// ReSharper disable once TooManyDependencies
public BackPropagationNeuralNetwork(NeuralNetworkConfiguration configuration)
{
_numberOfInputNodes = configuration.NumberOfInputNodes;
_numberOfHiddenNodes = configuration.NumberOfHiddenNodes;
_numberOfOutputNodes = configuration.NumberOfOutputNodes;
_hiddenBiases = new double[configuration.NumberOfHiddenNodes];
_hiddenOutputs = new double[configuration.NumberOfHiddenNodes];
_outputBiases = new double[configuration.NumberOfOutputNodes];
_outputs = new double[configuration.NumberOfOutputNodes];
_inputs = new double[configuration.NumberOfInputNodes];
_function = configuration.ActivationFunction;
_previousHiddenBiasesDelta = new double[configuration.NumberOfHiddenNodes];
_previousOutputBiasesDelta = new double[configuration.NumberOfOutputNodes];
_hiddenOutputWeights = NeuralNetworkHelpers.CreateMatrix(configuration.NumberOfHiddenNodes, configuration.NumberOfOutputNodes);
_hiddenInputWeights = NeuralNetworkHelpers.CreateMatrix(configuration.NumberOfInputNodes, configuration.NumberOfHiddenNodes);
_previousHiddenInputWeightDelta = NeuralNetworkHelpers.CreateMatrix(configuration.NumberOfInputNodes, configuration.NumberOfHiddenNodes);
_previousHiddenOutputWeightDelta = NeuralNetworkHelpers.CreateMatrix(configuration.NumberOfHiddenNodes, configuration.NumberOfOutputNodes);
_numberOfWeights = DetermineNumberOfWeightsAndBiases();
InitializeWeights();
}
private void openFileDialog1_FileOk(object sender, CancelEventArgs e)
{
Application.DoEvents();
textBox1.Text = openFileDialog1.FileName;
nnc = NeuralNetworkSerializationManager.Open(openFileDialog1.FileName);
lblToplogy.Text = nnc.TopoloyString;
lblLearningRate.Text = nnc.LearningRate.ToString();
lblNumberOfConnections.Text = nnc.WeightConfiguration.Count.ToString();
}
public FeedForwardNeuralNetwork(NeuralNetworkConfiguration configuration)
{
_configuration = configuration;
_hiddenBiases = new double[configuration.NumberOfHiddenNodes];
_hiddenOutputs = new double[configuration.NumberOfHiddenNodes];
_outputBiases = new double[configuration.NumberOfOutputNodes];
_outputs = new double[configuration.NumberOfOutputNodes];
_inputs = new double[configuration.NumberOfInputNodes];
_hiddenOutputWeights = NeuralNetworkHelpers.CreateMatrix(configuration.NumberOfHiddenNodes, configuration.NumberOfOutputNodes);
_hiddenInputWeights = NeuralNetworkHelpers.CreateMatrix(configuration.NumberOfInputNodes, configuration.NumberOfHiddenNodes);
_numberOfWeights = DetermineNumberOfWeightsAndBiases();
}
private static void Main()
{
Console.WriteLine("\nBegin neural network demo\n");
#region TrainData
double[][] normalizedData = new double[150][];
normalizedData[0] = new double[] { 5.1, 3.5, 1.4, 0.2, 0, 0, 1 }; // sepal length, sepal width, petal length, petal width ->
