public void OnTaskChanged(INetworkTask task)
{
var controls = NeuronsHolder.Children.OfType <OutputNeuronControl>().ToList();
for (int i = controls.Count; i < task.GetClasses().Count; ++i)
{
AddNeuron();
}
for (int i = task.GetClasses().Count; i < controls.Count; ++i)
{
NeuronsHolder.Children.RemoveAt(NeuronsHolder.Children.Count - 1);
}
}
public NetworkDataModel CreateNetworkDataModel(INetworkTask task, bool isCopy)
{
ErrorMatrix em1 = null;
if (task != null)
{
em1 = new ErrorMatrix(task.GetClasses());
var em2 = new ErrorMatrix(task.GetClasses());
em1.Next = em2;
em2.Next = em1;
}
var model = new NetworkDataModel(Id, GetLayersSize())
{
ErrorMatrix = em1,
Classes = task?.GetClasses(),
IsEnabled = CtlIsNetworkEnabled.IsOn,
Color = CtlColor.Foreground.GetColor(),
RandomizeMode = RandomizeMode,
RandomizerParamA = RandomizerParamA,
LearningRate = LearningRate,
InputInitial0 = ActivationFunction.Helper.GetInstance(InputLayer.ActivationFunc).Do(InputLayer.Initial0, InputLayer.ActivationFuncParamA),
InputInitial1 = ActivationFunction.Helper.GetInstance(InputLayer.ActivationFunc).Do(InputLayer.Initial1, InputLayer.ActivationFuncParamA),
CostFunction = CostFunction.Helper.GetInstance(CtlCostFunction.SelectedValue.ToString()),
IsAdjustFirstLayerWeights = InputLayer.IsAdjustFirstLayerWeights
};
model.ActivateNetwork();
LayerDataModel prevLayer = null;
var layers = GetLayersControls();
for (int ln = 0; ln < layers.Count; ++ln)
{
if (ln > 0)
{
prevLayer = model.Layers[ln - 1];
}
model.Layers[ln].VisualId = layers[ln].Id;
var neurons = layers[ln].GetNeuronsControls();
for (int nn = 0; nn < neurons.Count; ++nn)
{
var neuronModel = model.Layers[ln].Neurons[nn];
neuronModel.VisualId = neurons[nn].Id;
neuronModel.IsBias = neurons[nn].IsBias;
neuronModel.IsBiasConnected = neurons[nn].IsBiasConnected;
neuronModel.ActivationFunction = ActivationFunction.Helper.GetInstance(neurons[nn].ActivationFunc);
neuronModel.ActivationFuncParamA = neurons[nn].ActivationFuncParamA;
if (ln == 0 && !neuronModel.IsBias)
{
neuronModel.WeightsInitializer = InputLayer.WeightsInitializer;
neuronModel.WeightsInitializerParamA = InputLayer.WeightsInitializerParamA;
double initValue = InitializeMode.Helper.Invoke(neuronModel.WeightsInitializer, neuronModel.WeightsInitializerParamA);
if (!InitializeMode.Helper.IsSkipValue(initValue))
{
neuronModel.Weights.ForEach(w => w.Weight = InitializeMode.Helper.Invoke(neuronModel.WeightsInitializer, neuronModel.WeightsInitializerParamA));
}
}
else
{
neuronModel.WeightsInitializer = neurons[nn].WeightsInitializer;
neuronModel.WeightsInitializerParamA = neurons[nn].WeightsInitializerParamA;
double initValue = InitializeMode.Helper.Invoke(neuronModel.WeightsInitializer, neuronModel.WeightsInitializerParamA);
if (!InitializeMode.Helper.IsSkipValue(initValue))
{
neuronModel.Weights.ForEach(w => w.Weight = InitializeMode.Helper.Invoke(neuronModel.WeightsInitializer, neuronModel.WeightsInitializerParamA));
}
}
if (neuronModel.IsBias)
{
neuronModel.ActivationInitializer = neurons[nn].ActivationInitializer;
neuronModel.ActivationInitializerParamA = neurons[nn].ActivationInitializerParamA;
double initValue = InitializeMode.Helper.Invoke(neurons[nn].ActivationInitializer, neurons[nn].ActivationInitializerParamA);
if (!InitializeMode.Helper.IsSkipValue(initValue))
{
neuronModel.Activation = initValue;
}
}
if (!isCopy && prevLayer != null && prevLayer.Height > 0)
{
neuronModel.ForwardHelper = new ListX <ForwardNeuron>(prevLayer.Height);
var prevNeuron = prevLayer.Neurons[0];
while (prevNeuron != null)
{
if (!neuronModel.IsBias || (neuronModel.IsBiasConnected && prevNeuron.IsBias))
{
neuronModel.ForwardHelper.Add(new ForwardNeuron(prevNeuron, prevNeuron.WeightTo(neuronModel)));
}
prevNeuron = prevNeuron.Next;
}
}
}
}
model.Layers.Last().VisualId = Const.OutputLayerId;
{
var neurons = OutputLayer.GetNeuronsControls();
for (int i = 0; i < neurons.Count; ++i)
{
model.Layers.Last().Neurons[i].VisualId = neurons[i].Id;
}
}
if (!isCopy)
{
model.Copy = CreateNetworkDataModel(task, true);
}
return(model);
}