// Currently, this program creates a 3-layer network with 10 hidden nodes.
// The network accepts binary training data to learn [A XOR B] and ignore input C.
// With this structure and about 2000+ training epochs, the network slowly learns
// to perform binary logic, and scores full accuracy on the training data.
// In principle, there's nothing stopping this network from being used for all
// sorts of fun new data analysis now.
static void Main(string[] args)
{
// Create a new 3-10-1 NN
var network = new Structure.NeuralNetwork(new[] { 3, 10, 1 });
// Initialise training data and labels.
// Note: BinaryLow/High are premade values corresponding to 0.1d and 0.9d respectively,
// to avoid nasty sigmoid gradient problems.
var trainingData = new[]
{
new [] { BinaryLow, BinaryLow, BinaryLow },
new [] { BinaryLow, BinaryHigh, BinaryLow },
new [] { BinaryHigh, BinaryLow, BinaryLow },
new [] { BinaryHigh, BinaryHigh, BinaryLow },
new [] { BinaryLow, BinaryLow, BinaryHigh },
new [] { BinaryLow, BinaryHigh, BinaryHigh },
new [] { BinaryHigh, BinaryLow, BinaryHigh },
new [] { BinaryHigh, BinaryHigh, BinaryHigh }
};
var labels = new[]
{
new [] { BinaryLow },
new [] { BinaryHigh },
new [] { BinaryHigh },
new [] { BinaryLow },
new [] { BinaryLow },
new [] { BinaryHigh },
new [] { BinaryHigh },
new [] { BinaryLow }
};
// TrainConfig is a class used to store important training hyperparameters.
var myTrainConfig = new TrainConfig();
myTrainConfig.Epochs = 5000;
// Train the network using the config, network and data above.
Tasks.Train(network, trainingData, labels, myTrainConfig);
// Without further training, feed each data sample through the network and output to console.
foreach (var data in trainingData)
{
network.InputLayer.FeedForward(data);
Console.Write(Helpers.DataToString(data));
Console.Write(": ");
var output = Helpers.DataToString(network.OutputLayer.Read());
Console.WriteLine(output);
}
// Use the prebuilt regression accuracy task to get an avg error.
Console.WriteLine("Avg error: " + Tasks.TestRegressionAccuracy(network, trainingData, labels));
Console.ReadKey();
}
public static void Train(Structure.NeuralNetwork network, IEnumerable <IEnumerable <double> > trainingData, IEnumerable <IEnumerable <double> > trainingLabels, TrainConfig trainConfig)
{
var epochs = trainConfig.Epochs;
for (int epoch = 0; epoch < epochs; epoch++)
{
if (epoch % Math.Ceiling(epochs / 10d) == 0)
{
Console.WriteLine($"Trained for {epoch} epochs");
}
foreach (var dataLabelPair in trainingData.Zip(trainingLabels, (data, labels) => (data, labels)))
{
network.InputLayer.FeedForward(dataLabelPair.data);
network.OutputLayer.Backpropagate(dataLabelPair.labels);
}
}
}
public static double TestRegressionAccuracy(Structure.NeuralNetwork network, IEnumerable <IEnumerable <double> > testData, IEnumerable <IEnumerable <double> > testLabels)
{
var sumError = 0d;
foreach (var dataLabelsPair in testData.Zip(testLabels, (data, labels) => (data, labels)))
{
network.InputLayer.FeedForward(dataLabelsPair.data);
var readErrors = network.OutputLayer.Read().Zip(dataLabelsPair.labels, (read, label) => (read - label));
foreach (var error in readErrors)
{
sumError += Math.Abs(error);
}
}
var avgError = sumError / (testData.Count() * testLabels.First().Count());
return(avgError);
}
/// <summary>
/// Method stub for testing classification accuracy
/// </summary>
/// <param name="network"></param>
/// <param name="testData"></param>
/// <param name="testLabels"></param>
/// <returns></returns>
public static double TestClassificationAccuracy(Structure.NeuralNetwork network, IEnumerable <IEnumerable <double> > testData, IEnumerable <IEnumerable <double> > testLabels)
{
throw new NotImplementedException();
}