// method to compute a forward pass through the network
// input values are assumed to be scaled between 0 and 1
public double[] ForwardPass(double[] input)
{
// add bias to the input
double[] inputWithBias = LA.Append(input, 1.0);
// multiply input by first weight matrix
double[] hiddenLayerInput = LA.MatByVec(WeightMat1, inputWithBias);
// take sigmoid of terms
double[] hiddenLayerOutputNoBias = NNMath.VectorSigmoid(hiddenLayerInput);
// add bias term
double[] hiddenLayerOutput = LA.Append(hiddenLayerOutputNoBias, 1.0);
// multiply by second weight matrix
double[] outputLayerInput = LA.MatByVec(WeightMat2, hiddenLayerOutput);
// take sigmoid of terms
double[] networkOutput = NNMath.VectorSigmoid(outputLayerInput);
// return network output
return(networkOutput);
}
// method for backpropogation
// x is the network input, and target is the desired network output
public void Backprop(double[] x, double[] target)
{
// add bias to the input
double[] inputWithBias = LA.Append(x, 1.0);
// multiply biased input by first weight matrix
double[] hiddenLayerInput = LA.MatByVec(NeuralNet.WeightMat1, inputWithBias);
// take sigmoid of terms
double[] hiddenLayerOutput = NNMath.VectorSigmoid(hiddenLayerInput);
// add bias term
double[] h = LA.Append(hiddenLayerOutput, 1.0);
// multiply by second weight matrix
double[] outputLayerInput = LA.MatByVec(NeuralNet.WeightMat2, h);
// take sigmoid of terms
double[] output = NNMath.VectorSigmoid(outputLayerInput);
// find error in network output
int dim = target.GetLength(0);
double[] e = new double[dim];
for (int i = 0; i < dim; i++)
{
e[i] = target[i] - output[i];
}
// find output layer deltas
double[] outputDeltas = new double[dim];
for (int i = 0; i < dim; i++)
{
outputDeltas[i] = e[i] * output[i] * (1 - output[i]);
}
// update second weight matrix
//Parallel.For(0, NeuralNet.WeightMat2.GetLength(0), i =>
for (int i = 0; i < NeuralNet.WeightMat2.GetLength(0); i++)
{
for (int j = 0; j < NeuralNet.WeightMat2.GetLength(1); j++)
{
NeuralNet.WeightMat2[i, j] += Eta * outputDeltas[i] * h[j] + MomentumMat2[i, j] * Momentum;
MomentumMat2[i, j] = Eta * outputDeltas[i] * h[j];
}
}
// find hidden layer deltas
double[] hiddenDeltas = new double[NumHidden];
double tempVal;
for (int j = 0; j < NumHidden; j++)
{
tempVal = 0.0;
for (int k = 0; k < NumOutputs; k++)
{
tempVal += NeuralNet.WeightMat2[k, j] * outputDeltas[k] * h[j] * (1 - h[j]);
}
hiddenDeltas[j] = tempVal;
}
// update first weight matrix
for (int i = 0; i < NeuralNet.WeightMat1.GetLength(0); i++)
{
for (int j = 0; j < NeuralNet.WeightMat1.GetLength(1); j++)
{
NeuralNet.WeightMat1[i, j] += Eta * hiddenDeltas[i] * inputWithBias[j] + MomentumMat1[i, j] * Momentum;
MomentumMat1[i, j] = Eta * hiddenDeltas[i] * inputWithBias[j];
}
}
}
