tf.feature_column.sequence_categorical_column_with_vocabulary_file

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A sequence of categorical terms where ids use a vocabulary file.

tf.feature_column.sequence_categorical_column_with_vocabulary_file(
    key, vocabulary_file, vocabulary_size=None, num_oov_buckets=0,
    default_value=None, dtype=tf.dtypes.string
)

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:

states = sequence_categorical_column_with_vocabulary_file(
    key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
    num_oov_buckets=5)
states_embedding = embedding_column(states, dimension=10)
columns = [states_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_file: The vocabulary file name.
• vocabulary_size: Number of the elements in the vocabulary. This must be no greater than length of vocabulary_file, if less than length, later values are ignored. If None, it is set to the length of vocabulary_file.
• 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 [vocabulary_size, vocabulary_size+num_oov_buckets) based on a hash of the input value. A positive num_oov_buckets can not be specified with default_value.
• 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.
• dtype: The type of features. Only string and integer types are supported.

Returns:

A SequenceCategoricalColumn.

Raises:

• ValueError: vocabulary_file is missing or cannot be opened.
• ValueError: vocabulary_size is missing or < 1.
• ValueError: num_oov_buckets is a negative integer.
• ValueError: num_oov_buckets and default_value are both specified.
• ValueError: dtype is neither string nor integer.