partial void test_ann_neuron_hiddenPercpetronToolStripMenuItem_Click(object sender, EventArgs e)
{
string log = "";
int epochs = 1;
int nofS = 2;
Synapse s;
double momentum = 0.5, learningRate = 0.08;
string c = "";
c += "activation=tanh(a=1.7159, b=0.6667); fieldsize=2";
HiddenPerceptron[] h = new HiddenPerceptron[2];
OutputPerceptron o = new OutputPerceptron();
o.Configure(c);
// instantiate with configuration string
h[0] = new HiddenPerceptron();
h[0].Configure(c);
h[1] = new HiddenPerceptron();
h[1].Configure(c);
Node[] src0 = new Node[nofS];
for (int i = 0; i < nofS; i++)
{
src0[i] = new Node(new double?[] { -1.0 + (i * 2.0), null });
}
// initialize inputs
for (int i = 0; i < nofS; i++)
{
h[0].Source = src0[i];
h[1].Source = src0[i];
}
// initialize inputs for output node
for (int i = 0; i < nofS; i++)
{
o.Source = h[i].Output;
}
// initialize weights
for (int i = 0; i <= nofS; i++)
{
h[0].Synapse[i].W = (Math.Daemon.Random.Next(6) + 1) * 0.35;
h[0].Synapse[i].dW = 0.0;
h[1].Synapse[i].W = (Math.Daemon.Random.Next(6) + 1) * 0.50;
h[1].Synapse[i].dW = 0.0;
o.Synapse[i].W = (Math.Daemon.Random.Next(6) + 1) * 0.20;
o.Synapse[i].dW = 0.0;
}
double[][] trainingSet = new double[4][];
trainingSet[0] = new double[] { 0.0, 0.0, 0.0 };
trainingSet[1] = new double[] { 0.0, 1.0, 1.0 };
trainingSet[2] = new double[] { 1.0, 0.0, 1.0 };
trainingSet[3] = new double[] { 1.0, 1.0, 0.0 };
for (int i = 0; i < epochs; i++)
{
log += "\n";
for (int j = 0; j < trainingSet.Length; j++)
{
((double?[])src0[0].Element)[Global.Sig] = trainingSet[j][0];
((double?[])src0[1].Element)[Global.Sig] = trainingSet[j][1];
// propagate signal
log += "\n\nPROPAGATE SIGNAL...///////////////////////////////////////";
h[0].Next(Propagate.Signal);
h[1].Next(Propagate.Signal);
o.Next(Propagate.Signal);
log += "\n\nepoch[" + i.ToString("00") + "]";
log += "\n\n" + h[0].ToString();
log += "\n" + h[1].ToString();
log += "\n" + o.ToString();
// set target output value
log += "\n\nSET TARGET...////////////////////////////////////////////";
((double?[])h[0].Output.Element)[Global.Err] = 0.0;
((double?[])h[1].Output.Element)[Global.Err] = 0.0;
((double?[])o.Output.Element)[Global.Err] = trainingSet[j][2];
log += "\n\n" + h[0].ToString();
log += "\n" + h[1].ToString();
log += "\n" + o.ToString();
// propagate error
log += "\n\nPROPAGATE ERROR...///////////////////////////////////////";
o.Next(Propagate.Error);
h[0].Next(Propagate.Error);
h[1].Next(Propagate.Error);
log += "\n\n" + h[0].ToString();
log += "\n" + h[1].ToString();
log += "\n" + o.ToString();
// adjust weight
log += "\n\nADJUST WEIGHTS...///////////////////////////////////////";
for (int k = 0; k < o.Synapse.Count; k++)
{
s = o.Synapse[k];
s.dW = (momentum * s.dW) + (learningRate * o.Gradient * ((double?[])s.Source.Element)[Global.Sig].Value);
s.W += s.dW;
}
for (int k = 0; k < h[0].Synapse.Count; k++)
{
s = h[0].Synapse[k];
s.dW = (momentum * s.dW) + (learningRate * h[0].Gradient * ((double?[])s.Source.Element)[Global.Sig].Value);
s.W += s.dW;
}
for (int k = 0; k < h[1].Synapse.Count; k++)
{
s = h[1].Synapse[k];
s.dW = (momentum * s.dW) + (learningRate * h[1].Gradient * ((double?[])s.Source.Element)[Global.Sig].Value);
s.W += s.dW;
}
log += "\n\n" + h[0].ToString();
log += "\n" + h[1].ToString();
log += "\n" + o.ToString();
}
}
richTextBox.Text = log;
}
/// <summary>
/// tests output perceptron
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
partial void test_ann_neuron_outputPerceptronToolStripMenuItem_Click(object sender, EventArgs e)
{
string log = "";
int epochs = 1, nofS = 2;
string c = "act=tanh(a=1.7159, b=0.6667); outputfieldsize=2";
Synapse s;
double momentum = 0.5, learningRate = 0.08;
OutputPerceptron o = new OutputPerceptron();
o.Configure(c);
((double?[])o.Output.Element)[Global.Err] = 1.0;
Node[] source = new Node[nofS];
for (int i = 0; i < nofS; i++)
{
source[i] = new Node(new double?[] { 1.0, null });
}
for (int i = 0; i < nofS; i++)
{
o.Source = source[i];
}
for (int i = 0; i <= nofS; i++)
{
o.Synapse[i].W = (Math.Daemon.Random.Next(6) + 1) * 0.1;
o.Synapse[i].dW = 0.0;
}
double[][] trainingSet = new double[4][];
trainingSet[0] = new double[] { 0.0, 0.0, 0.0 };
trainingSet[1] = new double[] { 0.0, 1.0, 1.0 };
trainingSet[2] = new double[] { 1.0, 0.0, 1.0 };
trainingSet[3] = new double[] { 1.0, 1.0, 1.0 };
for (int i = 0; i < epochs; i++)
{
log += "\n";
for (int j = 0; j < trainingSet.Length; j++)
{
((double?[])source[0].Element)[Global.Sig] = trainingSet[j][0];
((double?[])source[1].Element)[Global.Sig] = trainingSet[j][1];
// propagate signal
o.Next(Propagate.Signal);
log += "\n\nepoch[" + i.ToString("00") + "]";
log += "\n\npropagate signal...";
log += "\n" + o.ToString();
// set target output value
((double?[])o.Output.Element)[Global.Err] = trainingSet[j][2];
log += "\n\nSet target...";
log += "\n" + o.ToString();
// propagate error
o.Next(Propagate.Error);
log += "\n\npropagate error...";
log += "\n" + o.ToString();
// adjust weight
for (int k = 0; k < o.Synapse.Count; k++)
{
s = o.Synapse[k];
s.dW = (momentum * s.dW) + (learningRate * o.Gradient * ((double?[])s.Source.Element)[Global.Sig].Value);
s.W += s.dW;
}
log += "\n\nadjust weights...";
log += "\n" + o.ToString();
}
}
richTextBox.Text = log;
}