tf.feature_column.categorical_column_with_hash_bucket

Represents sparse feature where ids are set by hashing. (deprecated)

Warning: tf.feature_column is not recommended for new code. Instead, feature preprocessing can be done directly using either Keras preprocessing layers or through the one-stop utility tf.keras.utils.FeatureSpace built on top of them. See the migration guide for details.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.feature_column.categorical_column_with_hash_bucket

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
Estimators	Classify structured data with feature columns

Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use Keras preprocessing layers instead, either directly or via the tf.keras.utils.FeatureSpace utility. Each of tf.feature_column.* has a functional equivalent in tf.keras.layers for feature preprocessing when training a Keras model.

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:

import tensorflow as tf
keywords = tf.feature_column.categorical_column_with_hash_bucket("keywords",
10000)
columns = [keywords]
features = {'keywords': tf.constant([['Tensorflow', 'Keras', 'RNN', 'LSTM',
'CNN'], ['LSTM', 'CNN', 'Tensorflow', 'Keras', 'RNN'], ['CNN', 'Tensorflow',
'LSTM', 'Keras', 'RNN']])}
linear_prediction, _, _ = tf.compat.v1.feature_column.linear_model(features,
columns)

# or
import tensorflow as tf
keywords = tf.feature_column.categorical_column_with_hash_bucket("keywords",
10000)
keywords_embedded = tf.feature_column.embedding_column(keywords, 16)
columns = [keywords_embedded]
features = {'keywords': tf.constant([['Tensorflow', 'Keras', 'RNN', 'LSTM',
'CNN'], ['LSTM', 'CNN', 'Tensorflow', 'Keras', 'RNN'], ['CNN', 'Tensorflow',
'LSTM', 'Keras', 'RNN']])}
input_layer = tf.keras.layers.DenseFeatures(columns)
dense_tensor = input_layer(features)

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.