Retrieve the object by deserializing the config dict.

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>

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'>

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)

## <<r-namespace:keras3>.ModifiedMeanSquaredError object>

# Returns the `ModifiedMeanSquaredError` object

Usage

deserialize_keras_object(config, custom_objects = NULL, safe_mode = TRUE, ...)

Arguments

config: Named list describing the object.
custom_objects: Named list containing a mapping between custom object names the corresponding classes or functions.
safe_mode: 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.

Value

The object described by the config dictionary.

Usage

Arguments

Value

See also