tf.data.IteratorSpec

Type specification for tf.data.Iterator.

Inherits From: TypeSpec

tf.data.IteratorSpec(
    element_spec
)

For instance, tf.data.IteratorSpec can be used to define a tf.function that takes tf.data.Iterator as an input argument:

@tf.function(input_signature=[tf.data.IteratorSpec(
  tf.TensorSpec(shape=(), dtype=tf.int32, name=None))])
def square(iterator):
  x = iterator.get_next()
  return x * x
dataset = tf.data.Dataset.from_tensors(5)
iterator = iter(dataset)
print(square(iterator))
tf.Tensor(25, shape=(), dtype=int32)

element_spec A (nested) structure of tf.TypeSpec objects that represents the type specification of the iterator elements.
value_type The Python type for values that are compatible with this TypeSpec.

In particular, all values that are compatible with this TypeSpec must be an instance of this type.

Methods

from_value

View source

@staticmethod
from_value(
    value
)

is_compatible_with

View source

is_compatible_with(
    spec_or_value
)

Returns true if spec_or_value is compatible with this TypeSpec.

most_specific_compatible_type

View source

most_specific_compatible_type(
    other: 'TypeSpec'
) -> 'TypeSpec'

Returns the most specific TypeSpec compatible with self and other.

Args
other A TypeSpec.

Raises
ValueError If there is no TypeSpec that is compatible with both self and other.

__eq__

View source

__eq__(
    other
) -> bool

Return self==value.

__ne__

View source

__ne__(
    other
) -> bool

Return self!=value.