public static void Main(string[] args)
{
//// inputs
Byte[] inputByte = MyMath.GetRandomBytes(2);
double n = 0.1;
int layersCount = 2;
Layer[] layers = new Layer[layersCount + 1];
layers[0] = new Layer();
layers[0].cellCount = inputByte.Length;
layers[0].output = new double[inputByte.Length];
for (int i = 0; i < inputByte.Length; i++)
{
layers[0].output[i] = inputByte[i] / 255.0;
}
layers[1] = new Layer();
layers[1].cellCount = 3;
layers[1].function = TransferFunction.Linear;
layers[1].bias = MyMath.GetRandomDoubles(layers[1].cellCount);
layers[1].weights = MyMath.GetRandom(layers[0].cellCount, layers[1].cellCount);
layers[2] = new Layer();
layers[2].cellCount = 1;
layers[2].function = TransferFunction.Linear;
layers[2].bias = MyMath.GetRandomDoubles(layers[2].cellCount);
layers[2].weights = MyMath.GetRandom(layers[1].cellCount, layers[2].cellCount);
for (int z = 0; z < 50; z++)
{
//feed forward all
for (int i = 1; i < layers.Length; i++)
{
layers[i].FeedForward(layers[i - 1].output);
}
//backpropagate last layer
int l = layers.Length - 1;
double[] targetOutput = new double[layers[l].cellCount];
//calculate errors
layers[l].errors = new double[layers[l].cellCount];
for (int i = 0; i < targetOutput.Length; i++)
{
layers[l].errors[i] = layers[l].output[i] - targetOutput[i];
}
Console.WriteLine("FeedForward error: " + Math.Round(MyMath.Sum(layers[l].errors), 4));
//calculate weights
ChangeWeights(n, layers, l);
//backpropagate other layers
for (l = layers.Length - 2; l > 0; l--)
{
//calculate errors
layers[l].errors = new double[layers[l].cellCount];
for (int i = 0; i < layers[l].cellCount; i++)
{
for (int j = 0; j < layers[l + 1].cellCount; j++)
{
layers[l].errors[i] = layers[l + 1].errors[j] * layers[l + 1].weights[i, j];
}
}
//calculate weights
ChangeWeights(n, layers, l);
}
}
Console.ReadKey();
}
public void FeedForward(double[] input)
{
output = MyMath.Multiply(input, weights);
for (int i = 0; i < output.Length; i++)
{
output[i] += bias[i];
}
double max = 0;
if (function == TransferFunction.Competitive)
{
max = MyMath.GetMax(output);
}
for (int i = 0; i < cellCount; i++)
{
double n = output[i];
double a = 0;
switch (function)
{
case TransferFunction.Hard_Limit:
{
a = (n >= 0) ? 1 : 0;
}
break;
case TransferFunction.Symmetrical_Hard_Limit:
{
a = (n >= 0) ? 1 : -1;
}
break;
case TransferFunction.Linear:
{
a = n;
}
break;
case TransferFunction.Saturating_Linear:
{
if (n > 1)
{
a = 1;
}
else if (n < 0)
{
a = 0;
}
else
{
a = n;
}
}
break;
case TransferFunction.Symmetric_Saturating_Linear:
{
if (n > 1)
{
a = 1;
}
else if (n < -1)
{
a = 0;
}
else
{
a = n;
}
}
break;
case TransferFunction.Log_Sigmoid:
{
double e = Math.Exp(-n);
a = 1.0 / (1 + e);
}
break;
case TransferFunction.Hyperbolic_Tangent_Sigmoid:
{
double e1 = Math.Exp(n);
double e2 = Math.Exp(-n);
a = (e1 - e2) / (e1 + e2);
}
break;
case TransferFunction.Positive_Linear:
{
a = (n < 0) ? 0 : n;
}
break;
case TransferFunction.Competitive:
{
a = (n == max) ? 1 : 0;
}
break;
}
output[i] = a;
}
}
//Back Propagate for Hidden Neuron
