// Train the net with all the data
void NeuralNet::train_net( int num_data, int num_input, float** input,int num_output, float ** output)
//void train_net(FANN::neural_net &net,unsigned int num_data, unsigned int num_input, fann_type **input,unsigned int num_output,fann_type **output)
{
net = new FANN::neural_net();
const float learning_rate = 0.07f;
const unsigned int num_layers=3;
int num_hidden=num_input/2;
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);
const float desired_error = 0.001f;
const unsigned int max_iterations = 1000;
const unsigned int iterations_between_reports = 1000;
FANN::training_data data;
data.set_train_data(num_data, num_input, input, num_output, output);
// Initialize and train the network with the data
net->init_weights(data);
net->train_on_data(data, max_iterations,0, desired_error);
data.save_train("ddumbassfile.save");
}
void Execution::initializeWeights()
{
assert(net && "Invalid program state, net uninitialized");
const int numInputs = net->get_num_input();
if (trainingInputsSelection.isValid() == 0)
{
QMessageBox::warning(this, tr("Information"),
tr("Select inputs columns for Training in Execution tab to initialize weights"));
return;
}
if (numInputs != trainingInputsSelection.numColumns())
{
QMessageBox::warning(this, tr("Information"),
tr("Selected inputs columns must match net inputs"));
return;
}
double **inputPatterns = trainingInputsSelection.getData();
FANN::training_data data;
data.set_train_data(trainingInputsSelection.numRows(), numInputs, inputPatterns, 0, 0);
net->init_weights(data);
}
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 Execution::runTraining()
{
assert(net && "Illegal program state, netuninitialized");
if (isTrainingSelectionValid() == 0)
{
return;
}
emit preparingToTrain(this);
double **inputPatterns = trainingInputsSelection.getData();
double **referenceOutputs = trainingReferenceSelection.getData();
const int numPatterns = trainingInputsSelection.numRows();
FANN::training_data data;
data.set_train_data(numPatterns, net->get_num_input(), inputPatterns,
net->get_num_output(), referenceOutputs);
resetErrorButton->setDisabled(true);
for (int i = 0; i < maxEpochs; ++i)
{
Selection& s = trainingErrorSelection;
std::cout << i << std::endl;
double mse = net->train_epoch(data);
if (s.isValid() && (i < s.numRows()))
{
client->writeToCell(s.getSheet(), s.getStartR() + i, s.getStartC(), mse);
}
if (mse < desiredMSE)
{
QMessageBox::information(this, tr("Information"),
tr("Net has reached the desired MSE of ") + QString::number(mse) +
tr(" after ") + QString::number(i) + tr(" epochs."));
break;
}
}
resetErrorButton->setEnabled(true);
}