public void TrainCorrectThreePasses()
{
Layer layer = new Layer(new double[, ] {
{ 1, 1 }
});
List <TrainingVector> trainingVectors = new List <TrainingVector>
{
new TrainingVector(
new NetworkVector(new double[] { 0, 0 }),
new NetworkVector(new double[] { 1 })
),
new TrainingVector(
new NetworkVector(new double[] { 1, 0 }),
new NetworkVector(new double[] { 0 })
),
new TrainingVector(
new NetworkVector(new double[] { 0, 1 }),
new NetworkVector(new double[] { 0 })
),
new TrainingVector(
new NetworkVector(new double[] { 1, 1 }),
new NetworkVector(new double[] { 1 })
)
};
OnlineTrainer trainer = new OnlineTrainer(layer, trainingVectors);
trainer.Train();
trainer.Train();
trainer.Train();
double[,] weightsCheck = new double[, ] {
{ 3, 7 }
};
double[] biasesCheck = new double[] { -1 };
for (int i = 0; i < layer.NumberOfOutputs; i++)
{
Assert.AreEqual(biasesCheck[i], layer.State.Biases[i]);
for (int j = 0; j < layer.NumberOfInputs; j++)
{
Assert.AreEqual(weightsCheck[i, j], layer.State.Weights[i, j]);
}
}
}
public void CanRunOnlineTrainer()
{
Layer layer = new Layer(new double[, ] {
{ 1, 1 }
});
List <TrainingVector> trainingVectors = new List <TrainingVector>
{
new TrainingVector(
new NetworkVector(new double[] { 0, 0 }),
new NetworkVector(new double[] { 1 })
)
};
OnlineTrainer trainer = new OnlineTrainer(layer, trainingVectors);
trainer.Train();
}
public void CanMakeOnlineTrainer()
{
Layer layer = new Layer(new double[, ] {
{ 1, 1 }
});
List <TrainingVector> trainingVectors = new List <TrainingVector>
{
new TrainingVector(
new NetworkVector(new double[] { 1, 1 }),
new NetworkVector(new double[] { 1 })
)
};
OnlineTrainer trainer = new OnlineTrainer(layer, trainingVectors);
Assert.AreNotEqual(null, trainer);
}
static void Main(string[] args)
{
string learningFileName = @"C:\Users\Jakub\Desktop\approximation_train_1.txt";
string testFileName = @"C:\Users\Jakub\Desktop\approximation_test.txt";
LayerCharacteristic[] layers = new LayerCharacteristic[2];
layers[0] = new LayerCharacteristic(2, new SigmoidUnipolarFunction());
layers[1] = new LayerCharacteristic(1, new IdentityFunction());
var network = new NeuralNetwork(1, layers);
ILearningProvider dataProvider = new LearningApproximationDataProvider(learningFileName, testFileName, 1, 1, true);
//dataProvider.LearnSet[0] = new Datum(Vector<double>.Build.Dense(2, 1), Vector<double>.Build.Dense(2, 1));
//dataProvider.LearnSet[1] = new Datum(Vector<double>.Build.Dense(2, 1), Vector<double>.Build.Dense(2, 0));
var trainer = new OnlineTrainer(new MeanSquareErrorCalculator(), dataProvider, new BackPropagationAlgorithm(new LearningRateHandler(0.01, 0.7, 1.05, 1.04), 0.2, 1.05));
network.ConsoleDisplay();
Console.ReadLine();
#region old demo
//LayerCharacteristic[] layers = new LayerCharacteristic[2];
//layers[0] = new LayerCharacteristic(2, new SigmoidUnipolarFunction());
//layers[1] = new LayerCharacteristic(2, new IdentityFunction());
//var network = new NeuralNetwork(1, layers);
//var input = Vector<double>.Build.Dense(2);
//input[0] = 1;
//input[1] = 2;
//foreach (var layer in network.Layers)
//{
// Console.Write("\nNext layer:");
// foreach (var row in layer.Weights.ToRowArrays())
// {
// Console.WriteLine("\nrow: ");
// foreach (var cell in row)
// {
// Console.Write($"{cell} | ");
// }
// }
//}
//Console.WriteLine($"\nCalculated first: {network.CalculateOutput(input)[0]}");
//Console.WriteLine($"\nCalculated seond: {network.CalculateOutput(input)[1]}");
//var r = network.CalculateOutput(input);
//Console.ReadLine();
#endregion
#region multiplty demo
//var m = Matrix<double>.Build.Random(2,1);
//var v = Vector<double>.Build.Dense(2,2);
//Console.WriteLine($"vector: {v[0]} {v[1]}");
//Console.Write("\nMatrix:");
//foreach (var row in m.ToRowArrays())
//{
// Console.WriteLine("\nrow: ");
// foreach (var cell in row)
// {
// Console.Write($"{cell} | ");
// }
//}
//var res = v * m;
//Console.WriteLine($"res: {res.ToString()}");
//Console.ReadLine();
#endregion
}
