old_contents: from dictlearn.config_registry import ConfigRegistry
  
  lm_config_registry = ConfigRegistry()
  lm_config_registry.set_root_config({
      # data
      'data_path' : "",
      'dict_path' : "",
      'layout' : 'standard',
      'top_k_words' : 10000,
      'max_length' : 100,
      'batch_size' : 32,
      'batch_size_valid' : 32,
      'max_def_length' : 1000,
      'exclude_top_k' : -1,
  
      # model
      'dim' : 128,
      'standalone_def_rnn' : True,
      'disregard_word_embeddings' : False,
      'learning_rate' : 0.001,
      'grad_clip_threshold' : 5.0,
  
      # monitoring and checkpointing
      'mon_freq_train' : 10,
      'mon_freq_valid' : 1000,
      'save_freq_batches' : 1000,
  })
  
  c = lm_config_registry['root']
  c['data_path'] = '/data/lisatmp4/bahdanau/data/lambada'
  c['layout'] = 'lambada'
  # In LAMBADA validation and test sets have quite long sentences.
  # And this implementation for now is very memory inefficient.
  c['batch_size_valid'] = 16
  c['top_k_words'] = 60000
  c['dim'] = 512
  c['mon_freq_train'] = 100
  lm_config_registry['lambada'] = c
  
new_contents: from dictlearn.config_registry import ConfigRegistry
  
  lm_config_registry = ConfigRegistry()
  lm_config_registry.set_root_config({
      # data
      'data_path' : "",
      'dict_path' : "",
      'layout' : 'standard',
      'top_k_words' : 10000,
      'max_length' : 100,
      'batch_size' : 32,
      'batch_size_valid' : 32,
      'max_def_length' : 1000,
      'exclude_top_k' : -1,
  
      # model
      'dim' : 128,
      'standalone_def_rnn' : True,
      'disregard_word_embeddings' : False,
      'learning_rate' : 0.001,
      'grad_clip_threshold' : 5.0,
  
      # monitoring and checkpointing
      'mon_freq_train' : 10,
      'mon_freq_valid' : 1000,
      'save_freq_batches' : 1000,
  })
  
  c = lm_config_registry['root']
  c['data_path'] = '/data/lisatmp4/bahdanau/data/lambada'
  c['layout'] = 'lambada'
  # In LAMBADA validation and test sets have quite long sentences.
  # And this implementation for now is very memory inefficient.
  c['batch_size_valid'] = 16
  c['top_k_words'] = 60000
  c['dim'] = 512
  c['mon_freq_train'] = 100
  c['grad_clip_threshold'] = 20.
  lm_config_registry['lambada'] = c
  
subject: Change gradie clipping threshold