feature_column.py 文件源码

def embedding_column(sparse_id_column,
                     dimension,
                     combiner="mean",
                     initializer=None,
                     ckpt_to_load_from=None,
                     tensor_name_in_ckpt=None,
                     max_norm=None):
  """Creates an `_EmbeddingColumn` for feeding sparse data into a DNN.

  Args:
    sparse_id_column: A `_SparseColumn` which is created by for example
      `sparse_column_with_*` or crossed_column functions. Note that `combiner`
      defined in `sparse_id_column` is ignored.
    dimension: An integer specifying dimension of the embedding.
    combiner: A string specifying how to reduce if there are multiple entries
      in a single row. Currently "mean", "sqrtn" and "sum" are supported, with
      "mean" the default. "sqrtn" often achieves good accuracy, in particular
      with bag-of-words columns. Each of this can be thought as example level
      normalizations on the column:
        * "sum": do not normalize
        * "mean": do l1 normalization
        * "sqrtn": do l2 normalization
      For more information: `tf.embedding_lookup_sparse`.
    initializer: A variable initializer function to be used in embedding
      variable initialization. If not specified, defaults to
      `tf.truncated_normal_initializer` with mean 0.0 and standard deviation
      1/sqrt(sparse_id_column.length).
    ckpt_to_load_from: (Optional). String representing checkpoint name/pattern
      to restore the column weights. Required if `tensor_name_in_ckpt` is not
      None.
    tensor_name_in_ckpt: (Optional). Name of the `Tensor` in the provided
      checkpoint from which to restore the column weights. Required if
      `ckpt_to_load_from` is not None.
    max_norm: (Optional). If not None, embedding values are l2-normalized to
      the value of max_norm.

  Returns:
    An `_EmbeddingColumn`.
  """
  return _EmbeddingColumn(sparse_id_column, dimension, combiner, initializer,
                          ckpt_to_load_from, tensor_name_in_ckpt,
                          max_norm=max_norm)