normalizedData[1] = new double[] { 4.9, 3.0, 1.4, 0.2, 0, 0, 1 }; // Iris setosa = 0 0 1, Iris versicolor = 0 1 0, Iris virginica = 1 0 0
normalizedData[2] = new double[] { 4.7, 3.2, 1.3, 0.2, 0, 0, 1 };
normalizedData[3] = new double[] { 4.6, 3.1, 1.5, 0.2, 0, 0, 1 };
normalizedData[4] = new double[] { 5.0, 3.6, 1.4, 0.2, 0, 0, 1 };
normalizedData[5] = new double[] { 5.4, 3.9, 1.7, 0.4, 0, 0, 1 };
normalizedData[6] = new double[] { 4.6, 3.4, 1.4, 0.3, 0, 0, 1 };
normalizedData[7] = new double[] { 5.0, 3.4, 1.5, 0.2, 0, 0, 1 };
normalizedData[8] = new double[] { 4.4, 2.9, 1.4, 0.2, 0, 0, 1 };
normalizedData[9] = new double[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
normalizedData[10] = new double[] { 5.4, 3.7, 1.5, 0.2, 0, 0, 1 };
normalizedData[11] = new double[] { 4.8, 3.4, 1.6, 0.2, 0, 0, 1 };
normalizedData[12] = new double[] { 4.8, 3.0, 1.4, 0.1, 0, 0, 1 };
normalizedData[13] = new double[] { 4.3, 3.0, 1.1, 0.1, 0, 0, 1 };
normalizedData[14] = new double[] { 5.8, 4.0, 1.2, 0.2, 0, 0, 1 };
normalizedData[15] = new double[] { 5.7, 4.4, 1.5, 0.4, 0, 0, 1 };
normalizedData[16] = new double[] { 5.4, 3.9, 1.3, 0.4, 0, 0, 1 };
normalizedData[17] = new double[] { 5.1, 3.5, 1.4, 0.3, 0, 0, 1 };
normalizedData[18] = new double[] { 5.7, 3.8, 1.7, 0.3, 0, 0, 1 };
normalizedData[19] = new double[] { 5.1, 3.8, 1.5, 0.3, 0, 0, 1 };
normalizedData[20] = new double[] { 5.4, 3.4, 1.7, 0.2, 0, 0, 1 };
normalizedData[21] = new double[] { 5.1, 3.7, 1.5, 0.4, 0, 0, 1 };
normalizedData[22] = new double[] { 4.6, 3.6, 1.0, 0.2, 0, 0, 1 };
normalizedData[23] = new double[] { 5.1, 3.3, 1.7, 0.5, 0, 0, 1 };
normalizedData[24] = new double[] { 4.8, 3.4, 1.9, 0.2, 0, 0, 1 };
normalizedData[25] = new double[] { 5.0, 3.0, 1.6, 0.2, 0, 0, 1 };
normalizedData[26] = new double[] { 5.0, 3.4, 1.6, 0.4, 0, 0, 1 };
normalizedData[27] = new double[] { 5.2, 3.5, 1.5, 0.2, 0, 0, 1 };
normalizedData[28] = new double[] { 5.2, 3.4, 1.4, 0.2, 0, 0, 1 };
normalizedData[29] = new double[] { 4.7, 3.2, 1.6, 0.2, 0, 0, 1 };
normalizedData[30] = new double[] { 4.8, 3.1, 1.6, 0.2, 0, 0, 1 };
normalizedData[31] = new double[] { 5.4, 3.4, 1.5, 0.4, 0, 0, 1 };
normalizedData[32] = new double[] { 5.2, 4.1, 1.5, 0.1, 0, 0, 1 };
normalizedData[33] = new double[] { 5.5, 4.2, 1.4, 0.2, 0, 0, 1 };
normalizedData[34] = new double[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
normalizedData[35] = new double[] { 5.0, 3.2, 1.2, 0.2, 0, 0, 1 };
normalizedData[36] = new double[] { 5.5, 3.5, 1.3, 0.2, 0, 0, 1 };
normalizedData[37] = new double[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
normalizedData[38] = new double[] { 4.4, 3.0, 1.3, 0.2, 0, 0, 1 };
normalizedData[39] = new double[] { 5.1, 3.4, 1.5, 0.2, 0, 0, 1 };
normalizedData[40] = new double[] { 5.0, 3.5, 1.3, 0.3, 0, 0, 1 };
