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tesseract/lstm/lstmtrainer.cpp
Stefan Weil 9e0da72818 lstm: Fix possible float division by zero 
Coverity report:

CID 1366441 (#1 of 1): Division or modulo by float zero (DIVIDE_BY_ZERO)
5. divide_by_zero: In expression
 static_cast<double>(char_errors) / truth_size, division by expression
 truth_size which may be zero has undefined behavior.

Signed-off-by: Stefan Weil <sw@weilnetz.de>
2016-12-01 14:42:54 +01:00

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///////////////////////////////////////////////////////////////////////
// File: lstmtrainer.cpp
// Description: Top-level line trainer class for LSTM-based networks.
// Author: Ray Smith
// Created: Fir May 03 09:14:06 PST 2013
//
// (C) Copyright 2013, Google Inc.
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
///////////////////////////////////////////////////////////////////////
#include "lstmtrainer.h"
#include <string>
#include "allheaders.h"
#include "boxread.h"
#include "ctc.h"
#include "imagedata.h"
#include "input.h"
#include "networkbuilder.h"
#include "ratngs.h"
#include "recodebeam.h"
#ifdef INCLUDE_TENSORFLOW
#include "tfnetwork.h"
#endif
#include "tprintf.h"
#include "callcpp.h"
namespace tesseract {
// Min actual error rate increase to constitute divergence.
const double kMinDivergenceRate = 50.0;
// Min iterations since last best before acting on a stall.
const int kMinStallIterations = 10000;
// Fraction of current char error rate that sub_trainer_ has to be ahead
// before we declare the sub_trainer_ a success and switch to it.
const double kSubTrainerMarginFraction = 3.0 / 128;
// Factor to reduce learning rate on divergence.
const double kLearningRateDecay = sqrt(0.5);
// LR adjustment iterations.
const int kNumAdjustmentIterations = 100;
// How often to add data to the error_graph_.
const int kErrorGraphInterval = 1000;
// Number of training images to train between calls to MaintainCheckpoints.
const int kNumPagesPerBatch = 100;
// Min percent error rate to consider start-up phase over.
const int kMinStartedErrorRate = 75;
// Error rate at which to transition to stage 1.
const double kStageTransitionThreshold = 10.0;
// How often to test for flipping.
const int kFlipTestRate = 20;
// Confidence beyond which the truth is more likely wrong than the recognizer.
const double kHighConfidence = 0.9375; // 15/16.
// Fraction of weight sign-changing total to constitute a definite improvement.
const double kImprovementFraction = 15.0 / 16.0;
// Fraction of last written best to make it worth writing another.
const double kBestCheckpointFraction = 31.0 / 32.0;
// Scale factor for display of target activations of CTC.
const int kTargetXScale = 5;
const int kTargetYScale = 100;
LSTMTrainer::LSTMTrainer()
: training_data_(0),
file_reader_(LoadDataFromFile),
file_writer_(SaveDataToFile),
checkpoint_reader_(
NewPermanentTessCallback(this, &LSTMTrainer::ReadTrainingDump)),
checkpoint_writer_(
NewPermanentTessCallback(this, &LSTMTrainer::SaveTrainingDump)),
sub_trainer_(NULL) {
EmptyConstructor();
debug_interval_ = 0;
}
LSTMTrainer::LSTMTrainer(FileReader file_reader, FileWriter file_writer,
CheckPointReader checkpoint_reader,
CheckPointWriter checkpoint_writer,
const char* model_base, const char* checkpoint_name,
int debug_interval, inT64 max_memory)
: training_data_(max_memory),
file_reader_(file_reader),
file_writer_(file_writer),
checkpoint_reader_(checkpoint_reader),
checkpoint_writer_(checkpoint_writer),
sub_trainer_(NULL) {
EmptyConstructor();
if (file_reader_ == NULL) file_reader_ = LoadDataFromFile;
if (file_writer_ == NULL) file_writer_ = SaveDataToFile;
if (checkpoint_reader_ == NULL) {
checkpoint_reader_ =
NewPermanentTessCallback(this, &LSTMTrainer::ReadTrainingDump);
}
if (checkpoint_writer_ == NULL) {
checkpoint_writer_ =
NewPermanentTessCallback(this, &LSTMTrainer::SaveTrainingDump);
}
debug_interval_ = debug_interval;
model_base_ = model_base;
checkpoint_name_ = checkpoint_name;
}
LSTMTrainer::~LSTMTrainer() {
delete align_win_;
delete target_win_;
delete ctc_win_;
delete recon_win_;
delete checkpoint_reader_;
delete checkpoint_writer_;
delete sub_trainer_;
}
// Tries to deserialize a trainer from the given file and silently returns
// false in case of failure.
bool LSTMTrainer::TryLoadingCheckpoint(const char* filename) {
GenericVector<char> data;
if (!(*file_reader_)(filename, &data)) return false;
tprintf("Loaded file %s, unpacking...\n", filename);
return checkpoint_reader_->Run(data, this);
}
// Initializes the character set encode/decode mechanism.
// train_flags control training behavior according to the TrainingFlags
// enum, including character set encoding.
// script_dir is required for TF_COMPRESS_UNICHARSET, and, if provided,
// fully initializes the unicharset from the universal unicharsets.
// Note: Call before InitNetwork!
void LSTMTrainer::InitCharSet(const UNICHARSET& unicharset,
const STRING& script_dir, int train_flags) {
EmptyConstructor();
training_flags_ = train_flags;
ccutil_.unicharset.CopyFrom(unicharset);
null_char_ = GetUnicharset().has_special_codes() ? UNICHAR_BROKEN
: GetUnicharset().size();
SetUnicharsetProperties(script_dir);
}
// Initializes the character set encode/decode mechanism directly from a
// previously setup UNICHARSET and UnicharCompress.
// ctc_mode controls how the truth text is mapped to the network targets.
// Note: Call before InitNetwork!
void LSTMTrainer::InitCharSet(const UNICHARSET& unicharset,
const UnicharCompress& recoder) {
EmptyConstructor();
int flags = TF_COMPRESS_UNICHARSET;
training_flags_ = static_cast<TrainingFlags>(flags);
ccutil_.unicharset.CopyFrom(unicharset);
recoder_ = recoder;
null_char_ = GetUnicharset().has_special_codes() ? UNICHAR_BROKEN
: GetUnicharset().size();
RecodedCharID code;
recoder_.EncodeUnichar(null_char_, &code);
null_char_ = code(0);
// Space should encode as itself.
recoder_.EncodeUnichar(UNICHAR_SPACE, &code);
ASSERT_HOST(code(0) == UNICHAR_SPACE);
}
// Initializes the trainer with a network_spec in the network description
// net_flags control network behavior according to the NetworkFlags enum.
// There isn't really much difference between them - only where the effects
// are implemented.
// For other args see NetworkBuilder::InitNetwork.
// Note: Be sure to call InitCharSet before InitNetwork!
bool LSTMTrainer::InitNetwork(const STRING& network_spec, int append_index,
int net_flags, float weight_range,
float learning_rate, float momentum) {
// Call after InitCharSet.
