tf.feature_column.sequence_categorical_column_with_hash_bucket

A sequence of categorical terms 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.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.feature_column.sequence_categorical_column_with_hash_bucket

tf.feature_column.sequence_categorical_column_with_hash_bucket(
    key,
    hash_bucket_size,
    dtype=tf.dtypes.string
)

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.

Pass this to embedding_column or indicator_column to convert sequence categorical data into dense representation for input to sequence NN, such as RNN.

Example:

tokens = sequence_categorical_column_with_hash_bucket(
    'tokens', hash_bucket_size=1000)
tokens_embedding = embedding_column(tokens, dimension=10)
columns = [tokens_embedding]

features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
sequence_feature_layer = SequenceFeatures(columns)
sequence_input, sequence_length = sequence_feature_layer(features)
sequence_length_mask = tf.sequence_mask(sequence_length)

rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size)
rnn_layer = tf.keras.layers.RNN(rnn_cell)
outputs, state = rnn_layer(sequence_input, mask=sequence_length_mask)

Args
key	A unique string identifying the input feature.
hash_bucket_size	An int > 1. The number of buckets.
dtype	The type of features. Only string and integer types are supported.

Returns
A SequenceCategoricalColumn.

Raises
ValueError	hash_bucket_size is not greater than 1.
ValueError	dtype is neither string nor integer.