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Represents sparse feature where ids are set by hashing.


  • tf.compat.v1.feature_column.categorical_column_with_hash_bucket
  • tf.compat.v2.feature_column.categorical_column_with_hash_bucket

Used in the guide:

Used in the tutorials:

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.


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.