private Tuple <MyVector[], My2DMatrix[]> Backpropagate(MyVector input, MyVector diseredOutput, Cancel cancelFnc)
{
MyVector[] parcDerivBiases = new MyVector[NumOfLayers];
My2DMatrix[] parcDerivWeights = new My2DMatrix[NumOfLayers];
MyVector activation = new MyVector(input);
List <MyVector> activations = new List <MyVector>(NumOfLayers)
{
activation
};
// Feedfoward
List <MyVector> weightedInputs = new List <MyVector>(NumOfLayers);
for (int i = 1; i < NumOfLayers; i++)
{
if (cancelFnc != null && cancelFnc())
{
return(null);
}
MyVector weightedInput = (m_weights[i] * activation) + m_biases[i];
weightedInputs.Add(weightedInput);
activation = weightedInput.ApplyFunction(Sigmoid);
activations.Add(activation);
}
// Backpropagate
MyVector delta = CrossEntrophyDelta(activations[activations.Count - 1], diseredOutput);
parcDerivBiases[parcDerivBiases.Length - 1] = delta;
parcDerivWeights[parcDerivWeights.Length - 1] = new My2DMatrix(delta, activations[activations.Count - 2]);
for (int l = 2; l < NumOfLayers; l++)
{
if (cancelFnc != null && cancelFnc())
{
return(null);
}
MyVector weightedInput = weightedInputs[weightedInputs.Count - l];
var spv = weightedInput.ApplyFunction(SigmoidPrime);
delta = (m_weights[m_weights.Length - l + 1].Transpose() * delta) * spv;
parcDerivBiases[parcDerivBiases.Length - l] = delta; // the error
parcDerivWeights[parcDerivWeights.Length - l] = new My2DMatrix(delta, activations[activations.Count - l - 1]); // error times the activation from the previous layer (vector times vector^T creates matrix)
}
return(new Tuple <MyVector[], My2DMatrix[]>(parcDerivBiases, parcDerivWeights));
}