ASSERT_HOST(GetUnicharset().size() > SPECIAL_UNICHAR_CODES_COUNT);
weight_range_ = weight_range;
learning_rate_ = learning_rate;
momentum_ = momentum;
int num_outputs = null_char_ == GetUnicharset().size()
? null_char_ + 1
: GetUnicharset().size();
if (IsRecoding()) num_outputs = recoder_.code_range();
if (!NetworkBuilder::InitNetwork(num_outputs, network_spec, append_index,
net_flags, weight_range, &randomizer_,
&network_)) {
return false;
}
network_str_ += network_spec;
tprintf("Built network:%s from request %s\n",
network_->spec().string(), network_spec.string());
tprintf("Training parameters:\n Debug interval = %d,"
" weights = %g, learning rate = %g, momentum=%g\n",
debug_interval_, weight_range_, learning_rate_, momentum_);
return true;
}
// Initializes a trainer from a serialized TFNetworkModel proto.
// Returns the global step of TensorFlow graph or 0 if failed.
int LSTMTrainer::InitTensorFlowNetwork(const std::string& tf_proto) {
#ifdef INCLUDE_TENSORFLOW
delete network_;
TFNetwork* tf_net = new TFNetwork("TensorFlow");
training_iteration_ = tf_net->InitFromProtoStr(tf_proto);
if (training_iteration_ == 0) {
tprintf("InitFromProtoStr failed!!\n");
return 0;
}
network_ = tf_net;
ASSERT_HOST(recoder_.code_range() == tf_net->num_classes());
return training_iteration_;
#else
tprintf("TensorFlow not compiled in! -DINCLUDE_TENSORFLOW\n");
return 0;
#endif
}
// Resets all the iteration counters for fine tuning or traininng a head,
// where we want the error reporting to reset.
void LSTMTrainer::InitIterations() {
sample_iteration_ = 0;
training_iteration_ = 0;
learning_iteration_ = 0;
prev_sample_iteration_ = 0;
best_error_rate_ = 100.0;
best_iteration_ = 0;
worst_error_rate_ = 0.0;
worst_iteration_ = 0;
stall_iteration_ = kMinStallIterations;
improvement_steps_ = kMinStallIterations;
perfect_delay_ = 0;
last_perfect_training_iteration_ = 0;
for (int i = 0; i < ET_COUNT; ++i) {
best_error_rates_[i] = 100.0;
worst_error_rates_[i] = 0.0;
error_buffers_[i].init_to_size(kRollingBufferSize_, 0.0);
error_rates_[i] = 100.0;
}
error_rate_of_last_saved_best_ = kMinStartedErrorRate;
}
// If the training sample is usable, grid searches for the optimal
// dict_ratio/cert_offset, and returns the results in a string of space-
// separated triplets of ratio,offset=worderr.
Trainability LSTMTrainer::GridSearchDictParams(
const ImageData* trainingdata, int iteration, double min_dict_ratio,
double dict_ratio_step, double max_dict_ratio, double min_cert_offset,
double cert_offset_step, double max_cert_offset, STRING* results) {
sample_iteration_ = iteration;
NetworkIO fwd_outputs, targets;
Trainability result =
PrepareForBackward(trainingdata, &fwd_outputs, &targets);
if (result == UNENCODABLE || result == HI_PRECISION_ERR || dict_ == NULL)
return result;
// Encode/decode the truth to get the normalization.
GenericVector<int> truth_labels, ocr_labels, xcoords;
ASSERT_HOST(EncodeString(trainingdata->transcription(), &truth_labels));
// NO-dict error.
RecodeBeamSearch base_search(recoder_, null_char_, SimpleTextOutput(), NULL);
base_search.Decode(fwd_outputs, 1.0, 0.0, RecodeBeamSearch::kMinCertainty,
NULL);
base_search.ExtractBestPathAsLabels(&ocr_labels, &xcoords);
STRING truth_text = DecodeLabels(truth_labels);
STRING ocr_text = DecodeLabels(ocr_labels);
double baseline_error = ComputeWordError(&truth_text, &ocr_text);
results->add_str_double("0,0=", baseline_error);
RecodeBeamSearch search(recoder_, null_char_, SimpleTextOutput(), dict_);
for (double r = min_dict_ratio; r < max_dict_ratio; r += dict_ratio_step) {
for (double c = min_cert_offset; c < max_cert_offset;
c += cert_offset_step) {
search.Decode(fwd_outputs, r, c, RecodeBeamSearch::kMinCertainty, NULL);
search.ExtractBestPathAsLabels(&ocr_labels, &xcoords);
truth_text = DecodeLabels(truth_labels);
ocr_text = DecodeLabels(ocr_labels);
// This is destructive on both strings.
double word_error = ComputeWordError(&truth_text, &ocr_text);
if ((r == min_dict_ratio && c == min_cert_offset) ||
!std::isfinite(word_error)) {
STRING t = DecodeLabels(truth_labels);
STRING o = DecodeLabels(ocr_labels);
tprintf("r=%g, c=%g, truth=%s, ocr=%s, wderr=%g, truth[0]=%d\n", r, c,
t.string(), o.string(), word_error, truth_labels[0]);
}
results->add_str_double(" ", r);
results->add_str_double(",", c);
results->add_str_double("=", word_error);
}
}
return result;
}
// Provides output on the distribution of weight values.
void LSTMTrainer::DebugNetwork() {
network_->DebugWeights();
}
// Loads a set of lstmf files that were created using the lstm.train config to
// tesseract into memory ready for training. Returns false if nothing was
// loaded.
bool LSTMTrainer::LoadAllTrainingData(const GenericVector<STRING>& filenames) {
training_data_.Clear();
return training_data_.LoadDocuments(filenames, "eng", CacheStrategy(),
file_reader_);
}
// Keeps track of best and locally worst char error_rate and launches tests
// using tester, when a new min or max is reached.
// Writes checkpoints at appropriate times and builds and returns a log message
// to indicate progress. Returns false if nothing interesting happened.
bool LSTMTrainer::MaintainCheckpoints(TestCallback tester, STRING* log_msg) {
PrepareLogMsg(log_msg);
double error_rate = CharError();
int iteration = learning_iteration();
if (iteration >= stall_iteration_ &&
error_rate > best_error_rate_ * (1.0 + kSubTrainerMarginFraction) &&
best_error_rate_ < kMinStartedErrorRate && !best_trainer_.empty()) {
// It hasn't got any better in a long while, and is a margin worse than the
// best, so go back to the best model and try a different learning rate.
StartSubtrainer(log_msg);
}
SubTrainerResult sub_trainer_result = STR_NONE;
if (sub_trainer_ != NULL) {
sub_trainer_result = UpdateSubtrainer(log_msg);
if (sub_trainer_result == STR_REPLACED) {
// Reset the inputs, as we have overwritten *this.
error_rate = CharError();
iteration = learning_iteration();
PrepareLogMsg(log_msg);
}
}
bool result = true; // Something interesting happened.
