static void Main(string[] args)
{
int[] layerSizes = new int[3] {
3, 4, 1
};
TransferFunction[] TFuncs = new TransferFunction[3] {
TransferFunction.None,
TransferFunction.Sigmoid,
TransferFunction.Linear
};
BackPropagationNetwork bpn = new BackPropagationNetwork(layerSizes, TFuncs);
double[] input = new double[] { 4, 6, 8 };//, new double[] {4, 7, 5}, new double[] {7, 4, 8}, new double[] {6, 7, 5}, new double[] {7, 7, 8}};
/*for(int i = 0; i < input.GetUpperBound(0); i++)
* {
* input[i] = new double[3];
* for (int j = 0; j < input[i].Length; j++)
* {
*
* }
* }*/
double[] desired = new double[] { -0.86 };//, new double[] {0.15}, new double[] {0.72 }, new double[] {0.53 }, new double[] { 0.44 } };
double[] output = new double[1];
double error = 0.0;
for (int i = 0; i < 10; i++)
{
error = bpn.Train(ref input, ref desired, 0.15, 0.1);
bpn.Run(ref input, out output);
if (i % 1 == 0)
{
Console.WriteLine("Iteration {0}: \n\t Input {1:0.000} {2:0.000} {3:0.000} Output {4:0.000} error{5:0.000}", i, input[0], input[1], input[2], output[0], error);
}
/*for (int k = 0; k < 4; k++)
* Console.WriteLine("{0:0.000}", bpn.layerOtput[0][k]);*/
}
Console.ReadKey();
}
static void Main(string[] args)
{
string filePath = @"E:\Work\NeuralXOR.xml";
int[] layerSizes = new int[3] {
2, 2, 1
};
TransferFunction[] tFuncs = new TransferFunction[3] {
TransferFunction.None,
TransferFunction.Sigmoid,
TransferFunction.Linear
};
BackPropagationNetwork bpn = new BackPropagationNetwork(layerSizes, tFuncs);
//Example XOR-Gate
bpn.Name = "XOR-Gate";
//Define the cases
double[][] input, ouput;
input = new double[4][]; ouput = new double[4][];
for (int i = 0; i < 4; i++)
{
input[i] = new double[2]; ouput[i] = new double[1];
}
input[0][0] = 0.0; input[0][1] = 0.0; ouput[0][0] = 0.0; //false XOR false = false
input[1][0] = 1.0; input[1][1] = 0.0; ouput[1][0] = 1.0; //true XOR false = true
input[2][0] = 0.0; input[2][1] = 1.0; ouput[2][0] = 1.0; //false XOR true = true
input[3][0] = 1.0; input[3][1] = 1.0; ouput[3][0] = 0.0; //true XOR true = false
//Train the network
double error = 0.0;
int max_count = 1000, count = 0;
do
{
//Prepare for training Epoch
count++;
error = 0.0;
//Train
for (int i = 0; i < 4; i++)
{
error += bpn.Train(ref input[i], ref ouput[i], 0.15, 0.10);
}
//Show Progress
if (count % 100 == 0)
{
Console.WriteLine("Epoch {0} completed with error {1:0.0000}", count, error);
}
} while (error > 0.00001 && count <= max_count);
//Display results!
