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;
}
public Vector <double> BackPropagation(TrainTuple train, double speed)
{
var activations = new List <Vector <double> >
{
Vector <double> .Build.DenseOfEnumerable(train.Input.Select(it => (double)it))
};
var weightedSums = new List <Vector <double> >();
var biasesWithWeights = Biases.Zip(Weights, (vector, matrix) => (vector, matrix));
foreach (var(biases, weights) in biasesWithWeights)
{
var weightedSum = weights * activations[^ 0] + biases;
