public void Train(int samples)
{
if (samples > 0 && TryLoadTrainingSet(samples))
{
return;
}
List<string> knowledge = new List<string>();
foreach (string file in knowledgeFiles)
{
knowledge.AddRange(File.ReadAllLines(file));
}
string[] lines = knowledge.ToArray();
samples = lines.Length; ;
int pixels = DimensionY * DimensionX;
int classes = netKeys.Count;
Emgu.CV.Matrix<Single> training = new Emgu.CV.Matrix<Single>(samples, pixels);
Emgu.CV.Matrix<Single> class_training = new Emgu.CV.Matrix<Single>(samples, classes);
Emgu.CV.Matrix<int> layers = new Emgu.CV.Matrix<int>(3, 1);
layers[0, 0] = pixels;
layers[1, 0] = (int)(factor * netKeys.Count);
layers[2, 0] = classes;
for (int i = 0; i < samples; i++)
{
LetterInfo info = LetterInfo.ReadLetterInfoLine(lines[i]);
byte[] bytes = Convert.FromBase64String(info.Base64);
float[] input = AdjustInput(bytes);
for (int j = 0; j < pixels; j++)
{
training[i, j] = input[j];
}
/*
for (int a = -1; a <= 1; a++)
for (int b = -1; b <= 1; b++)
for (int c = 0; c < DimensionX; c++)
for (int d = 0; d < DimensionY; d++)
{
if (0 > c + a || c + a >= DimensionX) continue;
if (0 > d + b || d + b >= DimensionY) continue;
training[i, d * DimensionX + c] = bytes[(b + d) * DimensionX + (a + c)];
} */
int d = netKeys.IndexOf(info.Char);
class_training[i, d] = 1;
}
nnet = new Emgu.CV.ML.ANN_MLP(layers, Emgu.CV.ML.MlEnum.ANN_MLP_ACTIVATION_FUNCTION.SIGMOID_SYM, 0.6, 1);
Emgu.CV.ML.Structure.MCvANN_MLP_TrainParams p = new Emgu.CV.ML.Structure.MCvANN_MLP_TrainParams();
p.term_crit.type = Emgu.CV.CvEnum.TERMCRIT.CV_TERMCRIT_EPS | Emgu.CV.CvEnum.TERMCRIT.CV_TERMCRIT_ITER;
p.term_crit.max_iter = 1000;
p.term_crit.epsilon = 0.000001;
p.train_method = Emgu.CV.ML.MlEnum.ANN_MLP_TRAIN_METHOD.BACKPROP;
p.bp_dw_scale = 0.1;
p.bp_moment_scale = 0.1;
bool success = false;
try
{
if (File.Exists(saveFile))
{
nnet.Load(saveFile);
success = true;
}
}
catch
{
}
if (!success)
{
int iteration = nnet.Train(training, class_training, null, p, Emgu.CV.ML.MlEnum.ANN_MLP_TRAINING_FLAG.DEFAULT);
if (saveFile != null)
{
Directory.CreateDirectory(Path.GetDirectoryName(saveFile));
nnet.Save(saveFile);
}
}
}
private bool TryLoadTrainingSet(int samples)
{
int pixels = DimensionY * DimensionX;
int classes = netKeys.Count;
Emgu.CV.Matrix<Single> training = new Emgu.CV.Matrix<Single>(samples, pixels);
Emgu.CV.Matrix<Single> class_training = new Emgu.CV.Matrix<Single>(samples, classes);
Emgu.CV.Matrix<int> layers = new Emgu.CV.Matrix<int>(3, 1);
layers[0, 0] = pixels;
layers[1, 0] = (int)(factor * netKeys.Count);
layers[2, 0] = classes;
nnet = new Emgu.CV.ML.ANN_MLP(layers, Emgu.CV.ML.MlEnum.ANN_MLP_ACTIVATION_FUNCTION.SIGMOID_SYM, 0.6, 1);
Emgu.CV.ML.Structure.MCvANN_MLP_TrainParams p = new Emgu.CV.ML.Structure.MCvANN_MLP_TrainParams();
p.term_crit.type = Emgu.CV.CvEnum.TERMCRIT.CV_TERMCRIT_EPS | Emgu.CV.CvEnum.TERMCRIT.CV_TERMCRIT_ITER;
p.term_crit.max_iter = 1000;
p.term_crit.epsilon = 0.000001;
p.train_method = Emgu.CV.ML.MlEnum.ANN_MLP_TRAIN_METHOD.BACKPROP;
p.bp_dw_scale = 0.1;
p.bp_moment_scale = 0.1;
try
{
if (File.Exists(saveFile))
{
nnet.Load(saveFile);
return true;
}
}
catch
{
}
return false;
}
