static void Main(string[] args)
{
// create a network
NeuralNetwork _network = new NeuralNetwork(2, 2, 5, 1);
Program program = new Program();
// dataset
// a set for learning the logical AND gate
DataSet trainingData = new DataSet();
trainingData.Inputs.Add(new List <double> {
0, 0
});
trainingData.Outputs.Add(new List <double> {
0
});
trainingData.Inputs.Add(new List <double> {
0, 1
});
trainingData.Outputs.Add(new List <double> {
0
});
trainingData.Inputs.Add(new List <double> {
1, 0
});
trainingData.Outputs.Add(new List <double> {
0
});
trainingData.Inputs.Add(new List <double> {
1, 1
});
trainingData.Outputs.Add(new List <double> {
1
});
// train
for (int i = 0; i < 100000; i++)
{
double og_error = 0.0;
double test_error = 0.0;
// calculate current network error
for (int j = 0; j < trainingData.Inputs.Count; j++)
{
_network.SetInputs(trainingData.Inputs[j]);
_network.ForwardPropagate();
List <double> Outputs = _network.GetOutputs();
for (int k = 0; k < Outputs.Count; k++)
{
og_error += Math.Pow(trainingData.Outputs[j][k] - Outputs[k], 2); // squared error cost function
}
}
// create a new network based off of the last one with gaussian distribution
NeuralNetwork _testNetwork = new NeuralNetwork(_network);
// change hidden layer Weights and biases
foreach (Layer layer in _testNetwork.HiddenLayers)
{
foreach (Neuron neuron in layer.Neurons)
{
for (int j = 0; j < neuron.Weights.Count; j++)
{
neuron.Weights[j] += program.NextGaussian(0, 0.025);
}
neuron.Bias += program.NextGaussian(0, 0.05);
}
}
// change output layer Weights and biases
foreach (Neuron neuron in _testNetwork.OutputLayer.Neurons)
{
for (int j = 0; j < neuron.Weights.Count; j++)
{
neuron.Weights[j] += program.NextGaussian(0, 0.5);
}
neuron.Bias += program.NextGaussian(0, 0.5);
}
// calculate test network error
for (int j = 0; j < trainingData.Inputs.Count; j++)
{
_testNetwork.SetInputs(trainingData.Inputs[j]);
_testNetwork.ForwardPropagate();
List <double> Outputs = _testNetwork.GetOutputs();
for (int k = 0; k < Outputs.Count; k++)
{
test_error += Math.Pow(trainingData.Outputs[j][k] - Outputs[k], 2); // squared error cost function
}
}
// if test has less error then replace the network
Console.SetCursorPosition(0, 0);
Console.WriteLine("og_error: " + og_error);
Console.WriteLine("test_error: " + test_error);
if (test_error < og_error)
{
Console.WriteLine("updated");
_network = _testNetwork;
}
else
{
Console.WriteLine("not updated");
}
}
// test the network
while (true)
{
List <double> test_inputs = new List <double>();
for (int i = 0; i < _network.InputLayer.Width; i++)
{
Console.WriteLine("input" + i + ": ");
double one = Convert.ToDouble(Console.ReadLine());
test_inputs.Add(one);
}
_network.SetInputs(test_inputs);
_network.ForwardPropagate();
foreach (double x in _network.GetOutputs())
{
Console.WriteLine("output: " + x);
}
}
}
public static void CreateAndTrain(string location)
{
string imageFileName = @"C:\Users\user\source\Repos\NumberRecognitionNN\Neural Network\Neural Network\train-images.idx3-ubyte";
string labelFileName = @"C:\Users\user\source\Repos\NumberRecognitionNN\Neural Network\Neural Network\train-labels.idx1-ubyte";
byte[] imageFile = File.ReadAllBytes(imageFileName);
byte[] labelFile = File.ReadAllBytes(labelFileName);
NeuralNetwork network = new NeuralNetwork(new double[784], new int[] { 16, 10 });
int imagesIndex = 16;
