TypeError: get_tr_stream() missing 1 required positional argument: 'trg_data'

[ghost]

When run
python training_adam.py I get the error:

Traceback (most recent call last): File "training_adam.py", line 175, in main(configuration, get_tr_stream(**configuration), TypeError: get_tr_stream() missing 1 required positional argument: 'trg_data'

[SwordYork]

Hi, I think you have not prepared the configurations.py file properly. Maybe the following lines is missing.

    # Source and target datasets
    config['src_data'] = datadir + 'all.{}-{}.{}.tok.shuf'.format(config['source_language'], config['target_language'],
                                                                  config['source_language'])
    config['trg_data'] = datadir + 'all.{}-{}.{}.tok.shuf'.format(config['source_language'], config['target_language'],
                                                                  config['target_language'])

Could you please paste your configurations.py ?

[SwordYork]

Let's forget the new version of DCNMT, the old version is much faster.

[ghost]

import os

def get_config(): config = {}

# prepare data
config['source_language'] = 'data/cls_train.txt.shuf'
config['target_language'] = 'data/mdn_train.txt.shuf'

# Where to save model, this corresponds to 'prefix' in groundhog
config['saveto'] = 'dcnmt_{}2{}'.format(config['source_language'], config['target_language'])

# Model related -----------------------------------------------------------

# Sequences longer than this will be discarded
config['max_src_seq_char_len'] = 300
config['max_src_seq_word_len'] = 50
config['max_trg_seq_char_len'] = 300
config['max_trg_seq_word_len'] = 50

# Number of hidden units in encoder/decoder GRU
config['src_dgru_nhids'] = 512
# We use half units in weight gru
assert config['src_dgru_nhids'] % 2 == 0
config['enc_nhids'] = 1024
config['dec_nhids'] = 1024
config['trg_dgru_nhids'] = 512
config['trg_igru_nhids'] = 1024

# Dimension of the word embedding matrix in encoder/decoder
config['enc_embed'] = 1024
config['dec_embed'] = 1024
config['src_dgru_depth'] = 1
config['bidir_encoder_depth'] = 2  # crucial
config['transition_depth'] = 1
config['trg_dgru_depth'] = 1
config['trg_igru_depth'] = 1

# Optimization related ----------------------------------------------------

# Batch size
config['batch_size'] = 32  # if use 450 length, you should set it to 56 to fit into 12G RAM

# This many batches will be read ahead and sorted
config['sort_k_batches'] = 12

# Gradient clipping threshold
config['step_clipping'] = 1.

# Std of weight initialization
config['weight_scale'] = 0.01

# Vocabulary/dataset related ----------------------------------------------

# Root directory for dataset
datadir = './data/'

# Module name of the stream that will be used
config['stream'] = 'stream'

# Source and target vocabularies
config['src_vocab'] = 'data/vocab.cls-mdn.cls.pkl'
config['trg_vocab'] = 'data/vocab.cls-mdn.mdn.pkl'

# Source and target datasets
config['src_data'] = 'data/cls_train.txt.shuf'
config['tgt_data'] = 'data/mdn_train.txt.shuf'

# Source and target vocabulary sizes, should include bos, eos, unk tokens
config['src_vocab_size'] = 3000
config['trg_vocab_size'] = 3000

# Special tokens and indexes
config['unk_id'] = 1
config['bos_token'] = '<S>'
config['eos_token'] = '</S>'
config['unk_token'] = '<UNK>'

# Early stopping based on val related ------------------------------------

# Normalize cost according to sequence length after beam-search
config['normalized_val'] = True

# Normalize cost according to sequence length after beam-search
config['normalized_bleu'] = True

# Bleu script that will be used (moses multi-perl in this case)
config['bleu_script'] = datadir + 'multi-bleu.perl'

# Test set source file
config['test_set'] = datadir + 'cls_test.txt'

# Test set gold file
config['test_set_grndtruth'] = datadir + 'mdn_test.txt'

# Test output file
config['test_set_out'] = '{}_model_out.txt'.format(os.path.basename(config['test_set_grndtruth']))

# Beam-size
config['beam_size'] = 12

# Timing/monitoring related -----------------------------------------------

# Maximum number of updates
config['finish_after'] = 1000000

# Reload model from files if exist
config['reload'] = True

# Save model after this many updates
config['save_freq'] = 500

# Print training status after this many updates
config['print_freq'] = 50

# Show samples from model after this many updates
config['sampling_freq'] = 50

# Show this many samples at each sampling
config['hook_samples'] = 2

# Dump model every this many updates
config['dump_freq'] = 20000

return config

[SwordYork]

tgt_data -> trg_data

[ghost]

Thank you! Sorry for this silly mistake.

[SwordYork]

Never mind. Please try old version, instead of the master branch.