000];
char MSE_buffer[64];
char bit_fail_buffer[64];
float diferenca_MSE;
float MSE;
int bit_fail = 0;
int diferenca_bit_fail = 0;
CCerebro *cerebro = (CCerebro*)_cerebro;
if(dados_teste){
//rede_neural.reset_MSE();
//printf("%f ", rede_neural.get_MSE());
MSE = rede_neural.test_data(*dados_teste);
//printf("%f\n", MSE);
}
MSE = rede_neural.get_MSE();
bit_fail = rede_neural.get_bit_fail();
//rede_neural.reset_MSE();
//printf("%f %f %d\n", MSE, last_MSE, (MSE - last_MSE) > 0.000001);
if(last_MSE)
diferenca_MSE = (last_MSE - MSE);
else
diferenca_MSE = 0;
if(last_bit_fail)
diferenca_bit_fail = last_bit_fail - bit_fail;
else
diferenca_bit_fail = 0;
if(last_bit_fail == bit_fail){
if(bit_fail_counter < BIT_FAIL_COUNTER)
bit_fail_counter++;
}else if(bit_fail_counter > 0)
bit_fail_counter--;// = bit_fail_counter * 0.85;
if(fabs(diferenca_MSE) < margem_de_erro){
if(MSE_counter < MSE_COUNTER)
MSE_counter++;
}else if(MSE_counter > 0)
MSE_counter--;// = MSE_counter * 0.9;
//printf("%d\n", bit_fail_counter);
//printf("%f %f\n",last_MSE, diferenca_MSE);
if(last_MSE){
fprintf(plot, "%d %f %f %d %d\n",turnos, MSE, diferenca_MSE, bit_fail_counter, MSE_counter);
fflush(plot);
total = total + diferenca_MSE;
}
last_MSE = MSE;
last_bit_fail = bit_fail;
if(set_aprendizagem){
if(!aprendizagem_inicial)
aprendizagem_inicial = cerebro->rede_neural->get_learning_rate();
aprendizagem = (1-((float)(MSE_counter + bit_fail_counter) / 200)) * aprendizagem_inicial;
if(!last_aprendizagem)
last_aprendizagem = aprendizagem;
if(last_aprendizagem > aprendizagem && !((MSE_counter + bit_fail_counter)%2)){
rede_neural.set_learning_rate(aprendizagem);
last_aprendizagem = aprendizagem;
}
}
if(set_steepness){
if(!steepness)
steepness = rede_neural.get_activation_steepness(1,0);
rede_neural.set_activation_steepness_layer(
(1-((float)(MSE_counter + bit_fail_counter) / 100))*steepness
,1);
//set_sneepness = true;
}
loops++;
if(turnos > 1){
if(loops < LOOPS && (bit_fail_counter < BIT_FAIL_COUNTER || MSE_counter < MSE_COUNTER) )
return 0;
}else{
bit_fail_counter = 0;
MSE_counter = 0;
last_aprendizagem = 0;
if(dados_teste)
free(dados_teste);
if(plot)
fclose(plot);
/*
dados_teste = new training_data();
if(cerebro->getDadosIdentificadosEscalados()){
dados_teste->set_train_data(cerebro->num_dados_identificados,
cerebro->num_caracteristicas, cerebro->dados_identificados_escalados,
cerebro->num_caracteristicas, cerebro->dados_identificados_escalados);
printf("Utilizando massa de testes identificada com %d casos.\n",cerebro->num_dados_identificados );
}else if(cerebro->getDadosEscalados()){
dados_teste->set_train_data(cerebro->num_dados,
cerebro->num_caracteristicas, cerebro->dados_escalados,
cerebro->num_caracteristicas, cerebro->dados_escalados);
printf("Utilizando massa de testes não identificada com %d casos.\n",cerebro->num_dados );
}else{
printf("Sem dados para treinar.\n");
delete dados_teste;
return -1;
}
*/
printf("Plotando em %s.\n",cerebro->plot_file);
plot = fopen(cerebro->plot_file, "w");
}
loops = 0;
memset(buffer,0,20000);
//clear_screen();
cor_nivel(bit_fail_counter,bit_fail_buffer);
cor_nivel(MSE_counter,MSE_buffer);
//printf("%f\n",round((float)bit_fail_counter/10));
sprintf((char*)(buffer + strlen(buffer)), "%08d turnos, precisão %1.8f(%-1.8f)(%s%%), nivel de adaptacao %d(%d)(%s%%)",
turnos, MSE, diferenca_MSE, MSE_buffer, bit_fail, diferenca_bit_fail, bit_fail_buffer);
sprintf((char*)(buffer + strlen(buffer)),", depuracao %-1.6f, aprendizagem %f, passo %f", total,last_aprendizagem,steepness);
sprintf((char*)(buffer + strlen(buffer)),"\n");
printf(buffer);
//textcolor(RESET, WHITE, BLACK);
//rede_neural.print_connections(buffer);
//printf(buffer);
if(bit_fail_counter == BIT_FAIL_COUNTER && MSE_counter == MSE_COUNTER){
printf("Treino completado em %08d, adaptacao %d, MSE %f\n",turnos, bit_fail, MSE);
return -1;
}
return 0;
}
void NextEpoch(vector<dna>& dnas)
{
size_t cnt = dnas.size();
#pragma omp parallel
#pragma omp for
for (short it = 0; it < cnt; ++it)
{
FANN::neural_net net;
net.create_standard(3, data.num_input_train_data(), dnas[it].numberOfHidden, data.num_output_train_data());
net.set_learning_rate(dnas[it].learningRate);
net.set_learning_momentum(dnas[it].momentum);
net.set_activation_steepness_hidden(1.0);
net.set_activation_steepness_output(1.0);
net.set_activation_function_hidden(FANN::LINEAR);
net.set_activation_function_output(FANN::LINEAR);
net.set_callback(print_fake, NULL);
net.set_error_log(NULL);
net.train_on_data(data, dnas[it].maxIteration,
-1, dnas[it].desiredError);
dnas[it].fitness = (1. - net.test_data(testdata)) ;
}
}
void neuralNetworkTraining(std::string training_data_file)
{
/*
* Parameters for create_standard method.
