public static void TestTanhLearningOnSinus()
{
NNetwork network = NNetwork.HyperbolicNetwork(new int[] { 1, 2, 1 });
network.RandomizeWeights(1, 2);
NetworkTrainer trainer = new NetworkTrainer(network);
double[][] inputs = SinusTrainSet()[0];
double[][] outputs = SinusTrainSet()[1];
double error = 1;
double delta = 1;
int j = 0;
for (; error > 0.01 && !(delta <= 0.000001) || j == 1; j++)
{
trainer.TrainClassification(inputs, outputs);
double new_cost = trainer.GetError();
delta = error - new_cost;
error = new_cost;
}
double[][] input_test = SinusTrainSet(20)[0];
double[][] output_test = SinusTrainSet(20)[1];
trainer.IsLearning = false;
trainer.TrainClassification(input_test, output_test);
error = trainer.GetError();
Console.Out.WriteLine(error);
for (int i = 0; i < input_test.Length; i++)
{
network.SetInput(input_test[i]);
Show(new [] { input_test[i][0], network.GetOutput()[0], Math.Sin(input_test[i][0]) });
}
}
private void buttonCreateNetwork_Click(object sender, EventArgs e)
{
String[] layers_string = textLayers.Text.Split(";".ToCharArray());
int[] layers = new int[layers_string.Length];
for (int i = 0; i < layers_string.Length; i++)
{
layers[i] = int.Parse(layers_string[i]);
}
if (radioHyperbolic.Checked)
{
network = NNetwork.HyperbolicNetwork(layers);
is_hyperbolic = true;
is_sigmoid = false;
}
if (radioSigmoid.Checked)
{
network = NNetwork.SigmoidNetwork(layers);
is_hyperbolic = false;
is_sigmoid = true;
}
if (radioCombined.Checked)
{
network = NNetwork.CombinedNetwork(layers);
is_hyperbolic = false;
is_sigmoid = true;
}
bool two_steps = network.OutputCount() >= 2;
bool three_steps = network.OutputCount() >= 3;
checkTrain2.Enabled = two_steps;
checkTest2.Enabled = two_steps;
checkTrain3.Enabled = three_steps;
checkTest3.Enabled = three_steps;
groupWeights.Enabled = true;
}
public void TestTanhDerivative()
{
// SO-SO test =(
NNetwork n = NNetwork.HyperbolicNetwork(new int[] { 2, 2, 1 });
n.RandomizeWeights(-1, 10);
Random random = new Random();
double x;
double y;
double z;
x = random.NextDouble();
y = random.NextDouble();
z = some_function(x, y);
n.SetInput(new double[] { x, y });
n.SetAnswers(new double[] { z });
n.BackPropagate();
double[] ders = n.Derivatives();
double[] ests = n.Estimation(0.0001);
var koeff = ests[0] / ders[0];
for (int i = 0; i < ders.Length; i++)
{
MyAssert.CloseTo(ests[i] / ders[i], koeff, 0.00001);
}
}
public void TestTanhNetwork()
{
NNetwork n = NNetwork.HyperbolicNetwork(new int[] { 2, 4, 4, 2 });
INeuron[][] neurons = n.Neurons;
Assert.IsInstanceOf(typeof(TanhNeuron), neurons[2][2]);
}
public void TestTanhLearningOnSinus()
{
NNetwork network = NNetwork.HyperbolicNetwork(new int[] { 1, 2, 1 });
network.RandomizeWeights(1, 2);
NetworkTrainer trainer = new NetworkTrainer(network);
double[][] inputs = SinusTrainSet()[0];
double[][] outputs = SinusTrainSet()[1];
double error = 1;
double delta = 1;
int j = 0;
for (; error > 0.01 && !(delta <= 0.000001) || j == 1; j++)
{
trainer.TrainClassification(inputs, outputs);
double new_cost = trainer.GetError();
delta = error - new_cost;
error = new_cost;
}
double[][] input_test = SinusTrainSet(20)[0];
double[][] output_test = SinusTrainSet(20)[1];
trainer.IsLearning = false;
trainer.TrainClassification(input_test, output_test);
error = trainer.GetError();
Assert.Less(error, 0.53);
}
public static void TestTanhDerivative()
{
NNetwork n = NNetwork.HyperbolicNetwork(new int[] { 2, 2, 1 });
n.RandomizeWeights(-1, 10);
Random random = new Random();
double x;
double y;
double z;
x = random.NextDouble();
y = random.NextDouble();
z = some_function(x, y);
n.SetInput(new double[] { x, y });
n.SetAnswers(new double[] { z });
n.BackPropagate();
double[] ders = n.Derivatives();
double[] ests = n.Estimation(0.0001);
for (int i = 0; i < ders.Length; i++)
{
Show(new[] { ders[i], ests[i], ests[i] / ders[i] });
}
}