private void InitializeNeuralNetwork()
{
NeuralNetwork[] networks = new NeuralNetwork[4];
int[] layers = new int[] { 784, 16, 16, 10 };
NeuralNetwork.LayerActivation[] layerType = new NeuralNetwork.LayerActivation[]
{ NeuralNetwork.LayerActivation.Relu, NeuralNetwork.LayerActivation.LeakyRelu, NeuralNetwork.LayerActivation.Sigmoid };
//neuralNetwork = new NeuralNetwork(layers, layerType);
networks[0] = new NeuralNetwork(layers, layerType);
layers = new int[] { 784, 16, 16, 10 };
layerType = new NeuralNetwork.LayerActivation[]
{ NeuralNetwork.LayerActivation.Sigmoid, NeuralNetwork.LayerActivation.Sigmoid, NeuralNetwork.LayerActivation.Sigmoid };
networks[1] = new NeuralNetwork(layers, layerType);
layers = new int[] { 784, 20, 10 };
layerType = new NeuralNetwork.LayerActivation[]
{ NeuralNetwork.LayerActivation.Sigmoid, NeuralNetwork.LayerActivation.Sigmoid, };
networks[2] = new NeuralNetwork(layers, layerType);
layers = new int[] { 784, 16, 16, 16, 10 };
layerType = new NeuralNetwork.LayerActivation[]
{ NeuralNetwork.LayerActivation.LeakyRelu, NeuralNetwork.LayerActivation.LeakyRelu, NeuralNetwork.LayerActivation.LeakyRelu, NeuralNetwork.LayerActivation.Sigmoid, };
networks[3] = new NeuralNetwork(layers, layerType);
network = democracy = new Democracy(networks);
}
public NeuralNetworkLayer(int inputSize, int outputSize, NeuralNetwork.LayerActivation layerType = NeuralNetwork.LayerActivation.Tanh, NeuralNetworkLayer previousLayer = null)
{
InputSize = inputSize;
OutputSize = outputSize;
Size = inputSize * outputSize;
Bias = new float[outputSize];
Weight = new float[Size];
if (previousLayer != null)
{
previousLayer.nextLayer = this;
}
switch (layerType)
{
case NeuralNetwork.LayerActivation.Tanh:
FlatFunction = Tanh;
FlatDeriv = TanhD;
break;
case NeuralNetwork.LayerActivation.Sigmoid:
FlatFunction = Sigmoid;
FlatDeriv = SigmoidD;
break;
case NeuralNetwork.LayerActivation.Relu:
FlatFunction = Relu;
FlatDeriv = ReluD;
break;
case NeuralNetwork.LayerActivation.LeakyRelu:
FlatFunction = LeakyRelu;
FlatDeriv = LeakyReluD;
break;
default:
FlatFunction = Relu;
FlatDeriv = ReluD;
break;
}
LayerActivation = layerType;
InitializeWeights();
InitializeBiases();
}
public Democracy(NeuralNetwork[] participants)
{
NeuralNetwork.LayerActivation type = participants[0].Layers.Last().LayerActivation;
InputSize = participants[0].InputSize;
OutputSize = participants[0].OutputSize;
foreach (NeuralNetwork neuralNetwork in participants)
{
if (neuralNetwork.InputSize != InputSize || neuralNetwork.OutputSize != OutputSize)
{
throw new Exception("Neural networks are not identical in input/output size!");
}
if (neuralNetwork.Layers.Last().LayerActivation != type)
{
throw new Exception("All participants must end with the same output activation!");
}
}
Participants = participants;
Reviewer = new NeuralNetwork(new int[] { OutputSize *participants.Length, OutputSize * 2, OutputSize },
new NeuralNetwork.LayerActivation[] { NeuralNetwork.LayerActivation.LeakyRelu, type });;
}
