public void TestMethod1()
{
// 创建输入层、隐层和输出层
var inputLayer = new LinearLayer(1);
var hiddenLayer = new LinearLayer(5);
var outputLayer = new LinearLayer(1);
// 创建层之间的关联
new BackpropagationConnector(inputLayer, hiddenLayer, ConnectionMode.Complete);
new BackpropagationConnector(hiddenLayer, outputLayer, ConnectionMode.Complete);
// 创建神经网络
var network = new BackpropagationNetwork(inputLayer, outputLayer);
//network.SetLearningRate(new LinearFunction(0.1, 0.6));
network.Initialize();
// 训练
var ran = new Random();
for (var i = 0; i < 100; i++)
{
var inputVector = new double[] { i };
var outputVector = new double[] { Math.PI *i };
var trainingSample = new TrainingSample(inputVector, outputVector);
network.Learn(trainingSample, i, 100);
}
// 预测
var testInput = new double[] { 1 };
var testOutput = network.Run(testInput);
Console.WriteLine(testOutput[0]);
}
void CreateNewNetwork()
{
LinearLayer inputLayer = new LinearLayer(neurons);
SigmoidLayer hiddenLayer = new SigmoidLayer(hidden1Neurons);
SigmoidLayer hiddenLayer2 = new SigmoidLayer(hidden2Neurons);
LinearLayer outputLayer = new LinearLayer(outputNum);
BackpropagationConnector conn1 = new BackpropagationConnector(inputLayer, hiddenLayer);
conn1.Initializer = new RandomFunction(0d, 0.001d);
BackpropagationConnector conn3 = new BackpropagationConnector(hiddenLayer, hiddenLayer2);
conn3.Initializer = new RandomFunction(0d, 0.001d);
BackpropagationConnector conn2 = new BackpropagationConnector(hiddenLayer2, outputLayer);
conn2.Initializer = new RandomFunction(0d, 0.001d);
conn1.Initialize();
conn2.Initialize();
conn3.Initialize();
neuralNetwork = new BackpropagationNetwork(inputLayer, outputLayer);
neuralNetwork.SetLearningRate(learningRate);
neuralNetwork.Initialize();
}
private void Form1_OnLoad(object sender, EventArgs e)
{
inputTier = new LinearLayer(35);
hiddenTier = new SigmoidLayer(3);
outputTier = new SigmoidLayer(5);
_ = new BackpropagationConnector(inputTier, hiddenTier);
_ = new BackpropagationConnector(hiddenTier, outputTier);
neuralNetwork = new BackpropagationNetwork(inputTier, outputTier);
neuralNetwork.Initialize();
}
private void WFAnnRecognition_Load(object sender, EventArgs e)
{
lstLog.Items.Insert(0, "Initialize ANN model");
inputLayer = new LinearLayer(35);
hiddenLayer = new SigmoidLayer(3);
outputLayer = new SigmoidLayer(5);
BackpropagationConnector connector = new BackpropagationConnector(inputLayer, hiddenLayer);
BackpropagationConnector connector2 = new BackpropagationConnector(hiddenLayer, outputLayer);
network = new BackpropagationNetwork(inputLayer, outputLayer);
network.Initialize();
}
private void Form1_Load(object sender, EventArgs e)
{
int gizlikatmansayisi = Convert.ToInt32(Microsoft.VisualBasic.Interaction.InputBox("Gizli Katman Sayısını Giriniz", "Bilgi Girişi", "Örn: 3", 0, 0));
giriskatmanı = new LinearLayer(35);
gizlikatman = new SigmoidLayer(gizlikatmansayisi);
cikiskatmani = new SigmoidLayer(5);
BackpropagationConnector giris_gizli_baglanti = new BackpropagationConnector(giriskatmanı, gizlikatman);
BackpropagationConnector gizli_cikis_baglanti = new BackpropagationConnector(gizlikatman, cikiskatmani);
ag = new BackpropagationNetwork(giriskatmanı, cikiskatmani);
ag.Initialize();
}
private void button8_Click(object sender, EventArgs e)
{
LinearLayer inputLayer = new LinearLayer(Convert.ToInt32(textBox3.Text));
SigmoidLayer hiddenLayer = new SigmoidLayer(Convert.ToInt32(textBox4.Text));
SigmoidLayer outputLayer = new SigmoidLayer(Convert.ToInt32(textBox5.Text));
BackpropagationConnector conn1 = new BackpropagationConnector(inputLayer, hiddenLayer);
BackpropagationConnector conn2 = new BackpropagationConnector(hiddenLayer, outputLayer);
network = new BackpropagationNetwork(inputLayer, outputLayer);
network.Initialize();
MessageBox.Show("Rete generata con successo.");
}
async Task LearnNetworkAsync()
{
_network = new BackpropagationNetwork(_inputLayer, _outputLayer);
_network.Initialize();
var trainingSet = new TrainingSet(1, 3);
foreach (var b in from bomb in Enum.GetValues(typeof(BombTypes)).Cast <BombTypes>() where bomb != BombTypes.Mine select BombFabric.CreateBomb(bomb) into b where b != null select b)
{
trainingSet.Add(new TrainingSample(new double[] { b.BeepsLevel },
new double[] {
(int)b.FirstStageDisarming,
(int)b.SecondStageDisarming,
(int)b.ThirdStageDisarming
}));
}
_network.Learn(trainingSet, 100000);
}
public void InitializeNetwork(int n)
