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 bool Equals(NeuralNetworkLayerModel?other)
{
if (other is null)
{
return(false);
}
if (ReferenceEquals(this, other))
{
return(true);
}
return
((Id == null && other.Id == null ||
Id?.Equals(other.Id) == true) &&
Index == other.Index &&
Inputs == other.Inputs &&
Outputs == other.Outputs &&
(Weights == null && other.Weights == null ||
Weights?.Length == other.Weights?.Length &&
Weights?.Zip(other.Weights).All(t => MathF.Abs(t.First - t.Second) < 0.001f) == true) &&
(Biases == null && other.Biases == null ||
Biases?.Length == other.Biases?.Length &&
Biases?.Zip(other.Biases).All(t => MathF.Abs(t.First - t.Second) < 0.001f) == true) &&
Activation == other.Activation &&
LastUpdateTime.Equals(other.LastUpdateTime));
}
public Vector <double> Feedforward(IEnumerable <double> input)
{
var output = Vector <double> .Build.DenseOfEnumerable(input);
var weightsWithBiases = Biases.Zip(Weights, (vector, matrix) => (vector, matrix));
foreach (var(bias, weights) in weightsWithBiases)
{
output = (weights.TransposeThisAndMultiply(output) + bias).Map(Sigmoid);
}
return(output);
}
public Vector <double> Feedforward(IEnumerable <byte> input)
{
var output = Vector <double> .Build.DenseOfEnumerable(input.Select(it => (double)it));
var biasesWithWeights = Biases.Zip(Weights, (vector, matrix) => (vector, matrix));
foreach (var(bias, weights) in biasesWithWeights)
{
output = Sigmoid(weights.Multiply(output) + bias);
}
return(output);
}
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;
