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String to Id table wrapper that assigns out-of-vocabulary keys to buckets.
tf.lookup.StaticVocabularyTable(
initializer, num_oov_buckets, lookup_key_dtype=None, name=None
)
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.
num_oov_buckets = 3
input_tensor = tf.constant(["emerson", "lake", "palmer", "king", "crimnson"])
table = tf.lookup.StaticVocabularyTable(
tf.TextFileIdTableInitializer(filename), 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).key_dtype
: The table key dtype.name
: The name of the table.resource_handle
: Returns the resource handle associated with this Resource.value_dtype
: The table value dtype.ValueError
: when num_oov_buckets
is not positive.TypeError
: when lookup_key_dtype or initializer.key_dtype are not
integer or string. Also when initializer.value_dtype != int64.lookup
lookup(
keys, name=None
)
Looks up keys
in the table, outputs the corresponding values.
It assigns out-of-vocabulary keys to buckets based in their hashes.
keys
: Keys to look up. May be either a SparseTensor
or dense Tensor
.name
: Optional name for the op.A SparseTensor
if keys are sparse, otherwise a dense Tensor
.
TypeError
: when keys
doesn't match the table key data type.size
size(
name=None
)
Compute the number of elements in this table.