public Network(string name, int inputs, List<int> hiddens, int outputs, Termination termination, NetworkParameters parameters = null, int constants = 1)
{
Name = name;
Termination = termination;
if (parameters != null)
{
parameters.Assert();
Parameters = parameters;
}
HiddensLayout = hiddens;
List<List<Neuron>> levels = new List<List<Neuron>>();
levels.Add(Enumerable.Range(0, inputs).Select(i => new Neuron(NeuronType.Input)).ToList());
foreach (int hidden in hiddens)
levels.Add(Enumerable.Range(0, hidden).Select(i => new Neuron(NeuronType.Hidden)).ToList());
levels.Add(Enumerable.Range(0, outputs).Select(i => new Neuron(NeuronType.Output)).ToList());
// Connects to all other neurons.. except input ones (this is used as the additive "constant" value attached to each sigmoid unit.)
List<Neuron> connectToAll = Enumerable.Range(0, constants).Select(i => new Neuron(NeuronType.Constant) { Value = 1 }).ToList();
for (int i = 0; i + 1 < levels.Count; ++i)
{
foreach (Neuron n in levels[i])
foreach (Neuron m in levels[i + 1])
n.Attach(m, new Weight(Parameters.InitialWeightInterval));
foreach (Neuron c in connectToAll)
foreach (Neuron m in levels[i + 1])
c.Attach(m, new Weight(Parameters.InitialWeightInterval));
}
Neurons = levels.Aggregate<List<Neuron>>((aggregated, level) => { aggregated.AddRange(level); return aggregated; }).ToList();
Neurons.AddRange(connectToAll);
}
private void btnStart_Click(object sender, RoutedEventArgs e)
{
try
{
if (Status == TrainStatus.Running)
{
Status = TrainStatus.Paused;
Save();
return;
}
if (Thread != null && Thread.IsAlive)
Thread.Join(); // Wait for last thread to finish working before starting again.
Termination termination;
if (cbTerminationType.Text == "ByValidationSet")
termination = Termination.ByValidationSet(DataParser.ValidationSet, ToInt(tbValidateCycle, x => x > 0, "Validation cycle must be > 0"));
else if (cbTerminationType.Text == "ByIteration")
termination = Termination.ByIteration(ToInt(tbIterations, x => x > 0, "Iterations must be > 0"));
else
throw new Exception("Did not recognize termination type: " + cbTerminationType.Text);
NetworkParameters parameters = new NetworkParameters()
{
InitialWeightInterval = new Tuple<double, double>(ToDouble(tbInitialWeightMin), ToDouble(tbInitialWeightMax)),
LearningRate = ToDouble(tbLearningRate, x => x > 0, "Learning rate must be > 0"),
LearningRateDecay = ToDouble(tbLearningRateDecay),
Momentum = ToDouble(tbMomentum, x => x >= 0 && x < 1, "Momentum must be in [0,1)"),
MomentumDecay = ToDouble(tbMomentumDecay)
};
string name = ToString(tbName, s => s.Trim() != string.Empty, "Network name cannot be empty.");
if (Status == TrainStatus.Create && Directory.Exists(name))
{
if (MessageBox.Show("Neural Net folder '" + name + "' already exists. Delete contents?\r\nIf not, use a unique name.", "Error", MessageBoxButton.YesNo) == MessageBoxResult.Yes)
(new DirectoryInfo(name)).Delete(true);
else
return;
}
if (Status == TrainStatus.Create)
{
Network = new Network(
name,
ToInt(tbInputs, x => x > 0, "Number of input nodes must be > 0"),
tbHiddens.Text.Split(new char[] { ' ', ',', ';' }, StringSplitOptions.RemoveEmptyEntries).Select(x => ToInt(x, tbHiddens, xx => xx > 0, "Number of hidden nodes must be > 0 per layer")).ToList<int>(),
ToInt(tbOutputs, x => x > 0, "Number of output nodes must be > 0"),
termination,
parameters);
}
else
{
Network.Termination.Type = termination.Type;
Network.Termination.ValidationSet = termination.ValidationSet;
Network.Termination.ValidateCycle = termination.ValidateCycle;
Network.Termination.TotalIterations = termination.TotalIterations;
Network.Parameters = parameters;
Network.Name = name;
}
Status = TrainStatus.Running;
Thread = new Thread(new ThreadStart(DoTrain)) { IsBackground = true };
Thread.Start();
}
catch (Exception x)
{
MessageBox.Show(x.Message, "Error");
}
}
public void New()
{
Save();
if (Thread != null && Thread.IsAlive)
Thread.Join(); // Wait for last thread to finish working before starting again.
tbName.Text = "";
tbInputs.Text = "82";
tbHiddens.Text = "100";
tbOutputs.Text = "1";
NetworkParameters parameters = new NetworkParameters();
tbLearningRate.Text = parameters.LearningRate.ToString();
tbLearningRateDecay.Text = parameters.LearningRateDecay.ToString();
tbMomentum.Text = parameters.Momentum.ToString();
tbMomentumDecay.Text = parameters.MomentumDecay.ToString();
tbInitialWeightMin.Text = parameters.InitialWeightInterval.Item1.ToString();
tbInitialWeightMax.Text = parameters.InitialWeightInterval.Item2.ToString();
cbTerminationType.Text = "ByValidationSet";
tbIterations.Text = "20000";
tbValidateCycle.Text = "500";
ValidationPlot.Collection.Clear();
TrainingPlot.Collection.Clear();
CurrentErrorHistoryIndex = 0;
Status = TrainStatus.Create;
lbError.Content = lbIteration.Content = lbTimeElapsed.Content = "0";
}
public Network(string name, int inputs, int hiddens, int outputs, Termination termination, NetworkParameters parameters = null)
: this(name, inputs, new List<int>(){hiddens}, outputs, termination, parameters)
{
}
