tesseract 4.1.1
Loading...
Searching...
No Matches
tesseract::LSTMTrainer Class Reference

#include <lstmtrainer.h>

Inheritance diagram for tesseract::LSTMTrainer:
tesseract::LSTMRecognizer

Public Member Functions

 LSTMTrainer ()
 
 LSTMTrainer (FileReader file_reader, FileWriter file_writer, CheckPointReader checkpoint_reader, CheckPointWriter checkpoint_writer, const char *model_base, const char *checkpoint_name, int debug_interval, int64_t max_memory)
 
virtual ~LSTMTrainer ()
 
bool TryLoadingCheckpoint (const char *filename, const char *old_traineddata)
 
void InitCharSet (const std::string &traineddata_path)
 
void InitCharSet (const TessdataManager &mgr)
 
bool InitNetwork (const STRING &network_spec, int append_index, int net_flags, float weight_range, float learning_rate, float momentum, float adam_beta)
 
int InitTensorFlowNetwork (const std::string &tf_proto)
 
void InitIterations ()
 
double ActivationError () const
 
double CharError () const
 
const double * error_rates () const
 
double best_error_rate () const
 
int best_iteration () const
 
int learning_iteration () const
 
int32_t improvement_steps () const
 
void set_perfect_delay (int delay)
 
const GenericVector< char > & best_trainer () const
 
double NewSingleError (ErrorTypes type) const
 
double LastSingleError (ErrorTypes type) const
 
const DocumentCachetraining_data () const
 
DocumentCachemutable_training_data ()
 
Trainability GridSearchDictParams (const ImageData *trainingdata, int iteration, double min_dict_ratio, double dict_ratio_step, double max_dict_ratio, double min_cert_offset, double cert_offset_step, double max_cert_offset, STRING *results)
 
void DebugNetwork ()
 
bool LoadAllTrainingData (const GenericVector< STRING > &filenames, CachingStrategy cache_strategy, bool randomly_rotate)
 
bool MaintainCheckpoints (TestCallback tester, STRING *log_msg)
 
bool MaintainCheckpointsSpecific (int iteration, const GenericVector< char > *train_model, const GenericVector< char > *rec_model, TestCallback tester, STRING *log_msg)
 
void PrepareLogMsg (STRING *log_msg) const
 
void LogIterations (const char *intro_str, STRING *log_msg) const
 
bool TransitionTrainingStage (float error_threshold)
 
int CurrentTrainingStage () const
 
bool Serialize (SerializeAmount serialize_amount, const TessdataManager *mgr, TFile *fp) const
 
bool DeSerialize (const TessdataManager *mgr, TFile *fp)
 
void StartSubtrainer (STRING *log_msg)
 
SubTrainerResult UpdateSubtrainer (STRING *log_msg)
 
void ReduceLearningRates (LSTMTrainer *samples_trainer, STRING *log_msg)
 
int ReduceLayerLearningRates (double factor, int num_samples, LSTMTrainer *samples_trainer)
 
bool EncodeString (const STRING &str, GenericVector< int > *labels) const
 
const ImageDataTrainOnLine (LSTMTrainer *samples_trainer, bool batch)
 
Trainability TrainOnLine (const ImageData *trainingdata, bool batch)
 
Trainability PrepareForBackward (const ImageData *trainingdata, NetworkIO *fwd_outputs, NetworkIO *targets)
 
bool SaveTrainingDump (SerializeAmount serialize_amount, const LSTMTrainer *trainer, GenericVector< char > *data) const
 
bool ReadTrainingDump (const GenericVector< char > &data, LSTMTrainer *trainer) const
 
bool ReadSizedTrainingDump (const char *data, int size, LSTMTrainer *trainer) const
 
bool ReadLocalTrainingDump (const TessdataManager *mgr, const char *data, int size)
 
void SetupCheckpointInfo ()
 
bool SaveTraineddata (const STRING &filename)
 
void SaveRecognitionDump (GenericVector< char > *data) const
 
STRING DumpFilename () const
 
void FillErrorBuffer (double new_error, ErrorTypes type)
 
std::vector< int > MapRecoder (const UNICHARSET &old_chset, const UnicharCompress &old_recoder) const
 
- Public Member Functions inherited from tesseract::LSTMRecognizer
 LSTMRecognizer ()
 
 LSTMRecognizer (const STRING language_data_path_prefix)
 
 ~LSTMRecognizer ()
 
int NumOutputs () const
 
int training_iteration () const
 
int sample_iteration () const
 
double learning_rate () const
 
LossType OutputLossType () const
 
bool SimpleTextOutput () const
 
bool IsIntMode () const
 
bool IsRecoding () const
 
bool IsTensorFlow () const
 
GenericVector< STRINGEnumerateLayers () const
 
NetworkGetLayer (const STRING &id) const
 
float GetLayerLearningRate (const STRING &id) const
 
void ScaleLearningRate (double factor)
 
void ScaleLayerLearningRate (const STRING &id, double factor)
 
void ConvertToInt ()
 
const UNICHARSETGetUnicharset () const
 
const UnicharCompressGetRecoder () const
 
const DictGetDict () const
 
void SetIteration (int iteration)
 
int NumInputs () const
 
int null_char () const
 
bool Load (const ParamsVectors *params, const char *lang, TessdataManager *mgr)
 
bool Serialize (const TessdataManager *mgr, TFile *fp) const
 
bool DeSerialize (const TessdataManager *mgr, TFile *fp)
 
bool LoadCharsets (const TessdataManager *mgr)
 
bool LoadRecoder (TFile *fp)
 
bool LoadDictionary (const ParamsVectors *params, const char *lang, TessdataManager *mgr)
 
void RecognizeLine (const ImageData &image_data, bool invert, bool debug, double worst_dict_cert, const TBOX &line_box, PointerVector< WERD_RES > *words, int lstm_choice_mode=0)
 
void OutputStats (const NetworkIO &outputs, float *min_output, float *mean_output, float *sd)
 
bool RecognizeLine (const ImageData &image_data, bool invert, bool debug, bool re_invert, bool upside_down, float *scale_factor, NetworkIO *inputs, NetworkIO *outputs)
 
STRING DecodeLabels (const GenericVector< int > &labels)
 
void DisplayForward (const NetworkIO &inputs, const GenericVector< int > &labels, const GenericVector< int > &label_coords, const char *window_name, ScrollView **window)
 
void LabelsFromOutputs (const NetworkIO &outputs, GenericVector< int > *labels, GenericVector< int > *xcoords)
 

Static Public Member Functions

static bool EncodeString (const STRING &str, const UNICHARSET &unicharset, const UnicharCompress *recoder, bool simple_text, int null_char, GenericVector< int > *labels)
 

Protected Member Functions

void InitCharSet ()
 
void SetNullChar ()
 
void EmptyConstructor ()
 
bool DebugLSTMTraining (const NetworkIO &inputs, const ImageData &trainingdata, const NetworkIO &fwd_outputs, const GenericVector< int > &truth_labels, const NetworkIO &outputs)
 
void DisplayTargets (const NetworkIO &targets, const char *window_name, ScrollView **window)
 
bool ComputeTextTargets (const NetworkIO &outputs, const GenericVector< int > &truth_labels, NetworkIO *targets)
 
bool ComputeCTCTargets (const GenericVector< int > &truth_labels, NetworkIO *outputs, NetworkIO *targets)
 
double ComputeErrorRates (const NetworkIO &deltas, double char_error, double word_error)
 
double ComputeRMSError (const NetworkIO &deltas)
 
double ComputeWinnerError (const NetworkIO &deltas)
 
double ComputeCharError (const GenericVector< int > &truth_str, const GenericVector< int > &ocr_str)
 
double ComputeWordError (STRING *truth_str, STRING *ocr_str)
 
void UpdateErrorBuffer (double new_error, ErrorTypes type)
 
void RollErrorBuffers ()
 
STRING UpdateErrorGraph (int iteration, double error_rate, const GenericVector< char > &model_data, TestCallback tester)
 
- Protected Member Functions inherited from tesseract::LSTMRecognizer
void SetRandomSeed ()
 
void DisplayLSTMOutput (const GenericVector< int > &labels, const GenericVector< int > &xcoords, int height, ScrollView *window)
 
void DebugActivationPath (const NetworkIO &outputs, const GenericVector< int > &labels, const GenericVector< int > &xcoords)
 
void DebugActivationRange (const NetworkIO &outputs, const char *label, int best_choice, int x_start, int x_end)
 
void LabelsViaReEncode (const NetworkIO &output, GenericVector< int > *labels, GenericVector< int > *xcoords)
 
void LabelsViaSimpleText (const NetworkIO &output, GenericVector< int > *labels, GenericVector< int > *xcoords)
 
const char * DecodeLabel (const GenericVector< int > &labels, int start, int *end, int *decoded)
 
const char * DecodeSingleLabel (int label)
 

Protected Attributes

ScrollViewalign_win_
 
ScrollViewtarget_win_
 
ScrollViewctc_win_
 
ScrollViewrecon_win_
 
int debug_interval_
 
int checkpoint_iteration_
 
STRING model_base_
 
STRING checkpoint_name_
 
bool randomly_rotate_
 
DocumentCache training_data_
 
STRING best_model_name_
 
int num_training_stages_
 
FileReader file_reader_
 
FileWriter file_writer_
 
CheckPointReader checkpoint_reader_
 
CheckPointWriter checkpoint_writer_
 
double best_error_rate_
 
double best_error_rates_ [ET_COUNT]
 
int best_iteration_
 
double worst_error_rate_
 
double worst_error_rates_ [ET_COUNT]
 
int worst_iteration_
 
int stall_iteration_
 
GenericVector< char > best_model_data_
 
GenericVector< char > worst_model_data_
 
GenericVector< char > best_trainer_
 
LSTMTrainersub_trainer_
 
float error_rate_of_last_saved_best_
 
int training_stage_
 
GenericVector< double > best_error_history_
 
GenericVector< int > best_error_iterations_
 
int32_t improvement_steps_
 
int learning_iteration_
 
int prev_sample_iteration_
 
int perfect_delay_
 
int last_perfect_training_iteration_
 
GenericVector< double > error_buffers_ [ET_COUNT]
 
double error_rates_ [ET_COUNT]
 
TessdataManager mgr_
 
- Protected Attributes inherited from tesseract::LSTMRecognizer
Networknetwork_
 
CCUtil ccutil_
 
UnicharCompress recoder_
 
STRING network_str_
 
int32_t training_flags_
 
int32_t training_iteration_
 
int32_t sample_iteration_
 
int32_t null_char_
 
float learning_rate_
 
float momentum_
 
float adam_beta_
 
TRand randomizer_
 
NetworkScratch scratch_space_
 
Dictdict_
 
RecodeBeamSearchsearch_
 
ScrollViewdebug_win_
 

Static Protected Attributes

static const int kRollingBufferSize_ = 1000
 

Detailed Description

Definition at line 89 of file lstmtrainer.h.

