public void BackPropagation(TrainTuple train)
{
var nablaB = Biases.Select(it => Vector <double> .Build.Dense(it.Count, 0)).ToList();
var nablaW = Weights.Select(it =>
Matrix <double> .Build.Dense(it.RowCount, it.ColumnCount, 0)).ToList();
var activation = Vector <double> .Build.DenseOfEnumerable(train.Input.Select(it => (double)it));
var activations = new List <Vector <double> > {
activation
};
var zs = new List <Vector <double> >();
var weightsWithBiases = Biases.Zip(Weights, (vector, matrix) => (vector, matrix));
foreach (var(bias, weights) in weightsWithBiases)
{
var z = weights.TransposeThisAndMultiply(activation) + bias;
zs.Add(z);
activations.Add(z.Map(Sigmoid));
}
var expected = Vector <double> .Build.DenseOfEnumerable(train.Output.Select(it => (double)it));
var delta = CostDerivative(activations.Last(), expected) *
zs.Last().Map(SigmoidPrime);
//nablaB[^0] = delta;
}
/// <summary>
/// Save wieghts and biases within a file
/// </summary>
/// <param name="file">path to the file</param>
public void Save(string file)
{
NetworkMemory memory = new NetworkMemory
{
Biases = Biases.Select(b => b.mat).ToArray(),
Weights = Weights.Select(b => b.mat).ToArray(),
Sizes = Sizes
};
JsonSerializer serializer = new JsonSerializer();
using (StreamWriter sw = new StreamWriter(file))
using (JsonWriter writer = new JsonTextWriter(sw))
{
serializer.Serialize(writer, memory);
}
}
/// <summary>
/// Update a mini batch of results representing a small portion of the neural network
/// </summary>
/// <param name="miniBatch">small batch of datas</param>
/// <param name="eta">leaning rate</param>
private void UpdateMiniBatch(List <Data> miniBatch, float eta)
{
Matrix[] nablaBiases = Biases.Select(b => new Matrix(new float[b.mat.GetLength(0), b.mat.GetLength(1)])).ToArray();
Matrix[] nablaWeights = Weights.Select(w => new Matrix(new float[w.mat.GetLength(0), w.mat.GetLength(1)])).ToArray();
float K = eta / miniBatch.Count;
for (int n = 0; n < miniBatch.Count; n++)
{
DeltaNabla deltaNabla = BackPropagation(miniBatch[n]);
for (int l = 0; l < NumberOfLayer - 1; l++)
{
nablaBiases[l] += deltaNabla.Biases[l];
nablaWeights[l] += deltaNabla.Weights[l];
}
}
for (int l = 0; l < NumberOfLayer - 1; l++)
{
Biases[l] -= K * nablaBiases[l];
Weights[l] -= K * nablaWeights[l];
}
}
