tesseract  4.1.1
lstmtrainer.cpp
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1 // File: lstmtrainer.cpp
3 // Description: Top-level line trainer class for LSTM-based networks.
4 // Author: Ray Smith
5 //
6 // (C) Copyright 2013, Google Inc.
7 // Licensed under the Apache License, Version 2.0 (the "License");
8 // you may not use this file except in compliance with the License.
9 // You may obtain a copy of the License at
10 // http://www.apache.org/licenses/LICENSE-2.0
11 // Unless required by applicable law or agreed to in writing, software
12 // distributed under the License is distributed on an "AS IS" BASIS,
13 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 // See the License for the specific language governing permissions and
15 // limitations under the License.
17 
18 #define _USE_MATH_DEFINES // needed to get definition of M_SQRT1_2
19 
20 // Include automatically generated configuration file if running autoconf.
21 #ifdef HAVE_CONFIG_H
22 #include "config_auto.h"
23 #endif
24 
25 #include "lstmtrainer.h"
26 #include <string>
27 
28 #include "allheaders.h"
29 #include "boxread.h"
30 #include "ctc.h"
31 #include "imagedata.h"
32 #include "input.h"
33 #include "networkbuilder.h"
34 #include "ratngs.h"
35 #include "recodebeam.h"
36 #ifdef INCLUDE_TENSORFLOW
37 #include "tfnetwork.h"
38 #endif
39 #include "tprintf.h"
40 
41 #include "callcpp.h"
42 
43 namespace tesseract {
44 
45 // Min actual error rate increase to constitute divergence.
46 const double kMinDivergenceRate = 50.0;
47 // Min iterations since last best before acting on a stall.
48 const int kMinStallIterations = 10000;
49 // Fraction of current char error rate that sub_trainer_ has to be ahead
50 // before we declare the sub_trainer_ a success and switch to it.
51 const double kSubTrainerMarginFraction = 3.0 / 128;
52 // Factor to reduce learning rate on divergence.
53 const double kLearningRateDecay = M_SQRT1_2;
54 // LR adjustment iterations.
55 const int kNumAdjustmentIterations = 100;
56 // How often to add data to the error_graph_.
57 const int kErrorGraphInterval = 1000;
58 // Number of training images to train between calls to MaintainCheckpoints.
59 const int kNumPagesPerBatch = 100;
60 // Min percent error rate to consider start-up phase over.
61 const int kMinStartedErrorRate = 75;
62 // Error rate at which to transition to stage 1.
63 const double kStageTransitionThreshold = 10.0;
64 // Confidence beyond which the truth is more likely wrong than the recognizer.
65 const double kHighConfidence = 0.9375; // 15/16.
66 // Fraction of weight sign-changing total to constitute a definite improvement.
67 const double kImprovementFraction = 15.0 / 16.0;
68 // Fraction of last written best to make it worth writing another.
69 const double kBestCheckpointFraction = 31.0 / 32.0;
70 // Scale factor for display of target activations of CTC.
71 const int kTargetXScale = 5;
72 const int kTargetYScale = 100;
73 
75  : randomly_rotate_(false),
76  training_data_(0),
77  file_reader_(LoadDataFromFile),
78  file_writer_(SaveDataToFile),
79  checkpoint_reader_(
80  NewPermanentTessCallback(this, &LSTMTrainer::ReadTrainingDump)),
81  checkpoint_writer_(
82  NewPermanentTessCallback(this, &LSTMTrainer::SaveTrainingDump)),
83  sub_trainer_(nullptr) {
85  debug_interval_ = 0;
86 }
87 
89  CheckPointReader checkpoint_reader,
90  CheckPointWriter checkpoint_writer,
91  const char* model_base, const char* checkpoint_name,
92  int debug_interval, int64_t max_memory)
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) {
102  if (file_reader_ == nullptr) file_reader_ = LoadDataFromFile;
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 }
116 
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 }
126 
127 // Tries to deserialize a trainer from the given file and silently returns
128 // false in case of failure.
129 bool LSTMTrainer::TryLoadingCheckpoint(const char* filename,
130  const char* old_traineddata) {
131  GenericVector<char> data;
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;
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(),
142  recoder_.code_range());
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 }
165 
166 // Initializes the trainer with a network_spec in the network description
167 // net_flags control network behavior according to the NetworkFlags enum.
168 // There isn't really much difference between them - only where the effects
169 // are implemented.
170 // For other args see NetworkBuilder::InitNetwork.
