public void Process()
{
Console.WriteLine("Please wait, reading MNIST training data.");
var dir = AppDomain.CurrentDomain.BaseDirectory;
var trainingReader = LearnDigitsBackprop.LoadMNIST(dir, true, MNIST_DEPTH);
var validationReader = LearnDigitsBackprop.LoadMNIST(dir, false, MNIST_DEPTH);
Console.WriteLine("Training set size: " + trainingReader.NumImages);
Console.WriteLine("Validation set size: " + validationReader.NumImages);
var inputCount = trainingReader.Data[0].Input.Length;
var outputCount = trainingReader.Data[0].Ideal.Length;
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, inputCount));
network.AddLayer(new BasicLayer(new ActivationReLU(), true, 100));
network.AddLayer(new DropoutLayer(new ActivationReLU(), true, 50, 0.5));
network.AddLayer(new BasicLayer(new ActivationReLU(), true, 25));
network.AddLayer(new BasicLayer(new ActivationSoftMax(), false, outputCount));
network.FinalizeStructure();
network.Reset();
// train the neural network
Console.WriteLine("Training neural network.");
var train = new BackPropagation(network, trainingReader.Data, 1e-4, 0.9);
train.L1 = 0;
train.L2 = 1e-11;
PerformIterationsClassifyEarlyStop(train, network, validationReader.Data, 5);
}
/// <summary>
/// The entry point for this example. If you would like to make this example
/// stand alone, then add to its own project and rename to Main.
/// </summary>
/// <param name="args">Not used.</param>
public static void ExampleMain(string[] args)
{
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, 2));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 5));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
network.FinalizeStructure();
network.Reset();
var trainingData = BasicData.ConvertArrays(XOR_INPUT, XOR_IDEAL);
// train the neural network
var train = new BackPropagation(network, trainingData, 0.7, 0.9);
var epoch = 1;
do
{
train.Iteration();
Console.WriteLine("Epoch #" + epoch + " Error:" + train.LastError);
epoch++;
} while (train.LastError > 0.01);
// test the neural network
Console.WriteLine("Neural Network Results:");
for (var i = 0; i < XOR_INPUT.Length; i++)
{
var output = network.ComputeRegression(XOR_INPUT[i]);
Console.WriteLine(string.Join(",", XOR_INPUT[i])
+ ", actual=" + string.Join(",", output)
+ ",ideal=" + string.Join(",", XOR_IDEAL[i]));
}
}
public BasicNetwork GetNetwork()
{
var network = new BasicNetwork();
foreach (NeuralLayerInfo layer in Layers)
{
var objLayer = layer.GetLayer();
network.AddLayer(objLayer);
}
network.FinalizeStructure();
network.Reset();
return(network);
}
public void Process()
{
IList <BasicData> trainingData = LoadSunspots();
BasicNetwork network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, this.INPUT_WINDOW));
network.AddLayer(new BasicLayer(new ActivationReLU(), true, 50));
network.AddLayer(new BasicLayer(new ActivationLinear(), false, 1));
network.FinalizeStructure();
network.Reset();
BackPropagation train = new BackPropagation(network, trainingData, 1e-9, 0.5);
train.BatchSize = 0;
PerformIterations(train, 100000, 650, true);
Query(network, trainingData);
}
/// <summary>
/// Run the example.
/// </summary>
public void Process()
{
// read the iris data from the resources
var assembly = Assembly.GetExecutingAssembly();
var res = assembly.GetManifestResourceStream("AIFH_Vol3.Resources.iris.csv");
// did we fail to read the resouce
if (res == null)
{
Console.WriteLine("Can't read iris data from embedded resources.");
return;
}
// load the data
var istream = new StreamReader(res);
var ds = DataSet.Load(istream);
istream.Close();
// The following ranges are setup for the Iris data set. If you wish to normalize other files you will
// need to modify the below function calls other files.
ds.NormalizeRange(0, 0, 1);
ds.NormalizeRange(1, 0, 1);
ds.NormalizeRange(2, 0, 1);
ds.NormalizeRange(3, 0, 1);
var species = ds.EncodeOneOfN(4);
var trainingData = ds.ExtractSupervised(0, 4, 4, 3);
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, 4));
network.AddLayer(new BasicLayer(new ActivationReLU(), true, 20));
network.AddLayer(new BasicLayer(new ActivationSoftMax(), false, 3));
network.FinalizeStructure();
network.Reset();
var train = new BackPropagation(network, trainingData, 0.001, 0.9);
PerformIterations(train, 100000, 0.02, true);
QueryOneOfN(network, trainingData, species);
}
/// <summary>
/// Run the example.
/// </summary>
public void Process()
{
// read the iris data from the resources
var assembly = Assembly.GetExecutingAssembly();
var res = assembly.GetManifestResourceStream("AIFH_Vol3.Resources.auto-mpg.data.csv");
// did we fail to read the resouce
if (res == null)
{
Console.WriteLine("Can't read auto MPG data from embedded resources.");
return;
}
// load the data
var istream = new StreamReader(res);
var ds = DataSet.Load(istream);
istream.Close();
// The following ranges are setup for the Auto MPG data set. If you wish to normalize other files you will
// need to modify the below function calls other files.
// First remove some columns that we will not use:
ds.DeleteColumn(8); // Car name
ds.DeleteColumn(7); // Car origin
ds.DeleteColumn(6); // Year
ds.DeleteUnknowns();
ds.NormalizeZScore(1);
ds.NormalizeZScore(2);
ds.NormalizeZScore(3);
ds.NormalizeZScore(4);
ds.NormalizeZScore(5);
var trainingData = ds.ExtractSupervised(1, 4, 0, 1);
var splitList = DataUtil.Split(trainingData, 0.75);
trainingData = splitList[0];
var validationData = splitList[1];
Console.WriteLine("Size of dataset: " + ds.Count);
Console.WriteLine("Size of training set: " + trainingData.Count);
Console.WriteLine("Size of validation set: " + validationData.Count);
var inputCount = trainingData[0].Input.Length;
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, inputCount));
network.AddLayer(new BasicLayer(new ActivationReLU(), true, 50));
network.AddLayer(new BasicLayer(new ActivationReLU(), true, 25));
network.AddLayer(new BasicLayer(new ActivationReLU(), true, 5));
network.AddLayer(new BasicLayer(new ActivationLinear(), false, 1));
network.FinalizeStructure();
network.Reset();
var train = new BackPropagation(network, trainingData, 0.000001, 0.9);
PerformIterationsEarlyStop(train, network, validationData, 20, new ErrorCalculationMSE());
Query(network, validationData);
}