ffn.hpp

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#ifndef MLPACK_METHODS_ANN_FFN_HPP
#define MLPACK_METHODS_ANN_FFN_HPP
 
#include <mlpack/prereqs.hpp>
 
#include "visitor/delete_visitor.hpp"
#include "visitor/delta_visitor.hpp"
#include "visitor/output_height_visitor.hpp"
#include "visitor/output_parameter_visitor.hpp"
#include "visitor/output_width_visitor.hpp"
#include "visitor/reset_visitor.hpp"
#include "visitor/weight_size_visitor.hpp"
#include "visitor/copy_visitor.hpp"
#include "visitor/loss_visitor.hpp"
 
#include "init_rules/network_init.hpp"
 
#include <mlpack/methods/ann/layer/layer_types.hpp>
#include <mlpack/methods/ann/layer/layer.hpp>
#include <mlpack/methods/ann/init_rules/random_init.hpp>
#include <mlpack/methods/ann/layer/layer_traits.hpp>
#include <ensmallen.hpp>
 
namespace mlpack {
namespace ann  {
 
template<
  typename OutputLayerType = NegativeLogLikelihood<>,
  typename InitializationRuleType = RandomInitialization,
  typename... CustomLayers
>
class FFN
{
 public:
  using NetworkType = FFN<OutputLayerType, InitializationRuleType>;
 
  FFN(OutputLayerType outputLayer = OutputLayerType(),
      InitializationRuleType initializeRule = InitializationRuleType());
 
  FFN(const FFN&);
 
  FFN(FFN&&);
 
  FFN& operator = (FFN);
 
  ~FFN();
 
  template<typename OptimizerType>
  typename std::enable_if<
      HasMaxIterations<OptimizerType, size_t&(OptimizerType::*)()>
      ::value, void>::type
  WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const;
 
  template<typename OptimizerType>
  typename std::enable_if<
      !HasMaxIterations<OptimizerType, size_t&(OptimizerType::*)()>
      ::value, void>::type
  WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const;
 
  template<typename OptimizerType, typename... CallbackTypes>
  double Train(arma::mat predictors,
               arma::mat responses,
               OptimizerType& optimizer,
               CallbackTypes&&... callbacks);
 
  template<typename OptimizerType = ens::RMSProp, typename... CallbackTypes>
  double Train(arma::mat predictors,
               arma::mat responses,
               CallbackTypes&&... callbacks);
 
  void Predict(arma::mat predictors, arma::mat& results);
 
  template<typename PredictorsType, typename ResponsesType>
  double Evaluate(const PredictorsType& predictors,
                  const ResponsesType& responses);
 
  double Evaluate(const arma::mat& parameters);
 
  double Evaluate(const arma::mat& parameters,
                  const size_t begin,
                  const size_t batchSize,
                  const bool deterministic);
 
  double Evaluate(const arma::mat& parameters,
                  const size_t begin,
                  const size_t batchSize);
 
  template<typename GradType>
  double EvaluateWithGradient(const arma::mat& parameters, GradType& gradient);
 
  template<typename GradType>
  double EvaluateWithGradient(const arma::mat& parameters,
                              const size_t begin,
                              GradType& gradient,
                              const size_t batchSize);
 
  void Gradient(const arma::mat& parameters,
                const size_t begin,
                arma::mat& gradient,
                const size_t batchSize);
 
  void Shuffle();
 
  /*
   * Add a new module to the model.
   *
   * @param args The layer parameter.
   */
  template <class LayerType, class... Args>
  void Add(Args... args) { network.push_back(new LayerType(args...)); }
 
  /*
   * Add a new module to the model.
   *
   * @param layer The Layer to be added to the model.
   */
  void Add(LayerTypes<CustomLayers...> layer) { network.push_back(layer); }
 
  const std::vector<LayerTypes<CustomLayers...> >& Model() const
  {
    return network;
  }
  std::vector<LayerTypes<CustomLayers...> >& Model() { return network; }
 
  size_t NumFunctions() const { return numFunctions; }
 
  const arma::mat& Parameters() const { return parameter; }
  arma::mat& Parameters() { return parameter; }
 
  const arma::mat& Responses() const { return responses; }
  arma::mat& Responses() { return responses; }
 
  const arma::mat& Predictors() const { return predictors; }
  arma::mat& Predictors() { return predictors; }
 
  void ResetParameters();
 
  template<typename Archive>
  void serialize(Archive& ar, const unsigned int /* version */);
 
  template<typename PredictorsType, typename ResponsesType>
  void Forward(const PredictorsType& inputs, ResponsesType& results);
 
  template<typename PredictorsType, typename ResponsesType>
  void Forward(const PredictorsType& inputs ,
               ResponsesType& results,
               const size_t begin,
               const size_t end);
 
  template<typename PredictorsType,
           typename TargetsType,
           typename GradientsType>
  double Backward(const PredictorsType& inputs,
                  const TargetsType& targets,
                  GradientsType& gradients);
 
 private:
  // Helper functions.
  template<typename InputType>
  void Forward(const InputType& input);
 
  void ResetData(arma::mat predictors, arma::mat responses);
 
  void Backward();
 
  template<typename InputType>
  void Gradient(const InputType& input);
 
  void ResetDeterministic();
 
  void ResetGradients(arma::mat& gradient);
 
  void Swap(FFN& network);
 
  OutputLayerType outputLayer;
 
  InitializationRuleType initializeRule;
 
  size_t width;
 
  size_t height;
 
  bool reset;
 
  std::vector<LayerTypes<CustomLayers...> > network;
 
  arma::mat predictors;
 
  arma::mat responses;
 
  arma::mat parameter;
 
  size_t numFunctions;
 
  arma::mat error;
 
  DeltaVisitor deltaVisitor;
 
  OutputParameterVisitor outputParameterVisitor;
 
  WeightSizeVisitor weightSizeVisitor;
 
  OutputWidthVisitor outputWidthVisitor;
 
  OutputHeightVisitor outputHeightVisitor;
 
  LossVisitor lossVisitor;
 
  ResetVisitor resetVisitor;
 
  DeleteVisitor deleteVisitor;
 
  bool deterministic;
 
  arma::mat delta;
 
  arma::mat inputParameter;
 
  arma::mat outputParameter;
 
  arma::mat gradient;
 
  CopyVisitor<CustomLayers...> copyVisitor;
 
  // The GAN class should have access to internal members.
  template<
    typename Model,
    typename InitializerType,
    typename NoiseType,
    typename PolicyType
  >
  friend class GAN;
}; // class FFN
 
} // namespace ann
} // namespace mlpack
 
namespace boost {
namespace serialization {
 
template<typename OutputLayerType,
         typename InitializationRuleType,
         typename... CustomLayer>
struct version<
    mlpack::ann::FFN<OutputLayerType, InitializationRuleType, CustomLayer...>>
{
  BOOST_STATIC_CONSTANT(int, value = 2);
};
 
} // namespace serialization
} // namespace boost
 
// Include implementation.
#include "ffn_impl.hpp"
 
#endif