public static void Incremental(FileInfo dataDir, string networkfiletosave, string trainingfile)
{
FileInfo file = FileUtil.CombinePath(dataDir, networkfiletosave);
FileInfo trainfile = FileUtil.CombinePath(dataDir, trainingfile);
if (!trainfile.Exists)
{
Console.WriteLine(@"Can't read file: " + trainfile);
return;
}
IMLDataSet training = EncogUtility.LoadEGB2Memory(trainfile);
var pattern = new FeedForwardPattern
{
InputNeurons = training.InputSize,
OutputNeurons = training.IdealSize,
ActivationFunction = new ActivationTANH()
};
var prune = new PruneIncremental(training, pattern, 100, 1, 10,
new ConsoleStatusReportable());
prune.AddHiddenLayer(5, 50);
prune.AddHiddenLayer(0, 50);
prune.Process();
Encog.Util.NetworkUtil.NetworkUtility.SaveTraining(dataDir.Directory.FullName, trainingfile, training);
EncogDirectoryPersistence.SaveObject(file, prune.BestNetwork);
}
public static BasicNetwork CreateNetwork()
{
var pattern = new FeedForwardPattern {InputNeurons = 3};
pattern.AddHiddenLayer(50);
pattern.OutputNeurons = 1;
pattern.ActivationFunction = new ActivationTANH();
var network = (BasicNetwork) pattern.Generate();
network.Reset();
return network;
}
private IMLMethod CreateFeedforwardNetwork()
{
// construct a feedforward type network
FeedForwardPattern pattern = new FeedForwardPattern();
pattern.ActivationFunction = new ActivationSigmoid();
pattern.InputNeurons = 1;
pattern.AddHiddenLayer(6);
pattern.OutputNeurons = 1;
return pattern.Generate();
}
public static BasicNetwork createNetwork()
{
var pattern = new FeedForwardPattern { InputNeurons = (Board.SIZE * Board.SIZE) };
pattern.AddHiddenLayer (NEURONS_HIDDEN_1);
pattern.OutputNeurons = 1;
pattern.ActivationFunction = new ActivationTANH ();
var network = (BasicNetwork)pattern.Generate ();
network.Reset ();
return network;
}
/// <summary>
/// Creates the feedforward network.
/// </summary>
/// <param name="inputsize">The inputsize.</param>
/// <param name="outputsize">The outputsize.</param>
/// <param name="hiddenlayers">The hiddenlayers.</param>
/// <param name="hidden2Layers">The hidden2layers.</param>
/// <returns></returns>
public static BasicNetwork CreateFeedforwardNetwork(int inputsize, int outputsize, int hiddenlayers,
int hidden2Layers)
{
// construct an Elman type network
var pattern = new FeedForwardPattern {ActivationFunction = new ActivationTANH(), InputNeurons = inputsize};
pattern.AddHiddenLayer(hiddenlayers);
pattern.AddHiddenLayer(hidden2Layers);
pattern.OutputNeurons = outputsize;
IMLMethod network = pattern.Generate();
return (BasicNetwork) network;
}
private static IMLMethod CreateFeedforwardNetwork(int inputs, int outputs, int hidden)
{
// construct a feedforward type network
var pattern = new FeedForwardPattern();
pattern.ActivationFunction = new ActivationSigmoid();
pattern.InputNeurons = inputs;
pattern.AddHiddenLayer(hidden);
pattern.OutputNeurons = outputs;
return pattern.Generate();
}
/// <summary>
/// Create a simple feedforward neural network.
/// </summary>
/// <param name="input">The number of input neurons.</param>
/// <param name="hidden1">The number of hidden layer 1 neurons.</param>
/// <param name="hidden2">The number of hidden layer 2 neurons.</param>
/// <param name="output">The number of output neurons.</param>
/// <param name="tanh">True to use hyperbolic tangent activation function, false to
/// use the sigmoid activation function.</param>
/// <returns>The neural network.</returns>
public static BasicNetwork SimpleFeedForward(int input,
int hidden1, int hidden2, int output,
bool tanh)
{
var pattern = new FeedForwardPattern {InputNeurons = input, OutputNeurons = output};
if (tanh)
{
pattern.ActivationFunction = new ActivationTANH();
}
else
{
pattern.ActivationFunction = new ActivationSigmoid();
}
if (hidden1 > 0)
{
pattern.AddHiddenLayer(hidden1);
}
if (hidden2 > 0)
{
pattern.AddHiddenLayer(hidden2);
}
var network = (BasicNetwork) pattern.Generate();
network.Reset();
return network;
}
public static BasicNetwork SimpleFeedForward(int input, int hidden1, int hidden2, int output, bool tanh)
{
FeedForwardPattern pattern;
BasicNetwork network;
FeedForwardPattern pattern2 = new FeedForwardPattern();
if (((uint) output) < 0)
{
goto Label_002E;
}
pattern2.InputNeurons = input;
goto Label_00BC;
Label_000D:
pattern.AddHiddenLayer(hidden2);
goto Label_001A;
Label_0016:
if (hidden2 > 0)
{
goto Label_000D;
}
Label_001A:
network = (BasicNetwork) pattern.Generate();
network.Reset();
return network;
Label_002E:
if ((((uint) tanh) | 3) != 0)
{
goto Label_0067;
}
if ((((uint) output) - ((uint) hidden2)) < 0)
{
goto Label_000D;
}
Label_005E:
pattern.AddHiddenLayer(hidden1);
goto Label_0016;
Label_0067:
if (hidden1 > 0)
{
goto Label_005E;
}
if ((((uint) input) + ((uint) hidden1)) >= 0)
{
goto Label_0016;
}
return network;
Label_00BC:
pattern2.OutputNeurons = output;
pattern = pattern2;
if (tanh)
{
pattern.ActivationFunction = new ActivationTANH();
goto Label_0067;
}
pattern.ActivationFunction = new ActivationSigmoid();
if ((((uint) hidden2) + ((uint) output)) >= 0)
{
goto Label_002E;
}
goto Label_00BC;
}