Constructor & Destructor Documentation

◆ LSTMTrainer() [1/2]

tesseract::LSTMTrainer::LSTMTrainer ( )

Definition at line 74 of file lstmtrainer.cpp.

75 : randomly_rotate_(false),
83 sub_trainer_(nullptr) {
86}
_ConstTessMemberResultCallback_5_0< false, R, T1, P1, P2, P3, P4, P5 >::base * NewPermanentTessCallback(const T1 *obj, R(T2::*member)(P1, P2, P3, P4, P5) const, typename Identity< P1 >::type p1, typename Identity< P2 >::type p2, typename Identity< P3 >::type p3, typename Identity< P4 >::type p4, typename Identity< P5 >::type p5)
Definition: tesscallback.h:258
bool SaveDataToFile(const GenericVector< char > &data, const STRING &filename)
bool LoadDataFromFile(const char *filename, GenericVector< char > *data)
bool ReadTrainingDump(const GenericVector< char > &data, LSTMTrainer *trainer) const
Definition: lstmtrainer.h:291
CheckPointReader checkpoint_reader_
Definition: lstmtrainer.h:424
LSTMTrainer * sub_trainer_
Definition: lstmtrainer.h:450
CheckPointWriter checkpoint_writer_
Definition: lstmtrainer.h:425
DocumentCache training_data_
Definition: lstmtrainer.h:414
bool SaveTrainingDump(SerializeAmount serialize_amount, const LSTMTrainer *trainer, GenericVector< char > *data) const

◆ LSTMTrainer() [2/2]

tesseract::LSTMTrainer::LSTMTrainer ( FileReader  file_reader,
FileWriter  file_writer,
CheckPointReader  checkpoint_reader,
CheckPointWriter  checkpoint_writer,
const char *  model_base,
const char *  checkpoint_name,
int  debug_interval,
int64_t  max_memory 
)

Definition at line 88 of file lstmtrainer.cpp.

93 : randomly_rotate_(false),
94 training_data_(max_memory),
95 file_reader_(file_reader),
96 file_writer_(file_writer),
97 checkpoint_reader_(checkpoint_reader),
98 checkpoint_writer_(checkpoint_writer),
99 sub_trainer_(nullptr),
100 mgr_(file_reader) {
103 if (file_writer_ == nullptr) file_writer_ = SaveDataToFile;
104 if (checkpoint_reader_ == nullptr) {
107 }
108 if (checkpoint_writer_ == nullptr) {
111 }
112 debug_interval_ = debug_interval;
113 model_base_ = model_base;
114 checkpoint_name_ = checkpoint_name;
115}
TessdataManager mgr_
Definition: lstmtrainer.h:483

◆ ~LSTMTrainer()

tesseract::LSTMTrainer::~LSTMTrainer ( )
virtual

Definition at line 117 of file lstmtrainer.cpp.

117 {
118 delete align_win_;
119 delete target_win_;
120 delete ctc_win_;
121 delete recon_win_;
122 delete checkpoint_reader_;
123 delete checkpoint_writer_;
124 delete sub_trainer_;
125}
ScrollView * target_win_
Definition: lstmtrainer.h:399
ScrollView * recon_win_
Definition: lstmtrainer.h:403
ScrollView * ctc_win_
Definition: lstmtrainer.h:401
ScrollView * align_win_
Definition: lstmtrainer.h:397

Member Function Documentation

◆ ActivationError()

double tesseract::LSTMTrainer::ActivationError ( ) const
inline

Definition at line 136 of file lstmtrainer.h.

136 {
137 return error_rates_[ET_DELTA];
138 }
double error_rates_[ET_COUNT]
Definition: lstmtrainer.h:481

◆ best_error_rate()

double tesseract::LSTMTrainer::best_error_rate ( ) const
inline

Definition at line 143 of file lstmtrainer.h.

143 {
144 return best_error_rate_;
145 }

◆ best_iteration()

int tesseract::LSTMTrainer::best_iteration ( ) const
inline

Definition at line 146 of file lstmtrainer.h.

146 {
147 return best_iteration_;
148 }

◆ best_trainer()

const GenericVector< char > & tesseract::LSTMTrainer::best_trainer ( ) const
inline

Definition at line 152 of file lstmtrainer.h.

152{ return best_trainer_; }
GenericVector< char > best_trainer_
Definition: lstmtrainer.h:447

◆ CharError()

double tesseract::LSTMTrainer::CharError ( ) const
inline

Definition at line 139 of file lstmtrainer.h.

139{ return error_rates_[ET_CHAR_ERROR]; }
@ ET_CHAR_ERROR
Definition: lstmtrainer.h:41

◆ ComputeCharError()

double tesseract::LSTMTrainer::ComputeCharError ( const GenericVector< int > &  truth_str,
const GenericVector< int > &  ocr_str 
)
protected

Definition at line 1191 of file lstmtrainer.cpp.

1192 {
1193 GenericVector<int> label_counts;
1194 label_counts.init_to_size(NumOutputs(), 0);
1195 int truth_size = 0;
1196 for (int i = 0; i < truth_str.size(); ++i) {
1197 if (truth_str[i] != null_char_) {
1198 ++label_counts[truth_str[i]];
1199 ++truth_size;
1200 }
1201 }
1202 for (int i = 0; i < ocr_str.size(); ++i) {
1203 if (ocr_str[i] != null_char_) {
1204 --label_counts[ocr_str[i]];
1205 }
1206 }
1207 int char_errors = 0;
1208 for (int i = 0; i < label_counts.size(); ++i) {
1209 char_errors += abs(label_counts[i]);
1210 }
1211 if (truth_size == 0) {
1212 return (char_errors == 0) ? 0.0 : 1.0;
1213 }
1214 return static_cast<double>(char_errors) / truth_size;
1215}
void init_to_size(int size, const T &t)
int size() const
Definition: genericvector.h:72

◆ ComputeCTCTargets()

bool tesseract::LSTMTrainer::ComputeCTCTargets ( const GenericVector< int > &  truth_labels,
NetworkIO outputs,
NetworkIO targets 
)
protected

Definition at line 1123 of file lstmtrainer.cpp.

1124 {
1125 // Bottom-clip outputs to a minimum probability.
1126 CTC::NormalizeProbs(outputs);
1127 return CTC::ComputeCTCTargets(truth_labels, null_char_,
1128 outputs->float_array(), targets);
1129}
static bool ComputeCTCTargets(const GenericVector< int > &truth_labels, int null_char, const GENERIC_2D_ARRAY< float > &outputs, NetworkIO *targets)
Definition: ctc.cpp:54
static void NormalizeProbs(NetworkIO *probs)
Definition: ctc.h:36

◆ ComputeErrorRates()

double tesseract::LSTMTrainer::ComputeErrorRates ( const NetworkIO deltas,
double  char_error,
double  word_error 
)
protected

Definition at line 1134 of file lstmtrainer.cpp.

1135 {
1137 // Delta error is the fraction of timesteps with >0.5 error in the top choice
1138 // score. If zero, then the top choice characters are guaranteed correct,
1139 // even when there is residue in the RMS error.
1140 double delta_error = ComputeWinnerError(deltas);
1141 UpdateErrorBuffer(delta_error, ET_DELTA);
1142 UpdateErrorBuffer(word_error, ET_WORD_RECERR);
1143 UpdateErrorBuffer(char_error, ET_CHAR_ERROR);
1144 // Skip ratio measures the difference between sample_iteration_ and
1145 // training_iteration_, which reflects the number of unusable samples,
1146 // usually due to unencodable truth text, or the text not fitting in the
1147 // space for the output.
1148 double skip_count = sample_iteration_ - prev_sample_iteration_;
1149 UpdateErrorBuffer(skip_count, ET_SKIP_RATIO);
1150 return delta_error;
1151}
@ ET_WORD_RECERR
Definition: lstmtrainer.h:40
@ ET_SKIP_RATIO
Definition: lstmtrainer.h:42
double ComputeRMSError(const NetworkIO &deltas)
double ComputeWinnerError(const NetworkIO &deltas)
void UpdateErrorBuffer(double new_error, ErrorTypes type)

◆ ComputeRMSError()

double tesseract::LSTMTrainer::ComputeRMSError ( const NetworkIO deltas)
protected

Definition at line 1154 of file lstmtrainer.cpp.

1154 {
1155 double total_error = 0.0;
1156 int width = deltas.Width();
1157 int num_classes = deltas.NumFeatures();
1158 for (int t = 0; t < width; ++t) {
1159 const float* class_errs = deltas.f(t);
1160 for (int c = 0; c < num_classes; ++c) {
1161 double error = class_errs[c];
1162 total_error += error * error;
1163 }
1164 }
1165 return sqrt(total_error / (width * num_classes));
1166}

◆ ComputeTextTargets()

bool tesseract::LSTMTrainer::ComputeTextTargets ( const NetworkIO outputs,
const GenericVector< int > &  truth_labels,
NetworkIO targets 
)
protected

Definition at line 1103 of file lstmtrainer.cpp.

1105 {
1106 if (truth_labels.size() > targets->Width()) {
1107 tprintf("Error: transcription %s too long to fit into target of width %d\n",
1108 DecodeLabels(truth_labels).string(), targets->Width());
1109 return false;
1110 }
1111 for (int i = 0; i < truth_labels.size() && i < targets->Width(); ++i) {
1112 targets->SetActivations(i, truth_labels[i], 1.0);
1113 }
1114 for (int i = truth_labels.size(); i < targets->Width(); ++i) {
1115 targets->SetActivations(i, null_char_, 1.0);
1116 }
1117 return true;
1118}
DLLSYM void tprintf(const char *format,...)
Definition: tprintf.cpp:35
STRING DecodeLabels(const GenericVector< int > &labels)

◆ ComputeWinnerError()

double tesseract::LSTMTrainer::ComputeWinnerError ( const NetworkIO deltas)
protected

Definition at line 1173 of file lstmtrainer.cpp.

1173 {
1174 int num_errors = 0;
1175 int width = deltas.Width();
1176 int num_classes = deltas.NumFeatures();
1177 for (int t = 0; t < width; ++t) {
1178 const float* class_errs = deltas.f(t);
1179 for (int c = 0; c < num_classes; ++c) {
1180 float abs_delta = fabs(class_errs[c]);
1181 // TODO(rays) Filtering cases where the delta is very large to cut out
1182 // GT errors doesn't work. Find a better way or get better truth.
1183 if (0.5 <= abs_delta)
1184 ++num_errors;
1185 }
1186 }
1187 return static_cast<double>(num_errors) / width;
1188}

◆ ComputeWordError()

double tesseract::LSTMTrainer::ComputeWordError ( STRING truth_str,
STRING ocr_str 
)
protected

Definition at line 1219 of file lstmtrainer.cpp.