{
neuronCount = n;
_neuralNetwork.Initialize();
}
static void Main(string[] args)
{
Boolean finished = false;
//Layer
LinearLayer inputLayer = new LinearLayer(25);
SigmoidLayer hiddenLayer1 = new SigmoidLayer(100);
SigmoidLayer outputLayer = new SigmoidLayer(4);
//Connectors
BackpropagationConnector connector = new BackpropagationConnector(inputLayer, hiddenLayer1);
BackpropagationConnector connector3 = new BackpropagationConnector(hiddenLayer1, outputLayer);
network = new BackpropagationNetwork(inputLayer, outputLayer);
network.Initialize();
labyrinth laby = new labyrinth();
player plyr = new player();
laby.Update(laby);
Console.WriteLine(laby.GetPlayerPosition(laby.Laby));
do
{
string pressedKey = Console.ReadKey(true).Key.ToString();
switch (pressedKey)
{
case "RightArrow":
if (laby.Laby[plyr.getPlayerPosition().Item1, plyr.getPlayerPosition().Item2 + 1] != laby.Wall)
{
laby.Laby = laby.SetPosition(plyr.getPlayerPosition(), plyr.move("R"), laby.Laby);
laby.Update(laby);
}
break;
case "LeftArrow":
if (laby.Laby[plyr.getPlayerPosition().Item1, plyr.getPlayerPosition().Item2 - 1] != laby.Wall)
{
laby.Laby = laby.SetPosition(plyr.getPlayerPosition(), plyr.move("L"), laby.Laby);
laby.Update(laby);
}
break;
case "UpArrow":
if (laby.Laby[plyr.getPlayerPosition().Item1 - 1, plyr.getPlayerPosition().Item2] != laby.Wall)
{
laby.Laby = laby.SetPosition(plyr.getPlayerPosition(), plyr.move("U"), laby.Laby);
laby.Update(laby);
}
break;
case "DownArrow":
if (laby.Laby[plyr.getPlayerPosition().Item1 + 1, plyr.getPlayerPosition().Item2] != laby.Wall)
{
laby.Laby = laby.SetPosition(plyr.getPlayerPosition(), plyr.move("D"), laby.Laby);
laby.Update(laby);
}
break;
}
} while (finished != true);
Console.Read();
}
private void Train(object sender, EventArgs e)
{
// btnTrain.Enabled = false;
int cycles = 200;
// if (!int.TryParse(txtCycles.Text, out cycles)) { cycles = 200; }
// txtCycles.Text = cycles.ToString();
int currentCombination = 0;
//int totalCombinations = Alphabet.LetterCount * (Alphabet.LetterCount - 1) / 2;
for (int i = 0; i < Alphabet.LetterCount; i++)
{
for (int j = i + 1; j < Alphabet.LetterCount; j++)
{
ActivationLayer inputLayer = new LinearLayer(400);
ActivationLayer hiddenLayer = new SigmoidLayer(4);
ActivationLayer outputLayer = new SigmoidLayer(2);
new BackpropagationConnector(inputLayer, hiddenLayer);
new BackpropagationConnector(hiddenLayer, outputLayer);
BackpropagationNetwork network = new BackpropagationNetwork(inputLayer, outputLayer);
TrainingSet trainingSet = new TrainingSet(400, 2);
Alphabet ithLetter = Alphabet.GetLetter(i);
Alphabet jthLetter = Alphabet.GetLetter(j);
foreach (Letter instance in ithLetter.Instances)
{
trainingSet.Add(new TrainingSample(instance.GetEquivalentVector(20, 20), new double[] { 1d, 0d }));
}
foreach (Letter instance in jthLetter.Instances)
{
trainingSet.Add(new TrainingSample(instance.GetEquivalentVector(20, 20), new double[] { 0d, 1d }));
}
//progressTraining.Value = 100 * currentCombination / totalCombinations;
Application.DoEvents();
bool correct = false;
int currentCycles = 35;
int count = trainingSet.TrainingSampleCount;
while (correct == false & currentCycles <= cycles)
{
network.Initialize();
network.Learn(trainingSet, currentCycles);
correct = true;
for (int sampleIndex = 0; sampleIndex < count; sampleIndex++)
{
double[] op = network.Run(trainingSet[sampleIndex].InputVector);
if (((trainingSet[sampleIndex].OutputVector[0] > trainingSet[sampleIndex].OutputVector[1]) && op[0] - op[1] < 0.4) || ((trainingSet[sampleIndex].OutputVector[0] < trainingSet[sampleIndex].OutputVector[1]) && op[1] - op[0] < 0.4))
{
correct = false;
trainingSet.Add(trainingSet[sampleIndex]);
}
}
currentCycles *= 2;
}
//lstLog.Items.Add(cboAplhabet.Items[i] + " & " + cboAplhabet.Items[j] + " = " + network.MeanSquaredError.ToString("0.0000"));
// lstLog.TopIndex = lstLog.Items.Count - (int)(lstLog.Height / lstLog.ItemHeight);
try
{
using (Stream stream = File.Open(Application.StartupPath + @"\Networks\" + i.ToString("00") + j.ToString("00") + ".ndn", FileMode.Create))
{
IFormatter formatter = new BinaryFormatter();
formatter.Serialize(stream, network);
}
}
catch (Exception)
{
MessageBox.Show("Failed to save trained neural networks", "Critical Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
currentCombination++;
}
}
// progressTraining.Value = 0;
// btnTrain.Enabled = false;
}
