// Worker thread
void SearchSolution( )
{
bool reducedNetwork = ( ( classesCount == 2 ) && ( useOneNeuronForTwoClasses ) );
// prepare learning data
double[][] input = new double[samples][];
double[][] output = new double[samples][];
for ( int i = 0; i < samples; i++ )
{
input[i] = new double[variables];
output[i] = new double[neuronsCount];
// set input
for ( int j = 0; j < variables; j++ )
input[i][j] = data[i, j];
// set output
if ( reducedNetwork )
{
output[i][0] = classes[i];
}
else
{
output[i][classes[i]] = 1;
}
}
// create perceptron
ActivationNetwork network = new ActivationNetwork(
new SigmoidFunction( sigmoidAlphaValue ), variables, neuronsCount );
ActivationLayer layer = network[0];
// create teacher
DeltaRuleLearning teacher = new DeltaRuleLearning( network );
// set learning rate
teacher.LearningRate = learningRate;
// iterations
int iteration = 1;
// statistic files
StreamWriter errorsFile = null;
StreamWriter weightsFile = null;
try
{
// check if we need to save statistics to files
if ( saveStatisticsToFiles )
{
// open files
errorsFile = File.CreateText( "errors.csv" );
weightsFile = File.CreateText( "weights.csv" );
}
// erros list
ArrayList errorsList = new ArrayList( );
// loop
while ( !needToStop )
{
// save current weights
if ( weightsFile != null )
{
for ( int i = 0; i < neuronsCount; i++ )
{
weightsFile.Write( "neuron" + i + "," );
for ( int j = 0; j < variables; j++ )
weightsFile.Write( layer[i][j] + "," );
weightsFile.WriteLine( layer[i].Threshold );
}
}
// run epoch of learning procedure
double error = teacher.RunEpoch( input, output ) / samples;
errorsList.Add( error );
// save current error
if ( errorsFile != null )
{
errorsFile.WriteLine( error );
}
// show current iteration & error
SetText( currentIterationBox, iteration.ToString( ) );
SetText( currentErrorBox, error.ToString( ) );
iteration++;
// check if we need to stop
if ( ( useErrorLimit ) && ( error <= learningErrorLimit ) )
break;
if ( ( !useErrorLimit ) && ( iterationLimit != 0 ) && ( iteration > iterationLimit ) )
break;
}
// show perceptron's weights
ClearList( weightsList );
for ( int i = 0; i < neuronsCount; i++ )
{
string neuronName = string.Format( "Neuron {0}", i + 1 );
ListViewItem item = null;
// add all weights
for ( int j = 0; j < variables; j++ )
{
item = AddListItem( weightsList, neuronName );
AddListSubitem( item, string.Format( "Weight {0}", j + 1 ) );
AddListSubitem( item, layer[i][0].ToString( "F6" ) );
}
// threshold
item = AddListItem( weightsList, neuronName );
AddListSubitem( item, "Threshold");
AddListSubitem( item, layer[i].Threshold.ToString( "F6" ) );
}
// show error's dynamics
double[,] errors = new double[errorsList.Count, 2];
for ( int i = 0, n = errorsList.Count; i < n; i++ )
{
errors[i, 0] = i;
errors[i, 1] = (double) errorsList[i];
}
errorChart.RangeX = new DoubleRange( 0, errorsList.Count - 1 );
errorChart.UpdateDataSeries( "error", errors );
}
catch ( IOException )
{
MessageBox.Show( "Failed writing file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error );
}
finally
{
// close files
if ( errorsFile != null )
errorsFile.Close( );
if ( weightsFile != null )
weightsFile.Close( );
}
// enable settings controls
EnableControls( true );
}
public void startTrain()
{
int class_count = 0;
if ("NN".Equals(Constants.NN_SVM_SURF))
{
double sigmoidAlphaValue = 1.0;
double learningRate = 100;
int max_epoch = 50;
double min_err = 0.000000001;
List<FileInfo> trainingFiles = FileTools.getTrainingFiles(ref class_count);
int samples = trainingFiles.Count;
// prepare learning data
double[][] input = new double[samples][];
Dictionary<int, double[][]> outputs = new Dictionary<int, double[][]>();
for (int i = 0; i < samples; i++)
{
int currentImageClass = Int32.Parse(trainingFiles[i].Directory.Name);
Bitmap bmp = (Bitmap)Bitmap.FromFile(trainingFiles[i].FullName, false);
int com_x = 0, com_y = 0;
ByteTools.imageCoM(bmp, ref com_x, ref com_y);
input[i] = new double[numOfinputs];
