tfm.optimization.AdamWeightDecayConfig

Configuration for Adam optimizer with weight decay.

Inherits From: BaseOptimizerConfig, Config, ParamsDict

tfm.optimization.AdamWeightDecayConfig(
    default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None,
    restrictions: dataclasses.InitVar[Optional[List[str]]] = None,
    clipnorm: Optional[float] = None,
    clipvalue: Optional[float] = None,
    global_clipnorm: Optional[float] = None,
    name: str = 'AdamWeightDecay',
    beta_1: float = 0.9,
    beta_2: float = 0.999,
    epsilon: float = 1e-07,
    amsgrad: bool = False,
    weight_decay_rate: float = 0.0,
    include_in_weight_decay: Optional[List[str]] = None,
    exclude_from_weight_decay: Optional[List[str]] = None,
    gradient_clip_norm: float = 1.0
)

name name of the optimizer.
beta_1 decay rate for 1st order moments.
beta_2 decay rate for 2st order moments.
epsilon epsilon value used for numerical stability in the optimizer.
amsgrad boolean. Whether to apply AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and beyond".
weight_decay_rate float. Weight decay rate. Default to 0.
include_in_weight_decay list[str], or None. List of weight names to include in weight decay.
exclude_from_weight_decay list[str], or None. List of weight names to not include in weight decay.
gradient_clip_norm A positive float. Clips the gradients to this maximum L2-norm. Default to 1.0.
BUILDER

default_params Dataclass field
restrictions Dataclass field
clipnorm Dataclass field
clipvalue Dataclass field
global_clipnorm Dataclass field

Methods

as_dict

View source

as_dict()

Returns a dict representation of params_dict.ParamsDict.

For the nested params_dict.ParamsDict, a nested dict will be returned.

from_args

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@classmethod
from_args(
    *args, **kwargs
)

Builds a config from the given list of arguments.

from_json

View source

@classmethod
from_json(
    file_path: str
)

Wrapper for from_yaml.

from_yaml

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@classmethod
from_yaml(
    file_path: str
)

get

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get(
    key, value=None
)

Accesses through built-in dictionary get method.

lock

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lock()

Makes the ParamsDict immutable.

override

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override(
    override_params, is_strict=True
)

Override the ParamsDict with a set of given params.

Args
override_params a dict or a ParamsDict specifying the parameters to be overridden.
is_strict a boolean specifying whether override is strict or not. If True, keys in override_params must be present in the ParamsDict. If False, keys in override_params can be different from what is currently defined in the ParamsDict. In this case, the ParamsDict will be extended to include the new keys.

replace

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replace(
    **kwargs
)

Overrides/returns a unlocked copy with the current config unchanged.

validate

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validate()

Validate the parameters consistency based on the restrictions.

This method validates the internal consistency using the pre-defined list of restrictions. A restriction is defined as a string which specifies a binary operation. The supported binary operations are {'==', '!=', '<', '<=', '>', '>='}. Note that the meaning of these operators are consistent with the underlying Python immplementation. Users should make sure the define restrictions on their type make sense.

For example, for a ParamsDict like the following

a:
  a1: 1
  a2: 2
b:
  bb:
    bb1: 10
    bb2: 20
  ccc:
    a1: 1
    a3: 3

one can define two restrictions like this ['a.a1 == b.ccc.a1', 'a.a2 <= b.bb.bb2']

What it enforces are

  • a.a1 = 1 == b.ccc.a1 = 1
  • a.a2 = 2 <= b.bb.bb2 = 20

Raises
KeyError if any of the following happens (1) any of parameters in any of restrictions is not defined in ParamsDict, (2) any inconsistency violating the restriction is found.
ValueError if the restriction defined in the string is not supported.

__contains__

View source

__contains__(
    key
)

Implements the membership test operator.

__eq__

__eq__(
    other
)

IMMUTABLE_TYPES (<class 'str'>, <class 'int'>, <class 'float'>, <class 'bool'>, <class 'NoneType'>)
RESERVED_ATTR ['_locked', '_restrictions']
SEQUENCE_TYPES (<class 'list'>, <class 'tuple'>)
amsgrad False
beta_1 0.9
beta_2 0.999
clipnorm None
clipvalue None
default_params None
epsilon 1e-07
exclude_from_weight_decay None
global_clipnorm None
gradient_clip_norm 1.0
include_in_weight_decay None
name 'AdamWeightDecay'
restrictions None
weight_decay_rate 0.0