public Network(int numInputParameters, int numNeuronsInHiddenLayer, int numOutputParameters, int maxEpochs)
{
InputLayer = new Layer();
HiddenLayer = new Layer();
OutputLayer = new Layer();
MaxEpochs = maxEpochs;
//Setup Input Layer
for (var i = 0; i < numInputParameters; i++)
{
InputLayer.Neurons.Add(new Neuron(1, true));
}
//Setup Hidden Layer
for (var i = 0; i < numNeuronsInHiddenLayer; i++)
{
var neuron = new Neuron(numInputParameters);
neuron.RandomizeWeights();
HiddenLayer.Neurons.Add(neuron);
}
//Setup Output Layer
for (var i = 0; i < numOutputParameters; i++)
{
var neuron = new Neuron(HiddenLayer.Neurons.Count);
neuron.RandomizeWeights();
OutputLayer.Neurons.Add(neuron);
}
}
private void BackPropagate(Neuron outputNeuron)
{
for (var neuronIndex = 0; neuronIndex < HiddenLayer.Neurons.Count; neuronIndex++)
{
var neuron = HiddenLayer.Neurons[neuronIndex];
neuron.Error = Sigmoid.Derivative(neuron.Output) * outputNeuron.Error * outputNeuron.Weights[neuronIndex];
neuron.AdjustWeights();
}
}