171 // Note: Be sure to call InitCharSet before InitNetwork!
172 bool LSTMTrainer::InitNetwork(const STRING& network_spec, int append_index,
173  int net_flags, float weight_range,
174  float learning_rate, float momentum,
175  float adam_beta) {
176  mgr_.SetVersionString(mgr_.VersionString() + ":" + network_spec.string());
177  adam_beta_ = adam_beta;
179  momentum_ = momentum;
180  SetNullChar();
181  if (!NetworkBuilder::InitNetwork(recoder_.code_range(), network_spec,
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",
192  debug_interval_, weight_range, learning_rate_, momentum_);
193  tprintf("null char=%d\n", null_char_);
194  return true;
195 }
196 
197 // Initializes a trainer from a serialized TFNetworkModel proto.
198 // Returns the global step of TensorFlow graph or 0 if failed.
199 #ifdef INCLUDE_TENSORFLOW
200 int LSTMTrainer::InitTensorFlowNetwork(const std::string& tf_proto) {
201  delete network_;
202  TFNetwork* tf_net = new TFNetwork("TensorFlow");
203  training_iteration_ = tf_net->InitFromProtoStr(tf_proto);
204  if (training_iteration_ == 0) {
205  tprintf("InitFromProtoStr failed!!\n");
206  return 0;
207  }
208  network_ = tf_net;
209  ASSERT_HOST(recoder_.code_range() == tf_net->num_classes());
210  return training_iteration_;
211 }
212 #endif
213 
214 // Resets all the iteration counters for fine tuning or traininng a head,
215 // where we want the error reporting to reset.
217  sample_iteration_ = 0;
221  best_error_rate_ = 100.0;
222  best_iteration_ = 0;
223  worst_error_rate_ = 0.0;
224  worst_iteration_ = 0;
227  perfect_delay_ = 0;
229  for (int i = 0; i < ET_COUNT; ++i) {
230  best_error_rates_[i] = 100.0;
231  worst_error_rates_[i] = 0.0;
233  error_rates_[i] = 100.0;
234  }
236 }
237 
238 // If the training sample is usable, grid searches for the optimal
239 // dict_ratio/cert_offset, and returns the results in a string of space-
240 // separated triplets of ratio,offset=worderr.
242  const ImageData* trainingdata, int iteration, double min_dict_ratio,
243  double dict_ratio_step, double max_dict_ratio, double min_cert_offset,
244  double cert_offset_step, double max_cert_offset, STRING* results) {
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 
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 }
289 
290 // Provides output on the distribution of weight values.
293 }
294 
295 // Loads a set of lstmf files that were created using the lstm.train config to
296 // tesseract into memory ready for training. Returns false if nothing was
297 // loaded.
299  CachingStrategy cache_strategy,
300  bool randomly_rotate) {
301  randomly_rotate_ = randomly_rotate;
303  return training_data_.LoadDocuments(filenames, cache_strategy, file_reader_);
304 }
305 
306 // Keeps track of best and locally worst char error_rate and launches tests
307 // using tester, when a new min or max is reached.
308 // Writes checkpoints at appropriate times and builds and returns a log message
309 // to indicate progress. Returns false if nothing interesting happened.
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.
365  GenericVector<char> revert_data(best_trainer_);
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 }
396 
397 // Builds a string containing a progress message with current error rates.
398 void LSTMTrainer::PrepareLogMsg(STRING* log_msg) const {
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 }
407 
408 // Appends <intro_str> iteration learning_iteration()/training_iteration()/
409 // sample_iteration() to the log_msg.
410 void LSTMTrainer::LogIterations(const char* intro_str, STRING* log_msg) const {
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 }
416 
417 // Returns true and increments the training_stage_ if the error rate has just
418 // passed through the given threshold for the first time.
419 bool LSTMTrainer::TransitionTrainingStage(float error_threshold) {
420  if (best_error_rate_ < error_threshold &&
422  ++training_stage_;
423  return true;
424  }
425  return false;
426 }
427 
428 // Writes to the given file. Returns false in case of error.
430  const TessdataManager* mgr, TFile* fp) const {
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 }
463 
464 // Reads from the given file. Returns false in case of error.
465 // NOTE: It is assumed that the trainer is never read cross-endian.
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 {
504  sub_trainer_ = new LSTMTrainer();
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 }
511 
512 // De-serializes the saved best_trainer_ into sub_trainer_, and adjusts the
513 // learning rates (by scaling reduction, or layer specific, according to
514 // NF_LAYER_SPECIFIC_LR).