*
* num_layers : The total number of layers including the input and the output layer.
* num_input_neurons : The number of neurons in the input layer.
* num_hidden_one_neurons : The number of neurons in the first hidden layer.
* num_hidden_two_neurons : The number of neurons in the second hidden layer.
* num_output_neurons : The number of neurons in the output layer.
*/
const unsigned int num_layers = 3;
const unsigned int num_input_neurons = 8;
const unsigned int num_hidden_neurons = 5;
const unsigned int num_output_neurons = 1;
/*
* Parameters for train_on_data method.
*
* desired_errors : The desired get_MSE or get_bit_fail, depending on which stop function is chosen by set_train_stop_function.
* max_epochs : The maximum number of epochs the training should continue.
* epochs_between_reports : The number of epochs between printing a status report to stdout. A value of zero means no reports should be printed.
*/
const float desired_error = DESIRED_ERROR;
const unsigned int max_epochs = MAX_EPOCHS;
const unsigned int epochs_between_reports = EPOCHS_BETWEEN_REPORTS;
FANN::neural_net net;
// Create a standard fully connected backpropagation neural network.
net.create_standard(num_layers, num_input_neurons, num_hidden_neurons, num_output_neurons);
net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE); // Set the activation function for all of the hidden layers.
net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE); // Set the activation function for the output layer.
net.set_training_algorithm(FANN::TRAIN_RPROP); // Set the training algorithm.
net.randomize_weights(-INIT_EPSILON, INIT_EPSILON); // Give each connection a random weight between -INIT_EPSILON and INIT_EPSILON.
std::cout << std::endl << "Network Type : ";
switch (net.get_network_type())
{
case FANN::LAYER:
std::cout << "LAYER" << std::endl;
break;
case FANN::SHORTCUT:
std::cout << "SHORTCUT" << std::endl;
break;
default:
std::cout << "UNKNOWN" << std::endl;
break;
}
net.print_parameters();
std::cout << std::endl << "Training Network." << std::endl;
FANN::training_data data;
if (data.read_train_from_file(training_data_file))
{
std::cout << "Max Epochs: " << std::setw(8) << max_epochs << ". " << "Desired Error: " << std::left << desired_error << std::right << std::endl;
net.set_callback(printCallback, NULL); // Sets the callback function for use during training.
net.train_on_data(data, max_epochs, epochs_between_reports, desired_error); // Trains on an entire dataset, for a period of time.
std::cout << "Saving Network." << std::endl;
net.save("neural_network_controller_float.net");
unsigned int decimal_point = net.save_to_fixed("neural_network_controller_fixed.net");
data.save_train_to_fixed("neural_network_controller_fixed.data", decimal_point);
}
}
void train(Configuration *cfg)
{
QString fileName(QDir::homePath() + "/" + QCoreApplication::applicationName() + ".ini");
qDebug() << "using config file:" << fileName;
QSettings settings(fileName, QSettings::IniFormat);
const float learningRate = settings.value("learningRate", 0.8).toFloat();
const unsigned int numLayers = settings.value("numLayers", 3).toInt();
const unsigned int numInput = settings.value("numInput", 1024).toInt();
const unsigned int numHidden = settings.value("numHidden", 32).toInt();
const unsigned int numOutput = settings.value("numOutput", 1).toInt();
const float desiredError = settings.value("desiredError", 0.0001f).toFloat();
const unsigned int maxIterations = settings.value("maxIterations", 3000).toInt();
const unsigned int iterationsBetweenReports = settings.value("iterationsBetweenReports", 100).toInt();
FANN::neural_net net;
net.create_standard(numLayers, numInput, numHidden, numOutput);
net.set_learning_rate(learningRate);
net.set_activation_steepness_hidden(0.5);
net.set_activation_steepness_output(0.5);
net.set_learning_momentum(0.6);
net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC);
net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC);
net.set_training_algorithm(FANN::TRAIN_RPROP);
net.print_parameters();
FANN::training_data data;
if (data.read_train_from_file(cfg->getDataSavePath().toStdString()))
{
qDebug() << "Wczytano dane";
//inicjalizacja wag
net.init_weights(data);
data.shuffle_train_data();
net.set_callback(printCallback, NULL);
net.train_on_data(data, maxIterations,
iterationsBetweenReports, desiredError);
net.save(cfg->getNetSavePath().toStdString());
qDebug() << "Nauczono i zapisano siec";
}
}
