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Error during decoding

See original GitHub issue

After training the model,I tried decoding it with two iterations and i am getting the following error.

python sample_ensemble.py --models trained_models/EuTrans_esen_AttentionRNNEncoderDecoder_src_emb_250_bidir_True_enc_LSTM_32_dec_ConditionalLSTM_32_deepout_linear_trg_emb_250_Adam_0.001/epoch_1 trained_models/EuTrans_esen_AttentionRNNEncoderDecoder_src_emb_250_bidir_True_enc_LSTM_32_dec_ConditionalLSTM_32_deepout_linear_trg_emb_250_Adam_0.001/epoch_2 --dataset datasets/Dataset_EuTrans_esen.pkl --text examples/EuTrans/test.en 
[01/11/2018 12:49:07] Reading parameters from config.py
[01/11/2018 12:49:07] <<< Cupy not available. Using numpy. >>>
Using Theano backend.
[01/11/2018 12:49:08] <<< Cupy not available. Using numpy. >>>
[01/11/2018 12:49:08] Using an ensemble of 2 models
[01/11/2018 12:49:08] <<< Loading model from trained_models/EuTrans_esen_AttentionRNNEncoderDecoder_src_emb_250_bidir_True_enc_LSTM_32_dec_ConditionalLSTM_32_deepout_linear_trg_emb_250_Adam_0.001/epoch_1_Model_Wrapper.pkl ... >>>
[01/11/2018 12:49:08] <<< Loading model from trained_models/EuTrans_esen_AttentionRNNEncoderDecoder_src_emb_250_bidir_True_enc_LSTM_32_dec_ConditionalLSTM_32_deepout_linear_trg_emb_250_Adam_0.001/epoch_1.h5 ... >>>
[01/11/2018 12:49:12] <<< Loading optimized model... >>>
[01/11/2018 12:49:15] <<< Optimized model loaded. >>>
[01/11/2018 12:49:15] <<< Model loaded in 7.5578 seconds. >>>
[01/11/2018 12:49:15] <<< Loading model from trained_models/EuTrans_esen_AttentionRNNEncoderDecoder_src_emb_250_bidir_True_enc_LSTM_32_dec_ConditionalLSTM_32_deepout_linear_trg_emb_250_Adam_0.001/epoch_2_Model_Wrapper.pkl ... >>>
[01/11/2018 12:49:15] <<< Loading model from trained_models/EuTrans_esen_AttentionRNNEncoderDecoder_src_emb_250_bidir_True_enc_LSTM_32_dec_ConditionalLSTM_32_deepout_linear_trg_emb_250_Adam_0.001/epoch_2.h5 ... >>>
[01/11/2018 12:49:17] <<< Loading optimized model... >>>
[01/11/2018 12:49:21] <<< Optimized model loaded. >>>
[01/11/2018 12:49:21] <<< Model loaded in 5.2314 seconds. >>>
[01/11/2018 12:49:21] <<< Loading Dataset instance from datasets/Dataset_EuTrans_esen.pkl ... >>>
[01/11/2018 12:49:21] <<< Dataset instance loaded >>>
[01/11/2018 12:49:21] Removed "val" set outputs of type "text" with id "target_text.
[01/11/2018 12:49:21] 	Applying tokenization function: "tokenize_none".
[01/11/2018 12:49:21] Loaded "val" set inputs of data_type "text" with data_id "source_text" and length 2996.
[01/11/2018 12:49:21] Loaded "val" set inputs of data_type "ghost" with data_id "state_below" and length 2996.
[01/11/2018 12:49:21] Loaded "val" set inputs of type "file-name" with id "raw_source_text".
[01/11/2018 12:49:21] 
 <<< Predicting outputs of val set >>>
[01/11/2018 12:49:28] We did not find a dynamic library in the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.
[01/11/2018 12:49:29] We did not find a dynamic library in the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.
Traceback (most recent call last):
  File "sample_ensemble.py", line 61, in <module>
    sample_ensemble(args, params)
  File "/home/Vinay737/nmt-keras/nmt_keras/apply_model.py", line 94, in sample_ensemble
    predictions = beam_searcher.predictBeamSearchNet()[s]
  File "/home/Vinay737/nmt-keras/src/keras-wrapper/keras_wrapper/model_ensemble.py", line 249, in predictBeamSearchNet
    return_alphas=self.return_alphas, model_ensemble=True, n_models=len(self.models))
  File "/home/Vinay737/nmt-keras/src/keras-wrapper/keras_wrapper/search.py", line 135, in beam_search
    new_hyp_samples.append(hyp_samples[ti] + [wi])
IndexError: list index out of range

Any fixes ?

Issue Analytics

State:
Created 5 years ago
Comments:5 (5 by maintainers)

Top GitHub Comments

1reaction

VP007-pycommented, Nov 2, 2018

Working…!!

How about integrating evaluation metrics after this step with bleu or toolkits like this

0reactions

lvapeabcommented, Nov 2, 2018

I’d rather to maintain the sampling process independent of the evaluation. After sampling, you can run your desired evaluator. There is a script (utils/evaluate_from_file.py) for computing several metrics according to the coco evaluation package. You can use this (or any other) for computing BLEU.

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