// ################################################
void NeuralNetwork::Backprop(std::vector<arma::vec>& delta_nabla_b, // is to be changed
std::vector<arma::mat>& delta_nabla_w, // is to be changed
const arma::vec& input, int output){
std::vector<arma::vec> activations;
activations.push_back(input);
std::vector<arma::vec> zValues;
for (unsigned int i = 0; i < vecBiases.size(); ++i){
arma::vec z = matWeights[i] * activations[i] + vecBiases[i];
zValues.push_back(z);
activations.push_back(ActivationFunction(z));
}
arma::vec trueoutput;
trueoutput.zeros(neurons[neurons.size()-1]);
trueoutput[output] = 1;
arma::vec delta = CostFunctionDerivative(activations[activations.size()-1], trueoutput, zValues[zValues.size()-1]);
delta_nabla_b[delta_nabla_b.size()-1] = delta;
delta_nabla_w[delta_nabla_w.size()-1] = delta * activations[activations.size() - 2].t();
for (unsigned int i = 2; i < neurons.size(); ++i){
arma::vec z = zValues[zValues.size() - i];
arma::vec sp = ActivationFunctionDerivative(z);
delta = matWeights[matWeights.size()-i+1].t() * delta % sp;
delta_nabla_b[delta_nabla_b.size()-i] = delta;
delta_nabla_w[delta_nabla_w.size()-i] = delta * activations[activations.size()-i-1].t();
}
return;
}
// ################################################
// Feed forwards input and returns output of neural network
arma::vec NeuralNetwork::FeedForward(std::vector<double> input){
std::vector<arma::vec > vecActivations;
vecActivations.resize(neurons.size());
vecActivations.at(0) = input;
for (unsigned int i = 0; i < vecActivations.size()-1; ++i){
vecActivations.at(i+1) = ActivationFunction((matWeights.at(i) * vecActivations.at(i)) + vecBiases.at(i));
}
return vecActivations.at(vecActivations.size()-1); // return last vector
}
void Neuron::CalculateNeuronResponse()
{
float totalInputValue = 0;
for(int i = 0; i < m_numberOfInputConnections; i++)
{
totalInputValue += m_inputValues[i];
}
m_unweightedOutputVal = ActivationFunction(totalInputValue, m_activationFunctionVariable, m_activationFunction);
}
// ################################################
arma::vec NeuralNetwork::ActivationFunctionDerivative(arma::vec vec){
arma::vec ones;
ones.ones(vec.n_rows);
vec = ActivationFunction(vec) % (ones - ActivationFunction(vec));
return vec;
}
