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.

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

__init__

__init__(
    *args,
    **kwargs
)

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

__contains__

__contains__(key)

__eq__

__eq__(other)

__getitem__

__getitem__(key)

__iter__

__iter__()

__len__

__len__()

__ne__

__ne__(other)

__setitem__

__setitem__(
    key,
    value
)

__subclasshook__

__subclasshook__(
    cls,
    C
)

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() -> list of D's (key, value) pairs, as 2-tuples

iteritems

iteritems()

D.iteritems() -> an iterator over the (key, value) items of D

iterkeys

iterkeys()

D.iterkeys() -> an iterator over the keys of D

itervalues

itervalues()

D.itervalues() -> an iterator over the values of D

keys

keys()

D.keys() -> list of D's keys

update

update(
    *args,
    **kwargs
)

values

values()

D.values() -> list of D's values