double[] networkOutput = new double[1];
for (int i = 0; i < 4; i++)
{
bpn.Run(ref input[i], out networkOutput);
Console.WriteLine("Case {3}: {0:0.0} xor {1:0.0} = {2:0.0000}", input[i][0], input[i][1], networkOutput[0], i + 1);
}
bpn.Save(filePath);
Console.ReadLine();
}
static void Main(string[] args)
{
var layerSizes = new[] { 2, 2, 1 };
var transferFunctions = new[]
{ TransferFunction.None, TransferFunction.Sigmoid, TransferFunction.Linear };
var backPropagationNetwork = new BackPropagationNetwork(layerSizes, transferFunctions)
{
Name = "XOR-Gate Example"
};
var input = new double[4][];
var expected = new double[4][];
for (int i = 0; i < 4; i++)
{
input[i] = new double[2];
expected[i] = new double[1];
}
input[0][0] = 0.0;
input[0][1] = 0.0;
expected[0][0] = 0; // false xor false = false
input[1][0] = 1.0;
input[1][1] = 0.0;
expected[1][0] = 1; // true xor false = true
input[2][0] = 0.0;
input[2][1] = 1.0;
expected[2][0] = 1; // false xor true = true
input[3][0] = 1.0;
input[3][1] = 1.0;
expected[3][0] = 0; // true xor true = false
double error = 0.0;
const int maxCount = 10;
int count = 0;
Stopwatch watch = Stopwatch.StartNew();
do
{
// prepare for training epic
count++;
error = 0;
// train
for (int i = 0; i < 4; i++)
{
error += backPropagationNetwork.Train(ref input[i], ref expected[i], .15, .1);
}
if (count % 1 == 0)
{
Console.WriteLine("Epoch {0} completed with error {1:0.0000}", count, error);
}
} while (error > 0.0001 && count <= maxCount);
watch.Stop();
var output = new double[4][];
for (int i = 0; i < 4; i++)
{
backPropagationNetwork.Run(ref input[i], out output[i]);
}
for (int i = 0; i < 4; i++)
{
Console.WriteLine("For inputs {0} and {1}, output is {2}", input[i][0], input[i][1], output[i][0]);
}
Console.WriteLine("Time Elapsed :" + watch.Elapsed);
Console.WriteLine("Hit Enter...");
Console.ReadLine();
}
private void навчитиToolStripMenuItem_Click(object sender, EventArgs e)
{
dataGridView1.DataSource = errors;
dataGridView2.DataSource = function;
dataGridView3.DataSource = weightsHidden;
dataGridView4.DataSource = weightsOutput;
switch (comboBox1.Text)
{
case "ln|cosx1| + tgx2 + ctgx3":
{
errors.Columns.Add("iteration");
errors.Columns.Add("function");
errors.Columns.Add("error");
errors.Columns.Add("output[d1]");
errors.Columns.Add("output[d2]");
double sum = 0.0;
for (int i = 0; i < dt.Rows.Count; i++)
{
desired[i][0] = Math.Sin(input[i][0]) + Math.Sin(input[i][1]) - Math.Sin(input[i][2]); /* Math.Log(Math.Abs(Math.Cos(input[i][0]))) + Math.Tan(input[i][1]) + 1/Math.Tan(input[i][2]);/*Math.Cos(input[i][0]) + Math.Tan(input[i][1]) + 1 / Math.Tan(input[i][2]);*/
sum += desired[i][0];
}
function.Columns.Add("i");
function.Columns.Add("x1");
function.Columns.Add("x2");
function.Columns.Add("x3");
function.Columns.Add("d1");
function.Columns.Add("d2");
function.Columns.Add("average");
for (int i = 0; i < desired.GetUpperBound(0) + 1; i++)
{
DataRow row = function.NewRow();
row["i"] = i;
row["x1"] = input[i][0];
row["x2"] = input[i][1];
row["x3"] = input[i][2];
row["d1"] = desired[i][0];
function.Rows.Add(row);
}
double[] output = new double[2];
// Навчання
int example = 1;
int h = 0;
double[] value = new double[3] {
4, 5, 4
};
Random rdn = new Random();
do
{
error = 0.0;
input.Reverse();
for (int j = 0; j < dt.Rows.Count; j++)
{
error += bpn.Train(ref input[j], ref desired[j], TrainingRate, Momentum);
}
if (h % 100 == 0)
{
bpn.Run(ref value, out output);
DataRow row = errors.NewRow();
row["iteration"] = h;
row["function"] = example;
row["error"] = error / 20;
row["output[d1]"] = output[0];
//row["output[d2]"] = output[1];
errors.Rows.Add(row);
}
h++;
if (h == 200000)
{
break;
}
} while ((error / 20) > 0.00127);
// Виведення скоригованих ваг прихованого шару
hidden = bpn.GetWeights(0);
weightsHidden.Columns.Add("Початковий/Прихований");
