/TensorFlow 2.4


Returns a feature column that represents sequences of integers.

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


watches = sequence_categorical_column_with_identity(
    'watches', num_buckets=1000)
watches_embedding = embedding_column(watches, dimension=10)
columns = [watches_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)
key A unique string identifying the input feature.
num_buckets Range of inputs. Namely, inputs are expected to be in the range [0, num_buckets).
default_value If None, this column's graph operations will fail for out-of-range inputs. Otherwise, this value must be in the range [0, num_buckets), and will replace out-of-range inputs.
A SequenceCategoricalColumn.
ValueError if num_buckets is less than one.
ValueError if default_value is not in range [0, num_buckets).

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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.