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)

Attributes
`element_spec`	A nested structure of `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
)

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
)

Return self==value.

`ne`

View source

__ne__(
    other
)

Return self!=value.

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/data/IteratorSpec