public static (BaseModel model, double accuracy) TrainAndTest(BaseModel model)
{
// Global parameters
string datasetPath = @"Sudoku.NeuralNetwork\Dataset\sudoku.csv.gz";
int numSudokus = 1000;
// ML parameters
double testPercent = 0.2;
float learningRate = .001F;
int batchSize = 32;
int epochs = 2;
Console.WriteLine("Initialize dataset");
var(sPuzzles, sSols) = DataSetHelper.ParseCSV(datasetPath, numSudokus);
var(_sPuzzzlesTrain, _sPuzzlesTest) = DataSetHelper.SplitDataSet(sPuzzles, testPercent);
var(_sSolsTrain, _sSolsTest) = DataSetHelper.SplitDataSet(sSols, testPercent);
Console.WriteLine("Preprocess data");
var sPuzzzlesTrain = DataSetHelper.PreprocessSudokus(_sPuzzzlesTrain);
var sSolsTrain = DataSetHelper.PreprocessSudokus(_sSolsTrain);
var sPuzzlesTest = DataSetHelper.PreprocessSudokus(_sPuzzlesTest);
var sSolsTest = DataSetHelper.PreprocessSudokus(_sSolsTest);
// Add optimizer
var adam = new Keras.Optimizers.Adam(learningRate);
model.Compile(loss: "sparse_categorical_crossentropy", optimizer: adam);
Console.WriteLine("Train model");
model.Fit(sPuzzzlesTrain, sSolsTrain, batch_size: batchSize, epochs: epochs);
// Test model
int correct = 0;
for (int i = 0; i < 1; i++)
{
Console.WriteLine("Testing " + i);
// Predict result
var prediction = Solve(sPuzzlesTest[i], model);
// Convert to sudoku
var sPredict = new Core.Sudoku()
{
Cells = prediction.flatten().astype(np.int32).GetData <int>().ToList()
};
var sSol = new Core.Sudoku()
{
Cells = ((sSolsTest[i] + 0.5) * 9).flatten().astype(np.int32).GetData <int>().ToList()
};
// Compare sudoku
var same = true;
for (int j = 0; j < 9; j++)
{
for (int k = 0; k < 9; k++)
{
if (sPredict.GetCell(j, k) != sSol.GetCell(j, k))
{
same = false;
}
}
}
Console.WriteLine("Prediction : \n" + sPredict);
Console.WriteLine("Solution : \n" + sSol);
if (same)
{
correct += 1;
}
}
// Calculate accuracy
var accuracy = (correct / sPuzzlesTest.size) * 100;
// Return
return(model, accuracy);
}