1219 {
1220 using StrMap = std::unordered_map<std::string, int, std::hash<std::string>>;
1221 GenericVector<STRING> truth_words, ocr_words;
1222 truth_str->split(' ', &truth_words);
1223 if (truth_words.empty()) return 0.0;
1224 ocr_str->split(' ', &ocr_words);
1225 StrMap word_counts;
1226 for (int i = 0; i < truth_words.size(); ++i) {
1227 std::string truth_word(truth_words[i].string());
1228 auto it = word_counts.find(truth_word);
1229 if (it == word_counts.end())
1230 word_counts.insert(std::make_pair(truth_word, 1));
1231 else
1232 ++it->second;
1233 }
1234 for (int i = 0; i < ocr_words.size(); ++i) {
1235 std::string ocr_word(ocr_words[i].string());
1236 auto it = word_counts.find(ocr_word);
1237 if (it == word_counts.end())
1238 word_counts.insert(std::make_pair(ocr_word, -1));
1239 else
1240 --it->second;
1241 }
1242 int word_recall_errs = 0;
1243 for (StrMap::const_iterator it = word_counts.begin(); it != word_counts.end();
1244 ++it) {
1245 if (it->second > 0) word_recall_errs += it->second;
1246 }
1247 return static_cast<double>(word_recall_errs) / truth_words.size();
1248}
bool empty() const
Definition: genericvector.h:91
void split(char c, GenericVector< STRING > *splited)
Definition: strngs.cpp:282

◆ CurrentTrainingStage()

int tesseract::LSTMTrainer::CurrentTrainingStage ( ) const
inline

Definition at line 211 of file lstmtrainer.h.

211{ return training_stage_; }

◆ DebugLSTMTraining()

bool tesseract::LSTMTrainer::DebugLSTMTraining ( const NetworkIO inputs,
const ImageData trainingdata,
const NetworkIO fwd_outputs,
const GenericVector< int > &  truth_labels,
const NetworkIO outputs 
)
protected

Definition at line 1029 of file lstmtrainer.cpp.

1033 {
1034 const STRING& truth_text = DecodeLabels(truth_labels);
1035 if (truth_text.string() == nullptr || truth_text.length() <= 0) {
1036 tprintf("Empty truth string at decode time!\n");
1037 return false;
1038 }
1039 if (debug_interval_ != 0) {
1040 // Get class labels, xcoords and string.
1041 GenericVector<int> labels;
1042 GenericVector<int> xcoords;
1043 LabelsFromOutputs(outputs, &labels, &xcoords);
1044 STRING text = DecodeLabels(labels);
1045 tprintf("Iteration %d: GROUND TRUTH : %s\n",
1046 training_iteration(), truth_text.string());
1047 if (truth_text != text) {
1048 tprintf("Iteration %d: ALIGNED TRUTH : %s\n",
1049 training_iteration(), text.string());
1050 }
1052 tprintf("TRAINING activation path for truth string %s\n",
1053 truth_text.string());
1054 DebugActivationPath(outputs, labels, xcoords);
1055 DisplayForward(inputs, labels, xcoords, "LSTMTraining", &align_win_);
1056 if (OutputLossType() == LT_CTC) {
1057 DisplayTargets(fwd_outputs, "CTC Outputs", &ctc_win_);
1058 DisplayTargets(outputs, "CTC Targets", &target_win_);
1059 }
1060 }
1061 }
1062 return true;
1063}
Definition: strngs.h:45
int32_t length() const
Definition: strngs.cpp:189
const char * string() const
Definition: strngs.cpp:194
LossType OutputLossType() const
void DebugActivationPath(const NetworkIO &outputs, const GenericVector< int > &labels, const GenericVector< int > &xcoords)
void LabelsFromOutputs(const NetworkIO &outputs, GenericVector< int > *labels, GenericVector< int > *xcoords)
void DisplayForward(const NetworkIO &inputs, const GenericVector< int > &labels, const GenericVector< int > &label_coords, const char *window_name, ScrollView **window)
void DisplayTargets(const NetworkIO &targets, const char *window_name, ScrollView **window)

◆ DebugNetwork()

void tesseract::LSTMTrainer::DebugNetwork ( )

Definition at line 291 of file lstmtrainer.cpp.

291 {
293}
virtual void DebugWeights()=0

◆ DeSerialize()

bool tesseract::LSTMTrainer::DeSerialize ( const TessdataManager mgr,
TFile fp 
)

Definition at line 466 of file lstmtrainer.cpp.

466 {
467 if (!LSTMRecognizer::DeSerialize(mgr, fp)) return false;
468 if (!fp->DeSerialize(&learning_iteration_)) {
469 // Special case. If we successfully decoded the recognizer, but fail here
470 // then it means we were just given a recognizer, so issue a warning and
471 // allow it.
472 tprintf("Warning: LSTMTrainer deserialized an LSTMRecognizer!\n");
475 return true;
476 }
477 if (!fp->DeSerialize(&prev_sample_iteration_)) return false;
478 if (!fp->DeSerialize(&perfect_delay_)) return false;
479 if (!fp->DeSerialize(&last_perfect_training_iteration_)) return false;
480 for (auto & error_buffer : error_buffers_) {
481 if (!error_buffer.DeSerialize(fp)) return false;
482 }
483 if (!fp->DeSerialize(&error_rates_[0], countof(error_rates_))) return false;
484 if (!fp->DeSerialize(&training_stage_)) return false;
485 uint8_t amount;
486 if (!fp->DeSerialize(&amount)) return false;
487 if (amount == LIGHT) return true; // Don't read the rest.
488 if (!fp->DeSerialize(&best_error_rate_)) return false;
489 if (!fp->DeSerialize(&best_error_rates_[0], countof(best_error_rates_))) return false;
490 if (!fp->DeSerialize(&best_iteration_)) return false;
491 if (!fp->DeSerialize(&worst_error_rate_)) return false;
492 if (!fp->DeSerialize(&worst_error_rates_[0], countof(worst_error_rates_))) return false;
493 if (!fp->DeSerialize(&worst_iteration_)) return false;
494 if (!fp->DeSerialize(&stall_iteration_)) return false;
495 if (!best_model_data_.DeSerialize(fp)) return false;
496 if (!worst_model_data_.DeSerialize(fp)) return false;
497 if (amount != NO_BEST_TRAINER && !best_trainer_.DeSerialize(fp)) return false;
498 GenericVector<char> sub_data;
499 if (!sub_data.DeSerialize(fp)) return false;
500 delete sub_trainer_;
501 if (sub_data.empty()) {
502 sub_trainer_ = nullptr;
503 } else {
505 if (!ReadTrainingDump(sub_data, sub_trainer_)) return false;
506 }
507 if (!best_error_history_.DeSerialize(fp)) return false;
508 if (!best_error_iterations_.DeSerialize(fp)) return false;
509 return fp->DeSerialize(&improvement_steps_);
510}
constexpr size_t countof(T const (&)[N]) noexcept
Definition: serialis.h:43
@ TS_ENABLED
Definition: network.h:95
@ NO_BEST_TRAINER
Definition: lstmtrainer.h:58
bool DeSerialize(bool swap, FILE *fp)
bool DeSerialize(const TessdataManager *mgr, TFile *fp)
GenericVector< double > error_buffers_[ET_COUNT]
Definition: lstmtrainer.h:479
GenericVector< char > best_model_data_
Definition: lstmtrainer.h:444
double worst_error_rates_[ET_COUNT]
Definition: lstmtrainer.h:438
GenericVector< double > best_error_history_
Definition: lstmtrainer.h:457
GenericVector< int > best_error_iterations_
Definition: lstmtrainer.h:458
GenericVector< char > worst_model_data_
Definition: lstmtrainer.h:445
double best_error_rates_[ET_COUNT]
Definition: lstmtrainer.h:432
virtual void SetEnableTraining(TrainingState state)
Definition: network.cpp:110

◆ DisplayTargets()

void tesseract::LSTMTrainer::DisplayTargets ( const NetworkIO targets,
const char *  window_name,
ScrollView **  window 
)
protected

Definition at line 1066 of file lstmtrainer.cpp.

1067 {
1068#ifndef GRAPHICS_DISABLED // do nothing if there's no graphics.
1069 int width = targets.Width();
1070 int num_features = targets.NumFeatures();
1071 Network::ClearWindow(true, window_name, width * kTargetXScale, kTargetYScale,
1072 window);
1073 for (int c = 0; c < num_features; ++c) {
1074 int color = c % (ScrollView::GREEN_YELLOW - 1) + 2;
1075 (*window)->Pen(static_cast<ScrollView::Color>(color));
1076 int start_t = -1;
1077 for (int t = 0; t < width; ++t) {
1078 double target = targets.f(t)[c];
1079 target *= kTargetYScale;
1080 if (target >= 1) {
1081 if (start_t < 0) {
1082 (*window)->SetCursor(t - 1, 0);
1083 start_t = t;
1084 }
1085 (*window)->DrawTo(t, target);
1086 } else if (start_t >= 0) {
1087 (*window)->DrawTo(t, 0);
1088 (*window)->DrawTo(start_t - 1, 0);
1089 start_t = -1;
1090 }
1091 }
1092 if (start_t >= 0) {
1093 (*window)->DrawTo(width, 0);
1094 (*window)->DrawTo(start_t - 1, 0);
1095 }
1096 }
1097 (*window)->Update();
1098#endif // GRAPHICS_DISABLED
1099}
const int kTargetYScale
Definition: lstmtrainer.cpp:72
const int kTargetXScale
Definition: lstmtrainer.cpp:71
static void ClearWindow(bool tess_coords, const char *window_name, int width, int height, ScrollView **window)
Definition: network.cpp:312

◆ DumpFilename()

STRING tesseract::LSTMTrainer::DumpFilename ( ) const

Definition at line 940 of file lstmtrainer.cpp.

940 {
941 STRING filename;
943 filename.add_str_int("_", best_iteration_);
944 filename += ".checkpoint";
945 return filename;
946}
void add_str_int(const char *str, int number)
Definition: strngs.cpp:377
void add_str_double(const char *str, double number)
Definition: strngs.cpp:387

◆ EmptyConstructor()

void tesseract::LSTMTrainer::EmptyConstructor ( )
protected

Definition at line 1014 of file lstmtrainer.cpp.

1014 {
1015 align_win_ = nullptr;
1016 target_win_ = nullptr;
1017 ctc_win_ = nullptr;
1018 recon_win_ = nullptr;
1020 training_stage_ = 0;
1023}

◆ EncodeString() [1/2]

bool tesseract::LSTMTrainer::EncodeString ( const STRING str,
const UNICHARSET unicharset,
const UnicharCompress recoder,
bool  simple_text,
int  null_char,
GenericVector< int > *  labels 
)
static

Definition at line 716 of file lstmtrainer.cpp.

