private static void Main(string[] args)
{
if (NETWORK_SIZE % 4 > 0)
{
throw new Exception("Network size must be a multiple of 4!");
}
System.Threading.Thread.CurrentThread.CurrentCulture = System.Globalization.CultureInfo.GetCultureInfo("en-US");
rand = new Random();
DeepNeuralNetwork[] nn = new DeepNeuralNetwork[NETWORK_SIZE];
// Construct initial neural networks
for (int i = 0; i < nn.Length; i++)
{
nn[i] = CreateNewNN();
}
dtLastIteration = DateTime.Now;
// Perform training iterations
for (int i = 0; i < ITERATION_LIMIT; i++)
{
PerformEvolution(ref nn, i);
}
// Print training summary
Console.WriteLine(Environment.NewLine + "The top quarter neural networks have " + GetAverageDeviation(nn).ToString() + " average deviation.");
Console.WriteLine("The best neural network has " + nn[0].AverageDeviation.ToString() + " deviation over " + (nn[0].IterationsAlive / SUB_ITERATION_COUNT).ToString() + " iterations.");
// Let the user try the NN with his own input values
while (true)
{
Console.Write(Environment.NewLine + "Enter input values: ");
string strInput = Console.ReadLine();
// Empty input is an exit request
if (strInput == string.Empty)
{
break;
}
// Separate individual values on the line
double[] userInput = CSVParser.ParseLine(strInput);
// Get output values from the NN
double[] predictedOutput = nn[0].ProcessData(userInput);
Console.WriteLine("Output: " + string.Join(" ", predictedOutput.AsEnumerable().Select(x => x.ToString())));
}
}
// Keep 1/4 of the NNs as survivors
// Replace 2/4 of the NNs with modifications of the survivors
// Fill the remaining 1/4 with completely new NNs
private static void PerformEvolution(ref DeepNeuralNetwork[] networks, int iterationIdx)
{
for (int i = 0; i < SUB_ITERATION_COUNT; i++)
{
FeedInputToNetworks(networks, rand.Next(sourceData.Count));
}
// Order NNs by their average deviation
networks = networks.OrderBy(x => x.AverageDeviation).ToArray();
if ((DateTime.Now - dtLastIteration) >= new TimeSpan(0, 0, 1))
{
dtLastIteration = DateTime.Now;
// Print average deviation for this iteration
Console.WriteLine("Iteration: " + (iterationIdx + 1).ToString().PadLeft(ITERATION_LIMIT.ToString().Length) + " - Deviation: " + GetAverageDeviation(networks).ToString());
}
// Don't modify the NNs on the last iteration
if (iterationIdx == ITERATION_LIMIT - 1)
{
return;
}
// Replace 2/4 of NNs with children of survivors
for (int i = 0; i < 2; i++)
{
for (int j = 0; j < NETWORK_QUARTER; j++)
{
// NN to be replaced with one of the children
ref DeepNeuralNetwork nn = ref networks[((i + 1) * NETWORK_QUARTER) + j];
// Don't use networks with no deviation as parents
// They would just produce their exact clones and that's pointless
if (networks[j].AverageDeviation == 0.0)
{
// Generate a completely new NN with random weights instead
nn = CreateNewNN();
}
else
{
// Replace the NN with a slightly modifier version of one of the survivors
nn = networks[j].ShakeWeights();
}
}
}