mlp.hpp

//| This file is a part of the sferes2 framework.
//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
//|
//| This software is a computer program whose purpose is to facilitate
//| experiments in evolutionary computation and evolutionary robotics.
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//| This software is governed by the CeCILL license under French law
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#ifndef _NN_MLP_HPP_
#define _NN_MLP_HPP_

#include "nn.hpp"
#include "connection.hpp"
#include "neuron.hpp"

namespace nn {
  // a basic multi-layer perceptron (feed-forward neural network)
  // only one hidden layer in this version
  // there's one autmatically added input for the bias
  template<typename N, typename C>
  class Mlp : public NN<N, C> {
   public:
    typedef nn::NN<N, C> nn_t;
    typedef typename nn_t::io_t io_t;
    typedef typename nn_t::vertex_desc_t vertex_desc_t;
    typedef typename nn_t::edge_desc_t edge_desc_t;
    typedef typename nn_t::adj_it_t adj_it_t;
    typedef typename nn_t::graph_t graph_t;
    typedef N neuron_t;
    typedef C conn_t;

    Mlp(size_t nb_inputs,
        size_t nb_hidden,
        size_t nb_outputs) {
      // neurons
      this->set_nb_inputs(nb_inputs + 1);
      this->set_nb_outputs(nb_outputs);
      for (size_t i = 0; i < nb_hidden; ++i)
        _hidden_neurons.
        push_back(this->add_neuron(std::string("h") + boost::lexical_cast<std::string>(i)));
      // connections
      this->full_connect(this->_inputs, this->_hidden_neurons,
                         trait<typename N::weight_t>::zero());
      this->full_connect(this->_hidden_neurons, this->_outputs,
                         trait<typename N::weight_t>::zero());
      // bias outputs too
      for (size_t i = 0; i < nb_outputs; ++i)
        this->add_connection(this->get_input(nb_inputs), this->get_output(i),
                             trait<typename N::weight_t>::zero());
    }
    unsigned get_nb_inputs() const {
      return this->_inputs.size() - 1;
    }
    void step(const std::vector<io_t>& in) {
      assert(in.size() == this->get_nb_inputs());
      std::vector<io_t> inf = in;
      inf.push_back(1.0f);
      nn_t::_step(inf);
    }
   protected:
    std::vector<vertex_desc_t> _hidden_neurons;
  };

  // a basic MLP with float weights
  typedef Mlp<Neuron<PfWSum<>, AfSigmoidNoBias<> >, Connection<> > mlp_t;

}

#endif