private void LoadTrainData()
{
OpenFileDialog openFile = new OpenFileDialog();
if (openFile.ShowDialog() == DialogResult.OK)
{
try
{
network = networkService.ReadDataTrain(openFile.FileName);
}
catch (Exception)
{
MessageBox.Show("No se pudo cargar el archivo");
}
}
if (network != null)
{
txtInputs.Text = network.NoInputs.ToString();
txtOutputs.Text = network.NoOutputs.ToString();
txtPatterns.Text = network.NoPatterns.ToString();
InitializeWeights(network.NoInputs, network.NoOutputs);
FillDataGridWeights(weights);
threshold = new double[network.NoOutputs];
threshold[0] = 0.3;
trainParams = new TrainParams();
trainParams.Weights = weights;
trainParams.Threshold = threshold;
FillDataGridThreshold(trainParams.Threshold);
}
}
public double[] simulateNetwork(TrainParams trainParams, double [] x)
{
int noOutputs = trainParams.Weights.GetLength(0);
int noInputs = trainParams.Weights.GetLength(1);
double[] yr = new double[noOutputs];
var s = new double[noOutputs];
for (int i = 0; i < noOutputs; i++)
{
for (int j = 0; j < noInputs; j++)
{
var xj = x[j];
var wji = trainParams.Weights[i, j];
s[i] = s[i] + (xj * wji);
Console.WriteLine("xi: " + xj);
Console.WriteLine("wji: " + wji);
}
Console.WriteLine("si: " + s[i]);
s[i] = s[i] - trainParams.Threshold[i];
Console.WriteLine(i + " Si: " + s[i]);
yr[i] = calculateEscalon(s[i]);
}
return(yr);
}
private void btnLoadNetworkParams_Click(object sender, EventArgs e)
{
try
{
OpenFileDialog openFileDialog = new OpenFileDialog();
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
trainParams = networkService.ReadNetworkTrainParams(openFileDialog.FileName);
}
FillDataGridWeights(trainParams.Weights);
FillDataGridThreshold(trainParams.Threshold);
}
catch (Exception)
{
throw;
}
}
private void LoadTrainParams()
{
TrainParams AuxParams;
OpenFileDialog openFileDialog = new OpenFileDialog();
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
AuxParams = networkService.ReadNetworkTrainParams(openFileDialog.FileName);
if (AuxParams.Weights.GetLength(0) == network.NoOutputs && AuxParams.Weights.GetLength(1) == network.NoInputs)
{
trainParams = AuxParams;
}
else
{
MessageBox.Show("incoherencia entre los datos de entrenamiento y los pesos o umbrales");
}
}
FillDataGridWeights(trainParams.Weights);
FillDataGridThreshold(trainParams.Threshold);
}
public TrainParams ReadNetworkTrainParams(string path)
{
StreamReader sr = File.OpenText(path);
StreamReader auxSr = File.OpenText(path);
string s = "";
int columns = 0;
int rows = 0;
while ((s = sr.ReadLine()) != null)
{
var a = s.Split(';');
var weights = a[0].Split('/');
var threshold = a[1];
if (rows == 0)
{
columns = weights.Length;
}
rows++;
}
_weights = new double[rows, columns];
_threshold = new double[rows];
int currentRow = 0;
while ((s = auxSr.ReadLine()) != null)
{
var a = s.Split(';');
var weights = a[0].Split('/');
var threshold = a[1];
for (int i = 0; i < weights.Length; i++)
{
_weights[currentRow, i] = double.Parse(weights[i]);
}
_threshold[currentRow] = double.Parse(threshold);
currentRow++;
}
TrainParams trainParams = new TrainParams();
trainParams.Threshold = _threshold;
trainParams.Weights = _weights;
return(trainParams);
}
/// <summary>
/// Точка входа в приложение
/// </summary>
/// <param name="args">Параметры командной строки</param>
private static void Main(string[] args)
{
var scalingFactory = new ScalingFactory <INeuralNetworkImageScaling>();
var neuralNetwork = scalingFactory.GetScaleObject(typeof(SRGAN));
neuralNetwork.ScaleLogger = new ConsoleScaleLogger();
TrainParams trainParams;
if (!File.Exists(ConfigPath))
{
trainParams = new TrainParams();
File.WriteAllText(ConfigPath, JsonConvert.SerializeObject(trainParams));
}
else
{
var data = File.ReadAllText(ConfigPath);
trainParams = JsonConvert.DeserializeObject <TrainParams>(data);
}
neuralNetwork.Train(trainParams);
Console.WriteLine("Press any key to exit");
Console.ReadKey();
}
private List <string> CreateParamsList(TrainParams trainParams)
{
string delimiter = "/";
List <string> data = new List <string>();
string a = "";
for (int i = 0; i < trainParams.Weights.GetLength(0); i++)
{
for (int j = 0; j < trainParams.Weights.GetLength(1); j++)
{
if (j == trainParams.Weights.GetLength(1) - 1)
{
a = a + trainParams.Weights[i, j] + ";";
}
else
{
a = a + trainParams.Weights[i, j] + delimiter;
}
}
a = a + trainParams.Threshold[i];
data.Add(a);
}
return(data);
}
