/// <summary>
/// Constructor to be used for non testing code.
/// </summary>
/// <param name="config"></param>
public FeedForwardNeuralNetwork(NeuralNetworkConfig config)
{
int numberOfInputNeurons = config
.GetParameterAsInteger(NUMBER_OF_INPUTS);
int numberOfHiddenNeurons = config
.GetParameterAsInteger(NUMBER_OF_HIDDEN_NEURONS);
int numberOfOutputNeurons = config
.GetParameterAsInteger(NUMBER_OF_OUTPUTS);
double lowerLimitForWeights = config
.GetParameterAsDouble(LOWER_LIMIT_WEIGHTS);
double upperLimitForWeights = config
.GetParameterAsDouble(UPPER_LIMIT_WEIGHTS);
hiddenLayer = new Layer(numberOfHiddenNeurons, numberOfInputNeurons,
lowerLimitForWeights, upperLimitForWeights,
new LogSigActivationFunction());
outputLayer = new Layer(numberOfOutputNeurons, numberOfHiddenNeurons,
lowerLimitForWeights, upperLimitForWeights,
new PureLinearActivationFunction());
}
/// <summary>
/// Constructor to be used for non testing code.
/// </summary>
/// <param name="config"></param>
public FeedForwardDeepNeuralNetwork(NeuralNetworkConfig config, IActivationFunction activationFunction)
{
if (config.GetParameterAsInteger(NUMBER_OF_HIDDEN_LAYERS) < 1)
{
throw new ArgumentOutOfRangeException("NUMBER_OF_HIDDEN_LAYERS must be >= 1");
}
if (null == activationFunction)
{
activationFunction = new LogSigActivationFunction();
}
this.config = config;
int numberOfInputNeurons = config.GetParameterAsInteger(NUMBER_OF_INPUTS);
int numberOfOutputNeurons = config.GetParameterAsInteger(NUMBER_OF_OUTPUTS);
double lowerLimitForWeights = config.GetParameterAsDouble(LOWER_LIMIT_WEIGHTS);
double upperLimitForWeights = config.GetParameterAsDouble(UPPER_LIMIT_WEIGHTS);
hiddenLayers = new Layer[config.GetParameterAsInteger(NUMBER_OF_HIDDEN_LAYERS)];
//TODO: Create Hidden layers here
}
