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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.
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.