GenericVector<char> rec_model_data;
if (error_rate < best_error_rate_) {
SaveRecognitionDump(&rec_model_data);
log_msg->add_str_double(" New best char error = ", error_rate);
*log_msg += UpdateErrorGraph(iteration, error_rate, rec_model_data, tester);
// If sub_trainer_ is not NULL, either *this beat it to a new best, or it
// just overwrote *this. In either case, we have finished with it.
delete sub_trainer_;
sub_trainer_ = NULL;
stall_iteration_ = learning_iteration() + kMinStallIterations;
if (TransitionTrainingStage(kStageTransitionThreshold)) {
log_msg->add_str_int(" Transitioned to stage ", CurrentTrainingStage());
}
checkpoint_writer_->Run(NO_BEST_TRAINER, this, &best_trainer_);
if (error_rate < error_rate_of_last_saved_best_ * kBestCheckpointFraction) {
STRING best_model_name = DumpFilename();
if (!(*file_writer_)(best_trainer_, best_model_name)) {
*log_msg += " failed to write best model:";
} else {
*log_msg += " wrote best model:";
error_rate_of_last_saved_best_ = best_error_rate_;
}
*log_msg += best_model_name;
}
} else if (error_rate > worst_error_rate_) {
SaveRecognitionDump(&rec_model_data);
log_msg->add_str_double(" New worst char error = ", error_rate);
*log_msg += UpdateErrorGraph(iteration, error_rate, rec_model_data, tester);
if (worst_error_rate_ > best_error_rate_ + kMinDivergenceRate &&
best_error_rate_ < kMinStartedErrorRate && !best_trainer_.empty()) {
// Error rate has ballooned. Go back to the best model.
*log_msg += "\nDivergence! ";
// Copy best_trainer_ before reading it, as it will get overwritten.
GenericVector<char> revert_data(best_trainer_);
if (checkpoint_reader_->Run(revert_data, this)) {
LogIterations("Reverted to", log_msg);
ReduceLearningRates(this, log_msg);
} else {
LogIterations("Failed to Revert at", log_msg);
}
// If it fails again, we will wait twice as long before reverting again.
stall_iteration_ = iteration + 2 * (iteration - learning_iteration());
// Re-save the best trainer with the new learning rates and stall
// iteration.
checkpoint_writer_->Run(NO_BEST_TRAINER, this, &best_trainer_);
}
} else {
// Something interesting happened only if the sub_trainer_ was trained.
result = sub_trainer_result != STR_NONE;
}
if (checkpoint_writer_ != NULL && file_writer_ != NULL &&
checkpoint_name_.length() > 0) {
// Write a current checkpoint.
GenericVector<char> checkpoint;
if (!checkpoint_writer_->Run(FULL, this, &checkpoint) ||
!(*file_writer_)(checkpoint, checkpoint_name_)) {
*log_msg += " failed to write checkpoint.";
} else {
*log_msg += " wrote checkpoint.";
}
}
*log_msg += "\n";
return result;
}
// Builds a string containing a progress message with current error rates.
void LSTMTrainer::PrepareLogMsg(STRING* log_msg) const {
LogIterations("At", log_msg);
log_msg->add_str_double(", Mean rms=", error_rates_[ET_RMS]);
log_msg->add_str_double("%, delta=", error_rates_[ET_DELTA]);
log_msg->add_str_double("%, char train=", error_rates_[ET_CHAR_ERROR]);
log_msg->add_str_double("%, word train=", error_rates_[ET_WORD_RECERR]);
log_msg->add_str_double("%, skip ratio=", error_rates_[ET_SKIP_RATIO]);
*log_msg += "%, ";
}
// Appends <intro_str> iteration learning_iteration()/training_iteration()/
// sample_iteration() to the log_msg.
void LSTMTrainer::LogIterations(const char* intro_str, STRING* log_msg) const {
*log_msg += intro_str;
log_msg->add_str_int(" iteration ", learning_iteration());
log_msg->add_str_int("/", training_iteration());
log_msg->add_str_int("/", sample_iteration());
}
// Returns true and increments the training_stage_ if the error rate has just
// passed through the given threshold for the first time.
bool LSTMTrainer::TransitionTrainingStage(float error_threshold) {
if (best_error_rate_ < error_threshold &&
training_stage_ + 1 < num_training_stages_) {
++training_stage_;
return true;
}
return false;
}
// Writes to the given file. Returns false in case of error.
bool LSTMTrainer::Serialize(TFile* fp) const {
if (!LSTMRecognizer::Serialize(fp)) return false;
if (fp->FWrite(&learning_iteration_, sizeof(learning_iteration_), 1) != 1)
return false;
if (fp->FWrite(&prev_sample_iteration_, sizeof(prev_sample_iteration_), 1) !=
1)
return false;
if (fp->FWrite(&perfect_delay_, sizeof(perfect_delay_), 1) != 1) return false;
if (fp->FWrite(&last_perfect_training_iteration_,
sizeof(last_perfect_training_iteration_), 1) != 1)
return false;
for (int i = 0; i < ET_COUNT; ++i) {
if (!error_buffers_[i].Serialize(fp)) return false;
}
if (fp->FWrite(&error_rates_, sizeof(error_rates_), 1) != 1) return false;
if (fp->FWrite(&training_stage_, sizeof(training_stage_), 1) != 1)
return false;
uinT8 amount = serialize_amount_;
if (fp->FWrite(&amount, sizeof(amount), 1) != 1) return false;
if (amount == LIGHT) return true; // We are done.
if (fp->FWrite(&best_error_rate_, sizeof(best_error_rate_), 1) != 1)
return false;
if (fp->FWrite(&best_error_rates_, sizeof(best_error_rates_), 1) != 1)
return false;
if (fp->FWrite(&best_iteration_, sizeof(best_iteration_), 1) != 1)
return false;
if (fp->FWrite(&worst_error_rate_, sizeof(worst_error_rate_), 1) != 1)
return false;
if (fp->FWrite(&worst_error_rates_, sizeof(worst_error_rates_), 1) != 1)
return false;
if (fp->FWrite(&worst_iteration_, sizeof(worst_iteration_), 1) != 1)
return false;
if (fp->FWrite(&stall_iteration_, sizeof(stall_iteration_), 1) != 1)
return false;
if (!best_model_data_.Serialize(fp)) return false;
if (!worst_model_data_.Serialize(fp)) return false;
if (amount != NO_BEST_TRAINER && !best_trainer_.Serialize(fp)) return false;
GenericVector<char> sub_data;
if (sub_trainer_ != NULL && !SaveTrainingDump(LIGHT, sub_trainer_, &sub_data))
return false;
if (!sub_data.Serialize(fp)) return false;
if (!best_error_history_.Serialize(fp)) return false;
if (!best_error_iterations_.Serialize(fp)) return false;
if (fp->FWrite(&improvement_steps_, sizeof(improvement_steps_), 1) != 1)
return false;
return true;
}
// Reads from the given file. Returns false in case of error.
