public XORNetworkLayout()
{
var neuronInitializer = new RandomLayerInitializer();
var outputActivation = new SigmoidActivation();
layers = new List <Layer>
{
new Layer(2, 3, neuronInitializer, outputActivation),
new Layer(3, 1, neuronInitializer, outputActivation),
};
ConnectLayers();
}
public CustomNetworkLayout(LayerConfiguration firstHiddenConfig, params LayerConfiguration[] nextHiddenConfigs)
{
var initializer = new RandomLayerInitializer();
layers = new List <Layer>
{
new Layer(784, firstHiddenConfig.NeuronCount, initializer, firstHiddenConfig.OutputActivation)
};
int previousLayerNeuronCount = firstHiddenConfig.NeuronCount;
foreach (LayerConfiguration layerConfig in nextHiddenConfigs)
{
layers.Add(new Layer(previousLayerNeuronCount, layerConfig.NeuronCount, initializer, layerConfig.OutputActivation));
previousLayerNeuronCount = layerConfig.NeuronCount;
}
layers.Add(new Layer(previousLayerNeuronCount, 10, initializer, new SigmoidActivation()));
ConnectLayers();
}
/// <summary>
/// Предоставляет класс для конфигурации структуры нейронной сети.
/// </summary>
/// <param name="layerConfigurations">Коллекция конфигураций слоя нейронной сети</param>
public NeuraNetLayout(LayerConfiguration[] layerConfigurations)
{
if (layerConfigurations == null)
{
throw new ArgumentNullException("layerConfigurations");
}
if (layerConfigurations.Length < 2)
{
throw new ArgumentException("Минимальное количество слоев в нейронной сети должно быть равно двум", "layerConfigurations");
}
var initializer = new RandomLayerInitializer();
layers = Enumerable.Range(1, layerConfigurations.Length - 1).Select(index =>
{
var activation = GetActivationByName(layerConfigurations[index].OutputActivationName);
return(new Layer(layerConfigurations[index - 1].NeuronCount, layerConfigurations[index].NeuronCount, initializer, activation));
}).ToList();
ConnectLayers();
}
