private void ReadWeights()
{
_handwrittenDigitRecogNn = new HandwrittenDigitRecognitionNn(new List <TrainingSample>(), NnInputs, NnHidden, NnOutputs, 0.002);
var weightsFile = new StreamReader("weights.txt");
var currentLayer = _handwrittenDigitRecogNn.HiddenLayer;
var weights = new List <double>();
var j = 0;
while (!weightsFile.EndOfStream)
{
var currentLine = weightsFile.ReadLine();
// End of weights for current unit.
if (currentLine == "*")
{
currentLayer.Units[j].Weights = new List <double>(weights);
j++;
weights.Clear();
continue;
}
// End of layer.
if (currentLine == "-")
{
currentLayer = _handwrittenDigitRecogNn.OutPutLayer;
j = 0;
weights.Clear();
continue;
}
weights.Add(double.Parse(currentLine));
}
weightsFile.Close();
}
private void TrainBtnClick(object sender, EventArgs e)
{
var trainingDataSet = new List <TrainingSample>();
var trainingDataSetFiles = Directory.GetFiles(Directory.GetCurrentDirectory() + "\\Digits");
foreach (var file in trainingDataSetFiles)
{
var name = file.Remove(file.LastIndexOf(".")).Substring(file.LastIndexOf("\\") + 1);
var @class = int.Parse(name.Substring(0, 1));
var classVec = new[] { 0.0, 0.0, 0.0 };
classVec[@class - 1] = 1;
var imgMatrix = GetImage(new Bitmap(file));
var imgVector = imgMatrix.Cast <double>().Select(c => c).ToArray();
trainingDataSet.Add(new TrainingSample(imgVector, @class, classVec));
}
_handwrittenDigitRecogNn = new HandwrittenDigitRecognitionNn(trainingDataSet, NnInputs, NnHidden, NnOutputs, 0.002);
_handwrittenDigitRecogNn.Training();
var fileWeights = new StreamWriter("weights.txt", false);
foreach (var layer in _handwrittenDigitRecogNn.Layers)
{
foreach (var unit in layer.Units)
{
foreach (var w in unit.Weights)
{
fileWeights.WriteLine(w);
}
fileWeights.WriteLine("*");
}
fileWeights.WriteLine("-");
}
fileWeights.Close();
MessageBox.Show("Training Complete!", "Message");
}
