beam_aligner.py 文件源码

def initialize(self):
        """Initialize the decoder.
        Args:
          name: Name scope for any created operations.
        Returns:
          `(finished, start_inputs, initial_state)`.
        """
        start_inputs = self._embedding_fn(self._tiled_start_tokens)
        print('start_inputs', start_inputs)
        finished = tf.zeros((self.batch_size, self._beam_width), dtype=tf.bool)

        self._initial_num_available_beams = tf.ones((self._batch_size,), dtype=tf.int32)
        self._full_num_available_beams = tf.fill((self._batch_size,), self._beam_width)

        with tf.name_scope('first_beam_mask'):
            self._first_beam_mask = self._make_beam_mask(self._initial_num_available_beams)
        with tf.name_scope('full_beam_mask'):
            self._full_beam_mask = self._make_beam_mask(self._full_num_available_beams)
        with tf.name_scope('minus_inifinity_scores'):
            self._minus_inifinity_scores = tf.fill((self.batch_size, self._beam_width, self._output_size), -1e+8)

        self._batch_size_range = tf.range(self.batch_size)
        initial_state = BeamSearchOptimizationDecoderState(
            cell_state=self._tiled_initial_cell_state,
            previous_logits=tf.zeros([self.batch_size, self._beam_width, self._output_size], dtype=tf.float32),
            previous_score=tf.zeros([self.batch_size, self._beam_width], dtype=tf.float32),
            # During the first time step we only consider the initial beam
            num_available_beams=self._initial_num_available_beams,
            gold_beam_id=tf.zeros([self.batch_size], dtype=tf.int32),
            finished=finished)

        return (finished, start_inputs, initial_state)