/// <summary>
/// Главный метод нейросети - обучение на картинках 28x28 pixels
/// </summary>
public void Learn(DataNumberDTO_28x28_Set[] dataSet)
{
// Поэтапная тренировка по каждой картинке
for (var i = 0; i < dataSet.Length; i++)
{
var rightAnswer = dataSet[i].Number;
var neural3NetworkHelper = new Neural3NetworkHelper(Neural3NetworkCreator);
// Трансформирование RGB - компонент в входной сигнал для нейронов входного слоя
var RGBComponents = dataSet[i].RGBComponents;
var signalsFromInputLayer = neural3NetworkHelper.TransformWhiteBlackPixelsToSignals(RGBComponents);
var inputLayer = Neural3NetworkCreator.InputLayer;
var hiddenLayer = Neural3NetworkCreator.HiddenLayer;
var outputLayer = Neural3NetworkCreator.OutputLayer;
var inputHiddenRelations = neural3NetworkHelper.GetInputHiddenRelations();
var hiddenOutputRelations = neural3NetworkHelper.GetHiddenOutputRelations();
// Вычисление комбинированного и сглаженного сигнала, пропущенного через сигмоиду,
// данный сигнал прошел через все узлы и вышел из output layer
var signalsFromHiddenLayer = neural3NetworkHelper.CalcSignalsFromLayer(signalsFromInputLayer, inputLayer, hiddenLayer, inputHiddenRelations, _funcActivation);
var signalsFromOutputLayer = neural3NetworkHelper.CalcSignalsFromLayer(signalsFromHiddenLayer, hiddenLayer, outputLayer, hiddenOutputRelations, _funcActivation);
var neural3NetworkWeightsUpdater = new Neural3NetworkWeightsUpdater(this, signalsFromInputLayer, signalsFromHiddenLayer, signalsFromOutputLayer);
// Обновление весов на нужных ребрах, в зависимости от ошибки и правильного ответа
var errorsHiddenLayer = neural3NetworkWeightsUpdater.UpdateWeights(hiddenOutputRelations, signalsFromHiddenLayer, signalsFromOutputLayer, rightAnswer);
neural3NetworkWeightsUpdater.UpdateWeights(errorsHiddenLayer, inputHiddenRelations, signalsFromInputLayer, signalsFromHiddenLayer);
}
}
public RightAnswerForm(int resultNumber, Neural3NetworkWeightsUpdater neural3NetworkWeightsUpdater, Neural3NetworkHelper neural3NetworkHelper)
{
InitializeComponent();
_resultNumN.Value = resultNumber;
_neural3NetworkWeightsUpdater = neural3NetworkWeightsUpdater;
_neural3NetworkHelper = neural3NetworkHelper;
}
/// <summary>
/// Проверка нейросети.
/// Нейросеть должна предсказать, что это за цифра, отрисованная в редакторе.
/// </summary>
private void _checkNNBtn_Click(object sender, EventArgs e)
{
var id = int.MaxValue;
int number = int.MaxValue;
var neural3NetworkChecker = new Neural3NetworkChecker(_neural3NetworkCreator);
var colors = GetRGBComponents28x28FromEditor();
var dataNumberDTO_28x28_Set = new DataNumberDTO_28x28_Set(id, number, colors);
var signalsFromInputLayer = new double[0];
var signalsFromHiddenLayer = new double[0];
var signalsFromOutputLayer = new double[0];
var result = neural3NetworkChecker.Check(dataNumberDTO_28x28_Set, out signalsFromInputLayer, out signalsFromHiddenLayer, out signalsFromOutputLayer);
var neural3NetworkWeightsUpdater = new Neural3NetworkWeightsUpdater(_neural3NetworkTeacher, signalsFromInputLayer, signalsFromHiddenLayer, signalsFromOutputLayer);
var neural3NetworkHelper = new Neural3NetworkHelper(_neural3NetworkCreator);
var rightAnswerForm = new RightAnswerForm(result.NeuronNumber, neural3NetworkWeightsUpdater, neural3NetworkHelper);
rightAnswerForm.Show();
}
/// <summary>
/// Главный метод нейросети - обучение на картинках 28x28 pixels
/// </summary>
public OutputSignalDTO Check(DataNumberDTO_28x28_Set imageDataSet, out double[] signalsFromInputLayer, out double[] signalsFromHiddenLayer, out double[] signalsFromOutputLayer)
{
var rightAnswer = imageDataSet.Number;
var neural3NetworkHelper = new Neural3NetworkHelper(_neural3NetworkCreator);
// Трансформирование RGB - компонент в входной сигнал для нейронов входного слоя
var RGBComponents = imageDataSet.RGBComponents;
signalsFromInputLayer = neural3NetworkHelper.TransformWhiteBlackPixelsToSignals(RGBComponents);
var inputLayer = _neural3NetworkCreator.InputLayer;
var hiddenLayer = _neural3NetworkCreator.HiddenLayer;
var outputLayer = _neural3NetworkCreator.OutputLayer;
var inputHiddenRelations = neural3NetworkHelper.GetInputHiddenRelations();
var hiddenOutputRelations = neural3NetworkHelper.GetHiddenOutputRelations();
// Вычисление комбинированного и сглаженного сигнала, пропущенного через сигмоиду,
// данный сигнал прошел через все узлы и вышел из output layer
signalsFromHiddenLayer = neural3NetworkHelper.CalcSignalsFromLayer(signalsFromInputLayer, inputLayer, hiddenLayer, inputHiddenRelations, _funcActivation);
signalsFromOutputLayer = neural3NetworkHelper.CalcSignalsFromLayer(signalsFromHiddenLayer, hiddenLayer, outputLayer, hiddenOutputRelations, _funcActivation);
return(neural3NetworkHelper.GetOutputSignalDTO(signalsFromOutputLayer));
}