// If swap is true, assumes a big/little-endian swap is needed.
bool LSTMTrainer::DeSerialize(bool swap, TFile* fp) {
if (!LSTMRecognizer::DeSerialize(swap, fp)) return false;
if (fp->FRead(&learning_iteration_, sizeof(learning_iteration_), 1) != 1) {
// Special case. If we successfully decoded the recognizer, but fail here
// then it means we were just given a recognizer, so issue a warning and
// allow it.
tprintf("Warning: LSTMTrainer deserialized an LSTMRecognizer!\n");
learning_iteration_ = 0;
network_->SetEnableTraining(TS_RE_ENABLE);
return true;
}
if (fp->FRead(&prev_sample_iteration_, sizeof(prev_sample_iteration_), 1) !=
1)
return false;
if (fp->FRead(&perfect_delay_, sizeof(perfect_delay_), 1) != 1) return false;
if (fp->FRead(&last_perfect_training_iteration_,
sizeof(last_perfect_training_iteration_), 1) != 1)
return false;
for (int i = 0; i < ET_COUNT; ++i) {
if (!error_buffers_[i].DeSerialize(swap, fp)) return false;
}
if (fp->FRead(&error_rates_, sizeof(error_rates_), 1) != 1) return false;
if (fp->FRead(&training_stage_, sizeof(training_stage_), 1) != 1)
return false;
uinT8 amount;
if (fp->FRead(&amount, sizeof(amount), 1) != 1) return false;
if (amount == LIGHT) return true; // Don't read the rest.
if (fp->FRead(&best_error_rate_, sizeof(best_error_rate_), 1) != 1)
return false;
if (fp->FRead(&best_error_rates_, sizeof(best_error_rates_), 1) != 1)
return false;
if (fp->FRead(&best_iteration_, sizeof(best_iteration_), 1) != 1)
return false;
if (fp->FRead(&worst_error_rate_, sizeof(worst_error_rate_), 1) != 1)
return false;
if (fp->FRead(&worst_error_rates_, sizeof(worst_error_rates_), 1) != 1)
return false;
if (fp->FRead(&worst_iteration_, sizeof(worst_iteration_), 1) != 1)
return false;
if (fp->FRead(&stall_iteration_, sizeof(stall_iteration_), 1) != 1)
return false;
if (!best_model_data_.DeSerialize(swap, fp)) return false;
if (!worst_model_data_.DeSerialize(swap, fp)) return false;
if (amount != NO_BEST_TRAINER && !best_trainer_.DeSerialize(swap, fp))
return false;
GenericVector<char> sub_data;
if (!sub_data.DeSerialize(swap, fp)) return false;
delete sub_trainer_;
if (sub_data.empty()) {
sub_trainer_ = NULL;
} else {
sub_trainer_ = new LSTMTrainer();
if (!ReadTrainingDump(sub_data, sub_trainer_)) return false;
}
if (!best_error_history_.DeSerialize(swap, fp)) return false;
if (!best_error_iterations_.DeSerialize(swap, fp)) return false;
if (fp->FRead(&improvement_steps_, sizeof(improvement_steps_), 1) != 1)
return false;
return true;
}
// De-serializes the saved best_trainer_ into sub_trainer_, and adjusts the
// learning rates (by scaling reduction, or layer specific, according to
// NF_LAYER_SPECIFIC_LR).
void LSTMTrainer::StartSubtrainer(STRING* log_msg) {
delete sub_trainer_;
sub_trainer_ = new LSTMTrainer();
if (!checkpoint_reader_->Run(best_trainer_, sub_trainer_)) {
*log_msg += " Failed to revert to previous best for trial!";
delete sub_trainer_;
sub_trainer_ = NULL;
} else {
log_msg->add_str_int(" Trial sub_trainer_ from iteration ",
sub_trainer_->training_iteration());
// Reduce learning rate so it doesn't diverge this time.
sub_trainer_->ReduceLearningRates(this, log_msg);
// If it fails again, we will wait twice as long before reverting again.
int stall_offset = learning_iteration() - sub_trainer_->learning_iteration();
stall_iteration_ = learning_iteration() + 2 * stall_offset;
sub_trainer_->stall_iteration_ = stall_iteration_;
// Re-save the best trainer with the new learning rates and stall iteration.
checkpoint_writer_->Run(NO_BEST_TRAINER, sub_trainer_, &best_trainer_);
}
}
// While the sub_trainer_ is behind the current training iteration and its
// training error is at least kSubTrainerMarginFraction better than the
// current training error, trains the sub_trainer_, and returns STR_UPDATED if
// it did anything. If it catches up, and has a better error rate than the
// current best, as well as a margin over the current error rate, then the
// trainer in *this is replaced with sub_trainer_, and STR_REPLACED is
// returned. STR_NONE is returned if the subtrainer wasn't good enough to
// receive any training iterations.
SubTrainerResult LSTMTrainer::UpdateSubtrainer(STRING* log_msg) {
double training_error = CharError();
double sub_error = sub_trainer_->CharError();
double sub_margin = (training_error - sub_error) / sub_error;
if (sub_margin >= kSubTrainerMarginFraction) {
log_msg->add_str_double(" sub_trainer=", sub_error);
log_msg->add_str_double(" margin=", 100.0 * sub_margin);
*log_msg += "\n";
// Catch up to current iteration.
int end_iteration = training_iteration();
while (sub_trainer_->training_iteration() < end_iteration &&
sub_margin >= kSubTrainerMarginFraction) {
int target_iteration =
sub_trainer_->training_iteration() + kNumPagesPerBatch;
while (sub_trainer_->training_iteration() < target_iteration) {
sub_trainer_->TrainOnLine(this, false);
}
STRING batch_log = "Sub:";
sub_trainer_->PrepareLogMsg(&batch_log);
batch_log += "\n";
tprintf("UpdateSubtrainer:%s", batch_log.string());
*log_msg += batch_log;
sub_error = sub_trainer_->CharError();
sub_margin = (training_error - sub_error) / sub_error;
}
if (sub_error < best_error_rate_ &&
sub_margin >= kSubTrainerMarginFraction) {
// The sub_trainer_ has won the race to a new best. Switch to it.
GenericVector<char> updated_trainer;
SaveTrainingDump(LIGHT, sub_trainer_, &updated_trainer);
ReadTrainingDump(updated_trainer, this);
log_msg->add_str_int(" Sub trainer wins at iteration ",
training_iteration());
*log_msg += "\n";
return STR_REPLACED;
}
return STR_UPDATED;
}
return STR_NONE;
}
// Reduces network learning rates, either for everything, or for layers
// independently, according to NF_LAYER_SPECIFIC_LR.
void LSTMTrainer::ReduceLearningRates(LSTMTrainer* samples_trainer,
STRING* log_msg) {
if (network_->TestFlag(NF_LAYER_SPECIFIC_LR)) {
int num_reduced = ReduceLayerLearningRates(
kLearningRateDecay, kNumAdjustmentIterations, samples_trainer);
log_msg->add_str_int("\nReduced learning rate on layers: ", num_reduced);
} else {
ScaleLearningRate(kLearningRateDecay);
log_msg->add_str_double("\nReduced learning rate to :", learning_rate_);
}
*log_msg += "\n";
}
// Considers reducing the learning rate independently for each layer down by
// factor(<1), or leaving it the same, by double-training the given number of
// samples and minimizing the amount of changing of sign of weight updates.
