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)`

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__()