feature_column.py 文件源码

def __new__(cls,
              sparse_id_column,
              dimension,
              combiner="sqrtn",
              initializer=None,
              ckpt_to_load_from=None,
              tensor_name_in_ckpt=None,
              shared_embedding_name=None,
              shared_vocab_size=None):
    if initializer is not None and not callable(initializer):
      raise ValueError("initializer must be callable if specified. "
                       "Embedding of column_name: {}".format(
                           sparse_id_column.name))

    if (ckpt_to_load_from is None) != (tensor_name_in_ckpt is None):
      raise ValueError("Must specify both `ckpt_to_load_from` and "
                       "`tensor_name_in_ckpt` or none of them.")
    if initializer is None:
      stddev = 1 / math.sqrt(sparse_id_column.length)
      # TODO(b/25671353): Better initial value?
      initializer = init_ops.truncated_normal_initializer(
          mean=0.0, stddev=stddev)
    return super(_EmbeddingColumn, cls).__new__(cls, sparse_id_column,
                                                dimension, combiner,
                                                initializer, ckpt_to_load_from,
                                                tensor_name_in_ckpt,
                                                shared_embedding_name,
                                                shared_vocab_size)