718 {
719 if (str.string() == nullptr || str.length() <= 0) {
720 tprintf("Empty truth string!\n");
721 return false;
722 }
723 int err_index;
724 GenericVector<int> internal_labels;
725 labels->truncate(0);
726 if (!simple_text) labels->push_back(null_char);
727 std::string cleaned = unicharset.CleanupString(str.string());
728 if (unicharset.encode_string(cleaned.c_str(), true, &internal_labels, nullptr,
729 &err_index)) {
730 bool success = true;
731 for (int i = 0; i < internal_labels.size(); ++i) {
732 if (recoder != nullptr) {
733 // Re-encode labels via recoder.
734 RecodedCharID code;
735 int len = recoder->EncodeUnichar(internal_labels[i], &code);
736 if (len > 0) {
737 for (int j = 0; j < len; ++j) {
738 labels->push_back(code(j));
739 if (!simple_text) labels->push_back(null_char);
740 }
741 } else {
742 success = false;
743 err_index = 0;
744 break;
745 }
746 } else {
747 labels->push_back(internal_labels[i]);
748 if (!simple_text) labels->push_back(null_char);
749 }
750 }
751 if (success) return true;
752 }
753 tprintf("Encoding of string failed! Failure bytes:");
754 while (err_index < cleaned.size()) {
755 tprintf(" %x", cleaned[err_index++]);
756 }
757 tprintf("\n");
758 return false;
759}
int push_back(T object)
void truncate(int size)
bool encode_string(const char *str, bool give_up_on_failure, GenericVector< UNICHAR_ID > *encoding, GenericVector< char > *lengths, int *encoded_length) const
Definition: unicharset.cpp:259
static std::string CleanupString(const char *utf8_str)
Definition: unicharset.h:246

◆ EncodeString() [2/2]

bool tesseract::LSTMTrainer::EncodeString ( const STRING str,
GenericVector< int > *  labels 
) const
inline

Definition at line 246 of file lstmtrainer.h.

246 {
247 return EncodeString(str, GetUnicharset(), IsRecoding() ? &recoder_ : nullptr,
248 SimpleTextOutput(), null_char_, labels);
249 }
const UNICHARSET & GetUnicharset() const
bool EncodeString(const STRING &str, GenericVector< int > *labels) const
Definition: lstmtrainer.h:246

◆ error_rates()

const double * tesseract::LSTMTrainer::error_rates ( ) const
inline

Definition at line 140 of file lstmtrainer.h.

140 {
141 return error_rates_;
142 }

◆ FillErrorBuffer()

void tesseract::LSTMTrainer::FillErrorBuffer ( double  new_error,
ErrorTypes  type 
)

Definition at line 949 of file lstmtrainer.cpp.

949 {
950 for (int i = 0; i < kRollingBufferSize_; ++i)
951 error_buffers_[type][i] = new_error;
952 error_rates_[type] = 100.0 * new_error;
953}
static const int kRollingBufferSize_
Definition: lstmtrainer.h:478

◆ GridSearchDictParams()

Trainability tesseract::LSTMTrainer::GridSearchDictParams ( const ImageData trainingdata,
int  iteration,
double  min_dict_ratio,
double  dict_ratio_step,
double  max_dict_ratio,
double  min_cert_offset,
double  cert_offset_step,
double  max_cert_offset,
STRING results 
)

Definition at line 241 of file lstmtrainer.cpp.

244 {
245 sample_iteration_ = iteration;
246 NetworkIO fwd_outputs, targets;
247 Trainability result =
248 PrepareForBackward(trainingdata, &fwd_outputs, &targets);
249 if (result == UNENCODABLE || result == HI_PRECISION_ERR || dict_ == nullptr)
250 return result;
251
252 // Encode/decode the truth to get the normalization.
253 GenericVector<int> truth_labels, ocr_labels, xcoords;
254 ASSERT_HOST(EncodeString(trainingdata->transcription(), &truth_labels));
255 // NO-dict error.
256 RecodeBeamSearch base_search(recoder_, null_char_, SimpleTextOutput(), nullptr);
257 base_search.Decode(fwd_outputs, 1.0, 0.0, RecodeBeamSearch::kMinCertainty,
258 nullptr);
259 base_search.ExtractBestPathAsLabels(&ocr_labels, &xcoords);
260 STRING truth_text = DecodeLabels(truth_labels);
261 STRING ocr_text = DecodeLabels(ocr_labels);
262 double baseline_error = ComputeWordError(&truth_text, &ocr_text);
263 results->add_str_double("0,0=", baseline_error);
264
265 RecodeBeamSearch search(recoder_, null_char_, SimpleTextOutput(), dict_);
266 for (double r = min_dict_ratio; r < max_dict_ratio; r += dict_ratio_step) {
267 for (double c = min_cert_offset; c < max_cert_offset;
268 c += cert_offset_step) {
269 search.Decode(fwd_outputs, r, c, RecodeBeamSearch::kMinCertainty, nullptr);
270 search.ExtractBestPathAsLabels(&ocr_labels, &xcoords);
271 truth_text = DecodeLabels(truth_labels);
272 ocr_text = DecodeLabels(ocr_labels);
273 // This is destructive on both strings.
274 double word_error = ComputeWordError(&truth_text, &ocr_text);
275 if ((r == min_dict_ratio && c == min_cert_offset) ||
276 !std::isfinite(word_error)) {
277 STRING t = DecodeLabels(truth_labels);
278 STRING o = DecodeLabels(ocr_labels);
279 tprintf("r=%g, c=%g, truth=%s, ocr=%s, wderr=%g, truth[0]=%d\n", r, c,
280 t.string(), o.string(), word_error, truth_labels[0]);
281 }
282 results->add_str_double(" ", r);
283 results->add_str_double(",", c);
284 results->add_str_double("=", word_error);
285 }
286 }
287 return result;
288}
#define ASSERT_HOST(x)
Definition: errcode.h:88
LIST search(LIST list, void *key, int_compare is_equal)
Definition: oldlist.cpp:258
@ HI_PRECISION_ERR
Definition: lstmtrainer.h:51
Trainability PrepareForBackward(const ImageData *trainingdata, NetworkIO *fwd_outputs, NetworkIO *targets)
double ComputeWordError(STRING *truth_str, STRING *ocr_str)
static constexpr float kMinCertainty
Definition: recodebeam.h:222

◆ improvement_steps()

int32_t tesseract::LSTMTrainer::improvement_steps ( ) const
inline

Definition at line 150 of file lstmtrainer.h.

150{ return improvement_steps_; }

◆ InitCharSet() [1/3]

void tesseract::LSTMTrainer::InitCharSet ( )
protected

Definition at line 992 of file lstmtrainer.cpp.

992 {
995 // Initialize the unicharset and recoder.
996 if (!LoadCharsets(&mgr_)) {
998 "Must provide a traineddata containing lstm_unicharset and"
999 " lstm_recoder!\n" != nullptr);
1000 }
1001 SetNullChar();
1002}
@ TF_COMPRESS_UNICHARSET
bool LoadCharsets(const TessdataManager *mgr)

◆ InitCharSet() [2/3]

void tesseract::LSTMTrainer::InitCharSet ( const std::string &  traineddata_path)
inline

Definition at line 109 of file lstmtrainer.h.

109 {
110 ASSERT_HOST(mgr_.Init(traineddata_path.c_str()));
111 InitCharSet();
112 }
bool Init(const char *data_file_name)

◆ InitCharSet() [3/3]

void tesseract::LSTMTrainer::InitCharSet ( const TessdataManager mgr)
inline

Definition at line 113 of file lstmtrainer.h.

113 {
114 mgr_ = mgr;
115 InitCharSet();
116 }

◆ InitIterations()

void tesseract::LSTMTrainer::InitIterations ( )

Definition at line 216 of file lstmtrainer.cpp.

◆ InitNetwork()

bool tesseract::LSTMTrainer::InitNetwork ( const STRING network_spec,
int  append_index,
int  net_flags,
float  weight_range,
float  learning_rate,
float  momentum,
float  adam_beta 
)

Definition at line 172 of file lstmtrainer.cpp.

175 {
176 mgr_.SetVersionString(mgr_.VersionString() + ":" + network_spec.string());
177 adam_beta_ = adam_beta;
179 momentum_ = momentum;
180 SetNullChar();
182 append_index, net_flags, weight_range,
183 &randomizer_, &network_)) {
184 return false;
185 }
186 network_str_ += network_spec;
187 tprintf("Built network:%s from request %s\n",
188 network_->spec().string(), network_spec.string());
189 tprintf(
190 "Training parameters:\n Debug interval = %d,"
191 " weights = %g, learning rate = %g, momentum=%g\n",
193 tprintf("null char=%d\n", null_char_);
194 return true;
195}
std::string VersionString() const
void SetVersionString(const std::string &v_str)
double learning_rate() const
virtual STRING spec() const
Definition: network.h:141
static bool InitNetwork(int num_outputs, STRING network_spec, int append_index, int net_flags, float weight_range, TRand *randomizer, Network **network)

◆ InitTensorFlowNetwork()

int tesseract::LSTMTrainer::InitTensorFlowNetwork ( const std::string &  tf_proto)

◆ LastSingleError()

double tesseract::LSTMTrainer::LastSingleError ( ErrorTypes  type) const
inline

Definition at line 160 of file lstmtrainer.h.

160 {
161 return error_buffers_[type]
164 }

◆ learning_iteration()

int tesseract::LSTMTrainer::learning_iteration ( ) const
inline

Definition at line 149 of file lstmtrainer.h.

149{ return learning_iteration_; }

◆ LoadAllTrainingData()

bool tesseract::LSTMTrainer::LoadAllTrainingData ( const GenericVector< STRING > &  filenames,
CachingStrategy  cache_strategy,
bool  randomly_rotate 
)

Definition at line 298 of file lstmtrainer.cpp.

300 {
301 randomly_rotate_ = randomly_rotate;
303 return training_data_.LoadDocuments(filenames, cache_strategy, file_reader_);
304}
bool LoadDocuments(const GenericVector< STRING > &filenames, CachingStrategy cache_strategy, FileReader reader)
Definition: imagedata.cpp:580

◆ LogIterations()

void tesseract::LSTMTrainer::LogIterations ( const char *  intro_str,
STRING log_msg 
) const

Definition at line 410 of file lstmtrainer.cpp.

410 {
411 *log_msg += intro_str;
412 log_msg->add_str_int(" iteration ", learning_iteration());
413 log_msg->add_str_int("/", training_iteration());
414 log_msg->add_str_int("/", sample_iteration());
415}
int learning_iteration() const
Definition: lstmtrainer.h:149

◆ MaintainCheckpoints()

bool tesseract::LSTMTrainer::MaintainCheckpoints ( TestCallback  tester,
STRING log_msg 
)

Definition at line 310 of file lstmtrainer.cpp.

