def embedding_column(sparse_id_column,
dimension,
combiner=None,
initializer=None,
ckpt_to_load_from=None,
tensor_name_in_ckpt=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. Each
of this can be considered an example level normalization 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.
Returns:
An `_EmbeddingColumn`.
"""
if combiner is None:
logging.warn("The default value of combiner will change from \"mean\" "
"to \"sqrtn\" after 2016/11/01.")
combiner = "mean"
return _EmbeddingColumn(sparse_id_column, dimension, combiner, initializer,
ckpt_to_load_from, tensor_name_in_ckpt)
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