lemga::FeedForwardNN

FeedForwardNN Class Reference

#include <feedforwardnn.h>

Inheritance diagram for FeedForwardNN:

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Collaboration diagram for FeedForwardNN:

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List of all members.

Public Types

typedef std::vector< NNLayer::WVEC > WEIGHT
enum  TRAIN_METHOD {
  GRADIENT_DESCENT, LINE_SEARCH, CONJUGATE_GRADIENT, WEIGHT_DECAY,
  ADAPTIVE_LEARNING_RATE
}

Public Member Functions

 FeedForwardNN ()
 FeedForwardNN (const FeedForwardNN &)
 FeedForwardNN (std::istream &is)
virtual ~FeedForwardNN ()
const FeedForwardNN & operator= (const FeedForwardNN &)
virtual const id_t & id () const
virtual FeedForwardNN * create () const
 Create a new object using the default constructor.
virtual FeedForwardNN * clone () const
 Create a new object by replicating itself.
UINT size () const
const NNLayer & operator[] (UINT n) const
void add_top (const NNLayer &)
void add_bottom (const NNLayer &)
void set_batch_mode (bool b=true)
void set_train_method (TRAIN_METHOD m)
void set_parameter (REAL lr, REAL mincst, UINT maxrun)
virtual bool support_weighted_data () const
 Whether the learning model/algorithm supports unequally weighted data.
virtual void initialize ()
virtual void train ()
 Train with preset data set and sample weight.
virtual Output operator() (const Input &) const
WEIGHT weight () const
void set_weight (const WEIGHT &)
REAL cost (UINT idx) const
REAL cost () const
WEIGHT gradient (UINT idx) const
WEIGHT gradient () const
void clear_gradient () const
bool stop_opt (UINT step, REAL cst)

Protected Member Functions

virtual bool serialize (std::ostream &, ver_list &) const
virtual bool unserialize (std::istream &, ver_list &, const id_t &=NIL_ID)
virtual REAL _cost (const Output &F, const Output &y) const
virtual Output _cost_deriv (const Output &F, const Output &y) const
virtual void log_cost (UINT epoch, REAL err)

Protected Attributes

UINT n_layer
 # of layers == layer.size()-1.
std::vector< NNLayer * > layer
 layer pointers (layer[0] == 0).
std::vector< Output > _y
 buffer for outputs.
std::vector< Output > _dy
 buffer for derivatives.
bool online_learn
TRAIN_METHOD train_method
REAL learn_rate
REAL min_cst
UINT max_run

Detailed Description

Todo:
documentation

Definition at line 18 of file feedforwardnn.h.

Member Typedef Documentation

typedef std::vector<NNLayer::WVEC> WEIGHT
 

Definition at line 28 of file feedforwardnn.h.

Member Enumeration Documentation

enum TRAIN_METHOD
 

Enumerator:
GRADIENT_DESCENT 
LINE_SEARCH 
CONJUGATE_GRADIENT 
WEIGHT_DECAY 
ADAPTIVE_LEARNING_RATE 

Definition at line 29 of file feedforwardnn.h.

Constructor & Destructor Documentation

FeedForwardNN (   ) 
 

Todo:
Online learning is not implemented

Definition at line 22 of file feedforwardnn.cpp.

References FeedForwardNN::layer.

Referenced by FeedForwardNN::clone(), and FeedForwardNN::create().

FeedForwardNN (  const FeedForwardNN &   ) 
 

Definition at line 31 of file feedforwardnn.cpp.

References FeedForwardNN::layer, and FeedForwardNN::n_layer.

FeedForwardNN (  std::istream &  is  )  [inline, explicit]
 

Definition at line 51 of file feedforwardnn.h.

~FeedForwardNN (   )  [virtual]
 

Definition at line 42 of file feedforwardnn.cpp.

Member Function Documentation

virtual REAL _cost (  const Output &  F,
const Output &  y
)  const [inline, protected, virtual]
 

Definition at line 84 of file feedforwardnn.h.

References LearnModel::r_error().

Referenced by FeedForwardNN::cost().

Output _cost_deriv (  const Output &  F,
const Output &  y
)  const [protected, virtual]
 

Definition at line 211 of file feedforwardnn.cpp.

References LearnModel::_n_out, and LearnModel::n_output().

Referenced by FeedForwardNN::gradient().

void add_bottom (  const NNLayer &   ) 
 

void add_top (  const NNLayer &   ) 
 

Definition at line 129 of file feedforwardnn.cpp.

References FeedForwardNN::_dy, LearnModel::_n_in, LearnModel::_n_out, FeedForwardNN::_y, NNLayer::clone(), FeedForwardNN::layer, LearnModel::n_input(), FeedForwardNN::n_layer, and LearnModel::n_output().

void clear_gradient (   )  const
 

Definition at line 276 of file feedforwardnn.cpp.

References FeedForwardNN::layer, and FeedForwardNN::n_layer.

Referenced by FeedForwardNN::gradient().

virtual FeedForwardNN* clone (   )  const [inline, virtual]
 

Create a new object by replicating itself.

