description: Represents an iterator of a tf.data.Dataset.

tf.data.Iterator

Represents an iterator of a tf.data.Dataset.

tf.data.Iterator is the primary mechanism for enumerating elements of a tf.data.Dataset. It supports the Python Iterator protocol, which means it can be iterated over using a for-loop:

>>> dataset = tf.data.Dataset.range(2)
>>> for element in dataset:
...   print(element)
tf.Tensor(0, shape=(), dtype=int64)
tf.Tensor(1, shape=(), dtype=int64)

or by fetching individual elements explicitly via get_next():

>>> dataset = tf.data.Dataset.range(2)
>>> iterator = iter(dataset)
>>> print(iterator.get_next())
tf.Tensor(0, shape=(), dtype=int64)
>>> print(iterator.get_next())
tf.Tensor(1, shape=(), dtype=int64)

In addition, non-raising iteration is supported via get_next_as_optional(), which returns the next element (if available) wrapped in a tf.experimental.Optional.

>>> dataset = tf.data.Dataset.from_tensors(42)
>>> iterator = iter(dataset)
>>> optional = iterator.get_next_as_optional()
>>> print(optional.has_value())
tf.Tensor(True, shape=(), dtype=bool)
>>> optional = iterator.get_next_as_optional()
>>> print(optional.has_value())
tf.Tensor(False, shape=(), dtype=bool)

Attributes

element_spec The type specification of an element of this iterator.

>>> dataset = tf.data.Dataset.from_tensors(42)
>>> iterator = iter(dataset)
>>> iterator.element_spec
tf.TensorSpec(shape=(), dtype=tf.int32, name=None)

Methods

get_next

View source

@abc.abstractmethod
get_next()

Returns a nested structure of tf.Tensors containing the next element.

>>> dataset = tf.data.Dataset.from_tensors(42)
>>> iterator = iter(dataset)
>>> print(iterator.get_next())
tf.Tensor(42, shape=(), dtype=int32)

Returns
A nested structure of tf.Tensor objects.

Raises
tf.errors.OutOfRangeError: If the end of the iterator has been reached.

get_next_as_optional

View source

@abc.abstractmethod
get_next_as_optional()

Returns a tf.experimental.Optional which contains the next element.

If the iterator has reached the end of the sequence, the returned tf.experimental.Optional will have no value.

>>> dataset = tf.data.Dataset.from_tensors(42)
>>> iterator = iter(dataset)
>>> optional = iterator.get_next_as_optional()
>>> print(optional.has_value())
tf.Tensor(True, shape=(), dtype=bool)
>>> print(optional.get_value())
tf.Tensor(42, shape=(), dtype=int32)
>>> optional = iterator.get_next_as_optional()
>>> print(optional.has_value())
tf.Tensor(False, shape=(), dtype=bool)

Returns
A tf.experimental.Optional object representing the next element.

__iter__

__iter__()