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)
| 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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/feature_column/categorical_column_with_hash_bucket