/// <summary>
///
/// </summary>
/// <param name="dataFilename"></param>
/// <param name="filenameToSave"></param>
/// <param name="filenameToLoad"></param>
private void BuildMlpClassifier(string dataFilename, string filenameToSave, string filenameToLoad)
{
const int ClassCount = 26;
CvMat data = null;
CvMat trainData = null;
CvMat responses = null;
CvMat mlpResponse = null;
CvMat layerSizes = null;
int nsamplesAll = 0, ntrainSamples = 0;
double trainHr = 0, testHr = 0;
CvANN_MLP mlp = new CvANN_MLP();
try
{
ReadNumClassData(dataFilename, 16, out data, out responses);
}
catch
{
Console.WriteLine("Could not read the database {0}", dataFilename);
return;
}
Console.WriteLine("The database {0} is loaded.", dataFilename);
nsamplesAll = data.Rows;
ntrainSamples = (int)(nsamplesAll * 0.8);
// Create or load MLP classifier
if (filenameToLoad != null)
{
// load classifier from the specified file
mlp.Load(filenameToLoad);
ntrainSamples = 0;
if (mlp.GetLayerCount() == 0)
{
Console.WriteLine("Could not read the classifier {0}", filenameToLoad);
return;
}
Console.WriteLine("The classifier {0} is loaded.", filenameToLoad);
}
else
{
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
//
// MLP does not support categorical variables by explicitly.
// So, instead of the output class label, we will use
// a binary vector of <class_count> components for training and,
// therefore, MLP will give us a vector of "probabilities" at the
// prediction stage
//
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
using (CvMat newResponses = new CvMat(ntrainSamples, ClassCount, MatrixType.F32C1))
{
// 1. unroll the responses
Console.WriteLine("Unrolling the responses...");
unsafe
{
for (int i = 0; i < ntrainSamples; i++)
{
int clsLabel = Cv.Round(responses.DataArraySingle[i]) - 'A';
float *bitVec = (float *)(newResponses.DataByte + i * newResponses.Step);
for (int j = 0; j < ClassCount; j++)
{
bitVec[j] = 0.0f;
}
bitVec[clsLabel] = 1.0f;
}
}
Cv.GetRows(data, out trainData, 0, ntrainSamples);
// 2. train classifier
int[] layerSizesData = { data.Cols, 100, 100, ClassCount };
layerSizes = new CvMat(1, layerSizesData.Length, MatrixType.S32C1, layerSizesData);
mlp.Create(layerSizes);
Console.Write("Training the classifier (may take a few minutes)...");
mlp.Train(
trainData, newResponses, null, null,
new CvANN_MLP_TrainParams(new CvTermCriteria(300, 0.01), MLPTrainingMethod.RPROP, 0.01)
);
}
Console.WriteLine();
}
mlpResponse = new CvMat(1, ClassCount, MatrixType.F32C1);
// compute prediction error on train and test data
for (int i = 0; i < nsamplesAll; i++)
{
int bestClass;
CvMat sample;
CvPoint minLoc, maxLoc;
Cv.GetRow(data, out sample, i);
mlp.Predict(sample, mlpResponse);
mlpResponse.MinMaxLoc(out minLoc, out maxLoc, null);
bestClass = maxLoc.X + 'A';
int r = (Math.Abs((double)bestClass - responses.DataArraySingle[i]) < float.Epsilon) ? 1 : 0;
if (i < ntrainSamples)
{
trainHr += r;
}
else
{
testHr += r;
}
}
testHr /= (double)(nsamplesAll - ntrainSamples);
trainHr /= (double)ntrainSamples;
Console.WriteLine("Recognition rate: train = {0:F1}%, test = {1:F1}%", trainHr * 100.0, testHr * 100.0);
// Save classifier to file if needed
if (filenameToSave != null)
{
mlp.Save(filenameToSave);
}
Console.Read();
mlpResponse.Dispose();
data.Dispose();
responses.Dispose();
if (layerSizes != null)
{
layerSizes.Dispose();
}
mlp.Dispose();