516  delete sub_trainer_;
517  sub_trainer_ = new LSTMTrainer();
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 }
536 
537 // While the sub_trainer_ is behind the current training iteration and its
538 // training error is at least kSubTrainerMarginFraction better than the
539 // current training error, trains the sub_trainer_, and returns STR_UPDATED if
540 // it did anything. If it catches up, and has a better error rate than the
541 // current best, as well as a margin over the current error rate, then the
542 // trainer in *this is replaced with sub_trainer_, and STR_REPLACED is
543 // returned. STR_NONE is returned if the subtrainer wasn't good enough to
544 // receive any training iterations.
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 }
585 
586 // Reduces network learning rates, either for everything, or for layers
587 // independently, according to NF_LAYER_SPECIFIC_LR.
589  STRING* log_msg) {
591  int num_reduced = ReduceLayerLearningRates(
592  kLearningRateDecay, kNumAdjustmentIterations, samples_trainer);
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 }
600 
601 // Considers reducing the learning rate independently for each layer down by
602 // factor(<1), or leaving it the same, by double-training the given number of
603 // samples and minimizing the amount of changing of sign of weight updates.
604 // Even if it looks like all weights should remain the same, an adjustment
605 // will be made to guarantee a different result when reverting to an old best.
606 // Returns the number of layer learning rates that were reduced.
607 int LSTMTrainer::ReduceLayerLearningRates(double factor, int num_samples,
608  LSTMTrainer* samples_trainer) {
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 }
712 
713 // Converts the string to integer class labels, with appropriate null_char_s
714 // in between if not in SimpleTextOutput mode. Returns false on failure.
715 /* static */
716 bool LSTMTrainer::EncodeString(const STRING& str, const UNICHARSET& unicharset,
717  const UnicharCompress* recoder, bool simple_text,
718  int null_char, GenericVector<int>* labels) {
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 }
760 
761 // Performs forward-backward on the given trainingdata.
762 // Returns a Trainability enum to indicate the suitability of the sample.
764  bool batch) {
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);
782  training_iteration_ + 1);
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 }
793 
794 // Prepares the ground truth, runs forward, and prepares the targets.
795 // Returns a Trainability enum to indicate the suitability of the sample.
797  NetworkIO* fwd_outputs,
798  NetworkIO* targets) {
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.
816  SetRandomSeed();
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;
893  return TRAINABLE;
894 }
895 
896 // Writes the trainer to memory, so that the current training state can be
897 // restored. *this must always be the master trainer that retains the only
898 // copy of the training data and language model. trainer is the model that is
899 // actually serialized.
901  const LSTMTrainer* trainer,
902  GenericVector<char>* data) const {
903  TFile fp;
904  fp.OpenWrite(data);
905  return trainer->Serialize(serialize_amount, &mgr_, &fp);
906 }
907 
908 // Restores the model to *this.
910  const char* data, int size) {
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 }
919 
920 // Writes the full recognition traineddata to the given filename.
921 bool LSTMTrainer::SaveTraineddata(const STRING& filename) {
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 }
928 
929 // Writes the recognizer to memory, so that it can be used for testing later.
931  TFile fp;
932  fp.OpenWrite(data);
936 }
937 
938 // Returns a suitable filename for a training dump, based on the model_base_,
939 // the iteration and the error rates.
941  STRING filename;
943  filename.add_str_int("_", best_iteration_);
944  filename += ".checkpoint";
945  return filename;
946 }
947 
948 // Fills the whole error buffer of the given type with the given value.
950  for (int i = 0; i < kRollingBufferSize_; ++i)
951  error_buffers_[type][i] = new_error;
952  error_rates_[type] = 100.0 * new_error;
953 }
954 
955 // Helper generates a map from each current recoder_ code (ie softmax index)
956 // to the corresponding old_recoder code, or -1 if there isn't one.
957 std::vector<int> LSTMTrainer::MapRecoder(
958  const UNICHARSET& old_chset, const UnicharCompress& old_recoder) const {
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 }
989 
990 // Private version of InitCharSet above finishes the job after initializing
991 // the mgr_ data member.
995  // Initialize the unicharset and recoder.
996  if (!LoadCharsets(&mgr_)) {
997  ASSERT_HOST(
998  "Must provide a traineddata containing lstm_unicharset and"
999  " lstm_recoder!\n" != nullptr);
1000  }
1001  SetNullChar();
1002 }
1003 
1004 // Helper computes and sets the null_char_.