List<Ipoint> featureList = fillFeatures(bmp, com_x, com_y, input[i]);
if (!outputs.ContainsKey(currentImageClass))
{
outputs.Add(currentImageClass, new double[samples][]);
for (int j = 0; j < samples; j++)
{
outputs[currentImageClass][j] = new double[] { 0d };
}
}
outputs[currentImageClass][i][0] = 1d;
}
Dictionary<int, ActivationNetwork> networks = new Dictionary<int, ActivationNetwork>();
int[] availSigns = outputs.Keys.ToArray();
foreach (int sign in availSigns)
{
ActivationNetwork network = new ActivationNetwork(new SigmoidFunction(sigmoidAlphaValue),
numOfinputs, new int[] { Constants.NUM_OF_NN_HIDDEN_LAYER_NODES, 1 });
Accord.Neuro.Learning.LevenbergMarquardtLearning teacher = new Accord.Neuro.Learning.LevenbergMarquardtLearning(network);
teacher.LearningRate = learningRate;
int epoch = 0;
double error;
while (true)
{
// run epoch of learning procedure
error = teacher.RunEpoch(input, outputs[sign]) / samples;
Console.WriteLine("Epoch:" + epoch + " Error:" + error);
if (epoch++ > max_epoch || error < min_err)
break;
}
networks.Add(sign, network);
network.Save(Constants.base_folder + "nn_12x12_" + sign + ".dat");
Logger.log("Error: " + error + " Epoch:" + epoch);
}
}
else if ("NN_SURF".Equals(Constants.NN_SVM_SURF) || "NN_12SIMPLE".Equals(Constants.NN_SVM_SURF))
{
double sigmoidAlphaValue = 1.0;
if ("NN_SURF".Equals(Constants.NN_SVM_SURF)) {
if ("triangle".Equals(Constants.CIRCLE_TRIANGLE))
sigmoidAlphaValue = 6.0;
if ("circle".Equals(Constants.CIRCLE_TRIANGLE))
sigmoidAlphaValue = 6.0;
}
else if ("NN_12SIMPLE".Equals(Constants.NN_SVM_SURF))
{
if ("triangle".Equals(Constants.CIRCLE_TRIANGLE))
sigmoidAlphaValue = 1.0;
if ("circle".Equals(Constants.CIRCLE_TRIANGLE))
sigmoidAlphaValue = 1.0;
}
double learningRate = 1.00;
int max_epoch = 3000;
double min_err = 0.000001;
ActivationNetwork network = new ActivationNetwork(new SigmoidFunction(sigmoidAlphaValue),
numOfinputs, Constants.NUM_OF_SIGN_TYPES);
DeltaRuleLearning teacher = new DeltaRuleLearning(network);
teacher.LearningRate = learningRate;
/*
ActivationNetwork network = new ActivationNetwork(new SigmoidFunction(sigmoidAlphaValue),
numOfinputs, new int[] { (numOfinputs + Constants.NUM_OF_SIGN_TYPES)/2, Constants.NUM_OF_SIGN_TYPES });
BackPropagationLearning teacher = new BackPropagationLearning(network);
teacher.LearningRate = learningRate;
//teacher.Momentum = momentum;
*/
List<FileInfo> trainingFiles = new List<FileInfo>(1000);
DirectoryInfo di = new DirectoryInfo(Constants.base_folder + "train_" + Constants.CIRCLE_TRIANGLE);
DirectoryInfo[] dirs = di.GetDirectories("*");
foreach (DirectoryInfo dir in dirs)
{
int i = 0;
FileInfo[] files = dir.GetFiles("*.bmp");
foreach (FileInfo fi in files)
{
trainingFiles.Add(fi);
if (i++ > Constants.MAX_TRAIN_SAMPLE)
break;
}
}
// List<FileInfo> trainingFiles = FileTools.getTrainingFiles(ref class_count);
int samples = trainingFiles.Count;
// prepare learning data
double[][] input = new double[samples][];
double[][] output = new double[samples][];
for (int i = 0; i < samples; i++)
{
Bitmap bmp = (Bitmap)Bitmap.FromFile(trainingFiles[i].FullName, false);
int com_x = 0, com_y = 0;
ByteTools.imageCoM(bmp, ref com_x, ref com_y);
input[i] = new double[numOfinputs];
output[i] = new double[Constants.NUM_OF_SIGN_TYPES];
bmp.Tag = trainingFiles[i].Directory.Name + "_" + trainingFiles[i].Name;
fillFeatures_SURF(bmp, com_x, com_y, input[i]);
output[i][Int32.Parse(trainingFiles[i].Directory.Name) - 1] = 1d;
}
int epoch = 0;
double error = 0;
while (true)
{
// run epoch of learning procedure
error = teacher.RunEpoch(input, output) / samples;
Console.WriteLine("Epoch:" + epoch + " Error:" + error);
if (epoch++ > max_epoch || error < min_err)
break;
}
network.Save(Constants.base_folder + Constants.NN_SVM_SURF + "_" + Constants.CIRCLE_TRIANGLE + ".dat");
Logger.log("NNTrain [" + error + "]: " + Constants.NN_SVM_SURF + ", " + Constants.CIRCLE_TRIANGLE + ", " + learningRate + ", " + sigmoidAlphaValue);
}
}