# tf.feature_column.categorical_column_with_hash_bucket

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


Represents sparse feature where ids are set by hashing.

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 SparseTensor. If Tensor, missing values can be represented by -1 for int and '' for string. Note that these values are independent of the default_value argument.

Example:

keywords = categorical_column_with_hash_bucket("keywords", 10K)
columns = [keywords, ...]
features = tf.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.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)


#### Args:

• 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.

#### Returns:

A _HashedCategoricalColumn.

#### Raises:

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