public static void BackPropogation_TrainingSession(NeuralNet net, double[] input, double[] desiredResult)
{
PreparePerceptionLayerForPulse(net, input);
net.Pulse();
CalculateErrors(net, desiredResult);
CalculateAndAppendTransformation(net);
}
private void btnTest_Click(object sender, EventArgs e)
{
#region Declarations
bool result, verbose;
StringBuilder bld;
#endregion
#region Initialization
verbose = false;
bld = new StringBuilder();
#endregion
#region Execution
net.PerceptionLayer[0].Output = (ckA.Checked) ? high : low;
net.PerceptionLayer[1].Output = (ckB.Checked) ? high : low;
net.Pulse();
result = net.OutputLayer[0].Output > .5;
#region verbose
if (verbose)
{
bld.Remove(0, bld.Length);
bld.Append("PERCEPTION LAYER <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n");
foreach (Neuron pn in net.PerceptionLayer)
{
AppendNeuronInfo(bld, pn);
}
bld.Append("\nHIDDEN LAYER <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n");
foreach (Neuron hn in net.HiddenLayer)
{
AppendNeuronInfo(bld, hn);
}
bld.Append("\nOUTPUT LAYER <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n");
foreach (Neuron on in net.OutputLayer)
{
AppendNeuronInfo(bld, on);
}
bld.Append("\n");
MessageBox.Show(bld.ToString());
}
#endregion
lblResult.Text = result.ToString();
#endregion
}
private void btnTrain_Click(object sender, EventArgs e)
{
#region Declarations
double ll, lh, hl, hh;
int count, iterations;
bool verbose;
double[][] input, output;
StringBuilder bld;
#endregion
#region Initialization
net = new NeuralNet();
bld = new StringBuilder();
input = new double[4][];
input[0] = new double[] { high, high };
input[1] = new double[] { low, high };
input[2] = new double[] { high, low };
input[3] = new double[] { low, low };
output = new double[4][];
output[0] = new double[] { low };
output[1] = new double[] { high };
output[2] = new double[] { high };
output[3] = new double[] { low };
verbose = false;
count = 0;
iterations = 5;
#endregion
#region Execution
// initialize with
// 2 perception neurons
// 2 hidden layer neurons
// 1 output neuron
net.Initialize(1, 2, 2, 1);
do
{
count++;
net.LearningRate = 3;
net.Train(input, output, TrainingType.BackPropogation, iterations);
net.PerceptionLayer[0].Output = low;
net.PerceptionLayer[1].Output = low;
net.Pulse();
ll = net.OutputLayer[0].Output;
net.PerceptionLayer[0].Output = high;
net.PerceptionLayer[1].Output = low;
net.Pulse();
hl = net.OutputLayer[0].Output;
net.PerceptionLayer[0].Output = low;
net.PerceptionLayer[1].Output = high;
net.Pulse();
lh = net.OutputLayer[0].Output;
net.PerceptionLayer[0].Output = high;
net.PerceptionLayer[1].Output = high;
net.Pulse();
hh = net.OutputLayer[0].Output;
#region verbose
if (verbose)
{
bld.Remove(0, bld.Length);
bld.Append("PERCEPTION LAYER <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n");
foreach (Neuron pn in net.PerceptionLayer)
{
AppendNeuronInfo(bld, pn);
}
bld.Append("\nHIDDEN LAYER <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n");
foreach (Neuron hn in net.HiddenLayer)
{
AppendNeuronInfo(bld, hn);
}
bld.Append("\nOUTPUT LAYER <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n");
foreach (Neuron on in net.OutputLayer)
{
AppendNeuronInfo(bld, on);
}
bld.Append("\n");
bld.Append("hh: \t").Append(hh.ToString()).Append(" \t< .5\n");
bld.Append("ll: \t").Append(ll.ToString()).Append(" \t< .5\n");
bld.Append("hl: \t").Append(hl.ToString()).Append(" \t> .5\n");
bld.Append("lh: \t").Append(lh.ToString()).Append(" \t> .5\n");
MessageBox.Show(bld.ToString());
}
#endregion
}
// really train this thing well...
while (hh > (mid + low) / 2 ||
lh < (mid + high) / 2 ||
hl < (mid + low) / 2 ||
ll > (mid + high) / 2);
net.PerceptionLayer[0].Output = low;
net.PerceptionLayer[1].Output = low;
net.Pulse();
ll = net.OutputLayer[0].Output;
net.PerceptionLayer[0].Output = high;
net.PerceptionLayer[1].Output = low;
net.Pulse();
hl = net.OutputLayer[0].Output;
net.PerceptionLayer[0].Output = low;
net.PerceptionLayer[1].Output = high;
net.Pulse();
lh = net.OutputLayer[0].Output;
net.PerceptionLayer[0].Output = high;
net.PerceptionLayer[1].Output = high;
net.Pulse();
hh = net.OutputLayer[0].Output;
bld.Remove(0, bld.Length);
bld.Append((count * iterations).ToString()).Append(" iterations required for training\n");
bld.Append("hh: ").Append(hh.ToString()).Append(" < .5\n");
bld.Append("ll: ").Append(ll.ToString()).Append(" < .5\n");
bld.Append("hl: ").Append(hl.ToString()).Append(" > .5\n");
bld.Append("lh: ").Append(lh.ToString()).Append(" > .5\n");
MessageBox.Show(bld.ToString());
#endregion
}
