// Replaces three steps - (b), (c) and (d) - with one.
public void Backpropagate()
{
BackpropagationNeuron targetNeuron = TargetNeuron as BackpropagationNeuron;
gradient = targetNeuron.Gradient * targetNeuron.Derivative * SourceNeuron.Output;
error += gradient;
}
/// <summary>
/// Calculates the gradient using the backpropagation algorithm.
/// <em>(2.19)</em>
/// </summary>
public void CalculateGradient()
{
// Initialize the error.
gradient = 0.0;
// Back-propagate the weighted error from the target neuron to the source neuron via this synapse.
foreach (BackpropagationSynapse targetSynapse in TargetSynapses)
{
BackpropagationNeuron targetNeuron = targetSynapse.TargetNeuron as BackpropagationNeuron;
gradient += targetNeuron.Gradient * targetNeuron.Derivative * targetSynapse.Weight;
}
}
/// <summary>
///
/// </summary>
/// <param name="layer">The (activation) layer to be decorated as backpropagation (activation) layer.</param>
/// <param name="parnetNetwork">The parnet network.</param>
public BackpropagationLayer(IActivationLayer activationLayer, INetwork parnetNetwork)
: base(activationLayer,parnetNetwork)
{
// Ensure the activation function of the neuron is derivable.
if (!(ActivationFunction is IDerivableActivationFunction))
{
// TODO: Throw an exception informing the client that in order for the neuron to undergo training
// using the error backpropagation algorithm, its activation function has to be derivable
// (i.e. it has to implement the IDerivableActivationFunction interface)
throw new Exception();
}
// Decorate the neurons.
for (int i = 0; i < NeuronCount; i++)
{
Neurons[ i ] = new BackpropagationNeuron(Neurons[i],this);
}
}
/// <summary>
/// Calculatates the gradient of the synapse using the backpropagation algorithm.
/// <em>(2.17)</em>
/// </summary>
public void CalculateGradient()
{
BackpropagationNeuron targetNeuron = TargetNeuron as BackpropagationNeuron;
gradient = targetNeuron.Gradient * targetNeuron.Derivative * SourceNeuron.Output;
}
