public static Network BuildNetwork(Random random,
ICostFunction costFunction, IRegularizationFunction regularizationFunction,
WeightIntializerType weightIntializerType,
DropoutLayerOptions dropoutLayerOptions,
int inputNeuronCount, int outputNeuronCount, params int[] hiddenLayerCounts)
{
Network network = new Network(costFunction, regularizationFunction,
dropoutLayerOptions, random);
network.InputLayer = InputLayer.BuildInputLayer(null, inputNeuronCount, random);
Layer previousLayer = network.InputLayer;
int dropoutLayerIndex = 1;
bool isDropoutLayer = false;
IWeightBuilder weightBuilder = null;
for (int c = 0; c < hiddenLayerCounts.Length; c++)
{
isDropoutLayer = dropoutLayerOptions.DropoutLayerIndices.Contains(dropoutLayerIndex);
int currentLayerCount = hiddenLayerCounts[c];
switch (weightIntializerType)
{
case WeightIntializerType.RandomGaussianWithNeuronCount:
weightBuilder = new RandomGaussianWithNeuronCount(previousLayer.Neurons.Count, 0, random);
break;
case WeightIntializerType.RandomNormal:
weightBuilder = new RandomGaussian(0, 1, random);
break;
}
HiddenLayer hiddenLayer = HiddenLayer.BuildHiddenLayer(weightBuilder, previousLayer,
currentLayerCount, isDropoutLayer ? dropoutLayerOptions.ProbabilityOfDropout : 0, random);
network.HiddenLayers.Add(hiddenLayer);
previousLayer = hiddenLayer;
dropoutLayerIndex++;
}
isDropoutLayer = dropoutLayerOptions.DropoutLayerIndices.Contains(dropoutLayerIndex);
switch (weightIntializerType)
{
case WeightIntializerType.RandomGaussianWithNeuronCount:
weightBuilder = new RandomGaussianWithNeuronCount(previousLayer.Neurons.Count, 0, random);
break;
case WeightIntializerType.RandomNormal:
weightBuilder = new RandomGaussian(0, 1, random);
break;
}
network.OutputLayer = OutputLayer.BuildOutputLayer(weightBuilder, (HiddenLayer)previousLayer,
outputNeuronCount, isDropoutLayer ? dropoutLayerOptions.ProbabilityOfDropout : 0, random);
return(network);
}
public static InputLayer BuildInputLayer(IWeightBuilder weightBuilder, int numberOfNeurons, Random random)
{
InputLayer toReturn = new InputLayer(numberOfNeurons, random);
for (int c = 0; c < numberOfNeurons; c++)
{
toReturn.Neurons.Add(Neuron.BuildNeuron(weightBuilder, null));
}
return(toReturn);
}