1007  : GetUnicharset().size();
1008  RecodedCharID code;
1010  null_char_ = code(0);
1011 }
1012 
1013 // Factored sub-constructor sets up reasonable default values.
1015  align_win_ = nullptr;
1016  target_win_ = nullptr;
1017  ctc_win_ = nullptr;
1018  recon_win_ = nullptr;
1020  training_stage_ = 0;
1022  InitIterations();
1023 }
1024 
1025 // Outputs the string and periodically displays the given network inputs
1026 // as an image in the given window, and the corresponding labels at the
1027 // corresponding x_starts.
1028 // Returns false if the truth string is empty.
1030  const ImageData& trainingdata,
1031  const NetworkIO& fwd_outputs,
1032  const GenericVector<int>& truth_labels,
1033  const NetworkIO& outputs) {
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  }
1051  if (debug_interval_ > 0 && training_iteration() % debug_interval_ == 0) {
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 }
1064 
1065 // Displays the network targets as line a line graph.
1067  const char* window_name, ScrollView** window) {
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 }
1100 
1101 // Builds a no-compromises target where the first positions should be the
1102 // truth labels and the rest is padded with the null_char_.
1104  const GenericVector<int>& truth_labels,
1105  NetworkIO* targets) {
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 }
1119 
1120 // Builds a target using standard CTC. truth_labels should be pre-padded with
1121 // nulls wherever desired. They don't have to be between all labels.
1122 // outputs is input-output, as it gets clipped to minimum probability.
1124  NetworkIO* outputs, NetworkIO* targets) {
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 }
1130 
1131 // Computes network errors, and stores the results in the rolling buffers,
1132 // along with the supplied text_error.
1133 // Returns the delta error of the current sample (not running average.)
1135  double char_error, double word_error) {
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 }
1152 
1153 // Computes the network activation RMS error rate.
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 }
1167 
1168 // Computes network activation winner error rate. (Number of values that are
1169 // in error by >= 0.5 divided by number of time-steps.) More closely related
1170 // to final character error than RMS, but still directly calculable from
1171 // just the deltas. Because of the binary nature of the targets, zero winner
1172 // error is a sufficient but not necessary condition for zero char error.
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 }
1189 
1190 // Computes a very simple bag of chars char error rate.
1192  const GenericVector<int>& ocr_str) {
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 }
1216 
1217 // Computes word recall error rate using a very simple bag of words algorithm.
1218 // NOTE that this is destructive on both input strings.
1219 double LSTMTrainer::ComputeWordError(STRING* truth_str, STRING* ocr_str) {
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 }
1249 
1250 // Updates the error buffer and corresponding mean of the given type with
1251 // the new_error.
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 }
1263 
1264 // Rolls error buffers and reports the current means.
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 }
1279 
1280 // Given that error_rate is either a new min or max, updates the best/worst
1281 // error rates, and record of progress.
1282 // Tester is an externally supplied callback function that tests on some
1283 // data set with a given model and records the error rates in a graph.
1284 STRING LSTMTrainer::UpdateErrorGraph(int iteration, double error_rate,
1285  const GenericVector<char>& model_data,
1286  TestCallback tester) {
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;
1316  memcpy(best_error_rates_, error_rates_, sizeof(error_rates_));
1317  best_iteration_ = iteration;
1318  best_error_history_.push_back(error_rate);
1319  best_error_iterations_.push_back(iteration);
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;
1352  memcpy(worst_error_rates_, error_rates_, sizeof(error_rates_));
1353  worst_iteration_ = iteration;
1354  return result;
1355 }
1356 
1357 } // namespace tesseract.
LossType OutputLossType() const
bool LoadDataFromFile(const char *filename, GenericVector< char > *data)
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LIST search(LIST list, void *key, int_compare is_equal)
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Trainability PrepareForBackward(const ImageData *trainingdata, NetworkIO *fwd_outputs, NetworkIO *targets)
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const double kMinDivergenceRate
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const STRING & imagefilename() const
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std::string VersionString() const
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int size() const
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const UNICHARSET & GetUnicharset() const
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CheckPointReader checkpoint_reader_
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const char * c_str() const
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float GetLayerLearningRate(const STRING &id) const
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virtual R Run(A1, A2)=0
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const char * string() const
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virtual void DebugWeights()=0
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CachingStrategy
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