tf.lookup

A generic hash table that is immutable once initialized.

tf.lookup.StaticHashTable(
    initializer, default_value, name=None
)

Used in the notebooks

Used in the tutorials
Federated Learning for Text Generation Preprocessing data with TensorFlow Transform

Example usage:

keys_tensor = tf.constant(['a', 'b', 'c'])
vals_tensor = tf.constant([7, 8, 9])
input_tensor = tf.constant(['a', 'f'])
table = tf.lookup.StaticHashTable(
    tf.lookup.KeyValueTensorInitializer(keys_tensor, vals_tensor),
    default_value=-1)
table.lookup(input_tensor).numpy()
array([ 7, -1], dtype=int32)

Or for more pythonic code:

table[input_tensor].numpy()
array([ 7, -1], dtype=int32)

The result of a lookup operation has the same shape as the argument:

input_tensor = tf.constant([['a', 'b'], ['c', 'd']])
table[input_tensor].numpy()
array([[ 7,  8],
       [ 9, -1]], dtype=int32)

Args
`initializer`	The table initializer to use. See `HashTable` kernel for supported key and value types.
`default_value`	The value to use if a key is missing in the table.
`name`	A name for the operation (optional).

Attributes
`default_value`	The default value of the table.
`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.

Methods

`export`

View source

export(
    name=None
)

Returns tensors of all keys and values in the table.

Args
`name`	A name for the operation (optional).

Returns
A pair of tensors with the first tensor containing all keys and the second tensors containing all values in the table.

`lookup`

View source

lookup(
    keys, name=None
)

Looks up keys in a table, outputs the corresponding values.

The default_value is used for keys not present in the table.

Args
`keys`	Keys to look up. May be either a `SparseTensor` or dense `Tensor`.
`name`	A name for the operation (optional).

Returns
A `SparseTensor` if keys are sparse, a `RaggedTensor` if keys are ragged, otherwise a dense `Tensor`.

Raises
`TypeError`	when `keys` or `default_value` doesn't match the table data types.

`size`

View source

size(
    name=None
)

Compute the number of elements in this table.

Args
`name`	A name for the operation (optional).

Returns
A scalar tensor containing the number of elements in this table.

`getitem`

View source

__getitem__(
    keys
)

Looks up keys in a table, outputs the corresponding values.

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.lookup / Overview

Used in the notebooks

Example usage:

Args

Attributes

Methods

export

lookup

size

__getitem__

`export`

`lookup`

`size`

`getitem`