int labelIndex = 8;
int percentDone = (int)(((double)imagesIndex / (double)imageFile.Length) * 100);
while (imagesIndex < imageFile.Length)
{
double[] imageFeed = new double[784];
for (int i = 0; i < 784; i++)
{
imageFeed[i] = (imageFile[imagesIndex]) / 256.0;
imagesIndex++;
}
network.Feed(imageFeed);
network.Backpropagation(NumberToDesiredOutput(labelFile[labelIndex]));
labelIndex++;
if (percentDone != (int)(((double)imagesIndex / (double)imageFile.Length) * 100))
{
percentDone = (int)(((double)imagesIndex / (double)imageFile.Length) * 100);
Console.Write(percentDone + "%\n");
}
}
network.Save(location);
#region printregion
/*
* int imagesIndex = 16;
* int labelIndex = 8;
*
* while (imagesIndex < 1000)
* {
* for (int i = 0; i < 28; i++)
* {
* for (int j = 0; j < 28; j++)
* {
* if (((double)imageFile[imagesIndex]) / 256.0 > 0.5)
* {
* Console.Write(1 + " ");
* }
* else
* {
* Console.Write(0 + " ");
* }
* imagesIndex++;
* }
* Console.Write("\n");
* }
*
*
* Console.Write("-----" + labelFile[labelIndex] + "-----");
* labelIndex++;
* Console.Write("\n");
* Console.Write("\n");
* }
*/
#endregion
}
private static void Main()
{
// Formarea unei retele neuronale este procesul de a gasi un set de valori de greutate numerice astfel incat, pentru un
// anumit set de date cu valori de intrare si iesire cunoscute, valorile de iesire calculate retelei se potrivesc indeaproape
// valorilor de ieșire cunoscute. Dupa ce s-au gasit cele mai bune valori de greutate, ele pot fi plasate in reteaua neuronala
// si utilizate pentru a prezice noi date de iesire care au datele de intrare cunoscute.
//Acest program foloseste optimizarea evolutionara pentru a forma o retea neuronala care prezice speciile unei flori Iris
//("setosa," "versicolor," "virginica") in functie lungimea sepalei florii (frunze modificate care alcatuiesc caliciul unei
// flori), latimea sepalei, lungimea petalei si latimea petalei. Avem 24 de date pentru formare. Dupa terminarea formarii,
// cel mai bun set de lungimi este adaugat in reteaua neuronala. Reteaua prezice corect specia a 5/6 din datele de test.
Console.WriteLine("Avem 30 de date de intrare pentru floarea Iris");
Console.Write("Datele sunt lungimea sepalei, latimea sepalei, lungimea petalei, latimea petalei");
Console.Write("\nSpeciile de Iris: Iris setosa = 0 0 1, Iris versicolor = 0 1 0, Iris virginica = 1 0 0 ");
// !!! PASUL 1: DATELE DE INTRARE !!!
var trainData = new double[24][];
trainData[0] = new[] { 6.3, 2.9, 5.6, 1.8, 1, 0, 0 };
trainData[1] = new[] { 6.9, 3.1, 4.9, 1.5, 0, 1, 0 };
trainData[2] = new[] { 4.6, 3.4, 1.4, 0.3, 0, 0, 1 };
trainData[3] = new[] { 7.2, 3.6, 6.1, 2.5, 1, 0, 0 };
trainData[4] = new[] { 4.7, 3.2, 1.3, 0.2, 0, 0, 1 };
trainData[5] = new[] { 4.9, 3, 1.4, 0.2, 0, 0, 1 };
trainData[6] = new[] { 7.6, 3, 6.6, 2.1, 1, 0, 0 };
trainData[7] = new[] { 4.9, 2.4, 3.3, 1, 0, 1, 0 };
trainData[8] = new[] { 5.4, 3.9, 1.7, 0.4, 0, 0, 1 };
trainData[9] = new[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
trainData[10] = new[] { 5, 3.6, 1.4, 0.2, 0, 0, 1 };
trainData[11] = new[] { 6.4, 3.2, 4.5, 1.5, 0, 1, 0 };
trainData[12] = new[] { 4.4, 2.9, 1.4, 0.2, 0, 0, 1 };
trainData[13] = new[] { 5.8, 2.7, 5.1, 1.9, 1, 0, 0 };
trainData[14] = new[] { 6.3, 3.3, 6, 2.5, 1, 0, 0 };
trainData[15] = new[] { 5.2, 2.7, 3.9, 1.4, 0, 1, 0 };
trainData[16] = new[] { 7, 3.2, 4.7, 1.4, 0, 1, 0 };
