ParametersRecord

class ParametersRecord(*args: Any, **kwargs: dict[str, Any])

기반 클래스: ArrayRecord

Deprecated class ParametersRecord, use ArrayRecord instead.

This class exists solely for backward compatibility with legacy code that previously used ParametersRecord. It has been renamed to ArrayRecord.

경고

ParametersRecord is deprecated and will be removed in a future release. Use ArrayRecord instead.

예제

Legacy (deprecated) usage:

from flwr.common import ParametersRecord

record = ParametersRecord()

Updated usage:

from flwr.common import ArrayRecord

record = ArrayRecord()

메소드

clear()
count_bytes()	이 객체에 저장된 바이트 수를 반환합니다.
from_array_dict(array_dict, *[, keep_input])	Create ArrayRecord from a dictionary of Array.
from_numpy_ndarrays(ndarrays, *[, keep_input])	Create ArrayRecord from a list of NumPy ndarray.
from_torch_state_dict(state_dict, *[, ...])	Create ArrayRecord from PyTorch state_dict.
get(k[,d])
items()
keys()
pop(k[,d])	키를 찾을 수 없으면 주어진 경우 d가 반환되고, 그렇지 않으면 KeyError가 발생합니다.
popitem()	as a 2-tuple; but raise KeyError if D is empty.
setdefault(k[,d])
to_numpy_ndarrays(*[, keep_input])	Return the ArrayRecord as a list of NumPy ndarray.
to_torch_state_dict(*[, keep_input])	Return the ArrayRecord as a PyTorch state_dict.
update([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
values()

clear() → None.  Remove all items from D.

count_bytes() → int

이 객체에 저장된 바이트 수를 반환합니다.

역직렬화에 필요한 직렬화된 객체의 메타데이터(예: NumPy 배열)에 해당하는 소량의 바이트도 이 카운팅에 포함될 수 있습니다.

classmethod from_array_dict(array_dict: OrderedDict[str, Array], *, keep_input: bool = True) → ArrayRecord

Create ArrayRecord from a dictionary of Array.

classmethod from_numpy_ndarrays(ndarrays: list[ndarray[Any, dtype[Any]]], *, keep_input: bool = True) → ArrayRecord

Create ArrayRecord from a list of NumPy ndarray.

classmethod from_torch_state_dict(state_dict: OrderedDict[str, torch.Tensor], *, keep_input: bool = True) → ArrayRecord

Create ArrayRecord from PyTorch state_dict.

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

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

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

pop(k[, d]) → v, remove specified key and return the corresponding value.

키를 찾을 수 없으면 주어진 경우 d가 반환되고, 그렇지 않으면 KeyError가 발생합니다.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D

to_numpy_ndarrays(*, keep_input: bool = True) → list[ndarray[Any, dtype[Any]]]

Return the ArrayRecord as a list of NumPy ndarray.

to_torch_state_dict(*, keep_input: bool = True) → OrderedDict[str, torch.Tensor]

Return the ArrayRecord as a PyTorch state_dict.

update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

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