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 TestBackPropWithKnownValues()
{
NNetwork n = NetworkTest.XorNetwork();
n.SetInput(new double[] { 1, 1 });
n.SetAnswers(new double[] { 0 });
n.BackPropagate();
double[] deltas = n.GetDeltasForLayer(2);
Assert.AreNotEqual(deltas[0], 0);
Assert.AreNotEqual(deltas[1], 0);
MyAssert.CloseTo(deltas[0], 0, 0.001);
MyAssert.CloseTo(deltas[1], 0, 0.001);
}
public void CanApplyTrainingForWholeNetwork()
{
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 1, 2, 2, 1 });
n.SetInput(new double[] { 0.3 });
n.SetAnswers(new double[] { 0.8 });
n.BackPropagate();
var output_before = n.GetOutput();
n.ApplyTraining();
var output_after = n.GetOutput();
Assert.AreNotEqual(output_after, output_before);
}
public void TestNetworkSetAnswerAndGetDelta()
{
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 2, 3, 2 });
n.SetInput(new double[] { 0, 0 });
double[] outputs = n.GetOutput();
double[] answers = new double[] { 0.1, 0.9 };
n.SetAnswers(answers);
n.BackPropagate();
double[] deltas = n.GetDeltasForLayer(3);
for (int i = 0; i < answers.Length; i++)
{
MyAssert.CloseTo(deltas[i], answers[i] - outputs[i]);
}
}
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] });
}
}
public void TestDerivative()
{
//Fails with square error function
NNetwork n = NNetwork.SigmoidNetwork(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++)
{
MyAssert.CloseTo(ders[i], ests[i], 0.0001);
}
}