public void IfInputNeuronsWereChangedThanBeforeCalculatingOutputInvalidateCache()
{
NNetwork n = XorNetwork();
n.CacheEnabled = true;
n.SetInput(new double[] { 0, 0 });
var first = n.GetOutput();
n.SetInput(new double[] { 1, 0 });
var second = n.GetOutput();
Assert.AreNotEqual(first, second);
}
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]) });
}
}
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 TestInputEqualsOutput()
{
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 1 });
n.SetWeightMatrix(new double[][]
{
new double[] { 1, 1 }
});
n.SetInput(new double[] { 9 });
Assert.AreEqual(n.GetOutput()[0], 9);
}
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 ActivateCachingAcrossAllNetwork()
{
NNetwork n = XorNetwork();
n.CacheEnabled = false;
n.SetInput(new double[] { 0, 0 });
long without_cache = MyAssert.MeasureMethod(() => n.GetOutput(), 400);
n.CacheEnabled = true;
long with_cache = MyAssert.MeasureMethod(() => n.GetOutput(), 400);
Assert.Greater(without_cache / with_cache, 1.9);
}
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 void TestXNORAuto()
{
NNetwork n = XorNetwork();
double[] input = new double[] { 0, 0 };
n.SetInput(input);
double[] output = n.GetOutput();
MyAssert.CloseTo(output[0], 1);
input = new double[] { 0, 1 };
n.SetInput(input);
output = n.GetOutput();
MyAssert.CloseTo(output[0], 0);
input = new double[] { 1, 0 };
n.SetInput(input);
output = n.GetOutput();
MyAssert.CloseTo(output[0], 0);
input = new double[] { 1, 1 };
n.SetInput(input);
output = n.GetOutput();
MyAssert.CloseTo(output[0], 1);
}
public void TestSimplestConnection()
{
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 1, 1 });
n.SetWeightMatrix(new double[][]
{
new double[] { 1, 1 },
new double[] { 1, 1 }
});
n.SetInput(new double[] { 1 });
var output = n.GetOutput()[0];
var desired = 1 / (1 + Math.Pow(Math.E, -2));
MyAssert.CloseTo(output, desired);
}
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 static void Main()
// {
// TrainPrediction();
//// Sinus();
//// TestTanhLearningOnSinus();
//// TestTanhDerivative();
//
// }
public static void TrainPrediction()
{
NNetwork network = NNetwork.SigmoidNetwork(new int[] { 5, 1 });
network.RandomizeWeights(-1, 20);
NetworkTrainer trainer = new NetworkTrainer(network);
List <double> tr = new List <double>();
for (double i = 0; i <= 1; i = i + 0.05)
{
tr.Add(i);
}
double[] train_set = tr.ToArray();//new double[] { 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 };
double error = 1;
double delta = 1;
int j = 0;
for (; error > 0.01 && !(delta <= 0.00001) || j == 1; j++)
{
trainer.TrainPrediction(train_set, 0.0001, 0.2);
double new_cost = trainer.GetError();
delta = error - new_cost;
error = new_cost;
}
Console.Out.WriteLine(j + ": " + error);
for (double i = 0; i <= 0.5; i = i + 0.05)
{
network.SetInput(new double[] { i + 0.0, i + 0.1, i + 0.2, i + 0.3, i + 0.4 });
Show(new double[]
{
i + 0.5,
network.GetOutput()[0],
// network.GetOutput()[1]
});
}
}
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);
}
}