Returns an index lookup table based on the given dataset.
tf.data.experimental.index_table_from_dataset(
dataset=None, num_oov_buckets=0, vocab_size=None, default_value=-1,
hasher_spec=lookup_ops.FastHashSpec, key_dtype=tf.dtypes.string, name=None
)
This operation constructs a lookup table based on the given dataset of keys.
Any lookup of an out-of-vocabulary token will return a bucket ID based on its
hash if num_oov_buckets
is greater than zero. Otherwise it is assigned the
default_value
.
The bucket ID range is
[vocabulary size, vocabulary size + num_oov_buckets - 1]
.
Sample Usages:
ds = tf.data.Dataset.range(100).map(lambda x: tf.strings.as_string(x * 2))
table = tf.data.experimental.index_table_from_dataset(
ds, key_dtype=dtypes.int64)
table.lookup(tf.constant(['0', '2', '4'], dtype=tf.string)).numpy()
array([0, 1, 2])
Args |
dataset
|
A dataset of keys.
|
num_oov_buckets
|
The number of out-of-vocabulary buckets.
|
vocab_size
|
Number of the elements in the vocabulary, if known.
|
default_value
|
The value to use for out-of-vocabulary feature values.
Defaults to -1.
|
hasher_spec
|
A HasherSpec to specify the hash function to use for
assignation of out-of-vocabulary buckets.
|
key_dtype
|
The key data type.
|
name
|
A name for this op (optional).
|
Returns |
The lookup table based on the given dataset.
|
Raises |
ValueError
|
If
num_oov_buckets is negative
vocab_size is not greater than zero
- The
key_dtype is not integer or string
|