}
/// <summary>
///
/// </summary>
/// <param name="dataFilename"></param>
/// <param name="filenameToSave"></param>
/// <param name="filenameToLoad"></param>
private void BuildMlpClassifier(string dataFilename, string filenameToSave, string filenameToLoad)
{
const int ClassCount = 26;
CvMat data = null;
CvMat trainData = null;
CvMat responses = null;
CvMat mlpResponse = null;
CvMat layerSizes = null;
int nsamplesAll = 0, ntrainSamples = 0;
double trainHr = 0, testHr = 0;
CvANN_MLP mlp = new CvANN_MLP();
try
{
ReadNumClassData(dataFilename, 16, out data, out responses);
}
catch
{
Console.WriteLine("Could not read the database {0}", dataFilename);
return;
}
Console.WriteLine("The database {0} is loaded.", dataFilename);
nsamplesAll = data.Rows;
ntrainSamples = (int)(nsamplesAll * 0.8);
// Create or load MLP classifier
if (filenameToLoad != null)
{
// load classifier from the specified file
mlp.Load(filenameToLoad);
ntrainSamples = 0;
if (mlp.GetLayerCount() == 0)
{
Console.WriteLine("Could not read the classifier {0}", filenameToLoad);
return;
}
Console.WriteLine("The classifier {0} is loaded.", filenameToLoad);
}
else
{
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
//
// MLP does not support categorical variables by explicitly.
// So, instead of the output class label, we will use
// a binary vector of <class_count> components for training and,
// therefore, MLP will give us a vector of "probabilities" at the
// prediction stage
//
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
using (CvMat newResponses = new CvMat(ntrainSamples, ClassCount, MatrixType.F32C1))
{
// 1. unroll the responses
Console.WriteLine("Unrolling the responses...");
unsafe
{
for (int i = 0; i < ntrainSamples; i++)
{
int clsLabel = Cv.Round(responses.DataArraySingle[i]) - 'A';
float* bitVec = (float*)(newResponses.DataByte + i * newResponses.Step);
for (int j = 0; j < ClassCount; j++)
{
bitVec[j] = 0.0f;
}
bitVec[clsLabel] = 1.0f;
}
}
Cv.GetRows(data, out trainData, 0, ntrainSamples);
// 2. train classifier
int[] layerSizesData = { data.Cols, 100, 100, ClassCount };
layerSizes = new CvMat(1, layerSizesData.Length, MatrixType.S32C1, layerSizesData);
mlp.Create(layerSizes);
Console.Write("Training the classifier (may take a few minutes)...");
mlp.Train(
trainData, newResponses, null, null,
new CvANN_MLP_TrainParams(new CvTermCriteria(300, 0.01), MLPTrainingMethod.RPROP, 0.01)
);
}
Console.WriteLine();
}
mlpResponse = new CvMat(1, ClassCount, MatrixType.F32C1);
// compute prediction error on train and test data
for (int i = 0; i < nsamplesAll; i++)
{
int bestClass;
CvMat sample;
CvPoint minLoc, maxLoc;
Cv.GetRow(data, out sample, i);
mlp.Predict(sample, mlpResponse);
mlpResponse.MinMaxLoc(out minLoc, out maxLoc, null);
bestClass = maxLoc.X + 'A';
int r = (Math.Abs((double)bestClass - responses.DataArraySingle[i]) < float.Epsilon) ? 1 : 0;
if (i < ntrainSamples)
trainHr += r;
else
testHr += r;
}
testHr /= (double)(nsamplesAll - ntrainSamples);
trainHr /= (double)ntrainSamples;
Console.WriteLine("Recognition rate: train = {0:F1}%, test = {1:F1}%", trainHr * 100.0, testHr * 100.0);
// Save classifier to file if needed
if (filenameToSave != null)
{
mlp.Save(filenameToSave);
}
Console.Read();
mlpResponse.Dispose();
data.Dispose();
responses.Dispose();
if (layerSizes != null) layerSizes.Dispose();
mlp.Dispose();
}