310 {
311 PrepareLogMsg(log_msg);
312 double error_rate = CharError();
313 int iteration = learning_iteration();
314 if (iteration >= stall_iteration_ &&
315 error_rate > best_error_rate_ * (1.0 + kSubTrainerMarginFraction) &&
317 // It hasn't got any better in a long while, and is a margin worse than the
318 // best, so go back to the best model and try a different learning rate.
319 StartSubtrainer(log_msg);
320 }
321 SubTrainerResult sub_trainer_result = STR_NONE;
322 if (sub_trainer_ != nullptr) {
323 sub_trainer_result = UpdateSubtrainer(log_msg);
324 if (sub_trainer_result == STR_REPLACED) {
325 // Reset the inputs, as we have overwritten *this.
326 error_rate = CharError();
327 iteration = learning_iteration();
328 PrepareLogMsg(log_msg);
329 }
330 }
331 bool result = true; // Something interesting happened.
332 GenericVector<char> rec_model_data;
333 if (error_rate < best_error_rate_) {
334 SaveRecognitionDump(&rec_model_data);
335 log_msg->add_str_double(" New best char error = ", error_rate);
336 *log_msg += UpdateErrorGraph(iteration, error_rate, rec_model_data, tester);
337 // If sub_trainer_ is not nullptr, either *this beat it to a new best, or it
338 // just overwrote *this. In either case, we have finished with it.
339 delete sub_trainer_;
340 sub_trainer_ = nullptr;
343 log_msg->add_str_int(" Transitioned to stage ", CurrentTrainingStage());
344 }
347 STRING best_model_name = DumpFilename();
348 if (!(*file_writer_)(best_trainer_, best_model_name.c_str())) {
349 *log_msg += " failed to write best model:";
350 } else {
351 *log_msg += " wrote best model:";
353 }
354 *log_msg += best_model_name;
355 }
356 } else if (error_rate > worst_error_rate_) {
357 SaveRecognitionDump(&rec_model_data);
358 log_msg->add_str_double(" New worst char error = ", error_rate);
359 *log_msg += UpdateErrorGraph(iteration, error_rate, rec_model_data, tester);
362 // Error rate has ballooned. Go back to the best model.
363 *log_msg += "\nDivergence! ";
364 // Copy best_trainer_ before reading it, as it will get overwritten.
366 if (checkpoint_reader_->Run(revert_data, this)) {
367 LogIterations("Reverted to", log_msg);
368 ReduceLearningRates(this, log_msg);
369 } else {
370 LogIterations("Failed to Revert at", log_msg);
371 }
372 // If it fails again, we will wait twice as long before reverting again.
373 stall_iteration_ = iteration + 2 * (iteration - learning_iteration());
374 // Re-save the best trainer with the new learning rates and stall
375 // iteration.
377 }
378 } else {
379 // Something interesting happened only if the sub_trainer_ was trained.
380 result = sub_trainer_result != STR_NONE;
381 }
382 if (checkpoint_writer_ != nullptr && file_writer_ != nullptr &&
383 checkpoint_name_.length() > 0) {
384 // Write a current checkpoint.
385 GenericVector<char> checkpoint;
386 if (!checkpoint_writer_->Run(FULL, this, &checkpoint) ||
387 !(*file_writer_)(checkpoint, checkpoint_name_.c_str())) {
388 *log_msg += " failed to write checkpoint.";
389 } else {
390 *log_msg += " wrote checkpoint.";
391 }
392 }
393 *log_msg += "\n";
394 return result;
395}
@ STR_REPLACED
Definition: lstmtrainer.h:66
const double kSubTrainerMarginFraction
Definition: lstmtrainer.cpp:51
const double kMinDivergenceRate
Definition: lstmtrainer.cpp:46
const double kBestCheckpointFraction
Definition: lstmtrainer.cpp:69
const double kStageTransitionThreshold
Definition: lstmtrainer.cpp:63
virtual R Run(A1, A2)=0
const char * c_str() const
Definition: strngs.cpp:205
virtual R Run(A1, A2, A3)=0
bool TransitionTrainingStage(float error_threshold)
void PrepareLogMsg(STRING *log_msg) const
void ReduceLearningRates(LSTMTrainer *samples_trainer, STRING *log_msg)
double CharError() const
Definition: lstmtrainer.h:139
void StartSubtrainer(STRING *log_msg)
void SaveRecognitionDump(GenericVector< char > *data) const
STRING UpdateErrorGraph(int iteration, double error_rate, const GenericVector< char > &model_data, TestCallback tester)
int CurrentTrainingStage() const
Definition: lstmtrainer.h:211
STRING DumpFilename() const
void LogIterations(const char *intro_str, STRING *log_msg) const
SubTrainerResult UpdateSubtrainer(STRING *log_msg)

◆ MaintainCheckpointsSpecific()

bool tesseract::LSTMTrainer::MaintainCheckpointsSpecific ( int  iteration,
const GenericVector< char > *  train_model,
const GenericVector< char > *  rec_model,
TestCallback  tester,
STRING log_msg 
)

◆ MapRecoder()

std::vector< int > tesseract::LSTMTrainer::MapRecoder ( const UNICHARSET old_chset,
const UnicharCompress old_recoder 
) const

Definition at line 957 of file lstmtrainer.cpp.

958 {
959 int num_new_codes = recoder_.code_range();
960 int num_new_unichars = GetUnicharset().size();
961 std::vector<int> code_map(num_new_codes, -1);
962 for (int c = 0; c < num_new_codes; ++c) {
963 int old_code = -1;
964 // Find all new unichar_ids that recode to something that includes c.
965 // The <= is to include the null char, which may be beyond the unicharset.
966 for (int uid = 0; uid <= num_new_unichars; ++uid) {
967 RecodedCharID codes;
968 int length = recoder_.EncodeUnichar(uid, &codes);
969 int code_index = 0;
970 while (code_index < length && codes(code_index) != c) ++code_index;
971 if (code_index == length) continue;
972 // The old unicharset must have the same unichar.
973 int old_uid =
974 uid < num_new_unichars
975 ? old_chset.unichar_to_id(GetUnicharset().id_to_unichar(uid))
976 : old_chset.size() - 1;
977 if (old_uid == INVALID_UNICHAR_ID) continue;
978 // The encoding of old_uid at the same code_index is the old code.
979 RecodedCharID old_codes;
980 if (code_index < old_recoder.EncodeUnichar(old_uid, &old_codes)) {
981 old_code = old_codes(code_index);
982 break;
983 }
984 }
985 code_map[c] = old_code;
986 }
987 return code_map;
988}
int EncodeUnichar(int unichar_id, RecodedCharID *code) const
int size() const
Definition: unicharset.h:341
UNICHAR_ID unichar_to_id(const char *const unichar_repr) const
Definition: unicharset.cpp:210

◆ mutable_training_data()

DocumentCache * tesseract::LSTMTrainer::mutable_training_data ( )
inline

Definition at line 168 of file lstmtrainer.h.

168{ return &training_data_; }

◆ NewSingleError()

double tesseract::LSTMTrainer::NewSingleError ( ErrorTypes  type) const
inline

Definition at line 154 of file lstmtrainer.h.

◆ PrepareForBackward()

Trainability tesseract::LSTMTrainer::PrepareForBackward ( const ImageData trainingdata,
NetworkIO fwd_outputs,
NetworkIO targets 
)

Definition at line 796 of file lstmtrainer.cpp.

798 {
799 if (trainingdata == nullptr) {
800 tprintf("Null trainingdata.\n");
801 return UNENCODABLE;
802 }
803 // Ensure repeatability of random elements even across checkpoints.
804 bool debug = debug_interval_ > 0 &&
806 GenericVector<int> truth_labels;
807 if (!EncodeString(trainingdata->transcription(), &truth_labels)) {
808 tprintf("Can't encode transcription: '%s' in language '%s'\n",
809 trainingdata->transcription().string(),
810 trainingdata->language().string());
811 return UNENCODABLE;
812 }
813 bool upside_down = false;
814 if (randomly_rotate_) {
815 // This ensures consistent training results.
817 upside_down = randomizer_.SignedRand(1.0) > 0.0;
818 if (upside_down) {
819 // Modify the truth labels to match the rotation:
820 // Apart from space and null, increment the label. This is changes the
821 // script-id to the same script-id but upside-down.
822 // The labels need to be reversed in order, as the first is now the last.
823 for (int c = 0; c < truth_labels.size(); ++c) {
824 if (truth_labels[c] != UNICHAR_SPACE && truth_labels[c] != null_char_)
825 ++truth_labels[c];
826 }
827 truth_labels.reverse();
828 }
829 }
830 int w = 0;
831 while (w < truth_labels.size() &&
832 (truth_labels[w] == UNICHAR_SPACE || truth_labels[w] == null_char_))
833 ++w;
834 if (w == truth_labels.size()) {
835 tprintf("Blank transcription: %s\n",
836 trainingdata->transcription().string());
837 return UNENCODABLE;
838 }
839 float image_scale;
840 NetworkIO inputs;
841 bool invert = trainingdata->boxes().empty();
842 if (!RecognizeLine(*trainingdata, invert, debug, invert, upside_down,
843 &image_scale, &inputs, fwd_outputs)) {
844 tprintf("Image not trainable\n");
845 return UNENCODABLE;
846 }
847 targets->Resize(*fwd_outputs, network_->NumOutputs());
848 LossType loss_type = OutputLossType();
849 if (loss_type == LT_SOFTMAX) {
850 if (!ComputeTextTargets(*fwd_outputs, truth_labels, targets)) {
851 tprintf("Compute simple targets failed!\n");
852 return UNENCODABLE;
853 }
854 } else if (loss_type == LT_CTC) {
855 if (!ComputeCTCTargets(truth_labels, fwd_outputs, targets)) {
856 tprintf("Compute CTC targets failed!\n");
857 return UNENCODABLE;
858 }
859 } else {
860 tprintf("Logistic outputs not implemented yet!\n");
861 return UNENCODABLE;
862 }
863 GenericVector<int> ocr_labels;
864 GenericVector<int> xcoords;
865 LabelsFromOutputs(*fwd_outputs, &ocr_labels, &xcoords);
866 // CTC does not produce correct target labels to begin with.
867 if (loss_type != LT_CTC) {
868 LabelsFromOutputs(*targets, &truth_labels, &xcoords);
869 }
870 if (!DebugLSTMTraining(inputs, *trainingdata, *fwd_outputs, truth_labels,
871 *targets)) {
872 tprintf("Input width was %d\n", inputs.Width());
873 return UNENCODABLE;
874 }
875 STRING ocr_text = DecodeLabels(ocr_labels);
876 STRING truth_text = DecodeLabels(truth_labels);
877 targets->SubtractAllFromFloat(*fwd_outputs);
878 if (debug_interval_ != 0) {
879 if (truth_text != ocr_text) {
880 tprintf("Iteration %d: BEST OCR TEXT : %s\n",
881 training_iteration(), ocr_text.string());
882 }
883 }
884 double char_error = ComputeCharError(truth_labels, ocr_labels);
885 double word_error = ComputeWordError(&truth_text, &ocr_text);
886 double delta_error = ComputeErrorRates(*targets, char_error, word_error);
887 if (debug_interval_ != 0) {
888 tprintf("File %s line %d %s:\n", trainingdata->imagefilename().string(),
889 trainingdata->page_number(), delta_error == 0.0 ? "(Perfect)" : "");
890 }
891 if (delta_error == 0.0) return PERFECT;
892 if (targets->AnySuspiciousTruth(kHighConfidence)) return HI_PRECISION_ERR;
893 return TRAINABLE;
894}
@ UNICHAR_SPACE
Definition: unicharset.h:34
const double kHighConfidence
Definition: lstmtrainer.cpp:65
double SignedRand(double range)
Definition: helpers.h:55
void RecognizeLine(const ImageData &image_data, bool invert, bool debug, double worst_dict_cert, const TBOX &line_box, PointerVector< WERD_RES > *words, int lstm_choice_mode=0)
double ComputeErrorRates(const NetworkIO &deltas, double char_error, double word_error)
bool ComputeTextTargets(const NetworkIO &outputs, const GenericVector< int > &truth_labels, NetworkIO *targets)
bool ComputeCTCTargets(const GenericVector< int > &truth_labels, NetworkIO *outputs, NetworkIO *targets)
double ComputeCharError(const GenericVector< int > &truth_str, const GenericVector< int > &ocr_str)
bool DebugLSTMTraining(const NetworkIO &inputs, const ImageData &trainingdata, const NetworkIO &fwd_outputs, const GenericVector< int > &truth_labels, const NetworkIO &outputs)
int NumOutputs() const
Definition: network.h:123

◆ PrepareLogMsg()

void tesseract::LSTMTrainer::PrepareLogMsg ( STRING log_msg) const

Definition at line 398 of file lstmtrainer.cpp.

