# tf.contrib.layers.sparse_column_with_hash_bucket

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


See the guide: Layers (contrib) > Feature columns

Creates a _SparseColumn with hashed bucket configuration.

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 unit64s 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.