The config dict is a Python dictionary that consists of a set of key-value
pairs, and represents a Keras object, such as an Optimizer, Layer,
Metrics, etc. The saving and loading library uses the following keys to
record information of a Keras object:
class_name: String. This is the name of the class,
as exactly defined in the source
code, such as "LossesContainer".
config: Named List. Library-defined or user-defined key-value pairs that store
the configuration of the object, as obtained by object$get_config().
module: String. The path of the python module. Built-in Keras classes
expect to have prefix keras.
registered_name: String. The key the class is registered under via
register_keras_serializable(package, name) API. The
key has the format of '{package}>{name}', where package and name are
the arguments passed to register_keras_serializable(). If name is not
provided, it uses the class name. If registered_name successfully
resolves to a class (that was registered), the class_name and config
values in the config dict will not be used. registered_name is only used for
non-built-in classes.
For example, the following config list represents the built-in Adam optimizer with the relevant config:
config <- list(
class_name = "Adam",
config = list(
amsgrad = FALSE,
beta_1 = 0.8999999761581421,
beta_2 = 0.9990000128746033,
epsilon = 1e-07,
learning_rate = 0.0010000000474974513,
name = "Adam"
),
module = "keras.optimizers",
registered_name = NULL
)
# Returns an `Adam` instance identical to the original one.
deserialize_keras_object(config)
## <keras.src.optimizers.adam.Adam object at 0x0>If the class does not have an exported Keras namespace, the library tracks
it by its module and class_name. For example:
config <- list(
class_name = "MetricsList",
config = list(
...
),
module = "keras.trainers.compile_utils",
registered_name = "MetricsList"
)# Returns a `MetricsList` instance identical to the original one.
deserialize_keras_object(config)
And the following config represents a user-customized MeanSquaredError
loss:
# define a custom object
loss_modified_mse <- Loss(
"ModifiedMeanSquaredError",
inherit = loss_mean_squared_error)# register the custom object
register_keras_serializable(loss_modified_mse)
# confirm object is registered
get_custom_objects()
## $`keras3>ModifiedMeanSquaredError`
## <class '<r-namespace:keras3>.ModifiedMeanSquaredError'>
## signature: (
## reduction='sum_over_batch_size',
## name='mean_squared_error',
## dtype=None
## )get_registered_name(loss_modified_mse)
## [1] "keras3>ModifiedMeanSquaredError"# now custom object instances can be serialized
full_config <- serialize_keras_object(loss_modified_mse())# the `config` arguments will be passed to loss_modified_mse()
str(full_config)
## List of 4
## $ module : chr "<r-namespace:keras3>"
## $ class_name : chr "ModifiedMeanSquaredError"
## $ config :List of 2
## ..$ name : chr "mean_squared_error"
## ..$ reduction: chr "sum_over_batch_size"
## $ registered_name: chr "keras3>ModifiedMeanSquaredError"# and custom object instances can be deserialized
deserialize_keras_object(full_config)
## <LossFunctionWrapper(<function mean_squared_error at 0x0>, kwargs={})>
## signature: (y_true, y_pred, sample_weight=None)# Returns the `ModifiedMeanSquaredError` object
deserialize_keras_object(config, custom_objects = NULL, safe_mode = TRUE, ...)The object described by the config dictionary.
Named list describing the object.
Named list containing a mapping between custom object names the corresponding classes or functions.
Boolean, whether to disallow unsafe lambda deserialization.
When safe_mode=FALSE, loading an object has the potential to
trigger arbitrary code execution. This argument is only
applicable to the Keras v3 model format. Defaults to TRUE.
For forward/backward compatability.
Other serialization utilities:
get_custom_objects()
get_registered_name()
get_registered_object()
register_keras_serializable()
serialize_keras_object()
with_custom_object_scope()