tf.contrib.checkpoint.Mapping

Class Mapping

Defined in tensorflow/python/training/checkpointable/data_structures.py.

An append-only checkpointable mapping data structure with string keys.

Maintains checkpoint dependencies on its contents (which must also be checkpointable), named based on its keys.

Note that once a key has been added, it may not be deleted or replaced. If names may not be unique, see tf.contrib.checkpoint.UniqueNameTracker.

__init__

__init__(
    *args,
    **kwargs
)

Construct a new sequence. Arguments are passed to dict().

Properties

layers

losses

Aggregate losses from any Layer instances.

non_trainable_variables

non_trainable_weights

trainable_variables

trainable_weights

updates

Aggregate updates from any Layer instances.

variables

weights

Methods

__contains__

__contains__(key)

__deepcopy__

__deepcopy__(memo)

__eq__

__eq__(other)

Return self==value.

__getitem__

__getitem__(key)

__iter__

__iter__()

__len__

__len__()

__setitem__

__setitem__(
    key,
    value
)

__subclasshook__

__subclasshook__(
    cls,
    C
)

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.subclasscheck(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

get

get(
    key,
    default=None
)

D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.

items

items()

D.items() -> a set-like object providing a view on D's items

keys

keys()

D.keys() -> a set-like object providing a view on D's keys

update

update(
    *args,
    **kwargs
)

values

values()

D.values() -> an object providing a view on D's values