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

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is_compatible_with(
    spec_or_value
)

Returns true if spec_or_value is compatible with this TypeSpec.

most_specific_compatible_type

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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__

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__eq__(
    other
)

Return self==value.

__ne__

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__ne__(
    other
)

Return self!=value.