Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/training/unicharset/lstmtrainer.h @ 46:7ee69f120f19 default tip
>>>>> tag v1.26.5+1 for changeset b74429b0f5c4
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Sat, 11 Oct 2025 17:17:30 +0200 |
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1 /////////////////////////////////////////////////////////////////////// |
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2 // File: lstmtrainer.h |
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3 // Description: Top-level line trainer class for LSTM-based networks. |
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4 // Author: Ray Smith |
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5 // |
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6 // (C) Copyright 2013, Google Inc. |
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7 // Licensed under the Apache License, Version 2.0 (the "License"); |
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8 // you may not use this file except in compliance with the License. |
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9 // You may obtain a copy of the License at |
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10 // http://www.apache.org/licenses/LICENSE-2.0 |
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11 // Unless required by applicable law or agreed to in writing, software |
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12 // distributed under the License is distributed on an "AS IS" BASIS, |
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13 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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14 // See the License for the specific language governing permissions and |
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15 // limitations under the License. |
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16 /////////////////////////////////////////////////////////////////////// |
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17 |
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18 #ifndef TESSERACT_LSTM_LSTMTRAINER_H_ |
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19 #define TESSERACT_LSTM_LSTMTRAINER_H_ |
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20 |
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21 #include "export.h" |
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22 |
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23 #include "imagedata.h" // for DocumentCache |
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24 #include "lstmrecognizer.h" |
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25 #include "rect.h" |
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26 |
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27 #include <functional> // for std::function |
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28 #include <sstream> // for std::stringstream |
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29 |
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30 namespace tesseract { |
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31 |
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32 class LSTM; |
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33 class LSTMTester; |
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34 class LSTMTrainer; |
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35 class Parallel; |
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36 class Reversed; |
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37 class Softmax; |
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38 class Series; |
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39 |
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40 // Enum for the types of errors that are counted. |
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41 enum ErrorTypes { |
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42 ET_RMS, // RMS activation error. |
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43 ET_DELTA, // Number of big errors in deltas. |
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44 ET_WORD_RECERR, // Output text string word recall error. |
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45 ET_CHAR_ERROR, // Output text string total char error. |
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46 ET_SKIP_RATIO, // Fraction of samples skipped. |
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47 ET_COUNT // For array sizing. |
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48 }; |
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49 |
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50 // Enum for the trainability_ flags. |
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51 enum Trainability { |
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52 TRAINABLE, // Non-zero delta error. |
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53 PERFECT, // Zero delta error. |
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54 UNENCODABLE, // Not trainable due to coding/alignment trouble. |
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55 HI_PRECISION_ERR, // Hi confidence disagreement. |
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56 NOT_BOXED, // Early in training and has no character boxes. |
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57 }; |
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58 |
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59 // Enum to define the amount of data to get serialized. |
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60 enum SerializeAmount { |
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61 LIGHT, // Minimal data for remote training. |
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62 NO_BEST_TRAINER, // Save an empty vector in place of best_trainer_. |
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63 FULL, // All data including best_trainer_. |
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64 }; |
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65 |
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66 // Enum to indicate how the sub_trainer_ training went. |
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67 enum SubTrainerResult { |
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68 STR_NONE, // Did nothing as not good enough. |
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69 STR_UPDATED, // Subtrainer was updated, but didn't replace *this. |
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70 STR_REPLACED // Subtrainer replaced *this. |
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71 }; |
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72 |
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73 class LSTMTrainer; |
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74 // Function to compute and record error rates on some external test set(s). |
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75 // Args are: iteration, mean errors, model, training stage. |
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76 // Returns a string containing logging information about the tests. |
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77 using TestCallback = std::function<std::string(int, const double *, |
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78 const TessdataManager &, int)>; |
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79 |
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80 // Trainer class for LSTM networks. Most of the effort is in creating the |
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81 // ideal target outputs from the transcription. A box file is used if it is |
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82 // available, otherwise estimates of the char widths from the unicharset are |
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83 // used to guide a DP search for the best fit to the transcription. |
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84 class TESS_UNICHARSET_TRAINING_API LSTMTrainer : public LSTMRecognizer { |
