private void LoadNetworkButton_Click(object sender, RoutedEventArgs e)
{
try
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.DefaultExt = ".nn";
ofd.Filter = "Neural Network definition files (.nn)|*.nn";
if (ofd.ShowDialog().GetValueOrDefault())
{
loadedNetwork = new DeepNeuralNetwork(ofd.FileName);
string layerString = string.Empty;
foreach (int ls in loadedNetwork.GetLayerCounts())
{
layerString += ls.ToString() + " ";
}
model.LoadedNNName = System.IO.Path.GetFileName(ofd.FileName) + " ( " + layerString + ")";
}
}
catch
{
MessageBox.Show("Error loading file.");
}
}
private async void StartButton_Click(object sender, RoutedEventArgs e)
{
if (model.TrainingActive)
{
MessageBox.Show("Training is already active.");
return;
}
if (dataContainer == null)
{
MessageBox.Show("No data loaded.");
return;
}
model.TrainingActive = true;
//prepare network
if (model.UseLoadedNN && loadedNetwork != null &&
loadedNetwork.GetLayerCounts()[0] == dataContainer.GetInputDataSize() && loadedNetwork.GetLayerCounts().Last() == dataContainer.GetOutputDataSize())
{
neuralNetwork = loadedNetwork;
}
else
{
string[] tokens = model.HiddenLayers.Split(' ').Where(s => !string.IsNullOrWhiteSpace(s)).ToArray();
List <int> hlList = new List <int>();
foreach (string token in tokens)
{
if (int.TryParse(token, out int hl))
{
hlList.Add(hl);
}
}
neuralNetwork = new DeepNeuralNetwork(dataContainer.GetInputDataSize(), dataContainer.GetOutputDataSize(), hlList.ToArray());
}
//hyper-params
int epochs = 0, batchSize = 0;
double learningRate = 0;
bool okToStart = true;
okToStart = okToStart && int.TryParse(model.Epochs, out epochs);
okToStart = okToStart && int.TryParse(model.BatchSize, out batchSize);
okToStart = okToStart && double.TryParse(model.LearningRate, out learningRate);
if (!okToStart)
{
return;
}
_epochs = epochs;
string layerString = string.Empty;
foreach (int ls in neuralNetwork.GetLayerCounts())
{
layerString += ls.ToString() + " ";
}
model.ConsoleString += $"{layerString} - epochs = {epochs}, batch size = {batchSize}, learning rate = {learningRate}\n\n\n";
//clear temp csv file
File.Delete(_tempCsvFileName);
//train
await Task.Run(() =>
Trainer.Sgd(neuralNetwork, dataContainer.TrainingSetAsLabeledDataArray(), batchSize, epochs, learningRate,
UpdateTrainingStatus, dataContainer.TestingSetAsLabeledDataArray(), dataContainer.CheckIfOutputIsCorrect));
MessageBox.Show("Training done.");
model.TrainingActive = false;
}
