/// <summary>
/// Routine to train the network
/// </summary>
/// <param name="cnnProject">The CNN project.</param>
/// <param name="numberOfRounds">The number of training rounds.</param>
public void TrainPattern(CNNProject cnnProject, long numberOfTrainingRounds)
{
// 1. preparation - both project's imageLists generalized images to the shuffled trainingItem-list
#region 1. preparation
List<TrainingItem> trainingItemList = new List<TrainingItem>();
#region fills the trainingItemList
ImageList imgList;
bool matching;
for (int i = 0; i < 2; i++)
{
// at first we include the matching images
if (i == 0)
{
imgList = cnnProject.Matching;
matching = true;
}
// and then the not matching images
else
{
imgList = cnnProject.Matching;
matching = false;
}
foreach (Image img in imgList.Images)
{
// generalizes the image
Image generalizedImg = ImageHandling.GeneralizeImage(img);
trainingItemList.Add(new TrainingItem(generalizedImg, matching));
}
}
#endregion
// filled list gets shuffled
// (maybe this optimizes the result)
StaticClasses.Shuffle<TrainingItem>(trainingItemList);
#endregion
// 2. build of training data items and add it to the helper
#region 2. trainingItem
// used later on to create the training thread
NetworkHelper helper = new NetworkHelper(_network);
foreach (TrainingItem trainingItem in trainingItemList)
{
Image img = trainingItem.Image;
ArrayList arryListInput;
#region fills arryListInput
// Converts an image of any size to a pattern that can be feed to the network.
ImageProcessingHelper imgHelper = new ImageProcessingHelper();
//note: this is a (monochrome) collection of 0's and 1's !!
arryListInput = imgHelper.ArrayListFromImage(img);
if (img.Width * img.Height != _network.InputLayer.Count)
{
throw new InvalidInputException("The number of pixels in the input image doesn't match the number of input layer neurons!", null);
}
#region Debugging
/*
// Convert an arrayList by rounding each value to a pattern of 0s and 1s
PatternProcessingHelper patHelper = new PatternProcessingHelper();
String tmpPatern = patHelper.PatternFromArraylist(tmpImgList);
Debug.WriteLine("Added : " + tmpPatern);
*/
#endregion
#endregion
ArrayList arryListOutput;
#region fills arryListOutput
arryListOutput = new ArrayList();
// true is going to be a single 1, false a single 0
arryListOutput.Add(trainingItem.Matching ? 1 : 0);
#endregion
// a training data item is used for a single training round
TrainingData trainingDataItem = new TrainingData(arryListInput, arryListOutput);
// this could be also used; one training round directly
//_network.TrainNetwork(trainingDataItem);
helper.AddTrainingData(trainingDataItem);
}
#endregion
// Let's go!
_trainStart = DateTime.Now;
// 3. training
#region 3. training
// ShowProgress delegate
helper.TrainingProgress += new NetworkHelper.TrainingProgressEventHandler(ShowProgress);
// Start training
// --- here we are going to wait --
helper.Train(numberOfTrainingRounds, true); // <--
// releasing
helper.TrainingProgress -= new NetworkHelper.TrainingProgressEventHandler(ShowProgress);
// show message box
if (StopTrainingSilently == false)
{
MessageBox.Show("Training of the neuronal network completed at " + DateTime.Now,
"Training Completed",
MessageBoxButtons.OK,
MessageBoxIcon.Information);
}
#endregion
}
/// <summary>
/// Routine to detect an image (right sized!)
/// </summary>
/// <param name="img">The resized and generalized image.</param>
/// <returns></returns>
public bool DetectPattern(Image img)
{
//Step 1 : Convert the image to an arrayList
ArrayList arryListInput;
#region fills arryListInput
// Converts an image of any size to a pattern that can be feed to the network.
ImageProcessingHelper imgHelper = new ImageProcessingHelper();
//note: this is a (monochrome) collection of 0's and 1's !!
arryListInput = imgHelper.ArrayListFromImage(img);
if (img.Width * img.Height != _network.InputLayer.Count)
{
throw new InvalidInputException("The number of pixels in the input image doesn't match the number of input layer neurons!", null);
}
#endregion
//Step 2: Run the network and obtain the output
ArrayList output = null;
output = _network.RunNetwork(arryListInput);
// finally: the result is in the first and only index
float result = (float)output[0];
// displays rounded result (better readable)
Debug.WriteLine("Detection Result: " + Math.Round(result, 3) + " (" + result + ")");
//TODO: what is the best value for this?
if (result <= 0.5)
{
return false;
}
else
{
return true;
}
}