public void BackPropag(vNeuron[,] net, int mPos, double learnRate, double bLearnRate)
{
for (int i = 0; i < this.inputNeurons.Count; i++)
{
List <List <double> > error = MyMath.matMulti(MyMath.transposeMat(this.weights[i]), MyMath.makeVertiColMat(this.handedDownDelta[0]));
List <double> fPrime = new List <double>();
if (this.actFunc == actFuncType.logistic)
{
for (int j = 0; j < this.output.Count; j++)
{
fPrime.Add(MyMath.logisticPrimeFunc(this.output[j]));
}
}
else if (this.actFunc == actFuncType.identity)
{
for (int j = 0; j < this.output.Count; j++)
{
fPrime.Add(1d);
}
}
List <List <double> > result = MyMath.transposeMat(MyMath.pointwiseMatMulti(error, MyMath.makeVertiColMat(fPrime)));
this.delta = result[0];
this.beta = MyMath.matSub(MyMath.makeHorizonColMat(this.beta), MyMath.scalarProd(bLearnRate, MyMath.makeHorizonColMat(this.delta)))[0];
int m = inputNeurons[i][0];
int n = inputNeurons[i][1];
if (net[m, n].handedDownDelta.Count > 0)
{
net[m, n].handedDownDelta[0] /*.Add(this.delta);*/ = MyMath.colAdd(net[m, n].handedDownDelta[0], this.delta);
}
else
{
net[m, n].handedDownDelta.Add(Enumerable.Repeat(0d, delta.Count).ToList());
net[m, n].handedDownDelta[0] /*.Add(this.delta);*/ = MyMath.colAdd(net[m, n].handedDownDelta[0], this.delta);
}
List <List <double> > matrix = this.weights[i];
this.newWeights.Add(MyMath.matSub(matrix, MyMath.scalarProd(learnRate, MyMath.matMulti(MyMath.makeVertiColMat(this.delta), MyMath.makeHorizonColMat(this.inputs[i])))));
}
}
//Back Propagate for Output Neuron
public void BackPropag(mNeuron[,] net, List <List <double> > expOut, double learnRate, double bLearnRate)
{
List <List <double> > error = main.createNewMatrix(this.postActOutput.Count, this.postActOutput[0].Count);
for (int i = 0; i < expOut.Count; i++)
{
for (int j = 0; j < expOut[0].Count; j++)
{
if (expOut[i][j] != -2)
{
error[i][j] = this.postActOutput[i][j] - expOut[i][j];
}
else
{
error[i][j] = 0;
}
}
}
List <List <double> > fPrime = new List <List <double> >();
if (this.actFunc == actFuncType.logistic)
{
for (int i = 0; i < this.output.Count; i++)
{
fPrime.Add(new List <double>());
for (int j = 0; j < this.output[0].Count; j++)
{
fPrime[i].Add(MyMath.logisticPrimeFunc(this.output[i][j]));
}
}
}
else if (this.actFunc == actFuncType.identity)
{
for (int i = 0; i < this.output.Count; i++)
{
fPrime.Add(new List <double>());
for (int j = 0; j < this.output[0].Count; j++)
{
fPrime[i].Add(1d);
}
}
}
List <List <double> > result = MyMath.pointwiseMatMulti(error, fPrime);
this.delta = result;
this.beta = MyMath.matSub(this.beta, MyMath.scalarProd(bLearnRate, this.delta));
for (int i = 0; i < this.inputNeurons.Count; i++)
{
int m = inputNeurons[i][0];
int n = inputNeurons[i][1];
net[m, n].handedDownDelta = MyMath.matAdd(net[m, n].handedDownDelta, this.delta);
List <List <double> > matrix = this.weights[i];
List <List <double> > gradient = MyMath.matMulti(this.delta, MyMath.transposeMat(this.inputs[i]));
this.newWeights.Add(MyMath.matSub(matrix, MyMath.scalarProd(learnRate, gradient)));
}
}