Returns:
A pointer to the new copy.
The code for a derived class Derived is always 
 return new Derived(*this);
Though seemingly redundant, it helps to copy an object without knowing the real type of the object.
See also:
C++ FAQ Lite 20.6

Implements LearnModel.

Definition at line 57 of file feedforwardnn.h.

References FeedForwardNN::FeedForwardNN().

REAL cost (   )  const
 

Definition at line 224 of file feedforwardnn.cpp.

References LearnModel::ptd, and LearnModel::ptw.

REAL cost (  UINT  idx  )  const
 

Definition at line 220 of file feedforwardnn.cpp.

References FeedForwardNN::_cost(), LearnModel::get_output(), and LearnModel::ptd.

virtual FeedForwardNN* create (   )  const [inline, virtual]
 

Create a new object using the default constructor.

The code for a derived class Derived is always 

 return new Derived();

Implements LearnModel.

Definition at line 56 of file feedforwardnn.h.

References FeedForwardNN::FeedForwardNN().

FeedForwardNN::WEIGHT gradient (   )  const
 

Definition at line 250 of file feedforwardnn.cpp.

References FeedForwardNN::_cost_deriv(), FeedForwardNN::_dy, LearnModel::_n_out, FeedForwardNN::_y, FeedForwardNN::clear_gradient(), FeedForwardNN::layer, FeedForwardNN::n_layer, LearnModel::ptd, LearnModel::ptw, and FeedForwardNN::size().

FeedForwardNN::WEIGHT gradient (  UINT  idx  )  const
 

Definition at line 233 of file feedforwardnn.cpp.

References FeedForwardNN::_cost_deriv(), FeedForwardNN::_dy, FeedForwardNN::_y, FeedForwardNN::clear_gradient(), FeedForwardNN::layer, FeedForwardNN::n_layer, and LearnModel::ptd.

virtual const id_t& id (   )  const [virtual]
 

Returns:
Class ID string (class name)

Implements Object.

void initialize (   )  [virtual]
 

Reimplemented from LearnModel.

Definition at line 146 of file feedforwardnn.cpp.

References FeedForwardNN::layer, and FeedForwardNN::n_layer.

void log_cost (  UINT  epoch,
REAL  err
)  [protected, virtual]
 

Definition at line 182 of file feedforwardnn.cpp.

References FeedForwardNN::learn_rate, and LearnModel::logf.

Referenced by FeedForwardNN::stop_opt().

Output operator() (  const Input &   )  const [virtual]
 

Implements LearnModel.

Definition at line 190 of file feedforwardnn.cpp.

References FeedForwardNN::_y, LearnModel::n_input(), and FeedForwardNN::n_layer.

const FeedForwardNN & operator= (  const FeedForwardNN &   ) 
 

Definition at line 46 of file feedforwardnn.cpp.

References FeedForwardNN::_dy, FeedForwardNN::_y, FeedForwardNN::layer, FeedForwardNN::learn_rate, FeedForwardNN::max_run, FeedForwardNN::min_cst, FeedForwardNN::n_layer, FeedForwardNN::online_learn, and FeedForwardNN::train_method.

const NNLayer& operator[] (  UINT  n  )  const [inline]
 

Definition at line 61 of file feedforwardnn.h.

References FeedForwardNN::layer.

bool serialize (  std::ostream &  ,
ver_list & 
)  const [protected, virtual]
 

Reimplemented from LearnModel.

Definition at line 67 of file feedforwardnn.cpp.

References FeedForwardNN::layer, FeedForwardNN::learn_rate, FeedForwardNN::max_run, FeedForwardNN::min_cst, FeedForwardNN::n_layer, FeedForwardNN::online_learn, and SERIALIZE_PARENT.

void set_batch_mode (  bool  b = true  )  [inline]
 

Definition at line 65 of file feedforwardnn.h.

References FeedForwardNN::online_learn.

void set_parameter (  REAL  lr,
REAL  mincst,
UINT  maxrun
)  [inline]
 

Parameters:
lr learning rate.
mincst minimal cost (error) need to be achieved during training.
maxrun maximal # of epochs the training should take.

Definition at line 72 of file feedforwardnn.h.

References FeedForwardNN::learn_rate, FeedForwardNN::max_run, and FeedForwardNN::min_cst.

void set_train_method (  TRAIN_METHOD  m  )  [inline]
 

Definition at line 66 of file feedforwardnn.h.

References FeedForwardNN::train_method.

void set_weight (  const WEIGHT &   ) 
 

Definition at line 205 of file feedforwardnn.cpp.

References FeedForwardNN::layer, and FeedForwardNN::n_layer.

UINT size (   )  const [inline]
 

Definition at line 60 of file feedforwardnn.h.

References FeedForwardNN::n_layer.

Referenced by FeedForwardNN::gradient().

bool stop_opt (  UINT  step,
REAL  cst
) 
 

Definition at line 281 of file feedforwardnn.cpp.

