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//creating the structure of the nn in a graph that will help in performing backpropagation and forward propagation
#pragma once
#include <cstdlib>
#include "neuralClasses.h"
#include <Eigen/Dense>
namespace nplm
{
template <class X>
class Node {
public:
X * param; //what parameter is this
//vector <void *> children;
//vector <void *> parents;
Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> fProp_matrix;
Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> bProp_matrix;
int minibatch_size;
public:
Node() : param(NULL), minibatch_size(0) { }
Node(X *input_param, int minibatch_size)
: param(input_param),
minibatch_size(minibatch_size)
{
resize(minibatch_size);
}
void resize(int minibatch_size)
{
this->minibatch_size = minibatch_size;
if (param->n_outputs() != -1)
{
fProp_matrix.setZero(param->n_outputs(), minibatch_size);
}
if (param->n_inputs() != -1)
{
bProp_matrix.setZero(param->n_inputs(), minibatch_size);
}
}
void resize() { resize(minibatch_size); }
/*
void Fprop(Matrix<double,Dynamic,Dynamic> & input,int n_cols)
{
param->fProp(input,fProp_matrix,0,0,n_cols);
}
void Fprop(Matrix<double,1,Dynamic> & input,int n_cols)
{
param->fProp(input,fProp_matrix,0,0,n_cols);
}
*/
//for f prop, just call the fProp node of the particular parameter.
};
} // namespace nplm
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