// Even if it looks like all weights should remain the same, an adjustment
// will be made to guarantee a different result when reverting to an old best.
// Returns the number of layer learning rates that were reduced.
int LSTMTrainer::ReduceLayerLearningRates(double factor, int num_samples,
LSTMTrainer* samples_trainer) {
enum WhichWay {
LR_DOWN, // Learning rate will go down by factor.
LR_SAME, // Learning rate will stay the same.
LR_COUNT // Size of arrays.
};
// Epsilon is so small that it may as well be zero, but still positive.
const double kEpsilon = 1.0e-30;
GenericVector<STRING> layers = EnumerateLayers();
int num_layers = layers.size();
GenericVector<int> num_weights;
num_weights.init_to_size(num_layers, 0);
GenericVector<double> bad_sums[LR_COUNT];
GenericVector<double> ok_sums[LR_COUNT];
for (int i = 0; i < LR_COUNT; ++i) {
bad_sums[i].init_to_size(num_layers, 0.0);
ok_sums[i].init_to_size(num_layers, 0.0);
}
double momentum_factor = 1.0 / (1.0 - momentum_);
GenericVector<char> orig_trainer;
SaveTrainingDump(LIGHT, this, &orig_trainer);
for (int i = 0; i < num_layers; ++i) {
Network* layer = GetLayer(layers[i]);
num_weights[i] = layer->IsTraining() ? layer->num_weights() : 0;
}
int iteration = sample_iteration();
for (int s = 0; s < num_samples; ++s) {
// Which way will we modify the learning rate?
for (int ww = 0; ww < LR_COUNT; ++ww) {
// Transfer momentum to learning rate and adjust by the ww factor.
float ww_factor = momentum_factor;
if (ww == LR_DOWN) ww_factor *= factor;
// Make a copy of *this, so we can mess about without damaging anything.
LSTMTrainer copy_trainer;
copy_trainer.ReadTrainingDump(orig_trainer, &copy_trainer);
// Clear the updates, doing nothing else.
copy_trainer.network_->Update(0.0, 0.0, 0);
// Adjust the learning rate in each layer.
for (int i = 0; i < num_layers; ++i) {
if (num_weights[i] == 0) continue;
copy_trainer.ScaleLayerLearningRate(layers[i], ww_factor);
}
copy_trainer.SetIteration(iteration);
// Train on the sample, but keep the update in updates_ instead of
// applying to the weights.
const ImageData* trainingdata =
copy_trainer.TrainOnLine(samples_trainer, true);
if (trainingdata == NULL) continue;
// We'll now use this trainer again for each layer.
GenericVector<char> updated_trainer;
SaveTrainingDump(LIGHT, &copy_trainer, &updated_trainer);
for (int i = 0; i < num_layers; ++i) {
if (num_weights[i] == 0) continue;
LSTMTrainer layer_trainer;
layer_trainer.ReadTrainingDump(updated_trainer, &layer_trainer);
Network* layer = layer_trainer.GetLayer(layers[i]);
// Update the weights in just the layer, and also zero the updates
// matrix (to epsilon).
layer->Update(0.0, kEpsilon, 0);
// Train again on the same sample, again holding back the updates.
layer_trainer.TrainOnLine(trainingdata, true);
// Count the sign changes in the updates in layer vs in copy_trainer.
float before_bad = bad_sums[ww][i];
float before_ok = ok_sums[ww][i];
layer->CountAlternators(*copy_trainer.GetLayer(layers[i]),
&ok_sums[ww][i], &bad_sums[ww][i]);
float bad_frac =
bad_sums[ww][i] + ok_sums[ww][i] - before_bad - before_ok;
if (bad_frac > 0.0f)
bad_frac = (bad_sums[ww][i] - before_bad) / bad_frac;
}
}
++iteration;
}
int num_lowered = 0;
for (int i = 0; i < num_layers; ++i) {
if (num_weights[i] == 0) continue;
Network* layer = GetLayer(layers[i]);
float lr = GetLayerLearningRate(layers[i]);
double total_down = bad_sums[LR_DOWN][i] + ok_sums[LR_DOWN][i];
double total_same = bad_sums[LR_SAME][i] + ok_sums[LR_SAME][i];
double frac_down = bad_sums[LR_DOWN][i] / total_down;
double frac_same = bad_sums[LR_SAME][i] / total_same;
tprintf("Layer %d=%s: lr %g->%g%%, lr %g->%g%%", i, layer->name().string(),
lr * factor, 100.0 * frac_down, lr, 100.0 * frac_same);
if (frac_down < frac_same * kImprovementFraction) {
tprintf(" REDUCED\n");
ScaleLayerLearningRate(layers[i], factor);
++num_lowered;
} else {
tprintf(" SAME\n");
}
}
if (num_lowered == 0) {
// Just lower everything to make sure.
for (int i = 0; i < num_layers; ++i) {
if (num_weights[i] > 0) {
ScaleLayerLearningRate(layers[i], factor);
++num_lowered;
}
}
}
return num_lowered;
}
// Converts the string to integer class labels, with appropriate null_char_s
// in between if not in SimpleTextOutput mode. Returns false on failure.
/* static */
bool LSTMTrainer::EncodeString(const STRING& str, const UNICHARSET& unicharset,
const UnicharCompress* recoder, bool simple_text,
int null_char, GenericVector<int>* labels) {
if (str.string() == NULL || str.length() <= 0) {
tprintf("Empty truth string!\n");
return false;
}
int err_index;
GenericVector<int> internal_labels;
labels->truncate(0);
if (!simple_text) labels->push_back(null_char);
if (unicharset.encode_string(str.string(), true, &internal_labels, NULL,
&err_index)) {
bool success = true;
for (int i = 0; i < internal_labels.size(); ++i) {
if (recoder != NULL) {
// Re-encode labels via recoder.
RecodedCharID code;
int len = recoder->EncodeUnichar(internal_labels[i], &code);
if (len > 0) {
for (int j = 0; j < len; ++j) {
labels->push_back(code(j));
if (!simple_text) labels->push_back(null_char);
}
} else {
success = false;
err_index = 0;
break;
}
} else {
labels->push_back(internal_labels[i]);
if (!simple_text) labels->push_back(null_char);
}
}
if (success) return true;
}
tprintf("Encoding of string failed! Failure bytes:");
while (err_index < str.length()) {
tprintf(" %x", str[err_index++]);
}
tprintf("\n");
return false;
}
// Performs forward-backward on the given trainingdata.
// Returns a Trainability enum to indicate the suitability of the sample.
Trainability LSTMTrainer::TrainOnLine(const ImageData* trainingdata,
bool batch) {
NetworkIO fwd_outputs, targets;
Trainability trainable =
PrepareForBackward(trainingdata, &fwd_outputs, &targets);
++sample_iteration_;
if (trainable == UNENCODABLE || trainable == NOT_BOXED) {
return trainable; // Sample was unusable.
}
bool debug = debug_interval_ > 0 &&
training_iteration() % debug_interval_ == 0;
// Run backprop on the output.
