tfm.optimization.CosineLrConfig

Configuration for Cosine learning rate decay.

Inherits From: Config, ParamsDict

tfm.optimization.CosineLrConfig(
    default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None,
    restrictions: dataclasses.InitVar[Optional[List[str]]] = None,
    name: str = 'CosineDecay',
    initial_learning_rate: Optional[float] = None,
    decay_steps: Optional[int] = None,
    alpha: float = 0.0,
    offset: int = 0
)

This class is a containers for the cosine learning rate decay configs, tf.keras.experimental.CosineDecay.

name The name of the learning rate schedule. Defaults to CosineDecay. initial_learning_rate A float. The initial learning rate. Defaults to None. decay_steps A positive integer that is used for decay computation. Defaults to None. alpha A float. Minimum learning rate value as a fraction of initial_learning_rate. offset An int. The offset applied to steps. Defaults to 0. BUILDER

default_params Dataclass field restrictions Dataclass field

Methods

as_dict

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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

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@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__

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__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'>) alpha 0.0 decay_steps None default_params None initial_learning_rate None name 'CosineDecay' offset 0 restrictions None