tesseract::Network Class Reference

#include <network.h>

Inheritance diagram for tesseract::Network:

Public Member Functions
	Network ()
	Network (NetworkType type, const std::string &name, int ni, int no)
virtual	~Network ()=default
NetworkType	type () const
bool	IsTraining () const
bool	needs_to_backprop () const
int	num_weights () const
int	NumInputs () const
int	NumOutputs () const
virtual StaticShape	InputShape () const
virtual StaticShape	OutputShape (const StaticShape &input_shape) const
const std::string &	name () const
virtual std::string	spec () const
bool	TestFlag (NetworkFlags flag) const
virtual bool	IsPlumbingType () const
virtual void	SetEnableTraining (TrainingState state)
virtual void	SetNetworkFlags (uint32_t flags)
virtual int	InitWeights (float range, TRand *randomizer)
virtual int	RemapOutputs ([[maybe_unused]] int old_no, [[maybe_unused]] const std::vector< int > &code_map)
virtual void	ConvertToInt ()
virtual void	SetRandomizer (TRand *randomizer)
virtual bool	SetupNeedsBackprop (bool needs_backprop)
virtual int	XScaleFactor () const
virtual void	CacheXScaleFactor ([[maybe_unused]] int factor)
virtual void	DebugWeights ()=0
virtual bool	Serialize (TFile *fp) const
virtual bool	DeSerialize (TFile *fp)=0
virtual void	Update ([[maybe_unused]] float learning_rate, [[maybe_unused]] float momentum, [[maybe_unused]] float adam_beta, [[maybe_unused]] int num_samples)
virtual void	CountAlternators ([[maybe_unused]] const Network &other, [[maybe_unused]] TFloat *same, [[maybe_unused]] TFloat *changed) const
virtual void	Forward (bool debug, const NetworkIO &input, const TransposedArray *input_transpose, NetworkScratch *scratch, NetworkIO *output)=0
virtual bool	Backward (bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch, NetworkIO *back_deltas)=0
void	DisplayForward (const NetworkIO &matrix)
void	DisplayBackward (const NetworkIO &matrix)

Static Public Member Functions
static Network *	CreateFromFile (TFile *fp)
static void	ClearWindow (bool tess_coords, const char *window_name, int width, int height, ScrollView **window)
static int	DisplayImage (Image pix, ScrollView *window)

Protected Member Functions
TFloat	Random (TFloat range)

Protected Attributes
NetworkType	type_
TrainingState	training_
bool	needs_to_backprop_
int32_t	network_flags_
int32_t	ni_
int32_t	no_
int32_t	num_weights_
std::string	name_
ScrollView *	forward_win_
ScrollView *	backward_win_
TRand *	randomizer_

Detailed Description

Definition at line 103 of file network.h.

Constructor & Destructor Documentation

Network() [1/2]

tesseract::Network::Network

(

)

Definition at line 80 of file network.cpp.

    : type_(NT_NONE)
    , training_(TS_ENABLED)
    , needs_to_backprop_(true)
    , network_flags_(0)
    , ni_(0)
    , no_(0)
    , num_weights_(0)
    , forward_win_(nullptr)
    , backward_win_(nullptr)
    , randomizer_(nullptr) {}

Network() [2/2]

tesseract::Network::Network	(	NetworkType	type,
		const std::string &	name,
		int	ni,
		int	no
	)

Definition at line 91 of file network.cpp.

    : type_(type)
    , training_(TS_ENABLED)
    , needs_to_backprop_(true)
    , network_flags_(0)
    , ni_(ni)
    , no_(no)
    , num_weights_(0)
    , name_(name)
    , forward_win_(nullptr)
    , backward_win_(nullptr)
    , randomizer_(nullptr) {}

~Network()

virtual tesseract::Network::~Network ( )

virtualdefault

Member Function Documentation

Backward()

virtual bool tesseract::Network::Backward ( bool  debug, const NetworkIO &  fwd_deltas, NetworkScratch *  scratch, NetworkIO *  back_deltas  )

pure virtual

Implemented in tesseract::Series, tesseract::Reversed, tesseract::Reconfig, tesseract::Parallel, tesseract::Maxpool, tesseract::LSTM, tesseract::Input, tesseract::FullyConnected, and tesseract::Convolve.