trainData[17] = new[] { 6.5, 2.8, 4.6, 1.5, 0, 1, 0 };
trainData[18] = new[] { 4.9, 2.5, 4.5, 1.7, 1, 0, 0 };
trainData[19] = new[] { 5.7, 2.8, 4.5, 1.3, 0, 1, 0 };
trainData[20] = new[] { 5, 3.4, 1.5, 0.2, 0, 0, 1 };
trainData[21] = new[] { 6.5, 3, 5.8, 2.2, 1, 0, 0 };
trainData[22] = new[] { 5.5, 2.3, 4, 1.3, 0, 1, 0 };
trainData[23] = new[] { 6.7, 2.5, 5.8, 1.8, 1, 0, 0 };
var testData = new double[6][];
testData[0] = new[] { 4.6, 3.1, 1.5, 0.2, 0, 0, 1 };
testData[1] = new[] { 7.1, 3, 5.9, 2.1, 1, 0, 0 };
testData[2] = new[] { 5.1, 3.5, 1.4, 0.2, 0, 0, 1 };
testData[3] = new[] { 6.3, 3.3, 4.7, 1.6, 0, 1, 0 };
testData[4] = new[] { 6.6, 2.9, 4.6, 1.3, 0, 1, 0 };
testData[5] = new[] { 7.3, 2.9, 6.3, 1.8, 1, 0, 0 };
Console.WriteLine("\nDatele pentru formare sunt:");
ShowMatrix(trainData, trainData.Length, 1, true);
Console.WriteLine("Datele de test sunt:");
ShowMatrix(testData, testData.Length, 1, true);
Console.WriteLine("Cream reteaua neuronala initiala");
// Numarul de tipuri de date de intrare (lungimea sepalei, latimea sepalei, lungimea petalei, latimea petalei)
const int numInput = 4;
// Numarul de noduri ascunse, se alege aleator
const int numHidden = 6;
// Numarul de tipuri de date de iesire ("setosa," "versicolor," "virginica")
const int numOutput = 3;
var nn = new NeuralNetwork(numInput, numHidden, numOutput);
// Parametrii de formare specifici optimizarii evolutionare
// Marimea populatiei este numarul de indivizi. Cu cat sunt mai multi indivizi, cu atat este mai buna solutia, dar scade si
// performanta.
var popSize = 8;
// Numarul de iteratii maxim pe care optimizarea evolutionara le va executa in procesul de selectie-incrucisare-mutatie
var maxGeneration = 500;
// Marja de eroare pentru un set de date pentru care se iese mai repede din proces
var exitError = 0.0;
// Rata de mutatie controleaza cate gene dintr-un cromozom nou vor suferi o mutatie
var mutateRate = 0.20;
// Magnitudinea schimbarii genei care a suferit o mutatie
var mutateChange = 0.01;
// Tau controleaza probabilitatea ca cei mai buni doi indivizi din populatie sa fie selectati ca parinti pentru reproducere.
// Cu cat tau este mai mare, cu atat sansa ca cei mai buni doi indivizi sa fie selectati creste.
var tau = 0.40;
Console.WriteLine("\nSetam marimea populatiei = " +
popSize);
Console.WriteLine("Setam numarul maxim de generatii = " + maxGeneration);
Console.Write("Setam marja de eroare = ");
Console.WriteLine(exitError.ToString("F3"));
Console.Write("Setam rata de mutatie = ");
Console.WriteLine(mutateRate.ToString("F3"));
Console.Write("Setam magnitudinea schimbarii genei = ");
Console.WriteLine(mutateChange.ToString("F3"));
Console.Write("Setam tau = ");
Console.WriteLine(tau.ToString("F3"));
Console.WriteLine("\nIncepem formarea");
var bestWeights = nn.Train(trainData, popSize, maxGeneration, exitError,
mutateRate, mutateChange, tau);
Console.WriteLine("Formarea incheiata");
//Console.WriteLine("\nValori finale:");
//ShowVector(bestWeights, 10, 3, true);
nn.SetWeights(bestWeights);
var trainAcc = nn.Accuracy(trainData);
Console.Write("\nAcuratetea datelor de formare = ");
Console.WriteLine(trainAcc.ToString("F4"));
var testAcc = nn.Accuracy(testData);
Console.Write("\nAcuratetea datelor de test = ");
Console.WriteLine(testAcc.ToString("F4"));
Console.ReadKey();
}
public NeuralNetwork Copy()
{
NeuralNetwork NN = this;
return(NN);
}
