tf.keras.saving.deserialize_keras_object

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Retrieve the object by deserializing the config dict.

tf.keras.saving.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, such as "keras.engine.compile_utils". 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": "LossesContainer",
  "config": {
      "losses": [...],
      "total_loss_mean": {...},
  },
  "module": "keras.engine.compile_utils",
  "registered_name": "LossesContainer"
}

# Returns a `LossesContainer` 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)