CacheXScaleFactor()

virtual void tesseract::Network::CacheXScaleFactor ( [[maybe_unused] ] int  factor )

inlinevirtual

Definition at line 220 of file network.h.

{}

ClearWindow()

void tesseract::Network::ClearWindow ( bool  tess_coords, const char *  window_name, int  width, int  height, ScrollView **  window  )

static

Definition at line 350 of file network.cpp.

                                               {
  if (*window == nullptr) {
    int min_size = std::min(width, height);
    if (min_size < kMinWinSize) {
      if (min_size < 1) {
        min_size = 1;
      }
      width = width * kMinWinSize / min_size;
      height = height * kMinWinSize / min_size;
    }
    width += kXWinFrameSize;
    height += kYWinFrameSize;
    if (width > kMaxWinSize) {
      width = kMaxWinSize;
    }
    if (height > kMaxWinSize) {
      height = kMaxWinSize;
    }
    *window = new ScrollView(window_name, 80, 100, width, height, width, height, tess_coords);
    tprintf("Created window %s of size %d, %d\n", window_name, width, height);
  } else {
    (*window)->Clear();
  }
}

ConvertToInt()

virtual void tesseract::Network::ConvertToInt ( )

inlinevirtual

Reimplemented in tesseract::Plumbing, tesseract::LSTM, and tesseract::FullyConnected.

Definition at line 196 of file network.h.

{}

CountAlternators()

virtual void tesseract::Network::CountAlternators ( [[maybe_unused] ] const Network &  other, [[maybe_unused] ] TFloat *  same, [[maybe_unused] ] TFloat *  changed  ) const

inlinevirtual

Definition at line 242 of file network.h.

                                                                        {}

CreateFromFile()

Network * tesseract::Network::CreateFromFile ( TFile *  fp )

static

Definition at line 217 of file network.cpp.

                                          {
  NetworkType type;       // Type of the derived network class.
  TrainingState training; // Are we currently training?
  bool needs_to_backprop; // This network needs to output back_deltas.
  int32_t network_flags;  // Behavior control flags in NetworkFlags.
  int32_t ni;             // Number of input values.
  int32_t no;             // Number of output values.
  int32_t num_weights;    // Number of weights in this and sub-network.
  std::string name;       // A unique name for this layer.
  int8_t data;
  Network *network = nullptr;
  type = getNetworkType(fp);
  if (!fp->DeSerialize(&data)) {
    return nullptr;
  }
  training = data == TS_ENABLED ? TS_ENABLED : TS_DISABLED;
  if (!fp->DeSerialize(&data)) {
    return nullptr;
  }
  needs_to_backprop = data != 0;
  if (!fp->DeSerialize(&network_flags)) {
    return nullptr;
  }
  if (!fp->DeSerialize(&ni)) {
    return nullptr;
  }
  if (!fp->DeSerialize(&no)) {
    return nullptr;
  }
  if (!fp->DeSerialize(&num_weights)) {
    return nullptr;
  }
  if (!fp->DeSerialize(name)) {
    return nullptr;
  }
 
