protected virtual void CalculateGradient()
{
var weightedSum = 0.0;
foreach (var outputConnection in _outputConnections)
{
weightedSum += outputConnection.TargetNeuron.Gradient * outputConnection.Weight;
}
this.Gradient = weightedSum * _activationFunction.CalculateDerivative(GetWeightedInput());
}
private double Activation(double[] activations, double[,] weights, double bias, ActivationFunction activationFunction, int outputIndex)
{
var zj = 0D;
for (var inputIndex = 0; inputIndex < activations.Length; inputIndex++)
{
zj += activations[inputIndex] * weights[inputIndex, outputIndex];
}
zj += bias;
switch (activationFunction)
{
case ActivationFunction.Sigmoid:
return(sigmoidActivationFunction.CalculateDerivative(zj));
default:
return(zj);
}
}
public float CalculateDerivative(float value)
{
return(_activationFunction.CalculateDerivative(value));
}