tf.feature_column.sequence_categorical_column_with_identity

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Returns a feature column that represents sequences of integers.

Aliases:

• tf.compat.v1.feature_column.sequence_categorical_column_with_identity
• tf.compat.v2.feature_column.sequence_categorical_column_with_identity

tf.feature_column.sequence_categorical_column_with_identity(
    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.

Example:

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)

Args:

• 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.

Returns:

A SequenceCategoricalColumn.

Raises:

• ValueError: if num_buckets is less than one.
• ValueError: if default_value is not in range [0, num_buckets).