398 {
399 LogIterations("At", log_msg);
400 log_msg->add_str_double(", Mean rms=", error_rates_[ET_RMS]);
401 log_msg->add_str_double("%, delta=", error_rates_[ET_DELTA]);
402 log_msg->add_str_double("%, char train=", error_rates_[ET_CHAR_ERROR]);
403 log_msg->add_str_double("%, word train=", error_rates_[ET_WORD_RECERR]);
404 log_msg->add_str_double("%, skip ratio=", error_rates_[ET_SKIP_RATIO]);
405 *log_msg += "%, ";
406}

◆ ReadLocalTrainingDump()

bool tesseract::LSTMTrainer::ReadLocalTrainingDump ( const TessdataManager mgr,
const char *  data,
int  size 
)

Definition at line 909 of file lstmtrainer.cpp.

910 {
911 if (size == 0) {
912 tprintf("Warning: data size is 0 in LSTMTrainer::ReadLocalTrainingDump\n");
913 return false;
914 }
915 TFile fp;
916 fp.Open(data, size);
917 return DeSerialize(mgr, &fp);
918}
bool DeSerialize(const TessdataManager *mgr, TFile *fp)

◆ ReadSizedTrainingDump()

bool tesseract::LSTMTrainer::ReadSizedTrainingDump ( const char *  data,
int  size,
LSTMTrainer trainer 
) const
inline

Definition at line 296 of file lstmtrainer.h.

297 {
298 return trainer->ReadLocalTrainingDump(&mgr_, data, size);
299 }

◆ ReadTrainingDump()

bool tesseract::LSTMTrainer::ReadTrainingDump ( const GenericVector< char > &  data,
LSTMTrainer trainer 
) const
inline

Definition at line 291 of file lstmtrainer.h.

292 {
293 if (data.empty()) return false;
294 return ReadSizedTrainingDump(&data[0], data.size(), trainer);
295 }
bool ReadSizedTrainingDump(const char *data, int size, LSTMTrainer *trainer) const
Definition: lstmtrainer.h:296

◆ ReduceLayerLearningRates()

int tesseract::LSTMTrainer::ReduceLayerLearningRates ( double  factor,
int  num_samples,
LSTMTrainer samples_trainer 
)

Definition at line 607 of file lstmtrainer.cpp.

608 {
609 enum WhichWay {
610 LR_DOWN, // Learning rate will go down by factor.
611 LR_SAME, // Learning rate will stay the same.
612 LR_COUNT // Size of arrays.
613 };
615 int num_layers = layers.size();
616 GenericVector<int> num_weights;
617 num_weights.init_to_size(num_layers, 0);
618 GenericVector<double> bad_sums[LR_COUNT];
619 GenericVector<double> ok_sums[LR_COUNT];
620 for (int i = 0; i < LR_COUNT; ++i) {
621 bad_sums[i].init_to_size(num_layers, 0.0);
622 ok_sums[i].init_to_size(num_layers, 0.0);
623 }
624 double momentum_factor = 1.0 / (1.0 - momentum_);
625 GenericVector<char> orig_trainer;
626 samples_trainer->SaveTrainingDump(LIGHT, this, &orig_trainer);
627 for (int i = 0; i < num_layers; ++i) {
628 Network* layer = GetLayer(layers[i]);
629 num_weights[i] = layer->IsTraining() ? layer->num_weights() : 0;
630 }
631 int iteration = sample_iteration();
632 for (int s = 0; s < num_samples; ++s) {
633 // Which way will we modify the learning rate?
634 for (int ww = 0; ww < LR_COUNT; ++ww) {
635 // Transfer momentum to learning rate and adjust by the ww factor.
636 float ww_factor = momentum_factor;
637 if (ww == LR_DOWN) ww_factor *= factor;
638 // Make a copy of *this, so we can mess about without damaging anything.
639 LSTMTrainer copy_trainer;
640 samples_trainer->ReadTrainingDump(orig_trainer, &copy_trainer);
641 // Clear the updates, doing nothing else.
642 copy_trainer.network_->Update(0.0, 0.0, 0.0, 0);
643 // Adjust the learning rate in each layer.
644 for (int i = 0; i < num_layers; ++i) {
645 if (num_weights[i] == 0) continue;
646 copy_trainer.ScaleLayerLearningRate(layers[i], ww_factor);
647 }
648 copy_trainer.SetIteration(iteration);
649 // Train on the sample, but keep the update in updates_ instead of
650 // applying to the weights.
651 const ImageData* trainingdata =
652 copy_trainer.TrainOnLine(samples_trainer, true);
653 if (trainingdata == nullptr) continue;
654 // We'll now use this trainer again for each layer.
655 GenericVector<char> updated_trainer;
656 samples_trainer->SaveTrainingDump(LIGHT, &copy_trainer, &updated_trainer);
657 for (int i = 0; i < num_layers; ++i) {
658 if (num_weights[i] == 0) continue;
659 LSTMTrainer layer_trainer;
660 samples_trainer->ReadTrainingDump(updated_trainer, &layer_trainer);
661 Network* layer = layer_trainer.GetLayer(layers[i]);
662 // Update the weights in just the layer, using Adam if enabled.
663 layer->Update(0.0, momentum_, adam_beta_,
664 layer_trainer.training_iteration_ + 1);
665 // Zero the updates matrix again.
666 layer->Update(0.0, 0.0, 0.0, 0);
667 // Train again on the same sample, again holding back the updates.
668 layer_trainer.TrainOnLine(trainingdata, true);
669 // Count the sign changes in the updates in layer vs in copy_trainer.
670 float before_bad = bad_sums[ww][i];
671 float before_ok = ok_sums[ww][i];
672 layer->CountAlternators(*copy_trainer.GetLayer(layers[i]),
673 &ok_sums[ww][i], &bad_sums[ww][i]);
674 float bad_frac =
675 bad_sums[ww][i] + ok_sums[ww][i] - before_bad - before_ok;
676 if (bad_frac > 0.0f)
677 bad_frac = (bad_sums[ww][i] - before_bad) / bad_frac;
678 }
679 }
680 ++iteration;
681 }
682 int num_lowered = 0;
683 for (int i = 0; i < num_layers; ++i) {
684 if (num_weights[i] == 0) continue;
685 Network* layer = GetLayer(layers[i]);
686 float lr = GetLayerLearningRate(layers[i]);
687 double total_down = bad_sums[LR_DOWN][i] + ok_sums[LR_DOWN][i];
688 double total_same = bad_sums[LR_SAME][i] + ok_sums[LR_SAME][i];
689 double frac_down = bad_sums[LR_DOWN][i] / total_down;
690 double frac_same = bad_sums[LR_SAME][i] / total_same;
691 tprintf("Layer %d=%s: lr %g->%g%%, lr %g->%g%%", i, layer->name().string(),
692 lr * factor, 100.0 * frac_down, lr, 100.0 * frac_same);
693 if (frac_down < frac_same * kImprovementFraction) {
694 tprintf(" REDUCED\n");
695 ScaleLayerLearningRate(layers[i], factor);
696 ++num_lowered;
697 } else {
698 tprintf(" SAME\n");
699 }
700 }
701 if (num_lowered == 0) {
702 // Just lower everything to make sure.
703 for (int i = 0; i < num_layers; ++i) {
704 if (num_weights[i] > 0) {
705 ScaleLayerLearningRate(layers[i], factor);
706 ++num_lowered;
707 }
708 }
709 }
710 return num_lowered;
711}
const double kImprovementFraction
Definition: lstmtrainer.cpp:67
Network * GetLayer(const STRING &id) const
float GetLayerLearningRate(const STRING &id) const
void ScaleLayerLearningRate(const STRING &id, double factor)
GenericVector< STRING > EnumerateLayers() const

◆ ReduceLearningRates()

void tesseract::LSTMTrainer::ReduceLearningRates ( LSTMTrainer samples_trainer,
STRING log_msg 
)

Definition at line 588 of file lstmtrainer.cpp.

589 {
591 int num_reduced = ReduceLayerLearningRates(
593 log_msg->add_str_int("\nReduced learning rate on layers: ", num_reduced);
594 } else {
596 log_msg->add_str_double("\nReduced learning rate to :", learning_rate_);
597 }
598 *log_msg += "\n";
599}
const double kLearningRateDecay
Definition: lstmtrainer.cpp:53
@ NF_LAYER_SPECIFIC_LR
Definition: network.h:87
const int kNumAdjustmentIterations
Definition: lstmtrainer.cpp:55
void ScaleLearningRate(double factor)
int ReduceLayerLearningRates(double factor, int num_samples, LSTMTrainer *samples_trainer)
bool TestFlag(NetworkFlags flag) const
Definition: network.h:144

◆ RollErrorBuffers()

void tesseract::LSTMTrainer::RollErrorBuffers ( )
protected

Definition at line 1265 of file lstmtrainer.cpp.

1265 {
1267 if (NewSingleError(ET_DELTA) > 0.0)
1269 else
1272 if (debug_interval_ != 0) {
1273 tprintf("Mean rms=%g%%, delta=%g%%, train=%g%%(%g%%), skip ratio=%g%%\n",
1277 }
1278}
double NewSingleError(ErrorTypes type) const
Definition: lstmtrainer.h:154

◆ SaveRecognitionDump()

void tesseract::LSTMTrainer::SaveRecognitionDump ( GenericVector< char > *  data) const

Definition at line 930 of file lstmtrainer.cpp.

