View source on GitHub |
A generic hash table that is immutable once initialized.
Inherits From: TrackableResource
tf.lookup.StaticHashTable(
initializer, default_value, name=None, experimental_is_anonymous=False
)
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)
Methods
export
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
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
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__
__getitem__(
keys
)
Looks up keys
in a table, outputs the corresponding values.