private void startIrisMachine()
{
Layer hidden = new Layer(
new Neuron[] {
new Neuron(VectorTools.zeros(4), 1, 0, ActivationFunctions.sigmoid, ActivationFunctions.sigmoidDiff),
new Neuron(VectorTools.zeros(4), 1, 0, ActivationFunctions.sigmoid, ActivationFunctions.sigmoidDiff),
new Neuron(VectorTools.zeros(4), 1, 0, ActivationFunctions.sigmoid, ActivationFunctions.sigmoidDiff)
}
);
Layer output = new Layer(
new Neuron[] {
new Neuron(VectorTools.zeros(3), 1, 0, ActivationFunctions.sigmoid, ActivationFunctions.sigmoidDiff),
new Neuron(VectorTools.zeros(3), 1, 0, ActivationFunctions.sigmoid, ActivationFunctions.sigmoidDiff),
new Neuron(VectorTools.zeros(3), 1, 0, ActivationFunctions.sigmoid, ActivationFunctions.sigmoidDiff)
}
);
double eta = (double)numEta.Value;
int epochs = (int)numMaxEpochs.Value;
network = new BackPropagation(new Layer[] { hidden, output }, irisSet, eta, epochs);
network.train(30);
int[,] testMatrix = network.test(20);
UiTools.drawMatrix(gridConfMatrix, testMatrix);
double accuracy = VectorTools.confusionAccuracy(testMatrix);
lblAccuracy.Text = Math.Round(accuracy, 4).ToString();
}
static void Main()
{
Console.WriteLine("Создание нейронной сети...");
DataItem data = DataItemFactory.GetNumericData();
int inputCount = data.Input.First().Length;
var neuronCount = new[] { 10 };
IActivationFunction function = new BipolarSigmoidFunction();
double learningRate = 0.1;
double momentum = 0;
int maxEpochNumber = 1000000;
double minErrorChange = 0.000001;
double minError = 0.001;
var network = MultilayerNeuralNetwork.CreateNetwork(neuronCount, inputCount, function);
network.Randomize();
Console.WriteLine("Создание нейронной сети завершено.");
var teacher = new PerceptronLearning(network)
{
LearningRate = learningRate
};
int epochNumber = 1;
double lastError = double.MaxValue;
double error;
double errorChange;
Console.WriteLine("Start learning...");
do
{
DateTime dtStart = DateTime.Now;
error = teacher.RunEpoch(data.Input, data.Output) / data.Input.Length;
Console.WriteLine(
$"Epoch #{epochNumber} finished; " +
$"current error is {error}; " +
$"it takes: {(DateTime.Now - dtStart).Duration()}");
errorChange = Math.Abs(lastError - error);
lastError = error;
epochNumber++;
}while (epochNumber < maxEpochNumber &&
error > minError &&
errorChange > minErrorChange);
for (int i = 0; i < data.Input.Length; i++)
{
double[] outputs = network.Compute(data.Input[i]);
string strIn = "";
foreach (var input in data.Output[i])
{
strIn += $"{input},";
}
string strOut = "";
foreach (var output in outputs)
{
strOut += $"{Math.Abs(output):0.00},";
}
Console.WriteLine($"{i}. Expected {strIn} Actual {strOut}");
}
}