public void Configure()
{
int numInput = 7;
int numHidden = 6;
int numOutput = 1;
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");
m_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] = (rnd.NextDouble() - 0.5d) * 0.1d;
}
//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");
m_Bnn.SetWeights(initWeights);
double learnRate = 0.03; // learning rate - controls the maginitude of the increase in the change in weights.
double momentum = learnRate / 10d; // momentum - to discourage oscillation.
Console.WriteLine("Setting learning rate = " + learnRate.ToString("F2") + " and momentum = " + momentum.ToString("F2"));
int maxEpochs = 8000000;
double errorThresh = 0.01;
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
{
double result = 0d; // GetGameResult(gameNum);
double[] predictedResult = m_Bnn.ComputeOutputs(new double[2]);
m_Bnn.UpdateWeights(new[] { result }, learnRate, momentum);
++epoch;
if (epoch % 20000 == 0)
{
error = 0d; // GetTotalError(m_Bnn, true);
if (error < errorThresh)
{
Console.WriteLine("Found weights and bias values that meet the error criterion at epoch " + epoch);
break;
}
Console.WriteLine("epoch = " + epoch);
Console.WriteLine("error = " + error);
}
} // train loop
double[] finalWeights = m_Bnn.GetWeights();
Console.WriteLine("");
//Console.WriteLine("Final neural network weights and bias values are:");
//Helpers.ShowVector(finalWeights, 5, 8, true);
}
public void Configure()
{
int numInput = 7;
int numHidden = 6;
int numOutput = 1;
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");
m_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] = (rnd.NextDouble() - 0.5d) * 0.1d;
//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");
m_Bnn.SetWeights(initWeights);
double learnRate = 0.03; // learning rate - controls the maginitude of the increase in the change in weights.
double momentum = learnRate / 10d; // momentum - to discourage oscillation.
Console.WriteLine("Setting learning rate = " + learnRate.ToString("F2") + " and momentum = " + momentum.ToString("F2"));
int maxEpochs = 8000000;
double errorThresh = 0.01;
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
{
double result = 0d; // GetGameResult(gameNum);
double[] predictedResult = m_Bnn.ComputeOutputs(new double[2]);
m_Bnn.UpdateWeights(new[] { result }, learnRate, momentum);
++epoch;
if (epoch % 20000 == 0)
{
error = 0d; // GetTotalError(m_Bnn, true);
if (error < errorThresh)
{
Console.WriteLine("Found weights and bias values that meet the error criterion at epoch " + epoch);
break;
}
Console.WriteLine("epoch = " + epoch);
Console.WriteLine("error = " + error);
}
} // train loop
double[] finalWeights = m_Bnn.GetWeights();
Console.WriteLine("");
//Console.WriteLine("Final neural network weights and bias values are:");
//Helpers.ShowVector(finalWeights, 5, 8, true);
}