  switch (type) {
    case NT_CONVOLVE:
      network = new Convolve(name.c_str(), ni, 0, 0);
      break;
    case NT_INPUT:
      network = new Input(name.c_str(), ni, no);
      break;
    case NT_LSTM:
    case NT_LSTM_SOFTMAX:
    case NT_LSTM_SOFTMAX_ENCODED:
    case NT_LSTM_SUMMARY:
      network = new LSTM(name.c_str(), ni, no, no, false, type);
      break;
    case NT_MAXPOOL:
      network = new Maxpool(name.c_str(), ni, 0, 0);
      break;
    // All variants of Parallel.
    case NT_PARALLEL:
    case NT_REPLICATED:
    case NT_PAR_RL_LSTM:
    case NT_PAR_UD_LSTM:
    case NT_PAR_2D_LSTM:
      network = new Parallel(name.c_str(), type);
      break;
    case NT_RECONFIG:
      network = new Reconfig(name.c_str(), ni, 0, 0);
      break;
    // All variants of reversed.
    case NT_XREVERSED:
    case NT_YREVERSED:
    case NT_XYTRANSPOSE:
      network = new Reversed(name.c_str(), type);
      break;
    case NT_SERIES:
      network = new Series(name.c_str());
      break;
    case NT_TENSORFLOW:
#ifdef INCLUDE_TENSORFLOW
      network = new TFNetwork(name.c_str());
#else
      tprintf("TensorFlow not compiled in! -DINCLUDE_TENSORFLOW\n");
#endif
      break;
    // All variants of FullyConnected.
    case NT_SOFTMAX:
    case NT_SOFTMAX_NO_CTC:
    case NT_RELU:
    case NT_TANH:
    case NT_LINEAR:
    case NT_LOGISTIC:
    case NT_POSCLIP:
    case NT_SYMCLIP:
      network = new FullyConnected(name.c_str(), ni, no, type);
      break;
    default:
      break;
  }
  if (network) {
    network->training_ = training;
    network->needs_to_backprop_ = needs_to_backprop;
    network->network_flags_ = network_flags;
    network->num_weights_ = num_weights;
    if (!network->DeSerialize(fp)) {
      delete network;
      network = nullptr;
    }
  }
  return network;
}

DebugWeights()

virtual void tesseract::Network::DebugWeights ( )

pure virtual

Implemented in tesseract::Plumbing, tesseract::LSTM, and tesseract::FullyConnected.

DeSerialize()

virtual bool tesseract::Network::DeSerialize ( TFile *  fp )

pure virtual

Implemented in tesseract::Reconfig, tesseract::Plumbing, tesseract::Maxpool, tesseract::LSTM, tesseract::Input, tesseract::FullyConnected, and tesseract::Convolve.

DisplayBackward()

void tesseract::Network::DisplayBackward

(

const NetworkIO &

matrix

)

Definition at line 341 of file network.cpp.

                                                     {
  Image image = matrix.ToPix();
  std::string window_name = name_ + "-back";
  ClearWindow(false, window_name.c_str(), pixGetWidth(image), pixGetHeight(image), &backward_win_);
  DisplayImage(image, backward_win_);
  backward_win_->Update();
}

DisplayForward()

void tesseract::Network::DisplayForward

(

const NetworkIO &

matrix

)

Definition at line 333 of file network.cpp.

                                                    {
  Image image = matrix.ToPix();
  ClearWindow(false, name_.c_str(), pixGetWidth(image), pixGetHeight(image), &forward_win_);
  DisplayImage(image, forward_win_);
  forward_win_->Update();
}

DisplayImage()

int tesseract::Network::DisplayImage ( Image  pix, ScrollView *  window  )

static

Definition at line 378 of file network.cpp.

                                                       {
  int height = pixGetHeight(pix);
  window->Draw(pix, 0, 0);
  pix.destroy();
  return height;
}

Forward()

virtual void tesseract::Network::Forward ( bool  debug, const NetworkIO &  input, const TransposedArray *  input_transpose, NetworkScratch *  scratch, NetworkIO *  output  )

pure virtual

Implemented in tesseract::Series, tesseract::Reversed, tesseract::Reconfig, tesseract::Parallel, tesseract::Maxpool, tesseract::LSTM, tesseract::Input, tesseract::FullyConnected, and tesseract::Convolve.

InitWeights()

int tesseract::Network::InitWeights ( float  range, TRand *  randomizer  )

virtual

Reimplemented in tesseract::Series, tesseract::Plumbing, tesseract::LSTM, and tesseract::FullyConnected.

