public static void Main(String[] args)
{
//var sourceDir = Network.Default.DatasetDirectory + "\\Base";
//var outputDir = Network.Default.DatasetDirectory;
//CharacterDatasetHandler.GenerateDatasetEntry(sourceDir, outputDir);
var trainAlphabet = new AlphabetDataset(EnumDatasetType.Train);
var validationAlphabet = new AlphabetDataset(EnumDatasetType.Validation);
var testAlphabet = new AlphabetDataset(EnumDatasetType.Test);
var cycles = DatasetUtils.BuildCyclesFromDataset(trainAlphabet, validationAlphabet, testAlphabet);
var network = new MultiLayerNetwork(cycles);
network.InitializeNetwork();
ObservableCollection <string> lettersReturned;
var likelyResults = "";
Console.WriteLine("\nThe Network has initialized. The test of the Network will start\n");
Console.ReadLine();
var numberOfExamples = 0;
var numberOfCorrectResults = 0;
for (int i = 0; i < network.Cycles.Count; i++)
{
//network.Cycles[i].ExamplesTest = network.RandomizeList(network.Cycles[i].ExamplesTest);
foreach (Example example in network.Cycles[i].ExamplesTest)
{
var outputFromTrainedNetwork = network.CheckElement(example);
lettersReturned = DatasetUtils.GetLettersFromOutputArray(outputFromTrainedNetwork);
likelyResults = string.Join(" ", lettersReturned);
Console.WriteLine("Cycle: " + (i + 1) + " Expected result: " + example.Name + ". Likely results: " + likelyResults);
numberOfExamples++;
if (lettersReturned.Contains(example.Name))
{
numberOfCorrectResults++;
}
likelyResults = "";
}
}
var result = ((float)numberOfCorrectResults / (float)numberOfExamples) * 100;
Console.WriteLine("Accuraty rate of Network: " + result + "%");
Console.ReadLine();
}
public static ObservableCollection <Cycle> BuildCyclesFromDataset(AlphabetDataset trainAlphabet, AlphabetDataset validationAlphabet, AlphabetDataset testAlphabet)
{
var cycles = new ObservableCollection <Cycle>();
var imagesPerLetterTrain = trainAlphabet.Letters.FirstOrDefault().ImagesPath.Count;
var imagesPerLetterValidation = validationAlphabet.Letters.FirstOrDefault().ImagesPath.Count;
var imagesPerLetterTest = testAlphabet.Letters.FirstOrDefault().ImagesPath.Count;
var letterCount = trainAlphabet.Letters.Count;
for (int i = 0; i < imagesPerLetterTrain; i++)
{
var cycle = new Cycle();
for (int j = 0; j < letterCount; j++)
{
var letter = trainAlphabet.Letters[j];
var example = new Example()
{
Name = letter.Name,
InputValues = letter.GetImagePixels(i),
WantedValues = GetLetterWantedValues(letter.Name)
};
cycle.ExamplesTrain.Add(example);
if (i < imagesPerLetterValidation && i < imagesPerLetterTest)
{
var letterValidation = validationAlphabet.Letters[j];
var exampleValidation = new Example()
{
Name = letterValidation.Name,
InputValues = letterValidation.GetImagePixels(i),
WantedValues = GetLetterWantedValues(letterValidation.Name)
};
cycle.ExamplesValidation.Add(exampleValidation);
var letterTest = testAlphabet.Letters[j];
var exampleTest = new Example()
{
Name = letterTest.Name,
InputValues = letterTest.GetImagePixels(i),
WantedValues = GetLetterWantedValues(letterTest.Name)
};
cycle.ExamplesTest.Add(exampleTest);
}
}
cycles.Add(cycle);
}
return(cycles);
}
