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
)
Used in the notebooks
| 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.
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)
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.
|
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TensorFlow 1 version
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