column_categorical_with_hash_bucket: Represents Sparse Feature where IDs are set by Hashing

In tfestimators: Interface to 'TensorFlow' Estimators

Description

Use this when your sparse features are in string or integer format, and you want to distribute your inputs into a finite number of buckets by hashing. output_id = Hash(input_feature_string) % bucket_size For input dictionary features, features$key$ is either tensor or sparse tensor object. If it's tensor object, missing values can be represented by -1 for int and '' for string. Note that these values are independent of the default_value argument.

Usage

column_categorical_with_hash_bucket(..., hash_bucket_size, dtype = tf$string)

Arguments

...	Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns.
hash_bucket_size	An int > 1. The number of buckets.
dtype	The type of features. Only string and integer types are supported.

Value

A _HashedCategoricalColumn.

Raises

• ValueError: hash_bucket_size is not greater than 1.

• ValueError: dtype is neither string nor integer.

See Also

Other feature column constructors: column_bucketized(), column_categorical_weighted(), column_categorical_with_identity(), column_categorical_with_vocabulary_file(), column_categorical_with_vocabulary_list(), column_crossed(), column_embedding(), column_numeric(), input_layer()

tfestimators documentation built on Aug. 10, 2021, 1:06 a.m.