NetworkIO bp_deltas;
if (network_->IsTraining() &&
(trainable != PERFECT ||
training_iteration() >
last_perfect_training_iteration_ + perfect_delay_)) {
network_->Backward(debug, targets, &scratch_space_, &bp_deltas);
network_->Update(learning_rate_, batch ? -1.0f : momentum_,
training_iteration_ + 1);
}
#ifndef GRAPHICS_DISABLED
if (debug_interval_ == 1 && debug_win_ != NULL) {
delete debug_win_->AwaitEvent(SVET_CLICK);
}
#endif // GRAPHICS_DISABLED
// Roll the memory of past means.
RollErrorBuffers();
return trainable;
}
// Prepares the ground truth, runs forward, and prepares the targets.
// Returns a Trainability enum to indicate the suitability of the sample.
Trainability LSTMTrainer::PrepareForBackward(const ImageData* trainingdata,
NetworkIO* fwd_outputs,
NetworkIO* targets) {
if (trainingdata == NULL) {
tprintf("Null trainingdata.\n");
return UNENCODABLE;
}
// Ensure repeatability of random elements even across checkpoints.
bool debug = debug_interval_ > 0 &&
training_iteration() % debug_interval_ == 0;
GenericVector<int> truth_labels;
if (!EncodeString(trainingdata->transcription(), &truth_labels)) {
tprintf("Can't encode transcription: %s\n",
trainingdata->transcription().string());
return UNENCODABLE;
}
int w = 0;
while (w < truth_labels.size() &&
(truth_labels[w] == UNICHAR_SPACE || truth_labels[w] == null_char_))
++w;
if (w == truth_labels.size()) {
tprintf("Blank transcription: %s\n",
trainingdata->transcription().string());
return UNENCODABLE;
}
float image_scale;
NetworkIO inputs;
bool invert = trainingdata->boxes().empty();
if (!RecognizeLine(*trainingdata, invert, debug, invert, 0.0f, &image_scale,
&inputs, fwd_outputs)) {
tprintf("Image not trainable\n");
return UNENCODABLE;
}
targets->Resize(*fwd_outputs, network_->NumOutputs());
double text_error = 100.0;
LossType loss_type = OutputLossType();
if (loss_type == LT_SOFTMAX) {
if (!ComputeTextTargets(*fwd_outputs, truth_labels, targets)) {
tprintf("Compute simple targets failed!\n");
return UNENCODABLE;
}
} else if (loss_type == LT_CTC) {
if (!ComputeCTCTargets(truth_labels, fwd_outputs, targets)) {
tprintf("Compute CTC targets failed!\n");
return UNENCODABLE;
}
} else {
tprintf("Logistic outputs not implemented yet!\n");
return UNENCODABLE;
}
GenericVector<int> ocr_labels;
GenericVector<int> xcoords;
LabelsFromOutputs(*fwd_outputs, 0.0f, &ocr_labels, &xcoords);
// CTC does not produce correct target labels to begin with.
if (loss_type != LT_CTC) {
LabelsFromOutputs(*targets, 0.0f, &truth_labels, &xcoords);
}
if (!DebugLSTMTraining(inputs, *trainingdata, *fwd_outputs, truth_labels,
*targets)) {
tprintf("Input width was %d\n", inputs.Width());
return UNENCODABLE;
}
STRING ocr_text = DecodeLabels(ocr_labels);
STRING truth_text = DecodeLabels(truth_labels);
targets->SubtractAllFromFloat(*fwd_outputs);
if (debug_interval_ != 0) {
tprintf("Iteration %d: BEST OCR TEXT : %s\n", training_iteration(),
ocr_text.string());
}
double char_error = ComputeCharError(truth_labels, ocr_labels);
double word_error = ComputeWordError(&truth_text, &ocr_text);
double delta_error = ComputeErrorRates(*targets, char_error, word_error);
if (debug_interval_ != 0) {
tprintf("File %s page %d %s:\n", trainingdata->imagefilename().string(),
trainingdata->page_number(), delta_error == 0.0 ? "(Perfect)" : "");
}
if (delta_error == 0.0) return PERFECT;
if (targets->AnySuspiciousTruth(kHighConfidence)) return HI_PRECISION_ERR;
return TRAINABLE;
}
// Writes the trainer to memory, so that the current training state can be
// restored.
bool LSTMTrainer::SaveTrainingDump(SerializeAmount serialize_amount,
const LSTMTrainer* trainer,
GenericVector<char>* data) const {
TFile fp;
fp.OpenWrite(data);
trainer->serialize_amount_ = serialize_amount;
return trainer->Serialize(&fp);
}
// Reads previously saved trainer from memory.
bool LSTMTrainer::ReadTrainingDump(const GenericVector<char>& data,
LSTMTrainer* trainer) {
return trainer->ReadSizedTrainingDump(&data[0], data.size());
}
bool LSTMTrainer::ReadSizedTrainingDump(const char* data, int size) {
TFile fp;
fp.Open(data, size);
return DeSerialize(false, &fp);
}
// Writes the recognizer to memory, so that it can be used for testing later.
void LSTMTrainer::SaveRecognitionDump(GenericVector<char>* data) const {
TFile fp;
fp.OpenWrite(data);
network_->SetEnableTraining(TS_TEMP_DISABLE);
ASSERT_HOST(LSTMRecognizer::Serialize(&fp));
network_->SetEnableTraining(TS_RE_ENABLE);
}
// Reads and returns a previously saved recognizer from memory.
LSTMRecognizer* LSTMTrainer::ReadRecognitionDump(
const GenericVector<char>& data) {
TFile fp;
fp.Open(&data[0], data.size());
LSTMRecognizer* recognizer = new LSTMRecognizer;
ASSERT_HOST(recognizer->DeSerialize(false, &fp));
return recognizer;
}
// Returns a suitable filename for a training dump, based on the model_base_,
// the iteration and the error rates.
STRING LSTMTrainer::DumpFilename() const {
STRING filename;
filename.add_str_double(model_base_.string(), best_error_rate_);
filename.add_str_int("_", best_iteration_);
filename += ".lstm";
return filename;
}
// Fills the whole error buffer of the given type with the given value.
void LSTMTrainer::FillErrorBuffer(double new_error, ErrorTypes type) {
for (int i = 0; i < kRollingBufferSize_; ++i)
error_buffers_[type][i] = new_error;
error_rates_[type] = 100.0 * new_error;
}
// Factored sub-constructor sets up reasonable default values.
void LSTMTrainer::EmptyConstructor() {
align_win_ = NULL;
target_win_ = NULL;
ctc_win_ = NULL;
recon_win_ = NULL;
checkpoint_iteration_ = 0;
serialize_amount_ = FULL;
training_stage_ = 0;
num_training_stages_ = 2;
InitIterations();
}
// Sets the unicharset properties using the given script_dir as a source of
// script unicharsets. If the flag TF_COMPRESS_UNICHARSET is true, also sets
// up the recoder_ to simplify the unicharset.
void LSTMTrainer::SetUnicharsetProperties(const STRING& script_dir) {
tprintf("Setting unichar properties\n");
for (int s = 0; s < GetUnicharset().get_script_table_size(); ++s) {
if (strcmp("NULL", GetUnicharset().get_script_from_script_id(s)) == 0)
continue;
// Load the unicharset for the script if available.
