using Accord.Neuro;

using Accord.Neuro.ActivationFunctions;

using Accord.Neuro.Learning;

using Accord.Neuro.Networks;

using Accord.Math;

using System;

using System.Collections.Generic;

using System.Linq;

using System.Text;

using System.Threading.Tasks;

using AForge.Neuro.Learning;

using System.IO;

namespace DeepLearning

{

class Program

{

static void Main(string[] args)

{

// We'll use a simple XOR function as input.

double[][] inputs = new double[][] {

new double[] { 0, 0 },

new double[] { 0, 1 },

new double[] { 1, 0 },

new double[] { 1, 1 }

};

// XOR output, cooresponding to the input.

double[][] outputs = new double[][] {

new double[] { 0 },

new double[] { 1 },

new double[] { 1 },

new double[] { 0 }

};

// Setup the deep belief network (2 inputs, 3 hidden, 1 output) and initialize with random weights.

DeepBeliefNetwork network = new DeepBeliefNetwork(2, 3, 1);

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.

for (int layerIndex = 0; layerIndex < network.Machines.Count - 1; layerIndex++)

{

teacher.LayerIndex = layerIndex;

layerData = teacher.GetLayerInput(batches);

for (int i = 0; i < 5000; 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 < 5000; 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(inputs[i]);

double outputResult = outputValues.First() >= 0.5 ? 1 : 0;

if (outputResult == outputs[i].First())

{

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();

}

}

}