TensorFlow 1 version View source on GitHub

Returns a feature column that represents sequences of integers.

    key, num_buckets, default_value=None

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 =, 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).