930 {
931 TFile fp;
932 fp.OpenWrite(data);
936}
@ TS_TEMP_DISABLE
Definition: network.h:97
@ TS_RE_ENABLE
Definition: network.h:99
bool Serialize(const TessdataManager *mgr, TFile *fp) const

◆ SaveTraineddata()

bool tesseract::LSTMTrainer::SaveTraineddata ( const STRING filename)

Definition at line 921 of file lstmtrainer.cpp.

921 {
922 GenericVector<char> recognizer_data;
923 SaveRecognitionDump(&recognizer_data);
924 mgr_.OverwriteEntry(TESSDATA_LSTM, &recognizer_data[0],
925 recognizer_data.size());
926 return mgr_.SaveFile(filename, file_writer_);
927}
void OverwriteEntry(TessdataType type, const char *data, int size)
bool SaveFile(const STRING &filename, FileWriter writer) const

◆ SaveTrainingDump()

bool tesseract::LSTMTrainer::SaveTrainingDump ( SerializeAmount  serialize_amount,
const LSTMTrainer trainer,
GenericVector< char > *  data 
) const

Definition at line 900 of file lstmtrainer.cpp.

902 {
903 TFile fp;
904 fp.OpenWrite(data);
905 return trainer->Serialize(serialize_amount, &mgr_, &fp);
906}

◆ Serialize()

bool tesseract::LSTMTrainer::Serialize ( SerializeAmount  serialize_amount,
const TessdataManager mgr,
TFile fp 
) const

Definition at line 429 of file lstmtrainer.cpp.

430 {
431 if (!LSTMRecognizer::Serialize(mgr, fp)) return false;
432 if (!fp->Serialize(&learning_iteration_)) return false;
433 if (!fp->Serialize(&prev_sample_iteration_)) return false;
434 if (!fp->Serialize(&perfect_delay_)) return false;
435 if (!fp->Serialize(&last_perfect_training_iteration_)) return false;
436 for (const auto & error_buffer : error_buffers_) {
437 if (!error_buffer.Serialize(fp)) return false;
438 }
439 if (!fp->Serialize(&error_rates_[0], countof(error_rates_))) return false;
440 if (!fp->Serialize(&training_stage_)) return false;
441 uint8_t amount = serialize_amount;
442 if (!fp->Serialize(&amount)) return false;
443 if (serialize_amount == LIGHT) return true; // We are done.
444 if (!fp->Serialize(&best_error_rate_)) return false;
445 if (!fp->Serialize(&best_error_rates_[0], countof(best_error_rates_))) return false;
446 if (!fp->Serialize(&best_iteration_)) return false;
447 if (!fp->Serialize(&worst_error_rate_)) return false;
448 if (!fp->Serialize(&worst_error_rates_[0], countof(worst_error_rates_))) return false;
449 if (!fp->Serialize(&worst_iteration_)) return false;
450 if (!fp->Serialize(&stall_iteration_)) return false;
451 if (!best_model_data_.Serialize(fp)) return false;
452 if (!worst_model_data_.Serialize(fp)) return false;
453 if (serialize_amount != NO_BEST_TRAINER && !best_trainer_.Serialize(fp))
454 return false;
455 GenericVector<char> sub_data;
456 if (sub_trainer_ != nullptr && !SaveTrainingDump(LIGHT, sub_trainer_, &sub_data))
457 return false;
458 if (!sub_data.Serialize(fp)) return false;
459 if (!best_error_history_.Serialize(fp)) return false;
460 if (!best_error_iterations_.Serialize(fp)) return false;
461 return fp->Serialize(&improvement_steps_);
462}
bool Serialize(FILE *fp) const

◆ set_perfect_delay()

void tesseract::LSTMTrainer::set_perfect_delay ( int  delay)
inline

Definition at line 151 of file lstmtrainer.h.

151{ perfect_delay_ = delay; }

◆ SetNullChar()

void tesseract::LSTMTrainer::SetNullChar ( )
protected

Definition at line 1005 of file lstmtrainer.cpp.

1005 {
1007 : GetUnicharset().size();
1008 RecodedCharID code;
1010 null_char_ = code(0);
1011}
@ UNICHAR_BROKEN
Definition: unicharset.h:36
bool has_special_codes() const
Definition: unicharset.h:722

◆ SetupCheckpointInfo()

void tesseract::LSTMTrainer::SetupCheckpointInfo ( )

◆ StartSubtrainer()

void tesseract::LSTMTrainer::StartSubtrainer ( STRING log_msg)

Definition at line 515 of file lstmtrainer.cpp.

515 {
516 delete sub_trainer_;
519 *log_msg += " Failed to revert to previous best for trial!";
520 delete sub_trainer_;
521 sub_trainer_ = nullptr;
522 } else {
523 log_msg->add_str_int(" Trial sub_trainer_ from iteration ",
525 // Reduce learning rate so it doesn't diverge this time.
526 sub_trainer_->ReduceLearningRates(this, log_msg);
527 // If it fails again, we will wait twice as long before reverting again.
528 int stall_offset =
530 stall_iteration_ = learning_iteration() + 2 * stall_offset;
532 // Re-save the best trainer with the new learning rates and stall iteration.
534 }
535}

◆ training_data()

const DocumentCache & tesseract::LSTMTrainer::training_data ( ) const
inline

Definition at line 165 of file lstmtrainer.h.

165 {
166 return training_data_;
167 }

◆ TrainOnLine() [1/2]

Trainability tesseract::LSTMTrainer::TrainOnLine ( const ImageData trainingdata,
bool  batch 
)

Definition at line 763 of file lstmtrainer.cpp.

764 {
765 NetworkIO fwd_outputs, targets;
766 Trainability trainable =
767 PrepareForBackward(trainingdata, &fwd_outputs, &targets);
769 if (trainable == UNENCODABLE || trainable == NOT_BOXED) {
770 return trainable; // Sample was unusable.
771 }
772 bool debug = debug_interval_ > 0 &&
774 // Run backprop on the output.
775 NetworkIO bp_deltas;
776 if (network_->IsTraining() &&
777 (trainable != PERFECT ||
780 network_->Backward(debug, targets, &scratch_space_, &bp_deltas);
783 }
784#ifndef GRAPHICS_DISABLED
785 if (debug_interval_ == 1 && debug_win_ != nullptr) {
787 }
788#endif // GRAPHICS_DISABLED
789 // Roll the memory of past means.
791 return trainable;
792}
@ SVET_CLICK
Definition: scrollview.h:48
NetworkScratch scratch_space_
virtual bool Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch, NetworkIO *back_deltas)=0
bool IsTraining() const
Definition: network.h:115
virtual void Update(float learning_rate, float momentum, float adam_beta, int num_samples)
Definition: network.h:230
SVEvent * AwaitEvent(SVEventType type)
Definition: scrollview.cpp:443

◆ TrainOnLine() [2/2]

const ImageData * tesseract::LSTMTrainer::TrainOnLine ( LSTMTrainer samples_trainer,
bool  batch 
)
inline

Definition at line 259 of file lstmtrainer.h.

259 {
260 int sample_index = sample_iteration();
261 const ImageData* image =
262 samples_trainer->training_data_.GetPageBySerial(sample_index);
263 if (image != nullptr) {
264 Trainability trainable = TrainOnLine(image, batch);
265 if (trainable == UNENCODABLE || trainable == NOT_BOXED) {
266 return nullptr; // Sample was unusable.
267 }
268 } else {
270 }
271 return image;
272 }
const ImageData * TrainOnLine(LSTMTrainer *samples_trainer, bool batch)
Definition: lstmtrainer.h:259

◆ TransitionTrainingStage()

bool tesseract::LSTMTrainer::TransitionTrainingStage ( float  error_threshold)

Definition at line 419 of file lstmtrainer.cpp.

419 {
420 if (best_error_rate_ < error_threshold &&
423 return true;
424 }
425 return false;
426}

◆ TryLoadingCheckpoint()

bool tesseract::LSTMTrainer::TryLoadingCheckpoint ( const char *  filename,
const char *  old_traineddata 
)

Definition at line 129 of file lstmtrainer.cpp.

130 {
132 if (!(*file_reader_)(filename, &data)) return false;
133 tprintf("Loaded file %s, unpacking...\n", filename);
134 if (!checkpoint_reader_->Run(data, this)) return false;
135 StaticShape shape = network_->OutputShape(network_->InputShape());
136 if (((old_traineddata == nullptr || *old_traineddata == '\0') &&
138 filename == old_traineddata) {
139 return true; // Normal checkpoint load complete.
140 }
141 tprintf("Code range changed from %d to %d!\n", network_->NumOutputs(),
143 if (old_traineddata == nullptr || *old_traineddata == '\0') {
144 tprintf("Must supply the old traineddata for code conversion!\n");
145 return false;
146 }
147 TessdataManager old_mgr;
148 ASSERT_HOST(old_mgr.Init(old_traineddata));
149 TFile fp;
150 if (!old_mgr.GetComponent(TESSDATA_LSTM_UNICHARSET, &fp)) return false;
151 UNICHARSET old_chset;
152 if (!old_chset.load_from_file(&fp, false)) return false;
153 if (!old_mgr.GetComponent(TESSDATA_LSTM_RECODER, &fp)) return false;
154 UnicharCompress old_recoder;
155 if (!old_recoder.DeSerialize(&fp)) return false;
156 std::vector<int> code_map = MapRecoder(old_chset, old_recoder);
157 // Set the null_char_ to the new value.
158 int old_null_char = null_char_;
159 SetNullChar();
160 // Map the softmax(s) in the network.
161 network_->RemapOutputs(old_recoder.code_range(), code_map);
162 tprintf("Previous null char=%d mapped to %d\n", old_null_char, null_char_);
163 return true;
164}
@ TESSDATA_LSTM_UNICHARSET
@ TESSDATA_LSTM_RECODER
bool load_from_file(const char *const filename, bool skip_fragments)
Definition: unicharset.h:388
std::vector< int > MapRecoder(const UNICHARSET &old_chset, const UnicharCompress &old_recoder) const
virtual int RemapOutputs(int old_no, const std::vector< int > &code_map)
Definition: network.h:186
virtual StaticShape OutputShape(const StaticShape &input_shape) const
Definition: network.h:133
virtual StaticShape InputShape() const
Definition: network.h:127

◆ UpdateErrorBuffer()

void tesseract::LSTMTrainer::UpdateErrorBuffer ( double  new_error,
ErrorTypes  type 
)
protected

Definition at line 1252 of file lstmtrainer.cpp.

1252 {
1254 error_buffers_[type][index] = new_error;
1255 // Compute the mean error.
1256 int mean_count = std::min(training_iteration_ + 1, error_buffers_[type].size());
1257 double buffer_sum = 0.0;
1258 for (int i = 0; i < mean_count; ++i) buffer_sum += error_buffers_[type][i];
1259 double mean = buffer_sum / mean_count;
1260 // Trim precision to 1/1000 of 1%.
1261 error_rates_[type] = IntCastRounded(100000.0 * mean) / 1000.0;
1262}
int IntCastRounded(double x)
Definition: helpers.h:175

◆ UpdateErrorGraph()

STRING tesseract::LSTMTrainer::UpdateErrorGraph ( int  iteration,
double  error_rate,
const GenericVector< char > &  model_data,
TestCallback  tester 
)
protected

Definition at line 1284 of file lstmtrainer.cpp.