STRING filename = script_dir + "/" +
GetUnicharset().get_script_from_script_id(s) +
".unicharset";
UNICHARSET script_set;
GenericVector<char> data;
if ((*file_reader_)(filename, &data) &&
script_set.load_from_inmemory_file(&data[0], data.size())) {
tprintf("Setting properties for script %s\n",
GetUnicharset().get_script_from_script_id(s));
ccutil_.unicharset.SetPropertiesFromOther(script_set);
}
}
if (IsRecoding()) {
STRING filename = script_dir + "/radical-stroke.txt";
GenericVector<char> data;
if ((*file_reader_)(filename, &data)) {
data += '\0';
STRING stroke_table = &data[0];
if (recoder_.ComputeEncoding(GetUnicharset(), null_char_,
&stroke_table)) {
RecodedCharID code;
recoder_.EncodeUnichar(null_char_, &code);
null_char_ = code(0);
// Space should encode as itself.
recoder_.EncodeUnichar(UNICHAR_SPACE, &code);
ASSERT_HOST(code(0) == UNICHAR_SPACE);
return;
}
} else {
tprintf("Failed to load radical-stroke info from: %s\n",
filename.string());
}
training_flags_ &= ~TF_COMPRESS_UNICHARSET;
}
}
// Outputs the string and periodically displays the given network inputs
// as an image in the given window, and the corresponding labels at the
// corresponding x_starts.
// Returns false if the truth string is empty.
bool LSTMTrainer::DebugLSTMTraining(const NetworkIO& inputs,
const ImageData& trainingdata,
const NetworkIO& fwd_outputs,
const GenericVector<int>& truth_labels,
const NetworkIO& outputs) {
const STRING& truth_text = DecodeLabels(truth_labels);
if (truth_text.string() == NULL || truth_text.length() <= 0) {
tprintf("Empty truth string at decode time!\n");
return false;
}
if (debug_interval_ != 0) {
// Get class labels, xcoords and string.
GenericVector<int> labels;
GenericVector<int> xcoords;
LabelsFromOutputs(outputs, 0.0f, &labels, &xcoords);
STRING text = DecodeLabels(labels);
tprintf("Iteration %d: ALIGNED TRUTH : %s\n",
training_iteration(), text.string());
if (debug_interval_ > 0 && training_iteration() % debug_interval_ == 0) {
tprintf("TRAINING activation path for truth string %s\n",
truth_text.string());
DebugActivationPath(outputs, labels, xcoords);
DisplayForward(inputs, labels, xcoords, "LSTMTraining", &align_win_);
if (OutputLossType() == LT_CTC) {
DisplayTargets(fwd_outputs, "CTC Outputs", &ctc_win_);
DisplayTargets(outputs, "CTC Targets", &target_win_);
}
}
}
return true;
}
// Displays the network targets as line a line graph.
void LSTMTrainer::DisplayTargets(const NetworkIO& targets,
const char* window_name, ScrollView** window) {
#ifndef GRAPHICS_DISABLED // do nothing if there's no graphics.
int width = targets.Width();
int num_features = targets.NumFeatures();
Network::ClearWindow(true, window_name, width * kTargetXScale, kTargetYScale,
window);
for (int c = 0; c < num_features; ++c) {
int color = c % (ScrollView::GREEN_YELLOW - 1) + 2;
(*window)->Pen(static_cast<ScrollView::Color>(color));
int start_t = -1;
for (int t = 0; t < width; ++t) {
double target = targets.f(t)[c];
target *= kTargetYScale;
if (target >= 1) {
if (start_t < 0) {
(*window)->SetCursor(t - 1, 0);
start_t = t;
}
(*window)->DrawTo(t, target);
} else if (start_t >= 0) {
(*window)->DrawTo(t, 0);
(*window)->DrawTo(start_t - 1, 0);
start_t = -1;
}
}
if (start_t >= 0) {
(*window)->DrawTo(width, 0);
(*window)->DrawTo(start_t - 1, 0);
}
}
(*window)->Update();
#endif // GRAPHICS_DISABLED
}
// Builds a no-compromises target where the first positions should be the
// truth labels and the rest is padded with the null_char_.
bool LSTMTrainer::ComputeTextTargets(const NetworkIO& outputs,
const GenericVector<int>& truth_labels,
NetworkIO* targets) {
if (truth_labels.size() > targets->Width()) {
tprintf("Error: transcription %s too long to fit into target of width %d\n",
DecodeLabels(truth_labels).string(), targets->Width());
return false;
}
for (int i = 0; i < truth_labels.size() && i < targets->Width(); ++i) {
targets->SetActivations(i, truth_labels[i], 1.0);
}
for (int i = truth_labels.size(); i < targets->Width(); ++i) {
targets->SetActivations(i, null_char_, 1.0);
}
return true;
}
// Builds a target using standard CTC. truth_labels should be pre-padded with
// nulls wherever desired. They don't have to be between all labels.
// outputs is input-output, as it gets clipped to minimum probability.
bool LSTMTrainer::ComputeCTCTargets(const GenericVector<int>& truth_labels,
NetworkIO* outputs, NetworkIO* targets) {
// Bottom-clip outputs to a minimum probability.
CTC::NormalizeProbs(outputs);
return CTC::ComputeCTCTargets(truth_labels, null_char_,
outputs->float_array(), targets);
}
// Computes network errors, and stores the results in the rolling buffers,
// along with the supplied text_error.
// Returns the delta error of the current sample (not running average.)
double LSTMTrainer::ComputeErrorRates(const NetworkIO& deltas,
double char_error, double word_error) {
UpdateErrorBuffer(ComputeRMSError(deltas), ET_RMS);
// Delta error is the fraction of timesteps with >0.5 error in the top choice
// score. If zero, then the top choice characters are guaranteed correct,
// even when there is residue in the RMS error.
double delta_error = ComputeWinnerError(deltas);
UpdateErrorBuffer(delta_error, ET_DELTA);
UpdateErrorBuffer(word_error, ET_WORD_RECERR);
UpdateErrorBuffer(char_error, ET_CHAR_ERROR);
// Skip ratio measures the difference between sample_iteration_ and
// training_iteration_, which reflects the number of unusable samples,
// usually due to unencodable truth text, or the text not fitting in the
// space for the output.
double skip_count = sample_iteration_ - prev_sample_iteration_;
UpdateErrorBuffer(skip_count, ET_SKIP_RATIO);
return delta_error;
}
// Computes the network activation RMS error rate.
double LSTMTrainer::ComputeRMSError(const NetworkIO& deltas) {
double total_error = 0.0;
int width = deltas.Width();
int num_classes = deltas.NumFeatures();
for (int t = 0; t < width; ++t) {
const float* class_errs = deltas.f(t);
for (int c = 0; c < num_classes; ++c) {
double error = class_errs[c];
total_error += error * error;
}
}
return sqrt(total_error / (width * num_classes));
}
// Computes network activation winner error rate. (Number of values that are
// in error by >= 0.5 divided by number of time-steps.) More closely related
// to final character error than RMS, but still directly calculable from
// just the deltas. Because of the binary nature of the targets, zero winner
// error is a sufficient but not necessary condition for zero char error.
