/// <summary>
/// This method uses the cross entropy cost function to caculate the losses.
/// </summary>
/// <param name="AL">The final prediction of the network ranging from 0 to 1.</param>
/// <param name="_y">The true label 0 or 1.</param>
/// <param name="lambda">The L2 regularization hyper-parameter.</param>
/// <param name="theta">Dictionary containing weights and bias'.</param>
/// <param name="dims">Number of neurons in each layer of the network.</param>
/// <returns>A float value which is the calculated loss as well as its derivative.</returns>
public float ComputeCost(MatrixVectors AL, MatrixVectors _y, float lambda, Dictionary <string, MatrixVectors> theta, int[] dims)
{
if (AL.columns > 1 || _y.columns > 1 || !AL.CompareShape(_y))
{
Console.WriteLine("Invalid YShape");
return(0f);
}
float crossEntropyCost = 0;
float regularizedCost = 0;
for (int l = 1; l < dims.Length; l++)
{
regularizedCost += MatrixCalculations.MatrixSummation(MatrixCalculations.Square(theta["W" + l]));
}
regularizedCost *= lambda / 2;
for (int y = 0; y < _y.rows; y++)
{
float currentAL = AL.MatrixVector[0, y];
float currentY = _y.MatrixVector[0, y];
float currentCost = (float)-(currentY * Math.Log10(currentAL) + (1 - currentY) * Math.Log10(1 - currentAL));
crossEntropyCost += currentCost;
}
float totalCost = crossEntropyCost + regularizedCost;
return(totalCost);
}
/// <summary>
/// Executes the non-linear ReLu activation function on some linear function Z.
/// </summary>
/// <param name="Z">The linear function of the weights biases and previous layers activations.</param>
/// <returns>A vector containing the non-linear sigmoid activations of the linear function z.</returns>
private MatrixVectors Relu(MatrixVectors Z)
{
MatrixVectors activationsVector = MatrixCalculations.Maximum(Z, 0);
return(activationsVector);
}
