static void Main()
{
TicTacToeValueLoader.LoadAllUniqueStates(Storage.Instance, TicTacToeValueNetwork.DefaultInputTransform);
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
Application.Run(new TicTacToeForm());
}
public TicTacToeValueNetwork CreateTrainedNetwork()
{
var trainingData = TicTacToeValueLoader.LoadAllUniqueStates(Storage.Instance);
var trainer = new TicTacToeValueNetworkTrainer(trainingData, 0);
return(trainer.Train(30));
}
public static void LoadValueNetworkAndTestOnAllStates()
{
string modelPath = @"C:\Users\pstepnowski\Source\Repos\fdafadf\basics\Workspace\TicTacToeKerasModel.bin";
Console.WriteLine($"Loading Testing Data");
var testingData = TicTacToeValueLoader.LoadAllUniqueStates(null);
//TicTacToeTrainingData.Load(TicTacToeNeuralIOLoader.InputTransforms.Bipolar, out double[][] inputs, out TicTacToeResultProbabilities[] outputs);
Console.WriteLine($"Loading Model");
var model = new KerasModel(() => BaseModel.LoadModel(modelPath));
Console.WriteLine($"Testing Model (All Possible States)");
TestModel(model, testingData, (l, p, i) => false, out int correct, out int wrong);;
Console.WriteLine(string.Format("Correct: {0}", correct));
Console.WriteLine(string.Format("Wrong: {0}", wrong));
}
public static void Compare3()
{
int epoches = 30;
var data = TicTacToeValueLoader.LoadAllUniqueStates(Storage.Instance);
List <Optimizer> optimizers = new List <Optimizer>();
optimizers.Add(SGDMomentum(Network(RELU, 9, 3, 72, 72, 72, 36, 36, 36, 18, 18), 0.001, 0.04));
optimizers.Add(SGDMomentum(Network(LEAKY, 9, 3, 72, 72, 72, 36, 36, 36, 18, 18), 0.001, 0.04));
ChartForm chartForm = new ChartForm("TicTacToe");
foreach (var optimizer in optimizers)
{
chartForm.Add(optimizer, Train(optimizer, data, epoches, 1, Monitors(MSE)));
}
chartForm.ShowDialog();
}
public static void Compare4()
{
int epoches = 600;
var data = TicTacToeValueLoader.LoadAllUniqueStates(Storage.Instance);
//List<Optimizer> optimizers = new List<Optimizer>();
var optimizer1 = SGDMomentum(Network(RELU, 9, 3, 72, 72, 72, 36, 36, 36, 18, 18), 0.001, 0.04);
var optimizer2 = SGDMomentum(Network(RELU, 9, 3, 72, 72, 72, 36, 36, 36, 18, 18), 0.001, 0.04);
var optimizer3 = SGDMomentum(Network(RELU, 9, 3, 72, 72, 72, 36, 36, 36, 18, 18), 0.001, 0.04);
var optimizer4 = AdaGrad(Network(RELU, 9, 3, 72, 72, 72, 36, 36, 36, 18, 18), 0.01);
//optimizers.Add(AdaGrad(Network(RELU, 9, 3, 72, 72, 72, 36, 36, 36, 18, 18), 0.001));
ChartForm chartForm = new ChartForm("TicTacToe");
//chartForm.Add(optimizer1, Train(optimizer1, @in, @out, epoches, 1, Monitors(MSE)));
//chartForm.Add(optimizer2, Train(optimizer2, @in, @out, epoches, 8, Monitors(MSE)));
chartForm.Add(optimizer3, Train(optimizer3, data, epoches, 32, Monitors(MSE)));
chartForm.Add(optimizer4, Train(optimizer4, data, epoches, 32, Monitors(MSE)));
chartForm.ShowDialog();
}
private void Training(TrainingSettings settings)
{
var trainingData = TicTacToeValueLoader.LoadAllUniqueStates(Storage.Instance, TicTacToeValueNetwork.DefaultInputTransform);
var trainer = new TicTacToeValueNetworkTrainer(trainingData, 0)
{
HiddenLayerSizes = settings.Layers,
LearingRate = settings.LearningRate,
Momentum = settings.Momentum,
};
TrainedNetwork = trainer.Train(settings.Epoches, new TrainingMonitor(this));
Invoke((MethodInvoker) delegate()
{
var accuracy = AccuracyMonitor.CalculateAccuracy(TrainedNetwork, trainingData);
labelAccuracy.Text = $"{accuracy.CorrectPredictions}/{accuracy.TestingSetSize} ({accuracy.Value * 100.0:f2}%)";
buttonTrained.Enabled = true;
});
}