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;
}