Definition at line 137 of file network.cpp.

                                                                        {
  randomizer_ = randomizer;
  return 0;
}

InputShape()

virtual StaticShape tesseract::Network::InputShape ( ) const

inlinevirtual

Reimplemented in tesseract::Plumbing, and tesseract::Input.

Definition at line 129 of file network.h.

                                         {
    StaticShape result;
    return result;
  }

IsPlumbingType()

virtual bool tesseract::Network::IsPlumbingType ( ) const

inlinevirtual

Reimplemented in tesseract::Plumbing.

Definition at line 154 of file network.h.

                                      {
    return false;
  }

IsTraining()

bool tesseract::Network::IsTraining ( ) const

inline

Definition at line 113 of file network.h.

                          {
    return training_ == TS_ENABLED;
  }

name()

const std::string& tesseract::Network::name ( ) const

inline

Definition at line 140 of file network.h.

                                {
    return name_;
  }

needs_to_backprop()

bool tesseract::Network::needs_to_backprop ( ) const

inline

Definition at line 116 of file network.h.

                                 {
    return needs_to_backprop_;
  }

num_weights()

int tesseract::Network::num_weights ( ) const

inline

Definition at line 119 of file network.h.

                          {
    return num_weights_;
  }

NumInputs()

int tesseract::Network::NumInputs ( ) const

inline

Definition at line 122 of file network.h.

                        {
    return ni_;
  }

NumOutputs()

int tesseract::Network::NumOutputs ( ) const

inline

Definition at line 125 of file network.h.

                         {
    return no_;
  }

OutputShape()

virtual StaticShape tesseract::Network::OutputShape ( const StaticShape &  input_shape ) const

inlinevirtual

Reimplemented in tesseract::Series, tesseract::Reversed, tesseract::Reconfig, tesseract::Parallel, tesseract::LSTM, and tesseract::FullyConnected.

Definition at line 135 of file network.h.

                                                                        {
    StaticShape result(input_shape);
    result.set_depth(no_);
    return result;
  }

Random()

TFloat tesseract::Network::Random ( TFloat  range )

protected

Definition at line 324 of file network.cpp.

                                   {
  ASSERT_HOST(randomizer_ != nullptr);
  return randomizer_->SignedRand(range);
}

RemapOutputs()

virtual int tesseract::Network::RemapOutputs ( [[maybe_unused] ] int  old_no, [[maybe_unused] ] const std::vector< int > &  code_map  )

inlinevirtual

Definition at line 190 of file network.h.

                                                                            {
    return 0;
  }

Serialize()

bool tesseract::Network::Serialize ( TFile *  fp ) const

virtual

Reimplemented in tesseract::Reconfig, tesseract::Plumbing, tesseract::LSTM, tesseract::Input, tesseract::FullyConnected, and tesseract::Convolve.

Definition at line 158 of file network.cpp.

                                       {
  int8_t data = NT_NONE;
  if (!fp->Serialize(&data)) {
    return false;
  }
  std::string type_name = kTypeNames[type_];
  if (!fp->Serialize(type_name)) {
    return false;
  }
  data = training_;
  if (!fp->Serialize(&data)) {
    return false;
  }
  data = needs_to_backprop_;
  if (!fp->Serialize(&data)) {
    return false;
  }
  if (!fp->Serialize(&network_flags_)) {
    return false;
  }
  if (!fp->Serialize(&ni_)) {
    return false;
  }
  if (!fp->Serialize(&no_)) {
    return false;
  }
  if (!fp->Serialize(&num_weights_)) {
    return false;
  }
  uint32_t length = name_.length();
  if (!fp->Serialize(&length)) {
    return false;
  }
  return fp->Serialize(name_.c_str(), length);
}

SetEnableTraining()

void tesseract::Network::SetEnableTraining ( TrainingState  state )

virtual

Reimplemented in tesseract::Plumbing, tesseract::LSTM, and tesseract::FullyConnected.

