private void ComputeDeltas(Matrix <float> target)
{
Matrix <float> a = Output._beta.Clone();
// Perform the derivative of the activation function on the values of the nodes
Matrix <float> b = Output.ActivationFunctionDerivative(Output._alpha);
// Adds the negative of each column vector in the target matrix to the respective column vector in ______
//a.EnumerateColumnsIndexed().Select(t => t.Item2.Add(target.Column(t.Item1).Negate()));
a = a - target;
Matrix <float> OutLayerDeltas = a.PointwiseMultiply(b);
Output._delta = OutLayerDeltas;
NodeLayer current = Output.Previous;
bool inputLayer = false;
while (!inputLayer)
{
Matrix <float> transWeight = current.Next.Weights().TransposeThisAndMultiply(current.Next._delta);
// MISSING CODE HERE
if (current.Previous == null)
{
inputLayer = true;
}
}
}
// ------------------------
// End Variables
// ------------------------
// ------------------------
// Begin Constructors
// ------------------------
/// <summary>
/// Initialize variables.
/// </summary>
protected NodeLayer()
{
Previous = null;
Next = null;
_omega = null;
_delta = null;
_beta = null;
_alpha = null;
Nodes = 0;
Passes = 1;
}
/// <summary>
/// Adds reference to the previous layer.
/// </summary>
/// <param name="nodes">Number of nodes in this layer. (number of rows)</param>
/// <param name="numPasses">Number of input passes. (number of columns)</param>
/// <param name="prev">Reference to the previous layer in the network.</param>
protected NodeLayer(int nodes, int numPasses, NodeLayer prev)
: this(nodes, numPasses)
{
Previous = prev;
}
