tf.feature_column.categorical_column_with_hash_bucket

Represents sparse feature where ids are set by hashing.

tf.feature_column.categorical_column_with_hash_bucket(
    key, hash_bucket_size, dtype=tf.dtypes.string
)

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 string type input. For int type input, the value is converted to its string representation first and then hashed by the same formula.

For input dictionary features, features[key] is either Tensor or SparseTensor. If Tensor, missing values can be represented by -1 for int and '' for string, which will be dropped by this feature column.

Example:

keywords = categorical_column_with_hash_bucket("keywords", 10K)
columns = [keywords, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)

# or
keywords_embedded = embedding_column(keywords, 16)
columns = [keywords_embedded, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)

key A unique string identifying the input feature. It is used as the column name and the dictionary key for feature parsing configs, feature Tensor objects, 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.

A HashedCategoricalColumn.

ValueError hash_bucket_size is not greater than 1.
ValueError dtype is neither string nor integer.