Definition at line 113 of file network.cpp.

                                                   {
  if (state == TS_RE_ENABLE) {
    // Enable only from temp disabled.
    if (training_ == TS_TEMP_DISABLE) {
      training_ = TS_ENABLED;
    }
  } else if (state == TS_TEMP_DISABLE) {
    // Temp disable only from enabled.
    if (training_ == TS_ENABLED) {
      training_ = state;
    }
  } else {
    training_ = state;
  }
}

SetNetworkFlags()

void tesseract::Network::SetNetworkFlags ( uint32_t  flags )

virtual

Reimplemented in tesseract::Plumbing.

Definition at line 131 of file network.cpp.

                                            {
  network_flags_ = flags;
}

SetRandomizer()

void tesseract::Network::SetRandomizer ( TRand *  randomizer )

virtual

Reimplemented in tesseract::Plumbing.

Definition at line 145 of file network.cpp.

                                             {
  randomizer_ = randomizer;
}

SetupNeedsBackprop()

bool tesseract::Network::SetupNeedsBackprop ( bool  needs_backprop )

virtual

Reimplemented in tesseract::Series, and tesseract::Plumbing.

Definition at line 152 of file network.cpp.

                                                    {
  needs_to_backprop_ = needs_backprop;
  return needs_backprop || num_weights_ > 0;
}

spec()

virtual std::string tesseract::Network::spec ( ) const

inlinevirtual

Reimplemented in tesseract::Series, tesseract::Reversed, tesseract::Reconfig, tesseract::Plumbing, tesseract::Parallel, tesseract::Maxpool, tesseract::LSTM, tesseract::Input, tesseract::FullyConnected, and tesseract::Convolve.

Definition at line 143 of file network.h.

                                 {
    return "?";
  }

TestFlag()

bool tesseract::Network::TestFlag ( NetworkFlags  flag ) const

inline

Definition at line 146 of file network.h.

                                         {
    return (network_flags_ & flag) != 0;
  }

type()

NetworkType tesseract::Network::type ( ) const

inline

Definition at line 110 of file network.h.

                           {
    return type_;
  }

Update()

virtual void tesseract::Network::Update ( [[maybe_unused] ] float  learning_rate, [[maybe_unused] ] float  momentum, [[maybe_unused] ] float  adam_beta, [[maybe_unused] ] int  num_samples  )

inlinevirtual

Definition at line 235 of file network.h.

                                                        {}

XScaleFactor()

virtual int tesseract::Network::XScaleFactor ( ) const

inlinevirtual

Reimplemented in tesseract::Series, tesseract::Reconfig, tesseract::Plumbing, and tesseract::Input.

Definition at line 214 of file network.h.

                                   {
    return 1;
  }

Member Data Documentation

backward_win_

ScrollView* tesseract::Network::backward_win_

protected

Definition at line 311 of file network.h.

forward_win_

ScrollView* tesseract::Network::forward_win_

protected

Definition at line 310 of file network.h.

name_

std::string tesseract::Network::name_

protected

Definition at line 307 of file network.h.

needs_to_backprop_

bool tesseract::Network::needs_to_backprop_

protected

Definition at line 302 of file network.h.

network_flags_

int32_t tesseract::Network::network_flags_

protected

Definition at line 303 of file network.h.

ni_

int32_t tesseract::Network::ni_

protected

Definition at line 304 of file network.h.

no_

int32_t tesseract::Network::no_

protected

Definition at line 305 of file network.h.

num_weights_

int32_t tesseract::Network::num_weights_

protected

Definition at line 306 of file network.h.

randomizer_

TRand* tesseract::Network::randomizer_

protected

Definition at line 312 of file network.h.

training_

TrainingState tesseract::Network::training_

protected

Definition at line 301 of file network.h.

type_

NetworkType tesseract::Network::type_

protected

Definition at line 300 of file network.h.

---

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

• src/lstm/network.h
• src/lstm/network.cpp