/// <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);
}
private static IMLMethod CreateFeedforwardNetwork(int input)
{
// construct a feedforward type network
var pattern = new FeedForwardPattern();
pattern.ActivationFunction = new ActivationSigmoid();
pattern.InputNeurons = input;
pattern.AddHiddenLayer(5);
pattern.OutputNeurons = 1;
return(pattern.Generate());
}
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);
}
/// <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
FeedForwardPattern pattern = new FeedForwardPattern();
pattern.ActivationFunction = new ActivationTANH();
pattern.InputNeurons = inputsize;
pattern.AddHiddenLayer(hiddenlayers);
pattern.AddHiddenLayer(hidden2layers);
pattern.OutputNeurons = outputsize;
var network = pattern.Generate();
return((BasicNetwork)network);
}
private BasicNetwork CreateNetwork()
{
var pattern = new FeedForwardPattern {
InputNeurons = 1
};
for (int i = 0; i < simSettings.HiddenLayers; i++)
{
pattern.AddHiddenLayer(simSettings.HiddenLayerNeurons);
}
pattern.OutputNeurons = 1;
pattern.ActivationFunction = new ActivationTANH();
var network = (BasicNetwork)pattern.Generate();
network.Reset();
return(network);
}
public static BasicNetwork CreateNetwork(int start, int end, params int[] layers)
{
var pattern = new FeedForwardPattern {
InputNeurons = start
};
foreach (int i in layers)
{
pattern.AddHiddenLayer(i);
}
pattern.OutputNeurons = end;
pattern.ActivationFunction = new ActivationTANH();
var network = (BasicNetwork)pattern.Generate();
network.Reset();
return(network);
}
/// <see cref="INetwork.BuildFeedforwardNetwork"/>
public INetwork BuildFeedforwardNetwork()
{
if (TrainedNetworkFile.Exists)
{
return(this);
}
var pattern = new FeedForwardPattern {
ActivationFunction = new ActivationSigmoid(),
InputNeurons = InputCount,
OutputNeurons = OutputCount
};
var network = (BasicNetwork)pattern.Generate();
EncogDirectoryPersistence.SaveObject(TrainedNetworkFile, network);
TrainedNetworkFile = new FileInfo(TrainedNetworkFile.FullName);
return(this);
}
public EncogMaze(MazeInfo maze, int hiddenLayers = 1, int?hiddenLayerNeurons = null)
{
this.maze = maze;
filePath = Path.Combine(Environment.CurrentDirectory, maze.Name + "." + ENCOG_FILE_EXTENSION);
if (!LoadNetwork())
{
// create new network
var pattern = new FeedForwardPattern()
{
InputNeurons = INPUT_RNEURONS, OutputNeurons = OUTPUT_RNEURONS
};
for (int i = 0; i < hiddenLayers; i++)
{
pattern.AddHiddenLayer(hiddenLayerNeurons == null ? maze.Width * maze.Height : hiddenLayerNeurons.Value);
}
pattern.ActivationFunction = new ActivationTANH();
network = (BasicNetwork)pattern.Generate();
network.Reset();
}
}