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tf.compat.v1.lookup.StaticHashTable

A generic hash table that is immutable once initialized.

Inherits From: StaticHashTable, TrackableResource

tf.compat.v1.lookup.StaticHashTable(
    initializer, default_value, name=None, experimental_is_anonymous=False
)

When running in graph mode, you must evaluate the tensor returned by tf.tables_initializer() before evaluating the tensor returned by this class's lookup() method. Example usage in graph mode:

keys_tensor = tf.constant([1, 2])
vals_tensor = tf.constant([3, 4])
input_tensor = tf.constant([1, 5])
table = tf.lookup.StaticHashTable(
    tf.lookup.KeyValueTensorInitializer(keys_tensor, vals_tensor), -1)
out = table.lookup(input_tensor)
with tf.Session() as sess:
    sess.run(tf.tables_initializer())
    print(sess.run(out))

Note that in graph mode if you set experimental_is_anonymous to True, you should only call Session.run once, otherwise each Session.run will create (and destroy) a new table unrelated to each other, leading to errors such as "Table not initialized". You can do so like this:

keys_tensor = tf.constant([1, 2])
vals_tensor = tf.constant([3, 4])
input_tensor = tf.constant([1, 5])
table = tf.lookup.StaticHashTable(
    tf.lookup.KeyValueTensorInitializer(keys_tensor, vals_tensor), -1,
    experimental_is_anonymous=True)
with tf.control_dependencies([tf.tables_initializer()]):
  out = table.lookup(input_tensor)
with tf.Session() as sess:
  print(sess.run(out))

In eager mode, no special code is needed to initialize the table. Example usage in eager mode:

tf.enable_eager_execution()
keys_tensor = tf.constant([1, 2])
vals_tensor = tf.constant([3, 4])
input_tensor = tf.constant([1, 5])
table = tf.lookup.StaticHashTable(
    tf.lookup.KeyValueTensorInitializer(keys_tensor, vals_tensor), -1)
print(table.lookup(input_tensor))
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).
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.
Attributes
default_value The default value of the table.
initializer
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.

© 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/versions/r2.9/api_docs/python/tf/compat/v1/lookup/StaticHashTable