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
tf.contrib.checkpoint.Mapping.__contains__
__contains__(key)
tf.contrib.checkpoint.Mapping.__deepcopy__
__deepcopy__(memo)
tf.contrib.checkpoint.Mapping.__eq__
__eq__(other)
Return self==value.
tf.contrib.checkpoint.Mapping.__getitem__
__getitem__(key)
tf.contrib.checkpoint.Mapping.__iter__
__iter__()
tf.contrib.checkpoint.Mapping.__len__
__len__()
tf.contrib.checkpoint.Mapping.__setitem__
__setitem__(
key,
value
)
tf.contrib.checkpoint.Mapping.__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).
tf.contrib.checkpoint.Mapping.get
get(
key,
default=None
)
D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.
tf.contrib.checkpoint.Mapping.items
items()
D.items() -> a set-like object providing a view on D's items
tf.contrib.checkpoint.Mapping.keys
keys()
D.keys() -> a set-like object providing a view on D's keys
tf.contrib.checkpoint.Mapping.update
update(
*args,
**kwargs
)
tf.contrib.checkpoint.Mapping.values
values()
D.values() -> an object providing a view on D's values