normalizedData[41] = new double[] { 4.5, 2.3, 1.3, 0.3, 0, 0, 1 };
normalizedData[42] = new double[] { 4.4, 3.2, 1.3, 0.2, 0, 0, 1 };
normalizedData[43] = new double[] { 5.0, 3.5, 1.6, 0.6, 0, 0, 1 };
normalizedData[44] = new double[] { 5.1, 3.8, 1.9, 0.4, 0, 0, 1 };
normalizedData[45] = new double[] { 4.8, 3.0, 1.4, 0.3, 0, 0, 1 };
normalizedData[46] = new double[] { 5.1, 3.8, 1.6, 0.2, 0, 0, 1 };
normalizedData[47] = new double[] { 4.6, 3.2, 1.4, 0.2, 0, 0, 1 };
normalizedData[48] = new double[] { 5.3, 3.7, 1.5, 0.2, 0, 0, 1 };
normalizedData[49] = new double[] { 5.0, 3.3, 1.4, 0.2, 0, 0, 1 };
normalizedData[50] = new double[] { 7.0, 3.2, 4.7, 1.4, 0, 1, 0 };
normalizedData[51] = new double[] { 6.4, 3.2, 4.5, 1.5, 0, 1, 0 };
normalizedData[52] = new double[] { 6.9, 3.1, 4.9, 1.5, 0, 1, 0 };
normalizedData[53] = new double[] { 5.5, 2.3, 4.0, 1.3, 0, 1, 0 };
normalizedData[54] = new double[] { 6.5, 2.8, 4.6, 1.5, 0, 1, 0 };
normalizedData[55] = new double[] { 5.7, 2.8, 4.5, 1.3, 0, 1, 0 };
normalizedData[56] = new double[] { 6.3, 3.3, 4.7, 1.6, 0, 1, 0 };
normalizedData[57] = new double[] { 4.9, 2.4, 3.3, 1.0, 0, 1, 0 };
normalizedData[58] = new double[] { 6.6, 2.9, 4.6, 1.3, 0, 1, 0 };
normalizedData[59] = new double[] { 5.2, 2.7, 3.9, 1.4, 0, 1, 0 };
normalizedData[60] = new double[] { 5.0, 2.0, 3.5, 1.0, 0, 1, 0 };
normalizedData[61] = new double[] { 5.9, 3.0, 4.2, 1.5, 0, 1, 0 };
normalizedData[62] = new double[] { 6.0, 2.2, 4.0, 1.0, 0, 1, 0 };
normalizedData[63] = new double[] { 6.1, 2.9, 4.7, 1.4, 0, 1, 0 };
normalizedData[64] = new double[] { 5.6, 2.9, 3.6, 1.3, 0, 1, 0 };
normalizedData[65] = new double[] { 6.7, 3.1, 4.4, 1.4, 0, 1, 0 };
normalizedData[66] = new double[] { 5.6, 3.0, 4.5, 1.5, 0, 1, 0 };
normalizedData[67] = new double[] { 5.8, 2.7, 4.1, 1.0, 0, 1, 0 };
normalizedData[68] = new double[] { 6.2, 2.2, 4.5, 1.5, 0, 1, 0 };
normalizedData[69] = new double[] { 5.6, 2.5, 3.9, 1.1, 0, 1, 0 };
normalizedData[70] = new double[] { 5.9, 3.2, 4.8, 1.8, 0, 1, 0 };
normalizedData[71] = new double[] { 6.1, 2.8, 4.0, 1.3, 0, 1, 0 };
normalizedData[72] = new double[] { 6.3, 2.5, 4.9, 1.5, 0, 1, 0 };
normalizedData[73] = new double[] { 6.1, 2.8, 4.7, 1.2, 0, 1, 0 };
normalizedData[74] = new double[] { 6.4, 2.9, 4.3, 1.3, 0, 1, 0 };
normalizedData[75] = new double[] { 6.6, 3.0, 4.4, 1.4, 0, 1, 0 };
normalizedData[76] = new double[] { 6.8, 2.8, 4.8, 1.4, 0, 1, 0 };
normalizedData[77] = new double[] { 6.7, 3.0, 5.0, 1.7, 0, 1, 0 };
normalizedData[78] = new double[] { 6.0, 2.9, 4.5, 1.5, 0, 1, 0 };
normalizedData[79] = new double[] { 5.7, 2.6, 3.5, 1.0, 0, 1, 0 };
normalizedData[80] = new double[] { 5.5, 2.4, 3.8, 1.1, 0, 1, 0 };
normalizedData[81] = new double[] { 5.5, 2.4, 3.7, 1.0, 0, 1, 0 };
normalizedData[82] = new double[] { 5.8, 2.7, 3.9, 1.2, 0, 1, 0 };
