tf.lookup.StaticVocabularyTable

String to Id table wrapper that assigns out-of-vocabulary keys to buckets.

Used in the notebooks

Used in the tutorials

For example, if an instance of StaticVocabularyTable is initialized with a string-to-id initializer that maps:

  • emerson -> 0
  • lake -> 1
  • palmer -> 2

The Vocabulary object will performs the following mapping:

  • emerson -> 0
  • lake -> 1
  • palmer -> 2
  • <other term> -> bucket_id, where bucket_id will be between 3 and 3 + num_oov_buckets - 1, calculated by: hash(<term>) % num_oov_buckets + vocab_size

If input_tensor is ["emerson", "lake", "palmer", "king", "crimson"], the lookup result is [0, 1, 2, 4, 7].

If initializer is None, only out-of-vocabulary buckets are used.

Example usage:

num_oov_buckets = 3
input_tensor = tf.constant(["emerson", "lake", "palmer", "king", "crimnson"])
table = tf.lookup.StaticVocabularyTable(
    tf.lookup.TextFileInitializer(
        filename,
        key_dtype=tf.string, key_index=tf.lookup.TextFileIndex.WHOLE_LINE,
        value_dtype=tf.int64, value_index=tf.lookup.TextFileIndex.LINE_NUMBER,
        delimiter="\t"),
    num_oov_buckets)
out = table.lookup(input_tensor).
table.init.run()
print(out.eval())

The hash function used for generating out-of-vocabulary buckets ID is Fingerprint64.

initializer A TableInitializerBase object that contains the data used to initialize the table. If None, then we only use out-of-vocab buckets.
num_oov_buckets Number of buckets to use for out-of-vocabulary keys. Must be greater than zero.
lookup_key_dtype Data type of keys passed to lookup. Defaults to initializer.key_dtype if initializer is specified, otherwise tf.string. Must be string or integer, and must be castable to initializer.key_dtype.
name A name for the operation (optional).

ValueError when num_oov_buckets is not