tf.feature_column.categorical_column_with_hash_bucket  |  TensorFlow v2.16.1 (original) (raw)

tf.feature_column.categorical_column_with_hash_bucket

Stay organized with collections Save and categorize content based on your preferences.

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

View aliases

Compat aliases for migration

SeeMigration 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

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 orSparseTensor. 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)