normalizedData[83] = new double[] { 6.0, 2.7, 5.1, 1.6, 0, 1, 0 };
normalizedData[84] = new double[] { 5.4, 3.0, 4.5, 1.5, 0, 1, 0 };
normalizedData[85] = new double[] { 6.0, 3.4, 4.5, 1.6, 0, 1, 0 };
normalizedData[86] = new double[] { 6.7, 3.1, 4.7, 1.5, 0, 1, 0 };
normalizedData[87] = new double[] { 6.3, 2.3, 4.4, 1.3, 0, 1, 0 };
normalizedData[88] = new double[] { 5.6, 3.0, 4.1, 1.3, 0, 1, 0 };
normalizedData[89] = new double[] { 5.5, 2.5, 4.0, 1.3, 0, 1, 0 };
normalizedData[90] = new double[] { 5.5, 2.6, 4.4, 1.2, 0, 1, 0 };
normalizedData[91] = new double[] { 6.1, 3.0, 4.6, 1.4, 0, 1, 0 };
normalizedData[92] = new double[] { 5.8, 2.6, 4.0, 1.2, 0, 1, 0 };
normalizedData[93] = new double[] { 5.0, 2.3, 3.3, 1.0, 0, 1, 0 };
normalizedData[94] = new double[] { 5.6, 2.7, 4.2, 1.3, 0, 1, 0 };
normalizedData[95] = new double[] { 5.7, 3.0, 4.2, 1.2, 0, 1, 0 };
normalizedData[96] = new double[] { 5.7, 2.9, 4.2, 1.3, 0, 1, 0 };
normalizedData[97] = new double[] { 6.2, 2.9, 4.3, 1.3, 0, 1, 0 };
normalizedData[98] = new double[] { 5.1, 2.5, 3.0, 1.1, 0, 1, 0 };
normalizedData[99] = new double[] { 5.7, 2.8, 4.1, 1.3, 0, 1, 0 };
normalizedData[100] = new double[] { 6.3, 3.3, 6.0, 2.5, 1, 0, 0 };
normalizedData[101] = new double[] { 5.8, 2.7, 5.1, 1.9, 1, 0, 0 };
normalizedData[102] = new double[] { 7.1, 3.0, 5.9, 2.1, 1, 0, 0 };
normalizedData[103] = new double[] { 6.3, 2.9, 5.6, 1.8, 1, 0, 0 };
normalizedData[104] = new double[] { 6.5, 3.0, 5.8, 2.2, 1, 0, 0 };
normalizedData[105] = new double[] { 7.6, 3.0, 6.6, 2.1, 1, 0, 0 };
normalizedData[106] = new double[] { 4.9, 2.5, 4.5, 1.7, 1, 0, 0 };
normalizedData[107] = new double[] { 7.3, 2.9, 6.3, 1.8, 1, 0, 0 };
normalizedData[108] = new double[] { 6.7, 2.5, 5.8, 1.8, 1, 0, 0 };
normalizedData[109] = new double[] { 7.2, 3.6, 6.1, 2.5, 1, 0, 0 };
normalizedData[110] = new double[] { 6.5, 3.2, 5.1, 2.0, 1, 0, 0 };
normalizedData[111] = new double[] { 6.4, 2.7, 5.3, 1.9, 1, 0, 0 };
normalizedData[112] = new double[] { 6.8, 3.0, 5.5, 2.1, 1, 0, 0 };
normalizedData[113] = new double[] { 5.7, 2.5, 5.0, 2.0, 1, 0, 0 };
normalizedData[114] = new double[] { 5.8, 2.8, 5.1, 2.4, 1, 0, 0 };
normalizedData[115] = new double[] { 6.4, 3.2, 5.3, 2.3, 1, 0, 0 };
normalizedData[116] = new double[] { 6.5, 3.0, 5.5, 1.8, 1, 0, 0 };
normalizedData[117] = new double[] { 7.7, 3.8, 6.7, 2.2, 1, 0, 0 };
normalizedData[118] = new double[] { 7.7, 2.6, 6.9, 2.3, 1, 0, 0 };
normalizedData[119] = new double[] { 6.0, 2.2, 5.0, 1.5, 1, 0, 0 };
normalizedData[120] = new double[] { 6.9, 3.2, 5.7, 2.3, 1, 0, 0 };
normalizedData[121] = new double[] { 5.6, 2.8, 4.9, 2.0, 1, 0, 0 };
normalizedData[122] = new double[] { 7.7, 2.8, 6.7, 2.0, 1, 0, 0 };
normalizedData[123] = new double[] { 6.3, 2.7, 4.9, 1.8, 1, 0, 0 };
normalizedData[124] = new double[] { 6.7, 3.3, 5.7, 2.1, 1, 0, 0 };
