tf.feature_column.sequence_categorical_column_with_vocabulary_list

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A sequence of categorical terms where ids use an in-memory list.

tf.feature_column.sequence_categorical_column_with_vocabulary_list(
    key, vocabulary_list, dtype=None, default_value=-1, num_oov_buckets=0
)

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:

colors = sequence_categorical_column_with_vocabulary_list(
    key='colors', vocabulary_list=('R', 'G', 'B', 'Y'),
    num_oov_buckets=2)
colors_embedding = embedding_column(colors, dimension=3)
columns = [colors_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.
• vocabulary_list: An ordered iterable defining the vocabulary. Each feature is mapped to the index of its value (if present) in vocabulary_list. Must be castable to dtype.
• dtype: The type of features. Only string and integer types are supported. If None, it will be inferred from vocabulary_list.
• default_value: The integer ID value to return for out-of-vocabulary feature values, defaults to -1. This can not be specified with a positive num_oov_buckets.
• num_oov_buckets: Non-negative integer, the number of out-of-vocabulary buckets. All out-of-vocabulary inputs will be assigned IDs in the range [len(vocabulary_list), len(vocabulary_list)+num_oov_buckets) based on a hash of the input value. A positive num_oov_buckets can not be specified with default_value.

Returns:

A SequenceCategoricalColumn.

Raises:

• ValueError: if vocabulary_list is empty, or contains duplicate keys.
• ValueError: num_oov_buckets is a negative integer.
• ValueError: num_oov_buckets and default_value are both specified.
• ValueError: if dtype is not integer or string.