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);
}
public override void Initialize()
{
_Network = new DeepBeliefNetwork(Length + 1, new int[] { _MiddleCount, FrameOut.Length + 1 }); // 斉次座標
new GaussianWeights(_Network).Randomize();
_Network.UpdateVisibleWeights();
InitializeTeacher();
}
public MainViewModel()
{
// Create settings for Optidigits dataset
Network = new DeepBeliefNetwork(new BernoulliFunction(), 1024, 50, 10);
Database = new Optdigits()
{
IsNormalized = false
};
new GaussianWeights(Network).Randomize();
Network.UpdateVisibleWeights();
Learn = new LearnViewModel(this);
Use = new UseViewModel(this);
Dream = new DreamViewModel(this);
Discover = new DiscoverViewModel(this);
NewLayerNeurons = 10;
}
private static DeepBeliefNetwork createNetwork(double[][] inputs)
{
DeepBeliefNetwork network = new DeepBeliefNetwork(6, 2, 1);
network.Machines[0].Hidden.Neurons[0].Weights[0] = 0.00461421;
network.Machines[0].Hidden.Neurons[0].Weights[1] = 0.04337112;
network.Machines[0].Hidden.Neurons[0].Weights[2] = -0.10839599;
network.Machines[0].Hidden.Neurons[0].Weights[3] = -0.06234004;
network.Machines[0].Hidden.Neurons[0].Weights[4] = -0.03017057;
network.Machines[0].Hidden.Neurons[0].Weights[5] = 0.09520391;
network.Machines[0].Hidden.Neurons[0].Threshold = 0;
network.Machines[0].Hidden.Neurons[1].Weights[0] = 0.08263872;
network.Machines[0].Hidden.Neurons[1].Weights[1] = -0.118437;
network.Machines[0].Hidden.Neurons[1].Weights[2] = -0.21710971;
network.Machines[0].Hidden.Neurons[1].Weights[3] = 0.02332903;
network.Machines[0].Hidden.Neurons[1].Weights[4] = 0.00953116;
network.Machines[0].Hidden.Neurons[1].Weights[5] = 0.09870652;
network.Machines[0].Hidden.Neurons[1].Threshold = 0;
network.Machines[0].Visible.Neurons[0].Threshold = 0;
network.Machines[0].Visible.Neurons[1].Threshold = 0;
network.Machines[0].Visible.Neurons[2].Threshold = 0;
network.Machines[0].Visible.Neurons[3].Threshold = 0;
network.Machines[0].Visible.Neurons[4].Threshold = 0;
network.Machines[0].Visible.Neurons[5].Threshold = 0;
network.UpdateVisibleWeights();
DeepBeliefNetworkLearning target = new DeepBeliefNetworkLearning(network)
{
Algorithm = (h, v, i) => new ContrastiveDivergenceLearning(h, v)
};
for (int layer = 0; layer < 2; layer++)
{
target.LayerIndex = layer;
double[][] layerInputs = target.GetLayerInput(inputs);
int iterations = 5000;
double[] errors = new double[iterations];
for (int i = 0; i < iterations; i++)
errors[i] = target.RunEpoch(layerInputs);
}
return network;
}
static void Main(string[] args)
{
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();
// Setup the deep belief network and initialize with random weights.
DeepBeliefNetwork network = new DeepBeliefNetwork(inputs.First().Length, 10, 10);
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 < 200; 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 BackPropagationLearning(network)
{
LearningRate = 0.1,
Momentum = 0.5
};
// Run supervised learning.
for (int i = 0; i < 500; 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) + "%");
Console.Write("Press any key to quit ..");
Console.ReadKey();
}
