/// <summary>
/// Tests the network over a given test dataset. Returns the error ( sum(|a - y(x)|^2)/n ). The out param will count the data that was correctly categorized using a given function.
/// </summary>
private static double TestNetwork(DeepNeuralNetwork nn, int testingDataSetSize, GetNextDataDelegate getNextData, out int correctCount, CheckIfCorrectDelegate checkCorrect)
{
correctCount = 0;
Vector <double> error = new DenseVector(nn.OutputLayer.GetNeuronCount());
for (int i = 0; i < testingDataSetSize; ++i)
{
LabeledData labeledData = getNextData(i);
Vector <double> result = nn.ProcessInput(labeledData.InputValues);
if (checkCorrect(result.AsArray(), labeledData.OutputValues.AsArray()))
{
++correctCount;
}
Vector <double> diff = labeledData.OutputValues - result;
error += diff.PointwiseMultiply(diff);
}
error = error.Divide(testingDataSetSize);
return(error.Average());
}
private static (Matrix <double>[] nablaW, Vector <double>[] nablaB) BackProp(DeepNeuralNetwork nn, LabeledData trainingData)
{
Matrix <double>[] nablaW;
Vector <double>[] nablaB;
(nablaW, nablaB) = GenParamsZero(nn);
//activation before applying sigm function
Vector <double> z;
List <Vector <double> > zs = new List <Vector <double> >();
//activation vector
Vector <double> a = trainingData.InputValues;
List <Vector <double> > activations = new List <Vector <double> >();
activations.Add(a);
//feedforward
foreach (ComputedLayer layer in nn.ComputedLayers)
{
z = layer.Weights * a + layer.Biasses;
zs.Add(z);
a = Utils.Sigmoid(z);
activations.Add(a);
}
//backward pass
Vector <double> delta = CostDerivative(activations.Last(), trainingData.OutputValues).PointwiseMultiply(Utils.SigmoidPrime(zs.Last()));
nablaB[nn.ComputedLayers.Length - 1] = delta;
nablaW[nn.ComputedLayers.Length - 1] = delta.ToColumnMatrix() * activations[activations.Count - 2].ToRowMatrix();
for (int i = nn.ComputedLayers.Length - 2; i >= 0; --i)
{
delta = nn.ComputedLayers[i + 1].Weights.TransposeThisAndMultiply(delta).PointwiseMultiply(Utils.SigmoidPrime(zs[i]));
nablaB[i] = delta;
nablaW[i] = delta.ToColumnMatrix() * activations[i].ToRowMatrix();
//note: activations[i] is actualy the activation of the previous layer since it counts the input layer as well
}
return(nablaW, nablaB);
}
