Resilient backpropagation neural network - question about gradient
- by Raf
Hello Guys,
First I want to say that I'm really new to neural networks and I don't understand it very good ;)
I've made my first C# implementation of the backpropagation neural network. I've tested it using XOR and it looks it work.
Now I would like change my implementation to use resilient backpropagation (Rprop - http://en.wikipedia.org/wiki/Rprop).
The definition says: "Rprop takes into account only the sign of the partial derivative over all patterns (not the magnitude), and acts independently on each "weight".
Could somebody tell me what partial derivative over all patterns is? And how should I compute this partial derivative for a neuron in hidden layer.
Thanks a lot
UPDATE:
My implementation base on this Java code: www_.dia.fi.upm.es/~jamartin/downloads/bpnn.java
My backPropagate method looks like this:
public double backPropagate(double[] targets)
{
double error, change;
// calculate error terms for output
double[] output_deltas = new double[outputsNumber];
for (int k = 0; k < outputsNumber; k++)
{
error = targets[k] - activationsOutputs[k];
output_deltas[k] = Dsigmoid(activationsOutputs[k]) * error;
}
// calculate error terms for hidden
double[] hidden_deltas = new double[hiddenNumber];
for (int j = 0; j < hiddenNumber; j++)
{
error = 0.0;
for (int k = 0; k < outputsNumber; k++)
{
error = error + output_deltas[k] * weightsOutputs[j, k];
}
hidden_deltas[j] = Dsigmoid(activationsHidden[j]) * error;
}
//update output weights
for (int j = 0; j < hiddenNumber; j++)
{
for (int k = 0; k < outputsNumber; k++)
{
change = output_deltas[k] * activationsHidden[j];
weightsOutputs[j, k] = weightsOutputs[j, k] + learningRate * change + momentumFactor * lastChangeWeightsForMomentumOutpus[j, k];
lastChangeWeightsForMomentumOutpus[j, k] = change;
}
}
// update input weights
for (int i = 0; i < inputsNumber; i++)
{
for (int j = 0; j < hiddenNumber; j++)
{
change = hidden_deltas[j] * activationsInputs[i];
weightsInputs[i, j] = weightsInputs[i, j] + learningRate * change + momentumFactor * lastChangeWeightsForMomentumInputs[i, j];
lastChangeWeightsForMomentumInputs[i, j] = change;
}
}
// calculate error
error = 0.0;
for (int k = 0; k < outputsNumber; k++)
{
error = error + 0.5 * (targets[k] - activationsOutputs[k]) * (targets[k] - activationsOutputs[k]);
}
return error;
}
So can I use change = hidden_deltas[j] * activationsInputs[i] variable as a gradient (partial derivative) for checking the sing?
