/// <summary>
/// Creates matrices (for weights) and vectors (for biasses) initialized with 0 values coresponding to every computed layer of the NN.
/// </summary>
private static (Matrix <double>[] nablaW, Vector <double>[] nablaB) GenParamsZero(DeepNeuralNetwork nn)
{
Matrix <double>[] nablaW = new Matrix <double> [nn.ComputedLayers.Length];
Vector <double>[] nablaB = new Vector <double> [nn.ComputedLayers.Length];
for (int i = 0; i < nn.ComputedLayers.Length; ++i)
{
ComputedLayer layer = nn.ComputedLayers[i];
nablaW[i] = new DenseMatrix(layer.Weights.RowCount, layer.Weights.ColumnCount);
nablaB[i] = new DenseVector(layer.Biasses.Count);
}
return(nablaW, nablaB);
}
/// <summary>
/// Train the network using the given training set. The starting weights and biasses are the ones present in the network when the call to this function is made.
/// </summary>
public static void TrainWithBatch(DeepNeuralNetwork nn, IEnumerable <LabeledData> batch, int batchSize, double learningRate)
{
Matrix <double>[] nablaW;
Vector <double>[] nablaB;
(nablaW, nablaB) = GenParamsZero(nn);
foreach (LabeledData trainingData in batch)
{
var delta = BackProp(nn, trainingData);
for (int i = 0; i < nn.ComputedLayers.Length; ++i)
{
nablaW[i] += delta.nablaW[i];
nablaB[i] += delta.nablaB[i];
}
}
double ratio = learningRate / batchSize;
for (int i = 0; i < nn.ComputedLayers.Length; ++i)
{
ComputedLayer layer = nn.ComputedLayers[i];
layer.Weights = layer.Weights - ratio * nablaW[i];
layer.Biasses = layer.Biasses - ratio * nablaB[i];
}
}