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85 public: |
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86 LSTMTrainer(); |
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87 LSTMTrainer(const std::string &model_base, |
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88 const std::string &checkpoint_name, |
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89 int debug_interval, int64_t max_memory); |
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90 virtual ~LSTMTrainer(); |
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91 |
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92 // Tries to deserialize a trainer from the given file and silently returns |
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93 // false in case of failure. If old_traineddata is not null, then it is |
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94 // assumed that the character set is to be re-mapped from old_traineddata to |
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95 // the new, with consequent change in weight matrices etc. |
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96 bool TryLoadingCheckpoint(const char *filename, const char *old_traineddata); |
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97 |
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98 // Initializes the character set encode/decode mechanism directly from a |
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99 // previously setup traineddata containing dawgs, UNICHARSET and |
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100 // UnicharCompress. Note: Call before InitNetwork! |
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101 bool InitCharSet(const std::string &traineddata_path) { |
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102 bool success = mgr_.Init(traineddata_path.c_str()); |
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103 if (success) { |
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104 InitCharSet(); |
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105 } |
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106 return success; |
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107 } |
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108 void InitCharSet(const TessdataManager &mgr) { |
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109 mgr_ = mgr; |
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110 InitCharSet(); |
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111 } |
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112 |
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113 // Initializes the trainer with a network_spec in the network description |
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114 // net_flags control network behavior according to the NetworkFlags enum. |
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115 // There isn't really much difference between them - only where the effects |
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116 // are implemented. |
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117 // For other args see NetworkBuilder::InitNetwork. |
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118 // Note: Be sure to call InitCharSet before InitNetwork! |
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119 bool InitNetwork(const char *network_spec, int append_index, int net_flags, |
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120 float weight_range, float learning_rate, float momentum, |
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121 float adam_beta); |
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122 // Resets all the iteration counters for fine tuning or training a head, |
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123 // where we want the error reporting to reset. |
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124 void InitIterations(); |
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125 |
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126 // Accessors. |
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127 double ActivationError() const { |
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128 return error_rates_[ET_DELTA]; |
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129 } |
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130 double CharError() const { |
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131 return error_rates_[ET_CHAR_ERROR]; |
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132 } |
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133 const double *error_rates() const { |
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134 return error_rates_; |
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135 } |
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136 double best_error_rate() const { |
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137 return best_error_rate_; |
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138 } |
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139 int best_iteration() const { |
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140 return best_iteration_; |
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141 } |
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142 int learning_iteration() const { |
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143 return learning_iteration_; |
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144 } |
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145 int32_t improvement_steps() const { |
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146 return improvement_steps_; |
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147 } |
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148 void set_perfect_delay(int delay) { |
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149 perfect_delay_ = delay; |
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150 } |
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151 const std::vector<char> &best_trainer() const { |
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152 return best_trainer_; |
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153 } |
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154 // Returns the error that was just calculated by PrepareForBackward. |
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155 double NewSingleError(ErrorTypes type) const { |
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156 return error_buffers_[type][training_iteration() % kRollingBufferSize_]; |
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157 } |
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158 // Returns the error that was just calculated by TrainOnLine. Since |
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159 // TrainOnLine rolls the error buffers, this is one further back than |
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160 // NewSingleError. |
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161 double LastSingleError(ErrorTypes type) const { |
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162 return error_buffers_[type] |
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163 [(training_iteration() + kRollingBufferSize_ - 1) % |
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164 kRollingBufferSize_]; |
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165 } |
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166 const DocumentCache &training_data() const { |
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167 return training_data_; |
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168 } |
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169 DocumentCache *mutable_training_data() { |
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170 return &training_data_; |
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171 } |
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172 |
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173 // If the training sample is usable, grid searches for the optimal |