normalizedData[125] = new double[] { 7.2, 3.2, 6.0, 1.8, 1, 0, 0 };
normalizedData[126] = new double[] { 6.2, 2.8, 4.8, 1.8, 1, 0, 0 };
normalizedData[127] = new double[] { 6.1, 3.0, 4.9, 1.8, 1, 0, 0 };
normalizedData[128] = new double[] { 6.4, 2.8, 5.6, 2.1, 1, 0, 0 };
normalizedData[129] = new double[] { 7.2, 3.0, 5.8, 1.6, 1, 0, 0 };
normalizedData[130] = new double[] { 7.4, 2.8, 6.1, 1.9, 1, 0, 0 };
normalizedData[131] = new double[] { 7.9, 3.8, 6.4, 2.0, 1, 0, 0 };
normalizedData[132] = new double[] { 6.4, 2.8, 5.6, 2.2, 1, 0, 0 };
normalizedData[133] = new double[] { 6.3, 2.8, 5.1, 1.5, 1, 0, 0 };
normalizedData[134] = new double[] { 6.1, 2.6, 5.6, 1.4, 1, 0, 0 };
normalizedData[135] = new double[] { 7.7, 3.0, 6.1, 2.3, 1, 0, 0 };
normalizedData[136] = new double[] { 6.3, 3.4, 5.6, 2.4, 1, 0, 0 };
normalizedData[137] = new double[] { 6.4, 3.1, 5.5, 1.8, 1, 0, 0 };
normalizedData[138] = new double[] { 6.0, 3.0, 4.8, 1.8, 1, 0, 0 };
normalizedData[139] = new double[] { 6.9, 3.1, 5.4, 2.1, 1, 0, 0 };
normalizedData[140] = new double[] { 6.7, 3.1, 5.6, 2.4, 1, 0, 0 };
normalizedData[141] = new double[] { 6.9, 3.1, 5.1, 2.3, 1, 0, 0 };
normalizedData[142] = new double[] { 5.8, 2.7, 5.1, 1.9, 1, 0, 0 };
normalizedData[143] = new double[] { 6.8, 3.2, 5.9, 2.3, 1, 0, 0 };
normalizedData[144] = new double[] { 6.7, 3.3, 5.7, 2.5, 1, 0, 0 };
normalizedData[145] = new double[] { 6.7, 3.0, 5.2, 2.3, 1, 0, 0 };
normalizedData[146] = new double[] { 6.3, 2.5, 5.0, 1.9, 1, 0, 0 };
normalizedData[147] = new double[] { 6.5, 3.0, 5.2, 2.0, 1, 0, 0 };
normalizedData[148] = new double[] { 6.2, 3.4, 5.4, 2.3, 1, 0, 0 };
normalizedData[149] = new double[] { 5.9, 3.0, 5.1, 1.8, 1, 0, 0 };
var trainData = normalizedData.Take((int) (normalizedData.Length * 0.8)).ToArray();
#endregion
#region Test Data
var testData = normalizedData.Skip((int) (normalizedData.Length * 0.8)).ToArray();
#endregion
Console.WriteLine("Number of input nodes: ");
int numInput = int.Parse(Console.ReadLine());
Console.WriteLine("Number of hidden nodes: ");
int numHidden = int.Parse(Console.ReadLine());
Console.WriteLine("Number of output nodes: ");
int numOutput = int.Parse(Console.ReadLine());
Console.WriteLine("Creating a neural netwotk with {0} input node(s), {1} hidden node(s) and {2} output node(s)", numInput, numHidden, numOutput);
var configuration = new NeuralNetworkConfiguration
{
ActivationFunction = ActivationFunctionEnum.HyperTan,
NumberOfHiddenNodes = numHidden,
NumberOfInputNodes = numInput,
NumberOfOutputNodes = numOutput
};
var nn = new BackPropagationNeuralNetwork(configuration);
int maxEpochs = int.Parse(ConfigurationManager.AppSettings["MaxEpochs"]);
double learnRate = double.Parse(ConfigurationManager.AppSettings["LearnRate"]);
