public void RunTest2()
{
// Example from Edwin Chen, Introduction to Restricted Boltzmann Machines
// http://blog.echen.me/2011/07/18/introduction-to-restricted-Boltzmann-machines/
double[][] inputs =
{
new double[] { 1, 1, 1, 0, 0, 0 },
new double[] { 1, 0, 1, 0, 0, 0 },
new double[] { 1, 1, 1, 0, 0, 0 },
new double[] { 0, 0, 1, 1, 1, 0 },
new double[] { 0, 0, 1, 1, 0, 0 },
new double[] { 0, 0, 1, 1, 1, 0 }
};
Accord.Math.Tools.SetupGenerator(0);
BernoulliFunction.Random = new ThreadSafeRandom(0);
GaussianFunction.Random.SetSeed(0);
RestrictedBoltzmannMachine network =
RestrictedBoltzmannMachine.CreateGaussianBernoulli(6, 2);
new GaussianWeights(network).Randomize();
network.UpdateVisibleWeights();
ContrastiveDivergenceLearning target = new ContrastiveDivergenceLearning(network);
target.Momentum = 0;
target.LearningRate = 0.1;
target.Decay = 0;
int iterations = 5000;
double[] errors = new double[iterations];
for (int i = 0; i < iterations; i++)
{
errors[i] = target.RunEpoch(inputs);
}
double startError = errors[0];
double lastError = errors[iterations - 1];
Assert.IsTrue(startError > lastError);
{
double[] output = network.GenerateOutput(new double[] { 0, 0, 0, 1, 1, 0 });
Assert.AreEqual(2, output.Length);
Assert.AreEqual(1, output[0]);
Assert.AreEqual(0, output[1]);
}
{
double[] output = network.GenerateOutput(new double[] { 1, 1, 1, 0, 0, 0 });
Assert.AreEqual(2, output.Length);
Assert.AreEqual(1, output[0]);
Assert.AreEqual(1, output[1]);
}
}
public void RunTest()
{
Accord.Math.Tools.SetupGenerator(0);
// Example from Edwin Chen, Introduction to Restricted Boltzmann Machines
// http://blog.echen.me/2011/07/18/introduction-to-restricted-Boltzmann-machines/
double[][] inputs =
{
new double[] { 1, 1, 1, 0, 0, 0 },
new double[] { 1, 0, 1, 0, 0, 0 },
new double[] { 1, 1, 1, 0, 0, 0 },
new double[] { 0, 0, 1, 1, 1, 0 },
new double[] { 0, 0, 1, 1, 0, 0 },
new double[] { 0, 0, 1, 1, 1, 0 }
};
BernoulliFunction activation = new BernoulliFunction();
BernoulliFunction.Random = new ThreadSafeRandom(2);
RestrictedBoltzmannMachine network = new RestrictedBoltzmannMachine(activation, 6, 2);
network.Hidden.Neurons[0].Weights[0] = 0.00461421;
network.Hidden.Neurons[0].Weights[1] = 0.04337112;
network.Hidden.Neurons[0].Weights[2] = -0.10839599;
network.Hidden.Neurons[0].Weights[3] = -0.06234004;
network.Hidden.Neurons[0].Weights[4] = -0.03017057;
network.Hidden.Neurons[0].Weights[5] = 0.09520391;
network.Hidden.Neurons[0].Threshold = 0;
network.Hidden.Neurons[1].Weights[0] = 0.08263872;
network.Hidden.Neurons[1].Weights[1] = -0.118437;
network.Hidden.Neurons[1].Weights[2] = -0.21710971;
network.Hidden.Neurons[1].Weights[3] = 0.02332903;
network.Hidden.Neurons[1].Weights[4] = 0.00953116;
network.Hidden.Neurons[1].Weights[5] = 0.09870652;
network.Hidden.Neurons[1].Threshold = 0;
network.Visible.Neurons[0].Threshold = 0;
network.Visible.Neurons[1].Threshold = 0;
network.Visible.Neurons[2].Threshold = 0;
network.Visible.Neurons[3].Threshold = 0;
network.Visible.Neurons[4].Threshold = 0;
network.Visible.Neurons[5].Threshold = 0;
network.Visible.CopyReversedWeightsFrom(network.Hidden);
ContrastiveDivergenceLearning target = new ContrastiveDivergenceLearning(network);
target.Momentum = 0;
target.LearningRate = 0.1;
target.Decay = 0;
int iterations = 5000;
double[] errors = new double[iterations];
for (int i = 0; i < iterations; i++)
{
errors[i] = target.RunEpoch(inputs);
}
double startError = errors[0];
double lastError = errors[iterations - 1];
Assert.IsTrue(startError > lastError);
Assert.AreEqual(9.5400234262580224, startError, 0.1);
Assert.AreEqual(1.3364496250348414, lastError, 0.3);
{
double[] output = network.GenerateOutput(new double[] { 0, 0, 0, 1, 1, 0 });
Assert.AreEqual(2, output.Length);
Assert.AreEqual(0, output[0]);
Assert.AreEqual(1, output[1]);
}
{
double[] output = network.GenerateOutput(new double[] { 1, 1, 1, 0, 0, 0 });
Assert.AreEqual(2, output.Length);
Assert.AreEqual(1, output[0]);
Assert.AreEqual(0, output[1]);
}
}