internal async Task InitTrain()
{
await Reset();
CurrentDemo.TrainingEnded -= TrainingEnded;
CurrentDemo.TrainingEnded += TrainingEnded;
if (string.IsNullOrWhiteSpace(HiddenLayers) == false)
{
CurrentDemo.HiddenLayers = HiddenLayers.Split(',').Select(l => int.Parse(l)).ToArray();
}
else
{
CurrentDemo.HiddenLayers = new int[] { };
}
CurrentDemo.Speed = SpeedValue;
CurrentDemo.LearningRate = LearningRate;
CurrentDemo.TargetError = TargetError;
CurrentDemo.CreateNeuralNetwork();
TotalSteps = CurrentDemo.TotalSteps;
TotalError = CurrentDemo.TotalError;
Input = string.Join(",", CurrentDemo.TrainingSet[0]);
Output = string.Join(",", CurrentDemo.NeuralNetwork.Update(CurrentDemo.TrainingSet[0]));
TargetOutput = string.Join(",", CurrentDemo.Targets[0]);
var scene = new NeuralNetworkScene(CurrentDemo.NeuralNetwork);
await _game.Start(scene);
}
public TrainingModel Prepare()
{
var hiddenLayers = HiddenLayers.Split(';')
.Select(x => Convert.ToInt32(x))
.ToList();
return(new TrainingModel
{
TrainingData = _trainingDataModel,
LearningRate = Convert.ToDouble(LearningRate),
MinError = Convert.ToDouble(MinError),
NoOfEpochs = Convert.ToInt32(_noEpochs),
HiddenLayers = hiddenLayers,
ActivationFunction = _selectedActivationFunction,
NeuralNetworkName = _neuralNetworkName,
NeuralNetworkSavePath = _neuralNetworkSavePath,
});
}