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174 // dict_ratio/cert_offset, and returns the results in a string of space- |
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175 // separated triplets of ratio,offset=worderr. |
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176 Trainability GridSearchDictParams( |
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177 const ImageData *trainingdata, int iteration, double min_dict_ratio, |
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178 double dict_ratio_step, double max_dict_ratio, double min_cert_offset, |
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179 double cert_offset_step, double max_cert_offset, std::string &results); |
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180 |
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181 // Provides output on the distribution of weight values. |
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182 void DebugNetwork(); |
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183 |
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184 // Loads a set of lstmf files that were created using the lstm.train config to |
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185 // tesseract into memory ready for training. Returns false if nothing was |
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186 // loaded. |
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187 bool LoadAllTrainingData(const std::vector<std::string> &filenames, |
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188 CachingStrategy cache_strategy, |
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189 bool randomly_rotate); |
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190 |
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191 // Keeps track of best and locally worst error rate, using internally computed |
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192 // values. See MaintainCheckpointsSpecific for more detail. |
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193 bool MaintainCheckpoints(const TestCallback &tester, std::stringstream &log_msg); |
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194 // Keeps track of best and locally worst error_rate (whatever it is) and |
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195 // launches tests using rec_model, when a new min or max is reached. |
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196 // Writes checkpoints using train_model at appropriate times and builds and |
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197 // returns a log message to indicate progress. Returns false if nothing |
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198 // interesting happened. |
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199 bool MaintainCheckpointsSpecific(int iteration, |
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200 const std::vector<char> *train_model, |
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201 const std::vector<char> *rec_model, |
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202 TestCallback tester, std::stringstream &log_msg); |
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203 // Builds a progress message with current error rates. |
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204 void PrepareLogMsg(std::stringstream &log_msg) const; |
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205 // Appends <intro_str> iteration learning_iteration()/training_iteration()/ |
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206 // sample_iteration() to the log_msg. |
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207 void LogIterations(const char *intro_str, std::stringstream &log_msg) const; |
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208 |
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209 // TODO(rays) Add curriculum learning. |
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210 // Returns true and increments the training_stage_ if the error rate has just |
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211 // passed through the given threshold for the first time. |
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212 bool TransitionTrainingStage(float error_threshold); |
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213 // Returns the current training stage. |
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214 int CurrentTrainingStage() const { |
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215 return training_stage_; |
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216 } |
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217 |
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218 // Writes to the given file. Returns false in case of error. |
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219 bool Serialize(SerializeAmount serialize_amount, const TessdataManager *mgr, |
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220 TFile *fp) const; |
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221 // Reads from the given file. Returns false in case of error. |
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222 bool DeSerialize(const TessdataManager *mgr, TFile *fp); |
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223 |
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224 // De-serializes the saved best_trainer_ into sub_trainer_, and adjusts the |
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225 // learning rates (by scaling reduction, or layer specific, according to |
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226 // NF_LAYER_SPECIFIC_LR). |
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227 void StartSubtrainer(std::stringstream &log_msg); |
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228 // While the sub_trainer_ is behind the current training iteration and its |
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229 // training error is at least kSubTrainerMarginFraction better than the |
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230 // current training error, trains the sub_trainer_, and returns STR_UPDATED if |
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231 // it did anything. If it catches up, and has a better error rate than the |
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232 // current best, as well as a margin over the current error rate, then the |
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233 // trainer in *this is replaced with sub_trainer_, and STR_REPLACED is |
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234 // returned. STR_NONE is returned if the subtrainer wasn't good enough to |
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235 // receive any training iterations. |
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236 SubTrainerResult UpdateSubtrainer(std::stringstream &log_msg); |
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237 // Reduces network learning rates, either for everything, or for layers |
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238 // independently, according to NF_LAYER_SPECIFIC_LR. |
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239 void ReduceLearningRates(LSTMTrainer *samples_trainer, std::stringstream &log_msg); |
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240 // Considers reducing the learning rate independently for each layer down by |
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241 // factor(<1), or leaving it the same, by double-training the given number of |
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242 // samples and minimizing the amount of changing of sign of weight updates. |
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243 // Even if it looks like all weights should remain the same, an adjustment |
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244 // will be made to guarantee a different result when reverting to an old best. |
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245 // Returns the number of layer learning rates that were reduced. |
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246 int ReduceLayerLearningRates(TFloat factor, int num_samples, |
