Python text_encoder.NUM_RESERVED_TOKENS屬性代碼示例

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import NUM_RESERVED_TOKENS [as 別名]
def generate_data(self, data_dir, _, task_id=-1):

    def generator_eos(nbr_symbols, max_length, nbr_cases):
      """Shift by NUM_RESERVED_IDS and append EOS token."""
      for case in self.generator(nbr_symbols, max_length, nbr_cases):
        new_case = {}
        for feature in case:
          new_case[feature] = [
              i + text_encoder.NUM_RESERVED_TOKENS for i in case[feature]
          ] + [text_encoder.EOS_ID]
        yield new_case

    utils.generate_dataset_and_shuffle(
        generator_eos(self.num_symbols, self.train_length, self.train_size),
        self.training_filepaths(data_dir, self.num_shards, shuffled=True),
        generator_eos(self.num_symbols, self.dev_length, self.dev_size),
        self.dev_filepaths(data_dir, 1, shuffled=True),
        shuffle=False) 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import NUM_RESERVED_TOKENS [as 別名]
def hparams(self, defaults, unused_model_hparams):
    p = defaults
    vocab_size = self.num_symbols + text_encoder.NUM_RESERVED_TOKENS
    p.input_modality = {"inputs": (registry.Modalities.SYMBOL, vocab_size)}
    p.target_modality = (registry.Modalities.SYMBOL, vocab_size)
    p.input_space_id = problem.SpaceID.DIGIT_0
    p.target_space_id = problem.SpaceID.DIGIT_1 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import NUM_RESERVED_TOKENS [as 別名]
def __init__(self,
               chunk_size=1,
               num_reserved_ids=text_encoder.NUM_RESERVED_TOKENS):
    super(DNAEncoder, self).__init__(num_reserved_ids=num_reserved_ids)
    # Build a vocabulary of chunks of size chunk_size
    self._chunk_size = chunk_size
    tokens = self._tokens()
    tokens.sort()
    ids = range(self._num_reserved_ids, len(tokens) + self._num_reserved_ids)
    self._ids_to_tokens = dict(zip(ids, tokens))
    self._tokens_to_ids = dict(zip(tokens, ids)) 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import NUM_RESERVED_TOKENS [as 別名]
def hparams(self, defaults, unused_model_hparams):
    p = defaults
    vocab_size = self.num_symbols + text_encoder.NUM_RESERVED_TOKENS
    p.modality = {"inputs": modalities.ModalityType.SYMBOL,
                  "targets": modalities.ModalityType.SYMBOL}
    p.vocab_size = {"inputs": vocab_size,
                    "targets": vocab_size}
    p.input_space_id = problem.SpaceID.DIGIT_0
    p.target_space_id = problem.SpaceID.DIGIT_1 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import NUM_RESERVED_TOKENS [as 別名]
def hparams(self, defaults, unused_model_hparams):
    p = defaults
    vocab_size = self.num_symbols + text_encoder.NUM_RESERVED_TOKENS
    p.modality = {"inputs": modalities.SymbolModality,
                  "targets": modalities.SymbolModality}
    p.vocab_size = {"inputs": vocab_size,
                    "targets": vocab_size}
    p.input_space_id = problem.SpaceID.DIGIT_0
    p.target_space_id = problem.SpaceID.DIGIT_1 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import NUM_RESERVED_TOKENS [as 別名]
def __init__(self,
               vocab_filename=None,
               vocab_list=None,
               separator="",
               num_reserved_ids=text_encoder.NUM_RESERVED_TOKENS):
    """Initialize from a file or list, one token per line.

    Handling of reserved tokens works as follows:
    - When initializing from a list, we add reserved tokens to the vocab.
    - When initializing from a file, we do not add reserved tokens to the vocab.
    - When saving vocab files, we save reserved tokens to the file.

    Args:
      vocab_filename: If not None, the full filename to read vocab from. If this
         is not None, then vocab_list should be None.
      vocab_list: If not None, a list of elements of the vocabulary. If this is
         not None, then vocab_filename should be None.
      separator: separator between symbols in original file.
      num_reserved_ids: Number of IDs to save for reserved tokens like <EOS>.
    """
    super(GraphemePhonemeEncoder, self).__init__(
        num_reserved_ids=num_reserved_ids)
    if vocab_filename and os.path.exists(vocab_filename):
      self._init_vocab_from_file(vocab_filename)
    else:
      assert vocab_list is not None
      self._init_vocab_from_list(vocab_list)
    self._separator = separator