protected override ICollection<ConnectableLayer> CreateLayers()
{
int hiddenLayersCount = HiddenLayers != null ? HiddenLayers.Count : 0;
var layers = new List<ConnectableLayer>(1 + hiddenLayersCount + 1);
var inputLayer = new InputLayer(InputSize).Connectable();
layers.Add(inputLayer);
var prevLayer = inputLayer;
if (hiddenLayersCount != 0)
{
foreach (var hl in HiddenLayers)
{
var hiddenLayer = hl.Connectable();
prevLayer.LowerLayers.Add(hiddenLayer);
layers.Add(hiddenLayer);
prevLayer = hiddenLayer;
}
}
var outputLayer = OutputLayer.Connectable();
prevLayer.LowerLayers.Add(outputLayer);
layers.Add(outputLayer);
return layers;
}
private static void TestNet()
{
var initRule = new NoisedWeightInitializationRule();
var gdRule = new GradientDescentRule();
var input = new InputLayer(6).Connectable();
var mid1 = new ActivationLayer(9, new SigmoidActivationFunction(), initRule, gdRule).Connectable();
var mid2 = new ActivationLayer(11, new SigmoidActivationFunction(), initRule, gdRule).Connectable();
var mid3 = new ActivationLayer(5, new SigmoidActivationFunction(), initRule, gdRule).Connectable();
var output = new ActivationLayer(2, new LinearActivationFunction(), initRule, gdRule).Connectable();
input.LowerLayers.Add(mid1);
input.LowerLayers.Add(mid2);
mid1.LowerLayers.Add(output);
mid3.LowerLayers.Add(output);
mid2.LowerLayers.Add(mid3);
var layers = new[] { input, mid1, mid2, mid3, output };
var groups = new GroupedLayers(layers);
Console.WriteLine(string.Join(" - ", from g in groups.LayerGroups
select string.Join(",", g.Select(l => l.Size))));
var cpuNN = new CPUNeuralNetwork(layers);
cpuNN.Iteration();
}