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247 LSTMTrainer *samples_trainer); |
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248 |
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249 // Converts the string to integer class labels, with appropriate null_char_s |
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250 // in between if not in SimpleTextOutput mode. Returns false on failure. |
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251 bool EncodeString(const std::string &str, std::vector<int> *labels) const { |
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252 return EncodeString(str, GetUnicharset(), |
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253 IsRecoding() ? &recoder_ : nullptr, SimpleTextOutput(), |
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254 null_char_, labels); |
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255 } |
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256 // Static version operates on supplied unicharset, encoder, simple_text. |
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257 static bool EncodeString(const std::string &str, const UNICHARSET &unicharset, |
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258 const UnicharCompress *recoder, bool simple_text, |
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259 int null_char, std::vector<int> *labels); |
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260 |
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261 // Performs forward-backward on the given trainingdata. |
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262 // Returns the sample that was used or nullptr if the next sample was deemed |
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263 // unusable. samples_trainer could be this or an alternative trainer that |
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264 // holds the training samples. |
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265 const ImageData *TrainOnLine(LSTMTrainer *samples_trainer, bool batch) { |
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266 int sample_index = sample_iteration(); |
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267 const ImageData *image = |
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268 samples_trainer->training_data_.GetPageBySerial(sample_index); |
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269 if (image != nullptr) { |
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270 Trainability trainable = TrainOnLine(image, batch); |
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271 if (trainable == UNENCODABLE || trainable == NOT_BOXED) { |
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272 return nullptr; // Sample was unusable. |
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273 } |
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274 } else { |
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275 ++sample_iteration_; |
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276 } |
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277 return image; |
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278 } |
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279 Trainability TrainOnLine(const ImageData *trainingdata, bool batch); |
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280 |
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281 // Prepares the ground truth, runs forward, and prepares the targets. |
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282 // Returns a Trainability enum to indicate the suitability of the sample. |
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283 Trainability PrepareForBackward(const ImageData *trainingdata, |
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284 NetworkIO *fwd_outputs, NetworkIO *targets); |
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285 |
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286 // Writes the trainer to memory, so that the current training state can be |
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287 // restored. *this must always be the master trainer that retains the only |
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288 // copy of the training data and language model. trainer is the model that is |
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289 // actually serialized. |
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290 bool SaveTrainingDump(SerializeAmount serialize_amount, |
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291 const LSTMTrainer &trainer, |
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292 std::vector<char> *data) const; |
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293 |
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294 // Reads previously saved trainer from memory. *this must always be the |
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295 // master trainer that retains the only copy of the training data and |
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296 // language model. trainer is the model that is restored. |
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297 bool ReadTrainingDump(const std::vector<char> &data, |
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298 LSTMTrainer &trainer) const { |
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299 if (data.empty()) { |
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300 return false; |
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301 } |
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302 return ReadSizedTrainingDump(&data[0], data.size(), trainer); |
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303 } |
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304 bool ReadSizedTrainingDump(const char *data, int size, |
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305 LSTMTrainer &trainer) const { |
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306 return trainer.ReadLocalTrainingDump(&mgr_, data, size); |
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307 } |
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308 // Restores the model to *this. |
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309 bool ReadLocalTrainingDump(const TessdataManager *mgr, const char *data, |
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310 int size); |
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311 |
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312 // Sets up the data for MaintainCheckpoints from a light ReadTrainingDump. |
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313 void SetupCheckpointInfo(); |
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314 |
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315 // Writes the full recognition traineddata to the given filename. |
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316 bool SaveTraineddata(const char *filename); |
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317 |
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318 // Writes the recognizer to memory, so that it can be used for testing later. |
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319 void SaveRecognitionDump(std::vector<char> *data) const; |
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320 |
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321 // Returns a suitable filename for a training dump, based on the model_base_, |
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322 // the iteration and the error rates. |
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323 std::string DumpFilename() const; |
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324 |
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325 // Fills the whole error buffer of the given type with the given value. |
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326 void FillErrorBuffer(double new_error, ErrorTypes type); |
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327 // Helper generates a map from each current recoder_ code (ie softmax index) |