References FeedForwardNN::log_cost(), FeedForwardNN::max_run, and FeedForwardNN::min_cst.

virtual bool support_weighted_data (   )  const [inline, virtual]
 

Whether the learning model/algorithm supports unequally weighted data.

Returns:
true if supporting; false otherwise. The default is false, just for safety.
See also:
set_train_data()

Reimplemented from LearnModel.

Definition at line 75 of file feedforwardnn.h.

void train (   )  [virtual]
 

Train with preset data set and sample weight.

Implements LearnModel.

Definition at line 151 of file feedforwardnn.cpp.

References FeedForwardNN::ADAPTIVE_LEARNING_RATE, FeedForwardNN::CONJUGATE_GRADIENT, FeedForwardNN::GRADIENT_DESCENT, lemga::iterative_optimize(), FeedForwardNN::learn_rate, FeedForwardNN::LINE_SEARCH, FeedForwardNN::n_layer, LearnModel::ptd, LearnModel::ptw, FeedForwardNN::train_method, and FeedForwardNN::WEIGHT_DECAY.

bool unserialize (  std::istream &  ,
ver_list &  ,
const id_t &  = NIL_ID
)  [protected, virtual]
 

Reimplemented from LearnModel.

Definition at line 80 of file feedforwardnn.cpp.

References FeedForwardNN::_dy, LearnModel::_n_in, LearnModel::_n_out, FeedForwardNN::_y, Object::create(), FeedForwardNN::learn_rate, FeedForwardNN::max_run, FeedForwardNN::min_cst, Object::NIL_ID, FeedForwardNN::online_learn, and UNSERIALIZE_PARENT.

FeedForwardNN::WEIGHT weight (   )  const
 

Definition at line 198 of file feedforwardnn.cpp.

References FeedForwardNN::layer, and FeedForwardNN::n_layer.

Member Data Documentation

std::vector<Output> _dy [mutable, protected]
 

buffer for derivatives.

Definition at line 41 of file feedforwardnn.h.

Referenced by FeedForwardNN::add_top(), FeedForwardNN::gradient(), FeedForwardNN::operator=(), and FeedForwardNN::unserialize().

std::vector<Output> _y [mutable, protected]
 

buffer for outputs.

Definition at line 40 of file feedforwardnn.h.

Referenced by FeedForwardNN::add_top(), FeedForwardNN::gradient(), FeedForwardNN::operator()(), FeedForwardNN::operator=(), and FeedForwardNN::unserialize().

std::vector<NNLayer*> layer [protected]
 

layer pointers (layer[0] == 0).

Definition at line 39 of file feedforwardnn.h.

Referenced by FeedForwardNN::add_top(), FeedForwardNN::clear_gradient(), FeedForwardNN::FeedForwardNN(), FeedForwardNN::gradient(), FeedForwardNN::initialize(), FeedForwardNN::operator=(), FeedForwardNN::operator[](), FeedForwardNN::serialize(), FeedForwardNN::set_weight(), and FeedForwardNN::weight().

REAL learn_rate [protected]
 

Definition at line 45 of file feedforwardnn.h.

Referenced by FeedForwardNN::log_cost(), FeedForwardNN::operator=(), FeedForwardNN::serialize(), FeedForwardNN::set_parameter(), FeedForwardNN::train(), and FeedForwardNN::unserialize().

UINT max_run [protected]
 

Definition at line 46 of file feedforwardnn.h.

Referenced by FeedForwardNN::operator=(), FeedForwardNN::serialize(), FeedForwardNN::set_parameter(), FeedForwardNN::stop_opt(), and FeedForwardNN::unserialize().

REAL min_cst [protected]
 

Definition at line 45 of file feedforwardnn.h.

Referenced by FeedForwardNN::operator=(), FeedForwardNN::serialize(), FeedForwardNN::set_parameter(), FeedForwardNN::stop_opt(), and FeedForwardNN::unserialize().

UINT n_layer [protected]
 

# of layers == layer.size()-1.

Definition at line 38 of file feedforwardnn.h.

Referenced by FeedForwardNN::add_top(), FeedForwardNN::clear_gradient(), FeedForwardNN::FeedForwardNN(), FeedForwardNN::gradient(), FeedForwardNN::initialize(), FeedForwardNN::operator()(), FeedForwardNN::operator=(), FeedForwardNN::serialize(), FeedForwardNN::set_weight(), FeedForwardNN::size(), FeedForwardNN::train(), and FeedForwardNN::weight().

bool online_learn [protected]
 

Definition at line 43 of file feedforwardnn.h.

Referenced by FeedForwardNN::operator=(), FeedForwardNN::serialize(), FeedForwardNN::set_batch_mode(), and FeedForwardNN::unserialize().

TRAIN_METHOD train_method [protected]
 

Definition at line 44 of file feedforwardnn.h.

Referenced by FeedForwardNN::operator=(), FeedForwardNN::set_train_method(), and FeedForwardNN::train().

The documentation for this class was generated from the following files:
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