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($"Testing Model (All Possible States)");
TestModel(model, testingData, (l, p, i) => false, out int correct, out int wrong);;
Console.WriteLine(string.Format("Correct: {0}", correct));
Console.WriteLine(string.Format("Wrong: {0}", wrong));
}
public static void PredictTimeTest()
{
global::Keras.Keras.DisablePySysConsoleLog = true;
string modelPath = @"C:\Users\pstepnowski\Source\Repos\fdafadf\basics\Workspace\TicTacToeKerasModel.bin";
Console.WriteLine($"Loading Model");
var model = new KerasModel(() => BaseModel.LoadModel(modelPath));
var startTime = DateTime.Now;
//Parallel.ForEach(Enumerable.Range(1, 100), (a) =>
//{
//});
for (int i = 0; i < 100; i++)
{
model.Predict(new double[] { 1, 0, 1, 0, 1, 0, 1, 0, 1 });
}
var elapsedTime = DateTime.Now - startTime;
Console.WriteLine($"{elapsedTime}");
List <double[]> inputs = new List <double[]>();
var startTime2 = DateTime.Now;
for (int i = 0; i < 100; i++)
{
int a = i & 1;
int b = i & 2;
int c = i & 4;
int d = i & 8;
int e = i & 16;
int f = i & 32;
int g = i & 64;
inputs.Add(new double[] { a, b, c, d, e, f, g, 0, 1 });
}
model.Predict(inputs.ToArray());
var elapsedTime2 = DateTime.Now - startTime2;
Console.WriteLine($"{elapsedTime2}");
}
private static void TestModel <TState, TLabel>(KerasModel model, LabeledState <TState, TLabel>[] testingData, Func <TLabel, float[], int, bool> outputComparer, out int correct, out int wrong)
{
correct = 0;
wrong = 0;
string boardLineToString(double[] input, int line)
{
string result = string.Empty;
for (int x = 0; x < 3; x++)
{
double v = input[line * 3 + x];
result += v < 0 ? "X" : (v > 0 ? "O" : ".");
}
return(result);
}
float[] outputs = model.Predict(testingData.Select(item => item.Input).ToArray());
for (int i = 0; i < testingData.Length; i++)
{
double[] input = testingData[i].Input;
TLabel expectedOutput = testingData[i].Label;
if (outputComparer(expectedOutput, outputs, i))
{
correct++;
}
else
{
//Console.WriteLine($"{boardLineToString(input, 0)} O: {output[0]:f3} O: {expectedOutput[0]:f3}");
//Console.WriteLine($"{boardLineToString(input, 1)} X: {output[1]:f3} X: {expectedOutput[1]:f3}");
//Console.WriteLine($"{boardLineToString(input, 2)} {output[2]:f3} {expectedOutput[2]:f3}");
wrong++;
}
}
}
public KerasPVNetwork(KerasModel model)
{
Model = model;
}
public TicTacToeKerasAI(string modelPath)
{
//PythonEngine.BeginAllowThreads();
model = new KerasModel(() => BaseModel.LoadModel(modelPath));
}