A sequence of categorical terms where ids are set by hashing. (deprecated)
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.