/// <summary>
/// Interpret an output from neural network in a form of label using tag in EigenValueTags class
/// </summary>
/// <param name="eigenVTags">
/// A <see cref="EigenValueTags"/>
/// </param>
/// <param name="f">
/// A <see cref="System.Double[]"/>
/// </param>
/// <returns>
/// A <see cref="System.String"/>
/// </returns>
public string AnalyseNetworkOutput(EigenValueTags eigenVTags, double[] f)
{
double max = f[0];
int maxIndex = 0;
for(int i=0;i<f.Length;i++){
if(f[i] > max){
maxIndex = i;
max = f[i];
}
}
Log.Debug("AnalyseNetwork... max = "+max);
if(max < 0.70)
return null;
string[] labels = eigenVTags.FacesLabel;
return labels[maxIndex];
}
/// <summary>
/// load eigen value in EigenValueTags class
/// </summary>
/// <param name="eigenRec">
/// A <see cref="EigenObjectRecognizer"/>
/// </param>
/// <returns>
/// A <see cref="EigenValueTags"/>
/// </returns>
public EigenValueTags RecordEigenValue(EigenObjectRecognizer eigenRec)
{
//DELETEME
//FaceClassifier.WriteEigenValueFile(eigenRec,"","eigenvalue");
EigenValueTags eigenValueTags = new EigenValueTags();
const int MAX_EIGEN_LENGTH = 30;
int nums_train = eigenRec.Labels.Length;
float[][] eigenMatrix = new float[nums_train][];
int max_eigenvalueLength = Math.Min(MAX_EIGEN_LENGTH, 4 + nums_train/5);
if(nums_train < 5)
max_eigenvalueLength = nums_train;
for(int i=0;i<nums_train;i++){
Emgu.CV.Matrix<float> eigenValue = eigenRec.EigenValues[i];
float[] temp = new float[max_eigenvalueLength];
for(int k=0; k<max_eigenvalueLength; k++){
temp[k] = eigenValue.Data[k,0];
}
eigenValueTags.Add(new VTag(temp, eigenRec.Labels[i]));
}
Log.Debug("eigenVTags Length = "+ eigenValueTags.eigenTaglist.Count);
return eigenValueTags;
}
/// <summary>
/// train and save as the spcified path
/// </summary>
/// <param name="eigen">
/// A <see cref="EigenValueTags"/>
/// </param>
private static void TrainNetwork(EigenValueTags eigen)
{
Log.Debug("================ Train Started ================ ");
string[] dLabels = eigen.FacesLabel;
int numInstances = eigen.eigenTaglist.Count;
int inputNodes = eigen.eigenTaglist[0].val.Length;
int outputNodes = dLabels.Length;
int hiddenNodes = inputNodes+outputNodes;
float[][] trainInput = new float[numInstances][];
float[][] trainOutput = new float[numInstances][];
//Random r = new Random();
int numstrain = 0;
for(int i=0;i<numInstances;i++){
trainInput[numstrain] = new float[inputNodes];
trainOutput[numstrain] = new float[outputNodes];
for(int j=0;j<dLabels.Length;j++){
if(eigen.eigenTaglist[i].tag.Equals(dLabels[j]))
trainOutput[numstrain][j] = 0.9f;
else
trainOutput[numstrain][j] = 0.1f;
}
for(int j=0;j<inputNodes;j++){
trainInput[numstrain][j] = eigen.eigenTaglist[i].val[j];
}
numstrain++;
}
// convert to double
Log.Debug("nums train = "+ numstrain);
double[][] trainInputD = new double[numstrain][];
double[][] trainOutputD = new double[numstrain][];
for(int i=0;i<numstrain;i++){
trainInputD[i] = new double[inputNodes];
trainOutputD[i] = new double[outputNodes];
for(int j=0;j<outputNodes;j++){
trainOutputD[i][j] = trainOutput[i][j];
}
for(int j=0;j<inputNodes;j++){
trainInputD[i][j] = trainInput[i][j];
}
}
// TimeSpan tp = System.DateTime.Now.TimeOfDay;
