//Adjust the weights of the inputs
public void AdjustWeights(NeuralNetwork neuralNetwork, int targetOutput, NeuralData neuralData, double learningRate)
{
double initialError = neuralData.Error; //Store the initial Error
Queue <double> initialWeights = new Queue <double>();
GetWeights(initialWeights);
int count = 10;
while (neuralData.Error >= initialError)
{
foreach (Conection conection in Conections)
{ //Loop through every conection
if ((conection.ConectedFrom.Output > 0 && targetOutput == 0))
{ //If the conected neuron fired and we wanted the neuron to not fire
conection.weight -= learningRate;
conection.ConectedFrom.AdjustWeights(targetOutput, learningRate);
}
else if ((conection.ConectedFrom.Output) == 0 && targetOutput == 1)
{ //If the conected neuron didn't fire and we wanted it to fire.
conection.weight += learningRate;
conection.ConectedFrom.AdjustWeights(targetOutput, learningRate);
}
neuralData = neuralNetwork.CalculateOutputs(neuralData);
neuralData.CalculateError();
if (neuralData.Error < initialError)
{
return;
}
}
count--;
if (count < 0)
{
//we were unable to improve the error, we must fix the weights
SetWeights(initialWeights);
neuralNetwork.CalculateOutputs(neuralData);
neuralData.CalculateError();
break;
}
}
}
public void Train()
{
Random rand = new Random();
for (int i = 0; i < Globals.XmlData.TrainingIterations; i++)
{
//Generate the input and target output
NeuralData neuralData = GenerateTrainingData(rand);
//Calculate the actual outputs from our generated inputs
neuralData = CalculateOutputs(neuralData);
//Calculate the error and print it
double initialError = neuralData.CalculateError();
neuralData.PrintInput();
neuralData.PrintTarget();
neuralData.PrintActual();
Console.WriteLine("Initial error: {0}", Math.Round(initialError, 3));
//Adjust the weights. If our error is zero, don't change anything
if (initialError > 0)
{
Console.WriteLine("Adjusting weights.");
//loop through each output and compare actual to target
for (int j = 0; j < neuralData.TargetOutput.Count; j++)
{
if (neuralData.ActualOutput[j] != neuralData.TargetOutput[j])
{ //Actual output does not match target output
NeuralLayers.Last().Neurons[j].AdjustWeights(this, neuralData.TargetOutput[j], neuralData, Math.Pow(neuralData.ActualOutput[j] - neuralData.TargetOutput[j], 2));
}
}
Console.WriteLine("New error: {0}", Math.Round(neuralData.CalculateError(), 3));
neuralData.PrintActual();
}
Console.WriteLine("");
}
}