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NeuralLayer.cpp
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NeuralLayer.cpp
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#include "NeuralLayer.h"
#include <iostream>
using namespace std;
namespace nncpp {
NeuralLayer::NeuralLayer() {
}
NeuralLayer::NeuralLayer(const NeuralLayer &b)
{
this->activation = b.activation;
this->regularization = b.regularization;
this->isSoftmax = b.isSoftmax;
this->neurons = vector<Neuron>(b.neurons.size());
for (int i = 0; i < this->neurons.size(); i++)
this->neurons[i] = b.neurons[i];
}
NeuralLayer::NeuralLayer(int size, mathFunction *activation, mathFunction *regularization, bool isSoftmax)
{
this->neurons = vector<Neuron>(size);
this->activation = activation;
for (int i = 0; i < this->neurons.size(); i++)
{
this->neurons[i] = Neuron(std::to_string(i), activation);
}
this->isSoftmax = isSoftmax;
this->regularization = regularization;
}
void NeuralLayer::update()
{
int i;
if (this->isSoftmax)
{
double sum = 0;
for (i = 0; i < this->size(); i++)
sum += exp(this->neurons[i].update());
for (i = 0; i < this->size(); i++)
this->neurons[i].output = exp(this->neurons[i].totalInput) / sum;
}
else
{
for (i = 0; i < this->neurons.size(); i++)
this->neurons[i].output = this->activation->f(this->neurons[i].update());
}//make this better
}
int NeuralLayer::size()
{
return this->neurons.size();
}
Neuron& NeuralLayer::operator[](int i)
{
return this->neurons[i];
}
vector<double> NeuralLayer::toVector()
{
vector<double> res(this->size());
for (int i = 0; i < this->size(); i++)
res[i] = this->neurons[i].output;
return res;
}
string NeuralLayer::toString()
{
string res = "";
res += std::to_string(this->size()) + "\n";
for (int i = 0; i < this->size(); i++)
{
res += this->neurons[i].toString();
}
return res;
}
void NeuralLayer::updateWeights(double learningRate, double regularizationRate)
{
for (int i = 0; i < this->size(); i++)
this->neurons[i].updateWeights(learningRate, regularizationRate);
}
void NeuralLayer::link(NeuralLayer &prev)
{
int i, j;
for (i = 0; i < prev.size(); i++)
{
for (j = 0; j < this->size(); j++)
{
NeuralLink *link = new NeuralLink(&prev[i], &this->neurons[j], this->regularization);
prev[i].outputs.push_back(link);
this->neurons[j].inputs.push_back(link);
}
}
}
NeuralLayer* NeuralLayer::clone() const
{
return new NeuralLayer(*this);
}
}