static void Main(string[] args)
{
try
{
Console.WriteLine("\nBegin Neural Network training using Back-Propagation demo\n");
Random rnd = new Random(1); // for random weights. not used.
double[] xValues = new double[3] { 1.0, -2.0, 3.0 }; // inputs
double[] yValues; // outputs
double[] tValues = new double[2] { 0.1234, 0.8766 }; // target values
Console.WriteLine("The fixed input xValues are:");
Helpers.ShowVector(xValues, 1, 8, true);
Console.WriteLine("The fixed target tValues are:");
Helpers.ShowVector(tValues, 4, 8, true);
int numInput = 3;
int numHidden = 4;
int numOutput = 2;
int numWeights = (numInput * numHidden) + (numHidden * numOutput) + (numHidden + numOutput);
Console.WriteLine("Creating a " + numInput + "-input, " + numHidden + "-hidden, " + numOutput + "-output neural network");
Console.WriteLine("Using hard-coded tanh function for hidden layer activation");
Console.WriteLine("Using hard-coded log-sigmoid function for output layer activation");
BackPropNeuralNet bnn = new BackPropNeuralNet(numInput, numHidden, numOutput);
//Console.WriteLine("\nGenerating random initial weights and bias values");
//double[] initWeights = new double[numWeights];
//for (int i = 0; i < initWeights.Length; ++i)
// initWeights[i] = (0.1 - 0.01) * rnd.NextDouble() + 0.01;
Console.WriteLine("\nCreating arbitrary initial weights and bias values");
double[] initWeights = new double[26] {
0.001, 0.002, 0.003, 0.004,
0.005, 0.006, 0.007, 0.008,
0.009, 0.010, 0.011, 0.012,
0.013, 0.014, 0.015, 0.016,
0.017, 0.018,
0.019, 0.020,
0.021, 0.022,
0.023, 0.024,
0.025, 0.026 };
Console.WriteLine("\nInitial weights and biases are:");
Helpers.ShowVector(initWeights, 3, 8, true);
Console.WriteLine("Loading neural network initial weights and biases into neural network");
bnn.SetWeights(initWeights);
double learnRate = 0.5; // learning rate - controls the maginitude of the increase in the change in weights.
double momentum = 0.1; // momentum - to discourage oscillation.
Console.WriteLine("Setting learning rate = " + learnRate.ToString("F2") + " and momentum = " + momentum.ToString("F2"));
int maxEpochs = 10000;
double errorThresh = 0.00001;
Console.WriteLine("\nSetting max epochs = " + maxEpochs + " and error threshold = " + errorThresh.ToString("F6"));
int epoch = 0;
double error = double.MaxValue;
Console.WriteLine("\nBeginning training using back-propagation\n");
while (epoch < maxEpochs) // train
{
if (epoch % 20 == 0) Console.WriteLine("epoch = " + epoch);
yValues = bnn.ComputeOutputs(xValues);
error = Helpers.Error(tValues, yValues);
if (error < errorThresh)
{
Console.WriteLine("Found weights and bias values that meet the error criterion at epoch " + epoch);
break;
}
bnn.UpdateWeights(tValues, learnRate, learnRate);
++epoch;
} // train loop
double[] finalWeights = bnn.GetWeights();
Console.WriteLine("");
Console.WriteLine("Final neural network weights and bias values are:");
Helpers.ShowVector(finalWeights, 5, 8, true);
yValues = bnn.ComputeOutputs(xValues);
Console.WriteLine("\nThe yValues using final weights are:");
Helpers.ShowVector(yValues, 8, 8, true);
double finalError = Helpers.Error(tValues, yValues);
Console.WriteLine("\nThe final error is " + finalError.ToString("F8"));
Console.WriteLine("\nEnd Neural Network Back-Propagation demo\n");
Console.ReadLine();
}
catch (Exception ex)
{
Console.WriteLine("Fatal: " + ex.Message);
Console.ReadLine();
}
}
static void Main(string[] args)
{
try
{
Console.WriteLine("\nBegin Neural Network training using Back-Propagation demo\n");
Random rnd = new Random(1); // for random weights. not used.
