udc_inputs.py 文件源码

def get_feature_columns(mode):
  feature_columns = []

  feature_columns.append(tf.contrib.layers.real_valued_column(
    column_name="context", dimension=TEXT_FEATURE_SIZE, dtype=tf.int64))
  feature_columns.append(tf.contrib.layers.real_valued_column(
      column_name="context_len", dimension=1, dtype=tf.int64))
  feature_columns.append(tf.contrib.layers.real_valued_column(
      column_name="utterance", dimension=TEXT_FEATURE_SIZE, dtype=tf.int64))
  feature_columns.append(tf.contrib.layers.real_valued_column(
      column_name="utterance_len", dimension=1, dtype=tf.int64))

  if mode == tf.contrib.learn.ModeKeys.TRAIN:
    # During training we have a label feature
    feature_columns.append(tf.contrib.layers.real_valued_column(
      column_name="label", dimension=1, dtype=tf.int64))

  if mode == tf.contrib.learn.ModeKeys.EVAL:
    # During evaluation we have distractors
    for i in range(9):
      feature_columns.append(tf.contrib.layers.real_valued_column(
        column_name="distractor_{}".format(i), dimension=TEXT_FEATURE_SIZE, dtype=tf.int64))
      feature_columns.append(tf.contrib.layers.real_valued_column(
        column_name="distractor_{}_len".format(i), dimension=1, dtype=tf.int64))

  return set(feature_columns)