tf.lookup.experimental.DenseHashTable

A mutable hash table with faster lookups and higher memory usage.

Inherits From: TrackableResource

View aliases

Compat aliases for migration

tf.compat.v1.lookup.experimental.DenseHashTable

tf.lookup.experimental.DenseHashTable(
    key_dtype,
    value_dtype,
    default_value,
    empty_key,
    deleted_key,
    initial_num_buckets=None,
    name='MutableDenseHashTable',
    checkpoint=True,
    experimental_is_anonymous=False
)

Data can be inserted by calling the insert method and removed by calling the remove method. It does not support initialization via the init method.

Compared to MutableHashTable, DenseHashTable offers generally faster insert, remove and lookup operations, in exchange for a higher overall memory footprint.

It uses "open addressing" with quadratic reprobing to resolve collisions. This requires specifying two keys in the key space, empty_key and deleted_key, that can never inserted into the table.

Unlike MutableHashTable, DenseHashTable does not require additional memory for temporary tensors created during checkpointing and restore operations.

Example usage:

table = tf.lookup.experimental.DenseHashTable(
    key_dtype=tf.string,
    value_dtype=tf.int64,
    default_value=-1,
    empty_key='',
    deleted_key='$')
keys = tf.constant(['a', 'b', 'c'])
values = tf.constant([0, 1, 2], dtype=tf.int64)
table.insert(keys, values)
table.remove(tf.constant(['c']))
table.lookup(tf.constant(['a', 'b', 'c','d'])).numpy()
array([ 0,  1, -1, -1])

Args
`key_dtype`	the type of the key tensors.
`value_dtype`	the type of the value tensors.
`default_value`	The value to use if a key is missing in the table.
`empty_key`	the key to use to represent empty buckets internally. Must not be used in insert, remove or lookup operations.
`deleted_key`	the key to use to represent deleted buckets internally. Must not be used in insert, remove or lookup operations and be different from the empty_key.
`initial_num_buckets`	the initial number of buckets (optional, default to 2^17=131072). Note that the default value is relatively large (~1MB), so if you are going to create many tables (likely the case when `experimental_is_anonymous` is `True`), you should set `initial_num_buckets` to a smaller value to reduce memory usage.
`name`	A name for the operation (optional).
`checkpoint`	if True, the contents of the table are saved to and restored from checkpoints. If `shared_name` is empty for a checkpointed table, it is shared using the table node name.
`experimental_is_anonymous`	Whether to use anonymous mode for the table (default is False). In anonymous mode, the table resource can only be accessed via a resource handle. It can't be looked up by a name. When all resource handles pointing to that resource are gone, the resource will be deleted automatically.

Raises
`ValueError`	If checkpoint is True and no name was specified.

Attributes
`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

`erase`

View source

erase(
    keys, name=None
)

Removes keys and its associated values from the table.

If a key is not present in the table, it is silently ignored.

Args
`keys`	Keys to remove. Can be a tensor of any shape. Must match the table's key type.
`name`	A name for the operation (optional).

Returns
The created Operation.

Raises
`TypeError`	when `keys` do not match the table data types.

`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.

`insert`

View source

insert(
    keys, values, name=None
)

Associates keys with values.

Args
`keys`	Keys to insert. Can be a tensor of any shape. Must match the table's key type.
`values`	Values to be associated with keys. Must be a tensor of the same shape as `keys` and match the table's value type.
`name`	A name for the operation (optional).

Returns
The created Operation.

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

`insert_or_assign`

View source

insert_or_assign(
    keys, values, name=None
)

Associates keys with values.

Args
`keys`	Keys to insert. Can be a tensor of any shape. Must match the table's key type.
`values`	Values to be associated with keys. Must be a tensor of the same shape as `keys` and match the table's value type.
`name`	A name for the operation (optional).

Returns
The created Operation.

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

`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. Can be a tensor of any shape. Must match the table's key_dtype.
`name`	A name for the operation (optional).

Returns
A tensor containing the values in the same shape as `keys` using the table's value type.

Raises
`TypeError`	when `keys` do not match the table data types.

`remove`

View source

remove(
    keys, name=None
)

Removes keys and its associated values from the table.

If a key is not present in the table, it is silently ignored.

Args
`keys`	Keys to remove. Can be a tensor of any shape. Must match the table's key type.
`name`	A name for the operation (optional).

Returns
The created Operation.

Raises
`TypeError`	when `keys` do not 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.

© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/lookup/experimental/DenseHashTable