tf.contrib.lookup.HashTable

Class HashTable

Inherits From: InitializableLookupTableBase

Defined in tensorflow/python/ops/lookup_ops.py.

A generic hash table implementation.

Example usage:

table = tf.HashTable(
    tf.KeyValueTensorInitializer(keys, values), -1)
out = table.lookup(input_tensor)
table.init.run()
print(out.eval())

Properties

default_value

The default value of the table.

init

The table initialization op.

key_dtype

The table key dtype.

name

The name of the table.

table_ref

Get the underlying table reference.

value_dtype

The table value dtype.

Methods

__init__

__init__(
    initializer,
    default_value,
    shared_name=None,
    name=None
)

Creates a non-initialized HashTable object.

Creates a table, the type of its keys and values are specified by the initializer. Before using the table you will have to initialize it. After initialization the table will be immutable.

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.
• shared_name: If non-empty, this table will be shared under the given name across multiple sessions.
• name: A name for the operation (optional).

Returns:

A HashTable object.

lookup

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, otherwise a dense Tensor.

Raises:

• TypeError: when keys or default_value doesn't match the table data types.

size

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.