double LSTMTrainer::ComputeWinnerError(const NetworkIO& deltas) {
int num_errors = 0;
int width = deltas.Width();
int num_classes = deltas.NumFeatures();
for (int t = 0; t < width; ++t) {
const float* class_errs = deltas.f(t);
for (int c = 0; c < num_classes; ++c) {
float abs_delta = fabs(class_errs[c]);
// TODO(rays) Filtering cases where the delta is very large to cut out
// GT errors doesn't work. Find a better way or get better truth.
if (0.5 <= abs_delta)
++num_errors;
}
}
return static_cast<double>(num_errors) / width;
}
// Computes a very simple bag of chars char error rate.
double LSTMTrainer::ComputeCharError(const GenericVector<int>& truth_str,
const GenericVector<int>& ocr_str) {
GenericVector<int> label_counts;
label_counts.init_to_size(NumOutputs(), 0);
int truth_size = 0;
for (int i = 0; i < truth_str.size(); ++i) {
if (truth_str[i] != null_char_) {
++label_counts[truth_str[i]];
++truth_size;
}
}
for (int i = 0; i < ocr_str.size(); ++i) {
if (ocr_str[i] != null_char_) {
--label_counts[ocr_str[i]];
}
}
int char_errors = 0;
for (int i = 0; i < label_counts.size(); ++i) {
char_errors += abs(label_counts[i]);
}
if (truth_size == 0) {
return (char_errors == 0) ? 0.0 : 1.0;
}
return static_cast<double>(char_errors) / truth_size;
}
// Computes word recall error rate using a very simple bag of words algorithm.
// NOTE that this is destructive on both input strings.
double LSTMTrainer::ComputeWordError(STRING* truth_str, STRING* ocr_str) {
typedef TessHashMap<std::string, int, std::hash<std::string> > StrMap;
GenericVector<STRING> truth_words, ocr_words;
truth_str->split(' ', &truth_words);
if (truth_words.empty()) return 0.0;
ocr_str->split(' ', &ocr_words);
StrMap word_counts;
for (int i = 0; i < truth_words.size(); ++i) {
std::string truth_word(truth_words[i].string());
StrMap::iterator it = word_counts.find(truth_word);
if (it == word_counts.end())
word_counts.insert(make_pair(truth_word, 1));
else
++it->second;
}
for (int i = 0; i < ocr_words.size(); ++i) {
std::string ocr_word(ocr_words[i].string());
StrMap::iterator it = word_counts.find(ocr_word);
if (it == word_counts.end())
word_counts.insert(make_pair(ocr_word, -1));
else
--it->second;
}
int word_recall_errs = 0;
for (StrMap::const_iterator it = word_counts.begin(); it != word_counts.end();
++it) {
if (it->second > 0) word_recall_errs += it->second;
}
return static_cast<double>(word_recall_errs) / truth_words.size();
}
// Updates the error buffer and corresponding mean of the given type with
// the new_error.
void LSTMTrainer::UpdateErrorBuffer(double new_error, ErrorTypes type) {
int index = training_iteration_ % kRollingBufferSize_;
error_buffers_[type][index] = new_error;
// Compute the mean error.
int mean_count = MIN(training_iteration_ + 1, error_buffers_[type].size());
double buffer_sum = 0.0;
for (int i = 0; i < mean_count; ++i) buffer_sum += error_buffers_[type][i];
double mean = buffer_sum / mean_count;
// Trim precision to 1/1000 of 1%.
error_rates_[type] = IntCastRounded(100000.0 * mean) / 1000.0;
}
// Rolls error buffers and reports the current means.
void LSTMTrainer::RollErrorBuffers() {
prev_sample_iteration_ = sample_iteration_;
if (NewSingleError(ET_DELTA) > 0.0)
++learning_iteration_;
else
last_perfect_training_iteration_ = training_iteration_;
++training_iteration_;
if (debug_interval_ != 0) {
tprintf("Mean rms=%g%%, delta=%g%%, train=%g%%(%g%%), skip ratio=%g%%\n",
error_rates_[ET_RMS], error_rates_[ET_DELTA],
error_rates_[ET_CHAR_ERROR], error_rates_[ET_WORD_RECERR],
error_rates_[ET_SKIP_RATIO]);
}
}
// Given that error_rate is either a new min or max, updates the best/worst
// error rates, and record of progress.
// Tester is an externally supplied callback function that tests on some
// data set with a given model and records the error rates in a graph.
STRING LSTMTrainer::UpdateErrorGraph(int iteration, double error_rate,
const GenericVector<char>& model_data,
TestCallback tester) {
if (error_rate > best_error_rate_
&& iteration < best_iteration_ + kErrorGraphInterval) {
// Too soon to record a new point.
if (tester != NULL)
return tester->Run(worst_iteration_, NULL, worst_model_data_,
CurrentTrainingStage());
else
return "";
}
STRING result;
// NOTE: there are 2 asymmetries here:
// 1. We are computing the global minimum, but the local maximum in between.
// 2. If the tester returns an empty string, indicating that it is busy,
// call it repeatedly on new local maxima to test the previous min, but
// not the other way around, as there is little point testing the maxima
// between very frequent minima.
if (error_rate < best_error_rate_) {
// This is a new (global) minimum.
if (tester != NULL) {
result = tester->Run(worst_iteration_, worst_error_rates_,
worst_model_data_, CurrentTrainingStage());
worst_model_data_.truncate(0);
best_model_data_ = model_data;
}
best_error_rate_ = error_rate;
memcpy(best_error_rates_, error_rates_, sizeof(error_rates_));
best_iteration_ = iteration;
best_error_history_.push_back(error_rate);
best_error_iterations_.push_back(iteration);
// Compute 2% decay time.
double two_percent_more = error_rate + 2.0;
int i;
for (i = best_error_history_.size() - 1;
i >= 0 && best_error_history_[i] < two_percent_more; --i) {
}
int old_iteration = i >= 0 ? best_error_iterations_[i] : 0;
improvement_steps_ = iteration - old_iteration;
tprintf("2 Percent improvement time=%d, best error was %g @ %d\n",
improvement_steps_, i >= 0 ? best_error_history_[i] : 100.0,
old_iteration);
} else if (error_rate > best_error_rate_) {
// This is a new (local) maximum.
if (tester != NULL) {
if (best_model_data_.empty()) {
// Allow for multiple data points with "worst" error rate.
result = tester->Run(worst_iteration_, worst_error_rates_,
worst_model_data_, CurrentTrainingStage());
} else {
result = tester->Run(best_iteration_, best_error_rates_,
best_model_data_, CurrentTrainingStage());
}
if (result.length() > 0)
best_model_data_.truncate(0);
worst_model_data_ = model_data;
}
}
worst_error_rate_ = error_rate;
memcpy(worst_error_rates_, error_rates_, sizeof(error_rates_));
worst_iteration_ = iteration;
return result;
}
} // namespace tesseract.
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