double momentum = double.Parse(ConfigurationManager.AppSettings["Momentum"]);
Console.WriteLine("Setting maxEpochs = " + maxEpochs);
Console.WriteLine("Setting learnRate = " + learnRate);
Console.WriteLine("Setting momentum = " + momentum);
Console.WriteLine("\nBeginning training using back-propagation\n");
var trainingData = new NeuralNetworkTrainingData
{
LearnRate = learnRate,
MaxEpochs = maxEpochs,
Momentum = momentum,
TrainingData = trainData
};
nn.Train(trainingData);
Console.WriteLine("Training complete");
var bestWeights = nn.GetWeights();
Console.WriteLine("Final neural network weights and bias values:");
ShowVector(bestWeights, 10, 3, true);
nn.SetWeightsAndBiases(bestWeights);
var trainAcc = nn.Accuracy(trainData);
Console.WriteLine("\nAccuracy on training data = " +
trainAcc.ToString("F4"));
var testAcc = nn.Accuracy(testData);
Console.WriteLine("Accuracy on test data = " +
testAcc.ToString("F4"));
Console.WriteLine("\nEnd neural network back propagation demo\n");
//Console.WriteLine("Start particle swarm optimization demo");
//Console.WriteLine("Number of input nodes: ");
//int inputNum = int.Parse(Console.ReadLine());
//Console.WriteLine("Number of hidden nodes: ");
//int hiddenNum = int.Parse(Console.ReadLine());
//Console.WriteLine("Number of output nodes: ");
//int outputNum = int.Parse(Console.ReadLine());
//Console.WriteLine("Creating a particle swarm optimized neural netwotk with {0} input node(s), {1} hidden node(s) and {2} output node(s)", inputNum, hiddenNum, outputNum);
//var nn_conf = new NeuralNetworkConfiguration
//{
// ActivationFunction = ActivationFunctionEnum.HyperTan,
// NumberOfHiddenNodes = hiddenNum,
// NumberOfInputNodes = inputNum,
// NumberOfOutputNodes = outputNum
//};
//Console.WriteLine("Max number of epochs:");
//var epochs = int.Parse(Console.ReadLine());
//Console.WriteLine("Number of particles:");
//var particles = int.Parse(Console.ReadLine());
//Console.WriteLine("Dimension min & max:");
//var dimensionMinMax = double.Parse(Console.ReadLine());
//Console.WriteLine("Exit error condition:");
//var exitError = double.Parse(Console.ReadLine());
//var pso_conf = new ParticleSwarmConfiguration
//{
// MaxEpochs = epochs,
// NeuralNetwork = new FeedForwardNeuralNetwork(nn_conf),
// dataVectors = trainData,
// NumbeOfParticles = particles,
// Dimensions = (inputNum * hiddenNum) + (hiddenNum * outputNum) + outputNum + hiddenNum,
// DimensionMaximum = dimensionMinMax,
// DimensionMinimum = dimensionMinMax * (-1),
// ExitError = exitError,
// configuration = nn_conf
//};
//var psnn = new ParticleSwarmNeuralNetwork(nn_conf, pso_conf);
//psnn.Train(trainingData);
ShowVector(nn.GetWeights(), 10, 3, true);
Console.WriteLine("Accuracy on test data = " + nn.Accuracy(testData).ToString("F4"));
Console.WriteLine("End Neural network demo.");
Console.ReadLine();
}
