tf.lookup.StaticHashTable

TensorFlow 1 version View source on GitHub

Class StaticHashTable

A generic hash table that is immutable once initialized.

Example usage:

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))

__init__

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__init__(
    initializer,
    default_value,
    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.
  • name: A name for the operation (optional).

Returns:

A HashTable object.

Properties

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

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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

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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

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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.