private static void CheckNetworkAccuracy(Network network, List<string> classifications)
{
ChooseImageForCheck();
PrintNewLine(1);
GetInput();
var imagePaths = GetImageNames();
while (imagePaths.Count <= 0)
{
InvalidNumberOfImages();
ChooseImageForCheck();
imagePaths = GetImageNames();
}
foreach (var imagePath in imagePaths)
{
var imageMatrix = GetImageMatrix(imagePath);
var results = network.Compute(imageMatrix);
ShowSureness(results, imagePath, classifications);
}
Console.ReadLine();
}
private static void StartImageTraining()
{
var classifications = new List<string>();
for (var i = 0; i < NumClassifications; i++)
{
AskForClassificationName(i + 1);
classifications.Add(GetInput());
}
var dataSets = new List<DataSet>();
for (var i = 0; i < classifications.Count; i++)
{
RequestImageSelection(classifications[i]);
var imagePaths = GetImageNames();
while (imagePaths.Count <= 0)
{
InvalidNumberOfImages();
RequestImageSelection(classifications[i]);
imagePaths = GetImageNames();
}
foreach (var imagePath in imagePaths)
{
var values = GetImageMatrix(imagePath);
var targets = new double[classifications.Count];
targets[i] = 1;
dataSets.Add(new DataSet(values, targets));
}
}
TrainingNetwork();
var network = new Network(MaxWidth * MaxHeight * 3, HiddenLayerSize, NumClassifications);
network.Train(dataSets, TrainingType == TrainingType.Epoch ? MaxEpochs : MinimumError);
CheckNetworkAccuracy(network, classifications);
}
