on the lstmtraiing, Have you ever seen error like "Logisitc outputs not implemented yet!"

[notorio...@gmail.com]

Hi All

on the lstmtraiing, Have you ever seen error like "Logisitc outputs not implemented yet!"

ㅜㅜㅜㅜ

// Performs forward-backward on the given trainingdata. https://github.com/tesseract-ocr/tesseract/blob/master/lstm/lstmtrainer.cpp
	// 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_, adam_beta_,
	training_iteration_ + 1);
	}
	#ifndef GRAPHICS_DISABLED
	if (debug_interval_ == 1 && debug_win_ != nullptr) {
	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 == nullptr) {
	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' in language '%s'\n",
	trainingdata->transcription().string(),
	trainingdata->language().string());
	return UNENCODABLE;
	}
	bool upside_down = false;
	if (randomly_rotate_) {
	// This ensures consistent training results.
	SetRandomSeed();
	upside_down = randomizer_.SignedRand(1.0) > 0.0;
	if (upside_down) {
	// Modify the truth labels to match the rotation:
	// Apart from space and null, increment the label. This is changes the
	// script-id to the same script-id but upside-down.
	// The labels need to be reversed in order, as the first is now the last.
	for (int c = 0; c < truth_labels.size(); ++c) {
	if (truth_labels[c] != UNICHAR_SPACE && truth_labels[c] != null_char_)
	++truth_labels[c];
	}
	truth_labels.reverse();
	}
	}
	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, upside_down,
	&image_scale, &inputs, fwd_outputs)) {
	tprintf("Image not trainable\n");
	return UNENCODABLE;
	}
	targets->Resize(*fwd_outputs, network_->NumOutputs());
	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;