Creates a table initializer from a tf.data.Dataset
.
tf.lookup.experimental.DatasetInitializer( dataset )
keys = tf.data.Dataset.range(100) values = tf.data.Dataset.range(100).map( lambda x: string_ops.as_string(x * 2)) ds = tf.data.Dataset.zip((keys, values)) init = tf.lookup.experimental.DatasetInitializer(ds) table = tf.lookup.StaticHashTable(init, "") table.lookup(tf.constant([0, 1, 2], dtype=tf.int64)).numpy() array([b'0', b'2', b'4'], dtype=object)
Raises: ValueError if dataset
doesn't conform to specifications.
Args | |
---|---|
dataset | A tf.data.Dataset object that produces tuples of scalars. The first scalar is treated as a key and the second as value. |
Attributes | |
---|---|
dataset | A tf.data.Dataset object that produces tuples of scalars. The first scalar is treated as a key and the second as value. |
key_dtype | The expected table key dtype. |
value_dtype | The expected table value dtype. |
initialize
initialize( table )
Returns the table initialization op.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/lookup/experimental/DatasetInitializer