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Add separator and training_iteration to checkpoint name (#2752)
* Add separator and training_iteration to checkpoint name * specify modelname_N.NN_NN_NN.checkpoint for intermediate checkpoint
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@ -8,11 +8,11 @@ lstmeval - Evaluation program for LSTM-based networks.
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SYNOPSIS
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--------
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*lstmeval* --model 'lang.lstm|langtrain_checkpoint|pluscharsN.NNN_NN.checkpoint' [--traineddata lang/lang.traineddata] --eval_listfile 'lang.eval_files.txt' [--verbosity N] [--max_image_MB NNNN]
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*lstmeval* --model 'lang.lstm|modelname_checkpoint|modelname_N.NN_NN_NN.checkpoint' [--traineddata lang/lang.traineddata] --eval_listfile 'lang.eval_files.txt' [--verbosity N] [--max_image_MB NNNN]
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DESCRIPTION
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-----------
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lstmeval(1) evaluates LSTM-based networks. Either a recognition model or a training checkpoint can be given as input for evaluation along with a list of lstmf files. If evaluating a training checkpoint, '--traineddata' should also be specified.
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lstmeval(1) evaluates LSTM-based networks. Either a recognition model or a training checkpoint can be given as input for evaluation along with a list of lstmf files. If evaluating a training checkpoint, '--traineddata' should also be specified. Intermediate training checkpoints can also be used.
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OPTIONS
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-------
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@ -910,11 +910,13 @@ void LSTMTrainer::SaveRecognitionDump(GenericVector<char>* data) const {
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}
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// Returns a suitable filename for a training dump, based on the model_base_,
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// the iteration and the error rates.
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// best_error_rate_, best_iteration_ and training_iteration_.
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STRING LSTMTrainer::DumpFilename() const {
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STRING filename;
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filename.add_str_double(model_base_.c_str(), best_error_rate_);
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filename += model_base_.c_str();
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filename.add_str_double("_", best_error_rate_);
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filename.add_str_int("_", best_iteration_);
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filename.add_str_int("_", training_iteration_);
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filename += ".checkpoint";
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return filename;
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}
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