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328 // to the corresponding old_recoder code, or -1 if there isn't one. |
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329 std::vector<int> MapRecoder(const UNICHARSET &old_chset, |
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330 const UnicharCompress &old_recoder) const; |
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331 |
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332 protected: |
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333 // Private version of InitCharSet above finishes the job after initializing |
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334 // the mgr_ data member. |
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335 void InitCharSet(); |
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336 // Helper computes and sets the null_char_. |
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337 void SetNullChar(); |
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338 |
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339 // Factored sub-constructor sets up reasonable default values. |
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340 void EmptyConstructor(); |
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341 |
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342 // Outputs the string and periodically displays the given network inputs |
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343 // as an image in the given window, and the corresponding labels at the |
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344 // corresponding x_starts. |
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345 // Returns false if the truth string is empty. |
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346 bool DebugLSTMTraining(const NetworkIO &inputs, const ImageData &trainingdata, |
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347 const NetworkIO &fwd_outputs, |
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348 const std::vector<int> &truth_labels, |
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349 const NetworkIO &outputs); |
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350 // Displays the network targets as line a line graph. |
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351 void DisplayTargets(const NetworkIO &targets, const char *window_name, |
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352 ScrollView **window); |
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353 |
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354 // Builds a no-compromises target where the first positions should be the |
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355 // truth labels and the rest is padded with the null_char_. |
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356 bool ComputeTextTargets(const NetworkIO &outputs, |
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357 const std::vector<int> &truth_labels, |
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358 NetworkIO *targets); |
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359 |
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360 // Builds a target using standard CTC. truth_labels should be pre-padded with |
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361 // nulls wherever desired. They don't have to be between all labels. |
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362 // outputs is input-output, as it gets clipped to minimum probability. |
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363 bool ComputeCTCTargets(const std::vector<int> &truth_labels, |
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364 NetworkIO *outputs, NetworkIO *targets); |
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365 |
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366 // Computes network errors, and stores the results in the rolling buffers, |
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367 // along with the supplied text_error. |
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368 // Returns the delta error of the current sample (not running average.) |
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369 double ComputeErrorRates(const NetworkIO &deltas, double char_error, |
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370 double word_error); |
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371 |
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372 // Computes the network activation RMS error rate. |
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373 double ComputeRMSError(const NetworkIO &deltas); |
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374 |
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375 // Computes network activation winner error rate. (Number of values that are |
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376 // in error by >= 0.5 divided by number of time-steps.) More closely related |
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377 // to final character error than RMS, but still directly calculable from |
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378 // just the deltas. Because of the binary nature of the targets, zero winner |
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379 // error is a sufficient but not necessary condition for zero char error. |
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380 double ComputeWinnerError(const NetworkIO &deltas); |
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381 |
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382 // Computes a very simple bag of chars char error rate. |
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383 double ComputeCharError(const std::vector<int> &truth_str, |
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384 const std::vector<int> &ocr_str); |
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385 // Computes a very simple bag of words word recall error rate. |
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386 // NOTE that this is destructive on both input strings. |
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387 double ComputeWordError(std::string *truth_str, std::string *ocr_str); |
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388 |
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389 // Updates the error buffer and corresponding mean of the given type with |
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390 // the new_error. |
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391 void UpdateErrorBuffer(double new_error, ErrorTypes type); |
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392 |
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393 // Rolls error buffers and reports the current means. |
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394 void RollErrorBuffers(); |
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395 |
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396 // Given that error_rate is either a new min or max, updates the best/worst |
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397 // error rates, and record of progress. |
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398 std::string UpdateErrorGraph(int iteration, double error_rate, |
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399 const std::vector<char> &model_data, |
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400 const TestCallback &tester); |
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401 |
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402 protected: |
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403 #ifndef GRAPHICS_DISABLED |
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404 // Alignment display window. |
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405 ScrollView *align_win_; |
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406 // CTC target display window. |
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407 ScrollView *target_win_; |
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408 // CTC output display window. |
