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

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

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

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:
  • "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.

Returns:

A _SparseColumn with hashed bucket configuration

Raises:

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

Defined in tensorflow/contrib/layers/python/layers/feature_column.py.