Log.Debug("#in = {0}, #hid = {1}, #out = {2}",inputNodes,hiddenNodes,outputNodes);
NeuronDotNet.Core.Backpropagation.SigmoidLayer inputLayer = new NeuronDotNet.Core.Backpropagation.SigmoidLayer(inputNodes);
NeuronDotNet.Core.Backpropagation.SigmoidLayer hiddenlayer = new NeuronDotNet.Core.Backpropagation.SigmoidLayer(hiddenNodes);
NeuronDotNet.Core.Backpropagation.SigmoidLayer outputlayer = new NeuronDotNet.Core.Backpropagation.SigmoidLayer(outputNodes);
Log.Debug("BackpropagationConnector input_hidden = new BackpropagationConnector(inputLayer, hiddenlayer);");
BackpropagationConnector input_hidden = new BackpropagationConnector(inputLayer, hiddenlayer);
BackpropagationConnector hidden_output = new BackpropagationConnector(hiddenlayer, outputlayer);
input_hidden.Momentum = 0.3;
hidden_output.Momentum = 0.3;
Log.Debug("bpnet = new BackpropagationNetwork(inputLayer,outputlayer);");
bpnet = new BackpropagationNetwork(inputLayer,outputlayer);
Log.Debug("TrainingSet tset = new TrainingSet(inputNodes, outputNodes);");
TrainingSet tset = new TrainingSet(inputNodes, outputNodes);
for(int i=0;i<numstrain;i++)
tset.Add(new TrainingSample(trainInputD[i], trainOutputD[i]));
// prevent getting stuck in local minima
bpnet.JitterNoiseLimit = 0.0001;
bpnet.Initialize();
int numEpoch = 200;
bpnet.SetLearningRate(0.2);
bpnet.Learn(tset, numEpoch);
// Log.Debug("error = {0}",bpnet.MeanSquaredError);
// string savepath = facedbPath + "object/";
// if(!Directory.Exists(savepath))
// Directory.CreateDirectory(savepath);
// Serialize
string path = Path.Combine (FSpot.Global.BaseDirectory, "ann.dat");
SerializeUtil.Serialize(path, bpnet);
// Deserialize
//BackpropagationNetwork testnet = (BackpropagationNetwork)SerializeUtil.DeSerialize("nn.dat");
// Log.Debug("error = {0}",bpnet.MeanSquaredError);
//bpnet = (BackpropagationNetwork)SerializeUtil.DeSerialize("/home/hyperjump/nn.dat");
//Log.Debug("error = {0}",bpnet.MeanSquaredError);
// test by using training data
// int correct = 0;
// for(int i=0;i<numInstances;i++){
//
// double[] v = new double[inputNodes];
// for(int j=0;j<v.Length;j++){
// v[j] = (double)eigen.eigenTaglist[i].val[j];
//Console.Write("{0},",v[j]);
// }
//Console.WriteLine();
// double[] netOutput = bpnet.Run(v);
//Console.WriteLine("net out:");
// for(int j=0;j<netOutput.Length;j++)
// Console.Write("{0},",netOutput[j]);
// string result = FaceClassifier.Instance.AnalyseNetworkOutput(eigen, netOutput);
// if(eigen.eigenTaglist[i].tag.Equals(result))
// correct++;
// }
// Log.Debug("% correct = " + (float)correct/(float)numInstances * 100);
//Save Train Status
Log.Debug("Saving Train Status...");
List<Tstate> tstateList = new List<Tstate>();
int[] num = new int[dLabels.Length];
Log.Debug("num length = {0}",num.Length);
foreach(VTag vt in eigen.eigenTaglist){
for(int k=0;k<num.Length;k++)
if(vt.tag.Equals(dLabels[k]))
num[k]++;
}
for(int k=0;k<dLabels.Length;k++){
tstateList.Add(new Tstate(dLabels[k], num[k]));
}
FaceSpotDb.Instance.TrainingData.Trainstat = tstateList;
// Log.Debug("time ="+ System.DateTime.Now.TimeOfDay.Subtract(tp));
Log.Debug("================ Train ended ================ ");
}
