public static void test()
{
//double[][] inputs;
//double[][] outputs;
//double[][] testInputs;
//double[][] testOutputs;
//// Load ascii digits dataset.
//inputs = DataManager.Load(@"../../../data/data.txt", out outputs);
//// The first 500 data rows will be for training. The rest will be for testing.
//testInputs = inputs.Skip(500).ToArray();
//testOutputs = outputs.Skip(500).ToArray();
//inputs = inputs.Take(500).ToArray();
//outputs = outputs.Take(500).ToArray();
//double[][] inputs = new double[4][] {
// new double[] {0, 0}, new double[] {0, 1},
// new double[] {1, 0}, new double[] {1, 1}
//};
//double[][] outputs = new double[4][] {
// new double[] {1, 0}, new double[] {0, 1},
// new double[] {0, 1}, new double[] {1, 0}
//};
double[][] inputs =
{
// input output
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 0, 0, 1, 0 }, // 0
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 1, 1, 1, 1 }, // 2
new double[] { 1, 0, 1, 1 }, // 2
new double[] { 1, 1, 0, 1 }, // 2
new double[] { 0, 1, 1, 1 }, // 2
new double[] { 1, 1, 1, 1 }, // 2
};
double[][] outputs = // those are the class labels
{
new double[] { 1, 0, 0 },
new double[] { 1, 0, 0 },
new double[] { 1, 0, 0 },
new double[] { 1, 0, 0 },
new double[] { 1, 0, 0 },
new double[] { 0, 1, 0 },
new double[] { 0, 1, 0 },
new double[] { 0, 1, 0 },
new double[] { 0, 1, 0 },
new double[] { 0, 1, 0 },
new double[] { 0, 0, 1 },
new double[] { 0, 0, 1 },
new double[] { 0, 0, 1 },
new double[] { 0, 0, 1 },
new double[] { 0, 0, 1 },
};
// Setup the deep belief network and initialize with random weights.
Console.WriteLine(inputs.First().Length);
DeepBeliefNetwork network = new DeepBeliefNetwork(inputs.First().Length, 2, outputs.First().Length);
new GaussianWeights(network, 0.1).Randomize();
network.UpdateVisibleWeights();
// Setup the learning algorithm.
DeepBeliefNetworkLearning teacher = new DeepBeliefNetworkLearning(network)
{
Algorithm = (h, v, i) => new ContrastiveDivergenceLearning(h, v)
{
LearningRate = 0.1,
Momentum = 0.5,
Decay = 0.001,
}
};
// Setup batches of input for learning.
int batchCount = Math.Max(1, inputs.Length / 100);
// Create mini-batches to speed learning.
int[] groups = Accord.Statistics.Tools.RandomGroups(inputs.Length, batchCount);
double[][][] batches = inputs.Subgroups(groups);
// Learning data for the specified layer.
double[][][] layerData;
// Unsupervised learning on each hidden layer, except for the output layer.
for (int layerIndex = 0; layerIndex < network.Machines.Count - 1; layerIndex++)
{
teacher.LayerIndex = layerIndex;
layerData = teacher.GetLayerInput(batches);
for (int i = 0; i < 50000; i++)
{
double error = teacher.RunEpoch(layerData) / inputs.Length;
//if (i % 10 == 0)
//{
// Console.WriteLine(i + ", Error = " + error);
//}
}
}
// Supervised learning on entire network, to provide output classification.
var teacher2 = new Accord.Neuro.Learning.BackPropagationLearning(network)
{
LearningRate = 0.1,
Momentum = 0.5
};
// Run supervised learning.
for (int i = 0; i < 50000; i++)
{
double error = teacher2.RunEpoch(inputs, outputs) / inputs.Length;
//if (i % 10 == 0)
//{
// Console.WriteLine(i + ", Error = " + error);
//}
}
// Test the resulting accuracy.
