task.py 文件源码

def make_feature_columns():
  """Retrieve the feature columns required for training."""
  feature_columns = (make_query_feature_columns()
                     | make_candidate_feature_columns())
  # Add feature column for the label.
  target_rating_real_column = tf.contrib.layers.real_valued_column(
      column_name=LABEL_RATING_SCORE, dtype=tf.float32)
  feature_columns.add(target_rating_real_column)

  # Ranking candidate movies used only in eval graph to rank candidate movie
  # against.
  ranking_candidate_movie_ids = (
      tf.contrib.layers.sparse_column_with_integerized_feature(
          column_name=RANKING_CANDIDATE_MOVIE_IDS,
          bucket_size=MOVIE_VOCAB_SIZE))
  feature_columns.add(ranking_candidate_movie_ids)

  return feature_columns