double[] xValues = new double[3] {
1.0, -2.0, 3.0
}; // inputs
double[] yValues; // outputs
double[] tValues = new double[2] {
0.1234, 0.8766
}; // target values
Console.WriteLine("The fixed input xValues are:");
Helpers.ShowVector(xValues, 1, 8, true);
Console.WriteLine("The fixed target tValues are:");
Helpers.ShowVector(tValues, 4, 8, true);
int numInput = 3;
int numHidden = 4;
int numOutput = 2;
int numWeights = (numInput * numHidden) + (numHidden * numOutput) + (numHidden + numOutput);
Console.WriteLine("Creating a " + numInput + "-input, " + numHidden + "-hidden, " + numOutput + "-output neural network");
Console.WriteLine("Using hard-coded tanh function for hidden layer activation");
Console.WriteLine("Using hard-coded log-sigmoid function for output layer activation");
BackPropNeuralNet bnn = new BackPropNeuralNet(numInput, numHidden, numOutput);
//Console.WriteLine("\nGenerating random initial weights and bias values");
//double[] initWeights = new double[numWeights];
//for (int i = 0; i < initWeights.Length; ++i)
// initWeights[i] = (0.1 - 0.01) * rnd.NextDouble() + 0.01;
Console.WriteLine("\nCreating arbitrary initial weights and bias values");
double[] initWeights = new double[26] {
0.001, 0.002, 0.003, 0.004,
0.005, 0.006, 0.007, 0.008,
0.009, 0.010, 0.011, 0.012,
0.013, 0.014, 0.015, 0.016,
0.017, 0.018,
0.019, 0.020,
0.021, 0.022,
0.023, 0.024,
0.025, 0.026
};
Console.WriteLine("\nInitial weights and biases are:");
Helpers.ShowVector(initWeights, 3, 8, true);
Console.WriteLine("Loading neural network initial weights and biases into neural network");
bnn.SetWeights(initWeights);
double learnRate = 0.5; // learning rate - controls the maginitude of the increase in the change in weights.
double momentum = 0.1; // momentum - to discourage oscillation.
Console.WriteLine("Setting learning rate = " + learnRate.ToString("F2") + " and momentum = " + momentum.ToString("F2"));
int maxEpochs = 10000;
double errorThresh = 0.00001;
Console.WriteLine("\nSetting max epochs = " + maxEpochs + " and error threshold = " + errorThresh.ToString("F6"));
int epoch = 0;
double error = double.MaxValue;
Console.WriteLine("\nBeginning training using back-propagation\n");
while (epoch < maxEpochs) // train
{
if (epoch % 20 == 0)
{
Console.WriteLine("epoch = " + epoch);
}
yValues = bnn.ComputeOutputs(xValues);
error = Helpers.Error(tValues, yValues);
if (error < errorThresh)
{
Console.WriteLine("Found weights and bias values that meet the error criterion at epoch " + epoch);
break;
}
bnn.UpdateWeights(tValues, learnRate, learnRate);
++epoch;
} // train loop
double[] finalWeights = bnn.GetWeights();
Console.WriteLine("");
Console.WriteLine("Final neural network weights and bias values are:");
Helpers.ShowVector(finalWeights, 5, 8, true);
yValues = bnn.ComputeOutputs(xValues);
Console.WriteLine("\nThe yValues using final weights are:");
Helpers.ShowVector(yValues, 8, 8, true);
double finalError = Helpers.Error(tValues, yValues);
Console.WriteLine("\nThe final error is " + finalError.ToString("F8"));
Console.WriteLine("\nEnd Neural Network Back-Propagation demo\n");
Console.ReadLine();
}
catch (Exception ex)
{
Console.WriteLine("Fatal: " + ex.Message);
Console.ReadLine();
}
} // Main