tf.contrib.layers.sparse_column_with_hash_bucket

Creates a _SparseColumn with hashed bucket configuration.

tf.contrib.layers.sparse_column_with_hash_bucket(
    column_name, hash_bucket_size, combiner='sum', dtype=tf.dtypes.string,
    hash_keys=None
)

Use this when your sparse features are in string or integer format, but you don't have a vocab file that maps each value to an integer ID. output_id = Hash(input_feature_string) % bucket_size

When hash_keys is set, multiple integer IDs would be created with each key pair in the hash_keys. This is useful to reduce the collision of hashed ids.

Args
column_name	A string defining sparse column name.
hash_bucket_size	An int that is > 1. The number of buckets.
combiner	A string specifying how to reduce if the sparse column is multivalent. Currently "mean", "sqrtn" and "sum" are supported, with "sum" the default. "sqrtn" often achieves good accuracy, in particular with bag-of-words columns. "sum": do not normalize features in the column "mean": do l1 normalization on features in the column "sqrtn": do l2 normalization on features in the column For more information: tf.embedding_lookup_sparse.
dtype	The type of features. Only string and integer types are supported.
hash_keys	The hash keys to use. It is a list of lists of two uint64s. If None, simple and fast hashing algorithm is used. Otherwise, multiple strong hash ids would be produced with each two uint64s in this argument.

Returns
A _SparseColumn with hashed bucket configuration

Raises
ValueError	hash_bucket_size is not greater than 2.
ValueError	dtype is neither string nor integer.