1286 {
1287 if (error_rate > best_error_rate_
1288 && iteration < best_iteration_ + kErrorGraphInterval) {
1289 // Too soon to record a new point.
1290 if (tester != nullptr && !worst_model_data_.empty()) {
1293 return tester->Run(worst_iteration_, nullptr, mgr_, CurrentTrainingStage());
1294 } else {
1295 return "";
1296 }
1297 }
1298 STRING result;
1299 // NOTE: there are 2 asymmetries here:
1300 // 1. We are computing the global minimum, but the local maximum in between.
1301 // 2. If the tester returns an empty string, indicating that it is busy,
1302 // call it repeatedly on new local maxima to test the previous min, but
1303 // not the other way around, as there is little point testing the maxima
1304 // between very frequent minima.
1305 if (error_rate < best_error_rate_) {
1306 // This is a new (global) minimum.
1307 if (tester != nullptr && !worst_model_data_.empty()) {
1310 result = tester->Run(worst_iteration_, worst_error_rates_, mgr_,
1313 best_model_data_ = model_data;
1314 }
1315 best_error_rate_ = error_rate;
1317 best_iteration_ = iteration;
1318 best_error_history_.push_back(error_rate);
1320 // Compute 2% decay time.
1321 double two_percent_more = error_rate + 2.0;
1322 int i;
1323 for (i = best_error_history_.size() - 1;
1324 i >= 0 && best_error_history_[i] < two_percent_more; --i) {
1325 }
1326 int old_iteration = i >= 0 ? best_error_iterations_[i] : 0;
1327 improvement_steps_ = iteration - old_iteration;
1328 tprintf("2 Percent improvement time=%d, best error was %g @ %d\n",
1329 improvement_steps_, i >= 0 ? best_error_history_[i] : 100.0,
1330 old_iteration);
1331 } else if (error_rate > best_error_rate_) {
1332 // This is a new (local) maximum.
1333 if (tester != nullptr) {
1334 if (!best_model_data_.empty()) {
1337 result = tester->Run(best_iteration_, best_error_rates_, mgr_,
1339 } else if (!worst_model_data_.empty()) {
1340 // Allow for multiple data points with "worst" error rate.
1343 result = tester->Run(worst_iteration_, worst_error_rates_, mgr_,
1345 }
1346 if (result.length() > 0)
1348 worst_model_data_ = model_data;
1349 }
1350 }
1351 worst_error_rate_ = error_rate;
1353 worst_iteration_ = iteration;
1354 return result;
1355}
const int kErrorGraphInterval
Definition: lstmtrainer.cpp:57

◆ UpdateSubtrainer()

SubTrainerResult tesseract::LSTMTrainer::UpdateSubtrainer ( STRING log_msg)

Definition at line 545 of file lstmtrainer.cpp.

545 {
546 double training_error = CharError();
547 double sub_error = sub_trainer_->CharError();
548 double sub_margin = (training_error - sub_error) / sub_error;
549 if (sub_margin >= kSubTrainerMarginFraction) {
550 log_msg->add_str_double(" sub_trainer=", sub_error);
551 log_msg->add_str_double(" margin=", 100.0 * sub_margin);
552 *log_msg += "\n";
553 // Catch up to current iteration.
554 int end_iteration = training_iteration();
555 while (sub_trainer_->training_iteration() < end_iteration &&
556 sub_margin >= kSubTrainerMarginFraction) {
557 int target_iteration =
559 while (sub_trainer_->training_iteration() < target_iteration) {
560 sub_trainer_->TrainOnLine(this, false);
561 }
562 STRING batch_log = "Sub:";
563 sub_trainer_->PrepareLogMsg(&batch_log);
564 batch_log += "\n";
565 tprintf("UpdateSubtrainer:%s", batch_log.string());
566 *log_msg += batch_log;
567 sub_error = sub_trainer_->CharError();
568 sub_margin = (training_error - sub_error) / sub_error;
569 }
570 if (sub_error < best_error_rate_ &&
571 sub_margin >= kSubTrainerMarginFraction) {
572 // The sub_trainer_ has won the race to a new best. Switch to it.
573 GenericVector<char> updated_trainer;
574 SaveTrainingDump(LIGHT, sub_trainer_, &updated_trainer);
575 ReadTrainingDump(updated_trainer, this);
576 log_msg->add_str_int(" Sub trainer wins at iteration ",
578 *log_msg += "\n";
579 return STR_REPLACED;
580 }
581 return STR_UPDATED;
582 }
583 return STR_NONE;
584}
const int kNumPagesPerBatch
Definition: lstmtrainer.cpp:59

Member Data Documentation

◆ align_win_

ScrollView* tesseract::LSTMTrainer::align_win_
protected

Definition at line 397 of file lstmtrainer.h.

◆ best_error_history_

GenericVector<double> tesseract::LSTMTrainer::best_error_history_
protected

Definition at line 457 of file lstmtrainer.h.

◆ best_error_iterations_

GenericVector<int> tesseract::LSTMTrainer::best_error_iterations_
protected

Definition at line 458 of file lstmtrainer.h.

◆ best_error_rate_

double tesseract::LSTMTrainer::best_error_rate_
protected

Definition at line 430 of file lstmtrainer.h.

◆ best_error_rates_

double tesseract::LSTMTrainer::best_error_rates_[ET_COUNT]
protected

Definition at line 432 of file lstmtrainer.h.

◆ best_iteration_

int tesseract::LSTMTrainer::best_iteration_
protected

Definition at line 434 of file lstmtrainer.h.

◆ best_model_data_

GenericVector<char> tesseract::LSTMTrainer::best_model_data_
protected

Definition at line 444 of file lstmtrainer.h.

◆ best_model_name_

STRING tesseract::LSTMTrainer::best_model_name_
protected

Definition at line 416 of file lstmtrainer.h.

◆ best_trainer_

GenericVector<char> tesseract::LSTMTrainer::best_trainer_
protected

Definition at line 447 of file lstmtrainer.h.

◆ checkpoint_iteration_

int tesseract::LSTMTrainer::checkpoint_iteration_
protected

Definition at line 407 of file lstmtrainer.h.

◆ checkpoint_name_

STRING tesseract::LSTMTrainer::checkpoint_name_
protected

Definition at line 411 of file lstmtrainer.h.

◆ checkpoint_reader_

CheckPointReader tesseract::LSTMTrainer::checkpoint_reader_
protected

Definition at line 424 of file lstmtrainer.h.

◆ checkpoint_writer_

CheckPointWriter tesseract::LSTMTrainer::checkpoint_writer_
protected

Definition at line 425 of file lstmtrainer.h.

◆ ctc_win_

ScrollView* tesseract::LSTMTrainer::ctc_win_
protected

Definition at line 401 of file lstmtrainer.h.

◆ debug_interval_

int tesseract::LSTMTrainer::debug_interval_
protected

Definition at line 405 of file lstmtrainer.h.

◆ error_buffers_

GenericVector<double> tesseract::LSTMTrainer::error_buffers_[ET_COUNT]
protected

Definition at line 479 of file lstmtrainer.h.

◆ error_rate_of_last_saved_best_

float tesseract::LSTMTrainer::error_rate_of_last_saved_best_
protected

Definition at line 452 of file lstmtrainer.h.

◆ error_rates_

double tesseract::LSTMTrainer::error_rates_[ET_COUNT]
protected

Definition at line 481 of file lstmtrainer.h.

◆ file_reader_

FileReader tesseract::LSTMTrainer::file_reader_
protected

Definition at line 420 of file lstmtrainer.h.

◆ file_writer_

FileWriter tesseract::LSTMTrainer::file_writer_
protected

Definition at line 421 of file lstmtrainer.h.

◆ improvement_steps_

int32_t tesseract::LSTMTrainer::improvement_steps_
protected

Definition at line 460 of file lstmtrainer.h.

◆ kRollingBufferSize_

const int tesseract::LSTMTrainer::kRollingBufferSize_ = 1000
staticprotected

Definition at line 478 of file lstmtrainer.h.

◆ last_perfect_training_iteration_

int tesseract::LSTMTrainer::last_perfect_training_iteration_
protected

Definition at line 475 of file lstmtrainer.h.

◆ learning_iteration_

int tesseract::LSTMTrainer::learning_iteration_
protected

Definition at line 464 of file lstmtrainer.h.

◆ mgr_

TessdataManager tesseract::LSTMTrainer::mgr_
protected

Definition at line 483 of file lstmtrainer.h.

◆ model_base_

STRING tesseract::LSTMTrainer::model_base_
protected

Definition at line 409 of file lstmtrainer.h.

◆ num_training_stages_

int tesseract::LSTMTrainer::num_training_stages_
protected

Definition at line 418 of file lstmtrainer.h.

◆ perfect_delay_

int tesseract::LSTMTrainer::perfect_delay_
protected

Definition at line 472 of file lstmtrainer.h.

◆ prev_sample_iteration_

int tesseract::LSTMTrainer::prev_sample_iteration_
protected

Definition at line 466 of file lstmtrainer.h.

◆ randomly_rotate_

bool tesseract::LSTMTrainer::randomly_rotate_
protected

Definition at line 413 of file lstmtrainer.h.

◆ recon_win_

ScrollView* tesseract::LSTMTrainer::recon_win_
protected

Definition at line 403 of file lstmtrainer.h.

◆ stall_iteration_

int tesseract::LSTMTrainer::stall_iteration_
protected

Definition at line 442 of file lstmtrainer.h.

◆ sub_trainer_

LSTMTrainer* tesseract::LSTMTrainer::sub_trainer_
protected

Definition at line 450 of file lstmtrainer.h.

◆ target_win_

ScrollView* tesseract::LSTMTrainer::target_win_
protected

Definition at line 399 of file lstmtrainer.h.

◆ training_data_

DocumentCache tesseract::LSTMTrainer::training_data_
protected

Definition at line 414 of file lstmtrainer.h.

◆ training_stage_

int tesseract::LSTMTrainer::training_stage_
protected

Definition at line 454 of file lstmtrainer.h.

◆ worst_error_rate_

double tesseract::LSTMTrainer::worst_error_rate_
protected

Definition at line 436 of file lstmtrainer.h.

◆ worst_error_rates_

double tesseract::LSTMTrainer::worst_error_rates_[ET_COUNT]
protected

Definition at line 438 of file lstmtrainer.h.

◆ worst_iteration_

int tesseract::LSTMTrainer::worst_iteration_
protected

Definition at line 440 of file lstmtrainer.h.

◆ worst_model_data_

GenericVector<char> tesseract::LSTMTrainer::worst_model_data_
protected

Definition at line 445 of file lstmtrainer.h.


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