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409 ScrollView *ctc_win_; |
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410 // Reconstructed image window. |
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411 ScrollView *recon_win_; |
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412 #endif |
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413 // How often to display a debug image. |
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|
414 int debug_interval_; |
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415 // Iteration at which the last checkpoint was dumped. |
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416 int checkpoint_iteration_; |
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417 // Basename of files to save best models to. |
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418 std::string model_base_; |
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419 // Checkpoint filename. |
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420 std::string checkpoint_name_; |
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421 // Training data. |
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422 bool randomly_rotate_; |
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423 DocumentCache training_data_; |
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424 // Name to use when saving best_trainer_. |
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|
425 std::string best_model_name_; |
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426 // Number of available training stages. |
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427 int num_training_stages_; |
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428 |
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429 // ===Serialized data to ensure that a restart produces the same results.=== |
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430 // These members are only serialized when serialize_amount != LIGHT. |
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431 // Best error rate so far. |
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432 double best_error_rate_; |
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433 // Snapshot of all error rates at best_iteration_. |
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434 double best_error_rates_[ET_COUNT]; |
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|
435 // Iteration of best_error_rate_. |
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436 int best_iteration_; |
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|
437 // Worst error rate since best_error_rate_. |
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438 double worst_error_rate_; |
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439 // Snapshot of all error rates at worst_iteration_. |
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440 double worst_error_rates_[ET_COUNT]; |
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441 // Iteration of worst_error_rate_. |
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442 int worst_iteration_; |
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443 // Iteration at which the process will be thought stalled. |
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444 int stall_iteration_; |
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445 // Saved recognition models for computing test error for graph points. |
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446 std::vector<char> best_model_data_; |
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447 std::vector<char> worst_model_data_; |
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448 // Saved trainer for reverting back to last known best. |
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449 std::vector<char> best_trainer_; |
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450 // A subsidiary trainer running with a different learning rate until either |
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451 // *this or sub_trainer_ hits a new best. |
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452 std::unique_ptr<LSTMTrainer> sub_trainer_; |
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453 // Error rate at which last best model was dumped. |
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454 float error_rate_of_last_saved_best_; |
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455 // Current stage of training. |
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456 int training_stage_; |
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457 // History of best error rate against iteration. Used for computing the |
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458 // number of steps to each 2% improvement. |
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459 std::vector<double> best_error_history_; |
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460 std::vector<int32_t> best_error_iterations_; |
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461 // Number of iterations since the best_error_rate_ was 2% more than it is now. |
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462 int32_t improvement_steps_; |
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463 // Number of iterations that yielded a non-zero delta error and thus provided |
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464 // significant learning. learning_iteration_ <= training_iteration_. |
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465 // learning_iteration_ is used to measure rate of learning progress. |
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466 int learning_iteration_; |
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467 // Saved value of sample_iteration_ before looking for the next sample. |
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468 int prev_sample_iteration_; |
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469 // How often to include a PERFECT training sample in backprop. |
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470 // A PERFECT training sample is used if the current |
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471 // training_iteration_ > last_perfect_training_iteration_ + perfect_delay_, |
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472 // so with perfect_delay_ == 0, all samples are used, and with |
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473 // perfect_delay_ == 4, at most 1 in 5 samples will be perfect. |
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474 int perfect_delay_; |
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475 // Value of training_iteration_ at which the last PERFECT training sample |
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476 // was used in back prop. |
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477 int last_perfect_training_iteration_; |
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478 // Rolling buffers storing recent training errors are indexed by |
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479 // training_iteration % kRollingBufferSize_. |
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480 static const int kRollingBufferSize_ = 1000; |
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481 std::vector<double> error_buffers_[ET_COUNT]; |
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482 // Rounded mean percent trailing training errors in the buffers. |
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483 double error_rates_[ET_COUNT]; // RMS training error. |
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484 // Traineddata file with optional dawgs + UNICHARSET and recoder. |
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485 TessdataManager mgr_; |
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486 }; |
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487 |
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488 } // namespace tesseract. |
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489 |
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490 #endif // TESSERACT_LSTM_LSTMTRAINER_H_ |
