stagadish/nnplusplus

A small and easy to use neural net implementation for C++. Just download and #include!

nnplusplus's Issues

Some issues regarding your library

Hey,

Today I have got to know about your library thru your post in G+. I am also trying to write a similar library/toolbox to be used in optimization.

At first look at your library, I can say that it looks neat. However, it would be great if you considered using templates in your data structures. Or, if you would like to go for inheritance for some reason, then you can abstract some functionality in your base classes.

What I mean by the above comment is that, for example, you could use an activation function abstraction and keep a pointer inside your NeuralNet class to support different activation functions, such as:

#include <iostream>
#include <vector>

template <typename T>
class IFunction {
public:
  virtual T operator()(T variable) const = 0;
  virtual ~IFunction() = default;
};

template <typename T>
class PReLU : public IFunction<T> {
private:
  T alpha_;
public:
  PReLU(T alpha = T { 0 }) : alpha_ {alpha} { }
  T operator()(T variable) const override {
    T zero { 0 };
    return variable < zero ? alpha_*variable : variable;
  }
};

template <typename T>
class Identity : public IFunction<T> {
public:
  T operator()(T variable) const override {
    return variable;
  }
};

int main (int argc, char* argv[]) {
  Identity    <double>  identity_function {         };
  PReLU       <double>  relu_function     {         };
  PReLU       <double>  absolute_value    { -1.     };

  IFunction   <double>  *generic_func_ptr { nullptr };

  size_t numelems { 100ul };
  double xmin { -5. }, xmax { 5. }, dx {(xmax - xmin)/(numelems-1)};
  std::vector <double>  xvalues ( numelems );
  for (size_t idx = 0; idx < xvalues.size(); idx++)
    xvalues[idx] = xmin + idx*dx;

  // either directly call as if your instances were functions (actually, they
  // are functors right now, with their operator()'s overloaded)
  for (auto x : xvalues)
    std::cout << "x: " << x
              << ", identity_function(x): " << identity_function(x)
              << ", relu_function(x): " << relu_function(x)
              << std::endl;

  // or, use polymorphism
  generic_func_ptr = &identity_function;
  for (auto x : xvalues)
    std::cout << "x: " << x
              << ", generic_func_ptr->operator()(x): "
              << generic_func_ptr->operator()(x)
              << std::endl;

  // or, both
  generic_func_ptr = &relu_function;
  for (auto x : xvalues)
    std::cout << "x: " << x
              << ", (*generic_func_ptr)(x): " << (*generic_func_ptr)(x)
              << std::endl;

  // absolute value function
  for (auto x : xvalues)
    std::cout << "x: " << x
              << ", absolute_value(x): " << absolute_value(x)
              << std::endl;

  return 0;
}

I hope this helps :) Good luck with your code!...