//int correct = 0;
//for (int i = 0; i < inputs.Length; i++)
//{
// double[] outputValues = network.Compute(testInputs[i]);
// if (DataManager.FormatOutputResult(outputValues) == DataManager.FormatOutputResult(testOutputs[i]))
// {
// correct++;
// }
//}
//Console.WriteLine("Correct " + correct + "/" + inputs.Length + ", " + Math.Round(((double)correct / (double)inputs.Length * 100), 2) + "%");
//double[] probs = network.GenerateOutput(new double[] { 0, 0 });
//foreach (double p in probs)
//{
// Console.Write(p + ", ");
//}
for (int i = 0; i < inputs.Length; i++)
{
double[] output = network.Compute(inputs[i]);
int imax; output.Max(out imax);
Console.Write(imax + " -- ");
foreach (double p in output)
{
Console.Write(p + ", ");
}
Console.WriteLine("\n------------------");
}
}
private void learnNetworkSupervised()
{
if (!Main.CanClassify) return;
Dispatcher dispatcher = Dispatcher.CurrentDispatcher;
new Task(() =>
{
var teacher = new BackPropagationLearning(Main.Network)
{
LearningRate = LearningRate,
Momentum = Momentum
};
double[][] inputs, outputs;
Main.Database.Training.GetInstances(out inputs, out outputs);
// Start running the learning procedure
for (int i = 0; i < Epochs && !shouldStop; i++)
{
double error = teacher.RunEpoch(inputs, outputs);
dispatcher.BeginInvoke((Action<int, double>)updateError,
DispatcherPriority.ContextIdle, i + 1, error);
}
Main.Network.UpdateVisibleWeights();
IsLearning = false;
}).Start();
}
public void ExampleTest1()
{
Accord.Math.Tools.SetupGenerator(0);
// We'll use a simple XOR function as input.
double[][] inputs =
{
new double[] { 0, 0 }, // 0 xor 0
new double[] { 0, 1 }, // 0 xor 1
new double[] { 1, 0 }, // 1 xor 0
new double[] { 1, 1 }, // 1 xor 1
};
// XOR output, corresponding to the input.
double[][] outputs =
{
new double[] { 0 }, // 0 xor 0 = 0
new double[] { 1 }, // 0 xor 1 = 1
new double[] { 1 }, // 1 xor 0 = 1
new double[] { 0 }, // 1 xor 1 = 0
};
// Setup the deep belief network (2 inputs, 3 hidden, 1 output)
DeepBeliefNetwork network = new DeepBeliefNetwork(2, 3, 1);
// Initialize the network with Gaussian weights
new GaussianWeights(network, 0.1).Randomize();
// Update the visible layer with the new weights
network.UpdateVisibleWeights();
// Setup the learning algorithm.
DeepBeliefNetworkLearning teacher = new DeepBeliefNetworkLearning(network)
{
Algorithm = (h, v, i) => new ContrastiveDivergenceLearning(h, v)
{
LearningRate = 0.1,
Momentum = 0.5,
Decay = 0.001,
}
};
// Unsupervised learning on each hidden layer, except for the output.
for (int i = 0; i < network.Layers.Length - 1; i++)
{
teacher.LayerIndex = i;
// Compute the learning data with should be used
var layerInput = teacher.GetLayerInput(inputs);
// Train the layer iteratively
for (int j = 0; j < 5000; j++)
teacher.RunEpoch(layerInput);
}
// Supervised learning on entire network, to provide output classification.
var backpropagation = new BackPropagationLearning(network)
{
LearningRate = 0.1,
Momentum = 0.5
};
// Run supervised learning.
for (int i = 0; i < 5000; i++)
backpropagation.RunEpoch(inputs, outputs);
// Test the resulting accuracy.
int correct = 0;
for (int i = 0; i < inputs.Length; i++)
{
double[] outputValues = network.Compute(inputs[i]);
double outputResult = outputValues.First() >= 0.5 ? 1 : 0;
if (outputResult == outputs[i].First())
{
correct++;
}
}
Assert.AreEqual(4, correct);
}