public Model(Layer[] layers, Connection[] connections, CostFunctions cost_function)
{
Layers = layers;
Connections = connections;
OLIndex = layers.Length - 1;
switch (cost_function)
{
case CostFunctions.MeanSquareSrror:
if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.Sigmoid)
{
Delta_OutputLayer = DeltaMeanSquareErrorSigmoid;
}
else if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.TanH)
{
Delta_OutputLayer = DeltaMeanSquareErrorTanH;
}
break;
case CostFunctions.MeanAbsoluteError:
if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.Sigmoid)
{
Delta_OutputLayer = DeltaMeanAbsoluteErrorSigmoid;
}
else if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.TanH)
{
Delta_OutputLayer = DeltaMeanAbsoluteErrorTanH;
}
break;
case CostFunctions.CrossEntropy:
if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.Sigmoid)
{
Delta_OutputLayer = DeltaCorssEntorpySigmoid;
}
else if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.TanH)
{
Delta_OutputLayer = DeltaCorssEntorpyTanH;
}
else if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.Softmax)
{
Delta_OutputLayer = DeltaCorssEntorpySoftmax;
}
break;
default:
throw new ArgumentException("Cost function can't be set");
}
}
public NeuralNetwork(Layer[] layers, LConnection[] layers_connections, CostFunctions cost_function, double learing_rate)
{
Layers = layers;
LConnections = layers_connections;
OLIndex = layers.Length - 1;
foreach (var item in LConnections)
{
item.LearningRate = learing_rate;
}
switch (cost_function)
{
case CostFunctions.MeanSquareSrror:
if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.Sigmoid)
{
Delta_OutputLayer = DeltaMeanSquareErrorSigmoid;
}
else if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.TanH)
{
Delta_OutputLayer = DeltaMeanSquareErrorTanH;
}
break;
case CostFunctions.MeanAbsoluteError:
if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.Sigmoid)
{
Delta_OutputLayer = DeltaMeanAbsoluteErrorSigmoid;
}
else if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.TanH)
{
Delta_OutputLayer = DeltaMeanAbsoluteErrorTanH;
}
break;
case CostFunctions.CrossEntropy:
if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.Sigmoid)
{
Delta_OutputLayer = DeltaCorssEntorpySigmoid;
}
else if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.TanH)
{
Delta_OutputLayer = DeltaCorssEntorpyTanH;
}
else if (Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.Softmax)
{
Delta_OutputLayer = DeltaCorssEntorpySoftmax;
}
break;
default:
throw new ArgumentException("Cost function can't be set");
}
}
private int OLIndex; //Output layer Index of output model
public Model(NeuralNetwork[] neural_networks, NNConnection[] neural_networks_Connections, CostFunctions cost_function, double learing_rate = 0.1)
{
NeuralNetworks = neural_networks;
NNConnections = neural_networks_Connections;
ONNIndex = NeuralNetworks.Length - 1;
OLIndex = NeuralNetworks[ONNIndex].OLIndex;
switch (cost_function)
{
case CostFunctions.MeanSquareSrror:
if (NeuralNetworks[ONNIndex].Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.Sigmoid)
{
Delta_OutputLayer = DeltaMeanSquareErrorSigmoid;
}
else if (NeuralNetworks[ONNIndex].Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.TanH)
{
Delta_OutputLayer = DeltaMeanSquareErrorTanH;
}
break;
case CostFunctions.MeanAbsoluteError:
if (NeuralNetworks[ONNIndex].Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.Sigmoid)
{
Delta_OutputLayer = DeltaMeanAbsoluteErrorSigmoid;
}
else if (NeuralNetworks[ONNIndex].Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.TanH)
{
Delta_OutputLayer = DeltaMeanAbsoluteErrorTanH;
}
break;
case CostFunctions.CrossEntropy:
if (NeuralNetworks[ONNIndex].Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.Sigmoid)
{
Delta_OutputLayer = DeltaCorssEntorpySigmoid;
}
else if (NeuralNetworks[ONNIndex].Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.TanH)
{
Delta_OutputLayer = DeltaCorssEntorpyTanH;
}
else if (NeuralNetworks[ONNIndex].Layers[OLIndex].GetActivatonFunction() == Layer.ActivationFunction.Softmax)
{
Delta_OutputLayer = DeltaCorssEntorpySoftmax;
}
break;
default:
throw new ArgumentException("Cost function can't be set");
}
foreach (var item in NNConnections)
{
item.Model_Connection_LearningRate = learing_rate;
}
}