for (int i = 0; i < hidden[0].Count(); i++)
{
weightsHidden.Columns.Add(Convert.ToString(i));
}
for (int i = 0; i < hidden.GetUpperBound(0) + 1; i++)
{
DataRow row = weightsHidden.NewRow();
row["Початковий/Прихований"] = i;
for (int j = 1; j < weightsHidden.Columns.Count; j++)
{
row[j] = hidden[i][j - 1];
}
weightsHidden.Rows.Add(row);
}
// Виведення скоригованих ваг вихідного шару
outputWeights = bpn.GetWeights(1);
weightsOutput.Columns.Add("Прихований/Вихідний");
for (int i = 0; i < outputWeights[0].Count(); i++)
{
weightsOutput.Columns.Add(Convert.ToString(i));
}
for (int i = 0; i < outputWeights.GetUpperBound(0) + 1; i++)
{
DataRow row = weightsOutput.NewRow();
row["Прихований/Вихідний"] = i;
for (int j = 1; j < weightsOutput.Columns.Count; j++)
{
row[j] = outputWeights[i][j - 1];
}
weightsOutput.Rows.Add(row);
}
break;
}
}
}
private static void Main(string[] args)
{
Console.Out.WriteLine(" BACKPROPAGATION neural network demo.");
Console.Out.WriteLine("");
Console.Out.WriteLine(" Copyright(C) XP Idea.com 2001-2004 ");
Console.Out.WriteLine("");
Console.Out.WriteLine("The purpose of this demo is to show learning abilities of BACKPROP network.");
Console.Out.WriteLine("The BACKPROP network is able to learn much more complex data patterns, than");
Console.Out.WriteLine("Adaline network (please see OCR demo application). ");
Console.Out.WriteLine("This example simple shows that the Backprop network is able to learn ");
Console.Out.WriteLine("an 'exclusive OR' (XOR) operation, but the Adaline network is not able to do so.");
Console.Out.WriteLine("");
Console.Out.WriteLine(" false XOR false = false");
Console.Out.WriteLine(" true XOR false = true");
Console.Out.WriteLine(" false XOR true = true");
Console.Out.WriteLine(" true XOR true = false");
Console.Out.WriteLine("");
Console.Out.WriteLine(" As result of the training, the network will produce output ‘0’");
Console.Out.WriteLine("corresponding to logical ‘false’ or ‘1’ corresponding to logical ‘true’ value.");
Console.Out.WriteLine("");
Console.Out.WriteLine("PLEASE HIT ENTER TO CONTINUE");
Console.In.ReadLine();
Console.Out.WriteLine("");
Console.Out.WriteLine("During this demo you will be prompted to enter input values");
Console.Out.WriteLine("for the network. Then network will perform “XOR” operation on ");
Console.Out.WriteLine("the entered values and result will be displayed to you. ");
Console.Out.WriteLine("Please enter any values in range from 0 to 1 and hit [ENTER] when prompted. ");
Console.Out.WriteLine("");
Console.Out.WriteLine("NOW THE NETWORK IS READY TO LEARN FOLLOWING PATTERNS");
Console.Out.WriteLine("");
Console.Out.WriteLine(" false XOR false = false;");
Console.Out.WriteLine(" true XOR false = true;");
Console.Out.WriteLine(" false XOR true = true;");
Console.Out.WriteLine(" true XOR true = false;");
Console.Out.WriteLine("PLEASE HIT ENTER TO BEGIN TRAINING");
Console.In.ReadLine();
Console.Out.Write("TRAINING....");
double d;
BackPropagationNetwork BackPropNet;
var patterns = new PatternsCollection(TrainingSets, 2, 1);
SetPattern(patterns[0], 0, 0, 0);
SetPattern(patterns[1], 0, 1, 1);
SetPattern(patterns[2], 1, 0, 1);
SetPattern(patterns[3], 1, 1, 0);
//Network(0.55,0.6,
BackPropNet = new BackPropagationNetwork(0.55, 0.6, new int[3] {
2, 3, 1
});
BackPropNet.Train(patterns);
Console.Out.WriteLine("DONE!");
Console.Out.WriteLine("");
//BackPropNet.SaveToFile("test.net");
while (true)
{
try
{
Console.Out.Write("Enter 1st value: ");
d = double.Parse(Console.In.ReadLine());
BackPropNet.InputNode(0).Value = d;
Console.Out.Write("Enter 2nd value: ");
d = double.Parse(Console.In.ReadLine());
BackPropNet.InputNode(1).Value = d;
BackPropNet.Run();
Console.Out.WriteLine("Result: " + Math.Round(BackPropNet.OutputNode(0).Value));
Console.Out.WriteLine("");
}
catch
{
return;
}
}
}
