tf.keras.utils.deserialize_keras_object

Retrieve the object by deserializing the config dict.

tf.keras.utils.deserialize_keras_object(
    config, custom_objects=None, safe_mode=True, **kwargs
)

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: Dict. 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 keras.saving.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 dict will not be used. registered_name is only used for non-built-in classes.

For example, the following dictionary represents the built-in Adam optimizer with the relevant config:

dict_structure = {
    "class_name": "Adam",
    "config": {
        "amsgrad": false,
        "beta_1": 0.8999999761581421,
        "beta_2": 0.9990000128746033,
        "decay": 0.0,
        "epsilon": 1e-07,
        "learning_rate": 0.0010000000474974513,
        "name": "Adam"
    },
    "module": "keras.optimizers",
    "registered_name": None
}
# Returns an `Adam` instance identical to the original one.
deserialize_keras_object(dict_structure)

If the class does not have an exported Keras namespace, the library tracks it by its module and class_name. For example:

dict_structure = {
  "class_name": "MetricsList",
  "config": {
      ...
  },
  "module": "keras.trainers.compile_utils",
  "registered_name": "MetricsList"
}

# Returns a `MetricsList` instance identical to the original one.
deserialize_keras_object(dict_structure)

And the following dictionary represents a user-customized MeanSquaredError loss:

@keras.saving.register_keras_serializable(package='my_package')
class ModifiedMeanSquaredError(keras.losses.MeanSquaredError):
  ...

dict_structure = {
    "class_name": "ModifiedMeanSquaredError",
    "config": {
        "fn": "mean_squared_error",
        "name": "mean_squared_error",
        "reduction": "auto"
    },
    "registered_name": "my_package>ModifiedMeanSquaredError"
}
# Returns the `ModifiedMeanSquaredError` object
deserialize_keras_object(dict_structure)

config Python dict describing the object.
custom_objects Python dict 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.

The object described by the config dictionary.