tf.data.Iterator | TensorFlow v2.16.1 (original) (raw)
tf.data.Iterator
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Represents an iterator of a tf.data.Dataset.
tf.data.Iterator is the primary mechanism for enumerating elements of atf.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 atf.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) For more information, read this guide. |
Methods
get_next
@abc.abstractmethod
get_next()
Returns 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 values matching tf.data.Iterator.element_spec. |
| Raises |
|---|
| tf.errors.OutOfRangeError: If the end of the iterator has been reached. |
get_next_as_optional
@abc.abstractmethod
get_next_as_optional()
Returns the next element wrapped in tf.experimental.Optional.
If the iterator has reached the end of the sequence, the returnedtf.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__()