returns the optimizer configuration as a python dict args optimizer an optimizer instance to serialize returns python dict which contains the configuration of the optimizer returns a keras optimizer object via its configuration args config optimizer configuration dictionary customobjects optional dictionary mapping names strings to custom objects classes and functions to be considered during deserialization returns a keras optimizer instance make deserialization caseinsensitive for builtin optimizers retrieves a keras optimizer instance args identifier optimizer identifier one of string name of an optimizer dictionary configuration dictionary keras optimizer instance it will be returned unchanged returns a keras optimizer instance we will add this temporarily so that tensorflow packages that depend on estimators will continue to import there are a large number note that keras 3 will not work with the estimators api returns the optimizer configuration as a python dict args optimizer an optimizer instance to serialize returns python dict which contains the configuration of the optimizer returns a keras optimizer object via its configuration args config optimizer configuration dictionary custom_objects optional dictionary mapping names strings to custom objects classes and functions to be considered during deserialization returns a keras optimizer instance make deserialization case insensitive for built in optimizers retrieves a keras optimizer instance args identifier optimizer identifier one of string name of an optimizer dictionary configuration dictionary keras optimizer instance it will be returned unchanged returns a keras optimizer instance we will add this temporarily so that tensorflow packages that depend on estimators will continue to import there are a large number note that keras 3 will not work with the estimators api
from keras.api_export import keras_export
from keras.optimizers.adadelta import Adadelta
from keras.optimizers.adafactor import Adafactor
from keras.optimizers.adagrad import Adagrad
from keras.optimizers.adam import Adam
from keras.optimizers.adamax import Adamax
from keras.optimizers.adamw import AdamW
from keras.optimizers.ftrl import Ftrl
from keras.optimizers.lion import Lion
from keras.optimizers.loss_scale_optimizer import LossScaleOptimizer
from keras.optimizers.nadam import Nadam
from keras.optimizers.optimizer import Optimizer
from keras.optimizers.rmsprop import RMSprop
from keras.optimizers.sgd import SGD
from keras.saving import serialization_lib

ALL_OBJECTS = {
    Optimizer,
    Adam,
    SGD,
    RMSprop,
    Adadelta,
    AdamW,
    Adagrad,
    Adamax,
    Adafactor,
    Nadam,
    Ftrl,
    Lion,
    LossScaleOptimizer,
}
ALL_OBJECTS_DICT = {cls.__name__.lower(): cls for cls in ALL_OBJECTS}


@keras_export("keras.optimizers.serialize")
def serialize(optimizer):
    
    return serialization_lib.serialize_keras_object(optimizer)


@keras_export("keras.optimizers.deserialize")
def deserialize(config, custom_objects=None):
    
    
    if config["class_name"].lower() in ALL_OBJECTS_DICT:
        config["class_name"] = config["class_name"].lower()

    return serialization_lib.deserialize_keras_object(
        config,
        module_objects=ALL_OBJECTS_DICT,
        custom_objects=custom_objects,
    )


@keras_export("keras.optimizers.get")
def get(identifier):
    
    if identifier is None:
        return None
    elif isinstance(identifier, dict):
        obj = deserialize(identifier)
    elif isinstance(identifier, str):
        config = {"class_name": identifier, "config": {}}
        obj = deserialize(config)
    else:
        obj = identifier

    if isinstance(obj, Optimizer):
        return obj
    raise ValueError(f"Could not interpret optimizer identifier: {identifier}")





@keras_export(
    [
        "keras.optimizers.legacy.Adagrad",
        "keras.optimizers.legacy.Adam",
        "keras.optimizers.legacy.Ftrl",
        "keras.optimizers.legacy.RMSprop",
        "keras.optimizers.legacy.SGD",
        "keras.optimizers.legacy.Optimizer",
    ]
)
class LegacyOptimizerWarning:
    def __init__(self, *args, **kwargs):
        raise ImportError(
            "`keras.optimizers.legacy` is not supported in Keras 3. When using "
            "`tf.keras`, to continue using a `tf.keras.optimizers.legacy` "
            "optimizer, you can install the `tf_keras` package (Keras 2) and "
            "set the environment variable `TF_USE_LEGACY_KERAS=True` to "
            "configure TensorFlow to use `tf_keras` when accessing `tf.keras`."
        )